minor bugfixes

docs/Changelog.rst

@@ -19,11 +19,12 @@ Changelog
 
 **Bug Fixes**
 
+-    Crosshairs returned the labels as a float which resulted in numbers like 10.0 rather than 10 when decimals=0. The labels are now converted to int when decimals=0 so they will indeed return zero decimals when told to.
 
 **Other**
 
--    Added a documentation section which explains how some important algorithms work
-
+-    Added a documentation section (Reliability Theory) which explains how some important algorithms work
+-    Made Fit_Everything more tolerant of different names to exclude distributions. For example to exclude the Weibull_CR model, users may type "Weibull_CR", "CR", "Weibull_Competing_Risks", "Competing Risks" and many more variations.
 
 **Version: 0.7.0 --- Released: 8 Oct 2021**
 '''''''''''''''''''''''''''''''''''''''''''

reliability/Fitters.py

@@ -93,8 +93,9 @@
 
 class Fit_Everything:
     """
-    This function will fit all available distributions (excluding mixture and
-    competing risks) to the data provided.
+    This function will fit all available distributions to the data provided.
+    The only distributions not fitted are Weibull_DSZI and Weibull_ZI. The
+    Beta_2P distribution will only be fitted if the data are between 0 and 1.
 
     Parameters
     ----------
@@ -121,9 +122,10 @@ class Fit_Everything:
         distribution. True or False. Default = True.
     exclude : list, array, optional
         List or array of strings specifying which distributions to exclude.
-        Default is None. Options are Weibull_2P, Weibull_3P, Normal_2P,
-        Gamma_2P, Loglogistic_2P, Gamma_3P, Lognormal_2P, Lognormal_3P,
-        Loglogistic_3P, Gumbel_2P, Exponential_2P, Exponential_1P, Beta_2P.
+        Default is None. Options are Weibull_2P, Weibull_3P, Weibull_CR,
+        Weibull_Mixture, Weibull_DS, Normal_2P, Gamma_2P, Loglogistic_2P,
+        Gamma_3P, Lognormal_2P, Lognormal_3P, Loglogistic_3P, Gumbel_2P,
+        Exponential_2P, Exponential_1P, Beta_2P.
     print_results : bool, optional
         Will show the results of the fitted parameters and the goodness of fit
         tests in a dataframe. True/False. Defaults to True.
@@ -321,6 +323,7 @@ def __init__(
             elif item.upper() in ["BETA_2P", "BETA2P", "BETA2"]:
                 excluded_distributions.append("Beta_2P")
             elif item.upper() in [
+                "WEIBULL MIXTURE",
                 "WEIBULLMIXTURE",
                 "WEIBULL_MIXTURE",
                 "MIXTURE",
@@ -330,19 +333,30 @@ def __init__(
             ]:
                 excluded_distributions.append("Weibull_Mixture")
             elif item.upper() in [
+                "WEIBULL CR",
                 "WEIBULLCR",
                 "WEIBULL_CR",
                 "WEIBULL_COMPETING_RISKS",
                 "WEIBULL_COMPETINGRISKS",
                 "WEIBULLCOMPETINGRISKS",
+                "WEIBULL COMPETING RISKS",
+                "WEIBULL COMPETINGRISKS",
+                "COMPETINGRISKS",
+                "COMPETING RISKS",
+                "CR",
             ]:
                 excluded_distributions.append("Weibull_CR")
             elif item.upper() in [
                 "WEIBULLDS",
                 "WEIBULL_DS",
+                "WEIBULL DS",
                 "WEIBULL_DEFECTIVE_SUBPOPULATION",
                 "WEIBULL_DEFECTIVESUBPOPULATION",
                 "WEIBULLDEFECTIVESUBPOPULATION",
+                "WEIBULL DEFECTIVE SUBPOPULATION",
+                "DEFECTIVE SUBPOPULATION",
+                "DEFECTIVESUBPOPULATION",
+                "DS",
             ]:
                 excluded_distributions.append("Weibull_DS")
             else:

reliability/Other_functions.py

@@ -863,8 +863,8 @@ class make_right_censored_data:
         threshold). This is known as "singly censored data" as everything is
         censored at a single point. Default is None in which case the
         fraction_censored will be used. See the notes below.
-    fraction_censored : float, optional
-        Must be between 0 and 1. Default = 0.5. Censoring is done randomly. This
+    fraction_censored : int, float, optional
+        Must be >= 0 and < 1. Default = 0.5. Censoring is done randomly. This
         is known as "multiply censored data" as there are multiple times at
         which censoring occurs. See the notes below.
     seed : int, optional
@@ -911,12 +911,12 @@ def __init__(self, data, threshold=None, fraction_censored=None, seed=None):
             np.random.shuffle(data)
             # place a limit on the amount of the data that can be censored
             if (
-                fraction_censored <= 0
+                fraction_censored < 0
                 or fraction_censored >= 1
-                or type(fraction_censored) not in [float, np.float_]
+                or type(fraction_censored) not in [int, float, np.float_, np.int_]
             ):
                 raise ValueError(
-                    "fraction_censored must be a float between 0 and 1. The default is 0.5 which will right censor half the data"
+                    "fraction_censored must be >= 0 and < 1. The default is 0.5 which will right censor half the data"
                 )
             number_of_items_to_censor = int(np.floor(len(data) * fraction_censored))
             self.right_censored = data[0:number_of_items_to_censor] * np.random.rand(
@@ -1363,11 +1363,19 @@ def __add_lines_and_text_to_crosshairs(sel, decimals, dateformat, **kwargs):
         if type(dateformat) is str:
             x_string = time.strftime(dateformat, time.gmtime(x * 24 * 3600))
         else:
-            x_string = round(x, decimals)
+            if decimals == 0:
+                x_string = int(x)
+            else:
+                x_string = round_to_decimals(x, decimals)
+
+        if decimals == 0:
+            y_string = int(y)
+        else:
+            y_string = round_to_decimals(y, decimals)
 
         texts = [
             ax.text(
-                s=round(y, decimals),
+                s=y_string,
                 x=0,
                 y=y,
                 transform=ax.get_yaxis_transform(),
@@ -1406,7 +1414,15 @@ def __format_annotation(sel, decimals, dateformat, label):
         if type(dateformat) is str:
             x_string = time.strftime(dateformat, time.gmtime(x * 24 * 3600))
         else:
-            x_string = round(x, decimals)
+            if decimals == 0:
+                x_string = int(x)
+            else:
+                x_string = round_to_decimals(x, decimals)
+
+        if decimals == 0:
+            y_string = int(y)
+        else:
+            y_string = round_to_decimals(y, decimals)
 
         text = str(
             label[0]
@@ -1415,7 +1431,7 @@ def __format_annotation(sel, decimals, dateformat, label):
             + "\n"
             + label[1]
             + " = "
-            + str(round(y, decimals))
+            + str(y_string)
         )
         sel.annotation.set_text(text)
         sel.annotation.get_bbox_patch().set(fc="white")