updating docs

MatthewReid854 · Jul 23, 2021 · 9ae9331 · 9ae9331
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diff --git a/docs/DSZI models.rst b/docs/DSZI models.rst
@@ -276,7 +276,7 @@ In this example we will create a DSZI model with DS=0.7 and ZI=0.2.
 Based on these parameters, we expect the random samples to be around 70% failures and of those failures 20% of the total samples (failures + right censored) should be zeros due to the zero inflated fraction.
 We draw the random samples from the model and then fit a Weibull_DSZI model to the data.
 The result is surprisingly accurate showing DS=0.700005 and ZI=0.22, with the alpha and beta parameters closely resembling the parameters of the input Weibull Distribution.
-The plot below shows the CDF on the Weibull probability plot (top) and on linear axes (bottom) which each provide a different perspective of how the distribution models the failure points.
+The plot below shows the CDF on the Weibull probability plot (left) and on linear axes (right) which each provide a different perspective of how the distribution models the failure points.
 
 .. code:: python
     
@@ -288,15 +288,16 @@ The plot below shows the CDF on the Weibull probability plot (top) and on linear
     model = DSZI_Model(distribution=Weibull_Distribution(alpha=1200,beta=3),DS=0.7,ZI=0.2)
     failures, right_censored = model.random_samples(100,seed=5,right_censored_time=3000)
     
-    plt.subplot(211)
-    fit = Fit_Weibull_DSZI(failures=failures,right_censored=right_censored,show_probability_plot=True)
+    plt.subplot(121)
+    fit = Fit_Weibull_DSZI(failures=failures,right_censored=right_censored,show_probability_plot=True,label='fitted Weibull_DSZI')
     model.CDF(label='true model')
     plt.legend()
     
-    plt.subplot(212)
+    plt.subplot(122)
     fit.distribution.CDF(label='fitted Weibull_DSZI')
     model.CDF(label='true model')
     plot_points(failures=failures,right_censored=right_censored)
+    plt.legend()
     plt.tight_layout()
     plt.show()
 

diff --git a/docs/Optimizers.rst b/docs/Optimizers.rst
@@ -5,9 +5,6 @@
 Optimizers
 ''''''''''
 
-.. Note::
-   This documentation applies to version 0.6.0 (currently unreleased). Prior to version 0.6.0, the default optimizer is L-BFGS-B below 97% censored data and TNC above 97% censored data, and the nelder-mead optimizer was not available as a bounded optimizer. The option to specify "best" for the optimizer is new in version 0.6.0.
-
 **What is an optimizer?**
 
 An optimizer is an algorithm that uses two primary inputs; a target function and an initial guess. The optimizer's job is to figure out which input to the target function will minimise the output of the target function.

diff --git a/docs/images/DSZI_example6.png b/docs/images/DSZI_example6.png