updating the examples in the main readme

README.md

@@ -75,14 +75,14 @@ The returned value is a dictionary from the parameter of the model to one of the
 For example, for the `ode` defined above, it will be
 
 ```
-Dict{Any, Symbol} with 7 entries:
-  a12 => :locally
-  a21 => :globally
-  a01 => :locally
-  b   => :nonidentifiable
-  x2  => :globally
-  x1  => :locally
-  x3  => :nonidentifiable
+OrderedDict{Any, Symbol} with 7 entries:
+  x1(t) => :locally
+  x2(t) => :globally
+  x3(t) => :nonidentifiable
+  a01   => :locally
+  a12   => :locally
+  a21   => :globally
+  b     => :nonidentifiable
 ```
 
 If one is interested in the identifiability of particular functions of the parameter, one can pass a list of them as a second argument:
@@ -94,9 +94,9 @@ assess_identifiability(ode, funcs_to_check = [a01 + a12, a01 * a12])
 This will return:
 
 ```
-Dict{Any, Symbol} with 2 entries:
-  a12 + a01 => :globally
-  a12*a01   => :globally
+OrderedDict{Any, Symbol} with 2 entries:
+  a01 + a12 => :globally
+  a01*a12   => :globally
 ```
 
 ### Assessing local identifiability
@@ -111,14 +111,14 @@ assess_local_identifiability(ode)
 The returned value is a dictionary from parameters and state variables to `1` (is locally identifiable/observable) and `0` (not identifiable/observable) values. In our example:
 
 ```
-Dict{Nemo.fmpq_mpoly, Bool} with 7 entries:
-  a12 => 1
-  a21 => 1
-  x3  => 0
-  a01 => 1
-  x2  => 1
-  x1  => 1
-  b   => 0
+OrderedDict{Any, Bool} with 7 entries:
+  x1(t) => 1
+  x2(t) => 1
+  x3(t) => 0
+  a01   => 1
+  a12   => 1
+  a21   => 1
+  b     => 0
 ```
 
 As for `assess_identifiability`, one can assess local identifiability of arbitrary rational functions in the parameters (and also states) by providing a list of such functions as the second argument.