Merge branch 'codac2' into codac2_codac4matlab

Author: SimonRohou

doc/manual/index.rst

@@ -52,8 +52,7 @@ The solution set is approximated from an initial box :math:`[\mathbf{x}_0]=[0,2]
    int main()
    {
      VectorVar x(3);
-     AnalyticFunction f { {x},
-       {
+     AnalyticFunction f { {x}, {
          -(x[2]^2)+2*x[2]*sin(x[2]*x[0])+cos(x[2]*x[1]),
          2*x[2]*cos(x[2]*x[0])-sin(x[2]*x[1])
        }
@@ -191,7 +190,8 @@ User manual
 * :ref:`sec-intervals`
    * What is an interval?
    * :ref:`sec-intervals-class`
-   * Boolean intervals
+   * :ref:`sec-intervals-intervalvector-class`
+   * :ref:`sec-intervals-boolinterval-class`
 
 * :ref:`sec-linear`
    * :ref:`sec-linear-vecmat`
@@ -207,11 +207,11 @@ User manual
       * :ref:`sec-functions-analytic-operators`
       * Extension to custom expressions
       * Temporal functions
+      * :ref:`sec-functions-parallelepiped-eval`
+      * :ref:`sec-functions-peibos`
    * Set-membership functions
       * The class SetMembershipFunction
       * Extension to custom expressions
-   * :ref:`sec-functions-parallelepiped-eval`
-   * :ref:`sec-functions-peibos`
 
 * Tubes
    * What is a tube?
@@ -338,10 +338,10 @@ User manual
    * :ref:`sec-tools-registration`
    * :ref:`sec-tools-octasym`
 
-* Codac extensions
+* :ref:`sec-extensions`
    * :ref:`sec-extensions-capd`
+   * :ref:`sec-extensions-sympy`
    * Interface with the IBEX library
-   * Sympy (symbolic computation)
 
 * Frequently Asked Questions
 
@@ -356,9 +356,7 @@ How-to guides
 -------------
 
 * Robotics
-   * Non-linear state estimation
-   * State estimation by solving data association
-   * Range-only SLAM
+   * :ref:`sec-tuto-cprob`
    * Explored area
    * Loop detections and verifications
 

doc/manual/manual/functions/analytic/analytic_operators.rst

@@ -245,7 +245,7 @@ If you notice any mathematical operators missing from the list below, feel free
   +-----------------------------------------------------+----------------------+---------------+-------------------------------------+--------+--------+-------+------------+
   | :math:`\min(x_1,x_2)`                               | ``min(x1,x2)``       | |MinOp|       | ``x1``, ``x2``: scalar              ||okk|   ||okk|   ||okk|  ||okk|       |
   +-----------------------------------------------------+----------------------+---------------+-------------------------------------+--------+--------+-------+------------+
-  | :math:`x_1\bmod x_2`                                | --                   | |ModOp|       | --                                  ||nok|   ||nok|   ||nok|  ||bok|       |
+  | :math:`x_1\mod p`                                   | ``mod(x1,p)``        | |ModOp|       | ``x1``, ``p``: scalar               ||okk|   ||nok|   ||nok|  ||okk|       |
   +-----------------------------------------------------+----------------------+---------------+-------------------------------------+--------+--------+-------+------------+
   | :math:`(x_1)^{x_2}`                                 | | ``pow(x1,x2)``     | |PowOp|       | ``x1``, ``x2``: scalar              ||okk|   ||okk|   ||okk|  ||okk|       |
   |                                                     | | ``x1^x2``          |               |                                     |        |        |       |            |

doc/manual/manual/tools/registration.rst

@@ -27,7 +27,7 @@ Inputs ``src`` and ``dst`` can be provided either as a list of 2d points, or wit
   .. code-tab:: py
 
     def affine_transformation(src, dst):
-    # with src/dst: two lists of Vector objects, or two SampledVectorTraj objects
+    # with src/dst: two lists of Vector objects, or two SampledTraj_Vector objects
 
   .. code-tab:: c++
 
@@ -70,7 +70,7 @@ The following example estimates a transformation between two sampled trajectorie
 
     # Reconstructing the dst trajectory using the estimated transformation
 
-    dst_estim = SampledVectorTraj()
+    dst_estim = SampledTraj_Vector()
     for ti,src_i in src:
       dst_estim.set(ti, tr*src_i)