.gitignore

@@ -6,11 +6,16 @@
 # gitignore template for Jupyter Notebooks
 # website: http://jupyter.org/
 
+*.*~
+*.msg
+
 .ipynb_checkpoints
 */.ipynb_checkpoints/*
 *.so
 *.so.dSYM
 *.so.dSYM/*
+.nfs*
+*__pycache__*
 
 # IPython
 profile_default/
@@ -23,5 +28,8 @@ ipython_config.py
 
 # bocop files (build and exec)
 build/
-bocop
+bocopApp
+bocopwrapper.py
 !bocop/
+*.sol
+libbocop.dylib

.gitlab-ci.yml

 image: intelpython/intelpython3_full:latest
 
 pages:
+  tags:
+    - ci.inria.fr
+    - small
   stage: deploy
   before_script:
     - conda install -c conda-forge pandoc
   script:
     - python --version
-    - python -m pip install $(cat requirements.txt)
+    - python -m pip install -r pkg/requirements.txt
     - pip list
     - conda list
     - python -m sphinx -b html examples/ public

README.md

-# ct (control toolbox) gallery
+# Welcome to ct (control toolbox) gallery
 
-Jupyter notebooks for use cases using solvers of the nutopy package and others.
+Examples from the ct (control toolbox) project
+using [bocop](https://ct.gitlabpages.inria.fr/bocop3), [nutopy](https://ct.gitlabpages.inria.fr/nutopy) and other packages. Please visit the [gallery](https://ct.gitlabpages.inria.fr/gallery) or explore the dashboards apps or the notebooks codes by clicking on the following images!
 
-[![Online examples](https://img.shields.io/badge/ct-gallery-blue.svg)](https://ct.gitlabpages.inria.fr/gallery)
-[![Full directory](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgitlab.inria.fr%2Fct%2Fgallery.git/master?urlpath=lab/)
+[<img src="examples/notebook-logo.png" alt="Visit the notebooks" width="270"/>](https://ct.gitlabpages.inria.fr/gallery/notebooks.html)
+[<img src="examples/dashboard-logo.png" alt="Visit the dashboards" width="300"/>](https://ct.gitlabpages.inria.fr/gallery/dashboards.html)
 
+You can execute the code online and make your own experiments by clicking on <img alt="Binder badge" src="https://static.mybinder.org/badge_logo.svg" style="vertical-align:text-bottom"> when you find it on a notebook or a dashboard.
 
+Special thanks: [A. Hurtig](http://www.alain.les-hurtig.org) for the logos ligatures

environment.yml

-name: ct-gallery
-channels:
-  - control-toolbox
-  - conda-forge
-  - dtk-forge
-dependencies:
-  - scipy
-  - matplotlib=3.1.*
-  - nutopy=0.3.*
-  - bocop>3.0.0
-  - cmake
-  - gfortran_linux-64

examples/_static/custom.css

+:root {
+  --sd-color-primary: #f37726;
+}

examples/bacteria/.gitignore

+./*.def
+./*.out

examples/bacteria/bocopwrapper.py

+# This file was automatically generated by SWIG (http://www.swig.org).
+# Version 4.0.2
+#
+# Do not make changes to this file unless you know what you are doing--modify
+# the SWIG interface file instead.
+
+from sys import version_info as _swig_python_version_info
+if _swig_python_version_info < (2, 7, 0):
+    raise RuntimeError("Python 2.7 or later required")
+
+# Import the low-level C/C++ module
+if __package__ or "." in __name__:
+    from . import _bocopwrapper
+else:
+    import _bocopwrapper
+
+try:
+    import builtins as __builtin__
+except ImportError:
+    import __builtin__
+
+_swig_new_instance_method = _bocopwrapper.SWIG_PyInstanceMethod_New
+_swig_new_static_method = _bocopwrapper.SWIG_PyStaticMethod_New
+
+def _swig_repr(self):
+    try:
+        strthis = "proxy of " + self.this.__repr__()
+    except __builtin__.Exception:
+        strthis = ""
+    return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
+
+
+def _swig_setattr_nondynamic_instance_variable(set):
+    def set_instance_attr(self, name, value):
+        if name == "thisown":
+            self.this.own(value)
+        elif name == "this":
+            set(self, name, value)
+        elif hasattr(self, name) and isinstance(getattr(type(self), name), property):
+            set(self, name, value)
+        else:
+            raise AttributeError("You cannot add instance attributes to %s" % self)
+    return set_instance_attr
+
+
+def _swig_setattr_nondynamic_class_variable(set):
+    def set_class_attr(cls, name, value):
+        if hasattr(cls, name) and not isinstance(getattr(cls, name), property):
+            set(cls, name, value)
+        else:
+            raise AttributeError("You cannot add class attributes to %s" % cls)
+    return set_class_attr
+
+
+def _swig_add_metaclass(metaclass):
+    """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass"""
+    def wrapper(cls):
+        return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy())
+    return wrapper
+
+
+class _SwigNonDynamicMeta(type):
+    """Meta class to enforce nondynamic attributes (no new attributes) for a class"""
+    __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__)
+
+
+import weakref
+
+class SwigPyIterator(object):
+    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
+
+    def __init__(self, *args, **kwargs):
+        raise AttributeError("No constructor defined - class is abstract")
+    __repr__ = _swig_repr
+    __swig_destroy__ = _bocopwrapper.delete_SwigPyIterator
+    value = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_value)
+    incr = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_incr)
+    decr = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_decr)
+    distance = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_distance)
+    equal = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_equal)
+    copy = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_copy)
+    next = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_next)
+    __next__ = _swig_new_instance_method(_bocopwrapper.SwigPyIterator___next__)
+    previous = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_previous)
+    advance = _swig_new_instance_method(_bocopwrapper.SwigPyIterator_advance)
+    __eq__ = _swig_new_instance_method(_bocopwrapper.SwigPyIterator___eq__)
+    __ne__ = _swig_new_instance_method(_bocopwrapper.SwigPyIterator___ne__)
+    __iadd__ = _swig_new_instance_method(_bocopwrapper.SwigPyIterator___iadd__)
+    __isub__ = _swig_new_instance_method(_bocopwrapper.SwigPyIterator___isub__)
+    __add__ = _swig_new_instance_method(_bocopwrapper.SwigPyIterator___add__)
+    __sub__ = _swig_new_instance_method(_bocopwrapper.SwigPyIterator___sub__)
+    def __iter__(self):
+        return self
+
+# Register SwigPyIterator in _bocopwrapper:
+_bocopwrapper.SwigPyIterator_swigregister(SwigPyIterator)
+
+class VecDouble(object):
+    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
+    __repr__ = _swig_repr
+    iterator = _swig_new_instance_method(_bocopwrapper.VecDouble_iterator)
+    def __iter__(self):
+        return self.iterator()
+    __nonzero__ = _swig_new_instance_method(_bocopwrapper.VecDouble___nonzero__)
+    __bool__ = _swig_new_instance_method(_bocopwrapper.VecDouble___bool__)
+    __len__ = _swig_new_instance_method(_bocopwrapper.VecDouble___len__)
+    __getslice__ = _swig_new_instance_method(_bocopwrapper.VecDouble___getslice__)
+    __setslice__ = _swig_new_instance_method(_bocopwrapper.VecDouble___setslice__)
+    __delslice__ = _swig_new_instance_method(_bocopwrapper.VecDouble___delslice__)
+    __delitem__ = _swig_new_instance_method(_bocopwrapper.VecDouble___delitem__)
+    __getitem__ = _swig_new_instance_method(_bocopwrapper.VecDouble___getitem__)
+    __setitem__ = _swig_new_instance_method(_bocopwrapper.VecDouble___setitem__)
+    pop = _swig_new_instance_method(_bocopwrapper.VecDouble_pop)
+    append = _swig_new_instance_method(_bocopwrapper.VecDouble_append)
+    empty = _swig_new_instance_method(_bocopwrapper.VecDouble_empty)
+    size = _swig_new_instance_method(_bocopwrapper.VecDouble_size)
+    swap = _swig_new_instance_method(_bocopwrapper.VecDouble_swap)
+    begin = _swig_new_instance_method(_bocopwrapper.VecDouble_begin)
+    end = _swig_new_instance_method(_bocopwrapper.VecDouble_end)
+    rbegin = _swig_new_instance_method(_bocopwrapper.VecDouble_rbegin)
+    rend = _swig_new_instance_method(_bocopwrapper.VecDouble_rend)
+    clear = _swig_new_instance_method(_bocopwrapper.VecDouble_clear)
+    get_allocator = _swig_new_instance_method(_bocopwrapper.VecDouble_get_allocator)
+    pop_back = _swig_new_instance_method(_bocopwrapper.VecDouble_pop_back)
+    erase = _swig_new_instance_method(_bocopwrapper.VecDouble_erase)
+
+    def __init__(self, *args):
+        _bocopwrapper.VecDouble_swiginit(self, _bocopwrapper.new_VecDouble(*args))
+    push_back = _swig_new_instance_method(_bocopwrapper.VecDouble_push_back)
+    front = _swig_new_instance_method(_bocopwrapper.VecDouble_front)
+    back = _swig_new_instance_method(_bocopwrapper.VecDouble_back)
+    assign = _swig_new_instance_method(_bocopwrapper.VecDouble_assign)
+    resize = _swig_new_instance_method(_bocopwrapper.VecDouble_resize)
+    insert = _swig_new_instance_method(_bocopwrapper.VecDouble_insert)
+    reserve = _swig_new_instance_method(_bocopwrapper.VecDouble_reserve)
+    capacity = _swig_new_instance_method(_bocopwrapper.VecDouble_capacity)
+    __swig_destroy__ = _bocopwrapper.delete_VecDouble
+
+# Register VecDouble in _bocopwrapper:
+_bocopwrapper.VecDouble_swigregister(VecDouble)
+
+class VecVecdouble(object):
+    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
+    __repr__ = _swig_repr
+    iterator = _swig_new_instance_method(_bocopwrapper.VecVecdouble_iterator)
+    def __iter__(self):
+        return self.iterator()
+    __nonzero__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___nonzero__)
+    __bool__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___bool__)
+    __len__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___len__)
+    __getslice__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___getslice__)
+    __setslice__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___setslice__)
+    __delslice__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___delslice__)
+    __delitem__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___delitem__)
+    __getitem__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___getitem__)
+    __setitem__ = _swig_new_instance_method(_bocopwrapper.VecVecdouble___setitem__)
+    pop = _swig_new_instance_method(_bocopwrapper.VecVecdouble_pop)
+    append = _swig_new_instance_method(_bocopwrapper.VecVecdouble_append)
+    empty = _swig_new_instance_method(_bocopwrapper.VecVecdouble_empty)
+    size = _swig_new_instance_method(_bocopwrapper.VecVecdouble_size)
+    swap = _swig_new_instance_method(_bocopwrapper.VecVecdouble_swap)
+    begin = _swig_new_instance_method(_bocopwrapper.VecVecdouble_begin)
+    end = _swig_new_instance_method(_bocopwrapper.VecVecdouble_end)
+    rbegin = _swig_new_instance_method(_bocopwrapper.VecVecdouble_rbegin)
+    rend = _swig_new_instance_method(_bocopwrapper.VecVecdouble_rend)
+    clear = _swig_new_instance_method(_bocopwrapper.VecVecdouble_clear)
+    get_allocator = _swig_new_instance_method(_bocopwrapper.VecVecdouble_get_allocator)
+    pop_back = _swig_new_instance_method(_bocopwrapper.VecVecdouble_pop_back)
+    erase = _swig_new_instance_method(_bocopwrapper.VecVecdouble_erase)
+
+    def __init__(self, *args):
+        _bocopwrapper.VecVecdouble_swiginit(self, _bocopwrapper.new_VecVecdouble(*args))
+    push_back = _swig_new_instance_method(_bocopwrapper.VecVecdouble_push_back)
+    front = _swig_new_instance_method(_bocopwrapper.VecVecdouble_front)
+    back = _swig_new_instance_method(_bocopwrapper.VecVecdouble_back)
+    assign = _swig_new_instance_method(_bocopwrapper.VecVecdouble_assign)
+    resize = _swig_new_instance_method(_bocopwrapper.VecVecdouble_resize)
+    insert = _swig_new_instance_method(_bocopwrapper.VecVecdouble_insert)
+    reserve = _swig_new_instance_method(_bocopwrapper.VecVecdouble_reserve)
+    capacity = _swig_new_instance_method(_bocopwrapper.VecVecdouble_capacity)
+    __swig_destroy__ = _bocopwrapper.delete_VecVecdouble
+
+# Register VecVecdouble in _bocopwrapper:
+_bocopwrapper.VecVecdouble_swigregister(VecVecdouble)
+
+class CallbackF(object):
+    thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
+    __repr__ = _swig_repr
+    handle = _swig_new_instance_method(_bocopwrapper.CallbackF_handle)
+    __swig_destroy__ = _bocopwrapper.delete_CallbackF
+
+    def __init__(self):
+        if self.__class__ == CallbackF:
+            _self = None
+        else:
+            _self = self
+        _bocopwrapper.CallbackF_swiginit(self, _bocopwrapper.new_CallbackF(_self, ))
+    def __disown__(self):
+        self.this.disown()
+        _bocopwrapper.disown_CallbackF(self)
+        return weakref.proxy(self)
+
+# Register CallbackF in _bocopwrapper:
+_bocopwrapper.CallbackF_swigregister(CallbackF)
+
+solve = _bocopwrapper.solve
+
+import numpy as np
+import matplotlib.pyplot as plt
+from IPython.display import display, clear_output
+import ipywidgets as widgets
+
+class PyCallback(CallbackF):
+    def __init__(self):
+        super().__init__()
+        self.initialized = False
+        self.g_state = []         #for each iter, 2 dims np array
+        self.g_control = []
+        self.out = widgets.Output()
+        self.outText = widgets.Output(layout={'border': '1px solid black'})
+
+    def update(self, iteration):
+        if self.initialized:
+            len_state = len(self.g_state[iteration])
+            clear_output(wait=True)
+            for i in range(len_state):
+                self.axs[i].clear()
+                self.axs[i].plot(self.x_state ,self.g_state[iteration][i])
+                self.axs[i].title.set_text(f"State {i}")
+                self.axs[i].relim()
+                self.axs[i].autoscale()
+                self.fig.canvas.blit(self.axs[i].bbox)
+
+            for i in range(len(self.g_control[iteration])):
+                self.axs[len_state + i].clear()
+                self.axs[len_state + i].plot(self.x_control ,self.g_control[iteration][i], color='r')
+                self.axs[len_state + i].relim()
+                self.axs[len_state + i].set_title(f"Control {i}")
+                self.axs[len_state + i].autoscale()
+                self.fig.canvas.blit(self.axs[len_state + i].bbox)
+
+            self.outText.clear_output()
+            with self.outText:
+                objective = self.g_state[iteration][-1][-1]
+                print(f"iteration {iteration}  objective : {objective}")
+
+            display(self.fig)
+            display(self.outText)
+
+    def handle(self, state, control):
+        if not self.initialized:
+            self.initialized = True
+            plt.ioff()
+            nrows = len(state) + len(control)
+            with self.out:
+                self.fig, self.axs = plt.subplots(nrows=nrows, ncols=1)
+                self.fig.tight_layout()
+                self.fig.canvas.header_visible = False # Hide the Figure name at the top of the figure
+# If true then scrolling while the mouse is over the canvas will not move the entire notebook
+                self.fig.canvas.capture_scroll = True
+
+            self.x_state = np.arange(len(state[0]))
+            self.x_control = np.arange(len(control[0]))
+            plt.ion()
+
+        self.g_state.append(np.asarray(state))
+        self.g_control.append(np.asarray(control))
+        self.update(len(self.g_state) -1)
+
+
+
+

examples/bacteria/def/naiveproblem.def

+# Definition file
+
+# Dimensions
+dim.state 4
+dim.control 1
+dim.boundaryconditions 4
+dim.pathconstraints 0
+dim.parameters 0
+dim.constants 4
+
+# Time interval
+initial.time 0
+final.time 20
+
+# Constants
+constant.0 0.6
+constant.1 0.003
+constant.2 0.5
+constant.3 0.07
+
+# Time discretisation
+ode.discretization midpoint_implicit
+time.steps 200 
+
+# Bounds for constraints (initial conditions for our case)
+boundarycond.0.lowerbound 0.03
+boundarycond.0.upperbound 0.03
+boundarycond.1.lowerbound 0.1
+boundarycond.1.upperbound 0.1
+boundarycond.2.lowerbound 0.2
+boundarycond.2.upperbound 0.2
+boundarycond.3.lowerbound 0.003
+boundarycond.3.upperbound 0.003
+
+# Bounds for variables (dynamical bound for each variable and control)
+state.0.lowerbound 0
+state.1.lowerbound 0
+state.2.lowerbound 0
+state.3.lowerbound 0
+#Optimal value calculated for our initial conditions
+control.0.lowerbound 0.4679139313552532 
+control.0.upperbound 0.4679139313552532
+
+# Initialization for discretized problem (initial point for the optimization algorithm.. this we can touch only if the algorithm is not working, otherwise we can leave it like this)
+state.0.init 0.1
+state.1.init 0.1
+state.2.init 0.1
+state.3.init 0.1 
+control.0.init 0.4679139313552532 
+
+# Ipopt
+ipoptIntOption.print_level 5
+ipoptIntOption.max_iter 1000
+ipoptStrOption.mu_strategy adaptive
+ipoptNumOption.tol 1e-12
+
+# Misc
+ad.retape 0

examples/bacteria/def/problem1.def

+# Definition file
+
+# Dimensions
+dim.state 4
+dim.control 1
+dim.boundaryconditions 4
+dim.pathconstraints 0
+dim.parameters 0
+dim.constants 4
+
+# Time interval
+initial.time 0
+final.time 20
+
+# Constants
+constant.0 0.6
+constant.1 0.003
+constant.2 0.5
+constant.3 0.07
+
+# Time discretisation
+ode.discretization midpoint_implicit
+time.steps 200
+
+# Bounds for constraints (initial conditions for our case)
+boundarycond.0.lowerbound 0.03
+boundarycond.0.upperbound 0.03
+boundarycond.1.lowerbound 0.1
+boundarycond.1.upperbound 0.1
+boundarycond.2.lowerbound 0.2
+boundarycond.2.upperbound 0.2 
+boundarycond.3.lowerbound 0.003
+boundarycond.3.upperbound 0.003
+
+# Bounds for variables (dynamical bound for each variable and control)
+state.0.lowerbound 0
+state.1.lowerbound 0
+state.2.lowerbound 0
+state.3.lowerbound 0 
+control.0.lowerbound 0
+control.0.upperbound 1 
+
+# Initialization for discretized problem (initial point for the optimization algorithm.. this we can touch only if the algorithm is not working, otherwise we can leave it like this)
+state.0.init 0.1
+state.1.init 0.1
+state.2.init 0.1
+state.3.init 0.1 
+control.0.init 0.5
+
+# Ipopt
+ipoptIntOption.print_level 5
+ipoptIntOption.max_iter 1000
+ipoptStrOption.mu_strategy adaptive
+ipoptNumOption.tol 1e-12
+
+# Misc
+ad.retape 0

examples/bacteria/def/problem2.def

+# Definition file
+
+# Dimensions
+dim.state 4
+dim.control 1
+dim.boundaryconditions 4
+dim.pathconstraints 0
+dim.parameters 0
+dim.constants 4
+
+# Time interval
+initial.time 0
+final.time 20
+
+# Constants
+constant.0 0.7
+constant.1 0.003
+constant.2 0.5
+constant.3 0.07
+
+
+# Time discretisation
+ode.discretization midpoint_implicit
+time.steps 2000 
+
+# Bounds for constraints (initial conditions for our case)
+boundarycond.0.lowerbound 0.03
+boundarycond.0.upperbound 0.03
+boundarycond.1.lowerbound 0.17433823529411765
+boundarycond.1.upperbound 0.17433823529411765
+boundarycond.2.upperbound 0.3256617647058824
+boundarycond.2.upperbound 0.3256617647058824
+boundarycond.3.lowerbound 0.003
+boundarycond.3.upperbound 0.003
+
+# Bounds for variables (dynamical bound for each variable and control)
+state.0.lowerbound 0
+state.1.lowerbound 0
+state.2.lowerbound 0
+state.3.lowerbound 0 
+control.0.lowerbound 0
+control.0.upperbound 1 
+
+# Initialization for discretized problem (initial point for the optimization algorithm.. this we can touch only if the algorithm is not working, otherwise we can leave it like this)
+state.0.init 0.1
+state.1.init 0.1
+state.2.init 0.1
+state.3.init 0.1 
+control.0.init 0.5 
+
+# Ipopt
+ipoptIntOption.print_level 5
+ipoptIntOption.max_iter 1000
+ipoptStrOption.mu_strategy adaptive
+ipoptNumOption.tol 1e-12
+
+# Misc
+ad.retape 0

examples/bacteria/def/problem3.def

+# Definition file
+
+# Dimensions
+dim.state 4
+dim.control 1
+dim.boundaryconditions 4
+dim.pathconstraints 0
+dim.parameters 0
+dim.constants 4
+
+# Time interval
+initial.time 0
+final.time 20
+
+# Constants
+constant.0 0.7
+constant.1 0.003
+constant.2 0.3
+constant.3 0.07
+
+# Time discretisation
+ode.discretization midpoint_implicit
+time.steps 2000 
+
+# Bounds for constraints (initial conditions for our case)
+boundarycond.0.lowerbound 0.0458
+boundarycond.0.upperbound 0.0458
+boundarycond.1.lowerbound 0.249
+boundarycond.1.upperbound 0.249
+boundarycond.2.upperbound 0.25
+boundarycond.2.upperbound 0.25
+boundarycond.3.lowerbound 0.003
+boundarycond.3.upperbound 0.003
+
+# Bounds for variables (dynamical bound for each variable and control)
+state.0.lowerbound 0
+state.1.lowerbound 0
+state.2.lowerbound 0
+state.3.lowerbound 0 
+control.0.lowerbound 0
+control.0.upperbound 1 
+
+# Initialization for discretized problem (initial point for the optimization algorithm.. this we can touch only if the algorithm is not working, otherwise we can leave it like this)
+state.0.init 0.1
+state.1.init 0.1
+state.2.init 0.1
+state.3.init 0.1 
+control.0.init 0.5
+
+# Ipopt
+ipoptIntOption.print_level 5
+ipoptIntOption.max_iter 1000
+ipoptStrOption.mu_strategy adaptive
+ipoptNumOption.tol 1e-12
+
+# Misc
+ad.retape 0

examples/bacteria/ipopt.opt

+max_iter 2000
+print_level 5
+tol 1.0e-12
+output_file result.out
+mu_strategy adaptive

examples/bacteria/naiveproblem.def

+# Definition file
+
+# Dimensions
+dim.state 4
+dim.control 1
+dim.boundaryconditions 4
+dim.pathconstraints 0
+dim.parameters 0
+dim.constants 4
+
+# Time interval
+initial.time 0
+final.time 20
+
+# Constants
+constant.0 0.6
+constant.1 0.003
+constant.2 0.5
+constant.3 0.07
+
+# Time discretisation
+ode.discretization midpoint_implicit
+time.steps 200 
+
+# Bounds for constraints (initial conditions for our case)
+boundarycond.0.lowerbound 0.03
+boundarycond.0.upperbound 0.03
+boundarycond.1.lowerbound 0.1
+boundarycond.1.upperbound 0.1
+boundarycond.2.lowerbound 0.2
+boundarycond.2.upperbound 0.2
+boundarycond.3.lowerbound 0.003
+boundarycond.3.upperbound 0.003
+
+# Bounds for variables (dynamical bound for each variable and control)
+state.0.lowerbound 0
+state.1.lowerbound 0
+state.2.lowerbound 0
+state.3.lowerbound 0
+#Optimal value calculated for our initial conditions
+control.0.lowerbound 0.4679139313552532 
+control.0.upperbound 0.4679139313552532
+
+# Initialization for discretized problem (initial point for the optimization algorithm.. this we can touch only if the algorithm is not working, otherwise we can leave it like this)
+state.0.init 0.1
+state.1.init 0.1
+state.2.init 0.1
+state.3.init 0.1 
+control.0.init 0.4679139313552532 
+
+# Ipopt
+ipoptIntOption.print_level 5
+ipoptIntOption.max_iter 1000
+ipoptStrOption.mu_strategy adaptive
+ipoptNumOption.tol 1e-12
+
+# Misc
+ad.retape 0

examples/bacteria/problem.cpp

+// +++DRAFT+++ This class implements the OCP functions
+// It derives from the generic class bocop3OCPBase
+// OCP functions are defined with templates since they will be called
+// from both the NLP solver (double arguments) and AD tool (ad_double arguments)
+//#pragma once
+
+#include <OCP.h>
+// ///////////////////////////////////////////////////////////////////
+
+
+template <typename Variable>
+void OCP::finalCost(double initial_time, double final_time, const Variable *initial_state, const Variable *final_state, const Variable *parameters, const double *constants, Variable &final_cost)
+{
+  // maximize the volume of the bacteria
+  final_cost = -final_state[3];
+}
+
+
+template <typename Variable>
+void OCP::dynamics(double time, const Variable *state, const Variable *control, const Variable *parameters, const double *constants, Variable *state_dynamics)
+{
+  
+    Variable p = state[0];
+    Variable r = state[1];
+    Variable m = state[2];
+    Variable V = state[3];
+    Variable u = control[0];
+    
+    
+    
+    double Em = constants[0];
+    double K = constants[1];
+    double rmax = constants[2];
+    double rmin = constants[3];
+    
+    Variable  vR = (r-rmin)*p/(K+p);
+    
+    state_dynamics[0] = Em*m - (p+1)*vR;
+    state_dynamics[1] = (u*rmax-r)*vR;
+    state_dynamics[2] = ((1-u)*rmax-m)*vR;
+    state_dynamics[3] = vR*V;
+    
+}
+
+template <typename Variable>
+void OCP::boundaryConditions(double initial_time, double final_time, const Variable *initial_state, const Variable *final_state, const Variable *parameters, const double *constants, Variable *boundary_conditions)
+{
+    boundary_conditions[0] = initial_state[0];
+    boundary_conditions[1] = initial_state[1];
+    boundary_conditions[2] = initial_state[2];
+    boundary_conditions[3] = initial_state[3];
+}
+
+template <typename Variable>
+void OCP::pathConstraints(double time, const Variable *state, const Variable *control, const Variable *parameters, const double *constants, Variable *path_constraints)
+{}
+
+void OCP::preProcessing()
+{}
+
+// ///////////////////////////////////////////////////////////////////
+// explicit template instanciation for template functions, with double and double_ad 
+// +++ could be in an included separate file ? 
+// but needs to be done for aux functions too ? APPARENTLY NOT !
+template void OCP::finalCost<double>(double initial_time, double final_time, const double *initial_state, const double *final_state, const double *parameters, const double *constants, double &final_cost);
+template void OCP::dynamics<double>(double time, const double *state, const double *control, const double *parameters, const double *constants, double *state_dynamics);
+template void OCP::boundaryConditions<double>(double initial_time, double final_time, const double *initial_state, const double *final_state, const double *parameters, const double *constants, double *boundary_conditions);
+template void OCP::pathConstraints<double>(double time, const double *state, const double *control, const double *parameters, const double *constants, double *path_constraints);
+
+template void OCP::finalCost<double_ad>(double initial_time, double final_time, const double_ad *initial_state, const double_ad *final_state, const double_ad *parameters, const double *constants, double_ad &final_cost);
+template void OCP::dynamics<double_ad>(double time, const double_ad *state, const double_ad *control, const double_ad *parameters, const double *constants, double_ad *state_dynamics);
+template void OCP::boundaryConditions<double_ad>(double initial_time, double final_time, const double_ad *initial_state, const double_ad *final_state, const double_ad *parameters, const double *constants, double_ad *boundary_conditions);
+template void OCP::pathConstraints<double_ad>(double time, const double_ad *state, const double_ad *control, const double_ad *parameters, const double *constants, double_ad *path_constraints);

examples/bacteria/problem1.def

+# Definition file
+
+# Dimensions
+dim.state 4
+dim.control 1
+dim.boundaryconditions 4
+dim.pathconstraints 0
+dim.parameters 0
+dim.constants 4
+
+# Time interval
+initial.time 0
+final.time 20
+
+# Constants
+constant.0 0.6
+constant.1 0.003
+constant.2 0.5
+constant.3 0.07
+
+# Time discretisation
+ode.discretization midpoint_implicit
+time.steps 200
+
+# Bounds for constraints (initial conditions for our case)
+boundarycond.0.lowerbound 0.03
+boundarycond.0.upperbound 0.03
+boundarycond.1.lowerbound 0.1
+boundarycond.1.upperbound 0.1
+boundarycond.2.lowerbound 0.2
+boundarycond.2.upperbound 0.2
+boundarycond.3.lowerbound 0.003
+boundarycond.3.upperbound 0.003
+
+# Bounds for variables (dynamical bound for each variable and control)
+state.0.lowerbound 0
+state.1.lowerbound 0
+state.2.lowerbound 0
+state.3.lowerbound 0 
+control.0.lowerbound 0
+control.0.upperbound 1 
+
+# Initialization for discretized problem (initial point for the optimization algorithm.. this we can touch only if the algorithm is not working, otherwise we can leave it like this)
+state.0.init 0.1
+state.1.init 0.1
+state.2.init 0.1
+state.3.init 0.1 
+control.0.init 0.5
+
+# Ipopt
+ipoptIntOption.print_level 5
+ipoptIntOption.max_iter 1000
+ipoptStrOption.mu_strategy adaptive
+ipoptNumOption.tol 1e-12
+
+# Misc
+ad.retape 0