Updated the tests so they work again on CI!

.github/workflows/run_tests_linux.yml

@@ -59,18 +59,17 @@ jobs:
         if: matrix.shard == 1
 
       - name: Run tests with code coverage
-        run: pytest -v --color=yes --cov-report term-missing --cov=bioptim --cov-report=xml:coverage.xml tests/shard${{ matrix.shard }}
+        run: pytest -v --color=yes --cov-report term-missing --cov=bioptim tests/shard${{ matrix.shard }}
         if: matrix.os == 'ubuntu-latest'
-
+      
       - name: Archive coverage report
         id: archive
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
-          name: coverage${{ matrix.shard }}
-          path: |
-            coverage.xml
-            .coverage
-
+          name: coverage-${{ matrix.shard }}
+          path: .coverage
+          if-no-files-found: error
+          include-hidden-files: true
 
   merge-coverage:
     needs: build
@@ -84,59 +83,37 @@ jobs:
         run:
             echo "PREFIX=${{ env.PREFIX_LINUX }}" >> $GITHUB_ENV
 
-
       - name: Checkout code
         uses: actions/checkout@v3
 
-      - name: Setup environment
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          miniforge-version: latest
-          use-mamba: true
-          activate-environment: bioptim
-          environment-file: environment.yml
-
-      - name: Print mamba info
-        run: |
-          conda info
-#          mamba list
-
       - name: Install extra dependencies
         run: |
-          sudo apt-get install -y python3-pip
+          sudo apt install -y python3-pip
           pip3 install coverage
 
       - name: Download all workflow run artifacts
         id: download
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4
+        with:
+          pattern: coverage-*
 
       - name: Rename coverage files
         run: |
           for shard in {1,2,3,4,5,6}; do
-            mv coverage${shard}/coverage.xml coverage${shard}.xml
-            mv coverage${shard}/.coverage* .coverage${shard}
+            mv coverage-${shard}/.coverage .coverage${shard}
           done
 
-      - name: Show current dir content
-        run: pwd; ls -a -l
-
       - name: Merge coverage reports
-        run: coverage combine .coverage1 .coverage2 .coverage3 .coverage4
-
-      - name: Show current dir content with new .coverage file
-        run: pwd; ls -a
+        run: coverage combine .coverage1 .coverage2 .coverage3 .coverage4 .coverage5 .coverage6
 
       - name: Generate XML report
         run: |
           coverage xml
           coverage report -m
 
-      - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v2
+      - uses: codecov/codecov-action@v5
         with:
-          #token: ${{ secrets.CODECOV_TOKEN }}
-          file: ./coverage.xml
-          #files: ./coverage1.xml, ./coverage2.xml, ./coverage3.xml, ./coverage4.xml
+          files: ./coverage.xml
           flags: unittests
           fail_ci_if_error: true
-          verbose: true
+          verbose: true

bioptim/examples/__main__.py

@@ -3,13 +3,14 @@
 import keyword
 import os
 import re
-import sys
 import subprocess
+import sys
 
-import pyqtgraph as pg
-from functools import lru_cache
 from collections import OrderedDict
-from PyQt5 import QtCore, QtGui, QtWidgets
+from functools import lru_cache
+
+import pyqtgraph as pg
+from pyqtgraph.Qt import QT_LIB, QtCore, QtGui, QtWidgets
 
 try:
     import acados
@@ -137,6 +138,8 @@
     ]
 )
 
+app = pg.mkQApp()
+
 
 class Ui_Form(object):
     def setupUi(self, Form):
@@ -206,7 +209,6 @@
 
 path = os.path.abspath(os.path.dirname(__file__))
 sys.path.insert(0, path)
-app = pg.mkQApp()
 
 
 QRegularExpression = QtCore.QRegularExpression
@@ -740,7 +742,7 @@
     app = pg.mkQApp()
     loader = ExampleLoader()
     loader.ui.exampleTree.setCurrentIndex(loader.ui.exampleTree.model().index(0, 0))
-    app.exec()
+    pg.exec()
 
 
 if __name__ == "__main__":

bioptim/interfaces/acados_interface.py

@@ -163,7 +163,7 @@ def __acados_export_model(self, ocp):
         x_sym = ocp.nlp[0].states.scaled.cx_start
         u = ocp.nlp[0].controls.cx_start
         u_sym = ocp.nlp[0].controls.scaled.cx_start
-        p = ocp.nlp[0].parameters.cx
+        p = ocp.nlp[0].parameters.scaled.cx
         p_sym = ocp.nlp[0].parameters.scaled.cx
         a = ocp.nlp[0].algebraic_states.cx_start
         a_sym = ocp.nlp[0].algebraic_states.scaled.cx_start
@@ -294,7 +294,7 @@ def __set_constraints(self, ocp):
             dt = nlp.dt
             x = nlp.states.cx_start
             u = nlp.controls.cx_start
-            p = nlp.parameters.cx
+            p = nlp.parameters.scaled.cx
             a = nlp.algebraic_states.cx_start
             d = nlp.numerical_timeseries.cx
 

bioptim/interfaces/interface_utils.py

@@ -79,19 +79,19 @@
         interface.online_optim(interface.ocp, interface.opts.show_options)
 
     # Thread here on (f and all_g) instead of individually for each function?
-    interface.sqp_nlp = {"x": v, "f": sum1(all_objectives), "g": all_g}
+    interface.nlp = {"x": v, "f": sum1(all_objectives), "g": all_g}
     interface.c_compile = interface.opts.c_compile
     options = interface.opts.as_dict(interface)
 
     if interface.c_compile:
         if not interface.ocp_solver or interface.ocp.program_changed:
-            nlpsol("nlpsol", interface.solver_name.lower(), interface.sqp_nlp, options).generate_dependencies("nlp.c")
+            nlpsol("nlpsol", interface.solver_name.lower(), interface.nlp, options).generate_dependencies("nlp.c")
             interface.ocp_solver = nlpsol("nlpsol", interface.solver_name, Importer("nlp.c", "shell"), options)
             interface.ocp.program_changed = False
     else:
-        interface.ocp_solver = nlpsol("solver", interface.solver_name.lower(), interface.sqp_nlp, options)
+        interface.ocp_solver = nlpsol("solver", interface.solver_name.lower(), interface.nlp, options)
 
-    interface.sqp_limits = {
+    interface.limits = {
         "lbx": v_bounds[0],
         "ubx": v_bounds[1],
         "lbg": all_g_bounds.min,
@@ -100,13 +100,13 @@
     }
 
     if interface.lam_g is not None:
-        interface.sqp_limits["lam_g0"] = interface.lam_g
+        interface.limits["lam_g0"] = interface.lam_g
     if interface.lam_x is not None:
-        interface.sqp_limits["lam_x0"] = interface.lam_x
+        interface.limits["lam_x0"] = interface.lam_x
 
     # Solve the problem
     tic = perf_counter()
-    interface.out = {"sol": interface.ocp_solver.call(interface.sqp_limits)}
+    interface.out = {"sol": interface.ocp_solver.call(interface.limits)}
     interface.out["sol"]["solver_time_to_optimize"] = interface.ocp_solver.stats()["t_wall_total"]
     interface.out["sol"]["real_time_to_optimize"] = perf_counter() - tic
     interface.out["sol"]["iter"] = interface.ocp_solver.stats()["iter_count"]
@@ -172,7 +172,7 @@
         try:
             penalty = penalty.expand()
         except RuntimeError:
-            # This happens mostly when there is a Newton decent in the penalty
+            # This happens mostly when, for instance, there is a Newton decent in the penalty
             pass
     return penalty(vertcat(*dt, v[len(dt) :]))
 

bioptim/interfaces/ipopt_interface.py

@@ -10,11 +10,11 @@
 )
 from .solver_interface import SolverInterface
 from ..interfaces import Solver
-from bioptim.optimization.solution.solution import Solution
-from ..optimization.non_linear_program import NonLinearProgram
 from ..misc.enums import (
     SolverType,
 )
+from ..optimization.non_linear_program import NonLinearProgram
+from ..optimization.solution.solution import Solution
 
 
 class IpoptInterface(SolverInterface):
@@ -27,9 +27,9 @@ class IpoptInterface(SolverInterface):
         Options irrelevant of a specific ocp
     opts: IPOPT
         Options of the current ocp
-    ipopt_nlp: dict
+    nlp: dict
         The declaration of the variables Ipopt-friendly
-    ipopt_limits: dict
+    limits: dict
         The declaration of the bound Ipopt-friendly
     lam_g: np.ndarray
         The lagrange multiplier of the constraints to initialize the solver
@@ -64,8 +64,8 @@ def __init__(self, ocp):
         self.opts = Solver.IPOPT()
         self.solver_name = SolverType.IPOPT.value
 
-        self.ipopt_nlp = {}
-        self.ipopt_limits = {}
+        self.nlp = {}
+        self.limits = {}
         self.ocp_solver = None
         self.c_compile = False
 

bioptim/interfaces/sqp_interface.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+from bioptim.optimization.solution.solution import Solution
 from .interface_utils import (
     generic_online_optim,
     generic_solve,
@@ -9,11 +10,10 @@
 )
 from .solver_interface import SolverInterface
 from ..interfaces import Solver
-from bioptim.optimization.solution.solution import Solution
-from ..optimization.non_linear_program import NonLinearProgram
 from ..misc.enums import (
     SolverType,
 )
+from ..optimization.non_linear_program import NonLinearProgram
 
 
 class SQPInterface(SolverInterface):
@@ -26,9 +26,9 @@ class SQPInterface(SolverInterface):
         Options irrelevant of a specific ocp
     opts: SQP
         Options of the current ocp
-    sqp_nlp: dict
+    nlp: dict
         The declaration of the variables SQP-friendly
-    sqp_limits: dict
+    limits: dict
         The declaration of the bound SQP-friendly
     lam_g: np.ndarray
         The lagrange multiplier of the constraints to initialize the solver
@@ -63,8 +63,8 @@ def __init__(self, ocp):
         self.opts = Solver.SQP_METHOD()
         self.solver_name = SolverType.SQP.value
 
-        self.sqp_nlp = {}
-        self.sqp_limits = {}
+        self.nlp = {}
+        self.limits = {}
         self.ocp_solver = None
         self.c_compile = False
 

bioptim/models/biorbd/biorbd_model.py

@@ -1,3 +1,5 @@
+import os
+from pathlib import Path
 from typing import Callable
 
 import biorbd_casadi as biorbd
@@ -89,7 +91,7 @@ def name(self) -> str:
 
     @property
     def path(self) -> str:
-        return self.model.path().relativePath().to_string()
+        return self.model.path().absolutePath().to_string()
 
     def copy(self):
         return BiorbdModel(self.path)

bioptim/models/biorbd/multi_biorbd_model.py

@@ -90,11 +90,12 @@ def deep_copy(self, *args):
         raise NotImplementedError("Deep copy is not implemented yet for MultiBiorbdModel class")
 
     @property
-    def path(self) -> (list[str], list[str]):
+    def path(self) -> tuple[list[str], list[str]]:
         return [model.path for model in self.models], [model.path for model in self.extra_models]
 
     def copy(self):
-        return MultiBiorbdModel(tuple(self.path[0]), tuple(self.path[1]))
+        all_paths = self.path
+        return MultiBiorbdModel(tuple(all_paths[0]), tuple(all_paths[1]))
 
     def serialize(self) -> tuple[Callable, dict]:
         return MultiBiorbdModel, dict(bio_model=tuple(self.path[0]), extra_bio_models=tuple(self.path[1]))

bioptim/models/protocols/stochastic_biomodel.py

@@ -38,6 +38,10 @@ def sensory_reference(self, time, states, controls, parameters, algebraic_states
     def reshape_to_matrix(var, shape):
         """
         Restore the matrix form of the variables
+
+        See Also
+        --------
+        reshape_to_vector
         """
 
         if var.shape[0] != shape[0] * shape[1]:
@@ -83,6 +87,10 @@ def reshape_to_cholesky_matrix(var, shape):
     def reshape_to_vector(matrix):
         """
         Restore the vector form of the matrix
+
+        See Also
+        --------
+        reshape_to_matrix
         """
         shape_0, shape_1 = matrix.shape[0], matrix.shape[1]
         if isinstance(matrix, np.ndarray):

resources/plotting_server.py

@@ -1,7 +1,7 @@
 """
-This file is an example of how to run a bioptim Online plotting server. Apart on Macos, this is usually not the way to run a 
-bioptim server as it is easier to run it as an automatic multiprocess. This is achieved by setting 
-`Solver.IPOPT(online_optim=OnlineOptim.MULTIPROCESS_SERVER)`. 
+This file is an example of how to run a bioptim Online plotting server. Apart on Macos, this is usually not the way
+to run a bioptim server as it is easier to run it as an automatic multiprocess. This is achieved by setting
+`Solver.IPOPT(online_optim=OnlineOptim.MULTIPROCESS_SERVER)`.
 If set to OnlineOptim.SERVER, then the plotting server is mandatory.
 
 Since the server runs usings sockets, it is possible to run the server on a different machine than the one running the