diff --git a/api/poetry.lock b/api/poetry.lock
index e3e9a17..f3d5d9d 100644
--- a/api/poetry.lock
+++ b/api/poetry.lock
@@ -25,92 +25,92 @@ files = [
 
 [[package]]
 name = "aiohttp"
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+version = "3.11.16"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -239,13 +239,13 @@ type-checking = ["mypy (>=1.9,<2.0)", "types-docutils (>=0.20,<0.21)", "typing-e
 
 [[package]]
 name = "azure-core"
-version = "1.32.0"
+version = "1.33.0"
 description = "Microsoft Azure Core Library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -255,6 +255,7 @@ typing-extensions = ">=4.6.0"
 
 [package.extras]
 aio = ["aiohttp (>=3.0)"]
+tracing = ["opentelemetry-api (>=1.26,<2.0)"]
 
 [[package]]
 name = "azure-cosmos"
@@ -696,74 +697,74 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"
 
 [[package]]
 name = "coverage"
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 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.9"
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 [[package]]
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 [[package]]
@@ -2809,114 +2810,125 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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+    {file = "rpds_py-0.24.0.tar.gz", hash = "sha256:772cc1b2cd963e7e17e6cc55fe0371fb9c704d63e44cacec7b9b7f523b78919e"},
 ]
 
 [[package]]
@@ -3290,15 +3302,29 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.12.2"
+version = "4.13.1"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
-    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+    {file = "typing_extensions-4.13.1-py3-none-any.whl", hash = "sha256:4b6cf02909eb5495cfbc3f6e8fd49217e6cc7944e145cdda8caa3734777f9e69"},
+    {file = "typing_extensions-4.13.1.tar.gz", hash = "sha256:98795af00fb9640edec5b8e31fc647597b4691f099ad75f469a2616be1a76dff"},
 ]
 
+[[package]]
+name = "typing-inspection"
+version = "0.4.0"
+description = "Runtime typing introspection tools"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "typing_inspection-0.4.0-py3-none-any.whl", hash = "sha256:50e72559fcd2a6367a19f7a7e610e6afcb9fac940c650290eed893d61386832f"},
+    {file = "typing_inspection-0.4.0.tar.gz", hash = "sha256:9765c87de36671694a67904bf2c96e395be9c6439bb6c87b5142569dcdd65122"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.12.0"
+
 [[package]]
 name = "tzdata"
 version = "2025.2"
@@ -3405,13 +3431,13 @@ test = ["aiohttp (>=3.10.5)", "flake8 (>=5.0,<6.0)", "mypy (>=0.800)", "psutil",
 
 [[package]]
 name = "virtualenv"
-version = "20.29.3"
+version = "20.30.0"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "virtualenv-20.29.3-py3-none-any.whl", hash = "sha256:3e3d00f5807e83b234dfb6122bf37cfadf4be216c53a49ac059d02414f819170"},
-    {file = "virtualenv-20.29.3.tar.gz", hash = "sha256:95e39403fcf3940ac45bc717597dba16110b74506131845d9b687d5e73d947ac"},
+    {file = "virtualenv-20.30.0-py3-none-any.whl", hash = "sha256:e34302959180fca3af42d1800df014b35019490b119eba981af27f2fa486e5d6"},
+    {file = "virtualenv-20.30.0.tar.gz", hash = "sha256:800863162bcaa5450a6e4d721049730e7f2dae07720e0902b0e4040bd6f9ada8"},
 ]
 
 [package.dependencies]
diff --git a/api/pyproject.toml b/api/pyproject.toml
index 6b2263f..41215d8 100644
--- a/api/pyproject.toml
+++ b/api/pyproject.toml
@@ -96,7 +96,7 @@ ignore = [
 "./tests/v0/mockers/*.py" = ["C901"]  # Allow high code complexity in mockers
 "./src/v0/database/client.py" = ["B024"]  # Allow Abstract class without abstractmethod
 "./src/v0/services/base.py" = ["B024"]  # Allow Abstract class without abstractmethod
-"./src/services/classes/directed_graph.py" = ["B024"]  # Allow Abstract class without abstractmethod
+"./src/v0/services/classes/abstract_directed_graph.py" = ["B024"]  # Allow Abstract class without abstractmethod
 
 [tool.codespell]
 skip = "*.lock"
diff --git a/api/src/v0/models/structure.py b/api/src/v0/models/structure.py
index 19cba77..543996f 100644
--- a/api/src/v0/models/structure.py
+++ b/api/src/v0/models/structure.py
@@ -124,7 +124,7 @@ class DecisionTreeResponse(DOTModel):
                         "name": "Joe can test the car",
                         "shortname": "Test",
                         "uuid": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-                        "alternatives": ['"Test"', '" no Test"'],
+                        "alternatives": ['"Test"', '"no Test"'],
                     },
                     "children": [
                         {
diff --git a/api/src/v0/services/structure_utils/__init__.py b/api/src/v0/services/analysis/__init__.py
similarity index 100%
rename from api/src/v0/services/structure_utils/__init__.py
rename to api/src/v0/services/analysis/__init__.py
diff --git a/api/src/v0/services/analysis/id_to_dt.py b/api/src/v0/services/analysis/id_to_dt.py
new file mode 100644
index 0000000..3b76099
--- /dev/null
+++ b/api/src/v0/services/analysis/id_to_dt.py
@@ -0,0 +1,168 @@
+"""
+Conversion of influence diagram to decision tree.
+The decision tree format is used for display in the frontend.
+"""
+
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.decision_tree import DecisionTree
+from src.v0.services.classes.influence_diagram import InfluenceDiagram
+from src.v0.services.classes.node import NodeABC, UtilityNode
+from src.v0.services.errors import PartialOrderOutputModeError
+
+
+class InfluenceDiagramToDecisionTree:
+    def decision_elimination_order(
+        self, influence_diagram: InfluenceDiagram
+    ) -> list[NodeABC]:
+        """Decision Elimination Order algorithm
+
+        Args:
+            influence_diagram (InfluenceDiagram): the influence diagram object
+            to convert
+
+        Returns:
+            list[NodeABC] : the decision elimination order graph associated to
+                            the influence diagram. Nodes in the list are copies of the
+                            nodes of the influence diagram ones.
+
+        TODO: add description of what is the algorithm about
+        """
+        cid_copy = influence_diagram.copy()
+        decisions = []
+        decisions_count = cid_copy.decision_count
+        while decisions_count > 0:
+            nodes = list(cid_copy.graph.nodes())
+            for node in nodes:
+                if not cid_copy.has_children(node):
+                    if node.is_decision_node:
+                        decisions.append(node)
+                        decisions_count -= 1
+                    cid_copy.graph.remove_node(node)
+        return decisions
+
+    def calculate_partial_order(
+        self, influence_diagram: InfluenceDiagram, *, mode="view"
+    ) -> list[NodeABC]:
+        """Partial order algorithm
+
+
+        Args:
+            influence_diagram (InfluenceDiagram): the influence diagram object
+            to convert
+            mode (str): ["view"(default)|"copy"]
+                returns a view or a copy of the nodes
+
+        Returns
+            List[NodeABC]: list of nodes (copies or vioews) sorted in decision order
+
+        TODO: add description of what the algorithm is about
+        TODO: handle utility nodes
+        """
+        if mode not in ["view", "copy"]:
+            raise PartialOrderOutputModeError(mode)
+
+        # get all chance nodes
+        uncertainty_node = influence_diagram.get_uncertainty_nodes()
+        elimination_order = self.decision_elimination_order(influence_diagram)
+        # TODO: Add utility nodes
+        partial_order = []
+
+        while elimination_order:
+            decision = elimination_order.pop()
+            parent_decision_nodes = []
+            for parent in influence_diagram.get_parents(decision):
+                if not parent.is_decision_node:
+                    if parent in uncertainty_node:
+                        parent_decision_nodes.append(parent)
+                        uncertainty_node.remove(parent)
+
+            if len(parent_decision_nodes) > 0:
+                partial_order += parent_decision_nodes
+            partial_order.append(decision)
+
+        partial_order += uncertainty_node
+
+        if mode == "copy":
+            partial_order = [node.copy() for node in partial_order]
+
+        return partial_order
+
+    def _output_branches_from_node(
+        self, node: NodeABC, node_in_partial_order: NodeABC, flip=True
+    ) -> list[tuple[Arc, NodeABC]]:
+        """Make a list of output branches from a node
+
+            This method actually returns the states of the nodes.
+
+        Args
+            node (NodeABC): node to find the output branch from
+            node_in_partial_order (NodeABC): associated node in the partial order - to
+                                                keep reference too
+            flip (bool): if True (default), flip the list of branches so a generated
+                            decision tree is in the same order as the entered states.
+                            If False, the tree will be flipped horizontally.
+
+        Returns
+            List: the list of tuples (Arc, Node in partial order)
+                The edges have the input node as start endpoint and name given by the
+                state
+        """
+        if node.is_utility_node:
+            tree_stack = [
+                Arc(tail=node, head=None, label=utility) for utility in node.utility
+            ]
+        if node.is_decision_node:
+            tree_stack = [
+                Arc(tail=node, head=None, label=alternative)
+                for alternative in node.alternatives
+            ]
+        if node.is_uncertainty_node:
+            # This needs to be re-written according to the way we deal with probabilities
+            tree_stack = [
+                Arc(tail=node, head=None, label=outcome) for outcome in node.outcomes
+            ]
+        if flip:
+            tree_stack.reverse()
+
+        return zip(tree_stack, [node_in_partial_order] * len(tree_stack), strict=False)
+
+    def conversion(self, influence_diagram: InfluenceDiagram) -> DecisionTree:
+        """Convert the influence diagram into a DecisionTree object
+
+        Returns:
+            DecisionTree: The symmetric decision tree equivalent to the influence diagram
+
+        TODO: Update ID2DT according to way we deal with probabilities
+        """
+        partial_order = self.calculate_partial_order(influence_diagram)
+        root_node = partial_order[0]
+        # decision_tree = DecisionTree.initialize_with_root(root_node)
+        decision_tree = DecisionTree(root=root_node)
+        # tree_stack contains views of the partial order nodes
+        # decision_tree contains copy of the nodes (as they appear several times)
+        tree_stack = [(root_node, root_node)]
+
+        while tree_stack:
+            element = tree_stack.pop()
+
+            if isinstance(element[0], NodeABC):
+                tree_stack += self._output_branches_from_node(*element)
+
+            else:  # element is a branch
+                tail_index = partial_order.index(element[1])
+
+                if tail_index < len(partial_order) - 1:
+                    head = partial_order[tail_index + 1].copy()
+                    tree_stack.append((head, partial_order[tail_index + 1]))
+                else:
+                    # head = UtilityNode(
+                    #     name=element[0].name, tag=element[0].name.lower()
+                    #                           )
+                    head = UtilityNode(shortname="ut", description="Utility")
+
+                element[0].head = head
+                decision_tree.add_arc(
+                    element[0]
+                )  # node is added when the branch is added
+
+        return decision_tree
diff --git a/api/src/v0/services/analysis/id_to_pyagrum.py b/api/src/v0/services/analysis/id_to_pyagrum.py
new file mode 100644
index 0000000..7344971
--- /dev/null
+++ b/api/src/v0/services/analysis/id_to_pyagrum.py
@@ -0,0 +1,137 @@
+"""
+Conversion of influence diagram to pyAgrum format.
+
+.. seealso:
+    pyAgrum documentation https://pyagrum.readthedocs.io/
+"""
+
+from itertools import product
+
+import pyAgrum as gum
+
+from src.v0.services.classes.discrete_conditional_probability import (
+    DiscreteConditionalProbability,
+)
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+from src.v0.services.classes.influence_diagram import InfluenceDiagram
+from src.v0.services.classes.node import (
+    DecisionNode,
+    UncertaintyNode,
+    UtilityNode,
+)
+from src.v0.services.errors import (
+    ArcPyAgrumFormatError,
+    InfluenceDiagramNotAcyclicError,
+    ProbabilityPyAgrumFormatError,
+)
+
+
+class InfluenceDiagramToPyAgrum:
+    def probabilities_conversion(self, probability):
+        if isinstance(probability, DiscreteConditionalProbability):
+            return self.conditional_probabilities_conversion(probability)
+        if isinstance(probability, DiscreteUnconditionalProbability):
+            return self.unconditional_probabilities_conversion(probability)
+
+    def unconditional_probabilities_conversion(self, probability):
+        variables = probability.variables
+        if len(variables) != 1:
+            raise ProbabilityPyAgrumFormatError(variables)
+        return [
+            (
+                {},
+                [
+                    probability._cpt.sel(**{variables[0]: state})
+                    for state in probability.outcomes
+                ],
+            )
+        ]
+
+    def conditional_probabilities_conversion(self, probability):
+        # agrum = list()
+        coords = probability._cpt.coords
+        variables = {
+            key: coords[key].data.tolist() for key in coords if key is not coords.dims[0]
+        }
+        agrum_dict = {k: list(range(len(v))) for k, v in variables.items()}
+        agrum_dict = [
+            dict(zip(agrum_dict.keys(), values, strict=False))
+            for values in product(*agrum_dict.values())
+        ]
+        agrum_prob = [
+            probability.get_distribution(
+                **{key: coords[key][val] for key, val in item.items()}
+            ).data.tolist()
+            for item in agrum_dict
+        ]
+        agrum = list(zip(agrum_dict, agrum_prob, strict=False))
+        return agrum
+
+    def nodes_conversion(self, nodes, gum_id):
+        # create an uuid for gum as 8 bytes integer and keep relation to uuid
+        uuid_dot_to_gum = {}
+        uuid_gum_to_dot = {}
+        node_uuid = {}
+
+        for node in nodes:
+            labelized_variables = [node.shortname, node.description]
+            if isinstance(node, UncertaintyNode):
+                try:
+                    labelized_variables.append(node.outcomes)
+                    variable_id = gum_id.addChanceNode(
+                        gum.LabelizedVariable(*labelized_variables)
+                    )
+                except Exception as e:
+                    raise ProbabilityPyAgrumFormatError(e)
+            elif isinstance(node, DecisionNode):
+                # This works even when alternatives are [""] or None
+                labelized_variables.append(node.alternatives)
+                variable_id = gum_id.addDecisionNode(
+                    gum.LabelizedVariable(*labelized_variables)
+                )
+            elif isinstance(node, UtilityNode):
+                # Utility not yet implemented
+                labelized_variables.append(1)
+                variable_id = gum_id.addUtilityNode(
+                    gum.LabelizedVariable(*labelized_variables)
+                )
+
+            uuid_dot_to_gum[node.uuid] = variable_id
+            uuid_gum_to_dot[variable_id] = node.uuid
+            node_uuid[variable_id] = node
+
+        return uuid_dot_to_gum, uuid_gum_to_dot, node_uuid
+
+    def arcs_conversion(self, arcs, gum_id, uuid_dot_to_gum):
+        for arc in arcs:
+            tail = uuid_dot_to_gum[arc.tail.uuid]
+            head = uuid_dot_to_gum[arc.head.uuid]
+            try:
+                gum_id.addArc(tail, head)
+            except Exception as e:
+                raise ArcPyAgrumFormatError(e)
+        return None
+
+    def conversion(self, influence_diagram: InfluenceDiagram):
+        if not influence_diagram.is_acyclic:
+            raise InfluenceDiagramNotAcyclicError(False)
+
+        gum_id = gum.InfluenceDiagram()
+        variable_id = []
+
+        uuid_dot_to_gum, uuid_gum_to_dot, node_uuid = self.nodes_conversion(
+            influence_diagram.nodes, gum_id
+        )
+        # Add head and tail in gum_id
+        self.arcs_conversion(influence_diagram.arcs, gum_id, uuid_dot_to_gum)
+
+        for variable_id in uuid_gum_to_dot:
+            if isinstance(node_uuid[variable_id], UncertaintyNode):
+                for agrum_prob in self.probabilities_conversion(
+                    node_uuid[variable_id].probability
+                ):
+                    gum_id.cpt(variable_id)[agrum_prob[0]] = agrum_prob[1]
+
+        return gum_id
diff --git a/api/src/v0/services/structure_utils/decision_diagrams/__init__.py b/api/src/v0/services/class_validations/__init__.py
similarity index 100%
rename from api/src/v0/services/structure_utils/decision_diagrams/__init__.py
rename to api/src/v0/services/class_validations/__init__.py
diff --git a/api/src/v0/services/class_validations/validate_and_set_arc.py b/api/src/v0/services/class_validations/validate_and_set_arc.py
new file mode 100644
index 0000000..0859541
--- /dev/null
+++ b/api/src/v0/services/class_validations/validate_and_set_arc.py
@@ -0,0 +1,37 @@
+from typing import Any
+from uuid import UUID, uuid4
+
+from src.v0.services.classes.node import NodeABC
+from src.v0.services.errors import (
+    ArcLabelValidationError,
+    EndPointValidationError,
+    UUIDValidationError,
+)
+
+
+def label(arg: Any) -> str:
+    if not (isinstance(arg, str) or arg is None):
+        raise ArcLabelValidationError(arg)
+    return arg
+
+
+def edge(arg: Any) -> NodeABC:
+    if not (isinstance(arg, NodeABC) or arg is None):
+        raise EndPointValidationError(arg)
+    return arg
+
+
+def uuid(arg: Any) -> str:
+    if not (isinstance(arg, str | UUID) or arg is None):
+        raise UUIDValidationError(arg)
+    if arg is None:
+        return str(uuid4())
+    if isinstance(arg, UUID) and arg.version == 4:
+        return str(arg)
+    try:  # if arg is str
+        uuid_obj = UUID(arg)
+        if uuid_obj.version == 4:
+            return arg
+    except Exception as e:
+        raise UUIDValidationError(e)
+    raise UUIDValidationError(f"version {uuid_obj.version}")
diff --git a/api/src/v0/services/class_validations/validate_and_set_graph_model.py b/api/src/v0/services/class_validations/validate_and_set_graph_model.py
new file mode 100644
index 0000000..18bc707
--- /dev/null
+++ b/api/src/v0/services/class_validations/validate_and_set_graph_model.py
@@ -0,0 +1,36 @@
+from typing import Any
+
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.node import (
+    DecisionNode,
+    NodeABC,
+    UncertaintyNode,
+    UtilityNode,
+)
+from src.v0.services.errors import (
+    ArcTypeValidationError,
+    DTNodeTypeValidationError,
+    IDNodeTypeValidationError,
+)
+
+
+def id_node(arg: Any) -> DecisionNode | UncertaintyNode | UtilityNode:
+    if not isinstance(arg, DecisionNode | UncertaintyNode | UtilityNode):
+        raise IDNodeTypeValidationError(arg)
+    return arg
+
+
+def dt_node(arg: Any) -> DecisionNode | UncertaintyNode | UtilityNode:
+    if not isinstance(arg, DecisionNode | UncertaintyNode | UtilityNode):
+        raise DTNodeTypeValidationError(arg)
+    return arg
+
+
+def arc_to_graph(arg: Any) -> tuple[tuple[NodeABC, NodeABC], dict]:
+    if not isinstance(arg, Arc):
+        raise ArcTypeValidationError(arg)
+    return (arg.tail, arg.head), {
+        "dtype": arg.dtype,
+        "label": arg.label,
+        "uuid": arg.uuid,
+    }
diff --git a/api/src/v0/services/class_validations/validate_and_set_node.py b/api/src/v0/services/class_validations/validate_and_set_node.py
new file mode 100644
index 0000000..29706bf
--- /dev/null
+++ b/api/src/v0/services/class_validations/validate_and_set_node.py
@@ -0,0 +1,77 @@
+from collections.abc import Sequence
+from typing import Any
+from uuid import UUID, uuid4
+
+from src.v0.services.classes.abstract_probability import ProbabilityABC
+from src.v0.services.classes.discrete_conditional_probability import (
+    DiscreteConditionalProbability,
+)
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+from src.v0.services.errors import (
+    AlternativeValidationError,
+    DescriptionValidationError,
+    NameValidationError,
+    ProbabilityValidationError,
+    ShortnameValidationError,
+    UUIDValidationError,
+)
+
+
+def description(arg: Any) -> str:
+    if not isinstance(arg, str):
+        raise DescriptionValidationError(arg)
+    return arg
+
+
+def name(arg: Any) -> str:
+    if not isinstance(arg, str):
+        raise NameValidationError(arg)
+    return arg
+
+
+def shortname(arg: Any) -> str:
+    if not isinstance(arg, str):
+        raise ShortnameValidationError(arg)
+    return arg
+
+
+def uuid(arg: Any) -> str:
+    if not (isinstance(arg, str | UUID) or arg is None):
+        raise UUIDValidationError(arg)
+    if arg is None:
+        return str(uuid4())
+    if isinstance(arg, UUID) and arg.version == 4:
+        return str(arg)
+    try:  # if arg is str
+        uuid_obj = UUID(arg)
+        if uuid_obj.version == 4:
+            return arg
+    except Exception as e:
+        raise UUIDValidationError(e)
+    raise UUIDValidationError(f"version {uuid_obj.version}")
+
+
+def alternatives(arg: Any) -> Sequence | None:
+    if isinstance(arg, str):
+        raise AlternativeValidationError(arg)
+    if not (isinstance(arg, Sequence) or arg is None):
+        raise AlternativeValidationError(arg)
+    if arg is None:
+        return arg
+    if not all(isinstance(item, str) for item in arg):
+        raise AlternativeValidationError(arg)
+    if len(arg) != len(set(arg)):
+        raise AlternativeValidationError(arg)
+    return arg
+
+
+def probability(arg: Any) -> ProbabilityABC:
+    if not (
+        isinstance(arg, DiscreteConditionalProbability)
+        or isinstance(arg, DiscreteUnconditionalProbability)
+        or arg is None
+    ):
+        raise ProbabilityValidationError(arg)
+    return arg
diff --git a/api/src/v0/services/class_validations/validate_and_set_probability.py b/api/src/v0/services/class_validations/validate_and_set_probability.py
new file mode 100644
index 0000000..052ec6e
--- /dev/null
+++ b/api/src/v0/services/class_validations/validate_and_set_probability.py
@@ -0,0 +1,86 @@
+import re
+from typing import Any
+
+import numpy as np
+
+from src.v0.services.errors import (
+    DiscreteConditionalProbabilityFunctionValidationError,
+    DiscreteProbabilityVariableValidationError,
+    DiscreteUnconditionalProbabilityFunctionValidationError,
+)
+
+
+def discrete_variables(arg: Any) -> dict:
+    if not isinstance(arg, dict):
+        raise DiscreteProbabilityVariableValidationError(arg)
+    if not all(isinstance(v, list | np.ndarray) for v in arg.values()):
+        raise DiscreteProbabilityVariableValidationError(arg)
+    try:
+        if any(max(np.asarray(v).shape) != np.asarray(v).size for v in arg.values()):
+            pass
+    except Exception as e:
+        raise DiscreteProbabilityVariableValidationError(e)
+    if any(max(np.asarray(v).shape) != np.asarray(v).size for v in arg.values()):
+        raise DiscreteProbabilityVariableValidationError(arg)
+    # remove white spaces as the string is used as variable name by xarray
+    return {re.sub(r"\s+", "_", k): v for k, v in arg.items()}
+
+
+def discrete_conditional_probability_function(
+    arg: Any, conditioned_variables: dict, conditioning_variables: dict
+) -> dict:
+    def isnormalized(arr, cond_variables, threshold=1e-6):
+        axis = tuple(a for a in range(len(cond_variables.keys())))
+        return np.all(np.linalg.norm(arr.sum(axis=axis) - 1.0) < threshold)
+
+    if not isinstance(arg, np.ndarray):
+        raise DiscreteConditionalProbabilityFunctionValidationError(arg)
+
+    arg = arg.astype(float)  # in case of None
+
+    variables = {**conditioned_variables, **conditioning_variables}
+    has_consistent_shape = tuple([len(v) for v in variables.values()]) == arg.shape
+    is_all_nans = np.isnan(arg.astype(float)).all()
+    is_between_zero_and_one = np.all(np.logical_and(arg >= 0, arg <= 1))
+    is_normalized = isnormalized(arg, conditioned_variables)
+
+    if not has_consistent_shape:
+        raise DiscreteConditionalProbabilityFunctionValidationError(arg)
+
+    if not (is_all_nans ^ is_between_zero_and_one):
+        raise DiscreteConditionalProbabilityFunctionValidationError(arg)
+
+    if not (is_all_nans ^ is_normalized):
+        raise DiscreteConditionalProbabilityFunctionValidationError(arg)
+
+    return arg
+
+
+def discrete_unconditional_probability_function(arg: Any, variables: dict) -> dict:
+    def isnormalized(arr, threshold=1e-6):
+        return np.all(np.linalg.norm(arr.sum() - 1.0) < threshold)
+
+    if not isinstance(arg, np.ndarray):
+        raise DiscreteUnconditionalProbabilityFunctionValidationError(arg)
+
+    arg = arg.astype(float)  # in case of None
+
+    # if 1D case
+    # if np.prod(arg.shape) == np.prod(arg.flatten().shape):
+    if 1 in arg.shape:
+        arg = arg.flatten()
+    has_consistent_shape = tuple([len(v) for v in variables.values()]) == arg.shape
+    is_all_nans = np.isnan(arg).all()
+    is_between_zero_and_one = np.all(np.logical_and(arg >= 0, arg <= 1))
+    is_normalized = isnormalized(arg)
+
+    if not has_consistent_shape:
+        raise DiscreteUnconditionalProbabilityFunctionValidationError(arg)
+
+    if not (is_all_nans ^ is_between_zero_and_one):
+        raise DiscreteUnconditionalProbabilityFunctionValidationError(arg)
+
+    if not (is_all_nans ^ is_normalized):
+        raise DiscreteUnconditionalProbabilityFunctionValidationError(arg)
+
+    return arg
diff --git a/api/src/v0/services/structure_utils/probability/__init__.py b/api/src/v0/services/classes/__init__.py
similarity index 100%
rename from api/src/v0/services/structure_utils/probability/__init__.py
rename to api/src/v0/services/classes/__init__.py
diff --git a/api/src/v0/services/classes/abstract_directed_graph.py b/api/src/v0/services/classes/abstract_directed_graph.py
new file mode 100644
index 0000000..55fe72f
--- /dev/null
+++ b/api/src/v0/services/classes/abstract_directed_graph.py
@@ -0,0 +1,229 @@
+"""Module defining the ProbabilisticGraphModel Abstract class"""
+
+from __future__ import annotations
+
+from abc import ABC
+from collections.abc import Sequence
+from typing import TYPE_CHECKING
+
+import networkx as nx
+
+from src.v0.services.class_validations import validate_and_set_graph_model
+
+from ..errors import NodeInGraphError
+from .arc import Arc
+
+if TYPE_CHECKING:  # pragma: no cover
+    from .node import NodeABC
+
+
+class DirectedGraphType:
+    @staticmethod
+    def get_validation_node_method(self):
+        if type(self).__qualname__ == "InfluenceDiagram":
+            method = "id_node"
+        elif type(self).__qualname__ == "DecisionTree":
+            method = "dt_node"
+        return getattr(validate_and_set_graph_model, method)
+
+
+class DirectedGraphABC(ABC):
+    NODES_MODULE_PATH = "src.v0.services.classes.node"
+
+    """Directed  Graph
+
+    While nodes are unique in the graph, it may exist several relationship
+    between them, expressed as the possibility to have several arcs between
+    two given nodes.
+    """
+
+    def __init__(self):
+        """
+        Create an instance of a DirectedGraphABC (ABSTRACT class!!!)
+        It is a wrapper around networkx.Digraph
+
+        Attributes:
+            graph: networkx object
+        """
+        self.graph = nx.DiGraph()
+
+    @property
+    def is_acyclic(self) -> bool:
+        """test if the graph is acyclic or not.
+
+        An influence diagram or a decision tree are acyclic. However, during
+        the building process, the condition may not be true.
+
+        Returns:
+            bool: True if the graph is acyclic, False otherwise.
+        """
+        return nx.is_directed_acyclic_graph(self.graph)
+
+    @property
+    def nodes(self) -> list[NodeABC]:
+        """List of the graph nodes
+
+        Returns:
+            List[NodeABC]: the list of node instances
+        """
+        return list(self.graph.nodes)
+
+    @property
+    def arcs(self) -> list[Arc]:
+        """List of the graph arcs
+
+        Returns:
+            List[Arc]: the list of arc instances
+        """
+        g = nx.node_link_data(self.graph, source="tail", target="head", link="edges")
+        return [
+            Arc(
+                tail=item["tail"],
+                head=item["head"],
+                label=item["label"],
+                unique_id=item["uuid"],
+            )
+            for item in g["edges"]
+        ]
+
+    @property
+    def node_uuids(self) -> list:
+        """List of uuid's of the graph nodes
+
+        Returns:
+            List: the list of uuid's
+        """
+        return [node.uuid for node in self.graph]
+
+    def node_in(self, node: NodeABC) -> bool:
+        """Check if a node is in the graph.
+
+        The test is done through the uuid of the nodes.
+
+        Args:
+            node (NodeABC): the node to be tested
+
+        Returns:
+            bool: True if the node is already within the graph, False otherwise.
+        """
+        return node.uuid in self.node_uuids
+
+    def add_nodes(self, nodes: Sequence[NodeABC]):
+        """Add a list/tuple of nodes to a graph
+
+        Args:
+            nodes (Sequence[NodeABC]): list/tuple of the nodes to be added
+        """
+        for node in nodes:
+            self.add_node(node)
+
+    def add_arcs(self, arcs: Sequence[Arc]):
+        """Add a list/tuple of arcs to a graph.
+            Endpoints of the arcs which are not already in the graph are added.
+
+        Args:
+            arcs (Sequence[Arcs]): list/tuple of the arcs to be added
+        """
+        for arc in arcs:
+            self.add_arc(arc)
+
+    def add_node(self, node: NodeABC):
+        """Add a node to the graph
+
+        Args:
+            node (NodeABC): node to be added
+        """
+        validation_method = DirectedGraphType.get_validation_node_method(self)
+        if not self.node_in(node):
+            self.graph.add_node(validation_method(node))
+
+    def add_arc(self, arc: Arc):
+        """Add an arc to the graph
+
+        Args:
+            arc (Arc): arc to be added. If some of the end points do not exist,
+            they are added to the graph too.
+        """
+        arc_info = validate_and_set_graph_model.arc_to_graph(arc)
+        tail = (
+            arc_info[0][0]
+            if not self.node_in(arc_info[0][0])
+            else self.get_node_from_uuid(arc_info[0][0].uuid)
+        )
+        head = (
+            arc_info[0][1]
+            if not self.node_in(arc_info[0][1])
+            else self.get_node_from_uuid(arc_info[0][1].uuid)
+        )
+        self.graph.add_edge(tail, head, **arc_info[1])
+
+    def copy(self):
+        """copy the probabilistic graph model
+
+        Returns
+            ProbabilisticGraphModel: a copy of the graph. uuid's of
+            the nodes are copied too.
+        """
+        new_graph = type(self)()  # Need to instance from the concrete class
+        new_graph.graph = self.graph.copy()
+        return new_graph
+
+    def get_parents(self, node: NodeABC) -> list[NodeABC]:
+        """get parents of a given node
+
+        Args:
+            node (NodeABC): node to find the parents of
+
+        Returns
+            List[NodeABC]: the list of the parents of the given node
+        """
+        if not self.graph.has_node(node):
+            raise NodeInGraphError(node)
+        return list(self.graph.predecessors(node))
+
+    def get_children(self, node: NodeABC) -> list[NodeABC]:
+        """get children of a given node
+
+        Args:
+            node (NodeABC): node to find the children of
+
+        Returns
+            List[NodeABC]: the list of the children of the given node
+        """
+        if not self.graph.has_node(node):
+            raise NodeInGraphError(node)
+        return list(self.graph.successors(node))
+
+    def has_children(self, node: NodeABC) -> bool:
+        """Check for existence of children
+
+        Args:
+            node (NodeABC): node to check for children
+
+        Returns
+            bool: existence (True) or not (False) of children to the given node
+        """
+        return len(self.get_children(node)) > 0
+
+    def get_node_from_uuid(self, uuid: str) -> NodeABC:
+        """get a node given its uuid
+
+        Args:
+            uuid (str): uuid of node to look for
+
+        Returns:
+            NodeABC: node object having the given uuid
+        """
+        return [node for node in self.graph if node.uuid == uuid][0]
+
+    # def get_node_type(self, node: NodeABC) -> str:
+    #     """Return the type of a given node
+
+    #     Args:
+    #         node (NodeABC): node to examine
+
+    #     Returns
+    #         str: a string describing the type of the node (class name in
+    #         lower font and without the "Node" suffix)
+    #     """
+    #     return type(node).__name__.replace("Node", "").lower()
diff --git a/api/src/v0/services/classes/abstract_probability.py b/api/src/v0/services/classes/abstract_probability.py
new file mode 100644
index 0000000..ead1351
--- /dev/null
+++ b/api/src/v0/services/classes/abstract_probability.py
@@ -0,0 +1,27 @@
+import logging
+from abc import ABC, abstractmethod
+
+logger = logging.getLogger(__name__)
+
+
+class ProbabilityABC(ABC):
+    """ProbabilityABC"""
+
+    @classmethod
+    @abstractmethod
+    def initialize_nan(cls, *args, **kwargs):
+        raise NotImplementedError
+
+    @classmethod
+    @abstractmethod
+    def initialize_uniform(cls, *args, **kwargs):
+        raise NotImplementedError
+
+    @property
+    @abstractmethod
+    def variables(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_distribution(self, **kwargs):
+        raise NotImplementedError
diff --git a/api/src/v0/services/classes/arc.py b/api/src/v0/services/classes/arc.py
new file mode 100644
index 0000000..899c413
--- /dev/null
+++ b/api/src/v0/services/classes/arc.py
@@ -0,0 +1,127 @@
+"""Arc classes
+
+Raises:
+    EndpointTypeError: When endpoint cannot be set to start or end
+    UtilityNodeSuccessorError: When a UtilityNode has a successor which
+    is not another UtilityNode
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from src.v0.services.classes.node import (
+    DecisionNode,
+    UncertaintyNode,
+    UtilityNode,
+)
+
+if TYPE_CHECKING:  # pragma: no cover
+    from src.v0.services.classes.node import NodeABC
+
+from src.v0.services.class_validations import validate_and_set_arc
+
+from ..errors import UtilityNodeSuccessorError
+
+
+class Arc:
+    """Class of arcs"""
+
+    def __init__(
+        self, *, tail: NodeABC, head: NodeABC, label: str = None, unique_id=None
+    ):
+        """Instance of Arc
+
+        Args:
+            tail (NodeABC): start node of the arc
+            head (NodeABC): end node of the arc
+            label (str, optional): label of the arc. Defaults to None.
+            unique_id (str, optional): uuid. Defaults to None. If None,
+            the uuid is attributed at instantiation.
+
+        Private attributes:
+            _tail (NodeABC): start node of the arc
+            _head (NodeABC): end node of the arc
+            _label (str, optional): label of the arc. Defaults to None.
+            _uuid (str, optional): uuid. Defaults to None. If None, the uuid
+            is attributed at instantiation.
+        """
+        self._tail = validate_and_set_arc.edge(tail)
+        self._head = validate_and_set_arc.edge(head)
+        self._label = validate_and_set_arc.label(label)
+        self._uuid = validate_and_set_arc.uuid(unique_id)
+
+    @property
+    def tail(self) -> NodeABC:
+        """
+        Returns:
+            NodeABC: the node at start of the arc (tail)
+        """
+        return self._tail
+
+    @property
+    def head(self) -> NodeABC:
+        """
+        Returns:
+            NodeABC: the node at end of the arc (head)
+        """
+        return self._head
+
+    @property
+    def label(self) -> str:
+        """
+        Returns:
+            str: the label of the arc
+        """
+        return self._label
+
+    @property
+    def uuid(self) -> str:
+        """Return the `uuid` attribute
+
+        Returns:
+            str: uuid of the node
+        """
+        return self._uuid
+
+    @property
+    def dtype(self):
+        """type of the arc (informational/conditional)"""
+        if isinstance(self.head, DecisionNode):
+            return "informational"
+        elif isinstance(self.head, UncertaintyNode):
+            return "conditional"
+        elif isinstance(self.head, UtilityNode):
+            return "functional"
+        elif self.head is None:
+            return None
+
+    @label.setter
+    def label(self, value):
+        self._label = validate_and_set_arc.label(value)
+
+    @tail.setter
+    def tail(self, node):
+        if node.is_utility_node:
+            if not (self.head.is_utility_node or self.head is None):
+                raise UtilityNodeSuccessorError(f"{self.head.uuid}/{node.uuid}")
+        self._tail = validate_and_set_arc.edge(node)
+
+    @head.setter
+    def head(self, node):
+        if self.tail.is_utility_node and not node.is_utility_node:
+            raise UtilityNodeSuccessorError(f"{self.tail.uuid}/{node.uuid}")
+        self._head = validate_and_set_arc.edge(node)
+
+    def copy(self):
+        """copy an arc
+
+        Returns:
+            Arc: copied arc, endpoints keeping their uuids
+        """
+        # need to create the arc and then fill it so that
+        # the endpoints are the same including uuid
+        arc = Arc(tail=None, head=None, label=self.label)
+        arc.tail = self.tail
+        arc.head = self.head
+        return arc
diff --git a/api/src/v0/services/classes/decision_tree.py b/api/src/v0/services/classes/decision_tree.py
new file mode 100644
index 0000000..75c1079
--- /dev/null
+++ b/api/src/v0/services/classes/decision_tree.py
@@ -0,0 +1,59 @@
+"""Module defining the DecisionTree class
+
+A DecisionTree is a sub-class of DirectedGraphABC.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from src.v0.services.class_validations import validate_and_set_graph_model
+
+from .abstract_directed_graph import DirectedGraphABC
+
+if TYPE_CHECKING:  # pragma: no cover
+    from src.v0.services.classes.node import NodeABC
+
+
+class DecisionTree(DirectedGraphABC):
+    """Decision tree class"""
+
+    def __init__(self, root: NodeABC = None):
+        """Create an instance of a DecisionTree
+            It is a wrapper around networkx.Digraph
+
+        Attributes:
+            graph (networkx.DiGraph): networkx.DiGraph object containing the
+            decision tree (nodes and arcs)
+        """
+        super().__init__()
+        if root is not None:
+            self.add_node(validate_and_set_graph_model.dt_node(root))
+
+    @property
+    def root(self):
+        """return the root node of the graph (no incoming arcs)
+
+        Returns:
+            NodeABC | None : the root node in the graph or None if none (cyclic) or
+            more than 1 (graph under construction)
+        """
+        root_node = [n for n, d in self.graph.in_degree() if d == 0]
+        if len(root_node) != 1:
+            return None
+        return root_node[0]
+
+    def parent(self, node: NodeABC) -> NodeABC | None:
+        """return the parent node in the Decition Tree
+
+        In a Decision Tree, a node has a unique parent
+        (The root doesn't have any)
+
+        Args:
+            node (NodeABC): node to find the parent of
+
+        Returns
+            NodeABC | None: the parent node or None is no parent node has been found
+        """
+        parents = self.get_parents(node)
+        return parents[0] if len(parents) > 0 else None
diff --git a/api/src/v0/services/classes/discrete_conditional_probability.py b/api/src/v0/services/classes/discrete_conditional_probability.py
new file mode 100644
index 0000000..bdf6fb2
--- /dev/null
+++ b/api/src/v0/services/classes/discrete_conditional_probability.py
@@ -0,0 +1,163 @@
+import numpy as np
+import xarray as xr
+from numpy.typing import ArrayLike
+
+from src.v0.services.class_validations import validate_and_set_probability
+from src.v0.services.classes.abstract_probability import ProbabilityABC
+
+
+class DiscreteConditionalProbability(ProbabilityABC):
+    """DiscreteConditionalProbability"""
+
+    def __init__(self, probability_function: ArrayLike, variables: dict) -> None:
+        """
+         Parameters
+         -----------
+         probability_function: ArrayLike
+             gives the probability that a variable is equal to some value
+         variables: dict
+             involves the variable name and its values; values refers to the
+             set of possible outcomes
+
+        .. note:
+            Future:
+
+            conditioned_variables: dict
+                involves the variable name and its values; values refers to the
+                set of possible outcomes
+            conditioning_variables: dict
+                involves the variable name and its values; values refers to the
+                set of possible outcomes
+
+         Warning
+         --------
+             Spaces in variable names and values will be removed.
+
+         Notes
+         -----
+
+         For the Conditional Probability Table, P(Test Result | Test, State) such as
+
+                        || 1. Test  ||    yes     yes   |  no      no
+                        || 2. State ||   Peach | Lemon  | Peach | Lemon
+        -----------------------------------------------------------------
+         0. Test Result ||          ||         |        |       |
+        -----------------------------------------------------------------
+         no Test        ||          ||   0.1   |  0.05  |  0.85 |  0.46
+         Peach          ||          ||   0.7   |  0.35  |  0.12 |  0.26
+         Lemon          ||          ||   0.2   |  0.60  |  0.03 |  0.28
+
+         the probability_function will be
+             [
+                 [0.1, 0.05, 0.85, 0.46],
+                 [0.7, 0.35, 0.12, 0.26],
+                 [0.2, 0.60, 0.03, 0.28],
+             ]
+
+         and the variables
+             {
+                 "Test Result": ["no Test", "Peach", "Lemon"],
+                 "Test": ["yes", "no"],
+                 "State": ["Peach", "Lemon"],
+             }
+        """
+        super().__init__()
+        variable_list = list(variables.keys())
+        variable_values = list(variables.values())
+        conditioned_variables = {variable_list[0]: variable_values[0]}
+        conditioning_variables = {
+            variable_list[k]: variable_values[k] for k in range(1, len(variable_list))
+        }
+        conditioned_variables = validate_and_set_probability.discrete_variables(
+            conditioned_variables
+        )
+        conditioning_variables = validate_and_set_probability.discrete_variables(
+            conditioning_variables
+        )
+        probability_function = (
+            validate_and_set_probability.discrete_conditional_probability_function(
+                probability_function, conditioned_variables, conditioning_variables
+            )
+        )
+        self._cpt = xr.DataArray(
+            probability_function,
+            coords={**conditioned_variables, **conditioning_variables},
+            attrs={
+                "conditioned_variables": list(conditioned_variables.keys()),
+                "conditioning_variables": list(conditioning_variables.keys()),
+            },
+        )
+
+    @property
+    def outcomes(self):
+        variable_names = self.conditioned_variables
+        if len(variable_names) == 1:
+            return tuple(self._cpt.coords[variable_names[0]].data.tolist())
+        # FUTURE IMPLEMENTATION
+        # else:
+        #     return tuple(
+        #         product(
+        #             *tuple(tuple(self._cpt.coords[vn].data.tolist()) \
+        #                    for vn in variable_names)
+        #             )
+        #         )
+
+    @property
+    def variables(self):
+        return self._cpt.dims
+
+    @property
+    def conditioned_variables(self):
+        return tuple(
+            item
+            for item in self._cpt.dims
+            if item in self._cpt.attrs["conditioned_variables"]
+        )
+
+    @property
+    def conditioning_variables(self):
+        return tuple(
+            item
+            for item in self._cpt.dims
+            if item in self._cpt.attrs["conditioning_variables"]
+        )
+
+    @classmethod
+    def initialize_nan(cls, conditioned_variables: dict, conditioning_variables: dict):
+        data_shape = tuple(
+            np.asarray(v).shape[0]
+            for v in {**conditioned_variables, **conditioning_variables}.values()
+        )
+        data = np.full(data_shape, np.nan)
+        return cls(
+            probability_function=data,
+            variables=conditioned_variables | conditioning_variables,
+        )
+
+    @classmethod
+    def initialize_uniform(
+        cls, conditioned_variables: dict, conditioning_variables: dict
+    ):
+        data_shape = tuple(
+            np.asarray(v).shape[0]
+            for v in {**conditioned_variables, **conditioning_variables}.values()
+        )
+        data = np.ones(data_shape) / data_shape[0]
+        return cls(
+            probability_function=data,
+            variables=conditioned_variables | conditioning_variables,
+        )
+
+    def get_distribution(self, **variables):
+        """Return the probability distribution
+
+        Parameters
+        ----------
+        **variables:
+            variables (name=value) for which the distribution is desired
+
+        Return
+        ------
+        xr.DataArray
+        """
+        return self._cpt.sel(**variables)
diff --git a/api/src/v0/services/classes/discrete_unconditional_probability.py b/api/src/v0/services/classes/discrete_unconditional_probability.py
new file mode 100644
index 0000000..c56a3e7
--- /dev/null
+++ b/api/src/v0/services/classes/discrete_unconditional_probability.py
@@ -0,0 +1,109 @@
+from itertools import product
+
+import numpy as np
+import xarray as xr
+from numpy.typing import ArrayLike
+
+from src.v0.services.class_validations import validate_and_set_probability
+from src.v0.services.classes.abstract_probability import ProbabilityABC
+
+
+class DiscreteUnconditionalProbability(ProbabilityABC):
+    """DiscreteUnconditionalProbability"""
+
+    def __init__(self, probability_function: ArrayLike, variables: dict) -> None:
+        """
+         Parameters
+         -----------
+         probability_function: ArrayLike
+             gives the probability that a variable is equal to some value
+         variables: dict
+             involves the variable name and its values; values refers to the set
+             of possible outcomes
+
+         Warning
+         --------
+             Spaces in variable names and values will be removed.
+
+         Notes
+         -----
+
+         For the Probability P(A, B , C) such as
+
+                || 1. B  ||    b1       b1   |   b2     b2
+                || 2. C  ||    c1   |   c2   |   c1  |  c1
+        -----------------------------------------------------------------
+         0. A   ||       ||         |        |       |
+        -----------------------------------------------------------------
+         a1     ||       ||   0.00  |  0.13  |  0.20 |  0.06
+         a2     ||       ||   0.03  |  0.28  |  0.11 |  0.02
+         a3     ||       ||   0.12  |  0.04  |  0.10 |  0.01
+
+         the probability_function will be
+             [
+                 [0.00, 0.13, 0.20, 0.06],
+                 [0.03, 0.18, 0.11, 0.02],
+                 [0.12, 0.04, 0.10, 0.01],
+             ]
+
+         and the variables
+             {
+                 "A": ["a1", "a2", "a3"],
+                 "B": ["b1", "b2"],
+                 "C": ["c1", "c2"],
+             }
+        """
+        super().__init__()
+        variables = validate_and_set_probability.discrete_variables(variables)
+        probability_function = (
+            validate_and_set_probability.discrete_unconditional_probability_function(
+                probability_function, variables
+            )
+        )
+        self._cpt = xr.DataArray(probability_function, coords=variables)
+
+    @property
+    def outcomes(self):
+        variable_names = self._cpt.dims
+        if len(variable_names) == 1:
+            return tuple(self._cpt.coords[variable_names[0]].data.tolist())
+        else:
+            outcomes_as_tuples = tuple(
+                product(
+                    *tuple(
+                        tuple(self._cpt.coords[vn].data.tolist())
+                        for vn in variable_names
+                    )
+                )
+            )
+            return tuple(" - ".join(t) for t in outcomes_as_tuples)
+
+    @property
+    def variables(self):
+        return self._cpt.dims
+
+    @classmethod
+    def initialize_nan(cls, variables: dict):
+        data_shape = tuple(np.asarray(v).shape[0] for v in variables.values())
+        data = np.full(data_shape, np.nan)
+        return cls(data, variables=variables)
+
+    @classmethod
+    def initialize_uniform(cls, variables: dict):
+        data_shape = tuple(np.asarray(v).shape[0] for v in variables.values())
+        data = np.ones(data_shape) / np.prod(data_shape)
+        return cls(data, variables=variables)
+
+    def get_distribution(self, **variables):
+        """Return the probability distribution
+
+        Parameters
+        ----------
+        **variables:
+            variables (name=value) for which the distribution is desired
+
+        Return
+        ------
+        xr.DataArray
+        """
+        return self._cpt.sel(**variables)
diff --git a/api/src/v0/services/classes/influence_diagram.py b/api/src/v0/services/classes/influence_diagram.py
new file mode 100644
index 0000000..80c8999
--- /dev/null
+++ b/api/src/v0/services/classes/influence_diagram.py
@@ -0,0 +1,90 @@
+"""Module defining the InfluenceDiagram class
+
+An InfluenceDiagram is a sub-class of DirectedGraphABC.
+"""
+
+from src.v0.services.classes.node import (
+    DecisionNode,
+    UncertaintyNode,
+    UtilityNode,
+)
+
+from .abstract_directed_graph import DirectedGraphABC
+
+
+class InfluenceDiagram(DirectedGraphABC):
+    """Influence Diagram"""
+
+    def __init__(self):
+        """
+        Create an instance of an InfluenceDiagram
+        It is a wrapper around networkx.Digraph
+
+        Attributes:
+            graph (networkx.DiGraph): networkx.DiGraph object
+            containing the influence diagram (nodes and arcs)
+        """
+        super().__init__()
+
+    def get_decision_nodes(self) -> list[DecisionNode]:
+        """Return a list of decision nodes
+
+        Returns:
+            list[DecisionNode]
+        """
+        return [
+            node[0]
+            for node in list(self.graph.nodes(data=True))
+            if isinstance(node[0], DecisionNode)
+        ]
+
+    def get_uncertainty_nodes(self) -> list[UncertaintyNode]:
+        """Return a list of uncertainty nodes
+
+        Returns:
+            list[UncertaintyNode]
+        """
+        return [
+            node[0]
+            for node in list(self.graph.nodes(data=True))
+            if isinstance(node[0], UncertaintyNode)
+        ]
+
+    def get_utility_nodes(self) -> list[UtilityNode]:
+        """Return a list of utility nodes
+
+        Returns:
+            list[UtilityNode]
+        """
+        return [
+            node[0]
+            for node in list(self.graph.nodes(data=True))
+            if isinstance(node[0], UtilityNode)
+        ]
+
+    @property
+    def decision_count(self) -> int:
+        """Return the number of decision nodes
+
+        Returns:
+            int: the number of DecisionNode objects in the influence diagram
+        """
+        return len(self.get_decision_nodes())
+
+    @property
+    def uncertainty_count(self) -> int:
+        """Return the number of uncertainty nodes
+
+        Returns:
+            int: the number of UncertaintyNode objects in the influence diagram
+        """
+        return len(self.get_uncertainty_nodes())
+
+    @property
+    def utility_count(self) -> int:
+        """Return the number of utility nodes
+
+        Returns:
+            int:  the number of UtilityNode objects in the influence diagram
+        """
+        return len(self.get_utility_nodes())
diff --git a/api/src/v0/services/classes/node.py b/api/src/v0/services/classes/node.py
new file mode 100644
index 0000000..cac5f5a
--- /dev/null
+++ b/api/src/v0/services/classes/node.py
@@ -0,0 +1,287 @@
+"""Nodes classes
+
+NodeABC is an abstract class with 3 concretizations:
+- DecisionNode
+- UncertaintyNode
+- UtilityNode
+"""
+
+from abc import ABC, abstractmethod
+from collections.abc import Sequence
+
+from src.v0.services.class_validations import validate_and_set_node
+
+from .abstract_probability import ProbabilityABC
+
+
+class NodeABC(ABC):
+    """Abstract class of nodes"""
+
+    def __init__(self, *, description: str, shortname: str, uuid=None):
+        """instantiation of an (abstract) node
+
+        Args:
+            description (str): description of the node
+            shortname (str): shortname of the node
+            uuid (str, optional): uuid. Defaults to None.
+            If None, the uuid is attributed at instantiation.
+
+        Private attributes:
+            _description (str): description of the node
+            _shortname (str): shortname of the node
+            _uuid (str, optional): uuid. Defaults to None.
+            If None, the uuid is attributed at instantiation.
+        """
+        self._description = validate_and_set_node.description(description)
+        self._shortname = validate_and_set_node.shortname(shortname)
+        self._uuid = validate_and_set_node.uuid(uuid)
+
+    @property
+    def description(self) -> str:
+        """Return the `description` attribute
+
+        Returns:
+            str: description of the node
+        """
+        return self._description
+
+    @property
+    def shortname(self) -> str:
+        """Return the `shortname` attribute
+
+        Returns:
+            str: shortname of the node
+        """
+        return self._shortname
+
+    @property
+    def uuid(self) -> str:
+        """Return the `uuid` attribute
+
+        Returns:
+            str: uuid of the node
+        """
+        return self._uuid
+
+    @description.setter
+    def description(self, value):
+        self._description = validate_and_set_node.description(value)
+
+    @shortname.setter
+    def shortname(self, value):
+        self._shortname = validate_and_set_node.shortname(value)
+
+    @uuid.setter
+    def uuid(self, value):
+        self._uuid = validate_and_set_node.uuid(value)
+
+    @property
+    def is_decision_node(self) -> bool:
+        """
+        Returns:
+            bool: True of the node is a DecisionNode, false otherwise
+        """
+        return isinstance(self, DecisionNode)
+
+    @property
+    def is_uncertainty_node(self) -> bool:
+        """
+        Returns:
+            bool: True of the node is a UncertaintyNode, false otherwise
+        """
+        return isinstance(self, UncertaintyNode)
+
+    @property
+    def is_utility_node(self) -> bool:
+        """
+        Returns:
+            bool: True of the node is a UtilityNode, false otherwise
+        """
+        return isinstance(self, UtilityNode)
+
+    @property
+    @abstractmethod
+    def states(self):  # pragma: no cover
+        raise NotImplementedError
+
+    def copy(self):
+        """Copy of the node, associating a new uuid
+
+        Returns:
+            NodeABC: a copy of the node, with a new uuid
+        """
+        copied_node = type(self)(description=self.description, shortname=self.shortname)
+        for attribute, value in self.__dict__.items():
+            if attribute not in ["_description", "_shortname", "_uuid"]:
+                setattr(copied_node, attribute, value)
+        return copied_node
+
+
+class DecisionNode(NodeABC):
+    """Decision node class"""
+
+    def __init__(
+        self,
+        *,
+        description: str,
+        shortname: str,
+        uuid=None,
+        alternatives: Sequence[str] = None,
+    ):
+        """Create an instance of a DecisionNode
+
+        Args:
+            description (str): description of the node
+            shortname (str): shortname of the node
+            unique_id (str, optional): uuid. Defaults to None.
+            If None, the uuid is attributed at instantiation.
+            alternatives (Sequence[str], optional): alternatives (states)
+            of the decision. Defaults to None.
+
+        Private attributes:
+            _description (str): description of the node
+            _shortname (str): shortname of the node
+            _unique_id (str, optional): uuid. Defaults to None.
+            If None, the uuid is attributed at instantiation.
+            _alternatives (Sequence[str], optional): alternatives (states) of
+            the decision. Defaults to None.
+        """
+        super().__init__(description=description, shortname=shortname, uuid=uuid)
+        self._alternatives = validate_and_set_node.alternatives(alternatives)
+
+    @property
+    def alternatives(self) -> list[str]:
+        """
+        Returns:
+            list[str]: the alternatives (states) of the decision
+        """
+        if isinstance(self._alternatives, list):
+            return self._alternatives
+        if isinstance(self._alternatives, Sequence):
+            return list(self._alternatives)
+        return []
+
+    @alternatives.setter
+    def alternatives(self, value):
+        self._alternatives = validate_and_set_node.alternatives(value)
+
+    @property
+    def states(self) -> list[str]:
+        """
+        Returns:
+            list[str]: the alternatives (states) of the decision
+        """
+        return self.alternatives
+
+
+class UncertaintyNode(NodeABC):
+    """Uncertainty (or chance) node class"""
+
+    def __init__(
+        self,
+        *,
+        description: str,
+        shortname: str,
+        uuid=None,
+        probability: ProbabilityABC = None,
+    ):
+        """Create an instance of a UncertaintyNode
+
+        Args:
+            description (str): description of the node
+            shortname (str): shortname of the node
+            unique_id (str, optional): uuid. Defaults to None.
+            If None, the uuid is attributed at instantiation.
+            probability (ProbabilityABC, optional): probability associated to the node.
+            Defaults to None.
+
+        Private attributes:
+            _description (str): description of the node
+            _shortname (str): shortname of the node
+            _unique_id (str, optional): uuid. Defaults to None.
+            If None, the uuid is attributed at instantiation.
+            _probability (ProbabilityABC, optional): probability associated to the node.
+            Defaults to None.
+        """
+        super().__init__(description=description, shortname=shortname, uuid=uuid)
+        self._probability = validate_and_set_node.probability(probability)
+
+    @property
+    def probability(self) -> ProbabilityABC:
+        """
+        Returns:
+            ProbabilityABC: the probabibility associated to the node
+        """
+        return self._probability
+
+    @probability.setter
+    def probability(self, value):
+        self._probability = validate_and_set_node.probability(value)
+
+    @property
+    def states(self) -> list[str]:
+        """
+        Returns:
+            list[str]: the outcomes (states) of the uncertainty
+        """
+        return self.outcomes
+
+    @property
+    def outcomes(self) -> list[str]:
+        """
+        Returns:
+            list[str]: the outcomes (states) of the uncertainty
+        """
+        if self.probability is None:
+            return ()
+        return self._probability.outcomes
+
+
+class UtilityNode(NodeABC):
+    """Utility (or value) node class"""
+
+    def __init__(self, *, description: str, shortname: str, uuid=None):
+        """Create an instance of a UtilityNode
+
+        Args:
+            description (str): description of the node
+            shortname (str): shortname of the node
+            unique_id (str, optional): uuid. Defaults to None.
+            If None, the uuid is attributed at instantiation.
+
+        Private attributes:
+            _description (str): description of the node
+            _shortname (str): shortname of the node
+            _unique_id (str, optional): uuid. Defaults to None.
+            If None, the uuid is attributed at instantiation.
+
+        TODO: implement utility matrix attribute through a Utility class.
+        So far it is rather a place holder.
+        TODO: implement the value metric in addition to the consequence
+        TODO: the total objective is a combination of gains and costs of each
+        decision and how we combine them into a desired objective
+        """
+        super().__init__(description=description, shortname=shortname, uuid=uuid)
+        self._utility = None  # TODO: implement it!
+
+    @property
+    def utility(self) -> list[str]:
+        """
+        Returns:
+            list[str]: the utility (states) of the utility
+        """
+        if isinstance(self._utility, Sequence):
+            return list(self._utility)
+        return []
+
+    @utility.setter
+    def utility(self, value):
+        self._utility = value
+
+    @property
+    def states(self) -> list[str]:
+        """
+        Returns:
+            list[str]: the consequence entries (states) of the utility
+        """
+        return self.utility
diff --git a/api/src/v0/services/errors.py b/api/src/v0/services/errors.py
new file mode 100644
index 0000000..d70259d
--- /dev/null
+++ b/api/src/v0/services/errors.py
@@ -0,0 +1,252 @@
+"""
+Error for services.classes errors
+"""
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class ProbabilityTypeError(Exception):
+    def __init__(self, arg):
+        error_message = f"Unreckonized probability type: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class DiscreteConditionalProbabilityTypeError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"Data cannot be used to create a DiscreteConditionalProbability: {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class DiscreteUnconditionalProbabilityTypeError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"Data cannot be used to create a DiscreteUnconditionalProbability: {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class DecisionNodeTypeError(Exception):
+    def __init__(self, arg):
+        error_message = f"Data cannot be used to create a DecisionNode: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class UncertaintyNodeTypeError(Exception):
+    def __init__(self, arg):
+        error_message = f"Data cannot be used to create a UncertaintyNode: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class UtilityNodeTypeError(Exception):
+    def __init__(self, arg):
+        error_message = f"Data cannot be used to create a UtilityNode: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class UtilityNodeSuccessorError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"Utility node can only have other utility nodes as successor: {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class NodeInGraphError(Exception):
+    def __init__(self, arg):
+        error_message = f"The node is not in the graph: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class InfluenceDiagramNodeTypeError(Exception):
+    def __init__(self, arg):
+        error_message = f"Data cannot be used to create an influence diagram Node: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class NodeTypeError(Exception):
+    def __init__(self, arg):
+        error_message = f"Data is not an InfluenceDiagram Node: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class DescriptionValidationError(Exception):
+    def __init__(self, arg):
+        error_message = f"Input description is not a string: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class NameValidationError(Exception):
+    def __init__(self, arg):
+        error_message = f"Input name is not a string: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class ArcLabelValidationError(Exception):
+    def __init__(self, arg):
+        error_message = f"Input label is neither a string nor None: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class EndPointValidationError(Exception):
+    def __init__(self, arg):
+        error_message = f"Endpoint of arcs should be Node or None: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class ArcTypeError(Exception):
+    def __init__(self, arg):
+        error_message = f"Data cannot be used to create an Arc: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class InfluenceDiagramTypeError(Exception):
+    def __init__(self, arg):
+        error_message = f"Data cannot be used to create an InfluenceDiagram: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class ArcTypeValidationError(Exception):
+    def __init__(self, arg):
+        error_message = f"Added arc is not of instance Arc: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class IDNodeTypeValidationError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"Added node is not of instance "
+            f"(DecisionNode, UncertaintyNode, UtilityNode): {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class DTNodeTypeValidationError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"Added node is not of instance "
+            f"(DecisionNode, UncertaintyNode, UtilityNode): {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class ShortnameValidationError(Exception):
+    def __init__(self, arg):
+        error_message = f"Input shortname is not a string: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class UUIDValidationError(Exception):
+    def __init__(self, arg):
+        error_message = f"Input uuid is neither a valid uuid (version 4) nor None: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class AlternativeValidationError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"Input alternatives is neither a list or "
+            f"tuple of unique strings nor None: {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class DiscreteProbabilityVariableValidationError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"One of the variables is not a dictionary with "
+            f"element being able to be interpreted as 1D: {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class DiscreteConditionalProbabilityFunctionValidationError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"The conditional probability function is not well formed size "
+            f"(not compatible with variables or content is not normalized): {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class DiscreteUnconditionalProbabilityFunctionValidationError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"The unconditional probability function is not well formed size "
+            f"(not compatible with variables or content is not normalized): {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class ProbabilityValidationError(Exception):
+    def __init__(self, arg):
+        error_message = (
+            f"Input probability is neither a well formed probability nor None: {arg}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class InfluenceDiagramNotAcyclicError(Exception):
+    def __init__(self, arg):
+        error_message = f"the influence diagram is not acyclic: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class RootNodeNotFound(Exception):
+    def __init__(self, arg):
+        error_message = f"Decision tree has no defined root node: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class PartialOrderOutputModeError(Exception):
+    def __init__(self, mode):
+        error_message = (
+            f"output mode should be [view|copy] and have been entered as {mode}"
+        )
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class ArcPyAgrumFormatError(Exception):
+    def __init__(self, arg):
+        error_message = f"Input arc cannot be used in pyagrum with error: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
+
+
+class ProbabilityPyAgrumFormatError(Exception):
+    def __init__(self, arg):
+        error_message = f"Input probability cannot be used in pyagrum with error: {arg}"
+        super().__init__(error_message)
+        logger.critical(error_message)
diff --git a/api/tests/v0/services/structure_utils/__init__.py b/api/src/v0/services/format_conversions/__init__.py
similarity index 100%
rename from api/tests/v0/services/structure_utils/__init__.py
rename to api/src/v0/services/format_conversions/__init__.py
diff --git a/api/src/v0/services/format_conversions/arc.py b/api/src/v0/services/format_conversions/arc.py
new file mode 100644
index 0000000..3a451c9
--- /dev/null
+++ b/api/src/v0/services/format_conversions/arc.py
@@ -0,0 +1,33 @@
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.node import NodeABC
+from src.v0.services.errors import ArcTypeError
+
+from .node import InfluenceDiagramNodeConversion
+
+
+class ArcConversion:
+    def from_json(self, edge: dict, issues: list[dict]) -> Arc:
+        if not ("outV" in edge.keys() and "inV" in edge.keys()):
+            raise ArcTypeError(list(edge.keys()))
+
+        tails = [issue for issue in issues if issue["uuid"] == edge["outV"]]
+        heads = [issue for issue in issues if issue["uuid"] == edge["inV"]]
+
+        if not (len(tails) == 1 and len(heads) == 1):
+            raise ArcTypeError((tails, heads))
+
+        return Arc(
+            tail=InfluenceDiagramNodeConversion().from_json(tails[0]),
+            head=InfluenceDiagramNodeConversion().from_json(heads[0]),
+        )
+
+    def to_json(self, arc: Arc, nodes: list[NodeABC]) -> dict:
+        outV = [node.uuid for node in nodes if node == arc.tail][0]
+        inV = [node.uuid for node in nodes if node == arc.head][0]
+        return {
+            "outV": outV,
+            "inV": inV,
+            "uuid": arc.uuid,
+            "id": arc.uuid,
+            "label": "influences",
+        }
diff --git a/api/src/v0/services/format_conversions/base.py b/api/src/v0/services/format_conversions/base.py
new file mode 100644
index 0000000..1510077
--- /dev/null
+++ b/api/src/v0/services/format_conversions/base.py
@@ -0,0 +1,60 @@
+"""
+General utilities for data format conversions between database and service layer-
+"""
+
+import datetime
+from abc import ABC, abstractmethod
+
+from src.v0.models.meta import EdgeMetaDataResponse, VertexMetaDataResponse
+
+
+class ConversionABC(ABC):
+    """
+    Abstract class for data format conversions between database and service layer.
+    """
+
+    @abstractmethod
+    def to_json(self, data):
+        raise NotImplementedError
+
+    @abstractmethod
+    def from_json(self, data):
+        raise NotImplementedError
+
+
+class MetadataCreate:
+    """
+    Creation of metadata response
+    """
+
+    def vertex(uuid: str) -> VertexMetaDataResponse:
+        """Create metadata for vertex
+
+        Args:
+            uuid (str): UUID of vertex as string
+
+        Returns:
+            VertexMetaDataResponse: vertex metadata
+        """
+        version = "v0"
+        ct = datetime.datetime.now()
+        date = str(ct)
+        timestamp = str(ct.timestamp())
+        uuid = uuid
+        return VertexMetaDataResponse.model_validate(
+            {"version": version, "date": date, "timestamp": timestamp, "uuid": uuid}
+        )
+
+    def edge(uuid: str) -> EdgeMetaDataResponse:
+        """Create metadata for edge
+
+        Args:
+            uuid (str): UUID of edge as string
+
+        Returns:
+            EdgeMetaDataResponse: edge metadata
+
+        """
+        version = "v0"
+        uuid = uuid
+        return EdgeMetaDataResponse.model_validate({"version": version, "uuid": uuid})
diff --git a/api/src/v0/services/format_conversions/directed_graph.py b/api/src/v0/services/format_conversions/directed_graph.py
new file mode 100644
index 0000000..c27ffd5
--- /dev/null
+++ b/api/src/v0/services/format_conversions/directed_graph.py
@@ -0,0 +1,79 @@
+import json
+
+import networkx as nx
+
+from src.v0.services.classes.decision_tree import DecisionTree
+from src.v0.services.classes.influence_diagram import InfluenceDiagram
+from src.v0.services.errors import InfluenceDiagramTypeError, RootNodeNotFound
+from src.v0.services.format_conversions.node import DecisionTreeNodeConversion
+
+from .arc import ArcConversion
+from .node import InfluenceDiagramNodeConversion
+
+
+class InfluenceDiagramConversion:
+    def from_json(self, influence_diagram: dict) -> InfluenceDiagram:
+        if (nodes := influence_diagram.get("vertices", None)) is None:
+            raise InfluenceDiagramTypeError(None)
+
+        diagram = InfluenceDiagram()
+        try:
+            for node_of_id in nodes:
+                diagram.add_node(InfluenceDiagramNodeConversion().from_json(node_of_id))
+            diagram.add_arcs(
+                [
+                    ArcConversion().from_json(arc, nodes)
+                    for arc in influence_diagram["edges"]
+                ]
+            )
+            return diagram
+        except Exception:
+            raise InfluenceDiagramTypeError(None)
+
+    def to_json(self, influence_diagram: InfluenceDiagram) -> dict:
+        return {
+            "vertices": [
+                InfluenceDiagramNodeConversion().to_json(item)
+                for item in influence_diagram.nodes
+            ],
+            "edges": [
+                ArcConversion().to_json(item, influence_diagram.nodes)
+                for item in influence_diagram.arcs
+            ],
+        }
+
+
+class DecisionTreeConversion:
+    def from_json(self, decision_tree: dict) -> DecisionTree:
+        raise NotImplementedError
+
+    def to_json(self, decision_tree: DecisionTree) -> dict:
+        def propagate_branch_name(decision_tree, node, names):
+            predecessor = decision_tree.parent(node)
+            if not predecessor:
+                return ""
+            n = names[(predecessor, node)]
+            return n if isinstance(n, str) else "-".join(n)
+
+        if decision_tree.root is None:
+            raise RootNodeNotFound(None)
+
+        edges_name = nx.get_edge_attributes(decision_tree.graph, "label")
+        tg = nx.readwrite.json_graph.tree_data(decision_tree.graph, decision_tree.root)
+        json_object = json.dumps(
+            tg,
+            default=lambda node: {
+                **DecisionTreeNodeConversion().to_json(
+                    DecisionTreeNodeConversion().from_json(
+                        InfluenceDiagramNodeConversion().to_json(node)
+                        | {
+                            "branch_name": propagate_branch_name(
+                                decision_tree, node, edges_name
+                            )
+                        }
+                    )
+                )
+            },
+            indent=2,
+        )
+        return json.loads(json_object)
diff --git a/api/src/v0/services/format_conversions/node.py b/api/src/v0/services/format_conversions/node.py
new file mode 100644
index 0000000..b9aa308
--- /dev/null
+++ b/api/src/v0/services/format_conversions/node.py
@@ -0,0 +1,237 @@
+"""
+This module converts issue related data between the database formats
+and the service layer formats
+"""
+
+from collections.abc import Sequence
+
+from src.v0.models.structure import DecisionTreeNodeData
+from src.v0.services.classes.node import (
+    DecisionNode,
+    NodeABC,
+    UncertaintyNode,
+    UtilityNode,
+)
+from src.v0.services.errors import (
+    DecisionNodeTypeError,
+    InfluenceDiagramNodeTypeError,
+    NodeTypeError,
+    UncertaintyNodeTypeError,
+    UtilityNodeTypeError,
+)
+
+from .base import ConversionABC, MetadataCreate
+from .probability import ProbabilityConversion
+
+
+def add_metadata(uuid: str) -> dict:
+    metadata = MetadataCreate.vertex(uuid)
+    return {
+        "uuid": metadata.uuid,
+        "timestamp": metadata.timestamp,
+        "date": metadata.date,
+        "id": metadata.uuid,
+        "label": "issue",
+    }
+
+
+class DecisionJSONConversion:
+    """
+    Conversion to json of fields relevant for decision
+    """
+
+    def states(self, node):
+        return (
+            None
+            if (isinstance(node.alternatives, Sequence) and len(node.alternatives) == 0)
+            else node.alternatives
+        )
+
+    def decision_type(self, node):
+        return "Focus"
+
+
+class UncertaintyJSONConversion:
+    """
+    Conversion to json of fields relevant for uncertainty
+    """
+
+    def probability(self, node):
+        return (
+            None
+            if (node.probability is None)
+            else ProbabilityConversion().to_json(node.probability)
+        )
+
+    def key_uncertainty(self, node):
+        return "true"
+
+    def source(self, node):
+        return ""
+
+
+class DecisionNodeConversion(ConversionABC):
+    """
+    Concrete implementation of `from_json` and `to_json` for decisions.
+    """
+
+    def from_json(self, issue: dict) -> DecisionNode:
+        if issue.get("category") != "Decision":
+            raise DecisionNodeTypeError(issue.get("category"))
+        return DecisionNode(
+            description=issue.get("description"),
+            shortname=issue.get("shortname"),
+            uuid=issue.get("uuid"),
+            alternatives=issue.get("alternatives"),
+        )
+
+    def to_json(self, node: DecisionNode) -> dict:
+        data = {
+            "category": "Decision",
+            "shortname": node.shortname,
+            "description": node.description,
+            "alternatives": DecisionJSONConversion().states(node),
+            "decisionType": DecisionJSONConversion().decision_type(node),
+            "boundary": "in",
+        }
+        return data | add_metadata(node.uuid)
+
+
+class UncertaintyNodeConversion(ConversionABC):
+    """
+    Concrete implementation of `from_json` and `to_json` for uncertainties.
+    """
+
+    def from_json(self, issue: dict) -> UncertaintyNode:
+        if issue.get("category") != "Uncertainty":
+            raise UncertaintyNodeTypeError(issue.get("category"))
+        try:
+            probability = ProbabilityConversion().from_json(issue.get("probabilities"))
+        except Exception as e:
+            raise UncertaintyNodeTypeError(e)
+        return UncertaintyNode(
+            description=issue.get("description"),
+            shortname=issue.get("shortname"),
+            uuid=issue.get("uuid"),
+            probability=probability,
+        )
+
+    def to_json(self, node: UncertaintyNode) -> dict:
+        try:
+            probability = ProbabilityConversion().to_json(node.probability)
+        except Exception as e:
+            raise UncertaintyNodeTypeError(e)
+        data = {
+            "category": "Uncertainty",
+            "shortname": node.shortname,
+            "description": node.description,
+            "probabilities": probability,
+            "keyUncertainty": "true",
+            "boundary": "in",
+        }
+        return data | add_metadata(node.uuid)
+
+
+class UtilityNodeConversion(ConversionABC):
+    """
+    Concrete implementation of `from_json` and `to_json` for utilities.
+
+    .. warning:
+        In this implementation we assume Utility is Value Metric which is not the
+        case and will be modified in future version
+    """
+
+    def from_json(self, issue: dict) -> UtilityNode:
+        if issue.get("category") != "Value Metric":
+            raise UtilityNodeTypeError(issue.get("category"))
+        return UtilityNode(
+            description=issue.get("description"),
+            shortname=issue.get("shortname"),
+            uuid=issue.get("uuid"),
+        )
+
+    def to_json(self, node: UtilityNode) -> dict:
+        data = {
+            "category": "Utility",
+            "shortname": node.shortname,
+            "description": node.description,
+            "boundary": "in",
+        }
+        return data | add_metadata(node.uuid)
+
+
+class InfluenceDiagramNodeConversion(ConversionABC):
+    def from_json(self, issue: dict) -> NodeABC:
+        """Create a node from a json stream.
+
+        Only key uncertainties, focus decisions and utilities are converted.
+
+        Args:
+            issue (Dict): issue as a dictionary
+
+        Raises:
+            InfluenceDiagramTypeError: _description_
+
+        Returns:
+            NodeABC: The converted node.
+        """
+        if issue.get("category") not in ["Decision", "Uncertainty", "Value Metric"]:
+            raise InfluenceDiagramNodeTypeError(f'category: {issue.get("category")}')
+        if issue.get("boundary") not in ["in", "on"]:
+            raise InfluenceDiagramNodeTypeError(f'boundary: {issue.get("boundary")}')
+        if not issue.get("shortname"):
+            raise InfluenceDiagramNodeTypeError(f'shortname: {issue.get("shortname")}')
+        if issue["category"] == "Decision":
+            if issue.get("decisionType") != "Focus":
+                raise InfluenceDiagramNodeTypeError(
+                    f'decisionType: {issue.get("decisionType")}'
+                )
+            return DecisionNodeConversion().from_json(issue)
+        if issue["category"] == "Uncertainty":
+            if issue["keyUncertainty"] != "true":
+                raise InfluenceDiagramNodeTypeError(
+                    f'keyUncertainty: {issue.get("keyUncertainty")}'
+                )
+            return UncertaintyNodeConversion().from_json(issue)
+        if issue["category"] == "Value Metric":
+            return UtilityNodeConversion().from_json(issue)
+
+    def to_json(self, node: NodeABC) -> dict:
+        if isinstance(node, DecisionNode):
+            return DecisionNodeConversion().to_json(node)
+        if isinstance(node, UncertaintyNode):
+            return UncertaintyNodeConversion().to_json(node)
+        if isinstance(node, UtilityNode):
+            return UtilityNodeConversion().to_json(node)
+        raise NodeTypeError(node)
+
+
+class DecisionTreeNodeConversion(ConversionABC):
+    def from_json(self, node: dict) -> DecisionTreeNodeData:
+        return DecisionTreeNodeData.model_validate(
+            {
+                "node_type": node.get("category"),
+                "shortname": node.get("shortname"),
+                "description": node.get("description"),
+                "branch_name": node.get("branch_name"),
+                "alternatives": node.get("alternatives"),
+                "probabilities": node.get("probabilities"),
+                "utility": node.get("utility"),
+                "uuid": node.get("uuid"),
+            }
+        )
+
+    def to_json(self, node: DecisionTreeNodeData) -> dict:
+        data = {
+            "node_type": node.node_type,
+            "shortname": node.shortname,
+            "description": node.description,
+            "branch_name": node.branch_name,
+            "alternatives": node.alternatives,
+            "probabilities": node.probabilities.model_dump()
+            if node.probabilities
+            else None,
+            "utility": node.utility,
+            "uuid": node.uuid,
+        }
+        return data
diff --git a/api/src/v0/services/format_conversions/probability.py b/api/src/v0/services/format_conversions/probability.py
new file mode 100644
index 0000000..0a52111
--- /dev/null
+++ b/api/src/v0/services/format_conversions/probability.py
@@ -0,0 +1,94 @@
+"""
+This module converts probability related data between the database formats
+and the service layer formats
+"""
+
+import numpy as np
+
+from src.v0.services.classes.abstract_probability import ProbabilityABC
+from src.v0.services.classes.discrete_conditional_probability import (
+    DiscreteConditionalProbability,
+)
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+from src.v0.services.errors import (
+    DiscreteConditionalProbabilityTypeError,
+    DiscreteUnconditionalProbabilityTypeError,
+    ProbabilityTypeError,
+)
+
+from .base import ConversionABC
+
+
+class DiscreteConditionalProbabilityConversion(ConversionABC):
+    def from_json(self, probability: dict) -> DiscreteConditionalProbability:
+        if probability.get("dtype") != "DiscreteConditionalProbability":
+            raise DiscreteConditionalProbabilityTypeError(probability.get("dtype"))
+        array_size = tuple([len(v) for v in probability["variables"].values()])
+        distribution = np.reshape(
+            np.asarray(probability["probability_function"]), array_size
+        )
+        return DiscreteConditionalProbability(distribution, probability["variables"])
+
+    def to_json(self, probability: DiscreteConditionalProbability) -> dict:
+        array_size = (
+            probability._cpt.data.shape[0],
+            np.astype(np.prod(probability._cpt.data.shape[1:]), int),
+        )
+        distribution = np.reshape(probability._cpt.data, array_size).tolist()
+
+        return {
+            "dtype": "DiscreteConditionalProbability",
+            "probability_function": distribution,
+            "variables": {
+                k: v.data.tolist() for k, v in probability._cpt.coords.items()
+            },
+        }
+
+
+class DiscreteUnconditionalProbabilityConversion(ConversionABC):
+    def from_json(self, probability: dict) -> DiscreteUnconditionalProbability:
+        if probability.get("dtype") != "DiscreteUnconditionalProbability":
+            raise DiscreteUnconditionalProbabilityTypeError(probability.get("dtype"))
+        array_size = tuple([len(v) for v in probability["variables"].values()])
+        distribution = np.reshape(
+            np.asarray(probability["probability_function"]), array_size
+        )
+        return DiscreteUnconditionalProbability(distribution, probability["variables"])
+
+    def to_json(self, probability: DiscreteUnconditionalProbability) -> dict:
+        array_size = (
+            probability._cpt.data.shape[0],
+            np.astype(np.prod(probability._cpt.data.shape[1:]), int),
+        )
+        distribution = np.reshape(probability._cpt.data, array_size).tolist()
+        return {
+            "dtype": "DiscreteUnconditionalProbability",
+            "probability_function": distribution,
+            "variables": {
+                k: v.data.tolist() for k, v in probability._cpt.coords.items()
+            },
+        }
+
+
+class ProbabilityConversion(ConversionABC):
+    def from_json(self, probability: dict | None) -> ProbabilityABC:
+        if probability is None:
+            return None
+        if not isinstance(probability, dict):
+            raise ProbabilityTypeError(probability)
+        if probability.get("dtype") == "DiscreteUnconditionalProbability":
+            return DiscreteUnconditionalProbabilityConversion().from_json(probability)
+        if probability.get("dtype") == "DiscreteConditionalProbability":
+            return DiscreteConditionalProbabilityConversion().from_json(probability)
+        raise ProbabilityTypeError(probability)
+
+    def to_json(self, probability: ProbabilityABC | None) -> dict:
+        if probability is None:
+            return None
+        if isinstance(probability, DiscreteUnconditionalProbability):
+            return DiscreteUnconditionalProbabilityConversion().to_json(probability)
+        if isinstance(probability, DiscreteConditionalProbability):
+            return DiscreteConditionalProbabilityConversion().to_json(probability)
+        raise ProbabilityTypeError(probability)
diff --git a/api/src/v0/services/structure.py b/api/src/v0/services/structure.py
index c1bc6fe..fa27ae8 100644
--- a/api/src/v0/services/structure.py
+++ b/api/src/v0/services/structure.py
@@ -1,7 +1,7 @@
-import json
-
-from src.v0.services.structure_utils.decision_diagrams.influence_diagram import (
-    InfluenceDiagram,
+from src.v0.services.analysis.id_to_dt import InfluenceDiagramToDecisionTree
+from src.v0.services.format_conversions.directed_graph import (
+    DecisionTreeConversion,
+    InfluenceDiagramConversion,
 )
 
 from ..models.filter import Filter
@@ -52,13 +52,13 @@ def read_influence_diagram(self, project_uuid: str) -> InfluenceDiagramResponse:
         issues_list = [
             IssueResponse.model_validate(v.model_dump()) for v in vertices if v
         ]
-        edges = edge_repository.read_all_edges_from_sub_project(
+        arcs = edge_repository.read_all_edges_from_sub_project(
             project_uuid=project_uuid,
             edge_label="influences",
             vertex_uuid=[issue.uuid for issue in issues_list],
         )
 
-        influence_diagram = InfluenceDiagramResponse(vertices=issues_list, edges=edges)
+        influence_diagram = InfluenceDiagramResponse(vertices=issues_list, edges=arcs)
         return influence_diagram
 
     def create_decision_tree(self, project_uuid: str) -> DecisionTreeResponse:
@@ -70,11 +70,11 @@ def create_decision_tree(self, project_uuid: str) -> DecisionTreeResponse:
         Returns
             DecisionTreeResponse: Dict of vertices
         """
-        influence_diagram = self.read_influence_diagram(project_uuid=project_uuid)
-        local_id = InfluenceDiagram.from_db(influence_diagram)
-        # local_id.to_json("id.json")
-        local_dt = local_id.convert_to_decision_tree()
-        # dt_json = json.loads(local_dt.to_json("dt.json"))
-        dt_json = json.loads(local_dt.to_json())
+        influence_diagram = self.read_influence_diagram(
+            project_uuid=project_uuid
+        ).model_dump()
+        local_id = InfluenceDiagramConversion().from_json(influence_diagram)
+        local_dt = InfluenceDiagramToDecisionTree().conversion(local_id)
+        dt_json = DecisionTreeConversion().to_json(local_dt)
         decision_tree = DecisionTreeResponse.model_validate(dt_json)
         return decision_tree
diff --git a/api/src/v0/services/structure_utils/decision_diagrams/decision_tree.py b/api/src/v0/services/structure_utils/decision_diagrams/decision_tree.py
deleted file mode 100644
index 00b0fd0..0000000
--- a/api/src/v0/services/structure_utils/decision_diagrams/decision_tree.py
+++ /dev/null
@@ -1,152 +0,0 @@
-"""Module defining the DecisionTree class
-
-A DecisionTree is a sub-class of ProbabilisticGraphModel.
-
-    Raises:
-        RootNodeNotFound: When trying to define a decision tree without giving the root
-                          node
-
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from typing import TYPE_CHECKING
-
-import networkx as nx
-
-from ..decision_diagrams.probabilistic_graph_model import ProbabilisticGraphModelABC
-
-if TYPE_CHECKING:  # pragma: no cover
-    from ..decision_diagrams.node import NodeABC
-
-
-logger = logging.getLogger(__name__)
-
-
-class RootNodeNotFound(Exception):
-    def __init__(self):
-        error_message = "Decision tree has no defined root node"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class DecisionTree(ProbabilisticGraphModelABC):
-    """Decision tree class"""
-
-    def __init__(self, *args, **kwargs):
-        """Create an instance of a DecisionTree
-            It is a wrapper around networkx.Digraph
-
-        Args:
-            *args, **kwargs: arguments for networkx.DiGraph
-
-        Attributes:
-            nx (networkx.DiGraph): networkx.DiGraph object containing the decision tree
-                                  (nodes and arcs)
-        """
-        super().__init__(*args, **kwargs)
-        self.root = kwargs.get("root", None)
-        if self.root is not None:
-            self.nx.add_node(self.root)
-
-    @classmethod
-    def initialize_diagram(cls, data: dict):
-        """Initialize a DecisionTree from data
-
-        It looks for the node without parent for setting it
-        first in the list of nodes
-
-        Args:
-            data (Dict): dictionary describing the graph model
-                {"nodes": List[NodeABC], "edges": List[Edge]}
-
-            Only the 'edges' attribute is actually used.
-
-        Returns:
-            DecisionTree: a new DecisionTree instance
-
-        Example:
-            >>> n0 = UncertaintyNode("Uncertainty node 0", "u0")
-            >>> n1 = UncertaintyNode("Uncertainty node 1", "u1")
-            >>> n2 = UncertaintyNode("Uncertainty node 2", "u2")
-            >>> n3 = DecisionNode("Decision node 0", "d0")
-            >>> n4 = DecisionNode("Decision node 1", "d1")
-            >>> n5 = DecisionNode("Decision node 2", "d2")
-            >>> e0 = Edge(n0, n1, name="e0")
-            >>> e1 = Edge(n0, n2, name="e1")
-            >>> e2 = Edge(n1, n3, name="e2")
-            >>> e3 = Edge(n1, n4, name="e3")
-            >>> e4 = Edge(n2, n5, name="e2")
-            >>> graph = {"nodes": [n0, n1, n2, n3, n4, n5], \
-            ...  "edges": [e0, e1, e2, e3, e4],}
-            >>> DecisionTree.initialize_diagram(graph)
-        """
-        roots = []
-        g = nx.DiGraph([(arc.endpoint_start, arc.endpoint_end) for arc in data["edges"]])
-        roots = [node for node in g.nodes if not list(g.predecessors(node))]
-        if not len(roots) == 1:
-            raise RootNodeNotFound
-        return cls(root=roots[0])
-
-    def set_root(self, root: NodeABC):
-        """set the root to a DecisionTree when this one has not been given
-            Typically, used if an empty decision tree is first generated and
-            then a node is given as its root.
-
-        Args:
-            root (NodeABC): the root node
-        """
-        self.root = root
-        if not self.nx.has_node(root):
-            self.add_node(root)
-
-    def parent(self, node: NodeABC) -> NodeABC | None:
-        """return the parent node in the Decition Tree
-
-        In a Decision Tree, a node has a unique parent
-        (The root doesn't have any)
-
-        Args:
-            node (NodeABC): node to find the parent of
-
-        Returns
-            Union[NodeABC, None]: the parent node or None is no parent node has been
-                                  found
-        """
-        parents = self.get_parents(node)
-        return parents[0] if len(parents) > 0 else None
-
-    def _to_json_stream(self) -> dict:
-        """convert the decision tree instance into a dictionary
-            It uses the method `networkx.readwrite.json_graph.tree_data()`
-
-        Raises:
-            RootNodeNotFound: Raised when no root has been set in the decision tree
-
-        Returns:
-            Dict: a representation of the tree
-        """
-
-        def propagate_branch_name(self, node, names):
-            predecessor = self.parent(node)
-            if not predecessor:
-                return ""
-            n = names[(predecessor, node)]
-            return n if isinstance(n, str) else "-".join(n)
-
-        if self.root is None:
-            raise RootNodeNotFound
-
-        edges_name = nx.get_edge_attributes(self.nx, "name")
-        tg = nx.readwrite.json_graph.tree_data(self.nx, self.root)
-        json_object = json.dumps(
-            tg,
-            default=lambda o: {
-                **o.to_dict(),
-                **{"branch_name": propagate_branch_name(self, o, edges_name)},
-            },
-            indent=2,
-        )
-        return json_object
diff --git a/api/src/v0/services/structure_utils/decision_diagrams/edge.py b/api/src/v0/services/structure_utils/decision_diagrams/edge.py
deleted file mode 100644
index 395c7dd..0000000
--- a/api/src/v0/services/structure_utils/decision_diagrams/edge.py
+++ /dev/null
@@ -1,197 +0,0 @@
-"""Edges classes
-
-Raises:
-    EndpointTypeError: When endpoint cannot be set to start or end
-    UtilityNodeSuccessorError: When a UtilityNode has a successor which is not another
-                               UtilityNode
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import TYPE_CHECKING
-
-from ..decision_diagrams.node import DecisionNode, UncertaintyNode, UtilityNode
-
-if TYPE_CHECKING:  # pragma: no cover
-    from ....models.edge import EdgeResponse
-    from ..decision_diagrams.node import NodeABC
-    from ..decision_diagrams.probabilistic_graph_model import ProbabilisticGraphModelABC
-
-
-logger = logging.getLogger(__name__)
-
-
-class EndpointTypeError(Exception):
-    def __init__(self, mode):
-        self.mode = mode
-        error_message = f"endpoint cannot be set to mode {mode}"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class UtilityNodeSuccessorError(Exception):
-    def __init__(self):
-        error_message = "utility node can only have other utility nodes as successor"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class Edge:
-    """Class of nodes"""
-
-    def __init__(
-        self,
-        endpoint_start: NodeABC,
-        endpoint_end: NodeABC,
-        name: str = None,
-        **kwargs,
-    ):
-        """Instance of Edge
-
-        Args:
-            endpoint_start (NodeABC): start node of the edge
-            endpoint_end (NodeABC): end node of the edge
-            name (str, optional): name of the edge. Defaults to None.
-
-        Private attributes:
-            _endpoint_start (NodeABC): start node of the edge
-            _endpoint_end (NodeABC): end node of the edge
-            _name (str, optional): name of the edge. Defaults to None.
-            _arc_type (str): type of the arc/edge, depending on the head node
-
-        """
-        self._endpoint_start = endpoint_start
-        self._endpoint_end = endpoint_end
-        self._name = name
-        self._arc_type = None
-        self._set_arc_type()
-
-    @classmethod
-    def from_dict(cls, edge: dict, pgm: ProbabilisticGraphModelABC):
-        """Create an Edge instance from dictionary information
-
-        endpoint nodes need to have already been added to the graph
-
-        Args
-            edge (Dict): description of the edage as dictionary.
-                    'from' the uuid of the start endpoint of the edge (tail)
-                    'to' the uuid of the end endpoint of the edge (head)
-                    'name' the name of the edge
-            pgm (ProbabilisticGraphModel): the diagram into which the edge is added.
-                                           This is only used to get the nodes from
-                                           their uuid's.
-
-        Returns:
-            Edge: a new edge instance is created.
-
-        :warning: The Edge is not added to the diagram !!!
-        """
-        endpoint_start = pgm.get_node_from_uuid(edge["from"])
-        endpoint_end = pgm.get_node_from_uuid(edge["to"])
-        attributes = {k: v for k, v in edge.items() if k not in ["from", "to", "id"]}
-        return cls(endpoint_start, endpoint_end, attributes.get("name", None))
-
-    @property
-    def endpoint_start(self) -> NodeABC:
-        """
-        Returns:
-            NodeABC: the node at start of the edge (tail)
-        """
-        return self._endpoint_start
-
-    @property
-    def endpoint_end(self) -> NodeABC:
-        """
-        Returns:
-            NodeABC: the node at end of the edge (head)
-        """
-        return self._endpoint_end
-
-    @property
-    def name(self) -> str:
-        """
-        Returns:
-            str: the name of the edge
-        """
-        return self._name
-
-    def copy(self):
-        """copy an edge
-
-        Returns:
-            Edge: copied edge, endpoints keeping their uuids
-        """
-        # need to create the edge and then fill it so that
-        # the endpoints are the same including uuid
-        edge = Edge(None, None, self.name)
-        edge.set_endpoint(self.endpoint_start, mode="start")
-        edge.set_endpoint(self.endpoint_end, mode="end")
-        return edge
-
-    def _set_arc_type(self):
-        """set (if possible) the type of the arc/edge (informational/conditional)"""
-        if isinstance(self._endpoint_end, DecisionNode):
-            self._arc_type = "informational"
-        elif isinstance(self._endpoint_end, UncertaintyNode):
-            self._arc_type = "conditional"
-        elif isinstance(self._endpoint_end, UtilityNode):
-            self._arc_type = "functional"
-
-    def set_endpoint(self, node: NodeABC, mode="end"):
-        """Add an endpoint to an edge
-
-            When creating an Edge, it does not need to have endpoints. Those endpoints
-            can be added at a later stage. This is actually necessary when converting an
-            influence diagram to a decision tree.
-
-        Args:
-            node (NodeABC): node to add as an endpoint
-            mode (str, optional): where to add the node ['start'|'end'].
-                                  Defaults to "end".
-
-        Raises:
-            EndpointTypeError: _description_
-            UtilityNodeSuccessorError: _description_
-        """
-        if mode == "end":
-            self._endpoint_end = node
-        elif mode == "start":
-            self._endpoint_start = node
-        else:
-            raise EndpointTypeError(mode)
-        if mode == "end" and self._endpoint_start.is_utility_node:
-            if not node.is_utility_node:
-                raise UtilityNodeSuccessorError
-
-        self._set_arc_type()
-
-    def to_nx(self) -> tuple[tuple[NodeABC, NodeABC], dict]:
-        """convert the edge to a format compatible with the networkx format
-
-        Returns:
-            Tuple[Tuple[NodeABC, NodeABC], Dict]: The Tuple[NodeABC, NodeABC] represents
-                                                  the edge itself, and the Dictionary
-                                                  adds extra information to the edge.
-        """
-        return (self._endpoint_start, self._endpoint_end), {
-            "arc_type": self._arc_type,
-            "name": self._name,
-        }
-
-    @classmethod
-    def from_db(cls, response: EdgeResponse, nodes: list[NodeABC]):
-        """create an Edges defined as an EdgeResponse and given a list of NodeABC
-
-        Args:
-            response (EdgeResponse): response from DataBase defining the edge
-            nodes (list[NodeABC]): nodes existing in the diagram
-
-        Returns
-            Edge: An Edge between existing nodes (NodeABC)
-        """
-        uuid_tail = response.outV
-        uuid_head = response.inV
-        tail = [node for node in nodes if node.uuid == uuid_tail][0]
-        head = [node for node in nodes if node.uuid == uuid_head][0]
-        return Edge(tail, head, **response.__dict__)
diff --git a/api/src/v0/services/structure_utils/decision_diagrams/influence_diagram.py b/api/src/v0/services/structure_utils/decision_diagrams/influence_diagram.py
deleted file mode 100644
index 3d6406f..0000000
--- a/api/src/v0/services/structure_utils/decision_diagrams/influence_diagram.py
+++ /dev/null
@@ -1,402 +0,0 @@
-"""Module defining the InfluenceDiagram class
-
-An InfluenceDiagram is a sub-class of ProbabilisticGraphModel.
-
-    Raises:
-        PartialOrderOutputModeError: When input to the partial_order method is wrong
-
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from typing import TYPE_CHECKING
-
-import networkx as nx
-import pyAgrum as gum
-
-from ..decision_diagrams.decision_tree import DecisionTree
-from ..decision_diagrams.edge import Edge
-from ..decision_diagrams.node import DecisionNode, NodeABC, UncertaintyNode, UtilityNode
-from ..decision_diagrams.probabilistic_graph_model import ProbabilisticGraphModelABC
-
-if TYPE_CHECKING:  # pragma: no cover
-    from ....models.structure import InfluenceDiagramResponse
-
-
-logger = logging.getLogger(__name__)
-
-
-class PartialOrderOutputModeError(Exception):
-    def __init__(self, mode):
-        self.mode = mode
-        error_message = (
-            f"output mode should be [view|copy] and have been entered as {mode}"
-        )
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class InfluenceDiagramNotAcyclicError(Exception):
-    def __init__(self):
-        error_message = "the influence diagram is not acyclic."
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class ProbabilityFormatError(Exception):
-    def __init__(self, error):
-        self.mode = error
-        error_message = (
-            f"Input probability cannot be used in pyagrum with error: {error}"
-        )
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class ArcFormatError(Exception):
-    def __init__(self, error):
-        self.mode = error
-        error_message = f"Input arc cannot be used in pyagrum with error: {error}"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class InfluenceDiagram(ProbabilisticGraphModelABC):
-    """Influence Diagram"""
-
-    def __init__(self, *args, **kwargs):
-        """
-        Create an instance of an InfluenceDiagram
-        It is a wrapper around networkx.Digraph
-
-        Args:
-            *args, **kwargs: arguments for networkx.DiGraph
-
-        Attributes:
-            nx (networkx.DiGraph): networkx.DiGraph object containing
-                                   the influence diagram (nodes and arcs)
-        """
-        super().__init__(*args, **kwargs)
-
-    @classmethod
-    def initialize_diagram(cls, data: dict):
-        """initialize an influence diagram given nodes and edges in a dictionary
-
-        Args:
-            data (Dict): dictionary describing the graph model
-                {"nodes": List[NodeABC], "edges": List[Edge]}
-
-        Example:
-            >>> n0 = UncertaintyNode("Uncertainty node 0", "u0")
-            >>> n1 = UncertaintyNode("Uncertainty node 1", "u1")
-            >>> n2 = DecisionNode("Decision node 0", "d0")
-            >>> n3 = UncertaintyNode("Uncertainty node 2", "u2")
-            >>> e0 = Edge(n0, n2, name="e0")
-            >>> e1 = Edge(n1, n2, name="e1")
-            >>> e2 = Edge(n2, n3, name="e2")
-            >>> graph = {"nodes": [n0, n1, n2, n3], "edges": [e0, e1, e2],}
-            >>> InfluenceDiagram.initialize_diagram(graph)
-        """
-        return cls()
-
-    def get_decision_nodes(self) -> list[DecisionNode]:
-        """Return a list of decision nodes
-
-        Returns:
-            List[DecisionNode]
-        """
-        return self._get_nodes_from_type("DecisionNode")
-
-    def get_uncertainty_nodes(self) -> list[UncertaintyNode]:
-        """Return a list of uncertainty nodes
-
-        Returns:
-            List[UncertaintyNode]
-        """
-        return self._get_nodes_from_type("UncertaintyNode")
-
-    def get_utility_nodes(self) -> list[UtilityNode]:
-        """Return a list of utility nodes
-
-        Returns:
-            List[UtilityNode]
-        """
-        return self._get_nodes_from_type("UtilityNode")
-
-    @property
-    def decision_count(self) -> int:
-        """Return the number of decision nodes
-
-        Returns:
-            int: the number of DecisionNode objects in the influence diagram
-        """
-        return len(self.get_decision_nodes())
-
-    @property
-    def uncertainty_count(self) -> int:
-        """Return the number of uncertainty nodes
-
-        Returns:
-            int: the number of UncertaintyNode objects in the influence diagram
-        """
-        return len(self.get_uncertainty_nodes())
-
-    @property
-    def utility_count(self) -> int:
-        """Return the number of utility nodes
-
-        Returns:
-            int:  the number of UtilityNode objects in the influence diagram
-        """
-        return len(self.get_utility_nodes())
-
-    def _to_json_stream(self) -> dict:
-        """convert the influence diagram instance into a dictionary
-            It uses the method `networkx.node_link_data()`
-
-        Returns:
-            Dict: a representation of the diagram
-        """
-        data = nx.node_link_data(self.nx, source="from", target="to", link="edges")
-        data["edges"] = [
-            {k: v if not isinstance(v, NodeABC) else v.uuid for k, v in e.items()}
-            for e in data["edges"]
-        ]
-        json_object = json.dumps(data, default=lambda o: o.to_dict(), indent=4)
-        return json_object
-
-    @classmethod
-    def from_db(cls, response: InfluenceDiagramResponse):
-        """Create a diagram from the DataBase Response
-
-        Args:
-            response (InfluenceDiagramResponse): The influence diagram as read from
-                                                 the database
-
-        Returns:
-            ProbabilisticGraphModelABC: the decision diagram
-
-        Example:
-            Assume the database returns an influence diagram described in the
-            `json_stream`
-
-            >>> influence_diagram_response = \
-            ... InfluenceDiagramResponse(vertices=json_stream['vertices'], \
-            ... edges=json_stream['edges'])
-            >>> InfluenceDiagram.from_db(influence_diagram_response)
-        """
-        nodes = [NodeABC.from_db(vertex) for vertex in response.vertices]
-        arcs = [Edge.from_db(edge, nodes) for edge in response.edges]
-        return cls.from_dict({"nodes": nodes, "edges": arcs})
-
-    def decision_elimination_order(self) -> list[NodeABC]:
-        """Decision Elimination Order algorithm
-
-        Returns:
-            List[NodeABC] : the decision elimination order graph associated to
-                            the influence diagram. Nodes in the list are copies of the
-                            nodes of the influence diagram ones.
-
-        TODO: add description of what is the algorithm about
-        """
-        cid_copy = self.copy()
-        decisions = []
-        decisions_count = cid_copy.decision_count
-        while decisions_count > 0:
-            nodes = list(cid_copy.nx.nodes())
-            for node in nodes:
-                if not cid_copy.has_children(node):
-                    if node.is_decision_node:
-                        decisions.append(node)
-                        decisions_count -= 1
-                    cid_copy.nx.remove_node(node)
-        return decisions
-
-    def calculate_partial_order(self, mode="view") -> list[NodeABC]:
-        """Partial order algorithm
-
-
-        Args:
-            mode (str): ["view"(default)|"copy"]
-                returns a view or a copy of the nodes
-
-        Returns
-            List[NodeABC]: list of nodes (copies or vioews) sorted in decision order
-
-        TODO: add description of what the algorithm is about
-        TODO: handle utility nodes
-        """
-        if mode not in ["view", "copy"]:
-            raise PartialOrderOutputModeError(mode)
-
-        # get all chance nodes
-        uncertainty_node = self.get_uncertainty_nodes()
-        elimination_order = self.decision_elimination_order()
-        # TODO: Add utility nodes
-        partial_order = []
-
-        while elimination_order:
-            decision = elimination_order.pop()
-            parent_decision_nodes = []
-            for parent in self.get_parents(decision):
-                if not parent.is_decision_node:
-                    if parent in uncertainty_node:
-                        parent_decision_nodes.append(parent)
-                        uncertainty_node.remove(parent)
-
-            if len(parent_decision_nodes) > 0:
-                partial_order += parent_decision_nodes
-            partial_order.append(decision)
-
-        partial_order += uncertainty_node
-
-        if mode == "copy":
-            partial_order = [node.copy() for node in partial_order]
-
-        return partial_order
-
-    def _output_branches_from_node(
-        self, node: NodeABC, node_in_partial_order: NodeABC, flip=True
-    ) -> list[tuple[Edge, NodeABC]]:
-        """Make a list of output branches from a node
-
-            This method actually returns the states of the nodes.
-
-        Args
-            node (NodeABC): node to find the output branch from
-            node_in_partial_order (NodeABC): associated node in the partial order - to
-                                             keep reference too
-            flip (bool): if True (default), flip the list of branches so a generated
-                         decision tree is in the same order as the entered states.
-                         If False, the tree will be flipped horizontally.
-
-        Returns
-            List: the list of tuples (Edges, Node in partial order)
-                The edges have the input node as start endpoint and name given by the
-                state
-        """
-        if node.is_utility_node:
-            tree_stack = [Edge(node, None, name=utility) for utility in node.utility]
-        if node.is_decision_node:
-            tree_stack = [
-                Edge(node, None, name=alternative) for alternative in node.alternatives
-            ]
-        if node.is_uncertainty_node:
-            # This needs to be re-written according to the way we deal with probabilities
-            tree_stack = [Edge(node, None, name=outcome) for outcome in node.outcomes]
-        if flip:
-            tree_stack.reverse()
-
-        return zip(tree_stack, [node_in_partial_order] * len(tree_stack), strict=False)
-
-    def convert_to_decision_tree(self) -> DecisionTree:
-        """Convert the influence diagram into a DecisionTree object
-
-        Returns:
-            DecisionTree: The symmetric decision tree equivalent to the influence diagram
-
-        TODO: Update ID2DT according to way we deal with probabilities
-        """
-        partial_order = self.calculate_partial_order()
-        root_node = partial_order[0]
-        # decision_tree = DecisionTree.initialize_with_root(root_node)
-        decision_tree = DecisionTree(root=root_node)
-        # tree_stack contains views of the partial order nodes
-        # decision_tree contains copy of the nodes (as they appear several times)
-        tree_stack = [(root_node, root_node)]
-
-        while tree_stack:
-            element = tree_stack.pop()
-
-            if isinstance(element[0], NodeABC):
-                tree_stack += self._output_branches_from_node(*element)
-
-            else:  # element is a branch
-                endpoint_start_index = partial_order.index(element[1])
-
-                if endpoint_start_index < len(partial_order) - 1:
-                    endpoint_end = partial_order[endpoint_start_index + 1].copy()
-                    tree_stack.append(
-                        (endpoint_end, partial_order[endpoint_start_index + 1])
-                    )
-                else:
-                    # endpoint_end = UtilityNode(
-                    #     name=element[0].name, tag=element[0].name.lower()
-                    #                           )
-                    endpoint_end = UtilityNode(shortname="ut", description="Utility")
-
-                element[0].set_endpoint(endpoint_end)
-                decision_tree.add_edge(
-                    element[0]
-                )  # node is added when the branch is added
-
-        return decision_tree
-
-    @staticmethod
-    def _nodes_to_pyagrum(nodes, gum_id):
-        # create an uuid for gum as 8 bytes integer and keep relation to uuid
-        uuid_dot_to_gum = {}
-        uuid_gum_to_dot = {}
-        node_uuid = {}
-
-        for node in nodes:
-            labelized_variables = [node.shortname, node.description]
-            if isinstance(node, UncertaintyNode):
-                try:
-                    labelized_variables.append(node.outcomes)
-                    variable_id = gum_id.addChanceNode(
-                        gum.LabelizedVariable(*labelized_variables)
-                    )
-                except Exception as e:
-                    raise ProbabilityFormatError(e)
-            elif isinstance(node, DecisionNode):
-                # This works even when alternatives are [""] or None
-                labelized_variables.append(node.alternatives)
-                variable_id = gum_id.addDecisionNode(
-                    gum.LabelizedVariable(*labelized_variables)
-                )
-            elif isinstance(node, UtilityNode):
-                # Utility not yet implemented
-                labelized_variables.append(1)
-                variable_id = gum_id.addUtilityNode(
-                    gum.LabelizedVariable(*labelized_variables)
-                )
-
-            uuid_dot_to_gum[node.uuid] = variable_id
-            uuid_gum_to_dot[variable_id] = node.uuid
-            node_uuid[variable_id] = node
-
-        return uuid_dot_to_gum, uuid_gum_to_dot, node_uuid
-
-    @staticmethod
-    def _arcs_to_pyagrum(edges, gum_id, uuid_dot_to_gum):
-        for arc in edges:
-            tail = uuid_dot_to_gum[arc[0].uuid]
-            head = uuid_dot_to_gum[arc[1].uuid]
-            try:
-                gum_id.addArc(tail, head)
-            except Exception as e:
-                raise ArcFormatError(e)
-        return None
-
-    def to_pyagrum(self):
-        if not nx.is_directed_acyclic_graph(self.nx):
-            raise InfluenceDiagramNotAcyclicError
-
-        gum_id = gum.InfluenceDiagram()
-        variable_id = []
-
-        uuid_dot_to_gum, uuid_gum_to_dot, node_uuid = InfluenceDiagram._nodes_to_pyagrum(
-            self.nx.nodes, gum_id
-        )
-        # Add head and tail in gum_id
-        InfluenceDiagram._arcs_to_pyagrum(self.nx.edges, gum_id, uuid_dot_to_gum)
-
-        for variable_id in uuid_gum_to_dot:
-            if isinstance(node_uuid[variable_id], UncertaintyNode):
-                for agrum_prob in node_uuid[variable_id].probabilities.to_pyagrum():
-                    gum_id.cpt(variable_id)[agrum_prob[0]] = agrum_prob[1]
-
-        return gum_id
diff --git a/api/src/v0/services/structure_utils/decision_diagrams/node.py b/api/src/v0/services/structure_utils/decision_diagrams/node.py
deleted file mode 100644
index 74d4ec7..0000000
--- a/api/src/v0/services/structure_utils/decision_diagrams/node.py
+++ /dev/null
@@ -1,467 +0,0 @@
-"""Nodes classes
-
-NodeABC is an abstract class with 3 concretizations:
-- DecisionNode
-- UncertaintyNode
-- UtilityNode
-
-    Raises:
-        NodeTypeError: When failing to create an instance of a node
-"""
-
-from __future__ import annotations
-
-import ast
-import logging
-import re
-from abc import ABC, abstractmethod
-from importlib import import_module
-from typing import TYPE_CHECKING
-from uuid import uuid4
-
-from ..probability.discrete_conditional_probability import DiscreteConditionalProbability
-from ..probability.discrete_unconditional_probability import (
-    DiscreteUnconditionalProbability,
-)
-
-if TYPE_CHECKING:  # pragma: no cover
-    from ....models.issue import IssueResponse
-    from ..probability.abstract_probability import ProbabilityABC
-
-
-logger = logging.getLogger(__name__)
-
-
-class NodeTypeError(Exception):
-    def __init__(self, node_type):
-        error_message = f"failing instantiation of {node_type}"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class NodeABC(ABC):
-    """Abstract class of nodes"""
-
-    def __init__(self, shortname: str, description: str, unique_id=None):
-        """instantiation of an (abstract) node
-
-        Args:
-            description (str): description of the node
-            shortname (str): shortname of the node
-            unique_id (str, optional): uuid. Defaults to None. If None, the uuid is
-                                       attributed at instantiation.
-
-        Private attributes:
-            _description (str): description of the node
-            _shortname (str): shortname of the node
-            _unique_id (str, optional): uuid. Defaults to None. If None, the uuid is
-                                        attributed at instantiation.
-        """
-        self._shortname = shortname
-        self._description = description
-        self._uuid = unique_id if unique_id is not None else str(uuid4())
-
-    @property
-    def description(self) -> str:
-        """Return the `description` attribute
-
-        Returns:
-            str: description of the node
-        """
-        return self._description
-
-    @property
-    def shortname(self) -> str:
-        """Return the `shortname` attribute
-
-        Returns:
-            str: shortname of the node
-        """
-        return self._shortname
-
-    @property
-    def uuid(self) -> str:
-        """Return the `uuid` attribute
-
-        Returns:
-            str: uuid of the node
-        """
-        return self._uuid
-
-    @property
-    def is_decision_node(self) -> bool:
-        """
-        Returns:
-            bool: True of the node is a DecisionNode, false otherwise
-        """
-        return isinstance(self, DecisionNode)
-
-    @property
-    def is_uncertainty_node(self) -> bool:
-        """
-        Returns:
-            bool: True of the node is a UncertaintyNode, false otherwise
-        """
-        return isinstance(self, UncertaintyNode)
-
-    @property
-    def is_utility_node(self) -> bool:
-        """
-        Returns:
-            bool: True of the node is a UtilityNode, false otherwise
-        """
-        return isinstance(self, UtilityNode)
-
-    @property
-    @abstractmethod
-    def states(self):
-        raise NotImplementedError
-
-    def copy(self):
-        """Copy of the node
-
-        Returns:
-            NodeABC: a copy of the node, with a new uuid
-        """
-        shortname = self.shortname
-        description = self.description
-        copied_node = type(self)(shortname, description)
-        for attribute, value in self.__dict__.items():
-            if attribute not in ["_shortname", "_description", "_uuid"]:
-                setattr(copied_node, attribute, value)
-        return copied_node
-
-    def to_dict(self) -> dict:
-        """Convert the Node object into a dictionary
-
-        Each property becomes the key of a dictionary.
-        Private and restricted attributes loose their underscores
-
-        Returns:
-            Dict: a dictionary representing the node
-        """
-        d = {"node_type": self.__class__.__name__}
-        properties = self.__dict__
-        for property in properties:
-            key = property.lstrip("_")
-            if hasattr(getattr(self, key, None), "to_dict"):
-                d[key] = getattr(self, key).to_dict()
-            else:
-                d[key] = getattr(self, key, None)
-        return d
-
-    @staticmethod
-    @abstractmethod
-    def get_instance_input(issue):
-        raise NotImplementedError
-
-    @staticmethod
-    def from_dict(data: dict) -> NodeABC:
-        """Create a Node from data in a dictionary
-
-        Args
-            data (Dict): dictionary describing the node
-                keys are:
-                'node_type': the name of the Node instance as a string
-                relevant keys according to the type of node
-
-        Returns
-            NodeABC: the created concrete type of Node
-
-        Example:
-            >>> node = {
-            ...     'node_type': 'DecisionNode',
-            ...     'description': "a decision",
-            ...     'shortname': "D"
-            ...        }
-            >>> Node.from_dict(node)
-        """
-        node_type = data.pop("node_type")
-        try:
-            node = globals()[node_type](**data)
-        except Exception:
-            raise NodeTypeError(node_type)
-        return node
-
-    @staticmethod
-    def from_db(response: IssueResponse) -> NodeABC:
-        """Create a node from DataBase data
-
-        Args:
-            response (IssueResponse): response describing the node
-                attributes are:
-                'category': the name of the Node instance as a string
-                relevant attributes according to the type of node
-
-        Returns
-            NodeABC: the created concrete type of Node
-        """
-        node_type = response.category
-        node_type = "Utility" if node_type == "Value Metric" else node_type
-        node_type += "Node"
-        try:
-            issue_data = globals()[node_type].get_instance_input(response)
-        except Exception:
-            raise NodeTypeError(node_type)
-
-        issue_data_parsed = {}
-        for k, v in issue_data.items():
-            try:
-                v = ast.literal_eval(v)
-            except Exception as exc:
-                e = exc
-            finally:
-                if e:
-                    pass  # trick for passing ruff and bandit...
-                issue_data_parsed[k] = v
-        node = globals()[node_type]._from_db_model(**issue_data_parsed)
-        return node
-
-    @classmethod
-    @abstractmethod
-    def _from_db_model(cls, **kwargs):
-        raise NotImplementedError
-
-
-class DecisionNode(NodeABC):
-    """Decision node class"""
-
-    def __init__(
-        self,
-        shortname: str,
-        description: str,
-        uuid=None,
-        alternatives=None,
-        **kwargs,
-    ):
-        """Create an instance of a DecisionNode
-
-        Args:
-            description (str): description of the node
-            shortname (str): shortname of the node
-            unique_id (str, optional): uuid. Defaults to None. If None, the uuid is
-                                       attributed at instantiation.
-            alternatives (List[str], optional): alternatives (states) of the decision.
-                                                Defaults to None.
-
-        Private attributes:
-            _description (str): description of the node
-            _shortname (str): shortname of the node
-            _unique_id (str, optional): uuid. Defaults to None. If None, the uuid is
-                                        attributed at instantiation.
-            _alternatives (List[str], optional): alternatives (states) of the decision.
-                                                 Defaults to None.
-        """
-        super().__init__(shortname, description, uuid)
-        self._alternatives = alternatives
-
-    @staticmethod
-    def get_instance_input(issue: IssueResponse) -> dict:
-        """Get the relevant input for the instantiation
-
-        Args:
-            issue (IssueResponse): issue from the DataBase
-
-        Returns:
-            Dict: keys/values of the DataBase issue relevant for the instantiation
-        """
-        input_list = ["description", "shortname", "uuid", "alternatives"]
-        return {key: getattr(issue, key) for key in input_list}
-
-    @property
-    def states(self) -> list[str]:
-        """
-        Returns:
-            List[str]: the alternatives (states) of the decision
-        """
-        return self.alternatives
-
-    @property
-    def alternatives(self) -> list[str]:
-        """
-        Returns:
-            List[str]: the alternatives (states) of the decision
-        """
-        return self._alternatives if isinstance(self._alternatives, list) else []
-
-    @classmethod
-    def _from_db_model(cls, **kwargs):
-        """instantiation from the DataBase Model
-
-        Args:
-            kwargs: keys/values of the DataBase issue relevant for the instantiation
-        """
-        return cls(**kwargs)
-
-
-class UncertaintyNode(NodeABC):
-    """Uncertainty (or chance) node class"""
-
-    def __init__(
-        self,
-        shortname: str,
-        description: str,
-        uuid=None,
-        probabilities: ProbabilityABC = None,
-        **kwargs,
-    ):
-        """Create an instance of a UncertaintyNode
-
-        Args:
-            description (str): description of the node
-            shortname (str): shortname of the node
-            unique_id (str, optional): uuid. Defaults to None. If None, the uuid is
-                                       attributed at instantiation.
-            probabilities (ProbabilityABC, optional): probability associated to the node.
-                                                      Defaults to None.
-
-        Private attributes:
-            _description (str): description of the node
-            _shortname (str): shortname of the node
-            _unique_id (str, optional): uuid. Defaults to None. If None, the uuid is
-                                        attributed at instantiation.
-            _probabilities (ProbabilityABC, optional): probability associated to the
-                                                       node. Defaults to None.
-        """
-        super().__init__(shortname, description, uuid)
-        self._probabilities = probabilities
-
-    @staticmethod
-    def get_instance_input(issue: IssueResponse) -> dict:
-        """Get the relevant input for the instantiation
-
-        Args:
-            issue (IssueResponse): issue from the DataBase
-
-        Returns:
-            Dict: keys/values of the DataBase issue relevant for the instantiation
-        """
-        input_list = ["description", "shortname", "uuid", "probabilities"]
-        return {key: getattr(issue, key) for key in input_list}
-
-    @property
-    def probabilities(self) -> ProbabilityABC:
-        """
-        Returns:
-            ProbabilityABC: the probabibility associated to the node
-        """
-        return self._probabilities
-
-    @property
-    def states(self) -> list[str]:
-        """
-        Returns:
-            List[str]: the outcomes (states) of the uncertainty
-        """
-        return self.outcomes
-
-    @property
-    def outcomes(self) -> list[str]:
-        """
-        Returns:
-            List[str]: the outcomes (states) of the uncertainty
-        """
-        if not isinstance(
-            self._probabilities, DiscreteConditionalProbability
-        ) and not isinstance(self._probabilities, DiscreteUnconditionalProbability):
-            return ()
-        return self._probabilities.outcomes
-
-    @classmethod
-    def _from_db_model(cls, **kwargs):
-        """instantiation from the DataBase Model
-
-        Args:
-            kwargs: keys/values of the DataBase issue relevant for the instantiation
-        """
-        if kwargs["probabilities"] is not None:
-            ptype = kwargs["probabilities"].dtype
-            module_name = (
-                "src.v0.services.structure_utils.probability."
-                + re.sub(r"([a-z](?=[A-Z])|[A-Z](?=[A-Z][a-z]))", r"\1 ", ptype)
-                .replace(" ", "_")
-                .lower()
-            )
-            kwargs["probabilities"] = getattr(
-                import_module(module_name), ptype
-            ).from_db_model(kwargs["probabilities"])
-        return cls(**kwargs)
-
-
-class UtilityNode(NodeABC):
-    """Utility (or value) node class"""
-
-    def __init__(
-        self,
-        shortname: str,
-        description: str,
-        uuid=None,
-        utility=None,
-        **kwargs,
-    ):
-        """Create an instance of a UtilityNode
-
-        Args:
-            description (str): description of the node
-            shortname (str): shortname of the node
-            unique_id (str, optional): uuid. Defaults to None. If None, the uuid is
-                                             attributed at instantiation.
-            utility (List[str], optional): utility associated to the node. Defaults to
-                                           None.
-
-        Private attributes:
-            _description (str): description of the node
-            _shortname (str): shortname of the node
-            _unique_id (str, optional): uuid. Defaults to None. If None, the uuid is
-                                        attributed at instantiation.
-            _utility (List[str], optional): utility associated to the node. Defaults to
-                                            None.
-
-        TODO: implement utility matrix attribute through a Utility class. So far it is
-              rather a place holder.
-        """
-        super().__init__(shortname, description, uuid)
-        self._utility = utility  # The value is typically E[gain] but we could minimize
-        # the risk instead of maximizing the gain
-
-    @staticmethod
-    def get_instance_input(issue: IssueResponse) -> dict:
-        """Get the relevant input for the instantiation
-
-        Args:
-            issue (IssueResponse): issue from the DataBase
-
-        Returns:
-            Dict: keys/values of the DataBase issue relevant for the instantiation
-        """
-        input_list = [
-            "description",
-            "shortname",
-            "uuid",
-        ]  # TODO: Add utility field to DB
-        return {key: getattr(issue, key) for key in input_list}
-
-    @property
-    def states(self) -> list[str]:
-        """
-        Returns:
-            List[str]: the utility entries (states) of the utility
-        """
-        return self.utility
-
-    @property
-    def utility(self) -> list[str]:
-        """
-        Returns:
-            List[str]: the utility entries (states) of the utility
-        """
-        return self._utility if isinstance(self._utility, list) else []
-
-    @classmethod
-    def _from_db_model(cls, **kwargs):
-        """instantiation from the DataBase Model
-
-        Args:
-            kwargs: keys/values of the DataBase issue relevant for the instantiation
-        """
-        return cls(**kwargs)
diff --git a/api/src/v0/services/structure_utils/decision_diagrams/probabilistic_graph_model.py b/api/src/v0/services/structure_utils/decision_diagrams/probabilistic_graph_model.py
deleted file mode 100644
index c9c580c..0000000
--- a/api/src/v0/services/structure_utils/decision_diagrams/probabilistic_graph_model.py
+++ /dev/null
@@ -1,184 +0,0 @@
-"""Module defining the ProbabilisticGraphModel Abstract class"""
-
-from __future__ import annotations
-
-import importlib
-import logging
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import TYPE_CHECKING
-
-import networkx as nx
-
-if TYPE_CHECKING:  # pragma: no cover
-    from ..decision_diagrams.edge import Edge
-    from ..decision_diagrams.node import NodeABC
-
-
-logger = logging.getLogger(__name__)
-
-
-class ProbabilisticGraphModelABC(ABC):
-    NODES_MODULE_PATH = "src.v0.services.structure_utils.decision_diagrams.node"
-
-    """Probabilistic Graph Model"""
-
-    def __init__(self, *args, **kwargs):
-        """
-        Create an instance of a ProbabilisticGraphModel (ABSTRACT class!!!)
-        It is a wrapper around networkx.Digraph
-
-        Args:
-            *args, **kwargs: arguments for networkx.DiGraph
-
-        Attributes:
-            nx: networkx object
-        """
-        self.nx = nx.DiGraph(*args, **kwargs)
-
-    @classmethod
-    def from_dict(cls, data: dict):
-        """Create a probabilistic graph model from data in a dictionary
-
-        Args:
-            data (Dict): dictionary describing the graph model
-                {"nodes": List[NodeABC], "edges": List[Edge]}
-
-        Returns:
-            ProbabilisticGraphModelABC: the decision diagram
-        """
-        diagram = cls.initialize_diagram(data)
-        for node in data["nodes"]:
-            diagram.add_node(node)
-        for edge in data.get("edges", []):
-            diagram.add_edge(edge)
-        return diagram
-
-    @classmethod
-    @abstractmethod
-    def initialize_diagram(cls, data):
-        """Initialize a diagram with data"""
-        raise NotImplementedError
-
-    def add_node(self, node: NodeABC):
-        """Add a node to the graph
-
-        Args:
-            node (NodeABC): node to be added
-        """
-        self.nx.add_node(node)
-
-    def add_edge(self, edge: Edge):
-        """Add an edge to the graph
-
-        Args:
-            edge (Edge): Edge to be added. If some of the end points do not exist, they
-                         are added to the graph too.
-        """
-        nx_edge, nx_attributes = edge.to_nx()
-        self.nx.add_edge(nx_edge[0], nx_edge[1], **nx_attributes)
-
-    def copy(self):
-        """copy the probabilistic graph model
-
-        Returns
-            ProbabilisticGraphModel: a copy of the graph. uuid's of the nodes are copied
-                                     too.
-        """
-        new_id = type(self)()  # Need to instance from the concrete class
-        new_id.nx = self.nx.copy()
-        return new_id
-
-    def get_parents(self, node: NodeABC) -> list[NodeABC]:
-        """get parents of a given node
-
-        Args:
-            node (NodeABC): node to find the parents of
-
-        Returns
-            List[NodeABC]: the list of the parents of the given node
-        """
-        return list(self.nx.predecessors(node))
-
-    def get_children(self, node: NodeABC) -> list[NodeABC]:
-        """get children of a given node
-
-        Args:
-            node (NodeABC): node to find the children of
-
-        Returns
-            List[NodeABC]: the list of the children of the given node
-        """
-        return list(self.nx.successors(node))
-
-    def get_node_type(self, node: NodeABC) -> str:
-        """Return the type of a given node
-
-        Args:
-            node (NodeABC): node to examine
-
-        Returns
-            str: a string describing the type of the node (class name in lower font and
-                 without the "Node" suffix)
-        """
-        return type(node).__name__.replace("Node", "").lower()
-
-    def _get_nodes_from_type(self, node_type_string: str) -> list[NodeABC]:
-        """find all the nodes of a given type
-
-        Args:
-            node_type_str (str): type of the nodes to find as a string
-
-        Returns
-            List[NodeABC]: the list of the nodes of given type
-        """
-        node_type = getattr(
-            importlib.import_module(self.NODES_MODULE_PATH), node_type_string
-        )
-        node_list = []
-        for node in list(self.nx.nodes(data=True)):
-            if isinstance(node[0], node_type):
-                node_list.append(node[0])
-        return node_list
-
-    def has_children(self, node: NodeABC) -> bool:
-        """Check for existence of children
-
-        Args:
-            node (NodeABC): node to check for children
-
-        Returns
-            bool: existence (True) or not (False) of children to the given node
-        """
-        return len(self.get_children(node)) > 0
-
-    def to_json(self, filepath: Path = None) -> str:
-        """Convert the graph into a json object
-
-        Args:
-            filepath (Path, optional): Path where to copy the json file. None if no
-                                       output file.
-
-        Returns:
-            str: json object as a string
-        """
-        json_object = self._to_json_stream()
-        if filepath:
-            with open(filepath, "w") as outfile:
-                outfile.write(json_object)
-        return json_object
-
-    @abstractmethod
-    def _to_json_stream(self):
-        raise NotImplementedError
-
-    def get_node_from_uuid(self, uuid: str) -> NodeABC:
-        """get a node given its uuid
-
-        Args:
-            uuid (str): uuid of node to look for
-
-        Returns:
-            NodeABC: node object having the given uuid
-        """
-        return [node for node in self.nx if node.uuid == uuid][0]
diff --git a/api/src/v0/services/structure_utils/probability/abstract_probability.py b/api/src/v0/services/structure_utils/probability/abstract_probability.py
deleted file mode 100644
index f7cd486..0000000
--- a/api/src/v0/services/structure_utils/probability/abstract_probability.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import logging
-from abc import ABC, abstractmethod
-
-logger = logging.getLogger(__name__)
-
-
-class ProbabilityABC(ABC):
-    """ProbabilityABC"""
-
-    # def __init__(self):
-    #     pass
-
-    @classmethod
-    @abstractmethod
-    def from_db_model(cls, **data):
-        raise NotImplementedError
-
-    @abstractmethod
-    def initialize_nan(self):
-        raise NotImplementedError
-
-    @abstractmethod
-    def set_to_uniform(self):
-        raise NotImplementedError
-
-    @abstractmethod
-    def normalize(self):
-        raise NotImplementedError
-
-    @abstractmethod
-    def isnormalized(self):
-        raise NotImplementedError
-
-    @classmethod
-    @abstractmethod
-    def from_json(cls):
-        raise NotImplementedError
-
-    @abstractmethod
-    def to_json(self):
-        raise NotImplementedError
-
-    def to_dict(self):
-        raise NotImplementedError
-
-    @abstractmethod
-    def get_distribution(self, **kwargs):
-        raise NotImplementedError
-
-    @abstractmethod
-    def to_pyagrum(self):
-        raise NotImplementedError
-
-    @abstractmethod
-    def to_pycid(self):
-        raise NotImplementedError
diff --git a/api/src/v0/services/structure_utils/probability/discrete_conditional_probability.py b/api/src/v0/services/structure_utils/probability/discrete_conditional_probability.py
deleted file mode 100644
index a8ac3bc..0000000
--- a/api/src/v0/services/structure_utils/probability/discrete_conditional_probability.py
+++ /dev/null
@@ -1,183 +0,0 @@
-import json
-import logging
-import re
-from itertools import product
-
-import numpy as np
-import xarray as xr
-from numpy.typing import ArrayLike
-
-from ..probability.abstract_probability import ProbabilityABC
-
-logger = logging.getLogger(__name__)
-
-
-class VariableNot1D(Exception):
-    def __init__(self):
-        error_message = "One of the variables cannot be interpreted as 1D"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class CPTTypeError(Exception):
-    def __init__(self, cpt_type):
-        error_message = f"Expected DiscreteConditionalProbability dtype, got {cpt_type}"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class DiscreteConditionalProbability(ProbabilityABC):
-    """DiscreteConditionalProbability"""
-
-    def __init__(self, probability_function: ArrayLike, variables: dict) -> None:
-        """
-         Parameters
-         -----------
-         probability_function: ArrayLike
-             gives the probability that a variable is equal to some value
-         variables: dict
-             involves the variable name and its values; values refers to the set of
-             possible outcomes
-
-         Warning
-         --------
-             Spaces in variable names and values will be removed.
-
-         Notes
-         -----
-
-         For the Conditional Probability Table, P(Test Result | Test, State) such as
-
-                        || 1. Test  ||    yes     yes   |  no      no
-                        || 2. State ||   Peach | Lemon  | Peach | Lemon
-        -----------------------------------------------------------------
-         0. Test Result ||          ||         |        |       |
-        -----------------------------------------------------------------
-         no Test        ||          ||   0.1   |  0.05  |  0.85 |  0.46
-         Peach          ||          ||   0.7   |  0.35  |  0.12 |  0.26
-         Lemon          ||          ||   0.2   |  0.60  |  0.03 |  0.28
-
-         the probability_function will be
-             [
-                 [0.1, 0.05, 0.85, 0.46],
-                 [0.7, 0.35, 0.12, 0.26],
-                 [0.2, 0.60, 0.03, 0.28],
-             ]
-
-         and the variables
-             {
-                 "Test Result": ["no Test", "Peach", "Lemon"],
-                 "Test": ["yes", "no"],
-                 "State": ["Peach", "Lemon"],
-             }
-        """
-        super().__init__()
-        if any(
-            max(np.asarray(v).shape) != np.asarray(v).size for v in variables.values()
-        ):
-            raise VariableNot1D
-        # remove white spaces as the string is used as variable name by xarray
-        variables = {re.sub(r"\s+", "", k): v for k, v in variables.items()}
-        self._cpt = xr.DataArray(probability_function, coords=variables)
-
-    @property
-    def outcomes(self):
-        variable_name = self._cpt.dims[0]
-        return tuple(self._cpt.coords[variable_name].data.tolist())
-
-    @property
-    def variables(self):
-        return self._cpt.dims
-
-    @classmethod
-    def from_db_model(cls, data):
-        array_size = tuple([len(v) for v in data.variables.values()])
-        distribution = np.reshape(data.probability_function, array_size)
-        return cls(distribution, data.variables)
-
-    def add_conditioning_variable(self, variables: dict):
-        raise NotImplementedError
-
-    def remove_conditioning_variable(self, variables: dict):
-        raise NotImplementedError
-
-    @classmethod
-    def initialize_nan(cls, variables: dict):
-        data_shape = tuple(np.asarray(v).shape[0] for v in variables.values())
-        return cls(np.full(data_shape, np.nan), variables=variables)
-
-    def set_to_uniform(self):
-        data_shape = tuple(np.asarray(v).shape[0] for v in self._cpt.coords.values())
-        self._cpt.data = np.ones(data_shape) / data_shape[0]
-        return self
-
-    def normalize(self):
-        self._cpt = self._cpt / np.apply_over_axes(
-            np.sum, self._cpt, range(1, self._cpt.ndim)
-        )
-        return self
-
-    def isnormalized(self, threshold=1e-6):
-        return np.all(np.linalg.norm(self._cpt.sum(axis=0) - 1.0) < threshold)
-
-    @classmethod
-    def from_json(cls, json_str: str):
-        d = json.loads(json_str)
-        if d["dtype"] != cls.__name__:
-            raise CPTTypeError(d["dtype"])
-        return cls(d["probability_function"], d["variables"])
-
-    def to_json(self):
-        return json.dumps(self.to_dict())
-
-    def to_dict(self):
-        d_ = self._cpt.to_dict()
-        d = {
-            "dtype": self.__class__.__name__,
-            "probability_function": d_["data"],
-            "variables": {k: v["data"] for k, v in d_["coords"].items()},
-        }
-        return d
-
-    def get_distribution(self, **variables):
-        """Return the probability distribution
-
-        Parameters
-        ----------
-        **variables:
-            variables (name=value) for which the distribution is desired
-
-        Return
-        ------
-        xr.DataArray
-        """
-        return self._cpt.sel(**variables)
-
-    # just for asymmetric purposes, i.e., the NA outcome
-    # symbolizing the asymmetry; otherwise, if outcome data
-    # has changed, just remake the cpt
-    def add_na_outcomes(self):
-        raise NotImplementedError
-
-    def to_pyagrum(self):
-        # agrum = list()
-        coords = self._cpt.coords
-        variables = {
-            key: coords[key].data.tolist() for key in coords if key is not coords.dims[0]
-        }
-        agrum_dict = {k: list(range(len(v))) for k, v in variables.items()}
-        agrum_dict = [
-            dict(zip(agrum_dict.keys(), values, strict=False))
-            for values in product(*agrum_dict.values())
-        ]
-        agrum_prob = [
-            self.get_distribution(
-                **{key: coords[key][val] for key, val in item.items()}
-            ).data.tolist()
-            for item in agrum_dict
-        ]
-        agrum = list(zip(agrum_dict, agrum_prob, strict=False))
-        return agrum
-
-    def to_pycid(self):
-        raise NotImplementedError
diff --git a/api/src/v0/services/structure_utils/probability/discrete_unconditional_probability.py b/api/src/v0/services/structure_utils/probability/discrete_unconditional_probability.py
deleted file mode 100644
index 6f2c577..0000000
--- a/api/src/v0/services/structure_utils/probability/discrete_unconditional_probability.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import json
-import logging
-import re
-from itertools import product
-
-import numpy as np
-import xarray as xr
-from numpy.typing import ArrayLike
-
-from ..probability.abstract_probability import ProbabilityABC
-
-logger = logging.getLogger(__name__)
-
-
-class VariableNot1D(Exception):
-    def __init__(self):
-        error_message = "One of the variables cannot be interpreted as 1D"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class CPTTypeError(Exception):
-    def __init__(self, cpt_type):
-        error_message = (
-            f"Expected DiscreteUnconditionalProbability dtype, got {cpt_type}"
-        )
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class AgrumConversionError(Exception):
-    def __init__(self):
-        error_message = "pyAgrum only takes 1D variables in UncertaintyNode"
-        super().__init__(error_message)
-        logger.critical(error_message)
-
-
-class DiscreteUnconditionalProbability(ProbabilityABC):
-    """DiscreteUnconditionalProbability"""
-
-    def __init__(self, probability_function: ArrayLike, variables: dict) -> None:
-        """
-         Parameters
-         -----------
-         probability_function: ArrayLike
-             gives the probability that a variable is equal to some value
-         variables: dict
-             involves the variable name and its values; values refers to the set of
-             possible outcomes
-
-         Warning
-         --------
-             Spaces in variable names and values will be removed.
-
-         Notes
-         -----
-
-         For the Probability P(A, B , C) such as
-
-                || 1. B  ||    b1       b1   |   b2     b2
-                || 2. C  ||    c1   |   c2   |   c1  |  c1
-        -----------------------------------------------------------------
-         0. A   ||       ||         |        |       |
-        -----------------------------------------------------------------
-         a1     ||       ||   0.00  |  0.13  |  0.20 |  0.06
-         a2     ||       ||   0.03  |  0.28  |  0.11 |  0.02
-         a3     ||       ||   0.12  |  0.04  |  0.10 |  0.01
-
-         the probability_function will be
-             [
-                 [0.00, 0.13, 0.20, 0.06],
-                 [0.03, 0.18, 0.11, 0.02],
-                 [0.12, 0.04, 0.10, 0.01],
-             ]
-
-         and the variables
-             {
-                 "A": ["a1", "a2", "a3"],
-                 "B": ["b1", "b2"],
-                 "C": ["c1", "c2"],
-             }
-        """
-        super().__init__()
-        if any(
-            max(np.asarray(v).shape) != np.asarray(v).size for v in variables.values()
-        ):
-            raise VariableNot1D
-        variables = {re.sub(r"\s+", "", k): v for k, v in variables.items()}
-        self._cpt = xr.DataArray(probability_function, coords=variables)
-
-    @property
-    def outcomes(self):
-        variable_names = self._cpt.dims
-        if len(variable_names) == 1:
-            return tuple(self._cpt.coords[variable_names[0]].data.tolist())
-        else:
-            return tuple(
-                product(
-                    *tuple(
-                        tuple(self._cpt.coords[vn].data.tolist())
-                        for vn in variable_names
-                    )
-                )
-            )
-
-    @property
-    def variables(self):
-        return self._cpt.dims
-
-    @classmethod
-    def from_db_model(cls, data):
-        array_size = tuple([len(v) for v in data.variables.values()])
-        distribution = np.reshape(data.probability_function, array_size)
-        return cls(distribution, data.variables)
-
-    @classmethod
-    def initialize_nan(cls, variables: dict):
-        data_shape = tuple(np.asarray(v).shape[0] for v in variables.values())
-        return cls(np.full(data_shape, np.nan), variables=variables)
-
-    def set_to_uniform(self):
-        data_shape = tuple(np.asarray(v).shape[0] for v in self._cpt.coords.values())
-        self._cpt.data = np.ones(data_shape) / np.prod(data_shape)
-        return self
-
-    # does it even make sense to normalize?
-    def normalize(self):
-        # normalize columwise before normalizing wrt the whole matrix?
-        # self._cpt = np.apply_over_axes(np.sum, self._cpt, range(1, self._cpt.ndim))
-        self._cpt = self._cpt / self._cpt.sum()
-        return self
-
-    def isnormalized(self, threshold=1e-6):
-        return np.all(np.linalg.norm(self._cpt.sum() - 1.0) < threshold)
-
-    @classmethod
-    def from_json(cls, json_str: str):
-        d = json.loads(json_str)
-        if d["dtype"] != cls.__name__:
-            raise CPTTypeError(d["dtype"])
-        return cls(d["probability_function"], d["variables"])
-
-    def to_json(self):
-        return json.dumps(self.to_dict())
-
-    def to_dict(self):
-        d_ = self._cpt.to_dict()
-        pf = np.array(d_["data"])
-        d = {
-            "dtype": self.__class__.__name__,
-            "probability_function": np.reshape(pf, (pf.shape[0], -1)).tolist(),
-            "variables": {k: v["data"] for k, v in d_["coords"].items()},
-        }
-        return d
-
-    def get_distribution(self, **variables):
-        """Return the probability distribution
-
-        Parameters
-        ----------
-        **variables:
-            variables (name=value) for which the distribution is desired
-
-        Return
-        ------
-        xr.DataArray
-        """
-        return self._cpt.sel(**variables)
-
-    # just for asymmetric purposes, i.e., the NA outcome
-    # symbolizing the asymmetry; otherwise, if outcome data
-    # has changed, just remake the cpt
-    def add_na_outcomes(self):
-        raise NotImplementedError
-
-    def to_pyagrum(self):
-        variables = self.variables
-        if len(variables) != 1:
-            raise AgrumConversionError
-        return [
-            (
-                {},
-                [self._cpt.sel(**{variables[0]: state}) for state in self.outcomes],
-            )
-        ]
-
-    def to_pycid(self):
-        raise NotImplementedError
diff --git a/api/tests/v0/models/test_issue.py b/api/tests/v0/models/test_issue.py
index 443da84..5cdc503 100644
--- a/api/tests/v0/models/test_issue.py
+++ b/api/tests/v0/models/test_issue.py
@@ -19,10 +19,10 @@ def test_CommentData_fail():
             comment="a comment",
         )
     assert (
-        "1 validation error for CommentData\nauthor\n  Field required [type=missing, "
-        "input_value={'comment': 'a comment'}, input_type=dict]\n    For further "
-        "information visit https://errors.pydantic.dev/2.10/v/missing" in str(exc.value)
-    )
+        "1 validation error for CommentData\nauthor\n  Field required "
+        "[type=missing, input_value={'comment': 'a comment'}, input_type=dict]\n"
+        "    For further information visit https://errors.pydantic.dev/2.11/v/missing"
+    ) in str(exc.value)
 
 
 def test_ProbabilityData_default():
diff --git a/api/tests/v0/repositories/test_issue.py b/api/tests/v0/repositories/test_issue.py
index 18d3d0c..0a29b20 100644
--- a/api/tests/v0/repositories/test_issue.py
+++ b/api/tests/v0/repositories/test_issue.py
@@ -48,7 +48,7 @@ def issue():
         "tag": ['["junk"]'],
         "index": ["1234"],
         "category": ["today"],
-        "keyUncertainty": ["True"],
+        "keyUncertainty": ["true"],
         "decisionType": ["Tactical"],
         "alternatives": ['["yes", "no"]'],
         "probabilities": [
diff --git a/api/tests/v0/routes/test_issue.py b/api/tests/v0/routes/test_issue.py
index 846d359..37ee3e7 100644
--- a/api/tests/v0/routes/test_issue.py
+++ b/api/tests/v0/routes/test_issue.py
@@ -28,7 +28,7 @@ def issue():
         "tag": ["junk"],
         "index": "1234",
         "category": "today",
-        "keyUncertainty": "True",
+        "keyUncertainty": "true",
         "decisionType": "Tactical",
         "alternatives": ["yes", "no"],
         "probabilities": {
diff --git a/api/tests/v0/routes/test_structure.py b/api/tests/v0/routes/test_structure.py
index 350a70a..c6a73ae 100644
--- a/api/tests/v0/routes/test_structure.py
+++ b/api/tests/v0/routes/test_structure.py
@@ -96,12 +96,12 @@ def graph():
 
 
 def test_read_influence_diagram_success(mock_service, graph):
-    vertices = [
+    nodes = [
         IssueResponse.model_validate(graph[0]),
         IssueResponse.model_validate(graph[1]),
         IssueResponse.model_validate(graph[2]),
     ]
-    edges = [
+    arcs = [
         EdgeResponse(
             uuid="101", id="101", outV="11-aa", inV="22-bb", label="influences"
         ),
@@ -110,7 +110,7 @@ def test_read_influence_diagram_success(mock_service, graph):
         ),
     ]
     mock_service.return_value.read_influence_diagram.return_value = (
-        InfluenceDiagramResponse(vertices=vertices, edges=edges)
+        InfluenceDiagramResponse(vertices=nodes, edges=arcs)
     )
     project_uuid = "0"
     response = client.get(
diff --git a/api/tests/v0/services/structure_utils/test_probability/__init__.py b/api/tests/v0/services/analysis/__init__.py
similarity index 100%
rename from api/tests/v0/services/structure_utils/test_probability/__init__.py
rename to api/tests/v0/services/analysis/__init__.py
diff --git a/api/tests/v0/services/analysis/test_id_to_dt.py b/api/tests/v0/services/analysis/test_id_to_dt.py
new file mode 100644
index 0000000..09ebc52
--- /dev/null
+++ b/api/tests/v0/services/analysis/test_id_to_dt.py
@@ -0,0 +1,773 @@
+import numpy as np
+import pytest
+
+from src.v0.services.analysis.id_to_dt import InfluenceDiagramToDecisionTree
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.decision_tree import DecisionTree
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+from src.v0.services.classes.influence_diagram import InfluenceDiagram
+from src.v0.services.classes.node import DecisionNode, UncertaintyNode, UtilityNode
+
+
+@pytest.fixture
+def influence_diagram():
+    n0 = UncertaintyNode(shortname="u1", description="Uncertainty node 1")
+    n1 = UncertaintyNode(shortname="u2", description="Uncertainty node 2")
+    n2 = UncertaintyNode(shortname="u3", description="Uncertainty node 3")
+    n3 = UncertaintyNode(shortname="u4", description="Uncertainty node 4")
+    n4 = DecisionNode(shortname="d1", description="Decision node 1")
+    n5 = UncertaintyNode(shortname="u5", description="Uncertainty node 5")
+    n6 = DecisionNode(shortname="d2", description="Decision node 2")
+    n7 = UncertaintyNode(shortname="u6", description="Uncertainty node 6")
+    n8 = UncertaintyNode(shortname="u7", description="Uncertainty node 7")
+    n9 = UncertaintyNode(shortname="u8", description="Uncertainty node 8")
+    n10 = UtilityNode(shortname="v1", description="Utility node 1")
+
+    e0 = Arc(tail=n0, head=n4, label="e0")
+    e1 = Arc(tail=n1, head=n4, label="e1")
+    e2 = Arc(tail=n2, head=n4, label="e2")
+    e3 = Arc(tail=n3, head=n6, label="e3")
+    e4 = Arc(tail=n4, head=n6, label="e4")
+    e5 = Arc(tail=n4, head=n5, label="e5")
+    e6 = Arc(tail=n6, head=n7, label="e6")
+    e7 = Arc(tail=n6, head=n8, label="e7")
+    e8 = Arc(tail=n6, head=n9, label="e8")
+    e9 = Arc(tail=n5, head=n10, label="e9")
+
+    data = {
+        "nodes": [n0, n1, n2, n3, n4, n5, n6, n7, n8, n9, n10],
+        "arcs": [e0, e1, e2, e3, e4, e5, e6, e7, e8, e9],
+    }
+
+    diagram = InfluenceDiagram()
+    diagram.add_nodes(data["nodes"])
+    diagram.add_arcs(data["arcs"])
+    return diagram
+
+
+def test_decision_elimination_order(influence_diagram):
+    result = InfluenceDiagramToDecisionTree().decision_elimination_order(
+        influence_diagram
+    )
+    target = [influence_diagram.nodes[4], influence_diagram.nodes[6]]
+    assert all(item in target for item in result)
+    assert all(item in result for item in target)
+
+
+def test_calculate_partial_order(influence_diagram):
+    # Test is only reproducing result, not testing logic!
+    partial_order = InfluenceDiagramToDecisionTree().calculate_partial_order(
+        influence_diagram
+    )
+    result = [n.shortname for n in partial_order]
+    target = ["u1", "u2", "u3", "d1", "u4", "d2", "u5", "u6", "u7", "u8"]
+    assert result == target
+
+
+def test_calculate_partial_order_fail_mode(influence_diagram, caplog):
+    # Test is only reproducing result, not testing logic!
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramToDecisionTree().calculate_partial_order(
+            influence_diagram, mode="junk"
+        )
+    assert [r.msg for r in caplog.records] == [
+        "output mode should be [view|copy] and have been entered as junk"
+    ]
+    assert (
+        str(exc_info.value)
+        == "output mode should be [view|copy] and have been entered as junk"
+    )
+
+
+def test_calculate_partial_order_copy_mode(influence_diagram):
+    # Test is only reproducing result, not testing logic!
+    partial_order_0 = InfluenceDiagramToDecisionTree().calculate_partial_order(
+        influence_diagram, mode="view"
+    )
+    partial_order = InfluenceDiagramToDecisionTree().calculate_partial_order(
+        influence_diagram, mode="copy"
+    )
+    result = [n.shortname for n in partial_order]
+    target = ["u1", "u2", "u3", "d1", "u4", "d2", "u5", "u6", "u7", "u8"]
+    assert result == target
+    assert partial_order_0 != partial_order
+
+
+def test_output_branches_from_node_empty_lists(influence_diagram):
+    uncertainty_node = influence_diagram.nodes[0]
+    decision_node = influence_diagram.nodes[4]
+    utility_node = influence_diagram.nodes[10]
+
+    assert (
+        len(
+            list(
+                zip(
+                    *InfluenceDiagramToDecisionTree()._output_branches_from_node(
+                        uncertainty_node, uncertainty_node
+                    ),
+                    strict=False,
+                )
+            )
+        )
+        == 0
+    )
+    assert (
+        len(
+            list(
+                zip(
+                    *InfluenceDiagramToDecisionTree()._output_branches_from_node(
+                        decision_node, uncertainty_node
+                    ),
+                    strict=False,
+                )
+            )
+        )
+        == 0
+    )
+    assert (
+        len(
+            list(
+                zip(
+                    *InfluenceDiagramToDecisionTree()._output_branches_from_node(
+                        utility_node, uncertainty_node
+                    ),
+                    strict=False,
+                )
+            )
+        )
+        == 0
+    )
+
+
+def test_output_branches_from_node(influence_diagram):
+    uncertainty_node = influence_diagram.nodes[0]
+    uncertainty_node._probability = DiscreteUnconditionalProbability(
+        **{
+            "probability_function": np.array([[0.7], [0.2], [0.1]]),
+            "variables": {"States": ["pear", "lemon", "plum"]},
+        }
+    )
+    decision_node = influence_diagram.nodes[4]
+    decision_node._alternatives = ["wait", "pickup"]
+    utility_node = influence_diagram.nodes[10]
+    utility_node._utility = ["1000"]
+
+    result = list(
+        InfluenceDiagramToDecisionTree()._output_branches_from_node(
+            uncertainty_node, uncertainty_node
+        )
+    )
+    assert all(r[1] == uncertainty_node for r in result)
+    assert [r[0].label for r in result] == ["plum", "lemon", "pear"]
+    assert all(isinstance(r[0], Arc) for r in result)
+    assert all(r[0].tail == uncertainty_node for r in result)
+    assert all(r[0].head is None for r in result)
+
+    result = list(
+        InfluenceDiagramToDecisionTree()._output_branches_from_node(
+            decision_node, uncertainty_node
+        )
+    )
+    assert all(r[1] == uncertainty_node for r in result)
+    assert [r[0].label for r in result] == ["pickup", "wait"]
+    assert all(isinstance(r[0], Arc) for r in result)
+    assert all(r[0].tail == decision_node for r in result)
+    assert all(r[0].head is None for r in result)
+
+    result = list(
+        InfluenceDiagramToDecisionTree()._output_branches_from_node(
+            utility_node, uncertainty_node
+        )
+    )
+    assert all(r[1] == uncertainty_node for r in result)
+    assert [r[0].label for r in result] == ["1000"]
+    assert all(isinstance(r[0], Arc) for r in result)
+    assert all(r[0].tail == utility_node for r in result)
+    assert all(r[0].head is None for r in result)
+
+
+def test_output_branches_from_node_reverse_mode(influence_diagram):
+    uncertainty_node = influence_diagram.nodes[0]
+    uncertainty_node._probability = DiscreteUnconditionalProbability(
+        **{
+            "probability_function": np.array([[0.7], [0.2], [0.1]]),
+            "variables": {"States": ["pear", "lemon", "plum"]},
+        }
+    )
+
+    result = list(
+        InfluenceDiagramToDecisionTree()._output_branches_from_node(
+            uncertainty_node, uncertainty_node, flip=False
+        )
+    )
+    assert [r[0].label for r in result] == ["pear", "lemon", "plum"]
+
+
+def test_convert_to_decision_tree_symmetry():
+    # Medical Diagnosis Problem
+    # Data taken from
+    # DECISION TREES AND INFLUENCE DIAGRAMS
+    # Prakash P. Shenoy
+    # Encyclopedia of Life Support Systems, U Derigs (ed.),
+    # Optimization and Operations Research, Vol. 4, pp. 280–298, 2009
+    #
+    #
+    # Probabilities in the ID to be updated!!!
+    #
+    # The produced tree may be flipped horizontally compared to the expected one
+
+    probability_S = DiscreteUnconditionalProbability(
+        **{
+            "probability_function": np.array([[0.7], [0.3]]),
+            "variables": {"State": ["yes", "no"]},
+        }
+    )
+    probability_P = DiscreteUnconditionalProbability(
+        **{
+            "probability_function": np.array([[0.2], [0.8]]),
+            "variables": {"State": ["yes", "no"]},
+        }
+    )
+    probability_D = DiscreteUnconditionalProbability(
+        **{
+            "probability_function": np.array([[0.1], [0.9]]),
+            "variables": {"State": ["yes", "no"]},
+        }
+    )
+
+    id_decision_T = DecisionNode(
+        shortname="T", description="Treat for Disease", alternatives=["yes", "no"]
+    )
+    id_uncertainty_S = UncertaintyNode(
+        shortname="S", description="Symptom", probability=probability_S
+    )
+    id_uncertainty_P = UncertaintyNode(
+        shortname="P", description="Pathological state", probability=probability_P
+    )
+    id_uncertainty_D = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    id_utility_0 = UtilityNode(shortname="v", description="Utility")
+
+    id_e0 = Arc(tail=id_uncertainty_S, head=id_decision_T)
+    id_e1 = Arc(tail=id_uncertainty_P, head=id_uncertainty_S)
+    id_e2 = Arc(tail=id_uncertainty_D, head=id_uncertainty_P)
+    id_e3 = Arc(tail=id_decision_T, head=id_utility_0)
+    id_e4 = Arc(tail=id_uncertainty_P, head=id_utility_0)
+    id_e5 = Arc(tail=id_uncertainty_D, head=id_utility_0)
+
+    net = {
+        "nodes": [
+            id_decision_T,
+            id_uncertainty_S,
+            id_uncertainty_P,
+            id_uncertainty_D,
+            id_utility_0,
+        ],
+        "arcs": [id_e0, id_e1, id_e2, id_e3, id_e4, id_e5],
+    }
+
+    ID = InfluenceDiagram()
+    ID.add_nodes(net["nodes"])
+    ID.add_arcs(net["arcs"])
+
+    dt_uncertainty_S = UncertaintyNode(
+        shortname="S", description="Symptom", probability=probability_S
+    )
+    dt_decision_T_0 = DecisionNode(
+        shortname="T", description="Treat for Disease", alternatives=["yes", "no"]
+    )
+    dt_e0 = Arc(tail=dt_uncertainty_S, head=dt_decision_T_0, label="yes")
+    dt_uncertainty_P_0 = UncertaintyNode(
+        shortname="P", description="Pathological state", probability=probability_P
+    )
+    dt_e1 = Arc(tail=dt_decision_T_0, head=dt_uncertainty_P_0, label="yes")
+    dt_uncertainty_D_0 = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    dt_e2 = Arc(tail=dt_uncertainty_P_0, head=dt_uncertainty_D_0, label="yes")
+    dt_utility_0 = UtilityNode(shortname="v", description="Utility")
+    dt_e3 = Arc(tail=dt_uncertainty_D_0, head=dt_utility_0, label="yes")
+    dt_utility_1 = UtilityNode(shortname="v", description="Utility")
+    dt_e4 = Arc(tail=dt_uncertainty_D_0, head=dt_utility_1, label="no")
+    dt_uncertainty_D_1 = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    dt_e5 = Arc(tail=dt_uncertainty_P_0, head=dt_uncertainty_D_1, label="no")
+    dt_utility_2 = UtilityNode(shortname="v", description="Utility")
+    dt_e6 = Arc(tail=dt_uncertainty_D_1, head=dt_utility_2, label="yes")
+    dt_utility_3 = UtilityNode(shortname="v", description="Utility")
+    dt_e7 = Arc(tail=dt_uncertainty_D_1, head=dt_utility_3, label="no")
+    dt_uncertainty_P_1 = UncertaintyNode(
+        shortname="P", description="Pathological state", probability=probability_P
+    )
+    dt_e8 = Arc(tail=dt_decision_T_0, head=dt_uncertainty_P_1, label="no")
+    dt_uncertainty_D_2 = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    dt_e9 = Arc(tail=dt_uncertainty_P_1, head=dt_uncertainty_D_2, label="yes")
+    dt_utility_4 = UtilityNode(shortname="v", description="Utility")
+    dt_e10 = Arc(tail=dt_uncertainty_D_2, head=dt_utility_4, label="yes")
+    dt_utility_5 = UtilityNode(shortname="v", description="Utility")
+    dt_e11 = Arc(tail=dt_uncertainty_D_2, head=dt_utility_5, label="no")
+    dt_uncertainty_D_3 = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    dt_e12 = Arc(tail=dt_uncertainty_P_1, head=dt_uncertainty_D_3, label="no")
+    dt_utility_6 = UtilityNode(shortname="v", description="Utility")
+    dt_e13 = Arc(tail=dt_uncertainty_D_3, head=dt_utility_6, label="yes")
+    dt_utility_7 = UtilityNode(shortname="v", description="Utility")
+    dt_e14 = Arc(tail=dt_uncertainty_D_3, head=dt_utility_7, label="no")
+    dt_decision_T_1 = DecisionNode(
+        shortname="T", description="Treat for Disease", alternatives=["yes", "no"]
+    )
+    dt_e15 = Arc(tail=dt_uncertainty_S, head=dt_decision_T_1, label="no")
+    dt_uncertainty_P_2 = UncertaintyNode(
+        shortname="P", description="Pathological state", probability=probability_P
+    )
+    dt_e16 = Arc(tail=dt_decision_T_1, head=dt_uncertainty_P_2, label="yes")
+    dt_uncertainty_D_4 = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    dt_e17 = Arc(tail=dt_uncertainty_P_2, head=dt_uncertainty_D_4, label="yes")
+    dt_utility_8 = UtilityNode(shortname="v", description="Utility")
+    dt_e18 = Arc(tail=dt_uncertainty_D_4, head=dt_utility_8, label="yes")
+    dt_utility_9 = UtilityNode(shortname="v", description="Utility")
+    dt_e19 = Arc(tail=dt_uncertainty_D_4, head=dt_utility_9, label="no")
+    dt_uncertainty_D_5 = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    dt_e20 = Arc(tail=dt_uncertainty_P_2, head=dt_uncertainty_D_5, label="no")
+    dt_utility_10 = UtilityNode(shortname="v", description="Utility")
+    dt_e21 = Arc(tail=dt_uncertainty_D_5, head=dt_utility_10, label="yes")
+    dt_utility_11 = UtilityNode(shortname="v", description="Utility")
+    dt_e22 = Arc(tail=dt_uncertainty_D_5, head=dt_utility_11, label="no")
+    dt_uncertainty_P_3 = UncertaintyNode(
+        shortname="P", description="Pathological state", probability=probability_P
+    )
+    dt_e23 = Arc(tail=dt_decision_T_1, head=dt_uncertainty_P_3, label="no")
+    dt_uncertainty_D_6 = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    dt_e24 = Arc(tail=dt_uncertainty_P_3, head=dt_uncertainty_D_6, label="yes")
+    dt_utility_12 = UtilityNode(shortname="v", description="Utility")
+    dt_e25 = Arc(tail=dt_uncertainty_D_6, head=dt_utility_12, label="yes")
+    dt_utility_13 = UtilityNode(shortname="v", description="Utility")
+    dt_e26 = Arc(tail=dt_uncertainty_D_6, head=dt_utility_13, label="no")
+    dt_uncertainty_D_7 = UncertaintyNode(
+        shortname="D", description="Disease", probability=probability_D
+    )
+    dt_e27 = Arc(tail=dt_uncertainty_P_3, head=dt_uncertainty_D_7, label="no")
+    dt_utility_14 = UtilityNode(shortname="v", description="Utility")
+    dt_e28 = Arc(tail=dt_uncertainty_D_7, head=dt_utility_14, label="yes")
+    dt_utility_15 = UtilityNode(shortname="v", description="Utility")
+    dt_e29 = Arc(tail=dt_uncertainty_D_7, head=dt_utility_15, label="no")
+
+    nodes = [
+        dt_uncertainty_S,
+        dt_decision_T_0,
+        dt_uncertainty_P_0,
+        dt_uncertainty_D_0,
+        dt_utility_0,
+        dt_utility_1,
+        dt_uncertainty_D_1,
+        dt_utility_2,
+        dt_utility_3,
+        dt_uncertainty_P_1,
+        dt_uncertainty_D_2,
+        dt_utility_4,
+        dt_utility_5,
+        dt_uncertainty_D_3,
+        dt_utility_6,
+        dt_utility_7,
+        dt_decision_T_1,
+        dt_uncertainty_P_2,
+        dt_uncertainty_D_4,
+        dt_utility_8,
+        dt_utility_9,
+        dt_uncertainty_D_5,
+        dt_utility_10,
+        dt_utility_11,
+        dt_uncertainty_P_3,
+        dt_uncertainty_D_6,
+        dt_utility_12,
+        dt_utility_13,
+        dt_uncertainty_D_7,
+        dt_utility_14,
+        dt_utility_15,
+    ]
+    net = {
+        "nodes": nodes,
+        "arcs": [
+            dt_e0,
+            dt_e1,
+            dt_e2,
+            dt_e3,
+            dt_e4,
+            dt_e5,
+            dt_e6,
+            dt_e7,
+            dt_e8,
+            dt_e9,
+            dt_e10,
+            dt_e11,
+            dt_e12,
+            dt_e13,
+            dt_e14,
+            dt_e15,
+            dt_e16,
+            dt_e17,
+            dt_e18,
+            dt_e19,
+            dt_e20,
+            dt_e21,
+            dt_e22,
+            dt_e23,
+            dt_e24,
+            dt_e25,
+            dt_e26,
+            dt_e27,
+            dt_e28,
+            dt_e29,
+        ],
+    }
+
+    DT = DecisionTree(root=dt_uncertainty_S)
+    DT.add_nodes(net["nodes"])
+    DT.add_arcs(net["arcs"])
+
+    IDT = InfluenceDiagramToDecisionTree().conversion(ID)
+    for id_node, dt_node in zip(IDT.graph.nodes, DT.graph.nodes, strict=False):
+        assert type(id_node) is type(dt_node)
+        assert id_node.description == dt_node.description
+        if not isinstance(id_node, UtilityNode):
+            assert id_node.shortname == dt_node.shortname
+
+    for id_edge, dt_edge in zip(IDT.graph.edges, DT.graph.edges, strict=False):
+        assert type(id_edge) is type(dt_edge)
+        assert id_edge[0].description == dt_edge[0].description
+        assert id_edge[1].description == dt_edge[1].description
+        assert (
+            IDT.graph.edges[id_edge[0], id_edge[1]]["label"]
+            == DT.graph.edges[dt_edge[0], dt_edge[1]]["label"]
+        )
+        assert (
+            IDT.graph.edges[id_edge[0], id_edge[1]]["dtype"]
+            == DT.graph.edges[dt_edge[0], dt_edge[1]]["dtype"]
+        )
+
+
+def test_convert_to_decision_tree_simple_order_asymmetry():
+    probability_U1 = DiscreteUnconditionalProbability(
+        **{
+            "probability_function": np.array([[0.7], [0.3]]),
+            "variables": {"State": ["high", "low"]},
+        }
+    )
+    probability_U2 = DiscreteUnconditionalProbability(
+        **{
+            "probability_function": np.array([[0.2], [0.8]]),
+            "variables": {"State": ["yes", "no"]},
+        }
+    )
+
+    id_uncertainty_u1 = UncertaintyNode(
+        shortname="u1", description="U1", probability=probability_U1
+    )
+    id_uncertainty_u2 = UncertaintyNode(
+        shortname="u2", description="U2", probability=probability_U2
+    )
+    id_decision = DecisionNode(
+        shortname="D", description="D", alternatives=["green", "red"]
+    )
+
+    id_e0 = Arc(tail=id_uncertainty_u1, head=id_decision)
+    id_e1 = Arc(tail=id_uncertainty_u2, head=id_decision)
+
+    net = {
+        "nodes": [id_uncertainty_u1, id_uncertainty_u2, id_decision],
+        "arcs": [id_e0, id_e1],
+    }
+
+    ID = InfluenceDiagram()
+    ID.add_nodes(net["nodes"])
+    ID.add_arcs(net["arcs"])
+
+    dt_uncertainty_u1 = UncertaintyNode(
+        shortname="u1", description="U1", probability=probability_U1
+    )
+
+    dt_uncertainty_u2_1 = UncertaintyNode(
+        shortname="u2", description="U2", probability=probability_U2
+    )
+    dt_e0 = Arc(tail=dt_uncertainty_u1, head=dt_uncertainty_u2_1, label="high")
+    dt_decision_1 = DecisionNode(
+        shortname="D", description="D", alternatives=["green", "red"]
+    )
+    dt_e1 = Arc(tail=dt_uncertainty_u2_1, head=dt_decision_1, label="yes")
+    dt_utility_0 = UtilityNode(shortname="v", description="Utility")
+    dt_e2 = Arc(tail=dt_decision_1, head=dt_utility_0, label="green")
+    dt_utility_1 = UtilityNode(shortname="v", description="Utility")
+    dt_e3 = Arc(tail=dt_decision_1, head=dt_utility_1, label="red")
+
+    dt_decision_2 = DecisionNode(
+        shortname="D", description="D", alternatives=["green", "red"]
+    )
+    dt_e4 = Arc(tail=dt_uncertainty_u2_1, head=dt_decision_2, label="no")
+    dt_utility_2 = UtilityNode(shortname="v", description="Utility")
+    dt_e5 = Arc(tail=dt_decision_2, head=dt_utility_2, label="green")
+    dt_utility_3 = UtilityNode(shortname="v", description="Utility")
+    dt_e6 = Arc(tail=dt_decision_2, head=dt_utility_3, label="red")
+
+    dt_uncertainty_u2_2 = UncertaintyNode(
+        shortname="u2", description="U2", probability=probability_U2
+    )
+    dt_e7 = Arc(tail=dt_uncertainty_u1, head=dt_uncertainty_u2_2, label="low")
+    dt_decision_3 = DecisionNode(
+        shortname="D", description="D", alternatives=["green", "red"]
+    )
+    dt_e8 = Arc(tail=dt_uncertainty_u2_2, head=dt_decision_3, label="yes")
+    dt_utility_4 = UtilityNode(shortname="v", description="Utility")
+    dt_e9 = Arc(tail=dt_decision_3, head=dt_utility_4, label="green")
+    dt_utility_5 = UtilityNode(shortname="v", description="Utility")
+    dt_e10 = Arc(tail=dt_decision_3, head=dt_utility_5, label="red")
+
+    dt_decision_4 = DecisionNode(
+        shortname="D", description="D", alternatives=["green", "red"]
+    )
+    dt_e11 = Arc(tail=dt_uncertainty_u2_2, head=dt_decision_4, label="no")
+    dt_utility_6 = UtilityNode(shortname="v", description="Utility")
+    dt_e12 = Arc(tail=dt_decision_4, head=dt_utility_6, label="green")
+    dt_utility_7 = UtilityNode(shortname="v", description="Utility")
+    dt_e13 = Arc(tail=dt_decision_4, head=dt_utility_7, label="red")
+
+    nodes = [
+        dt_uncertainty_u1,
+        dt_uncertainty_u2_1,
+        dt_decision_1,
+        dt_utility_0,
+        dt_utility_1,
+        dt_decision_2,
+        dt_utility_2,
+        dt_utility_3,
+        dt_uncertainty_u2_2,
+        dt_decision_3,
+        dt_utility_4,
+        dt_utility_5,
+        dt_decision_4,
+        dt_utility_6,
+        dt_utility_7,
+    ]
+    net = {
+        "nodes": nodes,
+        "arcs": [
+            dt_e0,
+            dt_e1,
+            dt_e2,
+            dt_e3,
+            dt_e4,
+            dt_e5,
+            dt_e6,
+            dt_e7,
+            dt_e8,
+            dt_e9,
+            dt_e10,
+            dt_e11,
+            dt_e12,
+            dt_e13,
+        ],
+    }
+
+    DT = DecisionTree(root=dt_uncertainty_u1)
+    DT.add_nodes(net["nodes"])
+    DT.add_arcs(net["arcs"])
+
+    IDT = InfluenceDiagramToDecisionTree().conversion(ID)
+    for id_node, dt_node in zip(IDT.graph.nodes, DT.graph.nodes, strict=False):
+        assert type(id_node) is type(dt_node)
+        assert id_node.description == dt_node.description
+        if not isinstance(id_node, UtilityNode):
+            assert id_node.shortname == dt_node.shortname
+
+    for id_edge, dt_edge in zip(IDT.graph.edges, DT.graph.edges, strict=False):
+        assert type(id_edge) is type(dt_edge)
+        assert id_edge[0].description == dt_edge[0].description
+        assert id_edge[1].description == dt_edge[1].description
+        assert (
+            IDT.graph.edges[id_edge[0], id_edge[1]]["label"]
+            == DT.graph.edges[dt_edge[0], dt_edge[1]]["label"]
+        )
+        assert (
+            IDT.graph.edges[id_edge[0], id_edge[1]]["dtype"]
+            == DT.graph.edges[dt_edge[0], dt_edge[1]]["dtype"]
+        )
+
+
+# def test_convert_to_decision_tree_asymemetry():
+#  TEST NEEDS TO BE UPDATED !!!
+
+#   # Wildcatter example
+#   # Data taken from
+#   # An improved method for solving Hybrid Influence Diagrams
+#   # Barbaros Yet, Martin Neil, Norman Fenton, Anthony Constantinou,
+#   # Eugene Dementiev
+#   # International Journal of Approximate Reasoning, Volume 95, April 2018,
+#   # Pages 93-112
+#   # https://doi.org/10.1016/j.ijar.2018.01.006
+#   #
+#   #
+#   # Probabilities in the ID to be updated!!!
+#   #
+#   # The produced tree may be flipped horizontally compared to the expected one
+#   id_decision_T = DecisionNode("Seismic Test", "T", alternatives=["yes", "No"])
+#   id_decision_D = DecisionNode("Drill", "D", alternatives=["yes", "No"])
+#   id_uncertainty_R = UncertaintyNode("Test Result", "R", \
+#                  probabilities={'No': 0.410, 'Open': 0.350, 'Closed': 0.240})
+#   id_uncertainty_O = UncertaintyNode("Oil", "O", \
+#                  probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
+#   id_value_S = UtilityNode("Seismic Cost", "U1", utility=0)
+#   id_value_D = UtilityNode("Drilling Gain", "U2", utility=0)
+#   id_value_T = UtilityNode("Total", "U3", utility=0)
+
+#   id_edge_0 = Edge(id_decision_T, id_value_S)
+#   id_edge_1 = Edge(id_decision_T, id_uncertainty_R)
+#   id_edge_2 = Edge(id_decision_T, id_decision_D)
+#   id_edge_3 = Edge(id_uncertainty_R, id_decision_D)
+#   id_edge_4 = Edge(id_uncertainty_O, id_uncertainty_R)
+#   id_edge_5 = Edge(id_uncertainty_O, id_value_D)
+#   id_edge_6 = Edge(id_decision_D, id_value_D)
+#   id_edge_7 = Edge(id_value_S, id_value_T)
+#   id_edge_8 = Edge(id_value_D, id_value_T)
+
+#   net = {'nodes': [id_decision_T, id_decision_D, id_uncertainty_R, \
+#                        id_uncertainty_O, id_value_S, id_value_D, id_value_T],
+#          'edges': [id_edge_0, id_edge_1, id_edge_2, id_edge_3, id_edge_4, \
+#                        id_edge_5, id_edge_6, id_edge_7, id_edge_8]}
+
+#   ID = InfluenceDiagram()
+#   for node in net['nodes']:
+#       ID.add_node(node)
+#   for edge in net['edges']:
+#       ID.add_edge(edge)
+
+
+#   dt_decision_T_0 = DecisionNode("Seismic Test", "T", alternatives=["yes", "No"])
+#   dt_decision_R_0 = UncertaintyNode("Test Result", "R", \
+#                     probabilities={'No': 0.410, 'Open': 0.350, 'Closed': 0.240})
+#   dt_edge_0 = Edge(dt_decision_T_0, dt_decision_R_0, name="Yes")
+#   dt_decision_D_0 = DecisionNode("Drill", "D", alternatives=["yes", "No"])
+#   dt_edge_1 = Edge(dt_decision_R_0, dt_decision_D_0, name="No")
+#   dt_uncertainty_O_0 = UncertaintyNode("Oil", "O", \
+#                     probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
+#   dt_edge_2 = Edge(dt_decision_D_0, dt_uncertainty_O_0, name="Yes")
+#   dt_value_T_0 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_3 = Edge(dt_uncertainty_O_0, dt_value_T_0, name="Dry")
+#   dt_value_T_1 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_4 = Edge(dt_uncertainty_O_0, dt_value_T_1, name="Wet")
+#   dt_value_T_2 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_5 = Edge(dt_uncertainty_O_0, dt_value_T_2, name="Soaking")
+#   dt_value_T_3 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_6 = Edge(dt_decision_D_0, dt_value_T_3, name="No")
+#   dt_decision_D_1 = DecisionNode("Drill", "D", alternatives=["yes", "No"])
+#   dt_edge_7 = Edge(dt_decision_R_0, dt_decision_D_1, name="Open")
+#   dt_uncertainty_O_1 = UncertaintyNode("Oil", "O", \
+#                     probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
+#   dt_edge_8 = Edge(dt_decision_D_1, dt_uncertainty_O_1, name="Yes")
+#   dt_value_T_4 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_9 = Edge(dt_uncertainty_O_1, dt_value_T_4, name="Dry")
+#   dt_value_T_5 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_10 = Edge(dt_uncertainty_O_1, dt_value_T_5, name="Wet")
+#   dt_value_T_6 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_11 = Edge(dt_uncertainty_O_1, dt_value_T_6, name="Soaking")
+#   dt_value_T_7 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_12 = Edge(dt_decision_D_1, dt_value_T_7, name="No")
+#   dt_decision_D_2 = DecisionNode("Drill", "D", alternatives=["yes", "No"])
+#   dt_edge_13 = Edge(dt_decision_R_0, dt_decision_D_2, name="Closed")
+#   dt_uncertainty_O_2 = UncertaintyNode("Oil", "O", \
+#                     probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
+#   dt_edge_14 = Edge(dt_decision_D_2, dt_uncertainty_O_2, name="Yes")
+#   dt_value_T_8 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_15 = Edge(dt_uncertainty_O_2, dt_value_T_8, name="Dry")
+#   dt_value_T_9 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_16 = Edge(dt_uncertainty_O_2, dt_value_T_9, name="Wet")
+#   dt_value_T_10 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_17 = Edge(dt_uncertainty_O_2, dt_value_T_10, name="Soaking")
+#   dt_value_T_11 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_18 = Edge(dt_decision_D_2, dt_value_T_11, name="No")
+#   dt_decision_D_3 = DecisionNode("Drill", "D", alternatives=["yes", "No"])
+#   dt_edge_19 = Edge(dt_decision_T_0, dt_decision_D_3, name="No")
+#   dt_uncertainty_O_3 = UncertaintyNode("Oil", "O", \
+#                     probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
+#   dt_edge_20 = Edge(dt_decision_D_2, dt_uncertainty_O_3, name="Yes")
+#   dt_value_T_12 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_21 = Edge(dt_uncertainty_O_3, dt_value_T_12, name="Dry")
+#   dt_value_T_13 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_22 = Edge(dt_uncertainty_O_3, dt_value_T_13, name="Wet")
+#   dt_value_T_14 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_23 = Edge(dt_uncertainty_O_3, dt_value_T_14, name="Soaking")
+#   dt_value_T_15 = UtilityNode("Total", "U3", utility=0)
+#   dt_edge_24 = Edge(dt_decision_D_3, dt_value_T_15, name="No")
+
+
+#   net = {'nodes': [dt_decision_T_0,
+#                    dt_decision_R_0,
+#                    dt_decision_D_0,
+#                    dt_uncertainty_O_0,
+#                    dt_value_T_0,
+#                    dt_value_T_1,
+#                    dt_value_T_2,
+#                    dt_value_T_3,
+#                    dt_decision_D_1,
+#                    dt_uncertainty_O_1,
+#                    dt_value_T_4,
+#                    dt_value_T_5,
+#                    dt_value_T_6,
+#                    dt_value_T_7,
+#                    dt_decision_D_2,
+#                    dt_uncertainty_O_2,
+#                    dt_value_T_8,
+#                    dt_value_T_9,
+#                    dt_value_T_10,
+#                    dt_value_T_11,
+#                    dt_decision_D_3,
+#                    dt_uncertainty_O_3,
+#                    dt_value_T_12,
+#                    dt_value_T_13,
+#                    dt_value_T_14,
+#                    dt_value_T_15],
+#          'edges': [dt_edge_0,
+#                    dt_edge_1,
+#                    dt_edge_2,
+#                    dt_edge_3,
+#                    dt_edge_4,
+#                    dt_edge_5,
+#                    dt_edge_6,
+#                    dt_edge_7,
+#                    dt_edge_8,
+#                    dt_edge_8,
+#                    dt_edge_9,
+#                    dt_edge_10,
+#                    dt_edge_11,
+#                    dt_edge_12,
+#                    dt_edge_13,
+#                    dt_edge_14,
+#                    dt_edge_15,
+#                    dt_edge_16,
+#                    dt_edge_17,
+#                    dt_edge_18,
+#                    dt_edge_19,
+#                    dt_edge_20,
+#                    dt_edge_21,
+#                    dt_edge_22,
+#                    dt_edge_23,
+#                    dt_edge_24]}
+
+#   DT = DecisionTree()
+#   for node in net['nodes']:
+#       DT.add_node(node)
+#   for edge in net['edges']:
+#       DT.add_edge(edge)
+
+#   assert ID.convert_to_decision_tree() == DT
diff --git a/api/tests/v0/services/analysis/test_id_to_pyagrum.py b/api/tests/v0/services/analysis/test_id_to_pyagrum.py
new file mode 100644
index 0000000..2abf082
--- /dev/null
+++ b/api/tests/v0/services/analysis/test_id_to_pyagrum.py
@@ -0,0 +1,123 @@
+import json
+
+import numpy as np
+import pyAgrum as gum
+import pytest
+
+from src.v0.services.analysis.id_to_pyagrum import InfluenceDiagramToPyAgrum
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+from src.v0.services.format_conversions.directed_graph import InfluenceDiagramConversion
+
+TESTDATA = "v0/services/testdata"
+
+
+@pytest.fixture
+def influence_diagram(copy_testdata_tmpdir, tmp_path):
+    copy_testdata_tmpdir(TESTDATA)
+    with open(tmp_path / "used_car_buyer_problem.json") as f:
+        data = json.load(f)
+    issues = data["vertices"]["issues"]
+    issues = [
+        {"uuid" if k == "id" else k: v for k, v in issue.items()} for issue in issues
+    ]
+    data = {
+        "vertices": issues,
+        "edges": [edge for edge in data["edges"] if edge["label"] == "influences"],
+    }
+    diagram = InfluenceDiagramConversion().from_json(data)
+    return diagram
+
+
+def test_conversion_used_car_buyer_success(influence_diagram):
+    result = InfluenceDiagramToPyAgrum().conversion(influence_diagram)
+    assert isinstance(result, gum.InfluenceDiagram)
+    assert result.size() == 5
+    assert result.chanceNodeSize() == 2
+    assert result.decisionNodeSize() == 2
+    assert result.utilityNodeSize() == 1
+
+    # import pyAgrum.lib.image as gimg
+    # from shutil import copyfile
+    # gimg.export(result, "test2.png")
+    # result.saveBIFXML("test.bifxml")
+    # testroot = '/tmp/pytest-of-codespace/pytest-current/'
+    # testpath = testroot+'test_conversion_used_car_buyercurrent/'
+    # copyfile(testpath+"test2.png", "/workspaces/dot/test2.png")
+    # copyfile(testpath+"test.bifxml", "/workspaces/dot/test.bifxml")
+
+
+def test_to_pyagrum_used_car_buyer_not_acyclic_fail(influence_diagram, caplog):
+    node0 = list(influence_diagram.graph.nodes)[0]
+    node1 = list(influence_diagram.graph.nodes)[1]
+    edge1 = Arc(tail=node0, head=node1, label="cyclic")
+    edge2 = Arc(tail=node1, head=node0, label="cyclic")
+    influence_diagram.add_arc(edge1)
+    influence_diagram.add_arc(edge2)
+
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramToPyAgrum().conversion(influence_diagram)
+    assert [r.msg for r in caplog.records] == [
+        "the influence diagram is not acyclic: False"
+    ]
+    assert str(exc_info.value) == "the influence diagram is not acyclic: False"
+
+
+def test_to_pyagrum_used_car_buyer_uncertainty_2d_unconditional_fail(
+    influence_diagram, caplog
+):
+    for node in influence_diagram.nodes:
+        node.probability = DiscreteUnconditionalProbability(
+            **{
+                "probability_function": np.array([[0.1, 0.2], [0.3, 0.4]]),
+                "variables": {"v1": ["blue", "green"], "v2": ["high", "low"]},
+            }
+        )
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramToPyAgrum().conversion(influence_diagram)
+    assert (
+        "Input probability cannot be used in pyagrum with error:"
+        in [r.msg for r in caplog.records][0]
+    )
+    assert "Input probability cannot be used in pyagrum with error:" in str(
+        exc_info.value
+    )
+
+
+def test_to_pyagrum_used_car_buyer_uncertainty_fail(influence_diagram, caplog):
+    for node in influence_diagram.nodes:
+        node.probability = None
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramToPyAgrum().conversion(influence_diagram)
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Input probability cannot be used in pyagrum with error: [pyAgrum] "
+            "Invalid argument: Empty variable State:Labelized({}) "
+            "cannot be added in a Potential"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Input probability cannot be used in pyagrum with error: [pyAgrum] "
+        "Invalid argument: Empty variable State:Labelized({}) "
+        "cannot be added in a Potential"
+    )
+
+
+def test_to_pyagrum_used_car_buyer_decision_fail(influence_diagram, caplog):
+    for node in influence_diagram.nodes:
+        node.alternatives = None
+
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramToPyAgrum().conversion(influence_diagram)
+    assert [r.msg for r in caplog.records] == [
+        "Input arc cannot be used in pyagrum with error: "
+        "[pyAgrum] Invalid argument: Empty variable Test:Labelized({}) "
+        "cannot be added in a Potential"
+    ]
+    assert str(exc_info.value) == (
+        "Input arc cannot be used in pyagrum with error: "
+        "[pyAgrum] Invalid argument: Empty variable Test:Labelized({}) "
+        "cannot be added in a Potential"
+    )
diff --git a/api/tests/v0/services/structure_utils/test_decision_diagrams/__init__,py b/api/tests/v0/services/class_validations/__init__.py
similarity index 100%
rename from api/tests/v0/services/structure_utils/test_decision_diagrams/__init__,py
rename to api/tests/v0/services/class_validations/__init__.py
diff --git a/api/tests/v0/services/class_validations/test_validate_and_set_arc.py b/api/tests/v0/services/class_validations/test_validate_and_set_arc.py
new file mode 100644
index 0000000..714767f
--- /dev/null
+++ b/api/tests/v0/services/class_validations/test_validate_and_set_arc.py
@@ -0,0 +1,92 @@
+from uuid import UUID, uuid1, uuid4
+
+import pytest
+
+from src.v0.services.class_validations import validate_and_set_arc
+from src.v0.services.classes.node import DecisionNode
+
+
+def test_label_success_string():
+    assert validate_and_set_arc.label("None") == "None"
+
+
+def test_label_success_None():
+    assert validate_and_set_arc.label(None) is None
+
+
+def test_label_fail(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_arc.label(1.0)
+    assert [r.msg for r in caplog.records] == [
+        "Input label is neither a string nor None: 1.0"
+    ]
+    assert str(exc_info.value) == "Input label is neither a string nor None: 1.0"
+
+
+def test_edge_success_None():
+    assert validate_and_set_arc.edge(None) is None
+
+
+def test_edge_success_Node():
+    node = DecisionNode(description="decision", shortname="D")
+    assert isinstance(validate_and_set_arc.edge(node), DecisionNode)
+
+
+def test_edge_fail_neither_Node_nor_None(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_arc.edge(1.0)
+    assert [r.msg for r in caplog.records] == [
+        "Endpoint of arcs should be Node or None: 1.0"
+    ]
+    assert str(exc_info.value) == "Endpoint of arcs should be Node or None: 1.0"
+
+
+def test_uuid_success_uuid():
+    id = uuid4()
+    assert isinstance(validate_and_set_arc.uuid(id), str)
+    assert UUID(validate_and_set_arc.uuid(id)).version == 4
+    assert isinstance(validate_and_set_arc.uuid(str(id)), str)
+    assert UUID(validate_and_set_arc.uuid(str(id))).version == 4
+
+
+def test_uuid_success_None():
+    assert UUID(validate_and_set_arc.uuid(None)).version == 4
+
+
+def test_uuid_fail_not_None(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_arc.uuid(3.14)
+    assert [r.msg for r in caplog.records] == [
+        "Input uuid is neither a valid uuid (version 4) nor None: 3.14"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Input uuid is neither a valid uuid (version 4) nor None: 3.14"
+    )
+
+
+def test_uuid_fail_not_uuid(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_arc.uuid("3.14")
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Input uuid is neither a valid uuid (version 4) nor None: "
+            "badly formed hexadecimal UUID string"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Input uuid is neither a valid uuid (version 4) nor None: "
+        "badly formed hexadecimal UUID string"
+    )
+
+
+def test_uuid_fail_not_version_4(caplog):
+    id = str(uuid1())
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_arc.uuid(id)
+    assert [r.msg for r in caplog.records] == [
+        ("Input uuid is neither a valid " "uuid (version 4) nor None: version 1")
+    ]
+    assert str(exc_info.value) == (
+        "Input uuid is neither a valid " "uuid (version 4) nor None: version 1"
+    )
diff --git a/api/tests/v0/services/class_validations/test_validate_and_set_directed_graph.py b/api/tests/v0/services/class_validations/test_validate_and_set_directed_graph.py
new file mode 100644
index 0000000..83b5da7
--- /dev/null
+++ b/api/tests/v0/services/class_validations/test_validate_and_set_directed_graph.py
@@ -0,0 +1,80 @@
+import pytest
+
+from src.v0.services.class_validations import validate_and_set_graph_model
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.node import (
+    DecisionNode,
+    UncertaintyNode,
+    UtilityNode,
+)
+
+
+def test_id_node_success():
+    assert validate_and_set_graph_model.id_node(
+        DecisionNode(description="description", shortname="D")
+    )
+    assert validate_and_set_graph_model.id_node(
+        UncertaintyNode(description="description", shortname="C")
+    )
+    assert validate_and_set_graph_model.id_node(
+        UtilityNode(description="description", shortname="V")
+    )
+
+
+def test_id_fail(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_graph_model.id_node(None)
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Added node is not of instance "
+            "(DecisionNode, UncertaintyNode, UtilityNode): None"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Added node is not of instance "
+        "(DecisionNode, UncertaintyNode, UtilityNode): None"
+    )
+
+
+def test_dt_node_success():
+    assert validate_and_set_graph_model.dt_node(
+        DecisionNode(description="description", shortname="D")
+    )
+    assert validate_and_set_graph_model.dt_node(
+        UncertaintyNode(description="description", shortname="C")
+    )
+    assert validate_and_set_graph_model.dt_node(
+        UtilityNode(description="description", shortname="V")
+    )
+
+
+def test_dt_fail(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_graph_model.dt_node(None)
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Added node is not of instance "
+            "(DecisionNode, UncertaintyNode, UtilityNode): None"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Added node is not of instance "
+        "(DecisionNode, UncertaintyNode, UtilityNode): None"
+    )
+
+
+def test_arc_to_graph_success():
+    n1 = UncertaintyNode(description="description", shortname="n1")
+    n2 = DecisionNode(description="description", shortname="n2")
+    arc = Arc(tail=n1, head=n2, label="arc_label")
+    assert validate_and_set_graph_model.arc_to_graph(arc) == (
+        (n1, n2),
+        {"dtype": "informational", "label": "arc_label", "uuid": arc.uuid},
+    )
+
+
+def test_arc_to_graph_fail(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_graph_model.arc_to_graph(None)
+    assert [r.msg for r in caplog.records] == ["Added arc is not of instance Arc: None"]
+    assert str(exc_info.value) == "Added arc is not of instance Arc: None"
diff --git a/api/tests/v0/services/class_validations/test_validate_and_set_node.py b/api/tests/v0/services/class_validations/test_validate_and_set_node.py
new file mode 100644
index 0000000..78a953a
--- /dev/null
+++ b/api/tests/v0/services/class_validations/test_validate_and_set_node.py
@@ -0,0 +1,185 @@
+from uuid import UUID, uuid1, uuid4
+
+import numpy as np
+import pytest
+
+from src.v0.services.class_validations import validate_and_set_node
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+
+
+def test_description_success():
+    assert validate_and_set_node.description("None") == "None"
+
+
+def test_description_fail(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.description(None)
+    assert [r.msg for r in caplog.records] == ["Input description is not a string: None"]
+    assert str(exc_info.value) == "Input description is not a string: None"
+
+
+def test_name_success():
+    assert validate_and_set_node.name("None") == "None"
+
+
+def test_name_fail(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.name(None)
+    assert [r.msg for r in caplog.records] == ["Input name is not a string: None"]
+    assert str(exc_info.value) == "Input name is not a string: None"
+
+
+def test_shortname_success():
+    assert validate_and_set_node.shortname("None") == "None"
+
+
+def test_shortname_fail(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.shortname(None)
+    assert [r.msg for r in caplog.records] == ["Input shortname is not a string: None"]
+    assert str(exc_info.value) == "Input shortname is not a string: None"
+
+
+def test_uuid_success_uuid():
+    id = uuid4()
+    assert isinstance(validate_and_set_node.uuid(id), str)
+    assert UUID(validate_and_set_node.uuid(id)).version == 4
+    assert isinstance(validate_and_set_node.uuid(str(id)), str)
+    assert UUID(validate_and_set_node.uuid(str(id))).version == 4
+
+
+def test_uuid_success_None():
+    assert UUID(validate_and_set_node.uuid(None)).version == 4
+
+
+def test_uuid_fail_not_None(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.uuid(3.14)
+    assert [r.msg for r in caplog.records] == [
+        ("Input uuid is neither a valid " "uuid (version 4) nor None: 3.14")
+    ]
+    assert str(exc_info.value) == (
+        "Input uuid is neither a valid " "uuid (version 4) nor None: 3.14"
+    )
+
+
+def test_uuid_fail_not_uuid(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.uuid("3.14")
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Input uuid is neither a valid "
+            "uuid (version 4) nor None: badly formed hexadecimal UUID string"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Input uuid is neither a valid "
+        "uuid (version 4) nor None: badly formed hexadecimal UUID string"
+    )
+
+
+def test_uuid_fail_not_version_4(caplog):
+    id = str(uuid1())
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.uuid(id)
+    assert [r.msg for r in caplog.records] == [
+        ("Input uuid is neither a valid " "uuid (version 4) nor None: version 1")
+    ]
+    assert str(exc_info.value) == (
+        "Input uuid is neither a valid " "uuid (version 4) nor None: version 1"
+    )
+
+
+def test_alternatives_success_list_of_strings():
+    assert validate_and_set_node.alternatives(["1", "2", "3"]) == ["1", "2", "3"]
+
+
+def test_alternatives_success_tuple_of_strings():
+    assert validate_and_set_node.alternatives(("1", "2", "3")) == ("1", "2", "3")
+
+
+def test_alternatives_success_None():
+    assert validate_and_set_node.alternatives(None) is None
+
+
+def test_alternatives_fail_string(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.alternatives("3.14")
+    assert [r.msg for r in caplog.records] == [
+        "Input alternatives is neither a list "
+        "or tuple of unique strings nor None: 3.14"
+    ]
+    assert (
+        str(exc_info.value) == "Input alternatives is neither a "
+        "list or tuple of unique strings nor None: 3.14"
+    )
+
+
+def test_alternatives_fail_not_sequence(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.alternatives({"3.14"})
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Input alternatives is neither a list "
+            "or tuple of unique strings nor None: {'3.14'}"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Input alternatives is neither a list "
+        "or tuple of unique strings nor None: {'3.14'}"
+    )
+
+
+def test_alternatives_fail_list_of_lists(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.alternatives([["3.14"]])
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Input alternatives is neither a list "
+            "or tuple of unique strings nor None: [['3.14']]"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Input alternatives is neither a list "
+        "or tuple of unique strings nor None: [['3.14']]"
+    )
+
+
+def test_alternatives_fail_redundant_elements(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.alternatives(["a", "a", "b"])
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Input alternatives is neither a list or "
+            "tuple of unique strings nor None: ['a', 'a', 'b']"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Input alternatives is neither a list "
+        "or tuple of unique strings nor None: ['a', 'a', 'b']"
+    )
+
+
+def test_probability_success_None():
+    assert validate_and_set_node.probability(None) is None
+
+
+def test_probability_success_well_formed_probability():
+    values = np.array([0.1, 0.9])
+    coords = {"A": ["yes", "no"]}
+    probability = DiscreteUnconditionalProbability(values, coords)
+    assert validate_and_set_node.probability(probability) == probability
+
+
+def test_alternatives_fail_neither_None_nor_well_formed_probability(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_node.probability([["3.14"]])
+    assert [r.msg for r in caplog.records] == [
+        "Input probability is neither a well formed probability nor None: [['3.14']]"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Input probability is neither a well formed probability nor None: [['3.14']]"
+    )
diff --git a/api/tests/v0/services/class_validations/test_validate_and_set_probability.py b/api/tests/v0/services/class_validations/test_validate_and_set_probability.py
new file mode 100644
index 0000000..15c31c8
--- /dev/null
+++ b/api/tests/v0/services/class_validations/test_validate_and_set_probability.py
@@ -0,0 +1,305 @@
+import numpy as np
+import pytest
+
+from src.v0.services.class_validations import validate_and_set_probability
+
+
+def test_discrete_probability_variable_success_with_reformating():
+    assert validate_and_set_probability.discrete_variables(
+        {"v1": ["y", "n"], "v 2": ["r", "g", "b"]}
+    ) == {
+        "v1": ["y", "n"],
+        "v_2": ["r", "g", "b"],
+    }
+
+
+def test_discrete_probability_variable_fail_not_dict(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_variables(None)
+    assert [r.msg for r in caplog.records] == [
+        (
+            "One of the variables is not a dictionary with "
+            "element being able to be interpreted as 1D: None"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "One of the variables is not a dictionary with "
+        "element being able to be interpreted as 1D: None"
+    )
+
+
+def test_discrete_probability_variable_fail_outcomes_not_as_list(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_variables({"v1": ["y", "n"], "v 2": 3})
+    assert [r.msg for r in caplog.records] == [
+        (
+            "One of the variables is not a dictionary with "
+            "element being able to be interpreted as 1D: {'v1': ['y', 'n'], 'v 2': 3}"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "One of the variables is not a dictionary with "
+        "element being able to be interpreted as 1D: {'v1': ['y', 'n'], 'v 2': 3}"
+    )
+
+
+def test_discrete_probability_variable_fail_not_as_array(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_variables(
+            {"v1": ["y", "n"], "v 2": [["r", "g"], "b"]}
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "One of the variables is not a dictionary with "
+            "element being able to be interpreted as 1D: "
+            "setting an array element with a sequence. "
+            "The requested array has an inhomogeneous shape "
+            "after 1 dimensions. The detected shape was (2,) + inhomogeneous part."
+        )
+    ]
+    assert str(exc_info.value) == (
+        "One of the variables is not a dictionary with "
+        "element being able to be interpreted as 1D: "
+        "setting an array element with a sequence. "
+        "The requested array has an inhomogeneous shape "
+        "after 1 dimensions. The detected shape was (2,) + inhomogeneous part."
+    )
+
+
+def test_discrete_probability_variable_fail_not_as_1d(caplog):
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_variables(
+            {"v1": ["y", "n"], "v 2": [["r", "g"], ["b", "a"]]}
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "One of the variables is not a dictionary with "
+            "element being able to be interpreted as 1D: "
+            "{'v1': ['y', 'n'], 'v 2': [['r', 'g'], ['b', 'a']]}"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "One of the variables is not a dictionary with "
+        "element being able to be interpreted as 1D: "
+        "{'v1': ['y', 'n'], 'v 2': [['r', 'g'], ['b', 'a']]}"
+    )
+
+
+def test_discrete_conditional_probability_function_success_as_nan():
+    conditioned_variable = {"v1": ["y", "n"]}
+    conditioning_variable = {"v 2": ["r", "g", "b"]}
+    pdf = np.full((2, 3), np.nan)
+    result = validate_and_set_probability.discrete_conditional_probability_function(
+        pdf, conditioned_variable, conditioning_variable
+    )
+    assert all(np.isnan(result).tolist())
+
+
+def test_discrete_conditional_probability_function_success_as_normalized():
+    conditioned_variable = {"v1": ["y", "n"]}
+    conditioning_variable = {"v 2": ["r", "g", "b"]}
+    pdf = np.array([[0.5, 1, 0], [0.5, 0, 1.0]])
+    result = validate_and_set_probability.discrete_conditional_probability_function(
+        pdf, conditioned_variable, conditioning_variable
+    )
+    np.testing.assert_allclose(pdf, result)
+
+
+def test_discrete_conditional_probability_function_fail_not_array_like(caplog):
+    conditioned_variable = {"v1": ["y", "n"]}
+    conditioning_variable = {"v 2": ["r", "g", "b"]}
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_conditional_probability_function(
+            None, conditioned_variable, conditioning_variable
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "The conditional probability function is not well "
+            "formed size (not compatible with variables or "
+            "content is not normalized): None"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "The conditional probability function is not well "
+        "formed size (not compatible with variables or "
+        "content is not normalized): None"
+    )
+
+
+def test_discrete_conditional_probability_function_fail_not_normalized(caplog):
+    conditioned_variable = {"v1": ["y", "n"]}
+    conditioning_variable = {"v 2": ["r", "g", "b"]}
+    pdf = np.array([[0.95, 1, 0], [0.5, 0, 1.0]])
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_conditional_probability_function(
+            pdf, conditioned_variable, conditioning_variable
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "The conditional probability function is not well "
+            "formed size (not compatible with variables or content is not normalized): "
+            "[[0.95 1.   0.  ]\n [0.5  0.   1.  ]]"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "The conditional probability function is not well "
+        "formed size (not compatible with variables or content is not normalized): "
+        "[[0.95 1.   0.  ]\n [0.5  0.   1.  ]]"
+    )
+
+
+def test_discrete_conditional_probability_function_fail_not_between_zero_and_one(caplog):
+    conditioned_variable = {"v1": ["y", "n"]}
+    conditioning_variable = {"v 2": ["r", "g", "b"]}
+    pdf = np.array([[-1.5, 1, 0], [0.5, 0, 1.0]])
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_conditional_probability_function(
+            pdf, conditioned_variable, conditioning_variable
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "The conditional probability function is not well "
+            "formed size (not compatible with variables or content is not normalized): "
+            "[[-1.5  1.   0. ]\n [ 0.5  0.   1. ]]"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "The conditional probability function is not well "
+        "formed size (not compatible with variables or content is not normalized): "
+        "[[-1.5  1.   0. ]\n [ 0.5  0.   1. ]]"
+    )
+
+
+def test_discrete_conditional_probability_function_fail_not_consistent(caplog):
+    conditioned_variable = {"v1": ["y", "n"]}
+    conditioning_variable = {"v 2": ["r", "g", "b"]}
+    pdf = np.array([[0, 1, 0], [0.5, 0, 0.5], [1, 0, 0]])
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_conditional_probability_function(
+            pdf, conditioned_variable, conditioning_variable
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "The conditional probability function is not well "
+            "formed size (not compatible with variables or content is not normalized): "
+            "[[0.  1.  0. ]\n [0.5 0.  0.5]\n [1.  0.  0. ]]"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "The conditional probability function is not well "
+        "formed size (not compatible with variables or content is not normalized): "
+        "[[0.  1.  0. ]\n [0.5 0.  0.5]\n [1.  0.  0. ]]"
+    )
+
+
+def test_discrete_unconditional_probability_function_success_as_None():
+    variables = {"v1": ["y", "n"], "v 2": ["r", "g", "b"]}
+    probability_function = [[None, None, None], [None, None, None]]
+    pdf = np.array(probability_function)
+    result = validate_and_set_probability.discrete_unconditional_probability_function(
+        pdf, variables
+    )
+    assert all(np.isnan(result).tolist())
+
+
+def test_discrete_unconditional_probability_function_success_as_nan():
+    variables = {"v1": ["y", "n"], "v 2": ["r", "g", "b"]}
+    pdf = np.full((2, 3), np.nan)
+    result = validate_and_set_probability.discrete_unconditional_probability_function(
+        pdf, variables
+    )
+    assert all(np.isnan(result).tolist())
+
+
+def test_discrete_unconditional_probability_function_success_as_normalized():
+    variables = {"v1": ["y", "n"], "v 2": ["r", "g", "b"]}
+    pdf = np.array([[0, 0.25, 0], [0.25, 0, 0.5]])
+    result = validate_and_set_probability.discrete_unconditional_probability_function(
+        pdf, variables
+    )
+    np.testing.assert_allclose(pdf, result)
+
+
+def test_discrete_unconditional_probability_function_fail_not_array_like(caplog):
+    variables = {"v1": ["y", "n"], "v 2": ["r", "g", "b"]}
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_unconditional_probability_function(
+            None, variables
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "The unconditional probability function is not well "
+            "formed size (not compatible with variables or content "
+            "is not normalized): None"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "The unconditional probability function is not well "
+        "formed size (not compatible with variables or content "
+        "is not normalized): None"
+    )
+
+
+def test_discrete_unconditional_probability_function_fail_not_normalized(caplog):
+    variables = {"v1": ["y", "n"], "v 2": ["r", "g", "b"]}
+    pdf = np.array([[1, 1, 0], [0.5, 0, 0.5]])
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_unconditional_probability_function(
+            pdf, variables
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "The unconditional probability function is not well formed "
+            "size (not compatible with variables or content is not normalized): "
+            "[[1.  1.  0. ]\n [0.5 0.  0.5]]"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "The unconditional probability function is not well formed "
+        "size (not compatible with variables or content is not normalized): "
+        "[[1.  1.  0. ]\n [0.5 0.  0.5]]"
+    )
+
+
+def test_discrete_unconditional_probability_function_fail_not_between_zero_and_one(
+    caplog,
+):
+    variables = {"v1": ["y", "n"], "v 2": ["r", "g", "b"]}
+    pdf = np.array([[10, 1, 0], [0.5, 0, 0.5]])
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_unconditional_probability_function(
+            pdf, variables
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "The unconditional probability function is not well "
+            "formed size (not compatible with variables or content is not normalized): "
+            "[[10.   1.   0. ]\n [ 0.5  0.   0.5]]"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "The unconditional probability function is not well "
+        "formed size (not compatible with variables or content is not normalized): "
+        "[[10.   1.   0. ]\n [ 0.5  0.   0.5]]"
+    )
+
+
+def test_discrete_unconditional_probability_function_fail_not_consistent(caplog):
+    variables = {"v1": ["y", "n"], "v 2": ["r", "g", "b"]}
+    pdf = np.array([[0, 1, 0], [0.5, 0, 0.5], [1, 0, 0]])
+    with pytest.raises(Exception) as exc_info:
+        validate_and_set_probability.discrete_unconditional_probability_function(
+            pdf, variables
+        )
+    assert [r.msg for r in caplog.records] == [
+        (
+            "The unconditional probability function is not well "
+            "formed size (not compatible with variables or content is not normalized): "
+            "[[0.  1.  0. ]\n [0.5 0.  0.5]\n [1.  0.  0. ]]"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "The unconditional probability function is not well "
+        "formed size (not compatible with variables or content is not normalized): "
+        "[[0.  1.  0. ]\n [0.5 0.  0.5]\n [1.  0.  0. ]]"
+    )
diff --git a/api/tests/v0/services/classes/__init__.py b/api/tests/v0/services/classes/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/api/tests/v0/services/classes/test_abstract_probability.py b/api/tests/v0/services/classes/test_abstract_probability.py
new file mode 100644
index 0000000..cf0d372
--- /dev/null
+++ b/api/tests/v0/services/classes/test_abstract_probability.py
@@ -0,0 +1,24 @@
+import pytest
+
+from src.v0.services.classes.abstract_probability import ProbabilityABC
+
+
+def test_class_ProbabilityABC(monkeypatch):
+    monkeypatch.setattr(
+        ProbabilityABC,
+        "__abstractmethods__",
+        set(),
+    )
+    abstract_probability = ProbabilityABC()
+
+    with pytest.raises(NotImplementedError):
+        abstract_probability.initialize_nan(None)
+
+    with pytest.raises(NotImplementedError):
+        abstract_probability.initialize_uniform(None)
+
+    with pytest.raises(NotImplementedError):
+        abstract_probability.variables()
+
+    with pytest.raises(NotImplementedError):
+        abstract_probability.get_distribution()
diff --git a/api/tests/v0/services/classes/test_arc.py b/api/tests/v0/services/classes/test_arc.py
new file mode 100644
index 0000000..a178aa3
--- /dev/null
+++ b/api/tests/v0/services/classes/test_arc.py
@@ -0,0 +1,131 @@
+import pytest
+
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.node import DecisionNode, UncertaintyNode, UtilityNode
+
+
+def test_class_InformationalArc():
+    n1 = UncertaintyNode(description="junk", shortname="J")
+    n2 = DecisionNode(description="junky", shortname="H")
+    arc = Arc(tail=n1, head=n2, label="first")
+    assert arc.tail == n1
+    assert arc.head == n2
+    assert arc.label == "first"
+    assert arc.dtype == "informational"
+
+
+def test_class_InformationalArc_without_name():
+    n1 = UncertaintyNode(description="junk", shortname="J")
+    n2 = DecisionNode(description="junky", shortname="H")
+    arc = Arc(tail=n1, head=n2)
+    assert arc.label is None
+
+
+def test_class_set_name():
+    n1 = UncertaintyNode(description="junk", shortname="J")
+    n2 = DecisionNode(description="junky", shortname="H")
+    arc = Arc(tail=n1, head=n2)
+    arc.label = "C2H5OH"
+    assert arc.label == "C2H5OH"
+
+
+def test_class_ConditionalArc():
+    n1 = UncertaintyNode(description="junk", shortname="J")
+    n2 = UncertaintyNode(description="junky", shortname="H")
+    arc = Arc(tail=n1, head=n2, label="second")
+    assert arc.tail.description == "junk"
+    assert arc.tail.shortname == "J"
+    assert arc.head.description == "junky"
+    assert arc.head.shortname == "H"
+    assert arc.label == "second"
+    assert arc.dtype == "conditional"
+
+
+def test_class_FunctionalArc():
+    n1 = UncertaintyNode(description="junk", shortname="J")
+    n2 = UtilityNode(description="junky", shortname="H")
+    arc = Arc(tail=n1, head=n2, label="first")
+    assert arc.tail == n1
+    assert arc.head == n2
+    assert arc.label == "first"
+    assert arc.dtype == "functional"
+
+
+def test_copy():
+    n1 = UncertaintyNode(description="junk", shortname="J")
+    n2 = DecisionNode(description="junky", shortname="H")
+    arc = Arc(tail=n1, head=n2, label="first")
+    copied_arc = arc.copy()
+    assert copied_arc.tail == arc.tail
+    assert copied_arc.head == arc.head
+    assert copied_arc.label == arc.label
+
+
+def test_set_head():
+    n1 = UncertaintyNode(description="junk", shortname="J")
+    n2 = DecisionNode(description="junky", shortname="H")
+    arc = Arc(tail=n1, head=None, label="first")
+    assert arc.tail == n1
+    assert arc.head is None
+    assert arc.label == "first"
+    assert arc.dtype is None
+    arc.head = n2
+    assert arc.head == n2
+    assert arc.dtype == "informational"
+
+
+def test_set_head_fail(caplog):
+    n1 = UtilityNode(
+        description="junk", shortname="J", uuid="775e46e5-2dd4-4e34-add6-bb8c0626627d"
+    )
+    n2 = DecisionNode(
+        description="junky", shortname="H", uuid="66095d54-74dc-4a75-bcf4-49676a44a2a2"
+    )
+    arc = Arc(tail=n1, head=None, label="first")
+    with pytest.raises(Exception) as exc_info:
+        arc.head = n2
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Utility node can only have other utility nodes as successor: "
+            "775e46e5-2dd4-4e34-add6-bb8c0626627d/66095d54-74dc-4a75-bcf4-49676a44a2a2"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Utility node can only have other utility nodes as successor: "
+        "775e46e5-2dd4-4e34-add6-bb8c0626627d/66095d54-74dc-4a75-bcf4-49676a44a2a2"
+    )
+
+
+def test_set_tail():
+    n1 = UncertaintyNode(description="junk", shortname="J")
+    n2 = DecisionNode(description="junky", shortname="H")
+    arc = Arc(tail=None, head=n2, label="first")
+    assert arc.tail is None
+    assert arc.head == n2
+    assert arc.label == "first"
+    assert arc.dtype == "informational"
+    arc.tail = n1
+    assert arc.tail == n1
+    assert arc.dtype == "informational"
+
+
+def test_set_tail_fail(caplog):
+    n1 = UtilityNode(
+        description="junk", shortname="J", uuid="775e46e5-2dd4-4e34-add6-bb8c0626627d"
+    )
+    n2 = DecisionNode(
+        description="junky", shortname="H", uuid="66095d54-74dc-4a75-bcf4-49676a44a2a2"
+    )
+    arc = Arc(tail=None, head=n2, label="first")
+    with pytest.raises(Exception) as exc_info:
+        arc.tail = n1
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Utility node can only have other utility nodes as successor: "
+            "66095d54-74dc-4a75-bcf4-49676a44a2a2/775e46e5-2dd4-4e34-add6-bb8c0626627d"
+        )
+    ]
+    assert str(exc_info.value) == (
+        "Utility node can only have other utility nodes as successor: "
+        "66095d54-74dc-4a75-bcf4-49676a44a2a2/775e46e5-2dd4-4e34-add6-bb8c0626627d"
+    )
diff --git a/api/tests/v0/services/classes/test_decision_tree.py b/api/tests/v0/services/classes/test_decision_tree.py
new file mode 100644
index 0000000..fb5d5f2
--- /dev/null
+++ b/api/tests/v0/services/classes/test_decision_tree.py
@@ -0,0 +1,133 @@
+import networkx as nx
+import pytest
+
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.decision_tree import DecisionTree
+from src.v0.services.classes.node import (
+    DecisionNode,
+    UncertaintyNode,
+    UtilityNode,
+)
+
+
+@pytest.fixture
+def simple_graph():
+    n0 = UncertaintyNode(description="Uncertainty node 0", shortname="u0")
+    n1 = UncertaintyNode(description="Uncertainty node 1", shortname="u1")
+    n2 = UncertaintyNode(description="Uncertainty node 2", shortname="u2")
+    n3 = DecisionNode(description="Decision node 0", shortname="d0")
+    n4 = DecisionNode(description="Decision node 1", shortname="d1")
+    n5 = DecisionNode(description="Decision node 2", shortname="d2")
+    n6 = DecisionNode(description="Decision node 3", shortname="d3")
+    n7 = DecisionNode(description="Decision node 4", shortname="d4")
+    n8 = UtilityNode(description="Utility node 0", shortname="v0")
+    n9 = UtilityNode(description="Utility node 1", shortname="v1")
+    n10 = UtilityNode(description="Utility node 2", shortname="v2")
+    n11 = UtilityNode(description="Utility node 3", shortname="v3")
+    n12 = UncertaintyNode(description="Uncertainty node 3", shortname="u3")
+    n13 = UtilityNode(description="Utility node 4", shortname="v4")
+    n14 = UtilityNode(description="Utility node 5", shortname="v5")
+    n15 = UtilityNode(description="Utility node 6", shortname="v6")
+    n16 = UtilityNode(description="Utility node 7", shortname="v7")
+    n17 = UtilityNode(description="Utility node 8", shortname="v8")
+    n18 = UtilityNode(description="Utility node 9", shortname="v9")
+    n19 = UtilityNode(description="Utility node 10", shortname="v10")
+    n20 = UtilityNode(description="Utility node 11", shortname="v11")
+    n21 = UtilityNode(description="Utility node 12", shortname="v12")
+
+    e0 = Arc(tail=n0, head=n1, label="e0")
+    e1 = Arc(tail=n1, head=n3, label="e1")
+    e2 = Arc(tail=n3, head=n8, label="e2")
+    e3 = Arc(tail=n3, head=n9, label="e3")
+    e4 = Arc(tail=n1, head=n4, label="e4")
+    e5 = Arc(tail=n4, head=n10, label="e5")
+    e6 = Arc(tail=n4, head=n11, label="e6")
+    e7 = Arc(tail=n4, head=n12, label="e7")
+    e8 = Arc(tail=n12, head=n20, label="e8")
+    e9 = Arc(tail=n12, head=n21, label="e9")
+    e10 = Arc(tail=n0, head=n2, label="e10")
+    e11 = Arc(tail=n2, head=n5, label="e11")
+    e12 = Arc(tail=n5, head=n13, label="e12")
+    e13 = Arc(tail=n5, head=n14, label="e13")
+    e14 = Arc(tail=n2, head=n6, label="e14")
+    e15 = Arc(tail=n6, head=n15, label="e15")
+    e16 = Arc(tail=n6, head=n16, label="e16")
+    e17 = Arc(tail=n2, head=n7, label="e17")
+    e18 = Arc(tail=n7, head=n17, label="e18")
+    e19 = Arc(tail=n7, head=n18, label="e19")
+    e20 = Arc(tail=n7, head=n19, label="e20")
+
+    return {
+        "nodes": [
+            n0,
+            n1,
+            n2,
+            n3,
+            n4,
+            n5,
+            n6,
+            n7,
+            n8,
+            n9,
+            n10,
+            n11,
+            n12,
+            n13,
+            n14,
+            n15,
+            n16,
+            n17,
+            n18,
+            n19,
+            n20,
+            n21,
+        ],
+        "arcs": [
+            e0,
+            e1,
+            e2,
+            e3,
+            e4,
+            e5,
+            e6,
+            e7,
+            e8,
+            e9,
+            e10,
+            e11,
+            e12,
+            e13,
+            e14,
+            e15,
+            e16,
+            e17,
+            e18,
+            e19,
+            e20,
+        ],
+    }
+
+
+def graph_to_decision_tree(graph):
+    decision_tree = DecisionTree()
+    decision_tree.add_nodes(graph["nodes"])
+    decision_tree.add_arcs(graph["arcs"])
+    return decision_tree
+
+
+def test_class_DecisionTree():
+    dt = DecisionTree()
+    assert isinstance(dt.graph, nx.DiGraph)
+    assert dt.root is None
+    root = DecisionNode(description="junk", shortname="D")
+    dt = DecisionTree(root=root)
+    assert len(list(dt.graph)) == 1
+    assert dt.root == root
+
+
+def test_parent(simple_graph):
+    dt = DecisionTree()
+    dt = graph_to_decision_tree(simple_graph)
+    n5 = simple_graph["nodes"][5]
+    n2 = simple_graph["nodes"][2]
+    assert dt.parent(n5) == n2
diff --git a/api/tests/v0/services/classes/test_discrete_conditional_probability.py b/api/tests/v0/services/classes/test_discrete_conditional_probability.py
new file mode 100644
index 0000000..8b6c19a
--- /dev/null
+++ b/api/tests/v0/services/classes/test_discrete_conditional_probability.py
@@ -0,0 +1,94 @@
+import numpy as np
+import pytest
+import xarray as xr
+
+from src.v0.services.classes.discrete_conditional_probability import (
+    DiscreteConditionalProbability,
+)
+
+
+@pytest.fixture
+def cpt_2d():
+    values = np.array([[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]])
+    conditioned_variable = {"A": ["yes", "no"]}
+    conditioning_variable = {"B": ["low", "mid", "high"]}
+    return DiscreteConditionalProbability(
+        probability_function=values,
+        variables=conditioned_variable | conditioning_variable,
+    )
+
+
+def test_class_categorical_conditional_probability(cpt_2d):
+    assert isinstance(cpt_2d._cpt, xr.DataArray)
+    np.testing.assert_equal(cpt_2d._cpt.sel(A="yes"), np.array([0.1, 0.7, 0.6]))
+    np.testing.assert_equal(cpt_2d._cpt.sel(A="no"), np.array([0.9, 0.3, 0.4]))
+    np.testing.assert_equal(cpt_2d._cpt.sel(A="yes").sel(B="mid"), np.array([0.7]))
+
+
+def test_outcomes():
+    values = np.random.random((2, 3 * 3))
+    for k in range(9):
+        values[:, k] = values[:, k] / np.sum(values[:, k])
+    values = np.reshape(values, (2, 3, 3))
+    conditioned_variables = {"A": ["yes", "no"]}
+    conditioning_variable = {"B": ["R", "G", "B"], "C": ["low", "mid", "high"]}
+    cpt = DiscreteConditionalProbability(
+        probability_function=values,
+        variables=conditioned_variables | conditioning_variable,
+    )
+    assert cpt.outcomes == (("yes", "no"))
+
+
+def test_outcomes_1d(cpt_2d):
+    assert cpt_2d.outcomes == ("yes", "no")
+
+
+def test_variables(cpt_2d):
+    assert cpt_2d.variables == ("A", "B")
+
+
+def test_conditioned_variables(cpt_2d):
+    assert cpt_2d.conditioned_variables == ("A",)
+
+
+def test_conditioning_variables(cpt_2d):
+    assert cpt_2d.conditioning_variables == ("B",)
+
+
+def test_initialize_nan():
+    conditioned_variable = {"a": np.array([1, 2, 3])}
+    conditioning_variables = {"b": np.array([4, 5]), "c": ["yes", "no"]}
+    result = DiscreteConditionalProbability.initialize_nan(
+        conditioned_variable, conditioning_variables=conditioning_variables
+    )
+    assert result._cpt.shape == (3, 2, 2)
+    assert np.all(np.isnan(result._cpt))
+
+
+# def test_initialize_nan_fail():
+#     conditioned_variable = {
+#         "a": np.array([1, 2, 3]),
+#         "b": np.array([[4, 5, 6], [7, 8, 9]]),
+#         "c": ["yes", "no"]
+#         }
+#     with pytest.raises(Exception) as exc:
+#         DiscreteConditionalProbability.initialize_nan(variables=conditioned_variable)
+#     assert str(exc.value) == "One of the variables cannot be interpreted as 1D"
+
+
+def test_initialize_uniform():
+    conditioned_variable = {"a": np.array([1, 2])}
+    conditioning_variables = {"b": np.array([4, 5, 3]), "c": ["yes", "no"]}
+    result = DiscreteConditionalProbability.initialize_uniform(
+        conditioned_variables=conditioned_variable,
+        conditioning_variables=conditioning_variables,
+    )
+    assert result._cpt.shape == (2, 3, 2)
+    assert np.all(result._cpt == 0.5)
+
+
+def test_get_distribution(cpt_2d):
+    np.testing.assert_allclose(
+        cpt_2d.get_distribution(A="yes"), np.array([0.1, 0.7, 0.6])
+    )
+    assert cpt_2d.get_distribution(A="no", B="mid") == 0.3
diff --git a/api/tests/v0/services/classes/test_discrete_unconditional_probability.py b/api/tests/v0/services/classes/test_discrete_unconditional_probability.py
new file mode 100644
index 0000000..34a00ce
--- /dev/null
+++ b/api/tests/v0/services/classes/test_discrete_unconditional_probability.py
@@ -0,0 +1,75 @@
+import numpy as np
+import pytest
+import xarray as xr
+
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+
+
+@pytest.fixture
+def cpt_2d():
+    values = np.array([[0.1, 0.3, 0.2], [0.2, 0.1, 0.1]])
+    coords = {"A": ["yes", "no"], "B": ["low", "mid", "high"]}
+    return DiscreteUnconditionalProbability(values, coords)
+
+
+def test_class_categorical_conditional_probability(cpt_2d):
+    assert isinstance(cpt_2d._cpt, xr.DataArray)
+    np.testing.assert_equal(cpt_2d._cpt.sel(A="yes"), np.array([0.1, 0.3, 0.2]))
+    np.testing.assert_equal(cpt_2d._cpt.sel(A="no"), np.array([0.2, 0.1, 0.1]))
+    np.testing.assert_equal(cpt_2d._cpt.sel(A="yes").sel(B="mid"), np.array([0.3]))
+
+
+def test_outcomes(cpt_2d):
+    assert cpt_2d.outcomes == (
+        "yes - low",
+        "yes - mid",
+        "yes - high",
+        "no - low",
+        "no - mid",
+        "no - high",
+    )
+
+
+def test_outcomes_1d():
+    values = np.array([0.1, 0.9])
+    coords = {"A": ["yes", "no"]}
+    probability = DiscreteUnconditionalProbability(values, coords)
+    assert probability.outcomes == ("yes", "no")
+
+
+def test_variables(cpt_2d):
+    assert cpt_2d.variables == ("A", "B")
+
+
+def test_initialize_nan():
+    coords = {"a": np.array([1, 2, 3]), "b": np.array([4, 5]), "c": ["yes", "no"]}
+    result = DiscreteUnconditionalProbability.initialize_nan(variables=coords)
+    assert result._cpt.shape == (3, 2, 2)
+    assert np.all(np.isnan(result._cpt))
+
+
+# def test_initialize_nan_fail():
+#     coords = {
+#         "a": np.array([1, 2, 3]),
+#         "b": np.array([[4, 5, 6], [7, 8, 9]]),
+#         "c": ["yes", "no"]
+#         }
+#     with pytest.raises(Exception) as exc:
+#         DiscreteUnconditionalProbability.initialize_nan(variables=coords)
+#     assert str(exc.value) == "One of the variables cannot be interpreted as 1D."
+
+
+def test_initialize_uniform():
+    coords = {"a": np.array([1, 2, 3]), "b": np.array([4, 5]), "c": ["yes", "no"]}
+    result = DiscreteUnconditionalProbability.initialize_uniform(variables=coords)
+    assert result._cpt.shape == (3, 2, 2)
+    assert np.all(np.linalg.norm(result._cpt - 1 / 12) < 1e-12)
+
+
+def test_get_distribution(cpt_2d):
+    np.testing.assert_allclose(
+        cpt_2d.get_distribution(A="yes"), np.array([0.1, 0.3, 0.2])
+    )
+    assert cpt_2d.get_distribution(A="no", B="mid") == 0.1
diff --git a/api/tests/v0/services/classes/test_influence_diagram.py b/api/tests/v0/services/classes/test_influence_diagram.py
new file mode 100644
index 0000000..83bb87e
--- /dev/null
+++ b/api/tests/v0/services/classes/test_influence_diagram.py
@@ -0,0 +1,195 @@
+import networkx as nx
+import pytest
+
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.influence_diagram import InfluenceDiagram
+from src.v0.services.classes.node import (
+    DecisionNode,
+    UncertaintyNode,
+    UtilityNode,
+)
+
+
+@pytest.fixture
+def simple_graph():
+    n0 = UncertaintyNode(description="Uncertainty node 1", shortname="u1")
+    n1 = UncertaintyNode(description="Uncertainty node 2", shortname="u2")
+    n2 = UncertaintyNode(description="Uncertainty node 3", shortname="u3")
+    n3 = UncertaintyNode(description="Uncertainty node 4", shortname="u4")
+    n4 = DecisionNode(description="Decision node 1", shortname="d1")
+    n5 = UncertaintyNode(description="Uncertainty node 5", shortname="u5")
+    n6 = DecisionNode(description="Decision node 2", shortname="d2")
+    n7 = UncertaintyNode(description="Uncertainty node 6", shortname="u6")
+    n8 = UncertaintyNode(description="Uncertainty node 7", shortname="u7")
+    n9 = UncertaintyNode(description="Uncertainty node 8", shortname="u8")
+    n10 = UtilityNode(description="Utility node 1", shortname="v1")
+
+    e0 = Arc(tail=n0, head=n4, label="e0")
+    e1 = Arc(tail=n1, head=n4, label="e1")
+    e2 = Arc(tail=n2, head=n4, label="e2")
+    e3 = Arc(tail=n3, head=n6, label="e3")
+    e4 = Arc(tail=n4, head=n6, label="e4")
+    e5 = Arc(tail=n4, head=n5, label="e5")
+    e6 = Arc(tail=n6, head=n7, label="e6")
+    e7 = Arc(tail=n6, head=n8, label="e7")
+    e8 = Arc(tail=n6, head=n9, label="e8")
+    e9 = Arc(tail=n5, head=n10, label="e9")
+
+    return {
+        "nodes": [n0, n1, n2, n3, n4, n5, n6, n7, n8, n9, n10],
+        "arcs": [e0, e1, e2, e3, e4, e5, e6, e7, e8, e9],
+    }
+
+
+def graph_to_influence_diagram(graph):
+    influence_diagram = InfluenceDiagram()
+    influence_diagram.add_nodes(graph["nodes"])
+    influence_diagram.add_arcs(graph["arcs"])
+    return influence_diagram
+
+
+def test_class_InfluenceDiagram():
+    ID = InfluenceDiagram()
+    assert isinstance(ID.graph, nx.DiGraph)
+
+
+def test_copy():
+    ID = InfluenceDiagram()
+    assert nx.utils.graphs_equal(ID.graph, ID.copy().graph)
+
+
+def test_is_acyclic_true(simple_graph):
+    ID = InfluenceDiagram()
+    ID = graph_to_influence_diagram(simple_graph)
+    assert ID.is_acyclic
+
+
+def test_is_acyclic_false():
+    n0 = UncertaintyNode(description="Uncertainty node 1", shortname="u1")
+    n1 = UncertaintyNode(description="Uncertainty node 2", shortname="u2")
+    n2 = UncertaintyNode(description="Uncertainty node 3", shortname="u3")
+
+    e0 = Arc(tail=n0, head=n1, label="e0")
+    e1 = Arc(tail=n1, head=n2, label="e1")
+
+    ID = InfluenceDiagram()
+    ID = graph_to_influence_diagram({"nodes": [n0, n1, n2], "arcs": [e0, e1]})
+    assert ID.is_acyclic
+
+
+def test_nodes(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    assert len(ID.nodes) == 11
+    assert ID.nodes[0].description == "Uncertainty node 1"
+
+
+def test_arcs(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    assert len(ID.arcs) == 10
+    assert ID.arcs[0].tail.description == "Uncertainty node 1"
+    assert ID.arcs[0].label == "e0"
+
+
+def test_node_uuids(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    result = ID.node_uuids
+    assert len(result) == 11
+    assert set(result) == {item.uuid for item in simple_graph["nodes"]}
+
+
+def test_node_in(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    assert ID.node_in(simple_graph["nodes"][0])
+    assert not ID.node_in(
+        UncertaintyNode(description="Uncertainty node 1", shortname="u1")
+    )  # the created node should have a new uuid, so not in the graph
+
+
+def test_get_parents(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    n4 = simple_graph["nodes"][4]
+    result = ID.get_parents(n4)
+    target = simple_graph["nodes"][0:3]
+    assert result == target
+
+
+def test_get_parents_fail(caplog, simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    with pytest.raises(Exception) as exc_info:
+        ID.get_parents(DecisionNode(description="junk", shortname="D"))
+    assert all("The node is not in the graph:" in r.msg for r in caplog.records)
+    assert "The node is not in the graph:" in str(exc_info.value)
+
+
+def test_get_children(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    n6 = simple_graph["nodes"][6]
+    result = ID.get_children(n6)
+    target = simple_graph["nodes"][7:10]
+    assert result == target
+
+    n4 = simple_graph["nodes"][4]
+    result = ID.get_children(n4)
+    target = [simple_graph["nodes"][5], simple_graph["nodes"][6]]
+    assert all(item in target for item in result)
+    assert all(item in result for item in target)
+
+
+def test_get_children_fail(caplog, simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    with pytest.raises(Exception) as exc_info:
+        ID.get_children(DecisionNode(description="junk", shortname="D"))
+    assert all("The node is not in the graph:" in r.msg for r in caplog.records)
+    assert "The node is not in the graph:" in str(exc_info.value)
+
+
+def test_get_decision_nodes(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    assert ID.get_decision_nodes() == [
+        simple_graph["nodes"][4],
+        simple_graph["nodes"][6],
+    ]
+
+
+def test_get_utility_nodes(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    assert ID.get_utility_nodes() == [simple_graph["nodes"][10]]
+
+
+def test_get_uncertainty_nodes(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    assert ID.get_uncertainty_nodes() == [
+        simple_graph["nodes"][0],
+        simple_graph["nodes"][1],
+        simple_graph["nodes"][2],
+        simple_graph["nodes"][3],
+        simple_graph["nodes"][5],
+        simple_graph["nodes"][7],
+        simple_graph["nodes"][8],
+        simple_graph["nodes"][9],
+    ]
+
+
+def test_nodes_count(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    assert ID.decision_count == 2
+    assert ID.utility_count == 1
+    assert ID.uncertainty_count == 8
+
+
+def test_has_children(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    n4 = simple_graph["nodes"][4]
+    n8 = simple_graph["nodes"][8]
+    assert ID.has_children(n4)
+    assert not ID.has_children(n8)
+
+
+def test_get_node_from_uuid(simple_graph):
+    ID = graph_to_influence_diagram(simple_graph)
+    n4 = simple_graph["nodes"][4]
+    uuid = n4.uuid
+    node = ID.get_node_from_uuid(uuid)
+    assert isinstance(node, DecisionNode)
+    assert node.description == "Decision node 1"
+    assert node.shortname == "d1"
diff --git a/api/tests/v0/services/classes/test_node.py b/api/tests/v0/services/classes/test_node.py
new file mode 100644
index 0000000..eb59749
--- /dev/null
+++ b/api/tests/v0/services/classes/test_node.py
@@ -0,0 +1,114 @@
+import numpy as np
+
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+from src.v0.services.classes.node import (
+    DecisionNode,
+    UncertaintyNode,
+    UtilityNode,
+)
+
+
+def test_class_DecisionNode():
+    node = DecisionNode(description="junk", shortname="J")
+    assert node.description == "junk"
+    assert node.shortname == "J"
+    assert isinstance(node.uuid, str)
+    assert len(node.uuid) == 36
+    assert isinstance(node.alternatives, list) and not node.alternatives
+    assert isinstance(node.states, list) and not node.states
+    assert node.is_decision_node
+    assert not node.is_uncertainty_node
+    assert not node.is_utility_node
+
+
+def test_class_DecisionNode_setter_alternatives_as_list():
+    node = DecisionNode(description="junk", shortname="J")
+    node.description = "C2H5OH"
+    node.shortname = "ethanol"
+    node.uuid = None
+    node.alternatives = ["1", "2", "3"]
+    assert node.description == "C2H5OH"
+    assert node.shortname == "ethanol"
+    assert isinstance(node.uuid, str)
+    assert len(node.uuid) == 36
+    assert node.alternatives == ["1", "2", "3"]
+
+
+def test_class_DecisionNode_setter_alternatives_as_empty():
+    node = DecisionNode(description="junk", shortname="J")
+    node.description = "C2H5OH"
+    node.shortname = "ethanol"
+    node.uuid = None
+    node.alternatives = []
+    assert node.description == "C2H5OH"
+    assert node.shortname == "ethanol"
+    assert isinstance(node.uuid, str)
+    assert len(node.uuid) == 36
+    assert node.alternatives == []
+
+
+def test_class_DecisionNode_setter_alternatives_as_tuple():
+    node = DecisionNode(description="junk", shortname="J")
+    node.description = "C2H5OH"
+    node.shortname = "ethanol"
+    node.uuid = None
+    node.alternatives = ("1", "2", "3")
+    assert node.description == "C2H5OH"
+    assert node.shortname == "ethanol"
+    assert isinstance(node.uuid, str)
+    assert len(node.uuid) == 36
+    assert node.alternatives == ["1", "2", "3"]
+
+
+def test_class_UncertaintyNode():
+    node = UncertaintyNode(description="junk", shortname="J")
+    assert node.description == "junk"
+    assert node.shortname == "J"
+    assert isinstance(node.uuid, str)
+    assert len(node.uuid) == 36
+    assert node.probability is None
+    assert isinstance(node.outcomes, tuple) and not node.outcomes
+    assert isinstance(node.states, tuple) and not node.states
+    assert not node.is_decision_node
+    assert node.is_uncertainty_node
+    assert not node.is_utility_node
+
+
+def test_class_UncertaintyNode_setter():
+    node = UncertaintyNode(description="junk", shortname="J")
+    node.probability = DiscreteUnconditionalProbability(
+        probability_function=np.array([1, 0]), variables={"outcome": ["y", "n"]}
+    )
+    assert node.outcomes == ("y", "n")
+
+
+def test_class_UtilityNode():
+    node = UtilityNode(description="junk", shortname="J")
+    assert node.description == "junk"
+    assert node.shortname == "J"
+    assert isinstance(node.uuid, str)
+    assert len(node.uuid) == 36
+    assert node.states == []
+    assert not node.is_decision_node
+    assert not node.is_uncertainty_node
+    assert node.is_utility_node
+    node.utility = []
+    assert node.states == []
+
+
+def test_class_UtilityNode_setter():
+    node = UtilityNode(description="junk", shortname="J")
+    assert isinstance(node, UtilityNode)
+
+
+def test_copy():
+    node = DecisionNode(
+        description="junk", shortname="J", alternatives=["a0", "a1", "a2"]
+    )
+    copied_node = node.copy()
+    assert isinstance(copied_node, type(node))
+    assert copied_node.description == node.description
+    assert copied_node.shortname == node.shortname
+    assert copied_node.alternatives == node.alternatives
diff --git a/api/tests/v0/services/format_conversions/__init__.py b/api/tests/v0/services/format_conversions/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/api/tests/v0/services/format_conversions/test_arc.py b/api/tests/v0/services/format_conversions/test_arc.py
new file mode 100644
index 0000000..7fb331b
--- /dev/null
+++ b/api/tests/v0/services/format_conversions/test_arc.py
@@ -0,0 +1,94 @@
+import json
+
+import pytest
+
+from src.v0.services.classes.arc import Arc
+from src.v0.services.format_conversions import arc, node
+
+TESTDATA = "v0/services/testdata"
+
+
+@pytest.fixture
+def influence_diagram(copy_testdata_tmpdir, tmp_path):
+    copy_testdata_tmpdir(TESTDATA)
+    with open(tmp_path / "simple_id.json") as f:
+        data = json.load(f)
+    issues = data["vertices"]["issues"]
+    issues = [
+        {"uuid" if k == "id" else k: v for k, v in issue.items()} for issue in issues
+    ]
+    data = {
+        "issues": issues,
+        "edges": [edge for edge in data["edges"] if edge["label"] == "influences"],
+    }
+    return data
+
+
+def test_class_ArcConversion_from_json_fail_not_head(caplog):
+    as_json = {
+        "id": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
+        "outV": "98e3d193-d830-452f-9fe8-c21d258ef603",
+        "inV": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
+        "uuid": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
+        "label": "influences",
+    }
+    issues = [
+        {
+            "description": "Joe does not know the state of the car",
+            "uuid": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
+        },
+    ]
+    with pytest.raises(Exception) as exc_info:
+        arc.ArcConversion().from_json(as_json, issues)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an Arc: ([], [])"
+    ]
+    assert str(exc_info.value) == "Data cannot be used to create an Arc: ([], [])"
+
+
+def test_class_ArcConversion_from_json_fail_not_edge(caplog):
+    as_json = {
+        "id": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
+        "uuid": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
+        "label": "influences",
+    }
+    issues = [
+        {
+            "description": "Joe does not know the state of the car",
+            "uuid": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
+        },
+    ]
+    with pytest.raises(Exception) as exc_info:
+        arc.ArcConversion().from_json(as_json, issues)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an Arc: ['id', 'uuid', 'label']"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Data cannot be used to create an Arc: ['id', 'uuid', 'label']"
+    )
+
+
+def test_class_ArcConversion_from_json(influence_diagram):
+    # First "influences" edge is actually b -> v
+    result = arc.ArcConversion().from_json(
+        influence_diagram["edges"][0], influence_diagram["issues"]
+    )
+    assert isinstance(result, Arc)
+    assert result.tail.description == "b"
+    assert result.head.description == "v"
+
+
+def test_class_ArcConversion_to_json(influence_diagram):
+    # Arc b->v (issues 0 and 2)
+    tail = node.InfluenceDiagramNodeConversion().from_json(
+        influence_diagram["issues"][0]
+    )
+    head = node.InfluenceDiagramNodeConversion().from_json(
+        influence_diagram["issues"][2]
+    )
+    edge = Arc(tail=tail, head=head)
+    result = arc.ArcConversion().to_json(edge, [tail, head])
+    assert result["outV"] == "b1f3d18d-20a8-475b-981f-32449d1ee6bd"
+    assert result["inV"] == "8106127e-ab4a-4aa5-a865-8b55789e7d7a"
+    assert result["uuid"] == edge.uuid
diff --git a/api/tests/v0/services/format_conversions/test_base.py b/api/tests/v0/services/format_conversions/test_base.py
new file mode 100644
index 0000000..f212dc2
--- /dev/null
+++ b/api/tests/v0/services/format_conversions/test_base.py
@@ -0,0 +1,30 @@
+import pytest
+
+from src.v0.models.meta import EdgeMetaDataResponse, VertexMetaDataResponse
+from src.v0.services.format_conversions.base import ConversionABC, MetadataCreate
+
+
+def test_class_ConversionABC(monkeypatch):
+    monkeypatch.setattr(
+        ConversionABC,
+        "__abstractmethods__",
+        set(),
+    )
+    conversion = ConversionABC()
+    with pytest.raises(NotImplementedError):
+        conversion.from_json(None)
+    with pytest.raises(NotImplementedError):
+        conversion.to_json(None)
+
+
+def test_MetadataCreate_vertex():
+    metadata = MetadataCreate.vertex("1")
+    assert isinstance(metadata, VertexMetaDataResponse)
+    assert metadata.uuid == "1"
+    assert metadata.version == "v0"
+
+
+def test_MetadataCreate_edge():
+    metadata = MetadataCreate.edge("1")
+    assert isinstance(metadata, EdgeMetaDataResponse)
+    assert metadata.uuid == "1"
diff --git a/api/tests/v0/services/format_conversions/test_decision_node.py b/api/tests/v0/services/format_conversions/test_decision_node.py
new file mode 100644
index 0000000..12b6a5f
--- /dev/null
+++ b/api/tests/v0/services/format_conversions/test_decision_node.py
@@ -0,0 +1,80 @@
+from copy import deepcopy
+
+import pytest
+
+from src.v0.services.classes.node import DecisionNode
+from src.v0.services.format_conversions.node import DecisionNodeConversion
+
+
+@pytest.fixture
+def decision_node():
+    return {
+        "description": "testing node",
+        "shortname": "Node",
+        "boundary": "in",
+        "comments": [{"author": "Jr.", "comment": "Nope"}],
+        "category": "Decision",
+        "decisionType": "Focus",
+        "alternatives": ["yes", "no"],
+        "uuid": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
+    }
+
+
+def test_class_DecisionNodeConversion_from_json_fail(caplog):
+    as_json = {
+        "category": "Junk",
+        "description": "C2H5OH",
+        "shortname": "veni vidi vici",
+        "alternatives": None,
+    }
+    with pytest.raises(Exception) as exc_info:
+        DecisionNodeConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create a DecisionNode: Junk"
+    ]
+    assert str(exc_info.value) == "Data cannot be used to create a DecisionNode: Junk"
+
+
+def test_DecisionNodeConversion_from_json(decision_node):
+    result = DecisionNodeConversion().from_json(decision_node)
+    assert isinstance(result, DecisionNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+    assert result.uuid == "a6ab145e-2ca9-49e2-8c4f-9607688e57a9"
+    assert result.alternatives == ["yes", "no"]
+
+
+def test_DecisionNodeConversion_from_json_no_alternatives(decision_node):
+    local_node = deepcopy(decision_node)
+    local_node["alternatives"] = None
+    result = DecisionNodeConversion().from_json(local_node)
+    assert isinstance(result, DecisionNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+    assert result.alternatives == []
+
+
+def test_DecisionNodeConversion_to_json(decision_node):
+    data = DecisionNode(
+        description=decision_node["description"],
+        shortname=decision_node["shortname"],
+        alternatives=decision_node["alternatives"],
+    )
+    result = DecisionNodeConversion().to_json(data)
+    assert result["description"] == decision_node["description"]
+    assert result["shortname"] == decision_node["shortname"]
+    assert result["decisionType"] == "Focus"
+    assert result["uuid"] == data.uuid
+    assert result["alternatives"] == decision_node["alternatives"]
+
+
+def test_DecisionNodeConversion_to_json_no_alternatives(decision_node):
+    data = DecisionNode(
+        description=decision_node["description"], shortname=decision_node["shortname"]
+    )
+    result = DecisionNodeConversion().to_json(data)
+    assert result["shortname"] == decision_node["shortname"]
+    assert result["description"] == decision_node["description"]
+    assert result["decisionType"] == "Focus"
+    assert result["uuid"] == data.uuid
+    assert result["alternatives"] is None
diff --git a/api/tests/v0/services/format_conversions/test_directed_graph.py b/api/tests/v0/services/format_conversions/test_directed_graph.py
new file mode 100644
index 0000000..d64953c
--- /dev/null
+++ b/api/tests/v0/services/format_conversions/test_directed_graph.py
@@ -0,0 +1,228 @@
+import json
+
+import pytest
+
+from src.v0.services.classes.arc import Arc
+from src.v0.services.classes.decision_tree import DecisionTree
+from src.v0.services.classes.influence_diagram import InfluenceDiagram
+from src.v0.services.classes.node import DecisionNode
+from src.v0.services.format_conversions import arc, directed_graph, node
+
+TESTDATA = "v0/services/testdata"
+
+
+@pytest.fixture
+def influence_diagram(copy_testdata_tmpdir, tmp_path):
+    copy_testdata_tmpdir(TESTDATA)
+    with open(tmp_path / "used_car_buyer_problem.json") as f:
+        data = json.load(f)
+    issues = data["vertices"]["issues"]
+    issues = [
+        {"uuid" if k == "id" else k: v for k, v in issue.items()} for issue in issues
+    ]
+    data = {
+        "vertices": issues,
+        "edges": [edge for edge in data["edges"] if edge["label"] == "influences"],
+    }
+    return data
+
+
+def test_class_InfluenceDiagramConversion_from_json_fail_no_vertex(caplog):
+    as_json = {
+        "type": "Junk",
+        "name": "C2H5OH",
+        "shortname": "veni vidi vici",
+        "alternatives": None,
+    }
+    with pytest.raises(Exception) as exc_info:
+        directed_graph.InfluenceDiagramConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an InfluenceDiagram: None"
+    ]
+    assert (
+        str(exc_info.value) == "Data cannot be used to create an InfluenceDiagram: None"
+    )
+
+
+def test_class_InfluenceDiagramConversion_from_json_fail_no_id_nodes(caplog):
+    as_json = {
+        "vertices": [
+            {
+                "tag": ["State"],
+                "type": "junk",
+                "shortname": "State",
+                "description": "Issue description",
+            }
+        ]
+    }
+
+    with pytest.raises(Exception) as exc_info:
+        directed_graph.InfluenceDiagramConversion().from_json(as_json)
+    assert "Data cannot be used to create an InfluenceDiagram: None" in [
+        r.msg for r in caplog.records
+    ]
+    assert (
+        str(exc_info.value) == "Data cannot be used to create an InfluenceDiagram: None"
+    )
+
+
+def test_class_InfluenceDiagramConversion_from_json_fail_no_focus_decision(caplog):
+    as_json = {
+        "vertices": [
+            {
+                "tag": ["State"],
+                "type": "Decision",
+                "shortname": "State",
+                "description": "Issue description",
+                "keyUncertainty": "Key",
+                "alternatives": ["Peach", "Lemon"],
+            }
+        ]
+    }
+
+    with pytest.raises(Exception) as exc_info:
+        directed_graph.InfluenceDiagramConversion().from_json(as_json)
+    assert "Data cannot be used to create an InfluenceDiagram: None" in [
+        r.msg for r in caplog.records
+    ]
+    assert (
+        str(exc_info.value) == "Data cannot be used to create an InfluenceDiagram: None"
+    )
+
+
+def test_class_InfluenceDiagramConversion_from_json_fail_no_key_uncertainty(caplog):
+    as_json = {
+        "vertices": [
+            {
+                "tag": ["State"],
+                "type": "Uncertainty",
+                "shortname": "State",
+                "description": "Issue description",
+                "probabilities": {
+                    "type": "DiscreteUnconditionalProbability",
+                    "probability_function": [[0.8, 0.2]],
+                    "variables": {"State": ["Peach", "Lemon"]},
+                },
+            }
+        ]
+    }
+
+    with pytest.raises(Exception) as exc_info:
+        directed_graph.InfluenceDiagramConversion().from_json(as_json)
+    assert "Data cannot be used to create an InfluenceDiagram: None" in [
+        r.msg for r in caplog.records
+    ]
+    assert (
+        str(exc_info.value) == "Data cannot be used to create an InfluenceDiagram: None"
+    )
+
+
+def test_class_InfluenceDiagramConversion_from_json_fail_badly_formatted_node(caplog):
+    as_json = {
+        "vertices": [
+            {
+                "tag": ["State"],
+                "type": "Uncertainty",
+                "index": "0",
+            }
+        ]
+    }
+
+    with pytest.raises(Exception) as exc_info:
+        directed_graph.InfluenceDiagramConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an influence diagram Node: category: None",
+        "Data cannot be used to create an InfluenceDiagram: None",
+    ]
+    assert (
+        str(exc_info.value) == "Data cannot be used to create an InfluenceDiagram: None"
+    )
+
+
+def test_DecisionNodeConversion_from_json(influence_diagram):
+    result = directed_graph.InfluenceDiagramConversion().from_json(influence_diagram)
+    assert isinstance(result, InfluenceDiagram)
+    assert result.decision_count == 2
+    assert result.uncertainty_count == 2
+    assert result.utility_count == 1
+
+
+def test_DecisionNodeConversion_to_json(influence_diagram):
+    diagram = InfluenceDiagram()
+    diagram.add_nodes(
+        [
+            node.InfluenceDiagramNodeConversion().from_json(item)
+            for item in influence_diagram["vertices"]
+        ]
+    )
+    diagram.add_arcs(
+        [
+            arc.ArcConversion().from_json(item, influence_diagram["vertices"])
+            for item in influence_diagram["edges"]
+        ]
+    )
+    result = directed_graph.InfluenceDiagramConversion().to_json(diagram)
+    assert len(result["vertices"]) == 5
+    assert len(result["edges"]) == 6
+    assert result["vertices"][1]["category"] == "Decision"
+    assert result["edges"][1]["label"] == "influences"
+
+
+def test_DecisionTreeConversion():
+    with pytest.raises(NotImplementedError):
+        directed_graph.DecisionTreeConversion().from_json(None)
+
+    decision1 = DecisionNode(
+        shortname="D1", description="", uuid="11111111-9999-4444-9999-aaaaaaaaaaaa"
+    )
+    decision2 = DecisionNode(
+        shortname="D2", description="", uuid="22222222-9999-4444-9999-bbbbbbbbbbbb"
+    )
+    dt = DecisionTree()
+    dt.add_nodes((decision1, decision2))
+    dt.add_arc(Arc(tail=decision1, head=decision2, label="branch"))
+
+    assert directed_graph.DecisionTreeConversion().to_json(dt) == {
+        "id": {
+            "node_type": "Decision",
+            "shortname": "D1",
+            "description": "",
+            "branch_name": "",
+            "alternatives": None,
+            "probabilities": None,
+            "utility": None,
+            "uuid": "11111111-9999-4444-9999-aaaaaaaaaaaa",
+        },
+        "children": [
+            {
+                "id": {
+                    "node_type": "Decision",
+                    "shortname": "D2",
+                    "description": "",
+                    "branch_name": "branch",
+                    "alternatives": None,
+                    "probabilities": None,
+                    "utility": None,
+                    "uuid": "22222222-9999-4444-9999-bbbbbbbbbbbb",
+                }
+            }
+        ],
+    }
+
+
+def test_DecisionTreeConversion_fail_no_root(caplog):
+    decision1 = DecisionNode(
+        shortname="D1", description="", uuid="11111111-9999-4444-9999-aaaaaaaaaaaa"
+    )
+    decision2 = DecisionNode(
+        shortname="D2", description="", uuid="22222222-9999-4444-9999-bbbbbbbbbbbb"
+    )
+    dt = DecisionTree()
+    dt.add_nodes((decision1, decision2))
+
+    with pytest.raises(Exception) as exc_info:
+        directed_graph.DecisionTreeConversion().to_json(dt)
+    assert [r.msg for r in caplog.records] == [
+        "Decision tree has no defined root node: None"
+    ]
+    assert str(exc_info.value) == "Decision tree has no defined root node: None"
diff --git a/api/tests/v0/services/format_conversions/test_node.py b/api/tests/v0/services/format_conversions/test_node.py
new file mode 100644
index 0000000..b4f7304
--- /dev/null
+++ b/api/tests/v0/services/format_conversions/test_node.py
@@ -0,0 +1,251 @@
+import numpy as np
+import pytest
+
+from src.v0.services.classes.node import DecisionNode, UncertaintyNode, UtilityNode
+from src.v0.services.format_conversions.node import (
+    DecisionJSONConversion,
+    InfluenceDiagramNodeConversion,
+    UncertaintyJSONConversion,
+    add_metadata,
+)
+from src.v0.services.format_conversions.probability import ProbabilityConversion
+
+
+@pytest.fixture
+def node():
+    return {
+        "description": "testing node",
+        "shortname": "Node",
+        "boundary": "in",
+        "comments": [{"author": "Jr.", "comment": "Nope"}],
+        "uuid": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
+    }
+
+
+@pytest.fixture
+def decision_node(node):
+    return node | {
+        "category": "Decision",
+        "decisionType": "Focus",
+        "alternatives": ["yes", "no"],
+    }
+
+
+@pytest.fixture
+def uncertainty_node(node):
+    return node | {
+        "category": "Uncertainty",
+        "keyUncertainty": "true",
+        "probabilities": {
+            "dtype": "DiscreteUnconditionalProbability",
+            "probability_function": [[0.3], [0.7]],
+            "variables": {"States": ["s1", "s2"]},
+        },
+    }
+
+
+@pytest.fixture
+def value_metric_node(node):
+    return node | {"category": "Value Metric"}
+
+
+def test_add_metadata():
+    metadata = add_metadata("1")
+    assert metadata["uuid"] == "1"
+
+
+def test_DecisionJSONConversion(decision_node):
+    node = InfluenceDiagramNodeConversion().from_json(decision_node)
+    assert DecisionJSONConversion().states(node) == ["yes", "no"]
+    assert DecisionJSONConversion().decision_type(node) == "Focus"
+
+
+def test_UncertaintyJSONConversion(uncertainty_node):
+    node = InfluenceDiagramNodeConversion().from_json(uncertainty_node)
+    assert UncertaintyJSONConversion().probability(node) == {
+        "dtype": "DiscreteUnconditionalProbability",
+        "probability_function": [[0.3], [0.7]],
+        "variables": {"States": ["s1", "s2"]},
+    }
+    assert UncertaintyJSONConversion().key_uncertainty(node) == "true"
+    assert UncertaintyJSONConversion().source(node) == ""
+
+
+def test_InfluenceDiagramNodeConversion_from_json_fail_not_id_node_due_to_category(
+    caplog,
+):
+    as_json = {
+        "category": "Junk",
+        "boundary": "in",
+        "description": "C2H5OH",
+        "shortname": "veni vidi vici",
+        "consequences": None,
+    }
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramNodeConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an influence diagram Node: category: Junk"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Data cannot be used to create an influence diagram Node: category: Junk"
+    )
+
+
+def test_InfluenceDiagramNodeConversion_from_json_fail_not_id_node_due_to_no_shortname(
+    caplog,
+):
+    as_json = {
+        "category": "Decision",
+        "boundary": "in",
+        "description": "C2H5OH",
+        "consequences": None,
+    }
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramNodeConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an influence diagram Node: shortname: None"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Data cannot be used to create an influence diagram Node: shortname: None"
+    )
+
+
+def test_InfluenceDiagramNodeConversion_from_json_fail_not_focus_decision(caplog):
+    as_json = {
+        "category": "Decision",
+        "boundary": "in",
+        "decisionType": "junk",
+        "description": "C2H5OH",
+        "shortname": "veni vidi vici",
+        "consequences": None,
+    }
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramNodeConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an influence diagram Node: decisionType: junk"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Data cannot be used to create an influence diagram Node: decisionType: junk"
+    )
+
+
+def test_InfluenceDiagramNodeConversion_from_json_fail_not_key_uncertainty(caplog):
+    as_json = {
+        "category": "Uncertainty",
+        "boundary": "in",
+        "keyUncertainty": "junk",
+        "description": "C2H5OH",
+        "shortname": "veni vidi vici",
+        "consequences": None,
+    }
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramNodeConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an influence diagram Node: "
+        "keyUncertainty: junk"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Data cannot be used to create an influence diagram Node: "
+        "keyUncertainty: junk"
+    )
+
+
+def test_InfluenceDiagramNodeConversion_from_json_fail_not_in_on_boundary(caplog):
+    as_json = {
+        "category": "Uncertainty",
+        "boundary": "out",
+        "keyUncertainty": "true",
+        "description": "C2H5OH",
+        "shortname": "veni vidi vici",
+        "consequences": None,
+    }
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramNodeConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create an influence diagram Node: boundary: out"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Data cannot be used to create an influence diagram Node: boundary: out"
+    )
+
+
+def test_InfluenceDiagramNodeConversion_from_json_decision(decision_node):
+    result = InfluenceDiagramNodeConversion().from_json(decision_node)
+    assert isinstance(result, DecisionNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+    assert result.alternatives == ["yes", "no"]
+
+
+def test_InfluenceDiagramNodeConversion_from_json_uncertainty(uncertainty_node):
+    result = InfluenceDiagramNodeConversion().from_json(uncertainty_node)
+    assert isinstance(result, UncertaintyNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+    assert result.probability.outcomes == ("s1", "s2")
+    assert result.probability.variables == ("States",)
+    np.testing.assert_allclose(
+        result.probability.get_distribution(), np.array([0.3, 0.7])
+    )
+
+
+def test_InfluenceDiagramNodeConversion_from_json(value_metric_node):
+    result = InfluenceDiagramNodeConversion().from_json(value_metric_node)
+    assert isinstance(result, UtilityNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+
+
+def test_InfluenceDiagramNodeConversion_to_json_fail_not_influence_diagram_node(caplog):
+    with pytest.raises(Exception) as exc_info:
+        InfluenceDiagramNodeConversion().to_json(None)
+    assert [r.msg for r in caplog.records] == [
+        "Data is not an InfluenceDiagram Node: None"
+    ]
+    assert str(exc_info.value) == "Data is not an InfluenceDiagram Node: None"
+
+
+def test_InfluenceDiagramNodeConversion_to_json_decision(decision_node):
+    data = DecisionNode(
+        description=decision_node["description"],
+        shortname=decision_node["shortname"],
+        uuid=decision_node["uuid"],
+        alternatives=decision_node["alternatives"],
+    )
+    result = InfluenceDiagramNodeConversion().to_json(data)
+    assert result["description"] == "testing node"
+    assert result["shortname"] == "Node"
+    assert result["uuid"] == data.uuid
+    assert result["alternatives"] == ["yes", "no"]
+
+
+def test_InfluenceDiagramNodeConversion_to_json_uncertainty(uncertainty_node):
+    probabilities = ProbabilityConversion().from_json(uncertainty_node["probabilities"])
+    data = UncertaintyNode(
+        description=uncertainty_node["description"],
+        shortname=uncertainty_node["shortname"],
+        uuid=uncertainty_node["uuid"],
+        probability=probabilities,
+    )
+    result = InfluenceDiagramNodeConversion().to_json(data)
+    assert result["description"] == uncertainty_node["description"]
+    assert result["shortname"] == uncertainty_node["shortname"]
+    assert result["keyUncertainty"] == "true"
+    assert result["uuid"] == data.uuid
+    assert result["probabilities"] == uncertainty_node["probabilities"]
+
+
+def test_InfluenceDiagramNodeConversion_to_json_utility(value_metric_node):
+    data = UtilityNode(
+        description=value_metric_node["description"],
+        shortname=value_metric_node["shortname"],
+    )
+    result = InfluenceDiagramNodeConversion().to_json(data)
+    assert result["description"] == value_metric_node["description"]
+    assert result["shortname"] == value_metric_node["shortname"]
+    assert result["uuid"] == data.uuid
diff --git a/api/tests/v0/services/format_conversions/test_probability.py b/api/tests/v0/services/format_conversions/test_probability.py
new file mode 100644
index 0000000..ced62d2
--- /dev/null
+++ b/api/tests/v0/services/format_conversions/test_probability.py
@@ -0,0 +1,232 @@
+import numpy as np
+import pytest
+
+from src.v0.services.classes.discrete_conditional_probability import (
+    DiscreteConditionalProbability as DiscreteConditionalProbability,
+)
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability as DiscreteUnconditionalProbability,
+)
+from src.v0.services.format_conversions.probability import (
+    DiscreteConditionalProbabilityConversion,
+    DiscreteUnconditionalProbabilityConversion,
+    ProbabilityConversion,
+)
+
+
+def test_class_ProbabilityConversion_from_json_fail_not_dict(caplog):
+    with pytest.raises(Exception) as exc_info:
+        ProbabilityConversion().from_json("None")
+    assert [r.msg for r in caplog.records] == ["Unreckonized probability type: None"]
+    assert str(exc_info.value) == "Unreckonized probability type: None"
+
+
+def test_class_ProbabilityConversion_from_json_fail_wrong_probability(caplog):
+    with pytest.raises(Exception) as exc_info:
+        ProbabilityConversion().from_json({"type": "unknown"})
+    assert [r.msg for r in caplog.records] == [
+        "Unreckonized probability type: {'type': 'unknown'}"
+    ]
+    assert str(exc_info.value) == "Unreckonized probability type: {'type': 'unknown'}"
+
+
+def test_class_ProbabilityConversion_from_json_conditional():
+    as_json = {
+        "dtype": "DiscreteConditionalProbability",
+        "probability_function": [[0.4, 0.5], [0.6, 0.5]],
+        "variables": {
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+        "attributes": {
+            "conditioned_variables": ["Node1"],
+            "conditioning_variables": ["Node2"],
+        },
+    }
+    result = ProbabilityConversion().from_json(as_json)
+    assert isinstance(result, DiscreteConditionalProbability)
+    assert result._cpt.attrs == {
+        "conditioned_variables": ["Node1"],
+        "conditioning_variables": ["Node2"],
+    }
+    assert result._cpt.coords.dims == ("Node1", "Node2")
+    assert result._cpt.coords["Node1"].values.tolist() == ["Outcome1", "Outcome2"]
+    assert result._cpt.coords["Node2"].values.tolist() == ["Outcome21", "Outcome22"]
+    assert result._cpt.data.tolist() == [[0.4, 0.5], [0.6, 0.5]]
+
+
+def test_class_ProbabilityConversion_from_json_unconditional():
+    as_json = {
+        "dtype": "DiscreteUnconditionalProbability",
+        "probability_function": [[0.2, 0.4], [0.1, 0.3]],
+        "variables": {
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+        "attributes": {
+            "conditioned_variables": ["Node1"],
+            "conditioning_variables": ["Node2"],
+        },
+    }
+    result = ProbabilityConversion().from_json(as_json)
+    assert isinstance(result, DiscreteUnconditionalProbability)
+    assert result._cpt.attrs == {}
+    assert result._cpt.coords.dims == ("Node1", "Node2")
+    assert result._cpt.coords["Node1"].values.tolist() == ["Outcome1", "Outcome2"]
+    assert result._cpt.coords["Node2"].values.tolist() == ["Outcome21", "Outcome22"]
+    assert result._cpt.data.tolist() == [[0.2, 0.4], [0.1, 0.3]]
+
+
+def test_class_ProbabilityConversion_to_json_conditional():
+    values = np.array([[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]])
+    conditioned_variable = {"A": ["yes", "no"]}
+    conditioning_variable = {"B": ["low", "mid", "high"]}
+    variables = conditioned_variable | conditioning_variable
+    cpt = DiscreteConditionalProbability(values, variables)
+    result = ProbabilityConversion().to_json(cpt)
+
+    target = {
+        "dtype": "DiscreteConditionalProbability",
+        "probability_function": [[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]],
+        "variables": {"A": ["yes", "no"], "B": ["low", "mid", "high"]},
+    }
+    assert result == target
+
+
+def test_class_ProbabilityConversion_to_json_unconditional():
+    values = np.array([[0.2, 0.4], [0.1, 0.3]])
+    variables = {"A": ["yes", "no"], "B": ["low", "high"]}
+    cpt = DiscreteUnconditionalProbability(values, variables)
+    result = ProbabilityConversion().to_json(cpt)
+
+    target = {
+        "dtype": "DiscreteUnconditionalProbability",
+        "probability_function": [[0.2, 0.4], [0.1, 0.3]],
+        "variables": {"A": ["yes", "no"], "B": ["low", "high"]},
+    }
+    assert result == target
+
+
+def test_class_DiscreteConditionalProbabilityConversion_from_json_fail(caplog):
+    as_json = {
+        "dtype": "Junk",
+        "probability_function": [[0.4, 0.5], [0.6, 0.5]],
+        "variables": {
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+        "attributes": {
+            "conditioned_variables": ["Node1"],
+            "conditioning_variables": ["Node2"],
+        },
+    }
+    with pytest.raises(Exception) as exc_info:
+        DiscreteConditionalProbabilityConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create a DiscreteConditionalProbability: Junk"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Data cannot be used to create a DiscreteConditionalProbability: Junk"
+    )
+
+
+def test_class_DiscreteConditionalProbabilityConversion_from_json():
+    as_json = {
+        "dtype": "DiscreteConditionalProbability",
+        "probability_function": [[0.4, 0.5], [0.6, 0.5]],
+        "variables": {
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+        "attributes": {
+            "conditioned_variables": ["Node1"],
+            "conditioning_variables": ["Node2"],
+        },
+    }
+    result = DiscreteConditionalProbabilityConversion().from_json(as_json)
+    assert isinstance(result, DiscreteConditionalProbability)
+    assert result._cpt.attrs == {
+        "conditioned_variables": ["Node1"],
+        "conditioning_variables": ["Node2"],
+    }
+    assert result._cpt.coords.dims == ("Node1", "Node2")
+    assert result._cpt.coords["Node1"].values.tolist() == ["Outcome1", "Outcome2"]
+    assert result._cpt.coords["Node2"].values.tolist() == ["Outcome21", "Outcome22"]
+    assert result._cpt.data.tolist() == [[0.4, 0.5], [0.6, 0.5]]
+
+
+def test_class_DiscreteConditionalProbabilityConversion_to_json():
+    values = np.array([[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]])
+    conditioned_variable = {"A": ["yes", "no"]}
+    conditioning_variable = {"B": ["low", "mid", "high"]}
+    variables = conditioned_variable | conditioning_variable
+    cpt = DiscreteConditionalProbability(values, variables)
+    result = DiscreteConditionalProbabilityConversion().to_json(cpt)
+
+    target = {
+        "dtype": "DiscreteConditionalProbability",
+        "probability_function": [[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]],
+        "variables": {"A": ["yes", "no"], "B": ["low", "mid", "high"]},
+    }
+    assert result == target
+
+
+def test_class_DiscreteUnconditionalProbabilityConversion_from_json_fail(caplog):
+    as_json = {
+        "dtype": "Junk",
+        "probability_function": [[0.4, 0.5], [0.6, 0.5]],
+        "variables": {
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+        "attributes": {
+            "conditioned_variables": ["Node1"],
+            "conditioning_variables": ["Node2"],
+        },
+    }
+    with pytest.raises(Exception) as exc_info:
+        DiscreteUnconditionalProbabilityConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create a DiscreteUnconditionalProbability: Junk"
+    ]
+    assert (
+        str(exc_info.value)
+        == "Data cannot be used to create a DiscreteUnconditionalProbability: Junk"
+    )
+
+
+def test_class_DiscreteUnconditionalProbabilityConversion_from_json():
+    as_json = {
+        "dtype": "DiscreteUnconditionalProbability",
+        "probability_function": [[0.2, 0.4], [0.1, 0.3]],
+        "variables": {
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+        "attributes": {
+            "conditioned_variables": ["Node1"],
+            "conditioning_variables": ["Node2"],
+        },
+    }
+    result = DiscreteUnconditionalProbabilityConversion().from_json(as_json)
+    assert isinstance(result, DiscreteUnconditionalProbability)
+    assert result._cpt.attrs == {}
+    assert result._cpt.coords.dims == ("Node1", "Node2")
+    assert result._cpt.coords["Node1"].values.tolist() == ["Outcome1", "Outcome2"]
+    assert result._cpt.coords["Node2"].values.tolist() == ["Outcome21", "Outcome22"]
+    assert result._cpt.data.tolist() == [[0.2, 0.4], [0.1, 0.3]]
+
+
+def test_class_DiscreteUnconditionalProbabilityConversion_to_json():
+    values = np.array([[0.2, 0.4], [0.1, 0.3]])
+    variables = {"A": ["yes", "no"], "B": ["low", "high"]}
+    cpt = DiscreteUnconditionalProbability(values, variables)
+    result = DiscreteUnconditionalProbabilityConversion().to_json(cpt)
+
+    target = {
+        "dtype": "DiscreteUnconditionalProbability",
+        "probability_function": [[0.2, 0.4], [0.1, 0.3]],
+        "variables": {"A": ["yes", "no"], "B": ["low", "high"]},
+    }
+    assert result == target
diff --git a/api/tests/v0/services/format_conversions/test_uncertainty_node.py b/api/tests/v0/services/format_conversions/test_uncertainty_node.py
new file mode 100644
index 0000000..340595b
--- /dev/null
+++ b/api/tests/v0/services/format_conversions/test_uncertainty_node.py
@@ -0,0 +1,211 @@
+from copy import deepcopy
+
+import numpy as np
+import pytest
+
+from src.v0.services.classes.discrete_conditional_probability import (
+    DiscreteConditionalProbability,
+)
+from src.v0.services.classes.discrete_unconditional_probability import (
+    DiscreteUnconditionalProbability,
+)
+from src.v0.services.classes.node import UncertaintyNode
+from src.v0.services.format_conversions.node import UncertaintyNodeConversion
+
+
+@pytest.fixture
+def uncertainty_node():
+    return {
+        "description": "testing node",
+        "shortname": "Node",
+        "boundary": "in",
+        "comments": [{"author": "Jr.", "comment": "Nope"}],
+        "category": "Uncertainty",
+        "keyUncertainty": "true",
+        "probabilities": {
+            "dtype": "DiscreteUnconditionalProbability",
+            "probability_function": [[0.3], [0.7]],
+            "variables": {"States": ["s1", "s2"]},
+        },
+        "uuid": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
+    }
+
+
+def test_class_UncertaintyNodeConversion_from_json_fail(caplog):
+    as_json = {
+        "category": "Junk",
+        "description": "C2H5OH",
+        "shortname": "veni vidi vici",
+        "probabilities": None,
+    }
+    with pytest.raises(Exception) as exc_info:
+        UncertaintyNodeConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create a UncertaintyNode: Junk"
+    ]
+    assert str(exc_info.value) == "Data cannot be used to create a UncertaintyNode: Junk"
+
+
+def test_UncertaintyNodeConversion_from_json_no_probability(uncertainty_node):
+    local_node = deepcopy(uncertainty_node)
+    local_node["probabilities"] = None
+    result = UncertaintyNodeConversion().from_json(local_node)
+    assert isinstance(result, UncertaintyNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+    assert result.probability is None
+
+
+def test_UncertaintyNodeConversion_from_json_unconditional(uncertainty_node):
+    result = UncertaintyNodeConversion().from_json(uncertainty_node)
+    assert isinstance(result, UncertaintyNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+    assert result.probability.outcomes == (("s1", "s2"))
+    assert result.probability.variables == ("States",)
+    np.testing.assert_allclose(
+        result.probability.get_distribution(), np.array([0.3, 0.7])
+    )
+
+
+def test_UncertaintyNodeConversion_from_json_conditional(uncertainty_node):
+    local_node = deepcopy(uncertainty_node)
+    local_node["probabilities"]["dtype"] = "DiscreteConditionalProbability"
+    local_node["probabilities"]["probability_function"] = [[0.3, 0.6], [0.7, 0.4]]
+    local_node["probabilities"]["variables"] = {"A": ["a1", "a2"], "B": ["b1", "b2"]}
+    result = UncertaintyNodeConversion().from_json(local_node)
+    assert isinstance(result, UncertaintyNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+    assert result.probability.outcomes == ("a1", "a2")
+    assert result.probability.variables == ("A", "B")
+    np.testing.assert_allclose(
+        result.probability.get_distribution(), np.array([[0.3, 0.6], [0.7, 0.4]])
+    )
+
+
+def test_UncertaintyNodeConversion_from_json_uncertainty_other(caplog, uncertainty_node):
+    local_node = deepcopy(uncertainty_node)
+    local_node["probabilities"]["dtype"] = "junk"
+    with pytest.raises(Exception) as exc_info:
+        UncertaintyNodeConversion().from_json(local_node)
+    assert [r.msg for r in caplog.records] == [
+        (
+            "Unreckonized probability type: "
+            "{'dtype': 'junk', "
+            "'probability_function': [[0.3], [0.7]], "
+            "'variables': {'States': ['s1', 's2']}}"
+        ),
+        (
+            "Data cannot be used to create a UncertaintyNode: "
+            ""
+            "Unreckonized probability type: "
+            "{'dtype': 'junk', "
+            "'probability_function': [[0.3], [0.7]], "
+            "'variables': {'States': ['s1', 's2']}}"
+        ),
+    ]
+    assert str(exc_info.value) == (
+        "Data cannot be used to create a UncertaintyNode: "
+        ""
+        "Unreckonized probability type: "
+        "{'dtype': 'junk', "
+        "'probability_function': [[0.3], [0.7]], "
+        "'variables': {'States': ['s1', 's2']}}"
+    )
+
+
+def test_UncertaintyNodeConversion_to_json_no_probability(uncertainty_node):
+    data = UncertaintyNode(
+        description=uncertainty_node["description"],
+        shortname=uncertainty_node["shortname"],
+    )
+    result = UncertaintyNodeConversion().to_json(data)
+    assert result["description"] == uncertainty_node["description"]
+    assert result["shortname"] == uncertainty_node["shortname"]
+    assert result["keyUncertainty"] == "true"
+    assert result["uuid"] == data.uuid
+    assert result["probabilities"] is None
+
+
+def test_UncertaintyNodeConversion_to_json_unconditional(uncertainty_node):
+    probabilities = DiscreteUnconditionalProbability(
+        probability_function=np.array([[0.25, 0.2], [0.25, 0.3]]),
+        variables={
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+    )
+    data = UncertaintyNode(
+        description=uncertainty_node["description"],
+        shortname=uncertainty_node["shortname"],
+        probability=probabilities,
+    )
+    result = UncertaintyNodeConversion().to_json(data)
+    assert result["description"] == uncertainty_node["description"]
+    assert result["shortname"] == uncertainty_node["shortname"]
+    assert result["keyUncertainty"] == "true"
+    assert result["uuid"] == data.uuid
+    assert result["probabilities"] == {
+        "dtype": "DiscreteUnconditionalProbability",
+        "probability_function": [[0.25, 0.2], [0.25, 0.3]],
+        "variables": {
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+    }
+
+
+def test_UncertaintyNodeConversion_to_json_conditional(uncertainty_node):
+    probabilities = DiscreteConditionalProbability(
+        probability_function=np.array([[0.5, 0.4], [0.5, 0.6]]),
+        variables={
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+    )
+    data = UncertaintyNode(
+        description=uncertainty_node["description"],
+        shortname=uncertainty_node["shortname"],
+        probability=probabilities,
+    )
+    result = UncertaintyNodeConversion().to_json(data)
+    assert result["description"] == uncertainty_node["description"]
+    assert result["shortname"] == uncertainty_node["shortname"]
+    assert result["keyUncertainty"] == "true"
+    assert result["uuid"] == data.uuid
+    assert result["probabilities"]["dtype"] == "DiscreteConditionalProbability"
+    assert result["probabilities"]["variables"] == {
+        "Node1": ["Outcome1", "Outcome2"],
+        "Node2": ["Outcome21", "Outcome22"],
+    }
+    assert result["probabilities"]["probability_function"] == [[0.5, 0.4], [0.5, 0.6]]
+
+
+def test_UncertaintyNodeConversion_to_json_uncertainty_other(caplog, uncertainty_node):
+    probabilities = DiscreteConditionalProbability(
+        probability_function=np.array([[0.5, 0.4], [0.5, 0.6]]),
+        variables={
+            "Node1": ["Outcome1", "Outcome2"],
+            "Node2": ["Outcome21", "Outcome22"],
+        },
+    )
+    data = UncertaintyNode(
+        description=uncertainty_node["description"],
+        shortname=uncertainty_node["shortname"],
+        probability=probabilities,
+    )
+    data._probability = "None"
+    with pytest.raises(Exception) as exc_info:
+        UncertaintyNodeConversion().to_json(data)
+    assert [r.msg for r in caplog.records] == [
+        "Unreckonized probability type: None",
+        (
+            "Data cannot be used to create a UncertaintyNode: "
+            "Unreckonized probability type: None"
+        ),
+    ]
+    assert str(exc_info.value) == (
+        "Data cannot be used to create a UncertaintyNode: "
+        "Unreckonized probability type: None"
+    )
diff --git a/api/tests/v0/services/format_conversions/test_utility_node.py b/api/tests/v0/services/format_conversions/test_utility_node.py
new file mode 100644
index 0000000..3a725d1
--- /dev/null
+++ b/api/tests/v0/services/format_conversions/test_utility_node.py
@@ -0,0 +1,47 @@
+import pytest
+
+from src.v0.services.classes.node import UtilityNode
+from src.v0.services.format_conversions.node import UtilityNodeConversion
+
+
+@pytest.fixture
+def utility_node():
+    return {
+        "description": "testing node",
+        "shortname": "Node",
+        "boundary": "in",
+        "comments": [{"author": "Jr.", "comment": "Nope"}],
+        "category": "Value Metric",
+        "uuid": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
+    }
+
+
+def test_class_UtilityNodeConversion_from_json_fail(caplog):
+    as_json = {
+        "category": "Junk",
+        "description": "C2H5OH",
+        "shortname": "veni vidi vici",
+    }
+    with pytest.raises(Exception) as exc_info:
+        UtilityNodeConversion().from_json(as_json)
+    assert [r.msg for r in caplog.records] == [
+        "Data cannot be used to create a UtilityNode: Junk"
+    ]
+    assert str(exc_info.value) == "Data cannot be used to create a UtilityNode: Junk"
+
+
+def test_UtilityNodeConversion_from_json(utility_node):
+    result = UtilityNodeConversion().from_json(utility_node)
+    assert isinstance(result, UtilityNode)
+    assert result.description == "testing node"
+    assert result.shortname == "Node"
+
+
+def test_UtilityNodeConversion_to_json(utility_node):
+    data = UtilityNode(
+        description=utility_node["description"], shortname=utility_node["shortname"]
+    )
+    result = UtilityNodeConversion().to_json(data)
+    assert result["description"] == utility_node["description"]
+    assert result["shortname"] == utility_node["shortname"]
+    assert result["uuid"] == data.uuid
diff --git a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_decision_tree.py b/api/tests/v0/services/structure_utils/test_decision_diagrams/test_decision_tree.py
deleted file mode 100644
index cdd2c51..0000000
--- a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_decision_tree.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import json
-from unittest.mock import mock_open, patch
-
-import networkx as nx
-import numpy as np
-import pytest
-
-from src.v0.services.structure_utils.decision_diagrams.decision_tree import DecisionTree
-from src.v0.services.structure_utils.decision_diagrams.edge import Edge
-from src.v0.services.structure_utils.decision_diagrams.node import (
-    DecisionNode,
-    UncertaintyNode,
-    UtilityNode,
-)
-
-
-@pytest.fixture
-def graph_as_dict():
-    n0 = UncertaintyNode("u0", "Uncertainty node 0")
-    n1 = UncertaintyNode("u1", "Uncertainty node 1")
-    n2 = UncertaintyNode("u2", "Uncertainty node 2")
-    n3 = DecisionNode("d0", "Decision node 0")
-    n4 = DecisionNode("d1", "Decision node 1")
-    n5 = DecisionNode("d2", "Decision node 2")
-    n6 = DecisionNode("d3", "Decision node 3")
-    n7 = DecisionNode("d4", "Decision node 4")
-    n8 = UtilityNode("v0", "Utility node 0")
-    n9 = UtilityNode("v1", "Utility node 1")
-    n10 = UtilityNode("v2", "Utility node 2")
-    n11 = UtilityNode("v3", "Utility node 3")
-    n12 = UncertaintyNode("u3", "Uncertainty node 3")
-    n13 = UtilityNode("v4", "Utility node 4")
-    n14 = UtilityNode("v5", "Utility node 5")
-    n15 = UtilityNode("v6", "Utility node 6")
-    n16 = UtilityNode("v7", "Utility node 7")
-    n17 = UtilityNode("v8", "Utility node 8")
-    n18 = UtilityNode("v9", "Utility node 9")
-    n19 = UtilityNode("v10", "Utility node 10")
-    n20 = UtilityNode("v11", "Utility node 11")
-    n21 = UtilityNode("v12", "Utility node 12")
-
-    e0 = Edge(n0, n1, name="e0")
-    e1 = Edge(n1, n3, name="e1")
-    e2 = Edge(n3, n8, name="e2")
-    e3 = Edge(n3, n9, name="e3")
-    e4 = Edge(n1, n4, name="e4")
-    e5 = Edge(n4, n10, name="e5")
-    e6 = Edge(n4, n11, name="e6")
-    e7 = Edge(n4, n12, name="e7")
-    e8 = Edge(n12, n20, name="e8")
-    e9 = Edge(n12, n21, name="e9")
-    e10 = Edge(n0, n2, name="e10")
-    e11 = Edge(n2, n5, name="e11")
-    e12 = Edge(n5, n13, name="e12")
-    e13 = Edge(n5, n14, name="e13")
-    e14 = Edge(n2, n6, name="e14")
-    e15 = Edge(n6, n15, name="e15")
-    e16 = Edge(n6, n16, name="e16")
-    e17 = Edge(n2, n7, name="e17")
-    e18 = Edge(n7, n17, name="e18")
-    e19 = Edge(n7, n18, name="e19")
-    e20 = Edge(n7, n19, name="e20")
-
-    return {
-        "nodes": [
-            n0,
-            n1,
-            n2,
-            n3,
-            n4,
-            n5,
-            n6,
-            n7,
-            n8,
-            n9,
-            n10,
-            n11,
-            n12,
-            n13,
-            n14,
-            n15,
-            n16,
-            n17,
-            n18,
-            n19,
-            n20,
-            n21,
-        ],
-        "edges": [
-            e0,
-            e1,
-            e2,
-            e3,
-            e4,
-            e5,
-            e6,
-            e7,
-            e8,
-            e9,
-            e10,
-            e11,
-            e12,
-            e13,
-            e14,
-            e15,
-            e16,
-            e17,
-            e18,
-            e19,
-            e20,
-        ],
-    }
-
-
-def test_from_dict(graph_as_dict):
-    dt = DecisionTree.from_dict(graph_as_dict)
-    assert isinstance(dt.nx, nx.DiGraph)
-
-
-def test_class_DecisionTree():
-    dt = DecisionTree()
-    assert isinstance(dt.nx, nx.DiGraph)
-
-
-def test_set_root(graph_as_dict):
-    dt = DecisionTree()
-    root = graph_as_dict["nodes"][0]
-    dt.set_root(root)
-    assert isinstance(dt.nx, nx.DiGraph)
-    assert dt.parent(root) is None
-    assert len(dt.get_children(root)) == 0
-
-
-def test_initialize_decision_tree(graph_as_dict):
-    n0 = graph_as_dict["nodes"][0]
-    dt = DecisionTree(root=n0)
-    assert nx.number_of_nodes(dt.nx) == 1
-
-
-def test_initialize_decision_tree_fail():
-    n0 = UncertaintyNode("u0", "Uncertainty node 0")
-    n1 = UncertaintyNode("u1", "Uncertainty node 1")
-    n2 = UncertaintyNode("u2", "Uncertainty node 2")
-    n3 = DecisionNode("d0", "Decision node 0")
-    n4 = DecisionNode("d1", "Decision node 1")
-    n5 = DecisionNode("d2", "Decision node 2")
-
-    e0 = Edge(n0, n5, name="e0")
-    e1 = Edge(n1, n2, name="e1")
-    e2 = Edge(n2, n3, name="e2")
-    e3 = Edge(n2, n4, name="e3")
-
-    graph = {
-        "nodes": [n0, n1, n2, n3, n4, n5],
-        "edges": [e0, e1, e2, e3],
-    }
-
-    with pytest.raises(Exception) as exc:
-        DecisionTree.initialize_diagram(graph)
-    assert str(exc.value) == "Decision tree has no defined root node"
-
-
-def test_parent(graph_as_dict):
-    dt = DecisionTree.from_dict(graph_as_dict)
-    n5 = graph_as_dict["nodes"][5]
-    n2 = graph_as_dict["nodes"][2]
-    assert dt.parent(n5) == n2
-
-
-def test_to_json(graph_as_dict):
-    dt = DecisionTree.from_dict(graph_as_dict)
-    result = dt.to_json()
-    assert isinstance(json.loads(result), dict)
-    assert result.count("description") == 22
-    assert result.count("shortname") == 22
-    assert result.count("probabilities") == 4
-    assert result.count("alternatives") == 5
-    assert result.count("utility") == 13
-    assert result.count("children") == 9
-
-
-def test_to_json_with_file(graph_as_dict):
-    dt = DecisionTree.from_dict(graph_as_dict)
-    with patch("builtins.open", mock_open()) as m:
-        dt.to_json("junk")
-        m.assert_called_once_with("junk", "w")
-
-
-def test_to_json_fail_no_root(caplog):
-    probabilities = {
-        "type": "DiscreteUnconditionalProbability",
-        "probability_function": np.array([0.8, 0.2]),
-        "variables": {"State": ["Peach", "Lemon"]},
-    }
-    uncertainty = UncertaintyNode("Symptom", "S", probabilities=probabilities)
-
-    dt = DecisionTree()
-    dt.add_node(uncertainty)
-    with pytest.raises(Exception) as exc_info:
-        dt.to_json()
-    assert [r.msg for r in caplog.records] == ["Decision tree has no defined root node"]
-    assert str(exc_info.value) == "Decision tree has no defined root node"
diff --git a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_edge.py b/api/tests/v0/services/structure_utils/test_decision_diagrams/test_edge.py
deleted file mode 100644
index 5158adf..0000000
--- a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_edge.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import pytest
-
-from src.v0.services.structure_utils.decision_diagrams.edge import Edge
-from src.v0.services.structure_utils.decision_diagrams.influence_diagram import (
-    InfluenceDiagram,
-)
-from src.v0.services.structure_utils.decision_diagrams.node import (
-    DecisionNode,
-    UncertaintyNode,
-    UtilityNode,
-)
-
-
-def test_class_InformationalArc():
-    n1 = UncertaintyNode("J", "junk")
-    n2 = DecisionNode("H", "junky")
-    edge = Edge(n1, n2, "first")
-    assert edge.endpoint_start == n1
-    assert edge.endpoint_end == n2
-    assert edge.name == "first"
-    assert edge._arc_type == "informational"
-
-
-def test_class_InformationalArc_without_name():
-    n1 = UncertaintyNode("J", "junk")
-    n2 = DecisionNode("H", "junky")
-    edge = Edge(n1, n2)
-    assert edge.name is None
-
-
-def test_class_ConditionalArc():
-    n1 = UncertaintyNode("J", "junk")
-    n2 = UncertaintyNode("H", "junky")
-    edge = Edge(n1, n2, "second")
-    assert edge.endpoint_start.description == "junk"
-    assert edge.endpoint_start.shortname == "J"
-    assert edge.endpoint_end.description == "junky"
-    assert edge.endpoint_end.shortname == "H"
-    assert edge._name == "second"
-    assert edge._arc_type == "conditional"
-
-
-def test_class_FunctionalArc():
-    n1 = UncertaintyNode("J", "junk")
-    n2 = UtilityNode("H", "junky")
-    edge = Edge(n1, n2, "first")
-    assert edge.endpoint_start == n1
-    assert edge.endpoint_end == n2
-    assert edge.name == "first"
-    assert edge._arc_type == "functional"
-
-
-def test_copy():
-    n1 = UncertaintyNode("J", "junk")
-    n2 = DecisionNode("H", "junky")
-    edge = Edge(n1, n2, "first")
-    copied_edge = edge.copy()
-    assert copied_edge.endpoint_start == edge.endpoint_start
-    assert copied_edge.endpoint_end == edge.endpoint_end
-    assert copied_edge.name == edge.name
-
-
-def test_set_endpoint_with_mode_end():
-    n1 = UncertaintyNode("J", "junk")
-    n2 = DecisionNode("H", "junky")
-    edge = Edge(n1, None, "first")
-    assert edge.endpoint_start == n1
-    assert edge.endpoint_end is None
-    assert edge._name == "first"
-    assert edge._arc_type is None
-    edge.set_endpoint(n2)
-    assert edge.endpoint_end == n2
-    assert edge._arc_type == "informational"
-
-
-def test_set_endpoint_with_mode_end_failing_for_wrong_mode(caplog):
-    n1 = UncertaintyNode("J", "junk")
-    n2 = DecisionNode("H", "junky")
-    edge = Edge(n1, None, "first")
-    with pytest.raises(Exception) as exc_info:
-        edge.set_endpoint(n2, mode="junk")
-    assert [r.msg for r in caplog.records] == ["endpoint cannot be set to mode junk"]
-    assert str(exc_info.value) == "endpoint cannot be set to mode junk"
-
-
-def test_set_endpoint_with_mode_end_failing_for_wrong_end_node(caplog):
-    n1 = UtilityNode("J", "junk")
-    n2 = DecisionNode("H", "junky")
-    edge = Edge(n1, None, "first")
-    with pytest.raises(Exception) as exc_info:
-        edge.set_endpoint(n2, mode="end")
-    assert [r.msg for r in caplog.records] == [
-        "utility node can only have other utility nodes as successor"
-    ]
-    assert (
-        str(exc_info.value)
-        == "utility node can only have other utility nodes as successor"
-    )
-
-
-def test_set_endpoint_with_mode_start():
-    n1 = UncertaintyNode("J", "junk")
-    n2 = DecisionNode("H", "junky")
-    edge = Edge(None, n2, "first")
-    assert edge.endpoint_start is None
-    assert edge.endpoint_end == n2
-    assert edge._name == "first"
-    assert edge._arc_type == "informational"
-    edge.set_endpoint(n1, mode="start")
-    assert edge.endpoint_start == n1
-    assert edge._arc_type == "informational"
-
-
-def test_from_dict():
-    n1 = UncertaintyNode("J", "junk")
-    n2 = DecisionNode("H", "junky")
-    ID = InfluenceDiagram.from_dict({"nodes": [n1, n2]})
-    edge = {"from": n1.uuid, "to": n2.uuid, "name": "first"}
-    edge = Edge.from_dict(edge, ID)
-    assert edge.endpoint_start == n1
-    assert edge.endpoint_end == n2
-    assert edge.name == "first"
-    assert edge._arc_type == "informational"
diff --git a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_influence_diagram.py b/api/tests/v0/services/structure_utils/test_decision_diagrams/test_influence_diagram.py
deleted file mode 100644
index 67765b3..0000000
--- a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_influence_diagram.py
+++ /dev/null
@@ -1,1067 +0,0 @@
-import json
-
-import networkx as nx
-import pyAgrum as gum
-import pytest
-
-from src.v0.models.issue import ProbabilityData
-from src.v0.models.structure import InfluenceDiagramResponse
-from src.v0.services.structure_utils.decision_diagrams.decision_tree import DecisionTree
-from src.v0.services.structure_utils.decision_diagrams.edge import Edge
-from src.v0.services.structure_utils.decision_diagrams.influence_diagram import (
-    InfluenceDiagram,
-)
-from src.v0.services.structure_utils.decision_diagrams.node import (
-    DecisionNode,
-    UncertaintyNode,
-    UtilityNode,
-)
-from src.v0.services.structure_utils.probability.discrete_unconditional_probability import (  # noqa: E501
-    DiscreteUnconditionalProbability,
-)
-
-TESTDATA = "v0/services/testdata"
-
-
-@pytest.fixture
-def graph_as_dict():
-    n0 = UncertaintyNode("u1", "Uncertainty node 1")
-    n1 = UncertaintyNode("u2", "Uncertainty node 2")
-    n2 = UncertaintyNode("u3", "Uncertainty node 3")
-    n3 = UncertaintyNode("u4", "Uncertainty node 4")
-    n4 = DecisionNode("d1", "Decision node 1")
-    n5 = UncertaintyNode("u5", "Uncertainty node 5")
-    n6 = DecisionNode("d2", "Decision node 2")
-    n7 = UncertaintyNode("u6", "Uncertainty node 6")
-    n8 = UncertaintyNode("u7", "Uncertainty node 7")
-    n9 = UncertaintyNode("u8", "Uncertainty node 8")
-    n10 = UtilityNode("v1", "Utility node 1")
-
-    e0 = Edge(n0, n4, name="e0")
-    e1 = Edge(n1, n4, name="e1")
-    e2 = Edge(n2, n4, name="e2")
-    e3 = Edge(n3, n6, name="e3")
-    e4 = Edge(n4, n6, name="e4")
-    e5 = Edge(n4, n5, name="e5")
-    e6 = Edge(n6, n7, name="e6")
-    e7 = Edge(n6, n8, name="e7")
-    e8 = Edge(n6, n9, name="e8")
-    e9 = Edge(n5, n10, name="e9")
-
-    return {
-        "nodes": [n0, n1, n2, n3, n4, n5, n6, n7, n8, n9, n10],
-        "edges": [e0, e1, e2, e3, e4, e5, e6, e7, e8, e9],
-    }
-
-
-def test_from_dict(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    assert isinstance(ID.nx, nx.DiGraph)
-    assert ID.decision_count == 2
-    assert ID.uncertainty_count == 8
-    assert ID.utility_count == 1
-
-
-def test_from_db(copy_testdata_tmpdir, tmp_path):
-    copy_testdata_tmpdir(TESTDATA)
-    with open(tmp_path / "used_car_buyer_model_response.json") as f:
-        json_stream = json.load(f)
-    influence_diagram_response = InfluenceDiagramResponse(
-        vertices=json_stream["vertices"], edges=json_stream["edges"]
-    )
-    InfluenceDiagram.from_db(influence_diagram_response)
-
-    n0 = UncertaintyNode("State", "Joe does not know the state of the car")
-    n1 = UncertaintyNode("Test Result", "The result of the test is currently unknown")
-    n2 = DecisionNode("Buy", "We can buy the car")
-    n3 = DecisionNode("Test", "Joe can test the car")
-    n4 = UtilityNode("Value", "Value")
-
-    e0 = Edge(n2, n4, name="e0")
-    e1 = Edge(n0, n1, name="e1")
-    e2 = Edge(n0, n4, name="e2")
-    e3 = Edge(n1, n2, name="e3")
-    e4 = Edge(n3, n1, name="e4")
-
-    target = InfluenceDiagram.from_dict(
-        {"nodes": [n0, n1, n2, n3, n4], "edges": [e0, e1, e2, e3, e4]}
-    )
-    result = InfluenceDiagram.from_db(influence_diagram_response)
-
-    for result_node, target_node in zip(result.nx.nodes, target.nx.nodes, strict=False):
-        assert type(result_node) is type(target_node)
-        assert result_node.description == target_node.description
-        if not isinstance(result_node, UtilityNode):
-            assert result_node.shortname == target_node.shortname
-
-    for result_arc, target_arc in zip(result.nx.edges, target.nx.edges, strict=False):
-        assert type(result_arc) is type(target_arc)
-        assert result_arc[0].description == target_arc[0].description
-        assert result_arc[1].description == target_arc[1].description
-        assert (
-            result.nx.edges[result_arc[0], result_arc[1]]["arc_type"]
-            == target.nx.edges[target_arc[0], target_arc[1]]["arc_type"]
-        )
-
-
-def test_to_json(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    result = ID.to_json()
-    json_result = json.loads(result)
-    assert json_result["directed"]
-    assert not json_result["multigraph"]
-    assert len(json_result["nodes"]) == 11
-    assert len(json_result["edges"]) == 10
-    assert result.count("description") == 11
-    assert result.count("shortname") == 11
-    assert result.count("from") == 10
-    assert result.count("to") == 10
-
-
-def test_class_InfluenceDiagram():
-    ID = InfluenceDiagram()
-    assert isinstance(ID.nx, nx.DiGraph)
-
-
-def test_copy():
-    ID = InfluenceDiagram()
-    assert nx.utils.graphs_equal(ID.nx, ID.copy().nx)
-
-
-def test_get_parents(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    n4 = graph_as_dict["nodes"][4]
-    result = ID.get_parents(n4)
-    target = graph_as_dict["nodes"][0:3]
-    assert result == target
-
-
-def test_get_children(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    n6 = graph_as_dict["nodes"][6]
-    result = ID.get_children(n6)
-    target = graph_as_dict["nodes"][7:10]
-    assert result == target
-
-    n4 = graph_as_dict["nodes"][4]
-    result = ID.get_children(n4)
-    target = [graph_as_dict["nodes"][5], graph_as_dict["nodes"][6]]
-    assert all(item in target for item in result)
-    assert all(item in result for item in target)
-
-
-def test_get_node_type(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    n6 = graph_as_dict["nodes"][6]
-    assert ID.get_node_type(n6) == "decision"
-
-
-def test_get_nodes_from_type(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    assert ID._get_nodes_from_type("DecisionNode") == [
-        graph_as_dict["nodes"][4],
-        graph_as_dict["nodes"][6],
-    ]
-    assert ID._get_nodes_from_type("UtilityNode") == [graph_as_dict["nodes"][10]]
-    assert ID._get_nodes_from_type("UncertaintyNode") == [
-        graph_as_dict["nodes"][0],
-        graph_as_dict["nodes"][1],
-        graph_as_dict["nodes"][2],
-        graph_as_dict["nodes"][3],
-        graph_as_dict["nodes"][5],
-        graph_as_dict["nodes"][7],
-        graph_as_dict["nodes"][8],
-        graph_as_dict["nodes"][9],
-    ]
-
-
-def test_get_decision_nodes(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    assert ID.get_decision_nodes() == [
-        graph_as_dict["nodes"][4],
-        graph_as_dict["nodes"][6],
-    ]
-
-
-def test_get_utility_nodes(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    assert ID.get_utility_nodes() == [graph_as_dict["nodes"][10]]
-
-
-def test_get_uncertainty_nodes(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    assert ID.get_uncertainty_nodes() == [
-        graph_as_dict["nodes"][0],
-        graph_as_dict["nodes"][1],
-        graph_as_dict["nodes"][2],
-        graph_as_dict["nodes"][3],
-        graph_as_dict["nodes"][5],
-        graph_as_dict["nodes"][7],
-        graph_as_dict["nodes"][8],
-        graph_as_dict["nodes"][9],
-    ]
-
-
-def test_nodes_count(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    assert ID.decision_count == 2
-    assert ID.utility_count == 1
-    assert ID.uncertainty_count == 8
-
-
-def test_has_children(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    n4 = graph_as_dict["nodes"][4]
-    n8 = graph_as_dict["nodes"][8]
-    assert ID.has_children(n4)
-    assert not ID.has_children(n8)
-
-
-def test_get_node_from_uuid(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    n4 = graph_as_dict["nodes"][4]
-    uuid = n4.uuid
-    node = ID.get_node_from_uuid(uuid)
-    assert isinstance(node, DecisionNode)
-    assert node.description == "Decision node 1"
-    assert node.shortname == "d1"
-
-
-def test_decision_elimination_order(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    result = ID.decision_elimination_order()
-    target = [graph_as_dict["nodes"][4], graph_as_dict["nodes"][6]]
-    assert all(item in target for item in result)
-    assert all(item in result for item in target)
-
-
-def test_calculate_partial_order(graph_as_dict):
-    # Test is only reproducing result, not testing logic!
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    partial_order = ID.calculate_partial_order()
-    result = [n.shortname for n in partial_order]
-    target = ["u1", "u2", "u3", "d1", "u4", "d2", "u5", "u6", "u7", "u8"]
-    assert result == target
-
-
-def test_calculate_partial_order_fail_mode(graph_as_dict, caplog):
-    # Test is only reproducing result, not testing logic!
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    with pytest.raises(Exception) as exc_info:
-        ID.calculate_partial_order(mode="junk")
-    assert [r.msg for r in caplog.records] == [
-        "output mode should be [view|copy] and have been entered as junk"
-    ]
-    assert (
-        str(exc_info.value)
-        == "output mode should be [view|copy] and have been entered as junk"
-    )
-
-
-def test_calculate_partial_order_copy_mode(graph_as_dict):
-    # Test is only reproducing result, not testing logic!
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    partial_order_0 = ID.calculate_partial_order(mode="view")
-    partial_order = ID.calculate_partial_order(mode="copy")
-    result = [n.shortname for n in partial_order]
-    target = ["u1", "u2", "u3", "d1", "u4", "d2", "u5", "u6", "u7", "u8"]
-    assert result == target
-    assert partial_order_0 != partial_order
-
-
-def test_output_branches_from_node_empty_lists(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    uncertainty_node = graph_as_dict["nodes"][0]
-    decision_node = graph_as_dict["nodes"][4]
-    utility_node = graph_as_dict["nodes"][10]
-
-    assert (
-        len(
-            list(
-                zip(
-                    *ID._output_branches_from_node(uncertainty_node, uncertainty_node),
-                    strict=False,
-                )
-            )
-        )
-        == 0
-    )
-    assert (
-        len(
-            list(
-                zip(
-                    *ID._output_branches_from_node(decision_node, uncertainty_node),
-                    strict=False,
-                )
-            )
-        )
-        == 0
-    )
-    assert (
-        len(
-            list(
-                zip(
-                    *ID._output_branches_from_node(utility_node, uncertainty_node),
-                    strict=False,
-                )
-            )
-        )
-        == 0
-    )
-
-
-def test_output_branches_from_node(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    probability = ProbabilityData(
-        **{
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.7, 0.2, 0.1]],
-            "variables": {"State": ["pear", "lemon", "plum"]},
-        }
-    )
-    uncertainty_node = graph_as_dict["nodes"][0]
-    uncertainty_node._probabilities = DiscreteUnconditionalProbability.from_db_model(
-        probability
-    )
-    decision_node = graph_as_dict["nodes"][4]
-    decision_node._alternatives = ["wait", "pickup"]
-    utility_node = graph_as_dict["nodes"][10]
-    utility_node._utility = ["1000"]
-
-    result = list(ID._output_branches_from_node(uncertainty_node, uncertainty_node))
-    assert all(r[1] == uncertainty_node for r in result)
-    assert [r[0].name for r in result] == ["plum", "lemon", "pear"]
-    assert all(isinstance(r[0], Edge) for r in result)
-    assert all(r[0].endpoint_start == uncertainty_node for r in result)
-    assert all(r[0].endpoint_end is None for r in result)
-
-    result = list(ID._output_branches_from_node(decision_node, uncertainty_node))
-    assert all(r[1] == uncertainty_node for r in result)
-    assert [r[0].name for r in result] == ["pickup", "wait"]
-    assert all(isinstance(r[0], Edge) for r in result)
-    assert all(r[0].endpoint_start == decision_node for r in result)
-    assert all(r[0].endpoint_end is None for r in result)
-
-    result = list(ID._output_branches_from_node(utility_node, uncertainty_node))
-    assert all(r[1] == uncertainty_node for r in result)
-    assert [r[0].name for r in result] == ["1000"]
-    assert all(isinstance(r[0], Edge) for r in result)
-    assert all(r[0].endpoint_start == utility_node for r in result)
-    assert all(r[0].endpoint_end is None for r in result)
-
-
-def test_output_branches_from_node_reverse_mode(graph_as_dict):
-    ID = InfluenceDiagram.from_dict(graph_as_dict)
-    probability = ProbabilityData(
-        **{
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.7, 0.2, 0.1]],
-            "variables": {"State": ["pear", "lemon", "plum"]},
-        }
-    )
-    uncertainty_node = graph_as_dict["nodes"][0]
-    uncertainty_node._probabilities = DiscreteUnconditionalProbability.from_db_model(
-        probability
-    )
-
-    result = list(
-        ID._output_branches_from_node(uncertainty_node, uncertainty_node, flip=False)
-    )
-    assert [r[0].name for r in result] == ["pear", "lemon", "plum"]
-
-
-def test_convert_to_decision_tree_symmetry():
-    # Medical Diagnosis Problem
-    # Data taken from
-    # DECISION TREES AND INFLUENCE DIAGRAMS
-    # Prakash P. Shenoy
-    # Encyclopedia of Life Support Systems, U Derigs (ed.),
-    # Optimization and Operations Research, Vol. 4, pp. 280–298, 2009
-    #
-    #
-    # Probabilities in the ID to be updated!!!
-    #
-    # The produced tree may be flipped horizontally compared to the expected one
-
-    probability_S = ProbabilityData(
-        **{
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [
-                [
-                    0.7,
-                    0.3,
-                ]
-            ],
-            "variables": {
-                "State": [
-                    "yes",
-                    "no",
-                ]
-            },
-        }
-    )
-    probability_S = DiscreteUnconditionalProbability.from_db_model(probability_S)
-    probability_P = ProbabilityData(
-        **{
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [
-                [
-                    0.2,
-                    0.8,
-                ]
-            ],
-            "variables": {
-                "State": [
-                    "yes",
-                    "no",
-                ]
-            },
-        }
-    )
-    probability_P = DiscreteUnconditionalProbability.from_db_model(probability_P)
-    probability_D = ProbabilityData(
-        **{
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [
-                [
-                    0.1,
-                    0.9,
-                ]
-            ],
-            "variables": {
-                "State": [
-                    "yes",
-                    "no",
-                ]
-            },
-        }
-    )
-    probability_D = DiscreteUnconditionalProbability.from_db_model(probability_D)
-
-    id_decision_T = DecisionNode("T", "Treat for Disease", alternatives=["yes", "no"])
-    id_uncertainty_S = UncertaintyNode("S", "Symptom", probabilities=probability_S)
-    id_uncertainty_P = UncertaintyNode(
-        "P", "Pathological state", probabilities=probability_P
-    )
-    id_uncertainty_D = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    id_utility_0 = UtilityNode("v", "Utility", utility=None)
-
-    id_e0 = Edge(id_uncertainty_S, id_decision_T)
-    id_e1 = Edge(id_uncertainty_P, id_uncertainty_S)
-    id_e2 = Edge(id_uncertainty_D, id_uncertainty_P)
-    id_e3 = Edge(id_decision_T, id_utility_0)
-    id_e4 = Edge(id_uncertainty_P, id_utility_0)
-    id_e5 = Edge(id_uncertainty_D, id_utility_0)
-
-    net = {
-        "nodes": [
-            id_decision_T,
-            id_uncertainty_S,
-            id_uncertainty_P,
-            id_uncertainty_D,
-            id_utility_0,
-        ],
-        "edges": [id_e0, id_e1, id_e2, id_e3, id_e4, id_e5],
-    }
-
-    ID = InfluenceDiagram()
-    for node in net["nodes"]:
-        ID.add_node(node)
-    for edge in net["edges"]:
-        ID.add_edge(edge)
-
-    dt_uncertainty_S = UncertaintyNode("S", "Symptom", probabilities=probability_S)
-    dt_decision_T_0 = DecisionNode("T", "Treat for Disease", alternatives=["yes", "no"])
-    dt_e0 = Edge(dt_uncertainty_S, dt_decision_T_0, name="yes")
-    dt_uncertainty_P_0 = UncertaintyNode(
-        "P", "Pathological state", probabilities=probability_P
-    )
-    dt_e1 = Edge(dt_decision_T_0, dt_uncertainty_P_0, name="yes")
-    dt_uncertainty_D_0 = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    dt_e2 = Edge(dt_uncertainty_P_0, dt_uncertainty_D_0, name="yes")
-    dt_utility_0 = UtilityNode("v", "Utility", utility=None)
-    dt_e3 = Edge(dt_uncertainty_D_0, dt_utility_0, name="yes")
-    dt_utility_1 = UtilityNode("v", "Utility", utility=None)
-    dt_e4 = Edge(dt_uncertainty_D_0, dt_utility_1, name="no")
-    dt_uncertainty_D_1 = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    dt_e5 = Edge(dt_uncertainty_P_0, dt_uncertainty_D_1, name="no")
-    dt_utility_2 = UtilityNode("v", "Utility", utility=None)
-    dt_e6 = Edge(dt_uncertainty_D_1, dt_utility_2, name="yes")
-    dt_utility_3 = UtilityNode("v", "Utility", utility=None)
-    dt_e7 = Edge(dt_uncertainty_D_1, dt_utility_3, name="no")
-    dt_uncertainty_P_1 = UncertaintyNode(
-        "P", "Pathological state", probabilities=probability_P
-    )
-    dt_e8 = Edge(dt_decision_T_0, dt_uncertainty_P_1, name="no")
-    dt_uncertainty_D_2 = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    dt_e9 = Edge(dt_uncertainty_P_1, dt_uncertainty_D_2, name="yes")
-    dt_utility_4 = UtilityNode("v", "Utility", utility=None)
-    dt_e10 = Edge(dt_uncertainty_D_2, dt_utility_4, name="yes")
-    dt_utility_5 = UtilityNode("v", "Utility", utility=None)
-    dt_e11 = Edge(dt_uncertainty_D_2, dt_utility_5, name="no")
-    dt_uncertainty_D_3 = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    dt_e12 = Edge(dt_uncertainty_P_1, dt_uncertainty_D_3, name="no")
-    dt_utility_6 = UtilityNode("v", "Utility", utility=None)
-    dt_e13 = Edge(dt_uncertainty_D_3, dt_utility_6, name="yes")
-    dt_utility_7 = UtilityNode("v", "Utility", utility=None)
-    dt_e14 = Edge(dt_uncertainty_D_3, dt_utility_7, name="no")
-    dt_decision_T_1 = DecisionNode("T", "Treat for Disease", alternatives=["yes", "no"])
-    dt_e15 = Edge(dt_uncertainty_S, dt_decision_T_1, name="no")
-    dt_uncertainty_P_2 = UncertaintyNode(
-        "P", "Pathological state", probabilities=probability_P
-    )
-    dt_e16 = Edge(dt_decision_T_1, dt_uncertainty_P_2, name="yes")
-    dt_uncertainty_D_4 = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    dt_e17 = Edge(dt_uncertainty_P_2, dt_uncertainty_D_4, name="yes")
-    dt_utility_8 = UtilityNode("v", "Utility", utility=None)
-    dt_e18 = Edge(dt_uncertainty_D_4, dt_utility_8, name="yes")
-    dt_utility_9 = UtilityNode("v", "Utility", utility=None)
-    dt_e19 = Edge(dt_uncertainty_D_4, dt_utility_9, name="no")
-    dt_uncertainty_D_5 = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    dt_e20 = Edge(dt_uncertainty_P_2, dt_uncertainty_D_5, name="no")
-    dt_utility_10 = UtilityNode("v", "Utility", utility=None)
-    dt_e21 = Edge(dt_uncertainty_D_5, dt_utility_10, name="yes")
-    dt_utility_11 = UtilityNode("v", "Utility", utility=None)
-    dt_e22 = Edge(dt_uncertainty_D_5, dt_utility_11, name="no")
-    dt_uncertainty_P_3 = UncertaintyNode(
-        "P", "Pathological state", probabilities=probability_P
-    )
-    dt_e23 = Edge(dt_decision_T_1, dt_uncertainty_P_3, name="no")
-    dt_uncertainty_D_6 = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    dt_e24 = Edge(dt_uncertainty_P_3, dt_uncertainty_D_6, name="yes")
-    dt_utility_12 = UtilityNode("v", "Utility", utility=None)
-    dt_e25 = Edge(dt_uncertainty_D_6, dt_utility_12, name="yes")
-    dt_utility_13 = UtilityNode("v", "Utility", utility=None)
-    dt_e26 = Edge(dt_uncertainty_D_6, dt_utility_13, name="no")
-    dt_uncertainty_D_7 = UncertaintyNode("D", "Disease", probabilities=probability_D)
-    dt_e27 = Edge(dt_uncertainty_P_3, dt_uncertainty_D_7, name="no")
-    dt_utility_14 = UtilityNode("v", "Utility", utility=None)
-    dt_e28 = Edge(dt_uncertainty_D_7, dt_utility_14, name="yes")
-    dt_utility_15 = UtilityNode("v", "Utility", utility=None)
-    dt_e29 = Edge(dt_uncertainty_D_7, dt_utility_15, name="no")
-
-    nodes = [
-        dt_uncertainty_S,
-        dt_decision_T_0,
-        dt_uncertainty_P_0,
-        dt_uncertainty_D_0,
-        dt_utility_0,
-        dt_utility_1,
-        dt_uncertainty_D_1,
-        dt_utility_2,
-        dt_utility_3,
-        dt_uncertainty_P_1,
-        dt_uncertainty_D_2,
-        dt_utility_4,
-        dt_utility_5,
-        dt_uncertainty_D_3,
-        dt_utility_6,
-        dt_utility_7,
-        dt_decision_T_1,
-        dt_uncertainty_P_2,
-        dt_uncertainty_D_4,
-        dt_utility_8,
-        dt_utility_9,
-        dt_uncertainty_D_5,
-        dt_utility_10,
-        dt_utility_11,
-        dt_uncertainty_P_3,
-        dt_uncertainty_D_6,
-        dt_utility_12,
-        dt_utility_13,
-        dt_uncertainty_D_7,
-        dt_utility_14,
-        dt_utility_15,
-    ]
-    net = {
-        "nodes": nodes,
-        "edges": [
-            dt_e0,
-            dt_e1,
-            dt_e2,
-            dt_e3,
-            dt_e4,
-            dt_e5,
-            dt_e6,
-            dt_e7,
-            dt_e8,
-            dt_e9,
-            dt_e10,
-            dt_e11,
-            dt_e12,
-            dt_e13,
-            dt_e14,
-            dt_e15,
-            dt_e16,
-            dt_e17,
-            dt_e18,
-            dt_e19,
-            dt_e20,
-            dt_e21,
-            dt_e22,
-            dt_e23,
-            dt_e24,
-            dt_e25,
-            dt_e26,
-            dt_e27,
-            dt_e28,
-            dt_e29,
-        ],
-    }
-
-    DT = DecisionTree(root=dt_uncertainty_S)
-    for node in net["nodes"]:
-        DT.add_node(node)
-    for edge in net["edges"]:
-        DT.add_edge(edge)
-
-    IDT = ID.convert_to_decision_tree()
-    for id_node, dt_node in zip(IDT.nx.nodes, DT.nx.nodes, strict=False):
-        assert type(id_node) is type(dt_node)
-        assert id_node.description == dt_node.description
-        if not isinstance(id_node, UtilityNode):
-            assert id_node.shortname == dt_node.shortname
-
-    for id_edge, dt_edge in zip(IDT.nx.edges, DT.nx.edges, strict=False):
-        assert type(id_edge) is type(dt_edge)
-        assert id_edge[0].description == dt_edge[0].description
-        assert id_edge[1].description == dt_edge[1].description
-        assert (
-            IDT.nx.edges[id_edge[0], id_edge[1]]["name"]
-            == DT.nx.edges[dt_edge[0], dt_edge[1]]["name"]
-        )
-        assert (
-            IDT.nx.edges[id_edge[0], id_edge[1]]["arc_type"]
-            == DT.nx.edges[dt_edge[0], dt_edge[1]]["arc_type"]
-        )
-
-
-def test_convert_to_decision_tree_simple_order_asymmetry():
-    probability_U1 = ProbabilityData(
-        **{
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [
-                [
-                    0.7,
-                    0.3,
-                ]
-            ],
-            "variables": {
-                "State": [
-                    "high",
-                    "low",
-                ]
-            },
-        }
-    )
-    probability_U1 = DiscreteUnconditionalProbability.from_db_model(probability_U1)
-    probability_U2 = ProbabilityData(
-        **{
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [
-                [
-                    0.2,
-                    0.8,
-                ]
-            ],
-            "variables": {
-                "State": [
-                    "yes",
-                    "no",
-                ]
-            },
-        }
-    )
-    probability_U2 = DiscreteUnconditionalProbability.from_db_model(probability_U2)
-
-    id_uncertainty_u1 = UncertaintyNode("u1", "U1", probabilities=probability_U1)
-    id_uncertainty_u2 = UncertaintyNode("u2", "U2", probabilities=probability_U2)
-    id_decision = DecisionNode("D", "D", alternatives=["green", "red"])
-
-    id_e0 = Edge(id_uncertainty_u1, id_decision)
-    id_e1 = Edge(id_uncertainty_u2, id_decision)
-
-    net = {
-        "nodes": [id_uncertainty_u1, id_uncertainty_u2, id_decision],
-        "edges": [id_e0, id_e1],
-    }
-
-    ID = InfluenceDiagram()
-    for node in net["nodes"]:
-        ID.add_node(node)
-    for edge in net["edges"]:
-        ID.add_edge(edge)
-
-    dt_uncertainty_u1 = UncertaintyNode("u1", "U1", probabilities=probability_U1)
-
-    dt_uncertainty_u2_1 = UncertaintyNode("u2", "U2", probabilities=probability_U2)
-    dt_e0 = Edge(dt_uncertainty_u1, dt_uncertainty_u2_1, name="high")
-    dt_decision_1 = DecisionNode("D", "D", alternatives=["green", "red"])
-    dt_e1 = Edge(dt_uncertainty_u2_1, dt_decision_1, name="yes")
-    dt_utility_0 = UtilityNode("v", "Utility", utility=None)
-    dt_e2 = Edge(dt_decision_1, dt_utility_0, name="green")
-    dt_utility_1 = UtilityNode("v", "Utility", utility=None)
-    dt_e3 = Edge(dt_decision_1, dt_utility_1, name="red")
-
-    dt_decision_2 = DecisionNode("D", "D", alternatives=["green", "red"])
-    dt_e4 = Edge(dt_uncertainty_u2_1, dt_decision_2, name="no")
-    dt_utility_2 = UtilityNode("v", "Utility", utility=None)
-    dt_e5 = Edge(dt_decision_2, dt_utility_2, name="green")
-    dt_utility_3 = UtilityNode("v", "Utility", utility=None)
-    dt_e6 = Edge(dt_decision_2, dt_utility_3, name="red")
-
-    dt_uncertainty_u2_2 = UncertaintyNode("u2", "U2", probabilities=probability_U2)
-    dt_e7 = Edge(dt_uncertainty_u1, dt_uncertainty_u2_2, name="low")
-    dt_decision_3 = DecisionNode("D", "D", alternatives=["green", "red"])
-    dt_e8 = Edge(dt_uncertainty_u2_2, dt_decision_3, name="yes")
-    dt_utility_4 = UtilityNode("v", "Utility", utility=None)
-    dt_e9 = Edge(dt_decision_3, dt_utility_4, name="green")
-    dt_utility_5 = UtilityNode("v", "Utility", utility=None)
-    dt_e10 = Edge(dt_decision_3, dt_utility_5, name="red")
-
-    dt_decision_4 = DecisionNode("D", "D", alternatives=["green", "red"])
-    dt_e11 = Edge(dt_uncertainty_u2_2, dt_decision_4, name="no")
-    dt_utility_6 = UtilityNode("v", "Utility", utility=None)
-    dt_e12 = Edge(dt_decision_4, dt_utility_6, name="green")
-    dt_utility_7 = UtilityNode("v", "Utility", utility=None)
-    dt_e13 = Edge(dt_decision_4, dt_utility_7, name="red")
-
-    nodes = [
-        dt_uncertainty_u1,
-        dt_uncertainty_u2_1,
-        dt_decision_1,
-        dt_utility_0,
-        dt_utility_1,
-        dt_decision_2,
-        dt_utility_2,
-        dt_utility_3,
-        dt_uncertainty_u2_2,
-        dt_decision_3,
-        dt_utility_4,
-        dt_utility_5,
-        dt_decision_4,
-        dt_utility_6,
-        dt_utility_7,
-    ]
-    net = {
-        "nodes": nodes,
-        "edges": [
-            dt_e0,
-            dt_e1,
-            dt_e2,
-            dt_e3,
-            dt_e4,
-            dt_e5,
-            dt_e6,
-            dt_e7,
-            dt_e8,
-            dt_e9,
-            dt_e10,
-            dt_e11,
-            dt_e12,
-            dt_e13,
-        ],
-    }
-
-    DT = DecisionTree(root=dt_uncertainty_u1)
-    for node in net["nodes"]:
-        DT.add_node(node)
-    for edge in net["edges"]:
-        DT.add_edge(edge)
-
-    IDT = ID.convert_to_decision_tree()
-    for id_node, dt_node in zip(IDT.nx.nodes, DT.nx.nodes, strict=False):
-        assert type(id_node) is type(dt_node)
-        assert id_node.description == dt_node.description
-        if not isinstance(id_node, UtilityNode):
-            assert id_node.shortname == dt_node.shortname
-
-    for id_edge, dt_edge in zip(IDT.nx.edges, DT.nx.edges, strict=False):
-        assert type(id_edge) is type(dt_edge)
-        assert id_edge[0].description == dt_edge[0].description
-        assert id_edge[1].description == dt_edge[1].description
-        assert (
-            IDT.nx.edges[id_edge[0], id_edge[1]]["name"]
-            == DT.nx.edges[dt_edge[0], dt_edge[1]]["name"]
-        )
-        assert (
-            IDT.nx.edges[id_edge[0], id_edge[1]]["arc_type"]
-            == DT.nx.edges[dt_edge[0], dt_edge[1]]["arc_type"]
-        )
-
-
-def test_to_pyagrum_used_car_buyer_success(copy_testdata_tmpdir, tmp_path):
-    copy_testdata_tmpdir(TESTDATA)
-    with open(tmp_path / "id_used_car_buyer.json") as f:
-        json_stream = json.load(f)
-    influence_diagram_response = InfluenceDiagramResponse(
-        vertices=json_stream["vertices"], edges=json_stream["edges"]
-    )
-    ID = InfluenceDiagram.from_db(influence_diagram_response)
-
-    result = ID.to_pyagrum()
-    assert isinstance(result, gum.InfluenceDiagram)
-    assert result.size() == 5
-    assert result.chanceNodeSize() == 2
-    assert result.decisionNodeSize() == 2
-    assert result.utilityNodeSize() == 1
-
-    # import pyAgrum.lib.image as gimg
-    # from shutil import copyfile
-    # gimg.export(result, "test2.png")
-    # result.saveBIFXML("test.bifxml")
-    # testroot = '/tmp/pytest-of-codespace/pytest-current/'
-    # testpath = testroot+'test_to_pyagrum_used_car_buyercurrent/'
-    # copyfile(testpath+"test2.png", "/workspaces/dot/test2.png")
-    # copyfile(testpath+"test.bifxml", "/workspaces/dot/test.bifxml")
-
-
-def test_to_pyagrum_used_car_buyer_not_acyclic_fail(
-    caplog, copy_testdata_tmpdir, tmp_path
-):
-    copy_testdata_tmpdir(TESTDATA)
-    with open(tmp_path / "id_used_car_buyer.json") as f:
-        json_stream = json.load(f)
-    influence_diagram_response = InfluenceDiagramResponse(
-        vertices=json_stream["vertices"], edges=json_stream["edges"]
-    )
-    ID = InfluenceDiagram.from_db(influence_diagram_response)
-    node0 = list(ID.nx.nodes)[0]
-    node1 = list(ID.nx.nodes)[1]
-    edge1 = Edge(node0, node1, name="cyclic")
-    edge2 = Edge(node1, node0, name="cyclic")
-    ID.add_edge(edge1)
-    ID.add_edge(edge2)
-
-    with pytest.raises(Exception) as exc_info:
-        ID.to_pyagrum()
-    assert [r.msg for r in caplog.records] == ["the influence diagram is not acyclic."]
-    assert str(exc_info.value) == "the influence diagram is not acyclic."
-
-
-def test_to_pyagrum_used_car_buyer_uncertainty_fail(
-    caplog, copy_testdata_tmpdir, tmp_path
-):
-    copy_testdata_tmpdir(TESTDATA)
-    with open(tmp_path / "id_used_car_buyer.json") as f:
-        json_stream = json.load(f)
-    influence_diagram_response = InfluenceDiagramResponse(
-        vertices=json_stream["vertices"], edges=json_stream["edges"]
-    )
-    node_index = [
-        k
-        for k, node in enumerate(influence_diagram_response.vertices)
-        if node.category == "Uncertainty"
-    ][0]
-    influence_diagram_response.vertices[node_index].probabilities = None
-    ID = InfluenceDiagram.from_db(influence_diagram_response)
-
-    error = (
-        "[pyAgrum] Invalid argument: Empty variable State:Labelized({}) "
-        "cannot be added in a Potential"
-    )
-    with pytest.raises(Exception) as exc_info:
-        ID.to_pyagrum()
-    assert [r.msg for r in caplog.records] == [
-        f"Input probability cannot be used in pyagrum with error: {error}"
-    ]
-    assert (
-        str(exc_info.value)
-        == f"Input probability cannot be used in pyagrum with error: {error}"
-    )
-
-
-def test_to_pyagrum_used_car_buyer_decision_fail(caplog, copy_testdata_tmpdir, tmp_path):
-    copy_testdata_tmpdir(TESTDATA)
-    with open(tmp_path / "id_used_car_buyer.json") as f:
-        json_stream = json.load(f)
-    influence_diagram_response = InfluenceDiagramResponse(
-        vertices=json_stream["vertices"], edges=json_stream["edges"]
-    )
-    node_index = [
-        k
-        for k, node in enumerate(influence_diagram_response.vertices)
-        if node.category == "Decision"
-    ][0]
-    influence_diagram_response.vertices[node_index].alternatives = None
-    ID = InfluenceDiagram.from_db(influence_diagram_response)
-
-    error = (
-        "[pyAgrum] Invalid argument: Empty variable Buy:Labelized({}) "
-        "cannot be added in a Potential"
-    )
-    with pytest.raises(Exception) as exc_info:
-        ID.to_pyagrum()
-    assert [r.msg for r in caplog.records] == [
-        f"Input arc cannot be used in pyagrum with error: {error}"
-    ]
-    assert (
-        str(exc_info.value) == f"Input arc cannot be used in pyagrum with error: {error}"
-    )
-
-    # def test_convert_to_decision_tree_asymemetry():
-
-
-#   # Wildcatter example
-#   # Data taken from
-#   # An improved method for solving Hybrid Influence Diagrams
-#   # Barbaros Yet, Martin Neil, Norman Fenton, Anthony Constantinou,
-#   # Eugene Dementiev
-#   # International Journal of Approximate Reasoning, Volume 95, April 2018,
-#   # Pages 93-112
-#   # https://doi.org/10.1016/j.ijar.2018.01.006
-#   #
-#   #
-#   # Probabilities in the ID to be updated!!!
-#   #
-#   # The produced tree may be flipped horizontally compared to the expected one
-#   id_decision_T = DecisionNode("Seismic Test", "T", alternatives=["yes", "No"])
-#   id_decision_D = DecisionNode("Drill", "D", alternatives=["yes", "No"])
-#   id_uncertainty_R = UncertaintyNode("Test Result", "R", \
-#                  probabilities={'No': 0.410, 'Open': 0.350, 'Closed': 0.240})
-#   id_uncertainty_O = UncertaintyNode("Oil", "O", \
-#                  probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
-#   id_value_S = UtilityNode("Seismic Cost", "U1", utility=0)
-#   id_value_D = UtilityNode("Drilling Gain", "U2", utility=0)
-#   id_value_T = UtilityNode("Total", "U3", utility=0)
-
-#   id_edge_0 = Edge(id_decision_T, id_value_S)
-#   id_edge_1 = Edge(id_decision_T, id_uncertainty_R)
-#   id_edge_2 = Edge(id_decision_T, id_decision_D)
-#   id_edge_3 = Edge(id_uncertainty_R, id_decision_D)
-#   id_edge_4 = Edge(id_uncertainty_O, id_uncertainty_R)
-#   id_edge_5 = Edge(id_uncertainty_O, id_value_D)
-#   id_edge_6 = Edge(id_decision_D, id_value_D)
-#   id_edge_7 = Edge(id_value_S, id_value_T)
-#   id_edge_8 = Edge(id_value_D, id_value_T)
-
-#   net = {'nodes': [id_decision_T, id_decision_D, id_uncertainty_R, \
-#                        id_uncertainty_O, id_value_S, id_value_D, id_value_T],
-#          'edges': [id_edge_0, id_edge_1, id_edge_2, id_edge_3, id_edge_4, \
-#                        id_edge_5, id_edge_6, id_edge_7, id_edge_8]}
-
-#   ID = InfluenceDiagram()
-#   for node in net['nodes']:
-#       ID.add_node(node)
-#   for edge in net['edges']:
-#       ID.add_edge(edge)
-
-
-#   dt_decision_T_0 = DecisionNode("Seismic Test", "T", alternatives=["yes", "No"])
-#   dt_decision_R_0 = UncertaintyNode("Test Result", "R", \
-#                     probabilities={'No': 0.410, 'Open': 0.350, 'Closed': 0.240})
-#   dt_edge_0 = Edge(dt_decision_T_0, dt_decision_R_0, name="Yes")
-#   dt_decision_D_0 = DecisionNode("Drill", "D", alternatives=["yes", "No"])
-#   dt_edge_1 = Edge(dt_decision_R_0, dt_decision_D_0, name="No")
-#   dt_uncertainty_O_0 = UncertaintyNode("Oil", "O", \
-#                     probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
-#   dt_edge_2 = Edge(dt_decision_D_0, dt_uncertainty_O_0, name="Yes")
-#   dt_value_T_0 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_3 = Edge(dt_uncertainty_O_0, dt_value_T_0, name="Dry")
-#   dt_value_T_1 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_4 = Edge(dt_uncertainty_O_0, dt_value_T_1, name="Wet")
-#   dt_value_T_2 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_5 = Edge(dt_uncertainty_O_0, dt_value_T_2, name="Soaking")
-#   dt_value_T_3 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_6 = Edge(dt_decision_D_0, dt_value_T_3, name="No")
-#   dt_decision_D_1 = DecisionNode("Drill", "D", alternatives=["yes", "No"])
-#   dt_edge_7 = Edge(dt_decision_R_0, dt_decision_D_1, name="Open")
-#   dt_uncertainty_O_1 = UncertaintyNode("Oil", "O", \
-#                     probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
-#   dt_edge_8 = Edge(dt_decision_D_1, dt_uncertainty_O_1, name="Yes")
-#   dt_value_T_4 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_9 = Edge(dt_uncertainty_O_1, dt_value_T_4, name="Dry")
-#   dt_value_T_5 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_10 = Edge(dt_uncertainty_O_1, dt_value_T_5, name="Wet")
-#   dt_value_T_6 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_11 = Edge(dt_uncertainty_O_1, dt_value_T_6, name="Soaking")
-#   dt_value_T_7 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_12 = Edge(dt_decision_D_1, dt_value_T_7, name="No")
-#   dt_decision_D_2 = DecisionNode("Drill", "D", alternatives=["yes", "No"])
-#   dt_edge_13 = Edge(dt_decision_R_0, dt_decision_D_2, name="Closed")
-#   dt_uncertainty_O_2 = UncertaintyNode("Oil", "O", \
-#                     probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
-#   dt_edge_14 = Edge(dt_decision_D_2, dt_uncertainty_O_2, name="Yes")
-#   dt_value_T_8 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_15 = Edge(dt_uncertainty_O_2, dt_value_T_8, name="Dry")
-#   dt_value_T_9 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_16 = Edge(dt_uncertainty_O_2, dt_value_T_9, name="Wet")
-#   dt_value_T_10 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_17 = Edge(dt_uncertainty_O_2, dt_value_T_10, name="Soaking")
-#   dt_value_T_11 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_18 = Edge(dt_decision_D_2, dt_value_T_11, name="No")
-#   dt_decision_D_3 = DecisionNode("Drill", "D", alternatives=["yes", "No"])
-#   dt_edge_19 = Edge(dt_decision_T_0, dt_decision_D_3, name="No")
-#   dt_uncertainty_O_3 = UncertaintyNode("Oil", "O", \
-#                     probabilities={'Dry': 0.7, 'Wet': 0.2, 'Soaking': 0.1})
-#   dt_edge_20 = Edge(dt_decision_D_2, dt_uncertainty_O_3, name="Yes")
-#   dt_value_T_12 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_21 = Edge(dt_uncertainty_O_3, dt_value_T_12, name="Dry")
-#   dt_value_T_13 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_22 = Edge(dt_uncertainty_O_3, dt_value_T_13, name="Wet")
-#   dt_value_T_14 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_23 = Edge(dt_uncertainty_O_3, dt_value_T_14, name="Soaking")
-#   dt_value_T_15 = UtilityNode("Total", "U3", utility=0)
-#   dt_edge_24 = Edge(dt_decision_D_3, dt_value_T_15, name="No")
-
-
-#   net = {'nodes': [dt_decision_T_0,
-#                    dt_decision_R_0,
-#                    dt_decision_D_0,
-#                    dt_uncertainty_O_0,
-#                    dt_value_T_0,
-#                    dt_value_T_1,
-#                    dt_value_T_2,
-#                    dt_value_T_3,
-#                    dt_decision_D_1,
-#                    dt_uncertainty_O_1,
-#                    dt_value_T_4,
-#                    dt_value_T_5,
-#                    dt_value_T_6,
-#                    dt_value_T_7,
-#                    dt_decision_D_2,
-#                    dt_uncertainty_O_2,
-#                    dt_value_T_8,
-#                    dt_value_T_9,
-#                    dt_value_T_10,
-#                    dt_value_T_11,
-#                    dt_decision_D_3,
-#                    dt_uncertainty_O_3,
-#                    dt_value_T_12,
-#                    dt_value_T_13,
-#                    dt_value_T_14,
-#                    dt_value_T_15],
-#          'edges': [dt_edge_0,
-#                    dt_edge_1,
-#                    dt_edge_2,
-#                    dt_edge_3,
-#                    dt_edge_4,
-#                    dt_edge_5,
-#                    dt_edge_6,
-#                    dt_edge_7,
-#                    dt_edge_8,
-#                    dt_edge_8,
-#                    dt_edge_9,
-#                    dt_edge_10,
-#                    dt_edge_11,
-#                    dt_edge_12,
-#                    dt_edge_13,
-#                    dt_edge_14,
-#                    dt_edge_15,
-#                    dt_edge_16,
-#                    dt_edge_17,
-#                    dt_edge_18,
-#                    dt_edge_19,
-#                    dt_edge_20,
-#                    dt_edge_21,
-#                    dt_edge_22,
-#                    dt_edge_23,
-#                    dt_edge_24]}
-
-#   DT = DecisionTree()
-#   for node in net['nodes']:
-#       DT.add_node(node)
-#   for edge in net['edges']:
-#       DT.add_edge(edge)
-
-#   assert ID.convert_to_decision_tree() == DT
diff --git a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_node.py b/api/tests/v0/services/structure_utils/test_decision_diagrams/test_node.py
deleted file mode 100644
index 6287d40..0000000
--- a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_node.py
+++ /dev/null
@@ -1,340 +0,0 @@
-from unittest.mock import patch
-
-import pytest
-
-from src.v0.models.issue import IssueResponse
-from src.v0.services.structure_utils.decision_diagrams.node import (
-    DecisionNode,
-    NodeABC,
-    UncertaintyNode,
-    UtilityNode,
-)
-from src.v0.services.structure_utils.probability.discrete_unconditional_probability import (  # noqa: E501
-    DiscreteUnconditionalProbability,
-)
-
-
-def test_class_NodeABC(monkeypatch):
-    monkeypatch.setattr(NodeABC, "__abstractmethods__", set())
-    with pytest.raises(NotImplementedError):
-        NodeABC._from_db_model()
-
-    with pytest.raises(NotImplementedError):
-        states = NodeABC("J", "J").states  # assignment to variables only for ruff
-        assert states
-
-
-def test_NodeABC(monkeypatch):
-    monkeypatch.setattr(NodeABC, "__abstractmethods__", set())
-    with pytest.raises(NotImplementedError):
-        NodeABC.get_instance_input(None)
-
-
-def test_class_DecisionNode():
-    node = DecisionNode("J", "junk")
-    assert node.description == "junk"
-    assert node.shortname == "J"
-    assert isinstance(node.uuid, str)
-    assert len(node.uuid) == 36
-    assert isinstance(node.alternatives, list) and not node.alternatives
-    assert isinstance(node.states, list) and not node.states
-    assert node.is_decision_node
-    assert not node.is_uncertainty_node
-    assert not node.is_utility_node
-
-
-def test_class_UncertaintyNode():
-    node = UncertaintyNode("J", "junk")
-    assert node.description == "junk"
-    assert node.shortname == "J"
-    assert isinstance(node.uuid, str)
-    assert len(node.uuid) == 36
-    assert node.probabilities is None
-    assert isinstance(node.outcomes, tuple) and not node.outcomes
-    assert isinstance(node.states, tuple) and not node.states
-    assert not node.is_decision_node
-    assert node.is_uncertainty_node
-    assert not node.is_utility_node
-
-
-def test_class_UtilityNode():
-    node = UtilityNode("J", "junk")
-    assert node.description == "junk"
-    assert node.shortname == "J"
-    assert isinstance(node.uuid, str)
-    assert len(node.uuid) == 36
-    assert isinstance(node.utility, list) and not node.utility
-    assert isinstance(node.states, list) and not node.states
-    assert not node.is_decision_node
-    assert not node.is_uncertainty_node
-    assert node.is_utility_node
-
-
-def test_copy():
-    node = DecisionNode("J", "junk", alternatives=["a0", "a1", "a2"])
-    copied_node = node.copy()
-    assert isinstance(copied_node, type(node))
-    assert copied_node.description == node.description
-    assert copied_node.shortname == node.shortname
-    assert copied_node.alternatives == node.alternatives
-
-
-@patch("src.v0.services.structure_utils.decision_diagrams.node.uuid4")
-def test_DecisionNode_to_dict(uuid_mocker):
-    uuid_mocker.return_value = "mocked_uuid"
-    node = DecisionNode("J", "junk")
-    node.__junk = 3.14
-    node.junky = "Coke"
-    assert node.to_dict() == {
-        "node_type": "DecisionNode",
-        "description": "junk",
-        "shortname": "J",
-        "uuid": "mocked_uuid",
-        "alternatives": [],
-        "junk": None,
-        "junky": "Coke",
-    }
-
-
-@patch("src.v0.services.structure_utils.decision_diagrams.node.uuid4")
-def test_UncertaintyNode_to_dict(uuid_mocker):
-    uuid_mocker.return_value = "mocked_uuid"
-    probabilities = DiscreteUnconditionalProbability(
-        probability_function=[[0.5, 0.5], [0.4, 0.6]],
-        variables={
-            "Node1": ["Outcome1", "Outcome2"],
-            "Node2": ["Outcome21", "Outcome22"],
-        },
-    )
-    node = UncertaintyNode("J", "junk", probabilities=probabilities)
-    assert node.to_dict() == {
-        "node_type": "UncertaintyNode",
-        "description": "junk",
-        "shortname": "J",
-        "uuid": "mocked_uuid",
-        "probabilities": {
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.5, 0.5], [0.4, 0.6]],
-            "variables": {
-                "Node1": ["Outcome1", "Outcome2"],
-                "Node2": ["Outcome21", "Outcome22"],
-            },
-        },
-    }
-
-
-@patch("src.v0.services.structure_utils.decision_diagrams.node.uuid4")
-def test_UtilityNode_to_dict(uuid_mocker):
-    uuid_mocker.return_value = "mocked_uuid"
-    node = UtilityNode("J", "junk")
-    assert node.to_dict() == {
-        "node_type": "UtilityNode",
-        "description": "junk",
-        "shortname": "J",
-        "uuid": "mocked_uuid",
-        "utility": [],
-    }
-
-
-def test_from_dict():
-    node = {"node_type": "DecisionNode", "description": "junk", "shortname": "J"}
-    node = NodeABC.from_dict(node)
-    assert node.description == "junk"
-    assert node.shortname == "J"
-    assert isinstance(node.uuid, str)
-    assert len(node.uuid) == 36
-    assert isinstance(node.alternatives, list) and not node.alternatives
-    assert node.is_decision_node
-    assert not node.is_uncertainty_node
-    assert not node.is_utility_node
-
-
-def test_from_dict_fail(caplog):
-    node = {"node_type": "UnknownNode", "description": "junk", "shortname": "J"}
-    with pytest.raises(Exception) as exc_info:
-        NodeABC.from_dict(node)
-    assert [r.msg for r in caplog.records] == ["failing instantiation of UnknownNode"]
-    assert str(exc_info.value) == "failing instantiation of UnknownNode"
-
-
-def test_from_db_uncertainty_node_2d_unconditional():
-    json_object = {
-        "tag": ["State"],
-        "category": "Uncertainty",
-        "index": "0",
-        "description": "Joe does not know the state of the car",
-        "shortname": "State",
-        "keyUncertainty": "true",
-        "decisionType": "",
-        "alternatives": ["Peach", "Lemon"],
-        "probabilities": {
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.5, 0.5], [0.4, 0.6]],
-            "variables": {
-                "Node1": ["Outcome1", "Outcome2"],
-                "Node2": ["Outcome21", "Outcome22"],
-            },
-        },
-        "influenceNodeUUID": "",
-        "boundary": "",
-        "comments": [{"comment": "", "author": ""}],
-        "uuid": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-        "timestamp": "1712648453.1573343",
-        "date": "2024-04-09 07:40:53.157336",
-        "ids": "test",
-        "id": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-        "label": "issue",
-    }
-
-    response = IssueResponse(**json_object)
-    result = NodeABC.from_db(response)
-    assert isinstance(result, UncertaintyNode)
-    assert result.description == "Joe does not know the state of the car"
-    assert result.shortname == "State"
-    assert result.uuid == "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e"
-    assert isinstance(result.probabilities, DiscreteUnconditionalProbability)
-    assert result.states == (
-        ("Outcome1", "Outcome21"),
-        ("Outcome1", "Outcome22"),
-        ("Outcome2", "Outcome21"),
-        ("Outcome2", "Outcome22"),
-    )
-    assert (
-        result.probabilities.get_distribution(Node1="Outcome1", Node2="Outcome21") == 0.5
-    )
-
-
-def test_from_db_uncertainty_node_1d_unconditional():
-    json_object = {
-        "tag": ["State"],
-        "category": "Uncertainty",
-        "index": "0",
-        "description": "Joe does not know the state of the car",
-        "shortname": "State",
-        "keyUncertainty": "true",
-        "decisionType": "",
-        "alternatives": ["Peach", "Lemon"],
-        "probabilities": {
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.6, 0.4]],
-            "variables": {"Node1": ["Outcome1", "Outcome2"]},
-        },
-        "influenceNodeUUID": "",
-        "boundary": "",
-        "comments": [{"comment": "", "author": ""}],
-        "uuid": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-        "timestamp": "1712648453.1573343",
-        "date": "2024-04-09 07:40:53.157336",
-        "ids": "test",
-        "id": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-        "label": "issue",
-    }
-
-    response = IssueResponse(**json_object)
-    result = NodeABC.from_db(response)
-    assert isinstance(result, UncertaintyNode)
-    assert result.description == "Joe does not know the state of the car"
-    assert result.shortname == "State"
-    assert result.uuid == "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e"
-    assert isinstance(result.probabilities, DiscreteUnconditionalProbability)
-    assert result.states == ("Outcome1", "Outcome2")
-    assert result.probabilities.get_distribution(Node1="Outcome1") == 0.6
-
-
-def test_from_db_decision_node():
-    json_object = {
-        "tag": ["Test"],
-        "category": "Decision",
-        "index": "0",
-        "description": "Joe can test the car",
-        "shortname": "Test",
-        "keyUncertainty": "false",
-        "decisionType": "Focus",
-        "alternatives": ["Test", "no Test"],
-        "probabilities": {
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.6, 0.4]],
-            "variables": {"Node1": ["Outcome1", "Outcome2"]},
-        },
-        "influenceNodeUUID": "",
-        "boundary": "",
-        "comments": [{"comment": "", "author": ""}],
-        "uuid": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-        "timestamp": "1712648468.41026",
-        "date": "2024-04-09 07:41:08.410262",
-        "ids": "test",
-        "id": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-        "label": "issue",
-    }
-    response = IssueResponse(**json_object)
-    result = NodeABC.from_db(response)
-    assert isinstance(result, DecisionNode)
-    assert result.description == "Joe can test the car"
-    assert result.shortname == "Test"
-    assert result.uuid == "ad651f50-22de-4f85-a560-bf5fb2d9f706"
-    assert result.states == ["Test", "no Test"]
-    assert result.alternatives == ["Test", "no Test"]
-
-
-def test_from_db_utility_node():
-    json_object = {
-        "tag": ["Value"],
-        "category": "Value Metric",
-        "index": "0",
-        "description": "Value",
-        "shortname": "Value",
-        "keyUncertainty": "false",
-        "decisionType": "",
-        "alternatives": ["Test"],
-        "probabilities": {
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.6, 0.4]],
-            "variables": {"Node1": ["Outcome1", "Outcome2"]},
-        },
-        "influenceNodeUUID": "",
-        "boundary": "",
-        "comments": [{"comment": "", "author": ""}],
-        "uuid": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-        "timestamp": "1712648501.9647892",
-        "date": "2024-04-09 07:41:41.964793",
-        "ids": "test",
-        "id": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-        "label": "issue",
-    }
-    response = IssueResponse(**json_object)
-    result = NodeABC.from_db(response)
-    assert isinstance(result, UtilityNode)
-    assert result.description == "Value"
-    assert result.shortname == "Value"
-    assert result.uuid == "d52cadfd-c3b8-4531-8e3b-b7e966271edb"
-
-
-def test_from_db_fail_node_type():
-    json_object = {
-        "tag": ["Value"],
-        "category": "Junky",
-        "index": "0",
-        "description": "Value",
-        "shortname": "Value",
-        "keyUncertainty": "false",
-        "decisionType": "",
-        "alternatives": ["Test"],
-        "probabilities": {
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.6, 0.4]],
-            "variables": {"Node1": ["Outcome1", "Outcome2"]},
-        },
-        "influenceNodeUUID": "",
-        "boundary": "",
-        "comments": [{"comment": "", "author": ""}],
-        "uuid": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-        "timestamp": "1712648501.9647892",
-        "date": "2024-04-09 07:41:41.964793",
-        "ids": "test",
-        "id": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-        "label": "issue",
-    }
-    response = IssueResponse(**json_object)
-    with pytest.raises(Exception) as exc:
-        NodeABC.from_db(response)
-    assert str(exc.value) == "failing instantiation of JunkyNode"
diff --git a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_probabilistic_graph_model.py b/api/tests/v0/services/structure_utils/test_decision_diagrams/test_probabilistic_graph_model.py
deleted file mode 100644
index bc1a4d0..0000000
--- a/api/tests/v0/services/structure_utils/test_decision_diagrams/test_probabilistic_graph_model.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import pytest
-
-from src.v0.services.structure_utils.decision_diagrams.probabilistic_graph_model import (
-    ProbabilisticGraphModelABC,
-)
-
-
-def test_class_ProbabilisticGraphModelABC(monkeypatch):
-    monkeypatch.setattr(ProbabilisticGraphModelABC, "__abstractmethods__", set())
-    node = ProbabilisticGraphModelABC()
-    with pytest.raises(NotImplementedError):
-        node.initialize_diagram(None)
-    with pytest.raises(NotImplementedError):
-        node._to_json_stream()
diff --git a/api/tests/v0/services/structure_utils/test_probability/test_abstract_probability.py b/api/tests/v0/services/structure_utils/test_probability/test_abstract_probability.py
deleted file mode 100644
index 11f479e..0000000
--- a/api/tests/v0/services/structure_utils/test_probability/test_abstract_probability.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import pytest
-
-from src.v0.services.structure_utils.probability.abstract_probability import (
-    ProbabilityABC,
-)
-
-
-def test_class_ProbabilityABC(monkeypatch):
-    monkeypatch.setattr(
-        ProbabilityABC,
-        "__abstractmethods__",
-        set(),
-    )
-    abstract_probability = ProbabilityABC()
-    with pytest.raises(NotImplementedError):
-        ProbabilityABC.from_db_model()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.initialize_nan()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.set_to_uniform()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.normalize()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.isnormalized()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.from_json()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.to_json()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.to_dict()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.get_distribution()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.to_pyagrum()
-
-    with pytest.raises(NotImplementedError):
-        abstract_probability.to_pycid()
diff --git a/api/tests/v0/services/structure_utils/test_probability/test_discrete_conditional_probability.py b/api/tests/v0/services/structure_utils/test_probability/test_discrete_conditional_probability.py
deleted file mode 100644
index c92a157..0000000
--- a/api/tests/v0/services/structure_utils/test_probability/test_discrete_conditional_probability.py
+++ /dev/null
@@ -1,205 +0,0 @@
-import numpy as np
-import pytest
-import xarray as xr
-
-from src.v0.models.issue import ProbabilityData
-from src.v0.services.structure_utils.probability.discrete_conditional_probability import (  # noqa: E501
-    DiscreteConditionalProbability,
-)
-
-
-@pytest.fixture
-def cpt_2d():
-    values = np.array([[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]])
-    coords = {"A": ["yes", "no"], "B": ["low", "mid", "high"]}
-    return DiscreteConditionalProbability(values, coords)
-
-
-@pytest.fixture
-def cpt_3d():
-    data = ProbabilityData(
-        **{
-            "dtype": "DiscreteConditionalProbability",
-            "probability_function": [
-                [0.1, 0.05, 0.85, 0.46],
-                [0.7, 0.35, 0.12, 0.26],
-                [0.2, 0.60, 0.03, 0.28],
-            ],
-            "variables": {
-                "Test Result": ["no Test", "Peach", "Lemon"],
-                "Test": ["yes", "no"],
-                "State": ["Peach", "Lemon"],
-            },
-        }
-    )
-    return DiscreteConditionalProbability.from_db_model(data)
-
-
-def test_class_discrete_conditional_probability(cpt_2d):
-    assert isinstance(cpt_2d._cpt, xr.DataArray)
-    np.testing.assert_equal(cpt_2d._cpt.sel(A="yes"), np.array([0.1, 0.7, 0.6]))
-    np.testing.assert_equal(cpt_2d._cpt.sel(A="no"), np.array([0.9, 0.3, 0.4]))
-    np.testing.assert_equal(cpt_2d._cpt.sel(A="yes").sel(B="mid"), np.array([0.7]))
-
-
-def test_outcomes(cpt_2d):
-    assert cpt_2d.outcomes == ("yes", "no")
-
-
-def test_variables(cpt_2d):
-    assert cpt_2d.variables == ("A", "B")
-
-
-def test_from_db():
-    data = ProbabilityData(
-        **{
-            "dtype": "DiscreteConditionalProbability",
-            "probability_function": [
-                [0.1, 0.05, 0.85, 0.46],
-                [0.7, 0.35, 0.12, 0.26],
-                [0.2, 0.60, 0.03, 0.28],
-            ],
-            "variables": {
-                "Test Result": ["no Test", "Peach", "Lemon"],
-                "Test": ["yes", "no"],
-                "State": ["Peach", "Lemon"],
-            },
-        }
-    )
-    result = DiscreteConditionalProbability.from_db_model(data)
-    assert result._cpt.shape == (3, 2, 2)
-    assert result.get_distribution(TestResult="Peach", Test="yes", State="Peach") == 0.7
-    assert result.get_distribution(TestResult="Lemon", Test="yes", State="Peach") == 0.2
-    assert result.get_distribution(TestResult="Lemon", Test="yes", State="Lemon") == 0.6
-    assert result.get_distribution(TestResult="Lemon", Test="no", State="Peach") == 0.03
-    assert result.get_distribution(TestResult="Lemon", Test="no", State="Lemon") == 0.28
-
-
-def test_initialize_nan():
-    coords = {
-        "a": np.array([1, 2, 3]),
-        "b": np.array([4, 5]),
-        "c": ["yes", "no"],
-    }
-    result = DiscreteConditionalProbability.initialize_nan(variables=coords)
-    assert result._cpt.shape == (3, 2, 2)
-    assert np.all(np.isnan(result._cpt))
-
-
-def test_initialize_nan_fail():
-    coords = {
-        "a": np.array([1, 2, 3]),
-        "b": np.array([[4, 5, 6], [7, 8, 9]]),
-        "c": ["yes", "no"],
-    }
-    with pytest.raises(Exception) as exc:
-        DiscreteConditionalProbability.initialize_nan(variables=coords)
-    assert str(exc.value) == "One of the variables cannot be interpreted as 1D"
-
-
-def test_set_to_uniform(cpt_2d):
-    cpt_2d.set_to_uniform()
-    assert np.all(cpt_2d._cpt == 0.5)
-
-
-def test_normalize():
-    values = np.array([[0.2, 0.2], [0.8, 0.2], [0.6, 0.4]])
-    coords = {"A": ["low", "mid", "high"], "B": ["yes", "no"]}
-    result = DiscreteConditionalProbability(values, coords)
-    result.normalize()
-    np.testing.assert_equal(result._cpt, np.array([[0.5, 0.5], [0.8, 0.2], [0.6, 0.4]]))
-
-
-def test_normalize_2by2():
-    values = np.array([[0.2, 0.2], [0.7, 0.3]])
-    coords = {"A": ["low", "high"], "B": ["yes", "no"]}
-    result = DiscreteConditionalProbability(values, coords)
-    result.normalize()
-    np.testing.assert_equal(result._cpt, np.array([[0.5, 0.5], [0.7, 0.3]]))
-
-
-def test_isnormalized(cpt_2d):
-    assert cpt_2d.isnormalized()
-
-
-def test_from_json(cpt_2d):
-    stream = """{
-        "dtype": "DiscreteConditionalProbability",
-        "probability_function": [[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]],
-        "variables": {
-            "A": ["yes", "no"],
-            "B": ["low", "mid", "high"]
-            }
-    }"""
-    result = DiscreteConditionalProbability.from_json(stream)
-    xr.testing.assert_allclose(result._cpt, cpt_2d._cpt)
-
-
-def test_from_json_fail(cpt_2d):
-    stream = """{
-        "dtype": "Junk",
-        "probability_function": [[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]],
-        "variables": {
-            "A": ["yes", "no"],
-            "B": ["low", "mid", "high"]
-            }
-    }"""
-    with pytest.raises(Exception) as exc:
-        DiscreteConditionalProbability.from_json(stream)
-    assert str(exc.value) == "Expected DiscreteConditionalProbability dtype, got Junk"
-
-
-def test_to_json(cpt_2d):
-    assert cpt_2d.to_json() == (
-        '{"dtype": "DiscreteConditionalProbability", '
-        '"probability_function": [[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]], '
-        '"variables": {"A": ["yes", "no"], "B": ["low", "mid", "high"]}}'
-    )
-
-
-def test_get_distribution(cpt_2d):
-    np.testing.assert_allclose(
-        cpt_2d.get_distribution(A="yes"), np.array([0.1, 0.7, 0.6])
-    )
-    assert cpt_2d.get_distribution(A="no", B="mid") == 0.3
-
-
-def test_add_conditioning_variable(cpt_2d):
-    with pytest.raises(NotImplementedError):
-        cpt_2d.add_conditioning_variable(None)
-
-
-def test_remove_conditioning_variable(cpt_2d):
-    with pytest.raises(NotImplementedError):
-        cpt_2d.remove_conditioning_variable(None)
-
-
-def test_add_na_outcomes(cpt_2d):
-    with pytest.raises(NotImplementedError):
-        cpt_2d.add_na_outcomes()
-
-
-def test_to_pyagrum_2d(cpt_2d):
-    result = cpt_2d.to_pyagrum()
-    target = [
-        ({"B": 0}, [0.1, 0.9]),
-        ({"B": 1}, [0.7, 0.3]),
-        ({"B": 2}, [0.6, 0.4]),
-    ]
-    assert result == target
-
-
-def test_to_pyagrum_3d(cpt_3d):
-    result = cpt_3d.to_pyagrum()
-    target = [
-        ({"Test": 0, "State": 0}, [0.10, 0.70, 0.20]),
-        ({"Test": 0, "State": 1}, [0.05, 0.35, 0.60]),
-        ({"Test": 1, "State": 0}, [0.85, 0.12, 0.03]),
-        ({"Test": 1, "State": 1}, [0.46, 0.26, 0.28]),
-    ]
-    assert result == target
-
-
-def test_to_pycid(cpt_2d):
-    with pytest.raises(NotImplementedError):
-        cpt_2d.to_pycid()
diff --git a/api/tests/v0/services/structure_utils/test_probability/test_discrete_unconditional_probability.py b/api/tests/v0/services/structure_utils/test_probability/test_discrete_unconditional_probability.py
deleted file mode 100644
index f928299..0000000
--- a/api/tests/v0/services/structure_utils/test_probability/test_discrete_unconditional_probability.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import numpy as np
-import pytest
-import xarray as xr
-
-from src.v0.models.issue import ProbabilityData
-from src.v0.services.structure_utils.probability.discrete_unconditional_probability import (  # noqa: E501
-    DiscreteUnconditionalProbability,
-)
-
-
-@pytest.fixture
-def cpt_1d():
-    values = np.array([0.3, 0.5, 0.2])
-    coords = {"A": ["yes", "no", "maybe"]}
-    return DiscreteUnconditionalProbability(values, coords)
-
-
-@pytest.fixture
-def cpt_2d():
-    values = np.array([[0.1, 0.3, 0.2], [0.2, 0.1, 0.1]])
-    coords = {"A": ["yes", "no"], "B": ["low", "mid", "high"]}
-    return DiscreteUnconditionalProbability(values, coords)
-
-
-@pytest.fixture
-def cpt_3d():
-    values = np.array(
-        [
-            [[0.02, 0.05, 0.03], [0.05, 0.02, 0.03], [0.05, 0.25, 0.05]],
-            [[0.03, 0.02, 0.05], [0.05, 0.1, 0.02], [0.03, 0.05, 0.1]],
-        ]
-    )
-    coords = {
-        "A": ["yes", "no"],
-        "B": ["low", "mid", "high"],
-        "C": ["red", "green", "blue"],
-    }
-    return DiscreteUnconditionalProbability(values, coords)
-
-
-def test_class_discrete_conditional_probability(cpt_2d):
-    assert isinstance(cpt_2d._cpt, xr.DataArray)
-    np.testing.assert_equal(cpt_2d._cpt.sel(A="yes"), np.array([0.1, 0.3, 0.2]))
-    np.testing.assert_equal(cpt_2d._cpt.sel(A="no"), np.array([0.2, 0.1, 0.1]))
-    np.testing.assert_equal(cpt_2d._cpt.sel(A="yes").sel(B="mid"), np.array([0.3]))
-
-
-def test_outcomes(cpt_2d):
-    assert cpt_2d.outcomes == (
-        ("yes", "low"),
-        ("yes", "mid"),
-        ("yes", "high"),
-        ("no", "low"),
-        ("no", "mid"),
-        ("no", "high"),
-    )
-
-
-def test_variables(cpt_2d):
-    assert cpt_2d.variables == ("A", "B")
-
-
-def test_from_db():
-    data = ProbabilityData(
-        **{
-            "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [
-                [0.0, 0.0, 1.0, 1.0],
-                [1.0, 0.0, 0.0, 0.0],
-                [0.0, 1.0, 0.0, 0.0],
-            ],
-            "variables": {
-                "Test Result": ["no Test", "Peach", "Lemon"],
-                "Test": ["yes", "no"],
-                "State": ["Peach", "Lemon"],
-            },
-        }
-    )
-    result = DiscreteUnconditionalProbability.from_db_model(data)
-    assert result._cpt.shape == (3, 2, 2)
-    assert result.get_distribution(TestResult="Peach", Test="yes", State="Peach") == 1.0
-
-
-def test_initialize_nan():
-    coords = {
-        "a": np.array([1, 2, 3]),
-        "b": np.array([4, 5]),
-        "c": ["yes", "no"],
-    }
-    result = DiscreteUnconditionalProbability.initialize_nan(variables=coords)
-    assert result._cpt.shape == (3, 2, 2)
-    assert np.all(np.isnan(result._cpt))
-
-
-def test_initialize_nan_fail():
-    coords = {
-        "a": np.array([1, 2, 3]),
-        "b": np.array([[4, 5, 6], [7, 8, 9]]),
-        "c": ["yes", "no"],
-    }
-    with pytest.raises(Exception) as exc:
-        DiscreteUnconditionalProbability.initialize_nan(variables=coords)
-    assert str(exc.value) == "One of the variables cannot be interpreted as 1D"
-
-
-def test_set_to_uniform(cpt_2d):
-    cpt_2d.set_to_uniform()
-    assert np.all(np.linalg.norm(cpt_2d._cpt - 1 / 6) < 1e-12)
-
-
-def test_normalize():
-    values = np.array([[0.15, 0.2], [0.1, 0.05]])
-    coords = {"A": ["low", "high"], "B": ["yes", "no"]}
-    result = DiscreteUnconditionalProbability(values, coords)
-    result.normalize()
-    np.testing.assert_allclose(result._cpt, np.array([[0.3, 0.4], [0.2, 0.1]]))
-
-
-def test_isnormalized(cpt_2d):
-    assert cpt_2d.isnormalized()
-
-
-def test_from_json(cpt_2d):
-    stream = """{
-        "dtype": "DiscreteUnconditionalProbability",
-        "probability_function": [[0.1, 0.3, 0.2], [0.2, 0.1, 0.1]],
-        "variables": {
-            "A": ["yes", "no"],
-            "B": ["low", "mid", "high"]
-            }
-    }"""
-    result = DiscreteUnconditionalProbability.from_json(stream)
-    xr.testing.assert_allclose(result._cpt, cpt_2d._cpt)
-
-
-def test_from_json_fail(cpt_2d):
-    stream = """{
-        "dtype": "Junk",
-        "probability_function": [[0.1, 0.7, 0.6], [0.9, 0.3, 0.4]],
-        "variables": {
-            "A": ["yes", "no"],
-            "B": ["low", "mid", "high"]
-            }
-    }"""
-    with pytest.raises(Exception) as exc:
-        DiscreteUnconditionalProbability.from_json(stream)
-    assert str(exc.value) == "Expected DiscreteUnconditionalProbability dtype, got Junk"
-
-
-def test_to_json_1d(cpt_1d):
-    assert cpt_1d.to_json() == (
-        '{"dtype": "DiscreteUnconditionalProbability", '
-        '"probability_function": [[0.3], [0.5], [0.2]], '
-        '"variables": {"A": ["yes", "no", "maybe"]}}'
-    )
-
-
-def test_to_json_2d(cpt_2d):
-    assert cpt_2d.to_json() == (
-        '{"dtype": "DiscreteUnconditionalProbability", '
-        '"probability_function": [[0.1, 0.3, 0.2], [0.2, 0.1, 0.1]], '
-        '"variables": {"A": ["yes", "no"], "B": ["low", "mid", "high"]}}'
-    )
-
-
-def test_to_json_3d(cpt_3d):
-    assert cpt_3d.to_json() == (
-        '{"dtype": "DiscreteUnconditionalProbability", '
-        '"probability_function": '
-        "[[0.02, 0.05, 0.03, 0.05, 0.02, 0.03, 0.05, 0.25, 0.05], "
-        "[0.03, 0.02, 0.05, 0.05, 0.1, 0.02, 0.03, 0.05, 0.1]], "
-        '"variables": {"A": ["yes", "no"], '
-        '"B": ["low", "mid", "high"], '
-        '"C": ["red", "green", "blue"]}}'
-    )
-
-
-def test_get_distribution(cpt_2d):
-    np.testing.assert_allclose(
-        cpt_2d.get_distribution(A="yes"), np.array([0.1, 0.3, 0.2])
-    )
-    assert cpt_2d.get_distribution(A="no", B="mid") == 0.1
-
-
-def test_add_na_outcomes(cpt_2d):
-    with pytest.raises(NotImplementedError):
-        cpt_2d.add_na_outcomes()
-
-
-def test_to_pyagrum_1d(cpt_1d):
-    result = cpt_1d.to_pyagrum()
-    target = [
-        ({}, [0.3, 0.5, 0.2]),
-    ]
-    assert result == target
-
-
-def test_to_pyagrum_1d_failed(cpt_2d):
-    with pytest.raises(Exception) as exc:
-        cpt_2d.to_pyagrum()
-    assert str(exc.value) == "pyAgrum only takes 1D variables in UncertaintyNode"
-
-
-def test_to_pycid(cpt_2d):
-    with pytest.raises(NotImplementedError):
-        cpt_2d.to_pycid()
diff --git a/api/tests/v0/services/test_issue.py b/api/tests/v0/services/test_issue.py
index 515f3f9..31df3c8 100644
--- a/api/tests/v0/services/test_issue.py
+++ b/api/tests/v0/services/test_issue.py
@@ -57,7 +57,7 @@ def issue():
         "tag": ["junk"],
         "index": "1234",
         "category": "issue",
-        "keyUncertainty": "True",
+        "keyUncertainty": "true",
         "decisionType": "Tactical",
         "alternatives": ["yes", "no"],
         "probabilities": {
diff --git a/api/tests/v0/services/test_structure.py b/api/tests/v0/services/test_structure.py
index 41f0869..6a3ad4d 100644
--- a/api/tests/v0/services/test_structure.py
+++ b/api/tests/v0/services/test_structure.py
@@ -61,14 +61,14 @@ def graph():
         "alternatives": [""],
         "probabilities": {
             "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.9, 0.1], [0.8, 0.2]],
+            "probability_function": [[0.4, 0.1], [0.3, 0.2]],
             "variables": {"var1": ["out1", "out2"], "var2": ["in1", "in2"]},
         },
         "influenceNodeUUID": "",
         "boundary": "in",
         "comments": None,
-        "uuid": "11-aa",
-        "id": "11-aa",
+        "uuid": "11111111-9999-4444-9999-aaaaaaaaaaaa",
+        "id": "11111111-9999-4444-9999-aaaaaaaaaaaa",
     }
 
     decision = {
@@ -84,8 +84,8 @@ def graph():
         "influenceNodeUUID": "",
         "boundary": "in",
         "comments": None,
-        "uuid": "22-bb",
-        "id": "22-bb",
+        "uuid": "22222222-9999-4444-9999-bbbbbbbbbbbb",
+        "id": "22222222-9999-4444-9999-bbbbbbbbbbbb",
     }
 
     value_metric = {
@@ -101,8 +101,8 @@ def graph():
         "influenceNodeUUID": "",
         "boundary": "in",
         "comments": None,
-        "uuid": "33-cc",
-        "id": "33-cc",
+        "uuid": "33333333-9999-4444-9999-cccccccccccc",
+        "id": "33333333-9999-4444-9999-cccccccccccc",
     }
 
     metadata = {
@@ -132,15 +132,15 @@ def test_read_influence_diagram_success(mock_client, mock_edge_repository, graph
         EdgeResponse(
             uuid="101",
             id="101",
-            outV="11-aa",
-            inV="22-bb",
+            outV="11111111-9999-4444-9999-aaaaaaaaaaaa",
+            inV="22222222-9999-4444-9999-bbbbbbbbbbbb",
             label="influences",
         ),
         EdgeResponse(
             uuid="102",
             id="102",
-            outV="22-bb",
-            inV="33-cc",
+            outV="22222222-9999-4444-9999-bbbbbbbbbbbb",
+            inV="33333333-9999-4444-9999-cccccccccccc",
             label="influences",
         ),
     ]
@@ -175,15 +175,15 @@ def test_create_decision_tree_success(mock_client, graph):
         EdgeResponse(
             uuid="101",
             id="101",
-            outV="11-aa",
-            inV="22-bb",
+            outV="11111111-9999-4444-9999-aaaaaaaaaaaa",
+            inV="22222222-9999-4444-9999-bbbbbbbbbbbb",
             label="influences",
         ),
         EdgeResponse(
             uuid="102",
             id="102",
-            outV="22-bb",
-            inV="33-cc",
+            outV="22222222-9999-4444-9999-bbbbbbbbbbbb",
+            inV="33333333-9999-4444-9999-cccccccccccc",
             label="influences",
         ),
     ]
@@ -196,23 +196,26 @@ def test_create_decision_tree_success(mock_client, graph):
     with patch(
         "src.v0.services.structure.StructureService.read_influence_diagram"
     ) as mocked_id:
-        mocked_id.return_value = InfluenceDiagramResponse(vertices=vertices, edges=edges)
+        # mocked_id.return_value = InfluenceDiagramResponse(nodes=vertices, edges=edges)
+        nodes = [n.model_dump() for n in vertices]
+        arcs = [a.model_dump() for a in edges]
+        mocked_id.return_value = InfluenceDiagramResponse(vertices=nodes, edges=arcs)
         result = service.create_decision_tree(project_uuid="0")
 
     assert result.children[0].children[0].children is None
     assert len(result.children[0].children) == 2
     assert len(result.children) == 4
     assert result.id.model_dump() == {
-        "node_type": "UncertaintyNode",
+        "node_type": "Uncertainty",
         "shortname": "Issue ABC",
         "alternatives": None,
         "description": "Bla",
         "probabilities": {
             "dtype": "DiscreteUnconditionalProbability",
-            "probability_function": [[0.9, 0.1], [0.8, 0.2]],
+            "probability_function": [[0.4, 0.1], [0.3, 0.2]],
             "variables": {"var1": ["out1", "out2"], "var2": ["in1", "in2"]},
         },
         "branch_name": "",
         "utility": None,
-        "uuid": "11-aa",
+        "uuid": "11111111-9999-4444-9999-aaaaaaaaaaaa",
     }
diff --git a/api/tests/v0/services/testdata/id_used_car_buyer.json b/api/tests/v0/services/testdata/id_used_car_buyer.json
deleted file mode 100644
index 591b167..0000000
--- a/api/tests/v0/services/testdata/id_used_car_buyer.json
+++ /dev/null
@@ -1,192 +0,0 @@
-{
-    "vertices": [
-      {
-        "tag": ["State"],
-        "category": "Uncertainty",
-        "index": "0",
-        "shortname": "State",
-        "description": "Joe does not know the state of the car",
-        "keyUncertainty": "true",
-        "decisionType": "",
-        "alternatives": ["Peach", "Lemon"],
-        "probabilities": {
-          "dtype": "DiscreteUnconditionalProbability",
-          "probability_function": [
-            [
-              0.8,
-              0.2
-            ]
-          ],
-          "variables": {
-            "State": [
-              "Peach",
-              "Lemon"
-            ]
-          }
-        },
-        "influenceNodeUUID": "",
-        "boundary": "in",
-        "comments": null,
-        "uuid": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-        "timestamp": "1712648453.1573343",
-        "date": "2024-04-09 07:40:53.157336",
-        "ids": "test",
-        "id": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-        "label": "issue"
-      },
-      {
-        "tag": ["Test Result"],
-        "category": "Uncertainty",
-        "index": "0",
-        "description": "The result of the test is currently unknown",
-        "shortname": "Test Result",
-        "keyUncertainty": "true",
-        "decisionType": "",
-        "alternatives": ["no Test", "Peach", "Lemon"],
-        "probabilities": {
-          "dtype": "DiscreteConditionalProbability",
-          "probability_function": [
-            [
-              0.0,
-              0.0,
-              1.0,
-              1.0
-            ],
-            [
-              1.0,
-              0.0,
-              0.0,
-              0.0
-            ],
-            [
-              0.0,
-              1.0,
-              0.0,
-              0.0
-            ]
-          ],
-          "variables": {
-            "Test Result": [
-              "no Test",
-              "Peach",
-              "Lemon"
-            ],
-            "Test": [
-              "yes",
-              "no"
-            ],
-            "State": [
-              "Peach",
-              "Lemon"
-            ]
-          }
-        },
-        "influenceNodeUUID": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-        "boundary": "in",
-        "comments": null,
-        "uuid": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
-        "timestamp": "1712648489.467934",
-        "date": "2024-04-09 07:41:29.467935",
-        "ids": "test",
-        "id": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
-        "label": "issue"
-      },
-      {
-        "tag": ["Buy"],
-        "category": "Decision",
-        "index": "0",
-        "description": "We can buy the car",
-        "shortname": "Buy",
-        "keyUncertainty": "false",
-        "decisionType": "Focus",
-        "alternatives": ["Buy with guarantee", "Buy without guarantee", "Do not buy"],
-        "probabilities": null,
-        "influenceNodeUUID": "",
-        "boundary": "in",
-        "comments": null,
-        "uuid": "98e3d193-d830-452f-9fe8-c21d258ef603",
-        "timestamp": "1712648530.228483",
-        "date": "2024-04-09 07:42:10.228485",
-        "ids": "test",
-        "id": "98e3d193-d830-452f-9fe8-c21d258ef603",
-        "label": "issue"
-      },
-      {
-        "tag": ["Test"],
-        "category": "Decision",
-        "index": "0",
-        "description": "Joe can test the car",
-        "shortname": "Test",
-        "keyUncertainty": "false",
-        "decisionType": "Focus",
-        "alternatives": ["Test", "no Test"],
-        "probabilities": null,
-        "influenceNodeUUID": "",
-        "boundary": "in",
-        "comments": null,
-        "uuid": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-        "timestamp": "1712648468.41026",
-        "date": "2024-04-09 07:41:08.410262",
-        "ids": "test",
-        "id": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-        "label": "issue"
-      },
-      {
-        "tag": ["Value"],
-        "category": "Value Metric",
-        "index": "0",
-        "description": "Value",
-        "shortname": "Value",
-        "keyUncertainty": "false",
-        "decisionType": "",
-        "alternatives": ["Test"],
-        "probabilities": null,
-        "influenceNodeUUID": "",
-        "boundary": "in",
-        "comments": null,
-        "uuid": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-        "timestamp": "1712648501.9647892",
-        "date": "2024-04-09 07:41:41.964793",
-        "ids": "test",
-        "id": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-        "label": "issue"
-      }
-    ],
-    "edges": [
-      {
-        "id": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
-        "outV": "98e3d193-d830-452f-9fe8-c21d258ef603",
-        "inV": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-        "uuid": "a6ab145e-2ca9-49e2-8c4f-9607688e57a9",
-        "label": "influences"
-      },
-      {
-        "id": "44e4aed1-ec53-4d9c-9401-5204d913d7a5",
-        "outV": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-        "inV": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
-        "uuid": "44e4aed1-ec53-4d9c-9401-5204d913d7a5",
-        "label": "influences"
-      },
-      {
-        "id": "8e690c5f-8b0b-4163-a436-ff6b4d324737",
-        "outV": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-        "inV": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-        "uuid": "8e690c5f-8b0b-4163-a436-ff6b4d324737",
-        "label": "influences"
-      },
-      {
-        "id": "3149fabe-41f4-4f5f-8d7c-514624094099",
-        "outV": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
-        "inV": "98e3d193-d830-452f-9fe8-c21d258ef603",
-        "uuid": "3149fabe-41f4-4f5f-8d7c-514624094099",
-        "label": "influences"
-      },
-      {
-        "id": "1293a923-8a8a-47c7-a08b-1f0f409dc858",
-        "outV": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-        "inV": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
-        "uuid": "1293a923-8a8a-47c7-a08b-1f0f409dc858",
-        "label": "influences"
-      }
-    ]
-  }
diff --git a/api/tests/v0/services/testdata/oil_wildcatter_problem.json b/api/tests/v0/services/testdata/oil_wildcatter_problem.json
new file mode 100644
index 0000000..0f88c5d
--- /dev/null
+++ b/api/tests/v0/services/testdata/oil_wildcatter_problem.json
@@ -0,0 +1,253 @@
+{
+    "vertices": {
+        "project": {
+            "id": "1bfcc6da-f610-4e07-ad2e-b331b862333d",
+            "label": "project",
+            "sensitivity_label": "Open",
+            "name": "The Oil Wildcatter - v2",
+            "description": "The Oil Wildcatter"
+        },
+        "objectives": [
+            {
+                "id": "a725498b-b3bf-4350-b3ea-c980859fe36e",
+                "label": "objective",
+                "hierarchy": "Strategic",
+                "description": "Increase shareholder value",
+                "tag": [
+                    "Value"
+                ]
+            },
+            {
+                "id": "4f489715-4a53-4d2e-9987-f82983dc083c",
+                "label": "objective",
+                "hierarchy": "Fundamental",
+                "description": "Increase Utility",
+                "tag": [
+                    "Value"
+                ]
+            }
+        ],
+        "opportunities": [],
+        "issues": [
+            {
+                "id": "ea7e5e24-5ce3-49bd-9de2-b284dd43ae16",
+                "label": "issue",
+                "boundary": "in",
+                "description": "The utility will be measured by the NPV",
+                "shortname": "NPV",
+                "tag": [
+                    "Financial"
+                ],
+                "category": "Value Metric",
+                "probabilities": null
+            },
+            {
+                "id": "b79acf5c-e437-44fa-987c-a540917bea97",
+                "label": "issue",
+                "boundary": "in",
+                "keyUncertainty": "true",
+                "description": "We are uncertain about if the reservoir is dry, wet or soaking",
+                "shortname": "Reservoir",
+                "tag": [
+                    "Reservoir"
+                ],
+                "category": "Uncertainty",
+                "probabilities": {
+                    "dtype": "DiscreteUnconditionalProbability",
+                    "probability_function": [
+                        [
+                            0.7
+                        ],
+                        [
+                            0.2
+                        ],
+                        [
+                            0.1
+                        ]
+                    ],
+                    "variables": {
+                        "variable": [
+                            "dry",
+                            " wet",
+                            " soaking"
+                        ]
+                    }
+                }
+            },
+            {
+                "id": "2ce33e57-2484-4d2d-bc0d-fe1b6d6dec7c",
+                "label": "issue",
+                "boundary": "in",
+                "description": "We can conduct seismic acquisition",
+                "shortname": "Seismic",
+                "alternatives": [
+                    "Yes",
+                    "No"
+                ],
+                "decisionType": "Focus",
+                "tag": [
+                    "Seismic"
+                ],
+                "category": "Decision",
+                "probabilities": null
+            },
+            {
+                "id": "4c5c0d6f-d3fb-432a-9431-4144d56faa84",
+                "label": "issue",
+                "boundary": "in",
+                "keyUncertainty": "true",
+                "description": "The results of the seismic acquisition are uncertain and not\n                known yet",
+                "shortname": "Seismic Result",
+                "tag": [
+                    "Seismic"
+                ],
+                "category": "Uncertainty",
+                "probabilities": {
+                    "dtype": "DiscreteUnconditionalProbability",
+                    "probability_function": [
+                        [
+                            0.5
+                        ],
+                        [
+                            0.3
+                        ],
+                        [
+                            0.2
+                        ]
+                    ],
+                    "variables": {
+                        "variable": [
+                            "dry",
+                            " wet",
+                            " soaking"
+                        ]
+                    }
+                }
+            },
+            {
+                "id": "14ae2916-aa4f-4af3-adac-b5e8c895dea5",
+                "label": "issue",
+                "boundary": "in",
+                "description": "Should the reservoir be drilled or not?",
+                "shortname": "Drill?",
+                "alternatives": [
+                    "Yes",
+                    "No"
+                ],
+                "decisionType": "Focus",
+                "tag": [
+                    "Drilling"
+                ],
+                "category": "Decision",
+                "probabilities": null
+            }
+        ],
+        "merged_issues": []
+    },
+    "edges": [
+        {
+            "version": "v0",
+            "uuid": "0c4dbe58-3b30-4bc5-a1fc-e5cb1d31faf3",
+            "outV": "1bfcc6da-f610-4e07-ad2e-b331b862333d",
+            "inV": "ea7e5e24-5ce3-49bd-9de2-b284dd43ae16",
+            "id": "0c4dbe58-3b30-4bc5-a1fc-e5cb1d31faf3",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "ac8987cb-a9ef-4542-b3d9-d76370127667",
+            "outV": "1bfcc6da-f610-4e07-ad2e-b331b862333d",
+            "inV": "a725498b-b3bf-4350-b3ea-c980859fe36e",
+            "id": "ac8987cb-a9ef-4542-b3d9-d76370127667",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "3f6757d4-fcfe-417a-b406-6884adb19227",
+            "outV": "1bfcc6da-f610-4e07-ad2e-b331b862333d",
+            "inV": "4f489715-4a53-4d2e-9987-f82983dc083c",
+            "id": "3f6757d4-fcfe-417a-b406-6884adb19227",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "53ea6083-7d72-4748-aa9e-b0e89dc7dcac",
+            "outV": "1bfcc6da-f610-4e07-ad2e-b331b862333d",
+            "inV": "b79acf5c-e437-44fa-987c-a540917bea97",
+            "id": "53ea6083-7d72-4748-aa9e-b0e89dc7dcac",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "c7b07347-76be-405b-8f4b-de110d0bc165",
+            "outV": "1bfcc6da-f610-4e07-ad2e-b331b862333d",
+            "inV": "2ce33e57-2484-4d2d-bc0d-fe1b6d6dec7c",
+            "id": "c7b07347-76be-405b-8f4b-de110d0bc165",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "e2b5d662-e77a-4949-900b-ada21149b4b6",
+            "outV": "1bfcc6da-f610-4e07-ad2e-b331b862333d",
+            "inV": "4c5c0d6f-d3fb-432a-9431-4144d56faa84",
+            "id": "e2b5d662-e77a-4949-900b-ada21149b4b6",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "3abd2834-042c-4317-b966-b3a5f4f352f8",
+            "outV": "1bfcc6da-f610-4e07-ad2e-b331b862333d",
+            "inV": "14ae2916-aa4f-4af3-adac-b5e8c895dea5",
+            "id": "3abd2834-042c-4317-b966-b3a5f4f352f8",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "4ace5f1b-6d96-4c83-93f2-c7bd83dce43a",
+            "outV": "b79acf5c-e437-44fa-987c-a540917bea97",
+            "inV": "ea7e5e24-5ce3-49bd-9de2-b284dd43ae16",
+            "id": "4ace5f1b-6d96-4c83-93f2-c7bd83dce43a",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "4594f62b-a6a7-4719-8997-ad20265f27b0",
+            "outV": "b79acf5c-e437-44fa-987c-a540917bea97",
+            "inV": "4c5c0d6f-d3fb-432a-9431-4144d56faa84",
+            "id": "4594f62b-a6a7-4719-8997-ad20265f27b0",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "585c052d-9940-478a-a917-bd5b2ffe759f",
+            "outV": "2ce33e57-2484-4d2d-bc0d-fe1b6d6dec7c",
+            "inV": "4c5c0d6f-d3fb-432a-9431-4144d56faa84",
+            "id": "585c052d-9940-478a-a917-bd5b2ffe759f",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "45d31010-97b0-4126-8481-efa17fd795ad",
+            "outV": "2ce33e57-2484-4d2d-bc0d-fe1b6d6dec7c",
+            "inV": "ea7e5e24-5ce3-49bd-9de2-b284dd43ae16",
+            "id": "45d31010-97b0-4126-8481-efa17fd795ad",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "4493ba32-60c8-4703-a93a-041ccb18747b",
+            "outV": "4c5c0d6f-d3fb-432a-9431-4144d56faa84",
+            "inV": "14ae2916-aa4f-4af3-adac-b5e8c895dea5",
+            "id": "4493ba32-60c8-4703-a93a-041ccb18747b",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "dfe5c415-84e2-402a-8eff-9107ac76720f",
+            "outV": "14ae2916-aa4f-4af3-adac-b5e8c895dea5",
+            "inV": "ea7e5e24-5ce3-49bd-9de2-b284dd43ae16",
+            "id": "dfe5c415-84e2-402a-8eff-9107ac76720f",
+            "label": "influences"
+        }
+    ]
+}
diff --git a/api/tests/v0/services/testdata/simple_id.json b/api/tests/v0/services/testdata/simple_id.json
new file mode 100644
index 0000000..8f55736
--- /dev/null
+++ b/api/tests/v0/services/testdata/simple_id.json
@@ -0,0 +1,169 @@
+{
+    "vertices": {
+        "project": {
+            "id": "a1e0a9bb-4f83-4704-8e80-4359fe381e11",
+            "label": "project",
+            "name": "simple id"
+        },
+        "objectives": [
+            {
+                "id": "b674098b-36f4-4659-b38a-4239c89032c4",
+                "label": "objective",
+                "hierarchy": "Fundamental",
+                "description": "v",
+                "tag": [
+                    "v"
+                ]
+            }
+        ],
+        "opportunities": [
+            {
+                "id": "a971770d-17f2-4b17-9ac2-06672783c6b4",
+                "label": "opportunity",
+                "description": "a",
+                "tag": []
+            }
+        ],
+        "issues": [
+            {
+                "id": "b1f3d18d-20a8-475b-981f-32449d1ee6bd",
+                "label": "issue",
+                "boundary": "in",
+                "comments": [
+                    {
+                        "comment": "www",
+                        "author": "User"
+                    }
+                ],
+                "description": "b",
+                "shortname": "b",
+                "alternatives": [
+                    "y",
+                    "n"
+                ],
+                "decisionType": "Focus",
+                "tag": [
+                    "b"
+                ],
+                "category": "Decision",
+                "probabilities": {
+                    "dtype": "DiscreteUnconditionalProbability",
+                    "probability_function": [
+                        [
+                            null
+                        ]
+                    ],
+                    "variables": {
+                        "variable": [
+                            "outcome"
+                        ]
+                    }
+                }
+            },
+            {
+                "id": "c3d0dacc-990e-4557-8c5f-d33f9b3ede89",
+                "label": "issue",
+                "boundary": "in",
+                "keyUncertainty": "true",
+                "description": "a",
+                "shortname": "a",
+                "tag": [
+                    "a"
+                ],
+                "category": "Uncertainty",
+                "probabilities": null
+            },
+            {
+                "id": "8106127e-ab4a-4aa5-a865-8b55789e7d7a",
+                "label": "issue",
+                "boundary": "in",
+                "description": "v",
+                "shortname": "v",
+                "tag": [
+                    "v"
+                ],
+                "category": "Value Metric",
+                "probabilities": {
+                    "dtype": "DiscreteUnconditionalProbability",
+                    "probability_function": [
+                        [
+                            null
+                        ]
+                    ],
+                    "variables": {
+                        "variable": [
+                            "outcome"
+                        ]
+                    }
+                }
+            }
+        ],
+        "merged_issues": []
+    },
+    "edges": [
+        {
+            "version": "v0",
+            "uuid": "52ae073f-dae9-4427-aa94-567f033e3a24",
+            "outV": "a1e0a9bb-4f83-4704-8e80-4359fe381e11",
+            "inV": "b1f3d18d-20a8-475b-981f-32449d1ee6bd",
+            "id": "52ae073f-dae9-4427-aa94-567f033e3a24",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "3b1ae234-9c1b-478f-a664-4e2b944e33b1",
+            "outV": "a1e0a9bb-4f83-4704-8e80-4359fe381e11",
+            "inV": "b674098b-36f4-4659-b38a-4239c89032c4",
+            "id": "3b1ae234-9c1b-478f-a664-4e2b944e33b1",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "5170e558-83f2-46ca-877f-ed5073549393",
+            "outV": "a1e0a9bb-4f83-4704-8e80-4359fe381e11",
+            "inV": "c3d0dacc-990e-4557-8c5f-d33f9b3ede89",
+            "id": "5170e558-83f2-46ca-877f-ed5073549393",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "2c042b81-9c98-4730-a03c-2c908289982f",
+            "outV": "a1e0a9bb-4f83-4704-8e80-4359fe381e11",
+            "inV": "8106127e-ab4a-4aa5-a865-8b55789e7d7a",
+            "id": "2c042b81-9c98-4730-a03c-2c908289982f",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "47daef3f-c4ad-4aee-9fdc-2547661d44fb",
+            "outV": "a1e0a9bb-4f83-4704-8e80-4359fe381e11",
+            "inV": "a971770d-17f2-4b17-9ac2-06672783c6b4",
+            "id": "47daef3f-c4ad-4aee-9fdc-2547661d44fb",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "362a8e35-272f-4adb-a2ef-a21faa3fe1a1",
+            "outV": "b1f3d18d-20a8-475b-981f-32449d1ee6bd",
+            "inV": "8106127e-ab4a-4aa5-a865-8b55789e7d7a",
+            "id": "362a8e35-272f-4adb-a2ef-a21faa3fe1a1",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "9f4379ed-63d6-4a5f-8470-27cf6a575fc5",
+            "outV": "c3d0dacc-990e-4557-8c5f-d33f9b3ede89",
+            "inV": "b1f3d18d-20a8-475b-981f-32449d1ee6bd",
+            "id": "9f4379ed-63d6-4a5f-8470-27cf6a575fc5",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "77d09215-f210-40af-99db-f0ecdea81484",
+            "outV": "b674098b-36f4-4659-b38a-4239c89032c4",
+            "inV": "8106127e-ab4a-4aa5-a865-8b55789e7d7a",
+            "id": "77d09215-f210-40af-99db-f0ecdea81484",
+            "label": "has_value_metric"
+        }
+    ]
+}
diff --git a/api/tests/v0/services/testdata/used_car_buyer_model_response.json b/api/tests/v0/services/testdata/used_car_buyer_model_response.json
deleted file mode 100644
index 608f90f..0000000
--- a/api/tests/v0/services/testdata/used_car_buyer_model_response.json
+++ /dev/null
@@ -1,251 +0,0 @@
-{
-    "vertices": [
-        {
-            "tag": ["State"],
-            "category": "Uncertainty",
-            "index": "0",
-            "description": "Joe does not know the state of the car",
-            "shortname": "State",
-            "keyUncertainty": "true",
-            "decisionType": "",
-            "alternatives": ["Peach","Lemon"],
-            "probabilities": {
-                "dtype": "DiscreteUnconditionalProbability",
-                "probability_function": [
-                    [
-                        0.5,
-                        0.5
-                    ],
-                    [
-                        0.4,
-                        0.6
-                    ]
-                ],
-                "variables": {
-                    "Node1": [
-                        "Outcome1",
-                        "Outcome2"
-                    ],
-                    "Node2": [
-                        "Outcome21",
-                        "Outcome22"
-                    ]
-                }
-            },
-            "influenceNodeUUID": "",
-            "boundary": "in",
-            "comments": null,
-            "uuid": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-            "timestamp": "1712648453.1573343",
-            "date": "2024-04-09 07:40:53.157336",
-            "ids": "test",
-            "id": "51cd8e4f-aa04-48e2-8cdf-83a3c9ef978e",
-            "label": "issue"
-        },
-        {
-            "tag": ["Test Result"],
-            "category": "Uncertainty",
-            "index": "0",
-            "description": "The result of the test is currently unknown",
-            "shortname": "Test Result",
-            "keyUncertainty": "true",
-            "decisionType": "",
-            "alternatives": ["no Test","Peach","Lemon"],
-            "probabilities": {
-                "dtype": "DiscreteUnconditionalProbability",
-                "probability_function": [
-                    [
-                        0.5,
-                        0.5
-                    ],
-                    [
-                        0.4,
-                        0.6
-                    ]
-                ],
-                "variables": {
-                    "Node1": [
-                        "Outcome1",
-                        "Outcome2"
-                    ],
-                    "Node2": [
-                        "Outcome21",
-                        "Outcome22"
-                    ]
-                }
-            },
-            "influenceNodeUUID": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-            "boundary": "in",
-            "comments": null,
-            "uuid": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
-            "timestamp": "1712648489.467934",
-            "date": "2024-04-09 07:41:29.467935",
-            "ids": "test",
-            "id": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
-            "label": "issue"
-        },
-        {
-            "tag": ["Buy"],
-            "category": "Decision",
-            "index": "0",
-            "description": "We can buy the car",
-            "shortname": "Buy",
-            "keyUncertainty": "false",
-            "decisionType": "Focus",
-            "alternatives": ["Buy with guarantee","Buy without guarantee","Do not buy"],
-            "probabilities": {
-                "dtype": "DiscreteUnconditionalProbability",
-                "probability_function": [
-                    [
-                        0.5,
-                        0.5
-                    ],
-                    [
-                        0.4,
-                        0.6
-                    ]
-                ],
-                "variables": {
-                    "Node1": [
-                        "Outcome1",
-                        "Outcome2"
-                    ],
-                    "Node2": [
-                        "Outcome21",
-                        "Outcome22"
-                    ]
-                }
-            },
-            "influenceNodeUUID": "",
-            "boundary": "in",
-            "comments": null,
-            "uuid": "98e3d193-d830-452f-9fe8-c21d258ef603",
-            "timestamp": "1712648530.228483",
-            "date": "2024-04-09 07:42:10.228485",
-            "ids": "test",
-            "id": "98e3d193-d830-452f-9fe8-c21d258ef603",
-            "label": "issue"
-        },
-        {
-            "tag": ["Test"],
-            "category": "Decision",
-            "index": "0",
-            "description": "Joe can test the car",
-            "shortname": "Test",
-            "keyUncertainty": "false",
-            "decisionType": "Focus",
-            "alternatives": ["Test","no Test"],
-            "probabilities": {
-                "dtype": "DiscreteUnconditionalProbability",
-                "probability_function": [
-                    [
-                        0.5,
-                        0.5
-                    ],
-                    [
-                        0.4,
-                        0.6
-                    ]
-                ],
-                "variables": {
-                    "Node1": [
-                        "Outcome1",
-                        "Outcome2"
-                    ],
-                    "Node2": [
-                        "Outcome21",
-                        "Outcome22"
-                    ]
-                }
-            },
-            "influenceNodeUUID": "",
-            "boundary": "in",
-            "comments": null,
-            "uuid": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-            "timestamp": "1712648468.41026",
-            "date": "2024-04-09 07:41:08.410262",
-            "ids": "test",
-            "id": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-            "label": "issue"
-        },
-        {
-            "tag": ["Value"],
-            "category": "Value Metric",
-            "index": "0",
-            "description": "Value",
-            "shortname": "Value",
-            "keyUncertainty": "false",
-            "decisionType": "",
-            "alternatives": ["Test"],
-            "probabilities": {
-                "dtype": "DiscreteUnconditionalProbability",
-                "probability_function": [
-                    [
-                        0.5,
-                        0.5
-                    ],
-                    [
-                        0.4,
-                        0.6
-                    ]
-                ],
-                "variables": {
-                    "Node1": [
-                        "Outcome1",
-                        "Outcome2"
-                    ],
-                    "Node2": [
-                        "Outcome21",
-                        "Outcome22"
-                    ]
-                }
-            },
-            "influenceNodeUUID": "",
-            "boundary": "in",
-            "comments": null,
-            "uuid": "d52cadfd-c3b8-4531-8e3b-b7e966271edb",
-            "timestamp": "1712648501.9647892",
-            "date": "2024-04-09 07:41:41.964793",
-            "ids": "test",
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-            "label": "issue"
-        }
-    ],
-    "edges": [
-        {
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-        },
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-            "uuid": "8e690c5f-8b0b-4163-a436-ff6b4d324737",
-            "label": "influences"
-        },
-        {
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-            "outV": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
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-            "uuid": "3149fabe-41f4-4f5f-8d7c-514624094099",
-            "label": "influences"
-        },
-        {
-            "id": "1293a923-8a8a-47c7-a08b-1f0f409dc858",
-            "outV": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
-            "inV": "72a8cf93-7400-4511-ae64-f72c2de2c16c",
-            "uuid": "1293a923-8a8a-47c7-a08b-1f0f409dc858",
-            "label": "influences"
-        }
-    ]
-}
diff --git a/api/tests/v0/services/testdata/used_car_buyer_problem.json b/api/tests/v0/services/testdata/used_car_buyer_problem.json
new file mode 100644
index 0000000..ba96639
--- /dev/null
+++ b/api/tests/v0/services/testdata/used_car_buyer_problem.json
@@ -0,0 +1,286 @@
+{
+    "vertices": {
+        "project": {
+            "id": "72ba27a5-2e9c-4551-aa1a-6ad9d676d67b",
+            "label": "project",
+            "sensitivity_label": "Open",
+            "name": "The Used Car Buyer Problem",
+            "description": "The Used Car Buyer Problem"
+        },
+        "objectives": [
+            {
+                "id": "5e02460a-0f8e-463f-b80d-66056377d3a6",
+                "label": "objective",
+                "hierarchy": "Mean",
+                "description": "Increase wealth",
+                "index": "0",
+                "tag": [
+                    "Value"
+                ]
+            }
+        ],
+        "opportunities": [
+            {
+                "id": "67d4da1d-da3e-4ebe-bf1d-c5c9b73c3441",
+                "label": "opportunity",
+                "description": "Joe can buy a car with a price of 1000 USD while the value is\n                1100 USD",
+                "index": "0",
+                "tag": [
+                    "subsurface"
+                ]
+            }
+        ],
+        "issues": [
+            {
+                "id": "ab2b3630-46fe-425c-9a27-e91a5b74285d",
+                "label": "issue",
+                "boundary": "in",
+                "description": "Value",
+                "index": "0",
+                "shortname": "Value",
+                "alternatives": [
+                    "Test"
+                ],
+                "tag": [
+                    "Value"
+                ],
+                "category": "Value Metric"
+            },
+            {
+                "id": "e5ade2d8-4033-4bef-a93a-094f2d1c3b2f",
+                "label": "issue",
+                "boundary": "in",
+                "keyUncertainty": "None",
+                "description": "Joe can test the car",
+                "index": "0",
+                "shortname": "Test",
+                "alternatives": [
+                    "Test",
+                    " no Test"
+                ],
+                "decisionType": "Focus",
+                "tag": [
+                    "Test"
+                ],
+                "category": "Decision"
+            },
+            {
+                "id": "5077b271-7c20-412c-808d-6e8072e071bd",
+                "label": "issue",
+                "boundary": "in",
+                "keyUncertainty": "None",
+                "description": "We can buy the car",
+                "index": "0",
+                "shortname": "Buy",
+                "alternatives": [
+                    "Buy with guarantee",
+                    " Buy without guarantee",
+                    " Do not buy"
+                ],
+                "decisionType": "Focus",
+                "tag": [
+                    "Buy"
+                ],
+                "category": "Decision"
+            },
+            {
+                "id": "a9283f74-8569-480d-9dfd-b4be2eea4007",
+                "label": "issue",
+                "boundary": "in",
+                "keyUncertainty": "true",
+                "description": "Joe does not know the state of the car",
+                "index": "0",
+                "shortname": "State",
+                "alternatives": [
+                    "Peach",
+                    "Lemon"
+                ],
+                "tag": [
+                    "State"
+                ],
+                "category": "Uncertainty",
+                "probabilities": {
+                    "dtype": "DiscreteUnconditionalProbability",
+                    "probability_function": [
+                        [
+                            0.8
+                        ],
+                        [
+                            0.2
+                        ]
+                    ],
+                    "variables": {
+                        "Node1": [
+                            "Peach",
+                            "Lemon"
+                        ]
+                    }
+                }
+            },
+            {
+                "id": "8a935e05-ecf6-41e1-8b85-e6b11086f49b",
+                "label": "issue",
+                "boundary": "in",
+                "keyUncertainty": "true",
+                "description": "The result of the test is currently unknown",
+                "index": "0",
+                "shortname": "Test Result",
+                "influenceNodeUUID": "ad651f50-22de-4f85-a560-bf5fb2d9f706",
+                "alternatives": [
+                    "no Test",
+                    "Peach",
+                    "Lemon"
+                ],
+                "tag": [
+                    "Test Result"
+                ],
+                "category": "Uncertainty",
+                "probabilities": {
+                    "dtype": "DiscreteConditionalProbability",
+                    "probability_function": [
+                      [
+                        0.0,
+                        0.0,
+                        1.0,
+                        1.0
+                      ],
+                      [
+                        1.0,
+                        0.0,
+                        0.0,
+                        0.0
+                      ],
+                      [
+                        0.0,
+                        1.0,
+                        0.0,
+                        0.0
+                      ]
+                    ],
+                    "variables": {
+                      "Test Result": [
+                        "no Test",
+                        "Peach",
+                        "Lemon"
+                      ],
+                      "Test": [
+                        "yes",
+                        "no"
+                      ],
+                      "State": [
+                        "Peach",
+                        "Lemon"
+                      ]
+                    }
+                }
+            }
+        ],
+        "merged_issues": []
+    },
+    "edges": [
+        {
+            "version": "v0",
+            "uuid": "84552b54-e17d-4957-89ea-b9bdf6e3b11d",
+            "outV": "72ba27a5-2e9c-4551-aa1a-6ad9d676d67b",
+            "inV": "ab2b3630-46fe-425c-9a27-e91a5b74285d",
+            "id": "84552b54-e17d-4957-89ea-b9bdf6e3b11d",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "8d87da6e-73f6-46d6-9020-9806d8c20557",
+            "outV": "72ba27a5-2e9c-4551-aa1a-6ad9d676d67b",
+            "inV": "67d4da1d-da3e-4ebe-bf1d-c5c9b73c3441",
+            "id": "8d87da6e-73f6-46d6-9020-9806d8c20557",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "8946ecd7-b26b-4a1c-b6e3-13f4306d345e",
+            "outV": "72ba27a5-2e9c-4551-aa1a-6ad9d676d67b",
+            "inV": "e5ade2d8-4033-4bef-a93a-094f2d1c3b2f",
+            "id": "8946ecd7-b26b-4a1c-b6e3-13f4306d345e",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "ba347042-c89d-4fbb-923e-d4931fc964a3",
+            "outV": "72ba27a5-2e9c-4551-aa1a-6ad9d676d67b",
+            "inV": "5e02460a-0f8e-463f-b80d-66056377d3a6",
+            "id": "ba347042-c89d-4fbb-923e-d4931fc964a3",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "5962c776-8536-4c9e-87d5-c3e119422d31",
+            "outV": "72ba27a5-2e9c-4551-aa1a-6ad9d676d67b",
+            "inV": "5077b271-7c20-412c-808d-6e8072e071bd",
+            "id": "5962c776-8536-4c9e-87d5-c3e119422d31",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "be7b59b6-4d3e-470c-a706-83ca1928ec66",
+            "outV": "72ba27a5-2e9c-4551-aa1a-6ad9d676d67b",
+            "inV": "a9283f74-8569-480d-9dfd-b4be2eea4007",
+            "id": "be7b59b6-4d3e-470c-a706-83ca1928ec66",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "379b29b2-ddc0-44ab-984b-71d1a05f0533",
+            "outV": "72ba27a5-2e9c-4551-aa1a-6ad9d676d67b",
+            "inV": "8a935e05-ecf6-41e1-8b85-e6b11086f49b",
+            "id": "379b29b2-ddc0-44ab-984b-71d1a05f0533",
+            "label": "contains"
+        },
+        {
+            "version": "v0",
+            "uuid": "36a6987d-ef18-405d-a9ff-ce44f9b30a7e",
+            "outV": "e5ade2d8-4033-4bef-a93a-094f2d1c3b2f",
+            "inV": "8a935e05-ecf6-41e1-8b85-e6b11086f49b",
+            "id": "36a6987d-ef18-405d-a9ff-ce44f9b30a7e",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "28276cd6-48f9-4e07-982f-15ea114cfc0f",
+            "outV": "e5ade2d8-4033-4bef-a93a-094f2d1c3b2f",
+            "inV": "ab2b3630-46fe-425c-9a27-e91a5b74285d",
+            "id": "28276cd6-48f9-4e07-982f-15ea114cfc0f",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "4c1d56d6-0d27-40c7-b836-590cb4b69c88",
+            "outV": "5077b271-7c20-412c-808d-6e8072e071bd",
+            "inV": "ab2b3630-46fe-425c-9a27-e91a5b74285d",
+            "id": "4c1d56d6-0d27-40c7-b836-590cb4b69c88",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "3ce899ad-c452-436c-934f-380f93087225",
+            "outV": "a9283f74-8569-480d-9dfd-b4be2eea4007",
+            "inV": "8a935e05-ecf6-41e1-8b85-e6b11086f49b",
+            "id": "3ce899ad-c452-436c-934f-380f93087225",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "276fdf2b-236a-42bd-8b2e-c7fcc652c855",
+            "outV": "a9283f74-8569-480d-9dfd-b4be2eea4007",
+            "inV": "ab2b3630-46fe-425c-9a27-e91a5b74285d",
+            "id": "276fdf2b-236a-42bd-8b2e-c7fcc652c855",
+            "label": "influences"
+        },
+        {
+            "version": "v0",
+            "uuid": "79240580-cd65-43ec-98bd-5689a16e5044",
+            "outV": "8a935e05-ecf6-41e1-8b85-e6b11086f49b",
+            "inV": "5077b271-7c20-412c-808d-6e8072e071bd",
+            "id": "79240580-cd65-43ec-98bd-5689a16e5044",
+            "label": "influences"
+        }
+    ]
+}
diff --git a/test.bifxml b/test.bifxml
new file mode 100644
index 0000000..81e50cb
--- /dev/null
+++ b/test.bifxml
@@ -0,0 +1,67 @@
+<?xml version="1.0" ?>
+
+<BIF VERSION="0.3">
+<NETWORK>
+
+<!-- Variables -->
+<VARIABLE TYPE="utility">
+	<NAME>Value</NAME>
+	<PROPERTY>Value</PROPERTY>
+	<OUTCOME>0</OUTCOME>
+</VARIABLE>
+
+<VARIABLE TYPE="decision">
+	<NAME>Test</NAME>
+	<PROPERTY>Joe can test the car</PROPERTY>
+	<OUTCOME>Test</OUTCOME>
+	<OUTCOME> no Test</OUTCOME>
+</VARIABLE>
+
+<VARIABLE TYPE="decision">
+	<NAME>Buy</NAME>
+	<PROPERTY>We can buy the car</PROPERTY>
+	<OUTCOME>Buy with guarantee</OUTCOME>
+	<OUTCOME> Buy without guarantee</OUTCOME>
+	<OUTCOME> Do not buy</OUTCOME>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>State</NAME>
+	<PROPERTY>Joe does not know the state of the car</PROPERTY>
+	<OUTCOME>Peach</OUTCOME>
+	<OUTCOME>Lemon</OUTCOME>
+</VARIABLE>
+
+<VARIABLE TYPE="nature">
+	<NAME>Test Result</NAME>
+	<PROPERTY>The result of the test is currently unknown</PROPERTY>
+	<OUTCOME>no Test</OUTCOME>
+	<OUTCOME>Peach</OUTCOME>
+	<OUTCOME>Lemon</OUTCOME>
+</VARIABLE>
+
+<!-- Probability distributions -->
+<DEFINITION>
+	<FOR>Value</FOR><!--Value | State,Test,Buy,-->
+	<GIVEN>State</GIVEN>
+	<GIVEN>Buy</GIVEN>
+	<GIVEN>Test</GIVEN>
+	<TABLE>0 0 0 0 0 0 0 0 0 0 0 0 </TABLE>
+</DEFINITION>
+<DEFINITION>
+	<FOR>Buy</FOR><!--Buy | Test Result,-->
+	<GIVEN>Test Result</GIVEN>
+</DEFINITION>
+<DEFINITION>
+	<FOR>State</FOR><!--State | -->
+	<TABLE>0.8 0.2 </TABLE>
+</DEFINITION>
+<DEFINITION>
+	<FOR>Test Result</FOR><!--Test Result | State,Test,-->
+	<GIVEN>State</GIVEN>
+	<GIVEN>Test</GIVEN>
+	<TABLE>0 1 0 1 0 0 0 0 1 1 0 0 </TABLE>
+</DEFINITION>
+
+</NETWORK>
+</BIF>
diff --git a/test2.png b/test2.png
new file mode 100644
index 0000000..197086e
Binary files /dev/null and b/test2.png differ
diff --git a/web/src/features/visualization/cid/CIDGraph.jsx b/web/src/features/visualization/cid/CIDGraph.jsx
index f24c0a6..70f08b9 100644
--- a/web/src/features/visualization/cid/CIDGraph.jsx
+++ b/web/src/features/visualization/cid/CIDGraph.jsx
@@ -210,6 +210,8 @@ class CIDGraph extends abstractGraph {
   static MakeGraphNodes(obj) {
     // create an array with nodes
     //const groups = CIDGraph.formatNode(obj);
+    console.log("MakeGraphNode", Object.keys(obj));
+    console.log("MakeGraphNode", Object.keys(obj.vertices));
     let categories = Object.keys(obj.vertices);
     let nodes = [];
     for (let category of categories) {