From ff66bade6b7ba5b07005a1dc82aef5eca2715dd5 Mon Sep 17 00:00:00 2001
From: Tim Mensinger <mensingertim@gmail.com>
Date: Tue, 10 Sep 2024 18:27:12 +0200
Subject: [PATCH] Update model processing (#85)

---
 .github/workflows/main.yml                    |     6 +-
 .pre-commit-config.yaml                       |    70 +-
 README.md                                     |     2 +-
 codecov.yml                                   |     1 -
 examples/long_running.py                      |    72 +-
 explanations/dispatchers.ipynb                |   130 +-
 explanations/function_representation.ipynb    |   176 +-
 pixi.lock                                     | 11524 ++++++++--------
 pyproject.toml                                |    40 +-
 scripts/check_nbqa_version_mismatch.py        |   118 -
 src/lcm/__init__.py                           |    11 +-
 src/lcm/discrete_problem.py                   |    28 +-
 src/lcm/dispatchers.py                        |    15 +-
 src/lcm/entry_point.py                        |    49 +-
 src/lcm/exceptions.py                         |    18 +
 src/lcm/function_representation.py            |    23 +-
 src/lcm/functools.py                          |     6 +-
 src/lcm/grid_helpers.py                       |   111 +
 src/lcm/grids.py                              |   252 +-
 src/lcm/input_processing/__init__.py          |     3 +
 .../create_params_template.py                 |   104 +-
 src/lcm/input_processing/process_model.py     |   241 +
 src/lcm/input_processing/util.py              |   165 +
 src/lcm/interfaces.py                         |   122 +-
 src/lcm/mark.py                               |     5 +-
 src/lcm/model_functions.py                    |    13 +-
 src/lcm/next_state.py                         |    11 +-
 src/lcm/process_model.py                      |   439 -
 .../state_space_jax_versus_numba.ipynb        |    38 +-
 src/lcm/simulate.py                           |    16 +-
 src/lcm/solve_brute.py                        |     4 +-
 src/lcm/state_space.py                        |    12 +-
 src/lcm/typing.py                             |    12 +-
 src/lcm/user_model.py                         |   122 +
 tests/input_processing/__init__.py            |     0
 .../test_create_params_template.py}           |    81 +-
 .../test_process_model.py                     |   185 +-
 tests/test_analytical_solution.py             |     4 +-
 tests/test_argmax.py                          |     3 +-
 tests/test_discrete_problem.py                |    32 +-
 tests/test_dispatchers.py                     |    15 +-
 tests/test_entry_point.py                     |    44 +-
 tests/test_function_representation.py         |    56 +-
 tests/test_functools.py                       |    11 +-
 tests/test_grid_helpers.py                    |   153 +
 tests/test_grids.py                           |   236 +-
 tests/test_model.py                           |   134 +
 tests/test_model_functions.py                 |    12 +-
 tests/test_models/deterministic.py            |   121 +-
 tests/test_models/stochastic.py               |    60 +-
 tests/test_next_state.py                      |    10 +-
 tests/test_random_choice.py                   |     1 +
 tests/test_regression_test.py                 |     4 +-
 tests/test_simulate.py                        |    24 +-
 tests/test_solution_on_toy_model.py           |    76 +-
 tests/test_solve_brute.py                     |     5 +-
 tests/test_state_space.py                     |    14 +-
 tests/test_stochastic.py                      |    12 +-
 58 files changed, 7690 insertions(+), 7562 deletions(-)
 delete mode 100644 scripts/check_nbqa_version_mismatch.py
 create mode 100644 src/lcm/exceptions.py
 create mode 100644 src/lcm/grid_helpers.py
 create mode 100644 src/lcm/input_processing/__init__.py
 rename src/lcm/{ => input_processing}/create_params_template.py (51%)
 create mode 100644 src/lcm/input_processing/process_model.py
 create mode 100644 src/lcm/input_processing/util.py
 delete mode 100644 src/lcm/process_model.py
 create mode 100644 src/lcm/user_model.py
 create mode 100644 tests/input_processing/__init__.py
 rename tests/{test_create_params.py => input_processing/test_create_params_template.py} (57%)
 rename tests/{ => input_processing}/test_process_model.py (55%)
 create mode 100644 tests/test_grid_helpers.py
 create mode 100644 tests/test_model.py

diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index 94a78581..f0420fe4 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -28,7 +28,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: prefix-dev/setup-pixi@v0.8.0
         with:
-          pixi-version: v0.23.0
+          pixi-version: v0.29.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: test-cpu
@@ -48,7 +48,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: prefix-dev/setup-pixi@v0.8.0
         with:
-          pixi-version: v0.23.0
+          pixi-version: v0.29.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: mypy
@@ -62,7 +62,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: prefix-dev/setup-pixi@v0.8.0
         with:
-          pixi-version: v0.23.0
+          pixi-version: v0.29.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
           environments: test-cpu
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 88b55200..4912e6d8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,19 +6,9 @@ repos:
       - id: check-useless-excludes
         # - id: identity  # Prints all files passed to pre-commits. Debugging.
   - repo: https://github.com/lyz-code/yamlfix
-    rev: 1.16.0
+    rev: 1.17.0
     hooks:
       - id: yamlfix
-  - repo: local
-    hooks:
-      - id: check-nbqa-version-mismatch
-        name: Check for version mismatch between black, ruff, and nbQA
-        entry: python scripts/check_nbqa_version_mismatch.py
-        language: python
-        always_run: true
-        require_serial: true
-        additional_dependencies:
-          - pyyaml
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.6.0
     hooks:
@@ -55,30 +45,23 @@ repos:
     rev: v1.35.1
     hooks:
       - id: yamllint
-  - repo: https://github.com/psf/black
-    rev: 24.4.2
-    hooks:
-      - id: black
-        language_version: python3.12
-  - repo: https://github.com/asottile/blacken-docs
-    rev: 1.18.0
-    hooks:
-      - id: blacken-docs
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.0
+    rev: v0.6.4
     hooks:
+      # Run the linter.
       - id: ruff
-      #   args:
-      #     - --verbose
-  - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.8.5
-    hooks:
-      - id: nbqa-black
-        additional_dependencies:
-          - black==24.4.2
-      - id: nbqa-ruff
-        additional_dependencies:
-          - ruff==v0.5.0
+        types_or:
+          - python
+          - pyi
+          - jupyter
+        args:
+          - --fix
+      # Run the formatter.
+      - id: ruff-format
+        types_or:
+          - python
+          - pyi
+          - jupyter
   - repo: https://github.com/executablebooks/mdformat
     rev: 0.7.17
     hooks:
@@ -86,10 +69,31 @@ repos:
         additional_dependencies:
           - mdformat-gfm
           - mdformat-gfm-alerts
-          - mdformat-black
+          - mdformat-ruff
         args:
           - --wrap
           - '88'
         files: (README\.md)
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout
+        args:
+          - --drop-empty-cells
+          - --keep-output
+  - repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.11.2
+    hooks:
+      - id: mypy
+        files: src|tests
+        additional_dependencies:
+          - jax>=0.4.20
+          - numpy
+          - packaging
+          - pandas-stubs
+        args:
+          - --config=pyproject.toml
 ci:
   autoupdate_schedule: monthly
+  skip:
+    - mypy  # installing jax is not possible on pre-commit.ci due to size limits.
diff --git a/README.md b/README.md
index 2b81b33a..258f79f6 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Life Cycle Models
 
-[![image](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
 [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/OpenSourceEconomics/lcm/main.svg)](https://results.pre-commit.ci/latest/github/OpenSourceEconomics/lcm/main)
 [![image](https://codecov.io/gh/OpenSourceEconomics/lcm/branch/main/graph/badge.svg)](https://codecov.io/gh/OpenSourceEconomics/lcm)
 
diff --git a/codecov.yml b/codecov.yml
index 740448d8..c01f5dab 100644
--- a/codecov.yml
+++ b/codecov.yml
@@ -14,5 +14,4 @@ coverage:
       default:
         target: 90%
 ignore:
-  - setup.py
   - tests/**/*
diff --git a/examples/long_running.py b/examples/long_running.py
index d1787e45..9c54a5f0 100644
--- a/examples/long_running.py
+++ b/examples/long_running.py
@@ -2,17 +2,7 @@
 
 import jax.numpy as jnp
 
-# ======================================================================================
-# Numerical parameters and constants
-# ======================================================================================
-N_GRID_POINTS = {
-    "wealth": 100,
-    "health": 100,
-    "consumption": 100,
-    "exercise": 200,
-}
-
-RETIREMENT_AGE = 65
+from lcm import DiscreteGrid, LinspaceGrid, Model
 
 # ======================================================================================
 # Model functions
@@ -62,9 +52,12 @@ def consumption_constraint(consumption, wealth, labor_income):
 # ======================================================================================
 # Model specification and parameters
 # ======================================================================================
+RETIREMENT_AGE = 65
 
-MODEL_CONFIG = {
-    "functions": {
+
+MODEL_CONFIG = Model(
+    n_periods=RETIREMENT_AGE - 18,
+    functions={
         "utility": utility,
         "next_wealth": next_wealth,
         "next_health": next_health,
@@ -73,37 +66,32 @@ def consumption_constraint(consumption, wealth, labor_income):
         "wage": wage,
         "age": age,
     },
-    "choices": {
-        "working": {"options": [0, 1]},
-        "consumption": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 100,
-            "n_points": N_GRID_POINTS["consumption"],
-        },
-        "exercise": {
-            "grid_type": "linspace",
-            "start": 0,
-            "stop": 1,
-            "n_points": N_GRID_POINTS["exercise"],
-        },
+    choices={
+        "working": DiscreteGrid([0, 1]),
+        "consumption": LinspaceGrid(
+            start=1,
+            stop=100,
+            n_points=100,
+        ),
+        "exercise": LinspaceGrid(
+            start=0,
+            stop=1,
+            n_points=200,
+        ),
     },
-    "states": {
-        "wealth": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 100,
-            "n_points": N_GRID_POINTS["wealth"],
-        },
-        "health": {
-            "grid_type": "linspace",
-            "start": 0,
-            "stop": 1,
-            "n_points": N_GRID_POINTS["health"],
-        },
+    states={
+        "wealth": LinspaceGrid(
+            start=1,
+            stop=100,
+            n_points=100,
+        ),
+        "health": LinspaceGrid(
+            start=0,
+            stop=1,
+            n_points=100,
+        ),
     },
-    "n_periods": RETIREMENT_AGE - 18,
-}
+)
 
 PARAMS = {
     "beta": 0.95,
diff --git a/explanations/dispatchers.ipynb b/explanations/dispatchers.ipynb
index b6cea60f..81aae290 100644
--- a/explanations/dispatchers.ipynb
+++ b/explanations/dispatchers.ipynb
@@ -12,13 +12,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import jax.numpy as jnp\n",
     "import pytest\n",
     "from jax import vmap\n",
+    "\n",
     "from lcm.dispatchers import productmap, spacemap, vmap_1d"
    ]
   },
@@ -33,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -52,7 +53,7 @@
        "Array([1.  , 1.25, 1.5 , 1.75, 2.  ], dtype=float32)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -77,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -86,7 +87,7 @@
        "Array([1.  , 1.25, 1.5 , 1.75, 2.  ], dtype=float32)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -97,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -135,7 +136,7 @@
        "Array([1.  , 1.25, 1.5 , 1.75, 2.  ], dtype=float32)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -156,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -202,7 +203,7 @@
        "        [ 2,  3,  4,  5]]], dtype=int32)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -223,7 +224,7 @@
        "(2, 3, 4)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -269,10 +270,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from lcm import DiscreteGrid, LinspaceGrid, Model\n",
+    "\n",
+    "\n",
     "def utility(consumption, retirement, lagged_retirement, wealth):\n",
     "    working = 1 - retirement\n",
     "    retirement_habit = lagged_retirement * wealth\n",
@@ -283,48 +287,38 @@
     "    return jnp.logical_or(retirement == 1, lagged_retirement == 0)\n",
     "\n",
     "\n",
-    "MODEL_CONFIG = {\n",
-    "    \"functions\": {\n",
+    "model = Model(\n",
+    "    functions={\n",
     "        \"utility\": utility,\n",
     "        \"next_lagged_retirement\": lambda retirement: retirement,\n",
     "        \"next_wealth\": lambda wealth, consumption: wealth - consumption,\n",
     "        \"absorbing_retirement_filter\": absorbing_retirement_filter,\n",
     "    },\n",
-    "    \"choices\": {\n",
-    "        \"retirement\": {\"options\": [0, 1]},\n",
-    "        \"consumption\": {\n",
-    "            \"grid_type\": \"linspace\",\n",
-    "            \"start\": 1,\n",
-    "            \"stop\": 2,\n",
-    "            \"n_points\": 2,\n",
-    "        },\n",
+    "    n_periods=1,\n",
+    "    choices={\n",
+    "        \"retirement\": DiscreteGrid([0, 1]),\n",
+    "        \"consumption\": LinspaceGrid(start=1, stop=2, n_points=2),\n",
     "    },\n",
-    "    \"states\": {\n",
-    "        \"lagged_retirement\": {\"options\": [0, 1]},\n",
-    "        \"wealth\": {\n",
-    "            \"grid_type\": \"linspace\",\n",
-    "            \"start\": 1,\n",
-    "            \"stop\": 4,\n",
-    "            \"n_points\": 4,\n",
-    "        },\n",
+    "    states={\n",
+    "        \"lagged_retirement\": DiscreteGrid([0, 1]),\n",
+    "        \"wealth\": LinspaceGrid(start=1, stop=4, n_points=4),\n",
     "    },\n",
-    "    \"n_periods\": 1,\n",
-    "}"
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from lcm.process_model import process_model\n",
+    "from lcm.input_processing import process_model\n",
     "from lcm.state_space import create_state_choice_space\n",
     "\n",
-    "model = process_model(user_model=MODEL_CONFIG)\n",
+    "processed_model = process_model(model)\n",
     "\n",
     "sc_space, space_info, state_indexer, segments = create_state_choice_space(\n",
-    "    model,\n",
+    "    processed_model,\n",
     "    period=2,\n",
     "    is_last_period=False,\n",
     "    jit_filter=False,\n",
@@ -342,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -351,7 +345,7 @@
        "{'wealth': Array([1., 2., 3., 4.], dtype=float32)}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -372,7 +366,7 @@
        " 'retirement': Array([0, 1, 1], dtype=int32)}"
       ]
      },
-     "execution_count": 16,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -383,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -438,7 +432,7 @@
        "2                  1           1"
       ]
      },
-     "execution_count": 17,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -504,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -513,7 +507,7 @@
        "{'segment_ids': Array([0, 0, 1], dtype=int32), 'num_segments': 2}"
       ]
      },
-     "execution_count": 18,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -546,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -569,7 +563,7 @@
        "{'wealth': Array([1., 2., 3., 4.], dtype=float32)}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -580,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -590,7 +584,7 @@
        " 'retirement': Array([0, 1, 1], dtype=int32)}"
       ]
      },
-     "execution_count": 21,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -601,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -612,7 +606,7 @@
        "       [ 1. ,  2. ,  3. ,  4. ]], dtype=float32)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -628,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -637,7 +631,7 @@
        "(3, 4)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -655,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -666,7 +660,7 @@
        "       [ 1. ,  2. ,  3. ,  4. ]], dtype=float32)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -682,10 +676,8 @@
     "        strict=False,\n",
     "    ),\n",
     "):\n",
-    "\n",
     "    # loop over product of dense variables\n",
     "    for j, wealth in enumerate(sc_space.dense_vars[\"wealth\"]):\n",
-    "\n",
     "        u = utility(\n",
     "            wealth=wealth,\n",
     "            retirement=retirement,\n",
@@ -734,7 +726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -758,7 +750,7 @@
        "        [ 6.9914646,  7.9914646,  8.991465 ,  9.991465 ]]], dtype=float32)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -774,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -783,7 +775,7 @@
        "(2, 3, 4)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -809,7 +801,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.0"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/explanations/function_representation.ipynb b/explanations/function_representation.ipynb
index 179e6bc3..0e89ddb5 100644
--- a/explanations/function_representation.ipynb
+++ b/explanations/function_representation.ipynb
@@ -72,12 +72,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import jax.numpy as jnp\n",
     "\n",
+    "from lcm import DiscreteGrid, LinspaceGrid, Model\n",
+    "\n",
     "\n",
     "def utility(consumption, working, disutility_of_work):\n",
     "    return jnp.log(consumption) - disutility_of_work * working\n",
@@ -107,12 +109,13 @@
     "    return consumption <= wealth\n",
     "\n",
     "\n",
-    "model = {\n",
-    "    \"description\": (\n",
+    "model = Model(\n",
+    "    description=(\n",
     "        \"Starts from Iskhakov et al. (2017), removes filters and the lagged_retirement \"\n",
     "        \"state, and adds a wage function that depends on age.\"\n",
     "    ),\n",
-    "    \"functions\": {\n",
+    "    n_periods=2,\n",
+    "    functions={\n",
     "        \"utility\": utility,\n",
     "        \"next_wealth\": next_wealth,\n",
     "        \"consumption_constraint\": consumption_constraint,\n",
@@ -121,25 +124,14 @@
     "        \"wage\": wage,\n",
     "        \"age\": age,\n",
     "    },\n",
-    "    \"choices\": {\n",
-    "        \"retirement\": {\"options\": [0, 1]},\n",
-    "        \"consumption\": {\n",
-    "            \"grid_type\": \"linspace\",\n",
-    "            \"start\": 1,\n",
-    "            \"stop\": 400,\n",
-    "            \"n_points\": 20,\n",
-    "        },\n",
+    "    choices={\n",
+    "        \"retirement\": DiscreteGrid([0, 1]),\n",
+    "        \"consumption\": LinspaceGrid(start=1, stop=400, n_points=20),\n",
     "    },\n",
-    "    \"states\": {\n",
-    "        \"wealth\": {\n",
-    "            \"grid_type\": \"linspace\",\n",
-    "            \"start\": 1,\n",
-    "            \"stop\": 400,\n",
-    "            \"n_points\": 10,\n",
-    "        },\n",
+    "    states={\n",
+    "        \"wealth\": LinspaceGrid(start=1, stop=400, n_points=10),\n",
     "    },\n",
-    "    \"n_periods\": 2,\n",
-    "}\n",
+    ")\n",
     "\n",
     "\n",
     "params = {\n",
@@ -161,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -173,7 +165,7 @@
     }
    ],
    "source": [
-    "from lcm.process_model import process_model\n",
+    "from lcm.input_processing import process_model\n",
     "\n",
     "processed_model = process_model(model)"
    ]
@@ -191,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -200,7 +192,7 @@
        "<Signature (consumption, params, retirement, wealth)>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -256,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -265,7 +257,7 @@
        "dict_keys(['retirement', 'wealth', 'consumption'])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -276,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -308,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -317,7 +309,7 @@
        "(10,)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -329,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -338,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -368,9 +360,9 @@
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@@ -478,7 +470,7 @@
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@@ -487,7 +479,7 @@
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-     "execution_count": 11,
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@@ -536,7 +528,7 @@
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@@ -550,15 +542,15 @@
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        "                            \n",
-       "var gd = document.getElementById('bd601c97-8bab-4252-b399-d8edb9382250');\n",
+       "var gd = document.getElementById('a035f6cb-0ca2-421a-bc40-631e8622b930');\n",
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@@ -632,15 +624,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
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        "                            \n",
-       "var gd = document.getElementById('ab5eff03-fe98-44cf-a6b9-70816a365ea2');\n",
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@@ -728,7 +720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -737,7 +729,7 @@
        "<Signature (health)>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -751,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -760,7 +752,7 @@
        "3"
       ]
      },
-     "execution_count": 17,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -783,7 +775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -792,7 +784,7 @@
        "Array([0.       , 4.4426513, 5.351858 ], dtype=float32)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -804,7 +796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -813,7 +805,7 @@
        "<Signature (wealth_index, vf_arr)>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -827,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -836,7 +828,7 @@
        "Array([0.       , 4.4426513, 5.351858 ], dtype=float32)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -861,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -871,23 +863,20 @@
        "       222.66667 , 267.00003 , 311.33334 , 355.6667  , 400.      ],      dtype=float32)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from lcm.interfaces import ContinuousGridInfo\n",
-    "\n",
-    "wealth_grid_info = ContinuousGridInfo(start=1, stop=400, n_points=10)\n",
+    "wealth_grid = LinspaceGrid(start=1, stop=400, n_points=10)\n",
     "\n",
-    "# Assuming a linear spacing, grid_info correponds to the following grid:\n",
-    "jnp.linspace(1, 400, 10)"
+    "wealth_grid.to_jax()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -896,7 +885,7 @@
        "<Signature (wealth)>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -906,8 +895,7 @@
     "\n",
     "wealth_coordinate_finder = _get_coordinate_finder(\n",
     "    in_name=\"wealth\",\n",
-    "    grid_type=\"linspace\",\n",
-    "    grid_info=wealth_grid_info,\n",
+    "    grid=wealth_grid,\n",
     ")\n",
     "wealth_coordinate_finder.__signature__"
    ]
@@ -929,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -938,7 +926,7 @@
        "Array([0.        , 0.50000006, 8.774436  ], dtype=float32)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -958,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -967,7 +955,7 @@
        "<Signature (vf_arr, wealth_index)>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -986,7 +974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -995,7 +983,7 @@
        "Array([0.       , 1.8806003, 5.952807 ], dtype=float32)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1012,15 +1000,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "                            \n",
-       "var gd = document.getElementById('786f8d18-0c5f-4259-8c7e-04902d16111b');\n",
+       "var gd = document.getElementById('0f918bfe-26d5-4426-b153-d7cadfda58f4');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -1053,7 +1041,7 @@
     "fig = go.Figure(\n",
     "    data=[\n",
     "        go.Scatter(\n",
-    "            x=wealth_grid,\n",
+    "            x=wealth_grid.to_jax(),\n",
     "            y=vf_arr,\n",
     "            mode=\"markers\",\n",
     "            marker={\"color\": blue, \"size\": 10},\n",
@@ -1145,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1154,7 +1142,7 @@
        "{}"
       ]
      },
-     "execution_count": 28,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1171,7 +1159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1180,7 +1168,7 @@
        "[]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1193,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1214,12 +1202,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Step 4: (1) First we need to add a coordinate finder for the wealth state variable\n",
-    "from lcm.grids import get_linspace_coordinate\n",
+    "from lcm.grid_helpers import get_linspace_coordinate\n",
     "\n",
     "\n",
     "def wealth_coordinate_finder(wealth):\n",
@@ -1236,7 +1224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1261,7 +1249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1270,7 +1258,7 @@
        "<Signature (vf_arr, wealth)>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1288,24 +1276,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "vf_evaluated = value_function(wealth=wealth_grid, vf_arr=vf_arr)"
+    "vf_evaluated = value_function(wealth=wealth_grid.to_jax(), vf_arr=vf_arr)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "var gd = document.getElementById('93d9f0ed-a6f6-4a3f-a4ec-fcba50693c6d');\n",
+       "var gd = document.getElementById('9e63f26b-4a1c-4ecd-8125-1bd0317b21d9');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -1338,14 +1326,14 @@
     "fig = go.Figure(\n",
     "    data=[\n",
     "        go.Scatter(\n",
-    "            x=wealth_grid,\n",
+    "            x=wealth_grid.to_jax(),\n",
     "            y=vf_arr,\n",
     "            mode=\"markers\",\n",
     "            marker={\"color\": blue, \"size\": 10},\n",
     "            name=\"Pre-calculated values\",\n",
     "        ),\n",
     "        go.Scatter(\n",
-    "            x=wealth_grid,\n",
+    "            x=wealth_grid.to_jax(),\n",
     "            y=vf_evaluated,\n",
     "            mode=\"markers\",\n",
     "            marker={\"color\": orange, \"size\": 7},\n",
diff --git a/pixi.lock b/pixi.lock
index f5eaf12a..4f2c9a9f 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -9,225 +9,226 @@ environments:
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+  url: https://conda.anaconda.org/conda-forge/osx-64/jaxlib-0.4.31-cpu_py312h70ed142_1.conda
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+  md5: 311f1c93337bac90fc0f39b0cb1d1651
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+  - __osx >=10.13
+  - __osx >=10.14
   - libabseil * cxx17*
   - libabseil >=20240116.2,<20240117.0a0
-  - libgcc-ng >=12
+  - libcxx >=17
   - libgrpc >=1.62.2,<1.63.0a0
-  - libstdcxx-ng >=12
-  - libzlib >=1.2.13,<2.0.0a0
+  - libzlib >=1.3.1,<2.0a0
   - ml_dtypes >=0.2.0
-  - numpy >=1.26.4,<2.0a0
-  - openssl >=3.3.0,<4.0a0
+  - numpy >=1.19,<3
+  - openssl >=3.3.1,<4.0a0
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
   - scipy >=1.9
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-  - jax >=0.4.23
+  - jax >=0.4.31
   license: Apache-2.0
   license_family: APACHE
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-  size: 54817916
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+  - pkg:pypi/jaxlib?source=hash-mapping
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+  timestamp: 1725060949146
 - kind: conda
   name: jaxlib
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-  build: cuda120py312h6027bbc_202
-  build_number: 202
+  version: 0.4.31
+  build: cuda120py312h3995614_201
+  build_number: 201
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/jaxlib-0.4.23-cuda120py312h6027bbc_202.conda
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-  md5: af3cbd00594b28589166ae165e55da35
+  url: https://conda.anaconda.org/conda-forge/linux-64/jaxlib-0.4.31-cuda120py312h3995614_201.conda
+  sha256: 6469eab531e697b5d420e5a8b782ba45920d79a5e01a77ec0c3c5134690939f7
+  md5: 0db2cd991d38945cb2a60b9d00bf1e12
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   - __glibc >=2.17,<3.0.a0
@@ -6033,7 +5871,7 @@ packages:
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   - cuda-nvtx >=12.0.76,<13.0a0
   - cuda-version >=12.0,<13
-  - cudnn >=8.9.7.29,<9.0a0
+  - cudnn >=9.2.1.18,<10.0a0
   - libabseil * cxx17*
   - libabseil >=20240116.2,<20240117.0a0
   - libcublas >=12.0.1.189,<13.0a0
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   - libcusolver >=11.4.2.57,<12.0a0
   - libcusparse >=12.0.0.76,<13.0a0
-  - libgcc-ng >=12
+  - libgcc >=12
   - libgrpc >=1.62.2,<1.63.0a0
-  - libstdcxx-ng >=12
-  - libzlib >=1.2.13,<2.0.0a0
+  - libstdcxx >=12
+  - libzlib >=1.3.1,<2.0a0
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-  - nccl >=2.21.5.1,<3.0a0
-  - numpy >=1.26.4,<2.0a0
-  - openssl >=3.3.0,<4.0a0
+  - nccl >=2.22.3.1,<3.0a0
+  - numpy >=1.19,<3
+  - openssl >=3.3.1,<4.0a0
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
   - scipy >=1.9
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-  - jax >=0.4.23
+  - jax >=0.4.31
   license: Apache-2.0
   license_family: APACHE
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-  size: 87484322
-  timestamp: 1716398144536
+  - pkg:pypi/jaxlib?source=hash-mapping
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+  timestamp: 1725064373665
 - kind: conda
   name: jedi
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@@ -6075,7 +5913,7 @@ packages:
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   license_family: MIT
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+  - pkg:pypi/jedi?source=hash-mapping
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@@ -6093,7 +5931,7 @@ packages:
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   license_family: BSD
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+  - pkg:pypi/jinja2?source=hash-mapping
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 - kind: conda
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   license_family: APACHE
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+  - pkg:pypi/json5?source=hash-mapping
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 - kind: conda
   name: jsonpointer
-  version: '2.4'
-  build: py312h2e8e312_3
-  build_number: 3
+  version: 3.0.0
+  build: py312h2e8e312_1
+  build_number: 1
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-  url: https://conda.anaconda.org/conda-forge/win-64/jsonpointer-2.4-py312h2e8e312_3.conda
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-  md5: 9d9572e257bf4559f20629efb0d3511d
+  url: https://conda.anaconda.org/conda-forge/win-64/jsonpointer-3.0.0-py312h2e8e312_1.conda
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+  md5: e3ceda014d8461a11ca8552830a978f9
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-  - python >=3.12.0rc3,<3.13.0a0
+  - python >=3.12,<3.13.0a0
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   license: BSD-3-Clause
   license_family: BSD
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+  - pkg:pypi/jsonpointer?source=hash-mapping
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 - kind: conda
   name: jsonpointer
-  version: '2.4'
-  build: py312h7900ff3_3
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+  version: 3.0.0
+  build: py312h7900ff3_1
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-  md5: 50f62bdb9b60b13c2f6ae69957342e4d
+  url: https://conda.anaconda.org/conda-forge/linux-64/jsonpointer-3.0.0-py312h7900ff3_1.conda
+  sha256: 76ccb7bffc7761d1d3133ffbe1f7f1710a0f0d9aaa9f7ea522652e799f3601f4
+  md5: 6b51f7459ea4073eeb5057207e2e1e3d
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+  - python >=3.12,<3.13.0a0
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   license_family: BSD
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 - kind: conda
   name: jsonpointer
-  version: '2.4'
-  build: py312h81bd7bf_3
-  build_number: 3
+  version: 3.0.0
+  build: py312h81bd7bf_1
+  build_number: 1
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/jsonpointer-2.4-py312h81bd7bf_3.conda
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-  md5: 327361b24f5348cab04ad9b1f74e831d
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-  - python >=3.12.0rc3,<3.13.0a0 *_cpython
+  - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
   license: BSD-3-Clause
   license_family: BSD
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 - kind: conda
   name: jsonpointer
-  version: '2.4'
-  build: py312hb401068_3
-  build_number: 3
+  version: 3.0.0
+  build: py312hb401068_1
+  build_number: 1
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/jsonpointer-2.4-py312hb401068_3.conda
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-  md5: 637aa8f6c1c61f659f1496e9b2dc7552
+  url: https://conda.anaconda.org/conda-forge/osx-64/jsonpointer-3.0.0-py312hb401068_1.conda
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+  md5: 5dcf96bca4649d496d818a0f5cfb962e
   depends:
-  - python >=3.12.0rc3,<3.13.0a0
+  - python >=3.12,<3.13.0a0
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   license: BSD-3-Clause
   license_family: BSD
   purls:
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-  size: 18184
-  timestamp: 1695397820416
+  - pkg:pypi/jsonpointer?source=hash-mapping
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+  timestamp: 1725303027769
 - kind: conda
   name: jsonschema
-  version: 4.22.0
+  version: 4.23.0
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.22.0-pyhd8ed1ab_0.conda
-  sha256: 57a466e8c42635d8e930fa065dc6e461f4215aa259ab03873eacb03ddaeefc8a
-  md5: b9661a4b1200d6bc7d8a4cdafdc91468
+  url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.23.0-pyhd8ed1ab_0.conda
+  sha256: 7d0c4c0346b26be9f220682b7c5c0d84606d48c6dbc36fc238e4452dda733aff
+  md5: da304c192ad59975202859b367d0f6a2
   depends:
   - attrs >=22.2.0
   - importlib_resources >=1.4.0
@@ -6206,9 +6044,9 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/jsonschema?source=conda-forge-mapping
-  size: 74149
-  timestamp: 1714573245148
+  - pkg:pypi/jsonschema?source=hash-mapping
+  size: 74323
+  timestamp: 1720529611305
 - kind: conda
   name: jsonschema-specifications
   version: 2023.12.1
@@ -6225,34 +6063,33 @@ packages:
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   license_family: MIT
   purls:
-  - pkg:pypi/jsonschema-specifications?source=conda-forge-mapping
+  - pkg:pypi/jsonschema-specifications?source=hash-mapping
   size: 16431
   timestamp: 1703778502971
 - kind: conda
   name: jsonschema-with-format-nongpl
-  version: 4.22.0
-  build: pyhd8ed1ab_0
+  version: 4.23.0
+  build: hd8ed1ab_0
   subdir: noarch
-  noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.22.0-pyhd8ed1ab_0.conda
-  sha256: 3c98d791bebd477597fe083b3cec35132ac974c61ba1e481dc6c29fac78b419d
-  md5: 32ab666927ee17b9468c2c72bbd7ba1b
+  noarch: generic
+  url: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.23.0-hd8ed1ab_0.conda
+  sha256: 007a0a506a0d1805b099629cb0ee743ad0afe7d9749e57339f32c168119e0139
+  md5: 16b37612b3a2fd77f409329e213b530c
   depends:
   - fqdn
   - idna
   - isoduration
   - jsonpointer >1.13
-  - jsonschema >=4.22.0,<4.22.1.0a0
-  - python
+  - jsonschema >=4.23.0,<4.23.1.0a0
   - rfc3339-validator
   - rfc3986-validator >0.1.0
   - uri-template
-  - webcolors >=1.11
+  - webcolors >=24.6.0
   license: MIT
   license_family: MIT
   purls: []
-  size: 7441
-  timestamp: 1714573279350
+  size: 7143
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 - kind: conda
   name: jupyter-lsp
   version: 2.2.5
@@ -6269,7 +6106,7 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
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+  - pkg:pypi/jupyter-lsp?source=hash-mapping
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   license_family: BSD
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+  - pkg:pypi/jupyter-client?source=hash-mapping
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   license_family: BSD
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+  - pkg:pypi/jupyter-core?source=hash-mapping
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   license_family: BSD
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+  - pkg:pypi/jupyter-core?source=hash-mapping
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+  - pkg:pypi/jupyter-core?source=hash-mapping
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+  - pkg:pypi/jupyter-core?source=hash-mapping
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+  - pkg:pypi/jupyter-events?source=hash-mapping
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 - kind: conda
   name: jupyter_server
-  version: 2.14.1
+  version: 2.14.2
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.14.1-pyhd8ed1ab_0.conda
-  sha256: 58628ef004ba0f754cc01b33199b6aefd94f5aed7fbf7afd2b796d8b5c4ef22c
-  md5: 174af03c6e6038edd32021a48aa003c4
+  url: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.14.2-pyhd8ed1ab_0.conda
+  sha256: edab71a05feceac54bdb90e755a257545af7832b9911607c1a70f09be44ba985
+  md5: ca23c71f70a7c7935b3d03f0f1a5801d
   depends:
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-  - argon2-cffi
-  - jinja2
+  - argon2-cffi >=21.1
+  - jinja2 >=3.0.3
   - jupyter_client >=7.4.4
   - jupyter_core >=4.12,!=5.0.*
   - jupyter_events >=0.9.0
-  - jupyter_server_terminals
+  - jupyter_server_terminals >=0.4.4
   - nbconvert-core >=6.4.4
   - nbformat >=5.3.0
-  - overrides
-  - packaging
-  - prometheus_client
+  - overrides >=5.0
+  - packaging >=22.0
+  - prometheus_client >=0.9
   - python >=3.8
   - pyzmq >=24
   - send2trash >=1.8.2
   - terminado >=0.8.3
   - tornado >=6.2.0
   - traitlets >=5.6.0
-  - websocket-client
+  - websocket-client >=1.7
   license: BSD-3-Clause
   license_family: BSD
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+  size: 323978
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 - kind: conda
   name: jupyter_server_terminals
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@@ -6447,18 +6284,18 @@ packages:
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   license_family: BSD
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+  - pkg:pypi/jupyter-server-terminals?source=hash-mapping
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 - kind: conda
   name: jupyterlab
-  version: 4.2.1
+  version: 4.2.5
   build: pyhd8ed1ab_0
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   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.2.1-pyhd8ed1ab_0.conda
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-  md5: 3e7290af6190b29c7017d6a8fb0eaeea
+  url: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.2.5-pyhd8ed1ab_0.conda
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+  md5: 594762eddc55b82feac6097165a88e3c
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   - httpx >=0.25.0
@@ -6473,15 +6310,16 @@ packages:
   - notebook-shim >=0.2
   - packaging
   - python >=3.8
+  - setuptools >=40.1.0
   - tomli >=1.2.2
   - tornado >=6.2.0
   - traitlets
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   license_family: BSD
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 - kind: conda
   name: jupyterlab_pygments
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   license_family: BSD
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+  - pkg:pypi/jupyterlab-pygments?source=hash-mapping
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   name: jupyterlab_server
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+  version: 2.27.3
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-  md5: d1cb7b113daaadd89e5aa6a32b28bf0d
+  url: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.27.3-pyhd8ed1ab_0.conda
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+  md5: af8239bf1ba7e8c69b689f780f653488
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-  - numpy <2.3
+  - numpy >=1.22.4,<2.3
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   - python >=3.12,<3.13.0a0 *_cpython
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+  - xorg-xextproto >=7.3.0,<8.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 46794
+  timestamp: 1722108216651
 - kind: conda
   name: xorg-libxrender
   version: 0.9.11
@@ -14404,6 +14035,65 @@ packages:
   purls: []
   size: 37770
   timestamp: 1688300707994
+- kind: conda
+  name: xorg-libxtst
+  version: 1.2.5
+  build: h4bc722e_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-h4bc722e_0.conda
+  sha256: 0139b52c3cbce57bfd1d120c41637bc239430faff4aa0445f58de0adf4c4b976
+  md5: 185159d666308204eca00295599b0a5c
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc-ng >=12
+  - xorg-inputproto
+  - xorg-libx11 >=1.8.9,<2.0a0
+  - xorg-libxext 1.3.*
+  - xorg-libxext >=1.3.4,<2.0a0
+  - xorg-libxi 1.7.*
+  - xorg-libxi >=1.7.10,<2.0a0
+  - xorg-recordproto
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 32931
+  timestamp: 1722575571554
+- kind: conda
+  name: xorg-libxxf86vm
+  version: 1.1.5
+  build: h4bc722e_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.5-h4bc722e_1.conda
+  sha256: 109d6b1931d1482faa0bf6de83c7e6d9ca36bbf9d36a00a05df4f63b82fce5c3
+  md5: 0c90ad87101001080484b91bd9d2cdef
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libgcc-ng >=12
+  - xorg-libx11 >=1.8.9,<2.0a0
+  - xorg-libxext >=1.3.4,<2.0a0
+  - xorg-xextproto >=7.3.0,<8.0a0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 18443
+  timestamp: 1722110433983
+- kind: conda
+  name: xorg-recordproto
+  version: 1.14.2
+  build: h7f98852_1002
+  build_number: 1002
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-recordproto-1.14.2-h7f98852_1002.tar.bz2
+  sha256: 4b91d48fed368c83eafd03891ebfd5bae0a03adc087ebea8a680ae22da99a85f
+  md5: 2f835e6c386e73c6faaddfe9eda67e98
+  depends:
+  - libgcc-ng >=9.3.0
+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 8014
+  timestamp: 1621340029114
 - kind: conda
   name: xorg-renderproto
   version: 0.11.1
@@ -14436,22 +14126,6 @@ packages:
   purls: []
   size: 30270
   timestamp: 1677036833037
-- kind: conda
-  name: xorg-xf86vidmodeproto
-  version: 2.3.1
-  build: h7f98852_1002
-  build_number: 1002
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/xorg-xf86vidmodeproto-2.3.1-h7f98852_1002.tar.bz2
-  sha256: 43398aeacad5b8753b7a1c12cb6bca36124e0c842330372635879c350c430791
-  md5: 3ceea9668625c18f19530de98b15d5b0
-  depends:
-  - libgcc-ng >=9.3.0
-  license: MIT
-  license_family: MIT
-  purls: []
-  size: 23875
-  timestamp: 1620067286978
 - kind: conda
   name: xorg-xproto
   version: 7.0.31
@@ -14585,114 +14259,226 @@ packages:
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: h75354e8_4
-  build_number: 4
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h75354e8_4.conda
-  sha256: bc9aaee39e7be107d7daff237435dfd8f791aca460a98583a36a263615205262
-  md5: 03cc8d9838ad9dd0060ab532e81ccb21
+  build: h64debc3_5
+  build_number: 5
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h64debc3_5.conda
+  sha256: b4ba544a04129472651a5df3b8906ed68e7f43bf23e724fd0e368218083c920c
+  md5: c29dbe9343a0b55b027fa645644c59d9
   depends:
-  - krb5 >=1.21.2,<1.22.0a0
-  - libgcc-ng >=12
-  - libsodium >=1.0.18,<1.0.19.0a0
-  - libstdcxx-ng >=12
+  - __osx >=11.0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libcxx >=17
+  - libsodium >=1.0.20,<1.0.21.0a0
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 353229
-  timestamp: 1715607188837
+  size: 296355
+  timestamp: 1725430145243
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: hcc0f68c_4
-  build_number: 4
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-hcc0f68c_4.conda
-  sha256: c22520d6d66a80f17c5f2b3719ad4a6ee809b210b8ac87d6f05ab98b94b3abda
-  md5: 39fb79e7a7a880a03f82c1f2eb7f7c73
+  build: ha4adb4c_5
+  build_number: 5
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-ha4adb4c_5.conda
+  sha256: dd48adc07fcd029c86fbf82e68d0e4818c7744b768e08139379920b56b582814
+  md5: e8372041ebb377237db9d0d24c7b5962
   depends:
-  - __osx >=11.0
-  - krb5 >=1.21.2,<1.22.0a0
-  - libcxx >=16
-  - libsodium >=1.0.18,<1.0.19.0a0
+  - __glibc >=2.17,<3.0.a0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libgcc >=13
+  - libsodium >=1.0.20,<1.0.21.0a0
+  - libstdcxx >=13
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 298555
-  timestamp: 1715607628741
+  size: 353159
+  timestamp: 1725429777124
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: hde137ed_4
-  build_number: 4
+  build: hb33e954_5
+  build_number: 5
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/zeromq-4.3.5-hde137ed_4.conda
-  sha256: 871625ce993e6c61649b14659a3d1d6011fbb242b7d6a25cadbc6300b2356f32
-  md5: e56609055da6c658aa329d42a6c6b9f2
+  url: https://conda.anaconda.org/conda-forge/osx-64/zeromq-4.3.5-hb33e954_5.conda
+  sha256: 7e63a9ec19660666095ea9332a5b226329ff4f499018e8a281d0d160cbb60ca4
+  md5: a9735eb372d515c78f8211785406e36f
   depends:
   - __osx >=10.13
-  - krb5 >=1.21.2,<1.22.0a0
-  - libcxx >=16
-  - libsodium >=1.0.18,<1.0.19.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libcxx >=17
+  - libsodium >=1.0.20,<1.0.21.0a0
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 304498
-  timestamp: 1715607961981
+  size: 303596
+  timestamp: 1725430161260
 - kind: conda
   name: zeromq
   version: 4.3.5
-  build: he1f189c_4
-  build_number: 4
+  build: he1f189c_5
+  build_number: 5
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-he1f189c_4.conda
-  sha256: 0f375034a88659f764ce837f324698a883da227fcb517561ffaf6a89474211b4
-  md5: b755eb545c2728b9a53729f02e627834
+  url: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-he1f189c_5.conda
+  sha256: 7cfea95cc9f637ad5b651cde6bb22ddcd7989bd9b21e3c6df4958f618c13b807
+  md5: a6df1c5da1f16f02e872994611dc4dfb
   depends:
-  - krb5 >=1.21.2,<1.22.0a0
-  - libsodium >=1.0.18,<1.0.19.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libsodium >=1.0.20,<1.0.21.0a0
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 2707065
-  timestamp: 1715607874610
+  size: 2710711
+  timestamp: 1725430044838
 - kind: conda
   name: zipp
-  version: 3.17.0
+  version: 3.20.1
   build: pyhd8ed1ab_0
   subdir: noarch
   noarch: python
-  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda
-  sha256: bced1423fdbf77bca0a735187d05d9b9812d2163f60ab426fc10f11f92ecbe26
-  md5: 2e4d6bc0b14e10f895fc6791a7d9b26a
+  url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.20.1-pyhd8ed1ab_0.conda
+  sha256: 30762bd25b6fc8714d5520a223ccf20ad4a6792dc439c54b59bf44b60bf51e72
+  md5: 74a4befb4b38897e19a107693e49da20
   depends:
   - python >=3.8
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/zipp?source=conda-forge-mapping
-  size: 18954
-  timestamp: 1695255262261
+  - pkg:pypi/zipp?source=hash-mapping
+  size: 21110
+  timestamp: 1724731063145
 - kind: conda
   name: zlib
-  version: 1.2.13
-  build: h4ab18f5_6
-  build_number: 6
+  version: 1.3.1
+  build: h2466b09_1
+  build_number: 1
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/zlib-1.3.1-h2466b09_1.conda
+  sha256: 76409556e6c7cb91991cd94d7fc853c9272c2872bd7e3573ff35eb33d6fca5be
+  md5: f8e0a35bf6df768ad87ed7bbbc36ab04
+  depends:
+  - libzlib 1.3.1 h2466b09_1
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: Zlib
+  license_family: Other
+  purls: []
+  size: 108081
+  timestamp: 1716874767420
+- kind: conda
+  name: zlib
+  version: 1.3.1
+  build: h4ab18f5_1
+  build_number: 1
   subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.13-h4ab18f5_6.conda
-  sha256: 534824ea44939f3e59ca8ebb95e3ece6f50f9d2a0e69999fbc692311252ed6ac
-  md5: 559d338a4234c2ad6e676f460a093e67
+  url: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-h4ab18f5_1.conda
+  sha256: cee16ab07a11303de721915f0a269e8c7a54a5c834aa52f74b1cc3a59000ade8
+  md5: 9653f1bf3766164d0e65fa723cabbc54
   depends:
   - libgcc-ng >=12
-  - libzlib 1.2.13 h4ab18f5_6
+  - libzlib 1.3.1 h4ab18f5_1
   license: Zlib
   license_family: Other
   purls: []
-  size: 92883
-  timestamp: 1716874088980
+  size: 93004
+  timestamp: 1716874213487
+- kind: conda
+  name: zstandard
+  version: 0.23.0
+  build: py312h15fbf35_1
+  build_number: 1
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.23.0-py312h15fbf35_1.conda
+  sha256: d00ca25c1e28fd31199b26a94f8c96574475704a825d244d7a6351ad3745eeeb
+  md5: a4cde595509a7ad9c13b1a3809bcfe51
+  depends:
+  - __osx >=11.0
+  - cffi >=1.11
+  - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
+  - python_abi 3.12.* *_cp312
+  - zstd >=1.5.6,<1.5.7.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 330788
+  timestamp: 1725305806565
+- kind: conda
+  name: zstandard
+  version: 0.23.0
+  build: py312h7122b0e_1
+  build_number: 1
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.23.0-py312h7122b0e_1.conda
+  sha256: 2685dde42478fae0780fba5d1f8a06896a676ae105f215d32c9f9e76f3c6d8fd
+  md5: bd132ba98f3fc0a6067f355f8efe4cb6
+  depends:
+  - __osx >=10.13
+  - cffi >=1.11
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - zstd >=1.5.6,<1.5.7.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 410873
+  timestamp: 1725305688706
+- kind: conda
+  name: zstandard
+  version: 0.23.0
+  build: py312h7606c53_1
+  build_number: 1
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.23.0-py312h7606c53_1.conda
+  sha256: 3e0c718aa18dcac7f080844dbe0aea41a9cea75083019ce02e8a784926239826
+  md5: a92cc3435b2fd6f51463f5a4db5c50b1
+  depends:
+  - cffi >=1.11
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  - zstd >=1.5.6,<1.5.7.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 320624
+  timestamp: 1725305934189
+- kind: conda
+  name: zstandard
+  version: 0.23.0
+  build: py312hef9b889_1
+  build_number: 1
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
+  sha256: b97015e146437283f2213ff0e95abdc8e2480150634d81fbae6b96ee09f5e50b
+  md5: 8b7069e9792ee4e5b4919a7a306d2e67
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - cffi >=1.11
+  - libgcc >=13
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  - zstd >=1.5.6,<1.5.7.0a0
+  - zstd >=1.5.6,<1.6.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 419552
+  timestamp: 1725305670210
 - kind: conda
   name: zstd
   version: 1.5.6
diff --git a/pyproject.toml b/pyproject.toml
index 8abb86db..d589ec9d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -96,12 +96,10 @@ python = "3.12"
 scipy = "<=1.13"
 pybaum = "*"
 jupyterlab = "*"
-plotly = ">=5.2,<6"
-pdbpp = "*"
+plotly = ">=5.24,<6"
 pre-commit = "*"
 snakeviz = "*"
 memory_profiler = "*"
-pixi-kernel = ">=0.3.0,<0.4"
 
 [tool.pixi.target.unix.dependencies]
 jax = ">=0.4.20"
@@ -112,6 +110,7 @@ jaxlib = ">=0.4.20"
 
 [tool.pixi.pypi-dependencies]
 lcm = {path = ".", editable = true}
+pdbp = "*"
 
 [tool.pixi.target.win-64.pypi-dependencies]
 jax = { version = ">=0.4.20", extras = ["cpu"] }
@@ -140,7 +139,7 @@ tests-with-cov = "pytest tests --cov-report=xml --cov=./"
 explanation-notebooks = "jupyter execute explanations/*.ipynb"
 
 [tool.pixi.feature.mypy.dependencies]
-mypy = "*"
+mypy = ">=1.11.2"
 pandas-stubs = "*"
 matplotlib = "*"  # required because of pandas
 
@@ -164,6 +163,7 @@ test-gpu = {features = ["test", "cuda"], solve-group = "cuda"}
 [tool.ruff]
 target-version = "py312"
 fix = true
+exclude = ["src/lcm/sandbox"]
 
 [tool.ruff.lint]
 select = ["ALL"]
@@ -215,32 +215,27 @@ extend-ignore = [
 
     # long messages outside the exception class
     "TRY003",
+
+    # Missing docstring in magic method
+    "D105",
+
+    # Rules ignored to avoid conflict with formatter
+    # ----------------------------------------------------------------------------------
+    "COM812",
+    "ISC001",
 ]
 
 [tool.ruff.lint.per-file-ignores]
 "docs/source/conf.py" = ["E501", "ERA001", "DTZ005"]
-"tests/test_*.py" = ["PLR2004"]
+"tests/*" = ["PLR2004"]
 "examples/*" = ["INP001"]
 "explanations/*" = ["INP001", "B018", "T201", "E402", "PD008"]
 "scripts/*" = ["INP001", "D101", "RET503"]
+"**/*.ipynb" = ["FBT003", "E402"]
 
 [tool.ruff.lint.pydocstyle]
 convention = "google"
 
-# ======================================================================================
-# NBQA configuration
-# ======================================================================================
-
-[tool.nbqa.config]
-black = "pyproject.toml"
-
-[tool.nbqa.mutate]
-black = 1
-
-[tool.nbqa.exclude]
-ruff = "src/lcm/sandbox"
-black = "src/lcm/sandbox"
-
 # ======================================================================================
 # Pytest configuration
 # ======================================================================================
@@ -268,5 +263,10 @@ none_representation = "null"
 # ======================================================================================
 
 [[tool.mypy.overrides]]
-module = ["dags", "dags.signature"]
+module = "tests.*"
+disallow_untyped_defs = false
+ignore_errors = true
+
+[[tool.mypy.overrides]]
+module = ["dags", "dags.signature", "pybaum", "pdbp"]
 ignore_missing_imports = true
diff --git a/scripts/check_nbqa_version_mismatch.py b/scripts/check_nbqa_version_mismatch.py
deleted file mode 100644
index 2f0a0440..00000000
--- a/scripts/check_nbqa_version_mismatch.py
+++ /dev/null
@@ -1,118 +0,0 @@
-"""Make sure that black and ruff versions used with nbQA match the primary versions.
-
-For example, we could use ruff==v0.4.6 in the primary pre-commit config, but nbQA could
-be using ruff==v0.3.2. In this case, we raise an error.
-
-"""
-
-from pathlib import Path
-from typing import NotRequired, TypedDict
-
-import yaml
-
-
-class PreCommitRepo(TypedDict):
-    repo: str
-    hooks: list[dict]
-    rev: NotRequired[str]
-
-
-class PreCommitConfig(TypedDict):
-    repos: list[PreCommitRepo]
-    ci: dict
-
-
-class NbQAHook(TypedDict):
-    id: str
-    additional_dependencies: list[str]
-
-
-class NbQARepo(PreCommitRepo):
-    repo: str
-    hooks: list[NbQAHook]
-    rev: str
-
-
-TOOL_TO_REPO = {
-    "black": "https://github.com/psf/black",
-    "ruff": "https://github.com/astral-sh/ruff-pre-commit",
-    "nbQA": "https://github.com/nbQA-dev/nbQA",
-}
-
-
-def read_pre_commit_config() -> PreCommitConfig:
-    """Read a YAML file."""
-    with Path(".pre-commit-config.yaml").open() as stream:
-        try:
-            return yaml.safe_load(stream)
-        except yaml.YAMLError as error:
-            raise ValueError("Failed to parse .pre-commit-config.yaml file") from error
-
-
-def get_nbqa_repo(pre_commit_config: PreCommitConfig) -> NbQARepo:
-    """Get the nbQA repo from the pre-commit config.
-
-    Args:
-        pre_commit_config: The pre-commit config.
-
-    Returns:
-        The nbQA repo if found, otherwise None.
-
-    """
-    for repo in pre_commit_config["repos"]:
-        if repo["repo"] == TOOL_TO_REPO["nbQA"]:
-            return repo
-
-
-def get_primary_version(pre_commit_config: PreCommitConfig, tool: str) -> str:
-    """Get the primary version of the tool used in the pre-commit config.
-
-    Args:
-        pre_commit_config: The pre-commit config.
-        tool: The tool to get the primary version for. For example, ruff or black.
-
-    Returns:
-        The primary version of the tool, otherwise None.
-
-    """
-    for repo in pre_commit_config["repos"]:
-        if repo["repo"] == TOOL_TO_REPO[tool]:
-            return repo["rev"]
-
-
-def check_for_version_mismatch(
-    pre_commit_config: PreCommitConfig,
-    nbqa_repo: NbQARepo,
-) -> None:
-    """Check for version mismatch between primary versions and versions used by nbQA.
-
-    Args:
-        pre_commit_config: The pre-commit config.
-        nbqa_repo: The nbQA repo.
-
-    Raises:
-        ValueError: If there is a version mismatch.
-
-    """
-    version_mismatch = {}
-
-    for hook in nbqa_repo["hooks"]:
-        tool = hook["id"].removeprefix("nbqa-")
-        primary_version = get_primary_version(pre_commit_config, tool)
-        tool_dependency = hook["additional_dependencies"][0]
-        version_used_by_nbqa = tool_dependency.removeprefix(f"{tool}==")
-
-        if primary_version != version_used_by_nbqa:
-            version_mismatch[tool] = {
-                "primary": primary_version,
-                "nbQA": version_used_by_nbqa,
-            }
-
-    if version_mismatch:
-        raise ValueError(f"Versions mismatch in nbQA repo: {version_mismatch}")
-
-
-if __name__ == "__main__":
-    pre_commit_config = read_pre_commit_config()
-    nbqa_repo = get_nbqa_repo(pre_commit_config)
-    check_for_version_mismatch(pre_commit_config, nbqa_repo)
diff --git a/src/lcm/__init__.py b/src/lcm/__init__.py
index 5f9cbd46..8d1a197b 100644
--- a/src/lcm/__init__.py
+++ b/src/lcm/__init__.py
@@ -1,3 +1,12 @@
+import contextlib
+
+try:
+    import pdbp  # noqa: F401
+except ImportError:
+    contextlib.suppress(Exception)
+
 from lcm import mark
+from lcm.grids import DiscreteGrid, LinspaceGrid, LogspaceGrid
+from lcm.user_model import Model
 
-__all__ = ["mark"]
+__all__ = ["mark", "Model", "LinspaceGrid", "LogspaceGrid", "DiscreteGrid"]
diff --git a/src/lcm/discrete_problem.py b/src/lcm/discrete_problem.py
index 84c665f6..a26baab2 100644
--- a/src/lcm/discrete_problem.py
+++ b/src/lcm/discrete_problem.py
@@ -19,7 +19,6 @@
 
 from collections.abc import Callable
 from functools import partial
-from typing import Any, Literal
 
 import jax
 import jax.numpy as jnp
@@ -27,30 +26,28 @@
 from jax import Array
 from jax.ops import segment_max
 
-from lcm.typing import SegmentInfo
+from lcm.typing import ParamsDict, SegmentInfo, ShockType
 
 
 def get_solve_discrete_problem(
     *,
-    random_utility_shock_type: Literal["extreme_value"] | None,
+    random_utility_shock_type: ShockType,
     variable_info: pd.DataFrame,
     is_last_period: bool,
     choice_segments: SegmentInfo | None,
-    params: dict[str, Any],
 ) -> Callable[[Array], Array]:
     """Get function that computes the expected max. of conditional continuation values.
 
     The maximum is taken over the discrete and sparse choice variables in each state.
 
     Args:
-        random_utility_shock_type (Literal["extreme_value"] | None): Type of choice
-            shock. Currently only None is supported. Work for "extreme_value" is in
-            progress.
-        variable_info (pd.DataFrame): DataFrame with information about the variables.
-        is_last_period (bool): Whether the function is created for the last period.
-        choice_segments (SegmentInfo): Contains segment information of sparse choices.
-            If None, there are no sparse choices.
-        params (dict): Dictionary with model parameters.
+        random_utility_shock_type: Type of choice shock. Currently only Shock.NONE is
+            supported. Work for "extreme_value" is in progress.
+        variable_info: DataFrame with information about the variables.
+        is_last_period: Whether the function is created for the last period.
+        choice_segments: Contains segment information of sparse choices. If None, there
+            are no sparse choices.
+        params: Dictionary with model parameters.
 
     Returns:
         callable: Function that calculates the expected maximum of the conditional
@@ -63,9 +60,9 @@ def get_solve_discrete_problem(
 
     choice_axes = _determine_dense_discrete_choice_axes(variable_info)
 
-    if random_utility_shock_type is None:
+    if random_utility_shock_type == ShockType.NONE:
         func = _solve_discrete_problem_no_shocks
-    elif random_utility_shock_type == "extreme_value":
+    elif random_utility_shock_type == ShockType.EXTREME_VALUE:
         raise NotImplementedError("Extreme value shocks are not yet implemented.")
     else:
         raise ValueError(f"Invalid shock_type: {random_utility_shock_type}.")
@@ -74,7 +71,6 @@ def get_solve_discrete_problem(
         func,
         choice_axes=choice_axes,
         choice_segments=choice_segments,
-        params=params,
     )
 
 
@@ -87,7 +83,7 @@ def _solve_discrete_problem_no_shocks(
     cc_values: Array,
     choice_axes: tuple[int, ...] | None,
     choice_segments: SegmentInfo | None,
-    params: dict[str, Any],  # noqa: ARG001
+    params: ParamsDict,  # noqa: ARG001
 ) -> Array:
     """Reduce conditional continuation values over discrete choices.
 
diff --git a/src/lcm/dispatchers.py b/src/lcm/dispatchers.py
index ebe941a9..d6eed9b4 100644
--- a/src/lcm/dispatchers.py
+++ b/src/lcm/dispatchers.py
@@ -48,17 +48,20 @@ def spacemap(
     """
     # Check inputs and prepare function
     # ==================================================================================
-    if overlap := set(dense_vars).intersection(sparse_vars):
+    overlap = set(dense_vars).intersection(sparse_vars)
+    if overlap:
         raise ValueError(
             f"Dense and sparse variables must be disjoint. Overlap: {overlap}",
         )
 
-    if duplicates := {v for v in dense_vars if dense_vars.count(v) > 1}:
+    duplicates = {v for v in dense_vars if dense_vars.count(v) > 1}
+    if duplicates:
         raise ValueError(
             f"Same argument provided more than once in dense variables: {duplicates}",
         )
 
-    if duplicates := {v for v in sparse_vars if sparse_vars.count(v) > 1}:
+    duplicates = {v for v in sparse_vars if sparse_vars.count(v) > 1}
+    if duplicates:
         raise ValueError(
             f"Same argument provided more than once in sparse variables: {duplicates}",
         )
@@ -116,7 +119,8 @@ def vmap_1d(
         described above but there might be additional dimensions.
 
     """
-    if duplicates := {v for v in variables if variables.count(v) > 1}:
+    duplicates = {v for v in variables if variables.count(v) > 1}
+    if duplicates:
         raise ValueError(
             f"Same argument provided more than once in variables: {duplicates}",
         )
@@ -179,7 +183,8 @@ def productmap(func: F, variables: list[str]) -> F:
     """
     func = allow_args(func)  # jax.vmap cannot deal with keyword-only arguments
 
-    if duplicates := {v for v in variables if variables.count(v) > 1}:
+    duplicates = {v for v in variables if variables.count(v) > 1}
+    if duplicates:
         raise ValueError(
             f"Same argument provided more than once in variables: {duplicates}",
         )
diff --git a/src/lcm/entry_point.py b/src/lcm/entry_point.py
index 0c8c1250..2ccf5148 100644
--- a/src/lcm/entry_point.py
+++ b/src/lcm/entry_point.py
@@ -1,6 +1,7 @@
 import functools
+from collections.abc import Callable
 from functools import partial
-from typing import Literal
+from typing import Literal, cast
 
 import jax
 import jax.numpy as jnp
@@ -8,56 +9,55 @@
 from lcm.argmax import argmax
 from lcm.discrete_problem import get_solve_discrete_problem
 from lcm.dispatchers import productmap
+from lcm.input_processing import process_model
 from lcm.logging import get_logger
 from lcm.model_functions import (
     get_utility_and_feasibility_function,
 )
 from lcm.next_state import get_next_state_function
 from lcm.options import DefaultMapCoordinatesOptions
-from lcm.process_model import process_model
 from lcm.simulate import simulate
 from lcm.solve_brute import solve
 from lcm.state_space import create_state_choice_space
+from lcm.typing import ParamsDict
+from lcm.user_model import Model
 
 
 def get_lcm_function(
-    model: dict,
+    model: Model,
     targets: Literal["solve", "simulate", "solve_and_simulate"] = "solve",
     *,
     debug_mode: bool = True,
     jit: bool = True,
     interpolation_options: dict | None = None,
-):
+) -> tuple[Callable, ParamsDict]:
     """Entry point for users to get high level functions generated by lcm.
 
+    Return the function to solve and/or simulate a model along with a template for the
+    parameters.
+
     Advanced users might want to use lower level functions instead, but can read the
     source code of this function to see how the lower level components are meant to be
     used.
 
     Notes:
     -----
-    - Further targets could be "likelihood" or "simulate"
-    - We might need additional arguments such as solver_options that we want to take
-      separate from a model specification.
-    - create_params needs to work with a processed_model instead of a user model.
-    - currently all the preparations are hardcoded to generate the arguments needed
-      by solve_brute. In the long run, this needs to inspect the signature of the
-      solver, or generate only what is needed using dags.
-    - there is a hack to make the state_indexers empty in the last period which needs
+    - There is a hack to make the state_indexers empty in the last period which needs
       to be replaced by a better solution, when we want to allow for bequest motives.
 
     Args:
-        model (dict): User model specification.
-        targets (str or iterable): The requested function types. Currently only
-            "solve", "simulate" and "solve_and_simulate" are supported.
-        debug_mode (bool): Whether to log debug messages.
-        jit (bool): Whether to jit the returned function.
-        interpolation_options (dict): Dictionary of keyword arguments for interpolation
+        model: User model specification.
+        targets: The requested function types. Currently only "solve", "simulate" and
+            "solve_and_simulate" are supported.
+        debug_mode: Whether to log debug messages.
+        jit: Whether to jit the returned function.
+        interpolation_options: Dictionary of keyword arguments for interpolation
             via map_coordinates. If None, the default options are used.
 
     Returns:
-        callable: A function that takes params and returns the requested targets.
-        dict: A parameter dict where all parameter values are initialized to NaN.
+        - A function that takes params (and possibly other arguments, such as initial
+          states in the simulate case) and returns the requested targets.
+        - A parameter dictionary where all parameter values are initialized to NaN.
 
     """
     # ==================================================================================
@@ -66,7 +66,7 @@ def get_lcm_function(
     if targets not in {"solve", "simulate", "solve_and_simulate"}:
         raise NotImplementedError
 
-    _mod = process_model(user_model=model)
+    _mod = process_model(model=model)
     last_period = _mod.n_periods - 1
     interpolation_options = DefaultMapCoordinatesOptions | (interpolation_options or {})
 
@@ -152,14 +152,11 @@ def get_lcm_function(
 
         # create list of emax_calculators
         # ==============================================================================
-        _shock_type = _mod.shocks.get("additive_utility_shock", None)
-
         calculator = get_solve_discrete_problem(
-            random_utility_shock_type=_shock_type,
+            random_utility_shock_type=_mod.random_utility_shocks,
             variable_info=_mod.variable_info,
             is_last_period=is_last_period,
             choice_segments=choice_segments[period],
-            params=_mod.params,
         )
         emax_calculators.append(calculator)
 
@@ -196,7 +193,7 @@ def get_lcm_function(
     elif targets == "solve_and_simulate":
         _target = partial(simulate_model, solve_model=solve_model)
 
-    return _target, _mod.params
+    return cast(Callable, _target), _mod.params
 
 
 def create_compute_conditional_continuation_value(
diff --git a/src/lcm/exceptions.py b/src/lcm/exceptions.py
new file mode 100644
index 00000000..d58b0ba1
--- /dev/null
+++ b/src/lcm/exceptions.py
@@ -0,0 +1,18 @@
+class ModelInitilizationError(Exception):
+    """Raised when there is an error in the model initialization."""
+
+
+class GridInitializationError(Exception):
+    """Raised when there is an error in the grid initialization."""
+
+
+def format_messages(errors: str | list[str]) -> str:
+    """Convert message or list of messages into a single string."""
+    if isinstance(errors, str):
+        formatted = errors
+    elif len(errors) == 1:
+        formatted = errors[0]
+    else:
+        enumerated = "\n\n".join([f"{i}. {error}" for i, error in enumerate(errors, 1)])
+        formatted = f"The following errors occurred:\n\n{enumerated}"
+    return formatted
diff --git a/src/lcm/function_representation.py b/src/lcm/function_representation.py
index 96143cfd..2a90d98b 100644
--- a/src/lcm/function_representation.py
+++ b/src/lcm/function_representation.py
@@ -7,9 +7,9 @@
 from jax import Array
 from jax.scipy.ndimage import map_coordinates
 
-import lcm.grids as grids_module
 from lcm.functools import all_as_kwargs
-from lcm.interfaces import ContinuousGridInfo, ContinuousGridType, SpaceInfo
+from lcm.grids import ContinuousGrid
+from lcm.interfaces import SpaceInfo
 from lcm.typing import MapCoordinatesOptions
 
 
@@ -54,7 +54,7 @@ def get_function_representation(
     functions are called is determined by a DAG.
 
     Args:
-        space_info: Namedtuple containing all information needed to interpret the
+        space_info: Class containing all information needed to interpret the
             pre-calculated values of a function.
         name_of_values_on_grid: The name of the argument via which the pre-calculated
             values, that have been evaluated on the state-space grid, will be passed
@@ -120,8 +120,7 @@ def get_function_representation(
         for var, grid_spec in space_info.interpolation_info.items():
             funcs[f"__{var}_coord__"] = _get_coordinate_finder(
                 in_name=input_prefix + var,
-                grid_type=grid_spec.kind,
-                grid_info=grid_spec.info,
+                grid=grid_spec,  # type: ignore[arg-type]
             )
 
         # ==============================================================================
@@ -199,8 +198,7 @@ def lookup_wrapper(*args, **kwargs):
 
 def _get_coordinate_finder(
     in_name: str,
-    grid_type: ContinuousGridType,
-    grid_info: ContinuousGridInfo,
+    grid: ContinuousGrid,
 ) -> Callable[..., Array]:
     """Create a function that translates a value into coordinates on a grid.
 
@@ -210,22 +208,19 @@ def _get_coordinate_finder(
     Args:
         in_name: Name via which the value to be translated into coordinates will be
             passed into the resulting function.
-        grid_type: Type of the grid, e.g. "linspace" or "logspace". The type of grid
-            must be implemented in lcm.grids.
-        grid_info: Information on how to build the grid, e.g. start, stop, and n_points.
+        grid: The continuous grid on which the value is to be translated into
+            coordinates.
 
     Returns:
         callable: A callable with keyword-only argument [in_name] that translates a
             value into coordinates on a grid.
 
     """
-    raw_func = getattr(grids_module, f"get_{grid_type}_coordinate")
-    partialled_func = partial(raw_func, **grid_info._asdict())
 
     @with_signature(args=[in_name])
     def find_coordinate(*args, **kwargs):
         kwargs = all_as_kwargs(args, kwargs, arg_names=[in_name])
-        return partialled_func(kwargs[in_name])
+        return grid.get_coordinate(kwargs[in_name])
 
     return find_coordinate
 
@@ -269,7 +264,7 @@ def _fail_if_interpolation_axes_are_not_last(space_info: SpaceInfo) -> None:
     """Fail if the interpolation axes are not the last elements in axis_names.
 
     Args:
-        space_info: Namedtuple containing all information needed to interpret the
+        space_info: Class containing all information needed to interpret the
             precalculated values of a function.
 
     Raises:
diff --git a/src/lcm/functools.py b/src/lcm/functools.py
index 811fa875..58a1d9b7 100644
--- a/src/lcm/functools.py
+++ b/src/lcm/functools.py
@@ -41,12 +41,14 @@ def func_with_only_kwargs(*args, **kwargs):
                 ),
             )
 
-        if extra := set(kwargs).difference(parameters):
+        extra = set(kwargs).difference(parameters)
+        if extra:
             raise ValueError(
                 f"Expected arguments: {list(parameters)}, got extra: {extra}",
             )
 
-        if missing := set(parameters).difference(kwargs):
+        missing = set(parameters).difference(kwargs)
+        if missing:
             raise ValueError(
                 f"Expected arguments: {list(parameters)}, missing: {missing}",
             )
diff --git a/src/lcm/grid_helpers.py b/src/lcm/grid_helpers.py
new file mode 100644
index 00000000..3f4c235c
--- /dev/null
+++ b/src/lcm/grid_helpers.py
@@ -0,0 +1,111 @@
+"""Functions to generate and work with different kinds of grids.
+
+Grid generation functions must have the following signature:
+
+    Signature (start: Scalar, stop: Scalar, n_points: int) -> jax.Array
+
+They take start and end points and create a grid of points between them.
+
+
+Interpolation info functions must have the following signature:
+
+    Signature (
+        value: Scalar,
+        start: Scalar,
+        stop: Scalar,
+        n_points: int
+    ) -> Scalar
+
+They take the information required to generate a grid, and return an index corresponding
+to the value, which is a point in the space but not necessarily a grid point.
+
+Some of the arguments will not be used by all functions but the aligned interface makes
+it easy to call functions interchangeably.
+
+"""
+
+import jax.numpy as jnp
+from jax import Array
+
+from lcm.typing import Scalar
+
+
+def linspace(start: Scalar, stop: Scalar, n_points: int) -> Array:
+    """Wrapper around jnp.linspace.
+
+    Returns a linearly spaced grid between start and stop with n_points, including both
+    endpoints.
+
+    """
+    return jnp.linspace(start, stop, n_points)
+
+
+def get_linspace_coordinate(
+    value: Scalar,
+    start: Scalar,
+    stop: Scalar,
+    n_points: int,
+) -> Scalar:
+    """Map a value into the input needed for jax.scipy.ndimage.map_coordinates."""
+    step_length = (stop - start) / (n_points - 1)
+    return (value - start) / step_length
+
+
+def logspace(start: Scalar, stop: Scalar, n_points: int) -> Array:
+    """Wrapper around jnp.logspace.
+
+    Returns a logarithmically spaced grid between start and stop with n_points,
+    including both endpoints.
+
+    From the JAX documentation:
+
+        In linear space, the sequence starts at base ** start (base to the power of
+        start) and ends with base ** stop [...].
+
+    """
+    start_linear = jnp.log(start)
+    stop_linear = jnp.log(stop)
+    return jnp.logspace(start_linear, stop_linear, n_points, base=jnp.e)
+
+
+def get_logspace_coordinate(
+    value: Scalar,
+    start: Scalar,
+    stop: Scalar,
+    n_points: int,
+) -> Scalar:
+    """Map a value into the input needed for jax.scipy.ndimage.map_coordinates."""
+    # Transform start, stop, and value to linear scale
+    start_linear = jnp.log(start)
+    stop_linear = jnp.log(stop)
+    value_linear = jnp.log(value)
+
+    # Calculate coordinate in linear space
+    coordinate_in_linear_space = get_linspace_coordinate(
+        value_linear,
+        start_linear,
+        stop_linear,
+        n_points,
+    )
+
+    # Calculate rank of lower and upper point in logarithmic space
+    rank_lower_gridpoint = jnp.floor(coordinate_in_linear_space)
+    rank_upper_gridpoint = rank_lower_gridpoint + 1
+
+    # Calculate lower and upper point in logarithmic space
+    step_length_linear = (stop_linear - start_linear) / (n_points - 1)
+    lower_gridpoint = jnp.exp(start_linear + step_length_linear * rank_lower_gridpoint)
+    upper_gridpoint = jnp.exp(start_linear + step_length_linear * rank_upper_gridpoint)
+
+    # Calculate the decimal part of coordinate
+    logarithmic_step_size_at_coordinate = upper_gridpoint - lower_gridpoint
+    distance_from_lower_gridpoint = value - lower_gridpoint
+
+    # If the distance from the lower gridpoint is zero, the coordinate corresponds to
+    # the rank of the lower gridpoint. The other extreme is when the distance is equal
+    # to the logarithmic step size at the coordinate, in which case the coordinate
+    # corresponds to the rank of the upper gridpoint. For values in between, the
+    # coordinate lies on a linear scale between the ranks of the lower and upper
+    # gridpoints.
+    decimal_part = distance_from_lower_gridpoint / logarithmic_step_size_at_coordinate
+    return rank_lower_gridpoint + decimal_part
diff --git a/src/lcm/grids.py b/src/lcm/grids.py
index 3f4c235c..15c89bce 100644
--- a/src/lcm/grids.py
+++ b/src/lcm/grids.py
@@ -1,111 +1,197 @@
-"""Functions to generate and work with different kinds of grids.
+"""Collection of classes that are used by the user to define the model and grids."""
 
-Grid generation functions must have the following signature:
+from abc import ABC, abstractmethod
+from collections.abc import Collection
+from dataclasses import dataclass
 
-    Signature (start: Scalar, stop: Scalar, n_points: int) -> jax.Array
+import jax.numpy as jnp
+from jax import Array
 
-They take start and end points and create a grid of points between them.
+from lcm import grid_helpers
+from lcm.exceptions import GridInitializationError, format_messages
+from lcm.typing import Scalar
 
 
-Interpolation info functions must have the following signature:
+class Grid(ABC):
+    """LCM Grid base class."""
 
-    Signature (
-        value: Scalar,
-        start: Scalar,
-        stop: Scalar,
-        n_points: int
-    ) -> Scalar
+    @abstractmethod
+    def to_jax(self) -> jnp.ndarray:
+        """Convert the grid to a Jax array."""
 
-They take the information required to generate a grid, and return an index corresponding
-to the value, which is a point in the space but not necessarily a grid point.
 
-Some of the arguments will not be used by all functions but the aligned interface makes
-it easy to call functions interchangeably.
+@dataclass(frozen=True)
+class DiscreteGrid(Grid):
+    """A grid of discrete values.
 
-"""
+    Attributes:
+        options: The options in the grid. Must be an iterable of scalar int or float
+            values.
 
-import jax.numpy as jnp
-from jax import Array
+    """
+
+    options: Collection[int | float]
+
+    def __post_init__(self) -> None:
+        if not isinstance(self.options, Collection):
+            raise GridInitializationError(
+                "options must be a collection of scalar int or float values, e.g., a ",
+                "list or tuple",
+            )
+
+        errors = _validate_discrete_grid(self.options)
+        if errors:
+            msg = format_messages(errors)
+            raise GridInitializationError(msg)
+
+    def to_jax(self) -> Array:
+        """Convert the grid to a Jax array."""
+        return jnp.array(list(self.options))
 
-from lcm.typing import Scalar
 
+@dataclass(frozen=True, kw_only=True)
+class ContinuousGrid(Grid, ABC):
+    """LCM Continuous Grid base class."""
 
-def linspace(start: Scalar, stop: Scalar, n_points: int) -> Array:
-    """Wrapper around jnp.linspace.
+    start: int | float
+    stop: int | float
+    n_points: int
 
-    Returns a linearly spaced grid between start and stop with n_points, including both
-    endpoints.
+    def __post_init__(self) -> None:
+        errors = _validate_continuous_grid(
+            start=self.start,
+            stop=self.stop,
+            n_points=self.n_points,
+        )
+        if errors:
+            msg = format_messages(errors)
+            raise GridInitializationError(msg)
+
+    @abstractmethod
+    def to_jax(self) -> Array:
+        """Convert the grid to a Jax array."""
+
+    @abstractmethod
+    def get_coordinate(self, value: Scalar) -> Scalar:
+        """Get the generalized coordinate of a value in the grid."""
+
+
+class LinspaceGrid(ContinuousGrid):
+    """A linear grid of continuous values.
+
+    Example:
+    --------
+    Let `start = 1`, `stop = 100`, and `n_points = 3`. The grid is `[1, 50.5, 100]`.
+
+    Attributes:
+        start: The start value of the grid. Must be a scalar int or float value.
+        stop: The stop value of the grid. Must be a scalar int or float value.
+        n_points: The number of points in the grid. Must be an int greater than 0.
 
     """
-    return jnp.linspace(start, stop, n_points)
 
+    def to_jax(self) -> Array:
+        """Convert the grid to a Jax array."""
+        return grid_helpers.linspace(self.start, self.stop, self.n_points)
+
+    def get_coordinate(self, value: Scalar) -> Scalar:
+        """Get the generalized coordinate of a value in the grid."""
+        return grid_helpers.get_linspace_coordinate(
+            value, self.start, self.stop, self.n_points
+        )
 
-def get_linspace_coordinate(
-    value: Scalar,
-    start: Scalar,
-    stop: Scalar,
-    n_points: int,
-) -> Scalar:
-    """Map a value into the input needed for jax.scipy.ndimage.map_coordinates."""
-    step_length = (stop - start) / (n_points - 1)
-    return (value - start) / step_length
 
+class LogspaceGrid(ContinuousGrid):
+    """A logarithmic grid of continuous values.
 
-def logspace(start: Scalar, stop: Scalar, n_points: int) -> Array:
-    """Wrapper around jnp.logspace.
+    Example:
+    --------
+    Let `start = 1`, `stop = 100`, and `n_points = 3`. The grid is `[1, 10, 100]`.
 
-    Returns a logarithmically spaced grid between start and stop with n_points,
-    including both endpoints.
+    Attributes:
+        start: The start value of the grid. Must be a scalar int or float value.
+        stop: The stop value of the grid. Must be a scalar int or float value.
+        n_points: The number of points in the grid. Must be an int greater than 0.
+
+    """
 
-    From the JAX documentation:
+    def to_jax(self) -> Array:
+        """Convert the grid to a Jax array."""
+        return grid_helpers.logspace(self.start, self.stop, self.n_points)
 
-        In linear space, the sequence starts at base ** start (base to the power of
-        start) and ends with base ** stop [...].
+    def get_coordinate(self, value: Scalar) -> Scalar:
+        """Get the generalized coordinate of a value in the grid."""
+        return grid_helpers.get_logspace_coordinate(
+            value, self.start, self.stop, self.n_points
+        )
+
+
+# ======================================================================================
+# Validate user input
+# ======================================================================================
+
+
+def _validate_discrete_grid(options: Collection[int | float]) -> list[str]:
+    """Validate the discrete grid options.
+
+    Args:
+        options: The user options to validate.
+
+    Returns:
+        list[str]: A list of error messages.
 
     """
-    start_linear = jnp.log(start)
-    stop_linear = jnp.log(stop)
-    return jnp.logspace(start_linear, stop_linear, n_points, base=jnp.e)
+    error_messages = []
+
+    if not len(options) > 0:
+        error_messages.append("options must contain at least one element")
+
+    if not all(isinstance(option, int | float) for option in options):
+        error_messages.append("options must contain only scalar int or float values")
+
+    if len(options) != len(set(options)):
+        error_messages.append("options must contain unique values")
+
+    if list(options) != list(range(len(options))):
+        error_messages.append(
+            "options must be a list of consecutive integers starting from 0",
+        )
 
+    return error_messages
 
-def get_logspace_coordinate(
-    value: Scalar,
-    start: Scalar,
-    stop: Scalar,
+
+def _validate_continuous_grid(
+    start: float,
+    stop: float,
     n_points: int,
-) -> Scalar:
-    """Map a value into the input needed for jax.scipy.ndimage.map_coordinates."""
-    # Transform start, stop, and value to linear scale
-    start_linear = jnp.log(start)
-    stop_linear = jnp.log(stop)
-    value_linear = jnp.log(value)
-
-    # Calculate coordinate in linear space
-    coordinate_in_linear_space = get_linspace_coordinate(
-        value_linear,
-        start_linear,
-        stop_linear,
-        n_points,
-    )
-
-    # Calculate rank of lower and upper point in logarithmic space
-    rank_lower_gridpoint = jnp.floor(coordinate_in_linear_space)
-    rank_upper_gridpoint = rank_lower_gridpoint + 1
-
-    # Calculate lower and upper point in logarithmic space
-    step_length_linear = (stop_linear - start_linear) / (n_points - 1)
-    lower_gridpoint = jnp.exp(start_linear + step_length_linear * rank_lower_gridpoint)
-    upper_gridpoint = jnp.exp(start_linear + step_length_linear * rank_upper_gridpoint)
-
-    # Calculate the decimal part of coordinate
-    logarithmic_step_size_at_coordinate = upper_gridpoint - lower_gridpoint
-    distance_from_lower_gridpoint = value - lower_gridpoint
-
-    # If the distance from the lower gridpoint is zero, the coordinate corresponds to
-    # the rank of the lower gridpoint. The other extreme is when the distance is equal
-    # to the logarithmic step size at the coordinate, in which case the coordinate
-    # corresponds to the rank of the upper gridpoint. For values in between, the
-    # coordinate lies on a linear scale between the ranks of the lower and upper
-    # gridpoints.
-    decimal_part = distance_from_lower_gridpoint / logarithmic_step_size_at_coordinate
-    return rank_lower_gridpoint + decimal_part
+) -> list[str]:
+    """Validate the continuous grid parameters.
+
+    Args:
+        start: The start value of the grid.
+        stop: The stop value of the grid.
+        n_points: The number of points in the grid.
+
+    Returns:
+        list[str]: A list of error messages.
+
+    """
+    error_messages = []
+
+    valid_start_type = isinstance(start, int | float)
+    if not valid_start_type:
+        error_messages.append("start must be a scalar int or float value")
+
+    valid_stop_type = isinstance(stop, int | float)
+    if not valid_stop_type:
+        error_messages.append("stop must be a scalar int or float value")
+
+    if not isinstance(n_points, int) or n_points < 1:
+        error_messages.append(
+            f"n_points must be an int greater than 0 but is {n_points}",
+        )
+
+    if valid_start_type and valid_stop_type and start >= stop:
+        error_messages.append("start must be less than stop")
+
+    return error_messages
diff --git a/src/lcm/input_processing/__init__.py b/src/lcm/input_processing/__init__.py
new file mode 100644
index 00000000..849cbdc9
--- /dev/null
+++ b/src/lcm/input_processing/__init__.py
@@ -0,0 +1,3 @@
+from .process_model import process_model
+
+__all__ = ["process_model"]
diff --git a/src/lcm/create_params_template.py b/src/lcm/input_processing/create_params_template.py
similarity index 51%
rename from src/lcm/create_params_template.py
rename to src/lcm/input_processing/create_params_template.py
index 591fd742..7c550528 100644
--- a/src/lcm/create_params_template.py
+++ b/src/lcm/input_processing/create_params_template.py
@@ -1,57 +1,52 @@
 """Create a parameter template for a model specification."""
 
 import inspect
-from typing import Any
 
 import jax.numpy as jnp
 import pandas as pd
 from jax import Array
 
+from lcm.input_processing.util import get_grids, get_variable_info
+from lcm.typing import ParamsDict
+from lcm.user_model import Model
+
 
 def create_params_template(
-    model_spec: dict[str, Any],
-    variable_info: pd.DataFrame,
-    grids: dict[str, Array],
-    default_params: dict[str, float] | None = None,
-) -> dict[str, dict[str, Any] | float]:
+    model: Model,
+    default_params: dict[str, float] = {"beta": jnp.nan},  # noqa: B006
+) -> ParamsDict:
     """Create parameter template from a model specification.
 
     Args:
-        model_spec (dict): A model specification provided by the user. Has keys
-            - "functions": A dictionary of functions used in the model.
-            - "choices": A dictionary of choice variables.
-            - "states": A dictionary of state variables.
-            - "n_periods": Number of periods in the model (int).
-            - "shocks": A dictionary of shock variables (optional).
-        variable_info (pd.DataFrame): A dataframe with information about the variables.
-        grids (dict): A dictionary of grids consistent with model_spec..
-        default_params (dict): A dictionary of default parameters. Default is None. If
-            None, the default {"beta": np.nan} is used. For other lifetime reward
-            objectives, additional parameters may be required, for example {"beta":
-            np.nan, "delta": np.nan} for beta-delta discounting.
+        model: The model as provided by the user.
+        default_params: A dictionary of default parameters. Default is None. If None,
+            the default {"beta": np.nan} is used. For other lifetime reward objectives,
+            additional parameters may be required, for example {"beta": np.nan, "delta":
+            np.nan} for beta-delta discounting.
 
     Returns:
-        dict: A nested dictionary of model parameters.
+        A nested dictionary of model parameters.
 
     """
-    if default_params is None:
-        # The default lifetime reward objective in LCM is expected discounted utility.
-        # For this objective the only additional parameter is the discounting rate beta.
-        default_params = {"beta": jnp.nan}
-
-    params_template = default_params | _create_function_params(model_spec)
+    variable_info = get_variable_info(model)
+    grids = get_grids(model)
 
     if variable_info["is_stochastic"].any():
-        params_template["shocks"] = _create_stochastic_transition_params(
-            model_spec=model_spec,
+        stochastic_transitions = _create_stochastic_transition_params(
+            model=model,
             variable_info=variable_info,
             grids=grids,
         )
+        stochastic_transition_params = {"shocks": stochastic_transitions}
+    else:
+        stochastic_transition_params = {}
+
+    function_params = _create_function_params(model)
 
-    return params_template
+    return default_params | function_params | stochastic_transition_params
 
 
-def _create_function_params(model_spec: dict[str, Any]) -> dict[str, dict[str, Any]]:
+def _create_function_params(model: Model) -> dict[str, dict[str, float]]:
     """Get function parameters from a model specification.
 
     Explanation: We consider the arguments of all model functions, from which we exclude
@@ -60,34 +55,29 @@ def _create_function_params(model_spec: dict[str, Any]) -> dict[str, dict[str, A
     user.
 
     Args:
-        model_spec (dict): A model specification provided by the user. Has keys
-            - "functions": A dictionary of functions used in the model.
-            - "choices": A dictionary of choice variables.
-            - "states": A dictionary of state variables.
-            - "n_periods": Number of periods in the model (int).
-            - "shocks": A dictionary of shock variables (optional).
+        model: The model as provided by the user.
 
     Returns:
-        dict: A dictionary for each model function, containing a parameters required in
-            the model functions, initialized with jnp.nan.
+        A dictionary for each model function, containing a parameters required in the
+            model functions, initialized with jnp.nan.
 
     """
     # Collect all model variables, that includes choices, states, the period, and
     # auxiliary variables (model function names).
     variables = {
-        *model_spec["functions"],
-        *model_spec["choices"],
-        *model_spec["states"],
+        *model.functions,
+        *model.choices,
+        *model.states,
         "_period",
     }
 
-    if "shocks" in model_spec:
-        variables = variables | set(model_spec["shocks"])
+    if hasattr(model, "shocks"):
+        variables = variables | set(model.shocks)
 
     function_params = {}
     # For each model function, capture the arguments of the function that are not in the
     # set of model variables, and initialize them.
-    for name, func in model_spec["functions"].items():
+    for name, func in model.functions.items():
         arguments = set(inspect.signature(func).parameters)
         params = sorted(arguments.difference(variables))
         function_params[name] = {p: jnp.nan for p in params}
@@ -96,25 +86,20 @@ def _create_function_params(model_spec: dict[str, Any]) -> dict[str, dict[str, A
 
 
 def _create_stochastic_transition_params(
-    model_spec: dict[str, Any],
+    model: Model,
     variable_info: pd.DataFrame,
     grids: dict[str, Array],
 ) -> dict[str, Array]:
     """Create parameters for stochastic transitions.
 
     Args:
-        model_spec (dict): A model specification provided by the user. Has keys
-            - "functions": A dictionary of functions used in the model.
-            - "choices": A dictionary of choice variables.
-            - "states": A dictionary of state variables.
-            - "n_periods": Number of periods in the model (int).
-            - "shocks": A dictionary of shock variables (optional).
-        variable_info (pd.DataFrame): A dataframe with information about the variables.
-        grids (dict): A dictionary of grids consistent with model_spec.
+        model: The model as provided by the user.
+        variable_info: A dataframe with information about the variables.
+        grids: A dictionary of grids consistent with model.
 
     Returns:
-        dict: A dictionary of parameters required for stochastic transitions,
-            initialized with jnp.nan matrices of the correct dimensions.
+        A dictionary of parameters required for stochastic transitions, initialized with
+            jnp.nan matrices of the correct dimensions.
 
     """
     stochastic_variables = variable_info.query("is_stochastic").index.tolist()
@@ -123,7 +108,8 @@ def _create_stochastic_transition_params(
     # ==================================================================================
     discrete_state_vars = set(variable_info.query("is_state & is_discrete").index)
 
-    if invalid := set(stochastic_variables) - discrete_state_vars:
+    invalid = set(stochastic_variables) - discrete_state_vars
+    if invalid:
         raise ValueError(
             f"The following variables are stochastic, but are not discrete state "
             f"variables: {invalid}. This is currently not supported.",
@@ -139,19 +125,19 @@ def _create_stochastic_transition_params(
     invalid_dependencies = {}
 
     for var in stochastic_variables:
-
         # Retrieve corresponding next function and its arguments
-        next_var = model_spec["functions"][f"next_{var}"]
+        next_var = model.functions[f"next_{var}"]
         dependencies = list(inspect.signature(next_var).parameters)
 
         # If there are invalid dependencies, store them in a dictionary and continue
         # with the next variable to collect as many invalid arguments as possible.
-        if invalid := set(dependencies) - valid_vars:
+        invalid = set(dependencies) - valid_vars
+        if invalid:
             invalid_dependencies[var] = invalid
         else:
             # Get the dimensions of variables that influence the stochastic variable
             dimensions_of_deps = [
-                len(grids[arg]) if arg != "_period" else model_spec["n_periods"]
+                len(grids[arg]) if arg != "_period" else model.n_periods
                 for arg in dependencies
             ]
             # Add the dimension of the stochastic variable itself at the end
diff --git a/src/lcm/input_processing/process_model.py b/src/lcm/input_processing/process_model.py
new file mode 100644
index 00000000..179c6268
--- /dev/null
+++ b/src/lcm/input_processing/process_model.py
@@ -0,0 +1,241 @@
+import functools
+import inspect
+from collections.abc import Callable
+from copy import deepcopy
+
+import pandas as pd
+from dags.signature import with_signature
+from jax import Array
+
+from lcm.functools import all_as_args, all_as_kwargs
+from lcm.input_processing.create_params_template import create_params_template
+from lcm.input_processing.util import (
+    get_function_info,
+    get_grids,
+    get_gridspecs,
+    get_variable_info,
+)
+from lcm.interfaces import InternalModel
+from lcm.typing import ParamsDict, ShockType
+from lcm.user_model import Model
+
+
+def process_model(model: Model) -> InternalModel:
+    """Process the user model.
+
+    This entails the following steps:
+
+    - Set defaults where needed
+    - Generate derived information
+    - Check that the model specification is valid.
+
+    Args:
+        model: The model as provided by the user.
+
+    Returns:
+        The processed model.
+
+    """
+    params = create_params_template(model)
+
+    return InternalModel(
+        grids=get_grids(model),
+        gridspecs=get_gridspecs(model),
+        variable_info=get_variable_info(model),
+        functions=_get_internal_functions(model, params=params),
+        function_info=get_function_info(model),
+        params=params,
+        # currently no additive utility shocks are supported
+        random_utility_shocks=ShockType.NONE,
+        n_periods=model.n_periods,
+    )
+
+
+def _get_internal_functions(
+    model: Model,
+    params: ParamsDict,
+) -> dict[str, Callable]:
+    """Process the user provided model functions.
+
+    Args:
+        model: The model as provided by the user.
+        params: The parameters of the model.
+
+    Returns:
+        dict: Dictionary containing all functions of the model. The keys are
+            the names of the functions. The values are the processed functions.
+            The main difference between processed and unprocessed functions is that
+            processed functions take `params` as argument unless they are filter
+            functions.
+
+    """
+    variable_info = get_variable_info(model)
+    grids = get_grids(model)
+    function_info = get_function_info(model)
+
+    raw_functions = deepcopy(model.functions)
+
+    for var in model.states:
+        if variable_info.loc[var, "is_stochastic"]:
+            raw_functions[f"next_{var}"] = _get_stochastic_next_function(
+                raw_func=raw_functions[f"next_{var}"],
+                grid=grids[var],
+            )
+
+            raw_functions[f"weight_next_{var}"] = _get_stochastic_weight_function(
+                raw_func=raw_functions[f"next_{var}"],
+                name=var,
+                variable_info=variable_info,
+            )
+
+    # ==================================================================================
+    # Add 'params' argument to functions
+    # ==================================================================================
+    # We wrap the user functions such that they can be called with the 'params' argument
+    # instead of the individual parameters. This is done for all functions except for
+    # filter functions, because they cannot depend on model parameters; and dynamically
+    # generated weighting functions for stochastic next functions, since they are
+    # constructed to accept the 'params' argument by default.
+
+    functions = {}
+    for name, func in raw_functions.items():
+        is_weight_next_function = name.startswith("weight_next_")
+
+        if is_weight_next_function:
+            processed_func = func
+
+        else:
+            is_filter_function = function_info.loc[name, "is_filter"]
+            # params[name] contains the dictionary of parameters for the function, which
+            # is empty if the function does not depend on any model parameters.
+            depends_on_params = bool(params[name])
+
+            if is_filter_function:
+                if params.get(name, False):
+                    raise ValueError(
+                        f"filters cannot depend on model parameters, but {name} does."
+                    )
+                processed_func = func
+
+            elif depends_on_params:
+                processed_func = _replace_func_parameters_by_params(
+                    func=func,
+                    params=params,
+                    name=name,
+                )
+
+            else:
+                processed_func = _add_dummy_params_argument(func)
+
+        functions[name] = processed_func
+
+    return functions
+
+
+def _replace_func_parameters_by_params(
+    func: Callable, params: ParamsDict, name: str
+) -> Callable:
+    old_signature = list(inspect.signature(func).parameters)
+    new_kwargs = [
+        p
+        for p in old_signature
+        if p not in params[name]  # type: ignore[operator]
+    ] + ["params"]
+
+    @with_signature(args=new_kwargs)
+    @functools.wraps(func)
+    def processed_func(*args, **kwargs):
+        kwargs = all_as_kwargs(args, kwargs, arg_names=new_kwargs)
+        _kwargs = {k: v for k, v in kwargs.items() if k in new_kwargs and k != "params"}
+        return func(**_kwargs, **kwargs["params"][name])
+
+    return processed_func
+
+
+def _add_dummy_params_argument(func: Callable) -> Callable:
+    old_signature = list(inspect.signature(func).parameters)
+
+    new_kwargs = [*old_signature, "params"]
+
+    @with_signature(args=new_kwargs)
+    @functools.wraps(func)
+    def processed_func(*args, **kwargs):
+        kwargs = all_as_kwargs(args, kwargs, arg_names=new_kwargs)
+        _kwargs = {k: v for k, v in kwargs.items() if k != "params"}
+        return func(**_kwargs)
+
+    return processed_func
+
+
+def _get_stochastic_next_function(raw_func: Callable, grid: Array):
+    @functools.wraps(raw_func)
+    def next_func(*args, **kwargs):  # noqa: ARG001
+        return grid
+
+    return next_func
+
+
+def _get_stochastic_weight_function(
+    raw_func: Callable, name: str, variable_info: pd.DataFrame
+):
+    """Get a function that returns the transition weights of a stochastic variable.
+
+    Example:
+    Consider a stochastic variable 'health' that takes two values {0, 1}. The transition
+    matrix is thus 2x2. We create the weighting function and then select the weights
+    that correspond to the case where 'health' is 0.
+
+    >>> from lcm.mark import StochasticInfo
+    >>> def next_health(health):
+    >>>     pass
+    >>> next_health._stochastic_info = StochasticInfo()
+    >>> params = {"shocks": {"health": np.arange(4).reshape(2, 2)}}
+    >>> weight_func = _get_stochastic_weight_function(
+    >>>     raw_func=next_health,
+    >>>     name="health"
+    >>>     variable_info=variable_info,
+    >>>     grids=grids,
+    >>> )
+    >>> weight_func(health=0, params=params)
+    >>> array([0, 1])
+
+
+    Args:
+        raw_func: The raw next function of the stochastic variable.
+        name: The name of the stochastic variable.
+        variable_info: A table with information about model variables.
+
+    Returns:
+        callable: A function that returns the transition weights of the stochastic
+            variable.
+
+    """
+    function_parameters = list(inspect.signature(raw_func).parameters)
+
+    # Assert that stochastic next function only depends on discrete variables or period
+    invalid = {
+        arg
+        for arg in function_parameters
+        if arg != "_period" and not variable_info.loc[arg, "is_discrete"]
+    }
+
+    if invalid:
+        raise ValueError(
+            "Stochastic variables can only depend on discrete variables and '_period', "
+            f"but {name} depends on {invalid}.",
+        )
+
+    new_kwargs = [*function_parameters, "params"]
+
+    @with_signature(args=new_kwargs)
+    def weight_func(*args, **kwargs):
+        args = all_as_args(args, kwargs, arg_names=new_kwargs)
+        params = args[-1]  # by definition of new_kargs, params is the last argument
+        # By assumption, all discrete variables that LCM handles internally are
+        # themselves indices (i.e. a range starting at 0 with a step size of 1).
+        # Therefore, the arguments themselves are the indices. For the special variable
+        # '_period' the same holds.
+        indices = args[:-1]
+        return params["shocks"][name][*indices]
+
+    return weight_func
diff --git a/src/lcm/input_processing/util.py b/src/lcm/input_processing/util.py
new file mode 100644
index 00000000..70a1a304
--- /dev/null
+++ b/src/lcm/input_processing/util.py
@@ -0,0 +1,165 @@
+from collections.abc import Callable
+
+import pandas as pd
+from dags import get_ancestors
+
+from lcm.grids import ContinuousGrid, Grid
+from lcm.typing import Array
+from lcm.user_model import Model
+
+
+def get_function_info(model: Model) -> pd.DataFrame:
+    """Derive information about functions in the model.
+
+    Args:
+        model: The model as provided by the user.
+
+    Returns:
+        pd.DataFrame: A table with information about all functions in the model. The
+            index contains the name of a model function. The columns are booleans that
+            are True if the function has the corresponding property. The columns are:
+            is_next, is_stochastic_next, is_filter, is_constraint.
+
+    """
+    info = pd.DataFrame(index=list(model.functions))
+    info["is_filter"] = info.index.str.endswith("_filter")
+    info["is_constraint"] = info.index.str.endswith("_constraint")
+    info["is_next"] = (
+        info.index.str.startswith("next_") & ~info["is_constraint"] & ~info["is_filter"]
+    )
+    info["is_stochastic_next"] = [
+        hasattr(func, "_stochastic_info") for func in model.functions.values()
+    ]
+    return info
+
+
+def get_variable_info(model: Model) -> pd.DataFrame:
+    """Derive information about all variables in the model.
+
+    Args:
+        model: The model as provided by the user.
+
+    Returns:
+        pd.DataFrame: A table with information about all variables in the model. The
+            index contains the name of a model variable. The columns are booleans that
+            are True if the variable has the corresponding property. The columns are:
+            is_state, is_choice, is_continuous, is_discrete, is_sparse, is_dense.
+
+    """
+    function_info = get_function_info(model)
+
+    variables = model.states | model.choices
+
+    info = pd.DataFrame(index=list(variables))
+
+    info["is_state"] = info.index.isin(model.states)
+    info["is_choice"] = ~info["is_state"]
+
+    info["is_continuous"] = [
+        isinstance(spec, ContinuousGrid) for spec in variables.values()
+    ]
+    info["is_discrete"] = ~info["is_continuous"]
+
+    info["is_stochastic"] = [
+        (var in model.states and function_info.loc[f"next_{var}", "is_stochastic_next"])
+        for var in variables
+    ]
+
+    auxiliary_variables = _get_auxiliary_variables(
+        state_variables=info.query("is_state").index.tolist(),
+        function_info=function_info,
+        user_functions=model.functions,
+    )
+    info["is_auxiliary"] = [var in auxiliary_variables for var in variables]
+
+    filter_names = function_info.query("is_filter").index.tolist()
+    filtered_variables: set[str] = set()
+    for name in filter_names:
+        filtered_variables.update(get_ancestors(model.functions, name))
+
+    info["is_sparse"] = [var in filtered_variables for var in variables]
+    info["is_dense"] = ~info["is_sparse"]
+
+    order = info.query("is_sparse & is_state").index.tolist()
+    order += info.query("is_sparse & is_choice").index.tolist()
+    order += info.query("is_dense & is_discrete & is_state").index.tolist()
+    order += info.query("is_dense & is_discrete & is_choice").index.tolist()
+    order += info.query("is_dense & is_continuous & is_state").index.tolist()
+    order += info.query("is_dense & is_continuous & is_choice").index.tolist()
+
+    if set(order) != set(info.index):
+        raise ValueError("Order and index do not match.")
+
+    return info.loc[order]
+
+
+def _get_auxiliary_variables(
+    state_variables: list[str],
+    function_info: pd.DataFrame,
+    user_functions: dict[str, Callable],
+) -> list[str]:
+    """Get state variables that only occur in next functions.
+
+    Args:
+        state_variables: List of state variable names.
+        function_info: A table with information about all
+            functions in the model. The index contains the name of a function. The
+            columns are booleans that are True if the function has the corresponding
+            property. The columns are: is_filter, is_constraint, is_next.
+        user_functions: Dictionary that maps names of functions to functions.
+
+    Returns:
+        list[str]: List of state variable names that are only used in next functions.
+
+    """
+    non_next_functions = function_info.query("~is_next").index.tolist()
+    user_functions = {name: user_functions[name] for name in non_next_functions}
+    ancestors = get_ancestors(
+        user_functions,
+        targets=list(user_functions),
+        include_targets=True,
+    )
+    return list(set(state_variables).difference(set(ancestors)))
+
+
+def get_gridspecs(
+    model: Model,
+) -> dict[str, Grid]:
+    """Create a dictionary of grid specifications for each variable in the model.
+
+    Args:
+        model (dict): The model as provided by the user.
+
+    Returns:
+        dict: Dictionary containing all variables of the model. The keys are
+            the names of the variables. The values describe which values the variable
+            can take. For discrete variables these are the options. For continuous
+            variables this is information about how to build the grids.
+
+    """
+    variable_info = get_variable_info(model)
+
+    raw_variables = model.states | model.choices
+    order = variable_info.index.tolist()
+    return {k: raw_variables[k] for k in order}
+
+
+def get_grids(
+    model: Model,
+) -> dict[str, Array]:
+    """Create a dictionary of array grids for each variable in the model.
+
+    Args:
+        model: The model as provided by the user.
+
+    Returns:
+        dict: Dictionary containing all variables of the model. The keys are
+            the names of the variables. The values are the grids.
+
+    """
+    variable_info = get_variable_info(model)
+    gridspecs = get_gridspecs(model)
+
+    grids = {name: spec.to_jax() for name, spec in gridspecs.items()}
+    order = variable_info.index.tolist()
+    return {k: grids[k] for k in order}
diff --git a/src/lcm/interfaces.py b/src/lcm/interfaces.py
index 090f1930..9e71d810 100644
--- a/src/lcm/interfaces.py
+++ b/src/lcm/interfaces.py
@@ -1,12 +1,15 @@
-from typing import Literal, NamedTuple
+from collections.abc import Callable
+from dataclasses import dataclass
 
 import pandas as pd
 from jax import Array
 
-from lcm.typing import DiscreteLabels, Scalar
+from lcm.grids import ContinuousGrid, DiscreteGrid, Grid
+from lcm.typing import ParamsDict, ShockType
 
 
-class IndexerInfo(NamedTuple):
+@dataclass(frozen=True)
+class IndexerInfo:
     """Information needed to work with an indexer array.
 
     In particular, this contains enough information to wrap an indexer array into a
@@ -27,47 +30,8 @@ class IndexerInfo(NamedTuple):
     out_name: str
 
 
-class ContinuousGridInfo(NamedTuple):
-    """Information on how to build a grid for a continuous variable.
-
-    Attributes:
-        start (Scalar): Start of the grid.
-        stop (Scalar): End of the grid.
-        n_points (int): Number of points in the grid.
-
-    """
-
-    start: Scalar
-    stop: Scalar
-    n_points: int
-
-
-ContinuousGridType = Literal["linspace", "logspace"]
-
-
-class ContinuousGridSpec(NamedTuple):
-    """Specification of a grid for continuous variables.
-
-    Contains all information necessary to build and work with a grid of a continuous
-    variable.
-
-
-    Attributes:
-        kind (ContinuousGridType): Name of a grid type implemented in lcm.grids.
-        info (ContinuousGridInfo): Information on how to build the grid. E.g., start,
-            stop, and n_points.
-
-    """
-
-    kind: ContinuousGridType
-    info: ContinuousGridInfo
-
-
-DiscreteGridSpec = Array
-GridSpec = ContinuousGridSpec | DiscreteGridSpec
-
-
-class Space(NamedTuple):
+@dataclass(frozen=True)
+class Space:
     """Everything needed to evaluate a function on a space (e.g. state space).
 
     Attributes:
@@ -79,11 +43,12 @@ class Space(NamedTuple):
 
     """
 
-    sparse_vars: dict
-    dense_vars: dict
+    sparse_vars: dict[str, Array]
+    dense_vars: dict[str, Array]
 
 
-class SpaceInfo(NamedTuple):
+@dataclass(frozen=True)
+class SpaceInfo:
     """Everything needed to work with the output of a function evaluated on a space.
 
     Attributes:
@@ -98,46 +63,47 @@ class SpaceInfo(NamedTuple):
     """
 
     axis_names: list[str]
-    lookup_info: dict[str, DiscreteLabels]
-    interpolation_info: dict[str, ContinuousGridSpec]
+    lookup_info: dict[str, DiscreteGrid]
+    interpolation_info: dict[str, ContinuousGrid]
     indexer_infos: list[IndexerInfo]
 
 
-class Model(NamedTuple):
+@dataclass(frozen=True)
+class InternalModel:
     """Internal representation of a user model.
 
     Attributes:
-        grids (dict): Dictionary that maps names of model variables to grids of feasible
-            values for that variable.
-        gridspecs (dict): Dictionary that maps names of model variables to
-            specifications from which grids of feasible values can be built.
-        variable_info (pd.DataFrame): A table with information about all variables in
-            the model. The index contains the name of a model variable. The columns are
-            booleans that are True if the variable has the corresponding property. The
-            columns are: is_state, is_choice, is_continuous, is_discrete, is_sparse,
-            is_dense.
-        functions (dict): Dictionary that maps names of functions to functions. The
-            functions differ from the user functions in that they all except the
-            filter functions take ``params`` as keyword argument. If the original
-            function depended on model parameters, those are automatically extracted
-            from ``params`` and passed to the original function. Otherwise, the
-            ``params`` argument is simply ignored.
-        function_info (pd.DataFrame): A table with information about all functions in
-            the model. The index contains the name of a function. The columns are
-            booleans that are True if the function has the corresponding property. The
-            columns are: is_filter, is_constraint, is_next.
-        params (dict): Dict of model parameters.
-        shocks (dict): TODO
-        n_periods (int): TODO
+        grids: Dictionary that maps names of model variables to grids of feasible values
+            for that variable.
+        gridspecs: Dictionary that maps names of model variables to specifications from
+            which grids of feasible values can be built.
+        variable_info: A table with information about all variables in the model. The
+            index contains the name of a model variable. The columns are booleans that
+            are True if the variable has the corresponding property. The columns are:
+            is_state, is_choice, is_continuous, is_discrete, is_sparse, columns are:
+            is_state, is_choice, is_continuous, is_discrete, is_sparse, is_dense.
+        functions: Dictionary that maps names of functions to functions. The functions
+            differ from the user functions in that they all except the filter functions
+            take ``params`` as keyword argument. If the original function depended on
+            model parameters, those are automatically extracted from ``params`` and
+            passed to the original function. Otherwise, the ``params`` argument is
+            simply ignored.
+        function_info: A table with information about all functions in the model. The
+            index contains the name of a function. The columns are booleans that are
+            True if the function has the corresponding property. The columns are:
+            is_filter, is_constraint, is_next.
+        params: Dict of model parameters.
+        n_periods: Number of periods.
+        random_utility_shocks: Type of random utility shocks.
 
     """
 
-    grids: dict
-    gridspecs: dict
+    grids: dict[str, Array]
+    gridspecs: dict[str, Grid]
     variable_info: pd.DataFrame
-    functions: dict
+    functions: dict[str, Callable]
     function_info: pd.DataFrame
-    params: dict
-    # not really processed yet
-    shocks: dict
+    params: ParamsDict
     n_periods: int
+    # Not properly processed yet
+    random_utility_shocks: ShockType
diff --git a/src/lcm/mark.py b/src/lcm/mark.py
index 8e5574ae..854e49a4 100644
--- a/src/lcm/mark.py
+++ b/src/lcm/mark.py
@@ -1,10 +1,11 @@
 """Collection of LCM marking decorators."""
 
 import functools
-from typing import NamedTuple
+from dataclasses import dataclass
 
 
-class StochasticInfo(NamedTuple):
+@dataclass(frozen=True)
+class StochasticInfo:
     """Information on the stochastic nature of user provided functions."""
 
 
diff --git a/src/lcm/model_functions.py b/src/lcm/model_functions.py
index 2146a912..a744ec34 100644
--- a/src/lcm/model_functions.py
+++ b/src/lcm/model_functions.py
@@ -11,11 +11,12 @@
     all_as_kwargs,
     get_union_of_arguments,
 )
+from lcm.interfaces import InternalModel
 from lcm.next_state import get_next_state_function
 
 
 def get_utility_and_feasibility_function(
-    model,
+    model: InternalModel,
     space_info,
     name_of_values_on_grid,
     interpolation_options,
@@ -66,7 +67,7 @@ def get_utility_and_feasibility_function(
     # ==================================================================================
 
     arg_names = {"vf_arr"} | get_union_of_arguments(relevant_functions) - {"_period"}
-    arg_names = [arg for arg in arg_names if "next_" not in arg]
+    arg_names = [arg for arg in arg_names if "next_" not in arg]  # type: ignore[assignment]
 
     if is_last_period:
 
@@ -153,11 +154,11 @@ def _outer(*args, **kwargs):
     return productmap(_outer, variables=arg_names)
 
 
-def get_combined_constraint(model):
+def get_combined_constraint(model: InternalModel):
     """Create a function that combines all constraint functions into a single one.
 
     Args:
-        model (Model): The model object.
+        model: The internal model object.
 
     Returns:
         callable
@@ -179,7 +180,7 @@ def combined_constraint():
     return combined_constraint
 
 
-def get_current_u_and_f(model):
+def get_current_u_and_f(model: InternalModel):
     functions = {"feasibility": get_combined_constraint(model), **model.functions}
 
     return concatenate_functions(
@@ -189,7 +190,7 @@ def get_current_u_and_f(model):
     )
 
 
-def get_next_weights_function(model):
+def get_next_weights_function(model: InternalModel):
     targets = [
         f"weight_{name}"
         for name in model.function_info.query("is_stochastic_next").index.tolist()
diff --git a/src/lcm/next_state.py b/src/lcm/next_state.py
index 220cb77a..30aa410f 100644
--- a/src/lcm/next_state.py
+++ b/src/lcm/next_state.py
@@ -11,10 +11,11 @@
 from dags.signature import with_signature
 
 from lcm.functools import all_as_args
+from lcm.interfaces import InternalModel
 from lcm.random_choice import random_choice
 
 
-def get_next_state_function(model, target):
+def get_next_state_function(model: InternalModel, target):
     if target == "solve":
         out = _get_next_state_function_solution(model)
     elif target == "simulate":
@@ -30,11 +31,11 @@ def get_next_state_function(model, target):
 # ======================================================================================
 
 
-def _get_next_state_function_solution(model):
+def _get_next_state_function_solution(model: InternalModel):
     """Get function that computes the next states for the solution.
 
     Args:
-        model (Model): Model instance.
+        model: Model instance.
 
     Returns:
         callable: Function that computes the next states. Depends on states and choices
@@ -56,11 +57,11 @@ def _get_next_state_function_solution(model):
 # ======================================================================================
 
 
-def _get_next_state_function_simulation(model):
+def _get_next_state_function_simulation(model: InternalModel):
     """Get function that computes the next states for the simulation.
 
     Args:
-        model (Model): Model instance.
+        model: Model instance.
 
     Returns:
         callable: Function that computes the next states. Depends on states and choices
diff --git a/src/lcm/process_model.py b/src/lcm/process_model.py
deleted file mode 100644
index 20fd1511..00000000
--- a/src/lcm/process_model.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import functools
-import inspect
-from copy import deepcopy
-
-import jax.numpy as jnp
-import pandas as pd
-from dags import get_ancestors
-from dags.signature import with_signature
-from jax import Array
-
-import lcm.grids as grids_module
-from lcm.create_params_template import create_params_template
-from lcm.functools import all_as_args, all_as_kwargs
-from lcm.interfaces import ContinuousGridInfo, ContinuousGridSpec, GridSpec, Model
-
-
-def process_model(user_model):
-    """Process the user model.
-
-    This entails the following steps:
-
-    - Set defaults where needed
-    - Generate derived information
-    - Check that the model specification is valid.
-
-    Args:
-        user_model (dict): The model as provided by the user.
-
-    Returns:
-        lcm.interfaces.Model: The processed model.
-
-    """
-    _function_info = _get_function_info(user_model)
-    _variable_info = _get_variable_info(
-        user_model,
-        function_info=_function_info,
-    )
-    _gridspecs = _get_gridspecs(user_model, variable_info=_variable_info)
-    _grids = _get_grids(gridspecs=_gridspecs, variable_info=_variable_info)
-
-    _params = create_params_template(
-        user_model,
-        variable_info=_variable_info,
-        grids=_grids,
-    )
-
-    _functions = _get_functions(
-        user_model,
-        function_info=_function_info,
-        variable_info=_variable_info,
-        params=_params,
-        grids=_grids,
-    )
-    return Model(
-        grids=_grids,
-        gridspecs=_gridspecs,
-        variable_info=_variable_info,
-        functions=_functions,
-        function_info=_function_info,
-        params=_params,
-        shocks=user_model.get("shocks", {}),
-        n_periods=user_model["n_periods"],
-    )
-
-
-def _get_variable_info(user_model, function_info):
-    """Derive information about all variables in the model.
-
-    Args:
-        user_model (dict): The model as provided by the user.
-        function_info (pandas.DataFrame): A table with information about all
-            functions in the model. The index contains the name of a function. The
-            columns are booleans that are True if the function has the corresponding
-            property. The columns are: is_filter, is_constraint, is_next.
-
-    Returns:
-        pandas.DataFrame: A table with information about all variables in the model.
-            The index contains the name of a model variable. The columns are booleans
-            that are True if the variable has the corresponding property. The columns
-            are: is_state, is_choice, is_continuous, is_discrete, is_sparse, is_dense.
-
-    """
-    _variables = {
-        **user_model["states"],
-        **user_model["choices"],
-    }
-
-    info = pd.DataFrame(index=list(_variables))
-
-    info["is_state"] = info.index.isin(user_model["states"])
-    info["is_choice"] = ~info["is_state"]
-
-    info["is_discrete"] = ["options" in spec for spec in _variables.values()]
-    info["is_continuous"] = ~info["is_discrete"]
-
-    info["is_stochastic"] = [
-        (
-            var in user_model["states"]
-            and function_info.loc[f"next_{var}", "is_stochastic_next"]
-        )
-        for var in _variables
-    ]
-
-    _auxiliary_variables = _get_auxiliary_variables(
-        state_variables=info.query("is_state").index.tolist(),
-        function_info=function_info,
-        user_functions=user_model["functions"],
-    )
-    info["is_auxiliary"] = [var in _auxiliary_variables for var in _variables]
-
-    _filtered_variables = set()
-    _filter_names = function_info.query("is_filter").index.tolist()
-
-    for name in _filter_names:
-        _filtered_variables = _filtered_variables.union(
-            get_ancestors(user_model["functions"], name),
-        )
-
-    info["is_sparse"] = [var in _filtered_variables for var in _variables]
-    info["is_dense"] = ~info["is_sparse"]
-
-    order = info.query("is_sparse & is_state").index.tolist()
-    order += info.query("is_sparse & is_choice").index.tolist()
-    order += info.query("is_dense & is_discrete & is_state").index.tolist()
-    order += info.query("is_dense & is_discrete & is_choice").index.tolist()
-    order += info.query("is_dense & is_continuous & is_state").index.tolist()
-    order += info.query("is_dense & is_continuous & is_choice").index.tolist()
-
-    if set(order) != set(info.index):
-        raise ValueError("Order and index do not match.")
-
-    return info.loc[order]
-
-
-def _get_auxiliary_variables(state_variables, function_info, user_functions):
-    """Get state variables that only occur in next functions.
-
-    Args:
-        state_variables (list): List of state variable names.
-        function_info (pandas.DataFrame): A table with information about all
-            functions in the model. The index contains the name of a function. The
-            columns are booleans that are True if the function has the corresponding
-            property. The columns are: is_filter, is_constraint, is_next.
-        user_functions (dict): Dictionary that maps names of functions to functions.
-
-    Returns:
-        list: List of state variable names that are only used in next functions.
-
-    """
-    non_next_functions = function_info.query("~is_next").index.tolist()
-    user_functions = {name: user_functions[name] for name in non_next_functions}
-    ancestors = get_ancestors(
-        user_functions,
-        targets=list(user_functions),
-        include_targets=True,
-    )
-    return list(set(state_variables).difference(set(ancestors)))
-
-
-def _get_gridspecs(user_model, variable_info):
-    """Create a dictionary of grid specifications for each variable in the model.
-
-    Args:
-        user_model (dict): The model as provided by the user.
-        variable_info (pandas.DataFrame): A table with information about all
-            variables in the model. The index contains the name of a model variable.
-            The columns are booleans that are True if the variable has the
-            corresponding property. The columns are: is_state, is_choice, is_continuous,
-            is_discrete, is_sparse, is_dense.
-
-    Returns:
-        dict: Dictionary containing all variables of the model. The keys are
-            the names of the variables. The values describe which values the variable
-            can take. For discrete variables these are the options. For continuous
-            variables this is information about how to build the grids.
-
-    """
-    raw = {**user_model["states"], **user_model["choices"]}
-
-    errors = {}
-    variables = {}
-    for name, spec in raw.items():
-        if "options" in spec:
-            options = spec["options"]
-            if options != list(range(len(options))):
-                errors[name] = options
-            variables[name] = options
-        else:
-            grid_info = {k: v for k, v in spec.items() if k != "grid_type"}
-            variables[name] = ContinuousGridSpec(
-                kind=spec["grid_type"],
-                info=ContinuousGridInfo(**grid_info),
-            )
-
-    if errors:
-        example = next(iter(errors))  # access the first key of the dictionary
-        msg = (
-            "The following variables have discrete options that are not an integer "
-            f"range from 0 to n: [{', '.join(errors)}].\nPlease rewrite (for example) "
-            f"{{'{example}': {{'options': {errors[example]}}}}} to "
-            f"{{'{example}_index': {{'options': {list(range(len(errors[example])))}}}}}"
-            ".\nThen add a new function to the functions dictionary that maps the index"
-            " to the value of the variable."
-        )
-        raise ValueError(msg)
-
-    order = variable_info.index.tolist()
-    return {k: variables[k] for k in order}
-
-
-def _get_grids(
-    gridspecs: dict[str, GridSpec],
-    variable_info: pd.DataFrame,
-) -> dict[str, Array]:
-    """Create a dictionary of grids for each variable in the model.
-
-    Args:
-        gridspecs: Dictionary containing all variables of the model. The keys are the
-            names of the variables. The values describe which values the variable can
-            take. For discrete variables these are the options (jnp.array). For
-            continuous variables this is information about how to build the grids
-            (ContinuousGridSpec).
-        variable_info: A table with information about all variables in the model. The
-            index contains the name of a model variable. The columns are booleans that
-            are True if the variable has the corresponding property. The columns are:
-            is_state, is_choice, is_continuous, is_discrete, is_sparse, is_dense.
-
-    Returns:
-        dict: Dictionary containing all variables of the model. The keys are
-            the names of the variables. The values are the grids.
-
-    """
-    grids = {}
-    for name, grid_spec in gridspecs.items():
-        if isinstance(grid_spec, ContinuousGridSpec):
-            build_grid = getattr(grids_module, grid_spec.kind)
-            grids[name] = build_grid(**grid_spec.info._asdict())
-        else:
-            grids[name] = jnp.array(grid_spec)
-
-    order = variable_info.index.tolist()
-    return {k: grids[k] for k in order}
-
-
-def _get_function_info(user_model):
-    """Derive information about all functions in the model.
-
-    Args:
-        user_model (dict): The model as provided by the user.
-
-    Returns:
-        pandas.DataFrame: A table with information about all functions in the model.
-            The index contains the name of a model function. The columns are booleans
-            that are True if the function has the corresponding property. The columns
-            are: is_next, is_filter, is_constraint.
-
-    """
-    info = pd.DataFrame(index=list(user_model["functions"]))
-    info["is_stochastic_next"] = [
-        hasattr(func, "_stochastic_info") for func in user_model["functions"].values()
-    ]
-    info["is_filter"] = info.index.str.endswith("_filter")
-    info["is_constraint"] = info.index.str.endswith("_constraint")
-    info["is_next"] = (
-        info.index.str.startswith("next_") & ~info["is_constraint"] & ~info["is_filter"]
-    )
-
-    return info
-
-
-def _get_functions(user_model, function_info, variable_info, grids, params):
-    """Process the user provided model functions.
-
-    Args:
-        user_model (dict): The model as provided by the user.
-        function_info (pd.DataFrame): A table with information about model functions.
-        variable_info (pd.DataFrame): A table with information about model variables.
-        grids (dict): Dictionary containing all variables of the model. The keys are
-            the names of the variables. The values are the grids.
-        params (dict): The parameters of the model.
-
-    Returns:
-        dict: Dictionary containing all functions of the model. The keys are
-            the names of the functions. The values are the processed functions.
-            The main difference between processed and unprocessed functions is that
-            processed functions take `params` as argument unless they are filter
-            functions.
-
-    """
-    raw_functions = deepcopy(user_model["functions"])
-
-    for var in user_model["states"]:
-        if variable_info.loc[var, "is_stochastic"]:
-            raw_functions[f"next_{var}"] = _get_stochastic_next_function(
-                raw_func=raw_functions[f"next_{var}"],
-                grid=grids[var],
-            )
-
-            raw_functions[f"weight_next_{var}"] = _get_stochastic_weight_function(
-                raw_func=raw_functions[f"next_{var}"],
-                name=var,
-                variable_info=variable_info,
-            )
-
-    # ==================================================================================
-    # Add 'params' argument to functions
-    # ==================================================================================
-    # We wrap the user functions such that they can be called with the 'params' argument
-    # instead of the individual parameters. This is done for all functions except for
-    # filter functions, because they cannot depend on model parameters; and dynamically
-    # generated weighting functions for stochastic next functions, since they are
-    # constructed to accept the 'params' argument by default.
-
-    functions = {}
-    for name, func in raw_functions.items():
-        is_weight_next_function = name.startswith("weight_next_")
-
-        if is_weight_next_function:
-            processed_func = func
-
-        else:
-            is_filter_function = function_info.loc[name, "is_filter"]
-            # params[name] contains the dictionary of parameters for the function
-            depends_on_params = bool(params[name])
-
-            if is_filter_function:
-                if params.get(name, {}):
-                    raise ValueError("filters cannot depend on model parameters.")
-                processed_func = func
-
-            elif depends_on_params:
-                processed_func = _get_extracting_function(
-                    func=func,
-                    params=params,
-                    name=name,
-                )
-
-            else:
-                processed_func = _get_function_with_dummy_params(func=func)
-
-        functions[name] = processed_func
-
-    return functions
-
-
-def _get_extracting_function(func, params, name):
-    old_signature = list(inspect.signature(func).parameters)
-    new_kwargs = [p for p in old_signature if p not in params[name]] + ["params"]
-
-    @with_signature(args=new_kwargs)
-    @functools.wraps(func)
-    def processed_func(*args, **kwargs):
-        kwargs = all_as_kwargs(args, kwargs, arg_names=new_kwargs)
-        _kwargs = {k: v for k, v in kwargs.items() if k in new_kwargs and k != "params"}
-        return func(**_kwargs, **kwargs["params"][name])
-
-    return processed_func
-
-
-def _get_function_with_dummy_params(func):
-    old_signature = list(inspect.signature(func).parameters)
-
-    new_kwargs = [*old_signature, "params"]
-
-    @with_signature(args=new_kwargs)
-    @functools.wraps(func)
-    def processed_func(*args, **kwargs):
-        kwargs = all_as_kwargs(args, kwargs, arg_names=new_kwargs)
-        _kwargs = {k: v for k, v in kwargs.items() if k != "params"}
-        return func(**_kwargs)
-
-    return processed_func
-
-
-def _get_stochastic_next_function(raw_func, grid):
-    @functools.wraps(raw_func)
-    def next_func(*args, **kwargs):  # noqa: ARG001
-        return grid
-
-    return next_func
-
-
-def _get_stochastic_weight_function(raw_func, name, variable_info):
-    """Get a function that returns the transition weights of a stochastic variable.
-
-    Example:
-    Consider a stochastic variable 'health' that takes two values {0, 1}. The transition
-    matrix is thus 2x2. We create the weighting function and then select the weights
-    that correspond to the case where 'health' is 0.
-
-    >>> from lcm.mark import StochasticInfo
-    >>> def next_health(health):
-    >>>     pass
-    >>> next_health._stochastic_info = StochasticInfo()
-    >>> params = {"shocks": {"health": np.arange(4).reshape(2, 2)}}
-    >>> weight_func = _get_stochastic_weight_function(
-    >>>     raw_func=next_health,
-    >>>     name="health"
-    >>>     variable_info=variable_info,
-    >>>     grids=grids,
-    >>> )
-    >>> weight_func(health=0, params=params)
-    >>> array([0, 1])
-
-
-    Args:
-        raw_func (callable): The raw next function of the stochastic variable.
-        name (str): The name of the stochastic variable.
-        variable_info (pd.DataFrame): A table with information about model variables.
-
-    Returns:
-        callable: A function that returns the transition weights of the stochastic
-            variable.
-
-    """
-    function_parameters = list(inspect.signature(raw_func).parameters)
-
-    # Assert that stochastic next function only depends on discrete variables or period
-    for arg in function_parameters:
-        if arg != "_period" and not variable_info.loc[arg, "is_discrete"]:
-            raise ValueError(
-                f"Stochastic variables can only depend on discrete variables and "
-                f"'_period', but {name} depends on {arg}.",
-            )
-
-    new_kwargs = [*function_parameters, "params"]
-
-    @with_signature(args=new_kwargs)
-    def weight_func(*args, **kwargs):
-        args = all_as_args(args, kwargs, arg_names=new_kwargs)
-        params = args[-1]  # by definition of new_kargs, params is the last argument
-        # By assumption, all discrete variables that LCM handles internally are
-        # themselves indices (i.e. a range starting at 0 with a step size of 1).
-        # Therefore, the arguments themselves are the indices. For the special variable
-        # '_period' the same holds.
-        indices = args[:-1]
-        return params["shocks"][name][*indices]
-
-    return weight_func
diff --git a/src/lcm/sandbox/state_space_jax_versus_numba.ipynb b/src/lcm/sandbox/state_space_jax_versus_numba.ipynb
index eed62775..a8f034a6 100644
--- a/src/lcm/sandbox/state_space_jax_versus_numba.ipynb
+++ b/src/lcm/sandbox/state_space_jax_versus_numba.ipynb
@@ -7,22 +7,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import math\n",
     "import itertools\n",
-    "import numpy as np\n",
-    "from numba import njit\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
+    "import math\n",
     "\n",
     "import jax.numpy as jnp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "from jax import jit\n",
     "from jax.config import config\n",
+    "from numba import njit\n",
     "\n",
     "config.update(\"jax_enable_x64\", True)\n",
     "\n",
     "from dags import concatenate_functions\n",
     "from lcm.dispatchers import gridmap, productmap\n",
-    "\n",
     "from numpy.testing import assert_array_almost_equal as aaae"
    ]
   },
@@ -136,7 +134,8 @@
     "}\n",
     "\n",
     "utility_concat = concatenate_functions(\n",
-    "    functions=[_utility, _leisure, _consumption], targets=\"_utility\"\n",
+    "    functions=[_utility, _leisure, _consumption],\n",
+    "    targets=\"_utility\",\n",
     ")\n",
     "_decorated_func = productmap(utility_concat, [\"wage\", \"working\"])\n",
     "decorated_func_jit = jit(_decorated_func)\n",
@@ -184,7 +183,8 @@
     "\n",
     "def get_jax_runtime_2d():\n",
     "    utility_concat = concatenate_functions(\n",
-    "        functions=[_utility, _leisure, _consumption], targets=\"_utility\"\n",
+    "        functions=[_utility, _leisure, _consumption],\n",
+    "        targets=\"_utility\",\n",
     "    )\n",
     "    _decorated_func = productmap(utility_concat, [\"wage\", \"working\"])\n",
     "    decorated_func_jit = jit(_decorated_func)\n",
@@ -394,7 +394,7 @@
     "        np.linspace(5, 25, 5),\n",
     "        np.linspace(1, 5, 5),\n",
     "        np.linspace(2, 10, 5),\n",
-    "    )\n",
+    "    ),\n",
     ")"
    ]
   },
@@ -513,7 +513,9 @@
     ")\n",
     "\n",
     "_decorated_func = gridmap(\n",
-    "    utility_concat_4d, list(dense_variables), list(contingent_variables)\n",
+    "    utility_concat_4d,\n",
+    "    list(dense_variables),\n",
+    "    list(contingent_variables),\n",
     ")\n",
     "decorated_func_jit = jit(_decorated_func)\n",
     "rslt_jax = decorated_func_jit(**dense_variables, **contingent_variables)\n",
@@ -561,7 +563,8 @@
     "\n",
     "def get_jax_runtime(len_grids_arr):\n",
     "    utility_concat_4d = concatenate_functions(\n",
-    "        functions=[_utility_4d, _leisure_4d, _consumption_4d], targets=\"_utility_4d\"\n",
+    "        functions=[_utility_4d, _leisure_4d, _consumption_4d],\n",
+    "        targets=\"_utility_4d\",\n",
     "    )\n",
     "\n",
     "    _decorated_func = gridmap(utility_concat_4d, [\"a\", \"b\"], [\"c\", \"d\"])\n",
@@ -741,7 +744,8 @@
     "\n",
     "def get_jax_runtime_4d(len_grids_arr):\n",
     "    utility_concat_4d = concatenate_functions(\n",
-    "        functions=[_utility_4d, _leisure_4d, _consumption_4d], targets=\"_utility_4d\"\n",
+    "        functions=[_utility_4d, _leisure_4d, _consumption_4d],\n",
+    "        targets=\"_utility_4d\",\n",
     "    )\n",
     "\n",
     "    _decorated_func = productmap(utility_concat_4d, [\"a\", \"b\", \"c\", \"d\"])\n",
@@ -775,7 +779,7 @@
     "        np.linspace(5, 25, 5),\n",
     "        np.linspace(1, 5, 5),\n",
     "        np.linspace(2, 10, 5),\n",
-    "    )\n",
+    "    ),\n",
     ")"
    ]
   },
@@ -808,14 +812,6 @@
    "source": [
     "plot_runtime(n_points_4d, runtime_numba_4d, runtime_jax_4d)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/src/lcm/simulate.py b/src/lcm/simulate.py
index 05c286f1..b804d3f0 100644
--- a/src/lcm/simulate.py
+++ b/src/lcm/simulate.py
@@ -9,17 +9,17 @@
 
 from lcm.argmax import argmax, segment_argmax
 from lcm.dispatchers import spacemap, vmap_1d
-from lcm.interfaces import Space
+from lcm.interfaces import InternalModel, Space
 
 
 def simulate(
     params,
+    initial_states,
     state_indexers,
     continuous_choice_grids,
     compute_ccv_policy_functions,
-    model,
+    model: InternalModel,
     next_state,
-    initial_states,
     logger,
     solve_model=None,
     vf_arr_list=None,
@@ -30,6 +30,8 @@ def simulate(
 
     Args:
         params (dict): Dict of model parameters.
+        initial_states (list): List of initial states to start from. Typically from the
+            observed dataset.
         state_indexers (list): List of dicts of length n_periods. Each dict contains one
             or several state indexers.
         continuous_choice_grids (list): List of dicts of length n_periods. Each dict
@@ -41,8 +43,6 @@ def simulate(
             state and choice variables. For stochastic variables, it returns a random
             draw from the distribution of the next state.
         model (Model): Model instance.
-        initial_states (list): List of initial states to start from. Typically from the
-            observed dataset.
         logger (logging.Logger): Logger that logs to stdout.
         solve_model (callable): Function that solves the model. Is only required if
             vf_arr_list is not provided.
@@ -230,7 +230,7 @@ def solve_continuous_problem(
     """Solve the agent's continuous choices problem problem.
 
     Args:
-        data_scs (Space): Namedtuple with entries dense_vars and sparse_vars.
+        data_scs (Space): Class with entries dense_vars and sparse_vars.
         compute_ccv (callable): Function that returns the conditional continuation
             values for a given combination of states and discrete choices. The function
             depends on:
@@ -462,14 +462,14 @@ def retrieve_non_sparse_choices(indices, grids, grid_shape):
 
 def create_data_scs(
     states,
-    model,
+    model: InternalModel,
     period,
 ):
     """Create data state choice space.
 
     Args:
         states (dict): Dict with initial states.
-        model (Model): Model instance.
+        model: Model instance.
         period (int): Period.
 
     Returns:
diff --git a/src/lcm/solve_brute.py b/src/lcm/solve_brute.py
index 101846a3..a7f1ab82 100644
--- a/src/lcm/solve_brute.py
+++ b/src/lcm/solve_brute.py
@@ -69,7 +69,7 @@ def solve(
 
         # solve discrete problem by calculating expected maximum over discrete choices
         calculate_emax = emax_calculators[period]
-        vf_arr = calculate_emax(conditional_continuation_values)
+        vf_arr = calculate_emax(conditional_continuation_values, params=params)
         reversed_solution.append(vf_arr)
 
         logger.info("Period: %s", period)
@@ -88,7 +88,7 @@ def solve_continuous_problem(
     """Solve the agent's continuous choices problem problem.
 
     Args:
-        state_choice_space (Space): Namedtuple with entries dense_vars and sparse_vars.
+        state_choice_space (Space): Class with entries dense_vars and sparse_vars.
         compute_ccv (callable): Function that returns the conditional continuation
             values for a given combination of states and discrete choices. The function
             depends on:
diff --git a/src/lcm/state_space.py b/src/lcm/state_space.py
index 9408fc58..cb1e3e0c 100644
--- a/src/lcm/state_space.py
+++ b/src/lcm/state_space.py
@@ -8,10 +8,12 @@
 from dags import concatenate_functions
 
 from lcm.dispatchers import productmap, spacemap
-from lcm.interfaces import IndexerInfo, Space, SpaceInfo
+from lcm.interfaces import IndexerInfo, InternalModel, Space, SpaceInfo
 
 
-def create_state_choice_space(model, period, *, is_last_period, jit_filter):
+def create_state_choice_space(
+    model: InternalModel, period, *, is_last_period, jit_filter
+):
     """Create a state choice space for the model.
 
     A state_choice_space is a compressed representation of all feasible states and the
@@ -126,15 +128,15 @@ def create_state_choice_space(model, period, *, is_last_period, jit_filter):
 
     space_info = SpaceInfo(
         axis_names=axis_names,
-        lookup_info=lookup_info,
-        interpolation_info=interpolation_info,
+        lookup_info=lookup_info,  # type: ignore[arg-type]
+        interpolation_info=interpolation_info,  # type: ignore[arg-type]
         indexer_infos=indexer_infos,
     )
 
     return state_choice_space, space_info, state_indexers, choice_segments
 
 
-def create_filter_mask(model, subset, fixed_inputs=None, *, jit_filter):
+def create_filter_mask(model: InternalModel, subset, fixed_inputs=None, *, jit_filter):
     """Create mask for combinations of grid values that is True if all filters are True.
 
     Args:
diff --git a/src/lcm/typing.py b/src/lcm/typing.py
index dd8039e2..afef46d7 100644
--- a/src/lcm/typing.py
+++ b/src/lcm/typing.py
@@ -1,4 +1,5 @@
-from typing import Annotated, Literal, TypedDict
+from enum import Enum
+from typing import Any, Literal, TypedDict
 
 from jax import Array
 
@@ -6,7 +7,14 @@
 # includes zero dimensional jax arrays.
 Scalar = int | float | Array
 
-DiscreteLabels = Annotated[list[int], "Int range starting from 0 with increments of 1"]
+ParamsDict = dict[str, Any]
+
+
+class ShockType(Enum):
+    """Type of shocks."""
+
+    EXTREME_VALUE = "extreme_value"
+    NONE = None
 
 
 class SegmentInfo(TypedDict):
diff --git a/src/lcm/user_model.py b/src/lcm/user_model.py
new file mode 100644
index 00000000..c2ba24e3
--- /dev/null
+++ b/src/lcm/user_model.py
@@ -0,0 +1,122 @@
+"""Collection of classes that are used by the user to define the model and grids."""
+
+import dataclasses as dc
+from collections.abc import Callable
+from dataclasses import KW_ONLY, dataclass, field
+
+from lcm.exceptions import ModelInitilizationError, format_messages
+from lcm.grids import Grid
+
+
+@dataclass(frozen=True)
+class Model:
+    """A user model which can be processed into an internal model.
+
+    Attributes:
+        description: Description of the model.
+        n_periods: Number of periods in the model.
+        functions: Dictionary of user provided functions that define the functional
+            relationships between model variables. It must include at least a function
+            called 'utility'.
+        choices: Dictionary of user provided choices.
+        states: Dictionary of user provided states.
+
+    """
+
+    description: str | None = None
+    _: KW_ONLY
+    n_periods: int
+    functions: dict[str, Callable] = field(default_factory=dict)
+    choices: dict[str, Grid] = field(default_factory=dict)
+    states: dict[str, Grid] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        type_errors = _validate_attribute_types(self)
+
+        if type_errors:
+            msg = format_messages(type_errors)
+            raise ModelInitilizationError(msg)
+
+        logical_errors = _validate_logical_consistency(self)
+
+        if logical_errors:
+            msg = format_messages(logical_errors)
+            raise ModelInitilizationError(msg)
+
+    def replace(self, **kwargs) -> "Model":
+        """Replace the attributes of the model.
+
+        Args:
+            **kwargs: Keyword arguments to replace the attributes of the model.
+
+        Returns:
+            A new model with the replaced attributes.
+
+        """
+        return dc.replace(self, **kwargs)
+
+
+def _validate_attribute_types(model: Model) -> list[str]:
+    """Validate the types of the model attributes."""
+    error_messages = []
+
+    # Validate types of states and choices
+    # ----------------------------------------------------------------------------------
+    for attr_name in ("choices", "states"):
+        attr = getattr(model, attr_name)
+        if not isinstance(attr, dict):
+            error_messages.append(f"{attr_name} must be a dictionary.")
+        else:
+            for k, v in attr.items():
+                if not isinstance(k, str):
+                    error_messages.append(f"{attr_name} key {k} must be a string.")
+                if not isinstance(v, Grid):
+                    error_messages.append(f"{attr_name} value {v} must be an LCM grid.")
+
+    # Validate types of functions
+    # ----------------------------------------------------------------------------------
+    if not isinstance(model.functions, dict):
+        error_messages.append("functions must be a dictionary.")
+    else:
+        for k, v in model.functions.items():
+            if not isinstance(k, str):
+                error_messages.append(f"function keys must be a strings, but is {k}.")
+            if not callable(v):
+                error_messages.append(
+                    f"function values must be a callable, but is {v}."
+                )
+
+    return error_messages
+
+
+def _validate_logical_consistency(model: Model) -> list[str]:
+    """Validate the logical consistency of the model."""
+    error_messages = []
+
+    if model.n_periods < 1:
+        error_messages.append("Number of periods must be a positive integer.")
+
+    if "utility" not in model.functions:
+        error_messages.append(
+            "Utility function is not defined. LCM expects a function called 'utility' "
+            "in the functions dictionary.",
+        )
+
+    states_without_next_func = [
+        state for state in model.states if f"next_{state}" not in model.functions
+    ]
+    if states_without_next_func:
+        error_messages.append(
+            "Each state must have a corresponding next state function. For the "
+            "following states, no next state function was found: "
+            f"{states_without_next_func}.",
+        )
+
+    states_and_choices_overlap = set(model.states) & set(model.choices)
+    if states_and_choices_overlap:
+        error_messages.append(
+            "States and choices cannot have overlapping names. The following names "
+            f"are used in both states and choices: {states_and_choices_overlap}.",
+        )
+
+    return error_messages
diff --git a/tests/input_processing/__init__.py b/tests/input_processing/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/test_create_params.py b/tests/input_processing/test_create_params_template.py
similarity index 57%
rename from tests/test_create_params.py
rename to tests/input_processing/test_create_params_template.py
index 907f6d99..e0b9c0d5 100644
--- a/tests/test_create_params.py
+++ b/tests/input_processing/test_create_params_template.py
@@ -1,52 +1,70 @@
+from dataclasses import dataclass
+from typing import Any
+
 import numpy as np
 import pandas as pd
 import pytest
-from lcm.create_params_template import (
+from numpy.testing import assert_equal
+
+from lcm.grids import DiscreteGrid
+from lcm.input_processing.create_params_template import (
     _create_function_params,
     _create_stochastic_transition_params,
     create_params_template,
 )
-from numpy.testing import assert_equal
+
+
+@dataclass
+class ModelMock:
+    """A model mock for testing the params creation functions.
+
+    This dataclass has the same attributes as the Model dataclass, but does not perform
+    any checks, which helps us to test the params creation functions in isolation.
+
+    """
+
+    n_periods: int | None = None
+    functions: dict[str, Any] | None = None
+    choices: dict[str, Any] | None = None
+    states: dict[str, Any] | None = None
 
 
 def test_create_params_without_shocks():
-    model = {
-        "functions": {
+    model = ModelMock(
+        functions={
             "f": lambda a, b, c: None,  # noqa: ARG005
+            "next_b": lambda b: b,
         },
-        "choices": {
-            "a": None,
+        choices={
+            "a": DiscreteGrid([0, 1]),
         },
-        "states": {
-            "b": None,
+        states={
+            "b": DiscreteGrid([0, 1]),
         },
-    }
-    got = create_params_template(
-        model,
-        variable_info=pd.DataFrame({"is_stochastic": [False]}),
-        grids=None,
+        n_periods=None,
     )
-    assert got == {"beta": np.nan, "f": {"c": np.nan}}
+    got = create_params_template(model)
+    assert got == {"beta": np.nan, "f": {"c": np.nan}, "next_b": {}}
 
 
 def test_create_function_params():
-    model = {
-        "functions": {
+    model = ModelMock(
+        functions={
             "f": lambda a, b, c: None,  # noqa: ARG005
         },
-        "choices": {
+        choices={
             "a": None,
         },
-        "states": {
+        states={
             "b": None,
         },
-    }
+    )
     got = _create_function_params(model)
     assert got == {"f": {"c": np.nan}}
 
 
 def test_create_shock_params():
-    def next_a(a, _period):  # noqa: ARG001
+    def next_a(a, _period):
         pass
 
     variable_info = pd.DataFrame(
@@ -54,8 +72,13 @@ def next_a(a, _period):  # noqa: ARG001
         index=["a"],
     )
 
+    model = ModelMock(
+        n_periods=3,
+        functions={"next_a": next_a},
+    )
+
     got = _create_stochastic_transition_params(
-        model_spec={"functions": {"next_a": next_a}, "n_periods": 3},
+        model=model,
         variable_info=variable_info,
         grids={"a": np.array([1, 2])},
     )
@@ -63,7 +86,7 @@ def next_a(a, _period):  # noqa: ARG001
 
 
 def test_create_shock_params_invalid_variable():
-    def next_a(a):  # noqa: ARG001
+    def next_a(a):
         pass
 
     variable_info = pd.DataFrame(
@@ -71,16 +94,20 @@ def next_a(a):  # noqa: ARG001
         index=["a"],
     )
 
+    model = ModelMock(
+        functions={"next_a": next_a},
+    )
+
     with pytest.raises(ValueError, match="The following variables are stochastic, but"):
         _create_stochastic_transition_params(
-            model_spec={"functions": {"next_a": next_a}},
+            model=model,
             variable_info=variable_info,
             grids={"a": np.array([1, 2])},
         )
 
 
 def test_create_shock_params_invalid_dependency():
-    def next_a(a, b, _period):  # noqa: ARG001
+    def next_a(a, b, _period):
         pass
 
     variable_info = pd.DataFrame(
@@ -92,9 +119,13 @@ def next_a(a, b, _period):  # noqa: ARG001
         index=["a", "b"],
     )
 
+    model = ModelMock(
+        functions={"next_a": next_a},
+    )
+
     with pytest.raises(ValueError, match="Stochastic transition functions can only"):
         _create_stochastic_transition_params(
-            model_spec={"functions": {"next_a": next_a}},
+            model=model,
             variable_info=variable_info,
             grids={"a": np.array([1, 2])},
         )
diff --git a/tests/test_process_model.py b/tests/input_processing/test_process_model.py
similarity index 55%
rename from tests/test_process_model.py
rename to tests/input_processing/test_process_model.py
index cdd0d542..61a459f7 100644
--- a/tests/test_process_model.py
+++ b/tests/input_processing/test_process_model.py
@@ -1,72 +1,84 @@
+from dataclasses import dataclass
+from typing import Any
+
 import jax.numpy as jnp
-import lcm.grids as grids_module
 import numpy as np
 import pandas as pd
 import pytest
-from lcm.interfaces import ContinuousGridInfo, ContinuousGridSpec
-from lcm.mark import StochasticInfo
-from lcm.process_model import (
-    _get_function_info,
-    _get_grids,
-    _get_gridspecs,
-    _get_stochastic_weight_function,
-    _get_variable_info,
-    process_model,
-)
 from numpy.testing import assert_array_equal
 from pandas.testing import assert_frame_equal
 
+from lcm import DiscreteGrid, LinspaceGrid, grid_helpers
+from lcm.input_processing.process_model import (
+    _get_stochastic_weight_function,
+    get_function_info,
+    get_grids,
+    get_gridspecs,
+    get_variable_info,
+    process_model,
+)
+from lcm.mark import StochasticInfo
 from tests.test_models.deterministic import (
-    N_GRID_POINTS,
     get_model_config,
 )
 
 
-@pytest.fixture()
-def user_model():
+@dataclass
+class ModelMock:
+    """A model mock for testing the process_model function.
+
+    This dataclass has the same attributes as the Model dataclass, but does not perform
+    any checks, which helps us to test the process_model function in isolation.
+
+    """
+
+    n_periods: int
+    functions: dict[str, Any]
+    choices: dict[str, Any]
+    states: dict[str, Any]
+
+
+@pytest.fixture
+def model():
     def next_c(a, b):
         return a + b
 
-    return {
-        "functions": {
+    return ModelMock(
+        n_periods=2,
+        functions={
             "next_c": next_c,
         },
-        "choices": {
-            "a": {"options": [0, 1]},
+        choices={
+            "a": DiscreteGrid([0, 1]),
         },
-        "states": {
-            "c": {"options": [0, 1]},
+        states={
+            "c": DiscreteGrid([0, 1]),
         },
-        "n_periods": 2,
-    }
+    )
 
 
-def test_get_function_info(user_model):
-    got = _get_function_info(user_model)
+def test_get_function_info(model):
+    got = get_function_info(model)
     exp = pd.DataFrame(
         {
-            "is_stochastic_next": [False],
             "is_filter": [False],
             "is_constraint": [False],
             "is_next": [True],
+            "is_stochastic_next": [False],
         },
         index=["next_c"],
     )
     assert_frame_equal(got, exp)
 
 
-def test_get_variable_info(user_model):
-    function_info = _get_function_info(user_model)
-    got = _get_variable_info(
-        user_model,
-        function_info,
-    )
+def test_get_variable_info(model):
+    got = get_variable_info(model)
     exp = pd.DataFrame(
         {
             "is_state": [False, True],
             "is_choice": [True, False],
-            "is_discrete": [True, True],
             "is_continuous": [False, False],
+            "is_discrete": [True, True],
             "is_stochastic": [False, False],
             "is_auxiliary": [False, True],
             "is_sparse": [False, False],
@@ -77,29 +89,21 @@ def test_get_variable_info(user_model):
     assert_frame_equal(got.loc[exp.index], exp)  # we don't care about the id order here
 
 
-def test_get_gridspecs(user_model):
-    variable_info = _get_variable_info(
-        user_model,
-        function_info=_get_function_info(user_model),
-    )
-    got = _get_gridspecs(user_model, variable_info)
-    exp = {"a": [0, 1], "c": [0, 1]}
-    assert got == exp
+def test_get_gridspecs(model):
+    got = get_gridspecs(model)
+    assert got["a"] == DiscreteGrid([0, 1])
+    assert got["c"] == DiscreteGrid([0, 1])
 
 
-def test_get_grids(user_model):
-    variable_info = _get_variable_info(
-        user_model,
-        function_info=_get_function_info(user_model),
-    )
-    gridspecs = _get_gridspecs(user_model, variable_info)
-    got = _get_grids(gridspecs, variable_info)
+def test_get_grids(model):
+    got = get_grids(model)
     assert_array_equal(got["a"], jnp.array([0, 1]))
     assert_array_equal(got["c"], jnp.array([0, 1]))
 
 
 def test_process_model_iskhakov_et_al_2017():
-    model = process_model(get_model_config("iskhakov_et_al_2017", n_periods=3))
+    model_config = get_model_config("iskhakov_et_al_2017", n_periods=3)
+    model = process_model(model_config)
 
     # Variable Info
     assert (
@@ -118,34 +122,30 @@ def test_process_model_iskhakov_et_al_2017():
     ).all()
 
     # Gridspecs
-    wealth_specs = ContinuousGridSpec(
-        kind="linspace",
-        info=ContinuousGridInfo(start=1, stop=400, n_points=N_GRID_POINTS["wealth"]),
+    wealth_grid = LinspaceGrid(
+        start=1,
+        stop=400,
+        n_points=model_config.states["wealth"].n_points,
     )
 
-    assert model.gridspecs["wealth"] == wealth_specs
+    assert model.gridspecs["wealth"] == wealth_grid
 
-    consumption_specs = ContinuousGridSpec(
-        kind="linspace",
-        info=ContinuousGridInfo(
-            start=1,
-            stop=400,
-            n_points=N_GRID_POINTS["consumption"],
-        ),
+    consumption_grid = LinspaceGrid(
+        start=1,
+        stop=400,
+        n_points=model_config.choices["consumption"].n_points,
     )
-    assert model.gridspecs["consumption"] == consumption_specs
+    assert model.gridspecs["consumption"] == consumption_grid
 
-    assert model.gridspecs["retirement"] == [0, 1]
-    assert model.gridspecs["lagged_retirement"] == [0, 1]
+    assert model.gridspecs["retirement"] == DiscreteGrid([0, 1])
+    assert model.gridspecs["lagged_retirement"] == DiscreteGrid([0, 1])
 
     # Grids
-    func = getattr(grids_module, model.gridspecs["consumption"].kind)
-    asserted = func(**model.gridspecs["consumption"].info._asdict())
-    assert (asserted == model.grids["consumption"]).all()
+    expected = grid_helpers.linspace(**model_config.choices["consumption"].__dict__)
+    assert_array_equal(model.grids["consumption"], expected)
 
-    func = getattr(grids_module, model.gridspecs["wealth"].kind)
-    asserted = func(**model.gridspecs["wealth"].info._asdict())
-    assert (asserted == model.grids["wealth"]).all()
+    expected = grid_helpers.linspace(**model_config.states["wealth"].__dict__)
+    assert_array_equal(model.grids["wealth"], expected)
 
     assert (model.grids["retirement"] == jnp.array([0, 1])).all()
     assert (model.grids["lagged_retirement"] == jnp.array([0, 1])).all()
@@ -165,9 +165,8 @@ def test_process_model_iskhakov_et_al_2017():
 
 
 def test_process_model():
-    model = process_model(
-        get_model_config("iskhakov_et_al_2017_stripped_down", n_periods=3),
-    )
+    model_config = get_model_config("iskhakov_et_al_2017_stripped_down", n_periods=3)
+    model = process_model(model_config)
 
     # Variable Info
     assert ~(model.variable_info["is_sparse"].to_numpy()).any()
@@ -181,33 +180,29 @@ def test_process_model():
     ).all()
 
     # Gridspecs
-    wealth_specs = ContinuousGridSpec(
-        kind="linspace",
-        info=ContinuousGridInfo(start=1, stop=400, n_points=N_GRID_POINTS["wealth"]),
+    wealth_specs = LinspaceGrid(
+        start=1,
+        stop=400,
+        n_points=model_config.states["wealth"].n_points,
     )
 
     assert model.gridspecs["wealth"] == wealth_specs
 
-    consumption_specs = ContinuousGridSpec(
-        kind="linspace",
-        info=ContinuousGridInfo(
-            start=1,
-            stop=400,
-            n_points=N_GRID_POINTS["consumption"],
-        ),
+    consumption_specs = LinspaceGrid(
+        start=1,
+        stop=400,
+        n_points=model_config.choices["consumption"].n_points,
     )
     assert model.gridspecs["consumption"] == consumption_specs
 
-    assert model.gridspecs["retirement"] == [0, 1]
+    assert model.gridspecs["retirement"] == DiscreteGrid([0, 1])
 
     # Grids
-    func = getattr(grids_module, model.gridspecs["consumption"].kind)
-    asserted = func(**model.gridspecs["consumption"].info._asdict())
-    assert (asserted == model.grids["consumption"]).all()
+    expected = grid_helpers.linspace(**model_config.choices["consumption"].__dict__)
+    assert_array_equal(model.grids["consumption"], expected)
 
-    func = getattr(grids_module, model.gridspecs["wealth"].kind)
-    asserted = func(**model.gridspecs["wealth"].info._asdict())
-    assert (asserted == model.grids["wealth"]).all()
+    expected = grid_helpers.linspace(**model_config.states["wealth"].__dict__)
+    assert_array_equal(model.grids["wealth"], expected)
 
     assert (model.grids["retirement"] == jnp.array([0, 1])).all()
 
@@ -226,7 +221,7 @@ def test_process_model():
 
 
 def test_get_stochastic_weight_function():
-    def raw_func(health, wealth):  # noqa: ARG001
+    def raw_func(health, wealth):
         pass
 
     raw_func._stochastic_info = StochasticInfo()
@@ -250,7 +245,7 @@ def raw_func(health, wealth):  # noqa: ARG001
 
 
 def test_get_stochastic_weight_function_non_state_dependency():
-    def raw_func(health, wealth):  # noqa: ARG001
+    def raw_func(health, wealth):
         pass
 
     raw_func._stochastic_info = StochasticInfo()
@@ -269,16 +264,12 @@ def raw_func(health, wealth):  # noqa: ARG001
 
 
 def test_variable_info_with_continuous_filter_has_unique_index():
-    user_model = get_model_config("iskhakov_et_al_2017", n_periods=3)
+    model = get_model_config("iskhakov_et_al_2017", n_periods=3)
 
     def wealth_filter(wealth):
         return wealth > 200
 
-    user_model["functions"]["wealth_filter"] = wealth_filter
+    model.functions["wealth_filter"] = wealth_filter
 
-    function_info = _get_function_info(user_model)
-    got = _get_variable_info(
-        user_model,
-        function_info,
-    )
+    got = get_variable_info(model)
     assert got.index.is_unique
diff --git a/tests/test_analytical_solution.py b/tests/test_analytical_solution.py
index 22fc6976..7769e426 100644
--- a/tests/test_analytical_solution.py
+++ b/tests/test_analytical_solution.py
@@ -9,10 +9,10 @@
 
 import numpy as np
 import pytest
-from lcm._config import TEST_DATA
-from lcm.entry_point import get_lcm_function
 from numpy.testing import assert_array_almost_equal as aaae
 
+from lcm._config import TEST_DATA
+from lcm.entry_point import get_lcm_function
 from tests.test_models.deterministic import get_model_config, get_params
 
 # ======================================================================================
diff --git a/tests/test_argmax.py b/tests/test_argmax.py
index 76c4bb83..d515ea1d 100644
--- a/tests/test_argmax.py
+++ b/tests/test_argmax.py
@@ -1,8 +1,9 @@
 import jax.numpy as jnp
 from jax import jit
-from lcm.argmax import _flatten_last_n_axes, _move_axes_to_back, argmax, segment_argmax
 from numpy.testing import assert_array_equal
 
+from lcm.argmax import _flatten_last_n_axes, _move_axes_to_back, argmax, segment_argmax
+
 # Test jitted functions
 # ======================================================================================
 jitted_segment_argmax = jit(segment_argmax, static_argnums=2)
diff --git a/tests/test_discrete_problem.py b/tests/test_discrete_problem.py
index 9eba33bc..3f87f33e 100644
--- a/tests/test_discrete_problem.py
+++ b/tests/test_discrete_problem.py
@@ -5,6 +5,8 @@
 import pandas as pd
 import pytest
 from jax.ops import segment_max
+from numpy.testing import assert_array_almost_equal as aaae
+
 from lcm.discrete_problem import (
     _calculate_emax_extreme_value_shocks,
     _determine_dense_discrete_choice_axes,
@@ -13,10 +15,10 @@
     _solve_discrete_problem_no_shocks,
     get_solve_discrete_problem,
 )
-from numpy.testing import assert_array_almost_equal as aaae
+from lcm.typing import ShockType
 
 
-@pytest.fixture()
+@pytest.fixture
 def cc_values():
     """Conditional continuation values."""
     v_t = jnp.arange(20).reshape(2, 2, 5) / 2
@@ -24,7 +26,7 @@ def cc_values():
     return jnp.transpose(v_t, axes=(2, 0, 1))
 
 
-@pytest.fixture()
+@pytest.fixture
 def segment_info():
     return {
         "segment_ids": jnp.array([0, 0, 1, 1, 1]),
@@ -48,14 +50,13 @@ def test_aggregation_without_shocks(cc_values, segment_info, collapse, n_extra_a
     )
 
     solve_discrete_problem = get_solve_discrete_problem(
-        random_utility_shock_type=None,
+        random_utility_shock_type=ShockType.NONE,
         variable_info=var_info,
         is_last_period=False,
         choice_segments=segment_info,
-        params={},
     )
 
-    calculated = solve_discrete_problem(cc_values)
+    calculated = solve_discrete_problem(cc_values, params=None)
 
     expected = jnp.array([8, 9.5])
 
@@ -141,7 +142,7 @@ def _get_reshaped_cc_values_and_variable_info(cc_values, collapse, n_extra_axes)
 # ======================================================================================
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_get_solve_discrete_problem_illustrative():
     variable_info = pd.DataFrame(
         {
@@ -153,11 +154,10 @@ def test_get_solve_discrete_problem_illustrative():
     )  # leads to choice_axes = [1]
 
     solve_discrete_problem = get_solve_discrete_problem(
-        random_utility_shock_type=None,
+        random_utility_shock_type=ShockType.NONE,
         variable_info=variable_info,
         is_last_period=False,
         choice_segments=None,
-        params=None,
     )
 
     cc_values = jnp.array(
@@ -168,11 +168,11 @@ def test_get_solve_discrete_problem_illustrative():
         ],
     )
 
-    got = solve_discrete_problem(cc_values)
+    got = solve_discrete_problem(cc_values, params=None)
     aaae(got, jnp.array([1, 3, 5]))
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_solve_discrete_problem_no_shocks_illustrative():
     cc_values = jnp.array(
         [
@@ -213,7 +213,7 @@ def test_solve_discrete_problem_no_shocks_illustrative():
     aaae(got, jnp.array([3, 5]))
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_calculate_emax_extreme_value_shocks_illustrative():
     cc_values = jnp.array(
         [
@@ -259,7 +259,7 @@ def test_calculate_emax_extreme_value_shocks_illustrative():
 # ======================================================================================
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_segment_max_over_first_axis_illustrative():
     a = jnp.arange(4)
     segment_info = {
@@ -271,7 +271,7 @@ def test_segment_max_over_first_axis_illustrative():
     aaae(got, expected)
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_segment_extreme_value_emax_over_first_axis_illustrative():
     a = jnp.array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
 
@@ -289,7 +289,7 @@ def test_segment_extreme_value_emax_over_first_axis_illustrative():
     aaae(got, expected)
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_segment_logsumexp_illustrative():
     a = jnp.array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
 
@@ -308,7 +308,7 @@ def test_segment_logsumexp_illustrative():
 # ======================================================================================
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_determine_discrete_choice_axes_illustrative():
     # No discrete choice variable
     # ==================================================================================
diff --git a/tests/test_dispatchers.py b/tests/test_dispatchers.py
index 8b0a4bc7..99a91951 100644
--- a/tests/test_dispatchers.py
+++ b/tests/test_dispatchers.py
@@ -2,13 +2,14 @@
 
 import jax.numpy as jnp
 import pytest
+from numpy.testing import assert_array_almost_equal as aaae
+
 from lcm.dispatchers import (
     productmap,
     spacemap,
     vmap_1d,
 )
 from lcm.functools import allow_args
-from numpy.testing import assert_array_almost_equal as aaae
 
 
 def f(a, /, *, b, c):
@@ -40,7 +41,7 @@ def g(a, /, b, *, c, d):
 # ======================================================================================
 
 
-@pytest.fixture()
+@pytest.fixture
 def setup_productmap_f():
     return {
         "a": jnp.linspace(-5, 5, 10),
@@ -49,7 +50,7 @@ def setup_productmap_f():
     }
 
 
-@pytest.fixture()
+@pytest.fixture
 def expected_productmap_f():
     grids = {
         "a": jnp.linspace(-5, 5, 10),
@@ -61,7 +62,7 @@ def expected_productmap_f():
     return allow_args(f)(*helper).reshape(10, 7, 5)
 
 
-@pytest.fixture()
+@pytest.fixture
 def setup_productmap_g():
     return {
         "a": jnp.linspace(-5, 5, 10),
@@ -71,7 +72,7 @@ def setup_productmap_g():
     }
 
 
-@pytest.fixture()
+@pytest.fixture
 def expected_productmap_g():
     grids = {
         "a": jnp.linspace(-5, 5, 10),
@@ -185,7 +186,7 @@ def test_productmap_with_some_argument_mapped_twice():
 # ======================================================================================
 
 
-@pytest.fixture()
+@pytest.fixture
 def setup_spacemap():
     value_grid = {
         "a": jnp.array([1.0, 2, 3]),
@@ -206,7 +207,7 @@ def setup_spacemap():
     return value_grid, combination_grid
 
 
-@pytest.fixture()
+@pytest.fixture
 def expected_spacemap():
     value_grid = {
         "a": jnp.array([1.0, 2, 3]),
diff --git a/tests/test_entry_point.py b/tests/test_entry_point.py
index 2c6a813d..d93dddab 100644
--- a/tests/test_entry_point.py
+++ b/tests/test_entry_point.py
@@ -1,15 +1,15 @@
 import jax.numpy as jnp
 import pytest
+from pybaum import tree_equal, tree_map
+
 from lcm.entry_point import (
     create_compute_conditional_continuation_policy,
     create_compute_conditional_continuation_value,
     get_lcm_function,
 )
+from lcm.input_processing import process_model
 from lcm.model_functions import get_utility_and_feasibility_function
-from lcm.process_model import process_model
 from lcm.state_space import create_state_choice_space
-from pybaum import tree_equal, tree_map
-
 from tests.test_models.deterministic import get_model_config
 from tests.test_models.deterministic import utility as iskhakov_et_al_2017_utility
 
@@ -30,15 +30,15 @@
 
 
 @pytest.mark.parametrize(
-    "user_model",
+    "model",
     [
         get_model_config(name, n_periods=3)
         for name in ["iskhakov_et_al_2017", *STRIPPED_DOWN_AND_FULLY_DISCRETE_MODELS]
     ],
     ids=["iskhakov_et_al_2017", *STRIPPED_DOWN_AND_FULLY_DISCRETE_MODELS],
 )
-def test_get_lcm_function_with_solve_target(user_model):
-    solve_model, params_template = get_lcm_function(model=user_model)
+def test_get_lcm_function_with_solve_target(model):
+    solve_model, params_template = get_lcm_function(model=model)
 
     params = tree_map(lambda _: 0.2, params_template)
 
@@ -51,16 +51,16 @@ def test_get_lcm_function_with_solve_target(user_model):
 
 
 @pytest.mark.parametrize(
-    "user_model",
+    "model",
     [
         get_model_config(name, n_periods=3)
         for name in STRIPPED_DOWN_AND_FULLY_DISCRETE_MODELS
     ],
     ids=STRIPPED_DOWN_AND_FULLY_DISCRETE_MODELS,
 )
-def test_get_lcm_function_with_simulation_target_simple(user_model):
+def test_get_lcm_function_with_simulation_target_simple(model):
     simulate, params_template = get_lcm_function(
-        model=user_model,
+        model=model,
         targets="solve_and_simulate",
     )
     params = tree_map(lambda _: 0.2, params_template)
@@ -70,30 +70,30 @@ def test_get_lcm_function_with_simulation_target_simple(user_model):
         initial_states={
             "wealth": jnp.array([0.0, 10.0, 50.0]),
         },
-        additional_targets=["age"] if "age" in user_model["functions"] else None,
+        additional_targets=["age"] if "age" in model.functions else None,
     )
 
 
 @pytest.mark.parametrize(
-    "user_model",
+    "model",
     [
         get_model_config(name, n_periods=3)
         for name in STRIPPED_DOWN_AND_FULLY_DISCRETE_MODELS
     ],
     ids=STRIPPED_DOWN_AND_FULLY_DISCRETE_MODELS,
 )
-def test_get_lcm_function_with_simulation_is_coherent(user_model):
+def test_get_lcm_function_with_simulation_is_coherent(model):
     """Test that solve_and_simulate creates same output as solve then simulate."""
     # solve then simulate
     # ==================================================================================
 
     # solve
-    solve_model, params_template = get_lcm_function(model=user_model)
+    solve_model, params_template = get_lcm_function(model=model)
     params = tree_map(lambda _: 0.2, params_template)
     vf_arr_list = solve_model(params)
 
     # simulate using solution
-    simulate_model, _ = get_lcm_function(model=user_model, targets="simulate")
+    simulate_model, _ = get_lcm_function(model=model, targets="simulate")
 
     solve_then_simulate = simulate_model(
         params,
@@ -106,7 +106,7 @@ def test_get_lcm_function_with_simulation_is_coherent(user_model):
     # solve and simulate
     # ==================================================================================
     solve_and_simulate_model, _ = get_lcm_function(
-        model=user_model,
+        model=model,
         targets="solve_and_simulate",
     )
 
@@ -121,18 +121,18 @@ def test_get_lcm_function_with_simulation_is_coherent(user_model):
 
 
 @pytest.mark.parametrize(
-    "user_model",
+    "model",
     [get_model_config("iskhakov_et_al_2017", n_periods=3)],
     ids=["iskhakov_et_al_2017"],
 )
-def test_get_lcm_function_with_simulation_target_iskhakov_et_al_2017(user_model):
+def test_get_lcm_function_with_simulation_target_iskhakov_et_al_2017(model):
     # solve model
-    solve_model, params_template = get_lcm_function(model=user_model, targets="solve")
+    solve_model, params_template = get_lcm_function(model=model, targets="solve")
     params = tree_map(lambda _: 0.2, params_template)
     vf_arr_list = solve_model(params)
 
     # simulate using solution
-    simulate_model, _ = get_lcm_function(model=user_model, targets="simulate")
+    simulate_model, _ = get_lcm_function(model=model, targets="simulate")
 
     simulate_model(
         params,
@@ -358,13 +358,13 @@ def test_create_compute_conditional_continuation_policy_with_discrete_model():
 
 
 def test_get_lcm_function_with_period_argument_in_filter():
-    user_model = get_model_config("iskhakov_et_al_2017", n_periods=3)
+    model = get_model_config("iskhakov_et_al_2017", n_periods=3)
 
     def absorbing_retirement_filter(retirement, lagged_retirement, _period):
         return jnp.logical_or(retirement == 1, lagged_retirement == 0)
 
-    user_model["functions"]["absorbing_retirement_filter"] = absorbing_retirement_filter
+    model.functions["absorbing_retirement_filter"] = absorbing_retirement_filter
 
-    solve_model, params_template = get_lcm_function(model=user_model)
+    solve_model, params_template = get_lcm_function(model=model)
     params = tree_map(lambda _: 0.2, params_template)
     solve_model(params)
diff --git a/tests/test_function_representation.py b/tests/test_function_representation.py
index 887ea1a2..ef993948 100644
--- a/tests/test_function_representation.py
+++ b/tests/test_function_representation.py
@@ -3,6 +3,8 @@
 
 import jax.numpy as jnp
 import pytest
+
+from lcm import LinspaceGrid
 from lcm.dispatchers import productmap
 from lcm.function_representation import (
     _fail_if_interpolation_axes_are_not_last,
@@ -12,10 +14,7 @@
     _get_lookup_function,
     get_function_representation,
 )
-from lcm.grids import linspace
 from lcm.interfaces import (
-    ContinuousGridInfo,
-    ContinuousGridSpec,
     IndexerInfo,
     SpaceInfo,
 )
@@ -23,19 +22,18 @@
 
 
 def test_function_evaluator_with_one_continuous_variable():
-    grid_info = ContinuousGridInfo(start=-3, stop=3, n_points=7)
+    wealth_grid = LinspaceGrid(start=-3, stop=3, n_points=7)
 
     space_info = SpaceInfo(
         axis_names=["wealth"],
         lookup_info={},
         interpolation_info={
-            "wealth": ContinuousGridSpec(kind="linspace", info=grid_info),
+            "wealth": wealth_grid,
         },
         indexer_infos=[],
     )
 
-    grid = linspace(**grid_info._asdict())
-    vf_arr = jnp.pi * grid + 2
+    vf_arr = jnp.pi * wealth_grid.to_jax() + 2
 
     # create the evaluator
     evaluator = get_function_representation(
@@ -123,14 +121,8 @@ def test_function_evaluator():
 
     # create info on continuous grids
     interpolation_info = {
-        "wealth": ContinuousGridSpec(
-            kind="linspace",
-            info=ContinuousGridInfo(start=100, stop=1100, n_points=6),
-        ),
-        "human_capital": ContinuousGridSpec(
-            kind="linspace",
-            info=ContinuousGridInfo(start=-3, stop=3, n_points=7),
-        ),
+        "wealth": LinspaceGrid(start=100, stop=1100, n_points=6),
+        "human_capital": LinspaceGrid(start=-3, stop=3, n_points=7),
     }
 
     # create info on axis of value function array
@@ -207,14 +199,8 @@ def test_function_evaluator_longer_indexer():
 
     # create info on continuous grids
     interpolation_info = {
-        "wealth": ContinuousGridSpec(
-            kind="linspace",
-            info=ContinuousGridInfo(start=100, stop=1100, n_points=6),
-        ),
-        "human_capital": ContinuousGridSpec(
-            kind="linspace",
-            info=ContinuousGridInfo(start=-3, stop=3, n_points=7),
-        ),
+        "wealth": LinspaceGrid(start=100, stop=1100, n_points=6),
+        "human_capital": LinspaceGrid(start=-3, stop=3, n_points=7),
     }
 
     # create info on axis of value function array
@@ -285,8 +271,7 @@ def test_get_lookup_function():
 def test_get_coordinate_finder():
     find_coordinate = _get_coordinate_finder(
         in_name="wealth",
-        grid_type="linspace",
-        grid_info=ContinuousGridInfo(start=0, stop=10, n_points=21),
+        grid=LinspaceGrid(start=0, stop=10, n_points=21),
     )
 
     calculated = find_coordinate(wealth=5.75)
@@ -320,22 +305,20 @@ def _utility(wealth, working):
 # ======================================================================================
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_get_function_evaluator_illustrative():
-    grid_info = ContinuousGridInfo(start=0, stop=1, n_points=3)
+    a_grid = LinspaceGrid(start=0, stop=1, n_points=3)
 
     space_info = SpaceInfo(
         axis_names=["a"],
         lookup_info={},
         interpolation_info={
-            "a": ContinuousGridSpec(kind="linspace", info=grid_info),
+            "a": a_grid,
         },
         indexer_infos=[],
     )
 
-    grid = linspace(**grid_info._asdict())
-
-    values = jnp.pi * grid + 2
+    values = jnp.pi * a_grid.to_jax() + 2
 
     # create the evaluator
     evaluator = get_function_representation(
@@ -354,7 +337,7 @@ def test_get_function_evaluator_illustrative():
     assert jnp.allclose(got, expected)
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_get_lookup_function_illustrative():
     values = jnp.array([0, 1, 4])
     func = _get_lookup_function(array_name="xyz", axis_names=["a"])
@@ -363,12 +346,11 @@ def test_get_lookup_function_illustrative():
     assert pure_lookup_func(a=2) == 4
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_get_coordinate_finder_illustrative():
     find_coordinate = _get_coordinate_finder(
         in_name="a",
-        grid_type="linspace",
-        grid_info=ContinuousGridInfo(start=0, stop=1, n_points=3),
+        grid=LinspaceGrid(start=0, stop=1, n_points=3),
     )
 
     assert find_coordinate(a=0) == 0
@@ -377,7 +359,7 @@ def test_get_coordinate_finder_illustrative():
     assert find_coordinate(a=0.25) == 0.5
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_get_interpolator_illustrative():
     interpolate = _get_interpolator(
         name_of_values_on_grid="test_name",
@@ -398,7 +380,7 @@ def f(a, b):
     assert interpolate(test_name=values, a=0.5, b=1.5) == -1
 
 
-@pytest.mark.illustrative()
+@pytest.mark.illustrative
 def test_fail_if_interpolation_axes_are_not_last_illustrative():
     # Empty intersection of axis_names and interpolation_info
     # ==================================================================================
diff --git a/tests/test_functools.py b/tests/test_functools.py
index f55ffe0e..b0bff402 100644
--- a/tests/test_functools.py
+++ b/tests/test_functools.py
@@ -4,6 +4,8 @@
 import jax.numpy as jnp
 import pytest
 from jax import vmap
+from numpy.testing import assert_array_almost_equal as aaae
+
 from lcm.functools import (
     all_as_args,
     all_as_kwargs,
@@ -12,7 +14,6 @@
     convert_kwargs_to_args,
     get_union_of_arguments,
 )
-from numpy.testing import assert_array_almost_equal as aaae
 
 # ======================================================================================
 # get_union_of_arguments
@@ -20,10 +21,10 @@
 
 
 def test_get_union_of_arguments():
-    def f(a, b):  # noqa: ARG001
+    def f(a, b):
         pass
 
-    def g(b, c):  # noqa: ARG001
+    def g(b, c):
         pass
 
     got = get_union_of_arguments([f, g])
@@ -160,7 +161,7 @@ def f(a, /, b):
 
 
 def test_allow_only_kwargs_signature_change():
-    def f(a, /, b, *, c):  # noqa: ARG001
+    def f(a, /, b, *, c):
         pass
 
     decorated = allow_only_kwargs(f)
@@ -239,7 +240,7 @@ def f(a, *, b):
 
 
 def test_allow_args_signature_change():
-    def f(a, /, b, *, c):  # noqa: ARG001
+    def f(a, /, b, *, c):
         pass
 
     decorated = allow_args(f)
diff --git a/tests/test_grid_helpers.py b/tests/test_grid_helpers.py
new file mode 100644
index 00000000..8468030b
--- /dev/null
+++ b/tests/test_grid_helpers.py
@@ -0,0 +1,153 @@
+import numpy as np
+import pytest
+from jax.scipy.ndimage import map_coordinates
+from numpy.testing import assert_array_almost_equal as aaae
+
+from lcm.grid_helpers import (
+    get_linspace_coordinate,
+    get_logspace_coordinate,
+    linspace,
+    logspace,
+)
+
+
+def test_linspace():
+    calculated = linspace(start=1, stop=2, n_points=6)
+    expected = np.array([1, 1.2, 1.4, 1.6, 1.8, 2])
+    aaae(calculated, expected)
+
+
+def test_linspace_mapped_value():
+    """For reference of the grid values, see expected grid in `test_linspace`."""
+    # Get position corresponding to a value in the grid
+    calculated = get_linspace_coordinate(
+        value=1.2,
+        start=1,
+        stop=2,
+        n_points=6,
+    )
+    assert np.allclose(calculated, 1.0)
+
+    # Get position corresponding to a value that is between two grid points
+    # ----------------------------------------------------------------------------------
+    # Here, the value is 1.3, that is in the middle of 1.2 and 1.4, which have the
+    # positions 1 and 2, respectively. Therefore, we want the position to be 1.5.
+    calculated = get_linspace_coordinate(
+        value=1.3,
+        start=1,
+        stop=2,
+        n_points=6,
+    )
+    assert np.allclose(calculated, 1.5)
+
+    # Get position corresponding to a value that is outside the grid
+    calculated = get_linspace_coordinate(
+        value=0.6,
+        start=1,
+        stop=2,
+        n_points=6,
+    )
+    assert np.allclose(calculated, -2.0)
+
+
+def test_logspace():
+    calculated = logspace(start=1, stop=100, n_points=7)
+    expected = np.array(
+        [1.0, 2.15443469, 4.64158883, 10.0, 21.5443469, 46.41588834, 100.0],
+    )
+    aaae(calculated, expected)
+
+
+def test_logspace_mapped_value():
+    """For reference of the grid values, see expected grid in `test_logspace`."""
+    calculated = get_logspace_coordinate(
+        value=(2.15443469 + 4.64158883) / 2,
+        start=1,
+        stop=100,
+        n_points=7,
+    )
+    assert np.allclose(calculated, 1.5)
+
+
+@pytest.mark.illustrative
+def test_map_coordinates_linear():
+    """Illustrative test on how the output of get_linspace_coordinate can be used."""
+    grid_info = {
+        "start": 0,
+        "stop": 1,
+        "n_points": 3,
+    }
+
+    grid = linspace(**grid_info)  # [0, 0.5, 1]
+
+    values = 2 * grid  # [0, 1.0, 2.0]
+
+    # We choose a coordinate that is exactly in the middle between the first and second
+    # entry of the grid.
+    coordinate = get_linspace_coordinate(
+        value=0.25,
+        **grid_info,
+    )
+
+    # Perform the linear interpolation
+    interpolated_value = map_coordinates(values, [coordinate], order=1, mode="nearest")
+    assert np.allclose(interpolated_value, 0.5)
+
+
+@pytest.mark.illustrative
+def test_map_coordinates_logarithmic():
+    """Illustrative test on how the output of get_logspace_coordinate can be used."""
+    grid_info = {
+        "start": 1,
+        "stop": 2,
+        "n_points": 3,
+    }
+
+    grid = logspace(**grid_info)  # [1.0, 1.414213562373095, 2.0]
+
+    values = 2 * grid  # [2.0, 2.82842712474619, 4.0]
+
+    # We choose a coordinate that is exactly in the middle between the first and second
+    # entry of the grid.
+    coordinate = get_logspace_coordinate(
+        value=(1.0 + 1.414213562373095) / 2,
+        **grid_info,
+    )
+
+    # Perform the linear interpolation
+    interpolated_value = map_coordinates(values, [coordinate], order=1, mode="nearest")
+    assert np.allclose(interpolated_value, (2.0 + 2.82842712474619) / 2)
+
+
+@pytest.mark.illustrative
+def test_map_coordinates_linear_outside_grid():
+    """Illustrative test on what happens to values outside the grid.
+
+    If mode="nearest", the value corresponding to the closest coordinate that still lies
+    within the grid is returned.
+
+    """
+    grid_info = {
+        "start": 0,
+        "stop": 1,
+        "n_points": 2,
+    }
+
+    grid = linspace(**grid_info)  # [0, 1]
+
+    values = 2 * grid  # [0, 2.0]
+
+    # We choose a coordinate that is exactly in the middle between the first and second
+    # entry of the grid.
+    coordinate = get_linspace_coordinate(
+        value=-1,
+        **grid_info,
+    )
+
+    assert coordinate == -1.0
+
+    # Perform the linear interpolation
+    interpolated_value = map_coordinates(values, [coordinate], order=1, mode="nearest")
+
+    # Because mode="nearest", the value at the first grid point is returned
+    assert np.allclose(interpolated_value, 0.0)
diff --git a/tests/test_grids.py b/tests/test_grids.py
index ccafaac5..aad98b5b 100644
--- a/tests/test_grids.py
+++ b/tests/test_grids.py
@@ -1,152 +1,96 @@
 import numpy as np
 import pytest
-from jax.scipy.ndimage import map_coordinates
+
+from lcm.exceptions import GridInitializationError
 from lcm.grids import (
-    get_linspace_coordinate,
-    get_logspace_coordinate,
-    linspace,
-    logspace,
+    DiscreteGrid,
+    LinspaceGrid,
+    LogspaceGrid,
+    _validate_continuous_grid,
+    _validate_discrete_grid,
 )
-from numpy.testing import assert_array_almost_equal as aaae
-
-
-def test_linspace():
-    calculated = linspace(start=1, stop=2, n_points=6)
-    expected = np.array([1, 1.2, 1.4, 1.6, 1.8, 2])
-    aaae(calculated, expected)
-
-
-def test_linspace_mapped_value():
-    """For reference of the grid values, see expected grid in `test_linspace`."""
-    # Get position corresponding to a value in the grid
-    calculated = get_linspace_coordinate(
-        value=1.2,
-        start=1,
-        stop=2,
-        n_points=6,
-    )
-    assert np.allclose(calculated, 1.0)
-
-    # Get position corresponding to a value that is between two grid points
-    # ----------------------------------------------------------------------------------
-    # Here, the value is 1.3, that is in the middle of 1.2 and 1.4, which have the
-    # positions 1 and 2, respectively. Therefore, we want the position to be 1.5.
-    calculated = get_linspace_coordinate(
-        value=1.3,
-        start=1,
-        stop=2,
-        n_points=6,
-    )
-    assert np.allclose(calculated, 1.5)
-
-    # Get position corresponding to a value that is outside the grid
-    calculated = get_linspace_coordinate(
-        value=0.6,
-        start=1,
-        stop=2,
-        n_points=6,
-    )
-    assert np.allclose(calculated, -2.0)
-
-
-def test_logspace():
-    calculated = logspace(start=1, stop=100, n_points=7)
-    expected = np.array(
-        [1.0, 2.15443469, 4.64158883, 10.0, 21.5443469, 46.41588834, 100.0],
-    )
-    aaae(calculated, expected)
-
-
-def test_logspace_mapped_value():
-    """For reference of the grid values, see expected grid in `test_logspace`."""
-    calculated = get_logspace_coordinate(
-        value=(2.15443469 + 4.64158883) / 2,
-        start=1,
-        stop=100,
-        n_points=7,
-    )
-    assert np.allclose(calculated, 1.5)
-
-
-@pytest.mark.illustrative()
-def test_map_coordinates_linear():
-    """Illustrative test on how the output of get_linspace_coordinate can be used."""
-    grid_info = {
-        "start": 0,
-        "stop": 1,
-        "n_points": 3,
-    }
-
-    grid = linspace(**grid_info)  # [0, 0.5, 1]
-
-    values = 2 * grid  # [0, 1.0, 2.0]
-
-    # We choose a coordinate that is exactly in the middle between the first and second
-    # entry of the grid.
-    coordinate = get_linspace_coordinate(
-        value=0.25,
-        **grid_info,
-    )
-
-    # Perform the linear interpolation
-    interpolated_value = map_coordinates(values, [coordinate], order=1, mode="nearest")
-    assert np.allclose(interpolated_value, 0.5)
-
-
-@pytest.mark.illustrative()
-def test_map_coordinates_logarithmic():
-    """Illustrative test on how the output of get_logspace_coordinate can be used."""
-    grid_info = {
-        "start": 1,
-        "stop": 2,
-        "n_points": 3,
-    }
-
-    grid = logspace(**grid_info)  # [1.0, 1.414213562373095, 2.0]
-
-    values = 2 * grid  # [2.0, 2.82842712474619, 4.0]
-
-    # We choose a coordinate that is exactly in the middle between the first and second
-    # entry of the grid.
-    coordinate = get_logspace_coordinate(
-        value=(1.0 + 1.414213562373095) / 2,
-        **grid_info,
-    )
-
-    # Perform the linear interpolation
-    interpolated_value = map_coordinates(values, [coordinate], order=1, mode="nearest")
-    assert np.allclose(interpolated_value, (2.0 + 2.82842712474619) / 2)
-
-
-@pytest.mark.illustrative()
-def test_map_coordinates_linear_outside_grid():
-    """Illustrative test on what happens to values outside the grid.
-
-    If mode="nearest", the value corresponding to the closest coordinate that still lies
-    within the grid is returned.
-
-    """
-    grid_info = {
-        "start": 0,
-        "stop": 1,
-        "n_points": 2,
-    }
-
-    grid = linspace(**grid_info)  # [0, 1]
-
-    values = 2 * grid  # [0, 2.0]
-
-    # We choose a coordinate that is exactly in the middle between the first and second
-    # entry of the grid.
-    coordinate = get_linspace_coordinate(
-        value=-1,
-        **grid_info,
-    )
-
-    assert coordinate == -1.0
 
-    # Perform the linear interpolation
-    interpolated_value = map_coordinates(values, [coordinate], order=1, mode="nearest")
 
-    # Because mode="nearest", the value at the first grid point is returned
-    assert np.allclose(interpolated_value, 0.0)
+def test_validate_discrete_grid_empty():
+    assert _validate_discrete_grid([]) == ["options must contain at least one element"]
+
+
+def test_validate_discrete_grid_non_scalar_input():
+    assert _validate_discrete_grid([1, "a"]) == [
+        "options must contain only scalar int or float values",
+        "options must be a list of consecutive integers starting from 0",
+    ]
+
+
+def test_validate_discrete_grid_non_unique():
+    assert _validate_discrete_grid([1, 2, 2]) == [
+        "options must contain unique values",
+        "options must be a list of consecutive integers starting from 0",
+    ]
+
+
+def test_validate_discrete_grid_non_consecutive():
+    assert _validate_discrete_grid([1, 3, 2]) == [
+        "options must be a list of consecutive integers starting from 0"
+    ]
+
+
+def test_validate_continuous_grid_invalid_start():
+    assert _validate_continuous_grid("a", 1, 10) == [
+        "start must be a scalar int or float value"
+    ]
+
+
+def test_validate_continuous_grid_invalid_stop():
+    assert _validate_continuous_grid(1, "a", 10) == [
+        "stop must be a scalar int or float value"
+    ]
+
+
+def test_validate_continuous_grid_invalid_n_points():
+    assert _validate_continuous_grid(1, 2, "a") == [
+        "n_points must be an int greater than 0 but is a"
+    ]
+
+
+def test_validate_continuous_grid_negative_n_points():
+    assert _validate_continuous_grid(1, 2, -1) == [
+        "n_points must be an int greater than 0 but is -1"
+    ]
+
+
+def test_validate_continuous_grid_start_greater_than_stop():
+    assert _validate_continuous_grid(2, 1, 10) == ["start must be less than stop"]
+
+
+def test_linspace_grid_creation():
+    grid = LinspaceGrid(start=1, stop=5, n_points=5)
+    assert np.allclose(grid.to_jax(), np.linspace(1, 5, 5))
+
+
+def test_logspace_grid_creation():
+    grid = LogspaceGrid(start=1, stop=10, n_points=3)
+    assert np.allclose(grid.to_jax(), np.logspace(np.log10(1), np.log10(10), 3))
+
+
+def test_discrete_grid_creation():
+    grid = DiscreteGrid(options=[0, 1, 2])
+    assert np.allclose(grid.to_jax(), np.arange(3))
+
+
+def test_linspace_grid_invalid_start():
+    with pytest.raises(GridInitializationError, match="start must be less than stop"):
+        LinspaceGrid(start=1, stop=0, n_points=10)
+
+
+def test_logspace_grid_invalid_start():
+    with pytest.raises(GridInitializationError, match="start must be less than stop"):
+        LogspaceGrid(start=1, stop=0, n_points=10)
+
+
+def test_discrete_grid_invalid_options():
+    with pytest.raises(
+        GridInitializationError,
+        match="options must contain only scalar int or float values",
+    ):
+        DiscreteGrid(options=[1, "a"])
diff --git a/tests/test_model.py b/tests/test_model.py
new file mode 100644
index 00000000..547a7762
--- /dev/null
+++ b/tests/test_model.py
@@ -0,0 +1,134 @@
+import pytest
+
+from lcm.exceptions import ModelInitilizationError
+from lcm.grids import DiscreteGrid
+from lcm.user_model import Model
+
+
+def test_model_invalid_states():
+    with pytest.raises(ModelInitilizationError, match="states must be a dictionary"):
+        Model(
+            n_periods=2,
+            states="health",
+            choices={},
+            functions={"utility": lambda: 0},
+        )
+
+
+def test_model_invalid_choices():
+    with pytest.raises(ModelInitilizationError, match="choices must be a dictionary"):
+        Model(
+            n_periods=2,
+            states={},
+            choices="exercise",
+            functions={"utility": lambda: 0},
+        )
+
+
+def test_model_invalid_functions():
+    with pytest.raises(ModelInitilizationError, match="functions must be a dictionary"):
+        Model(
+            n_periods=2,
+            states={},
+            choices={},
+            functions="utility",
+        )
+
+
+def test_model_invalid_functions_values():
+    with pytest.raises(
+        ModelInitilizationError, match="function values must be a callable, but is 0."
+    ):
+        Model(
+            n_periods=2,
+            states={},
+            choices={},
+            functions={"utility": 0},
+        )
+
+
+def test_model_invalid_functions_keys():
+    with pytest.raises(
+        ModelInitilizationError, match="function keys must be a strings, but is 0."
+    ):
+        Model(
+            n_periods=2,
+            states={},
+            choices={},
+            functions={0: lambda: 0},
+        )
+
+
+def test_model_invalid_choices_values():
+    with pytest.raises(
+        ModelInitilizationError, match="choices value 0 must be an LCM grid."
+    ):
+        Model(
+            n_periods=2,
+            states={},
+            choices={"exercise": 0},
+            functions={"utility": lambda: 0},
+        )
+
+
+def test_model_invalid_states_values():
+    with pytest.raises(
+        ModelInitilizationError, match="states value 0 must be an LCM grid."
+    ):
+        Model(
+            n_periods=2,
+            states={"health": 0},
+            choices={},
+            functions={"utility": lambda: 0},
+        )
+
+
+def test_model_invalid_n_periods():
+    with pytest.raises(
+        ModelInitilizationError, match="Number of periods must be a positive integer."
+    ):
+        Model(
+            n_periods=0,
+            states={},
+            choices={},
+            functions={"utility": lambda: 0},
+        )
+
+
+def test_model_missing_next_func():
+    with pytest.raises(
+        ModelInitilizationError,
+        match="Each state must have a corresponding next state function.",
+    ):
+        Model(
+            n_periods=2,
+            states={"health": DiscreteGrid([0, 1])},
+            choices={"exercise": DiscreteGrid([0, 1])},
+            functions={"utility": lambda: 0},
+        )
+
+
+def test_model_missing_utility():
+    with pytest.raises(
+        ModelInitilizationError,
+        match="Utility function is not defined. LCM expects a function called 'utility",
+    ):
+        Model(
+            n_periods=2,
+            states={},
+            choices={},
+            functions={},
+        )
+
+
+def test_model_overlapping_states_choices():
+    with pytest.raises(
+        ModelInitilizationError,
+        match="States and choices cannot have overlapping names.",
+    ):
+        Model(
+            n_periods=2,
+            states={"health": DiscreteGrid([0, 1])},
+            choices={"health": DiscreteGrid([0, 1])},
+            functions={"utility": lambda: 0},
+        )
diff --git a/tests/test_model_functions.py b/tests/test_model_functions.py
index 57e80662..bd9a933d 100644
--- a/tests/test_model_functions.py
+++ b/tests/test_model_functions.py
@@ -1,15 +1,15 @@
 import jax.numpy as jnp
 import pandas as pd
-from lcm.interfaces import Model
+from numpy.testing import assert_array_equal
+
+from lcm.input_processing import process_model
+from lcm.interfaces import InternalModel
 from lcm.model_functions import (
     get_combined_constraint,
     get_multiply_weights,
     get_utility_and_feasibility_function,
 )
-from lcm.process_model import process_model
 from lcm.state_space import create_state_choice_space
-from numpy.testing import assert_array_equal
-
 from tests.test_models.deterministic import get_model_config, utility
 
 
@@ -27,14 +27,14 @@ def h():
         {"is_constraint": [True, True, False]},
         index=["f", "g", "h"],
     )
-    model = Model(
+    model = InternalModel(
         grids=None,
         gridspecs=None,
         variable_info=None,
         functions={"f": f, "g": g, "h": h},
         function_info=function_info,
         params=None,
-        shocks=None,
+        random_utility_shocks=None,
         n_periods=None,
     )
     combined_constraint = get_combined_constraint(model)
diff --git a/tests/test_models/deterministic.py b/tests/test_models/deterministic.py
index 8be2cb1e..0c103e9c 100644
--- a/tests/test_models/deterministic.py
+++ b/tests/test_models/deterministic.py
@@ -11,16 +11,7 @@
 
 import jax.numpy as jnp
 
-# ======================================================================================
-# Numerical parameters and constants
-# ======================================================================================
-
-N_GRID_POINTS = {
-    "wealth": 100,
-    "consumption": 500,
-}
-
-RETIREMENT_AGE = 65
+from lcm import DiscreteGrid, LinspaceGrid, Model
 
 # ======================================================================================
 # Model functions
@@ -113,13 +104,14 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
 # Model specifications
 # ======================================================================================
 
-ISKHAKOV_ET_AL_2017 = {
-    "description": (
+ISKHAKOV_ET_AL_2017 = Model(
+    description=(
         "Corresponds to the example model in Iskhakov et al. (2017). In comparison to "
         "the extensions below, wage is treated as a constant parameter and therefore "
         "there is no need for the wage and age functions."
     ),
-    "functions": {
+    n_periods=3,
+    functions={
         "utility": utility_with_filter,
         "next_wealth": next_wealth,
         "next_lagged_retirement": lambda retirement: retirement,
@@ -128,34 +120,32 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
         "labor_income": labor_income,
         "working": working,
     },
-    "choices": {
-        "retirement": {"options": [0, 1]},
-        "consumption": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 400,
-            "n_points": N_GRID_POINTS["consumption"],
-        },
+    choices={
+        "retirement": DiscreteGrid([0, 1]),
+        "consumption": LinspaceGrid(
+            start=1,
+            stop=400,
+            n_points=500,
+        ),
     },
-    "states": {
-        "wealth": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 400,
-            "n_points": N_GRID_POINTS["wealth"],
-        },
-        "lagged_retirement": {"options": [0, 1]},
+    states={
+        "wealth": LinspaceGrid(
+            start=1,
+            stop=400,
+            n_points=100,
+        ),
+        "lagged_retirement": DiscreteGrid([0, 1]),
     },
-    "n_periods": 3,
-}
+)
 
 
-ISKHAKOV_ET_AL_2017_STRIPPED_DOWN = {
-    "description": (
+ISKHAKOV_ET_AL_2017_STRIPPED_DOWN = Model(
+    description=(
         "Starts from Iskhakov et al. (2017), removes filters and the lagged_retirement "
         "state, and adds wage function that depends on age."
     ),
-    "functions": {
+    n_periods=3,
+    functions={
         "utility": utility,
         "next_wealth": next_wealth,
         "consumption_constraint": consumption_constraint,
@@ -164,33 +154,31 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
         "wage": wage,
         "age": age,
     },
-    "choices": {
-        "retirement": {"options": [0, 1]},
-        "consumption": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 400,
-            "n_points": N_GRID_POINTS["consumption"],
-        },
+    choices={
+        "retirement": DiscreteGrid([0, 1]),
+        "consumption": LinspaceGrid(
+            start=1,
+            stop=400,
+            n_points=500,
+        ),
     },
-    "states": {
-        "wealth": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 400,
-            "n_points": N_GRID_POINTS["wealth"],
-        },
+    states={
+        "wealth": LinspaceGrid(
+            start=1,
+            stop=400,
+            n_points=100,
+        ),
     },
-    "n_periods": 3,
-}
+)
 
 
-ISKHAKOV_ET_AL_2017_FULLY_DISCRETE = {
-    "description": (
+ISKHAKOV_ET_AL_2017_FULLY_DISCRETE = Model(
+    description=(
         "Starts from Iskhakov et al. (2017), removes filters and the lagged_retirement "
         "state, and makes the consumption decision discrete."
     ),
-    "functions": {
+    n_periods=3,
+    functions={
         "utility": utility_fully_discrete,
         "next_wealth": next_wealth,
         "consumption_constraint": consumption_constraint,
@@ -198,20 +186,18 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
         "working": working,
         "consumption": consumption,
     },
-    "choices": {
-        "retirement": {"options": [0, 1]},
-        "consumption_index": {"options": [0, 1]},
+    choices={
+        "retirement": DiscreteGrid([0, 1]),
+        "consumption_index": DiscreteGrid([0, 1]),
     },
-    "states": {
-        "wealth": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 400,
-            "n_points": N_GRID_POINTS["wealth"],
-        },
+    states={
+        "wealth": LinspaceGrid(
+            start=1,
+            stop=400,
+            n_points=100,
+        ),
     },
-    "n_periods": 3,
-}
+)
 
 
 # ======================================================================================
@@ -227,8 +213,7 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
 
 def get_model_config(model_name: str, n_periods: int):
     model_config = deepcopy(IMPLEMENTED_MODELS[model_name])
-    model_config["n_periods"] = n_periods
-    return model_config
+    return model_config.replace(n_periods=n_periods)
 
 
 def get_params(beta=0.95, disutility_of_work=0.25, interest_rate=0.05, wage=5.0):
diff --git a/tests/test_models/stochastic.py b/tests/test_models/stochastic.py
index 6ebdb2bb..e5e96005 100644
--- a/tests/test_models/stochastic.py
+++ b/tests/test_models/stochastic.py
@@ -12,16 +12,9 @@
 from copy import deepcopy
 
 import jax.numpy as jnp
-import lcm
 
-# ======================================================================================
-# Numerical parameters and constants
-# ======================================================================================
-
-N_GRID_POINTS = {
-    "wealth": 100,
-    "consumption": 200,
-}
+import lcm
+from lcm import DiscreteGrid, LinspaceGrid, Model
 
 # ======================================================================================
 # Model functions
@@ -63,12 +56,12 @@ def next_wealth(wealth, consumption, labor_income, interest_rate):
 # Stochastic state transitions
 # --------------------------------------------------------------------------------------
 @lcm.mark.stochastic
-def next_health(health, partner):  # noqa: ARG001
+def next_health(health, partner):
     pass
 
 
 @lcm.mark.stochastic
-def next_partner(_period, working, partner):  # noqa: ARG001
+def next_partner(_period, working, partner):
     pass
 
 
@@ -83,12 +76,13 @@ def consumption_constraint(consumption, wealth):
 # Model specification and parameters
 # ======================================================================================
 
-MODEL_CONFIG = {
-    "description": (
+MODEL_CONFIG = Model(
+    description=(
         "Starts from Iskhakov et al. (2017), removes filters and the lagged_retirement "
         "state, and adds discrete stochastic state variables health and partner."
     ),
-    "functions": {
+    n_periods=3,
+    functions={
         "utility": utility,
         "next_wealth": next_wealth,
         "next_health": next_health,
@@ -96,27 +90,24 @@ def consumption_constraint(consumption, wealth):
         "consumption_constraint": consumption_constraint,
         "labor_income": labor_income,
     },
-    "choices": {
-        "working": {"options": [0, 1]},
-        "consumption": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 100,
-            "n_points": N_GRID_POINTS["consumption"],
-        },
+    choices={
+        "working": DiscreteGrid([0, 1]),
+        "consumption": LinspaceGrid(
+            start=1,
+            stop=100,
+            n_points=200,
+        ),
     },
-    "states": {
-        "health": {"options": [0, 1]},
-        "partner": {"options": [0, 1]},
-        "wealth": {
-            "grid_type": "linspace",
-            "start": 1,
-            "stop": 100,
-            "n_points": N_GRID_POINTS["wealth"],
-        },
+    states={
+        "health": DiscreteGrid([0, 1]),
+        "partner": DiscreteGrid([0, 1]),
+        "wealth": LinspaceGrid(
+            start=1,
+            stop=100,
+            n_points=100,
+        ),
     },
-    "n_periods": 3,
-}
+)
 
 
 # ======================================================================================
@@ -130,8 +121,7 @@ def consumption_constraint(consumption, wealth):
 
 def get_model_config(model_name: str, n_periods: int):
     model_config = deepcopy(IMPLEMENTED_MODELS[model_name])
-    model_config["n_periods"] = n_periods
-    return model_config
+    return model_config.replace(n_periods=n_periods)
 
 
 def get_params(
diff --git a/tests/test_next_state.py b/tests/test_next_state.py
index 651c508f..7bdcbb27 100644
--- a/tests/test_next_state.py
+++ b/tests/test_next_state.py
@@ -1,10 +1,10 @@
 import jax.numpy as jnp
 import pandas as pd
-from lcm.interfaces import Model
-from lcm.next_state import _get_stochastic_next_func, get_next_state_function
-from lcm.process_model import process_model
 from pybaum import tree_equal
 
+from lcm.input_processing import process_model
+from lcm.interfaces import InternalModel
+from lcm.next_state import _get_stochastic_next_func, get_next_state_function
 from tests.test_models.deterministic import get_model_config
 
 # ======================================================================================
@@ -61,14 +61,14 @@ def f_weight_b(state):  # noqa: ARG001
         index=["a", "b"],
     )
 
-    model = Model(
+    model = InternalModel(
         functions=functions,
         grids=grids,
         function_info=function_info,
         gridspecs=None,
         variable_info=None,
         params=None,
-        shocks=None,
+        random_utility_shocks=None,
         n_periods=1,
     )
 
diff --git a/tests/test_random_choice.py b/tests/test_random_choice.py
index 714556ff..5335d02c 100644
--- a/tests/test_random_choice.py
+++ b/tests/test_random_choice.py
@@ -1,5 +1,6 @@
 import jax
 import jax.numpy as jnp
+
 from lcm.random_choice import random_choice
 
 
diff --git a/tests/test_regression_test.py b/tests/test_regression_test.py
index ec9b04cc..4a6b5edf 100644
--- a/tests/test_regression_test.py
+++ b/tests/test_regression_test.py
@@ -1,10 +1,10 @@
 import jax.numpy as jnp
 import pandas as pd
-from lcm._config import TEST_DATA
-from lcm.entry_point import get_lcm_function
 from numpy.testing import assert_array_almost_equal as aaae
 from pandas.testing import assert_frame_equal
 
+from lcm._config import TEST_DATA
+from lcm.entry_point import get_lcm_function
 from tests.test_models.deterministic import get_model_config, get_params
 
 
diff --git a/tests/test_simulate.py b/tests/test_simulate.py
index ff8b0988..a9ab723c 100644
--- a/tests/test_simulate.py
+++ b/tests/test_simulate.py
@@ -2,14 +2,17 @@
 import pandas as pd
 import pytest
 from jax import random
+from numpy.testing import assert_array_almost_equal, assert_array_equal
+from pybaum import tree_equal
+
 from lcm.entry_point import (
     create_compute_conditional_continuation_policy,
     get_lcm_function,
 )
+from lcm.input_processing import process_model
 from lcm.logging import get_logger
 from lcm.model_functions import get_utility_and_feasibility_function
 from lcm.next_state import _get_next_state_function_simulation
-from lcm.process_model import process_model
 from lcm.simulate import (
     _as_data_frame,
     _compute_targets,
@@ -24,11 +27,7 @@
     simulate,
 )
 from lcm.state_space import create_state_choice_space
-from numpy.testing import assert_array_almost_equal, assert_array_equal
-from pybaum import tree_equal
-
 from tests.test_models.deterministic import (
-    N_GRID_POINTS,
     get_model_config,
     get_params,
 )
@@ -38,7 +37,7 @@
 # ======================================================================================
 
 
-@pytest.fixture()
+@pytest.fixture
 def simulate_inputs():
     model_config = get_model_config("iskhakov_et_al_2017_stripped_down", n_periods=1)
     model = process_model(model_config)
@@ -66,10 +65,12 @@ def simulate_inputs():
         )
         compute_ccv_policy_functions.append(compute_ccv)
 
+    n_grid_points = model_config.choices["consumption"].n_points
+
     return {
         "state_indexers": [{}],
         "continuous_choice_grids": [
-            {"consumption": jnp.linspace(1, 100, num=N_GRID_POINTS["consumption"])},
+            {"consumption": jnp.linspace(1, 100, num=n_grid_points)},
         ],
         "compute_ccv_policy_functions": compute_ccv_policy_functions,
         "model": model,
@@ -103,19 +104,20 @@ def test_simulate_using_raw_inputs(simulate_inputs):
 # ======================================================================================
 
 
-@pytest.fixture()
+@pytest.fixture
 def iskhakov_et_al_2017_stripped_down_model_solution():
     def _model_solution(n_periods):
         model_config = get_model_config(
             "iskhakov_et_al_2017_stripped_down",
             n_periods=n_periods,
         )
-        model_config["functions"] = {
+        updated_functions = {
             # remove dependency on age, so that wage becomes a parameter
             name: func
-            for name, func in model_config["functions"].items()
+            for name, func in model_config.functions.items()
             if name not in ["age", "wage"]
         }
+        model_config = model_config.replace(functions=updated_functions)
         solve_model, _ = get_lcm_function(model_config, targets="solve")
 
         params = get_params()
@@ -159,7 +161,6 @@ def test_simulate_using_get_lcm_function(
     assert_array_equal(res.loc[last_period_index, :]["retirement"], 1)
 
     for period in range(n_periods):
-
         # assert that higher wealth leads to higher consumption in each period
         assert (res.loc[period]["consumption"].diff()[1:] >= 0).all()
 
@@ -254,7 +255,6 @@ def test_effect_of_disutility_of_work():
     # Asserting
     # ==================================================================================
     for period in range(5):
-
         # We expect that individuals with lower disutility of work, work (weakly) more
         # and thus consume (weakly) more
         assert (
diff --git a/tests/test_solution_on_toy_model.py b/tests/test_solution_on_toy_model.py
index 7f6f91aa..3da3b008 100644
--- a/tests/test_solution_on_toy_model.py
+++ b/tests/test_solution_on_toy_model.py
@@ -1,16 +1,19 @@
 """Test analytical solution and simulation with only discrete choices."""
 
 from copy import deepcopy
+from dataclasses import replace
 
 import jax.numpy as jnp
-import lcm
 import numpy as np
 import pandas as pd
 import pytest
-from lcm.entry_point import get_lcm_function
 from numpy.testing import assert_array_almost_equal as aaae
 from pandas.testing import assert_frame_equal
 
+import lcm
+from lcm import DiscreteGrid, LinspaceGrid, Model
+from lcm.entry_point import get_lcm_function
+
 
 # ======================================================================================
 # Model specification
@@ -27,36 +30,35 @@ def consumption_constraint(consumption, wealth):
     return consumption <= wealth
 
 
-DETERMINISTIC_MODEL = {
-    "functions": {
+DETERMINISTIC_MODEL = Model(
+    functions={
         "utility": utility,
         "next_wealth": next_wealth,
         "consumption_constraint": consumption_constraint,
     },
-    "choices": {
-        "consumption": {"options": [0, 1]},
-        "working": {"options": [0, 1]},
+    n_periods=2,
+    choices={
+        "consumption": DiscreteGrid([0, 1]),
+        "working": DiscreteGrid([0, 1]),
     },
-    "states": {
-        "wealth": {
-            "grid_type": "linspace",
-            "start": 0,
-            "stop": 2,
-            "n_points": None,
-        },
+    states={
+        "wealth": LinspaceGrid(
+            start=0,
+            stop=2,
+            n_points=1,
+        ),
     },
-    "n_periods": 2,
-}
+)
 
 
 @lcm.mark.stochastic
-def next_health(health):  # noqa: ARG001
+def next_health(health):
     pass
 
 
 STOCHASTIC_MODEL = deepcopy(DETERMINISTIC_MODEL)
-STOCHASTIC_MODEL["functions"]["next_health"] = next_health
-STOCHASTIC_MODEL["states"]["health"] = {"options": [0, 1]}
+STOCHASTIC_MODEL.functions["next_health"] = next_health
+STOCHASTIC_MODEL.states["health"] = DiscreteGrid([0, 1])
 
 
 # ======================================================================================
@@ -311,8 +313,9 @@ def analytical_simulate_stochastic(initial_wealth, initial_health, health_1, par
 def test_deterministic_solve(beta, n_wealth_points):
     # Update model
     # ==================================================================================
-    model = deepcopy(DETERMINISTIC_MODEL)
-    model["states"]["wealth"]["n_points"] = n_wealth_points
+    new_states = DETERMINISTIC_MODEL.states
+    new_states["wealth"] = replace(new_states["wealth"], n_points=n_wealth_points)
+    model = DETERMINISTIC_MODEL.replace(states=new_states)
 
     # Solve model using LCM
     # ==================================================================================
@@ -325,11 +328,10 @@ def test_deterministic_solve(beta, n_wealth_points):
 
     # Compute analytical solution
     # ==================================================================================
-    wealth_grid_spec = model["states"]["wealth"]
     wealth_grid = np.linspace(
-        start=wealth_grid_spec["start"],
-        stop=wealth_grid_spec["stop"],
-        num=wealth_grid_spec["n_points"],
+        start=model.states["wealth"].start,
+        stop=model.states["wealth"].stop,
+        num=model.states["wealth"].n_points,
     )
     expected = analytical_solve_deterministic(wealth_grid, params=params)
 
@@ -345,8 +347,9 @@ def test_deterministic_solve(beta, n_wealth_points):
 def test_deterministic_simulate(beta, n_wealth_points):
     # Update model
     # ==================================================================================
-    model = deepcopy(DETERMINISTIC_MODEL)
-    model["states"]["wealth"]["n_points"] = n_wealth_points
+    new_states = DETERMINISTIC_MODEL.states
+    new_states["wealth"] = replace(new_states["wealth"], n_points=n_wealth_points)
+    model = DETERMINISTIC_MODEL.replace(states=new_states)
 
     # Simulate model using LCM
     # ==================================================================================
@@ -380,12 +383,11 @@ def test_deterministic_simulate(beta, n_wealth_points):
 @pytest.mark.parametrize("n_wealth_points", [100, 1_000])
 @pytest.mark.parametrize("health_transition", HEALTH_TRANSITION)
 def test_stochastic_solve(beta, n_wealth_points, health_transition):
-    beta = 0.9
-    n_wealth_points = 100
     # Update model
     # ==================================================================================
-    model = deepcopy(STOCHASTIC_MODEL)
-    model["states"]["wealth"]["n_points"] = n_wealth_points
+    new_states = STOCHASTIC_MODEL.states
+    new_states["wealth"] = replace(new_states["wealth"], n_points=n_wealth_points)
+    model = STOCHASTIC_MODEL.replace(states=new_states)
 
     # Solve model using LCM
     # ==================================================================================
@@ -398,11 +400,10 @@ def test_stochastic_solve(beta, n_wealth_points, health_transition):
 
     # Compute analytical solution
     # ==================================================================================
-    wealth_grid_spec = model["states"]["wealth"]
     _wealth_grid = np.linspace(
-        start=wealth_grid_spec["start"],
-        stop=wealth_grid_spec["stop"],
-        num=wealth_grid_spec["n_points"],
+        start=model.states["wealth"].start,
+        stop=model.states["wealth"].stop,
+        num=model.states["wealth"].n_points,
     )
     _health_grid = np.array([0, 1])
 
@@ -432,8 +433,9 @@ def test_stochastic_solve(beta, n_wealth_points, health_transition):
 def test_stochastic_simulate(beta, n_wealth_points, health_transition):
     # Update model
     # ==================================================================================
-    model = deepcopy(STOCHASTIC_MODEL)
-    model["states"]["wealth"]["n_points"] = n_wealth_points
+    new_states = STOCHASTIC_MODEL.states
+    new_states["wealth"] = replace(new_states["wealth"], n_points=n_wealth_points)
+    model = STOCHASTIC_MODEL.replace(states=new_states)
 
     # Simulate model using LCM
     # ==================================================================================
diff --git a/tests/test_solve_brute.py b/tests/test_solve_brute.py
index 97687bf4..7fc11f2c 100644
--- a/tests/test_solve_brute.py
+++ b/tests/test_solve_brute.py
@@ -1,11 +1,12 @@
 import jax.numpy as jnp
 import numpy as np
 from jax.scipy.ndimage import map_coordinates
+from numpy.testing import assert_array_almost_equal as aaae
+
 from lcm.entry_point import create_compute_conditional_continuation_value
 from lcm.interfaces import Space
 from lcm.logging import get_logger
 from lcm.solve_brute import solve, solve_continuous_problem
-from numpy.testing import assert_array_almost_equal as aaae
 
 
 def test_solve_brute():
@@ -94,7 +95,7 @@ def _get_continuation_value(lazy, wealth, vf_arr):
     # create emax aggregators and choice segments
     # ==================================================================================
 
-    def calculate_emax(values):
+    def calculate_emax(values, params):  # noqa: ARG001
         """Take max over axis that corresponds to working."""
         return values.max(axis=1)
 
diff --git a/tests/test_state_space.py b/tests/test_state_space.py
index b1d15970..c5d9384c 100644
--- a/tests/test_state_space.py
+++ b/tests/test_state_space.py
@@ -2,8 +2,10 @@
 import numpy as np
 import pandas as pd
 import pytest
-from lcm.interfaces import Model
-from lcm.process_model import process_model
+from numpy.testing import assert_array_almost_equal as aaae
+
+from lcm.input_processing import process_model
+from lcm.interfaces import InternalModel
 from lcm.state_space import (
     create_combination_grid,
     create_filter_mask,
@@ -11,8 +13,6 @@
     create_indexers_and_segments,
     create_state_choice_space,
 )
-from numpy.testing import assert_array_almost_equal as aaae
-
 from tests.test_models.deterministic import get_model_config
 
 
@@ -28,7 +28,7 @@ def test_create_state_choice_space():
     )
 
 
-@pytest.fixture()
+@pytest.fixture
 def filter_mask_inputs():
     def age(period):
         return period + 18
@@ -62,13 +62,13 @@ def absorbing_retirement_filter(retirement, lagged_retirement):
 
     # create a model instance where some attributes are set to None because they
     # are not needed for create_filter_mask
-    return Model(
+    return InternalModel(
         grids=grids,
         gridspecs=None,
         variable_info=None,
         functions=functions,
         function_info=function_info,
-        shocks=None,
+        random_utility_shocks=None,
         n_periods=100,
         params={},
     )
diff --git a/tests/test_stochastic.py b/tests/test_stochastic.py
index fd206cf2..ab0c369f 100644
--- a/tests/test_stochastic.py
+++ b/tests/test_stochastic.py
@@ -1,11 +1,11 @@
 import jax.numpy as jnp
-import lcm
 import pandas as pd
 import pytest
+
+import lcm
 from lcm.entry_point import (
     get_lcm_function,
 )
-
 from tests.test_models.stochastic import get_model_config, get_params
 
 # ======================================================================================
@@ -63,7 +63,7 @@ def test_get_lcm_function_with_solve_target():
 # ======================================================================================
 
 
-@pytest.fixture()
+@pytest.fixture
 def model_and_params():
     """Return a simple deterministic and stochastic model with parameters.
 
@@ -76,14 +76,14 @@ def model_and_params():
     # Overwrite health transition with simple stochastic version and deterministic one
     # ==================================================================================
     @lcm.mark.stochastic
-    def next_health_stochastic(health):  # noqa: ARG001
+    def next_health_stochastic(health):
         pass
 
     def next_health_deterministic(health):
         return health
 
-    model_deterministic["functions"]["next_health"] = next_health_deterministic
-    model_stochastic["functions"]["next_health"] = next_health_stochastic
+    model_deterministic.functions["next_health"] = next_health_deterministic
+    model_stochastic.functions["next_health"] = next_health_stochastic
 
     params = get_params(
         beta=0.95,