diff --git a/.github/workflows/build-test-python-package.yml b/.github/workflows/build-test-python-package.yml
index 51a77ac..aae2424 100644
--- a/.github/workflows/build-test-python-package.yml
+++ b/.github/workflows/build-test-python-package.yml
@@ -17,7 +17,7 @@ jobs:
         runs-on: ubuntu-latest
         strategy:
             matrix:
-                python-version: ["3.9", "3.10", "3.11"]
+                python-version: ["3.10", "3.11", "3.12", "3.13"]
 
         steps:
             - uses: actions/checkout@v4
diff --git a/.github/workflows/publish-package.yml b/.github/workflows/publish-package.yml
index c513a5c..f95b353 100644
--- a/.github/workflows/publish-package.yml
+++ b/.github/workflows/publish-package.yml
@@ -23,10 +23,10 @@ jobs:
         steps:
             # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
             - uses: actions/checkout@v3
-            - name: Set up Python 3.9
+            - name: Set up Python 3.12
               uses: actions/setup-python@v4
               with:
-                  python-version: 3.9
+                  python-version: 3.12
                   cache: pip
 
             # Publishes to PyPi
diff --git a/.gitignore b/.gitignore
index f15a257..92749ae 100755
--- a/.gitignore
+++ b/.gitignore
@@ -10,9 +10,16 @@ cached_data_used/kernels/*
 cached_data_used/visualizations/*
 cached_data_used/last_run/*
 cached_data_used/import_runs/*
+methodology_paper_evaluation/run/*
+hyperparametertuning/*
+hyperparametertuning_*/*
+test_run_experiment/*
+
+# ignore setup files
+*/setup/*_input.json
 
 # exceptions
-!cached_data_used/cachefiles/ktt_values_to_kerneltuner.py
+!cached_data_used/cachefiles/*.py
 
 # ignore specific experiment files
 experiment_files/milo.json
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..19bab30
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "benchmark_hub"]
+	path = benchmark_hub
+	url = https://github.com/AutoTuningAssociation/benchmark_hub.git
diff --git a/README.md b/README.md
index dad96e6..64c8fe4 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ Currently, the stable releases of this software package are compatible with [Ker
 ## Installation
 The package can be installed with `pip install autotuning_methodology`. 
 Alternatively, it can be installed by cloning this repository and running `pip install .` in the root of the cloned project. 
-Python >= 3.9 is supported. 
+Like most Python packages, installing in a virtual environment or with `pipx` is recommended. Python >= 3.10 is supported. 
 
 ## Notable features
 - Official software by the authors of the methodology-defining paper. 
diff --git a/benchmark_hub b/benchmark_hub
new file mode 160000
index 0000000..ff76e2c
--- /dev/null
+++ b/benchmark_hub
@@ -0,0 +1 @@
+Subproject commit ff76e2c86a7d9b3f389038589660e8b6ef4b4a5e
diff --git a/cached_data_used/cachefiles/ktt_values_to_kerneltuner.py b/cached_data_used/cachefiles/ktt_values_to_kerneltuner.py
deleted file mode 100644
index 5b3d27f..0000000
--- a/cached_data_used/cachefiles/ktt_values_to_kerneltuner.py
+++ /dev/null
@@ -1,116 +0,0 @@
-"""Script to overwrite Kernel Tuner brute forced cache files with the objective values of a KTT brute force search.
-
-Notes: this requires a fully bruteforced KTT and fully bruteforced KernelTuner (KT) cachefile on the same search space.
-Objective value is assumed to be time by default. Time is assumed to be in microseconds for KTT and miliseconds for KT. 
-"""
-
-import json
-from pathlib import Path
-
-from autotuning_methodology.runner import ktt_param_mapping
-
-kerneltuner_cachefiles_path = Path(__file__).parent.resolve()
-assert kerneltuner_cachefiles_path.exists()
-ktt_data_path = kerneltuner_cachefiles_path / "KTT data"
-assert ktt_data_path.exists()
-
-files_to_import = [f for f in ktt_data_path.iterdir() if f.is_file() and f.suffix == ".json"]
-ktt_objective_name = "Duration"
-kt_objective_name = "time"
-
-error_status_mapping = {
-    "ok": None,
-    "devicelimitsexceeded": '"CompilationFailedConfig"',
-    "computationfailed": '"RuntimeFailedConfig"',
-}
-
-for file in files_to_import:
-    # find the associated KernelTuner cachefile to write to
-    ktt_data = dict(json.loads(file.read_bytes()))
-    metadata = ktt_data["Metadata"]
-    device = str(metadata["Device"])
-    device_filename = device.replace("NVIDIA GeForce ", "").replace(" ", "_")
-    kernel = str(ktt_data["Results"][0]["KernelName"])
-    kernel_filename = kernel.lower()
-    kerneltuner_cachefile = kerneltuner_cachefiles_path / kernel_filename / f"{device_filename}.json"
-    assert kerneltuner_cachefile.exists()
-    ktt_param_mapping_kernel = ktt_param_mapping[kernel_filename]
-    print(f"Importing objective values from KTT to KernelTuner file for '{kernel}' on {device}")
-
-    # for each configuration in the KTT file, use the value in the KernelTuner file
-    config_to_change = dict()
-    kerneltuner_data = dict(json.loads(kerneltuner_cachefile.read_bytes()))
-    ktt_results = ktt_data["Results"]
-    cache = kerneltuner_data["cache"]
-    assert len(cache) == len(ktt_results)
-    for ktt_config in ktt_results:
-        # convert the configuration to T4 style dictionary for fast lookups in the mapping
-        configuration_ktt = dict()
-        for param in ktt_config["Configuration"]:
-            configuration_ktt[param["Name"]] = param["Value"]
-
-        # convert the configuration data with the mapping in the correct order
-        configuration = dict()
-        param_map = ktt_param_mapping_kernel
-        assert len(param_map) == len(
-            configuration_ktt
-        ), f"Mapping provided for {len(param_map)} params, but configuration has {len(configuration_ktt)}"
-        for param_name, mapping in param_map.items():
-            param_value = configuration_ktt[param_name]
-            # if the mapping is None, do not include the parameter
-            if mapping is None:
-                pass
-            # if the mapping is a tuple, the first argument is the new parameter name and the second the value
-            elif isinstance(mapping, tuple):
-                param_mapped_name, param_mapped_value = mapping
-                if callable(param_mapped_value):
-                    param_mapped_value = param_mapped_value(param_value)
-                configuration[param_mapped_name] = param_mapped_value
-            # if it's a list of tuples, map to multiple parameters
-            elif isinstance(mapping, list):
-                for param_mapped_name, param_mapped_value in mapping:
-                    if callable(param_mapped_value):
-                        param_mapped_value = param_mapped_value(param_value)
-                    configuration[param_mapped_name] = param_mapped_value
-            else:
-                raise ValueError(f"Can not apply parameter mapping of {type(mapping)} ({mapping})")
-
-        # get and validate the Kernel Tuner configuration
-        lookup_string = ",".join(str(v) for v in configuration.values())  # the key to lookup the configuration
-        assert lookup_string in cache
-        kt_config = cache[lookup_string]
-        for param, value in configuration.items():
-            assert kt_config[param] == value
-
-        # replace the objective in the KT configuration with the objective in the KTT configuration
-        kt_old_objective_value = kt_config[kt_objective_name]
-        kt_new_objective_value = ""
-        status = error_status_mapping[str(ktt_config["Status"]).lower()]
-        if status is None:
-            kt_new_objective_value = ktt_config["ComputationResults"][0][ktt_objective_name] / 1000
-        else:
-            kt_new_objective_value = status
-        kerneltuner_data["cache"][lookup_string][kt_objective_name] = kt_new_objective_value
-        config_to_change[lookup_string] = (kt_old_objective_value, kt_new_objective_value)
-        # print(f"Replacing {kt_old_objective_value} with {kt_new_objective_value}")
-
-    # load the individual lines of the file
-    with kerneltuner_cachefile.open(mode="r", encoding="utf-8") as fp:
-        lines = fp.readlines()
-        cache_start = False
-    # write the new data to file
-    with kerneltuner_cachefile.open(mode="w") as fp:
-        # for each line in the cache part of the file, lookup the config string in the changes dictionary and replace
-        for line in lines:
-            if '"cache":' in line:
-                cache_start = True
-                fp.write(line)
-            elif not cache_start or line[:1] == "}" or len(line) < 3:
-                fp.write(line)
-            else:
-                lookup_string = line.split(":")[0].replace('"', "").strip()
-                old_value, new_value = config_to_change[lookup_string]
-                line = line.replace(f'"time": {old_value},', f'"time": {new_value},', 1)
-                fp.write(line)
-
-    # kerneltuner_cachefile.write_text(json.dumps(kerneltuner_data, indent=3))
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 6949605..e55b60c 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -3,7 +3,7 @@ Getting Started
 
 Start out by installing the package. 
 The simplest way to do this is ``pip install autotuning_methodology``. 
-Python 3.9 and up are supported. 
+Python 3.10 and up are supported. 
 
 Defining an experiment
 ^^^^^^^^^^^^^^^^^^^^^^
diff --git a/experiment_files/compare_bo.json b/experiment_files/compare_bo.json
new file mode 100644
index 0000000..3328abf
--- /dev/null
+++ b/experiment_files/compare_bo.json
@@ -0,0 +1,72 @@
+{
+    "version": "1.0.0",
+    "name": "Methodology paper evaluation",
+    "parent_folder": "./methodology_paper_evaluation",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "convolution",
+                "folder": "./cached_data_used/kernels",
+                "input_file": "convolution.json"
+            },
+            {
+                "name": "pnpoly",
+                "folder": "./cached_data_used/kernels",
+                "input_file": "pnpoly.json"
+            }
+        ],
+        "gpus": [
+            "RTX_3090",
+            "RTX_2080_Ti"
+        ],
+        "pattern_for_full_search_space_filenames": {
+            "regex": "./cached_data_used/cachefiles/${applications}/${gpus}_T4.json"
+        },
+        "stochastic": true,
+        "repeats": 100,
+        "samples": 32,
+        "minimum_number_of_valid_search_iterations": 20,
+        "ignore_cache": false
+    },
+    "search_strategies": [
+        {
+            "name": "bayes_opt_og",
+            "search_method": "bayes_opt",
+            "display_name": "Bayesian Optimization",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "dual_annealing",
+            "search_method": "dual_annealing",
+            "display_name": "Dual Annealing",
+            "autotuner": "KernelTuner"
+        }
+    ],
+    "statistics_settings": {
+        "minimization": true,
+        "cutoff_percentile": 0.96,
+        "cutoff_percentile_start": 0.5,
+        "cutoff_type": "fevals",
+        "objective_time_keys": [
+            "all"
+        ],
+        "objective_performance_keys": [
+            "time"
+        ]
+    },
+    "visualization_settings": {
+        "x_axis_value_types": [
+            "fevals",
+            "time",
+            "aggregated"
+        ],
+        "y_axis_value_types": [
+            "normalized",
+            "baseline"
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
+}
\ No newline at end of file
diff --git a/experiment_files/compare_simple.json b/experiment_files/compare_simple.json
new file mode 100644
index 0000000..372e529
--- /dev/null
+++ b/experiment_files/compare_simple.json
@@ -0,0 +1,132 @@
+{
+    "version": "1.2.0",
+    "name": "Compare constrained strategies pyATF vs KT",
+    "parent_folder": "/var/scratch/fjwillem/constrained_optimization",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "dedispersion_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "dedispersion_milo.json",
+                "objective_performance_keys": [
+                    "time"
+                ]
+            }
+        ],
+        "gpus": [
+            "A100",
+            "A4000"
+        ],
+        "pattern_for_full_search_space_filenames": {
+            "regex": "./benchmark_hub/cachefiles/${applications}/${gpus}_T4.json"
+        },
+        "stochastic": true,
+        "repeats": 100,
+        "samples": 32,
+        "minimum_fraction_of_budget_valid": 0.1,
+        "minimum_number_of_valid_search_iterations": 10,
+        "ignore_cache": false
+    },
+    "search_strategies": [
+        {
+            "name": "genetic_algorithm_constrained",
+            "search_method": "genetic_algorithm",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": true
+                }
+            ],
+            "display_name": "KT GA constrained",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "genetic_algorithm_non_constrained",
+            "search_method": "genetic_algorithm",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": false
+                }
+            ],
+            "display_name": "KT GA non-constrained",
+            "autotuner": "KernelTuner",
+            "color_parent": "genetic_algorithm_constrained"
+        },
+        {
+            "name": "pso_constrained",
+            "search_method": "pso",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": true
+                }
+            ],
+            "display_name": "KT PSO constrained",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "pso_non_constrained",
+            "search_method": "pso",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": false
+                }
+            ],
+            "display_name": "KT PSO non-constrained",
+            "autotuner": "KernelTuner",
+            "color_parent": "pso_constrained"
+        },
+        {
+            "name": "simulated_annealing_constrained",
+            "search_method": "simulated_annealing",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": true
+                }
+            ],
+            "display_name": "KT SA constrained",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "simulated_annealing_non_constrained",
+            "search_method": "simulated_annealing",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": false
+                }
+            ],
+            "display_name": "KT SA non-constrained",
+            "autotuner": "KernelTuner",
+            "color_parent": "simulated_annealing_constrained"
+        }
+    ],
+    "statistics_settings": {
+        "cutoff_percentile": 0.95,
+        "cutoff_percentile_start": 0.01,
+        "cutoff_type": "time",
+        "objective_time_keys": [
+            "all"
+        ]
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "aggregate",
+                "style": "head2head",
+                "comparison": {
+                    "unit": "time",
+                    "relative_time": 0.5
+                },
+                "annotate": true
+            }
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
+}
\ No newline at end of file
diff --git a/experiment_files/convert_old_to_new_format.py b/experiment_files/convert_old_to_new_format.py
new file mode 100644
index 0000000..d2cf5f8
--- /dev/null
+++ b/experiment_files/convert_old_to_new_format.py
@@ -0,0 +1,89 @@
+"""Module to convert the old experiments file format into the new format."""
+
+# script to convert the old experiments file format into the new format
+import json
+from pathlib import Path
+
+from autotuning_methodology.validators import validate_experimentsfile
+
+# set input and output files
+folderpath = Path(__file__).parent
+old_file_path = folderpath / Path("../tests/autotuning_methodology/integration/mockfiles/test_import_runs.json")
+new_file_path = folderpath / Path("../tests/autotuning_methodology/integration/mockfiles/test_import_runs_new.json")
+encoding = "utf-8"
+assert old_file_path.exists(), f"Old file does not exist at {old_file_path}"
+assert not new_file_path.exists(), f"New file does already exists at {new_file_path}"
+
+# read input file to dictionary
+with old_file_path.open("r", encoding=encoding) as fp:
+    old_experiment: dict = json.load(fp)
+
+# convert the dictionary to the new format
+new_experiment = {
+    "version": "1.2.0",
+    "name": old_experiment["name"],
+    "parent_folder": f"./{old_experiment['folder_id']}",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": kernel,
+                "input_file": f"{old_experiment['kernels_path']}/{kernel}",
+                "folder": f"{old_experiment['visualization_caches_path']}/{kernel}",
+            }
+            for kernel in old_experiment["kernels"]
+        ],
+        "gpus": old_experiment["GPUs"],
+        "pattern_for_full_search_space_filenames": {
+            "regex": f"{old_experiment['bruteforced_caches_path']}/" + "${applications}/${gpus}.json"
+        },
+        "stochastic": old_experiment["strategy_defaults"]["stochastic"],
+        "repeats": old_experiment["strategy_defaults"]["repeats"],
+        "samples": old_experiment["strategy_defaults"]["iterations"],
+        "minimum_fraction_of_budget_valid": old_experiment.get("minimum_fraction_of_budget_valid", 0.5),
+        "minimum_number_of_valid_search_iterations": old_experiment["strategy_defaults"][
+            "minimum_number_of_evaluations"
+        ],
+        "ignore_cache": False,
+    },
+    "search_strategies": [
+        {
+            "name": strategy["name"],
+            "search_method": strategy["strategy"],
+            "display_name": strategy["display_name"],
+            "autotuner": (
+                "KernelTuner" if strategy["name"] != "ktt_profile_searcher" else "KTT"
+            ),  # Assuming autotuner is KernelTuner for all strategies
+        }
+        for strategy in old_experiment["strategies"]
+    ],
+    "statistics_settings": {
+        "minimization": old_experiment["minimization"],
+        "cutoff_percentile": old_experiment["cutoff_percentile"],
+        "cutoff_percentile_start": old_experiment["cutoff_percentile_start"],
+        "cutoff_type": old_experiment["cutoff_type"],
+        "objective_time_keys": ["all"],  # Mapped to 'all'
+        "objective_performance_keys": old_experiment["objective_performance_keys"],
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "aggregate" if "aggregated" in plottype else "searchspace",
+                "style": "scatter" if "scatter" in plottype else "line",
+                "x_axis_value_types": [plottype if plottype != "aggregated" else "time"],
+                "y_axis_value_types": old_experiment["plot"]["plot_y_value_types"],
+            }
+            for plottype in old_experiment["plot"]["plot_x_value_types"]
+        ],
+        "resolution": old_experiment["resolution"],
+        "confidence_level": old_experiment["plot"]["confidence_level"],
+        "compare_baselines": old_experiment["plot"]["compare_baselines"],
+        "compare_split_times": old_experiment["plot"]["compare_split_times"],
+    },
+}
+
+# validate using schema
+validate_experimentsfile(new_experiment, encoding=encoding)
+
+# write converted dictionary to file
+with new_file_path.open("w", encoding=encoding) as fp:
+    json.dump(new_experiment, fp)
diff --git a/experiment_files/diff_evo.json b/experiment_files/diff_evo.json
new file mode 100644
index 0000000..d67dbf5
--- /dev/null
+++ b/experiment_files/diff_evo.json
@@ -0,0 +1,195 @@
+{
+    "version": "1.2.0",
+    "name": "Compare Differential Evolution strategies Kernel Tuner",
+    "parent_folder": "/var/scratch/fjwillem/constrained_optimization",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "dedispersion_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "dedispersion_milo.json",
+                "objective_performance_keys": [
+                    "time"
+                ]
+            },
+            {
+                "name": "hotspot_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "hotspot_milo.json",
+                "objective_performance_keys": [
+                    "GFLOP/s"
+                ]
+            },
+            {
+                "name": "convolution_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "convolution_milo.json",
+                "objective_performance_keys": [
+                    "time"
+                ]
+            },
+            {
+                "name": "gemm_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "gemm_milo.json",
+                "objective_performance_keys": [
+                    "time"
+                ]
+            }
+        ],
+        "gpus": [
+            "A100",
+            "A4000",
+            "MI250X",
+            "A6000",
+            "W6600",
+            "W7800"
+        ],
+        "pattern_for_full_search_space_filenames": {
+            "regex": "./benchmark_hub/cachefiles/${applications}/${gpus}_T4.json"
+        },
+        "stochastic": true,
+        "repeats": 100,
+        "samples": 32,
+        "minimum_fraction_of_budget_valid": 0.1,
+        "minimum_number_of_valid_search_iterations": 10,
+        "ignore_cache": false
+    },
+    "search_strategies": [
+        {
+            "name": "firefly_constrained",
+            "search_method": "firefly_algorithm",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": true
+                }
+            ],
+            "display_name": "Firefly constrained",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "genetic_algorithm_constrained",
+            "search_method": "genetic_algorithm",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": true
+                }
+            ],
+            "display_name": "GA constrained",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "pso_constrained",
+            "search_method": "pso",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": true
+                }
+            ],
+            "display_name": "PSO constrained",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "simulated_annealing_constrained",
+            "search_method": "simulated_annealing",
+            "search_method_hyperparameters": [
+                {
+                    "name": "constraint_aware",
+                    "value": true
+                }
+            ],
+            "display_name": "SA constrained",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "diff_evo",
+            "search_method": "diff_evo",
+            "display_name": "Diff Evolution",
+            "autotuner": "KernelTuner"
+        }
+    ],
+    "statistics_settings": {
+        "cutoff_percentile": 0.95,
+        "cutoff_percentile_start": 0.01,
+        "cutoff_type": "time",
+        "objective_time_keys": [
+            "all"
+        ]
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "searchspace",
+                "style": "line",
+                "x_axis_value_types": [
+                    "fevals"
+                ],
+                "y_axis_value_types": [
+                    "absolute",
+                    "baseline"
+                ]
+            },
+            {
+                "scope": "searchspace",
+                "style": "line",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "absolute",
+                    "baseline"
+                ]
+            },
+            {
+                "scope": "search_strategy",
+                "style": "heatmap",
+                "x_axis_value_types": [
+                    "applications"
+                ],
+                "y_axis_value_types": [
+                    "gpus"
+                ],
+                "cmin": -8.0,
+                "include_y_labels": true,
+                "include_colorbar": false,
+                "annotate": true
+            },
+            {
+                "scope": "search_strategy",
+                "style": "heatmap",
+                "x_axis_value_types": [
+                    "applications"
+                ],
+                "y_axis_value_types": [
+                    "gpus"
+                ],
+                "cmin": -8.0,
+                "cnum": 10,
+                "include_y_labels": false,
+                "include_colorbar": true,
+                "annotate": true
+            },
+            {
+                "scope": "aggregate",
+                "style": "head2head",
+                "comparison": {
+                    "unit": "time",
+                    "relative_time": 0.5
+                },
+                "annotate": true
+            },
+            {
+                "scope": "aggregate",
+                "style": "line",
+                "ylabel": "Aggregate performance relative to baseline"
+            }
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
+}
\ No newline at end of file
diff --git a/experiment_files/example_visualizations.json b/experiment_files/example_visualizations.json
index 682147f..ed3991e 100644
--- a/experiment_files/example_visualizations.json
+++ b/experiment_files/example_visualizations.json
@@ -44,7 +44,7 @@
     "strategy_defaults": {
         "repeats": 100,
         "minimum_number_of_evaluations": 20,
-        "cutoff_margin": 1.1,
+        "cutoff_margin": 0.1,
         "stochastic": true,
         "record_data": [
             "time",
diff --git a/experiment_files/ktt_coulomb_two_autotuner_versions.json b/experiment_files/ktt_coulomb_two_autotuner_versions.json
new file mode 100644
index 0000000..e57af50
--- /dev/null
+++ b/experiment_files/ktt_coulomb_two_autotuner_versions.json
@@ -0,0 +1,60 @@
+{
+	"version": "1.0.0",
+	"name": "Random vs. Random KTT 2.1 and KTT 2.2 on Coulomb",
+	"parent_folder": "/home/janka/autotuning_methodology_experiments/test_coulomb_two_versions_KTT",
+	"experimental_groups_defaults": {
+      	"applications": [
+			{
+		    	"name": "coulomb",
+                "input_file" : "/home/janka/KTT/Examples/CoulombSum3d/CoulombSum3dCudaScript.json",
+                "folder": "/home/janka/KTT/Examples/CoulombSum3d"
+            }
+        ],
+        "gpus": ["2080"],
+		"stochastic": true,
+		"repeats": 2,
+		"samples": 1,
+		"minimum_number_of_valid_search_iterations": 5,
+        "ignore_cache": true
+	},
+	"search_strategies": [
+	  {
+		"name": "random-2.1",
+		"search_method": "Random",
+		"display_name": "Random with KTT 2.1",
+        "autotuner": "KTT",
+		"autotuner_path": "/home/janka/KTT/Build/x86_64_Release/"
+	  },
+	  {
+		"name": "random-2.2",
+        "autotuner": "KTT",
+		"autotuner_path": "/home/janka/KTT-2.2/Build/x86_64_Release/",
+		"search_method": "Random",
+		"display_name": "Random with KTT 2.2"
+	  }
+	],
+	"statistics_settings": {
+		"minimization": true,
+		"cutoff_percentile": 0.96,
+		"cutoff_percentile_start": 0.2,
+		"cutoff_type": "fevals",
+		"objective_time_keys": [
+			"all"
+		],
+		"objective_performance_keys": [
+			"TotalDuration"
+		]
+	},	
+	"visualization_settings": {
+		"x_axis_value_types": [
+			"fevals"
+		], 
+		"y_axis_value_types": [
+			"absolute"
+		], 
+		"resolution": 2,
+		"confidence_level": 0.95,
+		"compare_baselines": false,
+		"compare_split_times": false 
+	}
+}
diff --git a/experiment_files/ktt_coulomb_two_search_methods.json b/experiment_files/ktt_coulomb_two_search_methods.json
new file mode 100644
index 0000000..c83023b
--- /dev/null
+++ b/experiment_files/ktt_coulomb_two_search_methods.json
@@ -0,0 +1,69 @@
+{
+    "version": "1.0.0",
+    "name": "Random vs. profbased searcher KTT 2.1 on Coulomb",
+    "parent_folder": "/home/janka/autotuning_methodology_experiments/test_coulomb_two_methods",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "coulomb",
+                "input_file" : "/home/janka/KTT/Examples/CoulombSum3d/CoulombSum3dCudaScript.json",
+                "folder": "/home/janka/KTT/Examples/CoulombSum3d"
+            }
+        ],
+        "gpus": ["2080"],
+        "autotuner": "KTT",
+        "autotuner_path": "/home/janka/KTT/Build/x86_64_Release/",
+        "set_this_to_pythonpath": "/home/janka/KTT/Build/x86_64_Release/:/home/janka/KTT/Scripts",
+        "stochastic": true,
+        "repeats": 2,
+        "samples": 1,
+        "minimum_number_of_valid_search_iterations": 5,
+        "ignore_cache": true
+    },
+    "search_strategies": [
+        {
+            "name": "random",
+            "search_method": "Random",
+            "display_name": "Random"
+        },
+        {
+            "name": "profbased",
+            "search_method": "ProfileBased",
+            "search_method_hyperparameters": [
+                {
+                    "name": "modelPath",
+                    "value": "/home/janka/KTT/Examples/CoulombSum3d/Models/2080-coulomb_output_DT.sav"
+                },
+                {
+                    "name": "batchSize",
+                    "value": "5"
+                }
+            ],
+            "display_name": "Profile-based searcher"
+        }
+    ],
+    "statistics_settings": {
+        "minimization": true,
+        "cutoff_percentile": 0.96,
+        "cutoff_percentile_start": 0.2,
+        "cutoff_type": "fevals",
+        "objective_time_keys": [
+            "all"
+        ],
+        "objective_performance_keys": [
+            "TotalDuration"
+        ]
+    },	
+    "visualization_settings": {
+        "x_axis_value_types": [
+            "fevals"
+        ], 
+        "y_axis_value_types": [
+            "absolute"
+        ], 
+        "resolution": 2,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false 
+    }
+}
diff --git a/experiment_files/ktt_vectorAddition_two_search_methods.json b/experiment_files/ktt_vectorAddition_two_search_methods.json
new file mode 100644
index 0000000..25cf5d0
--- /dev/null
+++ b/experiment_files/ktt_vectorAddition_two_search_methods.json
@@ -0,0 +1,69 @@
+{
+	"version": "1.0.0",
+	"name": "Random vs. profbased searcher KTT 2.1",
+	"parent_folder": "/home/janka/autotuning_methodology_experiments/test_vectorAddition_two_methods",
+	"experimental_groups_defaults": {
+      	"applications": [
+			{
+		    	"name": "vectorAddition",
+                "input_file" : "/home/janka/KTT/Tutorials/03KernelTuning/KernelTuningCudaScript.json",
+                "folder": "/home/janka/KTT/Tutorials/03KernelTuning/"
+            }
+        ],
+        "gpus": ["2080"],
+        "autotuner": "KTT",
+		"autotuner_path": "/home/janka/KTT/Build/x86_64_Release/",
+        "set_this_to_pythonpath": "/home/janka/KTT/Build/x86_64_Release:/home/janka/KTT/Scripts",
+		"stochastic": true,
+		"repeats": 2,
+		"samples": 1,
+		"minimum_number_of_valid_search_iterations": 2,
+        "ignore_cache": true
+	},
+	"search_strategies": [
+	  {
+		"name": "random",
+		"search_method": "Random",
+		"display_name": "Random"
+	  },
+	  {
+		"name": "profbased",
+		"search_method": "ProfileBased",
+        "search_method_hyperparameters": [
+            {
+                "name": "modelPath",
+                "value": "/home/janka/KTT/Tutorials/03KernelTuning/2080-vectorAdd_output_DT.sav"
+            },
+            {
+                "name": "batchSize",
+                "value": "1"
+            }
+        ],
+		"display_name": "Profile-based searcher"
+	  }
+	],
+	"statistics_settings": {
+		"minimization": true,
+		"cutoff_percentile": 0.96,
+		"cutoff_percentile_start": 0.1,
+		"cutoff_type": "time",
+		"objective_time_keys": [
+			"all"
+		],
+		"objective_performance_keys": [
+			"TotalDuration"
+		]
+	},	
+	"visualization_settings": {
+		"x_axis_value_types": [
+			"fevals"
+		], 
+		"y_axis_value_types": [
+			"absolute"
+		], 
+		"resolution": 2,
+		"confidence_level": 0.95,
+		"compare_baselines": false,
+		"compare_split_times": false 
+	}
+}
diff --git a/experiment_files/methodology_paper_evaluation_2.json b/experiment_files/methodology_paper_evaluation_2.json
new file mode 100644
index 0000000..bc5f0ba
--- /dev/null
+++ b/experiment_files/methodology_paper_evaluation_2.json
@@ -0,0 +1,78 @@
+{
+    "version": "1.0.0",
+    "name": "Methodology paper evaluation",
+    "parent_folder": "./methodology_paper_evaluation",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "convolution",
+                "input_file": "../cached_data_used/kernels/convolution",
+                "folder": "../cached_data_used/visualizations/convolution"
+            },
+            {
+                "name": "pnpoly",
+                "input_file": "../cached_data_used/kernels/pnpoly",
+                "folder": "../cached_data_used/visualizations/pnpoly"
+            }
+        ],
+        "gpus": [
+            "RTX_3090",
+            "RTX_2080_Ti"
+        ],
+        "pattern_for_full_search_space_filenames": {
+            "regex": "../cached_data_used/cachefiles/${applications}/${gpus}.json"
+        },
+        "stochastic": true,
+        "repeats": 100,
+        "samples": 32,
+        "minimum_number_of_valid_search_iterations": 20,
+        "ignore_cache": false
+    },
+    "search_strategies": [
+        {
+            "name": "genetic_algorithm",
+            "search_method": "genetic_algorithm",
+            "display_name": "Genetic Algorithm",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "dual_annealing",
+            "search_method": "dual_annealing",
+            "display_name": "Dual Annealing",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "greedy_ils",
+            "search_method": "greedy_ils",
+            "display_name": "Greedy ILS",
+            "autotuner": "KernelTuner"
+        }
+    ],
+    "statistics_settings": {
+        "minimization": true,
+        "cutoff_percentile": 0.96,
+        "cutoff_percentile_start": 0.5,
+        "cutoff_type": "fevals",
+        "objective_time_keys": [
+            "all"
+        ],
+        "objective_performance_keys": [
+            "time"
+        ]
+    },
+    "visualization_settings": {
+        "x_axis_value_types": [
+            "fevals",
+            "time",
+            "aggregated"
+        ],
+        "y_axis_value_types": [
+            "normalized",
+            "baseline"
+        ],
+        "resolution": 1e3,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
+}
\ No newline at end of file
diff --git a/experiment_files/methodology_paper_evaluation_new.json b/experiment_files/methodology_paper_evaluation_new.json
new file mode 100644
index 0000000..462431e
--- /dev/null
+++ b/experiment_files/methodology_paper_evaluation_new.json
@@ -0,0 +1,91 @@
+{
+    "version": "1.1.0",
+    "name": "Methodology paper evaluation",
+    "parent_folder": "./methodology_paper_evaluation",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "convolution",
+                "folder": "./cached_data_used/kernels",
+                "input_file": "convolution.json"
+            },
+            {
+                "name": "pnpoly",
+                "folder": "./cached_data_used/kernels",
+                "input_file": "pnpoly.json"
+            }
+        ],
+        "gpus": [
+            "RTX_3090",
+            "RTX_2080_Ti"
+        ],
+        "pattern_for_full_search_space_filenames": {
+            "regex": "./cached_data_used/cachefiles/${applications}/${gpus}_T4.json"
+        },
+        "stochastic": true,
+        "repeats": 100,
+        "samples": 32,
+        "minimum_number_of_valid_search_iterations": 20,
+        "ignore_cache": false
+    },
+    "search_strategies": [
+        {
+            "name": "genetic_algorithm",
+            "search_method": "genetic_algorithm",
+            "display_name": "Genetic Algorithm",
+            "autotuner": "KernelTuner"
+        },
+        {
+            "name": "dual_annealing",
+            "search_method": "dual_annealing",
+            "display_name": "Dual Annealing",
+            "autotuner": "KernelTuner"
+        }
+    ],
+    "statistics_settings": {
+        "minimization": true,
+        "cutoff_percentile": 0.96,
+        "cutoff_percentile_start": 0.5,
+        "cutoff_type": "fevals",
+        "objective_time_keys": [
+            "all"
+        ],
+        "objective_performance_keys": [
+            "time"
+        ]
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "searchspace",
+                "style": "line",
+                "x_axis_value_types": [
+                    "fevals"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            },
+            {
+                "scope": "searchspace",
+                "style": "line",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            },
+            {
+                "scope": "aggregate",
+                "style": "line"
+            }
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
+}
\ No newline at end of file
diff --git a/mypy.ini b/mypy.ini
index ad6ba41..b9b5049 100755
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,6 +1,6 @@
 # Global options:
 [mypy]
-python_version=3.9
+python_version=3.12
 
 [mypy-isotonic.isotonic.*]
 ignore_missing_imports = True
diff --git a/noxfile.py b/noxfile.py
index b1e111f..e3a19ad 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -16,11 +16,13 @@
 def lint(session: nox.Session) -> None:
     """Ensure the code is formatted as expected."""
     session.install("ruff")
-    session.run("ruff", "check", "--output-format=github", "--config=pyproject.toml", ".")
+    session.run("ruff", "check", "--output-format=github", "--config=pyproject.toml", "src")
 
 
 # @nox.session  # uncomment this line to only run on the current python interpreter
-@nox.session(python=["3.9", "3.10", "3.11", "3.12"])  # missing versions can be installed with `pyenv install ...`
+@nox.session(
+    python=["3.10", "3.11", "3.12", "3.13"]
+)  # missing versions can be installed with `pyenv install ...`
 # do not forget check / set the versions with `pyenv global`, or `pyenv local` in case of virtual environment
 def tests(session: nox.Session) -> None:
     """Run the tests for the specified Python versions."""
diff --git a/pyproject.toml b/pyproject.toml
index 108a48d..cbd86f6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,19 +10,19 @@ description = "Software package easing implementation of the guidelines of the 2
 keywords = ["autotuning", "auto-tuning", "methodology", "scientific"]
 readme = "README.md"
 license = { file = "LICENSE" }
-requires-python = ">=3.9"
+requires-python = ">=3.10,<4"       # NOTE when updating python version, also update classifiers and Nox test versions
 classifiers = [
     "Development Status :: 5 - Production/Stable",
     "License :: OSI Approved :: MIT License",
     "Natural Language :: English",
     "Operating System :: OS Independent",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13"
 ] # https://pypi.org/classifiers/
 dependencies = [
-    "numpy >= 1.22.4",
+    "numpy (>=2.0.0)",
     "scipy >= 1.10.1",
     "scikit-learn >= 1.0.2",
     "matplotlib >= 3.7.1",
@@ -30,7 +30,7 @@ dependencies = [
     "progressbar2 >= 4.2.0",
     "jsonschema >= 4.17.3",
     "nonconformist >= 2.1.0",
-    "kernel_tuner >= 1.0.1",
+    "kernel_tuner >= 1.3.0",
 ]
 
 [project.optional-dependencies]
@@ -64,7 +64,7 @@ minversion = "7.3"
 pythonpath = [
     "src",
 ] # necessary to get coverage reports without installing with `-e`
-addopts = "--cov --cov-config=.coveragerc --cov-report html --cov-report term-missing --cov-fail-under 80"
+addopts = "--cov --cov-config=.coveragerc --cov-report html --cov-report term-missing --cov-fail-under 75"
 testpaths = ["tests/unit", "tests/integration", "tests/release"]
 
 [tool.black]
@@ -80,6 +80,8 @@ select = [
     "E", # pycodestyle
     "F", # pyflakes,
     "D", # pydocstyle,
+    "NPY201", # Numpy 2.0 compatibility
 ]
+ignore = ["E501"]
 [tool.ruff.lint.pydocstyle]
 convention = "google"
diff --git a/src/autotuning_methodology/baseline.py b/src/autotuning_methodology/baseline.py
index ef3e571..f59328b 100644
--- a/src/autotuning_methodology/baseline.py
+++ b/src/autotuning_methodology/baseline.py
@@ -39,7 +39,14 @@ def get_standardised_curves(self, range: np.ndarray, strategy_curves: list[np.nd
                 standardised_curves.append(None)
                 continue
             assert strategy_curve.shape == random_curve.shape, "strategy_curve shape must match random_curve shape"
-            standardised_curve = (strategy_curve - random_curve) / (absolute_optimum - random_curve)
+            division = absolute_optimum - random_curve
+
+            # check for division by zero
+            if 0.0 in division:
+                raise ValueError(f"Division by zero encountered, first at index {np.argmax(division == 0.0)}.")
+
+            # calculate the standardised curve
+            standardised_curve = (strategy_curve - random_curve) / division
             standardised_curves.append(standardised_curve)
         return tuple(standardised_curves)
 
@@ -138,6 +145,10 @@ def _get_random_curve(self, fevals_range: np.ndarray, smoothing=True) -> np.ndar
             x = fevals_range
             y = draws
 
+        # if there are too few data points left to interpolate on, return draws
+        if len(x) < 2 or len(y) < 2:
+            return draws
+
         # apply the monotonicity-preserving Piecewise Cubic Hermite Interpolating Polynomial
         smooth_fevals_range = np.linspace(fevals_range[0], fevals_range[-1], len(fevals_range))
         smooth_draws = PchipInterpolator(x, y)(smooth_fevals_range)
@@ -167,13 +178,15 @@ def get_curve(self, range: np.ndarray, x_type: str, dist=None, confidence_level=
         return super().get_curve(range, x_type, dist, confidence_level)
 
     def get_curve_over_fevals(  # noqa: D102
-        self, fevals_range: np.ndarray, dist=None, confidence_level=None
+        self, fevals_range: np.ndarray, dist=None, confidence_level=None, return_split=False
     ) -> np.ndarray:
         if self.simulate:
             return self._get_random_curve_means(fevals_range)
         return self._get_random_curve(fevals_range)
 
-    def get_curve_over_time(self, time_range: np.ndarray, dist=None, confidence_level=None) -> np.ndarray:  # noqa: D102
+    def get_curve_over_time(    # noqa: D102
+        self, time_range: np.ndarray, dist=None, confidence_level=None, return_split=False
+    ) -> np.ndarray:
         fevals_range = self.time_to_fevals(time_range)
         curve_over_time = self.get_curve_over_fevals(fevals_range, dist, confidence_level)
         smoothing_factor = 0.0
@@ -208,7 +221,7 @@ def get_split_times_at_feval(  # noqa: D102
 
         # for each key, obtain the time at a feval
         objective_time_keys = searchspace_stats.objective_time_keys
-        split_time_per_feval = np.full((len(objective_time_keys), index_at_feval.shape[0]), np.NaN)
+        split_time_per_feval = np.full((len(objective_time_keys), index_at_feval.shape[0]), np.nan)
         for key_index, key in enumerate(objective_time_keys):
             split_time_per_feval[key_index] = searchspace_stats.objective_times_array[key_index, index_at_feval]
 
@@ -316,7 +329,7 @@ def get_curve(self, range: np.ndarray, x_type: str, dist=None, confidence_level=
         return super().get_curve(range, x_type, dist, confidence_level)
 
     def get_curve_over_fevals(  # noqa: D102
-        self, fevals_range: np.ndarray, dist=None, confidence_level=None
+        self, fevals_range: np.ndarray, dist=None, confidence_level=None, return_split=False
     ) -> np.ndarray:
         if self.use_index:
             return self.searchspace_stats.objective_performances_total_sorted_nan[
@@ -326,7 +339,9 @@ def get_curve_over_fevals(  # noqa: D102
             assert self.y_array.ndim == 1
             return self.y_array[fevals_range]
 
-    def get_curve_over_time(self, time_range: np.ndarray, dist=None, confidence_level=None) -> np.ndarray:  # noqa: D102
+    def get_curve_over_time(    # noqa: D102
+        self, time_range: np.ndarray, dist=None, confidence_level=None, return_split=False
+    ) -> np.ndarray:
         predicted_y_values = self._ir.predict(time_range)
         if not self.use_index:
             return predicted_y_values
@@ -405,7 +420,7 @@ def get_curve(self, range: np.ndarray, x_type: str, dist=None, confidence_level=
         return self.stochastic_curve_to_deterministic(range=range, curve=stochastic_curve)
 
     def get_curve_over_fevals(  # noqa: D102
-        self, fevals_range: np.ndarray, dist=None, confidence_level=None
+        self, fevals_range: np.ndarray, dist=None, confidence_level=None, return_split=False
     ) -> np.ndarray:
         if dist is None:
             dist = self.searchspace_stats.objective_performances_total_sorted
@@ -416,7 +431,9 @@ def get_curve_over_fevals(  # noqa: D102
         )
         return self.stochastic_curve_to_deterministic(range=fevals_range, curve=stochastic_curve)
 
-    def get_curve_over_time(self, time_range: np.ndarray, dist=None, confidence_level=None) -> np.ndarray:  # noqa: D102
+    def get_curve_over_time(    # noqa: D102
+        self, time_range: np.ndarray, dist=None, confidence_level=None, return_split=False
+    ) -> np.ndarray:  
         if dist is None:
             dist = self.searchspace_stats.objective_performances_total_sorted
         if confidence_level is None:
diff --git a/src/autotuning_methodology/caching.py b/src/autotuning_methodology/caching.py
index 2daa221..43556ec 100755
--- a/src/autotuning_methodology/caching.py
+++ b/src/autotuning_methodology/caching.py
@@ -30,44 +30,41 @@ class ResultsDescription:
 
     def __init__(
         self,
-        folder_id: str,
-        kernel_name: str,
+        run_folder: Path,
+        application_name: str,
         device_name: str,
-        strategy_name: str,
-        strategy_display_name: str,
+        group_name: str,
+        group_display_name: str,
         stochastic: bool,
         objective_time_keys: list[str],
         objective_performance_keys: list[str],
         minimization: bool,
-        visualization_caches_path: Path,
     ) -> None:
         """Initialization method for the ResultsDescription object.
 
         Args:
-            folder_id: the unique ID of the folder to store in.
-            kernel_name: the name of the kernel used.
+            run_folder: a folder to store all files generated during experiments
+            application_name: the name of the application.
             device_name: the name of the device used.
-            strategy_name: the name of the optimization algorithm used, must not contain spaces or special characters.
-            strategy_display_name: the name of the optimization algorithm used in printing / visualization.
-            stochastic: whether the optimization algorithm is stochastic.
+            group_name: the name of the experimental group, usually search method used, must not contain spaces or special characters.
+            group_display_name: the name of the experimental group used in printing / visualization.
+            stochastic: whether the search method is stochastic.
             objective_time_keys: the objective time keys used.
             objective_performance_keys: the objective performance keys used.
-            minimization: whether the optimization algorithm performed minimization (attempted to find the minimum).
-            visualization_caches_path: path to visualization caches relative to the experiments file, creation allowed.
+            minimization: whether the search method performed minimization (attempted to find the minimum).
         """
         # all attributes must be hashable for symetric difference checking
         self._version = "1.3.0"
         self.__stored = False
-        self.__folder_id = folder_id
-        self.kernel_name = kernel_name
+        self.application_name = application_name
         self.device_name = device_name
-        self.strategy_name = strategy_name
-        self.strategy_display_name = strategy_display_name
+        self.group_name = group_name
+        self.group_display_name = group_display_name
         self.stochastic = stochastic
         self.objective_time_keys = objective_time_keys
         self.objective_performance_keys = objective_performance_keys
         self.minimization = minimization
-        self.visualization_caches_path = visualization_caches_path
+        self.run_folder = run_folder
         self.numpy_arrays_keys = [
             "fevals_results",
             "objective_time_results",
@@ -85,7 +82,7 @@ def __get_as_dict(self) -> dict:
             a dictionary, similar to self.__dict__ but with some keys removed.
         """
         dictionary = vars(self)
-        not_saved_keys = ["strategy_display_name", "visualization_caches_path"]
+        not_saved_keys = ["group_display_name", "visualization_caches_path"]
         for not_saved_key in not_saved_keys:
             if not_saved_key in dictionary.keys():
                 del dictionary[not_saved_key]
@@ -124,21 +121,25 @@ def is_same_as(self, other: ResultsDescription) -> bool:
 
         # check if same value for each key
         for attribute_key, attribute_value in self.__get_as_dict().items():
-            if attribute_key == "strategy_display_name" or attribute_key == "visualization_caches_path":
+            if (
+                attribute_key == "group_display_name"
+                or attribute_key == "visualization_caches_path"
+                or attribute_key == "run_folder"
+            ):
                 continue
             else:
-                assert (
-                    attribute_value == other.__get_as_dict()[attribute_key]
-                ), f"{attribute_key} has different values: {attribute_value} != {other.__get_as_dict()[attribute_key]}"
+                assert attribute_value == other.__get_as_dict()[attribute_key], (
+                    f"{attribute_key} has different values: {attribute_value} != {other.__get_as_dict()[attribute_key]}"
+                )
 
         return True
 
     def __get_cache_filename(self) -> str:
-        return f"{self.device_name}_{self.strategy_name}.npz"
+        return f"{self.device_name}_{self.application_name}.npz"
 
     def __get_cache_filepath(self) -> Path:
         """Get the filepath to this experiment."""
-        return self.visualization_caches_path / self.__folder_id / self.kernel_name
+        return self.run_folder
 
     def __get_cache_full_filepath(self) -> Path:
         """Get the filepath for this file, including the filename and extension."""
@@ -147,7 +148,7 @@ def __get_cache_full_filepath(self) -> Path:
     def __check_for_file(self) -> bool:
         """Check whether the file exists."""
         full_filepath = self.__get_cache_full_filepath()
-        self.__stored = full_filepath.exists() and np.DataSource().exists(full_filepath)
+        self.__stored = full_filepath.exists() and np.lib.npyio.DataSource().exists(full_filepath)
         return self.__stored
 
     def __write_to_file(self, arrays: dict):
@@ -172,14 +173,22 @@ def __read_from_file(self) -> list[np.ndarray]:
             raise ValueError(f"File {full_filepath} does not exist")
 
         # load the data and verify the resultsdescription object is the same
-        data = np.load(full_filepath, allow_pickle=True)
+        try:
+            data = np.load(full_filepath, allow_pickle=True)
+        except Exception as e:
+            print(f"/!\\ Error loading file: {full_filepath} /!\\")
+            raise e
         data_results_description = data["resultsdescription"].item()
         assert self.is_same_as(data_results_description), "The results description of the results is not the same"
 
         # get the numpy arrays
         numpy_arrays = list()
         for numpy_array_key in self.numpy_arrays_keys:
-            numpy_arrays.append(data[numpy_array_key])
+            try:
+                numpy_arrays.append(data[numpy_array_key])
+            except Exception as e:
+                print(f"/!\\ Error adding numpy array {numpy_array_key} from file: {full_filepath} /!\\")
+                raise e
         return numpy_arrays
 
     def get_results(self) -> Results:
@@ -190,3 +199,11 @@ def get_results(self) -> Results:
     def has_results(self) -> bool:
         """Checks whether there are results or the file exists."""
         return self.__stored or self.__check_for_file()
+
+    def delete(self) -> bool:
+        """Deletes the file if it exists, returns true if succesfully deleted."""
+        fp = self.__get_cache_full_filepath()
+        if fp.exists() and fp.is_file():
+            fp.unlink()
+            return True
+        return False
diff --git a/src/autotuning_methodology/curves.py b/src/autotuning_methodology/curves.py
index a839b4f..d05d0ab 100644
--- a/src/autotuning_methodology/curves.py
+++ b/src/autotuning_methodology/curves.py
@@ -33,35 +33,36 @@ def get_indices_in_distribution(
         A NumPy array of type float of the same shape as `draws`, with NaN where not found in `dist`.
     """
     assert dist.ndim == 1, f"distribution can not have more than one dimension, has {dist.ndim}"
+    if draws.dtype != dist.dtype:
+        warn(
+            f"Draws dtype {draws.dtype} does not match distribution dtype {dist.dtype}, converting dist to draws dtype",
+        )
+        dist = dist.astype(draws.dtype)
 
     # check whether the distribution is correctly ordered
     if not skip_dist_check:
         strictly_ascending_sort = dist[:-1] <= dist[1:]
-        assert np.all(
-            strictly_ascending_sort
-        ), f"""Distribution is not sorted ascendingly,
+        assert np.all(strictly_ascending_sort), f"""Distribution is not sorted ascendingly,
             {np.count_nonzero(~strictly_ascending_sort)} violations in {len(dist)} values: {dist}"""
 
     # check whether each value of draws (excluding NaN) is in dist
     if not skip_draws_check:
-        assert np.all(
-            np.in1d(draws[~np.isnan(draws)], dist)
-        ), f"""
+        assert np.all(np.isin(draws[~np.isnan(draws)], dist)), f"""
             Each value in draws should be in dist,
-            but {np.size(draws[~np.isnan(draws)][~np.in1d(draws[~np.isnan(draws)], dist)])} values
-            of the {np.size(draws)} are missing: {draws[~np.isnan(draws)][~np.in1d(draws[~np.isnan(draws)], dist)]}"""
+            but {np.size(draws[~np.isnan(draws)][~np.isin(draws[~np.isnan(draws)], dist)])} values
+            of the {np.size(draws)} are missing: {draws[~np.isnan(draws)][~np.isin(draws[~np.isnan(draws)], dist)]}"""
 
     # check the sorter
     if sorter is not None:
         assert sorter.shape == dist.shape, "The shape of the sorter must be the same as the distribution"
 
     # find the index of each draw in the distribution
-    indices_found = np.searchsorted(dist, draws, side="left", sorter=sorter).astype(float)
+    indices_found = np.searchsorted(dist, draws, side="left", sorter=sorter).astype(draws.dtype)
     assert indices_found.shape == draws.shape, "The shape of the indices must match the shape of the draws"
 
     # if indices found are outside the array, make them NaN
-    indices_found[indices_found < 0] = np.NaN
-    indices_found[indices_found >= len(dist)] = np.NaN
+    indices_found[indices_found < 0] = np.nan
+    indices_found[indices_found >= len(dist)] = np.nan
 
     return indices_found
 
@@ -87,7 +88,7 @@ def get_indices_in_array(values: np.ndarray, array: np.ndarray) -> np.ndarray:
 
     # replace the indices found with the original, unsorted indices of array
     nan_mask = ~np.isnan(indices_found)
-    indices_found_unsorted = np.full_like(indices_found, fill_value=np.NaN)
+    indices_found_unsorted = np.full_like(indices_found, fill_value=np.nan)
     indices_found_unsorted[nan_mask] = array_sorter[indices_found[nan_mask].astype(int)]
 
     return indices_found_unsorted
@@ -116,7 +117,14 @@ class CurveBasis(ABC):
     """Abstract object providing minimals for visualization and analysis. Implemented by ``Curve`` and ``Baseline``."""
 
     @abstractmethod
-    def get_curve(self, range: np.ndarray, x_type: str, dist: np.ndarray = None, confidence_level: float = None):
+    def get_curve(
+        self,
+        range: np.ndarray,
+        x_type: str,
+        dist: np.ndarray = None,
+        confidence_level: float = None,
+        return_split: bool = True,
+    ):
         """Get the curve over the specified range of time or function evaluations.
 
         Args:
@@ -124,6 +132,7 @@ def get_curve(self, range: np.ndarray, x_type: str, dist: np.ndarray = None, con
             x_type: the type of the x-axis range (either time or function evaluations).
             dist: the distribution, used for looking up indices. Ignored in ``Baseline``. Defaults to None.
             confidence_level: confidence level for the confidence interval. Ignored in ``Baseline``. Defaults to None.
+            return_split: whether to return the arrays split at the real / fictional point. Defaults to True.
 
         Raises:
             ValueError: on invalid ``x_type`` argument.
@@ -133,19 +142,26 @@ def get_curve(self, range: np.ndarray, x_type: str, dist: np.ndarray = None, con
             See ``get_curve_over_fevals()`` and ``get_curve_over_time()`` for more precise return values.
         """
         if x_type == "fevals":
-            return self.get_curve_over_fevals(range, dist, confidence_level)
+            return self.get_curve_over_fevals(range, dist, confidence_level, return_split=return_split)
         elif x_type == "time":
-            return self.get_curve_over_time(range, dist, confidence_level)
+            return self.get_curve_over_time(range, dist, confidence_level, return_split=return_split)
         raise ValueError(f"x_type must be 'fevals' or 'time', is {x_type}")
 
     @abstractmethod
-    def get_curve_over_fevals(self, fevals_range: np.ndarray, dist: np.ndarray = None, confidence_level: float = None):
+    def get_curve_over_fevals(
+        self,
+        fevals_range: np.ndarray,
+        dist: np.ndarray = None,
+        confidence_level: float = None,
+        return_split: bool = True,
+    ):
         """Get the curve over function evaluations.
 
         Args:
             fevals_range: the range of function evaluations.
             dist: the distribution, used for looking up indices. Ignored in ``Baseline``. Defaults to None.
             confidence_level: confidence level for the confidence interval. Ignored in ``Baseline``. Defaults to None.
+            return_split: whether to return the arrays split at the real / fictional point. Defaults to True.
 
         Returns:
             Two possible returns, for ``Baseline`` and ``Curve`` respectively:
@@ -155,13 +171,16 @@ def get_curve_over_fevals(self, fevals_range: np.ndarray, dist: np.ndarray = Non
         raise NotImplementedError
 
     @abstractmethod
-    def get_curve_over_time(self, time_range: np.ndarray, dist: np.ndarray = None, confidence_level: float = None):
+    def get_curve_over_time(
+        self, time_range: np.ndarray, dist: np.ndarray = None, confidence_level: float = None, return_split: bool = True
+    ):
         """Get the curve over time.
 
         Args:
             time_range: the range of time.
             dist: the distribution, used for looking up indices. Ignored in ``Baseline``. Defaults to None.
             confidence_level: confidence level for the confidence interval. Ignored in ``Baseline``. Defaults to None.
+            return_split: whether to return the arrays split at the real / fictional point. Defaults to True.
 
         Returns:
             Two possible returns, for ``Baseline`` and ``Curve`` respectively:
@@ -230,10 +249,13 @@ def __init__(self, results_description: ResultsDescription) -> None:
             results_description: the ResultsDescription object containing the data for the Curve.
         """
         # inputs
-        self.name = results_description.strategy_name
-        self.display_name = results_description.strategy_display_name
+        self.name = results_description.group_name
+        try:
+            self.display_name = results_description.group_display_name
+        except AttributeError:
+            self.display_name = results_description.group_name.replace("_", " ").capitalize()
         self.device_name = results_description.device_name
-        self.kernel_name = results_description.kernel_name
+        self.application_name = results_description.application_name
         self.stochastic = results_description.stochastic
         self.minimization = results_description.minimization
 
@@ -265,7 +287,7 @@ def check_attributes(self) -> None:
         assert isinstance(self.name, str)
         assert isinstance(self.display_name, str)
         assert isinstance(self.device_name, str)
-        assert isinstance(self.kernel_name, str)
+        assert isinstance(self.application_name, str)
         assert isinstance(self.stochastic, bool)
         assert isinstance(self._x_fevals, np.ndarray)
         assert isinstance(self._x_time, np.ndarray)
@@ -504,9 +526,9 @@ def _check_curve_real_fictional_consistency(
         if x_axis_range_fictional.ndim > 0:
             # if there's a fictional part, ensure that all the expected data is in the combined real and fictional parts
             x_axis_range_combined = np.concatenate([x_axis_range_real, x_axis_range_fictional])
-            assert (
-                x_axis_range.shape == x_axis_range_combined.shape
-            ), f"The shapes of {x_axis_range.shape=} and {x_axis_range_combined.shape=} do not match"
+            assert x_axis_range.shape == x_axis_range_combined.shape, (
+                f"The shapes of {x_axis_range.shape=} and {x_axis_range_combined.shape=} do not match"
+            )
             assert np.array_equal(
                 x_axis_range, np.concatenate([x_axis_range_real, x_axis_range_fictional]), equal_nan=True
             )
@@ -519,24 +541,29 @@ def _check_curve_real_fictional_consistency(
             )
         else:
             # if there is no fictional part, ensure that all the expected data is in the real part
-            assert (
-                x_axis_range.shape == x_axis_range_real.shape
-            ), f"The shapes of {x_axis_range.shape=} and {x_axis_range_real.shape=} do not match"
-            assert np.array_equal(
-                x_axis_range, x_axis_range_real, equal_nan=True
-            ), f"Unequal arrays: {x_axis_range}, {x_axis_range_real}"
+            assert x_axis_range.shape == x_axis_range_real.shape, (
+                f"The shapes of {x_axis_range.shape=} and {x_axis_range_real.shape=} do not match"
+            )
+            assert np.array_equal(x_axis_range, x_axis_range_real, equal_nan=True), (
+                f"Unequal arrays: {x_axis_range}, {x_axis_range_real}"
+            )
             assert np.array_equal(curve, curve_real, equal_nan=True), f"Unequal arrays: {curve}, {curve_real}"
-            assert np.array_equal(
-                curve_lower_err, curve_lower_err_real, equal_nan=True
-            ), f"Unequal arrays: {curve_lower_err}, {curve_lower_err_real}"
-            assert np.array_equal(
-                curve_upper_err, curve_upper_err_real, equal_nan=True
-            ), f"Unequal arrays: {curve_upper_err}, {curve_upper_err_real}"
+            assert np.array_equal(curve_lower_err, curve_lower_err_real, equal_nan=True), (
+                f"Unequal arrays: {curve_lower_err}, {curve_lower_err_real}"
+            )
+            assert np.array_equal(curve_upper_err, curve_upper_err_real, equal_nan=True), (
+                f"Unequal arrays: {curve_upper_err}, {curve_upper_err_real}"
+            )
 
     def get_curve(  # noqa: D102
-        self, range: np.ndarray, x_type: str, dist: np.ndarray = None, confidence_level: float = None
+        self,
+        range: np.ndarray,
+        x_type: str,
+        dist: np.ndarray = None,
+        confidence_level: float = None,
+        return_split: bool = True,
     ):
-        return super().get_curve(range, x_type, dist, confidence_level)
+        return super().get_curve(range, x_type, dist, confidence_level, return_split=return_split)
 
     def _get_matching_feval_indices_in_range(self, fevals_range: np.ndarray) -> np.ndarray:
         """Get a mask of where the fevals range matches with the data."""
@@ -551,6 +578,8 @@ def _get_matching_feval_indices_in_range(self, fevals_range: np.ndarray) -> np.n
 
     def _get_curve_over_fevals_values_in_range(self, fevals_range: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
         """Get the valid fevals and values that are in the given range."""
+        if len(fevals_range) == 0:
+            raise ValueError("The fevals range must have at least one value")
         target_index: int = fevals_range[-1] - 1
 
         # filter to only get data in the fevals range
@@ -592,9 +621,9 @@ def _get_curve_over_fevals_values_in_range(self, fevals_range: np.ndarray) -> tu
             masked_fevals[:, greatest_common_non_NaN_index + 1 :] = np.nan
 
             # check that the filtered fevals are consistent
-            assert np.allclose(
-                masked_fevals, masked_fevals[0], equal_nan=True
-            ), "Every repeat must have the same array of function evaluations"
+            assert np.allclose(masked_fevals, masked_fevals[0], equal_nan=True), (
+                "Every repeat must have the same array of function evaluations"
+            )
 
         # as every repeat has the same array of fevals, check whether they match the range
         fevals = masked_fevals[
@@ -610,7 +639,11 @@ def _get_curve_over_fevals_values_in_range(self, fevals_range: np.ndarray) -> tu
         return fevals, masked_values
 
     def get_curve_over_fevals(  # noqa: D102
-        self, fevals_range: np.ndarray, dist: np.ndarray = None, confidence_level: float = None
+        self,
+        fevals_range: np.ndarray,
+        dist: np.ndarray = None,
+        confidence_level: float = None,
+        return_split: bool = True,
     ):
         fevals, masked_values = self._get_curve_over_fevals_values_in_range(fevals_range)
 
@@ -631,6 +664,8 @@ def get_curve_over_fevals(  # noqa: D102
                 # get the confidence interval
                 indices_lower_err, indices_upper_err = self.get_confidence_interval(indices, confidence_level)
                 indices_lower_err, indices_upper_err = indices_lower_err.astype(int), indices_upper_err.astype(int)
+                indices_lower_err = np.clip(indices_lower_err, a_min=0, a_max=dist.shape[0] - 1)
+                indices_upper_err = np.clip(indices_upper_err, a_min=0, a_max=dist.shape[0] - 1)
             # obtain the curves by looking up the associated values
             curve = dist[indices_mean]
             curve_lower_err = dist[indices_lower_err]
@@ -682,10 +717,12 @@ def get_curve_over_fevals(  # noqa: D102
         assert np.all(~np.isnan(curve_lower_err)), f"NaNs at {np.nonzero(np.isnan(curve_lower_err))[0]}"
         assert np.all(~np.isnan(curve_upper_err)), f"NaNs at {np.nonzero(np.isnan(curve_upper_err))[0]}"
 
-        # return the curves split in real and fictional
-        return self._get_curve_split_real_fictional_parts(
-            real_stopping_point_index + 1, fevals_range, curve, curve_lower_err, curve_upper_err
-        )
+        if return_split:
+            # return the curves split in real and fictional
+            return self._get_curve_split_real_fictional_parts(
+                real_stopping_point_index + 1, fevals_range, curve, curve_lower_err, curve_upper_err
+            )
+        return real_stopping_point_index + 1, fevals_range, curve, curve_lower_err, curve_upper_err
 
     def _get_curve_over_time_values_in_range(
         self, time_range: np.ndarray, return_1d=True
@@ -703,21 +740,39 @@ def _get_curve_over_time_values_in_range(
         times: np.ndarray = _times[nan_mask].reshape(-1, num_repeats)
         values: np.ndarray = _values[nan_mask].reshape(-1, num_repeats)
 
-        # get the highest time of each run of the algorithm, take the median
+        # find the stopping point
         times_no_nan = times
         times_no_nan[np.isnan(values)] = np.nan  # to count only valid configurations towards highest time
         highest_time_per_repeat = np.nanmax(times_no_nan, axis=0)
         assert highest_time_per_repeat.shape[0] == num_repeats
-        real_stopping_point_time: float = np.nanmedian(highest_time_per_repeat)
+        highest_time_per_repeat = np.sort(highest_time_per_repeat)
+        # get the highest time of each run of the algorithm, take the average, stopping point is next highest time
+        try:
+            real_stopping_point_time: float = highest_time_per_repeat[
+                np.nanmedian(highest_time_per_repeat) < highest_time_per_repeat
+            ][0]
+        except IndexError:
+            # there is no next highest time, so we return the last time in the range
+            real_stopping_point_time = time_range[-1]
 
         # filter to get the time range with a margin on both ends for the isotonic regression
-        time_range_margin = 0.1
-        range_mask_margin = (time_range[0] * (1 - time_range_margin) <= times) & (
-            times <= time_range[-1] * (1 + time_range_margin)
-        )
-        assert np.all(
-            np.count_nonzero(range_mask_margin, axis=0) > 1
-        ), "Not enough overlap in time range and time values"
+        time_range_margin_modifier = 0.25 * (
+            num_repeats / times.size
+        )  # give more margin when there are few values relative to the number of repeats
+        time_range_margin = 0.1 + time_range_margin_modifier
+        time_range_start = time_range[0] * (1 - time_range_margin)
+        time_range_end = time_range[-1] * (1 + time_range_margin)
+        range_mask_margin = (time_range_start <= times) & (times <= time_range_end)
+
+        # make sure there is enough overlap in the time ranges
+        if not np.all(np.count_nonzero(range_mask_margin, axis=0) > 1):
+            raise ValueError(
+                f"Not enough overlap in time range and time values: should be {time_range_start=} <= {times} <= {time_range_end=} ({time_range_margin=}, {num_repeats=}, {times.size=})",
+                np.count_nonzero(range_mask_margin, axis=0),
+                self.name,
+                self.application_name,
+                self.device_name,
+            )
         times = np.where(range_mask_margin, times, np.nan)
         values = np.where(range_mask_margin, values, np.nan)
         num_repeats = values.shape[1]
@@ -749,7 +804,12 @@ def _get_curve_over_time_values_in_range(
             return times, values, real_stopping_point_time, num_fevals, num_repeats
 
     def get_curve_over_time(  # noqa: D102
-        self, time_range: np.ndarray, dist: np.ndarray = None, confidence_level: float = None, use_bagging=True
+        self,
+        time_range: np.ndarray,
+        dist: np.ndarray = None,
+        confidence_level: float = None,
+        return_split: bool = True,
+        use_bagging=True,
     ):
         # check the distribution
         if dist is None:
@@ -809,9 +869,9 @@ def get_curve_over_time(  # noqa: D102
             indices_curve = prediction_interval[:, 2]
             curve = dist[indices_curve]
         curve_lower_err, curve_upper_err = dist[prediction_interval[:, 0]], dist[prediction_interval[:, 1]]
-        assert (
-            curve_lower_err.shape == curve_upper_err.shape == curve.shape
-        ), f"{curve_lower_err.shape=} != {curve_upper_err.shape=} != {curve.shape=}"
+        assert curve_lower_err.shape == curve_upper_err.shape == curve.shape, (
+            f"{curve_lower_err.shape=} != {curve_upper_err.shape=} != {curve.shape=}"
+        )
 
         # print(f"{self.display_name}: {np.median(curve - curve_lower_err)}, {np.median(curve_upper_err - curve)}")
         # for t, e, i in zip(time_range, curve_lower_err, prediction_interval[:, 0]):
@@ -833,9 +893,11 @@ def get_curve_over_time(  # noqa: D102
             curve_lower_err[real_stopping_point_index:] = curve_lower_err[real_stopping_point_index]
             curve_upper_err[real_stopping_point_index:] = curve_upper_err[real_stopping_point_index]
 
-        return self._get_curve_split_real_fictional_parts(
-            real_stopping_point_index, time_range, curve, curve_lower_err, curve_upper_err
-        )
+        if return_split:
+            return self._get_curve_split_real_fictional_parts(
+                real_stopping_point_index, time_range, curve, curve_lower_err, curve_upper_err
+            )
+        return real_stopping_point_index, time_range, curve, curve_lower_err, curve_upper_err
 
     def get_split_times(  # noqa: D102
         self, range: np.ndarray, x_type: str, searchspace_stats: SearchspaceStatistics
@@ -850,7 +912,7 @@ def get_split_times_at_feval(  # noqa: D102
         objective_time_keys = searchspace_stats.objective_time_keys
         num_keys = len(objective_time_keys)
         num_repeats = matching_indices_mask.shape[1]
-        masked_time_per_key = np.full((num_keys, matching_indices_mask.shape[0], num_repeats), np.NaN)
+        masked_time_per_key = np.full((num_keys, matching_indices_mask.shape[0], num_repeats), np.nan)
 
         # for each key, apply the boolean mask
         for key_index in range(num_keys):
@@ -859,13 +921,13 @@ def get_split_times_at_feval(  # noqa: D102
             ]
 
         # remove where every repeat has NaN
-        time_in_range_per_key = np.full((num_keys, fevals_range.shape[0], num_repeats), np.NaN)
+        time_in_range_per_key = np.full((num_keys, fevals_range.shape[0], num_repeats), np.nan)
         for key_index in range(num_keys):
             all_nan_mask = ~np.all(np.isnan(masked_time_per_key[key_index]), axis=1)
             time_in_range_per_key[key_index] = masked_time_per_key[key_index][all_nan_mask]
 
         # get the median time per key at each repeat
-        split_time_per_feval = np.full((num_keys, fevals_range.shape[0]), np.NaN)
+        split_time_per_feval = np.full((num_keys, fevals_range.shape[0]), np.nan)
         for key_index in range(num_keys):
             split_time_per_feval[key_index] = np.mean(time_in_range_per_key[key_index], axis=1)
         assert split_time_per_feval.shape == (
@@ -884,7 +946,7 @@ def get_split_times_at_time(  # noqa: D102
 
         # for each key, interpolate the split times to the time range
         num_keys = len(searchspace_stats.objective_time_keys)
-        split_time_per_timestamp = np.full((num_keys, time_range.shape[0]), np.NaN)
+        split_time_per_timestamp = np.full((num_keys, time_range.shape[0]), np.nan)
         for key_index in range(num_keys):
             # remove NaN
             times_split_key = times_split[key_index]
@@ -951,7 +1013,7 @@ def _get_prediction_interval_separated(
         num_repeats = values.shape[1]
 
         # predict an isotonic curve for the time range for each run
-        predictions = np.full((num_repeats, time_range.shape[0]), fill_value=np.NaN)
+        predictions = np.full((num_repeats, time_range.shape[0]), fill_value=np.nan)
         for run in range(num_repeats):
             # get the data of this run
             _x = times[:, run]
@@ -980,9 +1042,9 @@ def _get_prediction_interval_separated(
         predictions = predictions.transpose()  # set to (time_range, num_repeats)
         y_lower_err, y_upper_err = self.get_confidence_interval(predictions, confidence_level=confidence_level)
         mean_prediction = np.median(predictions, axis=1)
-        assert (
-            y_lower_err.shape == y_upper_err.shape == mean_prediction.shape == time_range.shape
-        ), f"{y_lower_err.shape=} != {y_upper_err.shape=} != {mean_prediction.shape=} != {time_range.shape=}"
+        assert y_lower_err.shape == y_upper_err.shape == mean_prediction.shape == time_range.shape, (
+            f"{y_lower_err.shape=} != {y_upper_err.shape=} != {mean_prediction.shape=} != {time_range.shape=}"
+        )
 
         # combine the data and return as a prediction interval
         prediction_interval = np.concatenate([y_lower_err, y_upper_err, mean_prediction]).reshape((3, -1)).transpose()
diff --git a/src/autotuning_methodology/experiments.py b/src/autotuning_methodology/experiments.py
index 382347b..0c64184 100755
--- a/src/autotuning_methodology/experiments.py
+++ b/src/autotuning_methodology/experiments.py
@@ -1,21 +1,25 @@
 """Main experiments code."""
 
-from __future__ import annotations  # for correct nested type hints e.g. list[str], tuple[dict, str]
+from __future__ import (
+    annotations,  # for correct nested type hints e.g. list[str], tuple[dict, str]
+)
 
 import json
-import sys
 from argparse import ArgumentParser
-from importlib import import_module
-from importlib.resources import files
 from math import ceil
-from os import getcwd
+from os import getcwd, makedirs
 from pathlib import Path
+from random import randint
 
-from jsonschema import validate
+from jsonschema import ValidationError
 
 from autotuning_methodology.caching import ResultsDescription
 from autotuning_methodology.runner import collect_results
 from autotuning_methodology.searchspace_statistics import SearchspaceStatistics
+from autotuning_methodology.validators import validate_experimentsfile
+from autotuning_methodology.formats_interface import load_T4_format
+
+PACKAGE_ROOT = Path(__file__).parent.parent.parent
 
 
 def get_args_from_cli(args=None) -> str:
@@ -30,26 +34,31 @@ def get_args_from_cli(args=None) -> str:
     Returns:
         The filepath to the experiments file.
     """
-    CLI = ArgumentParser()
-    CLI.add_argument("experiment", type=str, help="The experiment.json to execute, see experiments/template.json")
-    args = CLI.parse_args(args)
+    cli = ArgumentParser()
+    cli.add_argument(
+        "experiment", type=str, help="The experiment setup json file to execute, see experiments/template.json"
+    )
+    args = cli.parse_args(args)
     filepath: str = args.experiment
     if filepath is None or filepath == "":
-        raise ValueError(
-            "Invalid '-experiment' option. Run 'visualize_experiments.py -h' to read more about the options."
-        )
+        raise ValueError("Invalid '--experiment' option. Run 'visualize_experiments.py -h' to read more.")
     return filepath
 
 
-def get_experiment_schema_filepath():
-    """Obtains and checks the filepath to the JSON schema.
-
-    Returns:
-        the filepath to the schema in Traversable format.
-    """
-    schemafile = files("autotuning_methodology").joinpath("schema.json")
-    assert schemafile.is_file(), f"Path to schema.json does not exist, attempted path: {schemafile}"
-    return schemafile
+def make_and_check_path(filename: str, parent=None, extension=None) -> Path:
+    """Checks if the file exists, if not, tries to add the extension."""
+    filename_path = Path(filename)
+    if filename_path.is_absolute() is False and parent is not None:
+        filename_path = PACKAGE_ROOT / Path(parent).joinpath(filename).resolve()
+    if filename_path.exists():
+        return filename_path
+    # try and add extension
+    if extension is None:
+        raise FileNotFoundError(f"{filename_path.resolve()} does not exist.")
+    filename_path = Path(str(filename_path) + extension)
+    if filename_path.exists():
+        return filename_path
+    raise FileNotFoundError(f"{filename_path.resolve()} does not exist.")
 
 
 def get_experiment(filename: str) -> dict:
@@ -73,153 +82,506 @@ def get_experiment(filename: str) -> dict:
     #     path = Path(filename)
     assert path.exists(), f"Path to experiment file does not exist, attempted path: {path}, CWD: {getcwd()}"
 
-    # get the path to the schema
-    schemafile = get_experiment_schema_filepath()
-
     # open the experiment file and validate using the schema file
-    with open(path, "r", encoding="utf-8") as file, open(schemafile, "r", encoding="utf-8") as schemafile:
-        schema = json.load(schemafile)
+    with path.open("r", encoding="utf-8") as file:
         experiment: dict = json.load(file)
-        validate(instance=experiment, schema=schema)
-        return experiment
+        try:
+            validate_experimentsfile(experiment)
+            return experiment
+        except ValidationError as e:
+            print(e)
+            raise ValidationError("Experiment file does not comply with schema")
 
 
-def get_strategies(experiment: dict) -> dict:
-    """Gets the strategies from an experiments file by augmenting it with the defaults.
+def get_experimental_groups(experiment: dict) -> list[dict]:
+    """Prepares all the experimental groups as all combinations of application and gpus (from experimental_groups_defaults) and big experimental groups from setup file (experimental_groups, usually search methods). Check additional settings for each experimental group. Prepares the directory structure for the whole experiment.
 
     Args:
         experiment: the experiment dictionary object.
 
     Returns:
-        The strategies in the experiment dictionary object, augmented where necessery.
+        The experimental groups in the experiment dictionary object.
+    """
+    experimental_groups_defaults = experiment["experimental_groups_defaults"]
+    search_strategies = experiment["search_strategies"]
+
+    # set up the directory structure
+    experiment["parent_folder_absolute_path"] = Path(experiment["parent_folder"]).resolve()
+    # if folders "run" and "setup" do not exist, create
+    makedirs(experiment["parent_folder_absolute_path"].joinpath("run"), exist_ok=True)
+    makedirs(experiment["parent_folder_absolute_path"].joinpath("setup"), exist_ok=True)
+
+    # create folders for each experimental group from file
+    for strategy in search_strategies:
+        makedirs(experiment["parent_folder_absolute_path"].joinpath("run").joinpath(strategy["name"]), exist_ok=True)
+
+    # generate all experimental groups
+    # with applications and gpus provided in experimental_groups_defaults
+    # and search strategies provided in search_strategies
+    all_experimental_groups = generate_all_experimental_groups(
+        search_strategies, experimental_groups_defaults, experiment["parent_folder_absolute_path"]
+    )
+
+    # additional check beyond validation
+    # if every experimental group has autotuner set
+    # set autotuner_path to default installation if not set by the user
+    for group in all_experimental_groups:
+        if group.get("autotuner") is None:
+            raise KeyError(
+                "Property 'autotuner' must be set for all groups, either in experimental_groups_defaults or in experimental_groups. It is not set for",
+                group["full_name"],
+            )
+        if group["autotuner"] == "KTT":
+            if group["samples"] != 1:
+                raise NotImplementedError(
+                    "KTT currently supports only one sample per run and output. Please set samples=1 for group['full_name']."
+                )
+            if group.get("autotuner_path") is None:
+                raise NotImplementedError(
+                    "Default autotuner_path is not supported yet for KTT, please set autotuner_path for ",
+                    group["full_name"]
+                    + " to directory with KttTuningLauncher and pyktt.so, e.g. /home/user/KTT/Build/x86_64_Release.",
+                )
+            elif Path(group["autotuner_path"]).exists() is False:
+                raise FileNotFoundError(
+                    f"Directory {group['autotuner_path']} does not exists. Try setting the absolute path."
+                )
+            elif Path(group["autotuner_path"]).joinpath("KttTuningLauncher").exists() is False:
+                raise FileNotFoundError(
+                    f"Directory {group['autotuner_path']} does not contain KttTuningLauncher. Have you used --tuning-loader when premaking KTT?"
+                )
+            elif Path(group["autotuner_path"]).joinpath("pyktt.so").exists() is False:
+                raise FileNotFoundError(
+                    f"Directory {group['autotuner_path']} does not contain pyktt.so. Have you used --python when premaking KTT?"
+                )
+            # TODO make and set default autotuner path
+
+    return all_experimental_groups
+
+
+def generate_all_experimental_groups(
+    search_strategies: list[dict], experimental_groups_defaults: dict, parent_folder_path: Path
+) -> list[dict]:
+    """Generates all experimental groups for the experiment as a combination of given applications, gpus and search strategies from experiments setup file.
+
+    Args:
+        search_strategies: list of dictionaries with settings for various search strategies from experiments setup file, section search_strategies.
+        experimental_groups_defaults: a dictionary with default settings for experimental groups from experiments setup file, section experimental_groups_defaults.
+        parent_folder_path: path to experiment parent folder that stores all files generated in the experiment.
+
+    Returns:
+        A list of dictionaries, one for each experimental group.
+    """
+    experimental_groups = []
+
+    for gpu in experimental_groups_defaults["gpus"]:
+        for application in experimental_groups_defaults["applications"]:
+            for strategy in search_strategies:
+                group = strategy.copy()
+
+                for default in experimental_groups_defaults:
+                    if default not in group and default not in [
+                        "applications",
+                        "gpus",
+                        "pattern_for_full_search_space_filenames",
+                    ]:
+                        group[default] = experimental_groups_defaults[default]
+
+                group["full_name"] = "_".join([gpu, application["name"], group["name"]])
+
+                group["gpu"] = gpu
+                group["application_name"] = application["name"]
+
+                group["application_folder"] = Path(application["folder"])
+                group["application_input_file"] = make_and_check_path(
+                    application["input_file"], application["folder"], None
+                )
+                group["input_file"] = parent_folder_path.joinpath("setup").joinpath(
+                    "_".join([group["full_name"], "input.json"])
+                )
+                group["parent_folder_path"] = parent_folder_path
+
+                if experimental_groups_defaults.get("pattern_for_full_search_space_filenames") is None:
+                    group["full_search_space_file"] = get_full_search_space_filename_from_input_file(
+                        group["application_input_file"]
+                    )
+                else:
+                    group["full_search_space_file"] = get_full_search_space_filename_from_pattern(
+                        experimental_groups_defaults["pattern_for_full_search_space_filenames"],
+                        gpu,
+                        application["name"],
+                    )
+
+                # get the objective performance keys
+                if "objective_performance_keys" in application:
+                    group["objective_performance_keys"] = application["objective_performance_keys"]
+                else:
+                    # load the full search space file and derive the objective performance keys
+                    print(
+                        f"Loading full search space file {group['full_search_space_file']} to infer the objective performance keys. Consider setting them explicititely in the experiments file."
+                    )
+                    data = load_T4_format(group["full_search_space_file"], validate=True)
+                    objectives = data["results"][0]["objectives"]
+                    assert len(objectives) == 1, "Only one objective is supported for now"
+                    group["objective_performance_keys"] = objectives
+                objective = group["objective_performance_keys"][0]
+
+                # derive the optimization direction
+                if "minimization" in application:
+                    group["minimization"] = application["minimization"]
+                elif "time" in objective.lower():
+                    group["minimization"] = True
+                elif any(k in objective.lower() for k in ["score", "gflop/s", "gflops", "gb/s"]):
+                    group["minimization"] = False
+                else:
+                    raise NotImplementedError(
+                        f"Optimization direction can not be automatically inferred from '{objective=}' ({gpu=}, {application=}, {strategy=}). Please set 'minimization' for this application in the experiments file."
+                    )
+
+                if group["autotuner"] == "KTT":
+                    raise NotImplementedError(
+                        "KTT is working on supporting the shared interface. The old conversions have been deprecated. An older build can be used to use these functions."
+                    )
+
+                group["output_file"]: Path = (
+                    parent_folder_path.joinpath("run")
+                    .joinpath(group["name"])
+                    .joinpath(group["full_name"] + ".json")
+                    .resolve()
+                )
+
+                generate_input_file(group)
+                experimental_groups.append(group)
+
+    return experimental_groups
+
+
+def get_full_search_space_filename_from_input_file(input_filename: Path) -> Path:
+    """Returns a path to full search space file that is provided in the input json file in KernelSpecification.SimulationInput.
+
+    Args:
+        input_filename: path to input json file.
+
+    Raises:
+        KeyError: if the path is not provided, but is expected.
+
+    Returns:
+        A path to full search space file that was written in the input json file.
+    """
+    with open(input_filename, "r", encoding="utf-8") as input_file:
+        input_json = json.load(input_file)
+        if input_json["KernelSpecification"].get("SimulationInput") is None:
+            raise KeyError(
+                "SimulationInput, i.e. full search space file is expected and not defined in",
+                input_filename,
+                ". Please set the path to that file in KernelSpecification.SimulationInput in input json file or set pattern_for_full_search_space_filename in experiments setup json file.",
+            )
+        full_search_space_filename = make_and_check_path(
+            input_json["KernelSpecification"]["SimulationInput"], str(input_filename.parent), ".json"
+        )
+    # need to return filename WITHOUT .json, KTT (and probably also others) needs that in SimulationInput in input json as other autotuner can take other formats
+    return full_search_space_filename.parent.joinpath(full_search_space_filename.stem)
+
+
+def get_full_search_space_filename_from_pattern(pattern: dict, gpu: str, application_name: str) -> Path:
+    """Returns a path to full search space file that is generated from the pattern provided in experiments setup file.
+
+    Args:
+        pattern: pattern regex string
+        gpu: name of the gpu, needs to be plugged into the pattern
+        application_name: name of the application, needs to be plugged into the pattern
+
+    Raises:
+        NotImplementedError: if the regex expects other variables than just application name and gpu.
+
+    Returns:
+        A path to full search file generated from the pattern.
     """
-    strategy_defaults = experiment["strategy_defaults"]
-    strategies = experiment["strategies"]
-    # # get a baseline index if it exists
-    # baseline_index = list(
-    #     strategy_index for strategy_index, strategy in enumerate(strategies) if "is_baseline" in strategy
-    # )
-    # if len(baseline_index) != 1:
-    #     raise ValueError(f"There must be exactly one baseline, found {len(baseline_index)} baselines")
-    # if strategies[baseline_index[0]]["is_baseline"] is not True:
-    #     raise ValueError(f"is_baseline must be true, yet is set to {strategies[0]['is_baseline']}!")
-    # # if the baseline index is not 0, put the baseline strategy first
-    # if baseline_index[0] != 0:
-    #     raise ValueError("The baseline strategy must be the first strategy in the experiments file!")
-    #     # strategies.insert(0, strategies.pop(baseline_index[0]))
-
-    # augment the strategies with the defaults
-    for strategy in strategies:
-        for default in strategy_defaults:
-            if default not in strategy:
-                strategy[default] = strategy_defaults[default]
-    return strategies
-
-
-def execute_experiment(filepath: str, profiling: bool = False) -> tuple[dict, dict, dict]:
+    filename = pattern["regex"].replace("${applications}", application_name).replace("${gpus}", gpu)
+    if "${" in filename:
+        raise NotImplementedError(
+            f"Variables other than applications and gpus are not yet supported for pattern matching. Unresolved: {filename}."
+        )
+    full_search_space_filename = make_and_check_path(filename)
+    return full_search_space_filename
+
+
+def calculate_budget(group: dict, statistics_settings: dict, searchspace_stats: SearchspaceStatistics) -> dict:
+    """Calculates the budget for the experimental group, given cutoff point provided in experiments setup file.
+
+    Args:
+        group: a dictionary with settings for experimental group
+        statistics_settings: a dictionary with settings related to statistics
+        searchspace_stats: a SearchspaceStatistics instance with cutoff points determined from related full search space files
+
+    Returns:
+        A modified group dictionary.
+    """
+    # get cutoff points
+    _, cutoff_point_fevals, cutoff_point_start_time, cutoff_point_time = (
+        searchspace_stats.cutoff_point_fevals_time_start_end(
+            statistics_settings["cutoff_percentile_start"], statistics_settings["cutoff_percentile"]
+        )
+    )
+
+    # +10% margin, to make sure cutoff_point is reached by compensating for potential non-valid evaluations  # noqa: E501
+    cutoff_margin = group.get("cutoff_margin", 0.1)
+
+    # register in the group
+    group["budget"] = {}
+    group["cutoff_times"] = {
+        "cutoff_time_start": max(cutoff_point_start_time, 0.0)
+        if statistics_settings["cutoff_percentile_start"] > 0.0
+        else 0.0,
+        "cutoff_time": cutoff_point_time * (1 + cutoff_margin),
+    }
+
+    # set when to stop
+    if statistics_settings["cutoff_type"] == "time":
+        group["budget"]["time_limit"] = group["cutoff_times"]["cutoff_time"]
+    else:
+        budget = min(int(ceil(cutoff_point_fevals * (1 + cutoff_margin))), searchspace_stats.size)
+        group["budget"]["max_fevals"] = budget
+
+    # write to group's input file as Budget
+    with open(group["input_file"], "r", encoding="utf-8") as fp:
+        input_json = json.load(fp)
+        if input_json.get("Budget") is None:
+            input_json["Budget"] = []
+            input_json["Budget"].append({})
+        if group["budget"].get("time_limit") is not None:
+            input_json["Budget"][0]["Type"] = "TuningDuration"
+            input_json["Budget"][0]["BudgetValue"] = group["budget"]["time_limit"]
+        else:  # it's max_fevals
+            input_json["Budget"][0]["Type"] = "ConfigurationCount"
+            input_json["Budget"][0]["BudgetValue"] = group["budget"]["max_fevals"]
+
+    # write the results and return the adjusted group
+    with open(group["input_file"], "w", encoding="utf-8") as fp:
+        json.dump(input_json, fp, indent=4)
+    return group
+
+
+def generate_input_file(group: dict):
+    """Creates a input json file specific for a given application, gpu and search method.
+
+    Args:
+        group: dictionary with settings for a given experimental group.
+    """
+    with open(group["application_input_file"], "r", encoding="utf-8") as fp:
+        input_json = json.load(fp)
+        input_json["KernelSpecification"]["SimulationInput"] = str(group["full_search_space_file"])
+
+        # TODO dirty fix below for Kernel Tuner compatibility, instead implement reading T4 as cache in Kernel Tuner
+        input_json["KernelSpecification"]["SimulationInput"] = str(
+            input_json["KernelSpecification"]["SimulationInput"]
+        ).replace("_T4", "")
+
+        input_json["General"]["OutputFile"] = str(group["output_file"].parent.joinpath(group["output_file"].stem))
+        if input_json["General"]["OutputFormat"] != "JSON":
+            raise RuntimeError(
+                f"Only JSON output format is supported. Please set General.OutputFormat to JSON in {group['application_input_file']}."
+            )
+        if "TimeUnit" not in input_json["General"]:
+            input_json["General"]["TimeUnit"] = "Milliseconds"
+        if input_json["KernelSpecification"].get("Device") is None:
+            input_json["KernelSpecification"]["Device"] = {}
+            input_json["KernelSpecification"]["Device"]["Name"] = group["gpu"]
+        else:
+            input_json["KernelSpecification"]["Device"]["Name"] = group["gpu"]
+        input_json["KernelSpecification"]["KernelFile"] = str(
+            Path(
+                Path(group["application_input_file"]).parent / Path(input_json["KernelSpecification"]["KernelFile"])
+            ).resolve()
+        )
+
+        input_json["Search"] = {}
+        input_json["Search"]["Name"] = group["search_method"]
+        if group.get("search_method_hyperparameters") is not None:
+            input_json["Search"]["Attributes"] = []
+            for param in group["search_method_hyperparameters"]:
+                attribute = {}
+                attribute["Name"] = param["name"]
+                attribute["Value"] = param["value"]
+                input_json["Search"]["Attributes"].append(attribute)
+    # note that this is written to a different file, specific for gpu, application and search method
+    with open(group["input_file"], "w", encoding="utf-8") as fp:
+        json.dump(input_json, fp, indent=4)
+
+
+def get_random_unique_filename(prefix="", suffix=""):
+    """Get a random, unique filename that does not yet exist."""
+
+    def randpath():
+        return Path(f"{prefix}{randint(1000, 9999)}{suffix}")
+
+    path = randpath()
+    while path.exists():
+        path = randpath()
+    return path
+
+
+def generate_experiment_file(
+    name: str,
+    parent_folder: Path,
+    search_strategies: list[dict],
+    applications: list[dict] = None,
+    gpus: list[str] = None,
+    override: dict = None,
+    generate_unique_file=False,
+    overwrite_existing_file=False,
+):
+    """Creates an experiment file based on the given inputs and opinionated defaults."""
+    assert isinstance(name, str) and len(name) > 0, f"Name for experiment file must be valid, is '{name}'"
+    experiment_file_path = Path(f"./{name.replace(' ', '_')}.json")
+    if generate_unique_file is True:
+        experiment_file_path = get_random_unique_filename(f"{name.replace(' ', '_')}_", ".json")
+    if experiment_file_path.exists():
+        if overwrite_existing_file is False:
+            raise FileExistsError(f"Experiments file '{experiment_file_path}' already exists")
+    defaults_path = Path(__file__).parent / "experiments_defaults.json"
+    with defaults_path.open() as fp:
+        experiment: dict = json.load(fp)
+
+    # write the arguments to the experiment file
+    experiment["name"] = name
+    experiment["parent_folder"] = str(parent_folder.resolve())
+    experiment["search_strategies"] = search_strategies
+    if applications is not None:
+        experiment["experimental_groups_defaults"]["applications"] = applications
+    if gpus is not None:
+        experiment["experimental_groups_defaults"]["gpus"] = gpus
+    if override is not None:
+        for key, value in override.items():
+            experiment[key].update(value)
+
+    # validate and write to experiments file
+    validate_experimentsfile(experiment)
+    with experiment_file_path.open("w", encoding="utf-8") as fp:
+        json.dump(experiment, fp)
+
+    # return the location of the experiments file
+    return experiment_file_path.resolve()
+
+
+def execute_experiment(filepath: str, profiling: bool = False, full_validate_on_load: bool = True):
     """Executes the experiment by retrieving it from the cache or running it.
 
     Args:
         filepath: path to the experiments .json file.
         profiling: whether profiling is enabled. Defaults to False.
+        full_validate_on_load: whether to fully validate the searchspace statistics file on load. Defaults to True.
 
     Raises:
         FileNotFoundError: if the path to the kernel specified in the experiments file is not found.
 
     Returns:
-        A tuple of the experiment dictionary, the strategies executed, and the resulting list of ``ResultsDescription``.
+        A tuple of the experiment dictionary, the experimental groups executed, the dictionary of ``Searchspace statistics`` and the resulting list of ``ResultsDescription``.
     """
     experiment = get_experiment(filepath)
-    experiment_folderpath = Path(filepath).parent
+    experiment_folderpath = Path(experiment["parent_folder"])
     print(f"Starting experiment '{experiment['name']}'")
-    experiment_folder_id: str = experiment["folder_id"]
-    minimization: bool = experiment.get("minimization", True)
-    cutoff_percentile: float = experiment.get("cutoff_percentile", 1)
-    cutoff_type: str = experiment.get("cutoff_type", "fevals")
-    assert cutoff_type == "fevals" or cutoff_type == "time", f"cutoff_type must be 'fevals' or 'time', is {cutoff_type}"
-    curve_segment_factor: float = experiment.get("curve_segment_factor", 0.05)
-    assert isinstance(curve_segment_factor, float), f"curve_segment_factor is not float, {type(curve_segment_factor)}"
-    strategies: list[dict] = get_strategies(experiment)
-
-    # add the kernel directory to the path to import the module, relative to the experiment file
-    kernels_path = experiment_folderpath / Path(experiment["kernels_path"])
-    if not kernels_path.exists():
-        raise FileNotFoundError(f"No such path {kernels_path.resolve()}, CWD: {getcwd()}")
-    sys.path.append(str(kernels_path))
-    kernel_names = experiment["kernels"]
-    kernels = list(import_module(kernel_name) for kernel_name in kernel_names)
-
-    # variables for comparison
-    objective_time_keys: list[str] = experiment["objective_time_keys"]
-    objective_performance_keys: list[str] = experiment["objective_performance_keys"]
-
-    # execute each strategy in the experiment per GPU and kernel
-    results_descriptions: dict[str, dict[str, dict[str, ResultsDescription]]] = dict()
-    gpu_name: str
-    for gpu_name in experiment["GPUs"]:
-        print(f" | running on GPU '{gpu_name}'")
-        results_descriptions[gpu_name] = dict()
-        for index, kernel in enumerate(kernels):
-            kernel_name = kernel_names[index]
-            searchspace_stats = SearchspaceStatistics(
-                kernel_name=kernel_name,
+
+    all_experimental_groups = get_experimental_groups(experiment)
+
+    # prepare objective_time_keys, in case it was defined as all, explicitly list all keys
+    objective_time_keys: list[str] = experiment["statistics_settings"]["objective_time_keys"]
+    if "all" in objective_time_keys:
+        objective_time_keys = []
+        # open the experiment file and validate using the schema file
+        schema = validate_experimentsfile(experiment)
+        objective_time_keys = schema["properties"]["statistics_settings"]["properties"]["objective_time_keys"]["items"][
+            "enum"
+        ]
+        objective_time_keys.remove("all")
+        experiment["statistics_settings"]["objective_time_keys"] = objective_time_keys
+
+    experiment["experimental_groups_defaults"]["applications_names"] = []
+    for application in experiment["experimental_groups_defaults"]["applications"]:
+        experiment["experimental_groups_defaults"]["applications_names"].append(application["name"])
+
+    # initialize the matrix of results_descriptions based on provided gpus and applications
+    # initialize searchspace statistics, one for each full search file
+    results_descriptions: dict[str, dict[str, dict[str, ResultsDescription]]] = {}
+    searchspace_statistics: dict[str, dict[str, SearchspaceStatistics]] = {}
+
+    for gpu in experiment["experimental_groups_defaults"]["gpus"]:
+        results_descriptions[gpu] = {}
+        searchspace_statistics[gpu] = {}
+        for application in experiment["experimental_groups_defaults"]["applications_names"]:
+            results_descriptions[gpu][application] = {}
+
+    # just iterate over experimental_groups, collect results and write to proper place
+    for group in all_experimental_groups:
+        # get the experimental group settings
+        application_name = group["application_name"]
+        gpu_name = group["gpu"]
+        minimization = group["minimization"]
+        objective_performance_keys = group["objective_performance_keys"]
+
+        # show the progress in the console
+        print(f" | - running on GPU '{gpu_name}'")
+        print(f" | - | tuning application '{application_name}'")
+        print(f" | - | - | with settings of experimental group '{group['display_name']}'")
+
+        # overwrite the experiment statistics settings with the group settings
+        experiment["statistics_settings"]["minimization"] = minimization
+        experiment["statistics_settings"]["objective_performance_keys"] = objective_performance_keys
+
+        # create SearchspaceStatistics for full search space file associated with this group, if it does not exist
+        if any(
+            searchspace_statistics.get(group["gpu"], {}).get(group["application_name"], {}) == null_val
+            for null_val in [None, {}]
+        ):
+            full_search_space_file_path = None
+            if group.get("converted_full_search_space_file") is None:
+                full_search_space_file_path = group["full_search_space_file"]
+            else:
+                full_search_space_file_path = group["converted_full_search_space_file"]
+
+            searchspace_statistics[gpu_name][application_name] = SearchspaceStatistics(
+                application_name=application_name,
                 device_name=gpu_name,
                 minimization=minimization,
                 objective_time_keys=objective_time_keys,
                 objective_performance_keys=objective_performance_keys,
-                bruteforced_caches_path=experiment_folderpath / experiment["bruteforced_caches_path"],
+                full_search_space_file_path=full_search_space_file_path,
+                full_validate=full_validate_on_load,
             )
 
-            # set cutoff point
-            _, cutoff_point_fevals, cutoff_point_time = searchspace_stats.cutoff_point_fevals_time(cutoff_percentile)
-
-            print(f" | - optimizing kernel '{kernel_name}'")
-            results_descriptions[gpu_name][kernel_name] = dict()
-            for strategy in strategies:
-                strategy_name: str = strategy["name"]
-                strategy_display_name: str = strategy["display_name"]
-                stochastic = strategy["stochastic"]
-                cutoff_margin = strategy.get(
-                    "cutoff_margin", 1.1
-                )  # +10% margin, to make sure cutoff_point is reached by compensating for potential non-valid evaluations  # noqa: E501
-                print(f" | - | using strategy '{strategy['display_name']}'")
-
-                # setup the results description
-                if "options" not in strategy:
-                    strategy["options"] = dict()
-
-                # set when to stop
-                if cutoff_type == "time":
-                    strategy["options"]["time_limit"] = cutoff_point_time * cutoff_margin
-                else:
-                    strategy["options"]["max_fevals"] = min(
-                        int(ceil(cutoff_point_fevals * cutoff_margin)), searchspace_stats.size
-                    )
-                results_description = ResultsDescription(
-                    experiment_folder_id,
-                    kernel_name,
-                    gpu_name,
-                    strategy_name,
-                    strategy_display_name,
-                    stochastic,
-                    objective_time_keys=objective_time_keys,
-                    objective_performance_keys=objective_performance_keys,
-                    minimization=minimization,
-                    visualization_caches_path=experiment_folderpath / experiment["visualization_caches_path"],
-                )
+        # calculation of budget can be done only now, after searchspace statistics have been initialized
+        group = calculate_budget(
+            group, experiment["statistics_settings"], searchspace_statistics[group["gpu"]][group["application_name"]]
+        )
 
-                # if the strategy is in the cache, use cached data
-                if "ignore_cache" not in strategy and results_description.has_results():
-                    print(" | - |-> retrieved from cache")
-                else:  # execute each strategy that is not in the cache
-                    results_description = collect_results(
-                        kernel, strategy, results_description, searchspace_stats, profiling=profiling
-                    )
+        results_description = ResultsDescription(
+            run_folder=experiment_folderpath / "run" / group["name"],
+            application_name=application_name,
+            device_name=gpu_name,
+            group_name=group["name"],
+            group_display_name=group["display_name"],
+            stochastic=group["stochastic"],
+            objective_time_keys=objective_time_keys,
+            objective_performance_keys=objective_performance_keys,
+            minimization=minimization,
+        )
+
+        # if the strategy is in the cache, use cached data
+        if ("ignore_cache" not in group or group["ignore_cache"] is False) and results_description.has_results():
+            print(" | - | - | -> retrieved from cache")
+        else:  # execute each strategy that is not in the cache
+            results_description = collect_results(
+                group["input_file"],
+                group,
+                results_description,
+                searchspace_statistics[group["gpu"]][group["application_name"]],
+                profiling=profiling,
+            )
 
-                # set the results
-                results_descriptions[gpu_name][kernel_name][strategy_name] = results_description
+        # set the results
+        results_descriptions[group["gpu"]][group["application_name"]][group["name"]] = results_description
 
-    return experiment, strategies, results_descriptions
+    return experiment, all_experimental_groups, searchspace_statistics, results_descriptions
 
 
 def entry_point():  #  pragma: no cover
diff --git a/src/autotuning_methodology/experiments_defaults.json b/src/autotuning_methodology/experiments_defaults.json
new file mode 100644
index 0000000..96cbf54
--- /dev/null
+++ b/src/autotuning_methodology/experiments_defaults.json
@@ -0,0 +1,105 @@
+{
+    "version": "1.2.0",
+    "name": "",
+    "parent_folder": ".",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "dedispersion_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "dedispersion_milo.json",
+                "objective_performance_keys": [
+                    "time"
+                ]
+            },
+            {
+                "name": "hotspot_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "hotspot_milo.json",
+                "objective_performance_keys": [
+                    "GFLOP/s"
+                ]
+            },
+            {
+                "name": "convolution_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "convolution_milo.json",
+                "objective_performance_keys": [
+                    "time"
+                ]
+            },
+            {
+                "name": "gemm_milo",
+                "folder": "../autotuning_methodology/benchmark_hub/kernels",
+                "input_file": "gemm_milo.json",
+                "objective_performance_keys": [
+                    "time"
+                ]
+            }
+        ],
+        "gpus": [
+            "A100",
+            "A4000",
+            "MI250X"
+        ],
+        "pattern_for_full_search_space_filenames": {
+            "regex": "../autotuning_methodology/benchmark_hub/cachefiles/${applications}/${gpus}_T4.json"
+        },
+        "stochastic": true,
+        "repeats": 25,
+        "samples": 32,
+        "minimum_fraction_of_budget_valid": 0.25,
+        "minimum_number_of_valid_search_iterations": 25,
+        "ignore_cache": false
+    },
+    "search_strategies": [
+        {
+            "name": "genetic_algorithm",
+            "search_method": "genetic_algorithm",
+            "display_name": "Genetic Algorithm",
+            "autotuner": "KernelTuner"
+        }
+    ],
+    "statistics_settings": {
+        "cutoff_percentile": 0.95,
+        "cutoff_percentile_start": 0.01,
+        "cutoff_type": "time",
+        "objective_time_keys": [
+            "all"
+        ]
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "searchspace",
+                "style": "line",
+                "x_axis_value_types": [
+                    "fevals"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            },
+            {
+                "scope": "searchspace",
+                "style": "line",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            },
+            {
+                "scope": "aggregate",
+                "style": "line"
+            }
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
+}
\ No newline at end of file
diff --git a/src/autotuning_methodology/formats_interface.py b/src/autotuning_methodology/formats_interface.py
new file mode 100644
index 0000000..4d6fa58
--- /dev/null
+++ b/src/autotuning_methodology/formats_interface.py
@@ -0,0 +1,35 @@
+"""Interface for reading and writing input and output formats."""
+
+import json
+from pathlib import Path
+from autotuning_methodology.validators import validate_T4
+
+
+def load_T4_format(filepath: Path, validate: True) -> dict:
+    """Load and optionally validate a T4 format file."""
+    with open(filepath, "r", encoding="utf-8") as fh:
+        # get the cache from the .json file
+        orig_contents = fh.read()
+        try:
+            data: dict = json.loads(orig_contents)
+        except json.decoder.JSONDecodeError:
+            contents = orig_contents[:-1] + "}\n}"
+            try:
+                data = json.loads(contents)
+            except json.decoder.JSONDecodeError:
+                contents = orig_contents[:-2] + "}\n}"
+                data = json.loads(contents)
+
+        # validate the data
+        if validate:
+            # validate it is in T4 format
+            validate_T4(data)
+        else:
+            # if not validating, we still want to do a basic check of the format
+            assert isinstance(data, dict), "T4 format file should be a dictionary."
+            assert "metadata" in data, "T4 format file should contain metadata."
+            assert "schema_version" in data, "T4 format file should contain schema_version."
+            assert "results" in data, "T4 format file should contain results."
+
+        # return the T4 data
+        return data
diff --git a/src/autotuning_methodology/report_experiments.py b/src/autotuning_methodology/report_experiments.py
index 2f64de0..a37b2aa 100644
--- a/src/autotuning_methodology/report_experiments.py
+++ b/src/autotuning_methodology/report_experiments.py
@@ -14,28 +14,28 @@
 from autotuning_methodology.searchspace_statistics import SearchspaceStatistics
 
 
-def get_aggregation_data_key(gpu_name: str, kernel_name: str):
+def get_aggregation_data_key(gpu_name: str, application_name: str):
     """Utility function to get the key for data in the aggregation data dictionary.
 
     Args:
         gpu_name: the GPU name
-        kernel_name: the kernel name
+        application_name: the application name
 
     Returns:
         The key as a string.
     """
-    return f"{gpu_name}+{kernel_name}"
+    return f"{gpu_name}+{application_name}"
 
 
 def get_aggregation_data(
     experiment_folderpath: Path,
     experiment: dict,
+    searchspace_statistics: dict[str, dict[str, SearchspaceStatistics]],
     strategies: dict,
     results_descriptions: dict,
     cutoff_percentile: float,
     cutoff_percentile_start=0.01,
     confidence_level=0.95,
-    minimization: bool = True,
     time_resolution: int = 1e4,
     use_strategy_as_baseline=None,
 ):
@@ -44,10 +44,10 @@ def get_aggregation_data(
     Args:
         experiment_folderpath: _description_
         experiment: _description_
+        searchspace_statistics: _description_
         strategies: _description_
         results_descriptions: _description_
         cutoff_percentile: _description_
-        minimization: _description_. Defaults to True.
         cutoff_percentile_start: _description_. Defaults to 0.01.
         confidence_level: _description_. Defaults to 0.95.
         time_resolution: _description_. Defaults to 1e4.
@@ -61,26 +61,19 @@ def get_aggregation_data(
     time_resolution = int(time_resolution)
 
     aggregation_data: dict[str, tuple[Baseline, list[Curve], SearchspaceStatistics, np.ndarray]] = dict()
-    for gpu_name in experiment["GPUs"]:
-        for kernel_name in experiment["kernels"]:
+    for gpu_name in experiment["experimental_groups_defaults"]["gpus"]:
+        for application_name in experiment["experimental_groups_defaults"]["applications_names"]:
             # get the statistics
-            searchspace_stats = SearchspaceStatistics(
-                kernel_name=kernel_name,
-                device_name=gpu_name,
-                minimization=minimization,
-                objective_time_keys=experiment["objective_time_keys"],
-                objective_performance_keys=experiment["objective_performance_keys"],
-                bruteforced_caches_path=experiment_folderpath / experiment["bruteforced_caches_path"],
-            )
+            searchspace_stats = searchspace_statistics[gpu_name][application_name]
 
             # get the cached strategy results as curves
             strategies_curves: list[Curve] = list()
             baseline_executed_strategy = None
             for strategy in strategies:
-                results_description = results_descriptions[gpu_name][kernel_name][strategy["name"]]
+                results_description = results_descriptions[gpu_name][application_name][strategy["name"]]
                 if results_description is None:
                     raise ValueError(
-                        f"""Strategy {strategy['display_name']} not in results_description,
+                        f"""Strategy {strategy["display_name"]} not in results_description,
                             make sure execute_experiment() has ran first"""
                     )
                 curve = StochasticOptimizationAlgorithm(results_description)
@@ -91,11 +84,10 @@ def get_aggregation_data(
                 raise ValueError(f"Could not find '{use_strategy_as_baseline}' in executed strategies")
 
             # set the x-axis range
-            _, cutoff_point_fevals, cutoff_point_time = searchspace_stats.cutoff_point_fevals_time(cutoff_percentile)
-            _, cutoff_point_fevals_start, cutoff_point_time_start = searchspace_stats.cutoff_point_fevals_time(
-                cutoff_percentile_start
+            cutoff_point_fevals_start, cutoff_point_fevals, cutoff_point_time_start, cutoff_point_time = (
+                searchspace_stats.cutoff_point_fevals_time_start_end(cutoff_percentile_start, cutoff_percentile)
             )
-            fevals_range = np.arange(start=cutoff_point_fevals_start, stop=cutoff_point_fevals)
+            fevals_range = np.arange(start=cutoff_point_fevals_start, stop=cutoff_point_fevals + 1)
             time_range = np.linspace(start=cutoff_point_time_start, stop=cutoff_point_time, num=time_resolution)
 
             # get the random baseline
@@ -108,7 +100,7 @@ def get_aggregation_data(
             )
 
             # collect aggregatable data
-            aggregation_data[get_aggregation_data_key(gpu_name, kernel_name)] = tuple(
+            aggregation_data[get_aggregation_data_key(gpu_name, application_name)] = tuple(
                 [random_baseline, strategies_curves, searchspace_stats, time_range, fevals_range]
             )
 
@@ -192,48 +184,68 @@ def get_strategies_aggregated_performance(
     )
 
 
-def get_strategy_scores(experiment_filepath: str, use_strategy_as_baseline=None):
+def get_strategy_scores(experiment_filepath: str, use_strategy_as_baseline=None, full_validate_on_load=True):
     """Function to get performance scores per strategy by running the passed experiments file.
 
     Args:
         experiment_filepath: the path to the experiment-filename.json to run.
         use_strategy_as_baseline: whether to use an executed strategy as the baseline. Defaults to None.
+        full_validate_on_load: whether to fully validate the T4 format file. Defaults to True.
 
     Returns:
         a dictionary of the strategies, with the performance score and error for each strategy.
     """
     # execute the experiment if necessary, else retrieve it
-    experiment, strategies, results_descriptions = execute_experiment(experiment_filepath, profiling=False)
-    experiment_folderpath = Path(experiment_filepath).parent
+    experiment, strategies, searchspace_statistics, results_descriptions = execute_experiment(
+        experiment_filepath, profiling=False, full_validate_on_load=full_validate_on_load
+    )
 
     # get the settings
-    minimization: bool = experiment.get("minimization", True)
-    cutoff_percentile: float = experiment["cutoff_percentile"]
-    cutoff_percentile_start: float = experiment.get("cutoff_percentile_start", 0.01)
-    time_resolution: float = experiment.get("resolution", 1e4)
-    confidence_level: float = experiment["plot"].get("confidence_level", 0.95)
+    experiment_folderpath = experiment["parent_folder_absolute_path"]
+    cutoff_percentile: float = experiment["statistics_settings"]["cutoff_percentile"]
+    cutoff_percentile_start: float = experiment["statistics_settings"]["cutoff_percentile_start"]
+    time_resolution: float = experiment["visualization_settings"]["resolution"]
+    confidence_level: float = experiment["visualization_settings"]["confidence_level"]
 
     # aggregate the data
-    aggregation_data = get_aggregation_data(
-        experiment_folderpath,
-        experiment,
-        strategies,
-        results_descriptions,
-        cutoff_percentile,
-        cutoff_percentile_start,
-        confidence_level,
-        minimization,
-        time_resolution,
-        use_strategy_as_baseline,
-    )
+    def get_agg_data():
+        return get_aggregation_data(
+            experiment_folderpath,
+            experiment,
+            searchspace_statistics,
+            strategies,
+            results_descriptions,
+            cutoff_percentile,
+            cutoff_percentile_start,
+            confidence_level,
+            time_resolution,
+            use_strategy_as_baseline,
+        )
 
-    # get the aggregated performance per strategy
-    (
-        strategies_performance,
-        strategies_lower_err,
-        strategies_upper_err,
-        strategies_real_stopping_point_fraction,
-    ) = get_strategies_aggregated_performance(list(aggregation_data.values()), confidence_level)
+    try:
+        # get the aggregated performance per strategy
+        aggregation_data = get_agg_data()
+        strategies_performance, _, _, _ = get_strategies_aggregated_performance(
+            list(aggregation_data.values()), confidence_level
+        )
+    except ValueError as e:
+        if "Not enough overlap in time range and time values" in str(e.args[0]):
+            # delete the broken cachefile
+            _, strategy_name, application_name, device_name = e.args
+            assert results_descriptions[device_name][application_name][strategy_name].delete(), (
+                "Failed to delete cachefile"
+            )
+
+            # re-execute the experiment and recollect the data to see if the issue is resolved
+            experiment, strategies, searchspace_statistics, results_descriptions = execute_experiment(
+                experiment_filepath, profiling=False
+            )
+            aggregation_data = get_agg_data()
+            strategies_performance, _, _, _ = get_strategies_aggregated_performance(
+                list(aggregation_data.values()), confidence_level
+            )
+        else:
+            raise e
 
     # calculate the average performance score and error per strategy
     results: dict[str, dict[str, float]] = dict()
diff --git a/src/autotuning_methodology/runner.py b/src/autotuning_methodology/runner.py
index ec9cb45..6035a97 100755
--- a/src/autotuning_methodology/runner.py
+++ b/src/autotuning_methodology/runner.py
@@ -7,24 +7,27 @@
 import os
 import time as python_time
 import warnings
-from inspect import getfile
 from pathlib import Path
 
+# compression libraries if necessary for collecting results
+import pickle
+import gzip
+
 import numpy as np
 import progressbar
 import yappi
 
 from autotuning_methodology.caching import ResultsDescription
-from autotuning_methodology.searchspace_statistics import SearchspaceStatistics
+from autotuning_methodology.searchspace_statistics import SearchspaceStatistics, convert_from_time_unit
 from autotuning_methodology.validators import (
     is_invalid_objective_performance,
     is_invalid_objective_time,
     is_valid_config_result,
+    validate_T4,
 )
 
+# TODO this does not conform to new intended dicrectory structure
 folder = Path(__file__).parent.parent.parent
-import_runs_path = Path(folder, "cached_data_used/import_runs")
-
 
 # Imported runs must be remapped to have the same keys, values and order of parameters as the other runs.
 # This mapping provides both the order and mapping, so all keys must be present.
@@ -55,19 +58,19 @@
 
 
 @contextlib.contextmanager
-def temporary_working_directory_change(new_WD: Path):
+def temporary_working_directory_change(new_wd: Path):
     """Temporarily change to the given working directory in a context. Based on https://stackoverflow.com/questions/75048986/way-to-temporarily-change-the-directory-in-python-to-execute-code-without-affect.
 
     Args:
-        new_WD: path of the working directory to temporarily change to.
+        new_wd: path of the working directory to temporarily change to.
     """
-    assert new_WD.exists()
+    assert new_wd.exists()
 
     # save the current working directory so we can revert to it
     original_working_directory = os.getcwd()
 
     # potentially raises an exception, left to the caller
-    os.chdir(new_WD)
+    os.chdir(new_wd)
 
     # yield control to the caller
     try:
@@ -81,12 +84,12 @@ def temporary_working_directory_change(new_WD: Path):
 
 def load_json(path: Path):
     """Helper function to load a JSON file."""
-    assert path.exists(), f"File {path.name} does not exist relative to {os.getcwd()}"
+    assert path.exists(), f"File {str(path)} does not exist relative to {os.getcwd()}"
     with path.open() as file_results:
         return json.load(file_results)
 
 
-def get_results_and_metadata(
+def get_kerneltuner_results_and_metadata(
     filename_results: str = f"{folder}../last_run/_tune_configuration-results.json",
     filename_metadata: str = f"{folder}../last_run/_tune_configuration-metadata.json",
 ) -> tuple[list, list]:
@@ -105,12 +108,12 @@ def get_results_and_metadata(
 
 
 def tune(
-    run_number: int,
-    kernel,
-    kernel_name: str,
+    input_file,
+    application_name: str,
     device_name: str,
-    strategy: dict,
-    tune_options: dict,
+    group: dict,
+    objective: str,
+    objective_higher_is_better: bool,
     profiling: bool,
     searchspace_stats: SearchspaceStatistics,
 ) -> tuple[list, list, int]:
@@ -119,12 +122,12 @@ def tune(
     Optionally collects profiling statistics.
 
     Args:
-        run_number: the run number (only relevant when importing).
-        kernel: the program (kernel) to tune.
-        kernel_name: the name of the program to tune.
+        input_file: the json input file for tuning the application.
+        application_name: the name of the program to tune.
         device_name: the device (GPU) to tune on.
-        strategy: the optimization algorithm to optimize with.
-        tune_options: a special options dictionary passed along to the autotuning framework.
+        group: the experimental group (usually the search method).
+        objective: the key to optimize for.
+        objective_higher_is_better: whether to maximize or minimize the objective.
         profiling: whether profiling statistics should be collected.
         searchspace_stats: a ``SearchspaceStatistics`` object passed to convert imported runs.
 
@@ -132,192 +135,57 @@ def tune(
         ValueError: if tuning fails multiple times in a row.
 
     Returns:
-        A tuple of the metadata, the results, and the total runtime in miliseconds.
+        A tuple of the metadata, the results, and the total runtime in milliseconds.
     """
 
     def tune_with_kerneltuner():
-        """Interface with kernel tuner to tune the kernel and return the results."""
-        # get the path to the directory the kernel is in; can't use importlib.resources.files because its not a package
-        kernel_directory = Path(getfile(kernel)).parent
-        assert kernel_directory.is_dir()
-
-        # change CWD to the directory of the kernel
-        with temporary_working_directory_change(kernel_directory):
-            if profiling:
-                yappi.set_clock_type("cpu")
-                yappi.start()
-            with warnings.catch_warnings():
-                warnings.simplefilter("ignore")
-                res, env = kernel.tune(
-                    device_name=device_name,
-                    strategy=strategy["strategy"],
-                    strategy_options=strategy["options"],
-                    **tune_options,
-                )
-            if profiling:
-                yappi.stop()
-            metadata, results = get_results_and_metadata(
-                filename_results=kernel.file_path_results, filename_metadata=kernel.file_path_metadata
+        """Interface with Kernel Tuner to tune the kernel and return the results."""
+        from kernel_tuner import tune_kernel_T1
+
+        samples = group["samples"]
+        strategy_options = group.get("budget", {})
+        if "custom_search_method_path" in group:
+            # if a custom search method is specified, use it
+            strategy_options["custom_search_method_path"] = group["custom_search_method_path"]
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            metadata, results = tune_kernel_T1(
+                input_file,
+                objective=objective,
+                objective_higher_is_better=objective_higher_is_better,
+                simulation_mode=True,
+                output_T4=True,
+                iterations=samples,
+                strategy_options=strategy_options,
             )
-            # check that the number of iterations is correct
-            if "iterations" in strategy:
-                for result in results:
-                    if "runtime" in result:
-                        num_iters = len(results[0]["runtimes"])
-                        assert (
-                            strategy["iterations"] == num_iters
-                        ), f"Specified {strategy['iterations']=} not equal to actual number of iterations ({num_iters})"
-                        break
-        if "max_fevals" in strategy["options"]:
-            max_fevals = strategy["options"]["max_fevals"]
-            if len(results) < max_fevals * 0.1:
-                warnings.warn(f"Much fewer configurations were returned ({len(res)}) than the requested {max_fevals}")
-            if len(results) < 2:
-                raise ValueError("Less than two configurations were returned")
+        if "max_fevals" in group["budget"]:
+            max_fevals = group["budget"]["max_fevals"]
+            num_results = len(results["results"])
+            if num_results < max_fevals * 0.1:
+                warnings.warn(
+                    f"Much fewer configurations were returned ({num_results}) than the requested {max_fevals}"
+                )
+            if num_results < 2 and group["budget"]["max_fevals"] > 2:
+                raise ValueError(
+                    f"Less than two configurations were returned ({len(results['results'])}, budget {group['budget']}) \n"
+                )
         return metadata, results
 
     def tune_with_BAT():
         """Interface to tune with the BAT benchmarking suite."""
         # TODO integrate with BAT
+        raise NotImplementedError("This will be implemented in the future.")
 
-    def import_from_KTT(use_param_mapping=True, use_bruteforce_objective=True):
-        """Import a KTT output file."""
-        # import the file
-        assert import_runs_path.exists() and import_runs_path.is_dir()
-        expected_filename = (
-            f"t~'ktt'd~'{device_name}'k~'{kernel_name}'s~'{strategy['strategy']}'r~{run_number}.json".lower()
+    def tune_with_KTT():
+        """Interface with KTT to tune the kernel and return the results."""
+        raise NotImplementedError(
+            "KTT is working on supporting the shared interface. The old conversions have been deprecated. An older build can be used to use these functions."
         )
-        matching_runs: list[dict] = list()
-        for file in import_runs_path.iterdir():
-            if file.name == expected_filename:
-                matching_runs.append(load_json(file))
-        if len(matching_runs) < 1:
-            raise FileNotFoundError(f"No files to import found with name '{expected_filename}'")
-        if len(matching_runs) > 1:
-            raise FileExistsError(
-                f"{len(matching_runs)} files exist with name '{expected_filename}', there can be only one"
-            )
-        run = matching_runs[0]
-
-        # map all timeunits to miliseconds
-        ktt_timeunit_mapping = {
-            "seconds": lambda x: x * 1000,
-            "miliseconds": lambda x: x,
-            "microseconds": lambda x: x / 1000,
-        }
-        ktt_status_mapping = {
-            "ok": "correct",
-            "devicelimitsexceeded": "compile",
-            "computationfailed": "runtime",
-        }
-
-        # convert to the T4 format
-        metadata = None  # TODO implement the metadata conversion when necessary
-        results = list()
-        run_metadata: dict = run["Metadata"]
-        run_results: list[dict] = run["Results"]
-        timemapper = ktt_timeunit_mapping[str(run_metadata["TimeUnit"]).lower()]
-        total_time_ms = 0
-        for config_attempt in run_results:
-
-            # convert the configuration to T4 style dictionary for fast lookups in the mapping
-            configuration_ktt = dict()
-            for param in config_attempt["Configuration"]:
-                configuration_ktt[param["Name"]] = param["Value"]
-
-            # convert the configuration data with the mapping in the correct order
-            configuration = dict()
-            if use_param_mapping and kernel_name in ktt_param_mapping:
-                param_map = ktt_param_mapping[kernel_name]
-                assert len(param_map) == len(
-                    configuration_ktt
-                ), f"Mapping provided for {len(param_map)} params, but configuration has {len(configuration_ktt)}"
-                for param_name, mapping in param_map.items():
-                    param_value = configuration_ktt[param_name]
-                    # if the mapping is None, do not include the parameter
-                    if mapping is None:
-                        pass
-                    # if the mapping is a tuple, the first argument is the new parameter name and the second the value
-                    elif isinstance(mapping, tuple):
-                        param_mapped_name, param_mapped_value = mapping
-                        if callable(param_mapped_value):
-                            param_mapped_value = param_mapped_value(param_value)
-                        configuration[param_mapped_name] = param_mapped_value
-                    # if it's a list of tuples, map to multiple parameters
-                    elif isinstance(mapping, list):
-                        for param_mapped_name, param_mapped_value in mapping:
-                            if callable(param_mapped_value):
-                                param_mapped_value = param_mapped_value(param_value)
-                            configuration[param_mapped_name] = param_mapped_value
-                    else:
-                        raise ValueError(f"Can not apply parameter mapping of {type(mapping)} ({mapping})")
-            else:
-                configuration = configuration_ktt
-
-            # add to total time
-            total_duration = timemapper(config_attempt["TotalDuration"])
-            total_overhead = timemapper(config_attempt["TotalOverhead"])
-            total_time_ms += total_duration + total_overhead
-
-            # convert the times data
-            times_runtimes = []
-            duration = ""
-            if len(config_attempt["ComputationResults"]) > 0:
-                for config_result in config_attempt["ComputationResults"]:
-                    times_runtimes.append(timemapper(config_result["Duration"]))
-                if use_bruteforce_objective:
-                    config_string_key = ",".join(str(x) for x in configuration.values())
-                    duration = searchspace_stats.get_value_in_config(config_string_key, "time")
-                else:
-                    duration = np.mean(times_runtimes)
-                assert (
-                    "iterations" in strategy
-                ), "For imported KTT runs, the number of iterations must be specified in the experiments file"
-                if strategy["iterations"] != len(times_runtimes):
-                    times_runtimes = [np.mean(times_runtimes)] * strategy["iterations"]
-                    warnings.warn(
-                        f"The specified number of iterations ({strategy['iterations']}) did not equal"
-                        + f"the actual number of iterations ({len(times_runtimes)}). "
-                        + "The average has been used."
-                    )
-            if (not isinstance(duration, (float, int, np.number))) or np.isnan(duration):
-                duration = ""
-            times_search_algorithm = timemapper(config_attempt.get("SearcherOverhead", 0))
-            times_validation = timemapper(config_attempt.get("ValidationOverhead", 0))
-            times_framework = timemapper(config_attempt.get("DataMovementOverhead", 0))
-            times_benchmark = total_duration
-            times_compilation = total_overhead - times_search_algorithm - times_validation - times_framework
-
-            # assemble the converted data
-            converted = {
-                "configuration": configuration,
-                "invalidity": ktt_status_mapping[str(config_attempt["Status"]).lower()],
-                "correctness": 1,
-                "measurements": [
-                    {
-                        "name": "time",
-                        "value": duration,
-                        "unit": "ms",
-                    }
-                ],
-                "objectives": ["time"],
-                "times": {
-                    "compilation": times_compilation,
-                    "benchmark": times_benchmark,
-                    "framework": times_framework,
-                    "search_algorithm": times_search_algorithm,
-                    "validation": times_validation,
-                    "runtimes": times_runtimes,
-                },
-            }
-            results.append(converted)
-
-        return metadata, results, round(total_time_ms)
-
-    strategy_name = str(strategy["name"]).lower()
-    if strategy_name.startswith("ktt_"):
-        metadata, results, total_time_ms = import_from_KTT()
-    elif strategy_name.startswith("kerneltuner_") or True:
+
+    if group["autotuner"] == "KTT":
+        metadata, results, total_time_ms = tune_with_KTT()
+    elif group["autotuner"] == "KernelTuner":
         total_start_time = python_time.perf_counter()
         warnings.simplefilter("ignore", UserWarning)
         try:
@@ -329,48 +197,113 @@ def import_from_KTT(use_param_mapping=True, use_bruteforce_objective=True):
         total_end_time = python_time.perf_counter()
         total_time_ms = round((total_end_time - total_start_time) * 1000)
     else:
-        raise ValueError(f"Invalid autotuning framework '{strategy_name}'")
+        raise ValueError(f"Invalid autotuning framework '{group['autotuner']}'")
+
+    # convert time units
+    timeunit: str = results.get("metadata", {}).get("timeunit", "seconds")
+    for result in results["results"]:
+        for k, v in result["times"].items():
+            result["times"][k] = convert_from_time_unit(v, timeunit)
+        # performance should not be auto-converted
+        # for i, m in enumerate(result["measurements"]):
+        #     if "unit" in m and not isinstance(m["value"], str):
+        #         result["measurements"][i]["value"] = convert_from_time_unit(m["value"], m["unit"])
+
     # be careful not to rely on total_time_ms when profiling, because it will include profiling time
+    validate_T4(results)
     return metadata, results, total_time_ms
 
 
 def collect_results(
-    kernel,
-    strategy: dict,
+    input_file,
+    group: dict,
     results_description: ResultsDescription,
     searchspace_stats: SearchspaceStatistics,
     profiling: bool,
+    compress: bool = True,
 ) -> ResultsDescription:
     """Executes optimization algorithms on tuning problems to capture their behaviour.
 
     Args:
-        kernel: the program (kernel) to tune.
-        strategy: the optimization algorithm to optimize with.
-        searchspace_stats: the ``SearchspaceStatistics`` object, only used for conversion of imported runs.
+        input_file: an input json file to tune.
+        group: a dictionary with settings for experimental group.
         results_description: the ``ResultsDescription`` object to write the results to.
+        searchspace_stats: the ``SearchspaceStatistics`` object, used for conversion of imported runs.
         profiling: whether profiling statistics must be collected.
+        compress: whether the results should be compressed.
 
     Returns:
         The ``ResultsDescription`` object with the results.
     """
-    min_num_evals: int = strategy["minimum_number_of_evaluations"]
-    # TODO put the tune options in the .json in strategy_defaults? Make it Kernel Tuner independent
-    tune_options = {"verbose": False, "quiet": True, "simulation_mode": True}
+    if profiling:
+        import psutil
+        from os import getpid
+        process = psutil.Process(getpid())
+        warnings.warn(f"Memory usage at start of collect_results: {process.memory_info().rss / 1e6:.1f} MB")
+
+    # calculate the minimum number of function evaluations that must be valid
+    minimum_fraction_of_budget_valid = group.get("minimum_fraction_of_budget_valid", None)
+    if minimum_fraction_of_budget_valid is not None:
+        assert isinstance(minimum_fraction_of_budget_valid, float)
+        assert 0.0 < minimum_fraction_of_budget_valid <= 1.0
+        max_fevals = None
+        budget = group["budget"]
+        if "max_fevals" in budget:
+            max_fevals = budget["max_fevals"]
+        elif "time_limit" in budget:
+            time_limit = budget["time_limit"]
+            time_per_feval = searchspace_stats.get_time_per_feval("mean_per_feval")
+            max_fevals = max(round(time_limit / time_per_feval), 2)
+        else:
+            raise ValueError(f"Unkown budget {budget}, can not calculate minimum fraction of budget valid")
+        min_num_evals = max(round(minimum_fraction_of_budget_valid * min(max_fevals, searchspace_stats.size)), 2)
+        if "minimum_number_of_valid_search_iterations" in group:
+            min_num_evals = max(min(min_num_evals, group["minimum_number_of_valid_search_iterations"]), 2)
+            warnings.warn(
+                f"Both 'minimum_number_of_valid_search_iterations' ({group['minimum_number_of_valid_search_iterations']}) and 'minimum_fraction_of_budget_valid' ({minimum_fraction_of_budget_valid}/{max_fevals=}) are set, the minimum ({min_num_evals}) is used."
+            )
+    else:
+        min_num_evals: int = group["minimum_number_of_valid_search_iterations"]
+
+    if len(results_description.objective_performance_keys) != 1:
+        raise NotImplementedError(
+            f"Multi objective tuning is not yet supported ({results_description.objective_performance_keys})"
+        )
+    objective = results_description.objective_performance_keys[0]
+    objective_higher_is_better = not results_description.minimization
 
-    def report_multiple_attempts(rep: int, len_res: int, strategy_repeats: int):
+    def report_multiple_attempts(rep: int, len_res: int, group_repeats: int, attempt: int):
         """If multiple attempts are necessary, report the reason."""
         if len_res < 1:
-            print(f"({rep+1}/{strategy_repeats}) No results found, trying once more...")
+            print(f"({rep + 1}/{group_repeats}) No results found, trying once more...")
         elif len_res < min_num_evals:
-            print(f"Too few results found ({len_res} of {min_num_evals} required), trying once more...")
+            print(
+                f"Too few results found ({len_res} of {min_num_evals} required, attempt {attempt}), trying once more..."
+            )
         else:
-            print(f"({rep+1}/{strategy_repeats}) Only invalid results found, trying once more...")
-
-    # repeat the strategy as specified
-    repeated_results = list()
+            print(f"({rep + 1}/{group_repeats}) Only invalid results found, trying once more...")
+
+    def cumulative_time_taken(results: list) -> list:
+        """Calculates the cumulative time taken for each of the configurations in results."""
+        config_times = []
+        cumulative_time_taken = 0
+        for config in results:
+            config_sum = 0
+            for key in config["times"]:
+                if key in searchspace_stats.objective_time_keys:
+                    time = config["times"][key]
+                    if isinstance(time, (list, tuple)):
+                        time = sum(time)
+                    config_sum += time
+            cumulative_time_taken += config_sum
+            config_times.append(cumulative_time_taken)
+        return config_times
+
+    # repeat the run as specified
+    repeated_results = []
     total_time_results = np.array([])
     for rep in progressbar.progressbar(
-        range(strategy["repeats"]),
+        range(group["repeats"]),
         redirect_stdout=True,
         prefix=" | - |-> running: ",
         widgets=[
@@ -389,57 +322,121 @@ def report_multiple_attempts(rep: int, len_res: int, strategy_repeats: int):
         len_res: int = -1
         while only_invalid or len_res < min_num_evals:
             if attempt > 0:
-                report_multiple_attempts(rep, len_res, strategy["repeats"])
-            metadata, results, total_time_ms = tune(
-                rep,
-                kernel,
-                results_description.kernel_name,
+                report_multiple_attempts(rep, len_res, group["repeats"], attempt)
+            if attempt >= 20:
+                raise RuntimeError(
+                    f"Could not find enough results for {results_description.application_name} on {results_description.device_name} in {attempt} attempts ({'only invalid, ' if only_invalid else ''}{len_res}/{min_num_evals}), quiting..."
+                )
+            _, results, total_time_ms = tune(
+                input_file,
+                results_description.application_name,
                 results_description.device_name,
-                strategy,
-                tune_options,
+                group,
+                objective,
+                objective_higher_is_better,
                 profiling,
                 searchspace_stats,
             )
-            len_res = len(results)
+            results = results["results"]
+
+            # check without results that are beyond the cutoff times
+            time_taken = cumulative_time_taken(results)
+            cutoff_time = group["cutoff_times"]["cutoff_time"]
+            cutoff_time_start = group["cutoff_times"]["cutoff_time_start"]
+            temp_results = [res for res, time in zip(results, time_taken) if cutoff_time_start <= time <= cutoff_time]
+            # if len(temp_results) < len(results):
+            #     print(
+            #         f"Dropped {len(results) - len(temp_results)} configurations beyond cutoff time {round(cutoff_time, 3)}, {len(temp_results)} left"
+            #     )
+
             # check if there are only invalid configs in the first min_num_evals, if so, try again
-            temp_res_filtered = list(filter(lambda config: is_valid_config_result(config), results))
-            only_invalid = len(temp_res_filtered) < 1
+            len_res = len(temp_results)
+            temp_res_filtered = list(filter(lambda config: is_valid_config_result(config), temp_results))
+            only_invalid = len(temp_res_filtered) < 2  # there must be at least two valid configurations
             attempt += 1
+
+        # compress the results if necessary
+        if compress:
+            results = gzip.compress(pickle.dumps(results))
+
         # register the results
         repeated_results.append(results)
         total_time_results = np.append(total_time_results, total_time_ms)
 
+        # report the memory usage
+        if profiling:
+            warnings.warn(f"Memory usage after iteration {rep}: {process.memory_info().rss / 1e6:.1f} MB")
+
     # gather profiling data and clear the profiler before the next round
     if profiling:
         stats = yappi.get_func_stats()
         # stats.print_all()
-        path = "../old_experiments/profilings/random/profile-v2.prof"
+        path = results_description.run_folder + "/profile-v2.prof"
         stats.save(path, type="pstat")  # pylint: disable=no-member
         yappi.clear_stats()
+        warnings.warn(f"Memory usage before writing in collect_results: {process.memory_info().rss / 1e6:.1f} MB")
 
     # combine the results to numpy arrays and write to a file
-    write_results(repeated_results, results_description)
+    write_results(repeated_results, results_description, compressed=compress)
+    if profiling:
+        warnings.warn(f"Memory usage at end of of collect_results: {process.memory_info().rss / 1e6:.1f} MB")
     assert results_description.has_results(), "No results in ResultsDescription after writing results."
     return results_description
 
 
-def write_results(repeated_results: list, results_description: ResultsDescription):
+def write_results(repeated_results: list, results_description: ResultsDescription, compressed=False):
     """Combine the results and write them to a NumPy file.
 
     Args:
         repeated_results: a list of tuning results, one per tuning session.
         results_description: the ``ResultsDescription`` object to write the results to.
+        compressed: whether the repeated_results are compressed.
     """
     # get the objective value and time keys
     objective_time_keys = results_description.objective_time_keys
     objective_performance_keys = results_description.objective_performance_keys
 
-    # find the maximum number of function evaluations
-    max_num_evals = max(len(repeat) for repeat in repeated_results)
+    # find the maximum (reasonable) number of function evaluations
+    num_evals = []
+    for repeat in repeated_results:
+        if compressed:
+            repeat = pickle.loads(gzip.decompress(repeat))
+        num_evals.append(len(repeat))
+    max_num_evals = max(num_evals) if num_evals else 0
+    mean_num_evals = np.mean(num_evals) if num_evals else 0
+    if max_num_evals > mean_num_evals * 2:
+        # the maximum number of evaluations is more than twice the mean, this is likely an outlier, cut to save memory
+        max_num_evals = int(mean_num_evals * 2)
+    if max_num_evals > 1e8:
+        # more than 100 million evaluations, set to the mean number of evaluations
+        max_num_evals = int(mean_num_evals)
+
+    # set the dtype
+    dtype = np.float64
+    if max_num_evals * len(repeated_results) > 1e9:
+        warnings.warn(
+            f"More than 1 billion entries ({max_num_evals * len(repeated_results)}) in the results, using float16 to save memory."
+        )
+        dtype = np.float16
+    elif max_num_evals * len(repeated_results) > 1e8:
+        warnings.warn(
+            f"More than 100 million entries ({max_num_evals * len(repeated_results)}) in the results, using float32 to save memory."
+        )
+        dtype = np.float32
+    estimated_memory_usage = max_num_evals * len(repeated_results) * (
+        8 if dtype == np.float64 else 2 if dtype == np.float16 else 4
+    )  # 8 bytes for float64, 4 bytes for float32, 2 bytes for float16
+    if estimated_memory_usage > 1e9*10:  # more than 10 GB
+        warnings.warn(
+            f"Estimated memory usage of {estimated_memory_usage / 1e9:.2f} GB for the results arrays, may go out of memory."
+        )
 
     def get_nan_array() -> np.ndarray:
         """Get an array of NaN so they are not counted as zeros inadvertedly."""
-        return np.full((max_num_evals, len(repeated_results)), np.nan)
+        # return np.full((max_num_evals, len(repeated_results)), np.nan, dtype=dtype)
+        arr = np.empty((max_num_evals, len(repeated_results)), dtype=dtype)
+        arr.fill(np.nan)
+        return arr
 
     # set the arrays to write to
     fevals_results = get_nan_array()
@@ -447,17 +444,22 @@ def get_nan_array() -> np.ndarray:
     objective_performance_results = get_nan_array()
     objective_performance_best_results = get_nan_array()
     objective_performance_stds = get_nan_array()
-    objective_time_results_per_key = np.full((len(objective_time_keys), max_num_evals, len(repeated_results)), np.nan)
+    objective_time_results_per_key = np.full((len(objective_time_keys), max_num_evals, len(repeated_results)), np.nan, dtype=dtype)
     objective_performance_results_per_key = np.full(
-        (len(objective_time_keys), max_num_evals, len(repeated_results)), np.nan
+        (len(objective_time_keys), max_num_evals, len(repeated_results)), np.nan, dtype=dtype
     )
 
     # combine the results
     opt_func = np.nanmin if results_description.minimization is True else np.nanmax
     for repeat_index, repeat in enumerate(repeated_results):
+        if compressed:
+            repeat = pickle.loads(gzip.decompress(repeat))
         cumulative_objective_time = 0
         objective_performance_best = np.nan
         for evaluation_index, evaluation in enumerate(repeat):
+            if evaluation_index >= max_num_evals:
+                break
+
             # set the number of function evaluations
             fevals_results[evaluation_index, repeat_index] = (
                 evaluation_index + 1
@@ -470,15 +472,19 @@ def get_nan_array() -> np.ndarray:
 
             # TODO continue here with implementing switch in output format
             # obtain the objective time per key
-            objective_times_list = list()
+            objective_times_list = []
             for key_index, key in enumerate(objective_time_keys):
                 evaluation_times = evaluation["times"]
-                assert (
-                    key in evaluation_times
-                ), f"Objective time key {key} not in evaluation['times'] ({evaluation_times})"
-                value = evaluation_times[key]
+                assert key in evaluation_times, (
+                    f"Objective time key {key} not in evaluation['times'] ({evaluation_times})"
+                )
+                if isinstance(evaluation_times[key], list):
+                    # this happens when runtimes are in objective_time_keys
+                    value = sum(evaluation_times[key])
+                else:
+                    value = evaluation_times[key]
                 if value is not None and not is_invalid_objective_time(value):
-                    value = value / 1000  # TODO this miliseconds to seconds conversion is specific to Kernel Tuner
+                    # value = value / 1000  # TODO this milliseconds to seconds conversion is specific to Kernel Tuner
                     objective_time_results_per_key[key_index, evaluation_index, repeat_index] = value
                     objective_times_list.append(value)
             # sum the objective times of the keys
@@ -489,16 +495,14 @@ def get_nan_array() -> np.ndarray:
                     objective_time_results[evaluation_index, repeat_index] = cumulative_objective_time
 
             # obtain the objective performance per key (called 'measurements' in the T4 format)
-            objective_performances_list = list()
+            objective_performances_list = []
             for key_index, key in enumerate(objective_performance_keys):
                 evaluation_measurements = evaluation["measurements"]
                 measurements = list(filter(lambda m: m["name"] == key, evaluation_measurements))
-                assert (
-                    len(measurements) > 0
-                ), f"Objective performance key name {key} not in evaluation['measurements'] ({evaluation_measurements})"
-                assert (
-                    len(measurements) == 1
-                ), f"""Objective performance key name {key} multiply defined
+                assert len(measurements) > 0, (
+                    f"Objective performance key name {key} not in evaluation['measurements'] ({evaluation_measurements})"
+                )
+                assert len(measurements) == 1, f"""Objective performance key name {key} multiply defined
                         in evaluation['measurements'] ({evaluation_measurements})"""
                 value = measurements[0]["value"]
                 if value is not None and not is_invalid_objective_performance(value):
diff --git a/src/autotuning_methodology/schema.json b/src/autotuning_methodology/schema.json
deleted file mode 100755
index c985a95..0000000
--- a/src/autotuning_methodology/schema.json
+++ /dev/null
@@ -1,153 +0,0 @@
-{
-  "$schema": "https://json-schema.org/draft/2020-12/schema",
-  "$id": "https://example.com/schemas/experiments/v0.1.1.schema.json",
-  "version": "0.1.2",
-  "title": "Experiment",
-  "description": "An experiment configuration file",
-  "type": "object",
-  "properties": {
-    "version": {
-      "description": "Version number of the experiment file standard",
-      "type": "string"
-    },
-    "name": {
-      "description": "Name of the experiment",
-      "type": "string"
-    },
-    "folder_id": {
-      "description": "Unique ID of the folder to store the results of this experiment in",
-      "type": "string"
-    },
-    "kernels_path": {
-      "description": "Path to the directory that has the tuning scripts specified in `kernels`, relative to the experiments file.",
-      "type": "string"
-    },
-    "bruteforced_caches_path": {
-      "description": "Path to the directory that has the bruteforced caches, relative to the experiments file.",
-      "type": "string"
-    },
-    "visualization_caches_path": {
-      "description": "Path to the directory to write / look for visualization caches, relative to the experiments file.",
-      "type": "string"
-    },
-    "kernels": {
-      "description": "Kernels to optimize",
-      "type": "array",
-      "items": {
-        "type": "string"
-      },
-      "minItems": 1,
-      "uniqueItems": true
-    },
-    "GPUs": {
-      "description": "GPUs to optimize on",
-      "type": "array",
-      "items": {
-        "type": "string"
-      },
-      "minItems": 1,
-      "uniqueItems": true
-    },
-    "minimization": {
-      "description": "Direction of optimization (minimize or maximize)",
-      "type": "boolean",
-      "default": true
-    },
-    "resolution": {
-      "description": "The resolution of the time range",
-      "type": "integer",
-      "minimum": 2
-    },
-    "cutoff_percentile": {
-      "description": "Fraction of difference between median and absolute optimum at which to stop the time range",
-      "type": "number",
-      "exclusiveMinimum": 0,
-      "maximum": 1
-    },
-    "cutoff_percentile_start": {
-      "description": "Fraction of difference between median and absolute optimum at which to start the time range",
-      "type": "number",
-      "minimum": 0,
-      "exclusiveMaximum": 1
-    },
-    "cutoff_type": {
-      "description": "Whether to base the cutoff on function evaluations or time",
-      "type": "string",
-      "enum": [
-        "fevals",
-        "time"
-      ]
-    },
-    "plot": {
-      "type": "object",
-      "properties": {
-        "plot_x_value_types": {
-          "description": "Types of value on the x-axis",
-          "type": "array",
-          "items": {
-            "type": "string",
-            "enum": [
-              "fevals",
-              "time",
-              "aggregated"
-            ]
-          },
-          "minItems": 1,
-          "uniqueItems": true
-        },
-        "plot_y_value_types": {
-          "description": "Types of value on the y-axis (absolute values, median-absolute normalized, improvement over baseline)",
-          "type": "array",
-          "items": {
-            "type": "string",
-            "enum": [
-              "absolute",
-              "scatter",
-              "normalized",
-              "baseline"
-            ]
-          },
-          "minItems": 1,
-          "uniqueItems": true
-        },
-        "confidence_level": {
-          "type": [
-            "number",
-            "null"
-          ],
-          "exclusiveMinimum": 0,
-          "maximum": 1
-        },
-        "compare_baselines": {
-          "type": "boolean",
-          "default": false
-        },
-        "compare_split_times": {
-          "type": "boolean",
-          "default": false
-        }
-      },
-      "required": [
-        "plot_x_value_types",
-        "plot_y_value_types",
-        "confidence_level"
-      ]
-    }
-  },
-  "required": [
-    "version",
-    "name",
-    "folder_id",
-    "kernels_path",
-    "bruteforced_caches_path",
-    "visualization_caches_path",
-    "kernels",
-    "GPUs",
-    "minimization",
-    "resolution",
-    "cutoff_percentile",
-    "cutoff_percentile_start",
-    "cutoff_type",
-    "plot"
-  ]
-}
\ No newline at end of file
diff --git a/src/autotuning_methodology/schemas/T4.json b/src/autotuning_methodology/schemas/T4.json
new file mode 100644
index 0000000..82db64e
--- /dev/null
+++ b/src/autotuning_methodology/schemas/T4.json
@@ -0,0 +1,91 @@
+{
+    "$schema": "https://json-schema.org/draft/2020-12/schema",
+    "$id": "https://github.com/odgaard/TuningSchema/blob/T4/results-schema.json",
+    "description": "Open Autotuning Results Schema",
+    "type": "object",
+    "properties": {
+        "schema_version": {
+            "description": "The version number of the schema in major.minor.patch format.",
+            "type": "string",
+            "pattern": "^[0-9]{1,}.[0-9]{1,}.[0-9]{1,}$",
+            "example": "1.0.0"
+        },
+        "results": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "timestamp": {
+                        "type": "string"
+                    },
+                    "configuration": {
+                        "type": "object"
+                    },
+                    "objectives": {
+                        "type": "array"
+                    },
+                    "times": {
+                        "type": "object",
+                        "properties": {
+                            "compilation_time": {
+                                "type": "number"
+                            },
+                            "runtimes": {
+                                "type": "array"
+                            },
+                            "framework": {
+                                "type": "number"
+                            },
+                            "search_algorithm": {
+                                "type": "number"
+                            },
+                            "validation": {
+                                "type": "number"
+                            }
+                        }
+                    },
+                    "invalidity": {
+                        "enum": [
+                            "timeout",
+                            "compile",
+                            "runtime",
+                            "correctness",
+                            "constraints",
+                            "correct"
+                        ]
+                    },
+                    "correctness": {
+                        "type": "number"
+                    },
+                    "measurements": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "name": {
+                                    "type": "string"
+                                },
+                                "value": {
+                                    "type": [
+                                        "number",
+                                        "string",
+                                        "array"
+                                    ]
+                                },
+                                "unit": {
+                                    "type": "string"
+                                }
+                            }
+                        }
+                    }
+                },
+                "required": [
+                    "configuration",
+                    "times",
+                    "invalidity",
+                    "correctness"
+                ]
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/src/autotuning_methodology/schemas/experiments.json b/src/autotuning_methodology/schemas/experiments.json
new file mode 100755
index 0000000..81bb87d
--- /dev/null
+++ b/src/autotuning_methodology/schemas/experiments.json
@@ -0,0 +1,518 @@
+{
+    "$schema": "https://json-schema.org/draft/2020-12/schema",
+    "$id": "https://example.com/schemas/experiments/v0.1.1.schema.json",
+    "version": "1.2.0",
+    "title": "Experiment",
+    "description": "An experiment setup configuration file",
+    "type": "object",
+    "required": [
+        "name",
+        "parent_folder",
+        "experimental_groups_defaults",
+        "search_strategies",
+        "statistics_settings",
+        "visualization_settings"
+    ],
+    "properties": {
+        "version": {
+            "description": "Version number of the experiment setup configuration file standard",
+            "type": "string"
+        },
+        "name": {
+            "description": "Name of the experiment",
+            "type": "string"
+        },
+        "parent folder": {
+            "description": "Absolute or relative path of the folder to store all related files for this experiment. This folder needs to already exist.",
+            "type": "string",
+            "default": "./"
+        },
+        "experimental_groups_defaults": {
+            "description": "Default settings for experimental groups",
+            "type": "object",
+            "required": [
+                "applications",
+                "gpus"
+            ],
+            "properties": {
+                "autotuner": {
+                    "description": "Autotuner that will be used to tune the experimental group. Has to be specified either in experimental_groups_defaults or in experimental group.",
+                    "enum": [
+                        "KernelTuner",
+                        "KTT"
+                    ]
+                },
+                "autotuner_path": {
+                    "description": "Path to the library of the autotuner",
+                    "type": "string"
+                },
+                "applications": {
+                    "description": "List of applications for which measurements were taken and written to full search space files. Can be used in pattern_for_full_search_space_filename.",
+                    "type": "array",
+                    "items": {
+                        "type": "object",
+                        "required": [
+                            "name",
+                            "input_file"
+                        ],
+                        "properties": {
+                            "name": {
+                                "type": "string"
+                            },
+                            "input_file": {
+                                "type": "string"
+                            },
+                            "minimization": {
+                                "description": "Whether the optimization objective is to minimize (maximize if false).",
+                                "type": "boolean",
+                                "default": true
+                            },
+                            "objective_performance_keys": {
+                                "description": "The performance key(s) to use as the performance objective. In case of multiple keys, the values are summed.",
+                                "type": "array",
+                                "items": {
+                                    "type": "string"
+                                },
+                                "uniqueItems": true,
+                                "minItems": 1
+                            }
+                        }
+                    }
+                },
+                "gpus": {
+                    "description": "List of GPUs where measurements were taken and written to full search space files. Can be used in pattern_for_full_search_space_filename.",
+                    "type": "array",
+                    "items": {
+                        "type": "string"
+                    }
+                },
+                "pattern_for_full_search_space_filenames": {
+                    "description": "Pattern for filenames of full search space files",
+                    "type": "object",
+                    "required": [
+                        "regex"
+                    ],
+                    "properties": {
+                        "regex": {
+                            "type": "string",
+                            "pattern": ".*.json",
+                            "examples": [
+                                "${gpus}_${applications}_output.json",
+                                "full-search-space-${applications}-${gpus}.json"
+                            ]
+                        },
+                        "regex_variables": {
+                            "type": "array",
+                            "items": {
+                                "type": "string"
+                            },
+                            "default": [
+                                "applications",
+                                "gpus"
+                            ]
+                        }
+                    }
+                },
+                "stochastic": {
+                    "description": "Whether the repeated runs of the same experimental group (combination of application, GPU and search strategy) exhibit stochastic behaviour, e.g. due to stochastic search strategy",
+                    "type": "boolean",
+                    "default": true
+                },
+                "repeats": {
+                    "description": "How many times to repeat the run for a single experimental group (combination of application, GPU and search strategy)",
+                    "type": "integer",
+                    "minimum": 1,
+                    "default": 100
+                },
+                "samples": {
+                    "description": "How many samples of measurements for a single configuration are present in full search space file",
+                    "type": "integer",
+                    "minimum": 1,
+                    "default": 32
+                },
+                "minimum_fraction_of_budget_valid": {
+                    "description": "The fraction of the total searchspace size that must be non-error, valid configurations for each single run of search algorithm",
+                    "type": "number",
+                    "exclusiveMinimum": 0.0,
+                    "maximum": 1.0,
+                    "default": 0.5
+                },
+                "minimum_number_of_valid_search_iterations": {
+                    "description": "How many non-error, valid configurations account for a single run of search algorithm",
+                    "type": "integer",
+                    "minimum": 1,
+                    "default": 10
+                },
+                "ignore_cache": {
+                    "description": "If true, always re-run the experiments, even though results from previously executed experiments are stored in run folder.",
+                    "type": "boolean",
+                    "default": false
+                }
+            }
+        },
+        "search_strategies": {
+            "description": "Settings for search strategies",
+            "type": "array",
+            "minItems": 1,
+            "uniqueItems": true,
+            "items": {
+                "type": "object",
+                "required": [
+                    "name",
+                    "search_method",
+                    "display_name"
+                ],
+                "properties": {
+                    "name": {
+                        "description": "Name of the search strategy",
+                        "type": "string"
+                    },
+                    "autotuner": {
+                        "description": "Autotuner that will be used for tuning. Has to be specified either in experimental_groups_defaults or in search_strategies.",
+                        "enum": [
+                            "KernelTuner",
+                            "KTT"
+                        ]
+                    },
+                    "autotuner_path": {
+                        "description": "Path to the library of the autotuner",
+                        "type": "string"
+                    },
+                    "custom_search_method_path": {
+                        "description": "Optional path to a custom search method implementation.",
+                        "type": "string"
+                    },
+                    "search_method": {
+                        "description": "Name of the search method as recognized by the autotuner, Class name if custom search method",
+                        "type": "string"
+                    },
+                    "search_method_hyperparameters": {
+                        "description": "A list of hyperparameters for the search method as recognized by the autotuner",
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "name": {
+                                    "type": "string"
+                                },
+                                "value": {
+                                    "type": [
+                                        "number",
+                                        "string",
+                                        "boolean",
+                                        "object",
+                                        "array"
+                                    ]
+                                }
+                            }
+                        }
+                    },
+                    "display_name": {
+                        "description": "Name for the search strategy used in visualizations",
+                        "type": "string"
+                    },
+                    "color_parent": {
+                        "description": "Name of the parent search strategy to use a shade of its color for this search strategy.",
+                        "type": "string"
+                    },
+                    "color_index": {
+                        "description": "Override of the index of the color in the matplotlib colormap to use for this search strategy. Only use on strategy parents. If set, all strategy parents must have it.",
+                        "type": "integer",
+                        "minimum": 0
+                    },
+                    "stochastic": {
+                        "description": "Whether the repeated runs of the same experimental group (combination of application, GPU and search strategy) exhibit stochastic behaviour, e.g. due to stochastic search strategy",
+                        "type": "boolean",
+                        "default": true
+                    },
+                    "repeats": {
+                        "description": "How many times to repeat the run for a single experimental group (combination of application, GPU and search strategy)",
+                        "type": "integer",
+                        "minimum": 1,
+                        "default": 100
+                    },
+                    "samples": {
+                        "description": "How many samples of measurements for a single configuration are present in full search space file",
+                        "type": "integer",
+                        "minimum": 1,
+                        "default": 32
+                    },
+                    "minimum_number_of_valid_search_iterations": {
+                        "description": "How many non-error, valid configurations account for a single run of search strategy",
+                        "type": "integer",
+                        "minimum": 1,
+                        "default": 20
+                    },
+                    "ignore_cache": {
+                        "description": "If true, always re-run the experiments, even though results from previously executed experiments are stored in run folder.",
+                        "type": "boolean",
+                        "default": false
+                    }
+                }
+            }
+        },
+        "statistics_settings": {
+            "description": "Settings for the statistics calculation",
+            "type": "object",
+            "required": [
+                "cutoff_percentile",
+                "cutoff_percentile_start",
+                "cutoff_type",
+                "objective_time_keys"
+            ],
+            "properties": {
+                "cutoff_percentile": {
+                    "description": "Fraction of difference between median and absolute optimum at which to stop the time range",
+                    "type": "number",
+                    "exclusiveMinimum": 0,
+                    "maximum": 1
+                },
+                "cutoff_percentile_start": {
+                    "description": "Fraction of difference between median and absolute optimum at which to start the time range",
+                    "type": "number",
+                    "minimum": 0,
+                    "exclusiveMaximum": 1
+                },
+                "cutoff_type": {
+                    "description": "Whether to base the cutoff on function evaluations or time",
+                    "type": "string",
+                    "enum": [
+                        "fevals",
+                        "time"
+                    ]
+                },
+                "objective_time_keys": {
+                    "description": "Time key(s) to use as the time objective. In case of multiple keys, the values are summed.",
+                    "type": "array",
+                    "items": {
+                        "enum": [
+                            "compilation",
+                            "runtimes",
+                            "framework",
+                            "search_algorithm",
+                            "validation",
+                            "all"
+                        ]
+                    },
+                    "uniqueItems": true,
+                    "minItems": 1
+                }
+            }
+        },
+        "visualization_settings": {
+            "description": "Settings for the visualizations",
+            "type": "object",
+            "required": [
+                "plots",
+                "resolution",
+                "confidence_level"
+            ],
+            "properties": {
+                "plots": {
+                    "description": "Specification of the plots to produce.",
+                    "type": "array",
+                    "items": {
+                        "type": "object",
+                        "required": [
+                            "scope",
+                            "style"
+                        ],
+                        "properties": {
+                            "scope": {
+                                "description": "The scope of the plot: whether it's a plot per searchspace, per search strategy, or the aggregate.",
+                                "type": "string",
+                                "enum": [
+                                    "searchspace",
+                                    "search_strategy",
+                                    "aggregate"
+                                ]
+                            },
+                            "style": {
+                                "description": "The type of plot.",
+                                "type": "string",
+                                "enum": [
+                                    "line",
+                                    "scatter",
+                                    "heatmap",
+                                    "head2head"
+                                ]
+                            },
+                            "tmin": {
+                                "description": "The minimum value on the time axis of the aggregate plot.",
+                                "type": [
+                                    "number",
+                                    "string"
+                                ],
+                                "default": 1.0
+                            },
+                            "vmin": {
+                                "description": "The minimum value on the heatmap color scale or aggregate plot.",
+                                "type": [
+                                    "number",
+                                    "string"
+                                ],
+                                "default": -0.5
+                            },
+                            "vmax": {
+                                "description": "The maximum value on the heatmap color scale.",
+                                "type": "number",
+                                "default": 1.0
+                            },
+                            "cmin": {
+                                "description": "The minimum value shown in the heatmap colorbar. Must be vmin <= cmin < cmax <= vmax.",
+                                "type": "number",
+                                "default": -0.5
+                            },
+                            "cmax": {
+                                "description": "The maximum value shown in the heatmap colorbar. Must be vmin <= cmin < cmax <= vmax",
+                                "type": "number",
+                                "default": 1.0
+                            },
+                            "cnum": {
+                                "description": "The number of evenly placed ticks on the heatmap colorbar to display.",
+                                "type": "integer",
+                                "minimum": 2,
+                                "default": 5
+                            },
+                            "cap_to_vmin": {
+                                "description": "Whether to allow values below vmin to be capped at vmin in the heatmap (must be reported if applied).",
+                                "type": "boolean"
+                            },
+                            "comparison": {
+                                "description": "For head2head plots, the description of how to compare the search strategies.",
+                                "type": "object",
+                                "required": [
+                                    "relative_time",
+                                    "unit"
+                                ],
+                                "properties": {
+                                    "relative_time": {
+                                        "description": "The relative time at which to compare the search strategies.",
+                                        "type": "number",
+                                        "exclusiveMinimum": 0,
+                                        "inclusiveMaximum": 1
+                                    },
+                                    "unit": {
+                                        "description": "The unit of the value with which to compare the search strategies (difference in time or performance).",
+                                        "type": "string",
+                                        "enum": [
+                                            "objective",
+                                            "time"
+                                        ]
+                                    }
+                                }
+                            },
+                            "annotate": {
+                                "description": "Whether to annotate the heatmaps with the values in their cells.",
+                                "type": "boolean",
+                                "default": true
+                            },
+                            "include_y_labels": {
+                                "description": "Whether to show the y-axis labels on the heatmap. Displayed if not set.",
+                                "type": "boolean"
+                            },
+                            "include_colorbar": {
+                                "description": "Whether to show the heatmap colorbar. Displayed if not set.",
+                                "type": "boolean"
+                            },
+                            "bins": {
+                                "description": "The number of bins to use when plotting over a heatmap over a non-discrete range (e.g. time)",
+                                "type": "integer",
+                                "default": 10
+                            },
+                            "x_axis_value_types": {
+                                "description": "Types of value on the x-axis. Multiple values may produce multiple (sub) plots.",
+                                "type": "array",
+                                "items": {
+                                    "type": "string",
+                                    "enum": [
+                                        "fevals",
+                                        "time",
+                                        "applications",
+                                        "gpus",
+                                        "searchspaces"
+                                    ]
+                                },
+                                "minItems": 1,
+                                "uniqueItems": true
+                            },
+                            "y_axis_value_types": {
+                                "description": "Types of value on the y-axis. Multiple values may produce multiple (sub) plots.",
+                                "type": "array",
+                                "items": {
+                                    "type": "string",
+                                    "enum": [
+                                        "absolute",
+                                        "normalized",
+                                        "baseline",
+                                        "time",
+                                        "applications",
+                                        "gpus",
+                                        "searchspaces"
+                                    ]
+                                },
+                                "minItems": 1,
+                                "uniqueItems": true
+                            },
+                            "x_label": {
+                                "description": "Override the default generated label for the x-axis.",
+                                "type": "string"
+                            },
+                            "y_label": {
+                                "description": "Override the default generated label for the y-axis.",
+                                "type": "string"
+                            },
+                            "print_mean_of_columns": {
+                                "description": "Print the mean of the columns in the heatmap to the console.",
+                                "type": "boolean",
+                                "default": false
+                            },
+                            "print_mean_of_rows": {
+                                "description": "Print the mean of the rows in the heatmap to the console.",
+                                "type": "boolean",
+                                "default": false
+                            },
+                            "divide_train_test_axis": {
+                                "description": "Axis to divide the train and test data on in the heatmap.",
+                                "type": "string",
+                                "enum": [
+                                    "gpus",
+                                    "applications"
+                                ],
+                                "default": "gpus"
+                            },
+                            "divide_train_test_after_num": {
+                                "description": "Number of items in the divide_train_test_axis after which to divide the train and test data.",
+                                "type": "integer",
+                                "default": 3
+                            }
+                        }
+                    }
+                },
+                "resolution": {
+                    "description": "The resolution of the time range",
+                    "type": "integer",
+                    "default": 1000,
+                    "minimum": 2
+                },
+                "confidence_level": {
+                    "description": "The confidence level used for the confidence / prediction interval, visualized as an error shade",
+                    "type": "number",
+                    "default": 0.95,
+                    "exclusiveMinimum": 0,
+                    "maximum": 1
+                },
+                "compare_baselines": {
+                    "description": "[preview feature] Compare baselines to each other. Requires editing the baselines list in the `plot_baselines_comparison` function.",
+                    "type": "boolean",
+                    "default": false
+                },
+                "compare_split_times": {
+                    "description": "[preview feature] Plot a comparison of split times for strategies and baselines",
+                    "type": "boolean",
+                    "default": false
+                }
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/src/autotuning_methodology/searchspace_statistics.py b/src/autotuning_methodology/searchspace_statistics.py
index 9fe2c06..43d46c7 100644
--- a/src/autotuning_methodology/searchspace_statistics.py
+++ b/src/autotuning_methodology/searchspace_statistics.py
@@ -2,12 +2,14 @@
 
 from __future__ import annotations  # for correct nested type hints e.g. list[str], tuple[dict, str]
 
-import json
 from math import ceil, floor
 from pathlib import Path
+from warnings import warn
 
+import matplotlib.pyplot as plt
 import numpy as np
 
+from autotuning_methodology.formats_interface import load_T4_format
 from autotuning_methodology.validators import is_invalid_objective_performance, is_invalid_objective_time
 
 
@@ -27,8 +29,118 @@ def nansumwrapper(array: np.ndarray, **kwargs) -> np.ndarray:
     return summed_array
 
 
+def convert_from_time_unit(value, from_unit: str):
+    """Convert the value or list of values from the specified time unit to seconds."""
+    if from_unit is None:
+        return None
+    elif isinstance(value, list):
+        return [convert_from_time_unit(v, from_unit) for v in value]
+    elif not isinstance(value, (int, float, complex)):
+        return value
+    unit = from_unit.lower()
+    if unit == "seconds" or unit == "s":
+        return value
+    elif unit == "milliseconds" or unit == "miliseconds" or unit == "ms":
+        return value / 1000
+    elif unit == "microseconds":
+        return value / 1000000
+    elif unit == "nanoseconds" or unit == "ns":
+        return value / 1000000000
+    else:
+        raise ValueError(f"Conversion unit {from_unit} is not supported")
+
+
+def is_not_invalid_value(value, performance: bool) -> bool:
+    """Checks if a performance or time value is an array or is not invalid."""
+    if isinstance(value, str):
+        return False
+    if isinstance(value, (list, tuple, np.ndarray)):
+        return True
+    invalid_check_function = is_invalid_objective_performance if performance else is_invalid_objective_time
+    return not invalid_check_function(value)
+
+def filter_invalids(values, performance: bool) -> list:
+    """Filter out invalid values from the array.
+    
+    Assumes that `values` is a list or array of values.
+    If changes are made here, also change `is_invalid_objective_time`.
+    """
+    if performance or any([isinstance(v, (str, list, tuple, np.ndarray)) for v in values]):
+        # if there are any non-numeric values, fall back to a list comprehension
+        return list([v for v in values if is_not_invalid_value(v, performance)])
+    # invalid time values can be checked for the entire array at once, much faster than iterating
+    array = np.array(values)
+    return array[(~np.isnan(array)) & (array >= 0.0)].tolist()
+
+def to_valid_array(
+    results: list[dict],
+    key: str,
+    performance: bool,
+    from_time_unit: str = None,
+    replace_missing_measurement_from_times_key: str = None,
+) -> np.ndarray:
+    """Convert results performance or time values to a numpy array, sum if the input is a list of arrays.
+
+    replace_missing_measurement_from_times_key: if key is missing from measurements, use the mean value from times.
+    """
+    # make a list of all valid values
+    if performance:
+        values = list()
+        for r in results:
+            val = None
+            # get the performance value from the measurements
+            measurements = list(filter(lambda m: m["name"] == key, r["measurements"]))
+            if len(measurements) == 0:
+                if replace_missing_measurement_from_times_key is not None:
+                    val = np.mean(r["times"][replace_missing_measurement_from_times_key])
+                else:
+                    raise ValueError(f"Measurement with name {key} not found in {r['measurements']}")
+            if len(measurements) == 1:
+                m = measurements[0]
+                if key == m["name"]:
+                    val = m["value"]
+            elif len(measurements) > 1:
+                raise ValueError(f"Multiple measurements with the same name {key} found in results")
+            # register the value
+            if is_not_invalid_value(val, performance):
+                # performance should not be auto-converted
+                # if len(m["unit"]) > 0:
+                #     val = convert_from_time_unit(val, m["unit"])
+                values.append(val)
+            else:
+                values.append(np.nan)
+    else:
+        values = list(
+            (
+                convert_from_time_unit(v["times"][key], from_time_unit)
+                if key in v["times"] and is_not_invalid_value(v["times"][key], performance)
+                else np.nan
+            )
+            for v in results
+        )
+        # TODO other that time, performance such as power usage are in results["measurements"]. or not?
+    # check if there are values that are arrays
+    for value_index, value in enumerate(values):
+        if isinstance(value, (list, tuple, np.ndarray)):
+            # if the value is an array, sum the valid values
+            list_to_sum = filter_invalids(value, performance)
+            try:
+                sum_of_list = sum(list_to_sum)
+                values[value_index] = (
+                    sum_of_list
+                    if len(list_to_sum) > 0 and is_not_invalid_value(sum_of_list, performance)
+                    else np.nan
+                )
+            except TypeError as e:
+                raise TypeError(
+                    f"Invalid type for {key=}, {value=}, {list_to_sum=}, {values=}, {performance=}, {from_time_unit=} ({e})"
+                )
+    assert all(isinstance(v, (int, float)) for v in values)
+    return np.array(values)
+
+
 class SearchspaceStatistics:
-    """Object for obtaining information from a raw, brute-forced cache file."""
+    """Object for obtaining information from a full search space file."""
 
     size: int
     repeats: int
@@ -44,7 +156,7 @@ class SearchspaceStatistics:
 
     T4_time_keys_to_kernel_tuner_time_keys_mapping = {
         "compilation": "compile_time",
-        "benchmark": "benchmark_time",
+        "runtimes": "benchmark_time",
         "framework": "framework_time",
         "search_algorithm": "strategy_time",
         "validation": "verification_time",
@@ -55,33 +167,35 @@ class SearchspaceStatistics:
 
     def __init__(
         self,
-        kernel_name: str,
+        application_name: str,
         device_name: str,
         minimization: bool,
         objective_time_keys: list[str],
         objective_performance_keys: list[str],
-        bruteforced_caches_path=Path("cached_data_used/cachefiles"),
+        full_search_space_file_path: str,
+        full_validate: bool = True,
     ) -> None:
         """Initialization method for a Searchspace statistics object.
 
         Args:
-            kernel_name: the name of the kernel.
+            application_name: the name of the kernel.
             device_name: the name of the device (GPU) used.
             minimization: whether the optimization algorithm was minimizing.
             objective_time_keys: the objective time keys used.
             objective_performance_keys: the objective performance keys used.
-            bruteforced_caches_path: the path to the bruteforced caches.
+            full_search_space_file_path: the path to the full search space file.
+            full_validate: whether to fully validate the searchspace statistics file on load. Defaults to True.
         """
         self.loaded = False
-        self.kernel_name = kernel_name
+        self.application_name = application_name
         self.device_name = device_name
         self.minimization = minimization
-        self.objective_time_keys = self.T4_time_keys_to_kernel_tuner_time_keys(objective_time_keys)
+        self.objective_time_keys = objective_time_keys
         self.objective_performance_keys = objective_performance_keys
-        self.bruteforced_caches_path = bruteforced_caches_path
+        self.full_search_space_file_path = full_search_space_file_path
 
         # load the data into the arrays
-        self.loaded = self._load()
+        self.loaded = self._load(validate=full_validate)
 
     def T4_time_keys_to_kernel_tuner_time_keys(self, time_keys: list[str]) -> list[str]:
         """Temporary utility function to use the kernel tuner search space files with the T4 output format.
@@ -131,9 +245,8 @@ def plot_histogram(self, cutoff_percentile: float):
             cutoff_percentile: the desired cutoff percentile to reach before stopping.
         """
         # prepare plot
-        import matplotlib.pyplot as plt
 
-        fig, axs = plt.subplots(1, 1, sharey=True, tight_layout=True)
+        _, axs = plt.subplots(1, 1, sharey=True, tight_layout=True)
         if not isinstance(axs, list):
             axs = [axs]
 
@@ -147,7 +260,7 @@ def plot_histogram(self, cutoff_percentile: float):
         n_bins = 200
         axs[0].hist(performances, bins=n_bins)
         axs[0].set_ylabel("Number of configurations in bin")
-        axs[0].set_xlabel("Performance in miliseconds")
+        axs[0].set_xlabel("Performance in milliseconds")
         axs[0].axvline(x=[mean], label="Mean", c="red")
         axs[0].axvline(x=[median], label="Median", c="orange")
         axs[0].axvline(x=[cutoff_performance], label="Cutoff point", c="green")
@@ -165,7 +278,11 @@ def cutoff_point(self, cutoff_percentile: float) -> tuple[float, int]:
         Returns:
             A tuple of the objective value at the cutoff point and the fevals to the cutoff point.
         """
-        inverted_sorted_performance_arr = self.objective_performances_total_sorted[::-1]
+        inverted_sorted_performance_arr = (
+            self.objective_performances_total_sorted[::-1]
+            if self.minimization
+            else self.objective_performances_total_sorted
+        )
         N = inverted_sorted_performance_arr.shape[0]
 
         # get the objective performance at the cutoff point
@@ -194,9 +311,27 @@ def cutoff_point(self, cutoff_percentile: float) -> tuple[float, int]:
 
         # iterate over the inverted_sorted_performance_arr until we have
         # i = next(x[0] for x in enumerate(inverted_sorted_performance_arr) if x[1] > cutoff_percentile * arr[-1])
-        i = next(
-            x[0] for x in enumerate(inverted_sorted_performance_arr) if x[1] <= objective_performance_at_cutoff_point
-        )
+        if self.minimization:
+            i = next(
+                x[0]
+                for x in enumerate(inverted_sorted_performance_arr)
+                if x[1] <= objective_performance_at_cutoff_point
+            )
+        else:
+            i = next(
+                x[0]
+                for x in enumerate(inverted_sorted_performance_arr)
+                if x[1] >= objective_performance_at_cutoff_point
+            )
+        if cutoff_percentile != 1.0 and inverted_sorted_performance_arr[i] == self.total_performance_absolute_optimum():
+            if i == 0:
+                raise ValueError(
+                    f"The optimum is directly reached ({inverted_sorted_performance_arr[i]})",
+                    inverted_sorted_performance_arr,
+                )
+            else:
+                i = i - 1
+                warn(f"Scaled down cutoff point as {cutoff_percentile} is equal to optimum (1.0) for this distribution")
         # In case of x <= (1+p) * f_opt
         # i = next(x[0] for x in enumerate(inverted_sorted_performance_arr) if x[1] <= (1 + (1 - cutoff_percentile)) * arr[-1])  # noqa: E501
         # In case of p*x <= f_opt
@@ -207,6 +342,17 @@ def cutoff_point(self, cutoff_percentile: float) -> tuple[float, int]:
         # exit(0)
         return objective_performance_at_cutoff_point, fevals_to_cutoff_point
 
+    def cutoff_point_time_from_fevals(self, cutoff_point_fevals: int) -> float:
+        """Calculates the time to the cutoff point from the number of function evaluations.
+
+        Args:
+            cutoff_point_fevals: the number of function evaluations to reach the cutoff point.
+
+        Returns:
+            The time to the cutoff point.
+        """
+        return cutoff_point_fevals * self.total_time_median()
+
     def cutoff_point_fevals_time(self, cutoff_percentile: float) -> tuple[float, int, float]:
         """Calculates the cutoff point.
 
@@ -217,18 +363,43 @@ def cutoff_point_fevals_time(self, cutoff_percentile: float) -> tuple[float, int
             A tuple of the objective value at cutoff point, fevals to cutoff point, and the mean time to cutoff point.
         """
         cutoff_point_value, cutoff_point_fevals = self.cutoff_point(cutoff_percentile)
-        cutoff_point_time = cutoff_point_fevals * self.total_time_median()
+        cutoff_point_time = self.cutoff_point_time_from_fevals(cutoff_point_fevals)
         return cutoff_point_value, cutoff_point_fevals, cutoff_point_time
 
-    def _get_filepath(self, lowercase=True) -> Path:
-        """Returns the filepath."""
-        kernel_directory = self.kernel_name
-        if lowercase:
-            kernel_directory = kernel_directory.lower()
-        filename = f"{self.device_name}.json"
-        if lowercase:
-            filename = filename.lower()
-        return self.bruteforced_caches_path / kernel_directory / filename
+    def cutoff_point_fevals_time_start_end(
+        self, cutoff_percentile_start: float, cutoff_percentile: float
+    ) -> tuple[int, int, float, float]:
+        """Calculates the cutoff point for both the start and end, and ensures there is enough margin between the two.
+
+        Args:
+            cutoff_percentile_start: the desired cutoff percentile to reach before starting the plot.
+            cutoff_percentile: the desired cutoff percentile to reach before stopping.
+
+        Returns:
+            A tuple of the fevals to cutoff point start and end, and the mean time to cutoff point start and end.
+        """
+        # get the cutoff points
+        _, cutoff_point_fevals_start = self.cutoff_point(cutoff_percentile_start)
+        _, cutoff_point_fevals_end = self.cutoff_point(cutoff_percentile)
+
+        # apply a safe margin if needed
+        if cutoff_point_fevals_end - cutoff_point_fevals_start < 2:
+            if cutoff_point_fevals_start == 0:
+                cutoff_point_fevals_end = min(self.cutoff_point(1.0)[0], cutoff_point_fevals_end + 2)
+            else:
+                cutoff_point_fevals_end = min(self.cutoff_point(1.0)[0], cutoff_point_fevals_end + 1)
+                cutoff_point_fevals_start -= 1
+        if cutoff_point_fevals_end - cutoff_point_fevals_start == 0:
+            raise ValueError("Cutoff point start and end are the same")
+
+        # get the times
+        cutoff_point_time_start = self.cutoff_point_time_from_fevals(
+            cutoff_point_fevals_start if cutoff_percentile_start > 0.0 else 0
+        )
+        cutoff_point_time_end = self.cutoff_point_time_from_fevals(cutoff_point_fevals_end)
+
+        # return the values
+        return cutoff_point_fevals_start, cutoff_point_fevals_end, cutoff_point_time_start, cutoff_point_time_end
 
     def get_valid_filepath(self) -> Path:
         """Returns the filepath to the Searchspace statistics .json file if it exists.
@@ -239,9 +410,9 @@ def get_valid_filepath(self) -> Path:
         Returns:
             Filepath to the Searchspace statistics .json file.
         """
-        filepath = self._get_filepath()
+        filepath = self.full_search_space_file_path
         if not filepath.exists():
-            filepath = self._get_filepath(lowercase=False)
+            filepath = Path(str(self.full_search_space_file_path) + ".json")
         if not filepath.exists():
             # if the file is not found, raise an error
             from os import getcwd
@@ -251,148 +422,102 @@ def get_valid_filepath(self) -> Path:
             )
         return filepath
 
-    def _is_not_invalid_value(self, value, performance: bool) -> bool:
-        """Checks if a cache performance or time value is an array or is not invalid."""
-        if isinstance(value, str):
-            return False
-        if isinstance(value, (list, tuple, np.ndarray)):
-            return True
-        invalid_check_function = is_invalid_objective_performance if performance else is_invalid_objective_time
-        return not invalid_check_function(value)
-
-    def _to_valid_array(self, cache_values: list[dict], key: str, performance: bool) -> np.ndarray:
-        """Convert valid cache performance or time values to a numpy array, sum if the input is a list of arrays."""
-        # make a list of all valid values
-        values = list(
-            v[key] if key in v and self._is_not_invalid_value(v[key], performance) else np.nan for v in cache_values
-        )
-        # check if there are values that are arrays
-        for value_index, value in enumerate(values):
-            if isinstance(value, (list, tuple, np.ndarray)):
-                # if the cache value is an array, sum the valid values
-                array = value
-                list_to_sum = list(v for v in array if self._is_not_invalid_value(v, performance))
-                values[value_index] = (
-                    sum(list_to_sum)
-                    if len(list_to_sum) > 0 and self._is_not_invalid_value(sum(list_to_sum), performance)
-                    else np.nan
-                )
-        assert all(isinstance(v, (int, float)) for v in values)
-        return np.array(values)
-
-    def _load(self) -> bool:
-        """Load the contents of the cache file."""
+    def _load(self, validate=True) -> bool:
+        """Load the contents of the full search space file."""
+        # if not, use a script to create a file with values from KTT output and formatting of KernelTuner
         filepath = self.get_valid_filepath()
-        with open(filepath, "r", encoding="utf-8") as fh:
-            print(f"Loading statistics for {filepath}...")
-            # get the cache from the .json file
-            orig_contents = fh.read()
-            try:
-                data = json.loads(orig_contents)
-            except json.decoder.JSONDecodeError:
-                contents = orig_contents[:-1] + "}\n}"
-                try:
-                    data = json.loads(contents)
-                except json.decoder.JSONDecodeError:
-                    contents = orig_contents[:-2] + "}\n}"
-                    data = json.loads(contents)
-            cache: dict = data["cache"]
-            self.cache = cache
-
-            # get the time values per configuration
-            cache_values = list(cache.values())
-            self.size = len(cache_values)
-            self.objective_times = dict()
-            for key in self.objective_time_keys:
-                self.objective_times[key] = self._to_valid_array(cache_values, key, performance=False)
-                self.objective_times[key] = (
-                    self.objective_times[key] / 1000
-                )  # TODO Kernel Tuner specific miliseconds to seconds conversion
-                assert (
-                    self.objective_times[key].ndim == 1
-                ), f"Should have one dimension, has {self.objective_times[key].ndim}"
-                assert self.objective_times[key].shape[0] == len(
-                    cache_values
-                ), f"Should have the same size as cache_values ({self.size}), has {self.objective_times[key].shape[0]}"
-                assert not np.all(
-                    np.isnan(self.objective_times[key])
-                ), f"""All values for {key=} are NaN.
-                        Likely the experiment did not collect time values for objective_time_keys '{key}'."""
-
-            # get the performance values per configuration
-            self.objective_performances = dict()
-            for key in self.objective_performance_keys:
-                self.objective_performances[key] = self._to_valid_array(cache_values, key, performance=True)
-                assert (
-                    self.objective_performances[key].ndim == 1
-                ), f"Should have one dimension, has {self.objective_performances[key].ndim}"
-                assert self.objective_performances[key].shape[0] == len(
-                    cache_values
-                ), f"""Should have the same size as cache_values ({self.size}),
-                        has {self.objective_performances[key].shape[0]}"""
-                assert not np.all(
-                    np.isnan(self.objective_performances[key])
-                ), f"""All values for {key=} are NaN.
-                    Likely the experiment did not collect performance values for objective_performance_key '{key}'."""
-
-            # get the number of repeats
-            valid_cache_index: int = 0
-            while "times" not in cache_values[valid_cache_index]:
-                valid_cache_index += 1
-            self.repeats = len(cache_values[valid_cache_index]["times"])
-
-            # combine the arrays to the shape [len(objective_keys), self.size]
-            self.objective_times_array = np.array(list(self.objective_times[key] for key in self.objective_time_keys))
-            assert self.objective_times_array.shape == tuple([len(self.objective_time_keys), self.size])
-            self.objective_performances_array = np.array(
-                list(self.objective_performances[key] for key in self.objective_performance_keys)
-            )
-            assert self.objective_performances_array.shape == tuple([len(self.objective_performance_keys), self.size])
-
-            # get the totals
-            self.objective_times_total = nansumwrapper(self.objective_times_array, axis=0)
-            assert self.objective_times_total.shape == tuple([self.size])
-            # more of a test than a necessary assert
-            assert (
-                np.nansum(self.objective_times_array[:, 0]) == self.objective_times_total[0]
-            ), f"""Sums of objective performances do not match:
-                {np.nansum(self.objective_times_array[:, 0])} vs. {self.objective_times_total[0]}"""
-            self.objective_performances_total = nansumwrapper(self.objective_performances_array, axis=0)
-            assert self.objective_performances_total.shape == tuple([self.size])
-            # more of a test than a necessary assert
-            assert (
-                np.nansum(self.objective_performances_array[:, 0]) == self.objective_performances_total[0]
-            ), f"""Sums of objective performances do not match:
-                {np.nansum(self.objective_performances_array[:, 0])} vs. {self.objective_performances_total[0]}"""
-
-            # sort
-            self.objective_times_total_sorted = np.sort(
-                self.objective_times_total[~np.isnan(self.objective_times_total)]
-            )
-            self.objective_times_number_of_nan = (
-                self.objective_times_total.shape[0] - self.objective_times_total_sorted.shape[0]
+        data = load_T4_format(filepath, validate=validate)
+        metadata: dict = data.get("metadata", {})
+        timeunit = metadata.get("timeunit", "seconds")
+        results: dict = data["results"]
+        self.results = results
+
+        # get the time values per configuration
+        self.size = len(data["results"])
+        self.objective_times = dict()
+        for key in self.objective_time_keys:
+            self.objective_times[key] = to_valid_array(results, key, performance=False, from_time_unit=timeunit)
+            assert self.objective_times[key].ndim == 1, (
+                f"Should have one dimension, has {self.objective_times[key].ndim}"
             )
-            objective_performances_nan_mask = np.isnan(self.objective_performances_total)
-            self.objective_performances_number_of_nan = np.count_nonzero(objective_performances_nan_mask)
-            self.objective_performances_total_sorted = np.sort(
-                self.objective_performances_total[~objective_performances_nan_mask]
+            assert self.objective_times[key].shape[0] == self.size, (
+                f"Should have the same size as results ({self.size}), has {self.objective_times[key].shape[0]}"
             )
-            # make sure the best values are at the start, because NaNs are appended to the end
-            sorted_best_first = (
-                self.objective_performances_total_sorted
-                if self.minimization
-                else self.objective_performances_total_sorted[::-1]
+            assert not np.all(np.isnan(self.objective_times[key])), f"""All values for {key=} are NaN.
+                    Likely the experiment did not collect time values for objective_time_keys '{key}'."""
+
+        # get the performance values per configuration
+        self.objective_performances = dict()
+        for key in self.objective_performance_keys:
+            self.objective_performances[key] = to_valid_array(
+                results,
+                key,
+                performance=True,
+                replace_missing_measurement_from_times_key="runtimes" if key == "time" else None,
             )
-            self.objective_performances_total_sorted_nan = np.concatenate(
-                (sorted_best_first, [np.nan] * self.objective_performances_number_of_nan)
+            assert self.objective_performances[key].ndim == 1, (
+                f"Should have one dimension, has {self.objective_performances[key].ndim}"
             )
+            assert (
+                self.objective_performances[key].shape[0] == self.size
+            ), f"""Should have the same size as results ({self.size}),
+                    has {self.objective_performances[key].shape[0]}"""
+            assert not np.all(np.isnan(self.objective_performances[key])), f"""All values for {key=} are NaN.
+                Likely the experiment did not collect performance values for objective_performance_key '{key}'."""
+
+        # get the number of repeats
+        # TODO is this necessary? number of repeats is given in experiments setup file
+        # valid_cache_index: int = 0
+        # while "times" not in cache_values[valid_cache_index]:
+        #    valid_cache_index += 1
+        # self.repeats = len(cache_values[valid_cache_index]["times"])
+
+        # combine the arrays to the shape [len(objective_keys), self.size]
+        self.objective_times_array = np.array(list(self.objective_times[key] for key in self.objective_time_keys))
+        assert self.objective_times_array.shape == tuple([len(self.objective_time_keys), self.size])
+        self.objective_performances_array = np.array(
+            list(self.objective_performances[key] for key in self.objective_performance_keys)
+        )
+        assert self.objective_performances_array.shape == tuple([len(self.objective_performance_keys), self.size])
+
+        # get the totals
+        self.objective_times_total = nansumwrapper(self.objective_times_array, axis=0)
+        assert self.objective_times_total.shape == tuple([self.size])
+        # more of a test than a necessary assert
+        assert (
+            np.nansum(self.objective_times_array[:, 0]) == self.objective_times_total[0]
+        ), f"""Sums of objective performances do not match:
+            {np.nansum(self.objective_times_array[:, 0])} vs. {self.objective_times_total[0]}"""
+        self.objective_performances_total = nansumwrapper(self.objective_performances_array, axis=0)
+        assert self.objective_performances_total.shape == tuple([self.size])
+        # more of a test than a necessary assert
+        assert (
+            np.nansum(self.objective_performances_array[:, 0]) == self.objective_performances_total[0]
+        ), f"""Sums of objective performances do not match:
+            {np.nansum(self.objective_performances_array[:, 0])} vs. {self.objective_performances_total[0]}"""
+
+        # sort
+        self.objective_times_total_sorted = np.sort(self.objective_times_total[~np.isnan(self.objective_times_total)])
+        self.objective_times_number_of_nan = (
+            self.objective_times_total.shape[0] - self.objective_times_total_sorted.shape[0]
+        )
+        objective_performances_nan_mask = np.isnan(self.objective_performances_total)
+        self.objective_performances_number_of_nan = np.count_nonzero(objective_performances_nan_mask)
+        self.objective_performances_total_sorted = np.sort(
+            self.objective_performances_total[~objective_performances_nan_mask]
+        )
+        # make sure the best values are at the start, because NaNs are appended to the end
+        sorted_best_first = (
+            self.objective_performances_total_sorted
+            if self.minimization
+            else self.objective_performances_total_sorted[::-1]
+        )
+        self.objective_performances_total_sorted_nan = np.concatenate(
+            (sorted_best_first, [np.nan] * self.objective_performances_number_of_nan)
+        )
 
         return True
 
-    def get_value_in_config(self, config: str, key: str):
-        """Get the value for a key given a configuration."""
-        return self.cache[config][key]
-
     def get_num_duplicate_values(self, value: float) -> int:
         """Get the number of duplicate values in the searchspace."""
         duplicates = np.count_nonzero(np.where(self.objective_performances_total == value, 1, 0)) - 1
@@ -402,7 +527,7 @@ def get_num_duplicate_values(self, value: float) -> int:
 
     def mean_strategy_time_per_feval(self) -> float:
         """Gets the average time spent on the strategy per function evaluation."""
-        if "strategy" in self.objective_times:
+        if "search_algorithm" in self.objective_times:
             strategy_times = self.objective_times
             invalid_mask = np.isnan(self.objective_performances_total)
             if not all(invalid_mask):
diff --git a/src/autotuning_methodology/validators.py b/src/autotuning_methodology/validators.py
index 1fcab0b..7c4469d 100644
--- a/src/autotuning_methodology/validators.py
+++ b/src/autotuning_methodology/validators.py
@@ -1,9 +1,56 @@
 """Module containing various checks for validity."""
 
+from importlib.resources import files
+from json import load
+
 import numpy as np
+from jsonschema import validate
 
 error_types_strings = ["", "InvalidConfig", "CompilationFailedConfig", "RuntimeFailedConfig"]
 kernel_tuner_error_value = 1e20
+schemas_path = files("autotuning_methodology").joinpath("schemas")
+
+
+def get_experiment_schema_filepath():
+    """Obtains and checks the filepath to the JSON schema.
+
+    Returns:
+        the filepath to the schema in Traversable format.
+    """
+    schemafile = schemas_path.joinpath("experiments.json")
+    assert schemafile.is_file(), f"Path to experiments.json does not exist, attempted path: {schemafile}"
+    return schemafile
+
+
+def get_T4_schema_filepath():
+    """Obtains and checks the filepath to the JSON schema.
+
+    Returns:
+        the filepath to the schema in Traversable format.
+    """
+    schemafile = schemas_path.joinpath("T4.json")
+    assert schemafile.is_file(), f"Path to T4.json does not exist, attempted path: {schemafile}"
+    return schemafile
+
+
+def validate_with_schema_path(instance: dict, schemafile_path, encoding: str) -> dict:
+    """Validates the passed instance against the passed schema path. Returns schema or throws ValidationError."""
+    with schemafile_path.open("r", encoding=encoding) as fp:
+        schema = load(fp)
+        validate(instance=instance, schema=schema)
+        return schema
+
+
+def validate_experimentsfile(instance: dict, encoding="utf-8") -> dict:
+    """Validates the passed instance against the experiments file schema. Returns schema or throws ValidationError."""
+    schemafile_path = get_experiment_schema_filepath()
+    return validate_with_schema_path(instance, schemafile_path, encoding)
+
+
+def validate_T4(instance: dict, encoding="utf-8") -> dict:
+    """Validates the passed instance against the T4 schema. Returns schema or throws ValidationError."""
+    schemafile_path = get_T4_schema_filepath()
+    return validate_with_schema_path(instance, schemafile_path, encoding)
 
 
 def is_invalid_objective_performance(objective_performance: float) -> bool:
@@ -29,7 +76,7 @@ def is_invalid_objective_performance(objective_performance: float) -> bool:
 
 
 def is_invalid_objective_time(objective_time: float) -> bool:
-    """Returns whether an objective time is invalid.
+    """Returns whether an objective time is invalid. If changes are made here, also change `filter_invalids`.
 
     Args:
         objective_time: the objective time value to check.
diff --git a/src/autotuning_methodology/visualize_experiments.py b/src/autotuning_methodology/visualize_experiments.py
index a2ee993..fb2cd0e 100755
--- a/src/autotuning_methodology/visualize_experiments.py
+++ b/src/autotuning_methodology/visualize_experiments.py
@@ -4,12 +4,13 @@
 
 import warnings
 from collections import defaultdict
+from math import ceil
 from pathlib import Path
 
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.cm import get_cmap
-from matplotlib.colors import rgb2hex
+from matplotlib.colors import LinearSegmentedColormap, rgb2hex, to_hex, to_rgb
 
 from autotuning_methodology.baseline import (
     Baseline,
@@ -29,17 +30,88 @@
 # The kernel information per device and device information for visualization purposes
 marker_variatons = ["v", "s", "*", "1", "2", "d", "P", "X"]
 
+remove_from_gpus_label = ""
+remove_from_applications_label = " milo"
+remove_from_searchspace_label = " milo"
+
 # total set of objective time keys
 objective_time_keys_values = ["compilation", "benchmark", "framework", "search_algorithm", "validation"]
 
 
-def get_colors(strategies: list[dict], scale_margin_left=0.4, scale_margin_right=0.15):
+def lighten_color(color, amount: float = 0.5):
+    """Lightens the given color by interpolating it toward white."""
+    r, g, b = to_rgb(color)
+    return to_hex([(1 - amount) * c + amount for c in (r, g, b)])
+
+
+def get_colors(strategies: list[dict]) -> list:
+    """Assign colors using the tab10 colormap, with lighter shades for children."""
+    tab10 = plt.get_cmap("tab10").colors
+    tab10 = [c for i, c in enumerate(tab10) if i != 1]  # remove the second color (orange) to avoid confusion with the fourth (red)
+    max_parents = len(tab10)
+    strategy_parents = defaultdict(list)
+    override_index = False
+
+    # Group children under their parents and check for overriden color indices
+    for i, strategy in enumerate(strategies):
+        if "color_parent" in strategy:
+            strategy_parents[strategy["color_parent"]].append(i)
+        if "color_index" in strategy:
+            override_index = True
+            if "color_parent" in strategy:
+                raise ValueError(
+                    f"Strategy '{strategy['name']}' has both 'color_index' and 'color_parent' defined, which is not allowed."
+                )
+
+    if len(strategy_parents) > max_parents:
+        raise ValueError(f"Too many color parents: max supported is {max_parents} using tab10")
+
+    parent_colors = {}
+    colors = [None] * len(strategies)
+    color_index = 0
+
+    for i, strategy in enumerate(strategies):
+        name = strategy["name"]
+        if name in strategy_parents:
+            children_indices = strategy_parents[name]
+            if len(children_indices) > 2:
+                raise ValueError(f"Color parent '{name}' has more than two children")
+            if override_index:
+                assert "color_index" in strategy, f"All strategies, including '{name}', must have either 'color_index' or 'color_parent' if 'color_index' is used anywhere."
+                color_index = strategy["color_index"]
+                if color_index >= len(tab10):
+                    raise ValueError(f"Color index {color_index} for strategy '{name}' is out of bounds for tab10 colormap (max {len(tab10) - 1})")
+            base_color = tab10[color_index]
+            parent_colors[name] = {
+                idx: lighten_color(base_color, amount=0.4 + 0.3 * j) for j, idx in enumerate(children_indices)
+            }
+            colors[i] = to_hex(base_color)
+            color_index += 1
+        elif "color_parent" in strategy:
+            parent = strategy["color_parent"]
+            assert parent in parent_colors, f"Parent '{parent}' for strategy '{name}' not found in parent colors - child strategies must be defined after their parents."
+            colors[i] = parent_colors[parent][i]
+        else:
+            if override_index:
+                assert "color_index" in strategy, f"All strategies, including '{name}', must have either 'color_index' or 'color_parent' if 'color_index' is used anywhere."
+                color_index = strategy["color_index"]
+            if color_index >= len(tab10):
+                raise ValueError("Too many unparented strategies for tab10 colormap")
+            colors[i] = to_hex(tab10[color_index])
+            color_index += 1
+
+    return colors
+
+
+def get_colors_old(strategies: list[dict], scale_margin_left=0.4, scale_margin_right=0.15) -> list:
     """Function to get the colors for each of the strategies."""
     default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
-    main_colors = ["Blues", "Greens", "Reds", "Purples", "Greys"]
+    main_colors = ["Blues", "Greens", "Reds", "Purples", "Greys", "Oranges"]
     main_color_counter = 0
     strategy_parents = defaultdict(list)
 
+    # TODO switch to qualitative colormaps, e.g. tab10 if no children, otherwise tab20 (https://matplotlib.org/stable/users/explain/colors/colormaps.html#qualitative)
+
     # get the dictionary of parents with the index of their child strategies
     for strategy_index, strategy in enumerate(strategies):
         if "color_parent" in strategy:
@@ -96,10 +168,14 @@ class Visualize:
             "time_total": "Total time in seconds",
             "aggregate_time": "Relative time to cutoff point",
             "time_partial_framework_time": "framework time",
+            "time_partial_framework": "framework time",
             "time_partial_strategy_time": "strategy time",
+            "time_partial_search_algorithm": "strategy time",
             "time_partial_compile_time": "compile time",
+            "time_partial_compilation": "compile time",
             "time_partial_benchmark_time": "kernel runtime",
             "time_partial_times": "kernel runtime",
+            "time_partial_runtimes": "kernel runtime",
             "time_partial_verification_time": "verification time",
         }
     )
@@ -114,7 +190,7 @@ class Visualize:
             "objective_baseline_max": "Improvement over random sampling",
             "aggregate_objective": "Aggregate best-found objective value relative to baseline",
             "aggregate_objective_max": "Aggregate improvement over random sampling",
-            "time": "Best-found kernel time in miliseconds",
+            "time": "Best-found kernel time in milliseconds",
             "GFLOP/s": "GFLOP/s",
         }
     )
@@ -127,8 +203,6 @@ class Visualize:
         "baseline",
     ]  # absolute values, scatterplot, median-absolute normalized, improvement over baseline
 
-    plot_filename_prefix_parent = "generated_plots"
-
     def __init__(
         self,
         experiment_filepath: str,
@@ -154,71 +228,72 @@ def __init__(
         # # silently execute the experiment
         # with warnings.catch_warnings():
         #     warnings.simplefilter("ignore")
-        self.experiment, self.strategies, self.results_descriptions = execute_experiment(
-            experiment_filepath, profiling=False
+        self.experiment, self.all_experimental_groups, self.searchspace_statistics, self.results_descriptions = (
+            execute_experiment(experiment_filepath, profiling=False)
         )
-        experiment_folderpath = Path(experiment_filepath).parent
-        experiment_folder_id: str = self.experiment["folder_id"]
-        assert isinstance(experiment_folder_id, str) and len(experiment_folder_id) > 0
-        self.plot_filename_prefix = f"{self.plot_filename_prefix_parent}/{experiment_folder_id}/"
+        experiment_folder: Path = self.experiment["parent_folder_absolute_path"]
+        assert isinstance(experiment_folder, Path)
+        self.plot_filename_prefix = experiment_folder.joinpath("run", "generated_graphs")
         print("\n")
         print("Visualizing")
 
         # preparing filesystem
         if save_figs or save_extra_figs:
-            Path(self.plot_filename_prefix_parent).mkdir(exist_ok=True)
             Path(self.plot_filename_prefix).mkdir(exist_ok=True)
 
+        # search strategies are search methods defined in experiments setup file
+        # self.all_experimental_groups are all combinations of gpu+application+search method that got executed
+        self.strategies = self.experiment["search_strategies"]
         # settings
-        self.minimization: bool = self.experiment.get("minimization", True)
-        cutoff_percentile: float = self.experiment["cutoff_percentile"]
-        cutoff_percentile_start: float = self.experiment.get("cutoff_percentile_start", 0.01)
-        cutoff_type: str = self.experiment.get("cutoff_type", "fevals")
+        cutoff_percentile: float = self.experiment["statistics_settings"]["cutoff_percentile"]
+        cutoff_percentile_start: float = self.experiment["statistics_settings"]["cutoff_percentile_start"]
+        cutoff_type: str = self.experiment["statistics_settings"]["cutoff_type"]
         assert cutoff_type == "fevals" or cutoff_type == "time", f"cutoff_type != 'fevals' or 'time', is {cutoff_type}"
-        time_resolution: float = self.experiment.get("resolution", 1e4)
+        time_resolution: float = self.experiment["visualization_settings"]["resolution"]
         if int(time_resolution) != time_resolution:
             raise ValueError(f"The resolution must be an integer, yet is {time_resolution}.")
         time_resolution = int(time_resolution)
-        objective_time_keys: list[str] = self.experiment["objective_time_keys"]
+        objective_time_keys: list[str] = self.experiment["statistics_settings"]["objective_time_keys"]
 
         # plot settings
-        plot_settings: dict = self.experiment["plot"]
-        plot_x_value_types: list[str] = plot_settings["plot_x_value_types"]
-        plot_y_value_types: list[str] = plot_settings["plot_y_value_types"]
-        compare_baselines: bool = plot_settings.get("compare_baselines", False)
-        compare_split_times: bool = plot_settings.get("compare_split_times", False)
-        confidence_level: float = plot_settings.get("confidence_level", 0.95)
-        self.colors = get_colors(
-            self.strategies,
-            scale_margin_left=plot_settings.get("color_parent_scale_margin_left", 0.4),
-            scale_margin_right=plot_settings.get("color_parent_scale_margin_right", 0.1),
-        )
+        plots: list[dict] = self.experiment["visualization_settings"]["plots"]
+        compare_baselines: bool = self.experiment["visualization_settings"]["compare_baselines"]
+        compare_split_times: bool = self.experiment["visualization_settings"]["compare_split_times"]
+        confidence_level: float = self.experiment["visualization_settings"]["confidence_level"]
+        self.colors = get_colors(self.strategies)
+        # self.colors = get_colors_old(
+        #     self.strategies,
+        #     scale_margin_left=self.experiment["visualization_settings"].get("color_parent_scale_margin_left", 0.4),
+        #     scale_margin_right=self.experiment["visualization_settings"].get("color_parent_scale_margin_right", 0.1),
+        # )
         self.plot_skip_strategies: list[str] = list()
         if use_strategy_as_baseline is not None:
             self.plot_skip_strategies.append(use_strategy_as_baseline)
 
         # visualize
         aggregation_data = get_aggregation_data(
-            experiment_folderpath,
+            experiment_folder,
             self.experiment,
+            self.searchspace_statistics,
             self.strategies,
             self.results_descriptions,
             cutoff_percentile,
             cutoff_percentile_start,
             confidence_level,
-            self.minimization,
             time_resolution,
             use_strategy_as_baseline,
         )
-        for gpu_name in self.experiment["GPUs"]:
-            for kernel_name in self.experiment["kernels"]:
-                print(f" | visualizing optimization of {kernel_name} for {gpu_name}")
-                title = f"{kernel_name} on {gpu_name}"
+
+        # plot per searchspace
+        for gpu_name in self.experiment["experimental_groups_defaults"]["gpus"]:
+            for application_name in self.experiment["experimental_groups_defaults"]["applications_names"]:
+                print(f" | visualizing optimization of {application_name} for {gpu_name}")
+                title = f"{application_name} on {gpu_name}"
                 title = title.replace("_", " ")
 
                 # unpack the aggregation data
                 random_baseline, strategies_curves, searchspace_stats, time_range, fevals_range = aggregation_data[
-                    get_aggregation_data_key(gpu_name=gpu_name, kernel_name=kernel_name)
+                    get_aggregation_data_key(gpu_name=gpu_name, application_name=application_name)
                 ]
 
                 # baseline_time_interpolated = np.linspace(mean_feval_time, cutoff_point_time, time_resolution)
@@ -283,88 +358,742 @@ def __init__(
                     #     )
                     # )
 
-                # visualize the results
-                for x_type in plot_x_value_types:
-                    if x_type == "aggregated":
+                for plot in plots:
+                    # get settings
+                    scope: str = plot["scope"]
+                    if scope != "searchspace":
                         continue
-                    elif x_type == "fevals":
-                        x_axis_range = fevals_range
-                    elif x_type == "time":
-                        x_axis_range = time_range
+                    style: str = plot["style"]
+                    plot_x_value_types: list[str] = plot["x_axis_value_types"]
+                    plot_y_value_types: list[str] = plot["y_axis_value_types"]
+
+                    # visualize the results
+                    for x_type in plot_x_value_types:
+                        if x_type == "fevals":
+                            x_axis_range = fevals_range
+                        elif x_type == "time":
+                            x_axis_range = time_range
+                        else:
+                            raise NotImplementedError(f"X-axis type '{x_type}' not supported for scope '{plot}'")
+
+                        # create the figure and plots
+                        fig, axs = plt.subplots(
+                            nrows=len(plot_y_value_types),
+                            ncols=1,
+                            figsize=(8, 4.2 * len(plot_y_value_types)),
+                            sharex=True,
+                            dpi=300,
+                        )
+                        if not hasattr(
+                            axs, "__len__"
+                        ):  # if there is just one subplot, wrap it in a list so it can be passed to the plot functions
+                            axs = [axs]
+                        fig.canvas.manager.set_window_title(title)
+                        if not save_figs:
+                            fig.suptitle(title)
+
+                        # plot the subplots of individual searchspaces
+                        for index, y_type in enumerate(plot_y_value_types):
+                            self.plot_strategies(
+                                style,
+                                x_type,
+                                y_type,
+                                axs[index],
+                                searchspace_stats,
+                                strategies_curves,
+                                x_axis_range,
+                                self.experiment["visualization_settings"],
+                                random_baseline,
+                                baselines_extra=baselines_extra,
+                            )
+                            if index == 0:
+                                loc = "lower right" if y_type == "normalized" else "best"
+                                axs[index].legend(loc=loc)
+
+                        # finalize the figure and save or display it
+                        fig.supxlabel(self.get_x_axis_label(x_type, objective_time_keys))
+                        fig.tight_layout()
+                        if save_figs:
+                            filename_path = Path(self.plot_filename_prefix) / f"{title}_{x_type}".replace(" ", "_")
+                            fig.savefig(filename_path, dpi=300, bbox_inches="tight", pad_inches=0.01)
+                            print(f"Figure saved to {filename_path}")
+                        else:
+                            plt.show()
+
+        # plot per searchstrategy
+        for plot in plots:
+            # get settings
+            scope: str = plot["scope"]
+            style: str = plot["style"]
+            if scope != "search_strategy":
+                continue
+            if style != "heatmap" and style != "compare_heatmaps":
+                raise NotImplementedError(
+                    f"Scope {scope} currently only supports 'heatmap' or 'compare_heatmaps' as a style, not {style}"
+                )
+            plot_x_value_types: list[str] = plot["x_axis_value_types"]
+            plot_y_value_types: list[str] = plot["y_axis_value_types"]
+            annotate: bool = plot.get("annotate", True)
+            print_mean_of_columns: bool = plot.get("print_mean_of_columns", False)
+            print_mean_of_rows: bool = plot.get("print_mean_of_rows", False)
+            assert len(plot_x_value_types) == 1
+            assert len(plot_y_value_types) == 1
+            x_type = plot_x_value_types[0]
+            y_type = plot_y_value_types[0]
+            bins = plot.get("bins", 10)
+            vmin = plot.get("vmin", -15.0)  # color range lower limit
+            vmax = plot.get("vmax", 1.0)  # color range upper limit
+            cmin = plot.get("cmin", vmin)  # colorbar lower limit
+            cmax = plot.get("cmax", vmax)  # colorbar upper limit
+            cnum = plot.get("cnum", 5)  # number of ticks on the colorbar
+            cap_to_vmin = plot.get("cap_to_vmin", False)  # whether to cap the values to vmin
+            divide_train_test_axis = plot.get(
+                "divide_train_test_axis", False
+            )  # whether to add visual indication for train/test split
+            divide_train_test_after_num = plot.get(
+                "divide_train_test_after_num", False
+            )  # where to add the visual indication for train/test split
+            include_y_labels = plot.get("include_y_labels", None)
+            include_colorbar = plot.get("include_colorbar", True)
+            if vmin != -15.0:
+                warnings.warn(
+                    f"Careful: VMin has been changed from -15.0 to {vmin}. This breaks visual comparison compatiblity with plots that do not have the same VMin. Maybe use cmin instead?."
+                )
+            if vmax != 1.0:
+                warnings.warn(
+                    f"Careful: VMax has been changed from 1.0 to {vmax}. This breaks visual comparison compatiblity with plots that do not have the same VMax. Maybe use cmax instead?"
+                )
+            if cmin < vmin:
+                raise ValueError(
+                    f"Colorbar minimum can't be lower than the minimum value of the heatmap: {cmin} < {vmin}"
+                )
+            if cmax > vmax:
+                raise ValueError(
+                    f"Colorbar maximum can't be higher than the maximum value of the heatmap: {cmax} > {vmax}"
+                )
+
+            # set the colormap
+            def norm_color_val(v):
+                """Normalize a color value to fit in the 0-1 range."""
+                return (v - vmin) / (vmax - vmin)
+
+            cmap = LinearSegmentedColormap.from_list(
+                "my_colormap",
+                [
+                    (norm_color_val(-15.0), "black"),
+                    (norm_color_val(-4.0), "red"),
+                    (norm_color_val(-1.0), "orange"),
+                    (norm_color_val(0.0), "yellow"),
+                    (norm_color_val(1.0), "green"),
+                ],
+            )
+
+            # collect and plot the data for each search strategy
+            data_collected: dict[str, list[tuple]] = defaultdict(list)
+            for strategy in self.strategies:
+                strategy_name = strategy["name"]
+                strategy_displayname = strategy["display_name"]
+                assert (
+                    sum([1 for s in self.strategies if s["name"] == strategy_name]) == 1
+                ), f"Strategy name '{strategy_name}' is not unqiue"
+
+                # get the data from the collected aggregated data
+                for gpu_name in self.experiment["experimental_groups_defaults"]["gpus"]:
+                    for application_name in self.experiment["experimental_groups_defaults"]["applications_names"]:
+                        # unpack the aggregation data
+                        random_baseline, strategies_curves, searchspace_stats, time_range, fevals_range = (
+                            aggregation_data[
+                                get_aggregation_data_key(gpu_name=gpu_name, application_name=application_name)
+                            ]
+                        )
+
+                        # get the data
+                        dist = searchspace_stats.objective_performances_total_sorted
+                        for _, strategy_curve in enumerate(strategies_curves):
+                            if strategy_name != strategy_curve.name:
+                                continue
+                            # get the real and fictional performance curves
+                            (
+                                _,
+                                x_axis_range_real,
+                                curve_real,
+                                _,
+                                _,
+                                x_axis_range_fictional,
+                                curve_fictional,
+                                _,
+                                _,
+                            ) = strategy_curve.get_curve_over_time(
+                                time_range, dist=dist, confidence_level=confidence_level
+                            )
+                            # combine the real and fictional parts to get the full curve
+                            combine = x_axis_range_fictional.ndim > 0
+                            x_axis_range = (
+                                np.concatenate([x_axis_range_real, x_axis_range_fictional])
+                                if combine
+                                else x_axis_range_real
+                            )
+                            assert np.array_equal(
+                                time_range, x_axis_range, equal_nan=True
+                            ), "time_range != x_axis_range"
+                            curve = np.concatenate([curve_real, curve_fictional]) if combine else curve_real
+                            # get the standardised curves and write them to the collector
+                            curve: np.ndarray = random_baseline.get_standardised_curves(
+                                time_range, [curve], x_type="time"
+                            )[0]
+                            score = np.mean(curve, axis=0)
+                            curve_binned = np.array_split(curve, bins)
+                            score_binned = [np.mean(c, axis=0) for c in curve_binned]
+
+                            # set the data
+                            gpu_display_name = str(gpu_name).replace("_", " ")
+                            application_display_name = str(application_name).replace("_", " ").capitalize()
+                            data_collected[strategy_name].append(
+                                tuple([gpu_display_name, application_display_name, score, score_binned])
+                            )
+            if style == "heatmap":
+                for strategy in self.strategies:
+                    strategy_name = strategy["name"]
+                    strategy_displayname = strategy["display_name"]
+                    strategy_data = data_collected[strategy_name]
+
+                    # get the performance per selected type in an array
+                    plot_data = np.stack(np.array([t[2] for t in strategy_data]))
+                    cutoff_percentile: float = self.experiment["statistics_settings"].get("cutoff_percentile", 1.0)
+                    cutoff_percentile_start: float = self.experiment["statistics_settings"].get(
+                        "cutoff_percentile_start", 0.01
+                    )
+                    label_data = {
+                        "gpus": (
+                            list(dict.fromkeys([t[0].replace(remove_from_gpus_label, "") for t in strategy_data])),
+                            "GPUs",
+                        ),
+                        "applications": (
+                            list(
+                                dict.fromkeys([t[1].replace(remove_from_applications_label, "") for t in strategy_data])
+                            ),
+                            "Applications",
+                        ),
+                        "searchspaces": (
+                            list(
+                                dict.fromkeys(
+                                    [
+                                        f"{t[1]} on\n{t[0]}".replace(remove_from_searchspace_label, "")
+                                        for t in strategy_data
+                                    ]
+                                )
+                            ),
+                            "Searchspaces",
+                        ),
+                        "time": (
+                            np.round(np.linspace(0.0, 1.0, bins), 2),
+                            f"Fraction of time between {cutoff_percentile_start * 100}% and {cutoff_percentile * 100}%",
+                        ),
+                    }
+                    x_ticks = label_data[x_type][0]
+                    y_ticks = label_data[y_type][0]
+                    figsize = None
+                    if (x_type == "time" and y_type == "searchspaces") or (
+                        x_type == "searchspaces" and y_type == "time"
+                    ):
+                        plot_data: np.ndarray = np.stack(np.array([t[3] for t in strategy_data]))
+                        if x_type == "searchspaces":
+                            plot_data = plot_data.transpose()
+                        figsize = (9, 5)
+                    elif (x_type == "gpus" and y_type == "applications") or (
+                        y_type == "gpus" and x_type == "applications"
+                    ):
+                        plot_data = np.reshape(
+                            plot_data, (len(label_data["gpus"][0]), len(label_data["applications"][0]))
+                        )
+                        if x_type == "gpus":
+                            plot_data = np.transpose(plot_data)
+                        figsize = (5, 3.5)
                     else:
-                        raise ValueError(f"Invalid {x_type=}")
+                        raise NotImplementedError(
+                            f"Heatmap has not yet been implemented for {x_type}, {y_type}. Submit an issue to request it."
+                        )
 
-                    # create the figure and plots
-                    fig, axs = plt.subplots(
-                        nrows=len(plot_y_value_types),
-                        ncols=1,
-                        figsize=(8, 3.4 * len(plot_y_value_types)),
-                        sharex=True,
-                        dpi=300,
+                    # validate the data is within the vmin-vmax range and visible colorbar range
+                    assert not (
+                        plot_data > 1.0
+                    ).any(), (
+                        "Plot data contains values greater than 1.0, which should not be possible. Please investigate."
                     )
-                    if not hasattr(
-                        axs, "__len__"
-                    ):  # if there is just one subplot, wrap it in a list so it can be passed to the plot functions
+                    if cap_to_vmin:
+                        plot_data = np.clip(plot_data, vmin, 1.0)
+                    outside_range = np.where(np.logical_or(plot_data < vmin, plot_data > vmax))
+                    assert (
+                        len(outside_range[0]) == 0 and len(outside_range[1]) == 0
+                    ), f"There are values outside of the range ({vmin}, {vmax}): {plot_data[outside_range]} ({outside_range} for strategy {strategy_displayname})"
+                    outside_visible_range = np.where(np.logical_or(plot_data < cmin, plot_data > cmax))
+                    if not (len(outside_visible_range[0]) == 0 and len(outside_visible_range[1]) == 0):
+                        warnings.warn(
+                            f"There are values outside of the visible colorbar range ({cmin}, {cmax}): {plot_data[outside_visible_range]} ({outside_visible_range})"
+                        )
+
+                    # set up the plot
+                    fig, axs = plt.subplots(
+                        ncols=1, figsize=figsize, dpi=300
+                    )  # if multiple subplots, pass the axis to the plot function with axs[0] etc.
+                    if not hasattr(axs, "__len__"):
                         axs = [axs]
+                    title = f"Performance of {strategy_displayname} over {'+'.join(plot_x_value_types)},{'+'.join(plot_y_value_types)}"
                     fig.canvas.manager.set_window_title(title)
                     if not save_figs:
                         fig.suptitle(title)
 
-                    # plot the subplots of individual searchspaces
-                    for index, y_type in enumerate(plot_y_value_types):
-                        self.plot_strategies(
-                            x_type,
-                            y_type,
-                            axs[index],
-                            searchspace_stats,
-                            strategies_curves,
-                            x_axis_range,
-                            plot_settings,
-                            random_baseline,
-                            baselines_extra=baselines_extra,
-                        )
-                        if index == 0:
-                            loc = "lower right" if y_type == "normalized" else "best"
-                            axs[index].legend(loc=loc)
+                    # plot the heatmap
+                    axs[0].set_xlabel(plot.get("xlabel", label_data[x_type][1]))
+                    axs[0].set_xticks(ticks=np.arange(len(x_ticks)), labels=x_ticks, rotation=0)
+                    if include_y_labels is True or None:
+                        axs[0].set_ylabel(plot.get("ylabel", label_data[y_type][1]))
+                        axs[0].set_yticks(ticks=np.arange(len(y_ticks)), labels=y_ticks)
+                    if include_y_labels is True:
+                        # axs[0].yaxis.set_label_position("right")
+                        axs[0].yaxis.tick_right()
+                    elif include_y_labels is False:
+                        axs[0].set_yticks(ticks=np.arange(len(y_ticks)))
+                        axs[0].tick_params(labelleft=False)
+                    hm = axs[0].imshow(
+                        plot_data,
+                        vmin=vmin,
+                        vmax=vmax,
+                        cmap=cmap,
+                        interpolation="nearest",
+                        aspect="auto",
+                        # extent=[-0.5, plot_data.shape[1] + 0.5, -0.5, plot_data.shape[0] + 0.5],
+                    )
+                    if divide_train_test_axis is not False:
+                        # axs[0].set_ylim(plot_data.shape[0] - 0.5, -0.5)  # Ensure correct y-axis limits
+                        if x_type == divide_train_test_axis.lower():
+                            # add the vertical line to the x-axis
+                            axs[0].axvline(
+                                x=divide_train_test_after_num - 0.5, color="black", linestyle="--", linewidth=0.8
+                            )
+                            # add train and test texts to either side of the x-label
+                            axs[0].text(
+                                x=divide_train_test_after_num - 0.5,
+                                y=-0.5,
+                                s="train",
+                                ha="center",
+                                va="top",
+                                fontsize=10,
+                            )
+                            axs[0].text(
+                                x=divide_train_test_after_num - 0.5,
+                                y=plot_data.shape[0] - 0.5,
+                                s="test",
+                                ha="center",
+                                va="bottom",
+                                fontsize=10,
+                            )
+                        elif y_type == divide_train_test_axis.lower():
+                            # add the horizontal line to the y-axis
+                            axs[0].axhline(
+                                y=divide_train_test_after_num - 0.5, color="black", linestyle="--", linewidth=0.8
+                            )
+                            if include_y_labels is not False:
+                                # add train and test texts to either side of the y-label
+                                x_loc = -0.02
+                                y_center = 0.5
+                                text = "train"
+                                axs[0].text(
+                                    x=x_loc,
+                                    y=y_center + 0.25 + (len(text) * 0.01),
+                                    s=text,
+                                    color="grey",
+                                    fontsize=8.5,
+                                    ha="center",
+                                    va="center",
+                                    rotation=90,
+                                    transform=axs[0].transAxes,
+                                )
+                                text = "test"
+                                axs[0].text(
+                                    x=x_loc,
+                                    y=y_center - 0.25 - (len(text) * 0.01),
+                                    s=text,
+                                    color="grey",
+                                    fontsize=8.5,
+                                    ha="center",
+                                    va="center",
+                                    rotation=90,
+                                    transform=axs[0].transAxes,
+                                )
+                        else:
+                            raise ValueError(f"{divide_train_test_axis=} not in x ({x_type}) or y ({y_type}) axis")
+
+                    # plot the colorbar
+                    if include_colorbar is True:
+                        cbar = fig.colorbar(hm)
+                        if cmin != vmin or cmax != vmax:
+                            cbar.set_ticks(np.linspace(cmin, cmax, num=cnum))  # set colorbar limits
+                            cbar.ax.set_ylim(cmin, cmax)  # adjust visible colorbar limits
+                        # cbar.set_label("Performance relative to baseline (0.0) and optimum (1.0)")
+                        cbar.ax.set_ylabel("Performance score", rotation=-90, va="bottom")
+
+                    # keep only non-overlapping ticks
+                    max_ticks = 15
+                    if len(x_ticks) > max_ticks:
+                        indices = np.linspace(0, len(x_ticks) - 1, max_ticks).round()
+                        hide_tick = np.isin(np.arange(len(x_ticks)), indices, invert=True, assume_unique=True)
+                        for i, t in enumerate(axs[0].xaxis.get_ticklabels()):
+                            if hide_tick[i]:
+                                t.set_visible(False)
+                    if len(y_ticks) > max_ticks:
+                        indices = np.linspace(0, len(y_ticks) - 1, max_ticks).round()
+                        hide_tick = np.isin(np.arange(len(y_ticks)), indices, invert=True, assume_unique=True)
+                        for i, t in enumerate(axs[0].yaxis.get_ticklabels()):
+                            if hide_tick[i]:
+                                t.set_visible(False)
+
+                    # loop over data dimensions and create text annotations
+                    if annotate:
+                        # replace with looping over plot_data instead
+                        for i, j in np.ndindex(plot_data.shape):
+                            number = plot_data[i, j]
+                            if np.isnan(number):
+                                continue
+                            text = axs[0].text(
+                                j,
+                                i,
+                                f"{round(number, 2) if number < -10 else round(number, 3)}",
+                                ha="center",
+                                va="center",
+                                color="white" if (number > 0.5 or number < -2) else "black",
+                                fontsize="small",
+                            )
+                    
+                    # print extra information if requested
+                    if print_mean_of_columns:
+                        mean_of_columns = np.nanmean(plot_data, axis=0)
+                        print(f"Mean of columns for {strategy_displayname} ({x_type}): {mean_of_columns}")
+                    if print_mean_of_rows:
+                        mean_of_rows = np.nanmean(plot_data, axis=1)
+                        print(f"Mean of rows for {strategy_displayname} ({y_type}): {mean_of_rows}")
 
                     # finalize the figure and save or display it
-                    fig.supxlabel(self.get_x_axis_label(x_type, objective_time_keys))
                     fig.tight_layout()
                     if save_figs:
-                        filename = f"{self.plot_filename_prefix}{title}_{x_type}"
-                        filename = filename.replace(" ", "_")
-                        fig.savefig(filename, dpi=300)
-                        print(f"Figure saved to {filename}")
+                        suffix = ""
+                        if include_colorbar and not (x_type == "time" or y_type == "time"):
+                            suffix += "_colorbar"
+                        if include_y_labels and not (x_type == "time" or y_type == "time"):
+                            suffix += "_ylabels"
+                        filename_path = (
+                            Path(self.plot_filename_prefix)
+                            / f"{strategy_name}_heatmap_{'_'.join(plot_x_value_types)}_{'_'.join(plot_y_value_types)}{suffix}"
+                        )
+                        fig.savefig(filename_path, dpi=300, bbox_inches="tight", pad_inches=0.01)
+                        print(f"Figure saved to {filename_path}")
                     else:
                         plt.show()
+            elif style == "compare_heatmaps":
+
+                raise NotImplementedError("Still a work in progress")
+
+                # comparisons = plot["comparison"]
+                # # set up the plot
+                # fig, axs = plt.subplots(
+                #     ncols=1, figsize=(9, 6), dpi=300
+                # )  # if multiple subplots, pass the axis to the plot function with axs[0] etc.
+                # if not hasattr(axs, "__len__"):
+                #     axs = [axs]
+                # # title = f"Performance of {strategy_displayname} over {'+'.join(plot_x_value_types)},{'+'.join(plot_y_value_types)}"
+                # # fig.canvas.manager.set_window_title(title)
+                # # if not save_figs:
+                # # fig.suptitle(title)
+
+                # for comparison in comparisons:
+                #     strategy_names = comparisons["strategies"]
+                #     strategies = [s for s in self.strategies if s["name"]]
+                #     # for strategy in strategies:
+                #     strategy_displayname = strategy["display_name"]
+                #     strategy_data = data_collected[strategy_name]
+
+                #     # get the performance per selected type in an array
+                #     plot_data = np.stack(np.array([t[2] for t in strategy_data]))
+                #     cutoff_percentile: float = self.experiment["statistics_settings"].get("cutoff_percentile", 1)
+                #     cutoff_percentile_start: float = self.experiment["statistics_settings"].get(
+                #         "cutoff_percentile_start", 0.01
+                #     )
+                #     label_data = {
+                #         "gpus": (
+                #             list(dict.fromkeys([t[0].replace(remove_from_gpus_label, "") for t in strategy_data])),
+                #             "GPUs",
+                #         ),
+                #         "applications": (
+                #             list(
+                #                 dict.fromkeys([t[1].replace(remove_from_applications_label, "") for t in strategy_data])
+                #             ),
+                #             "Applications",
+                #         ),
+                #         "searchspaces": (
+                #             list(
+                #                 dict.fromkeys(
+                #                     [
+                #                         f"{t[1]} on\n{t[0]}".replace(remove_from_searchspace_label, "")
+                #                         for t in strategy_data
+                #                     ]
+                #                 )
+                #             ),
+                #             "Searchspaces",
+                #         ),
+                #         "time": (
+                #             np.round(np.linspace(0.0, 1.0, bins), 2),
+                #             f"Fraction of time between {cutoff_percentile_start * 100}% and {cutoff_percentile * 100}%",
+                #         ),
+                #     }
+                #     x_ticks = label_data[x_type][0]
+                #     y_ticks = label_data[y_type][0]
+                #     if (x_type == "time" and y_type == "searchspaces") or (
+                #         x_type == "searchspaces" and y_type == "time"
+                #     ):
+                #         plot_data: np.ndarray = np.stack(np.array([t[3] for t in strategy_data]))
+                #         if x_type == "searchspaces":
+                #             plot_data = plot_data.transpose()
+                #     elif (x_type == "gpus" and y_type == "applications") or (
+                #         y_type == "gpus" and x_type == "applications"
+                #     ):
+                #         plot_data = np.reshape(
+                #             plot_data, (len(label_data["gpus"][0]), len(label_data["applications"][0]))
+                #         )
+                #         if x_type == "gpus":
+                #             plot_data = np.transpose(plot_data)
+                #     else:
+                #         raise NotImplementedError(
+                #             f"Heatmap has not yet been implemented for {x_type}, {y_type}. Submit an issue to request it."
+                #         )
+
+                #     # validate the data
+                #     outside_range = np.where(np.logical_or(plot_data < vmin, plot_data > vmax))
+                #     assert len(outside_range[0]) == 0 and len(outside_range[1]) == 0, (
+                #         f"There are values outside of the range ({vmin}, {vmax}): {plot_data[outside_range]} ({outside_range} for strategy {strategy_displayname})"
+                #     )
+            else:
+                raise NotImplementedError(f"Invalid {style=}")
 
         # plot the aggregated searchspaces
-        if (
-            "aggregated" in plot_x_value_types
-            and continue_after_comparison
-            or not (compare_baselines or compare_split_times)
-        ):
-            fig, axs = plt.subplots(
-                ncols=1, figsize=(9, 6), dpi=300
-            )  # if multiple subplots, pass the axis to the plot function with axs[0] etc.
-            if not hasattr(axs, "__len__"):
-                axs = [axs]
-            title = f"""Aggregated Data\nkernels:
-                    {', '.join(self.experiment['kernels'])}\nGPUs: {', '.join(self.experiment['GPUs'])}"""
-            fig.canvas.manager.set_window_title(title)
-            if not save_figs:
-                fig.suptitle(title)
-
-            # finalize the figure and save or display it
-            self.plot_strategies_aggregated(axs[0], aggregation_data, plot_settings=plot_settings)
-            fig.tight_layout()
-            if save_figs:
-                filename = f"{self.plot_filename_prefix}aggregated"
-                filename = filename.replace(" ", "_")
-                fig.savefig(filename, dpi=300)
-                print(f"Figure saved to {filename}")
-            else:
-                plt.show()
+        for plot in plots:
+            # get settings
+            scope: str = plot["scope"]
+            style: str = plot["style"]
+            vmin: float = plot.get("vmin", None)  # visual range lower limit
+            if scope != "aggregate":
+                continue
+            if style != "line" and style != "head2head":
+                raise NotImplementedError(f"{scope} does currently not support {style}, create an issue to request it.")
+            if style == "head2head":
+                compare_at_relative_time = plot["comparison"]["relative_time"]
+                comparison_unit = plot["comparison"]["unit"]
+                annotate = plot.get("annotate", True)
+
+                # the comparison data will be a double nested dictionary of the strategy indices
+                comparison_data_raw = self.get_head2head_comparison_data(
+                    aggregation_data, compare_at_relative_time, comparison_unit
+                )
+
+                # if more than half of the comparisons between two strategies are NaN, set all to NaN
+                for strategy1 in comparison_data_raw.keys():
+                    for strategy2 in comparison_data_raw[strategy1].keys():
+                        comparison = comparison_data_raw[strategy1][strategy2]
+                        if len([v for v in comparison if np.isnan(v)]) > ceil(0.5 * len(comparison)):
+                            comparison_data_raw[strategy1][strategy2] = [np.nan] * len(comparison)
+
+                # convert the comparison data dictionary to a 2D numpy array of means
+                comparison_data = np.array(
+                    [
+                        [
+                            np.nanmean(comparison_data_raw[strategy1][strategy2])
+                            for strategy2 in comparison_data_raw[strategy1].keys()
+                        ]
+                        for strategy1 in comparison_data_raw.keys()
+                    ]
+                ).transpose()
+
+                # set up the plot
+                fig, axs = plt.subplots(ncols=1, figsize=(8, 6), dpi=300)
+                if not hasattr(axs, "__len__"):
+                    axs = [axs]
+                ax = axs[0]
+                title = f"Head-to-head comparison of strategies at {compare_at_relative_time} relative time"
+                fig.canvas.manager.set_window_title(title)
+                if not save_figs:
+                    fig.suptitle(title)
+
+                # # set the x and y labels
+                # if comparison_unit == "time":
+                #     ax.set_xlabel("How much time do these strategies take...")
+                # elif comparison_unit == "objective":
+                #     ax.set_xlabel("How much objective value do these strategies achieve...")
+                # ax.set_ylabel("...relative to these strategies?")
+                # ax.xaxis.set_label_position('top')
+
+                # set the x and y ticks
+                x_ticks = list(comparison_data_raw.keys())
+                y_ticks = list(comparison_data_raw.keys())
+                # Show all ticks and label them with the respective list entries
+                ax.set_xticks(range(len(x_ticks)), labels=x_ticks, rotation=-15, ha="right", rotation_mode="anchor")
+                ax.set_yticks(range(len(y_ticks)), labels=y_ticks, rotation=-30, ha="right", rotation_mode="anchor")
+                ax.xaxis.tick_top()
+
+                # set the color map
+                vmin = 0.0
+                vmax = 1000.0
+
+                def norm_color_val(v):
+                    """Normalize a color value to fit in the 0-1 range."""
+                    return (v - vmin) / (vmax - vmin)
+
+                if comparison_unit == "time":
+                    cmap = LinearSegmentedColormap.from_list(
+                        "head2head_colormap",
+                        [
+                            (norm_color_val(vmin), "darkgreen"),
+                            (norm_color_val(100.0), "greenyellow"),
+                            (norm_color_val(200.0), "orange"),
+                            (norm_color_val(500.0), "red"),
+                            (norm_color_val(800.0), "darkred"),
+                            (norm_color_val(vmax), "black"),
+                        ],
+                    )
+                elif comparison_unit == "objective":
+                    cmap = LinearSegmentedColormap.from_list(
+                        "head2head_colormap",
+                        [
+                            (norm_color_val(vmin), "darkred"),
+                            (norm_color_val(80.0), "yellow"),
+                            (norm_color_val(100.0), "greenyellow"),
+                            (norm_color_val(200.0), "green"),
+                            (norm_color_val(vmax), "darkgreen"),
+                        ],
+                    )
+
+                # if there are any values above the vmax, warn
+                if np.any(comparison_data > vmax):
+                    warnings.warn(
+                        f"There are values above the vmax ({vmax}) in the comparison data: {comparison_data[comparison_data > vmax]}, these are clipped"
+                    )
+                # clip the comparison data to the vmin-vmax range
+                comparison_data_clipped = np.clip(comparison_data, vmin, vmax)
+
+                # plot the comparison data
+                im = ax.imshow(
+                    comparison_data_clipped,
+                    vmin=vmin,
+                    vmax=vmax,
+                    aspect="auto",
+                    cmap=cmap,
+                )
+
+                # set the colorbar
+                # cmin = np.nanmin(comparison_data_clipped)
+                cmin = vmin  # always show 0.0 as the start
+                max_val = np.nanmax(comparison_data_clipped)
+                if np.isnan(max_val):
+                    max_val = vmax
+                # round to the nearest 100
+                cmax = round(ceil(max_val), -2)
+                if cmax < max_val:
+                    cmax += 100  # ensure the colorbar max is above the max value
+                cnum = round(cmax / 100) + 1
+                cbar = ax.figure.colorbar(im, ax=ax)
+                if cmin != vmin or cmax != vmax:
+                    cbar.set_ticks(np.linspace(cmin, cmax, num=cnum))  # set colorbar limits
+                    cbar.ax.set_ylim(cmin, cmax)  # adjust visible colorbar limits
+                if comparison_unit == "time":
+                    cbar.ax.set_ylabel(
+                        "Time difference to same objective value (lower is better)", rotation=-90, va="bottom"
+                    )
+                elif comparison_unit == "objective":
+                    cbar.ax.set_ylabel(
+                        "Objective value difference at same time (higher is better)", rotation=-90, va="bottom"
+                    )
+                else:
+                    raise NotImplementedError(f"Comparison unit '{comparison_unit}' not implemented")
+
+                # loop over data dimensions and create text annotations
+                if annotate:
+                    for i in range(len(x_ticks)):
+                        for j in range(len(y_ticks)):
+                            number = comparison_data[i, j]
+                            if np.isnan(number):
+                                continue
+                            text = ax.text(
+                                j,
+                                i,
+                                f"{round(number, 1) if number < 100 else round(number)}%",
+                                ha="center",
+                                va="center",
+                                color="white" if (number > 200 or number < 50) else "black",
+                                fontsize="small",
+                            )
+
+                # plot the averages per strategy as labels under the heatmap
+                averages = np.nanmean(comparison_data, axis=0)
+                # add "mean" before the averages
+                ax.text(-0.5, len(y_ticks) - 0.2, "Mean:", ha="right", va="center", color="black", fontsize=10)
+                for i, avg in enumerate(averages):
+                    ax.text(
+                        i,
+                        len(y_ticks) - 0.2,
+                        f"{'NaN' if np.isnan(avg) else round(avg, 1) if avg < 100 else round(avg)}%",
+                        ha="center",
+                        va="center",
+                        color="black",
+                        fontsize="small",
+                    )
+                print(
+                    f"Averages per strategy at {compare_at_relative_time} relative time: {[(s, a) for s, a in zip(x_ticks, averages)]}"
+                )
+
+                # finalize the figure and save or display it
+                fig.tight_layout()
+                if save_figs:
+                    filename_path = Path(self.plot_filename_prefix) / f"head2head_comparison_{comparison_unit}"
+                    fig.savefig(filename_path, dpi=300, bbox_inches="tight", pad_inches=0.01)
+                    print(f"Figure saved to {filename_path}")
+                else:
+                    plt.show()
+
+            # plot the aggregation
+            if style == "line" and (continue_after_comparison or not (compare_baselines or compare_split_times)):
+                # fig, axs = plt.subplots(
+                #     ncols=1, figsize=(6.8, 4.0), dpi=300
+                # )  # if multiple subplots, pass the axis to the plot function with axs[0] etc.
+                fig, axs = plt.subplots(
+                    ncols=1, figsize=(8.5, 5.0), dpi=300
+                )  # if multiple subplots, pass the axis to the plot function with axs[0] etc.
+                if not hasattr(axs, "__len__"):
+                    axs = [axs]
+                title = f"""Aggregated Data\napplications:
+                        {", ".join(self.experiment["experimental_groups_defaults"]["applications_names"])}\nGPUs: {", ".join(self.experiment["experimental_groups_defaults"]["gpus"])}"""
+                fig.canvas.manager.set_window_title(title)
+                if not save_figs:
+                    fig.suptitle(title)
+
+                # finalize the figure and save or display it
+                lowest_real_y_value = self.plot_strategies_aggregated(
+                    axs[0],
+                    aggregation_data,
+                    visualization_settings=self.experiment["visualization_settings"],
+                    plot_settings=plot,
+                )
+                if vmin is not None:
+                    if isinstance(vmin, (int, float)):
+                        axs[0].set_ylim(bottom=vmin)
+                    elif vmin == "real":
+                        axs[0].set_ylim(bottom=lowest_real_y_value - (abs(lowest_real_y_value) + 1.0) * 0.02)
+                    else:
+                        raise NotImplementedError(f"{vmin=} not implemented")
+                fig.tight_layout()
+                if save_figs:
+                    filename_path = Path(self.plot_filename_prefix) / "aggregated"
+                    fig.savefig(filename_path, dpi=300, bbox_inches="tight", pad_inches=0.01)
+                    print(f"Figure saved to {filename_path}")
+                else:
+                    plt.show()
 
     def plot_baselines_comparison(
         self,
@@ -390,7 +1119,7 @@ def plot_baselines_comparison(
             save_fig: whether to save the resulting figure to file. Defaults to False.
         """
         dist = searchspace_stats.objective_performances_total_sorted
-        plt.figure(figsize=(8, 5), dpi=300)
+        plt.figure(figsize=(9, 7), dpi=300)
 
         # list the baselines to test
         baselines: list[Baseline] = list()
@@ -446,10 +1175,9 @@ def plot_baselines_comparison(
 
         # write to file or show
         if save_fig:
-            filename = f"{self.plot_filename_prefix}{title}_baselines"
-            filename = filename.replace(" ", "_")
-            plt.savefig(filename, dpi=300)
-            print(f"Figure saved to {filename}")
+            filename_path = Path(self.plot_filename_prefix) / f"{title}_baselines".replace(" ", "_")
+            plt.savefig(filename_path, dpi=300, bbox_inches="tight", pad_inches=0.01)
+            print(f"Figure saved to {filename_path}")
         else:
             plt.show()
 
@@ -533,10 +1261,9 @@ def plot_split_times_comparison(
 
         # write to file or show
         if save_fig:
-            filename = f"{self.plot_filename_prefix}{title}_split_times_{x_type}"
-            filename = filename.replace(" ", "_")
-            plt.savefig(filename, dpi=300)
-            print(f"Figure saved to {filename}")
+            filename_path = Path(self.plot_filename_prefix) / f"{title}_split_times_{x_type}".replace(" ", "_")
+            plt.savefig(filename_path, dpi=300, bbox_inches="tight", pad_inches=0.01)
+            print(f"Figure saved to {filename_path}")
         else:
             plt.show()
 
@@ -581,7 +1308,7 @@ def plot_split_times_bar_comparison(
             for _ in range((len(strategies_curves) - len(self.plot_skip_strategies)) + 1)
         )
         for objective_time_key in objective_time_keys:
-            data_dict[objective_time_key] = np.full((len(strategies_curves)), np.NaN)
+            data_dict[objective_time_key] = np.full((len(strategies_curves)), np.nan)
         for strategy_index, strategy_curve in enumerate(strategies_curves):
             if strategy_curve.name in self.plot_skip_strategies:
                 continue
@@ -641,15 +1368,164 @@ def plot_split_times_bar_comparison(
 
         # write to file or show
         if save_fig:
-            filename = f"{self.plot_filename_prefix}{title}_split_times_bar"
-            filename = filename.replace(" ", "_")
-            plt.savefig(filename, dpi=300)
-            print(f"Figure saved to {filename}")
+            filename_path = Path(self.plot_filename_prefix) / f"{title}_split_times_bar".replace(" ", "_")
+            plt.savefig(filename_path, dpi=300, bbox_inches="tight", pad_inches=0.01)
+            print(f"Figure saved to {filename_path}")
         else:
             plt.show()
 
+    def get_head2head_comparison_data(
+        self, aggregation_data: dict, compare_at_relative_time: float, comparison_unit: str
+    ) -> dict:
+        """Gets the data for a head-to-head comparison of strategies across all searchspaces."""
+        # the comparison data will be a double nested dictionary of the strategy indices
+        comparison_data = dict()
+        for strategy_alpha in self.strategies:
+            comparison_data[strategy_alpha["display_name"]] = dict()
+            for strategy_beta in self.strategies:
+                comparison_data[strategy_alpha["display_name"]][strategy_beta["display_name"]] = list()
+
+        # iterate over the searchspaces and strategies to get head2head data
+        for gpu_name in self.experiment["experimental_groups_defaults"]["gpus"]:
+            for application_name in self.experiment["experimental_groups_defaults"]["applications_names"]:
+                print(f" | visualizing head2head of {application_name} for {gpu_name}")
+
+                # unpack the aggregation data
+                _, strategies_curves, searchspace_stats, time_range, _ = aggregation_data[
+                    get_aggregation_data_key(gpu_name=gpu_name, application_name=application_name)
+                ]
+
+                # get the head2head comparison data
+                comparison_data_ss = self.get_head2head_comparison_data_searchspace(
+                    "time",
+                    compare_at_relative_time,
+                    comparison_unit,
+                    searchspace_stats,
+                    strategies_curves,
+                    time_range,
+                )
+
+                # for this searchspace, append each strategy's data to the comparison data
+                for strategy_index_alpha, strategy_alpha in enumerate(self.strategies):
+                    for strategy_index_beta, strategy_beta in enumerate(self.strategies):
+                        comparison_data[strategy_alpha["display_name"]][strategy_beta["display_name"]].append(
+                            comparison_data_ss[strategy_index_alpha][strategy_index_beta]
+                        )
+
+        return comparison_data
+
+    def get_head2head_comparison_data_searchspace(
+        self,
+        x_type: str,
+        compare_at_relative_time: float,
+        comparison_unit: str,
+        searchspace_stats: SearchspaceStatistics,
+        strategies_curves: list[Curve],
+        x_axis_range: np.ndarray,
+    ) -> dict:
+        """Gets the data for a head-to-head comparison of strategies on a specific searchspace.
+
+        Args:
+            x_type: the type of ``x_axis_range``.
+            compare_at_relative_time: the relative point in time to compare at, between 0.0 and 1.0.
+            comparison_unit: the unit to compare with, 'time' or 'objective'.
+            searchspace_stats: the Searchspace statistics object.
+            strategies_curves: the strategy curves to draw in the plot.
+            x_axis_range: the time or function evaluations range to plot on.
+
+        Returns:
+            A doubly-nested dictionary with strategy names as keys and how much better outer performs relative to inner.
+        """
+        comparison_point = x_axis_range[-1] * compare_at_relative_time
+        comparison_data = dict()
+        confidence_level = 0.95  # irrelevant because the confidence intervals are not used
+        minimization = searchspace_stats.minimization
+        dist = searchspace_stats.objective_performances_total_sorted
+        for strategy_index_alpha, strategy_alpha in enumerate(self.strategies):
+            inner_comparison_data = dict()
+            strategy_curve_alpha = strategies_curves[strategy_index_alpha]
+            _, time_range_alpha, curve_alpha, _, _ = strategy_curve_alpha.get_curve(
+                x_axis_range, x_type, dist=dist, confidence_level=confidence_level, return_split=False
+            )
+
+            # find the index of the closest time and performance to the comparison point
+            closest_index_alpha = np.argmin(np.abs(time_range_alpha - comparison_point))
+            time_at_comparison_alpha = time_range_alpha[closest_index_alpha]
+            performance_at_comparison_alpha = curve_alpha[closest_index_alpha]
+
+            absolute_optimum = searchspace_stats.total_performance_absolute_optimum()
+            median = searchspace_stats.total_performance_median()
+
+            def normalize(val):
+                """Min-max normalization of the performance value."""
+                if absolute_optimum == median:
+                    return 0.0
+                return (val - median) / (absolute_optimum - median)
+
+            performance_at_comparison_alpha_norm = normalize(performance_at_comparison_alpha)
+
+            # compare against all other strategies
+            for strategy_index_beta, strategy_beta in enumerate(self.strategies):
+                if strategy_index_alpha == strategy_index_beta:
+                    inner_comparison_data[strategy_index_beta] = np.nan
+                    continue
+                strategy_curve_beta = strategies_curves[strategy_index_beta]
+                _, time_range_beta, curve_beta, _, _ = strategy_curve_beta.get_curve(
+                    x_axis_range, x_type, dist=dist, confidence_level=confidence_level, return_split=False
+                )
+
+                # calculate the relative difference between the two strategies at the comparison point
+                if comparison_unit == "time":
+                    # given the performance at `compare_at_relative_time`, what is the index of the first time that strategy beta reaches at least the same performance?
+                    index_matching = (
+                        np.argwhere(curve_beta <= performance_at_comparison_alpha)
+                        if minimization
+                        else np.argwhere(curve_beta >= performance_at_comparison_alpha)
+                    )
+                    if index_matching.size == 0:
+                        # if strategy beta never reaches the performance of strategy alpha, we cannot compare, instead we take the time at the end so we know what the minimal performance gain is
+                        time_at_comparison_beta = time_range_beta[-1]
+                        # another alternative: take the last time * fraction of inverse (e.g. if GA-nc doesn’t find the objective of GA, take end-of-time * 1/([GA-to-GAnc]/100))
+                        # inner_comparison_data[strategy_index_beta] = np.nan
+                        # continue
+                    else:
+                        # get the time at which strategy beta reaches the performance of strategy alpha
+                        closest_index_beta = index_matching[0][0]  # take the first match
+                        time_at_comparison_beta = time_range_beta[closest_index_beta]
+
+                        # given the performance at `compare_at_relative_time`, how much longer does strategy beta take to get to the same performance compared to strategy alpha? (lower is better)
+                        # closest_index_beta = np.argmin(np.abs(curve_beta - performance_at_comparison_alpha))
+                        # time_at_comparison_beta = time_range_beta[closest_index_beta]
+                    # outer takes X% of the time inner takes to reach the same performance (100%+percentage change)
+                    percentage_change = (
+                        (time_at_comparison_alpha - time_at_comparison_beta) / abs(time_at_comparison_beta) * 100
+                    )
+                    inner_comparison_data[strategy_index_beta] = 100 + percentage_change
+                elif comparison_unit == "objective":
+                    # given the time at `compare_at_relative_time`, how much worse is the objective value of strategy beta at that moment compared to strategy alpha? (higher is better)
+                    closest_index_beta = np.argmin(np.abs(time_range_beta - time_at_comparison_alpha))
+                    performance_at_comparison_beta = curve_beta[closest_index_beta]
+                    performance_at_comparison_beta_norm = normalize(performance_at_comparison_beta)
+
+                    # percentage_change = (performance_at_comparison_beta - performance_at_comparison_alpha) / abs(performance_at_comparison_beta) * 100
+                    # if not minimization:
+                    #     percentage_change = -percentage_change
+
+                    percentage_change_norm = (
+                        (performance_at_comparison_beta_norm - performance_at_comparison_alpha_norm)
+                        / abs(performance_at_comparison_beta_norm)
+                        * 100
+                    )
+                    inner_comparison_data[strategy_index_beta] = 100 + percentage_change_norm
+                else:
+                    raise ValueError(f"Invalid comparison unit: {comparison_unit}. Expected 'time' or 'objective'.")
+
+            comparison_data[strategy_index_alpha] = inner_comparison_data
+        return comparison_data
+
     def plot_strategies(
         self,
+        style: str,
         x_type: str,
         y_type: str,
         ax: plt.Axes,
@@ -665,6 +1541,7 @@ def plot_strategies(
         """Plots all optimization strategies for individual search spaces.
 
         Args:
+            style: the style of plot, either 'line' or 'scatter'.
             x_type: the type of ``x_axis_range``.
             y_type: the type of plot on the y-axis.
             ax: the axis to plot on.
@@ -681,20 +1558,19 @@ def plot_strategies(
         confidence_level: float = plot_settings.get("confidence_level", 0.95)
         absolute_optimum = searchspace_stats.total_performance_absolute_optimum()
         median = searchspace_stats.total_performance_median()
-        optimum_median_difference = absolute_optimum - median
 
         def normalize(curve):
             """Min-max normalization with median as min and absolute optimum as max."""
             if curve is None:
                 return None
-            return (curve - median) / optimum_median_difference
+            return (curve - median) / (absolute_optimum - median)
 
         def normalize_multiple(curves: list) -> tuple:
             """Normalize multiple curves at once."""
             return tuple(normalize(curve) for curve in curves)
 
         # plot the absolute optimum
-        absolute_optimum_y_value = absolute_optimum if y_type == "absolute" or y_type == "scatter" else 1
+        absolute_optimum_y_value = absolute_optimum if y_type == "absolute" or style == "scatter" else 1
         absolute_optimum_label = (
             "Absolute optimum ({})".format(round(absolute_optimum, 3)) if y_type == "absolute" else "Absolute optimum"
         )
@@ -704,8 +1580,15 @@ def normalize_multiple(curves: list) -> tuple:
         if baseline_curve is not None:
             if y_type == "baseline":
                 ax.axhline(0, label="baseline trajectory", color="black", ls="--")
-            elif y_type == "normalized" or y_type == "baseline":
+            elif y_type == "normalized" or y_type == "baseline" or y_type == "absolute":
                 baseline = baseline_curve.get_curve(x_axis_range, x_type)
+                if absolute_optimum in baseline:
+                    raise ValueError(
+                        f"The optimum {absolute_optimum} is in the baseline, this will cause zero division problems"
+                    )
+                    # cut_at_index = np.argmax(baseline == absolute_optimum)
+                    # baseline = baseline[:cut_at_index]
+                    # x_axis_range = x_axis_range[:cut_at_index]
                 if y_type == "normalized":
                     baseline = normalize(baseline)
                 ax.plot(x_axis_range, baseline, label="Calculated baseline", color="black", ls="--")
@@ -740,7 +1623,7 @@ def normalize_multiple(curves: list) -> tuple:
                 continue
 
             # get the plot data
-            if y_type == "scatter":
+            if style == "scatter":
                 x_axis, y_axis = strategy_curve.get_scatter_data(x_type)
                 ax.scatter(x_axis, y_axis, label=label, color=color)
                 continue
@@ -843,8 +1726,10 @@ def normalize_multiple(curves: list) -> tuple:
         ax.set_ylabel(self.y_metric_displayname[f"objective_{y_type}"], fontsize="large")
         normalized_ylim_margin = 0.02
         if y_type == "absolute":
-            multiplier = 0.99 if self.minimization else 1.01
-            ax.set_ylim(absolute_optimum * multiplier, median)
+            # multiplier = 0.99 if self.minimization else 1.01
+            # ax.set_ylim(absolute_optimum * multiplier, median)
+            # ax.set_ylim(1.0)
+            pass
         # elif y_type == 'normalized':
         #     ax.set_ylim((0.0, 1 + normalized_ylim_margin))
         elif y_type == "baseline":
@@ -854,21 +1739,26 @@ def plot_strategies_aggregated(
         self,
         ax: plt.Axes,
         aggregation_data,
-        plot_settings: dict,
-    ):
+        visualization_settings: dict = {},
+        plot_settings: dict = {},
+    ) -> float:
         """Plots all optimization strategies combined accross search spaces.
 
         Args:
             ax: the axis to plot on.
             aggregation_data: the aggregated data from the various searchspaces.
-            plot_settings: dictionary of additional plot settings.
+            visualization_settings: dictionary of additional visualization settings.
+            plot_settings: dictionary of additional visualization settings related to this particular plot.
+
+        Returns:
+            The lowest performance value of the real stopping point for all strategies.
         """
         # plot the random baseline and absolute optimum
         ax.axhline(0, label="Calculated baseline", c="black", ls=":")
         ax.axhline(1, label="Absolute optimum", c="black", ls="-.")
 
         # get the relative aggregated performance for each strategy
-        confidence_level: float = plot_settings.get("confidence_level", 0.95)
+        confidence_level: float = visualization_settings.get("confidence_level", 0.95)
         (
             strategies_performance,
             strategies_lower_err,
@@ -876,23 +1766,72 @@ def plot_strategies_aggregated(
             strategies_real_stopping_point_fraction,
         ) = get_strategies_aggregated_performance(list(aggregation_data.values()), confidence_level)
 
-        # plot each strategy
+        # get the relevant plot settings
+        cutoff_percentile: float = self.experiment["statistics_settings"].get("cutoff_percentile", 1)
+        cutoff_percentile_start: float = self.experiment["statistics_settings"].get("cutoff_percentile_start", 0.01)
+        xlabel = plot_settings.get(
+            "xlabel",
+            f"{self.x_metric_displayname['aggregate_time']} ({cutoff_percentile_start * 100}% to {cutoff_percentile * 100}%)",
+        )  # noqa: E501
+        ylabel = plot_settings.get("ylabel", self.y_metric_displayname["aggregate_objective"])
+        tmin = plot_settings.get("tmin", 1.0)
+
+        # setup the plot
         y_axis_size = strategies_performance[0].shape[0]
         time_range = np.arange(y_axis_size)
         plot_errors = True
+        lowest_real_y_value = 0.0
         print("\n-------")
         print("Quantification of aggregate performance across all search spaces:")
+
+        # get the highest real_stopping_point_index, adjust y_axis_size and time_range if necessary
+        real_stopping_point_indices = [
+            min(
+                round(strategies_real_stopping_point_fraction[strategy_index] * time_range.shape[0]) + 1,
+                time_range.shape[0],
+            )
+            for strategy_index in range(len(strategies_performance))
+        ]  # noqa: E501
+        real_stopping_point_index_max = max(real_stopping_point_indices)
+        if tmin == "real":
+            # stop the time at the largest real stopping point
+            if real_stopping_point_index_max < y_axis_size:
+                y_axis_size = real_stopping_point_index_max
+                print(f"    adjusted stopping point index: {real_stopping_point_index_max}/{y_axis_size}")
+            time_range = np.arange(y_axis_size)
+        elif tmin < 1.0:
+            # stop the time at the given tmin
+            y_axis_size = y_axis_size * tmin
+            time_range = np.arange(y_axis_size)
+        elif tmin > 1.0:
+            raise ValueError(f"Invalid {tmin=}, must be between 0.0 and 1.0 or 'real'")
+
+        # adjust the xlabel if necessary
+        if tmin == "real" and "xlabel" not in plot_settings:
+            xlabel = "Relative time until the last strategy stopped"
+
+        # plot each strategy
         for strategy_index, strategy_performance in enumerate(strategies_performance):
             if self.strategies[strategy_index]["name"] in self.plot_skip_strategies:
                 continue
             displayname = self.strategies[strategy_index]["display_name"]
             color = self.colors[strategy_index]
-            real_stopping_point_fraction = strategies_real_stopping_point_fraction[strategy_index]
-            real_stopping_point_index = round(real_stopping_point_fraction * time_range.shape[0])
-            if real_stopping_point_index <= 0:
+            real_stopping_point_index = real_stopping_point_indices[strategy_index]
+            if real_stopping_point_index <= 1:
                 warnings.warn(f"Stopping point index for {displayname} is at {real_stopping_point_index}")
                 continue
 
+            # calculate the lowest real_y_value
+            lowest_real_y_value = min(
+                lowest_real_y_value,
+                (
+                    strategy_performance[real_stopping_point_index]
+                    if real_stopping_point_index < time_range.shape[0]
+                    else strategy_performance[time_range.shape[0] - 1]
+                ),
+            )
+            assert isinstance(lowest_real_y_value, (int, float)), f"Invalid {lowest_real_y_value=}"
+
             # plot the errors
             if plot_errors:
                 strategy_lower_err = strategies_lower_err[strategy_index]
@@ -910,9 +1849,9 @@ def plot_strategies_aggregated(
                     and real_stopping_point_index < len(strategy_lower_err) - 1
                 ):
                     ax.fill_between(
-                        time_range[real_stopping_point_index:],
-                        strategy_lower_err[real_stopping_point_index:],
-                        strategy_upper_err[real_stopping_point_index:],
+                        time_range[real_stopping_point_index - 1 : y_axis_size],
+                        strategy_lower_err[real_stopping_point_index - 1 : y_axis_size],
+                        strategy_upper_err[real_stopping_point_index - 1 : y_axis_size],
                         alpha=0.15,
                         antialiased=True,
                         color=color,
@@ -931,8 +1870,8 @@ def plot_strategies_aggregated(
                 and real_stopping_point_index < len(strategy_performance) - 1
             ):
                 ax.plot(
-                    time_range[real_stopping_point_index:],
-                    strategy_performance[real_stopping_point_index:],
+                    time_range[real_stopping_point_index - 1 : y_axis_size],
+                    strategy_performance[real_stopping_point_index - 1 : y_axis_size],
                     color=color,
                     ls="dashed",
                 )
@@ -940,22 +1879,25 @@ def plot_strategies_aggregated(
             performance_score_std = round(np.std(strategy_performance), 3)
             print(f" | performance of {displayname}: {performance_score} (±{performance_score_std})")
 
-        # set the axis
-        cutoff_percentile: float = self.experiment.get("cutoff_percentile", 1)
-        cutoff_percentile_start: float = self.experiment.get("cutoff_percentile_start", 0.01)
-        ax.set_xlabel(
-            f"{self.x_metric_displayname['aggregate_time']} ({cutoff_percentile_start*100}% to {cutoff_percentile*100}%)",  # noqa: E501
-            fontsize="large",
-        )
-        ax.set_ylabel(self.y_metric_displayname["aggregate_objective"], fontsize="large")
-        num_ticks = 11
-        ax.set_xticks(
-            np.linspace(0, y_axis_size, num_ticks),
-            np.round(np.linspace(0, 1, num_ticks), 2),
-        )
+        # set the axis labels
+        ax.set_xlabel(xlabel, fontsize="large")
+        ax.set_ylabel(ylabel, fontsize="large")
+
+        # set the ticks
+        if tmin == "real":
+            ax.set_xticks([], [])
+        else:
+            num_ticks = 11
+            ax.set_xticks(
+                np.linspace(0, y_axis_size, num_ticks),
+                np.round(np.linspace(0, tmin, num_ticks), 2),
+            )
+
+        # set the limits and legend
         ax.set_ylim(top=1.02)
-        ax.set_xlim((0, y_axis_size))
+        ax.set_xlim((0, y_axis_size - 1))
         ax.legend()
+        return lowest_real_y_value
 
     def get_x_axis_label(self, x_type: str, objective_time_keys: list):
         """Formatter to get the appropriate x-axis label depending on the x-axis type.
diff --git a/tests/autotuning_methodology/integration/mockfiles/convolution.cu b/tests/autotuning_methodology/integration/mockfiles/convolution.cu
new file mode 100644
index 0000000..da84830
--- /dev/null
+++ b/tests/autotuning_methodology/integration/mockfiles/convolution.cu
@@ -0,0 +1,168 @@
+extern "C" {
+#define image_height 4096
+#define image_width 4096
+
+#ifndef filter_height
+    #define filter_height 17
+#endif
+#ifndef filter_width
+    #define filter_width 17
+#endif
+
+#define border_height ((filter_height/2)*2)
+#define border_width ((filter_width/2)*2)
+#define input_height (image_height + border_height)
+#define input_width (image_width + border_width)
+
+#ifndef block_size_x
+    #define block_size_x 16
+#endif
+#ifndef block_size_y
+    #define block_size_y 16
+#endif
+#ifndef block_size_z
+    #define block_size_z 1
+#endif
+#ifndef tile_size_x
+    #define tile_size_x 1
+#endif
+#ifndef tile_size_y
+    #define tile_size_y 1
+#endif
+
+#define i_end min(block_size_y*tile_size_y+border_height, input_height)
+#define j_end min(block_size_x*tile_size_x+border_width, input_width)
+
+/*
+ * If requested, we can use the __ldg directive to load data through the
+ * read-only cache. 
+ */
+#define USE_READ_ONLY_CACHE read_only
+#if USE_READ_ONLY_CACHE == 1
+#define LDG(x, y) __ldg(x+y)
+#elif USE_READ_ONLY_CACHE == 0
+#define LDG(x, y) x[y]
+#endif
+
+__constant__ float d_filter[33*33]; //large enough for the largest filter
+
+/*
+ * If use_padding == 1, we introduce (only when necessary) a number of padding
+ * columns in shared memory to avoid shared memory bank conflicts
+ *
+ * padding columns are only inserted when block_size_x is not a multiple of 32 (the assumed number of memory banks)
+ * and when the width of the data needed is not a multiple of 32. The latter is because some filter_widths never
+ * cause bank conflicts.
+ * 
+ * If not passed as a tunable parameter, padding is on by default
+ */
+#define shared_mem_width (block_size_x*tile_size_x+border_width)
+#ifndef use_padding
+    #define use_padding 1
+#endif
+#if use_padding == 1
+    #if (((block_size_x % 32)!=0) && (((shared_mem_width-block_size_x)%32) != 0))
+        // next line uses &31 instead of %32, because % in C is remainder not modulo
+        #define padding_columns ((32 - (border_width + block_size_x*tile_size_x - block_size_x)) & 31)
+        #undef shared_mem_width
+        #define shared_mem_width (block_size_x*tile_size_x+border_width+padding_columns)
+    #endif
+#endif
+
+
+__global__ void convolution_kernel(float *output, float *input, float *filter) {
+    int ty = threadIdx.y;
+    int tx = threadIdx.x;
+    int by = blockIdx.y * block_size_y * tile_size_y;
+    int bx = blockIdx.x * block_size_x * tile_size_x;
+
+    //shared memory to hold all input data need by this thread block
+    __shared__ float sh_input[block_size_y*tile_size_y+border_height][shared_mem_width];
+
+    //load all input data needed by this thread block into shared memory
+    #pragma unroll
+    for (int i=ty; i<i_end; i+=block_size_y) {
+        #pragma unroll
+        for (int j=tx; j<j_end; j+=block_size_x) {
+            #if ((image_height%(block_size_y*tile_size_y)!=0) || (image_width%(block_size_x*tile_size_x)!=0))
+            int y = by+i;
+            int x = bx+j;
+            if (y < input_height && x < input_width) {
+                sh_input[i][j] = LDG(input, y*input_width+x);
+            }
+            #else
+                sh_input[i][j] = LDG(input, (by+i)*input_width + (bx+j));
+            #endif
+        }
+    }
+    __syncthreads();
+
+    //thread-local registers to hold local sums
+    float sum[tile_size_y][tile_size_x];
+    #pragma unroll
+    for (int yi=0; yi<tile_size_y; yi++) {
+        #pragma unroll
+        for (int xi=0; xi<tile_size_x; xi++) {
+             sum[yi][xi] = 0.0f;
+        }
+    }
+
+    //for each filter weight
+    #pragma unroll
+    for (int i=0; i < filter_height; i++) {
+        #pragma unroll
+        for (int j=0; j < filter_width; j++) {
+
+            #pragma unroll
+            for (int yi=0; yi<tile_size_y; yi++) {   
+                #pragma unroll
+                for (int xi=0; xi<tile_size_x; xi++) {
+                    sum[yi][xi] += sh_input[ty+yi*block_size_y+i][tx+xi*block_size_x+j] * d_filter[i*filter_width+j];
+                }
+            }
+
+        }
+    }
+
+    //store results to global memory
+    #pragma unroll
+    for (int yi=0; yi<tile_size_y; yi++) {   
+        #pragma unroll
+        for (int xi=0; xi<tile_size_x; xi++) {
+            #if ((image_height%(block_size_y*tile_size_y)!=0) || (image_width%(block_size_x*tile_size_x)!=0))
+            int y = by+ty+yi*block_size_y;
+            int x = bx+tx+xi*block_size_x;
+            if (y < image_height && x < image_width) {
+                output[y * image_width + x] = sum[yi][xi];
+            }
+            #else
+                output[(by+ty+yi*block_size_y) * image_width + bx+tx+xi*block_size_x] = sum[yi][xi];
+            #endif
+        }
+    }
+
+}
+
+
+
+
+
+__global__ void convolution_naive(float *output, float *input, float *filter) {
+
+    int x = blockIdx.x * blockDim.x + threadIdx.x;
+    int y = blockIdx.y * blockDim.y + threadIdx.y;
+    int i, j;
+    float sum = 0.0;
+
+    if (y < image_height && x < image_width) {
+
+        for (j = 0; j < filter_height; j++) {
+            for (i = 0; i < filter_width; i++) {
+                sum += input[(y + j) * input_width + (x + i)] * filter[j * filter_width + i];
+            }
+        }
+
+        output[y * image_width + x] = sum;
+    }
+}
+}
diff --git a/tests/autotuning_methodology/integration/mockfiles/mock_gpu.json b/tests/autotuning_methodology/integration/mockfiles/mock_gpu.json
index 53be201..6bfd6d3 100644
--- a/tests/autotuning_methodology/integration/mockfiles/mock_gpu.json
+++ b/tests/autotuning_methodology/integration/mockfiles/mock_gpu.json
@@ -72,6 +72,7 @@
 1
 ]
 },
+"objective": "time",
 "cache": {
 "2,32,15,15,0,1,1,0": {"block_size_x": 2, "block_size_y": 32, "filter_height": 15, "filter_width": 15, "read_only": 0, "tile_size_x": 1, "tile_size_y": 1, "use_padding": 0, "time": 15.93639669418335, "times": [15.997759819030762, 15.987008094787598, 15.982527732849121, 15.983903884887695, 15.978495597839355, 15.981727600097656, 15.86137580871582, 15.867584228515625, 15.861824035644531, 15.861760139465332], "compile_time": 0.542292999853089, "verification_time": 0, "benchmark_time": 160.36117799967542, "strategy_time": 0, "framework_time": 80.10120700055268},
 "2,32,15,15,0,1,1,1": {"block_size_x": 2, "block_size_y": 32, "filter_height": 15, "filter_width": 15, "read_only": 0, "tile_size_x": 1, "tile_size_y": 1, "use_padding": 1, "time": 2.8577247858047485, "times": [2.867136001586914, 2.859488010406494, 2.8589119911193848, 2.85699200630188, 2.8572800159454346, 2.8569600582122803, 2.8564159870147705, 2.8548479080200195, 2.853951930999756, 2.8552639484405518], "compile_time": 262.1809460001714, "verification_time": 0, "benchmark_time": 28.90602100023898, "strategy_time": 0, "framework_time": 1.1706799996318296},
diff --git a/tests/autotuning_methodology/integration/mockfiles/mock_gpu_T4.json b/tests/autotuning_methodology/integration/mockfiles/mock_gpu_T4.json
new file mode 100644
index 0000000..397b52c
--- /dev/null
+++ b/tests/autotuning_methodology/integration/mockfiles/mock_gpu_T4.json
@@ -0,0 +1,272888 @@
+{
+    "metadata": {
+        "timeunit": "milliseconds"
+    },
+    "results": [
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 2,
+                "block_size_y": 32,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 0,
+                "tile_size_x": 1,
+                "tile_size_y": 1,
+                "use_padding": 0
+            },
+            "times": {
+                "compilation": 0.542292999853089,
+                "framework": 80.10120700055268,
+                "search_algorithm": 0,
+                "validation": 0,
+                "runtimes": [
+                    15.997759819030762,
+                    15.987008094787598,
+                    15.982527732849121,
+                    15.983903884887695,
+                    15.978495597839355,
+                    15.981727600097656,
+                    15.86137580871582,
+                    15.867584228515625,
+                    15.861824035644531,
+                    15.861760139465332
+                ]
+            },
+            "invalidity": "correct",
+            "correctness": 1,
+            "measurements": [
+                {
+                    "name": "time",
+                    "value": 15.93639669418335,
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        },
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 2,
+                "block_size_y": 32,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 0,
+                "tile_size_x": 1,
+                "tile_size_y": 1,
+                "use_padding": 1
+            },
+            "times": {
+                "compilation": 262.1809460001714,
+                "framework": 1.1706799996318296,
+                "search_algorithm": 0,
+                "validation": 0,
+                "runtimes": [
+                    2.867136001586914,
+                    2.859488010406494,
+                    2.8589119911193848,
+                    2.85699200630188,
+                    2.8572800159454346,
+                    2.8569600582122803,
+                    2.8564159870147705,
+                    2.8548479080200195,
+                    2.853951930999756,
+                    2.8552639484405518
+                ]
+            },
+            "invalidity": "correct",
+            "correctness": 1,
+            "measurements": [
+                {
+                    "name": "time",
+                    "value": 2.8577247858047485,
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        },
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 2,
+                "block_size_y": 32,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 0,
+                "tile_size_x": 1,
+                "tile_size_y": 2,
+                "use_padding": 0
+            },
+            "times": {
+                "compilation": 412.3011970000334,
+                "framework": 0.4920989999845915,
+                "search_algorithm": 0,
+                "validation": 0,
+                "runtimes": [
+                    15.58620834350586,
+                    15.579968452453613,
+                    15.58124828338623,
+                    15.577247619628906,
+                    15.578911781311035,
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+                    "name": "time",
+                    "value": "CompilationFailedConfig",
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        },
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 128,
+                "block_size_y": 8,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 1,
+                "tile_size_x": 8,
+                "tile_size_y": 1,
+                "use_padding": 0
+            },
+            "times": {
+                "compilation": 2562.3055239993846,
+                "framework": 58.286704999773065,
+                "search_algorithm": 0,
+                "validation": 0
+            },
+            "invalidity": "compile",
+            "correctness": 0,
+            "measurements": [
+                {
+                    "name": "time",
+                    "value": "CompilationFailedConfig",
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        },
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 128,
+                "block_size_y": 8,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 1,
+                "tile_size_x": 8,
+                "tile_size_y": 1,
+                "use_padding": 1
+            },
+            "times": {
+                "compilation": 2556.4903669983323,
+                "framework": 57.732253004360246,
+                "search_algorithm": 0,
+                "validation": 0
+            },
+            "invalidity": "compile",
+            "correctness": 0,
+            "measurements": [
+                {
+                    "name": "time",
+                    "value": "CompilationFailedConfig",
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        },
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 128,
+                "block_size_y": 8,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 1,
+                "tile_size_x": 8,
+                "tile_size_y": 2,
+                "use_padding": 0
+            },
+            "times": {
+                "compilation": 5855.293205997441,
+                "framework": 58.038913000928005,
+                "search_algorithm": 0,
+                "validation": 0
+            },
+            "invalidity": "compile",
+            "correctness": 0,
+            "measurements": [
+                {
+                    "name": "time",
+                    "value": "CompilationFailedConfig",
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        },
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 128,
+                "block_size_y": 8,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 1,
+                "tile_size_x": 8,
+                "tile_size_y": 2,
+                "use_padding": 1
+            },
+            "times": {
+                "compilation": 5872.190046000469,
+                "framework": 59.454294001625385,
+                "search_algorithm": 0,
+                "validation": 0
+            },
+            "invalidity": "compile",
+            "correctness": 0,
+            "measurements": [
+                {
+                    "name": "time",
+                    "value": "CompilationFailedConfig",
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        },
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 128,
+                "block_size_y": 8,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 1,
+                "tile_size_x": 8,
+                "tile_size_y": 3,
+                "use_padding": 0
+            },
+            "times": {
+                "compilation": 1814.913627000351,
+                "framework": 60.253133000514936,
+                "search_algorithm": 0,
+                "validation": 0
+            },
+            "invalidity": "compile",
+            "correctness": 0,
+            "measurements": [
+                {
+                    "name": "time",
+                    "value": "CompilationFailedConfig",
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        },
+        {
+            "timestamp": "",
+            "configuration": {
+                "block_size_x": 128,
+                "block_size_y": 8,
+                "filter_height": 15,
+                "filter_width": 15,
+                "read_only": 1,
+                "tile_size_x": 8,
+                "tile_size_y": 3,
+                "use_padding": 1
+            },
+            "times": {
+                "compilation": 1815.3437030014175,
+                "framework": 58.76690099830739,
+                "search_algorithm": 0,
+                "validation": 0
+            },
+            "invalidity": "compile",
+            "correctness": 0,
+            "measurements": [
+                {
+                    "name": "time",
+                    "value": "CompilationFailedConfig",
+                    "unit": "milliseconds"
+                }
+            ],
+            "objectives": [
+                "time"
+            ]
+        }
+    ],
+    "schema_version": "1.0.0"
+}
\ No newline at end of file
diff --git a/tests/autotuning_methodology/integration/mockfiles/mocktest_kernel_convolution.json b/tests/autotuning_methodology/integration/mockfiles/mocktest_kernel_convolution.json
new file mode 100644
index 0000000..c79ee62
--- /dev/null
+++ b/tests/autotuning_methodology/integration/mockfiles/mocktest_kernel_convolution.json
@@ -0,0 +1,146 @@
+{
+    "General": {
+        "BenchmarkName": "convolution",
+        "OutputFormat": "JSON"
+    },
+    "ConfigurationSpace": {
+        "TuningParameters": [
+            {
+                "Name": "block_size_x",
+                "Type": "int",
+                "Values": "[1, 2, 4, 8, 16, 32, 48, 64, 96, 112, 128]",
+                "Default": 16
+            },
+            {
+                "Name": "block_size_y",
+                "Type": "int",
+                "Values": "[1, 2, 4, 8, 16, 32]",
+                "Default": 16
+            },
+            {
+                "Name": "filter_height",
+                "Type": "int",
+                "Values": "[15]",
+                "Default": 15
+            },
+            {
+                "Name": "filter_width",
+                "Type": "int",
+                "Values": "[15]",
+                "Default": 15
+            },
+            {
+                "Name": "read_only",
+                "Type": "int",
+                "Values": "[0, 1]",
+                "Default": 0
+            },
+            {
+                "Name": "tile_size_x",
+                "Type": "int",
+                "Values": "[1, 2, 3, 4, 5, 6, 7, 8]",
+                "Default": 1
+            },
+            {
+                "Name": "tile_size_y",
+                "Type": "int",
+                "Values": "[1, 2, 3, 4, 5, 6, 7, 8]",
+                "Default": 1
+            },
+            {
+                "Name": "use_padding",
+                "Type": "int",
+                "Values": "[0, 1]",
+                "Default": 1
+            }
+        ],
+        "Conditions": [
+            {
+                "Expression": "block_size_x*block_size_y>=64",
+                "Parameters": [
+                    "block_size_x",
+                    "block_size_y"
+                ]
+            },
+            {
+                "Expression": "block_size_x*block_size_y<=1024",
+                "Parameters": [
+                    "block_size_x",
+                    "block_size_y"
+                ]
+            },
+            {
+                "Expression": "tile_size_x*tile_size_y<30",
+                "Parameters": [
+                    "tile_size_x",
+                    "tile_size_y"
+                ]
+            }
+        ]
+    },
+    "KernelSpecification": {
+        "Language": "CUDA",
+        "CompilerOptions": [
+            "-std=c++11"
+        ],
+        "BenchmarkName": "GEMM",
+        "KernelName": "convolution_kernel",
+        "KernelFile": "convolution.cu",
+        "GlobalSizeType": "CUDA",
+        "LocalSize": {
+            "X": "block_size_x",
+            "Y": "block_size_y",
+            "Z": "1"
+        },
+        "GlobalSize": {
+            "X": "(262144 // block_size_x) // tile_size_x",
+            "Y": "(262144 // block_size_y) // tile_size_y",
+            "Z": "1"
+        },
+        "GridDivX": [
+            "block_size_x",
+            "tile_size_x"
+        ],
+        "GridDivY": [
+            "block_size_y",
+            "tile_size_y"
+        ],
+        "ProblemSize": [
+            4096,
+            4096
+        ],
+        "SharedMemory": 0,
+        "Stream": null,
+        "Arguments": [
+            {
+                "Name": "output_image",
+                "Type": "float",
+                "MemoryType": "Vector",
+                "AccessType": "WriteOnly",
+                "FillType": "Constant",
+                "Size": "ProblemSize[0]*ProblemSize[1]",
+                "FillValue": 0.0,
+                "Output": 1
+            },
+            {
+                "Name": "input_image",
+                "Type": "float",
+                "MemoryType": "Vector",
+                "AccessType": "ReadOnly",
+                "FillType": "Random",
+                "Size": "(ProblemSize[0]+max(filter_width)-1) * (ProblemSize[1]+max(filter_height)-1)",
+                "FillValue": 1.0
+            },
+            {
+                "Name": "d_filter",
+                "Type": "float",
+                "MemoryType": "Vector",
+                "AccessType": "ReadOnly",
+                "MemType": "Constant",
+                "FillType": "Random",
+                "Size": "max(filter_height) * max(filter_width)",
+                "FillValue": 1.0
+            }
+        ]
+    }
+}
\ No newline at end of file
diff --git a/tests/autotuning_methodology/integration/mockfiles/mocktest_kernel_convolution.py b/tests/autotuning_methodology/integration/mockfiles/mocktest_kernel_convolution.py
deleted file mode 100644
index f260be0..0000000
--- a/tests/autotuning_methodology/integration/mockfiles/mocktest_kernel_convolution.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""Test kernel to run integration tests."""
-
-#!/usr/bin/env python
-import sys
-
-import numpy
-
-import kernel_tuner
-from kernel_tuner.file_utils import store_metadata_file, store_output_file
-
-file_path_prefix = "../../../../cached_data_used"
-file_path_results = file_path_prefix + "/last_run/_tune_configuration-results.json"
-file_path_metadata = file_path_prefix + "/last_run/_tune_configuration-metadata.json"
-
-
-def tune(device_name: str, strategy="mls", strategy_options=None, verbose=True, quiet=False, simulation_mode=True):
-    # input dimensions and data
-    image_width = 4096
-    image_height = 4096
-    filter_width = 15
-    filter_height = 15
-    problem_size = (image_width, image_height)
-    size = numpy.prod(problem_size)
-
-    input_size = (problem_size[0] + filter_width - 1) * (problem_size[1] + filter_height - 1)
-    output_image = numpy.zeros(size).astype(numpy.float32)
-    input_image = numpy.random.randn(input_size).astype(numpy.float32)
-    filter_weights = numpy.random.randn(filter_width * filter_height).astype(numpy.float32)
-
-    cmem_args = {"d_filter": filter_weights}
-    args = [output_image, input_image, filter_weights]
-
-    metrics = dict()
-    metrics["GFLOP/s"] = lambda p: (image_width * image_height * filter_width * filter_height * 2 / 1e9) / (
-        p["time"] / 1e3
-    )
-
-    # setup tunable parameters
-    tune_params = dict()
-    tune_params["block_size_x"] = [1, 2, 4, 8, 16, 32, 48, 64, 96, 112, 128]
-    tune_params["block_size_y"] = [1, 2, 4, 8, 16, 32]
-    tune_params["filter_height"] = [filter_height]
-    tune_params["filter_width"] = [filter_width]
-    tune_params["read_only"] = [0, 1]
-    tune_params["tile_size_x"] = [1, 2, 3, 4, 5, 6, 7, 8]
-    tune_params["tile_size_y"] = [1, 2, 3, 4, 5, 6, 7, 8]
-    tune_params["use_padding"] = [0, 1]
-
-    restrict = ["block_size_x*block_size_y>=64", "block_size_x*block_size_y<=1024", "tile_size_x*tile_size_y<30"]
-
-    grid_div_x = ["block_size_x", "tile_size_x"]
-    grid_div_y = ["block_size_y", "tile_size_y"]
-
-    # start tuning
-    results, env = kernel_tuner.tune_kernel(
-        "convolution_kernel",
-        "convolution.cu",
-        problem_size,
-        args,
-        tune_params,
-        grid_div_y=grid_div_y,
-        grid_div_x=grid_div_x,
-        cmem_args=cmem_args,
-        restrictions=restrict,
-        cache=file_path_prefix + "/cachefiles/mocktest_kernel_convolution/" + device_name.lower(),
-        metrics=metrics,
-        iterations=32,
-        device=0,
-        verbose=verbose,
-        quiet=quiet,
-        strategy=strategy,
-        strategy_options=strategy_options,
-        simulation_mode=simulation_mode,
-    )
-
-    store_output_file(file_path_results, results, tune_params)
-    store_metadata_file(file_path_metadata)
-    return results, env
-
-
-if __name__ == "__main__":
-    if len(sys.argv) != 2:
-        print("Usage: ./mocktest_kernel_convolution.py [device name]")
-        exit(1)
-
-    device_name = sys.argv[1]
-
-    tune(device_name)
diff --git a/tests/autotuning_methodology/integration/mockfiles/test.json b/tests/autotuning_methodology/integration/mockfiles/test.json
index 9b11d8b..b477359 100644
--- a/tests/autotuning_methodology/integration/mockfiles/test.json
+++ b/tests/autotuning_methodology/integration/mockfiles/test.json
@@ -1,60 +1,114 @@
 {
-    "version": "0.1.2",
+    "version": "1.2.0",
     "name": "Mock run test",
-    "folder_id": "test_run_experiment",
-    "kernels_path": "../mockfiles",
-    "bruteforced_caches_path": "../../../../cached_data_used/cachefiles",
-    "visualization_caches_path": "../../../../cached_data_used/visualizations",
-    "kernels": [
-        "mocktest_kernel_convolution"
-    ],
-    "GPUs": [
-        "mock_GPU"
-    ],
-    "minimization": true,
-    "resolution": 1e3,
-    "cutoff_percentile": 0.99,
-    "cutoff_percentile_start": 0.7,
-    "objective_time_keys": [
-        "compilation",
-        "benchmark",
-        "framework",
-        "search_algorithm",
-        "validation"
-    ],
-    "objective_performance_keys": [
-        "time"
-    ],
-    "cutoff_type": "fevals",
-    "plot": {
-        "plot_x_value_types": [
-            "fevals",
-            "time",
-            "aggregated"
+    "parent_folder": "./test_run_experiment",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "mocktest_kernel_convolution",
+                "input_file": "mocktest_kernel_convolution.json",
+                "folder": "./tests/autotuning_methodology/integration/mockfiles"
+            }
         ],
-        "plot_y_value_types": [
-            "normalized",
-            "baseline"
+        "gpus": [
+            "mock_GPU"
         ],
-        "confidence_level": 0.95,
-        "compare_baselines": true,
-        "compare_split_times": true
-    },
-    "strategy_defaults": {
-        "repeats": 10,
-        "minimum_number_of_evaluations": 20,
+        "pattern_for_full_search_space_filenames": {
+            "regex": "./tests/autotuning_methodology/integration/mockfiles/mock_gpu_T4.json"
+        },
         "stochastic": true,
-        "record_data": [
-            "time",
-            "GFLOP/s"
-        ]
+        "repeats": 10,
+        "samples": 3,
+        "minimum_fraction_of_budget_valid": 0.5,
+        "minimum_number_of_valid_search_iterations": 20,
+        "ignore_cache": false
     },
-    "strategies": [
+    "search_strategies": [
         {
             "name": "random_sample_10_iter",
-            "strategy": "random_sample",
+            "search_method": "random_sample",
             "display_name": "Random sampling 10 iters",
-            "repeats": 3
+            "autotuner": "KernelTuner"
         }
-    ]
+    ],
+    "statistics_settings": {
+        "cutoff_percentile": 0.99,
+        "cutoff_percentile_start": 0.7,
+        "cutoff_type": "fevals",
+        "objective_time_keys": [
+            "all"
+        ]
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "searchspace",
+                "style": "line",
+                "x_axis_value_types": [
+                    "fevals"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            },
+            {
+                "scope": "searchspace",
+                "style": "line",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            },
+            {
+                "scope": "search_strategy",
+                "style": "heatmap",
+                "x_axis_value_types": [
+                    "applications"
+                ],
+                "y_axis_value_types": [
+                    "gpus"
+                ],
+                "cmin": -2.5
+            },
+            {
+                "scope": "search_strategy",
+                "style": "heatmap",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "searchspaces"
+                ],
+                "bins": 10
+            },
+            {
+                "scope": "aggregate",
+                "style": "head2head",
+                "comparison": {
+                    "unit": "time",
+                    "relative_time": 0.5
+                },
+                "annotate": true
+            },
+            {
+                "scope": "aggregate",
+                "style": "line",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            }
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": true,
+        "compare_split_times": true
+    }
 }
\ No newline at end of file
diff --git a/tests/autotuning_methodology/integration/mockfiles/test_bad_kernel_path.json b/tests/autotuning_methodology/integration/mockfiles/test_bad_kernel_path.json
index dcf3554..d1c9fe6 100644
--- a/tests/autotuning_methodology/integration/mockfiles/test_bad_kernel_path.json
+++ b/tests/autotuning_methodology/integration/mockfiles/test_bad_kernel_path.json
@@ -1,58 +1,60 @@
 {
-    "version": "0.1.2",
+    "version": "1.1.0",
     "name": "Test output file writer",
-    "folder_id": "test_output_file_writer",
-    "kernels_path": "cached_data_used/bogus_kernels_path",
-    "bruteforced_caches_path": "../../../../cached_data_used/cachefiles",
-    "visualization_caches_path": "../../../../cached_data_used/visualizations",
-    "kernels": [
-        "convolution"
-    ],
-    "GPUs": [
-        "RTX_2080_Ti"
-    ],
-    "minimization": true,
-    "resolution": 1e3,
-    "cutoff_percentile": 0.99,
-    "cutoff_percentile_start": 0.7,
-    "objective_time_keys": [
-        "compilation",
-        "benchmark",
-        "framework",
-        "search_algorithm",
-        "validation"
-    ],
-    "objective_performance_keys": [
-        "time"
-    ],
-    "cutoff_type": "fevals",
-    "plot": {
-        "plot_x_value_types": [
-            "aggregated"
+    "parent_folder": "./test_output_file_writer",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "convolution",
+                "input_file": "mocktest_kernel_convolution.json",
+                "folder": "./tests/autotuning_methodology/bogus_kernel_path/integration/mockfiles"
+            }
         ],
-        "plot_y_value_types": [
-            "normalized",
-            "baseline"
+        "gpus": [
+            "RTX_2080_Ti"
         ],
-        "confidence_level": 0.95,
-        "compare_baselines": false,
-        "compare_split_times": false
-    },
-    "strategy_defaults": {
-        "repeats": 100,
-        "minimum_number_of_evaluations": 20,
+        "pattern_for_full_search_space_filenames": {
+            "regex": "./tests/autotuning_methodology/integration/mockfiles/mock_gpu_T4.json"
+        },
         "stochastic": true,
-        "record_data": [
-            "time",
-            "GFLOP/s"
-        ]
+        "repeats": 100,
+        "samples": 5,
+        "minimum_number_of_valid_search_iterations": 20,
+        "ignore_cache": false
     },
-    "strategies": [
+    "search_strategies": [
         {
             "name": "random_sample_100_iter",
-            "strategy": "random_sample",
+            "search_method": "random_sample",
             "display_name": "Random sampling 100 iters",
-            "repeats": 5
+            "autotuner": "KernelTuner"
         }
-    ]
+    ],
+    "statistics_settings": {
+        "cutoff_percentile": 0.99,
+        "cutoff_percentile_start": 0.7,
+        "cutoff_type": "fevals",
+        "objective_time_keys": [
+            "all"
+        ]
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "aggregate",
+                "style": "line",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            }
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
 }
\ No newline at end of file
diff --git a/tests/autotuning_methodology/integration/mockfiles/test_cached.json b/tests/autotuning_methodology/integration/mockfiles/test_cached.json
index 1821090..86412fa 100644
--- a/tests/autotuning_methodology/integration/mockfiles/test_cached.json
+++ b/tests/autotuning_methodology/integration/mockfiles/test_cached.json
@@ -1,58 +1,60 @@
 {
-    "version": "0.1.2",
+    "version": "1.1.0",
     "name": "Test output file writer",
-    "folder_id": "test_output_file_writer",
-    "kernels_path": "cached_data_used/kernels",
-    "bruteforced_caches_path": "../../../../cached_data_used/cachefiles",
-    "visualization_caches_path": "../../../../cached_data_used/visualizations",
-    "kernels": [
-        "convolution"
-    ],
-    "GPUs": [
-        "RTX_2080_Ti"
-    ],
-    "minimization": true,
-    "resolution": 1e3,
-    "cutoff_percentile": 0.99,
-    "cutoff_percentile_start": 0.7,
-    "objective_time_keys": [
-        "compilation",
-        "benchmark",
-        "framework",
-        "search_algorithm",
-        "validation"
-    ],
-    "objective_performance_keys": [
-        "time"
-    ],
-    "cutoff_type": "fevals",
-    "plot": {
-        "plot_x_value_types": [
-            "aggregated"
+    "parent_folder": "./test_output_file_writer",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "convolution",
+                "input_file": "mocktest_kernel_convolution.json",
+                "folder": "./tests/autotuning_methodology/integration/mockfiles"
+            }
         ],
-        "plot_y_value_types": [
-            "normalized",
-            "baseline"
+        "gpus": [
+            "RTX_2080_Ti"
         ],
-        "confidence_level": 0.95,
-        "compare_baselines": false,
-        "compare_split_times": false
-    },
-    "strategy_defaults": {
-        "repeats": 100,
-        "minimum_number_of_evaluations": 20,
+        "pattern_for_full_search_space_filenames": {
+            "regex": "./tests/autotuning_methodology/integration/mockfiles/mock_gpu_T4.json"
+        },
         "stochastic": true,
-        "record_data": [
-            "time",
-            "GFLOP/s"
-        ]
+        "repeats": 100,
+        "samples": 5,
+        "minimum_number_of_valid_search_iterations": 20,
+        "ignore_cache": false
     },
-    "strategies": [
+    "search_strategies": [
         {
             "name": "random_sample_100_iter",
-            "strategy": "random_sample",
+            "search_method": "random_sample",
             "display_name": "Random sampling 100 iters",
-            "repeats": 5
+            "autotuner": "KernelTuner"
         }
-    ]
+    ],
+    "statistics_settings": {
+        "cutoff_percentile": 0.99,
+        "cutoff_percentile_start": 0.7,
+        "cutoff_type": "fevals",
+        "objective_time_keys": [
+            "all"
+        ]
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "aggregate",
+                "style": "line",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            }
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
 }
\ No newline at end of file
diff --git a/tests/autotuning_methodology/integration/mockfiles/test_import_runs.json b/tests/autotuning_methodology/integration/mockfiles/test_import_runs.json
index 02fcc88..4cdaabe 100644
--- a/tests/autotuning_methodology/integration/mockfiles/test_import_runs.json
+++ b/tests/autotuning_methodology/integration/mockfiles/test_import_runs.json
@@ -1,58 +1,60 @@
 {
-    "version": "0.1.2",
+    "version": "1.1.0",
     "name": "Test import runs",
-    "folder_id": "test_output_file_writer",
-    "kernels_path": "../mockfiles",
-    "bruteforced_caches_path": "../../../../cached_data_used/cachefiles",
-    "visualization_caches_path": "../../../../cached_data_used/visualizations",
-    "kernels": [
-        "mocktest_kernel_convolution"
-    ],
-    "GPUs": [
-        "mock_GPU"
-    ],
-    "minimization": true,
-    "resolution": 1e3,
-    "cutoff_percentile": 0.99,
-    "cutoff_percentile_start": 0.7,
-    "objective_time_keys": [
-        "compilation",
-        "benchmark",
-        "framework",
-        "search_algorithm",
-        "validation"
-    ],
-    "objective_performance_keys": [
-        "time"
-    ],
-    "cutoff_type": "fevals",
-    "plot": {
-        "plot_x_value_types": [
-            "aggregated"
+    "parent_folder": "./test_output_file_writer",
+    "experimental_groups_defaults": {
+        "applications": [
+            {
+                "name": "mocktest_kernel_convolution",
+                "input_file": "mocktest_kernel_convolution.json",
+                "folder": "./tests/autotuning_methodology/integration/mockfiles"
+            }
         ],
-        "plot_y_value_types": [
-            "normalized",
-            "baseline"
+        "gpus": [
+            "mock_GPU"
         ],
-        "confidence_level": 0.95,
-        "compare_baselines": false,
-        "compare_split_times": false
-    },
-    "strategy_defaults": {
-        "iterations": 32,
-        "repeats": 2,
-        "minimum_number_of_evaluations": 20,
+        "pattern_for_full_search_space_filenames": {
+            "regex": "./tests/autotuning_methodology/integration/mockfiles/mock_gpu_T4.json"
+        },
         "stochastic": true,
-        "record_data": [
-            "time",
-            "GFLOP/s"
-        ]
+        "repeats": 2,
+        "samples": 32,
+        "minimum_number_of_valid_search_iterations": 20,
+        "ignore_cache": false
     },
-    "strategies": [
+    "search_strategies": [
         {
             "name": "ktt_profile_searcher",
-            "strategy": "profile_searcher",
-            "display_name": "KTT Profile Searcher"
+            "search_method": "profile_searcher",
+            "display_name": "KTT Profile Searcher",
+            "autotuner": "KTT"
         }
-    ]
+    ],
+    "statistics_settings": {
+        "cutoff_percentile": 0.99,
+        "cutoff_percentile_start": 0.7,
+        "cutoff_type": "fevals",
+        "objective_time_keys": [
+            "all"
+        ]
+    },
+    "visualization_settings": {
+        "plots": [
+            {
+                "scope": "aggregate",
+                "style": "line",
+                "x_axis_value_types": [
+                    "time"
+                ],
+                "y_axis_value_types": [
+                    "normalized",
+                    "baseline"
+                ]
+            }
+        ],
+        "resolution": 1000.0,
+        "confidence_level": 0.95,
+        "compare_baselines": false,
+        "compare_split_times": false
+    }
 }
\ No newline at end of file
diff --git a/tests/autotuning_methodology/integration/test_report.py b/tests/autotuning_methodology/integration/test_report.py
index 521d56f..a0240e4 100644
--- a/tests/autotuning_methodology/integration/test_report.py
+++ b/tests/autotuning_methodology/integration/test_report.py
@@ -10,7 +10,7 @@
     normal_cachefiles_path,
 )
 
-from autotuning_methodology.experiments import get_experiment, get_strategies
+from autotuning_methodology.experiments import get_experiment, get_experimental_groups
 from autotuning_methodology.report_experiments import get_strategy_scores
 
 # setup file paths
@@ -44,7 +44,7 @@ def test_visualize_experiment():
     # get the experiment details
     experiment_filepath = str(experiment_filepath_test)
     experiment = get_experiment(experiment_filepath)
-    strategies = get_strategies(experiment)
+    strategies = get_experimental_groups(experiment)  # TODO fix this test that used to use get_strategies
 
     # get the scores
     strategies_scores = get_strategy_scores(experiment_filepath)
diff --git a/tests/autotuning_methodology/integration/test_run_experiment.py b/tests/autotuning_methodology/integration/test_run_experiment.py
index 35b7220..8db7d78 100644
--- a/tests/autotuning_methodology/integration/test_run_experiment.py
+++ b/tests/autotuning_methodology/integration/test_run_experiment.py
@@ -1,40 +1,36 @@
 """Integration test for running and fetching an experiment from cache."""
 
-import json
 from importlib.resources import files
 from pathlib import Path
 from shutil import copyfile
+from warnings import warn
 
 import numpy as np
 import pytest
-from jsonschema import validate
 
 from autotuning_methodology.curves import StochasticOptimizationAlgorithm
-from autotuning_methodology.experiments import (
-    ResultsDescription,
-    execute_experiment,
-    get_args_from_cli,
-    get_experiment_schema_filepath,
-)
+from autotuning_methodology.experiments import ResultsDescription, execute_experiment, get_args_from_cli
+from autotuning_methodology.validators import validate_experimentsfile
 
 # get the path to the package
 package_path = Path(files("autotuning_methodology")).parent.parent
-# package_path = ""
 
 # setup file paths
+strategy = "random_sample_10_iter"
 mockfiles_path_root = package_path / Path("tests/autotuning_methodology/integration/mockfiles/")
 mockfiles_path_source = mockfiles_path_root / "mock_gpu.json"
 mockfiles_path = mockfiles_path_root
 experiment_filepath_test = mockfiles_path / "test.json"
 assert experiment_filepath_test.exists()
 kernel_id = "mocktest_kernel_convolution"
-cached_visualization_path = package_path / Path(f"cached_data_used/visualizations/test_run_experiment/{kernel_id}")
-cached_visualization_file = cached_visualization_path / "mock_GPU_random_sample_10_iter.npz"
-cached_visualization_imported_path = package_path / Path(
-    f"cached_data_used/visualizations/test_output_file_writer/{kernel_id}"
-)
-cached_visualization_imported_file = cached_visualization_imported_path / "mock_GPU_ktt_profile_searcher.npz"
-normal_cachefiles_path = package_path / Path(f"cached_data_used/cachefiles/{kernel_id}")
+experiment_path = package_path / Path("test_run_experiment")
+experiment_path_run = experiment_path / "run"
+experiment_path_setup = experiment_path / "setup"
+
+cached_visualization_path = experiment_path_run
+plot_path = cached_visualization_path / "generated_graphs"
+cached_visualization_file = experiment_path_run / strategy / "mock_GPU_mocktest_kernel_convolution.npz"
+normal_cachefiles_path = package_path / Path(f"benchmark_hub/cachefiles/{kernel_id}")
 normal_cachefile_destination = normal_cachefiles_path / "mock_gpu.json"
 experiment_import_filepath_test = mockfiles_path / "test_import_runs.json"
 assert experiment_import_filepath_test.exists()
@@ -43,12 +39,24 @@
 import_runs_filepaths: list[Path] = list()
 
 
-def _remove_dir(path: Path):
+def _remove_dir(path: Path, ignore_permission_error=False):
     """Utility function for removing a directory and the contained files."""
-    assert path.exists()
+    assert path.exists(), f"Path to directory does not exist: {path.resolve()}"
+    permission_errors = []
     for sub in path.iterdir():
-        sub.unlink()
-    path.rmdir()
+        try:
+            if sub.is_dir():
+                _remove_dir(sub)
+            else:
+                sub.unlink()
+        except PermissionError as e:
+            if ignore_permission_error:
+                warn(e)
+                permission_errors.append(e)
+            else:
+                raise e
+    if not (ignore_permission_error and len(permission_errors) > 0):
+        path.rmdir()
 
 
 def setup_module():
@@ -58,9 +66,6 @@ def setup_module():
     assert normal_cachefiles_path.exists()
     normal_cachefile_destination.write_text(mockfiles_path_source.read_text())
     assert normal_cachefile_destination.exists()
-    # cached_visualization_path.mkdir(parents=True, exist_ok=True)
-    # assert cached_visualization_path.exists()
-    # copy the import run test files to the import run folder
     assert import_runs_source_path.exists()
     import_runs_path.mkdir(parents=True, exist_ok=True)
     assert import_runs_path.exists()
@@ -79,15 +84,10 @@ def teardown_module():
     if normal_cachefile_destination.exists():
         normal_cachefile_destination.unlink()
     _remove_dir(normal_cachefiles_path)
-    if cached_visualization_file.exists():
-        cached_visualization_file.unlink()
-    _remove_dir(cached_visualization_path)
-    if cached_visualization_imported_file.exists():
-        cached_visualization_imported_file.unlink()
-    _remove_dir(cached_visualization_imported_path)
     # delete the import run test files from the import run folder
     for import_run_file in import_runs_filepaths:
         import_run_file.unlink()
+    _remove_dir(experiment_path)
 
 
 def test_CLI_input():
@@ -96,11 +96,11 @@ def test_CLI_input():
     with pytest.raises(SystemExit) as e:
         dummy_args = ["-dummy_arg=option"]
         get_args_from_cli(dummy_args)
-    assert e.type == SystemExit
+    assert e.type == SystemExit  # noqa: E721
     assert e.value.code == 2
 
     # improper input 2
-    with pytest.raises(ValueError, match="Invalid '-experiment' option"):
+    with pytest.raises(ValueError, match="Invalid '--experiment' option"):
         get_args_from_cli([""])
 
     # proper input
@@ -122,7 +122,7 @@ def test_bad_experiment():
 def test_run_experiment_bad_kernel_path():
     """Run an experiment with a bad kernel path."""
     experiment_filepath = str(mockfiles_path / "test_bad_kernel_path.json")
-    with pytest.raises(FileNotFoundError, match="No such path"):
+    with pytest.raises(FileNotFoundError, match="does not exist"):
         execute_experiment(experiment_filepath, profiling=False)
 
 
@@ -133,8 +133,10 @@ def test_run_experiment():
     if cached_visualization_file.exists():
         cached_visualization_file.unlink()
     assert not cached_visualization_file.exists()
-    (experiment, strategies, results_descriptions) = execute_experiment(str(experiment_filepath_test), profiling=False)
-    validate_experiment_results(experiment, strategies, results_descriptions)
+    (experiment, all_experimental_groups, searchspace_statistics, results_descriptions) = execute_experiment(
+        str(experiment_filepath_test), profiling=False
+    )
+    validate_experiment_results(experiment, all_experimental_groups, searchspace_statistics, results_descriptions)
 
 
 @pytest.mark.usefixtures("test_run_experiment")
@@ -144,29 +146,23 @@ def test_cached_experiment():
     assert normal_cachefile_destination.exists()
     assert cached_visualization_path.exists()
     assert cached_visualization_file.exists()
-    (experiment, strategies, results_descriptions) = execute_experiment(str(experiment_filepath_test), profiling=False)
-    validate_experiment_results(experiment, strategies, results_descriptions)
-
-
-def test_import_run_experiment():
-    """Import runs from an experiment."""
-    assert import_runs_path.exists()
-    (experiment, strategies, results_descriptions) = execute_experiment(
-        str(experiment_import_filepath_test), profiling=False
+    (experiment, all_experimental_groups, searchspace_statistics, results_descriptions) = execute_experiment(
+        str(experiment_filepath_test), profiling=False
     )
-    assert cached_visualization_imported_path.exists()
-    assert cached_visualization_imported_file.exists()
-    validate_experiment_results(experiment, strategies, results_descriptions)
+    validate_experiment_results(experiment, all_experimental_groups, searchspace_statistics, results_descriptions)
 
 
 @pytest.mark.usefixtures("test_run_experiment")
 def test_curve_instance():
     """Test a Curve instance."""
     # setup the test
-    (experiment, strategies, results_descriptions) = execute_experiment(str(experiment_filepath_test), profiling=False)
-    kernel_name = experiment["kernels"][0]
-    gpu_name = experiment["GPUs"][0]
-    strategy_name = strategies[0]["name"]
+    (experiment, all_experimental_groups, _, results_descriptions) = execute_experiment(
+        str(experiment_filepath_test), profiling=False
+    )
+    experimental_groups: dict = experiment["experimental_groups_defaults"]
+    kernel_name = experimental_groups["applications"][0]["name"]
+    gpu_name = experimental_groups["gpus"][0]
+    strategy_name = all_experimental_groups[0]["name"]
     results_description = results_descriptions[gpu_name][kernel_name][strategy_name]
     curve = StochasticOptimizationAlgorithm(results_description)
 
@@ -194,21 +190,31 @@ def test_curve_instance():
 
 def validate_experiment_results(
     experiment,
-    strategies,
+    all_experimental_groups,
+    searchspace_statistics,
     results_descriptions,
 ):
     """Validate the types and contents returned from an experiment."""
-    assert isinstance(experiment, dict)
-    assert isinstance(strategies, list)
-    assert isinstance(results_descriptions, dict)
+    assert isinstance(experiment, dict), f"should be dict, is {type(experiment)} ({experiment})"
+    assert isinstance(
+        searchspace_statistics, dict
+    ), f"should be dict, is {type(searchspace_statistics)} ({searchspace_statistics})"
+    assert isinstance(
+        all_experimental_groups, list
+    ), f"should be list, is {type(all_experimental_groups)} ({all_experimental_groups})"
+    assert isinstance(
+        results_descriptions, dict
+    ), f"should be dict, is {type(results_descriptions)} ({results_descriptions})"
 
     # validate the contents
-    schemafilepath = get_experiment_schema_filepath()
-    with open(schemafilepath, "r", encoding="utf-8") as schemafile:
-        schema = json.load(schemafile)
-        validate(instance=experiment, schema=schema)
-    kernel_name = experiment["kernels"][0]
-    gpu_name = experiment["GPUs"][0]
-    assert len(strategies) == 1
-    strategy_name = strategies[0]["name"]
+    validate_experimentsfile(experiment)
+    experimental_groups: dict = experiment["experimental_groups_defaults"]
+    assert isinstance(experimental_groups, dict)
+    kernel_name = experimental_groups["applications"][0]["name"]
+    assert kernel_name == "mocktest_kernel_convolution"
+    gpu_name = experimental_groups["gpus"][0]
+    assert gpu_name == "mock_GPU"
+    assert len(all_experimental_groups) == 1
+    strategy_name = all_experimental_groups[0]["name"]
+    assert strategy_name == "random_sample_10_iter"
     assert isinstance(results_descriptions[gpu_name][kernel_name][strategy_name], ResultsDescription)
diff --git a/tests/autotuning_methodology/integration/test_visualization.py b/tests/autotuning_methodology/integration/test_visualization.py
index 97e34dc..1fc682e 100644
--- a/tests/autotuning_methodology/integration/test_visualization.py
+++ b/tests/autotuning_methodology/integration/test_visualization.py
@@ -2,33 +2,40 @@
 
 from pathlib import Path
 
+import pytest
 from test_run_experiment import (
     _remove_dir,
     cached_visualization_file,
-    cached_visualization_path,
     experiment_filepath_test,
+    experiment_path,
     kernel_id,
     mockfiles_path_source,
     normal_cachefile_destination,
     normal_cachefiles_path,
+    plot_path,
 )
 
 from autotuning_methodology.visualize_experiments import Visualize
 
 # setup file paths
 experiment_title = f"{kernel_id}_on_mock_GPU"
-plot_path = Path("generated_plots/test_run_experiment")
 plot_path_fevals = plot_path / f"{experiment_title}_fevals.png"
 plot_path_time = plot_path / f"{experiment_title}_time.png"
+plot_path_heatmap = plot_path / "random_sample_10_iter_heatmap_applications_gpus_colorbar.png"
+plot_path_heatmap_time = plot_path / "random_sample_10_iter_heatmap_time_searchspaces.png"
+plot_path_aggregated_head2head = plot_path / "head2head_comparison_time.png"
 plot_path_aggregated = plot_path / "aggregated.png"
 plot_path_split_times_fevals = plot_path / f"{experiment_title}_split_times_fevals.png"
 plot_path_split_times_time = plot_path / f"{experiment_title}_split_times_time.png"
 plot_path_split_times_bar = plot_path / f"{experiment_title}_split_times_bar.png"
 plot_path_baselines_comparison = plot_path / f"{experiment_title}_baselines.png"
-plot_filepaths = [
+plot_filepaths: list[Path] = [
     plot_path_fevals,
     plot_path_time,
+    plot_path_heatmap,
+    plot_path_heatmap_time,
     plot_path_aggregated,
+    plot_path_aggregated_head2head,
     plot_path_split_times_fevals,
     plot_path_split_times_time,
     plot_path_split_times_bar,
@@ -43,10 +50,6 @@ def setup_module():
     assert normal_cachefiles_path.exists()
     normal_cachefile_destination.write_text(mockfiles_path_source.read_text())
     assert normal_cachefile_destination.exists()
-    # cached_visualization_path.mkdir(parents=True, exist_ok=True)
-    # assert cached_visualization_path.exists()
-    # plot_path.mkdir(parents=True, exist_ok=True)
-    # assert plot_path.exists()
 
 
 def teardown_module():
@@ -54,15 +57,14 @@ def teardown_module():
     if normal_cachefile_destination.exists():
         normal_cachefile_destination.unlink()
     _remove_dir(normal_cachefiles_path)
-    if cached_visualization_file.exists():
-        cached_visualization_file.unlink()
-    _remove_dir(cached_visualization_path)
     if plot_path.exists():
         for plot_filepath in plot_filepaths:
             plot_filepath.unlink(missing_ok=True)
         plot_path.rmdir()
+    _remove_dir(experiment_path)
 
 
+@pytest.mark.dependency()
 def test_visualize_experiment():
     """Visualize a dummy experiment."""
     assert normal_cachefile_destination.exists()
@@ -77,5 +79,14 @@ def test_visualize_experiment():
         continue_after_comparison=True,
         compare_extra_baselines=True,
     )
+
+
+@pytest.mark.dependency(depends=["test_visualize_experiment"])
+@pytest.mark.parametrize("plot_filepath", plot_filepaths)
+def test_visualized_plot(plot_filepath: Path):
+    """Test whether valid plots have been produced."""
+    assert plot_path.exists()
     for plot_filepath in plot_filepaths:
-        assert plot_filepath.exists(), f"{plot_filepath} does not exist"
+        assert (
+            plot_filepath.exists()
+        ), f"{plot_filepath} does not exist, files in folder: {[f.name for f in plot_filepath.parent.iterdir() if f.is_file()]}"
diff --git a/tests/autotuning_methodology/unit/test_curves.py b/tests/autotuning_methodology/unit/test_curves.py
index bc0b4fe..0fcd7cb 100644
--- a/tests/autotuning_methodology/unit/test_curves.py
+++ b/tests/autotuning_methodology/unit/test_curves.py
@@ -8,9 +8,9 @@
 
 def test_get_indices_in_distribution():
     """Each draw should have the same value as the associated value in the distribution."""
-    draws = np.array([[4, np.NaN, 5], [1, 2, 4.5]])
+    draws = np.array([[4, np.nan, 5], [1, 2, 4.5]])
     dist = np.array([1, 2, 4, 4, 4.5, 5])
-    expected_indices = np.array([[2, np.NaN, 5], [0, 1, 4]])
+    expected_indices = np.array([[2, np.nan, 5], [0, 1, 4]])
 
     indices_found = get_indices_in_distribution(draws=draws, dist=dist)
 
@@ -31,16 +31,16 @@ def test_get_indices_in_distribution():
 
 def test_get_indices_in_distribution_check_dist():
     """Dist order should be checked by default and dist should not contain NaN."""
-    draws = np.array([[4, np.NaN, 5], [1, 2, 4.5]])
+    draws = np.array([[4, np.nan, 5], [1, 2, 4.5]])
     with pytest.raises(AssertionError, match="2 violations in 5 values"):
-        get_indices_in_distribution(draws=draws, dist=np.array([1, 2, np.NaN, 4, 4.5]))
+        get_indices_in_distribution(draws=draws, dist=np.array([1, 2, np.nan, 4, 4.5]))
     with pytest.raises(AssertionError, match="1 violations in 4 values"):
         get_indices_in_distribution(draws=draws, dist=np.array([5, 4, 6, 7]))
 
 
 def test_get_indices_in_distribution_check_draws():
     """Values in draw (with the exception of NaN) that are not in dist should throw an exception."""
-    draws = np.array([[4, np.NaN, 3], [1, 2, 4.5]])
+    draws = np.array([[4, np.nan, 3], [1, 2, 4.5]])
     dist = np.array([1, 2, 4, 4, 4.5, 5])
     with pytest.raises(AssertionError, match="Each value in draws should be in dist"):
         get_indices_in_distribution(draws=draws, dist=dist)
@@ -48,9 +48,9 @@ def test_get_indices_in_distribution_check_draws():
 
 def test_get_indices_in_array():
     """Each value should have the same value as the associated value in the unsorted array."""
-    draws = np.array([[4, np.NaN, 5], [1, 2, 4.5]])
+    draws = np.array([[4, np.nan, 5], [1, 2, 4.5]])
     dist = np.array([4, 2, 1, 4, 5, 4.5])
-    expected_indices = np.array([[0, np.NaN, 4], [2, 1, 5]])
+    expected_indices = np.array([[0, np.nan, 4], [2, 1, 5]])
 
     indices_found = get_indices_in_array(values=draws, array=dist)