Merge branch 'master' into janis

34 files changed, 3833 insertions, 2844 deletions

diff --git a/README.md b/README.md
index 47c38df..5529261 100644
--- a/README.md
+++ b/README.md
@@ -8,8 +8,8 @@ sudo apt install python3-numpy python3-scipy python3-sklearn
 Code Style
 ---
 
-Please do not commit code with significant PEP8 violations. It's best to check
-this with a pre-commit hook:
+Please only commit blackened code. It's best to check this with a pre-commit
+hook:
 
 ```
 #!/bin/sh
@@ -25,5 +25,5 @@ fi
 # Redirect output to stderr.
 exec 1>&2
 
-git diff --cached $against | flake8 --diff
+black --check $(git diff --cached --name-only --diff-filter=ACM $against | grep '\.py$')
 ```
diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index 4531d86..8311f5c 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -61,18 +61,25 @@ Options:
     Specify traces which should be ignored due to bogus data. 1 is the first
     trace, 2 the second, and so on.
 
---discard-outliers=
-    not supported at the moment
-
 --cross-validate=<method>:<count>
     Perform cross validation when computing model quality.
     Only works with --show-quality=table at the moment.
+
     If <method> is "montecarlo": Randomly divide data into 2/3 training and 1/3
     validation, <count> times. Reported model quality is the average of all
     validation runs. Data is partitioned without regard for parameter values,
     so a specific parameter combination may be present in both training and
     validation sets or just one of them.
 
+    If <method> is "kfold": Perform k-fold cross validation with k=<count>.
+    Divide data into 1-1/k training and 1/k validation, <count> times.
+    In the first set, items 0, k, 2k, ... ard used for validation, in the
+    second set, items 1, k+1, 2k+1, ... and so on.
+    validation, <count> times. Reported model quality is the average of all
+    validation runs. Data is partitioned without regard for parameter values,
+    so a specific parameter combination may be present in both training and
+    validation sets or just one of them.
+
 --function-override=<name attribute function>[;<name> <attribute> <function>;...]
     Manually specify the function to fit for <name> <attribute>. A function
     specified this way bypasses parameter detection: It is always assigned,
@@ -94,17 +101,22 @@ Options:
 
 --export-energymodel=<model.json>
     Export energy model. Works out of the box for v1 and v2 logfiles. Requires --hwmodel for v0 logfiles.
+
+--no-cache
+    Do not load cached measurement results
 """
 
 import getopt
 import json
+import logging
 import random
 import re
 import sys
 from dfatool import plotter
-from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate
-from dfatool.dfatool import gplearn_to_function
-from dfatool.dfatool import CrossValidator
+from dfatool.loader import RawData, pta_trace_to_aggregate
+from dfatool.functions import gplearn_to_function
+from dfatool.model import PTAModel
+from dfatool.validation import CrossValidator
 from dfatool.utils import filter_aggregate_by_param
 from dfatool.automata import PTA
 
@@ -133,6 +145,15 @@ def format_quality_measures(result):
 
 
 def model_quality_table(result_lists, info_list):
+    print(
+        "{:20s} {:15s}       {:19s}       {:19s}       {:19s}".format(
+            "key",
+            "attribute",
+            "static".center(19),
+            "LUT".center(19),
+            "parameterized".center(19),
+        )
+    )
     for state_or_tran in result_lists[0]["by_name"].keys():
         for key in result_lists[0]["by_name"][state_or_tran].keys():
             buf = "{:20s} {:15s}".format(state_or_tran, key)
@@ -143,7 +164,7 @@ def model_quality_table(result_lists, info_list):
                     result = results["by_name"][state_or_tran][key]
                     buf += format_quality_measures(result)
                 else:
-                    buf += "{:6}----{:9}".format("", "")
+                    buf += "{:7}----{:8}".format("", "")
             print(buf)
 
 
@@ -279,7 +300,6 @@ def print_html_model_data(model, pm, pq, lm, lq, am, ai, aq):
 if __name__ == "__main__":
 
     ignored_trace_indexes = []
-    discard_outliers = None
     safe_functions_enabled = False
     function_override = {}
     show_models = []
@@ -292,11 +312,12 @@ if __name__ == "__main__":
 
     try:
         optspec = (
-            "info "
+            "info no-cache "
             "plot-unparam= plot-param= plot-traces= show-models= show-quality= "
-            "ignored-trace-indexes= discard-outliers= function-override= "
+            "ignored-trace-indexes= function-override= "
             "export-traces= "
             "filter-param= "
+            "log-level= "
             "cross-validate= "
             "with-safe-functions hwmodel= export-energymodel="
         )
@@ -313,9 +334,6 @@ if __name__ == "__main__":
             if 0 in ignored_trace_indexes:
                 print("[E] arguments to --ignored-trace-indexes start from 1")
 
-        if "discard-outliers" in opt:
-            discard_outliers = float(opt["discard-outliers"])
-
         if "function-override" in opt:
             for function_desc in opt["function-override"].split(";"):
                 state_or_tran, attribute, *function_str = function_desc.split(" ")
@@ -344,12 +362,21 @@ if __name__ == "__main__":
         if "hwmodel" in opt:
             pta = PTA.from_file(opt["hwmodel"])
 
+        if "log-level" in opt:
+            numeric_level = getattr(logging, opt["log-level"].upper(), None)
+            if not isinstance(numeric_level, int):
+                print(f"Invalid log level: {loglevel}", file=sys.stderr)
+                sys.exit(1)
+            logging.basicConfig(level=numeric_level)
+
     except getopt.GetoptError as err:
         print(err, file=sys.stderr)
         sys.exit(2)
 
     raw_data = RawData(
-        args, with_traces=("export-traces" in opt or "plot-traces" in opt)
+        args,
+        with_traces=("export-traces" in opt or "plot-traces" in opt),
+        skip_cache=("no-cache" in opt),
     )
 
     if "info" in opt:
@@ -357,9 +384,12 @@ if __name__ == "__main__":
         if raw_data.version <= 1:
             data_source = "MIMOSA"
         elif raw_data.version == 2:
-            data_source = "MSP430 EnergyTrace"
-        else:
-            data_source = "UNKNOWN"
+            if raw_data.ptalog and "sync" in raw_data.ptalog["opt"]["energytrace"]:
+                data_source = "MSP430 EnergyTrace, sync={}".format(
+                    raw_data.ptalog["opt"]["energytrace"]["sync"]
+                )
+            else:
+                data_source = "MSP430 EnergyTrace"
         print(f"    Data source ID: {raw_data.version} ({data_source})")
 
     preprocessed_data = raw_data.get_preprocessed_data()
@@ -434,7 +464,6 @@ if __name__ == "__main__":
         parameters,
         arg_count,
         traces=preprocessed_data,
-        discard_outliers=discard_outliers,
         function_override=function_override,
         pta=pta,
     )
@@ -495,21 +524,6 @@ if __name__ == "__main__":
                         model.stats.param_dependence_ratio(state, "power", param),
                     )
                 )
-                if model.stats.has_codependent_parameters(state, "power", param):
-                    print(
-                        "{:24s}  co-dependencies: {:s}".format(
-                            "",
-                            ", ".join(
-                                model.stats.codependent_parameters(
-                                    state, "power", param
-                                )
-                            ),
-                        )
-                    )
-                    for param_dict in model.stats.codependent_parameter_value_dicts(
-                        state, "power", param
-                    ):
-                        print("{:24s}  parameter-aware for {}".format("", param_dict))
 
         for trans in model.transitions():
             # Mean power is not a typical transition attribute, but may be present for debugging or analysis purposes
@@ -551,6 +565,8 @@ if __name__ == "__main__":
 
     if xv_method == "montecarlo":
         static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count)
+    elif xv_method == "kfold":
+        static_quality = xv.kfold(lambda m: m.get_static(), xv_count)
     else:
         static_quality = model.assess(static_model)
 
@@ -560,6 +576,8 @@ if __name__ == "__main__":
 
     if xv_method == "montecarlo":
         lut_quality = xv.montecarlo(lambda m: m.get_param_lut(fallback=True), xv_count)
+    elif xv_method == "kfold":
+        lut_quality = xv.kfold(lambda m: m.get_param_lut(fallback=True), xv_count)
     else:
         lut_quality = model.assess(lut_model)
 
@@ -616,21 +634,21 @@ if __name__ == "__main__":
 
     if "param" in show_models or "all" in show_models:
         if not model.stats.can_be_fitted():
-            print(
-                "[!] measurements have insufficient distinct numeric parameters for fitting. A parameter-aware model is not available."
+            logging.warning(
+                "measurements have insufficient distinct numeric parameters for fitting. A parameter-aware model is not available."
             )
         for state in model.states():
             for attribute in model.attributes(state):
                 if param_info(state, attribute):
                     print(
                         "{:10s}: {}".format(
-                            state, param_info(state, attribute)["function"]._model_str
+                            state,
+                            param_info(state, attribute)["function"].model_function,
                         )
                     )
                     print(
                         "{:10s}  {}".format(
-                            "",
-                            param_info(state, attribute)["function"]._regression_args,
+                            "", param_info(state, attribute)["function"].model_args
                         )
                     )
         for trans in model.transitions():
@@ -640,19 +658,19 @@ if __name__ == "__main__":
                         "{:10s}: {:10s}: {}".format(
                             trans,
                             attribute,
-                            param_info(trans, attribute)["function"]._model_str,
+                            param_info(trans, attribute)["function"].model_function,
                         )
                     )
                     print(
                         "{:10s}  {:10s}  {}".format(
-                            "",
-                            "",
-                            param_info(trans, attribute)["function"]._regression_args,
+                            "", "", param_info(trans, attribute)["function"].model_args
                         )
                     )
 
     if xv_method == "montecarlo":
         analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count)
+    elif xv_method == "kfold":
+        analytic_quality = xv.kfold(lambda m: m.get_fitted()[0], xv_count)
     else:
         analytic_quality = model.assess(param_model)
 
@@ -686,7 +704,7 @@ if __name__ == "__main__":
         )
 
     if "overall" in show_quality or "all" in show_quality:
-        print("overall static/param/lut MAE assuming equal state distribution:")
+        print("overall state static/param/lut MAE assuming equal state distribution:")
         print(
             "    {:6.1f}  /  {:6.1f}  /  {:6.1f}  µW".format(
                 model.assess_states(static_model),
@@ -694,15 +712,30 @@ if __name__ == "__main__":
                 model.assess_states(lut_model),
             )
         )
-        print("overall static/param/lut MAE assuming 95% STANDBY1:")
-        distrib = {"STANDBY1": 0.95, "POWERDOWN": 0.03, "TX": 0.01, "RX": 0.01}
-        print(
-            "    {:6.1f}  /  {:6.1f}  /  {:6.1f}  µW".format(
-                model.assess_states(static_model, distribution=distrib),
-                model.assess_states(param_model, distribution=distrib),
-                model.assess_states(lut_model, distribution=distrib),
+        distrib = dict()
+        num_states = len(model.states())
+        p95_state = None
+        for state in model.states():
+            distrib[state] = 1.0 / num_states
+
+        if "STANDBY1" in model.states():
+            p95_state = "STANDBY1"
+        elif "SLEEP" in model.states():
+            p95_state = "SLEEP"
+
+        if p95_state is not None:
+            for state in distrib.keys():
+                distrib[state] = 0.05 / (num_states - 1)
+            distrib[p95_state] = 0.95
+
+            print(f"overall state static/param/lut MAE assuming 95% {p95_state}:")
+            print(
+                "    {:6.1f}  /  {:6.1f}  /  {:6.1f}  µW".format(
+                    model.assess_states(static_model, distribution=distrib),
+                    model.assess_states(param_model, distribution=distrib),
+                    model.assess_states(lut_model, distribution=distrib),
+                )
             )
-        )
 
     if "summary" in show_quality or "all" in show_quality:
         model_summary_table(
diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py
index 4039f45..ddd49ec 100755
--- a/bin/analyze-timing.py
+++ b/bin/analyze-timing.py
@@ -75,12 +75,14 @@ Options:
 
 import getopt
 import json
+import logging
 import re
 import sys
 from dfatool import plotter
-from dfatool.dfatool import AnalyticModel, TimingData, pta_trace_to_aggregate
-from dfatool.dfatool import gplearn_to_function
-from dfatool.dfatool import CrossValidator
+from dfatool.loader import TimingData, pta_trace_to_aggregate
+from dfatool.functions import gplearn_to_function
+from dfatool.model import AnalyticModel
+from dfatool.validation import CrossValidator
 from dfatool.utils import filter_aggregate_by_param
 from dfatool.parameters import prune_dependent_parameters
 
@@ -170,7 +172,6 @@ def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq):
 if __name__ == "__main__":
 
     ignored_trace_indexes = []
-    discard_outliers = None
     safe_functions_enabled = False
     function_override = {}
     show_models = []
@@ -183,8 +184,9 @@ if __name__ == "__main__":
     try:
         optspec = (
             "plot-unparam= plot-param= show-models= show-quality= "
-            "ignored-trace-indexes= discard-outliers= function-override= "
+            "ignored-trace-indexes= function-override= "
             "filter-param= "
+            "log-level= "
             "cross-validate= "
             "corrcoef param-info "
             "with-safe-functions hwmodel= export-energymodel="
@@ -202,9 +204,6 @@ if __name__ == "__main__":
             if 0 in ignored_trace_indexes:
                 print("[E] arguments to --ignored-trace-indexes start from 1")
 
-        if "discard-outliers" in opt:
-            discard_outliers = float(opt["discard-outliers"])
-
         if "function-override" in opt:
             for function_desc in opt["function-override"].split(";"):
                 state_or_tran, attribute, *function_str = function_desc.split(" ")
@@ -237,6 +236,13 @@ if __name__ == "__main__":
         else:
             opt["filter-param"] = list()
 
+        if "log-level" in opt:
+            numeric_level = getattr(logging, opt["log-level"].upper(), None)
+            if not isinstance(numeric_level, int):
+                print(f"Invalid log level: {loglevel}", file=sys.stderr)
+                sys.exit(1)
+            logging.basicConfig(level=numeric_level)
+
     except getopt.GetoptError as err:
         print(err)
         sys.exit(2)
@@ -297,30 +303,6 @@ if __name__ == "__main__":
                         model.stats.param_dependence_ratio(trans, "duration", param),
                     )
                 )
-                if model.stats.has_codependent_parameters(trans, "duration", param):
-                    print(
-                        "{:24s}  co-dependencies: {:s}".format(
-                            "",
-                            ", ".join(
-                                model.stats.codependent_parameters(
-                                    trans, "duration", param
-                                )
-                            ),
-                        )
-                    )
-                    for param_dict in model.stats.codependent_parameter_value_dicts(
-                        trans, "duration", param
-                    ):
-                        print("{:24s}  parameter-aware for {}".format("", param_dict))
-        # import numpy as np
-        # safe_div = np.vectorize(lambda x,y: 0. if x == 0 else 1 - x/y)
-        # ratio_by_value = safe_div(model.stats.stats['write']['duration']['lut_by_param_values']['max_retry_count'], model.stats.stats['write']['duration']['std_by_param_values']['max_retry_count'])
-        # err_mode = np.seterr('warn')
-        # dep_by_value = ratio_by_value > 0.5
-        # np.seterr(**err_mode)
-        # Eigentlich sollte hier ein paar mal True stehen, ist aber nicht so...
-        # und warum ist da eine non-power-of-two Zahl von True-Einträgen in der Matrix? 3 stück ist komisch...
-        # print(dep_by_value)
 
     if xv_method == "montecarlo":
         static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count)
@@ -423,14 +405,12 @@ if __name__ == "__main__":
                         "{:10s}: {:10s}: {}".format(
                             trans,
                             attribute,
-                            param_info(trans, attribute)["function"]._model_str,
+                            param_info(trans, attribute)["function"].model_function,
                         )
                     )
                     print(
                         "{:10s}  {:10s}  {}".format(
-                            "",
-                            "",
-                            param_info(trans, attribute)["function"]._regression_args,
+                            "", "", param_info(trans, attribute)["function"].model_args
                         )
                     )
 
diff --git a/bin/cal-hist b/bin/cal-hist
index ba2ff47..a92ae1e 100755
--- a/bin/cal-hist
+++ b/bin/cal-hist
@@ -7,7 +7,7 @@ import struct
 import sys
 import tarfile
 import matplotlib.pyplot as plt
-from dfatool.dfatool import MIMOSA
+from dfatool.loader import MIMOSA
 from dfatool.utils import running_mean
 
 voltage = float(sys.argv[1])
@@ -18,50 +18,74 @@ mim = MIMOSA(voltage, shunt)
 
 charges, triggers = mim.load_data(mimfile)
 trigidx = mim.trigger_edges(triggers)
-cal_edges = mim.calibration_edges(running_mean(mim.currents_nocal(charges[0:trigidx[0]]), 10))
+cal_edges = mim.calibration_edges(
+    running_mean(mim.currents_nocal(charges[0 : trigidx[0]]), 10)
+)
+
+# charges = charges[charges > 20000]
+# charges = charges[charges < 21000]
 
-#charges = charges[charges > 20000]
-#charges = charges[charges < 21000]
 
 def show_hist(data):
-	bins = np.max(data) - np.min(data)
-	if bins == 0:
-		bins = 1
-	if bins > 1000:
-		bins = bins / 10
-	#bins = 500
-	n, bins, patches = plt.hist(data, bins, normed=0, facecolor='green', alpha=0.75)
-	plt.grid(True)
-	plt.show()
-	print(np.histogram(data, bins=bins))
+    bins = np.max(data) - np.min(data)
+    if bins == 0:
+        bins = 1
+    if bins > 1000:
+        bins = bins / 10
+    # bins = 500
+    n, bins, patches = plt.hist(data, bins, normed=0, facecolor="green", alpha=0.75)
+    plt.grid(True)
+    plt.show()
+    print(np.histogram(data, bins=bins))
+
 
-#show_hist(charges[cal_edges[0]:cal_edges[1]])
-#show_hist(charges[cal_edges[4]:cal_edges[5]])
-#show_hist(charges[cal_edges[2]:cal_edges[3]])
-#show_hist(charges[trigidx[7]:trigidx[8]])
-#show_hist(np.array(charges))
+# show_hist(charges[cal_edges[0]:cal_edges[1]])
+# show_hist(charges[cal_edges[4]:cal_edges[5]])
+# show_hist(charges[cal_edges[2]:cal_edges[3]])
+# show_hist(charges[trigidx[7]:trigidx[8]])
+# show_hist(np.array(charges))
 
-#print(charges[cal_edges[0]:cal_edges[1]])
-#print(charges[cal_edges[4]:cal_edges[5]])
-#print(charges[cal_edges[2]:cal_edges[3]])
+# print(charges[cal_edges[0]:cal_edges[1]])
+# print(charges[cal_edges[4]:cal_edges[5]])
+# print(charges[cal_edges[2]:cal_edges[3]])
 
-plt.hist(mim.charge_to_current_nocal(charges[cal_edges[2]:cal_edges[3]]) * 1e-3, 100, normed=0, facecolor='blue', alpha=0.8)
-plt.xlabel('mA MimosaCMD')
-plt.ylabel('#')
+plt.hist(
+    mim.charge_to_current_nocal(charges[cal_edges[2] : cal_edges[3]]) * 1e-3,
+    100,
+    normed=0,
+    facecolor="blue",
+    alpha=0.8,
+)
+plt.xlabel("mA MimosaCMD")
+plt.ylabel("#")
 plt.grid(True)
 plt.show()
-plt.hist(mim.charge_to_current_nocal(charges[cal_edges[4]:cal_edges[5]]) * 1e-3, 100, normed=0, facecolor='blue', alpha=0.8)
-plt.xlabel('mA MimosaCMD')
-plt.ylabel('#')
+plt.hist(
+    mim.charge_to_current_nocal(charges[cal_edges[4] : cal_edges[5]]) * 1e-3,
+    100,
+    normed=0,
+    facecolor="blue",
+    alpha=0.8,
+)
+plt.xlabel("mA MimosaCMD")
+plt.ylabel("#")
 plt.grid(True)
 plt.show()
-plt.hist(mim.charge_to_current_nocal(charges[cal_edges[0]:cal_edges[1]]) * 1e-3, 100, normed=0, facecolor='blue', alpha=0.8)
-plt.xlabel('mA MimosaCMD')
-plt.ylabel('#')
+plt.hist(
+    mim.charge_to_current_nocal(charges[cal_edges[0] : cal_edges[1]]) * 1e-3,
+    100,
+    normed=0,
+    facecolor="blue",
+    alpha=0.8,
+)
+plt.xlabel("mA MimosaCMD")
+plt.ylabel("#")
 plt.grid(True)
 plt.show()
-plt.hist(charges[cal_edges[0]:cal_edges[1]], 100, normed=0, facecolor='blue', alpha=0.8)
-plt.xlabel('Rohwert MimosaCMD')
-plt.ylabel('#')
+plt.hist(
+    charges[cal_edges[0] : cal_edges[1]], 100, normed=0, facecolor="blue", alpha=0.8
+)
+plt.xlabel("Rohwert MimosaCMD")
+plt.ylabel("#")
 plt.grid(True)
 plt.show()
diff --git a/bin/eval-accounting-overhead.py b/bin/eval-accounting-overhead.py
index 7ea0807..1c03bf8 100755
--- a/bin/eval-accounting-overhead.py
+++ b/bin/eval-accounting-overhead.py
@@ -13,7 +13,7 @@ providing overhead per transition and getEnergy overhead
 
 """
 
-from dfatool.dfatool import AnalyticModel, TimingData, pta_trace_to_aggregate
+from dfatool.loader import AnalyticModel, TimingData, pta_trace_to_aggregate
 import json
 import sys
 
diff --git a/bin/eval-online-model-accuracy.py b/bin/eval-online-model-accuracy.py
index 202ac28..97fd8e2 100755
--- a/bin/eval-online-model-accuracy.py
+++ b/bin/eval-online-model-accuracy.py
@@ -28,7 +28,7 @@ import itertools
 import yaml
 from dfatool.automata import PTA
 from dfatool.codegen import get_simulated_accountingmethod
-from dfatool.dfatool import regression_measures
+from dfatool.model import regression_measures
 import numpy as np
 
 opt = dict()
diff --git a/bin/eval-outlier-removal.py b/bin/eval-outlier-removal.py
index 14f0e60..c03266d 100755
--- a/bin/eval-outlier-removal.py
+++ b/bin/eval-outlier-removal.py
@@ -3,7 +3,8 @@
 import getopt
 import re
 import sys
-from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate
+from dfatool.loader import RawData, pta_trace_to_aggregate
+from dfatool.model import PTAModel
 
 opt = dict()
 
@@ -141,12 +142,12 @@ if __name__ == "__main__":
             if param_i1(state, attribute):
                 print(
                     "{:10s}: {}".format(
-                        state, param_i1(state, attribute)["function"]._model_str
+                        state, param_i1(state, attribute)["function"].model_function
                     )
                 )
                 print(
                     "{:10s}  {}".format(
-                        "", param_i1(state, attribute)["function"]._regression_args
+                        "", param_i1(state, attribute)["function"].model_args
                     )
                 )
     for trans in m1.transitions():
@@ -162,12 +163,12 @@ if __name__ == "__main__":
                     "{:10s}: {:10s}: {}".format(
                         trans,
                         attribute,
-                        param_i1(trans, attribute)["function"]._model_str,
+                        param_i1(trans, attribute)["function"].model_function,
                     )
                 )
                 print(
                     "{:10s}  {:10s}  {}".format(
-                        "", "", param_i1(trans, attribute)["function"]._regression_args
+                        "", "", param_i1(trans, attribute)["function"].model_args
                     )
                 )
     param_m2, param_i2 = m2.get_fitted()
@@ -176,12 +177,12 @@ if __name__ == "__main__":
             if param_i2(state, attribute):
                 print(
                     "{:10s}: {}".format(
-                        state, param_i2(state, attribute)["function"]._model_str
+                        state, param_i2(state, attribute)["function"].model_function
                     )
                 )
                 print(
                     "{:10s}  {}".format(
-                        "", param_i2(state, attribute)["function"]._regression_args
+                        "", param_i2(state, attribute)["function"].model_args
                     )
                 )
     for trans in m2.transitions():
@@ -197,12 +198,12 @@ if __name__ == "__main__":
                     "{:10s}: {:10s}: {}".format(
                         trans,
                         attribute,
-                        param_i2(trans, attribute)["function"]._model_str,
+                        param_i2(trans, attribute)["function"].model_function,
                     )
                 )
                 print(
                     "{:10s}  {:10s}  {}".format(
-                        "", "", param_i2(trans, attribute)["function"]._regression_args
+                        "", "", param_i2(trans, attribute)["function"].model_args
                     )
                 )
 
diff --git a/bin/eval-rel-energy.py b/bin/eval-rel-energy.py
index 8a2be13..aeaf88c 100755
--- a/bin/eval-rel-energy.py
+++ b/bin/eval-rel-energy.py
@@ -3,7 +3,8 @@
 import getopt
 import re
 import sys
-from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate
+from dfatool.loader import RawData, pta_trace_to_aggregate
+from dfatool.model import PTAModel
 
 opt = dict()
 
@@ -22,7 +23,6 @@ def get_file_groups(args):
 if __name__ == "__main__":
 
     ignored_trace_indexes = []
-    discard_outliers = None
     safe_functions_enabled = False
     function_override = {}
     show_models = []
@@ -31,7 +31,7 @@ if __name__ == "__main__":
     try:
         optspec = (
             "plot-unparam= plot-param= show-models= show-quality= "
-            "ignored-trace-indexes= discard-outliers= function-override= "
+            "ignored-trace-indexes= function-override= "
             "with-safe-functions"
         )
         raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" "))
@@ -47,9 +47,6 @@ if __name__ == "__main__":
                 if 0 in ignored_trace_indexes:
                     print("[E] arguments to --ignored-trace-indexes start from 1")
 
-            if "discard-outliers" in opt:
-                discard_outliers = float(opt["discard-outliers"])
-
             if "function-override" in opt:
                 for function_desc in opt["function-override"].split(";"):
                     state_or_tran, attribute, *function_str = function_desc.split(" ")
@@ -88,7 +85,6 @@ if __name__ == "__main__":
             arg_count,
             traces=preprocessed_data,
             ignore_trace_indexes=ignored_trace_indexes,
-            discard_outliers=discard_outliers,
             function_override=function_override,
             verbose=False,
         )
diff --git a/bin/generate-dfa-benchmark.py b/bin/generate-dfa-benchmark.py
index 478b221..2c53d9f 100755
--- a/bin/generate-dfa-benchmark.py
+++ b/bin/generate-dfa-benchmark.py
@@ -61,6 +61,10 @@ Options:
 
 --energytrace=[k=v,k=v,...]
     Perform energy measurements using MSP430 EnergyTrace hardware. Includes --timing.
+    Additional configuration settings:
+    sync = bar (Barcode mode (default): synchronize measurements via barcodes embedded in the energy trace)
+    sync = la (Logic Analyzer mode (WIP): An external logic analyzer captures transition timing)
+    sync = timing (Timing mode (WIP): The on-board cycle counter captures transition timing)
 
 --trace-filter=<transition,transition,transition,...>[ <transition,transition,transition,...> ...]
     Only consider traces whose beginning matches one of the provided transition sequences.
@@ -219,17 +223,11 @@ def benchmark_from_runs(
                     )
             elif opt["sleep"]:
                 if "energytrace" in opt:
-                    outbuf.write(
-                        "arch.sleep_ms({:d}); // {}\n".format(
-                            opt["sleep"], transition.destination.name
-                        )
-                    )
+                    outbuf.write(f"// -> {transition.destination.name}\n")
+                    outbuf.write(runner.sleep_ms(opt["sleep"], opt["arch"]))
                 else:
-                    outbuf.write(
-                        "arch.delay_ms({:d}); // {}\n".format(
-                            opt["sleep"], transition.destination.name
-                        )
-                    )
+                    outbuf.write(f"// -> {transition.destination.name}\n")
+                    outbuf.write("arch.delay_ms({:d});\n".format(opt["sleep"]))
 
         outbuf.write(harness.stop_run(num_traces))
         if dummy:
@@ -337,6 +335,7 @@ def run_benchmark(
         files = list()
         i = 0
         while i < opt["repeat"]:
+            print(f"""[RUN] flashing benchmark {i+1}/{opt["repeat"]}""")
             runner.flash(arch, app, run_args)
             if "mimosa" in opt:
                 monitor = runner.get_monitor(
@@ -353,7 +352,6 @@ def run_benchmark(
                 while not harness.done:
                     # possible race condition: if the benchmark completes at this
                     # exact point, it sets harness.done and unsets harness.synced.
-                    #                                                   vvv
                     if (
                         slept > 30
                         and slept < 40
@@ -372,11 +370,11 @@ def run_benchmark(
                     time.sleep(5)
                     slept += 5
                     print(
-                        "[RUN] {:d}/{:d} ({:.0f}%), current benchmark at {:.0f}%".format(
+                        "[RUN] {:d}/{:d} ({:.0f}%) at trace {:d}".format(
                             run_offset,
                             runs_total,
                             run_offset * 100 / runs_total,
-                            slept * 100 / run_timeout,
+                            harness.trace_id,
                         )
                     )
             except KeyboardInterrupt:
@@ -593,6 +591,9 @@ if __name__ == "__main__":
     if run_flags is None:
         run_flags = opt["run"].split()
 
+    if "msp430fr" in opt["arch"]:
+        run_flags.append("cpu_freq=8000000")
+
     runs = list(
         pta.dfs(
             opt["depth"],
@@ -630,9 +631,13 @@ if __name__ == "__main__":
             post_transition_delay_us=20,
         )
     elif "energytrace" in opt:
+        # Use barcode sync by default
+        gpio_mode = "bar"
+        if "sync" in opt["energytrace"] and opt["energytrace"]["sync"] != "bar":
+            gpio_mode = "around"
         harness = OnboardTimerHarness(
             gpio_pin=timer_pin,
-            gpio_mode="bar",
+            gpio_mode=gpio_mode,
             pta=pta,
             counter_limits=runner.get_counter_limits_us(opt["arch"]),
             log_return_values=need_return_values,
diff --git a/bin/gptest.py b/bin/gptest.py
index 82b4575..b5012e5 100755
--- a/bin/gptest.py
+++ b/bin/gptest.py
@@ -2,12 +2,11 @@
 
 import sys
 import numpy as np
-from dfatool.dfatool import (
-    PTAModel,
+from dfatool.loader import (
     RawData,
-    regression_measures,
     pta_trace_to_aggregate,
 )
+from dfatool.model import PTAModel, regression_measures
 from gplearn.genetic import SymbolicRegressor
 from multiprocessing import Pool
 
diff --git a/bin/gradient b/bin/gradient
index 8280794..ca60949 100755
--- a/bin/gradient
+++ b/bin/gradient
@@ -7,7 +7,7 @@ import struct
 import sys
 import tarfile
 import matplotlib.pyplot as plt
-from dfatool.dfatool import MIMOSA
+from dfatool.loader import MIMOSA
 from dfatool.utils import running_mean
 
 voltage = float(sys.argv[1])
@@ -17,17 +17,17 @@ mimfile = sys.argv[3]
 mim = MIMOSA(voltage, shunt)
 
 charges, triggers = mim.load_file(mimfile)
-#charges = charges[2000000:3000000]
+# charges = charges[2000000:3000000]
 
 currents = running_mean(mim.charge_to_current_nocal(charges), 10) * 1e-6
 xr = np.arange(len(currents)) * 1e-5
 threshold = 1e-5
 grad = np.gradient(currents, 2)
 tp = np.abs(grad) > threshold
-plt.plot( xr, currents, "r-")
-plt.plot( xr, grad, "y-")
-plt.plot( xr[tp], grad[tp], "bo")
-plt.xlabel('Zeit [s]')
-plt.ylabel('Strom [A]')
+plt.plot(xr, currents, "r-")
+plt.plot(xr, grad, "y-")
+plt.plot(xr[tp], grad[tp], "bo")
+plt.xlabel("Zeit [s]")
+plt.ylabel("Strom [A]")
 plt.grid(True)
 plt.show()
diff --git a/lib/keysightdlog.py b/bin/keysightdlog.py
index 89264b9..89264b9 100755
--- a/lib/keysightdlog.py
+++ b/bin/keysightdlog.py
diff --git a/bin/mim-vs-keysight.py b/bin/mim-vs-keysight.py
index c214f2f..c9a7249 100755
--- a/bin/mim-vs-keysight.py
+++ b/bin/mim-vs-keysight.py
@@ -3,7 +3,7 @@
 import numpy as np
 import sys
 import matplotlib.pyplot as plt
-from dfatool.dfatool import MIMOSA, KeysightCSV
+from dfatool.loader import MIMOSA, KeysightCSV
 from dfatool.utils import running_mean
 
 voltage = float(sys.argv[1])
diff --git a/bin/mimosa-etv b/bin/mimosa-etv
index e23b46c..9b6e897 100755
--- a/bin/mimosa-etv
+++ b/bin/mimosa-etv
@@ -8,13 +8,16 @@ import numpy as np
 import os
 import re
 import sys
-from dfatool.dfatool import aggregate_measures, MIMOSA
+from dfatool.loader import MIMOSA
+from dfatool.model import aggregate_measures
 from dfatool.utils import running_mean
 
 opt = dict()
 
+
 def show_help():
-    print('''mimosa-etv - MIMOSA Analyzer and Visualizer
+    print(
+        """mimosa-etv - MIMOSA Analyzer and Visualizer
 
 USAGE
 
@@ -41,7 +44,9 @@ OPTIONS
     Show power/time plot
   --stat
     Show mean voltage, current, and power as well as total energy consumption.
-    ''')
+    """
+    )
+
 
 def peak_search(data, lower, upper, direction_function):
     while upper - lower > 1e-6:
@@ -58,6 +63,7 @@ def peak_search(data, lower, upper, direction_function):
             upper = bs_test
     return None
 
+
 def peak_search2(data, lower, upper, check_function):
     for power in np.arange(lower, upper, 1e-6):
         peakcount = itertools.groupby(data, lambda x: x >= power)
@@ -67,38 +73,39 @@ def peak_search2(data, lower, upper, check_function):
             return power
     return None
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     try:
-        optspec = ('help skip= threshold= threshold-peakcount= plot stat')
-        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' '))
+        optspec = "help skip= threshold= threshold-peakcount= plot stat"
+        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" "))
 
         for option, parameter in raw_opts:
-            optname = re.sub(r'^--', '', option)
+            optname = re.sub(r"^--", "", option)
             opt[optname] = parameter
 
-        if 'help' in opt:
+        if "help" in opt:
             show_help()
             sys.exit(0)
 
-        if 'skip' in opt:
-            opt['skip'] = int(opt['skip'])
+        if "skip" in opt:
+            opt["skip"] = int(opt["skip"])
         else:
-            opt['skip'] = 0
+            opt["skip"] = 0
 
-        if 'threshold' in opt and opt['threshold'] != 'mean':
-            opt['threshold'] = float(opt['threshold'])
+        if "threshold" in opt and opt["threshold"] != "mean":
+            opt["threshold"] = float(opt["threshold"])
 
-        if 'threshold-peakcount' in opt:
-            opt['threshold-peakcount'] = int(opt['threshold-peakcount'])
+        if "threshold-peakcount" in opt:
+            opt["threshold-peakcount"] = int(opt["threshold-peakcount"])
 
     except getopt.GetoptError as err:
         print(err)
         sys.exit(2)
     except IndexError:
-        print('Usage: mimosa-etv <duration>')
+        print("Usage: mimosa-etv <duration>")
         sys.exit(2)
     except ValueError:
-        print('Error: duration or skip is not a number')
+        print("Error: duration or skip is not a number")
         sys.exit(2)
 
     voltage, shunt, inputfile = args
@@ -110,7 +117,7 @@ if __name__ == '__main__':
     currents = mim.charge_to_current_nocal(charges) * 1e-6
     powers = currents * voltage
 
-    if 'threshold-peakcount' in opt:
+    if "threshold-peakcount" in opt:
         bs_mean = np.mean(powers)
 
         # Finding the correct threshold is tricky. If #peaks < peakcont, our
@@ -126,42 +133,59 @@ if __name__ == '__main__':
         # #peaks != peakcount and threshold >= mean, we go down.
         # If that doesn't work, we fall back to a linear search in 1 µW steps
         def direction_function(peakcount, power):
-            if peakcount == opt['threshold-peakcount']:
+            if peakcount == opt["threshold-peakcount"]:
                 return 0
             if power < bs_mean:
                 return 1
             return -1
+
         threshold = peak_search(power, np.min(power), np.max(power), direction_function)
         if threshold == None:
-            threshold = peak_search2(power, np.min(power), np.max(power), direction_function)
+            threshold = peak_search2(
+                power, np.min(power), np.max(power), direction_function
+            )
 
         if threshold != None:
-            print('Threshold set to {:.0f} µW         : {:.9f}'.format(threshold * 1e6, threshold))
-            opt['threshold'] = threshold
+            print(
+                "Threshold set to {:.0f} µW         : {:.9f}".format(
+                    threshold * 1e6, threshold
+                )
+            )
+            opt["threshold"] = threshold
         else:
-            print('Found no working threshold')
+            print("Found no working threshold")
 
-    if 'threshold' in opt:
-        if opt['threshold'] == 'mean':
-            opt['threshold'] = np.mean(powers)
-            print('Threshold set to {:.0f} µW         : {:.9f}'.format(opt['threshold'] * 1e6, opt['threshold']))
+    if "threshold" in opt:
+        if opt["threshold"] == "mean":
+            opt["threshold"] = np.mean(powers)
+            print(
+                "Threshold set to {:.0f} µW         : {:.9f}".format(
+                    opt["threshold"] * 1e6, opt["threshold"]
+                )
+            )
 
         baseline_mean = 0
-        if np.any(powers < opt['threshold']):
-            baseline_mean = np.mean(powers[powers < opt['threshold']])
-            print('Baseline mean: {:.0f} µW           : {:.9f}'.format(
-                baseline_mean * 1e6, baseline_mean))
-        if np.any(powers >= opt['threshold']):
-            print('Peak mean: {:.0f} µW               : {:.9f}'.format(
-                np.mean(powers[powers >= opt['threshold']]) * 1e6,
-                np.mean(powers[powers >= opt['threshold']])))
+        if np.any(powers < opt["threshold"]):
+            baseline_mean = np.mean(powers[powers < opt["threshold"]])
+            print(
+                "Baseline mean: {:.0f} µW           : {:.9f}".format(
+                    baseline_mean * 1e6, baseline_mean
+                )
+            )
+        if np.any(powers >= opt["threshold"]):
+            print(
+                "Peak mean: {:.0f} µW               : {:.9f}".format(
+                    np.mean(powers[powers >= opt["threshold"]]) * 1e6,
+                    np.mean(powers[powers >= opt["threshold"]]),
+                )
+            )
 
         peaks = []
         peak_start = -1
         for i, dp in enumerate(powers):
-            if dp >= opt['threshold'] and peak_start == -1:
+            if dp >= opt["threshold"] and peak_start == -1:
                 peak_start = i
-            elif dp < opt['threshold'] and peak_start != -1:
+            elif dp < opt["threshold"] and peak_start != -1:
                 peaks.append((peak_start, i))
                 peak_start = -1
 
@@ -170,32 +194,55 @@ if __name__ == '__main__':
         for peak in peaks:
             duration = (peak[1] - peak[0]) * 1e-5
             total_energy += np.mean(powers[peak[0] : peak[1]]) * duration
-            delta_energy += (np.mean(powers[peak[0] : peak[1]]) - baseline_mean) * duration
+            delta_energy += (
+                np.mean(powers[peak[0] : peak[1]]) - baseline_mean
+            ) * duration
             delta_powers = powers[peak[0] : peak[1]] - baseline_mean
-            print('{:.2f}ms peak ({:f} -> {:f})'.format(duration * 1000,
-                peak[0], peak[1]))
-            print('    {:f} µJ / mean {:f} µW'.format(
-                np.mean(powers[peak[0] : peak[1]]) * duration * 1e6,
-                np.mean(powers[peak[0] : peak[1]]) * 1e6 ))
+            print(
+                "{:.2f}ms peak ({:f} -> {:f})".format(duration * 1000, peak[0], peak[1])
+            )
+            print(
+                "    {:f} µJ / mean {:f} µW".format(
+                    np.mean(powers[peak[0] : peak[1]]) * duration * 1e6,
+                    np.mean(powers[peak[0] : peak[1]]) * 1e6,
+                )
+            )
             measures = aggregate_measures(np.mean(delta_powers), delta_powers)
-            print('    {:f} µW delta mean = {:0.1f}% / {:f} µW error'.format(np.mean(delta_powers) * 1e6, measures['smape'], measures['rmsd'] * 1e6 ))
-        print('Peak energy mean: {:.0f} µJ         : {:.9f}'.format(
-            total_energy * 1e6 / len(peaks), total_energy / len(peaks)))
-        print('Average per-peak energy (delta over baseline): {:.0f} µJ         : {:.9f}'.format(
-            delta_energy * 1e6 / len(peaks), delta_energy / len(peaks)))
-
-
-    if 'stat' in opt:
+            print(
+                "    {:f} µW delta mean = {:0.1f}% / {:f} µW error".format(
+                    np.mean(delta_powers) * 1e6,
+                    measures["smape"],
+                    measures["rmsd"] * 1e6,
+                )
+            )
+        print(
+            "Peak energy mean: {:.0f} µJ         : {:.9f}".format(
+                total_energy * 1e6 / len(peaks), total_energy / len(peaks)
+            )
+        )
+        print(
+            "Average per-peak energy (delta over baseline): {:.0f} µJ         : {:.9f}".format(
+                delta_energy * 1e6 / len(peaks), delta_energy / len(peaks)
+            )
+        )
+
+    if "stat" in opt:
         mean_current = np.mean(currents)
         mean_power = np.mean(powers)
-        print('Mean current: {:.0f} µA       : {:.9f}'.format(mean_current * 1e6, mean_current))
-        print('Mean power: {:.0f} µW       : {:.9f}'.format(mean_power * 1e6, mean_power))
-
-    if 'plot' in opt:
+        print(
+            "Mean current: {:.0f} µA       : {:.9f}".format(
+                mean_current * 1e6, mean_current
+            )
+        )
+        print(
+            "Mean power: {:.0f} µW       : {:.9f}".format(mean_power * 1e6, mean_power)
+        )
+
+    if "plot" in opt:
         timestamps = np.arange(len(powers)) * 1e-5
-        pwrhandle, = plt.plot(timestamps, powers, 'b-', label='U*I', markersize=1)
+        (pwrhandle,) = plt.plot(timestamps, powers, "b-", label="U*I", markersize=1)
         plt.legend(handles=[pwrhandle])
-        plt.xlabel('Time [s]')
-        plt.ylabel('Power [W]')
+        plt.xlabel("Time [s]")
+        plt.ylabel("Power [W]")
         plt.grid(True)
         plt.show()
diff --git a/bin/mimplot b/bin/mimplot
index 2a888ee..a55a875 100755
--- a/bin/mimplot
+++ b/bin/mimplot
@@ -9,54 +9,52 @@ import struct
 import sys
 import tarfile
 import matplotlib.pyplot as plt
-from dfatool.dfatool import MIMOSA
+from dfatool.loader import MIMOSA
 from dfatool.utils import running_mean
 
 opt = dict()
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
-	try:
-		optspec = (
-			'export= '
-		)
+    try:
+        optspec = "export= "
 
-		raw_opts, args = getopt.getopt(sys.argv[1:], '', optspec.split())
+        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split())
 
-		for option, parameter in raw_opts:
-			optname = re.sub(r'^--', '', option)
-			opt[optname] = parameter
+        for option, parameter in raw_opts:
+            optname = re.sub(r"^--", "", option)
+            opt[optname] = parameter
 
-		if 'export' in opt:
-			opt['export'] = list(map(int, opt['export'].split(':')))
+        if "export" in opt:
+            opt["export"] = list(map(int, opt["export"].split(":")))
 
-	except getopt.GetoptError as err:
-		print(err)
-		sys.exit(2)
+    except getopt.GetoptError as err:
+        print(err)
+        sys.exit(2)
 
-	voltage = float(args[0])
-	shunt = float(args[1])
-	mimfile = args[2]
+    voltage = float(args[0])
+    shunt = float(args[1])
+    mimfile = args[2]
 
-	mim = MIMOSA(voltage, shunt)
+    mim = MIMOSA(voltage, shunt)
 
-	charges, triggers = mim.load_file(mimfile)
-	charges = charges[:3000000]
+    charges, triggers = mim.load_file(mimfile)
+    charges = charges[:3000000]
 
-	currents = running_mean(mim.charge_to_current_nocal(charges), 10) * 1e-6
-	powers = currents * voltage
-	xr = np.arange(len(currents)) * 1e-5
+    currents = running_mean(mim.charge_to_current_nocal(charges), 10) * 1e-6
+    powers = currents * voltage
+    xr = np.arange(len(currents)) * 1e-5
 
-	if 'export' in opt:
-		xr = xr[opt['export'][0] : opt['export'][1]]
-		currents = currents[opt['export'][0] : opt['export'][1]]
-		powers = powers[opt['export'][0] : opt['export'][1]]
+    if "export" in opt:
+        xr = xr[opt["export"][0] : opt["export"][1]]
+        currents = currents[opt["export"][0] : opt["export"][1]]
+        powers = powers[opt["export"][0] : opt["export"][1]]
 
-		for pair in zip(xr, powers):
-			print('{} {}'.format(*pair))
+        for pair in zip(xr, powers):
+            print("{} {}".format(*pair))
 
-	plt.plot( xr, powers, "r-")
-	plt.xlabel('Time [s]')
-	plt.ylabel('Power [W]')
-	plt.grid(True)
-	plt.show()
+    plt.plot(xr, powers, "r-")
+    plt.xlabel("Time [s]")
+    plt.ylabel("Power [W]")
+    plt.grid(True)
+    plt.show()
diff --git a/bin/test_corrcoef.py b/bin/test_corrcoef.py
index 0b1ca54..ccb3366 100755
--- a/bin/test_corrcoef.py
+++ b/bin/test_corrcoef.py
@@ -4,8 +4,9 @@ import getopt
 import re
 import sys
 from dfatool import plotter
-from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate
-from dfatool.dfatool import gplearn_to_function
+from dfatool.loader import RawData, pta_trace_to_aggregate
+from dfatool.functions import gplearn_to_function
+from dfatool.model import PTAModel
 
 opt = dict()
 
@@ -110,7 +111,6 @@ def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq):
 if __name__ == "__main__":
 
     ignored_trace_indexes = None
-    discard_outliers = None
     safe_functions_enabled = False
     function_override = {}
     show_models = []
@@ -119,7 +119,7 @@ if __name__ == "__main__":
     try:
         optspec = (
             "plot-unparam= plot-param= show-models= show-quality= "
-            "ignored-trace-indexes= discard-outliers= function-override= "
+            "ignored-trace-indexes= function-override= "
             "with-safe-functions"
         )
         raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" "))
@@ -135,9 +135,6 @@ if __name__ == "__main__":
                 if 0 in ignored_trace_indexes:
                     print("[E] arguments to --ignored-trace-indexes start from 1")
 
-            if "discard-outliers" in opt:
-                discard_outliers = float(opt["discard-outliers"])
-
             if "function-override" in opt:
                 for function_desc in opt["function-override"].split(";"):
                     state_or_tran, attribute, *function_str = function_desc.split(" ")
@@ -169,7 +166,6 @@ if __name__ == "__main__":
         arg_count,
         traces=preprocessed_data,
         ignore_trace_indexes=ignored_trace_indexes,
-        discard_outliers=discard_outliers,
         function_override=function_override,
         use_corrcoef=False,
     )
@@ -179,7 +175,6 @@ if __name__ == "__main__":
         arg_count,
         traces=preprocessed_data,
         ignore_trace_indexes=ignored_trace_indexes,
-        discard_outliers=discard_outliers,
         function_override=function_override,
         use_corrcoef=True,
     )
diff --git a/doc/generate-dfa-benchmark.md b/doc/generate-dfa-benchmark.md
new file mode 100644
index 0000000..48a991d
--- /dev/null
+++ b/doc/generate-dfa-benchmark.md
@@ -0,0 +1,85 @@
+Diese Anleitung beschreibt die Benchmarkgenerierung mit AEMR/dfatool. Sie geht
+von der folgenden Verzeichnisstruktur aus.
+
+* `data`: Benchmark-Messdaten
+* `data/cache`: Cache für teilweise ausgewertete Benchmarks
+* `dfatool`: dfatool-Repository
+* `multipass`: multipass-Repository
+
+*multipass* enthält Gerätetreiber mit zugehörigen PTA-Definitionen
+(Transitionen, Zustände und Parameter der Hardware) sowie Hilfsfunktionen für
+Benchmarks. Es verzichtet bewusst auf Tasking und System-Ticks, um Benchmarks
+nicht durch Timer Interrupts zu beeinflussen. In *dfatool* liegen die
+Generierungs- und Auswertungsskripte.
+
+## Benchmarkgenerierung
+
+Die Generierung und Vermessung von Benchmarks erfolgt immer mit
+`generate-dfa-benchmark.py`. Dieses muss vom multipass-Verzeichnis aus
+aufgerufen werden. Ein Benchmark läuft wie folgt ab.
+
+* Generierung von Läufen durch den PTA des zu vermessenden Geräts. Die Läufe
+  können u.a. mit `--depth`, `--shrink` und `--trace-filter` beeinflusst
+  werden.
+* Erzeugung einer C++-Anwendung (`src/app/aemr/main.cc`), welche die Hardware
+  durch die Läufe schickt und die ausgeführten Transitionen protokolliert. Sie
+  greift auf `include/object/ptalog.h` zurück.
+  * Die grundlegende Anwendungsstruktur (Header, Aufruf der Treiberfunktionen,
+    Wartezeit zwischen Funktionsaufrufen) wird von generate-dfa-benchmark
+    vorgegeben (`benchmark_from_runs`)
+  * Ein Test Harness aus `lib/harness.py` (OnboardTimerHarness für
+    energytrace/timing benchmarks, TransitionHarness für MIMOSA) erweitert
+    die generierte Anwendung um Synchronisierungsaufrufe und/oder zusätzliche
+    Messungen, z.B. mit einem Onboard-Timer. Dazu werden für jeden Lauf durch
+    den PTA `start_run` und `start_trace` aufgerufen ("ein neuer Lauf beginnt"),
+    dann für jeden Funktionsaufruf und jeden Zustand `append_transition`,
+    `append_state` und `pass_transition` und schließlich `stop_run`.
+    Das Harness speichert die zum generierten Code gehörenden Läufe und die
+    während eines Zustands / einer Transition gültigen PTA-Parameter intern als
+    `{"isa": "state", "name": ..., "parameter": dict(...)}` bzw.
+    `{"isa": "transition", "name": ..., "parameter: dict(...), "args": list(...)}`
+* Kompilieren der Anwendung in `run_benchmark` per `runner.build` (siehe
+  `runner.py`). Falls der Benchmark zu groß ist, wird er in mehrere
+  Anwendungen aufgeteilt, die nacheinander ausgeführt und vermessen werden.
+  Zusätzlich wird jede Messung mehrfach durchegführt, um Einflüsse durch
+  Messfehler zu minimieren.
+* Ausführung des Benchmarks. Der Code wird mittels `runner.flash` programmiert,
+  die Ansteuerung zusätzlicher Software (z.B. MIMOSA, EnergyTrace) erfolgt über
+  einen Monitor aus `lib/runner.py`. Sobald der Monitor mittels `get_monitor`
+  erzeugt wird, beginnt die Messung. Während der Messung werden Ausgaben
+  von der seriellen Konsole über den `parser_cb` des aktiven Test Harness
+  verarbeitet; auf diese Weise wird auch das Ende des Benchmarks erkannt.
+  `monitor.close()` beendet die Messung.
+* Nach Abschluss aller (Teil)benchmarks und Wiederholungen werden
+  die Benchmarkpläne (`harness.traces`), UART-Ausgaben (`monitor.get_lines()`)
+  und ggf. zusätzliche Logfiles (`monitor.get_files()`) in eine tar-Datei
+  archiviert.
+
+## Beispiel
+
+Wenn sich msp430-etv und energytrace in $PATH befinden, generiert der folgende
+Aufruf mit einem MSP430FR5994 Launchpad ohne Peripherie einen erfolgreichen
+Benchmark-Ablauf:
+
+```
+cd multipass
+../dfatool/bin/generate-dfa-benchmark.py --data=../data \
+--timer-pin=GPIO::p1_0 --sleep=200 --repeat=3 --arch=msp430fr5994lp \
+--energytrace=sync=bar model/driver/sharp96.dfa src/app/aemr/main.cc
+```
+
+Nach einigen Minuten wird unter `data` ein auf sharp96.tar endendes Archiv mit
+Benchmark-Setup (Treiber-PTA, energytrace-Config, Traces durch den
+Automaten) und Messdaten (energytrace-Logfiles) abgelegt. Dieses kann wie folgt
+analysiert werden:
+
+```
+cd dfatool
+bin/analyze-archive.py --info --show-model=all --show-quality=table ../data/...-sharp96.tar
+```
+
+Sofern sich die LED-Leistungsaufnahme des verwendeten Launchpads im üblichen
+Rahmen bewegt, funktioniert die Auswertung.  Hier sollten für POWEROFF und
+POWERON sehr ähnliche Werte herauskommen (da ja keine Peripherie angeschlossen
+war) und die writeLine-Transition deutlich mehr Zeit als die restlichen
+benötigen.
diff --git a/lib/automata.py b/lib/automata.py
index b3318e0..ebe1871 100755
--- a/lib/automata.py
+++ b/lib/automata.py
@@ -3,11 +3,14 @@
 from .functions import AnalyticFunction, NormalizationFunction
 from .utils import is_numeric
 import itertools
+import logging
 import numpy as np
 import json
 import queue
 import yaml
 
+logger = logging.getLogger(__name__)
+
 
 def _dict_to_list(input_dict: dict) -> list:
     return [input_dict[x] for x in sorted(input_dict.keys())]
@@ -100,7 +103,7 @@ class PTAAttribute:
     def __repr__(self):
         if self.function is not None:
             return "PTAATtribute<{:.0f}, {}>".format(
-                self.value, self.function._model_str
+                self.value, self.function.model_function
             )
         return "PTAATtribute<{:.0f}, None>".format(self.value)
 
@@ -134,8 +137,8 @@ class PTAAttribute:
         }
         if self.function:
             ret["function"] = {
-                "raw": self.function._model_str,
-                "regression_args": list(self.function._regression_args),
+                "raw": self.function.model_function,
+                "regression_args": list(self.function.model_args),
             }
             ret["function_error"] = self.function_error
         return ret
@@ -1305,8 +1308,8 @@ class PTA:
                                 "power"
                             ]
                 except KeyError:
-                    print(
-                        "[W] skipping model update of state {} due to missing data".format(
+                    logger.warning(
+                        "skipping model update of state {} due to missing data".format(
                             state.name
                         )
                     )
@@ -1353,8 +1356,8 @@ class PTA:
                         "timeout"
                     ]
             except KeyError:
-                print(
-                    "[W] skipping model update of transition {} due to missing data".format(
+                logger.warning(
+                    "skipping model update of transition {} due to missing data".format(
                         transition.name
                     )
                 )
diff --git a/lib/data_parameters.py b/lib/data_parameters.py
index 1150b71..84eacfd 100644
--- a/lib/data_parameters.py
+++ b/lib/data_parameters.py
@@ -7,9 +7,12 @@ length of lists, ane more.
 
 from .protocol_benchmarks import codegen_for_lib
 from . import cycles_to_energy, size_to_radio_energy, utils
+import logging
 import numpy as np
 import ubjson
 
+logger = logging.getLogger(__name__)
+
 
 def _string_value_length(json):
     if type(json) == str:
@@ -224,7 +227,7 @@ class Protolog:
                         except KeyError:
                             pass
                         except TypeError as e:
-                            print(
+                            logger.error(
                                 "TypeError in {} {} {} {}: {} -> {}".format(
                                     arch_lib,
                                     benchmark,
@@ -395,7 +398,7 @@ class Protolog:
                         except KeyError:
                             pass
                         except ValueError:
-                            print(
+                            logger.warning(
                                 "cycles_enc is NaN for {} -> {} -> {}".format(
                                     arch, lib, key
                                 )
@@ -410,7 +413,7 @@ class Protolog:
                         except KeyError:
                             pass
                         except ValueError:
-                            print(
+                            logger.warning(
                                 "cycles_ser is NaN for {} -> {} -> {}".format(
                                     arch, lib, key
                                 )
@@ -425,7 +428,7 @@ class Protolog:
                         except KeyError:
                             pass
                         except ValueError:
-                            print(
+                            logger.warning(
                                 "cycles_encser is NaN for {} -> {} -> {}".format(
                                     arch, lib, key
                                 )
@@ -440,7 +443,7 @@ class Protolog:
                         except KeyError:
                             pass
                         except ValueError:
-                            print(
+                            logger.warning(
                                 "cycles_des is NaN for {} -> {} -> {}".format(
                                     arch, lib, key
                                 )
@@ -455,7 +458,7 @@ class Protolog:
                         except KeyError:
                             pass
                         except ValueError:
-                            print(
+                            logger.warning(
                                 "cycles_dec is NaN for {} -> {} -> {}".format(
                                     arch, lib, key
                                 )
@@ -470,7 +473,7 @@ class Protolog:
                         except KeyError:
                             pass
                         except ValueError:
-                            print(
+                            logger.warning(
                                 "cycles_desdec is NaN for {} -> {} -> {}".format(
                                     arch, lib, key
                                 )
diff --git a/lib/functions.py b/lib/functions.py
index 6d8daa4..94b1aaf 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -5,12 +5,14 @@ This module provides classes and helper functions useful for least-squares
 regression and general handling of model functions.
 """
 from itertools import chain, combinations
+import logging
 import numpy as np
 import re
 from scipy import optimize
-from .utils import is_numeric, vprint
+from .utils import is_numeric
 
 arg_support_enabled = True
+logger = logging.getLogger(__name__)
 
 
 def powerset(iterable):
@@ -23,6 +25,47 @@ def powerset(iterable):
     return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))
 
 
+def gplearn_to_function(function_str: str):
+    """
+    Convert gplearn-style function string to Python function.
+
+    Takes a function string like "mul(add(X0, X1), X2)" and returns
+    a Python function implementing the specified behaviour,
+    e.g. "lambda x, y, z: (x + y) * z".
+
+    Supported functions:
+    add  --  x + y
+    sub  --  x - y
+    mul  --  x * y
+    div  --  x / y if |y| > 0.001, otherwise 1
+    sqrt --  sqrt(|x|)
+    log  --  log(|x|) if |x| > 0.001, otherwise 0
+    inv  --  1 / x if |x| > 0.001, otherwise 0
+    """
+    eval_globals = {
+        "add": lambda x, y: x + y,
+        "sub": lambda x, y: x - y,
+        "mul": lambda x, y: x * y,
+        "div": lambda x, y: np.divide(x, y) if np.abs(y) > 0.001 else 1.0,
+        "sqrt": lambda x: np.sqrt(np.abs(x)),
+        "log": lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 0.0,
+        "inv": lambda x: 1.0 / x if np.abs(x) > 0.001 else 0.0,
+    }
+
+    last_arg_index = 0
+    for i in range(0, 100):
+        if function_str.find("X{:d}".format(i)) >= 0:
+            last_arg_index = i
+
+    arg_list = []
+    for i in range(0, last_arg_index + 1):
+        arg_list.append("X{:d}".format(i))
+
+    eval_str = "lambda {}, *whatever: {}".format(",".join(arg_list), function_str)
+    logger.debug(eval_str)
+    return eval(eval_str, eval_globals)
+
+
 class ParamFunction:
     """
     A one-dimensional model function, ready for least squares optimization and similar.
@@ -118,9 +161,7 @@ class AnalyticFunction:
     packet length.
     """
 
-    def __init__(
-        self, function_str, parameters, num_args, verbose=True, regression_args=None
-    ):
+    def __init__(self, function_str, parameters, num_args, regression_args=None):
         """
         Create a new AnalyticFunction object from a function string.
 
@@ -135,18 +176,16 @@ class AnalyticFunction:
         :param num_args: number of local function arguments, if any. Set to 0 if
             the model attribute does not belong to a function or if function
             arguments are not included in the model.
-        :param verbose: complain about odd events
         :param regression_args: Initial regression variable values,
             both for function usage and least squares optimization.
             If unset, defaults to [1, 1, 1, ...]
         """
         self._parameter_names = parameters
         self._num_args = num_args
-        self._model_str = function_str
+        self.model_function = function_str
         rawfunction = function_str
         self._dependson = [False] * (len(parameters) + num_args)
         self.fit_success = False
-        self.verbose = verbose
 
         if type(function_str) == str:
             num_vars_re = re.compile(r"regression_arg\(([0-9]+)\)")
@@ -176,12 +215,12 @@ class AnalyticFunction:
             self._function = function_str
 
         if regression_args:
-            self._regression_args = regression_args.copy()
+            self.model_args = regression_args.copy()
             self._fit_success = True
         elif type(function_str) == str:
-            self._regression_args = list(np.ones((num_vars)))
+            self.model_args = list(np.ones((num_vars)))
         else:
-            self._regression_args = []
+            self.model_args = []
 
     def get_fit_data(self, by_param, state_or_tran, model_attribute):
         """
@@ -231,9 +270,8 @@ class AnalyticFunction:
                         else:
                             X[i].extend([np.nan] * len(val[model_attribute]))
             elif key[0] == state_or_tran and len(key[1]) != dimension:
-                vprint(
-                    self.verbose,
-                    "[W] Invalid parameter key length while gathering fit data for {}/{}. is {}, want {}.".format(
+                logger.warning(
+                    "Invalid parameter key length while gathering fit data for {}/{}. is {}, want {}.".format(
                         state_or_tran, model_attribute, len(key[1]), dimension
                     ),
                 )
@@ -263,30 +301,27 @@ class AnalyticFunction:
             error_function = lambda P, X, y: self._function(P, X) - y
             try:
                 res = optimize.least_squares(
-                    error_function, self._regression_args, args=(X, Y), xtol=2e-15
+                    error_function, self.model_args, args=(X, Y), xtol=2e-15
                 )
             except ValueError as err:
-                vprint(
-                    self.verbose,
-                    "[W] Fit failed for {}/{}: {} (function: {})".format(
-                        state_or_tran, model_attribute, err, self._model_str
+                logger.warning(
+                    "Fit failed for {}/{}: {} (function: {})".format(
+                        state_or_tran, model_attribute, err, self.model_function
                     ),
                 )
                 return
             if res.status > 0:
-                self._regression_args = res.x
+                self.model_args = res.x
                 self.fit_success = True
             else:
-                vprint(
-                    self.verbose,
-                    "[W] Fit failed for {}/{}: {} (function: {})".format(
-                        state_or_tran, model_attribute, res.message, self._model_str
+                logger.warning(
+                    "Fit failed for {}/{}: {} (function: {})".format(
+                        state_or_tran, model_attribute, res.message, self.model_function
                     ),
                 )
         else:
-            vprint(
-                self.verbose,
-                "[W] Insufficient amount of valid parameter keys, cannot fit {}/{}".format(
+            logger.warning(
+                "Insufficient amount of valid parameter keys, cannot fit {}/{}".format(
                     state_or_tran, model_attribute
                 ),
             )
@@ -314,9 +349,9 @@ class AnalyticFunction:
             corresponds to lexically first parameter, etc.
         :param arg_list: argument values (list of float), if arguments are used.
         """
-        if len(self._regression_args) == 0:
+        if len(self.model_args) == 0:
             return self._function(param_list, arg_list)
-        return self._function(self._regression_args, param_list)
+        return self._function(self.model_args, param_list)
 
 
 class analytic:
diff --git a/lib/harness.py b/lib/harness.py
index 3b279c0..ae9c28c 100644
--- a/lib/harness.py
+++ b/lib/harness.py
@@ -21,7 +21,7 @@ class TransitionHarness:
         * `name`: state or transition name
         * `parameter`: currently valid parameter values. If normalization is used, they are already normalized. Each parameter value is either a primitive
           int/float/str value (-> constant for each iteration) or a list of
-          primitive values (-> set by the return value of the current run, not necessarily constan)
+          primitive values (-> set by the return value of the current run, not necessarily constant)
         * `args`: function arguments, if isa == 'transition'
     """
 
@@ -229,6 +229,7 @@ class TransitionHarness:
             log_data_target["parameter"][parameter_name] = list()
         log_data_target["parameter"][parameter_name].append(parameter_value)
 
+    # Here Be Dragons
     def parser_cb(self, line):
         # print('[HARNESS] got line {}'.format(line))
         if re.match(r"\[PTA\] benchmark stop", line):
@@ -440,6 +441,7 @@ class OnboardTimerHarness(TransitionHarness):
             log_data_target["parameter"][parameter_name] = list()
         log_data_target["parameter"][parameter_name].append(parameter_value)
 
+    # Here Be Dragons
     def parser_cb(self, line):
         # print('[HARNESS] got line {}'.format(line))
         res = re.match(r"\[PTA\] nop=(\S+)/(\S+)", line)
diff --git a/lib/lex.py b/lib/lex.py
index 7bb3760..f698e8c 100644
--- a/lib/lex.py
+++ b/lib/lex.py
@@ -1,4 +1,7 @@
 from .sly import Lexer, Parser
+import logging
+
+logger = logging.getLogger(__name__)
 
 
 class TimedWordLexer(Lexer):
@@ -38,7 +41,7 @@ class TimedSequenceLexer(Lexer):
     FUNCTIONSEP = r";"
 
     def error(self, t):
-        print("Illegal character '%s'" % t.value[0])
+        logger.error("Illegal character '%s'" % t.value[0])
         if t.value[0] == "{" and t.value.find("}"):
             self.index += 1 + t.value.find("}")
         else:
@@ -153,11 +156,11 @@ class TimedSequenceParser(Parser):
 
     def error(self, p):
         if p:
-            print("Syntax error at token", p.type)
+            logger.error("Syntax error at token", p.type)
             # Just discard the token and tell the parser it's okay.
             self.errok()
         else:
-            print("Syntax error at EOF")
+            logger.error("Syntax error at EOF")
 
 
 class TimedWord:
diff --git a/lib/dfatool.py b/lib/loader.py
index 63639d3..4e07c92 100644
--- a/lib/dfatool.py
+++ b/lib/loader.py
@@ -3,26 +3,17 @@
 import csv
 import io
 import json
+import logging
 import numpy as np
 import os
 import re
-from scipy import optimize
-from sklearn.metrics import r2_score
 import struct
 import tarfile
 import hashlib
 from multiprocessing import Pool
-from .functions import analytic
-from .functions import AnalyticFunction
-from .parameters import ParamStats
-from .utils import (
-    vprint,
-    is_numeric,
-    soft_cast_int,
-    param_slice_eq,
-    remove_index_from_tuple,
-)
-from .utils import by_name_to_by_param, match_parameter_values, running_mean
+from .utils import running_mean, soft_cast_int
+
+logger = logging.getLogger(__name__)
 
 try:
     from .pubcode import Code128
@@ -36,135 +27,6 @@ except ImportError:
 arg_support_enabled = True
 
 
-def gplearn_to_function(function_str: str):
-    """
-    Convert gplearn-style function string to Python function.
-
-    Takes a function string like "mul(add(X0, X1), X2)" and returns
-    a Python function implementing the specified behaviour,
-    e.g. "lambda x, y, z: (x + y) * z".
-
-    Supported functions:
-    add  --  x + y
-    sub  --  x - y
-    mul  --  x * y
-    div  --  x / y if |y| > 0.001, otherwise 1
-    sqrt --  sqrt(|x|)
-    log  --  log(|x|) if |x| > 0.001, otherwise 0
-    inv  --  1 / x if |x| > 0.001, otherwise 0
-    """
-    eval_globals = {
-        "add": lambda x, y: x + y,
-        "sub": lambda x, y: x - y,
-        "mul": lambda x, y: x * y,
-        "div": lambda x, y: np.divide(x, y) if np.abs(y) > 0.001 else 1.0,
-        "sqrt": lambda x: np.sqrt(np.abs(x)),
-        "log": lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 0.0,
-        "inv": lambda x: 1.0 / x if np.abs(x) > 0.001 else 0.0,
-    }
-
-    last_arg_index = 0
-    for i in range(0, 100):
-        if function_str.find("X{:d}".format(i)) >= 0:
-            last_arg_index = i
-
-    arg_list = []
-    for i in range(0, last_arg_index + 1):
-        arg_list.append("X{:d}".format(i))
-
-    eval_str = "lambda {}, *whatever: {}".format(",".join(arg_list), function_str)
-    print(eval_str)
-    return eval(eval_str, eval_globals)
-
-
-def append_if_set(aggregate: dict, data: dict, key: str):
-    """Append data[key] to aggregate if key in data."""
-    if key in data:
-        aggregate.append(data[key])
-
-
-def mean_or_none(arr):
-    """
-    Compute mean of NumPy array `arr`, return -1 if empty.
-
-    :param arr: 1-Dimensional NumPy array
-    """
-    if len(arr):
-        return np.mean(arr)
-    return -1
-
-
-def aggregate_measures(aggregate: float, actual: list) -> dict:
-    """
-    Calculate error measures for model value on data list.
-
-    arguments:
-    aggregate -- model value (float or int)
-    actual -- real-world / reference values (list of float or int)
-
-    return value:
-    See regression_measures
-    """
-    aggregate_array = np.array([aggregate] * len(actual))
-    return regression_measures(aggregate_array, np.array(actual))
-
-
-def regression_measures(predicted: np.ndarray, actual: np.ndarray):
-    """
-    Calculate error measures by comparing model values to reference values.
-
-    arguments:
-    predicted -- model values (np.ndarray)
-    actual -- real-world / reference values (np.ndarray)
-
-    Returns a dict containing the following measures:
-    mae -- Mean Absolute Error
-    mape -- Mean Absolute Percentage Error,
-            if all items in actual are non-zero (NaN otherwise)
-    smape -- Symmetric Mean Absolute Percentage Error,
-             if no 0,0-pairs are present in actual and predicted (NaN otherwise)
-    msd -- Mean Square Deviation
-    rmsd -- Root Mean Square Deviation
-    ssr -- Sum of Squared Residuals
-    rsq -- R^2 measure, see sklearn.metrics.r2_score
-    count -- Number of values
-    """
-    if type(predicted) != np.ndarray:
-        raise ValueError("first arg must be ndarray, is {}".format(type(predicted)))
-    if type(actual) != np.ndarray:
-        raise ValueError("second arg must be ndarray, is {}".format(type(actual)))
-    deviations = predicted - actual
-    # mean = np.mean(actual)
-    if len(deviations) == 0:
-        return {}
-    measures = {
-        "mae": np.mean(np.abs(deviations), dtype=np.float64),
-        "msd": np.mean(deviations ** 2, dtype=np.float64),
-        "rmsd": np.sqrt(np.mean(deviations ** 2), dtype=np.float64),
-        "ssr": np.sum(deviations ** 2, dtype=np.float64),
-        "rsq": r2_score(actual, predicted),
-        "count": len(actual),
-    }
-
-    # rsq_quotient = np.sum((actual - mean)**2, dtype=np.float64) * np.sum((predicted - mean)**2, dtype=np.float64)
-
-    if np.all(actual != 0):
-        measures["mape"] = np.mean(np.abs(deviations / actual)) * 100  # bad measure
-    else:
-        measures["mape"] = np.nan
-    if np.all(np.abs(predicted) + np.abs(actual) != 0):
-        measures["smape"] = (
-            np.mean(np.abs(deviations) / ((np.abs(predicted) + np.abs(actual)) / 2))
-            * 100
-        )
-    else:
-        measures["smape"] = np.nan
-    # if np.all(rsq_quotient != 0):
-    #    measures['rsq'] = (np.sum((actual - mean) * (predicted - mean), dtype=np.float64)**2) / rsq_quotient
-
-    return measures
-
-
 class KeysightCSV:
     """Simple loader for Keysight CSV data, as exported by the windows software."""
 
@@ -194,162 +56,6 @@ class KeysightCSV:
         return timestamps, currents
 
 
-def _xv_partitions_kfold(length, num_slices):
-    pairs = []
-    indexes = np.arange(length)
-    for i in range(0, num_slices):
-        training = np.delete(indexes, slice(i, None, num_slices))
-        validation = indexes[i::num_slices]
-        pairs.append((training, validation))
-    return pairs
-
-
-def _xv_partition_montecarlo(length):
-    shuffled = np.random.permutation(np.arange(length))
-    border = int(length * float(2) / 3)
-    training = shuffled[:border]
-    validation = shuffled[border:]
-    return (training, validation)
-
-
-class CrossValidator:
-    """
-    Cross-Validation helper for model generation.
-
-    Given a set of measurements and a model class, it will partition the
-    data into training and validation sets, train the model on the training
-    set, and assess its quality on the validation set. This is repeated
-    several times depending on cross-validation algorithm and configuration.
-    Reports the mean model error over all cross-validation runs.
-    """
-
-    def __init__(self, model_class, by_name, parameters, arg_count):
-        """
-        Create a new CrossValidator object.
-
-        Does not perform cross-validation yet.
-
-        arguments:
-        model_class -- model class/type used for model synthesis,
-            e.g. PTAModel or AnalyticModel. model_class must have a
-            constructor accepting (by_name, parameters, arg_count, verbose = False)
-            and provide an assess method.
-        by_name -- measurements aggregated by state/transition/function/... name.
-            Layout: by_name[name][attribute] = list of data. Additionally,
-            by_name[name]['attributes'] must be set to the list of attributes,
-            e.g. ['power'] or ['duration', 'energy'].
-        """
-        self.model_class = model_class
-        self.by_name = by_name
-        self.names = sorted(by_name.keys())
-        self.parameters = sorted(parameters)
-        self.arg_count = arg_count
-
-    def montecarlo(self, model_getter, count=200):
-        """
-        Perform Monte Carlo cross-validation and return average model quality.
-
-        The by_name data is randomly divided into 2/3 training and 1/3
-        validation. After creating a model for the training set, the
-        model type returned by model_getter is evaluated on the validation set.
-        This is repeated count times (defaulting to 200); the average of all
-        measures is returned to the user.
-
-        arguments:
-        model_getter -- function with signature (model_object) -> model,
-            e.g. lambda m: m.get_fitted()[0] to evaluate the parameter-aware
-            model with automatic parameter detection.
-        count -- number of validation runs to perform, defaults to 200
-
-        return value:
-        dict of model quality measures.
-        {
-            'by_name' : {
-                for each name: {
-                    for each attribute: {
-                        'mae' : mean of all mean absolute errors
-                        'mae_list' : list of the individual MAE values encountered during cross-validation
-                        'smape' : mean of all symmetric mean absolute percentage errors
-                        'smape_list' : list of the individual SMAPE values encountered during cross-validation
-                    }
-                }
-            }
-        }
-        """
-        ret = {"by_name": dict()}
-
-        for name in self.names:
-            ret["by_name"][name] = dict()
-            for attribute in self.by_name[name]["attributes"]:
-                ret["by_name"][name][attribute] = {
-                    "mae_list": list(),
-                    "smape_list": list(),
-                }
-
-        for _ in range(count):
-            res = self._single_montecarlo(model_getter)
-            for name in self.names:
-                for attribute in self.by_name[name]["attributes"]:
-                    ret["by_name"][name][attribute]["mae_list"].append(
-                        res["by_name"][name][attribute]["mae"]
-                    )
-                    ret["by_name"][name][attribute]["smape_list"].append(
-                        res["by_name"][name][attribute]["smape"]
-                    )
-
-        for name in self.names:
-            for attribute in self.by_name[name]["attributes"]:
-                ret["by_name"][name][attribute]["mae"] = np.mean(
-                    ret["by_name"][name][attribute]["mae_list"]
-                )
-                ret["by_name"][name][attribute]["smape"] = np.mean(
-                    ret["by_name"][name][attribute]["smape_list"]
-                )
-
-        return ret
-
-    def _single_montecarlo(self, model_getter):
-        training = dict()
-        validation = dict()
-        for name in self.names:
-            training[name] = {"attributes": self.by_name[name]["attributes"]}
-            validation[name] = {"attributes": self.by_name[name]["attributes"]}
-
-            if "isa" in self.by_name[name]:
-                training[name]["isa"] = self.by_name[name]["isa"]
-                validation[name]["isa"] = self.by_name[name]["isa"]
-
-            data_count = len(self.by_name[name]["param"])
-            training_subset, validation_subset = _xv_partition_montecarlo(data_count)
-
-            for attribute in self.by_name[name]["attributes"]:
-                self.by_name[name][attribute] = np.array(self.by_name[name][attribute])
-                training[name][attribute] = self.by_name[name][attribute][
-                    training_subset
-                ]
-                validation[name][attribute] = self.by_name[name][attribute][
-                    validation_subset
-                ]
-
-            # We can't use slice syntax for 'param', which may contain strings and other odd values
-            training[name]["param"] = list()
-            validation[name]["param"] = list()
-            for idx in training_subset:
-                training[name]["param"].append(self.by_name[name]["param"][idx])
-            for idx in validation_subset:
-                validation[name]["param"].append(self.by_name[name]["param"][idx])
-
-        training_data = self.model_class(
-            training, self.parameters, self.arg_count, verbose=False
-        )
-        training_model = model_getter(training_data)
-        validation_data = self.model_class(
-            validation, self.parameters, self.arg_count, verbose=False
-        )
-
-        return validation_data.assess(training_model)
-
-
 def _preprocess_mimosa(measurement):
     setup = measurement["setup"]
     mim = MIMOSA(
@@ -457,9 +163,7 @@ class TimingData:
                 transitions = list(
                     filter(lambda x: x["isa"] == "transition", trace["trace"])
                 )
-                self.traces.append(
-                    {"id": trace["id"], "trace": transitions,}
-                )
+                self.traces.append({"id": trace["id"], "trace": transitions})
         for i, trace in enumerate(self.traces):
             trace["orig_id"] = trace["id"]
             trace["id"] = i
@@ -490,14 +194,13 @@ class TimingData:
                 self.traces_by_fileno.extend(log_data["traces"])
         self._concatenate_analyzed_traces()
 
-    def get_preprocessed_data(self, verbose=True):
+    def get_preprocessed_data(self):
         """
         Return a list of DFA traces annotated with timing and parameter data.
 
         Suitable for the PTAModel constructor.
         See PTAModel(...) docstring for format details.
         """
-        self.verbose = verbose
         if self.preprocessed:
             return self.traces
         if self.version == 0:
@@ -539,7 +242,7 @@ class RawData:
     file system, making subsequent loads near-instant.
     """
 
-    def __init__(self, filenames, with_traces=False):
+    def __init__(self, filenames, with_traces=False, skip_cache=False):
         """
         Create a new RawData object.
 
@@ -602,6 +305,7 @@ class RawData:
         self._parameter_names = None
         self.ignore_clipping = False
         self.pta = None
+        self.ptalog = None
 
         with tarfile.open(filenames[0]) as tf:
             for member in tf.getmembers():
@@ -612,9 +316,12 @@ class RawData:
                 elif ".etlog" in member.name:
                     self.version = 2
                     break
+            if self.version >= 1:
+                self.ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json")))
+                self.pta = self.ptalog["pta"]
 
         self.set_cache_file()
-        if not with_traces:
+        if not with_traces and not skip_cache:
             self.load_cache()
 
     def set_cache_file(self):
@@ -631,6 +338,8 @@ class RawData:
                 self.preprocessing_stats = cache_data["preprocessing_stats"]
                 if "pta" in cache_data:
                     self.pta = cache_data["pta"]
+                if "ptalog" in cache_data:
+                    self.ptalog = cache_data["ptalog"]
                 self.setup_by_fileno = cache_data["setup_by_fileno"]
                 self.preprocessed = True
 
@@ -647,6 +356,7 @@ class RawData:
                 "traces": self.traces,
                 "preprocessing_stats": self.preprocessing_stats,
                 "pta": self.pta,
+                "ptalog": self.ptalog,
                 "setup_by_fileno": self.setup_by_fileno,
             }
             json.dump(cache_data, f)
@@ -1050,7 +760,7 @@ class RawData:
             trace["id"] = i
         return trace_output
 
-    def get_preprocessed_data(self, verbose=True):
+    def get_preprocessed_data(self):
         """
         Return a list of DFA traces annotated with energy, timing, and parameter data.
         The list is cached on disk, unless the constructor was called with `with_traces` set.
@@ -1103,7 +813,6 @@ class RawData:
          * `args`: List of arguments the corresponding function call was called with. args entries are strings which are not necessarily numeric
          * `code`: List of function name (first entry) and arguments (remaining entries) of the corresponding function call
         """
-        self.verbose = verbose
         if self.preprocessed:
             return self.traces
         if self.version == 0:
@@ -1145,8 +854,7 @@ class RawData:
 
                 new_filenames = list()
                 with tarfile.open(filename) as tf:
-                    ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json")))
-                    self.pta = ptalog["pta"]
+                    ptalog = self.ptalog
 
                     # Benchmark code may be too large to be executed in a single
                     # run, so benchmarks (a benchmark is basically a list of DFA runs)
@@ -1200,8 +908,7 @@ class RawData:
 
                 new_filenames = list()
                 with tarfile.open(filename) as tf:
-                    ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json")))
-                    self.pta = ptalog["pta"]
+                    ptalog = self.ptalog
 
                     # Benchmark code may be too large to be executed in a single
                     # run, so benchmarks (a benchmark is basically a list of DFA runs)
@@ -1292,13 +999,12 @@ class RawData:
         for measurement in measurements:
 
             if "energy_trace" not in measurement:
-                vprint(
-                    self.verbose,
-                    "[W] Skipping {ar:s}/{m:s}: {e:s}".format(
+                logger.warning(
+                    "Skipping {ar:s}/{m:s}: {e:s}".format(
                         ar=self.filenames[measurement["fileno"]],
                         m=measurement["info"].name,
                         e="; ".join(measurement["datasource_errors"]),
-                    ),
+                    )
                 )
                 continue
 
@@ -1315,32 +1021,29 @@ class RawData:
                     self._merge_online_and_offline(measurement)
                     num_valid += 1
                 else:
-                    vprint(
-                        self.verbose,
-                        "[W] Skipping {ar:s}/{m:s}: {e:s}".format(
+                    logger.warning(
+                        "Skipping {ar:s}/{m:s}: {e:s}".format(
                             ar=self.filenames[measurement["fileno"]],
                             m=measurement["info"].name,
                             e=measurement["error"],
-                        ),
+                        )
                     )
             elif version == 2:
                 if self._measurement_is_valid_2(measurement):
                     self._merge_online_and_etlog(measurement)
                     num_valid += 1
                 else:
-                    vprint(
-                        self.verbose,
-                        "[W] Skipping {ar:s}/{m:s}: {e:s}".format(
+                    logger.warning(
+                        "Skipping {ar:s}/{m:s}: {e:s}".format(
                             ar=self.filenames[measurement["fileno"]],
                             m=measurement["info"].name,
                             e=measurement["error"],
-                        ),
+                        )
                     )
-        vprint(
-            self.verbose,
-            "[I] {num_valid:d}/{num_total:d} measurements are valid".format(
+        logger.info(
+            "{num_valid:d}/{num_total:d} measurements are valid".format(
                 num_valid=num_valid, num_total=len(measurements)
-            ),
+            )
         )
         if version == 0:
             self.traces = self._concatenate_traces(self.traces_by_fileno)
@@ -1357,597 +1060,6 @@ class RawData:
         }
 
 
-class ParallelParamFit:
-    """
-    Fit a set of functions on parameterized measurements.
-
-    One parameter is variale, all others are fixed. Reports the best-fitting
-    function type for each parameter.
-    """
-
-    def __init__(self, by_param):
-        """Create a new ParallelParamFit object."""
-        self.fit_queue = []
-        self.by_param = by_param
-
-    def enqueue(
-        self,
-        state_or_tran,
-        attribute,
-        param_index,
-        param_name,
-        safe_functions_enabled=False,
-        param_filter=None,
-    ):
-        """
-        Add state_or_tran/attribute/param_name to fit queue.
-
-        This causes fit() to compute the best-fitting function for this model part.
-        """
-        self.fit_queue.append(
-            {
-                "key": [state_or_tran, attribute, param_name, param_filter],
-                "args": [
-                    self.by_param,
-                    state_or_tran,
-                    attribute,
-                    param_index,
-                    safe_functions_enabled,
-                    param_filter,
-                ],
-            }
-        )
-
-    def fit(self):
-        """
-        Fit functions on previously enqueue data.
-
-        Fitting is one in parallel with one process per core.
-
-        Results can be accessed using the public ParallelParamFit.results object.
-        """
-        with Pool() as pool:
-            self.results = pool.map(_try_fits_parallel, self.fit_queue)
-
-
-def _try_fits_parallel(arg):
-    """
-    Call _try_fits(*arg['args']) and return arg['key'] and the _try_fits result.
-
-    Must be a global function as it is called from a multiprocessing Pool.
-    """
-    return {"key": arg["key"], "result": _try_fits(*arg["args"])}
-
-
-def _try_fits(
-    by_param,
-    state_or_tran,
-    model_attribute,
-    param_index,
-    safe_functions_enabled=False,
-    param_filter: dict = None,
-):
-    """
-    Determine goodness-of-fit for prediction of `by_param[(state_or_tran, *)][model_attribute]` dependence on `param_index` using various functions.
-
-    This is done by varying `param_index` while keeping all other parameters constant and doing one least squares optimization for each function and for each combination of the remaining parameters.
-    The value of the parameter corresponding to `param_index` (e.g. txpower or packet length) is the sole input to the model function.
-    Only numeric parameter values (as determined by `utils.is_numeric`) are used for fitting, non-numeric values such as None or enum strings are ignored.
-    Fitting is only performed if at least three distinct parameter values exist in `by_param[(state_or_tran, *)]`.
-
-    :returns:  a dictionary with the following elements:
-        best -- name of the best-fitting function (see `analytic.functions`). `None` in case of insufficient data.
-        best_rmsd -- mean Root Mean Square Deviation of best-fitting function over all combinations of the remaining parameters
-        mean_rmsd -- mean Root Mean Square Deviation of a reference model using the mean of its respective input data as model value
-        median_rmsd -- mean Root Mean Square Deviation of a reference model using the median of its respective input data as model value
-        results -- mean goodness-of-fit measures for the individual functions. See `analytic.functions` for keys and `aggregate_measures` for values
-
-    :param by_param: measurements partitioned by state/transition/... name and parameter values.
-    Example: `{('foo', (0, 2)): {'bar': [2]}, ('foo', (0, 4)): {'bar': [4]}, ('foo', (0, 6)): {'bar': [6]}}`
-
-    :param state_or_tran: state/transition/... name for which goodness-of-fit will be calculated (first element of by_param key tuple).
-    Example: `'foo'`
-
-    :param model_attribute: attribute for which goodness-of-fit will be calculated.
-    Example: `'bar'`
-
-    :param param_index: index of the parameter used as model input
-    :param safe_functions_enabled: Include "safe" variants of functions with limited argument range.
-    :param param_filter: Only use measurements whose parameters match param_filter for fitting.
-    """
-
-    functions = analytic.functions(safe_functions_enabled=safe_functions_enabled)
-
-    for param_key in filter(lambda x: x[0] == state_or_tran, by_param.keys()):
-        # We might remove elements from 'functions' while iterating over
-        # its keys. A generator will not allow this, so we need to
-        # convert to a list.
-        function_names = list(functions.keys())
-        for function_name in function_names:
-            function_object = functions[function_name]
-            if is_numeric(param_key[1][param_index]) and not function_object.is_valid(
-                param_key[1][param_index]
-            ):
-                functions.pop(function_name, None)
-
-    raw_results = dict()
-    raw_results_by_param = dict()
-    ref_results = {"mean": list(), "median": list()}
-    results = dict()
-    results_by_param = dict()
-
-    seen_parameter_combinations = set()
-
-    # for each parameter combination:
-    for param_key in filter(
-        lambda x: x[0] == state_or_tran
-        and remove_index_from_tuple(x[1], param_index)
-        not in seen_parameter_combinations
-        and len(by_param[x]["param"])
-        and match_parameter_values(by_param[x]["param"][0], param_filter),
-        by_param.keys(),
-    ):
-        X = []
-        Y = []
-        num_valid = 0
-        num_total = 0
-
-        # Ensure that each parameter combination is only optimized once. Otherwise, with parameters (1, 2, 5), (1, 3, 5), (1, 4, 5) and param_index == 1,
-        # the parameter combination (1, *, 5) would be optimized three times, both wasting time and biasing results towards more frequently occuring combinations of non-param_index parameters
-        seen_parameter_combinations.add(
-            remove_index_from_tuple(param_key[1], param_index)
-        )
-
-        # for each value of the parameter denoted by param_index (all other parameters remain the same):
-        for k, v in filter(
-            lambda kv: param_slice_eq(kv[0], param_key, param_index), by_param.items()
-        ):
-            num_total += 1
-            if is_numeric(k[1][param_index]):
-                num_valid += 1
-                X.extend([float(k[1][param_index])] * len(v[model_attribute]))
-                Y.extend(v[model_attribute])
-
-        if num_valid > 2:
-            X = np.array(X)
-            Y = np.array(Y)
-            other_parameters = remove_index_from_tuple(k[1], param_index)
-            raw_results_by_param[other_parameters] = dict()
-            results_by_param[other_parameters] = dict()
-            for function_name, param_function in functions.items():
-                if function_name not in raw_results:
-                    raw_results[function_name] = dict()
-                error_function = param_function.error_function
-                res = optimize.least_squares(
-                    error_function, [0, 1], args=(X, Y), xtol=2e-15
-                )
-                measures = regression_measures(param_function.eval(res.x, X), Y)
-                raw_results_by_param[other_parameters][function_name] = measures
-                for measure, error_rate in measures.items():
-                    if measure not in raw_results[function_name]:
-                        raw_results[function_name][measure] = list()
-                    raw_results[function_name][measure].append(error_rate)
-                # print(function_name, res, measures)
-            mean_measures = aggregate_measures(np.mean(Y), Y)
-            ref_results["mean"].append(mean_measures["rmsd"])
-            raw_results_by_param[other_parameters]["mean"] = mean_measures
-            median_measures = aggregate_measures(np.median(Y), Y)
-            ref_results["median"].append(median_measures["rmsd"])
-            raw_results_by_param[other_parameters]["median"] = median_measures
-
-    if not len(ref_results["mean"]):
-        # Insufficient data for fitting
-        # print('[W] Insufficient data for fitting {}/{}/{}'.format(state_or_tran, model_attribute, param_index))
-        return {"best": None, "best_rmsd": np.inf, "results": results}
-
-    for (
-        other_parameter_combination,
-        other_parameter_results,
-    ) in raw_results_by_param.items():
-        best_fit_val = np.inf
-        best_fit_name = None
-        results = dict()
-        for function_name, result in other_parameter_results.items():
-            if len(result) > 0:
-                results[function_name] = result
-                rmsd = result["rmsd"]
-                if rmsd < best_fit_val:
-                    best_fit_val = rmsd
-                    best_fit_name = function_name
-        results_by_param[other_parameter_combination] = {
-            "best": best_fit_name,
-            "best_rmsd": best_fit_val,
-            "mean_rmsd": results["mean"]["rmsd"],
-            "median_rmsd": results["median"]["rmsd"],
-            "results": results,
-        }
-
-    best_fit_val = np.inf
-    best_fit_name = None
-    results = dict()
-    for function_name, result in raw_results.items():
-        if len(result) > 0:
-            results[function_name] = {}
-            for measure in result.keys():
-                results[function_name][measure] = np.mean(result[measure])
-            rmsd = results[function_name]["rmsd"]
-            if rmsd < best_fit_val:
-                best_fit_val = rmsd
-                best_fit_name = function_name
-
-    return {
-        "best": best_fit_name,
-        "best_rmsd": best_fit_val,
-        "mean_rmsd": np.mean(ref_results["mean"]),
-        "median_rmsd": np.mean(ref_results["median"]),
-        "results": results,
-        "results_by_other_param": results_by_param,
-    }
-
-
-def _num_args_from_by_name(by_name):
-    num_args = dict()
-    for key, value in by_name.items():
-        if "args" in value:
-            num_args[key] = len(value["args"][0])
-    return num_args
-
-
-def get_fit_result(results, name, attribute, verbose=False, param_filter: dict = None):
-    """
-    Parse and sanitize fit results for state/transition/... 'name' and model attribute 'attribute'.
-
-    Filters out results where the best function is worse (or not much better than) static mean/median estimates.
-
-    :param results: fit results as returned by `paramfit.results`
-    :param name: state/transition/... name, e.g. 'TX'
-    :param attribute: model attribute, e.g. 'duration'
-    :param verbose: print debug message to stdout when deliberately not using a determined fit function
-    :param param_filter:
-    :returns: dict with fit result (see `_try_fits`) for each successfully fitted parameter. E.g. {'param 1': {'best' : 'function name', ...} }
-    """
-    fit_result = dict()
-    for result in results:
-        if (
-            result["key"][0] == name
-            and result["key"][1] == attribute
-            and result["key"][3] == param_filter
-            and result["result"]["best"] is not None
-        ):  # dürfte an ['best'] != None liegen-> Fit für gefilterten Kram schlägt fehl?
-            this_result = result["result"]
-            if this_result["best_rmsd"] >= min(
-                this_result["mean_rmsd"], this_result["median_rmsd"]
-            ):
-                vprint(
-                    verbose,
-                    "[I] Not modeling {} {} as function of {}: best ({:.0f}) is worse than ref ({:.0f}, {:.0f})".format(
-                        name,
-                        attribute,
-                        result["key"][2],
-                        this_result["best_rmsd"],
-                        this_result["mean_rmsd"],
-                        this_result["median_rmsd"],
-                    ),
-                )
-            # See notes on depends_on_param
-            elif this_result["best_rmsd"] >= 0.8 * min(
-                this_result["mean_rmsd"], this_result["median_rmsd"]
-            ):
-                vprint(
-                    verbose,
-                    "[I] Not modeling {} {} as function of {}: best ({:.0f}) is not much better than ref ({:.0f}, {:.0f})".format(
-                        name,
-                        attribute,
-                        result["key"][2],
-                        this_result["best_rmsd"],
-                        this_result["mean_rmsd"],
-                        this_result["median_rmsd"],
-                    ),
-                )
-            else:
-                fit_result[result["key"][2]] = this_result
-    return fit_result
-
-
-class AnalyticModel:
-    u"""
-    Parameter-aware analytic energy/data size/... model.
-
-    Supports both static and parameter-based model attributes, and automatic detection of parameter-dependence.
-
-    These provide measurements aggregated by (function/state/...) name
-    and (for by_param) parameter values. Layout:
-    dictionary with one key per name ('send', 'TX', ...) or
-    one key per name and parameter combination
-    (('send', (1, 2)), ('send', (2, 3)), ('TX', (1, 2)), ('TX', (2, 3)), ...).
-
-    Parameter values must be ordered corresponding to the lexically sorted parameter names.
-
-    Each element is in turn a dict with the following elements:
-    - param: list of parameter values in each measurement (-> list of lists)
-    - attributes: list of keys that should be analyzed,
-        e.g. ['power', 'duration']
-    - for each attribute mentioned in 'attributes': A list with measurements.
-      All list except for 'attributes' must have the same length.
-
-    For example:
-    parameters = ['foo_count', 'irrelevant']
-    by_name = {
-        'foo' : [1, 1, 2],
-        'bar' : [5, 6, 7],
-        'attributes' : ['foo', 'bar'],
-        'param' : [[1, 0], [1, 0], [2, 0]]
-    }
-
-    methods:
-    get_static -- return static (parameter-unaware) model.
-    get_param_lut -- return parameter-aware look-up-table model. Cannot model parameter combinations not present in by_param.
-    get_fitted -- return parameter-aware model using fitted functions for behaviour prediction.
-
-    variables:
-    names -- function/state/... names (i.e., the keys of by_name)
-    parameters -- parameter names
-    stats -- ParamStats object providing parameter-dependency statistics for each name and attribute
-    assess -- calculate model quality
-    """
-
-    def __init__(
-        self,
-        by_name,
-        parameters,
-        arg_count=None,
-        function_override=dict(),
-        verbose=True,
-        use_corrcoef=False,
-    ):
-        """
-        Create a new AnalyticModel and compute parameter statistics.
-
-        :param by_name: measurements aggregated by (function/state/...) name.
-            Layout: dictionary with one key per name ('send', 'TX', ...) or
-            one key per name and parameter combination
-            (('send', (1, 2)), ('send', (2, 3)), ('TX', (1, 2)), ('TX', (2, 3)), ...).
-
-            Parameter values must be ordered corresponding to the lexically sorted parameter names.
-
-            Each element is in turn a dict with the following elements:
-            - param: list of parameter values in each measurement (-> list of lists)
-            - attributes: list of keys that should be analyzed,
-                e.g. ['power', 'duration']
-            - for each attribute mentioned in 'attributes': A list with measurements.
-            All list except for 'attributes' must have the same length.
-
-            For example:
-            parameters = ['foo_count', 'irrelevant']
-            by_name = {
-                'foo' : [1, 1, 2],
-                'duration' : [5, 6, 7],
-                'attributes' : ['foo', 'duration'],
-                'param' : [[1, 0], [1, 0], [2, 0]]
-                # foo_count-^  ^-irrelevant
-            }
-        :param parameters: List of parameter names
-        :param function_override: dict of overrides for automatic parameter function generation.
-            If (state or transition name, model attribute) is present in function_override,
-            the corresponding text string is the function used for analytic (parameter-aware/fitted)
-            modeling of this attribute. It is passed to AnalyticFunction, see
-            there for the required format. Note that this happens regardless of
-            parameter dependency detection: The provided analytic function will be assigned
-            even if it seems like the model attribute is static / parameter-independent.
-        :param verbose: Print debug/info output while generating the model?
-        :param use_corrcoef: use correlation coefficient instead of stddev comparison to detect whether a model attribute depends on a parameter
-        """
-        self.cache = dict()
-        self.by_name = by_name
-        self.by_param = by_name_to_by_param(by_name)
-        self.names = sorted(by_name.keys())
-        self.parameters = sorted(parameters)
-        self.function_override = function_override.copy()
-        self.verbose = verbose
-        self._use_corrcoef = use_corrcoef
-        self._num_args = arg_count
-        if self._num_args is None:
-            self._num_args = _num_args_from_by_name(by_name)
-
-        self.stats = ParamStats(
-            self.by_name,
-            self.by_param,
-            self.parameters,
-            self._num_args,
-            verbose=verbose,
-            use_corrcoef=use_corrcoef,
-        )
-
-    def _get_model_from_dict(self, model_dict, model_function):
-        model = {}
-        for name, elem in model_dict.items():
-            model[name] = {}
-            for key in elem["attributes"]:
-                try:
-                    model[name][key] = model_function(elem[key])
-                except RuntimeWarning:
-                    vprint(self.verbose, "[W] Got no data for {} {}".format(name, key))
-                except FloatingPointError as fpe:
-                    vprint(
-                        self.verbose,
-                        "[W] Got no data for {} {}: {}".format(name, key, fpe),
-                    )
-        return model
-
-    def param_index(self, param_name):
-        if param_name in self.parameters:
-            return self.parameters.index(param_name)
-        return len(self.parameters) + int(param_name)
-
-    def param_name(self, param_index):
-        if param_index < len(self.parameters):
-            return self.parameters[param_index]
-        return str(param_index)
-
-    def get_static(self, use_mean=False):
-        """
-        Get static model function: name, attribute -> model value.
-
-        Uses the median of by_name for modeling.
-        """
-        getter_function = np.median
-
-        if use_mean:
-            getter_function = np.mean
-
-        static_model = self._get_model_from_dict(self.by_name, getter_function)
-
-        def static_model_getter(name, key, **kwargs):
-            return static_model[name][key]
-
-        return static_model_getter
-
-    def get_param_lut(self, fallback=False):
-        """
-        Get parameter-look-up-table model function: name, attribute, parameter values -> model value.
-
-        The function can only give model values for parameter combinations
-        present in by_param. By default, it raises KeyError for other values.
-
-        arguments:
-        fallback -- Fall back to the (non-parameter-aware) static model when encountering unknown parameter values
-        """
-        static_model = self._get_model_from_dict(self.by_name, np.median)
-        lut_model = self._get_model_from_dict(self.by_param, np.median)
-
-        def lut_median_getter(name, key, param, arg=[], **kwargs):
-            param.extend(map(soft_cast_int, arg))
-            try:
-                return lut_model[(name, tuple(param))][key]
-            except KeyError:
-                if fallback:
-                    return static_model[name][key]
-                raise
-
-        return lut_median_getter
-
-    def get_fitted(self, safe_functions_enabled=False):
-        """
-        Get paramete-aware model function and model information function.
-
-        Returns two functions:
-        model_function(name, attribute, param=parameter values) -> model value.
-        model_info(name, attribute) -> {'fit_result' : ..., 'function' : ... } or None
-        """
-        if "fitted_model_getter" in self.cache and "fitted_info_getter" in self.cache:
-            return self.cache["fitted_model_getter"], self.cache["fitted_info_getter"]
-
-        static_model = self._get_model_from_dict(self.by_name, np.median)
-        param_model = dict([[name, {}] for name in self.by_name.keys()])
-        paramfit = ParallelParamFit(self.by_param)
-
-        for name in self.by_name.keys():
-            for attribute in self.by_name[name]["attributes"]:
-                for param_index, param in enumerate(self.parameters):
-                    if self.stats.depends_on_param(name, attribute, param):
-                        paramfit.enqueue(name, attribute, param_index, param, False)
-                if arg_support_enabled and name in self._num_args:
-                    for arg_index in range(self._num_args[name]):
-                        if self.stats.depends_on_arg(name, attribute, arg_index):
-                            paramfit.enqueue(
-                                name,
-                                attribute,
-                                len(self.parameters) + arg_index,
-                                arg_index,
-                                False,
-                            )
-
-        paramfit.fit()
-
-        for name in self.by_name.keys():
-            num_args = 0
-            if name in self._num_args:
-                num_args = self._num_args[name]
-            for attribute in self.by_name[name]["attributes"]:
-                fit_result = get_fit_result(
-                    paramfit.results, name, attribute, self.verbose
-                )
-
-                if (name, attribute) in self.function_override:
-                    function_str = self.function_override[(name, attribute)]
-                    x = AnalyticFunction(function_str, self.parameters, num_args)
-                    x.fit(self.by_param, name, attribute)
-                    if x.fit_success:
-                        param_model[name][attribute] = {
-                            "fit_result": fit_result,
-                            "function": x,
-                        }
-                elif len(fit_result.keys()):
-                    x = analytic.function_powerset(
-                        fit_result, self.parameters, num_args
-                    )
-                    x.fit(self.by_param, name, attribute)
-
-                    if x.fit_success:
-                        param_model[name][attribute] = {
-                            "fit_result": fit_result,
-                            "function": x,
-                        }
-
-        def model_getter(name, key, **kwargs):
-            if "arg" in kwargs and "param" in kwargs:
-                kwargs["param"].extend(map(soft_cast_int, kwargs["arg"]))
-            if key in param_model[name]:
-                param_list = kwargs["param"]
-                param_function = param_model[name][key]["function"]
-                if param_function.is_predictable(param_list):
-                    return param_function.eval(param_list)
-            return static_model[name][key]
-
-        def info_getter(name, key):
-            if key in param_model[name]:
-                return param_model[name][key]
-            return None
-
-        self.cache["fitted_model_getter"] = model_getter
-        self.cache["fitted_info_getter"] = info_getter
-
-        return model_getter, info_getter
-
-    def assess(self, model_function):
-        """
-        Calculate MAE, SMAPE, etc. of model_function for each by_name entry.
-
-        state/transition/... name and parameter values are fed into model_function.
-        The by_name entries of this AnalyticModel are used as ground truth and
-        compared with the values predicted by model_function.
-
-        For proper model assessments, the data used to generate model_function
-        and the data fed into this AnalyticModel instance must be mutually
-        exclusive (e.g. by performing cross validation). Otherwise,
-        overfitting cannot be detected.
-        """
-        detailed_results = {}
-        for name, elem in sorted(self.by_name.items()):
-            detailed_results[name] = {}
-            for attribute in elem["attributes"]:
-                predicted_data = np.array(
-                    list(
-                        map(
-                            lambda i: model_function(
-                                name, attribute, param=elem["param"][i]
-                            ),
-                            range(len(elem[attribute])),
-                        )
-                    )
-                )
-                measures = regression_measures(predicted_data, elem[attribute])
-                detailed_results[name][attribute] = measures
-
-        return {
-            "by_name": detailed_results,
-        }
-
-    def to_json(self):
-        # TODO
-        pass
-
-
 def _add_trace_data_to_aggregate(aggregate, key, element):
     # Only cares about element['isa'], element['offline_aggregates'], and
     # element['plan']['level']
@@ -2049,540 +1161,6 @@ def pta_trace_to_aggregate(traces, ignore_trace_indexes=[]):
     return by_name, parameter_names, arg_count
 
 
-class PTAModel:
-    u"""
-    Parameter-aware PTA-based energy model.
-
-    Supports both static and parameter-based model attributes, and automatic detection of parameter-dependence.
-
-    The model heavily relies on two internal data structures:
-    PTAModel.by_name and PTAModel.by_param.
-
-    These provide measurements aggregated by state/transition name
-    and (for by_param) parameter values. Layout:
-    dictionary with one key per state/transition ('send', 'TX', ...) or
-    one key per state/transition and parameter combination
-    (('send', (1, 2)), ('send', (2, 3)), ('TX', (1, 2)), ('TX', (2, 3)), ...).
-    For by_param, parameter values are ordered corresponding to the lexically sorted parameter names.
-
-    Each element is in turn a dict with the following elements:
-    - isa: 'state' or 'transition'
-    - power: list of mean power measurements in µW
-    - duration: list of durations in µs
-    - power_std: list of stddev of power per state/transition
-    - energy: consumed energy (power*duration) in pJ
-    - paramkeys: list of parameter names in each measurement (-> list of lists)
-    - param: list of parameter values in each measurement (-> list of lists)
-    - attributes: list of keys that should be analyzed,
-        e.g. ['power', 'duration']
-    additionally, only if isa == 'transition':
-    - timeout: list of duration of previous state in µs
-    - rel_energy_prev: transition energy relative to previous state mean power in pJ
-    - rel_energy_next: transition energy relative to next state mean power in pJ
-    """
-
-    def __init__(
-        self,
-        by_name,
-        parameters,
-        arg_count,
-        traces=[],
-        ignore_trace_indexes=[],
-        discard_outliers=None,
-        function_override={},
-        verbose=True,
-        use_corrcoef=False,
-        pta=None,
-    ):
-        """
-        Prepare a new PTA energy model.
-
-        Actual model generation is done on-demand by calling the respective functions.
-
-        arguments:
-        by_name -- state/transition measurements aggregated by name, as returned by pta_trace_to_aggregate.
-        parameters -- list of parameter names, as returned by pta_trace_to_aggregate
-        arg_count -- function arguments, as returned by pta_trace_to_aggregate
-        traces -- list of preprocessed DFA traces, as returned by RawData.get_preprocessed_data()
-        ignore_trace_indexes -- list of trace indexes. The corresponding traces will be ignored.
-        discard_outliers -- currently not supported: threshold for outlier detection and removel (float).
-            Outlier detection is performed individually for each state/transition in each trace,
-            so it only works if the benchmark ran several times.
-            Given "data" (a set of measurements of the same thing, e.g. TX duration in the third benchmark trace),
-            "m" (the median of all attribute measurements with the same parameters, which may include data from other traces),
-            a data point X is considered an outlier if
-            | 0.6745 * (X - m) / median(|data - m|) | > discard_outliers .
-        function_override -- dict of overrides for automatic parameter function generation.
-            If (state or transition name, model attribute) is present in function_override,
-            the corresponding text string is the function used for analytic (parameter-aware/fitted)
-            modeling of this attribute. It is passed to AnalyticFunction, see
-            there for the required format. Note that this happens regardless of
-            parameter dependency detection: The provided analytic function will be assigned
-            even if it seems like the model attribute is static / parameter-independent.
-        verbose -- print informative output, e.g. when removing an outlier
-        use_corrcoef -- use correlation coefficient instead of stddev comparison
-            to detect whether a model attribute depends on a parameter
-        pta -- hardware model as `PTA` object
-        """
-        self.by_name = by_name
-        self.by_param = by_name_to_by_param(by_name)
-        self._parameter_names = sorted(parameters)
-        self._num_args = arg_count
-        self._use_corrcoef = use_corrcoef
-        self.traces = traces
-        self.stats = ParamStats(
-            self.by_name,
-            self.by_param,
-            self._parameter_names,
-            self._num_args,
-            self._use_corrcoef,
-            verbose=verbose,
-        )
-        self.cache = {}
-        np.seterr("raise")
-        self._outlier_threshold = discard_outliers
-        self.function_override = function_override.copy()
-        self.verbose = verbose
-        self.pta = pta
-        self.ignore_trace_indexes = ignore_trace_indexes
-        self._aggregate_to_ndarray(self.by_name)
-
-    def _aggregate_to_ndarray(self, aggregate):
-        for elem in aggregate.values():
-            for key in elem["attributes"]:
-                elem[key] = np.array(elem[key])
-
-    # This heuristic is very similar to the "function is not much better than
-    # median" checks in get_fitted. So far, doing it here as well is mostly
-    # a performance and not an algorithm quality decision.
-    # --df, 2018-04-18
-    def depends_on_param(self, state_or_trans, key, param):
-        return self.stats.depends_on_param(state_or_trans, key, param)
-
-    # See notes on depends_on_param
-    def depends_on_arg(self, state_or_trans, key, param):
-        return self.stats.depends_on_arg(state_or_trans, key, param)
-
-    def _get_model_from_dict(self, model_dict, model_function):
-        model = {}
-        for name, elem in model_dict.items():
-            model[name] = {}
-            for key in elem["attributes"]:
-                try:
-                    model[name][key] = model_function(elem[key])
-                except RuntimeWarning:
-                    vprint(self.verbose, "[W] Got no data for {} {}".format(name, key))
-                except FloatingPointError as fpe:
-                    vprint(
-                        self.verbose,
-                        "[W] Got no data for {} {}: {}".format(name, key, fpe),
-                    )
-        return model
-
-    def get_static(self, use_mean=False):
-        """
-        Get static model function: name, attribute -> model value.
-
-        Uses the median of by_name for modeling, unless `use_mean` is set.
-        """
-        getter_function = np.median
-
-        if use_mean:
-            getter_function = np.mean
-
-        static_model = self._get_model_from_dict(self.by_name, getter_function)
-
-        def static_model_getter(name, key, **kwargs):
-            return static_model[name][key]
-
-        return static_model_getter
-
-    def get_param_lut(self, fallback=False):
-        """
-        Get parameter-look-up-table model function: name, attribute, parameter values -> model value.
-
-        The function can only give model values for parameter combinations
-        present in by_param. By default, it raises KeyError for other values.
-
-        arguments:
-        fallback -- Fall back to the (non-parameter-aware) static model when encountering unknown parameter values
-        """
-        static_model = self._get_model_from_dict(self.by_name, np.median)
-        lut_model = self._get_model_from_dict(self.by_param, np.median)
-
-        def lut_median_getter(name, key, param, arg=[], **kwargs):
-            param.extend(map(soft_cast_int, arg))
-            try:
-                return lut_model[(name, tuple(param))][key]
-            except KeyError:
-                if fallback:
-                    return static_model[name][key]
-                raise
-
-        return lut_median_getter
-
-    def param_index(self, param_name):
-        if param_name in self._parameter_names:
-            return self._parameter_names.index(param_name)
-        return len(self._parameter_names) + int(param_name)
-
-    def param_name(self, param_index):
-        if param_index < len(self._parameter_names):
-            return self._parameter_names[param_index]
-        return str(param_index)
-
-    def get_fitted(self, safe_functions_enabled=False):
-        """
-        Get parameter-aware model function and model information function.
-
-        Returns two functions:
-        model_function(name, attribute, param=parameter values) -> model value.
-        model_info(name, attribute) -> {'fit_result' : ..., 'function' : ... } or None
-        """
-        if "fitted_model_getter" in self.cache and "fitted_info_getter" in self.cache:
-            return self.cache["fitted_model_getter"], self.cache["fitted_info_getter"]
-
-        static_model = self._get_model_from_dict(self.by_name, np.median)
-        param_model = dict(
-            [[state_or_tran, {}] for state_or_tran in self.by_name.keys()]
-        )
-        paramfit = ParallelParamFit(self.by_param)
-        for state_or_tran in self.by_name.keys():
-            for model_attribute in self.by_name[state_or_tran]["attributes"]:
-                fit_results = {}
-                for parameter_index, parameter_name in enumerate(self._parameter_names):
-                    if self.depends_on_param(
-                        state_or_tran, model_attribute, parameter_name
-                    ):
-                        paramfit.enqueue(
-                            state_or_tran,
-                            model_attribute,
-                            parameter_index,
-                            parameter_name,
-                            safe_functions_enabled,
-                        )
-                        for (
-                            codependent_param_dict
-                        ) in self.stats.codependent_parameter_value_dicts(
-                            state_or_tran, model_attribute, parameter_name
-                        ):
-                            paramfit.enqueue(
-                                state_or_tran,
-                                model_attribute,
-                                parameter_index,
-                                parameter_name,
-                                safe_functions_enabled,
-                                codependent_param_dict,
-                            )
-                if (
-                    arg_support_enabled
-                    and self.by_name[state_or_tran]["isa"] == "transition"
-                ):
-                    for arg_index in range(self._num_args[state_or_tran]):
-                        if self.depends_on_arg(
-                            state_or_tran, model_attribute, arg_index
-                        ):
-                            paramfit.enqueue(
-                                state_or_tran,
-                                model_attribute,
-                                len(self._parameter_names) + arg_index,
-                                arg_index,
-                                safe_functions_enabled,
-                            )
-        paramfit.fit()
-
-        for state_or_tran in self.by_name.keys():
-            num_args = 0
-            if (
-                arg_support_enabled
-                and self.by_name[state_or_tran]["isa"] == "transition"
-            ):
-                num_args = self._num_args[state_or_tran]
-            for model_attribute in self.by_name[state_or_tran]["attributes"]:
-                fit_results = get_fit_result(
-                    paramfit.results, state_or_tran, model_attribute, self.verbose
-                )
-
-                for parameter_name in self._parameter_names:
-                    if self.depends_on_param(
-                        state_or_tran, model_attribute, parameter_name
-                    ):
-                        for (
-                            codependent_param_dict
-                        ) in self.stats.codependent_parameter_value_dicts(
-                            state_or_tran, model_attribute, parameter_name
-                        ):
-                            pass
-                            # FIXME get_fit_result hat ja gar keinen Parameter als Argument...
-
-                if (state_or_tran, model_attribute) in self.function_override:
-                    function_str = self.function_override[
-                        (state_or_tran, model_attribute)
-                    ]
-                    x = AnalyticFunction(function_str, self._parameter_names, num_args)
-                    x.fit(self.by_param, state_or_tran, model_attribute)
-                    if x.fit_success:
-                        param_model[state_or_tran][model_attribute] = {
-                            "fit_result": fit_results,
-                            "function": x,
-                        }
-                elif len(fit_results.keys()):
-                    x = analytic.function_powerset(
-                        fit_results, self._parameter_names, num_args
-                    )
-                    x.fit(self.by_param, state_or_tran, model_attribute)
-                    if x.fit_success:
-                        param_model[state_or_tran][model_attribute] = {
-                            "fit_result": fit_results,
-                            "function": x,
-                        }
-
-        def model_getter(name, key, **kwargs):
-            if "arg" in kwargs and "param" in kwargs:
-                kwargs["param"].extend(map(soft_cast_int, kwargs["arg"]))
-            if key in param_model[name]:
-                param_list = kwargs["param"]
-                param_function = param_model[name][key]["function"]
-                if param_function.is_predictable(param_list):
-                    return param_function.eval(param_list)
-            return static_model[name][key]
-
-        def info_getter(name, key):
-            if key in param_model[name]:
-                return param_model[name][key]
-            return None
-
-        self.cache["fitted_model_getter"] = model_getter
-        self.cache["fitted_info_getter"] = info_getter
-
-        return model_getter, info_getter
-
-    def to_json(self):
-        static_model = self.get_static()
-        static_quality = self.assess(static_model)
-        param_model, param_info = self.get_fitted()
-        analytic_quality = self.assess(param_model)
-        self.pta.update(
-            static_model,
-            param_info,
-            static_error=static_quality["by_name"],
-            analytic_error=analytic_quality["by_name"],
-        )
-        return self.pta.to_json()
-
-    def states(self):
-        """Return sorted list of state names."""
-        return sorted(
-            list(
-                filter(lambda k: self.by_name[k]["isa"] == "state", self.by_name.keys())
-            )
-        )
-
-    def transitions(self):
-        """Return sorted list of transition names."""
-        return sorted(
-            list(
-                filter(
-                    lambda k: self.by_name[k]["isa"] == "transition",
-                    self.by_name.keys(),
-                )
-            )
-        )
-
-    def states_and_transitions(self):
-        """Return list of states and transition names."""
-        ret = self.states()
-        ret.extend(self.transitions())
-        return ret
-
-    def parameters(self):
-        return self._parameter_names
-
-    def attributes(self, state_or_trans):
-        return self.by_name[state_or_trans]["attributes"]
-
-    def assess(self, model_function):
-        """
-        Calculate MAE, SMAPE, etc. of model_function for each by_name entry.
-
-        state/transition/... name and parameter values are fed into model_function.
-        The by_name entries of this PTAModel are used as ground truth and
-        compared with the values predicted by model_function.
-
-        For proper model assessments, the data used to generate model_function
-        and the data fed into this AnalyticModel instance must be mutually
-        exclusive (e.g. by performing cross validation). Otherwise,
-        overfitting cannot be detected.
-        """
-        detailed_results = {}
-        for name, elem in sorted(self.by_name.items()):
-            detailed_results[name] = {}
-            for key in elem["attributes"]:
-                predicted_data = np.array(
-                    list(
-                        map(
-                            lambda i: model_function(name, key, param=elem["param"][i]),
-                            range(len(elem[key])),
-                        )
-                    )
-                )
-                measures = regression_measures(predicted_data, elem[key])
-                detailed_results[name][key] = measures
-
-        return {"by_name": detailed_results}
-
-    def assess_states(
-        self, model_function, model_attribute="power", distribution: dict = None
-    ):
-        """
-        Calculate overall model error assuming equal distribution of states
-        """
-        # TODO calculate mean power draw for distribution and use it to
-        # calculate relative error from MAE combination
-        model_quality = self.assess(model_function)
-        num_states = len(self.states())
-        if distribution is None:
-            distribution = dict(map(lambda x: [x, 1 / num_states], self.states()))
-
-        if not np.isclose(sum(distribution.values()), 1):
-            raise ValueError(
-                "distribution must be a probability distribution with sum 1"
-            )
-
-        # total_value = None
-        # try:
-        #     total_value = sum(map(lambda x: model_function(x, model_attribute) * distribution[x], self.states()))
-        # except KeyError:
-        #     pass
-
-        total_error = np.sqrt(
-            sum(
-                map(
-                    lambda x: np.square(
-                        model_quality["by_name"][x][model_attribute]["mae"]
-                        * distribution[x]
-                    ),
-                    self.states(),
-                )
-            )
-        )
-        return total_error
-
-    def assess_on_traces(self, model_function):
-        """
-        Calculate MAE, SMAPE, etc. of model_function for each trace known to this PTAModel instance.
-
-        :returns: dict of `duration_by_trace`, `energy_by_trace`, `timeout_by_trace`, `rel_energy_by_trace` and `state_energy_by_trace`.
-            Each entry holds regression measures for the corresponding measure. Note that the determined model quality heavily depends on the
-            traces: small-ish absolute errors in states which frequently occur may have more effect than large absolute errors in rarely occuring states
-        """
-        model_energy_list = []
-        real_energy_list = []
-        model_rel_energy_list = []
-        model_state_energy_list = []
-        model_duration_list = []
-        real_duration_list = []
-        model_timeout_list = []
-        real_timeout_list = []
-
-        for trace in self.traces:
-            if trace["id"] not in self.ignore_trace_indexes:
-                for rep_id in range(len(trace["trace"][0]["offline"])):
-                    model_energy = 0.0
-                    real_energy = 0.0
-                    model_rel_energy = 0.0
-                    model_state_energy = 0.0
-                    model_duration = 0.0
-                    real_duration = 0.0
-                    model_timeout = 0.0
-                    real_timeout = 0.0
-                    for i, trace_part in enumerate(trace["trace"]):
-                        name = trace_part["name"]
-                        prev_name = trace["trace"][i - 1]["name"]
-                        isa = trace_part["isa"]
-                        if name != "UNINITIALIZED":
-                            try:
-                                param = trace_part["offline_aggregates"]["param"][
-                                    rep_id
-                                ]
-                                prev_param = trace["trace"][i - 1][
-                                    "offline_aggregates"
-                                ]["param"][rep_id]
-                                power = trace_part["offline"][rep_id]["uW_mean"]
-                                duration = trace_part["offline"][rep_id]["us"]
-                                prev_duration = trace["trace"][i - 1]["offline"][
-                                    rep_id
-                                ]["us"]
-                                real_energy += power * duration
-                                if isa == "state":
-                                    model_energy += (
-                                        model_function(name, "power", param=param)
-                                        * duration
-                                    )
-                                else:
-                                    model_energy += model_function(
-                                        name, "energy", param=param
-                                    )
-                                    # If i == 1, the previous state was UNINITIALIZED, for which we do not have model data
-                                    if i == 1:
-                                        model_rel_energy += model_function(
-                                            name, "energy", param=param
-                                        )
-                                    else:
-                                        model_rel_energy += model_function(
-                                            prev_name, "power", param=prev_param
-                                        ) * (prev_duration + duration)
-                                        model_state_energy += model_function(
-                                            prev_name, "power", param=prev_param
-                                        ) * (prev_duration + duration)
-                                    model_rel_energy += model_function(
-                                        name, "rel_energy_prev", param=param
-                                    )
-                                    real_duration += duration
-                                    model_duration += model_function(
-                                        name, "duration", param=param
-                                    )
-                                    if (
-                                        "plan" in trace_part
-                                        and trace_part["plan"]["level"] == "epilogue"
-                                    ):
-                                        real_timeout += trace_part["offline"][rep_id][
-                                            "timeout"
-                                        ]
-                                        model_timeout += model_function(
-                                            name, "timeout", param=param
-                                        )
-                            except KeyError:
-                                # if states/transitions have been removed via --filter-param, this is harmless
-                                pass
-                    real_energy_list.append(real_energy)
-                    model_energy_list.append(model_energy)
-                    model_rel_energy_list.append(model_rel_energy)
-                    model_state_energy_list.append(model_state_energy)
-                    real_duration_list.append(real_duration)
-                    model_duration_list.append(model_duration)
-                    real_timeout_list.append(real_timeout)
-                    model_timeout_list.append(model_timeout)
-
-        return {
-            "duration_by_trace": regression_measures(
-                np.array(model_duration_list), np.array(real_duration_list)
-            ),
-            "energy_by_trace": regression_measures(
-                np.array(model_energy_list), np.array(real_energy_list)
-            ),
-            "timeout_by_trace": regression_measures(
-                np.array(model_timeout_list), np.array(real_timeout_list)
-            ),
-            "rel_energy_by_trace": regression_measures(
-                np.array(model_rel_energy_list), np.array(real_energy_list)
-            ),
-            "state_energy_by_trace": regression_measures(
-                np.array(model_state_energy_list), np.array(real_energy_list)
-            ),
-        }
-
-
 class EnergyTraceLog:
     """
     EnergyTrace log loader for DFA traces.
@@ -2617,7 +1195,6 @@ class EnergyTraceLog:
         self.state_duration = state_duration * 1e-3
         self.transition_names = transition_names
         self.with_traces = with_traces
-        self.verbose = False
         self.errors = list()
 
         # TODO auto-detect
@@ -2643,6 +1220,7 @@ class EnergyTraceLog:
         """
 
         if not zbar_available:
+            logger.error("zbar module is not available")
             self.errors.append(
                 'zbar module is not available. Try "apt install python3-zbar"'
             )
@@ -2675,11 +1253,10 @@ class EnergyTraceLog:
 
         self.sample_rate = data_count / (m_duration_us * 1e-6)
 
-        vprint(
-            self.verbose,
+        logger.debug(
             "got {} samples with {} seconds of log data ({} Hz)".format(
                 data_count, m_duration_us * 1e-6, self.sample_rate
-            ),
+            )
         )
 
         return (
@@ -2783,25 +1360,20 @@ class EnergyTraceLog:
         for name, duration in expected_transitions:
             bc, start, stop, end = self.find_barcode(next_barcode)
             if bc is None:
-                print('[!!!] did not find transition "{}"'.format(name))
+                logger.error('did not find transition "{}"'.format(name))
                 break
             next_barcode = end + self.state_duration + duration
-            vprint(
-                self.verbose,
+            logger.debug(
                 '{} barcode "{}" area: {:0.2f} .. {:0.2f} / {:0.2f} seconds'.format(
                     offline_index, bc, start, stop, end
-                ),
+                )
             )
             if bc != name:
-                vprint(
-                    self.verbose,
-                    '[!!!] mismatch: expected "{}", got "{}"'.format(name, bc),
-                )
-            vprint(
-                self.verbose,
+                logger.error('mismatch: expected "{}", got "{}"'.format(name, bc))
+            logger.debug(
                 "{} estimated transition area: {:0.3f} .. {:0.3f} seconds".format(
                     offline_index, end, end + duration
-                ),
+                )
             )
 
             transition_start_index = self.ts_to_index(end)
@@ -2811,13 +1383,12 @@ class EnergyTraceLog:
                 self.ts_to_index(end + duration + self.state_duration) + 1
             )
 
-            vprint(
-                self.verbose,
+            logger.debug(
                 "{} estimated transitionindex: {:0.3f} .. {:0.3f} seconds".format(
                     offline_index,
                     transition_start_index / self.sample_rate,
                     transition_done_index / self.sample_rate,
-                ),
+                )
             )
 
             transition_power_W = self.interval_power[
@@ -2912,11 +1483,10 @@ class EnergyTraceLog:
             + self.led_power / 3
         )
 
-        vprint(
-            self.verbose,
+        logger.debug(
             "looking for barcode starting at {:0.2f} s, threshold is {:0.1f} mW".format(
                 start_ts, sync_threshold_power * 1e3
-            ),
+            )
         )
 
         sync_area_start = None
@@ -2947,11 +1517,10 @@ class EnergyTraceLog:
 
         barcode_data = self.interval_power[sync_area_start:sync_area_end]
 
-        vprint(
-            self.verbose,
+        logger.debug(
             "barcode search area: {:0.2f} .. {:0.2f} seconds ({} samples)".format(
                 sync_start_ts, sync_end_ts, len(barcode_data)
-            ),
+            )
         )
 
         bc, start, stop, padding_bits = self.find_barcode_in_power_data(barcode_data)
@@ -3026,7 +1595,7 @@ class EnergyTraceLog:
 
             return content, sym_start, sym_end, padding_bits
         else:
-            vprint(self.verbose, "unable to find barcode")
+            logger.warning("unable to find barcode")
             return None, None, None, None
 
 
@@ -3046,17 +1615,15 @@ class MIMOSA:
     Resulting data is a list of state/transition/state/transition/... measurements.
     """
 
-    def __init__(self, voltage: float, shunt: int, verbose=True, with_traces=False):
+    def __init__(self, voltage: float, shunt: int, with_traces=False):
         """
         Initialize MIMOSA loader for a specific voltage and shunt setting.
 
         :param voltage: MIMOSA DUT supply voltage (V)
         :para mshunt: MIMOSA Shunt (Ohms)
-        :param verbose: print notices about invalid data on STDOUT?
         """
         self.voltage = voltage
         self.shunt = shunt
-        self.verbose = verbose
         self.with_traces = with_traces
         self.r1 = 984  # "1k"
         self.r2 = 99013  # "100k"
@@ -3254,7 +1821,7 @@ class MIMOSA:
         if cal_r2_mean > cal_0_mean:
             b_lower = (ua_r2 - 0) / (cal_r2_mean - cal_0_mean)
         else:
-            vprint(self.verbose, "[W] 0 uA == %.f uA during calibration" % (ua_r2))
+            logger.warning("0 uA == %.f uA during calibration" % (ua_r2))
             b_lower = 0
 
         b_upper = (ua_r1 - ua_r2) / (cal_r1_mean - cal_r2_mean)
@@ -3302,50 +1869,6 @@ class MIMOSA:
 
         return calfunc, caldata
 
-    """
-    def calcgrad(self, currents, threshold):
-        grad = np.gradient(running_mean(currents * self.voltage, 10))
-        # len(grad) == len(currents) - 9
-        subst = []
-        lastgrad = 0
-        for i in range(len(grad)):
-            # minimum substate duration: 10ms
-            if np.abs(grad[i]) > threshold and i - lastgrad > 50:
-                # account for skew introduced by running_mean and current
-                # ramp slope (parasitic capacitors etc.)
-                subst.append(i+10)
-                lastgrad = i
-        if lastgrad != i:
-            subst.append(i+10)
-        return subst
-
-    # TODO konfigurierbare min/max threshold und len(gradidx) > X, binaere
-    # Sache nach noetiger threshold. postprocessing mit
-    # "zwei benachbarte substates haben sehr aehnliche werte / niedrige stddev" -> mergen
-    # ... min/max muessen nicht vorgegeben werden, sind ja bekannt (0 / np.max(grad))
-    # TODO bei substates / index foo den offset durch running_mean beachten
-    # TODO ggf. clustering der 'abs(grad) > threshold' und bestimmung interessanter
-    # uebergaenge dadurch?
-    def gradfoo(self, currents):
-        gradients = np.abs(np.gradient(running_mean(currents * self.voltage, 10)))
-        gradmin = np.min(gradients)
-        gradmax = np.max(gradients)
-        threshold = np.mean([gradmin, gradmax])
-        gradidx = self.calcgrad(currents, threshold)
-        num_substates = 2
-        while len(gradidx) != num_substates:
-            if gradmax - gradmin < 0.1:
-                # We did our best
-                return threshold, gradidx
-            if len(gradidx) > num_substates:
-                gradmin = threshold
-            else:
-                gradmax = threshold
-            threshold = np.mean([gradmin, gradmax])
-            gradidx = self.calcgrad(currents, threshold)
-        return threshold, gradidx
-    """
-
     def analyze_states(self, charges, trigidx, ua_func):
         u"""
         Split log data into states and transitions and return duration, energy, and mean power for each element.
@@ -3380,30 +1903,6 @@ class MIMOSA:
         for idx in trigger_indices:
             range_raw = charges[previdx:idx]
             range_ua = ua_func(range_raw)
-            substates = {}
-
-            if previdx != 0 and idx - previdx > 200:
-                thr, subst = 0, []  # self.gradfoo(range_ua)
-                if len(subst):
-                    statelist = []
-                    prevsubidx = 0
-                    for subidx in subst:
-                        statelist.append(
-                            {
-                                "duration": (subidx - prevsubidx) * 10,
-                                "uW_mean": np.mean(
-                                    range_ua[prevsubidx:subidx] * self.voltage
-                                ),
-                                "uW_std": np.std(
-                                    range_ua[prevsubidx:subidx] * self.voltage
-                                ),
-                            }
-                        )
-                        prevsubidx = subidx
-                    substates = {
-                        "threshold": thr,
-                        "states": statelist,
-                    }
 
             isa = "state"
             if not is_state:
@@ -3422,12 +1921,6 @@ class MIMOSA:
             if self.with_traces:
                 data["uW"] = range_ua * self.voltage
 
-            if "states" in substates:
-                data["substates"] = substates
-                ssum = np.sum(list(map(lambda x: x["duration"], substates["states"])))
-                if ssum != data["us"]:
-                    vprint(self.verbose, "ERR: duration %d vs %d" % (data["us"], ssum))
-
             if isa == "transition":
                 # subtract average power of previous state
                 # (that is, the state from which this transition originates)
diff --git a/lib/model.py b/lib/model.py
new file mode 100644
index 0000000..bb4a45b
--- /dev/null
+++ b/lib/model.py
@@ -0,0 +1,1156 @@
+#!/usr/bin/env python3
+
+import logging
+import numpy as np
+from scipy import optimize
+from sklearn.metrics import r2_score
+from multiprocessing import Pool
+from .automata import PTA
+from .functions import analytic
+from .functions import AnalyticFunction
+from .parameters import ParamStats
+from .utils import is_numeric, soft_cast_int, param_slice_eq, remove_index_from_tuple
+from .utils import by_name_to_by_param, match_parameter_values
+
+logger = logging.getLogger(__name__)
+arg_support_enabled = True
+
+
+def aggregate_measures(aggregate: float, actual: list) -> dict:
+    """
+    Calculate error measures for model value on data list.
+
+    arguments:
+    aggregate -- model value (float or int)
+    actual -- real-world / reference values (list of float or int)
+
+    return value:
+    See regression_measures
+    """
+    aggregate_array = np.array([aggregate] * len(actual))
+    return regression_measures(aggregate_array, np.array(actual))
+
+
+def regression_measures(predicted: np.ndarray, actual: np.ndarray):
+    """
+    Calculate error measures by comparing model values to reference values.
+
+    arguments:
+    predicted -- model values (np.ndarray)
+    actual -- real-world / reference values (np.ndarray)
+
+    Returns a dict containing the following measures:
+    mae -- Mean Absolute Error
+    mape -- Mean Absolute Percentage Error,
+            if all items in actual are non-zero (NaN otherwise)
+    smape -- Symmetric Mean Absolute Percentage Error,
+             if no 0,0-pairs are present in actual and predicted (NaN otherwise)
+    msd -- Mean Square Deviation
+    rmsd -- Root Mean Square Deviation
+    ssr -- Sum of Squared Residuals
+    rsq -- R^2 measure, see sklearn.metrics.r2_score
+    count -- Number of values
+    """
+    if type(predicted) != np.ndarray:
+        raise ValueError("first arg must be ndarray, is {}".format(type(predicted)))
+    if type(actual) != np.ndarray:
+        raise ValueError("second arg must be ndarray, is {}".format(type(actual)))
+    deviations = predicted - actual
+    # mean = np.mean(actual)
+    if len(deviations) == 0:
+        return {}
+    measures = {
+        "mae": np.mean(np.abs(deviations), dtype=np.float64),
+        "msd": np.mean(deviations ** 2, dtype=np.float64),
+        "rmsd": np.sqrt(np.mean(deviations ** 2), dtype=np.float64),
+        "ssr": np.sum(deviations ** 2, dtype=np.float64),
+        "rsq": r2_score(actual, predicted),
+        "count": len(actual),
+    }
+
+    # rsq_quotient = np.sum((actual - mean)**2, dtype=np.float64) * np.sum((predicted - mean)**2, dtype=np.float64)
+
+    if np.all(actual != 0):
+        measures["mape"] = np.mean(np.abs(deviations / actual)) * 100  # bad measure
+    else:
+        measures["mape"] = np.nan
+    if np.all(np.abs(predicted) + np.abs(actual) != 0):
+        measures["smape"] = (
+            np.mean(np.abs(deviations) / ((np.abs(predicted) + np.abs(actual)) / 2))
+            * 100
+        )
+    else:
+        measures["smape"] = np.nan
+    # if np.all(rsq_quotient != 0):
+    #    measures['rsq'] = (np.sum((actual - mean) * (predicted - mean), dtype=np.float64)**2) / rsq_quotient
+
+    return measures
+
+
+class ParallelParamFit:
+    """
+    Fit a set of functions on parameterized measurements.
+
+    One parameter is variale, all others are fixed. Reports the best-fitting
+    function type for each parameter.
+    """
+
+    def __init__(self, by_param):
+        """Create a new ParallelParamFit object."""
+        self.fit_queue = []
+        self.by_param = by_param
+
+    def enqueue(
+        self,
+        state_or_tran,
+        attribute,
+        param_index,
+        param_name,
+        safe_functions_enabled=False,
+        param_filter=None,
+    ):
+        """
+        Add state_or_tran/attribute/param_name to fit queue.
+
+        This causes fit() to compute the best-fitting function for this model part.
+        """
+        self.fit_queue.append(
+            {
+                "key": [state_or_tran, attribute, param_name, param_filter],
+                "args": [
+                    self.by_param,
+                    state_or_tran,
+                    attribute,
+                    param_index,
+                    safe_functions_enabled,
+                    param_filter,
+                ],
+            }
+        )
+
+    def fit(self):
+        """
+        Fit functions on previously enqueue data.
+
+        Fitting is one in parallel with one process per core.
+
+        Results can be accessed using the public ParallelParamFit.results object.
+        """
+        with Pool() as pool:
+            self.results = pool.map(_try_fits_parallel, self.fit_queue)
+
+    def get_result(self, name, attribute, param_filter: dict = None):
+        """
+        Parse and sanitize fit results for state/transition/... 'name' and model attribute 'attribute'.
+
+        Filters out results where the best function is worse (or not much better than) static mean/median estimates.
+
+        :param name: state/transition/... name, e.g. 'TX'
+        :param attribute: model attribute, e.g. 'duration'
+        :param param_filter:
+        :returns: dict with fit result (see `_try_fits`) for each successfully fitted parameter. E.g. {'param 1': {'best' : 'function name', ...} }
+        """
+        fit_result = dict()
+        for result in self.results:
+            if (
+                result["key"][0] == name
+                and result["key"][1] == attribute
+                and result["key"][3] == param_filter
+                and result["result"]["best"] is not None
+            ):  # dürfte an ['best'] != None liegen-> Fit für gefilterten Kram schlägt fehl?
+                this_result = result["result"]
+                if this_result["best_rmsd"] >= min(
+                    this_result["mean_rmsd"], this_result["median_rmsd"]
+                ):
+                    logger.debug(
+                        "Not modeling {} {} as function of {}: best ({:.0f}) is worse than ref ({:.0f}, {:.0f})".format(
+                            name,
+                            attribute,
+                            result["key"][2],
+                            this_result["best_rmsd"],
+                            this_result["mean_rmsd"],
+                            this_result["median_rmsd"],
+                        )
+                    )
+                # See notes on depends_on_param
+                elif this_result["best_rmsd"] >= 0.8 * min(
+                    this_result["mean_rmsd"], this_result["median_rmsd"]
+                ):
+                    logger.debug(
+                        "Not modeling {} {} as function of {}: best ({:.0f}) is not much better than ref ({:.0f}, {:.0f})".format(
+                            name,
+                            attribute,
+                            result["key"][2],
+                            this_result["best_rmsd"],
+                            this_result["mean_rmsd"],
+                            this_result["median_rmsd"],
+                        )
+                    )
+                else:
+                    fit_result[result["key"][2]] = this_result
+        return fit_result
+
+
+def _try_fits_parallel(arg):
+    """
+    Call _try_fits(*arg['args']) and return arg['key'] and the _try_fits result.
+
+    Must be a global function as it is called from a multiprocessing Pool.
+    """
+    return {"key": arg["key"], "result": _try_fits(*arg["args"])}
+
+
+def _try_fits(
+    by_param,
+    state_or_tran,
+    model_attribute,
+    param_index,
+    safe_functions_enabled=False,
+    param_filter: dict = None,
+):
+    """
+    Determine goodness-of-fit for prediction of `by_param[(state_or_tran, *)][model_attribute]` dependence on `param_index` using various functions.
+
+    This is done by varying `param_index` while keeping all other parameters constant and doing one least squares optimization for each function and for each combination of the remaining parameters.
+    The value of the parameter corresponding to `param_index` (e.g. txpower or packet length) is the sole input to the model function.
+    Only numeric parameter values (as determined by `utils.is_numeric`) are used for fitting, non-numeric values such as None or enum strings are ignored.
+    Fitting is only performed if at least three distinct parameter values exist in `by_param[(state_or_tran, *)]`.
+
+    :returns:  a dictionary with the following elements:
+        best -- name of the best-fitting function (see `analytic.functions`). `None` in case of insufficient data.
+        best_rmsd -- mean Root Mean Square Deviation of best-fitting function over all combinations of the remaining parameters
+        mean_rmsd -- mean Root Mean Square Deviation of a reference model using the mean of its respective input data as model value
+        median_rmsd -- mean Root Mean Square Deviation of a reference model using the median of its respective input data as model value
+        results -- mean goodness-of-fit measures for the individual functions. See `analytic.functions` for keys and `aggregate_measures` for values
+
+    :param by_param: measurements partitioned by state/transition/... name and parameter values.
+    Example: `{('foo', (0, 2)): {'bar': [2]}, ('foo', (0, 4)): {'bar': [4]}, ('foo', (0, 6)): {'bar': [6]}}`
+
+    :param state_or_tran: state/transition/... name for which goodness-of-fit will be calculated (first element of by_param key tuple).
+    Example: `'foo'`
+
+    :param model_attribute: attribute for which goodness-of-fit will be calculated.
+    Example: `'bar'`
+
+    :param param_index: index of the parameter used as model input
+    :param safe_functions_enabled: Include "safe" variants of functions with limited argument range.
+    :param param_filter: Only use measurements whose parameters match param_filter for fitting.
+    """
+
+    functions = analytic.functions(safe_functions_enabled=safe_functions_enabled)
+
+    for param_key in filter(lambda x: x[0] == state_or_tran, by_param.keys()):
+        # We might remove elements from 'functions' while iterating over
+        # its keys. A generator will not allow this, so we need to
+        # convert to a list.
+        function_names = list(functions.keys())
+        for function_name in function_names:
+            function_object = functions[function_name]
+            if is_numeric(param_key[1][param_index]) and not function_object.is_valid(
+                param_key[1][param_index]
+            ):
+                functions.pop(function_name, None)
+
+    raw_results = dict()
+    raw_results_by_param = dict()
+    ref_results = {"mean": list(), "median": list()}
+    results = dict()
+    results_by_param = dict()
+
+    seen_parameter_combinations = set()
+
+    # for each parameter combination:
+    for param_key in filter(
+        lambda x: x[0] == state_or_tran
+        and remove_index_from_tuple(x[1], param_index)
+        not in seen_parameter_combinations
+        and len(by_param[x]["param"])
+        and match_parameter_values(by_param[x]["param"][0], param_filter),
+        by_param.keys(),
+    ):
+        X = []
+        Y = []
+        num_valid = 0
+        num_total = 0
+
+        # Ensure that each parameter combination is only optimized once. Otherwise, with parameters (1, 2, 5), (1, 3, 5), (1, 4, 5) and param_index == 1,
+        # the parameter combination (1, *, 5) would be optimized three times, both wasting time and biasing results towards more frequently occuring combinations of non-param_index parameters
+        seen_parameter_combinations.add(
+            remove_index_from_tuple(param_key[1], param_index)
+        )
+
+        # for each value of the parameter denoted by param_index (all other parameters remain the same):
+        for k, v in filter(
+            lambda kv: param_slice_eq(kv[0], param_key, param_index), by_param.items()
+        ):
+            num_total += 1
+            if is_numeric(k[1][param_index]):
+                num_valid += 1
+                X.extend([float(k[1][param_index])] * len(v[model_attribute]))
+                Y.extend(v[model_attribute])
+
+        if num_valid > 2:
+            X = np.array(X)
+            Y = np.array(Y)
+            other_parameters = remove_index_from_tuple(k[1], param_index)
+            raw_results_by_param[other_parameters] = dict()
+            results_by_param[other_parameters] = dict()
+            for function_name, param_function in functions.items():
+                if function_name not in raw_results:
+                    raw_results[function_name] = dict()
+                error_function = param_function.error_function
+                res = optimize.least_squares(
+                    error_function, [0, 1], args=(X, Y), xtol=2e-15
+                )
+                measures = regression_measures(param_function.eval(res.x, X), Y)
+                raw_results_by_param[other_parameters][function_name] = measures
+                for measure, error_rate in measures.items():
+                    if measure not in raw_results[function_name]:
+                        raw_results[function_name][measure] = list()
+                    raw_results[function_name][measure].append(error_rate)
+                # print(function_name, res, measures)
+            mean_measures = aggregate_measures(np.mean(Y), Y)
+            ref_results["mean"].append(mean_measures["rmsd"])
+            raw_results_by_param[other_parameters]["mean"] = mean_measures
+            median_measures = aggregate_measures(np.median(Y), Y)
+            ref_results["median"].append(median_measures["rmsd"])
+            raw_results_by_param[other_parameters]["median"] = median_measures
+
+    if not len(ref_results["mean"]):
+        # Insufficient data for fitting
+        # print('[W] Insufficient data for fitting {}/{}/{}'.format(state_or_tran, model_attribute, param_index))
+        return {"best": None, "best_rmsd": np.inf, "results": results}
+
+    for (
+        other_parameter_combination,
+        other_parameter_results,
+    ) in raw_results_by_param.items():
+        best_fit_val = np.inf
+        best_fit_name = None
+        results = dict()
+        for function_name, result in other_parameter_results.items():
+            if len(result) > 0:
+                results[function_name] = result
+                rmsd = result["rmsd"]
+                if rmsd < best_fit_val:
+                    best_fit_val = rmsd
+                    best_fit_name = function_name
+        results_by_param[other_parameter_combination] = {
+            "best": best_fit_name,
+            "best_rmsd": best_fit_val,
+            "mean_rmsd": results["mean"]["rmsd"],
+            "median_rmsd": results["median"]["rmsd"],
+            "results": results,
+        }
+
+    best_fit_val = np.inf
+    best_fit_name = None
+    results = dict()
+    for function_name, result in raw_results.items():
+        if len(result) > 0:
+            results[function_name] = {}
+            for measure in result.keys():
+                results[function_name][measure] = np.mean(result[measure])
+            rmsd = results[function_name]["rmsd"]
+            if rmsd < best_fit_val:
+                best_fit_val = rmsd
+                best_fit_name = function_name
+
+    return {
+        "best": best_fit_name,
+        "best_rmsd": best_fit_val,
+        "mean_rmsd": np.mean(ref_results["mean"]),
+        "median_rmsd": np.mean(ref_results["median"]),
+        "results": results,
+        "results_by_other_param": results_by_param,
+    }
+
+
+def _num_args_from_by_name(by_name):
+    num_args = dict()
+    for key, value in by_name.items():
+        if "args" in value:
+            num_args[key] = len(value["args"][0])
+    return num_args
+
+
+class AnalyticModel:
+    u"""
+    Parameter-aware analytic energy/data size/... model.
+
+    Supports both static and parameter-based model attributes, and automatic detection of parameter-dependence.
+
+    These provide measurements aggregated by (function/state/...) name
+    and (for by_param) parameter values. Layout:
+    dictionary with one key per name ('send', 'TX', ...) or
+    one key per name and parameter combination
+    (('send', (1, 2)), ('send', (2, 3)), ('TX', (1, 2)), ('TX', (2, 3)), ...).
+
+    Parameter values must be ordered corresponding to the lexically sorted parameter names.
+
+    Each element is in turn a dict with the following elements:
+    - param: list of parameter values in each measurement (-> list of lists)
+    - attributes: list of keys that should be analyzed,
+        e.g. ['power', 'duration']
+    - for each attribute mentioned in 'attributes': A list with measurements.
+      All list except for 'attributes' must have the same length.
+
+    For example:
+    parameters = ['foo_count', 'irrelevant']
+    by_name = {
+        'foo' : [1, 1, 2],
+        'bar' : [5, 6, 7],
+        'attributes' : ['foo', 'bar'],
+        'param' : [[1, 0], [1, 0], [2, 0]]
+    }
+
+    methods:
+    get_static -- return static (parameter-unaware) model.
+    get_param_lut -- return parameter-aware look-up-table model. Cannot model parameter combinations not present in by_param.
+    get_fitted -- return parameter-aware model using fitted functions for behaviour prediction.
+
+    variables:
+    names -- function/state/... names (i.e., the keys of by_name)
+    parameters -- parameter names
+    stats -- ParamStats object providing parameter-dependency statistics for each name and attribute
+    assess -- calculate model quality
+    """
+
+    def __init__(
+        self,
+        by_name,
+        parameters,
+        arg_count=None,
+        function_override=dict(),
+        use_corrcoef=False,
+    ):
+        """
+        Create a new AnalyticModel and compute parameter statistics.
+
+        :param by_name: measurements aggregated by (function/state/...) name.
+            Layout: dictionary with one key per name ('send', 'TX', ...) or
+            one key per name and parameter combination
+            (('send', (1, 2)), ('send', (2, 3)), ('TX', (1, 2)), ('TX', (2, 3)), ...).
+
+            Parameter values must be ordered corresponding to the lexically sorted parameter names.
+
+            Each element is in turn a dict with the following elements:
+            - param: list of parameter values in each measurement (-> list of lists)
+            - attributes: list of keys that should be analyzed,
+                e.g. ['power', 'duration']
+            - for each attribute mentioned in 'attributes': A list with measurements.
+            All list except for 'attributes' must have the same length.
+
+            For example:
+            parameters = ['foo_count', 'irrelevant']
+            by_name = {
+                'foo' : [1, 1, 2],
+                'duration' : [5, 6, 7],
+                'attributes' : ['foo', 'duration'],
+                'param' : [[1, 0], [1, 0], [2, 0]]
+                # foo_count-^  ^-irrelevant
+            }
+        :param parameters: List of parameter names
+        :param function_override: dict of overrides for automatic parameter function generation.
+            If (state or transition name, model attribute) is present in function_override,
+            the corresponding text string is the function used for analytic (parameter-aware/fitted)
+            modeling of this attribute. It is passed to AnalyticFunction, see
+            there for the required format. Note that this happens regardless of
+            parameter dependency detection: The provided analytic function will be assigned
+            even if it seems like the model attribute is static / parameter-independent.
+        :param use_corrcoef: use correlation coefficient instead of stddev comparison to detect whether a model attribute depends on a parameter
+        """
+        self.cache = dict()
+        self.by_name = by_name
+        self.by_param = by_name_to_by_param(by_name)
+        self.names = sorted(by_name.keys())
+        self.parameters = sorted(parameters)
+        self.function_override = function_override.copy()
+        self._use_corrcoef = use_corrcoef
+        self._num_args = arg_count
+        if self._num_args is None:
+            self._num_args = _num_args_from_by_name(by_name)
+
+        self.stats = ParamStats(
+            self.by_name,
+            self.by_param,
+            self.parameters,
+            self._num_args,
+            use_corrcoef=use_corrcoef,
+        )
+
+    def _get_model_from_dict(self, model_dict, model_function):
+        model = {}
+        for name, elem in model_dict.items():
+            model[name] = {}
+            for key in elem["attributes"]:
+                try:
+                    model[name][key] = model_function(elem[key])
+                except RuntimeWarning:
+                    logger.warning("Got no data for {} {}".format(name, key))
+                except FloatingPointError as fpe:
+                    logger.warning("Got no data for {} {}: {}".format(name, key, fpe))
+        return model
+
+    def param_index(self, param_name):
+        if param_name in self.parameters:
+            return self.parameters.index(param_name)
+        return len(self.parameters) + int(param_name)
+
+    def param_name(self, param_index):
+        if param_index < len(self.parameters):
+            return self.parameters[param_index]
+        return str(param_index)
+
+    def get_static(self, use_mean=False):
+        """
+        Get static model function: name, attribute -> model value.
+
+        Uses the median of by_name for modeling.
+        """
+        getter_function = np.median
+
+        if use_mean:
+            getter_function = np.mean
+
+        static_model = self._get_model_from_dict(self.by_name, getter_function)
+
+        def static_model_getter(name, key, **kwargs):
+            return static_model[name][key]
+
+        return static_model_getter
+
+    def get_param_lut(self, fallback=False):
+        """
+        Get parameter-look-up-table model function: name, attribute, parameter values -> model value.
+
+        The function can only give model values for parameter combinations
+        present in by_param. By default, it raises KeyError for other values.
+
+        arguments:
+        fallback -- Fall back to the (non-parameter-aware) static model when encountering unknown parameter values
+        """
+        static_model = self._get_model_from_dict(self.by_name, np.median)
+        lut_model = self._get_model_from_dict(self.by_param, np.median)
+
+        def lut_median_getter(name, key, param, arg=[], **kwargs):
+            param.extend(map(soft_cast_int, arg))
+            try:
+                return lut_model[(name, tuple(param))][key]
+            except KeyError:
+                if fallback:
+                    return static_model[name][key]
+                raise
+
+        return lut_median_getter
+
+    def get_fitted(self, safe_functions_enabled=False):
+        """
+        Get paramete-aware model function and model information function.
+
+        Returns two functions:
+        model_function(name, attribute, param=parameter values) -> model value.
+        model_info(name, attribute) -> {'fit_result' : ..., 'function' : ... } or None
+        """
+        if "fitted_model_getter" in self.cache and "fitted_info_getter" in self.cache:
+            return self.cache["fitted_model_getter"], self.cache["fitted_info_getter"]
+
+        static_model = self._get_model_from_dict(self.by_name, np.median)
+        param_model = dict([[name, {}] for name in self.by_name.keys()])
+        paramfit = ParallelParamFit(self.by_param)
+
+        for name in self.by_name.keys():
+            for attribute in self.by_name[name]["attributes"]:
+                for param_index, param in enumerate(self.parameters):
+                    if self.stats.depends_on_param(name, attribute, param):
+                        paramfit.enqueue(name, attribute, param_index, param, False)
+                if arg_support_enabled and name in self._num_args:
+                    for arg_index in range(self._num_args[name]):
+                        if self.stats.depends_on_arg(name, attribute, arg_index):
+                            paramfit.enqueue(
+                                name,
+                                attribute,
+                                len(self.parameters) + arg_index,
+                                arg_index,
+                                False,
+                            )
+
+        paramfit.fit()
+
+        for name in self.by_name.keys():
+            num_args = 0
+            if name in self._num_args:
+                num_args = self._num_args[name]
+            for attribute in self.by_name[name]["attributes"]:
+                fit_result = paramfit.get_result(name, attribute)
+
+                if (name, attribute) in self.function_override:
+                    function_str = self.function_override[(name, attribute)]
+                    x = AnalyticFunction(function_str, self.parameters, num_args)
+                    x.fit(self.by_param, name, attribute)
+                    if x.fit_success:
+                        param_model[name][attribute] = {
+                            "fit_result": fit_result,
+                            "function": x,
+                        }
+                elif len(fit_result.keys()):
+                    x = analytic.function_powerset(
+                        fit_result, self.parameters, num_args
+                    )
+                    x.fit(self.by_param, name, attribute)
+
+                    if x.fit_success:
+                        param_model[name][attribute] = {
+                            "fit_result": fit_result,
+                            "function": x,
+                        }
+
+        def model_getter(name, key, **kwargs):
+            if "arg" in kwargs and "param" in kwargs:
+                kwargs["param"].extend(map(soft_cast_int, kwargs["arg"]))
+            if key in param_model[name]:
+                param_list = kwargs["param"]
+                param_function = param_model[name][key]["function"]
+                if param_function.is_predictable(param_list):
+                    return param_function.eval(param_list)
+            return static_model[name][key]
+
+        def info_getter(name, key):
+            if key in param_model[name]:
+                return param_model[name][key]
+            return None
+
+        self.cache["fitted_model_getter"] = model_getter
+        self.cache["fitted_info_getter"] = info_getter
+
+        return model_getter, info_getter
+
+    def assess(self, model_function):
+        """
+        Calculate MAE, SMAPE, etc. of model_function for each by_name entry.
+
+        state/transition/... name and parameter values are fed into model_function.
+        The by_name entries of this AnalyticModel are used as ground truth and
+        compared with the values predicted by model_function.
+
+        For proper model assessments, the data used to generate model_function
+        and the data fed into this AnalyticModel instance must be mutually
+        exclusive (e.g. by performing cross validation). Otherwise,
+        overfitting cannot be detected.
+        """
+        detailed_results = {}
+        for name, elem in sorted(self.by_name.items()):
+            detailed_results[name] = {}
+            for attribute in elem["attributes"]:
+                predicted_data = np.array(
+                    list(
+                        map(
+                            lambda i: model_function(
+                                name, attribute, param=elem["param"][i]
+                            ),
+                            range(len(elem[attribute])),
+                        )
+                    )
+                )
+                measures = regression_measures(predicted_data, elem[attribute])
+                detailed_results[name][attribute] = measures
+
+        return {"by_name": detailed_results}
+
+    def to_json(self):
+        # TODO
+        pass
+
+
+class PTAModel:
+    u"""
+    Parameter-aware PTA-based energy model.
+
+    Supports both static and parameter-based model attributes, and automatic detection of parameter-dependence.
+
+    The model heavily relies on two internal data structures:
+    PTAModel.by_name and PTAModel.by_param.
+
+    These provide measurements aggregated by state/transition name
+    and (for by_param) parameter values. Layout:
+    dictionary with one key per state/transition ('send', 'TX', ...) or
+    one key per state/transition and parameter combination
+    (('send', (1, 2)), ('send', (2, 3)), ('TX', (1, 2)), ('TX', (2, 3)), ...).
+    For by_param, parameter values are ordered corresponding to the lexically sorted parameter names.
+
+    Each element is in turn a dict with the following elements:
+    - isa: 'state' or 'transition'
+    - power: list of mean power measurements in µW
+    - duration: list of durations in µs
+    - power_std: list of stddev of power per state/transition
+    - energy: consumed energy (power*duration) in pJ
+    - paramkeys: list of parameter names in each measurement (-> list of lists)
+    - param: list of parameter values in each measurement (-> list of lists)
+    - attributes: list of keys that should be analyzed,
+        e.g. ['power', 'duration']
+    additionally, only if isa == 'transition':
+    - timeout: list of duration of previous state in µs
+    - rel_energy_prev: transition energy relative to previous state mean power in pJ
+    - rel_energy_next: transition energy relative to next state mean power in pJ
+    """
+
+    def __init__(
+        self,
+        by_name,
+        parameters,
+        arg_count,
+        traces=[],
+        ignore_trace_indexes=[],
+        function_override={},
+        use_corrcoef=False,
+        pta=None,
+    ):
+        """
+        Prepare a new PTA energy model.
+
+        Actual model generation is done on-demand by calling the respective functions.
+
+        arguments:
+        by_name -- state/transition measurements aggregated by name, as returned by pta_trace_to_aggregate.
+        parameters -- list of parameter names, as returned by pta_trace_to_aggregate
+        arg_count -- function arguments, as returned by pta_trace_to_aggregate
+        traces -- list of preprocessed DFA traces, as returned by RawData.get_preprocessed_data()
+        ignore_trace_indexes -- list of trace indexes. The corresponding traces will be ignored.
+        function_override -- dict of overrides for automatic parameter function generation.
+            If (state or transition name, model attribute) is present in function_override,
+            the corresponding text string is the function used for analytic (parameter-aware/fitted)
+            modeling of this attribute. It is passed to AnalyticFunction, see
+            there for the required format. Note that this happens regardless of
+            parameter dependency detection: The provided analytic function will be assigned
+            even if it seems like the model attribute is static / parameter-independent.
+        use_corrcoef -- use correlation coefficient instead of stddev comparison
+            to detect whether a model attribute depends on a parameter
+        pta -- hardware model as `PTA` object
+        """
+        self.by_name = by_name
+        self.by_param = by_name_to_by_param(by_name)
+        self._parameter_names = sorted(parameters)
+        self._num_args = arg_count
+        self._use_corrcoef = use_corrcoef
+        self.traces = traces
+        self.stats = ParamStats(
+            self.by_name,
+            self.by_param,
+            self._parameter_names,
+            self._num_args,
+            self._use_corrcoef,
+        )
+        self.cache = {}
+        np.seterr("raise")
+        self.function_override = function_override.copy()
+        self.pta = pta
+        self.ignore_trace_indexes = ignore_trace_indexes
+        self._aggregate_to_ndarray(self.by_name)
+
+    def _aggregate_to_ndarray(self, aggregate):
+        for elem in aggregate.values():
+            for key in elem["attributes"]:
+                elem[key] = np.array(elem[key])
+
+    # This heuristic is very similar to the "function is not much better than
+    # median" checks in get_fitted. So far, doing it here as well is mostly
+    # a performance and not an algorithm quality decision.
+    # --df, 2018-04-18
+    def depends_on_param(self, state_or_trans, key, param):
+        return self.stats.depends_on_param(state_or_trans, key, param)
+
+    # See notes on depends_on_param
+    def depends_on_arg(self, state_or_trans, key, param):
+        return self.stats.depends_on_arg(state_or_trans, key, param)
+
+    def _get_model_from_dict(self, model_dict, model_function):
+        model = {}
+        for name, elem in model_dict.items():
+            model[name] = {}
+            for key in elem["attributes"]:
+                try:
+                    model[name][key] = model_function(elem[key])
+                except RuntimeWarning:
+                    logger.warning("Got no data for {} {}".format(name, key))
+                except FloatingPointError as fpe:
+                    logger.warning("Got no data for {} {}: {}".format(name, key, fpe))
+        return model
+
+    def get_static(self, use_mean=False):
+        """
+        Get static model function: name, attribute -> model value.
+
+        Uses the median of by_name for modeling, unless `use_mean` is set.
+        """
+        getter_function = np.median
+
+        if use_mean:
+            getter_function = np.mean
+
+        static_model = self._get_model_from_dict(self.by_name, getter_function)
+
+        def static_model_getter(name, key, **kwargs):
+            return static_model[name][key]
+
+        return static_model_getter
+
+    def get_param_lut(self, fallback=False):
+        """
+        Get parameter-look-up-table model function: name, attribute, parameter values -> model value.
+
+        The function can only give model values for parameter combinations
+        present in by_param. By default, it raises KeyError for other values.
+
+        arguments:
+        fallback -- Fall back to the (non-parameter-aware) static model when encountering unknown parameter values
+        """
+        static_model = self._get_model_from_dict(self.by_name, np.median)
+        lut_model = self._get_model_from_dict(self.by_param, np.median)
+
+        def lut_median_getter(name, key, param, arg=[], **kwargs):
+            param.extend(map(soft_cast_int, arg))
+            try:
+                return lut_model[(name, tuple(param))][key]
+            except KeyError:
+                if fallback:
+                    return static_model[name][key]
+                raise
+
+        return lut_median_getter
+
+    def param_index(self, param_name):
+        if param_name in self._parameter_names:
+            return self._parameter_names.index(param_name)
+        return len(self._parameter_names) + int(param_name)
+
+    def param_name(self, param_index):
+        if param_index < len(self._parameter_names):
+            return self._parameter_names[param_index]
+        return str(param_index)
+
+    def get_fitted(self, safe_functions_enabled=False):
+        """
+        Get parameter-aware model function and model information function.
+
+        Returns two functions:
+        model_function(name, attribute, param=parameter values) -> model value.
+        model_info(name, attribute) -> {'fit_result' : ..., 'function' : ... } or None
+        """
+        if "fitted_model_getter" in self.cache and "fitted_info_getter" in self.cache:
+            return self.cache["fitted_model_getter"], self.cache["fitted_info_getter"]
+
+        static_model = self._get_model_from_dict(self.by_name, np.median)
+        param_model = dict(
+            [[state_or_tran, {}] for state_or_tran in self.by_name.keys()]
+        )
+        paramfit = ParallelParamFit(self.by_param)
+        for state_or_tran in self.by_name.keys():
+            for model_attribute in self.by_name[state_or_tran]["attributes"]:
+                fit_results = {}
+                for parameter_index, parameter_name in enumerate(self._parameter_names):
+                    if self.depends_on_param(
+                        state_or_tran, model_attribute, parameter_name
+                    ):
+                        paramfit.enqueue(
+                            state_or_tran,
+                            model_attribute,
+                            parameter_index,
+                            parameter_name,
+                            safe_functions_enabled,
+                        )
+                if (
+                    arg_support_enabled
+                    and self.by_name[state_or_tran]["isa"] == "transition"
+                ):
+                    for arg_index in range(self._num_args[state_or_tran]):
+                        if self.depends_on_arg(
+                            state_or_tran, model_attribute, arg_index
+                        ):
+                            paramfit.enqueue(
+                                state_or_tran,
+                                model_attribute,
+                                len(self._parameter_names) + arg_index,
+                                arg_index,
+                                safe_functions_enabled,
+                            )
+        paramfit.fit()
+
+        for state_or_tran in self.by_name.keys():
+            num_args = 0
+            if (
+                arg_support_enabled
+                and self.by_name[state_or_tran]["isa"] == "transition"
+            ):
+                num_args = self._num_args[state_or_tran]
+            for model_attribute in self.by_name[state_or_tran]["attributes"]:
+                fit_results = paramfit.get_result(state_or_tran, model_attribute)
+
+                if (state_or_tran, model_attribute) in self.function_override:
+                    function_str = self.function_override[
+                        (state_or_tran, model_attribute)
+                    ]
+                    x = AnalyticFunction(function_str, self._parameter_names, num_args)
+                    x.fit(self.by_param, state_or_tran, model_attribute)
+                    if x.fit_success:
+                        param_model[state_or_tran][model_attribute] = {
+                            "fit_result": fit_results,
+                            "function": x,
+                        }
+                elif len(fit_results.keys()):
+                    x = analytic.function_powerset(
+                        fit_results, self._parameter_names, num_args
+                    )
+                    x.fit(self.by_param, state_or_tran, model_attribute)
+                    if x.fit_success:
+                        param_model[state_or_tran][model_attribute] = {
+                            "fit_result": fit_results,
+                            "function": x,
+                        }
+
+        def model_getter(name, key, **kwargs):
+            if "arg" in kwargs and "param" in kwargs:
+                kwargs["param"].extend(map(soft_cast_int, kwargs["arg"]))
+            if key in param_model[name]:
+                param_list = kwargs["param"]
+                param_function = param_model[name][key]["function"]
+                if param_function.is_predictable(param_list):
+                    return param_function.eval(param_list)
+            return static_model[name][key]
+
+        def info_getter(name, key):
+            if key in param_model[name]:
+                return param_model[name][key]
+            return None
+
+        self.cache["fitted_model_getter"] = model_getter
+        self.cache["fitted_info_getter"] = info_getter
+
+        return model_getter, info_getter
+
+    def to_json(self):
+        static_model = self.get_static()
+        static_quality = self.assess(static_model)
+        param_model, param_info = self.get_fitted()
+        analytic_quality = self.assess(param_model)
+        pta = self.pta
+        if pta is None:
+            pta = PTA(self.states(), parameters=self._parameter_names)
+        pta.update(
+            static_model,
+            param_info,
+            static_error=static_quality["by_name"],
+            analytic_error=analytic_quality["by_name"],
+        )
+        return pta.to_json()
+
+    def states(self):
+        """Return sorted list of state names."""
+        return sorted(
+            list(
+                filter(lambda k: self.by_name[k]["isa"] == "state", self.by_name.keys())
+            )
+        )
+
+    def transitions(self):
+        """Return sorted list of transition names."""
+        return sorted(
+            list(
+                filter(
+                    lambda k: self.by_name[k]["isa"] == "transition",
+                    self.by_name.keys(),
+                )
+            )
+        )
+
+    def states_and_transitions(self):
+        """Return list of states and transition names."""
+        ret = self.states()
+        ret.extend(self.transitions())
+        return ret
+
+    def parameters(self):
+        return self._parameter_names
+
+    def attributes(self, state_or_trans):
+        return self.by_name[state_or_trans]["attributes"]
+
+    def assess(self, model_function):
+        """
+        Calculate MAE, SMAPE, etc. of model_function for each by_name entry.
+
+        state/transition/... name and parameter values are fed into model_function.
+        The by_name entries of this PTAModel are used as ground truth and
+        compared with the values predicted by model_function.
+
+        For proper model assessments, the data used to generate model_function
+        and the data fed into this AnalyticModel instance must be mutually
+        exclusive (e.g. by performing cross validation). Otherwise,
+        overfitting cannot be detected.
+        """
+        detailed_results = {}
+        for name, elem in sorted(self.by_name.items()):
+            detailed_results[name] = {}
+            for key in elem["attributes"]:
+                predicted_data = np.array(
+                    list(
+                        map(
+                            lambda i: model_function(name, key, param=elem["param"][i]),
+                            range(len(elem[key])),
+                        )
+                    )
+                )
+                measures = regression_measures(predicted_data, elem[key])
+                detailed_results[name][key] = measures
+
+        return {"by_name": detailed_results}
+
+    def assess_states(
+        self, model_function, model_attribute="power", distribution: dict = None
+    ):
+        """
+        Calculate overall model error assuming equal distribution of states
+        """
+        # TODO calculate mean power draw for distribution and use it to
+        # calculate relative error from MAE combination
+        model_quality = self.assess(model_function)
+        num_states = len(self.states())
+        if distribution is None:
+            distribution = dict(map(lambda x: [x, 1 / num_states], self.states()))
+
+        if not np.isclose(sum(distribution.values()), 1):
+            raise ValueError(
+                "distribution must be a probability distribution with sum 1"
+            )
+
+        # total_value = None
+        # try:
+        #     total_value = sum(map(lambda x: model_function(x, model_attribute) * distribution[x], self.states()))
+        # except KeyError:
+        #     pass
+
+        total_error = np.sqrt(
+            sum(
+                map(
+                    lambda x: np.square(
+                        model_quality["by_name"][x][model_attribute]["mae"]
+                        * distribution[x]
+                    ),
+                    self.states(),
+                )
+            )
+        )
+        return total_error
+
+    def assess_on_traces(self, model_function):
+        """
+        Calculate MAE, SMAPE, etc. of model_function for each trace known to this PTAModel instance.
+
+        :returns: dict of `duration_by_trace`, `energy_by_trace`, `timeout_by_trace`, `rel_energy_by_trace` and `state_energy_by_trace`.
+            Each entry holds regression measures for the corresponding measure. Note that the determined model quality heavily depends on the
+            traces: small-ish absolute errors in states which frequently occur may have more effect than large absolute errors in rarely occuring states
+        """
+        model_energy_list = []
+        real_energy_list = []
+        model_rel_energy_list = []
+        model_state_energy_list = []
+        model_duration_list = []
+        real_duration_list = []
+        model_timeout_list = []
+        real_timeout_list = []
+
+        for trace in self.traces:
+            if trace["id"] not in self.ignore_trace_indexes:
+                for rep_id in range(len(trace["trace"][0]["offline"])):
+                    model_energy = 0.0
+                    real_energy = 0.0
+                    model_rel_energy = 0.0
+                    model_state_energy = 0.0
+                    model_duration = 0.0
+                    real_duration = 0.0
+                    model_timeout = 0.0
+                    real_timeout = 0.0
+                    for i, trace_part in enumerate(trace["trace"]):
+                        name = trace_part["name"]
+                        prev_name = trace["trace"][i - 1]["name"]
+                        isa = trace_part["isa"]
+                        if name != "UNINITIALIZED":
+                            try:
+                                param = trace_part["offline_aggregates"]["param"][
+                                    rep_id
+                                ]
+                                prev_param = trace["trace"][i - 1][
+                                    "offline_aggregates"
+                                ]["param"][rep_id]
+                                power = trace_part["offline"][rep_id]["uW_mean"]
+                                duration = trace_part["offline"][rep_id]["us"]
+                                prev_duration = trace["trace"][i - 1]["offline"][
+                                    rep_id
+                                ]["us"]
+                                real_energy += power * duration
+                                if isa == "state":
+                                    model_energy += (
+                                        model_function(name, "power", param=param)
+                                        * duration
+                                    )
+                                else:
+                                    model_energy += model_function(
+                                        name, "energy", param=param
+                                    )
+                                    # If i == 1, the previous state was UNINITIALIZED, for which we do not have model data
+                                    if i == 1:
+                                        model_rel_energy += model_function(
+                                            name, "energy", param=param
+                                        )
+                                    else:
+                                        model_rel_energy += model_function(
+                                            prev_name, "power", param=prev_param
+                                        ) * (prev_duration + duration)
+                                        model_state_energy += model_function(
+                                            prev_name, "power", param=prev_param
+                                        ) * (prev_duration + duration)
+                                    model_rel_energy += model_function(
+                                        name, "rel_energy_prev", param=param
+                                    )
+                                    real_duration += duration
+                                    model_duration += model_function(
+                                        name, "duration", param=param
+                                    )
+                                    if (
+                                        "plan" in trace_part
+                                        and trace_part["plan"]["level"] == "epilogue"
+                                    ):
+                                        real_timeout += trace_part["offline"][rep_id][
+                                            "timeout"
+                                        ]
+                                        model_timeout += model_function(
+                                            name, "timeout", param=param
+                                        )
+                            except KeyError:
+                                # if states/transitions have been removed via --filter-param, this is harmless
+                                pass
+                    real_energy_list.append(real_energy)
+                    model_energy_list.append(model_energy)
+                    model_rel_energy_list.append(model_rel_energy)
+                    model_state_energy_list.append(model_state_energy)
+                    real_duration_list.append(real_duration)
+                    model_duration_list.append(model_duration)
+                    real_timeout_list.append(real_timeout)
+                    model_timeout_list.append(model_timeout)
+
+        return {
+            "duration_by_trace": regression_measures(
+                np.array(model_duration_list), np.array(real_duration_list)
+            ),
+            "energy_by_trace": regression_measures(
+                np.array(model_energy_list), np.array(real_energy_list)
+            ),
+            "timeout_by_trace": regression_measures(
+                np.array(model_timeout_list), np.array(real_timeout_list)
+            ),
+            "rel_energy_by_trace": regression_measures(
+                np.array(model_rel_energy_list), np.array(real_energy_list)
+            ),
+            "state_energy_by_trace": regression_measures(
+                np.array(model_state_energy_list), np.array(real_energy_list)
+            ),
+        }
diff --git a/lib/parameters.py b/lib/parameters.py
index 8b562b6..5c6b978 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -1,11 +1,15 @@
 import itertools
+import logging
 import numpy as np
+import warnings
 from collections import OrderedDict
 from copy import deepcopy
 from multiprocessing import Pool
 from .utils import remove_index_from_tuple, is_numeric
 from .utils import filter_aggregate_by_param, by_name_to_by_param
 
+logger = logging.getLogger(__name__)
+
 
 def distinct_param_values(by_name, state_or_tran):
     """
@@ -78,25 +82,7 @@ def _reduce_param_matrix(matrix: np.ndarray, parameter_names: list) -> list:
     return list()
 
 
-def _codependent_parameters(param, lut_by_param_values, std_by_param_values):
-    """
-    Return list of parameters which affect whether a parameter affects a model attribute or not.
-    """
-    return list()
-    safe_div = np.vectorize(lambda x, y: 0.0 if x == 0 else 1 - x / y)
-    ratio_by_value = safe_div(lut_by_param_values, std_by_param_values)
-    err_mode = np.seterr("ignore")
-    dep_by_value = ratio_by_value > 0.5
-    np.seterr(**err_mode)
-
-    other_param_list = list(filter(lambda x: x != param, self._parameter_names))
-    influencer_parameters = _reduce_param_matrix(dep_by_value, other_param_list)
-    return influencer_parameters
-
-
-def _std_by_param(
-    by_param, all_param_values, state_or_tran, attribute, param_index, verbose=False
-):
+def _std_by_param(by_param, all_param_values, state_or_tran, attribute, param_index):
     u"""
     Calculate standard deviations for a static model where all parameters but `param_index` are constant.
 
@@ -162,12 +148,11 @@ def _std_by_param(
         #    vprint(verbose, '[W] parameter value partition for {} is empty'.format(param_value))
 
     if np.all(np.isnan(stddev_matrix)):
-        print(
-            "[W] {}/{} parameter #{} has no data partitions -- how did this even happen?".format(
-                state_or_tran, attribute, param_index
+        warnings.warn(
+            "{}/{} parameter #{} has no data partitions. stddev_matrix = {}".format(
+                state_or_tran, attribute, param_index, stddev_matrix
             )
         )
-        print("stddev_matrix = {}".format(stddev_matrix))
         return stddev_matrix, 0.0
 
     return (
@@ -202,13 +187,13 @@ def _corr_by_param(by_name, state_or_trans, attribute, param_index):
             # -> assume no correlation
             return 0.0
         except ValueError:
-            print(
-                "[!] Exception in _corr_by_param(by_name, state_or_trans={}, attribute={}, param_index={})".format(
+            logger.error(
+                "ValueError in _corr_by_param(by_name, state_or_trans={}, attribute={}, param_index={})".format(
                     state_or_trans, attribute, param_index
                 )
             )
-            print(
-                "[!] while executing np.corrcoef(by_name[{}][{}]={}, {}))".format(
+            logger.error(
+                "while executing np.corrcoef(by_name[{}][{}]={}, {}))".format(
                     state_or_trans,
                     attribute,
                     by_name[state_or_trans][attribute],
@@ -229,7 +214,6 @@ def _compute_param_statistics(
     attribute,
     distinct_values,
     distinct_values_by_param_index,
-    verbose=False,
 ):
     """
     Compute standard deviation and correlation coefficient for various data partitions.
@@ -252,7 +236,6 @@ def _compute_param_statistics(
     :param arg_count: dict providing the number of functions args ("local parameters") for each function.
     :param state_or_trans: state or transition name, e.g. 'send' or 'TX'
     :param attribute: model attribute, e.g. 'power' or 'duration'
-    :param verbose: print warning if some parameter partitions are too small for fitting
 
     :returns: a dict with the following content:
     std_static -- static parameter-unaware model error: stddev of by_name[state_or_trans][attribute]
@@ -267,6 +250,8 @@ def _compute_param_statistics(
     corr_by_param -- correlation coefficient
     corr_by_arg -- same, but ignoring a single function argument
         Only set if state_or_trans appears in arg_count, empty dict otherwise.
+    depends_on_param -- dict(parameter_name -> Bool). True if /attribute/ behaviour probably depends on /parameter_name/
+    depends_on_arg -- list(bool). Same, but for function arguments, if any.
     """
     ret = {
         "std_static": np.std(by_name[state_or_trans][attribute]),
@@ -287,7 +272,6 @@ def _compute_param_statistics(
         "corr_by_arg": [],
         "depends_on_param": {},
         "depends_on_arg": [],
-        "param_data": {},
     }
 
     np.seterr("raise")
@@ -299,7 +283,6 @@ def _compute_param_statistics(
             state_or_trans,
             attribute,
             param_idx,
-            verbose,
         )
         ret["std_by_param"][param] = mean_std
         ret["std_by_param_values"][param] = std_matrix
@@ -314,49 +297,6 @@ def _compute_param_statistics(
             ret["std_param_lut"],
         )
 
-        if ret["depends_on_param"][param]:
-            ret["param_data"][param] = {
-                "codependent_parameters": _codependent_parameters(
-                    param, lut_matrix, std_matrix
-                ),
-                "depends_for_codependent_value": dict(),
-            }
-
-            # calculate parameter dependence for individual values of codependent parameters
-            codependent_param_values = list()
-            for codependent_param in ret["param_data"][param]["codependent_parameters"]:
-                codependent_param_values.append(distinct_values[codependent_param])
-            for combi in itertools.product(*codependent_param_values):
-                by_name_part = deepcopy(by_name)
-                filter_list = list(
-                    zip(ret["param_data"][param]["codependent_parameters"], combi)
-                )
-                filter_aggregate_by_param(by_name_part, parameter_names, filter_list)
-                by_param_part = by_name_to_by_param(by_name_part)
-                # there may be no data for this specific parameter value combination
-                if state_or_trans in by_name_part:
-                    part_corr = _corr_by_param(
-                        by_name_part, state_or_trans, attribute, param_idx
-                    )
-                    part_std_lut = np.mean(
-                        [
-                            np.std(by_param_part[x][attribute])
-                            for x in by_param_part.keys()
-                            if x[0] == state_or_trans
-                        ]
-                    )
-                    _, part_std_param, _ = _std_by_param(
-                        by_param_part,
-                        distinct_values_by_param_index,
-                        state_or_trans,
-                        attribute,
-                        param_idx,
-                        verbose,
-                    )
-                    ret["param_data"][param]["depends_for_codependent_value"][
-                        combi
-                    ] = _depends_on_param(part_corr, part_std_param, part_std_lut)
-
     if state_or_trans in arg_count:
         for arg_index in range(arg_count[state_or_trans]):
             std_matrix, mean_std, lut_matrix = _std_by_param(
@@ -365,7 +305,6 @@ def _compute_param_statistics(
                 state_or_trans,
                 attribute,
                 len(parameter_names) + arg_index,
-                verbose,
             )
             ret["std_by_arg"].append(mean_std)
             ret["std_by_arg_values"].append(std_matrix)
@@ -447,8 +386,8 @@ def prune_dependent_parameters(by_name, parameter_names, correlation_threshold=0
                     correlation != np.nan
                     and np.abs(correlation) > correlation_threshold
                 ):
-                    print(
-                        "[!] Parameters {} <-> {} are correlated with coefficcient {}".format(
+                    logger.debug(
+                        "Parameters {} <-> {} are correlated with coefficcient {}".format(
                             parameter_names[index_1],
                             parameter_names[index_2],
                             correlation,
@@ -458,7 +397,7 @@ def prune_dependent_parameters(by_name, parameter_names, correlation_threshold=0
                         index_to_remove = index_1
                     else:
                         index_to_remove = index_2
-                    print(
+                    logger.debug(
                         "    Removing parameter {}".format(
                             parameter_names[index_to_remove]
                         )
@@ -495,13 +434,7 @@ class ParamStats:
     """
 
     def __init__(
-        self,
-        by_name,
-        by_param,
-        parameter_names,
-        arg_count,
-        use_corrcoef=False,
-        verbose=False,
+        self, by_name, by_param, parameter_names, arg_count, use_corrcoef=False,
     ):
         """
         Compute standard deviation and correlation coefficient on parameterized data partitions.
@@ -556,7 +489,6 @@ class ParamStats:
                             attribute,
                             self.distinct_values[state_or_tran],
                             self.distinct_values_by_param_index[state_or_tran],
-                            verbose,
                         ],
                     }
                 )
@@ -592,147 +524,21 @@ class ParamStats:
                     )
                     > 2
                 ):
-                    print(
-                        key,
-                        param,
-                        list(
-                            filter(
-                                lambda n: is_numeric(n),
-                                self.distinct_values[key][param],
-                            )
-                        ),
+                    logger.debug(
+                        "{} can be fitted for param {} on {}".format(
+                            key,
+                            param,
+                            list(
+                                filter(
+                                    lambda n: is_numeric(n),
+                                    self.distinct_values[key][param],
+                                )
+                            ),
+                        )
                     )
                     return True
         return False
 
-    def static_submodel_params(self, state_or_tran, attribute):
-        """
-        Return the union of all parameter values which decide whether another parameter influences the model or not.
-
-        I.e., the returned list of dicts contains one entry for each parameter value combination which (probably) does not have any parameter influencing the model.
-        If the current parameters matches one of these, a static sub-model built based on this subset of parameters can likely be used.
-        """
-        # TODO
-        pass
-
-    def has_codependent_parameters(
-        self, state_or_tran: str, attribute: str, param: str
-    ) -> bool:
-        """
-        Return whether there are parameters which determine whether `param` influences `state_or_tran` `attribute` or not.
-
-        :param state_or_tran: model state or transition
-        :param attribute: model attribute
-        :param param: parameter name
-        """
-        if len(self.codependent_parameters(state_or_tran, attribute, param)):
-            return True
-        return False
-
-    def codependent_parameters(
-        self, state_or_tran: str, attribute: str, param: str
-    ) -> list:
-        """
-        Return list of parameters which determine whether `param` influences `state_or_tran` `attribute` or not.
-
-        :param state_or_tran: model state or transition
-        :param attribute: model attribute
-        :param param: parameter name
-        """
-        if self.stats[state_or_tran][attribute]["depends_on_param"][param]:
-            return self.stats[state_or_tran][attribute]["param_data"][param][
-                "codependent_parameters"
-            ]
-        return list()
-
-    def has_codependent_parameters_union(
-        self, state_or_tran: str, attribute: str
-    ) -> bool:
-        """
-        Return whether there is a subset of parameters which decides whether `state_or_tran` `attribute` is static or parameter-dependent
-
-        :param state_or_tran: model state or transition
-        :param attribute: model attribute
-        """
-        depends_on_a_parameter = False
-        for param in self._parameter_names:
-            if self.stats[state_or_tran][attribute]["depends_on_param"][param]:
-                print("{}/{} depends on {}".format(state_or_tran, attribute, param))
-                depends_on_a_parameter = True
-                if (
-                    len(self.codependent_parameters(state_or_tran, attribute, param))
-                    == 0
-                ):
-                    print("has no codependent parameters")
-                    # Always depends on this parameter, regardless of other parameters' values
-                    return False
-        return depends_on_a_parameter
-
-    def codependent_parameters_union(self, state_or_tran: str, attribute: str) -> list:
-        """
-        Return list of parameters which determine whether any parameter influences `state_or_tran` `attribute`.
-
-        :param state_or_tran: model state or transition
-        :param attribute: model attribute
-        """
-        codependent_parameters = set()
-        for param in self._parameter_names:
-            if self.stats[state_or_tran][attribute]["depends_on_param"][param]:
-                if (
-                    len(self.codependent_parameters(state_or_tran, attribute, param))
-                    == 0
-                ):
-                    return list(self._parameter_names)
-                for codependent_param in self.codependent_parameters(
-                    state_or_tran, attribute, param
-                ):
-                    codependent_parameters.add(codependent_param)
-        return sorted(codependent_parameters)
-
-    def codependence_by_codependent_param_values(
-        self, state_or_tran: str, attribute: str, param: str
-    ) -> dict:
-        """
-        Return dict mapping codependent parameter values to a boolean indicating whether `param` influences `state_or_tran` `attribute`.
-
-        If a dict value is true, `attribute` depends on `param` for the corresponding codependent parameter values, otherwise it does not.
-
-        :param state_or_tran: model state or transition
-        :param attribute: model attribute
-        :param param: parameter name
-        """
-        if self.stats[state_or_tran][attribute]["depends_on_param"][param]:
-            return self.stats[state_or_tran][attribute]["param_data"][param][
-                "depends_for_codependent_value"
-            ]
-        return dict()
-
-    def codependent_parameter_value_dicts(
-        self, state_or_tran: str, attribute: str, param: str, kind="dynamic"
-    ):
-        """
-        Return dicts of codependent parameter key-value mappings for which `param` influences (or does not influence) `state_or_tran` `attribute`.
-
-        :param state_or_tran: model state or transition
-        :param attribute: model attribute
-        :param param: parameter name:
-        :param kind: 'static' or 'dynamic'. If 'dynamic' (the default), returns codependent parameter values for which `param` influences `attribute`. If 'static', returns codependent parameter values for which `param` does not influence `attribute`
-        """
-        codependent_parameters = self.stats[state_or_tran][attribute]["param_data"][
-            param
-        ]["codependent_parameters"]
-        codependence_info = self.stats[state_or_tran][attribute]["param_data"][param][
-            "depends_for_codependent_value"
-        ]
-        if len(codependent_parameters) == 0:
-            return
-        else:
-            for param_values, is_dynamic in codependence_info.items():
-                if (is_dynamic and kind == "dynamic") or (
-                    not is_dynamic and kind == "static"
-                ):
-                    yield dict(zip(codependent_parameters, param_values))
-
     def _generic_param_independence_ratio(self, state_or_trans, attribute):
         """
         Return the heuristic ratio of parameter independence for state_or_trans and attribute.
diff --git a/lib/protocol_benchmarks.py b/lib/protocol_benchmarks.py
index b42e821..d41979f 100755
--- a/lib/protocol_benchmarks.py
+++ b/lib/protocol_benchmarks.py
@@ -16,8 +16,11 @@ import io
 import os
 import re
 import time
+import logging
 from filelock import FileLock
 
+logger = logging.getLogger(__name__)
+
 
 class DummyProtocol:
     def __init__(self):
@@ -1838,14 +1841,14 @@ class Benchmark:
             this_result["data"] = data
         if value != None:
             this_result[key] = {"v": value, "ts": int(time.time())}
-            print(
+            logger.debug(
                 "{} {} {} ({}) :: {} -> {}".format(
                     libkey, bench_name, bench_index, data, key, value
                 )
             )
         else:
             this_result[key] = {"e": error, "ts": int(time.time())}
-            print(
+            logger.debug(
                 "{} {} {} ({}) :: {} -> [E] {}".format(
                     libkey, bench_name, bench_index, data, key, error[:500]
                 )
diff --git a/lib/runner.py b/lib/runner.py
index 16f0a29..77b7c68 100644
--- a/lib/runner.py
+++ b/lib/runner.py
@@ -31,7 +31,8 @@ class SerialReader(serial.threaded.Protocol):
         """Create a new SerialReader object."""
         self.callback = callback
         self.recv_buf = ""
-        self.lines = []
+        self.lines = list()
+        self.all_lines = list()
 
     def __call__(self):
         return self
@@ -47,7 +48,9 @@ class SerialReader(serial.threaded.Protocol):
             # Note: Do not call str.strip on lines[-1]! Otherwise, lines may be mangled
             lines = self.recv_buf.split("\n")
             if len(lines) > 1:
-                self.lines.extend(map(str.strip, lines[:-1]))
+                new_lines = list(map(str.strip, lines[:-1]))
+                self.lines.extend(new_lines)
+                self.all_lines.extend(new_lines)
                 self.recv_buf = lines[-1]
                 if self.callback:
                     for line in lines[:-1]:
@@ -120,7 +123,7 @@ class SerialMonitor:
         return self.reader.get_lines()
 
     def get_lines(self) -> list:
-        return self.reader.get_lines()
+        return self.reader.all_lines
 
     def get_files(self) -> list:
         return list()
@@ -143,6 +146,9 @@ class SerialMonitor:
 class EnergyTraceMonitor(SerialMonitor):
     """EnergyTraceMonitor captures serial timing output and EnergyTrace energy data."""
 
+    # Zusätzliche key-value-Argumente von generate-dfa-benchmark.py --energytrace=... landen hier
+    # (z.B. --energytrace=var1=bar,somecount=2 => EnerygTraceMonitor(..., var1="bar", somecount="2")).
+    # Soald das EnergyTraceMonitor-Objekt erzeugt wird, beginnt die Messung (d.h. hier: msp430-etv wird gestartet)
     def __init__(self, port: str, baud: int, callback=None, voltage=3.3):
         super().__init__(port=port, baud=baud, callback=callback)
         self._voltage = voltage
@@ -155,20 +161,31 @@ class EnergyTraceMonitor(SerialMonitor):
             cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True
         )
 
+    # Benchmark fertig -> externe Hilfsprogramme beenden
     def close(self):
         super().close()
         self._logger.send_signal(subprocess.signal.SIGINT)
         stdout, stderr = self._logger.communicate(timeout=15)
 
+    # Zusätzliche Dateien, die mit dem Benchmark-Log und -Plan abgespeichert werden sollen
+    # (hier: Die von msp430-etv generierten Logfiles)
     def get_files(self) -> list:
         return [self._output]
 
+    #
     def get_config(self) -> dict:
         return {
             "voltage": self._voltage,
         }
 
 
+class EnergyTraceLogicAnalyzerMonitor(EnergyTraceMonitor):
+    """EnergyTraceLogicAnalyzerMonitor captures EnergyTrace energy data and LogicAnalyzer timing output."""
+
+    def __init__(self, port: str, baud: int, callback=None, voltage=3.3):
+        super().__init__(port=port, baud=baud, callback=callback, voltage=voltage)
+
+
 class MIMOSAMonitor(SerialMonitor):
     """MIMOSAMonitor captures serial output and MIMOSA energy data for a specific amount of time."""
 
@@ -362,8 +379,14 @@ def get_monitor(arch: str, **kwargs) -> object:
                 mimosa_kwargs = kwargs.pop("mimosa")
                 return MIMOSAMonitor(port, arg, **mimosa_kwargs, **kwargs)
             elif "energytrace" in kwargs and kwargs["energytrace"] is not None:
-                energytrace_kwargs = kwargs.pop("energytrace")
-                return EnergyTraceMonitor(port, arg, **energytrace_kwargs, **kwargs)
+                energytrace_kwargs = kwargs.pop("energytrace").copy()
+                sync_mode = energytrace_kwargs.pop("sync")
+                if sync_mode == "la":
+                    return EnergyTraceLogicAnalyzerMonitor(
+                        port, arg, **energytrace_kwargs, **kwargs
+                    )
+                else:
+                    return EnergyTraceMonitor(port, arg, **energytrace_kwargs, **kwargs)
             else:
                 kwargs.pop("energytrace", None)
                 kwargs.pop("mimosa", None)
@@ -382,6 +405,23 @@ def get_counter_limits(arch: str) -> tuple:
     raise RuntimeError("Did not find Counter Overflow limits")
 
 
+def sleep_ms(duration: int, arch: str, cpu_freq: int = None) -> str:
+    max_sleep = None
+    if "msp430fr" in arch:
+        if cpu_freq is not None and cpu_freq > 8000000:
+            max_sleep = 250
+        else:
+            max_sleep = 500
+    if max_sleep is not None and duration > max_sleep:
+        sub_sleep_count = duration // max_sleep
+        tail_sleep = duration % max_sleep
+        ret = f"for (unsigned char i = 0; i < {sub_sleep_count}; i++) {{ arch.sleep_ms({max_sleep}); }}\n"
+        if tail_sleep > 0:
+            ret += f"arch.sleep_ms({tail_sleep});\n"
+        return ret
+    return "arch.sleep_ms({duration});\n"
+
+
 def get_counter_limits_us(arch: str) -> tuple:
     """Return duration of one counter step and one counter overflow in us."""
     cpu_freq = 0
diff --git a/lib/utils.py b/lib/utils.py
index 91dded0..d28ecda 100644
--- a/lib/utils.py
+++ b/lib/utils.py
@@ -1,17 +1,9 @@
 import numpy as np
 import re
+import logging
 
 arg_support_enabled = True
-
-
-def vprint(verbose, string):
-    """
-    Print `string` if `verbose`.
-
-    Prints string if verbose is a True value
-    """
-    if verbose:
-        print(string)
+logger = logging.getLogger(__name__)
 
 
 def running_mean(x: np.ndarray, N: int) -> np.ndarray:
@@ -222,7 +214,7 @@ def filter_aggregate_by_param(aggregate, parameters, parameter_filter):
                 )
             )
             if len(indices_to_keep) == 0:
-                print("??? {}->{}".format(parameter_filter, name))
+                logger.debug("??? {}->{}".format(parameter_filter, name))
                 names_to_remove.add(name)
             else:
                 for attribute in aggregate[name]["attributes"]:
diff --git a/lib/validation.py b/lib/validation.py
new file mode 100644
index 0000000..ee147fe
--- /dev/null
+++ b/lib/validation.py
@@ -0,0 +1,238 @@
+#!/usr/bin/env python3
+
+import logging
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+
+def _xv_partitions_kfold(length, k=10):
+    """
+    Return k pairs of training and validation sets for k-fold cross-validation on `length` items.
+
+    In k-fold cross-validation, every k-th item is used for validation and the remainder is used for training.
+    As there are k ways to do this (items 0, k, 2k, ... vs. items 1, k+1, 2k+1, ... etc), this function returns k pairs of training and validation set.
+
+    Note that this function operates on indices, not data.
+    """
+    pairs = []
+    num_slices = k
+    indexes = np.arange(length)
+    for i in range(num_slices):
+        training = np.delete(indexes, slice(i, None, num_slices))
+        validation = indexes[i::num_slices]
+        pairs.append((training, validation))
+    return pairs
+
+
+def _xv_partition_montecarlo(length):
+    """
+    Return training and validation set for Monte Carlo cross-validation on `length` items.
+
+    This function operates on indices, not data. It randomly partitions range(length) into a list of training indices and a list of validation indices.
+
+    The training set contains 2/3 of all indices; the validation set consits of the remaining 1/3.
+
+    Example: 9 items -> training = [7, 3, 8, 0, 4, 2], validation = [ 1, 6, 5]
+    """
+    shuffled = np.random.permutation(np.arange(length))
+    border = int(length * float(2) / 3)
+    training = shuffled[:border]
+    validation = shuffled[border:]
+    return (training, validation)
+
+
+class CrossValidator:
+    """
+    Cross-Validation helper for model generation.
+
+    Given a set of measurements and a model class, it will partition the
+    data into training and validation sets, train the model on the training
+    set, and assess its quality on the validation set. This is repeated
+    several times depending on cross-validation algorithm and configuration.
+    Reports the mean model error over all cross-validation runs.
+    """
+
+    def __init__(self, model_class, by_name, parameters, arg_count):
+        """
+        Create a new CrossValidator object.
+
+        Does not perform cross-validation yet.
+
+        arguments:
+        model_class -- model class/type used for model synthesis,
+            e.g. PTAModel or AnalyticModel. model_class must have a
+            constructor accepting (by_name, parameters, arg_count)
+            and provide an `assess` method.
+        by_name -- measurements aggregated by state/transition/function/... name.
+            Layout: by_name[name][attribute] = list of data. Additionally,
+            by_name[name]['attributes'] must be set to the list of attributes,
+            e.g. ['power'] or ['duration', 'energy'].
+        """
+        self.model_class = model_class
+        self.by_name = by_name
+        self.names = sorted(by_name.keys())
+        self.parameters = sorted(parameters)
+        self.arg_count = arg_count
+
+    def kfold(self, model_getter, k=10):
+        """
+        Perform k-fold cross-validation and return average model quality.
+
+        The by_name data is divided into 1-1/k training and 1/k validation in a deterministic manner.
+        After creating a model for the training set, the
+        model type returned by model_getter is evaluated on the validation set.
+        This is repeated k times; the average of all measures is returned to the user.
+
+        arguments:
+        model_getter -- function with signature (model_object) -> model,
+            e.g. lambda m: m.get_fitted()[0] to evaluate the parameter-aware
+            model with automatic parameter detection.
+        k -- step size for k-fold cross-validation. The validation set contains 100/k % of data.
+
+        return value:
+        dict of model quality measures.
+        {
+            'by_name' : {
+                for each name: {
+                    for each attribute: {
+                        'mae' : mean of all mean absolute errors
+                        'mae_list' : list of the individual MAE values encountered during cross-validation
+                        'smape' : mean of all symmetric mean absolute percentage errors
+                        'smape_list' : list of the individual SMAPE values encountered during cross-validation
+                    }
+                }
+            }
+        }
+        """
+
+        # training / validation subsets for each state and transition
+        subsets_by_name = dict()
+        training_and_validation_sets = list()
+
+        for name in self.names:
+            sample_count = len(self.by_name[name]["param"])
+            subsets_by_name[name] = list()
+            subsets_by_name[name] = _xv_partitions_kfold(sample_count, k)
+
+        for i in range(k):
+            training_and_validation_sets.append(dict())
+            for name in self.names:
+                training_and_validation_sets[i][name] = subsets_by_name[name][i]
+
+        return self._generic_xv(model_getter, training_and_validation_sets)
+
+    def montecarlo(self, model_getter, count=200):
+        """
+        Perform Monte Carlo cross-validation and return average model quality.
+
+        The by_name data is randomly divided into 2/3 training and 1/3
+        validation. After creating a model for the training set, the
+        model type returned by model_getter is evaluated on the validation set.
+        This is repeated count times (defaulting to 200); the average of all
+        measures is returned to the user.
+
+        arguments:
+        model_getter -- function with signature (model_object) -> model,
+            e.g. lambda m: m.get_fitted()[0] to evaluate the parameter-aware
+            model with automatic parameter detection.
+        count -- number of validation runs to perform, defaults to 200
+
+        return value:
+        dict of model quality measures.
+        {
+            'by_name' : {
+                for each name: {
+                    for each attribute: {
+                        'mae' : mean of all mean absolute errors
+                        'mae_list' : list of the individual MAE values encountered during cross-validation
+                        'smape' : mean of all symmetric mean absolute percentage errors
+                        'smape_list' : list of the individual SMAPE values encountered during cross-validation
+                    }
+                }
+            }
+        }
+        """
+
+        # training / validation subsets for each state and transition
+        subsets_by_name = dict()
+        training_and_validation_sets = list()
+
+        for name in self.names:
+            sample_count = len(self.by_name[name]["param"])
+            subsets_by_name[name] = list()
+            for _ in range(count):
+                subsets_by_name[name].append(_xv_partition_montecarlo(sample_count))
+
+        for i in range(count):
+            training_and_validation_sets.append(dict())
+            for name in self.names:
+                training_and_validation_sets[i][name] = subsets_by_name[name][i]
+
+        return self._generic_xv(model_getter, training_and_validation_sets)
+
+    def _generic_xv(self, model_getter, training_and_validation_sets):
+        ret = {"by_name": dict()}
+
+        for name in self.names:
+            ret["by_name"][name] = dict()
+            for attribute in self.by_name[name]["attributes"]:
+                ret["by_name"][name][attribute] = {
+                    "mae_list": list(),
+                    "rmsd_list": list(),
+                    "smape_list": list(),
+                }
+
+        for training_and_validation_by_name in training_and_validation_sets:
+            res = self._single_xv(model_getter, training_and_validation_by_name)
+            for name in self.names:
+                for attribute in self.by_name[name]["attributes"]:
+                    for measure in ("mae", "rmsd", "smape"):
+                        ret["by_name"][name][attribute][f"{measure}_list"].append(
+                            res["by_name"][name][attribute][measure]
+                        )
+
+        for name in self.names:
+            for attribute in self.by_name[name]["attributes"]:
+                for measure in ("mae", "rmsd", "smape"):
+                    ret["by_name"][name][attribute][measure] = np.mean(
+                        ret["by_name"][name][attribute][f"{measure}_list"]
+                    )
+
+        return ret
+
+    def _single_xv(self, model_getter, tv_set_dict):
+        training = dict()
+        validation = dict()
+        for name in self.names:
+            training[name] = {"attributes": self.by_name[name]["attributes"]}
+            validation[name] = {"attributes": self.by_name[name]["attributes"]}
+
+            if "isa" in self.by_name[name]:
+                training[name]["isa"] = self.by_name[name]["isa"]
+                validation[name]["isa"] = self.by_name[name]["isa"]
+
+            training_subset, validation_subset = tv_set_dict[name]
+
+            for attribute in self.by_name[name]["attributes"]:
+                self.by_name[name][attribute] = np.array(self.by_name[name][attribute])
+                training[name][attribute] = self.by_name[name][attribute][
+                    training_subset
+                ]
+                validation[name][attribute] = self.by_name[name][attribute][
+                    validation_subset
+                ]
+
+            # We can't use slice syntax for 'param', which may contain strings and other odd values
+            training[name]["param"] = list()
+            validation[name]["param"] = list()
+            for idx in training_subset:
+                training[name]["param"].append(self.by_name[name]["param"][idx])
+            for idx in validation_subset:
+                validation[name]["param"].append(self.by_name[name]["param"][idx])
+
+        training_data = self.model_class(training, self.parameters, self.arg_count)
+        training_model = model_getter(training_data)
+        validation_data = self.model_class(validation, self.parameters, self.arg_count)
+
+        return validation_data.assess(training_model)
diff --git a/test/test_codegen.py b/test/test_codegen.py
index 981117b..ce565d6 100755
--- a/test/test_codegen.py
+++ b/test/test_codegen.py
@@ -5,84 +5,74 @@ from dfatool.codegen import get_simulated_accountingmethod
 import unittest
 
 example_json_1 = {
-    'parameters': ['datarate', 'txbytes', 'txpower'],
-    'initial_param_values': [None, None, None],
-    'state': {
-        'IDLE': {
-            'power': {
-                'static': 5,
-            }
-        },
-        'TX': {
-            'power': {
-                'static': 100,
-                'function': {
-                    'raw': 'regression_arg(0) + regression_arg(1)'
-                    ' * parameter(txpower)',
-                    'regression_args': [100, 2]
+    "parameters": ["datarate", "txbytes", "txpower"],
+    "initial_param_values": [None, None, None],
+    "state": {
+        "IDLE": {"power": {"static": 5,}},
+        "TX": {
+            "power": {
+                "static": 100,
+                "function": {
+                    "raw": "regression_arg(0) + regression_arg(1)"
+                    " * parameter(txpower)",
+                    "regression_args": [100, 2],
                 },
             }
         },
     },
-    'transitions': [
+    "transitions": [
         {
-            'name': 'init',
-            'origin': ['UNINITIALIZED', 'IDLE'],
-            'destination': 'IDLE',
-            'duration': {
-                'static': 50000,
-            },
-            'set_param': {
-                'txpower': 10
-            },
+            "name": "init",
+            "origin": ["UNINITIALIZED", "IDLE"],
+            "destination": "IDLE",
+            "duration": {"static": 50000,},
+            "set_param": {"txpower": 10},
         },
         {
-            'name': 'setTxPower',
-            'origin': 'IDLE',
-            'destination': 'IDLE',
-            'duration': {'static': 120},
-            'energy ': {'static': 10000},
-            'arg_to_param_map': {0: 'txpower'},
-            'argument_values': [[10, 20, 30]],
+            "name": "setTxPower",
+            "origin": "IDLE",
+            "destination": "IDLE",
+            "duration": {"static": 120},
+            "energy ": {"static": 10000},
+            "arg_to_param_map": {0: "txpower"},
+            "argument_values": [[10, 20, 30]],
         },
         {
-            'name': 'send',
-            'origin': 'IDLE',
-            'destination': 'TX',
-            'duration': {
-                'static': 10,
-                'function': {
-                    'raw': 'regression_arg(0) + regression_arg(1)'
-                    ' * function_arg(1)',
-                    'regression_args': [48, 8],
+            "name": "send",
+            "origin": "IDLE",
+            "destination": "TX",
+            "duration": {
+                "static": 10,
+                "function": {
+                    "raw": "regression_arg(0) + regression_arg(1)" " * function_arg(1)",
+                    "regression_args": [48, 8],
                 },
             },
-            'energy': {
-                'static': 3,
-                'function': {
-                    'raw': 'regression_arg(0) + regression_arg(1)'
-                    ' * function_arg(1)',
-                    'regression_args': [3, 5],
+            "energy": {
+                "static": 3,
+                "function": {
+                    "raw": "regression_arg(0) + regression_arg(1)" " * function_arg(1)",
+                    "regression_args": [3, 5],
                 },
             },
-            'arg_to_param_map': {1: 'txbytes'},
-            'argument_values': [['"foo"', '"hodor"'], [3, 5]],
-            'argument_combination': 'zip',
+            "arg_to_param_map": {1: "txbytes"},
+            "argument_values": [['"foo"', '"hodor"'], [3, 5]],
+            "argument_combination": "zip",
         },
         {
-            'name': 'txComplete',
-            'origin': 'TX',
-            'destination': 'IDLE',
-            'is_interrupt': 1,
-            'timeout': {
-                'static': 2000,
-                'function': {
-                    'raw': 'regression_arg(0) + regression_arg(1)'
-                    ' * parameter(txbytes)',
-                    'regression_args': [500, 16],
+            "name": "txComplete",
+            "origin": "TX",
+            "destination": "IDLE",
+            "is_interrupt": 1,
+            "timeout": {
+                "static": 2000,
+                "function": {
+                    "raw": "regression_arg(0) + regression_arg(1)"
+                    " * parameter(txbytes)",
+                    "regression_args": [500, 16],
                 },
             },
-        }
+        },
     ],
 }
 
@@ -91,9 +81,11 @@ class TestCG(unittest.TestCase):
     def test_statetransition_immediate(self):
         pta = PTA.from_json(example_json_1)
         pta.set_random_energy_model()
-        pta.state['IDLE'].power.value = 9
-        cg = get_simulated_accountingmethod('static_statetransition_immediate')(pta, 1000000, 'uint8_t', 'uint8_t', 'uint8_t', 'uint8_t')
-        cg.current_state = pta.state['IDLE']
+        pta.state["IDLE"].power.value = 9
+        cg = get_simulated_accountingmethod("static_statetransition_immediate")(
+            pta, 1000000, "uint8_t", "uint8_t", "uint8_t", "uint8_t"
+        )
+        cg.current_state = pta.state["IDLE"]
         cg.sleep(7)
         self.assertEqual(cg.get_energy(), 9 * 7)
         pta.transitions[1].energy.value = 123
@@ -102,8 +94,10 @@ class TestCG(unittest.TestCase):
         cg.pass_transition(pta.transitions[1])
         self.assertEqual(cg.get_energy(), (9 * 7 + 123 + 123) % 256)
 
-        cg = get_simulated_accountingmethod('static_statetransition_immediate')(pta, 100000, 'uint8_t', 'uint8_t', 'uint8_t', 'uint8_t')
-        cg.current_state = pta.state['IDLE']
+        cg = get_simulated_accountingmethod("static_statetransition_immediate")(
+            pta, 100000, "uint8_t", "uint8_t", "uint8_t", "uint8_t"
+        )
+        cg.current_state = pta.state["IDLE"]
         cg.sleep(7)
         self.assertEqual(cg.get_energy(), 0)
         cg.sleep(15)
@@ -111,8 +105,10 @@ class TestCG(unittest.TestCase):
         cg.sleep(90)
         self.assertEqual(cg.get_energy(), 900 % 256)
 
-        cg = get_simulated_accountingmethod('static_statetransition_immediate')(pta, 100000, 'uint8_t', 'uint8_t', 'uint8_t', 'uint16_t')
-        cg.current_state = pta.state['IDLE']
+        cg = get_simulated_accountingmethod("static_statetransition_immediate")(
+            pta, 100000, "uint8_t", "uint8_t", "uint8_t", "uint16_t"
+        )
+        cg.current_state = pta.state["IDLE"]
         cg.sleep(7)
         self.assertEqual(cg.get_energy(), 0)
         cg.sleep(15)
@@ -120,10 +116,12 @@ class TestCG(unittest.TestCase):
         cg.sleep(90)
         self.assertEqual(cg.get_energy(), 900)
 
-        pta.state['IDLE'].power.value = 9  # -> 90 uW
+        pta.state["IDLE"].power.value = 9  # -> 90 uW
         pta.transitions[1].energy.value = 1  # -> 100 pJ
-        cg = get_simulated_accountingmethod('static_statetransition_immediate')(pta, 1000000, 'uint8_t', 'uint8_t', 'uint8_t', 'uint8_t', 1e-5, 1e-5, 1e-10)
-        cg.current_state = pta.state['IDLE']
+        cg = get_simulated_accountingmethod("static_statetransition_immediate")(
+            pta, 1000000, "uint8_t", "uint8_t", "uint8_t", "uint8_t", 1e-5, 1e-5, 1e-10
+        )
+        cg.current_state = pta.state["IDLE"]
         cg.sleep(10)  # 10 us
         self.assertEqual(cg.get_energy(), 90 * 10)
         cg.pass_transition(pta.transitions[1])
@@ -134,9 +132,11 @@ class TestCG(unittest.TestCase):
     def test_statetransition(self):
         pta = PTA.from_json(example_json_1)
         pta.set_random_energy_model()
-        pta.state['IDLE'].power.value = 9
-        cg = get_simulated_accountingmethod('static_statetransition')(pta, 1000000, 'uint8_t', 'uint8_t', 'uint8_t', 'uint8_t')
-        cg.current_state = pta.state['IDLE']
+        pta.state["IDLE"].power.value = 9
+        cg = get_simulated_accountingmethod("static_statetransition")(
+            pta, 1000000, "uint8_t", "uint8_t", "uint8_t", "uint8_t"
+        )
+        cg.current_state = pta.state["IDLE"]
         cg.sleep(7)
         self.assertEqual(cg.get_energy(), 9 * 7)
         pta.transitions[1].energy.value = 123
@@ -148,9 +148,11 @@ class TestCG(unittest.TestCase):
     def test_state_immediate(self):
         pta = PTA.from_json(example_json_1)
         pta.set_random_energy_model()
-        pta.state['IDLE'].power.value = 9
-        cg = get_simulated_accountingmethod('static_state_immediate')(pta, 1000000, 'uint8_t', 'uint8_t', 'uint8_t', 'uint8_t')
-        cg.current_state = pta.state['IDLE']
+        pta.state["IDLE"].power.value = 9
+        cg = get_simulated_accountingmethod("static_state_immediate")(
+            pta, 1000000, "uint8_t", "uint8_t", "uint8_t", "uint8_t"
+        )
+        cg.current_state = pta.state["IDLE"]
         cg.sleep(7)
         self.assertEqual(cg.get_energy(), 9 * 7)
         pta.transitions[1].energy.value = 123
@@ -162,9 +164,11 @@ class TestCG(unittest.TestCase):
     def test_state(self):
         pta = PTA.from_json(example_json_1)
         pta.set_random_energy_model()
-        pta.state['IDLE'].power.value = 9
-        cg = get_simulated_accountingmethod('static_state')(pta, 1000000, 'uint8_t', 'uint8_t', 'uint8_t', 'uint8_t')
-        cg.current_state = pta.state['IDLE']
+        pta.state["IDLE"].power.value = 9
+        cg = get_simulated_accountingmethod("static_state")(
+            pta, 1000000, "uint8_t", "uint8_t", "uint8_t", "uint8_t"
+        )
+        cg.current_state = pta.state["IDLE"]
         cg.sleep(7)
         self.assertEqual(cg.get_energy(), 9 * 7)
         pta.transitions[1].energy.value = 123
@@ -173,8 +177,10 @@ class TestCG(unittest.TestCase):
         cg.pass_transition(pta.transitions[1])
         self.assertEqual(cg.get_energy(), 9 * 7)
 
-        cg = get_simulated_accountingmethod('static_state')(pta, 1000000, 'uint8_t', 'uint16_t', 'uint16_t', 'uint16_t')
+        cg = get_simulated_accountingmethod("static_state")(
+            pta, 1000000, "uint8_t", "uint16_t", "uint16_t", "uint16_t"
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/test/test_parameters.py b/test/test_parameters.py
new file mode 100755
index 0000000..e36b1a1
--- /dev/null
+++ b/test/test_parameters.py
@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+
+from dfatool import parameters
+from dfatool.utils import by_name_to_by_param
+from dfatool.functions import analytic
+from dfatool.model import ParallelParamFit
+import unittest
+
+import numpy as np
+
+
+class TestModels(unittest.TestCase):
+    def test_distinct_param_values(self):
+        X = np.arange(35)
+        by_name = {
+            "TX": {
+                "param": [(x % 5, x % 7) for x in X],
+                "power": X,
+                "attributes": ["power"],
+            }
+        }
+        self.assertEqual(
+            parameters.distinct_param_values(by_name, "TX"),
+            [list(range(5)), list(range(7))],
+        )
+
+    def test_parameter_detection_linear(self):
+        # rng = np.random.default_rng(seed=1312) # requiresy NumPy >= 1.17
+        np.random.seed(1312)
+        X = np.arange(200) % 50
+        # Y = X + rng.normal(size=X.size) # requiry NumPy >= 1.17
+        Y = X + np.random.normal(size=X.size)
+        parameter_names = ["p_mod5", "p_linear"]
+
+        # Test input data:
+        # * param[0] ("p_mod5") == X % 5 (bogus data to test detection of non-influence)
+        # * param[1] ("p_linear") == X
+        # * TX power == X ± gaussian noise
+        # -> TX power depends linearly on "p_linear"
+        by_name = {
+            "TX": {
+                "param": [(x % 5, x) for x in X],
+                "power": Y,
+                "attributes": ["power"],
+            }
+        }
+        by_param = by_name_to_by_param(by_name)
+        stats = parameters.ParamStats(by_name, by_param, parameter_names, dict())
+
+        self.assertEqual(stats.depends_on_param("TX", "power", "p_mod5"), False)
+        self.assertEqual(stats.depends_on_param("TX", "power", "p_linear"), True)
+
+        # Fit individual functions for each parameter (only "p_linear" in this case)
+
+        paramfit = ParallelParamFit(by_param)
+        paramfit.enqueue("TX", "power", 1, "p_linear")
+        paramfit.fit()
+
+        fit_result = paramfit.get_result("TX", "power")
+        self.assertEqual(fit_result["p_linear"]["best"], "linear")
+        self.assertEqual("p_mod5" not in fit_result, True)
+
+        # Fit a single function for all parameters (still only "p_linear" in this case)
+
+        combined_fit = analytic.function_powerset(fit_result, parameter_names, 0)
+
+        self.assertEqual(
+            combined_fit.model_function,
+            "0 + regression_arg(0) + regression_arg(1) * parameter(p_linear)",
+        )
+        self.assertEqual(
+            combined_fit._function_str,
+            "0 + reg_param[0] + reg_param[1] * model_param[1]",
+        )
+
+        combined_fit.fit(by_param, "TX", "power")
+
+        self.assertEqual(combined_fit.fit_success, True)
+
+        self.assertEqual(combined_fit.is_predictable([None, None]), False)
+        self.assertEqual(combined_fit.is_predictable([None, 0]), True)
+        self.assertEqual(combined_fit.is_predictable([None, 50]), True)
+        self.assertEqual(combined_fit.is_predictable([0, None]), False)
+        self.assertEqual(combined_fit.is_predictable([50, None]), False)
+        self.assertEqual(combined_fit.is_predictable([0, 0]), True)
+        self.assertEqual(combined_fit.is_predictable([0, 50]), True)
+        self.assertEqual(combined_fit.is_predictable([50, 0]), True)
+        self.assertEqual(combined_fit.is_predictable([50, 50]), True)
+
+        # The function should be linear without offset or skew
+        for i in range(100):
+            self.assertAlmostEqual(combined_fit.eval([None, i]), i, places=0)
+
+    def test_parameter_detection_multi_dimensional(self):
+        # rng = np.random.default_rng(seed=1312) # requires NumPy >= 1.17
+        np.random.seed(1312)
+        # vary each parameter from 1 to 10
+        Xi = (np.arange(50) % 10) + 1
+        # Three parameters -> Build input array [[1, 1, 1], [1, 1, 2], ..., [10, 10, 10]]
+        X = np.array(np.meshgrid(Xi, Xi, Xi)).T.reshape(-1, 3)
+
+        f_lls = np.vectorize(
+            lambda x: 42 + 7 * x[0] + 10 * np.log(x[1]) - 0.5 * x[2] * x[2],
+            signature="(n)->()",
+        )
+        f_ll = np.vectorize(
+            lambda x: 23 + 5 * x[0] - 3 * x[0] / x[1], signature="(n)->()"
+        )
+
+        # Y_lls = f_lls(X) + rng.normal(size=X.shape[0]) # requires NumPy >= 1.17
+        # Y_ll = f_ll(X) + rng.normal(size=X.shape[0]) # requires NumPy >= 1.17
+        Y_lls = f_lls(X) + np.random.normal(size=X.shape[0])
+        Y_ll = f_ll(X) + np.random.normal(size=X.shape[0])
+
+        parameter_names = ["lin_lin", "log_inv", "square_none"]
+
+        by_name = {
+            "someKey": {
+                "param": X,
+                "lls": Y_lls,
+                "ll": Y_ll,
+                "attributes": ["lls", "ll"],
+            }
+        }
+        by_param = by_name_to_by_param(by_name)
+        stats = parameters.ParamStats(by_name, by_param, parameter_names, dict())
+
+        self.assertEqual(stats.depends_on_param("someKey", "lls", "lin_lin"), True)
+        self.assertEqual(stats.depends_on_param("someKey", "lls", "log_inv"), True)
+        self.assertEqual(stats.depends_on_param("someKey", "lls", "square_none"), True)
+
+        self.assertEqual(stats.depends_on_param("someKey", "ll", "lin_lin"), True)
+        self.assertEqual(stats.depends_on_param("someKey", "ll", "log_inv"), True)
+        self.assertEqual(stats.depends_on_param("someKey", "ll", "square_none"), False)
+
+        paramfit = ParallelParamFit(by_param)
+        paramfit.enqueue("someKey", "lls", 0, "lin_lin")
+        paramfit.enqueue("someKey", "lls", 1, "log_inv")
+        paramfit.enqueue("someKey", "lls", 2, "square_none")
+        paramfit.enqueue("someKey", "ll", 0, "lin_lin")
+        paramfit.enqueue("someKey", "ll", 1, "log_inv")
+        paramfit.fit()
+
+        fit_lls = paramfit.get_result("someKey", "lls")
+        self.assertEqual(fit_lls["lin_lin"]["best"], "linear")
+        self.assertEqual(fit_lls["log_inv"]["best"], "logarithmic")
+        self.assertEqual(fit_lls["square_none"]["best"], "square")
+
+        combined_fit_lls = analytic.function_powerset(fit_lls, parameter_names, 0)
+
+        self.assertEqual(
+            combined_fit_lls.model_function,
+            "0 + regression_arg(0) + regression_arg(1) * parameter(lin_lin)"
+            " + regression_arg(2) * np.log(parameter(log_inv))"
+            " + regression_arg(3) * (parameter(square_none))**2"
+            " + regression_arg(4) * parameter(lin_lin) * np.log(parameter(log_inv))"
+            " + regression_arg(5) * parameter(lin_lin) * (parameter(square_none))**2"
+            " + regression_arg(6) * np.log(parameter(log_inv)) * (parameter(square_none))**2"
+            " + regression_arg(7) * parameter(lin_lin) * np.log(parameter(log_inv)) * (parameter(square_none))**2",
+        )
+
+        combined_fit_lls.fit(by_param, "someKey", "lls")
+
+        self.assertEqual(combined_fit_lls.fit_success, True)
+
+        # Verify that f_lls parameters have been found
+        self.assertAlmostEqual(combined_fit_lls.model_args[0], 42, places=0)
+        self.assertAlmostEqual(combined_fit_lls.model_args[1], 7, places=0)
+        self.assertAlmostEqual(combined_fit_lls.model_args[2], 10, places=0)
+        self.assertAlmostEqual(combined_fit_lls.model_args[3], -0.5, places=1)
+        self.assertAlmostEqual(combined_fit_lls.model_args[4], 0, places=2)
+        self.assertAlmostEqual(combined_fit_lls.model_args[5], 0, places=2)
+        self.assertAlmostEqual(combined_fit_lls.model_args[6], 0, places=2)
+        self.assertAlmostEqual(combined_fit_lls.model_args[7], 0, places=2)
+
+        self.assertEqual(combined_fit_lls.is_predictable([None, None, None]), False)
+        self.assertEqual(combined_fit_lls.is_predictable([None, None, 11]), False)
+        self.assertEqual(combined_fit_lls.is_predictable([None, 11, None]), False)
+        self.assertEqual(combined_fit_lls.is_predictable([None, 11, 11]), False)
+        self.assertEqual(combined_fit_lls.is_predictable([11, None, None]), False)
+        self.assertEqual(combined_fit_lls.is_predictable([11, None, 11]), False)
+        self.assertEqual(combined_fit_lls.is_predictable([11, 11, None]), False)
+        self.assertEqual(combined_fit_lls.is_predictable([11, 11, 11]), True)
+
+        # Verify that fitted function behaves like input function
+        for i, x in enumerate(X):
+            self.assertAlmostEqual(combined_fit_lls.eval(x), f_lls(x), places=0)
+
+        fit_ll = paramfit.get_result("someKey", "ll")
+        self.assertEqual(fit_ll["lin_lin"]["best"], "linear")
+        self.assertEqual(fit_ll["log_inv"]["best"], "inverse")
+        self.assertEqual("quare_none" not in fit_ll, True)
+
+        combined_fit_ll = analytic.function_powerset(fit_ll, parameter_names, 0)
+
+        self.assertEqual(
+            combined_fit_ll.model_function,
+            "0 + regression_arg(0) + regression_arg(1) * parameter(lin_lin)"
+            " + regression_arg(2) * 1/(parameter(log_inv))"
+            " + regression_arg(3) * parameter(lin_lin) * 1/(parameter(log_inv))",
+        )
+
+        combined_fit_ll.fit(by_param, "someKey", "ll")
+
+        self.assertEqual(combined_fit_ll.fit_success, True)
+
+        # Verify that f_ll parameters have been found
+        self.assertAlmostEqual(combined_fit_ll.model_args[0], 23, places=0)
+        self.assertAlmostEqual(combined_fit_ll.model_args[1], 5, places=0)
+        self.assertAlmostEqual(combined_fit_ll.model_args[2], 0, places=1)
+        self.assertAlmostEqual(combined_fit_ll.model_args[3], -3, places=0)
+
+        self.assertEqual(combined_fit_ll.is_predictable([None, None, None]), False)
+        self.assertEqual(combined_fit_ll.is_predictable([None, None, 11]), False)
+        self.assertEqual(combined_fit_ll.is_predictable([None, 11, None]), False)
+        self.assertEqual(combined_fit_ll.is_predictable([None, 11, 11]), False)
+        self.assertEqual(combined_fit_ll.is_predictable([11, None, None]), False)
+        self.assertEqual(combined_fit_ll.is_predictable([11, None, 11]), False)
+        self.assertEqual(combined_fit_ll.is_predictable([11, 11, None]), True)
+        self.assertEqual(combined_fit_ll.is_predictable([11, 11, 11]), True)
+
+        # Verify that fitted function behaves like input function
+        for i, x in enumerate(X):
+            self.assertAlmostEqual(combined_fit_ll.eval(x), f_ll(x), places=0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/test/test_pta.py b/test/test_pta.py
index 9f0778d..d43e702 100755
--- a/test/test_pta.py
+++ b/test/test_pta.py
@@ -5,88 +5,79 @@ import unittest
 import yaml
 
 example_json_1 = {
-    'parameters': ['datarate', 'txbytes', 'txpower'],
-    'initial_param_values': [None, None, None],
-    'state': {
-        'IDLE': {
-            'power': {
-                'static': 5,
-            }
-        },
-        'TX': {
-            'power': {
-                'static': 10000,
-                'function': {
-                    'raw': 'regression_arg(0) + regression_arg(1)'
-                    ' * parameter(txpower)',
-                    'regression_args': [10000, 2]
+    "parameters": ["datarate", "txbytes", "txpower"],
+    "initial_param_values": [None, None, None],
+    "state": {
+        "IDLE": {"power": {"static": 5,}},
+        "TX": {
+            "power": {
+                "static": 10000,
+                "function": {
+                    "raw": "regression_arg(0) + regression_arg(1)"
+                    " * parameter(txpower)",
+                    "regression_args": [10000, 2],
                 },
             }
         },
     },
-    'transitions': [
+    "transitions": [
         {
-            'name': 'init',
-            'origin': ['UNINITIALIZED', 'IDLE'],
-            'destination': 'IDLE',
-            'duration': {
-                'static': 50000,
-            },
-            'set_param': {
-                'txpower': 10
-            },
+            "name": "init",
+            "origin": ["UNINITIALIZED", "IDLE"],
+            "destination": "IDLE",
+            "duration": {"static": 50000,},
+            "set_param": {"txpower": 10},
         },
         {
-            'name': 'setTxPower',
-            'origin': 'IDLE',
-            'destination': 'IDLE',
-            'duration': {'static': 120},
-            'energy ': {'static': 10000},
-            'arg_to_param_map': {0: 'txpower'},
-            'argument_values': [[10, 20, 30]],
+            "name": "setTxPower",
+            "origin": "IDLE",
+            "destination": "IDLE",
+            "duration": {"static": 120},
+            "energy ": {"static": 10000},
+            "arg_to_param_map": {0: "txpower"},
+            "argument_values": [[10, 20, 30]],
         },
         {
-            'name': 'send',
-            'origin': 'IDLE',
-            'destination': 'TX',
-            'duration': {
-                'static': 10,
-                'function': {
-                    'raw': 'regression_arg(0) + regression_arg(1)'
-                    ' * function_arg(1)',
-                    'regression_args': [48, 8],
+            "name": "send",
+            "origin": "IDLE",
+            "destination": "TX",
+            "duration": {
+                "static": 10,
+                "function": {
+                    "raw": "regression_arg(0) + regression_arg(1)" " * function_arg(1)",
+                    "regression_args": [48, 8],
                 },
             },
-            'energy': {
-                'static': 3,
-                'function': {
-                    'raw': 'regression_arg(0) + regression_arg(1)'
-                    ' * function_arg(1)',
-                    'regression_args': [3, 5],
+            "energy": {
+                "static": 3,
+                "function": {
+                    "raw": "regression_arg(0) + regression_arg(1)" " * function_arg(1)",
+                    "regression_args": [3, 5],
                 },
             },
-            'arg_to_param_map': {1: 'txbytes'},
-            'argument_values': [['"foo"', '"hodor"'], [3, 5]],
-            'argument_combination': 'zip',
+            "arg_to_param_map": {1: "txbytes"},
+            "argument_values": [['"foo"', '"hodor"'], [3, 5]],
+            "argument_combination": "zip",
         },
         {
-            'name': 'txComplete',
-            'origin': 'TX',
-            'destination': 'IDLE',
-            'is_interrupt': 1,
-            'timeout': {
-                'static': 2000,
-                'function': {
-                    'raw': 'regression_arg(0) + regression_arg(1)'
-                    ' * parameter(txbytes)',
-                    'regression_args': [500, 16],
+            "name": "txComplete",
+            "origin": "TX",
+            "destination": "IDLE",
+            "is_interrupt": 1,
+            "timeout": {
+                "static": 2000,
+                "function": {
+                    "raw": "regression_arg(0) + regression_arg(1)"
+                    " * parameter(txbytes)",
+                    "regression_args": [500, 16],
                 },
             },
-        }
+        },
     ],
 }
 
-example_yaml_1 = yaml.safe_load("""
+example_yaml_1 = yaml.safe_load(
+    """
 codegen:
   instance: cc1200
 
@@ -124,9 +115,11 @@ transition:
     src: [TX]
     dst: IDLE
     is_interrupt: true
-""")
+"""
+)
 
-example_yaml_2 = yaml.safe_load("""
+example_yaml_2 = yaml.safe_load(
+    """
 codegen:
   instance: cc1200
 
@@ -169,9 +162,11 @@ transition:
     src: [TX]
     dst: IDLE
     is_interrupt: true
-""")
+"""
+)
 
-example_yaml_3 = yaml.safe_load("""
+example_yaml_3 = yaml.safe_load(
+    """
 codegen:
   instance: nrf24l01
   includes: ['driver/nrf24l01.h']
@@ -260,12 +255,17 @@ transition:
       - name: blocking
         values: [1, 1, 1, 1, 1, 1]
     argument_combination: zip
-""")
+"""
+)
 
 
-def dfs_tran_to_name(runs: list, with_args: bool = False, with_param: bool = False) -> list:
+def dfs_tran_to_name(
+    runs: list, with_args: bool = False, with_param: bool = False
+) -> list:
     if with_param:
-        return list(map(lambda run: list(map(lambda x: (x[0].name, x[1], x[2]), run)), runs))
+        return list(
+            map(lambda run: list(map(lambda x: (x[0].name, x[1], x[2]), run)), runs)
+        )
     if with_args:
         return list(map(lambda run: list(map(lambda x: (x[0].name, x[1]), run)), runs))
     return list(map(lambda run: list(map(lambda x: (x[0].name), run)), runs))
@@ -273,117 +273,175 @@ def dfs_tran_to_name(runs: list, with_args: bool = False, with_param: bool = Fal
 
 class TestPTA(unittest.TestCase):
     def test_dfs(self):
-        pta = PTA(['IDLE', 'TX'])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init')
-        pta.add_transition('IDLE', 'TX', 'send')
-        pta.add_transition('TX', 'IDLE', 'txComplete')
-        self.assertEqual(dfs_tran_to_name(pta.dfs(0), False), [['init']])
-        self.assertEqual(dfs_tran_to_name(pta.dfs(1), False), [['init', 'send']])
-        self.assertEqual(dfs_tran_to_name(pta.dfs(2), False), [['init', 'send', 'txComplete']])
-        self.assertEqual(dfs_tran_to_name(pta.dfs(3), False), [['init', 'send', 'txComplete', 'send']])
-
-        pta = PTA(['IDLE'])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init')
-        pta.add_transition('IDLE', 'IDLE', 'set1')
-        pta.add_transition('IDLE', 'IDLE', 'set2')
-        self.assertEqual(dfs_tran_to_name(pta.dfs(0), False), [['init']])
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(1), False)), [['init', 'set1'], ['init', 'set2']])
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(2), False)), [['init', 'set1', 'set1'],
-                                                                       ['init', 'set1', 'set2'],
-                                                                       ['init', 'set2', 'set1'],
-                                                                       ['init', 'set2', 'set2']])
+        pta = PTA(["IDLE", "TX"])
+        pta.add_transition("UNINITIALIZED", "IDLE", "init")
+        pta.add_transition("IDLE", "TX", "send")
+        pta.add_transition("TX", "IDLE", "txComplete")
+        self.assertEqual(dfs_tran_to_name(pta.dfs(0), False), [["init"]])
+        self.assertEqual(dfs_tran_to_name(pta.dfs(1), False), [["init", "send"]])
+        self.assertEqual(
+            dfs_tran_to_name(pta.dfs(2), False), [["init", "send", "txComplete"]]
+        )
+        self.assertEqual(
+            dfs_tran_to_name(pta.dfs(3), False),
+            [["init", "send", "txComplete", "send"]],
+        )
+
+        pta = PTA(["IDLE"])
+        pta.add_transition("UNINITIALIZED", "IDLE", "init")
+        pta.add_transition("IDLE", "IDLE", "set1")
+        pta.add_transition("IDLE", "IDLE", "set2")
+        self.assertEqual(dfs_tran_to_name(pta.dfs(0), False), [["init"]])
+        self.assertEqual(
+            sorted(dfs_tran_to_name(pta.dfs(1), False)),
+            [["init", "set1"], ["init", "set2"]],
+        )
+        self.assertEqual(
+            sorted(dfs_tran_to_name(pta.dfs(2), False)),
+            [
+                ["init", "set1", "set1"],
+                ["init", "set1", "set2"],
+                ["init", "set2", "set1"],
+                ["init", "set2", "set2"],
+            ],
+        )
 
     def test_dfs_trace_filter(self):
-        pta = PTA(['IDLE'])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init')
-        pta.add_transition('IDLE', 'IDLE', 'set1')
-        pta.add_transition('IDLE', 'IDLE', 'set2')
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(2, trace_filter=[['init', 'set1', 'set2'], ['init', 'set2', 'set1']]), False)),
-                         [['init', 'set1', 'set2'], ['init', 'set2', 'set1']])
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(2, trace_filter=[['init', 'set1', '$'], ['init', 'set2', '$']]), False)),
-                         [['init', 'set1'], ['init', 'set2']])
+        pta = PTA(["IDLE"])
+        pta.add_transition("UNINITIALIZED", "IDLE", "init")
+        pta.add_transition("IDLE", "IDLE", "set1")
+        pta.add_transition("IDLE", "IDLE", "set2")
+        self.assertEqual(
+            sorted(
+                dfs_tran_to_name(
+                    pta.dfs(
+                        2,
+                        trace_filter=[
+                            ["init", "set1", "set2"],
+                            ["init", "set2", "set1"],
+                        ],
+                    ),
+                    False,
+                )
+            ),
+            [["init", "set1", "set2"], ["init", "set2", "set1"]],
+        )
+        self.assertEqual(
+            sorted(
+                dfs_tran_to_name(
+                    pta.dfs(
+                        2, trace_filter=[["init", "set1", "$"], ["init", "set2", "$"]]
+                    ),
+                    False,
+                )
+            ),
+            [["init", "set1"], ["init", "set2"]],
+        )
 
     def test_dfs_accepting(self):
-        pta = PTA(['IDLE', 'TX'], accepting_states=['IDLE'])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init')
-        pta.add_transition('IDLE', 'TX', 'send')
-        pta.add_transition('TX', 'IDLE', 'txComplete')
-        self.assertEqual(dfs_tran_to_name(pta.dfs(0), False), [['init']])
+        pta = PTA(["IDLE", "TX"], accepting_states=["IDLE"])
+        pta.add_transition("UNINITIALIZED", "IDLE", "init")
+        pta.add_transition("IDLE", "TX", "send")
+        pta.add_transition("TX", "IDLE", "txComplete")
+        self.assertEqual(dfs_tran_to_name(pta.dfs(0), False), [["init"]])
         self.assertEqual(dfs_tran_to_name(pta.dfs(1), False), [])
-        self.assertEqual(dfs_tran_to_name(pta.dfs(2), False), [['init', 'send', 'txComplete']])
+        self.assertEqual(
+            dfs_tran_to_name(pta.dfs(2), False), [["init", "send", "txComplete"]]
+        )
         self.assertEqual(dfs_tran_to_name(pta.dfs(3), False), [])
 
     def test_dfs_objects(self):
-        pta = PTA(['IDLE', 'TX'])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init')
-        pta.add_transition('IDLE', 'TX', 'send')
-        pta.add_transition('TX', 'IDLE', 'txComplete')
+        pta = PTA(["IDLE", "TX"])
+        pta.add_transition("UNINITIALIZED", "IDLE", "init")
+        pta.add_transition("IDLE", "TX", "send")
+        pta.add_transition("TX", "IDLE", "txComplete")
         traces = list(pta.dfs(2))
         self.assertEqual(len(traces), 1)
         trace = traces[0]
         self.assertEqual(len(trace), 3)
-        self.assertEqual(trace[0][0].name, 'init')
-        self.assertEqual(trace[1][0].name, 'send')
-        self.assertEqual(trace[2][0].name, 'txComplete')
+        self.assertEqual(trace[0][0].name, "init")
+        self.assertEqual(trace[1][0].name, "send")
+        self.assertEqual(trace[2][0].name, "txComplete")
         self.assertEqual(pta.get_transition_id(trace[0][0]), 0)
         self.assertEqual(pta.get_transition_id(trace[1][0]), 1)
         self.assertEqual(pta.get_transition_id(trace[2][0]), 2)
 
     def test_dfs_with_sleep(self):
-        pta = PTA(['IDLE', 'TX'])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init')
-        pta.add_transition('IDLE', 'TX', 'send')
-        pta.add_transition('TX', 'IDLE', 'txComplete')
+        pta = PTA(["IDLE", "TX"])
+        pta.add_transition("UNINITIALIZED", "IDLE", "init")
+        pta.add_transition("IDLE", "TX", "send")
+        pta.add_transition("TX", "IDLE", "txComplete")
         traces = list(pta.dfs(2, sleep=10))
         self.assertEqual(len(traces), 1)
         trace = traces[0]
         self.assertEqual(len(trace), 6)
         self.assertIsNone(trace[0][0])
-        self.assertEqual(trace[1][0].name, 'init')
+        self.assertEqual(trace[1][0].name, "init")
         self.assertIsNone(trace[2][0])
-        self.assertEqual(trace[3][0].name, 'send')
+        self.assertEqual(trace[3][0].name, "send")
         self.assertIsNone(trace[4][0])
-        self.assertEqual(trace[5][0].name, 'txComplete')
+        self.assertEqual(trace[5][0].name, "txComplete")
         self.assertEqual(pta.get_transition_id(trace[1][0]), 0)
         self.assertEqual(pta.get_transition_id(trace[3][0]), 1)
         self.assertEqual(pta.get_transition_id(trace[5][0]), 2)
 
     def test_bfs(self):
-        pta = PTA(['IDLE', 'TX'])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init')
-        pta.add_transition('IDLE', 'TX', 'send')
-        pta.add_transition('TX', 'IDLE', 'txComplete')
-        self.assertEqual(dfs_tran_to_name(pta.bfs(0), False), [['init']])
-        self.assertEqual(dfs_tran_to_name(pta.bfs(1), False), [['init'], ['init', 'send']])
-        self.assertEqual(dfs_tran_to_name(pta.bfs(2), False), [['init'], ['init', 'send'], ['init', 'send', 'txComplete']])
-        self.assertEqual(dfs_tran_to_name(pta.bfs(3), False), [['init'], ['init', 'send'], ['init', 'send', 'txComplete'], ['init', 'send', 'txComplete', 'send']])
-
-        pta = PTA(['IDLE'])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init')
-        pta.add_transition('IDLE', 'IDLE', 'set1')
-        pta.add_transition('IDLE', 'IDLE', 'set2')
-        self.assertEqual(dfs_tran_to_name(pta.bfs(0), False), [['init']])
-        self.assertEqual(sorted(dfs_tran_to_name(pta.bfs(1), False)), [['init'], ['init', 'set1'], ['init', 'set2']])
-        self.assertEqual(sorted(dfs_tran_to_name(pta.bfs(2), False)), [['init'],
-                                                                       ['init', 'set1'],
-                                                                       ['init', 'set1', 'set1'],
-                                                                       ['init', 'set1', 'set2'],
-                                                                       ['init', 'set2'],
-                                                                       ['init', 'set2', 'set1'],
-                                                                       ['init', 'set2', 'set2']])
+        pta = PTA(["IDLE", "TX"])
+        pta.add_transition("UNINITIALIZED", "IDLE", "init")
+        pta.add_transition("IDLE", "TX", "send")
+        pta.add_transition("TX", "IDLE", "txComplete")
+        self.assertEqual(dfs_tran_to_name(pta.bfs(0), False), [["init"]])
+        self.assertEqual(
+            dfs_tran_to_name(pta.bfs(1), False), [["init"], ["init", "send"]]
+        )
+        self.assertEqual(
+            dfs_tran_to_name(pta.bfs(2), False),
+            [["init"], ["init", "send"], ["init", "send", "txComplete"]],
+        )
+        self.assertEqual(
+            dfs_tran_to_name(pta.bfs(3), False),
+            [
+                ["init"],
+                ["init", "send"],
+                ["init", "send", "txComplete"],
+                ["init", "send", "txComplete", "send"],
+            ],
+        )
+
+        pta = PTA(["IDLE"])
+        pta.add_transition("UNINITIALIZED", "IDLE", "init")
+        pta.add_transition("IDLE", "IDLE", "set1")
+        pta.add_transition("IDLE", "IDLE", "set2")
+        self.assertEqual(dfs_tran_to_name(pta.bfs(0), False), [["init"]])
+        self.assertEqual(
+            sorted(dfs_tran_to_name(pta.bfs(1), False)),
+            [["init"], ["init", "set1"], ["init", "set2"]],
+        )
+        self.assertEqual(
+            sorted(dfs_tran_to_name(pta.bfs(2), False)),
+            [
+                ["init"],
+                ["init", "set1"],
+                ["init", "set1", "set1"],
+                ["init", "set1", "set2"],
+                ["init", "set2"],
+                ["init", "set2", "set1"],
+                ["init", "set2", "set2"],
+            ],
+        )
 
     def test_from_json(self):
         pta = PTA.from_json(example_json_1)
-        self.assertEqual(pta.parameters, ['datarate', 'txbytes', 'txpower'])
-        self.assertEqual(pta.state['UNINITIALIZED'].name, 'UNINITIALIZED')
-        self.assertEqual(pta.state['IDLE'].name, 'IDLE')
-        self.assertEqual(pta.state['TX'].name, 'TX')
+        self.assertEqual(pta.parameters, ["datarate", "txbytes", "txpower"])
+        self.assertEqual(pta.state["UNINITIALIZED"].name, "UNINITIALIZED")
+        self.assertEqual(pta.state["IDLE"].name, "IDLE")
+        self.assertEqual(pta.state["TX"].name, "TX")
         self.assertEqual(len(pta.transitions), 5)
-        self.assertEqual(pta.transitions[0].name, 'init')
-        self.assertEqual(pta.transitions[1].name, 'init')
-        self.assertEqual(pta.transitions[2].name, 'setTxPower')
-        self.assertEqual(pta.transitions[3].name, 'send')
-        self.assertEqual(pta.transitions[4].name, 'txComplete')
+        self.assertEqual(pta.transitions[0].name, "init")
+        self.assertEqual(pta.transitions[1].name, "init")
+        self.assertEqual(pta.transitions[2].name, "setTxPower")
+        self.assertEqual(pta.transitions[3].name, "send")
+        self.assertEqual(pta.transitions[4].name, "txComplete")
 
     # def test_to_json(self):
     #    pta = PTA.from_json(example_json_1)
@@ -394,368 +452,471 @@ class TestPTA(unittest.TestCase):
 
     def test_from_json_dfs_arg(self):
         pta = PTA.from_json(example_json_1)
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(1), False)), [['init', 'init'], ['init', 'send'], ['init', 'setTxPower']])
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(1, with_arguments=True), True)),
-                         [
-            [('init', ()), ('init', ())],
-            [('init', ()), ('send', ('"foo"', 3))],
-            [('init', ()), ('send', ('"hodor"', 5))],
-            [('init', ()), ('setTxPower', (10,))],
-            [('init', ()), ('setTxPower', (20,))],
-            [('init', ()), ('setTxPower', (30,))],
-        ]
+        self.assertEqual(
+            sorted(dfs_tran_to_name(pta.dfs(1), False)),
+            [["init", "init"], ["init", "send"], ["init", "setTxPower"]],
+        )
+        self.assertEqual(
+            sorted(dfs_tran_to_name(pta.dfs(1, with_arguments=True), True)),
+            [
+                [("init", ()), ("init", ())],
+                [("init", ()), ("send", ('"foo"', 3))],
+                [("init", ()), ("send", ('"hodor"', 5))],
+                [("init", ()), ("setTxPower", (10,))],
+                [("init", ()), ("setTxPower", (20,))],
+                [("init", ()), ("setTxPower", (30,))],
+            ],
         )
 
     def test_from_json_dfs_param(self):
         pta = PTA.from_json(example_json_1)
         no_param = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 10,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 10,
         }
         param_tx3 = {
-            'datarate': None,
-            'txbytes': 3,
-            'txpower': 10,
+            "datarate": None,
+            "txbytes": 3,
+            "txpower": 10,
         }
         param_tx5 = {
-            'datarate': None,
-            'txbytes': 5,
-            'txpower': 10,
+            "datarate": None,
+            "txbytes": 5,
+            "txpower": 10,
         }
         param_txp10 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 10,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 10,
         }
         param_txp20 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 20,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 20,
         }
         param_txp30 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 30,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 30,
         }
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(1, with_arguments=True, with_parameters=True), True, True)),
-                         [
-            [('init', (), no_param), ('init', (), no_param)],
-            [('init', (), no_param), ('send', ('"foo"', 3), param_tx3)],
-            [('init', (), no_param), ('send', ('"hodor"', 5), param_tx5)],
-            [('init', (), no_param), ('setTxPower', (10,), param_txp10)],
-            [('init', (), no_param), ('setTxPower', (20,), param_txp20)],
-            [('init', (), no_param), ('setTxPower', (30,), param_txp30)],
-        ]
+        self.assertEqual(
+            sorted(
+                dfs_tran_to_name(
+                    pta.dfs(1, with_arguments=True, with_parameters=True), True, True
+                )
+            ),
+            [
+                [("init", (), no_param), ("init", (), no_param)],
+                [("init", (), no_param), ("send", ('"foo"', 3), param_tx3)],
+                [("init", (), no_param), ("send", ('"hodor"', 5), param_tx5)],
+                [("init", (), no_param), ("setTxPower", (10,), param_txp10)],
+                [("init", (), no_param), ("setTxPower", (20,), param_txp20)],
+                [("init", (), no_param), ("setTxPower", (30,), param_txp30)],
+            ],
         )
 
     def test_from_json_function(self):
         pta = PTA.from_json(example_json_1)
-        self.assertEqual(pta.state['TX'].get_energy(1000, {'datarate': 10, 'txbytes': 6, 'txpower': 10}), 1000 * (10000 + 2 * 10))
-        self.assertEqual(pta.transitions[4].get_timeout({'datarate': 10, 'txbytes': 6, 'txpower': 10}), 500 + 16 * 6)
+        self.assertEqual(
+            pta.state["TX"].get_energy(
+                1000, {"datarate": 10, "txbytes": 6, "txpower": 10}
+            ),
+            1000 * (10000 + 2 * 10),
+        )
+        self.assertEqual(
+            pta.transitions[4].get_timeout(
+                {"datarate": 10, "txbytes": 6, "txpower": 10}
+            ),
+            500 + 16 * 6,
+        )
 
     def test_from_yaml_dfs_param(self):
         pta = PTA.from_yaml(example_yaml_1)
         no_param = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': None,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": None,
         }
         param_tx3 = {
-            'datarate': None,
-            'txbytes': 3,
-            'txpower': None,
+            "datarate": None,
+            "txbytes": 3,
+            "txpower": None,
         }
         param_tx5 = {
-            'datarate': None,
-            'txbytes': 5,
-            'txpower': None,
+            "datarate": None,
+            "txbytes": 5,
+            "txpower": None,
         }
         param_txp10 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 10,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 10,
         }
         param_txp20 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 20,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 20,
         }
         param_txp30 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 30,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 30,
         }
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(1, with_arguments=True, with_parameters=True), True, True)),
-                         [
-            [('init', (), no_param), ('init', (), no_param)],
-            [('init', (), no_param), ('send', ('"foo"', 3), param_tx3)],
-            [('init', (), no_param), ('send', ('"hodor"', 5), param_tx5)],
-            [('init', (), no_param), ('setTxPower', (10,), param_txp10)],
-            [('init', (), no_param), ('setTxPower', (20,), param_txp20)],
-            [('init', (), no_param), ('setTxPower', (30,), param_txp30)],
-        ]
+        self.assertEqual(
+            sorted(
+                dfs_tran_to_name(
+                    pta.dfs(1, with_arguments=True, with_parameters=True), True, True
+                )
+            ),
+            [
+                [("init", (), no_param), ("init", (), no_param)],
+                [("init", (), no_param), ("send", ('"foo"', 3), param_tx3)],
+                [("init", (), no_param), ("send", ('"hodor"', 5), param_tx5)],
+                [("init", (), no_param), ("setTxPower", (10,), param_txp10)],
+                [("init", (), no_param), ("setTxPower", (20,), param_txp20)],
+                [("init", (), no_param), ("setTxPower", (30,), param_txp30)],
+            ],
         )
 
     def test_normalization(self):
         pta = PTA.from_yaml(example_yaml_2)
         no_param = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': None,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": None,
         }
         param_tx3 = {
-            'datarate': None,
-            'txbytes': 3,
-            'txpower': None,
+            "datarate": None,
+            "txbytes": 3,
+            "txpower": None,
         }
         param_tx6 = {
-            'datarate': None,
-            'txbytes': 6,
-            'txpower': None,
+            "datarate": None,
+            "txbytes": 6,
+            "txpower": None,
         }
         param_txp10 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': -6,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": -6,
         }
         param_txp20 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 4,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 4,
         }
         param_txp30 = {
-            'datarate': None,
-            'txbytes': None,
-            'txpower': 14,
+            "datarate": None,
+            "txbytes": None,
+            "txpower": 14,
         }
-        self.assertEqual(sorted(dfs_tran_to_name(pta.dfs(1, with_arguments=True, with_parameters=True), True, True)),
-                         [
-            [('init', (), no_param), ('init', (), no_param)],
-            [('init', (), no_param), ('send', ('FOO',), param_tx3)],
-            [('init', (), no_param), ('send', ('LONGER',), param_tx6)],
-            [('init', (), no_param), ('setTxPower', (10,), param_txp10)],
-            [('init', (), no_param), ('setTxPower', (20,), param_txp20)],
-            [('init', (), no_param), ('setTxPower', (30,), param_txp30)],
-        ]
+        self.assertEqual(
+            sorted(
+                dfs_tran_to_name(
+                    pta.dfs(1, with_arguments=True, with_parameters=True), True, True
+                )
+            ),
+            [
+                [("init", (), no_param), ("init", (), no_param)],
+                [("init", (), no_param), ("send", ("FOO",), param_tx3)],
+                [("init", (), no_param), ("send", ("LONGER",), param_tx6)],
+                [("init", (), no_param), ("setTxPower", (10,), param_txp10)],
+                [("init", (), no_param), ("setTxPower", (20,), param_txp20)],
+                [("init", (), no_param), ("setTxPower", (30,), param_txp30)],
+            ],
         )
 
     def test_shrink(self):
         pta = PTA.from_yaml(example_yaml_3)
         pta.shrink_argument_values()
-        self.assertEqual(pta.transitions[0].name, 'setAutoAck')
-        self.assertEqual(pta.transitions[1].name, 'setPALevel')
-        self.assertEqual(pta.transitions[2].name, 'setRetries')
-        self.assertEqual(pta.transitions[3].name, 'setup')
-        self.assertEqual(pta.transitions[4].name, 'setup')
-        self.assertEqual(pta.transitions[5].name, 'write')
+        self.assertEqual(pta.transitions[0].name, "setAutoAck")
+        self.assertEqual(pta.transitions[1].name, "setPALevel")
+        self.assertEqual(pta.transitions[2].name, "setRetries")
+        self.assertEqual(pta.transitions[3].name, "setup")
+        self.assertEqual(pta.transitions[4].name, "setup")
+        self.assertEqual(pta.transitions[5].name, "write")
         self.assertEqual(pta.transitions[0].argument_values, [[0, 1]])
-        self.assertEqual(pta.transitions[1].argument_values, [['Nrf24l01::RF24_PA_MIN', 'Nrf24l01::RF24_PA_MAX']])
+        self.assertEqual(
+            pta.transitions[1].argument_values,
+            [["Nrf24l01::RF24_PA_MIN", "Nrf24l01::RF24_PA_MAX"]],
+        )
         self.assertEqual(pta.transitions[2].argument_values, [[0, 15], [0, 15]])
-        self.assertEqual(pta.transitions[5].argument_values, [['"foo"', '"foo"', '"foofoofoo"', '"foofoofoo"', '"123456789012345678901234567890"',
-                                                               '"123456789012345678901234567890"'], [3, 3, 9, 9, 30, 30], [0, 1, 0, 1, 0, 1], [1, 1, 1, 1, 1, 1]])
+        self.assertEqual(
+            pta.transitions[5].argument_values,
+            [
+                [
+                    '"foo"',
+                    '"foo"',
+                    '"foofoofoo"',
+                    '"foofoofoo"',
+                    '"123456789012345678901234567890"',
+                    '"123456789012345678901234567890"',
+                ],
+                [3, 3, 9, 9, 30, 30],
+                [0, 1, 0, 1, 0, 1],
+                [1, 1, 1, 1, 1, 1],
+            ],
+        )
 
     def test_simulation(self):
         pta = PTA()
-        pta.add_state('IDLE', power=5)
-        pta.add_state('TX', power=100)
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init', duration=50000)
-        pta.add_transition('IDLE', 'TX', 'send', energy=3, duration=10)
-        pta.add_transition('TX', 'IDLE', 'txComplete', timeout=2000, is_interrupt=True)
+        pta.add_state("IDLE", power=5)
+        pta.add_state("TX", power=100)
+        pta.add_transition("UNINITIALIZED", "IDLE", "init", duration=50000)
+        pta.add_transition("IDLE", "TX", "send", energy=3, duration=10)
+        pta.add_transition("TX", "IDLE", "txComplete", timeout=2000, is_interrupt=True)
         trace = [
-            ['init'],
+            ["init"],
             [None, 10000000],
-            ['send', 'foo', 3],
+            ["send", "foo", 3],
             [None, 5000000],
-            ['send', 'foo', 3]
+            ["send", "foo", 3],
         ]
-        expected_energy = 5. * 10000000 + 3 + 100 * 2000 + 5 * 5000000 + 3 + 100 * 2000
+        expected_energy = 5.0 * 10000000 + 3 + 100 * 2000 + 5 * 5000000 + 3 + 100 * 2000
         expected_duration = 50000 + 10000000 + 10 + 2000 + 5000000 + 10 + 2000
         result = pta.simulate(trace)
         self.assertAlmostEqual(result.energy, expected_energy * 1e-12, places=12)
         self.assertAlmostEqual(result.duration, expected_duration * 1e-6, places=6)
-        self.assertEqual(result.end_state.name, 'IDLE')
+        self.assertEqual(result.end_state.name, "IDLE")
         self.assertEqual(result.parameters, {})
 
     def test_simulation_param_none(self):
-        pta = PTA(parameters=['txpower', 'length'])
-        pta.add_state('IDLE', power=5)
-        pta.add_state('TX', power=100)
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init', energy=500000, duration=50000)
-        pta.add_transition('IDLE', 'TX', 'send', energy=3, duration=10)
-        pta.add_transition('TX', 'IDLE', 'txComplete', timeout=2000, is_interrupt=True)
+        pta = PTA(parameters=["txpower", "length"])
+        pta.add_state("IDLE", power=5)
+        pta.add_state("TX", power=100)
+        pta.add_transition(
+            "UNINITIALIZED", "IDLE", "init", energy=500000, duration=50000
+        )
+        pta.add_transition("IDLE", "TX", "send", energy=3, duration=10)
+        pta.add_transition("TX", "IDLE", "txComplete", timeout=2000, is_interrupt=True)
         trace = [
-            ['init'],
+            ["init"],
         ]
         expected_energy = 500000
         expected_duration = 50000
         result = pta.simulate(trace)
         self.assertAlmostEqual(result.energy, expected_energy * 1e-12, places=12)
         self.assertAlmostEqual(result.duration, expected_duration * 1e-6, places=6)
-        self.assertEqual(result.end_state.name, 'IDLE')
-        self.assertEqual(result.parameters, {
-            'txpower': None,
-            'length': None
-        })
+        self.assertEqual(result.end_state.name, "IDLE")
+        self.assertEqual(result.parameters, {"txpower": None, "length": None})
 
     def test_simulation_param_update_function(self):
-        pta = PTA(parameters=['txpower', 'length'])
-        pta.add_state('IDLE', power=5)
-        pta.add_state('TX', power=100)
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init', energy=500000, duration=50000)
-        pta.add_transition('IDLE', 'IDLE', 'setTxPower', energy=10000, duration=120,
-                           param_update_function=lambda param, arg: {**param, 'txpower': arg[0]})
-        pta.add_transition('IDLE', 'TX', 'send', energy=3, duration=10)
-        pta.add_transition('TX', 'IDLE', 'txComplete', timeout=2000, is_interrupt=True)
-        trace = [
-            ['init'],
-            ['setTxPower', 10]
-        ]
+        pta = PTA(parameters=["txpower", "length"])
+        pta.add_state("IDLE", power=5)
+        pta.add_state("TX", power=100)
+        pta.add_transition(
+            "UNINITIALIZED", "IDLE", "init", energy=500000, duration=50000
+        )
+        pta.add_transition(
+            "IDLE",
+            "IDLE",
+            "setTxPower",
+            energy=10000,
+            duration=120,
+            param_update_function=lambda param, arg: {**param, "txpower": arg[0]},
+        )
+        pta.add_transition("IDLE", "TX", "send", energy=3, duration=10)
+        pta.add_transition("TX", "IDLE", "txComplete", timeout=2000, is_interrupt=True)
+        trace = [["init"], ["setTxPower", 10]]
         expected_energy = 510000
         expected_duration = 50120
         result = pta.simulate(trace)
         self.assertAlmostEqual(result.energy, expected_energy * 1e-12, places=12)
         self.assertAlmostEqual(result.duration, expected_duration * 1e-6, places=6)
-        self.assertEqual(result.end_state.name, 'IDLE')
-        self.assertEqual(result.parameters, {
-            'txpower': 10,
-            'length': None
-        })
+        self.assertEqual(result.end_state.name, "IDLE")
+        self.assertEqual(result.parameters, {"txpower": 10, "length": None})
 
     def test_simulation_arg_to_param_map(self):
-        pta = PTA(parameters=['txpower', 'length'])
-        pta.add_state('IDLE', power=5)
-        pta.add_state('TX', power=100)
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init', energy=500000, duration=50000)
-        pta.add_transition('IDLE', 'IDLE', 'setTxPower', energy=10000, duration=120,
-                           arg_to_param_map={0: 'txpower'})
-        pta.add_transition('IDLE', 'TX', 'send', energy=3, duration=10)
-        pta.add_transition('TX', 'IDLE', 'txComplete', timeout=2000, is_interrupt=True)
-        trace = [
-            ['init'],
-            ['setTxPower', 10]
-        ]
+        pta = PTA(parameters=["txpower", "length"])
+        pta.add_state("IDLE", power=5)
+        pta.add_state("TX", power=100)
+        pta.add_transition(
+            "UNINITIALIZED", "IDLE", "init", energy=500000, duration=50000
+        )
+        pta.add_transition(
+            "IDLE",
+            "IDLE",
+            "setTxPower",
+            energy=10000,
+            duration=120,
+            arg_to_param_map={0: "txpower"},
+        )
+        pta.add_transition("IDLE", "TX", "send", energy=3, duration=10)
+        pta.add_transition("TX", "IDLE", "txComplete", timeout=2000, is_interrupt=True)
+        trace = [["init"], ["setTxPower", 10]]
         expected_energy = 510000
         expected_duration = 50120
         result = pta.simulate(trace)
         self.assertAlmostEqual(result.energy, expected_energy * 1e-12, places=12)
         self.assertAlmostEqual(result.duration, expected_duration * 1e-6, places=6)
-        self.assertEqual(result.end_state.name, 'IDLE')
-        self.assertEqual(result.parameters, {
-            'txpower': 10,
-            'length': None
-        })
+        self.assertEqual(result.end_state.name, "IDLE")
+        self.assertEqual(result.parameters, {"txpower": 10, "length": None})
 
     def test_simulation_set_param(self):
-        pta = PTA(parameters=['txpower', 'length'])
-        pta.add_state('IDLE', power=5)
-        pta.add_state('TX', power=100)
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init', energy=500000, duration=50000, set_param={'txpower': 10})
+        pta = PTA(parameters=["txpower", "length"])
+        pta.add_state("IDLE", power=5)
+        pta.add_state("TX", power=100)
+        pta.add_transition(
+            "UNINITIALIZED",
+            "IDLE",
+            "init",
+            energy=500000,
+            duration=50000,
+            set_param={"txpower": 10},
+        )
         trace = [
-            ['init'],
+            ["init"],
         ]
         expected_energy = 500000
         expected_duration = 50000
         result = pta.simulate(trace)
         self.assertAlmostEqual(result.energy, expected_energy * 1e-12, places=12)
         self.assertAlmostEqual(result.duration, expected_duration * 1e-6, places=6)
-        self.assertEqual(result.end_state.name, 'IDLE')
-        self.assertEqual(result.parameters, {
-            'txpower': 10,
-            'length': None
-        })
+        self.assertEqual(result.end_state.name, "IDLE")
+        self.assertEqual(result.parameters, {"txpower": 10, "length": None})
 
     def test_simulation_arg_function(self):
-        pta = PTA(parameters=['txpower', 'length'])
-        pta.add_state('IDLE', power=5)
-        pta.add_state('TX', power=100)
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init', energy=500000, duration=50000)
-        pta.add_transition('IDLE', 'IDLE', 'setTxPower', energy=10000, duration=120,
-                           param_update_function=lambda param, arg: {**param, 'txpower': arg[0]})
-        pta.add_transition('IDLE', 'TX', 'send', energy=3, duration=10,
-                           energy_function=lambda param, arg: 3 + 5 * arg[1],
-                           duration_function=lambda param, arg: 48 + 8 * arg[1])
-        pta.add_transition('TX', 'IDLE', 'txComplete', timeout=2000, is_interrupt=True)
+        pta = PTA(parameters=["txpower", "length"])
+        pta.add_state("IDLE", power=5)
+        pta.add_state("TX", power=100)
+        pta.add_transition(
+            "UNINITIALIZED", "IDLE", "init", energy=500000, duration=50000
+        )
+        pta.add_transition(
+            "IDLE",
+            "IDLE",
+            "setTxPower",
+            energy=10000,
+            duration=120,
+            param_update_function=lambda param, arg: {**param, "txpower": arg[0]},
+        )
+        pta.add_transition(
+            "IDLE",
+            "TX",
+            "send",
+            energy=3,
+            duration=10,
+            energy_function=lambda param, arg: 3 + 5 * arg[1],
+            duration_function=lambda param, arg: 48 + 8 * arg[1],
+        )
+        pta.add_transition("TX", "IDLE", "txComplete", timeout=2000, is_interrupt=True)
         trace = [
-            ['init'],
-            ['setTxPower', 10],
-            ['send', 'foo', 3],
+            ["init"],
+            ["setTxPower", 10],
+            ["send", "foo", 3],
         ]
         expected_energy = 500000 + 10000 + (3 + 5 * 3) + (2000 * 100)
         expected_duration = 50000 + 120 + (48 + 8 * 3) + 2000
         result = pta.simulate(trace)
         self.assertAlmostEqual(result.energy, expected_energy * 1e-12, places=12)
         self.assertAlmostEqual(result.duration, expected_duration * 1e-6, places=6)
-        self.assertEqual(result.end_state.name, 'IDLE')
-        self.assertEqual(result.parameters, {
-            'txpower': 10,
-            'length': None
-        })
-
-        pta = PTA(parameters=['txpower', 'length'])
-        pta.add_state('IDLE', power=5)
-        pta.add_state('TX', power=100)
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init', energy=500000, duration=50000)
-        pta.add_transition('IDLE', 'IDLE', 'setTxPower', energy=10000, duration=120,
-                           param_update_function=lambda param, arg: {**param, 'txpower': arg[0]})
-        pta.add_transition('IDLE', 'TX', 'send', energy=3, duration=10,
-                           energy_function=lambda param, arg: 3 + 5 * arg[1],
-                           duration_function=lambda param, arg: 48 + 8 * arg[1])
-        pta.add_transition('TX', 'IDLE', 'txComplete', timeout=2000, is_interrupt=True)
+        self.assertEqual(result.end_state.name, "IDLE")
+        self.assertEqual(result.parameters, {"txpower": 10, "length": None})
+
+        pta = PTA(parameters=["txpower", "length"])
+        pta.add_state("IDLE", power=5)
+        pta.add_state("TX", power=100)
+        pta.add_transition(
+            "UNINITIALIZED", "IDLE", "init", energy=500000, duration=50000
+        )
+        pta.add_transition(
+            "IDLE",
+            "IDLE",
+            "setTxPower",
+            energy=10000,
+            duration=120,
+            param_update_function=lambda param, arg: {**param, "txpower": arg[0]},
+        )
+        pta.add_transition(
+            "IDLE",
+            "TX",
+            "send",
+            energy=3,
+            duration=10,
+            energy_function=lambda param, arg: 3 + 5 * arg[1],
+            duration_function=lambda param, arg: 48 + 8 * arg[1],
+        )
+        pta.add_transition("TX", "IDLE", "txComplete", timeout=2000, is_interrupt=True)
         trace = [
-            ['init'],
-            ['setTxPower', 10],
-            ['send', 'foobar', 6],
+            ["init"],
+            ["setTxPower", 10],
+            ["send", "foobar", 6],
         ]
         expected_energy = 500000 + 10000 + (3 + 5 * 6) + (2000 * 100)
         expected_duration = 50000 + 120 + (48 + 8 * 6) + 2000
         result = pta.simulate(trace)
         self.assertAlmostEqual(result.energy, expected_energy * 1e-12, places=12)
         self.assertAlmostEqual(result.duration, expected_duration * 1e-6, places=6)
-        self.assertEqual(result.end_state.name, 'IDLE')
-        self.assertEqual(result.parameters, {
-            'txpower': 10,
-            'length': None
-        })
+        self.assertEqual(result.end_state.name, "IDLE")
+        self.assertEqual(result.parameters, {"txpower": 10, "length": None})
 
     def test_simulation_param_function(self):
-        pta = PTA(parameters=['length', 'txpower'])
-        pta.add_state('IDLE', power=5)
-        pta.add_state('TX', power=100,
-                      power_function=lambda param, arg: 1000 + 2 * param[1])
-        pta.add_transition('UNINITIALIZED', 'IDLE', 'init', energy=500000, duration=50000)
-        pta.add_transition('IDLE', 'IDLE', 'setTxPower', energy=10000, duration=120,
-                           param_update_function=lambda param, arg: {**param, 'txpower': arg[0]})
-        pta.add_transition('IDLE', 'TX', 'send', energy=3, duration=10,
-                           energy_function=lambda param, arg: 3 + 5 * arg[1],
-                           param_update_function=lambda param, arg: {**param, 'length': arg[1]})
-        pta.add_transition('TX', 'IDLE', 'txComplete', timeout=2000, is_interrupt=True,
-                           timeout_function=lambda param, arg: 500 + 16 * param[0])
+        pta = PTA(parameters=["length", "txpower"])
+        pta.add_state("IDLE", power=5)
+        pta.add_state(
+            "TX", power=100, power_function=lambda param, arg: 1000 + 2 * param[1]
+        )
+        pta.add_transition(
+            "UNINITIALIZED", "IDLE", "init", energy=500000, duration=50000
+        )
+        pta.add_transition(
+            "IDLE",
+            "IDLE",
+            "setTxPower",
+            energy=10000,
+            duration=120,
+            param_update_function=lambda param, arg: {**param, "txpower": arg[0]},
+        )
+        pta.add_transition(
+            "IDLE",
+            "TX",
+            "send",
+            energy=3,
+            duration=10,
+            energy_function=lambda param, arg: 3 + 5 * arg[1],
+            param_update_function=lambda param, arg: {**param, "length": arg[1]},
+        )
+        pta.add_transition(
+            "TX",
+            "IDLE",
+            "txComplete",
+            timeout=2000,
+            is_interrupt=True,
+            timeout_function=lambda param, arg: 500 + 16 * param[0],
+        )
         trace = [
-            ['init'],
-            ['setTxPower', 10],
-            ['send', 'foo', 3],
+            ["init"],
+            ["setTxPower", 10],
+            ["send", "foo", 3],
         ]
-        expected_energy = 500000 + 10000 + (3 + 5 * 3) + (1000 + 2 * 10) * (500 + 16 * 3)
+        expected_energy = (
+            500000 + 10000 + (3 + 5 * 3) + (1000 + 2 * 10) * (500 + 16 * 3)
+        )
         expected_duration = 50000 + 120 + 10 + (500 + 16 * 3)
         result = pta.simulate(trace)
         self.assertAlmostEqual(result.energy, expected_energy * 1e-12, places=12)
         self.assertAlmostEqual(result.duration, expected_duration * 1e-6, places=6)
-        self.assertEqual(result.end_state.name, 'IDLE')
-        self.assertEqual(result.parameters, {
-            'txpower': 10,
-            'length': 3
-        })
+        self.assertEqual(result.end_state.name, "IDLE")
+        self.assertEqual(result.parameters, {"txpower": 10, "length": 3})
 
     def test_get_X_expensive_state(self):
         pta = PTA.from_json(example_json_1)
-        self.assertEqual(pta.get_least_expensive_state(), pta.state['IDLE'])
-        self.assertEqual(pta.get_most_expensive_state(), pta.state['TX'])
+        self.assertEqual(pta.get_least_expensive_state(), pta.state["IDLE"])
+        self.assertEqual(pta.get_most_expensive_state(), pta.state["TX"])
         # self.assertAlmostEqual(pta.min_duration_until_energy_overflow(), (2**32 - 1) * 1e-12 / 10e-3, places=9)
         # self.assertAlmostEqual(pta.min_duration_until_energy_overflow(energy_granularity=1e-9), (2**32 - 1) * 1e-9 / 10e-3, places=9)
-        self.assertAlmostEqual(pta.max_duration_until_energy_overflow(), (2**32 - 1) * 1e-12 / 5e-6, places=9)
-        self.assertAlmostEqual(pta.max_duration_until_energy_overflow(energy_granularity=1e-9), (2**32 - 1) * 1e-9 / 5e-6, places=9)
+        self.assertAlmostEqual(
+            pta.max_duration_until_energy_overflow(),
+            (2 ** 32 - 1) * 1e-12 / 5e-6,
+            places=9,
+        )
+        self.assertAlmostEqual(
+            pta.max_duration_until_energy_overflow(energy_granularity=1e-9),
+            (2 ** 32 - 1) * 1e-9 / 5e-6,
+            places=9,
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/test/test_ptamodel.py b/test/test_ptamodel.py
index 7d501e6..e8905b1 100755
--- a/test/test_ptamodel.py
+++ b/test/test_ptamodel.py
@@ -1,248 +1,843 @@
 #!/usr/bin/env python3
 
-from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate
+from dfatool.loader import RawData, pta_trace_to_aggregate
+from dfatool.model import PTAModel
+from dfatool.utils import by_name_to_by_param
+from dfatool.validation import CrossValidator
 import os
 import unittest
 import pytest
 
+import numpy as np
 
-class TestModels(unittest.TestCase):
-    def test_model_singlefile_rf24(self):
-        raw_data = RawData(['test-data/20170220_164723_RF24_int_A.tar'])
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+
+class TestSynthetic(unittest.TestCase):
+    def test_model_validation(self):
+        # rng = np.random.default_rng(seed=1312) # requiresy NumPy >= 1.17
+        np.random.seed(1312)
+        X = np.arange(500) % 50
+        parameter_names = ["p_mod5", "p_linear"]
+
+        s1_duration_base = 70
+        s1_duration_scale = 2
+        s1_power_base = 50
+        s1_power_scale = 7
+        s2_duration_base = 700
+        s2_duration_scale = 1
+        s2_power_base = 1500
+        s2_power_scale = 10
+
+        by_name = {
+            "raw_state_1": {
+                "isa": "state",
+                "param": [(x % 5, x) for x in X],
+                "duration": s1_duration_base
+                + np.random.normal(size=X.size, scale=s1_duration_scale),
+                "power": s1_power_base
+                + X
+                + np.random.normal(size=X.size, scale=s1_power_scale),
+                "attributes": ["duration", "power"],
+            },
+            "raw_state_2": {
+                "isa": "state",
+                "param": [(x % 5, x) for x in X],
+                "duration": s2_duration_base
+                - 2 * X
+                + np.random.normal(size=X.size, scale=s2_duration_scale),
+                "power": s2_power_base
+                + X
+                + np.random.normal(size=X.size, scale=s2_power_scale),
+                "attributes": ["duration", "power"],
+            },
+        }
+        by_param = by_name_to_by_param(by_name)
+        model = PTAModel(by_name, parameter_names, dict())
+        static_model = model.get_static()
+
+        # x ∈ [0, 50] -> mean(X) is 25
+        self.assertAlmostEqual(
+            static_model("raw_state_1", "duration"), s1_duration_base, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("raw_state_1", "power"), s1_power_base + 25, delta=7
+        )
+        self.assertAlmostEqual(
+            static_model("raw_state_2", "duration"), s2_duration_base - 2 * 25, delta=2
+        )
+        self.assertAlmostEqual(
+            static_model("raw_state_2", "power"), s2_power_base + 25, delta=7
+        )
+
+        param_model, param_info = model.get_fitted()
+
+        self.assertAlmostEqual(
+            param_model("raw_state_1", "duration", param=[0, 10]),
+            s1_duration_base,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model("raw_state_1", "duration", param=[0, 50]),
+            s1_duration_base,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model("raw_state_1", "duration", param=[0, 70]),
+            s1_duration_base,
+            places=0,
+        )
+
+        self.assertAlmostEqual(
+            param_model("raw_state_1", "power", param=[0, 10]),
+            s1_power_base + 10,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model("raw_state_1", "power", param=[0, 50]),
+            s1_power_base + 50,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model("raw_state_1", "power", param=[0, 70]),
+            s1_power_base + 70,
+            places=0,
+        )
+
+        self.assertAlmostEqual(
+            param_model("raw_state_2", "duration", param=[0, 10]),
+            s2_duration_base - 2 * 10,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model("raw_state_2", "duration", param=[0, 50]),
+            s2_duration_base - 2 * 50,
+            places=0,
+        )
+        self.assertAlmostEqual(
+            param_model("raw_state_2", "duration", param=[0, 70]),
+            s2_duration_base - 2 * 70,
+            places=0,
+        )
+
+        self.assertAlmostEqual(
+            param_model("raw_state_2", "power", param=[0, 10]),
+            s2_power_base + 10,
+            delta=50,
+        )
+        self.assertAlmostEqual(
+            param_model("raw_state_2", "power", param=[0, 50]),
+            s2_power_base + 50,
+            delta=50,
+        )
+        self.assertAlmostEqual(
+            param_model("raw_state_2", "power", param=[0, 70]),
+            s2_power_base + 70,
+            delta=50,
+        )
+
+        static_quality = model.assess(static_model)
+        param_quality = model.assess(param_model)
+
+        # static quality reflects normal distribution scale for non-parameterized data
+
+        # the Root Mean Square Deviation must not be greater the scale (i.e., standard deviation) of the normal distribution
+        # Low Mean Absolute Error (< 2)
+        self.assertTrue(static_quality["by_name"]["raw_state_1"]["duration"]["mae"] < 2)
+        # Low Root Mean Square Deviation (< scale == 2)
+        self.assertTrue(
+            static_quality["by_name"]["raw_state_1"]["duration"]["rmsd"] < 2
+        )
+        # Relatively low error percentage (~~ MAE * 100% / s1_duration_base)
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["duration"]["mape"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["mae"]
+            * 100
+            / s1_duration_base,
+            places=1,
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["duration"]["smape"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["mae"]
+            * 100
+            / s1_duration_base,
+            places=1,
+        )
+
+        # static error is high for parameterized data
+
+        # MAE == mean(abs(actual value - model value))
+        # parameter range is [0, 50) -> mean 25, deviation range is [0, 25) -> mean deviation is 12.5 ± gauss scale
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["power"]["mae"], 12.5, delta=1
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["power"]["rmsd"], 16, delta=2
+        )
+        # high percentage error due to low s1_power_base
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["power"]["mape"], 19, delta=2
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["power"]["smape"], 19, delta=2
+        )
+
+        # parameter range is [0, 100) -> mean deviation is 25 ± gauss scale
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["duration"]["mae"], 25, delta=2
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["duration"]["rmsd"], 30, delta=2
+        )
+
+        # low percentage error due to high s2_duration_base (~~ 3.5 %)
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["duration"]["mape"],
+            25 * 100 / s2_duration_base,
+            delta=1,
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["duration"]["smape"],
+            25 * 100 / s2_duration_base,
+            delta=1,
+        )
+
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["power"]["mae"], 12.5, delta=2
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["power"]["rmsd"], 17, delta=2
+        )
+
+        # low percentage error due to high s2_power_base (~~ 1.7 %)
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["power"]["mape"],
+            25 * 100 / s2_power_base,
+            delta=1,
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["power"]["smape"],
+            25 * 100 / s2_power_base,
+            delta=1,
+        )
+
+        # raw_state_1/duration does not depend on parameters and delegates to the static model
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["duration"]["mae"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["mae"],
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["duration"]["rmsd"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["rmsd"],
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["duration"]["mape"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["mape"],
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["duration"]["smape"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["smape"],
+        )
+
+        # fitted param-model quality reflects normal distribution scale for all data
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["power"]["mape"], 0.9, places=1
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["power"]["smape"], 0.9, places=1
+        )
+
+        self.assertTrue(
+            param_quality["by_name"]["raw_state_1"]["power"]["mae"] < s1_power_scale
+        )
+        self.assertTrue(
+            param_quality["by_name"]["raw_state_1"]["power"]["rmsd"] < s1_power_scale
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["power"]["mape"], 7.5, delta=1
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["power"]["smape"], 7.5, delta=1
+        )
+
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["duration"]["mae"],
+            s2_duration_scale,
+            delta=0.2,
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["duration"]["rmsd"],
+            s2_duration_scale,
+            delta=0.2,
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["duration"]["mape"],
+            0.12,
+            delta=0.01,
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["duration"]["smape"],
+            0.12,
+            delta=0.01,
+        )
+
+        # ... unless the signal-to-noise ratio (parameter range = [0 .. 50] vs. scale = 10) is bad, leading to
+        # increased regression errors
+        self.assertTrue(param_quality["by_name"]["raw_state_2"]["power"]["mae"] < 15)
+        self.assertTrue(param_quality["by_name"]["raw_state_2"]["power"]["rmsd"] < 18)
+
+        # still: low percentage error due to high s2_power_base
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["power"]["mape"], 0.9, places=1
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["power"]["smape"], 0.9, places=1
+        )
+
+    def test_model_crossvalidation_10fold(self):
+        # rng = np.random.default_rng(seed=1312) # requiresy NumPy >= 1.17
+        np.random.seed(1312)
+        X = np.arange(500) % 50
+        parameter_names = ["p_mod5", "p_linear"]
+
+        s1_duration_base = 70
+        s1_duration_scale = 2
+        s1_power_base = 50
+        s1_power_scale = 7
+        s2_duration_base = 700
+        s2_duration_scale = 1
+        s2_power_base = 1500
+        s2_power_scale = 10
+
+        by_name = {
+            "raw_state_1": {
+                "isa": "state",
+                "param": [(x % 5, x) for x in X],
+                "duration": s1_duration_base
+                + np.random.normal(size=X.size, scale=s1_duration_scale),
+                "power": s1_power_base
+                + X
+                + np.random.normal(size=X.size, scale=s1_power_scale),
+                "attributes": ["duration", "power"],
+            },
+            "raw_state_2": {
+                "isa": "state",
+                "param": [(x % 5, x) for x in X],
+                "duration": s2_duration_base
+                - 2 * X
+                + np.random.normal(size=X.size, scale=s2_duration_scale),
+                "power": s2_power_base
+                + X
+                + np.random.normal(size=X.size, scale=s2_power_scale),
+                "attributes": ["duration", "power"],
+            },
+        }
+        by_param = by_name_to_by_param(by_name)
+        arg_count = dict()
+        model = PTAModel(by_name, parameter_names, arg_count)
+        validator = CrossValidator(PTAModel, by_name, parameter_names, arg_count)
+
+        static_quality = validator.kfold(lambda m: m.get_static(), 10)
+        param_quality = validator.kfold(lambda m: m.get_fitted()[0], 10)
+
+        print(static_quality)
+
+        # static quality reflects normal distribution scale for non-parameterized data
+
+        # the Root Mean Square Deviation must not be greater the scale (i.e., standard deviation) of the normal distribution
+        # Low Mean Absolute Error (< 2)
+        self.assertTrue(static_quality["by_name"]["raw_state_1"]["duration"]["mae"] < 2)
+        # Low Root Mean Square Deviation (< scale == 2)
+        self.assertTrue(
+            static_quality["by_name"]["raw_state_1"]["duration"]["rmsd"] < 2
+        )
+        # Relatively low error percentage (~~ MAE * 100% / s1_duration_base)
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["duration"]["smape"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["mae"]
+            * 100
+            / s1_duration_base,
+            places=1,
+        )
+
+        # static error is high for parameterized data
+
+        # MAE == mean(abs(actual value - model value))
+        # parameter range is [0, 50) -> mean 25, deviation range is [0, 25) -> mean deviation is 12.5 ± gauss scale
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["power"]["mae"], 12.5, delta=1
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["power"]["rmsd"], 16, delta=2
+        )
+        # high percentage error due to low s1_power_base
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_1"]["power"]["smape"], 19, delta=2
+        )
+
+        # parameter range is [0, 100) -> mean deviation is 25 ± gauss scale
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["duration"]["mae"], 25, delta=2
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["duration"]["rmsd"], 30, delta=2
+        )
+
+        # low percentage error due to high s2_duration_base (~~ 3.5 %)
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["duration"]["smape"],
+            25 * 100 / s2_duration_base,
+            delta=1,
+        )
+
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["power"]["mae"], 12.5, delta=2
+        )
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["power"]["rmsd"], 17, delta=2
+        )
+
+        # low percentage error due to high s2_power_base (~~ 1.7 %)
+        self.assertAlmostEqual(
+            static_quality["by_name"]["raw_state_2"]["power"]["smape"],
+            25 * 100 / s2_power_base,
+            delta=1,
+        )
+
+        # raw_state_1/duration does not depend on parameters and delegates to the static model
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["duration"]["mae"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["mae"],
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["duration"]["rmsd"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["rmsd"],
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["duration"]["smape"],
+            static_quality["by_name"]["raw_state_1"]["duration"]["smape"],
+        )
+
+        # fitted param-model quality reflects normal distribution scale for all data
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["power"]["smape"], 0.9, places=1
+        )
+
+        self.assertTrue(
+            param_quality["by_name"]["raw_state_1"]["power"]["mae"] < s1_power_scale
+        )
+        self.assertTrue(
+            param_quality["by_name"]["raw_state_1"]["power"]["rmsd"] < s1_power_scale
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_1"]["power"]["smape"], 7.5, delta=1
+        )
+
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["duration"]["mae"],
+            s2_duration_scale,
+            delta=0.2,
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["duration"]["rmsd"],
+            s2_duration_scale,
+            delta=0.2,
+        )
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["duration"]["smape"],
+            0.12,
+            delta=0.01,
+        )
+
+        # ... unless the signal-to-noise ratio (parameter range = [0 .. 50] vs. scale = 10) is bad, leading to
+        # increased regression errors
+        self.assertTrue(param_quality["by_name"]["raw_state_2"]["power"]["mae"] < 15)
+        self.assertTrue(param_quality["by_name"]["raw_state_2"]["power"]["rmsd"] < 18)
+
+        # still: low percentage error due to high s2_power_base
+        self.assertAlmostEqual(
+            param_quality["by_name"]["raw_state_2"]["power"]["smape"], 0.9, places=1
+        )
+
+
+class TestFromFile(unittest.TestCase):
+    def test_singlefile_rf24(self):
+        raw_data = RawData(["test-data/20170220_164723_RF24_int_A.tar"])
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-        model = PTAModel(by_name, parameters, arg_count, verbose=False)
-        self.assertEqual(model.states(), 'POWERDOWN RX STANDBY1 TX'.split(' '))
-        self.assertEqual(model.transitions(), 'begin epilogue powerDown powerUp setDataRate_num setPALevel_num startListening stopListening write_nb'.split(' '))
+        model = PTAModel(by_name, parameters, arg_count)
+        self.assertEqual(model.states(), "POWERDOWN RX STANDBY1 TX".split(" "))
+        self.assertEqual(
+            model.transitions(),
+            "begin epilogue powerDown powerUp setDataRate_num setPALevel_num startListening stopListening write_nb".split(
+                " "
+            ),
+        )
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('POWERDOWN', 'power'), 0, places=0)
-        self.assertAlmostEqual(static_model('RX', 'power'), 52254, places=0)
-        self.assertAlmostEqual(static_model('STANDBY1', 'power'), 7, places=0)
-        self.assertAlmostEqual(static_model('TX', 'power'), 18414, places=0)
-        self.assertAlmostEqual(static_model('begin', 'energy'), 1652249, places=0)
-        self.assertAlmostEqual(static_model('epilogue', 'energy'), 15449, places=0)
-        self.assertAlmostEqual(static_model('powerDown', 'energy'), 4547, places=0)
-        self.assertAlmostEqual(static_model('powerUp', 'energy'), 1641765, places=0)
-        self.assertAlmostEqual(static_model('setDataRate_num', 'energy'), 7749, places=0)
-        self.assertAlmostEqual(static_model('setPALevel_num', 'energy'), 4700, places=0)
-        self.assertAlmostEqual(static_model('startListening', 'energy'), 4309602, places=0)
-        self.assertAlmostEqual(static_model('stopListening', 'energy'), 193775, places=0)
-        self.assertAlmostEqual(static_model('write_nb', 'energy'), 218339, places=0)
-        self.assertAlmostEqual(static_model('begin', 'rel_energy_prev'), 1649571, places=0)
-        self.assertAlmostEqual(static_model('epilogue', 'rel_energy_prev'), -744114, places=0)
-        self.assertAlmostEqual(static_model('powerDown', 'rel_energy_prev'), 3854, places=0)
-        self.assertAlmostEqual(static_model('powerUp', 'rel_energy_prev'), 1641381, places=0)
-        self.assertAlmostEqual(static_model('setDataRate_num', 'rel_energy_prev'), 6777, places=0)
-        self.assertAlmostEqual(static_model('setPALevel_num', 'rel_energy_prev'), 3728, places=0)
-        self.assertAlmostEqual(static_model('startListening', 'rel_energy_prev'), 4307769, places=0)
-        self.assertAlmostEqual(static_model('stopListening', 'rel_energy_prev'), -13533693, places=0)
-        self.assertAlmostEqual(static_model('write_nb', 'rel_energy_prev'), 214618, places=0)
-        self.assertAlmostEqual(static_model('begin', 'duration'), 19830, places=0)
-        self.assertAlmostEqual(static_model('epilogue', 'duration'), 40, places=0)
-        self.assertAlmostEqual(static_model('powerDown', 'duration'), 90, places=0)
-        self.assertAlmostEqual(static_model('powerUp', 'duration'), 10030, places=0)
-        self.assertAlmostEqual(static_model('setDataRate_num', 'duration'), 140, places=0)
-        self.assertAlmostEqual(static_model('setPALevel_num', 'duration'), 90, places=0)
-        self.assertAlmostEqual(static_model('startListening', 'duration'), 260, places=0)
-        self.assertAlmostEqual(static_model('stopListening', 'duration'), 260, places=0)
-        self.assertAlmostEqual(static_model('write_nb', 'duration'), 510, places=0)
-
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('POWERDOWN', 'power', 'datarate'), 0, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('POWERDOWN', 'power', 'txbytes'), 0, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('POWERDOWN', 'power', 'txpower'), 0, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('RX', 'power', 'datarate'), 0.99, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('RX', 'power', 'txbytes'), 0, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('RX', 'power', 'txpower'), 0.01, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('STANDBY1', 'power', 'datarate'), 0.04, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('STANDBY1', 'power', 'txbytes'), 0.35, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('STANDBY1', 'power', 'txpower'), 0.32, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('TX', 'power', 'datarate'), 1, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('TX', 'power', 'txbytes'), 0.09, places=2)
-        self.assertAlmostEqual(model.stats.param_dependence_ratio('TX', 'power', 'txpower'), 1, places=2)
+        self.assertAlmostEqual(static_model("POWERDOWN", "power"), 0, places=0)
+        self.assertAlmostEqual(static_model("RX", "power"), 52254, places=0)
+        self.assertAlmostEqual(static_model("STANDBY1", "power"), 7, places=0)
+        self.assertAlmostEqual(static_model("TX", "power"), 18414, places=0)
+        self.assertAlmostEqual(static_model("begin", "energy"), 1652249, places=0)
+        self.assertAlmostEqual(static_model("epilogue", "energy"), 15449, places=0)
+        self.assertAlmostEqual(static_model("powerDown", "energy"), 4547, places=0)
+        self.assertAlmostEqual(static_model("powerUp", "energy"), 1641765, places=0)
+        self.assertAlmostEqual(
+            static_model("setDataRate_num", "energy"), 7749, places=0
+        )
+        self.assertAlmostEqual(static_model("setPALevel_num", "energy"), 4700, places=0)
+        self.assertAlmostEqual(
+            static_model("startListening", "energy"), 4309602, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("stopListening", "energy"), 193775, places=0
+        )
+        self.assertAlmostEqual(static_model("write_nb", "energy"), 218339, places=0)
+        self.assertAlmostEqual(
+            static_model("begin", "rel_energy_prev"), 1649571, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("epilogue", "rel_energy_prev"), -744114, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("powerDown", "rel_energy_prev"), 3854, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("powerUp", "rel_energy_prev"), 1641381, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("setDataRate_num", "rel_energy_prev"), 6777, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("setPALevel_num", "rel_energy_prev"), 3728, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("startListening", "rel_energy_prev"), 4307769, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("stopListening", "rel_energy_prev"), -13533693, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("write_nb", "rel_energy_prev"), 214618, places=0
+        )
+        self.assertAlmostEqual(static_model("begin", "duration"), 19830, places=0)
+        self.assertAlmostEqual(static_model("epilogue", "duration"), 40, places=0)
+        self.assertAlmostEqual(static_model("powerDown", "duration"), 90, places=0)
+        self.assertAlmostEqual(static_model("powerUp", "duration"), 10030, places=0)
+        self.assertAlmostEqual(
+            static_model("setDataRate_num", "duration"), 140, places=0
+        )
+        self.assertAlmostEqual(static_model("setPALevel_num", "duration"), 90, places=0)
+        self.assertAlmostEqual(
+            static_model("startListening", "duration"), 260, places=0
+        )
+        self.assertAlmostEqual(static_model("stopListening", "duration"), 260, places=0)
+        self.assertAlmostEqual(static_model("write_nb", "duration"), 510, places=0)
+
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("POWERDOWN", "power", "datarate"),
+            0,
+            places=2,
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("POWERDOWN", "power", "txbytes"),
+            0,
+            places=2,
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("POWERDOWN", "power", "txpower"),
+            0,
+            places=2,
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("RX", "power", "datarate"),
+            0.99,
+            places=2,
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("RX", "power", "txbytes"), 0, places=2
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("RX", "power", "txpower"), 0.01, places=2
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("STANDBY1", "power", "datarate"),
+            0.04,
+            places=2,
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("STANDBY1", "power", "txbytes"),
+            0.35,
+            places=2,
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("STANDBY1", "power", "txpower"),
+            0.32,
+            places=2,
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("TX", "power", "datarate"), 1, places=2
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("TX", "power", "txbytes"), 0.09, places=2
+        )
+        self.assertAlmostEqual(
+            model.stats.param_dependence_ratio("TX", "power", "txpower"), 1, places=2
+        )
 
         param_model, param_info = model.get_fitted()
-        self.assertEqual(param_info('POWERDOWN', 'power'), None)
-        self.assertEqual(param_info('RX', 'power')['function']._model_str,
-                         '0 + regression_arg(0) + regression_arg(1) * np.sqrt(parameter(datarate))')
-        self.assertAlmostEqual(param_info('RX', 'power')['function']._regression_args[0], 48530.7, places=0)
-        self.assertAlmostEqual(param_info('RX', 'power')['function']._regression_args[1], 117, places=0)
-        self.assertEqual(param_info('STANDBY1', 'power'), None)
-        self.assertEqual(param_info('TX', 'power')['function']._model_str,
-                         '0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * parameter(txpower) + regression_arg(3) * 1/(parameter(datarate)) * parameter(txpower)')
-        self.assertEqual(param_info('epilogue', 'timeout')['function']._model_str,
-                         '0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))')
-        self.assertEqual(param_info('stopListening', 'duration')['function']._model_str,
-                         '0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))')
-
-        self.assertAlmostEqual(param_model('RX', 'power', param=[1, None, None]), 48647, places=-1)
-
-    def test_model_singlefile_mmparam(self):
-        raw_data = RawData(['test-data/20161221_123347_mmparam.tar'])
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+        self.assertEqual(param_info("POWERDOWN", "power"), None)
+        self.assertEqual(
+            param_info("RX", "power")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * np.sqrt(parameter(datarate))",
+        )
+        self.assertAlmostEqual(
+            param_info("RX", "power")["function"].model_args[0], 48530.7, places=0
+        )
+        self.assertAlmostEqual(
+            param_info("RX", "power")["function"].model_args[1], 117, places=0
+        )
+        self.assertEqual(param_info("STANDBY1", "power"), None)
+        self.assertEqual(
+            param_info("TX", "power")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * parameter(txpower) + regression_arg(3) * 1/(parameter(datarate)) * parameter(txpower)",
+        )
+        self.assertEqual(
+            param_info("epilogue", "timeout")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))",
+        )
+        self.assertEqual(
+            param_info("stopListening", "duration")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))",
+        )
+
+        self.assertAlmostEqual(
+            param_model("RX", "power", param=[1, None, None]), 48647, places=-1
+        )
+
+    def test_singlefile_mmparam(self):
+        raw_data = RawData(["test-data/20161221_123347_mmparam.tar"])
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-        model = PTAModel(by_name, parameters, arg_count, verbose=False)
-        self.assertEqual(model.states(), 'OFF ON'.split(' '))
-        self.assertEqual(model.transitions(), 'off setBrightness'.split(' '))
+        model = PTAModel(by_name, parameters, arg_count)
+        self.assertEqual(model.states(), "OFF ON".split(" "))
+        self.assertEqual(model.transitions(), "off setBrightness".split(" "))
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('OFF', 'power'), 7124, places=0)
-        self.assertAlmostEqual(static_model('ON', 'power'), 17866, places=0)
-        self.assertAlmostEqual(static_model('off', 'energy'), 268079197, places=0)
-        self.assertAlmostEqual(static_model('setBrightness', 'energy'), 168912773, places=0)
-        self.assertAlmostEqual(static_model('off', 'rel_energy_prev'), 105040198, places=0)
-        self.assertAlmostEqual(static_model('setBrightness', 'rel_energy_prev'), 103745586, places=0)
-        self.assertAlmostEqual(static_model('off', 'duration'), 9130, places=0)
-        self.assertAlmostEqual(static_model('setBrightness', 'duration'), 9130, places=0)
+        self.assertAlmostEqual(static_model("OFF", "power"), 7124, places=0)
+        self.assertAlmostEqual(static_model("ON", "power"), 17866, places=0)
+        self.assertAlmostEqual(static_model("off", "energy"), 268079197, places=0)
+        self.assertAlmostEqual(
+            static_model("setBrightness", "energy"), 168912773, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("off", "rel_energy_prev"), 105040198, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("setBrightness", "rel_energy_prev"), 103745586, places=0
+        )
+        self.assertAlmostEqual(static_model("off", "duration"), 9130, places=0)
+        self.assertAlmostEqual(
+            static_model("setBrightness", "duration"), 9130, places=0
+        )
 
         param_lut_model = model.get_param_lut()
-        self.assertAlmostEqual(param_lut_model('OFF', 'power', param=[None, None]), 7124, places=0)
+        self.assertAlmostEqual(
+            param_lut_model("OFF", "power", param=[None, None]), 7124, places=0
+        )
         with self.assertRaises(KeyError):
-            param_lut_model('ON', 'power', param=[None, None])
-            param_lut_model('ON', 'power', param=['a'])
-            param_lut_model('ON', 'power', param=[0])
-        self.assertTrue(param_lut_model('ON', 'power', param=[0, 0]))
+            param_lut_model("ON", "power", param=[None, None])
+            param_lut_model("ON", "power", param=["a"])
+            param_lut_model("ON", "power", param=[0])
+        self.assertTrue(param_lut_model("ON", "power", param=[0, 0]))
         param_lut_model = model.get_param_lut(fallback=True)
-        self.assertAlmostEqual(param_lut_model('ON', 'power', param=[None, None]), 17866, places=0)
+        self.assertAlmostEqual(
+            param_lut_model("ON", "power", param=[None, None]), 17866, places=0
+        )
 
-    def test_model_multifile_lm75x(self):
+    def test_multifile_lm75x(self):
         testfiles = [
-            'test-data/20170116_124500_LM75x.tar',
-            'test-data/20170116_131306_LM75x.tar',
+            "test-data/20170116_124500_LM75x.tar",
+            "test-data/20170116_131306_LM75x.tar",
         ]
         raw_data = RawData(testfiles)
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-        model = PTAModel(by_name, parameters, arg_count, verbose=False)
-        self.assertEqual(model.states(), 'ACTIVE POWEROFF'.split(' '))
-        self.assertEqual(model.transitions(), 'getTemp setHyst setOS shutdown start'.split(' '))
+        model = PTAModel(by_name, parameters, arg_count)
+        self.assertEqual(model.states(), "ACTIVE POWEROFF".split(" "))
+        self.assertEqual(
+            model.transitions(), "getTemp setHyst setOS shutdown start".split(" ")
+        )
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('ACTIVE', 'power'), 332, places=0)
-        self.assertAlmostEqual(static_model('POWEROFF', 'power'), 7, places=0)
-        self.assertAlmostEqual(static_model('getTemp', 'energy'), 26016748, places=0)
-        self.assertAlmostEqual(static_model('setHyst', 'energy'), 22082226, places=0)
-        self.assertAlmostEqual(static_model('setOS', 'energy'), 21774238, places=0)
-        self.assertAlmostEqual(static_model('shutdown', 'energy'), 11808160, places=0)
-        self.assertAlmostEqual(static_model('start', 'energy'), 12445302, places=0)
-        self.assertAlmostEqual(static_model('getTemp', 'rel_energy_prev'), 21722720, places=0)
-        self.assertAlmostEqual(static_model('setHyst', 'rel_energy_prev'), 19001499, places=0)
-        self.assertAlmostEqual(static_model('setOS', 'rel_energy_prev'), 18693283, places=0)
-        self.assertAlmostEqual(static_model('shutdown', 'rel_energy_prev'), 11746224, places=0)
-        self.assertAlmostEqual(static_model('start', 'rel_energy_prev'), 12391462, places=0)
-        self.assertAlmostEqual(static_model('getTemp', 'duration'), 12740, places=0)
-        self.assertAlmostEqual(static_model('setHyst', 'duration'), 9140, places=0)
-        self.assertAlmostEqual(static_model('setOS', 'duration'), 9140, places=0)
-        self.assertAlmostEqual(static_model('shutdown', 'duration'), 6980, places=0)
-        self.assertAlmostEqual(static_model('start', 'duration'), 6980, places=0)
-
-    def test_model_multifile_sharp(self):
+        self.assertAlmostEqual(static_model("ACTIVE", "power"), 332, places=0)
+        self.assertAlmostEqual(static_model("POWEROFF", "power"), 7, places=0)
+        self.assertAlmostEqual(static_model("getTemp", "energy"), 26016748, places=0)
+        self.assertAlmostEqual(static_model("setHyst", "energy"), 22082226, places=0)
+        self.assertAlmostEqual(static_model("setOS", "energy"), 21774238, places=0)
+        self.assertAlmostEqual(static_model("shutdown", "energy"), 11808160, places=0)
+        self.assertAlmostEqual(static_model("start", "energy"), 12445302, places=0)
+        self.assertAlmostEqual(
+            static_model("getTemp", "rel_energy_prev"), 21722720, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("setHyst", "rel_energy_prev"), 19001499, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("setOS", "rel_energy_prev"), 18693283, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("shutdown", "rel_energy_prev"), 11746224, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("start", "rel_energy_prev"), 12391462, places=0
+        )
+        self.assertAlmostEqual(static_model("getTemp", "duration"), 12740, places=0)
+        self.assertAlmostEqual(static_model("setHyst", "duration"), 9140, places=0)
+        self.assertAlmostEqual(static_model("setOS", "duration"), 9140, places=0)
+        self.assertAlmostEqual(static_model("shutdown", "duration"), 6980, places=0)
+        self.assertAlmostEqual(static_model("start", "duration"), 6980, places=0)
+
+    def test_multifile_sharp(self):
         testfiles = [
-            'test-data/20170116_145420_sharpLS013B4DN.tar',
-            'test-data/20170116_151348_sharpLS013B4DN.tar',
+            "test-data/20170116_145420_sharpLS013B4DN.tar",
+            "test-data/20170116_151348_sharpLS013B4DN.tar",
         ]
         raw_data = RawData(testfiles)
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-        model = PTAModel(by_name, parameters, arg_count, verbose=False)
-        self.assertEqual(model.states(), 'DISABLED ENABLED'.split(' '))
-        self.assertEqual(model.transitions(), 'clear disable enable ioInit sendLine toggleVCOM'.split(' '))
+        model = PTAModel(by_name, parameters, arg_count)
+        self.assertEqual(model.states(), "DISABLED ENABLED".split(" "))
+        self.assertEqual(
+            model.transitions(),
+            "clear disable enable ioInit sendLine toggleVCOM".split(" "),
+        )
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('DISABLED', 'power'), 22, places=0)
-        self.assertAlmostEqual(static_model('ENABLED', 'power'), 24, places=0)
-        self.assertAlmostEqual(static_model('clear', 'energy'), 14059, places=0)
-        self.assertAlmostEqual(static_model('disable', 'energy'), 0, places=0)
-        self.assertAlmostEqual(static_model('enable', 'energy'), 0, places=0)
-        self.assertAlmostEqual(static_model('ioInit', 'energy'), 0, places=0)
-        self.assertAlmostEqual(static_model('sendLine', 'energy'), 37874, places=0)
-        self.assertAlmostEqual(static_model('toggleVCOM', 'energy'), 30991, places=0)
-        self.assertAlmostEqual(static_model('clear', 'rel_energy_prev'), 13329, places=0)
-        self.assertAlmostEqual(static_model('disable', 'rel_energy_prev'), 0, places=0)
-        self.assertAlmostEqual(static_model('enable', 'rel_energy_prev'), 0, places=0)
-        self.assertAlmostEqual(static_model('ioInit', 'rel_energy_prev'), 0, places=0)
-        self.assertAlmostEqual(static_model('sendLine', 'rel_energy_prev'), 33447, places=0)
-        self.assertAlmostEqual(static_model('toggleVCOM', 'rel_energy_prev'), 30242, places=0)
-        self.assertAlmostEqual(static_model('clear', 'duration'), 30, places=0)
-        self.assertAlmostEqual(static_model('disable', 'duration'), 0, places=0)
-        self.assertAlmostEqual(static_model('enable', 'duration'), 0, places=0)
-        self.assertAlmostEqual(static_model('ioInit', 'duration'), 0, places=0)
-        self.assertAlmostEqual(static_model('sendLine', 'duration'), 180, places=0)
-        self.assertAlmostEqual(static_model('toggleVCOM', 'duration'), 30, places=0)
-
-    def test_model_multifile_mmstatic(self):
+        self.assertAlmostEqual(static_model("DISABLED", "power"), 22, places=0)
+        self.assertAlmostEqual(static_model("ENABLED", "power"), 24, places=0)
+        self.assertAlmostEqual(static_model("clear", "energy"), 14059, places=0)
+        self.assertAlmostEqual(static_model("disable", "energy"), 0, places=0)
+        self.assertAlmostEqual(static_model("enable", "energy"), 0, places=0)
+        self.assertAlmostEqual(static_model("ioInit", "energy"), 0, places=0)
+        self.assertAlmostEqual(static_model("sendLine", "energy"), 37874, places=0)
+        self.assertAlmostEqual(static_model("toggleVCOM", "energy"), 30991, places=0)
+        self.assertAlmostEqual(
+            static_model("clear", "rel_energy_prev"), 13329, places=0
+        )
+        self.assertAlmostEqual(static_model("disable", "rel_energy_prev"), 0, places=0)
+        self.assertAlmostEqual(static_model("enable", "rel_energy_prev"), 0, places=0)
+        self.assertAlmostEqual(static_model("ioInit", "rel_energy_prev"), 0, places=0)
+        self.assertAlmostEqual(
+            static_model("sendLine", "rel_energy_prev"), 33447, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("toggleVCOM", "rel_energy_prev"), 30242, places=0
+        )
+        self.assertAlmostEqual(static_model("clear", "duration"), 30, places=0)
+        self.assertAlmostEqual(static_model("disable", "duration"), 0, places=0)
+        self.assertAlmostEqual(static_model("enable", "duration"), 0, places=0)
+        self.assertAlmostEqual(static_model("ioInit", "duration"), 0, places=0)
+        self.assertAlmostEqual(static_model("sendLine", "duration"), 180, places=0)
+        self.assertAlmostEqual(static_model("toggleVCOM", "duration"), 30, places=0)
+
+    def test_multifile_mmstatic(self):
         testfiles = [
-            'test-data/20170116_143516_mmstatic.tar',
-            'test-data/20170116_142654_mmstatic.tar',
+            "test-data/20170116_143516_mmstatic.tar",
+            "test-data/20170116_142654_mmstatic.tar",
         ]
         raw_data = RawData(testfiles)
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-        model = PTAModel(by_name, parameters, arg_count, verbose=False)
-        self.assertEqual(model.states(), 'B G OFF R'.split(' '))
-        self.assertEqual(model.transitions(), 'blue green off red'.split(' '))
+        model = PTAModel(by_name, parameters, arg_count)
+        self.assertEqual(model.states(), "B G OFF R".split(" "))
+        self.assertEqual(model.transitions(), "blue green off red".split(" "))
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('B', 'power'), 29443, places=0)
-        self.assertAlmostEqual(static_model('G', 'power'), 29432, places=0)
-        self.assertAlmostEqual(static_model('OFF', 'power'), 7057, places=0)
-        self.assertAlmostEqual(static_model('R', 'power'), 49068, places=0)
-        self.assertAlmostEqual(static_model('blue', 'energy'), 374440955, places=0)
-        self.assertAlmostEqual(static_model('green', 'energy'), 372026027, places=0)
-        self.assertAlmostEqual(static_model('off', 'energy'), 372999554, places=0)
-        self.assertAlmostEqual(static_model('red', 'energy'), 378936634, places=0)
-        self.assertAlmostEqual(static_model('blue', 'rel_energy_prev'), 105535587, places=0)
-        self.assertAlmostEqual(static_model('green', 'rel_energy_prev'), 102999371, places=0)
-        self.assertAlmostEqual(static_model('off', 'rel_energy_prev'), 103613698, places=0)
-        self.assertAlmostEqual(static_model('red', 'rel_energy_prev'), 110474331, places=0)
-        self.assertAlmostEqual(static_model('blue', 'duration'), 9140, places=0)
-        self.assertAlmostEqual(static_model('green', 'duration'), 9140, places=0)
-        self.assertAlmostEqual(static_model('off', 'duration'), 9140, places=0)
-        self.assertAlmostEqual(static_model('red', 'duration'), 9140, places=0)
-
-    @pytest.mark.skipif('TEST_SLOW' not in os.environ, reason="slow test, set TEST_SLOW=1 to run")
-    def test_model_multifile_cc1200(self):
+        self.assertAlmostEqual(static_model("B", "power"), 29443, places=0)
+        self.assertAlmostEqual(static_model("G", "power"), 29432, places=0)
+        self.assertAlmostEqual(static_model("OFF", "power"), 7057, places=0)
+        self.assertAlmostEqual(static_model("R", "power"), 49068, places=0)
+        self.assertAlmostEqual(static_model("blue", "energy"), 374440955, places=0)
+        self.assertAlmostEqual(static_model("green", "energy"), 372026027, places=0)
+        self.assertAlmostEqual(static_model("off", "energy"), 372999554, places=0)
+        self.assertAlmostEqual(static_model("red", "energy"), 378936634, places=0)
+        self.assertAlmostEqual(
+            static_model("blue", "rel_energy_prev"), 105535587, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("green", "rel_energy_prev"), 102999371, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("off", "rel_energy_prev"), 103613698, places=0
+        )
+        self.assertAlmostEqual(
+            static_model("red", "rel_energy_prev"), 110474331, places=0
+        )
+        self.assertAlmostEqual(static_model("blue", "duration"), 9140, places=0)
+        self.assertAlmostEqual(static_model("green", "duration"), 9140, places=0)
+        self.assertAlmostEqual(static_model("off", "duration"), 9140, places=0)
+        self.assertAlmostEqual(static_model("red", "duration"), 9140, places=0)
+
+    @pytest.mark.skipif(
+        "TEST_SLOW" not in os.environ, reason="slow test, set TEST_SLOW=1 to run"
+    )
+    def test_multifile_cc1200(self):
         testfiles = [
-            'test-data/20170125_125433_cc1200.tar',
-            'test-data/20170125_142420_cc1200.tar',
-            'test-data/20170125_144957_cc1200.tar',
-            'test-data/20170125_151149_cc1200.tar',
-            'test-data/20170125_151824_cc1200.tar',
-            'test-data/20170125_154019_cc1200.tar',
+            "test-data/20170125_125433_cc1200.tar",
+            "test-data/20170125_142420_cc1200.tar",
+            "test-data/20170125_144957_cc1200.tar",
+            "test-data/20170125_151149_cc1200.tar",
+            "test-data/20170125_151824_cc1200.tar",
+            "test-data/20170125_154019_cc1200.tar",
         ]
         raw_data = RawData(testfiles)
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-        model = PTAModel(by_name, parameters, arg_count, verbose=False)
-        self.assertEqual(model.states(), 'IDLE RX SLEEP SLEEP_EWOR SYNTH_ON TX XOFF'.split(' '))
-        self.assertEqual(model.transitions(), 'crystal_off eWOR idle init prepare_xmit receive send setSymbolRate setTxPower sleep txDone'.split(' '))
+        model = PTAModel(by_name, parameters, arg_count)
+        self.assertEqual(
+            model.states(), "IDLE RX SLEEP SLEEP_EWOR SYNTH_ON TX XOFF".split(" ")
+        )
+        self.assertEqual(
+            model.transitions(),
+            "crystal_off eWOR idle init prepare_xmit receive send setSymbolRate setTxPower sleep txDone".split(
+                " "
+            ),
+        )
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('IDLE', 'power'), 9500, places=0)
-        self.assertAlmostEqual(static_model('RX', 'power'), 85177, places=0)
-        self.assertAlmostEqual(static_model('SLEEP', 'power'), 143, places=0)
-        self.assertAlmostEqual(static_model('SLEEP_EWOR', 'power'), 81801, places=0)
-        self.assertAlmostEqual(static_model('SYNTH_ON', 'power'), 60036, places=0)
-        self.assertAlmostEqual(static_model('TX', 'power'), 92461, places=0)
-        self.assertAlmostEqual(static_model('XOFF', 'power'), 780, places=0)
-        self.assertAlmostEqual(static_model('crystal_off', 'energy'), 114658, places=0)
-        self.assertAlmostEqual(static_model('eWOR', 'energy'), 317556, places=0)
-        self.assertAlmostEqual(static_model('idle', 'energy'), 717713, places=0)
-        self.assertAlmostEqual(static_model('init', 'energy'), 23028941, places=0)
-        self.assertAlmostEqual(static_model('prepare_xmit', 'energy'), 378552, places=0)
-        self.assertAlmostEqual(static_model('receive', 'energy'), 380335, places=0)
-        self.assertAlmostEqual(static_model('send', 'energy'), 4282597, places=0)
-        self.assertAlmostEqual(static_model('setSymbolRate', 'energy'), 962060, places=0)
-        self.assertAlmostEqual(static_model('setTxPower', 'energy'), 288701, places=0)
-        self.assertAlmostEqual(static_model('sleep', 'energy'), 104445, places=0)
-        self.assertEqual(static_model('txDone', 'energy'), 0)
+        self.assertAlmostEqual(static_model("IDLE", "power"), 9500, places=0)
+        self.assertAlmostEqual(static_model("RX", "power"), 85177, places=0)
+        self.assertAlmostEqual(static_model("SLEEP", "power"), 143, places=0)
+        self.assertAlmostEqual(static_model("SLEEP_EWOR", "power"), 81801, places=0)
+        self.assertAlmostEqual(static_model("SYNTH_ON", "power"), 60036, places=0)
+        self.assertAlmostEqual(static_model("TX", "power"), 92461, places=0)
+        self.assertAlmostEqual(static_model("XOFF", "power"), 780, places=0)
+        self.assertAlmostEqual(static_model("crystal_off", "energy"), 114658, places=0)
+        self.assertAlmostEqual(static_model("eWOR", "energy"), 317556, places=0)
+        self.assertAlmostEqual(static_model("idle", "energy"), 717713, places=0)
+        self.assertAlmostEqual(static_model("init", "energy"), 23028941, places=0)
+        self.assertAlmostEqual(static_model("prepare_xmit", "energy"), 378552, places=0)
+        self.assertAlmostEqual(static_model("receive", "energy"), 380335, places=0)
+        self.assertAlmostEqual(static_model("send", "energy"), 4282597, places=0)
+        self.assertAlmostEqual(
+            static_model("setSymbolRate", "energy"), 962060, places=0
+        )
+        self.assertAlmostEqual(static_model("setTxPower", "energy"), 288701, places=0)
+        self.assertAlmostEqual(static_model("sleep", "energy"), 104445, places=0)
+        self.assertEqual(static_model("txDone", "energy"), 0)
 
         param_model, param_info = model.get_fitted()
-        self.assertEqual(param_info('IDLE', 'power'), None)
-        self.assertEqual(param_info('RX', 'power')['function']._model_str,
-                         '0 + regression_arg(0) + regression_arg(1) * np.log(parameter(symbolrate) + 1)')
-        self.assertEqual(param_info('SLEEP', 'power'), None)
-        self.assertEqual(param_info('SLEEP_EWOR', 'power'), None)
-        self.assertEqual(param_info('SYNTH_ON', 'power'), None)
-        self.assertEqual(param_info('XOFF', 'power'), None)
+        self.assertEqual(param_info("IDLE", "power"), None)
+        self.assertEqual(
+            param_info("RX", "power")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * np.log(parameter(symbolrate) + 1)",
+        )
+        self.assertEqual(param_info("SLEEP", "power"), None)
+        self.assertEqual(param_info("SLEEP_EWOR", "power"), None)
+        self.assertEqual(param_info("SYNTH_ON", "power"), None)
+        self.assertEqual(param_info("XOFF", "power"), None)
 
-        self.assertAlmostEqual(param_info('RX', 'power')['function']._regression_args[0], 84415, places=0)
-        self.assertAlmostEqual(param_info('RX', 'power')['function']._regression_args[1], 206, places=0)
+        self.assertAlmostEqual(
+            param_info("RX", "power")["function"].model_args[0], 84415, places=0
+        )
+        self.assertAlmostEqual(
+            param_info("RX", "power")["function"].model_args[1], 206, places=0
+        )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     unittest.main()
diff --git a/test/test_timingharness.py b/test/test_timingharness.py
index c8a422c..917e4e2 100755
--- a/test/test_timingharness.py
+++ b/test/test_timingharness.py
@@ -1,95 +1,157 @@
 #!/usr/bin/env python3
 
-from dfatool.dfatool import AnalyticModel, TimingData, pta_trace_to_aggregate
+from dfatool.loader import TimingData, pta_trace_to_aggregate
+from dfatool.model import AnalyticModel
 from dfatool.parameters import prune_dependent_parameters
 import unittest
 
 
 class TestModels(unittest.TestCase):
     def test_model_singlefile_rf24(self):
-        raw_data = TimingData(['test-data/20190815_111745_nRF24_no-rx.json'])
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+        raw_data = TimingData(["test-data/20190815_111745_nRF24_no-rx.json"])
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-        model = AnalyticModel(by_name, parameters, arg_count, verbose=False)
-        self.assertEqual(model.names, 'setPALevel setRetries setup write'.split(' '))
+        model = AnalyticModel(by_name, parameters, arg_count)
+        self.assertEqual(model.names, "setPALevel setRetries setup write".split(" "))
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('setPALevel', 'duration'), 146, places=0)
-        self.assertAlmostEqual(static_model('setRetries', 'duration'), 73, places=0)
-        self.assertAlmostEqual(static_model('setup', 'duration'), 6533, places=0)
-        self.assertAlmostEqual(static_model('write', 'duration'), 12634, places=0)
-
-        for transition in 'setPALevel setRetries setup write'.split(' '):
-            self.assertAlmostEqual(model.stats.param_dependence_ratio(transition, 'duration', 'channel'), 0, places=2)
+        self.assertAlmostEqual(static_model("setPALevel", "duration"), 146, places=0)
+        self.assertAlmostEqual(static_model("setRetries", "duration"), 73, places=0)
+        self.assertAlmostEqual(static_model("setup", "duration"), 6533, places=0)
+        self.assertAlmostEqual(static_model("write", "duration"), 12634, places=0)
+
+        for transition in "setPALevel setRetries setup write".split(" "):
+            self.assertAlmostEqual(
+                model.stats.param_dependence_ratio(transition, "duration", "channel"),
+                0,
+                places=2,
+            )
 
         param_model, param_info = model.get_fitted()
-        self.assertEqual(param_info('setPALevel', 'duration'), None)
-        self.assertEqual(param_info('setRetries', 'duration'), None)
-        self.assertEqual(param_info('setup', 'duration'), None)
-        self.assertEqual(param_info('write', 'duration')['function']._model_str, '0 + regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay)')
-
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[0], 1163, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[1], 464, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[2], 1, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[3], 1, places=0)
+        self.assertEqual(param_info("setPALevel", "duration"), None)
+        self.assertEqual(param_info("setRetries", "duration"), None)
+        self.assertEqual(param_info("setup", "duration"), None)
+        self.assertEqual(
+            param_info("write", "duration")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay)",
+        )
+
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[0], 1163, places=0,
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[1], 464, places=0,
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[2], 1, places=0
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[3], 1, places=0
+        )
 
     def test_dependent_parameter_pruning(self):
-        raw_data = TimingData(['test-data/20190815_103347_nRF24_no-rx.json'])
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+        raw_data = TimingData(["test-data/20190815_103347_nRF24_no-rx.json"])
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
         prune_dependent_parameters(by_name, parameters)
-        model = AnalyticModel(by_name, parameters, arg_count, verbose=False)
-        self.assertEqual(model.names, 'getObserveTx setPALevel setRetries setup write'.split(' '))
+        model = AnalyticModel(by_name, parameters, arg_count)
+        self.assertEqual(
+            model.names, "getObserveTx setPALevel setRetries setup write".split(" ")
+        )
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('getObserveTx', 'duration'), 75, places=0)
-        self.assertAlmostEqual(static_model('setPALevel', 'duration'), 146, places=0)
-        self.assertAlmostEqual(static_model('setRetries', 'duration'), 73, places=0)
-        self.assertAlmostEqual(static_model('setup', 'duration'), 6533, places=0)
-        self.assertAlmostEqual(static_model('write', 'duration'), 12634, places=0)
-
-        for transition in 'getObserveTx setPALevel setRetries setup write'.split(' '):
-            self.assertAlmostEqual(model.stats.param_dependence_ratio(transition, 'duration', 'channel'), 0, places=2)
+        self.assertAlmostEqual(static_model("getObserveTx", "duration"), 75, places=0)
+        self.assertAlmostEqual(static_model("setPALevel", "duration"), 146, places=0)
+        self.assertAlmostEqual(static_model("setRetries", "duration"), 73, places=0)
+        self.assertAlmostEqual(static_model("setup", "duration"), 6533, places=0)
+        self.assertAlmostEqual(static_model("write", "duration"), 12634, places=0)
+
+        for transition in "getObserveTx setPALevel setRetries setup write".split(" "):
+            self.assertAlmostEqual(
+                model.stats.param_dependence_ratio(transition, "duration", "channel"),
+                0,
+                places=2,
+            )
 
         param_model, param_info = model.get_fitted()
-        self.assertEqual(param_info('getObserveTx', 'duration'), None)
-        self.assertEqual(param_info('setPALevel', 'duration'), None)
-        self.assertEqual(param_info('setRetries', 'duration'), None)
-        self.assertEqual(param_info('setup', 'duration'), None)
-        self.assertEqual(param_info('write', 'duration')['function']._model_str, '0 + regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay)')
-
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[0], 1163, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[1], 464, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[2], 1, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[3], 1, places=0)
+        self.assertEqual(param_info("getObserveTx", "duration"), None)
+        self.assertEqual(param_info("setPALevel", "duration"), None)
+        self.assertEqual(param_info("setRetries", "duration"), None)
+        self.assertEqual(param_info("setup", "duration"), None)
+        self.assertEqual(
+            param_info("write", "duration")["function"].model_function,
+            "0 + regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay)",
+        )
+
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[0], 1163, places=0,
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[1], 464, places=0,
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[2], 1, places=0
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[3], 1, places=0
+        )
 
     def test_function_override(self):
-        raw_data = TimingData(['test-data/20190815_122531_nRF24_no-rx.json'])
-        preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
+        raw_data = TimingData(["test-data/20190815_122531_nRF24_no-rx.json"])
+        preprocessed_data = raw_data.get_preprocessed_data()
         by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
-        model = AnalyticModel(by_name, parameters, arg_count, verbose=False, function_override={('write', 'duration'): '(parameter(auto_ack!) * (regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay))) + ((1 - parameter(auto_ack!)) * regression_arg(4))'})
-        self.assertEqual(model.names, 'setAutoAck setPALevel setRetries setup write'.split(' '))
+        model = AnalyticModel(
+            by_name,
+            parameters,
+            arg_count,
+            function_override={
+                (
+                    "write",
+                    "duration",
+                ): "(parameter(auto_ack!) * (regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay))) + ((1 - parameter(auto_ack!)) * regression_arg(4))"
+            },
+        )
+        self.assertEqual(
+            model.names, "setAutoAck setPALevel setRetries setup write".split(" ")
+        )
         static_model = model.get_static()
-        self.assertAlmostEqual(static_model('setAutoAck', 'duration'), 72, places=0)
-        self.assertAlmostEqual(static_model('setPALevel', 'duration'), 146, places=0)
-        self.assertAlmostEqual(static_model('setRetries', 'duration'), 73, places=0)
-        self.assertAlmostEqual(static_model('setup', 'duration'), 6533, places=0)
-        self.assertAlmostEqual(static_model('write', 'duration'), 1181, places=0)
-
-        for transition in 'setAutoAck setPALevel setRetries setup write'.split(' '):
-            self.assertAlmostEqual(model.stats.param_dependence_ratio(transition, 'duration', 'channel'), 0, places=2)
+        self.assertAlmostEqual(static_model("setAutoAck", "duration"), 72, places=0)
+        self.assertAlmostEqual(static_model("setPALevel", "duration"), 146, places=0)
+        self.assertAlmostEqual(static_model("setRetries", "duration"), 73, places=0)
+        self.assertAlmostEqual(static_model("setup", "duration"), 6533, places=0)
+        self.assertAlmostEqual(static_model("write", "duration"), 1181, places=0)
+
+        for transition in "setAutoAck setPALevel setRetries setup write".split(" "):
+            self.assertAlmostEqual(
+                model.stats.param_dependence_ratio(transition, "duration", "channel"),
+                0,
+                places=2,
+            )
 
         param_model, param_info = model.get_fitted()
-        self.assertEqual(param_info('setAutoAck', 'duration'), None)
-        self.assertEqual(param_info('setPALevel', 'duration'), None)
-        self.assertEqual(param_info('setRetries', 'duration'), None)
-        self.assertEqual(param_info('setup', 'duration'), None)
-        self.assertEqual(param_info('write', 'duration')['function']._model_str, '(parameter(auto_ack!) * (regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay))) + ((1 - parameter(auto_ack!)) * regression_arg(4))')
-
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[0], 1162, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[1], 464, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[2], 1, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[3], 1, places=0)
-        self.assertAlmostEqual(param_info('write', 'duration')['function']._regression_args[4], 1086, places=0)
-
-
-if __name__ == '__main__':
+        self.assertEqual(param_info("setAutoAck", "duration"), None)
+        self.assertEqual(param_info("setPALevel", "duration"), None)
+        self.assertEqual(param_info("setRetries", "duration"), None)
+        self.assertEqual(param_info("setup", "duration"), None)
+        self.assertEqual(
+            param_info("write", "duration")["function"].model_function,
+            "(parameter(auto_ack!) * (regression_arg(0) + regression_arg(1) * parameter(max_retry_count) + regression_arg(2) * parameter(retry_delay) + regression_arg(3) * parameter(max_retry_count) * parameter(retry_delay))) + ((1 - parameter(auto_ack!)) * regression_arg(4))",
+        )
+
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[0], 1162, places=0,
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[1], 464, places=0,
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[2], 1, places=0
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[3], 1, places=0
+        )
+        self.assertAlmostEqual(
+            param_info("write", "duration")["function"].model_args[4], 1086, places=0,
+        )
+
+
+if __name__ == "__main__":
     unittest.main()