use black(1) for uniform code formatting

37 files changed, 7311 insertions, 4081 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index ecfda51..d248d1b 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -114,122 +114,168 @@ def print_model_quality(results):
     for state_or_tran in results.keys():
         print()
         for key, result in results[state_or_tran].items():
-            if 'smape' in result:
-                print('{:20s} {:15s} {:.2f}% / {:.0f}'.format(
-                    state_or_tran, key, result['smape'], result['mae']))
+            if "smape" in result:
+                print(
+                    "{:20s} {:15s} {:.2f}% / {:.0f}".format(
+                        state_or_tran, key, result["smape"], result["mae"]
+                    )
+                )
             else:
-                print('{:20s} {:15s} {:.0f}'.format(
-                    state_or_tran, key, result['mae']))
+                print("{:20s} {:15s} {:.0f}".format(state_or_tran, key, result["mae"]))
 
 
 def format_quality_measures(result):
-    if 'smape' in result:
-        return '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
+    if "smape" in result:
+        return "{:6.2f}% / {:9.0f}".format(result["smape"], result["mae"])
     else:
-        return '{:6}    {:9.0f}'.format('', result['mae'])
+        return "{:6}    {:9.0f}".format("", result["mae"])
 
 
 def model_quality_table(result_lists, info_list):
-    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)
+    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)
             for i, results in enumerate(result_lists):
                 info = info_list[i]
-                buf += '  |||  '
+                buf += "  |||  "
                 if info is None or info(state_or_tran, key):
-                    result = results['by_name'][state_or_tran][key]
+                    result = results["by_name"][state_or_tran][key]
                     buf += format_quality_measures(result)
                 else:
-                    buf += '{:6}----{:9}'.format('', '')
+                    buf += "{:6}----{:9}".format("", "")
             print(buf)
 
 
 def model_summary_table(result_list):
-    buf = 'transition duration'
+    buf = "transition duration"
     for results in result_list:
         if len(buf):
-            buf += '  |||  '
-        buf += format_quality_measures(results['duration_by_trace'])
+            buf += "  |||  "
+        buf += format_quality_measures(results["duration_by_trace"])
     print(buf)
-    buf = 'total energy       '
+    buf = "total energy       "
     for results in result_list:
         if len(buf):
-            buf += '  |||  '
-        buf += format_quality_measures(results['energy_by_trace'])
+            buf += "  |||  "
+        buf += format_quality_measures(results["energy_by_trace"])
     print(buf)
-    buf = 'rel total energy   '
+    buf = "rel total energy   "
     for results in result_list:
         if len(buf):
-            buf += '  |||  '
-        buf += format_quality_measures(results['rel_energy_by_trace'])
+            buf += "  |||  "
+        buf += format_quality_measures(results["rel_energy_by_trace"])
     print(buf)
-    buf = 'state-only energy  '
+    buf = "state-only energy  "
     for results in result_list:
         if len(buf):
-            buf += '  |||  '
-        buf += format_quality_measures(results['state_energy_by_trace'])
+            buf += "  |||  "
+        buf += format_quality_measures(results["state_energy_by_trace"])
     print(buf)
-    buf = 'transition timeout '
+    buf = "transition timeout "
     for results in result_list:
         if len(buf):
-            buf += '  |||  '
-        buf += format_quality_measures(results['timeout_by_trace'])
+            buf += "  |||  "
+        buf += format_quality_measures(results["timeout_by_trace"])
     print(buf)
 
 
 def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq):
-    print('')
-    print(r'key attribute $1 - \frac{\sigma_X}{...}$')
+    print("")
+    print(r"key attribute $1 - \frac{\sigma_X}{...}$")
     for state_or_tran in model.by_name.keys():
         for attribute in model.attributes(state_or_tran):
-            print('{} {} {:.8f}'.format(state_or_tran, attribute, model.stats.generic_param_dependence_ratio(state_or_tran, attribute)))
-
-    print('')
-    print(r'key attribute parameter $1 - \frac{...}{...}$')
+            print(
+                "{} {} {:.8f}".format(
+                    state_or_tran,
+                    attribute,
+                    model.stats.generic_param_dependence_ratio(
+                        state_or_tran, attribute
+                    ),
+                )
+            )
+
+    print("")
+    print(r"key attribute parameter $1 - \frac{...}{...}$")
     for state_or_tran in model.by_name.keys():
         for attribute in model.attributes(state_or_tran):
             for param in model.parameters():
-                print('{} {} {} {:.8f}'.format(state_or_tran, attribute, param, model.stats.param_dependence_ratio(state_or_tran, attribute, param)))
+                print(
+                    "{} {} {} {:.8f}".format(
+                        state_or_tran,
+                        attribute,
+                        param,
+                        model.stats.param_dependence_ratio(
+                            state_or_tran, attribute, param
+                        ),
+                    )
+                )
             if state_or_tran in model._num_args:
                 for arg_index in range(model._num_args[state_or_tran]):
-                    print('{} {} {:d} {:.8f}'.format(state_or_tran, attribute, arg_index, model.stats.arg_dependence_ratio(state_or_tran, attribute, arg_index)))
+                    print(
+                        "{} {} {:d} {:.8f}".format(
+                            state_or_tran,
+                            attribute,
+                            arg_index,
+                            model.stats.arg_dependence_ratio(
+                                state_or_tran, attribute, arg_index
+                            ),
+                        )
+                    )
 
 
 def print_html_model_data(model, pm, pq, lm, lq, am, ai, aq):
     state_attributes = model.attributes(model.states()[0])
 
-    print('<table><tr><th>state</th><th>' + '</th><th>'.join(state_attributes) + '</th></tr>')
+    print(
+        "<table><tr><th>state</th><th>"
+        + "</th><th>".join(state_attributes)
+        + "</th></tr>"
+    )
     for state in model.states():
-        print('<tr>', end='')
-        print('<td>{}</td>'.format(state), end='')
+        print("<tr>", end="")
+        print("<td>{}</td>".format(state), end="")
         for attribute in state_attributes:
-            unit = ''
-            if attribute == 'power':
-                unit = 'µW'
-            print('<td>{:.0f} {} ({:.1f}%)</td>'.format(pm(state, attribute), unit, pq['by_name'][state][attribute]['smape']), end='')
-        print('</tr>')
-    print('</table>')
+            unit = ""
+            if attribute == "power":
+                unit = "µW"
+            print(
+                "<td>{:.0f} {} ({:.1f}%)</td>".format(
+                    pm(state, attribute), unit, pq["by_name"][state][attribute]["smape"]
+                ),
+                end="",
+            )
+        print("</tr>")
+    print("</table>")
 
     trans_attributes = model.attributes(model.transitions()[0])
-    if 'rel_energy_prev' in trans_attributes:
-        trans_attributes.remove('rel_energy_next')
-
-    print('<table><tr><th>transition</th><th>' + '</th><th>'.join(trans_attributes) + '</th></tr>')
+    if "rel_energy_prev" in trans_attributes:
+        trans_attributes.remove("rel_energy_next")
+
+    print(
+        "<table><tr><th>transition</th><th>"
+        + "</th><th>".join(trans_attributes)
+        + "</th></tr>"
+    )
     for trans in model.transitions():
-        print('<tr>', end='')
-        print('<td>{}</td>'.format(trans), end='')
+        print("<tr>", end="")
+        print("<td>{}</td>".format(trans), end="")
         for attribute in trans_attributes:
-            unit = ''
-            if attribute == 'duration':
-                unit = 'µs'
-            elif attribute in ['energy', 'rel_energy_prev']:
-                unit = 'pJ'
-            print('<td>{:.0f} {} ({:.1f}%)</td>'.format(pm(trans, attribute), unit, pq['by_name'][trans][attribute]['smape']), end='')
-        print('</tr>')
-    print('</table>')
-
-
-if __name__ == '__main__':
+            unit = ""
+            if attribute == "duration":
+                unit = "µs"
+            elif attribute in ["energy", "rel_energy_prev"]:
+                unit = "pJ"
+            print(
+                "<td>{:.0f} {} ({:.1f}%)</td>".format(
+                    pm(trans, attribute), unit, pq["by_name"][trans][attribute]["smape"]
+                ),
+                end="",
+            )
+        print("</tr>")
+    print("</table>")
+
+
+if __name__ == "__main__":
 
     ignored_trace_indexes = []
     discard_outliers = None
@@ -245,264 +291,413 @@ if __name__ == '__main__':
 
     try:
         optspec = (
-            'plot-unparam= plot-param= plot-traces= param-info show-models= show-quality= '
-            'ignored-trace-indexes= discard-outliers= function-override= '
-            'export-traces= '
-            'filter-param= '
-            'cross-validate= '
-            'with-safe-functions hwmodel= export-energymodel='
+            "plot-unparam= plot-param= plot-traces= param-info show-models= show-quality= "
+            "ignored-trace-indexes= discard-outliers= function-override= "
+            "export-traces= "
+            "filter-param= "
+            "cross-validate= "
+            "with-safe-functions hwmodel= export-energymodel="
         )
-        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)
+            optname = re.sub(r"^--", "", option)
             opts[optname] = parameter
 
-        if 'ignored-trace-indexes' in opts:
-            ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(',')))
+        if "ignored-trace-indexes" in opts:
+            ignored_trace_indexes = list(
+                map(int, opts["ignored-trace-indexes"].split(","))
+            )
             if 0 in ignored_trace_indexes:
-                print('[E] arguments to --ignored-trace-indexes start from 1')
+                print("[E] arguments to --ignored-trace-indexes start from 1")
 
-        if 'discard-outliers' in opts:
-            discard_outliers = float(opts['discard-outliers'])
+        if "discard-outliers" in opts:
+            discard_outliers = float(opts["discard-outliers"])
 
-        if 'function-override' in opts:
-            for function_desc in opts['function-override'].split(';'):
-                state_or_tran, attribute, *function_str = function_desc.split(' ')
-                function_override[(state_or_tran, attribute)] = ' '.join(function_str)
+        if "function-override" in opts:
+            for function_desc in opts["function-override"].split(";"):
+                state_or_tran, attribute, *function_str = function_desc.split(" ")
+                function_override[(state_or_tran, attribute)] = " ".join(function_str)
 
-        if 'show-models' in opts:
-            show_models = opts['show-models'].split(',')
+        if "show-models" in opts:
+            show_models = opts["show-models"].split(",")
 
-        if 'show-quality' in opts:
-            show_quality = opts['show-quality'].split(',')
+        if "show-quality" in opts:
+            show_quality = opts["show-quality"].split(",")
 
-        if 'cross-validate' in opts:
-            xv_method, xv_count = opts['cross-validate'].split(':')
+        if "cross-validate" in opts:
+            xv_method, xv_count = opts["cross-validate"].split(":")
             xv_count = int(xv_count)
 
-        if 'filter-param' in opts:
-            opts['filter-param'] = list(map(lambda x: x.split('='), opts['filter-param'].split(',')))
+        if "filter-param" in opts:
+            opts["filter-param"] = list(
+                map(lambda x: x.split("="), opts["filter-param"].split(","))
+            )
         else:
-            opts['filter-param'] = list()
+            opts["filter-param"] = list()
 
-        if 'with-safe-functions' in opts:
+        if "with-safe-functions" in opts:
             safe_functions_enabled = True
 
-        if 'hwmodel' in opts:
-            pta = PTA.from_file(opts['hwmodel'])
+        if "hwmodel" in opts:
+            pta = PTA.from_file(opts["hwmodel"])
 
     except getopt.GetoptError as err:
         print(err)
         sys.exit(2)
 
-    raw_data = RawData(args, with_traces=('export-traces' in opts or 'plot-traces' in opts))
+    raw_data = RawData(
+        args, with_traces=("export-traces" in opts or "plot-traces" in opts)
+    )
 
     preprocessed_data = raw_data.get_preprocessed_data()
 
-    if 'export-traces' in opts:
+    if "export-traces" in opts:
         uw_per_sot = dict()
         for trace in preprocessed_data:
-            for state_or_transition in trace['trace']:
-                name = state_or_transition['name']
+            for state_or_transition in trace["trace"]:
+                name = state_or_transition["name"]
                 if name not in uw_per_sot:
                     uw_per_sot[name] = list()
-                for elem in state_or_transition['offline']:
-                    elem['uW'] = list(elem['uW'])
+                for elem in state_or_transition["offline"]:
+                    elem["uW"] = list(elem["uW"])
                 uw_per_sot[name].append(state_or_transition)
         for name, data in uw_per_sot.items():
             target = f"{opts['export-traces']}/{name}.json"
-            print(f'exporting {target} ...')
-            with open(target, 'w') as f:
+            print(f"exporting {target} ...")
+            with open(target, "w") as f:
                 json.dump(data, f)
 
-    if 'plot-traces' in opts:
+    if "plot-traces" in opts:
         traces = list()
         for trace in preprocessed_data:
-            for state_or_transition in trace['trace']:
-                if state_or_transition['name'] == opts['plot-traces']:
-                    traces.extend(map(lambda x: x['uW'], state_or_transition['offline']))
-        plotter.plot_y(traces, xlabel='t [1e-5 s]', ylabel='P [uW]', title=opts['plot-traces'], family=True)
+            for state_or_transition in trace["trace"]:
+                if state_or_transition["name"] == opts["plot-traces"]:
+                    traces.extend(
+                        map(lambda x: x["uW"], state_or_transition["offline"])
+                    )
+        plotter.plot_y(
+            traces,
+            xlabel="t [1e-5 s]",
+            ylabel="P [uW]",
+            title=opts["plot-traces"],
+            family=True,
+        )
 
-    if raw_data.preprocessing_stats['num_valid'] == 0:
-        print('No valid data available. Abort.')
+    if raw_data.preprocessing_stats["num_valid"] == 0:
+        print("No valid data available. Abort.")
         sys.exit(2)
 
     if pta is None and raw_data.pta is not None:
         pta = PTA.from_json(raw_data.pta)
 
-    by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes)
+    by_name, parameters, arg_count = pta_trace_to_aggregate(
+        preprocessed_data, ignored_trace_indexes
+    )
 
-    filter_aggregate_by_param(by_name, parameters, opts['filter-param'])
+    filter_aggregate_by_param(by_name, parameters, opts["filter-param"])
 
-    model = PTAModel(by_name, parameters, arg_count,
-                     traces=preprocessed_data,
-                     discard_outliers=discard_outliers,
-                     function_override=function_override,
-                     pta=pta)
+    model = PTAModel(
+        by_name,
+        parameters,
+        arg_count,
+        traces=preprocessed_data,
+        discard_outliers=discard_outliers,
+        function_override=function_override,
+        pta=pta,
+    )
 
     if xv_method:
         xv = CrossValidator(PTAModel, by_name, parameters, arg_count)
 
-    if 'param-info' in opts:
+    if "param-info" in opts:
         for state in model.states():
-            print('{}:'.format(state))
+            print("{}:".format(state))
             for param in model.parameters():
-                print('    {} = {}'.format(param, model.stats.distinct_values[state][param]))
+                print(
+                    "    {} = {}".format(
+                        param, model.stats.distinct_values[state][param]
+                    )
+                )
         for transition in model.transitions():
-            print('{}:'.format(transition))
+            print("{}:".format(transition))
             for param in model.parameters():
-                print('    {} = {}'.format(param, model.stats.distinct_values[transition][param]))
-
-    if 'plot-unparam' in opts:
-        for kv in opts['plot-unparam'].split(';'):
-            state_or_trans, attribute, ylabel = kv.split(':')
-            fname = 'param_y_{}_{}.pdf'.format(state_or_trans, attribute)
-            plotter.plot_y(model.by_name[state_or_trans][attribute], xlabel='measurement #', ylabel=ylabel, output=fname)
+                print(
+                    "    {} = {}".format(
+                        param, model.stats.distinct_values[transition][param]
+                    )
+                )
+
+    if "plot-unparam" in opts:
+        for kv in opts["plot-unparam"].split(";"):
+            state_or_trans, attribute, ylabel = kv.split(":")
+            fname = "param_y_{}_{}.pdf".format(state_or_trans, attribute)
+            plotter.plot_y(
+                model.by_name[state_or_trans][attribute],
+                xlabel="measurement #",
+                ylabel=ylabel,
+                output=fname,
+            )
 
     if len(show_models):
-        print('--- simple static model ---')
+        print("--- simple static model ---")
     static_model = model.get_static()
-    if 'static' in show_models or 'all' in show_models:
+    if "static" in show_models or "all" in show_models:
         for state in model.states():
-            print('{:10s}: {:.0f} µW  ({:.2f})'.format(
-                state,
-                static_model(state, 'power'),
-                model.stats.generic_param_dependence_ratio(state, 'power')))
+            print(
+                "{:10s}: {:.0f} µW  ({:.2f})".format(
+                    state,
+                    static_model(state, "power"),
+                    model.stats.generic_param_dependence_ratio(state, "power"),
+                )
+            )
             for param in model.parameters():
-                print('{:10s}  dependence on {:15s}: {:.2f}'.format(
-                    '',
-                    param,
-                    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))
+                print(
+                    "{:10s}  dependence on {:15s}: {:.2f}".format(
+                        "",
+                        param,
+                        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
             try:
-                print('{:10s}: {:.0f} µW  ({:.2f})'.format(
-                    trans,
-                    static_model(trans, 'power'),
-                    model.stats.generic_param_dependence_ratio(trans, 'power')))
+                print(
+                    "{:10s}: {:.0f} µW  ({:.2f})".format(
+                        trans,
+                        static_model(trans, "power"),
+                        model.stats.generic_param_dependence_ratio(trans, "power"),
+                    )
+                )
             except KeyError:
                 pass
             try:
-                print('{:10s}: {:.0f} / {:.0f} / {:.0f} pJ  ({:.2f} / {:.2f} / {:.2f})'.format(
-                    trans, static_model(trans, 'energy'),
-                    static_model(trans, 'rel_energy_prev'),
-                    static_model(trans, 'rel_energy_next'),
-                    model.stats.generic_param_dependence_ratio(trans, 'energy'),
-                    model.stats.generic_param_dependence_ratio(trans, 'rel_energy_prev'),
-                    model.stats.generic_param_dependence_ratio(trans, 'rel_energy_next')))
+                print(
+                    "{:10s}: {:.0f} / {:.0f} / {:.0f} pJ  ({:.2f} / {:.2f} / {:.2f})".format(
+                        trans,
+                        static_model(trans, "energy"),
+                        static_model(trans, "rel_energy_prev"),
+                        static_model(trans, "rel_energy_next"),
+                        model.stats.generic_param_dependence_ratio(trans, "energy"),
+                        model.stats.generic_param_dependence_ratio(
+                            trans, "rel_energy_prev"
+                        ),
+                        model.stats.generic_param_dependence_ratio(
+                            trans, "rel_energy_next"
+                        ),
+                    )
+                )
             except KeyError:
-                print('{:10s}: {:.0f} pJ  ({:.2f})'.format(
-                    trans, static_model(trans, 'energy'),
-                    model.stats.generic_param_dependence_ratio(trans, 'energy')))
-            print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration')))
-
-    if xv_method == 'montecarlo':
+                print(
+                    "{:10s}: {:.0f} pJ  ({:.2f})".format(
+                        trans,
+                        static_model(trans, "energy"),
+                        model.stats.generic_param_dependence_ratio(trans, "energy"),
+                    )
+                )
+            print("{:10s}: {:.0f} µs".format(trans, static_model(trans, "duration")))
+
+    if xv_method == "montecarlo":
         static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count)
     else:
         static_quality = model.assess(static_model)
 
     if len(show_models):
-        print('--- LUT ---')
+        print("--- LUT ---")
     lut_model = model.get_param_lut()
 
-    if xv_method == 'montecarlo':
+    if xv_method == "montecarlo":
         lut_quality = xv.montecarlo(lambda m: m.get_param_lut(fallback=True), xv_count)
     else:
         lut_quality = model.assess(lut_model)
 
     if len(show_models):
-        print('--- param model ---')
+        print("--- param model ---")
 
-    param_model, param_info = model.get_fitted(safe_functions_enabled=safe_functions_enabled)
+    param_model, param_info = model.get_fitted(
+        safe_functions_enabled=safe_functions_enabled
+    )
 
-    if 'paramdetection' in show_models or 'all' in show_models:
+    if "paramdetection" in show_models or "all" in show_models:
         for state in model.states_and_transitions():
             for attribute in model.attributes(state):
                 info = param_info(state, attribute)
-                print('{:10s} {:10s} non-param stddev {:f}'.format(
-                    state, attribute, model.stats.stats[state][attribute]['std_static']
-                ))
-                print('{:10s} {:10s} param-lut stddev {:f}'.format(
-                    state, attribute, model.stats.stats[state][attribute]['std_param_lut']
-                ))
-                for param in sorted(model.stats.stats[state][attribute]['std_by_param'].keys()):
-                    print('{:10s} {:10s} {:10s} stddev {:f}'.format(
-                        state, attribute, param, model.stats.stats[state][attribute]['std_by_param'][param]
-                    ))
+                print(
+                    "{:10s} {:10s} non-param stddev {:f}".format(
+                        state,
+                        attribute,
+                        model.stats.stats[state][attribute]["std_static"],
+                    )
+                )
+                print(
+                    "{:10s} {:10s} param-lut stddev {:f}".format(
+                        state,
+                        attribute,
+                        model.stats.stats[state][attribute]["std_param_lut"],
+                    )
+                )
+                for param in sorted(
+                    model.stats.stats[state][attribute]["std_by_param"].keys()
+                ):
+                    print(
+                        "{:10s} {:10s} {:10s} stddev {:f}".format(
+                            state,
+                            attribute,
+                            param,
+                            model.stats.stats[state][attribute]["std_by_param"][param],
+                        )
+                    )
                 if info is not None:
-                    for param_name in sorted(info['fit_result'].keys(), key=str):
-                        param_fit = info['fit_result'][param_name]['results']
+                    for param_name in sorted(info["fit_result"].keys(), key=str):
+                        param_fit = info["fit_result"][param_name]["results"]
                         for function_type in sorted(param_fit.keys()):
-                            function_rmsd = param_fit[function_type]['rmsd']
-                            print('{:10s} {:10s} {:10s} mean {:10s} RMSD {:.0f}'.format(
-                                state, attribute, str(param_name), function_type, function_rmsd
-                            ))
-
-    if 'param' in show_models or 'all' in show_models:
+                            function_rmsd = param_fit[function_type]["rmsd"]
+                            print(
+                                "{:10s} {:10s} {:10s} mean {:10s} RMSD {:.0f}".format(
+                                    state,
+                                    attribute,
+                                    str(param_name),
+                                    function_type,
+                                    function_rmsd,
+                                )
+                            )
+
+    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.')
+            print(
+                "[!] 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))
-                    print('{:10s}  {}'.format('', param_info(state, attribute)['function']._regression_args))
+                    print(
+                        "{:10s}: {}".format(
+                            state, param_info(state, attribute)["function"]._model_str
+                        )
+                    )
+                    print(
+                        "{:10s}  {}".format(
+                            "",
+                            param_info(state, attribute)["function"]._regression_args,
+                        )
+                    )
         for trans in model.transitions():
             for attribute in model.attributes(trans):
                 if param_info(trans, attribute):
-                    print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str))
-                    print('{:10s}  {:10s}  {}'.format('', '', param_info(trans, attribute)['function']._regression_args))
-
-    if xv_method == 'montecarlo':
+                    print(
+                        "{:10s}: {:10s}: {}".format(
+                            trans,
+                            attribute,
+                            param_info(trans, attribute)["function"]._model_str,
+                        )
+                    )
+                    print(
+                        "{:10s}  {:10s}  {}".format(
+                            "",
+                            "",
+                            param_info(trans, attribute)["function"]._regression_args,
+                        )
+                    )
+
+    if xv_method == "montecarlo":
         analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count)
     else:
         analytic_quality = model.assess(param_model)
 
-    if 'tex' in show_models or 'tex' in show_quality:
-        print_text_model_data(model, static_model, static_quality, lut_model, lut_quality, param_model, param_info, analytic_quality)
-
-    if 'html' in show_models or 'html' in show_quality:
-        print_html_model_data(model, static_model, static_quality, lut_model, lut_quality, param_model, param_info, analytic_quality)
-
-    if 'table' in show_quality or 'all' in show_quality:
-        model_quality_table([static_quality, analytic_quality, lut_quality], [None, param_info, None])
-
-    if 'overall' in show_quality or 'all' in show_quality:
-        print('overall static/param/lut MAE assuming equal state distribution:')
-        print('    {:6.1f}  /  {:6.1f}  /  {:6.1f}  µW'.format(
-            model.assess_states(static_model),
-            model.assess_states(param_model),
-            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)))
-
-    if 'summary' in show_quality or 'all' in show_quality:
-        model_summary_table([model.assess_on_traces(static_model), model.assess_on_traces(param_model), model.assess_on_traces(lut_model)])
-
-    if 'plot-param' in opts:
-        for kv in opts['plot-param'].split(';'):
-            state_or_trans, attribute, param_name, *function = kv.split(' ')
+    if "tex" in show_models or "tex" in show_quality:
+        print_text_model_data(
+            model,
+            static_model,
+            static_quality,
+            lut_model,
+            lut_quality,
+            param_model,
+            param_info,
+            analytic_quality,
+        )
+
+    if "html" in show_models or "html" in show_quality:
+        print_html_model_data(
+            model,
+            static_model,
+            static_quality,
+            lut_model,
+            lut_quality,
+            param_model,
+            param_info,
+            analytic_quality,
+        )
+
+    if "table" in show_quality or "all" in show_quality:
+        model_quality_table(
+            [static_quality, analytic_quality, lut_quality], [None, param_info, None]
+        )
+
+    if "overall" in show_quality or "all" in show_quality:
+        print("overall static/param/lut MAE assuming equal state distribution:")
+        print(
+            "    {:6.1f}  /  {:6.1f}  /  {:6.1f}  µW".format(
+                model.assess_states(static_model),
+                model.assess_states(param_model),
+                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),
+            )
+        )
+
+    if "summary" in show_quality or "all" in show_quality:
+        model_summary_table(
+            [
+                model.assess_on_traces(static_model),
+                model.assess_on_traces(param_model),
+                model.assess_on_traces(lut_model),
+            ]
+        )
+
+    if "plot-param" in opts:
+        for kv in opts["plot-param"].split(";"):
+            state_or_trans, attribute, param_name, *function = kv.split(" ")
             if len(function):
-                function = gplearn_to_function(' '.join(function))
+                function = gplearn_to_function(" ".join(function))
             else:
                 function = None
-            plotter.plot_param(model, state_or_trans, attribute, model.param_index(param_name), extra_function=function)
-
-    if 'export-energymodel' in opts:
+            plotter.plot_param(
+                model,
+                state_or_trans,
+                attribute,
+                model.param_index(param_name),
+                extra_function=function,
+            )
+
+    if "export-energymodel" in opts:
         if not pta:
-            print('[E] --export-energymodel requires --hwmodel to be set')
+            print("[E] --export-energymodel requires --hwmodel to be set")
             sys.exit(1)
         json_model = model.to_json()
-        with open(opts['export-energymodel'], 'w') as f:
+        with open(opts["export-energymodel"], "w") as f:
             json.dump(json_model, f, indent=2, sort_keys=True)
 
     sys.exit(0)
diff --git a/bin/analyze-timing.py b/bin/analyze-timing.py
index 6a458d9..e565c8f 100755
--- a/bin/analyze-timing.py
+++ b/bin/analyze-timing.py
@@ -91,55 +91,83 @@ def print_model_quality(results):
     for state_or_tran in results.keys():
         print()
         for key, result in results[state_or_tran].items():
-            if 'smape' in result:
-                print('{:20s} {:15s} {:.2f}% / {:.0f}'.format(
-                    state_or_tran, key, result['smape'], result['mae']))
+            if "smape" in result:
+                print(
+                    "{:20s} {:15s} {:.2f}% / {:.0f}".format(
+                        state_or_tran, key, result["smape"], result["mae"]
+                    )
+                )
             else:
-                print('{:20s} {:15s} {:.0f}'.format(
-                    state_or_tran, key, result['mae']))
+                print("{:20s} {:15s} {:.0f}".format(state_or_tran, key, result["mae"]))
 
 
 def format_quality_measures(result):
-    if 'smape' in result:
-        return '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
+    if "smape" in result:
+        return "{:6.2f}% / {:9.0f}".format(result["smape"], result["mae"])
     else:
-        return '{:6}    {:9.0f}'.format('', result['mae'])
+        return "{:6}    {:9.0f}".format("", result["mae"])
 
 
 def model_quality_table(result_lists, info_list):
-    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)
+    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)
             for i, results in enumerate(result_lists):
                 info = info_list[i]
-                buf += '  |||  '
+                buf += "  |||  "
                 if info is None or info(state_or_tran, key):
-                    result = results['by_name'][state_or_tran][key]
+                    result = results["by_name"][state_or_tran][key]
                     buf += format_quality_measures(result)
                 else:
-                    buf += '{:6}----{:9}'.format('', '')
+                    buf += "{:6}----{:9}".format("", "")
             print(buf)
 
 
 def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq):
-    print('')
-    print(r'key attribute $1 - \frac{\sigma_X}{...}$')
+    print("")
+    print(r"key attribute $1 - \frac{\sigma_X}{...}$")
     for state_or_tran in model.by_name.keys():
         for attribute in model.attributes(state_or_tran):
-            print('{} {} {:.8f}'.format(state_or_tran, attribute, model.stats.generic_param_dependence_ratio(state_or_tran, attribute)))
-
-    print('')
-    print(r'key attribute parameter $1 - \frac{...}{...}$')
+            print(
+                "{} {} {:.8f}".format(
+                    state_or_tran,
+                    attribute,
+                    model.stats.generic_param_dependence_ratio(
+                        state_or_tran, attribute
+                    ),
+                )
+            )
+
+    print("")
+    print(r"key attribute parameter $1 - \frac{...}{...}$")
     for state_or_tran in model.by_name.keys():
         for attribute in model.attributes(state_or_tran):
             for param in model.parameters():
-                print('{} {} {} {:.8f}'.format(state_or_tran, attribute, param, model.stats.param_dependence_ratio(state_or_tran, attribute, param)))
+                print(
+                    "{} {} {} {:.8f}".format(
+                        state_or_tran,
+                        attribute,
+                        param,
+                        model.stats.param_dependence_ratio(
+                            state_or_tran, attribute, param
+                        ),
+                    )
+                )
             if state_or_tran in model._num_args:
                 for arg_index in range(model._num_args[state_or_tran]):
-                    print('{} {} {:d} {:.8f}'.format(state_or_tran, attribute, arg_index, model.stats.arg_dependence_ratio(state_or_tran, attribute, arg_index)))
+                    print(
+                        "{} {} {:d} {:.8f}".format(
+                            state_or_tran,
+                            attribute,
+                            arg_index,
+                            model.stats.arg_dependence_ratio(
+                                state_or_tran, attribute, arg_index
+                            ),
+                        )
+                    )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     ignored_trace_indexes = []
     discard_outliers = None
@@ -154,56 +182,60 @@ if __name__ == '__main__':
 
     try:
         optspec = (
-            'plot-unparam= plot-param= show-models= show-quality= '
-            'ignored-trace-indexes= discard-outliers= function-override= '
-            'filter-param= '
-            'cross-validate= '
-            'corrcoef param-info '
-            'with-safe-functions hwmodel= export-energymodel='
+            "plot-unparam= plot-param= show-models= show-quality= "
+            "ignored-trace-indexes= discard-outliers= function-override= "
+            "filter-param= "
+            "cross-validate= "
+            "corrcoef param-info "
+            "with-safe-functions hwmodel= export-energymodel="
         )
-        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)
+            optname = re.sub(r"^--", "", option)
             opts[optname] = parameter
 
-        if 'ignored-trace-indexes' in opts:
-            ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(',')))
+        if "ignored-trace-indexes" in opts:
+            ignored_trace_indexes = list(
+                map(int, opts["ignored-trace-indexes"].split(","))
+            )
             if 0 in ignored_trace_indexes:
-                print('[E] arguments to --ignored-trace-indexes start from 1')
+                print("[E] arguments to --ignored-trace-indexes start from 1")
 
-        if 'discard-outliers' in opts:
-            discard_outliers = float(opts['discard-outliers'])
+        if "discard-outliers" in opts:
+            discard_outliers = float(opts["discard-outliers"])
 
-        if 'function-override' in opts:
-            for function_desc in opts['function-override'].split(';'):
-                state_or_tran, attribute, *function_str = function_desc.split(' ')
-                function_override[(state_or_tran, attribute)] = ' '.join(function_str)
+        if "function-override" in opts:
+            for function_desc in opts["function-override"].split(";"):
+                state_or_tran, attribute, *function_str = function_desc.split(" ")
+                function_override[(state_or_tran, attribute)] = " ".join(function_str)
 
-        if 'show-models' in opts:
-            show_models = opts['show-models'].split(',')
+        if "show-models" in opts:
+            show_models = opts["show-models"].split(",")
 
-        if 'show-quality' in opts:
-            show_quality = opts['show-quality'].split(',')
+        if "show-quality" in opts:
+            show_quality = opts["show-quality"].split(",")
 
-        if 'cross-validate' in opts:
-            xv_method, xv_count = opts['cross-validate'].split(':')
+        if "cross-validate" in opts:
+            xv_method, xv_count = opts["cross-validate"].split(":")
             xv_count = int(xv_count)
 
-        if 'with-safe-functions' in opts:
+        if "with-safe-functions" in opts:
             safe_functions_enabled = True
 
-        if 'hwmodel' in opts:
-            with open(opts['hwmodel'], 'r') as f:
+        if "hwmodel" in opts:
+            with open(opts["hwmodel"], "r") as f:
                 hwmodel = json.load(f)
 
-        if 'corrcoef' not in opts:
-            opts['corrcoef'] = False
+        if "corrcoef" not in opts:
+            opts["corrcoef"] = False
 
-        if 'filter-param' in opts:
-            opts['filter-param'] = list(map(lambda x: x.split('='), opts['filter-param'].split(',')))
+        if "filter-param" in opts:
+            opts["filter-param"] = list(
+                map(lambda x: x.split("="), opts["filter-param"].split(","))
+            )
         else:
-            opts['filter-param'] = list()
+            opts["filter-param"] = list()
 
     except getopt.GetoptError as err:
         print(err)
@@ -212,44 +244,74 @@ if __name__ == '__main__':
     raw_data = TimingData(args)
 
     preprocessed_data = raw_data.get_preprocessed_data()
-    by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes)
+    by_name, parameters, arg_count = pta_trace_to_aggregate(
+        preprocessed_data, ignored_trace_indexes
+    )
 
     prune_dependent_parameters(by_name, parameters)
 
-    filter_aggregate_by_param(by_name, parameters, opts['filter-param'])
+    filter_aggregate_by_param(by_name, parameters, opts["filter-param"])
 
-    model = AnalyticModel(by_name, parameters, arg_count, use_corrcoef=opts['corrcoef'], function_override=function_override)
+    model = AnalyticModel(
+        by_name,
+        parameters,
+        arg_count,
+        use_corrcoef=opts["corrcoef"],
+        function_override=function_override,
+    )
 
     if xv_method:
         xv = CrossValidator(AnalyticModel, by_name, parameters, arg_count)
 
-    if 'param-info' in opts:
+    if "param-info" in opts:
         for state in model.names:
-            print('{}:'.format(state))
+            print("{}:".format(state))
             for param in model.parameters:
-                print('    {} = {}'.format(param, model.stats.distinct_values[state][param]))
-
-    if 'plot-unparam' in opts:
-        for kv in opts['plot-unparam'].split(';'):
-            state_or_trans, attribute, ylabel = kv.split(':')
-            fname = 'param_y_{}_{}.pdf'.format(state_or_trans, attribute)
-            plotter.plot_y(model.by_name[state_or_trans][attribute], xlabel='measurement #', ylabel=ylabel)
+                print(
+                    "    {} = {}".format(
+                        param, model.stats.distinct_values[state][param]
+                    )
+                )
+
+    if "plot-unparam" in opts:
+        for kv in opts["plot-unparam"].split(";"):
+            state_or_trans, attribute, ylabel = kv.split(":")
+            fname = "param_y_{}_{}.pdf".format(state_or_trans, attribute)
+            plotter.plot_y(
+                model.by_name[state_or_trans][attribute],
+                xlabel="measurement #",
+                ylabel=ylabel,
+            )
 
     if len(show_models):
-        print('--- simple static model ---')
+        print("--- simple static model ---")
     static_model = model.get_static()
-    if 'static' in show_models or 'all' in show_models:
+    if "static" in show_models or "all" in show_models:
         for trans in model.names:
-            print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration')))
+            print("{:10s}: {:.0f} µs".format(trans, static_model(trans, "duration")))
             for param in model.parameters:
-                print('{:10s}  dependence on {:15s}: {:.2f}'.format(
-                    '',
-                    param,
-                    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))
+                print(
+                    "{:10s}  dependence on {:15s}: {:.2f}".format(
+                        "",
+                        param,
+                        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'])
@@ -260,81 +322,153 @@ if __name__ == '__main__':
         # 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':
+    if xv_method == "montecarlo":
         static_quality = xv.montecarlo(lambda m: m.get_static(), xv_count)
     else:
         static_quality = model.assess(static_model)
 
     if len(show_models):
-        print('--- LUT ---')
+        print("--- LUT ---")
     lut_model = model.get_param_lut()
 
-    if xv_method == 'montecarlo':
+    if xv_method == "montecarlo":
         lut_quality = xv.montecarlo(lambda m: m.get_param_lut(fallback=True), xv_count)
     else:
         lut_quality = model.assess(lut_model)
 
     if len(show_models):
-        print('--- param model ---')
+        print("--- param model ---")
 
-    param_model, param_info = model.get_fitted(safe_functions_enabled=safe_functions_enabled)
+    param_model, param_info = model.get_fitted(
+        safe_functions_enabled=safe_functions_enabled
+    )
 
-    if 'paramdetection' in show_models or 'all' in show_models:
+    if "paramdetection" in show_models or "all" in show_models:
         for transition in model.names:
-            for attribute in ['duration']:
+            for attribute in ["duration"]:
                 info = param_info(transition, attribute)
-                print('{:10s} {:10s} non-param stddev {:f}'.format(
-                    transition, attribute, model.stats.stats[transition][attribute]['std_static']
-                ))
-                print('{:10s} {:10s} param-lut stddev {:f}'.format(
-                    transition, attribute, model.stats.stats[transition][attribute]['std_param_lut']
-                ))
-                for param in sorted(model.stats.stats[transition][attribute]['std_by_param'].keys()):
-                    print('{:10s} {:10s} {:10s} stddev {:f}'.format(
-                        transition, attribute, param, model.stats.stats[transition][attribute]['std_by_param'][param]
-                    ))
-                    print('{:10s} {:10s} dependence on {:15s}: {:.2f}'.format(
-                        transition, attribute, param, model.stats.param_dependence_ratio(transition, attribute, param)))
-                for i, arg_stddev in enumerate(model.stats.stats[transition][attribute]['std_by_arg']):
-                    print('{:10s} {:10s} arg{:d} stddev {:f}'.format(
-                        transition, attribute, i, arg_stddev
-                    ))
-                    print('{:10s} {:10s} dependence on arg{:d}: {:.2f}'.format(
-                        transition, attribute, i, model.stats.arg_dependence_ratio(transition, attribute, i)))
+                print(
+                    "{:10s} {:10s} non-param stddev {:f}".format(
+                        transition,
+                        attribute,
+                        model.stats.stats[transition][attribute]["std_static"],
+                    )
+                )
+                print(
+                    "{:10s} {:10s} param-lut stddev {:f}".format(
+                        transition,
+                        attribute,
+                        model.stats.stats[transition][attribute]["std_param_lut"],
+                    )
+                )
+                for param in sorted(
+                    model.stats.stats[transition][attribute]["std_by_param"].keys()
+                ):
+                    print(
+                        "{:10s} {:10s} {:10s} stddev {:f}".format(
+                            transition,
+                            attribute,
+                            param,
+                            model.stats.stats[transition][attribute]["std_by_param"][
+                                param
+                            ],
+                        )
+                    )
+                    print(
+                        "{:10s} {:10s} dependence on {:15s}: {:.2f}".format(
+                            transition,
+                            attribute,
+                            param,
+                            model.stats.param_dependence_ratio(
+                                transition, attribute, param
+                            ),
+                        )
+                    )
+                for i, arg_stddev in enumerate(
+                    model.stats.stats[transition][attribute]["std_by_arg"]
+                ):
+                    print(
+                        "{:10s} {:10s} arg{:d} stddev {:f}".format(
+                            transition, attribute, i, arg_stddev
+                        )
+                    )
+                    print(
+                        "{:10s} {:10s} dependence on arg{:d}: {:.2f}".format(
+                            transition,
+                            attribute,
+                            i,
+                            model.stats.arg_dependence_ratio(transition, attribute, i),
+                        )
+                    )
                 if info is not None:
-                    for param_name in sorted(info['fit_result'].keys(), key=str):
-                        param_fit = info['fit_result'][param_name]['results']
+                    for param_name in sorted(info["fit_result"].keys(), key=str):
+                        param_fit = info["fit_result"][param_name]["results"]
                         for function_type in sorted(param_fit.keys()):
-                            function_rmsd = param_fit[function_type]['rmsd']
-                            print('{:10s} {:10s} {:10s} mean {:10s} RMSD {:.0f}'.format(
-                                transition, attribute, str(param_name), function_type, function_rmsd
-                            ))
-
-    if 'param' in show_models or 'all' in show_models:
+                            function_rmsd = param_fit[function_type]["rmsd"]
+                            print(
+                                "{:10s} {:10s} {:10s} mean {:10s} RMSD {:.0f}".format(
+                                    transition,
+                                    attribute,
+                                    str(param_name),
+                                    function_type,
+                                    function_rmsd,
+                                )
+                            )
+
+    if "param" in show_models or "all" in show_models:
         for trans in model.names:
-            for attribute in ['duration']:
+            for attribute in ["duration"]:
                 if param_info(trans, attribute):
-                    print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str))
-                    print('{:10s}  {:10s}  {}'.format('', '', param_info(trans, attribute)['function']._regression_args))
-
-    if xv_method == 'montecarlo':
+                    print(
+                        "{:10s}: {:10s}: {}".format(
+                            trans,
+                            attribute,
+                            param_info(trans, attribute)["function"]._model_str,
+                        )
+                    )
+                    print(
+                        "{:10s}  {:10s}  {}".format(
+                            "",
+                            "",
+                            param_info(trans, attribute)["function"]._regression_args,
+                        )
+                    )
+
+    if xv_method == "montecarlo":
         analytic_quality = xv.montecarlo(lambda m: m.get_fitted()[0], xv_count)
     else:
         analytic_quality = model.assess(param_model)
 
-    if 'tex' in show_models or 'tex' in show_quality:
-        print_text_model_data(model, static_model, static_quality, lut_model, lut_quality, param_model, param_info, analytic_quality)
+    if "tex" in show_models or "tex" in show_quality:
+        print_text_model_data(
+            model,
+            static_model,
+            static_quality,
+            lut_model,
+            lut_quality,
+            param_model,
+            param_info,
+            analytic_quality,
+        )
 
-    if 'table' in show_quality or 'all' in show_quality:
-        model_quality_table([static_quality, analytic_quality, lut_quality], [None, param_info, None])
+    if "table" in show_quality or "all" in show_quality:
+        model_quality_table(
+            [static_quality, analytic_quality, lut_quality], [None, param_info, None]
+        )
 
-    if 'plot-param' in opts:
-        for kv in opts['plot-param'].split(';'):
-            state_or_trans, attribute, param_name, *function = kv.split(' ')
+    if "plot-param" in opts:
+        for kv in opts["plot-param"].split(";"):
+            state_or_trans, attribute, param_name, *function = kv.split(" ")
             if len(function):
-                function = gplearn_to_function(' '.join(function))
+                function = gplearn_to_function(" ".join(function))
             else:
                 function = None
-            plotter.plot_param(model, state_or_trans, attribute, model.param_index(param_name), extra_function=function)
+            plotter.plot_param(
+                model,
+                state_or_trans,
+                attribute,
+                model.param_index(param_name),
+                extra_function=function,
+            )
 
     sys.exit(0)
diff --git a/bin/eval-accounting-overhead.py b/bin/eval-accounting-overhead.py
index d4b1d2c..7ea0807 100755
--- a/bin/eval-accounting-overhead.py
+++ b/bin/eval-accounting-overhead.py
@@ -18,17 +18,21 @@ import json
 import sys
 
 for filename in sys.argv[1:]:
-    with open(filename, 'r') as f:
+    with open(filename, "r") as f:
         measurement = json.load(f)
     raw_data = TimingData([filename])
     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)
     static_model = model.get_static()
-    if 'setup' in model.names:
-        transition_duration = static_model('setup', 'duration')
-    elif 'init' in model.names:
-        transition_duration = static_model('init', 'duration')
-    get_energy_duration = static_model('getEnergy', 'duration')
+    if "setup" in model.names:
+        transition_duration = static_model("setup", "duration")
+    elif "init" in model.names:
+        transition_duration = static_model("init", "duration")
+    get_energy_duration = static_model("getEnergy", "duration")
 
-    print('{:60s}: {:.0f} / {:.0f} µs'.format(measurement['opt']['accounting'], transition_duration, get_energy_duration))
+    print(
+        "{:60s}: {:.0f} / {:.0f} µs".format(
+            measurement["opt"]["accounting"], transition_duration, get_energy_duration
+        )
+    )
diff --git a/bin/eval-online-model-accuracy.py b/bin/eval-online-model-accuracy.py
index 21e7a1e..202ac28 100755
--- a/bin/eval-online-model-accuracy.py
+++ b/bin/eval-online-model-accuracy.py
@@ -33,72 +33,74 @@ import numpy as np
 
 opt = dict()
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     try:
         optspec = (
-            'accounting= '
-            'arch= '
-            'app= '
-            'depth= '
-            'dummy= '
-            'instance= '
-            'repeat= '
-            'run= '
-            'sleep= '
-            'timer-pin= '
-            'trace-filter= '
-            'timer-freq= '
-            'timer-type= '
-            'timestamp-type= '
-            'energy-type= '
-            'power-type= '
-            'timestamp-granularity= '
-            'energy-granularity= '
-            'power-granularity= '
+            "accounting= "
+            "arch= "
+            "app= "
+            "depth= "
+            "dummy= "
+            "instance= "
+            "repeat= "
+            "run= "
+            "sleep= "
+            "timer-pin= "
+            "trace-filter= "
+            "timer-freq= "
+            "timer-type= "
+            "timestamp-type= "
+            "energy-type= "
+            "power-type= "
+            "timestamp-granularity= "
+            "energy-granularity= "
+            "power-granularity= "
         )
-        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(' '))
+        raw_opts, args = getopt.getopt(sys.argv[1:], "", optspec.split(" "))
 
         opt_default = {
-            'depth': 3,
-            'sleep': 0,
-            'timer-freq': 1e6,
-            'timer-type': 'uint16_t',
-            'timestamp-type': 'uint16_t',
-            'energy-type': 'uint32_t',
-            'power-type': 'uint16_t',
-            'timestamp-granularity': 1e-6,
-            'power-granularity': 1e-6,
-            'energy-granularity': 1e-12,
+            "depth": 3,
+            "sleep": 0,
+            "timer-freq": 1e6,
+            "timer-type": "uint16_t",
+            "timestamp-type": "uint16_t",
+            "energy-type": "uint32_t",
+            "power-type": "uint16_t",
+            "timestamp-granularity": 1e-6,
+            "power-granularity": 1e-6,
+            "energy-granularity": 1e-12,
         }
 
         for option, parameter in raw_opts:
-            optname = re.sub(r'^--', '', option)
+            optname = re.sub(r"^--", "", option)
             opt[optname] = parameter
 
-        for key in 'depth sleep'.split():
+        for key in "depth sleep".split():
             if key in opt:
                 opt[key] = int(opt[key])
             else:
                 opt[key] = opt_default[key]
 
-        for key in 'timer-freq timestamp-granularity energy-granularity power-granularity'.split():
+        for (
+            key
+        ) in "timer-freq timestamp-granularity energy-granularity power-granularity".split():
             if key in opt:
                 opt[key] = float(opt[key])
             else:
                 opt[key] = opt_default[key]
 
-        for key in 'timer-type timestamp-type energy-type power-type'.split():
+        for key in "timer-type timestamp-type energy-type power-type".split():
             if key not in opt:
                 opt[key] = opt_default[key]
 
-        if 'trace-filter' in opt:
+        if "trace-filter" in opt:
             trace_filter = []
-            for trace in opt['trace-filter'].split():
-                trace_filter.append(trace.split(','))
-            opt['trace-filter'] = trace_filter
+            for trace in opt["trace-filter"].split():
+                trace_filter.append(trace.split(","))
+            opt["trace-filter"] = trace_filter
         else:
-            opt['trace-filter'] = None
+            opt["trace-filter"] = None
 
     except getopt.GetoptError as err:
         print(err)
@@ -109,81 +111,120 @@ if __name__ == '__main__':
     pta = PTA.from_file(modelfile)
 
     enum = dict()
-    if '.json' not in modelfile:
-        with open(modelfile, 'r') as f:
+    if ".json" not in modelfile:
+        with open(modelfile, "r") as f:
             driver_definition = yaml.safe_load(f)
-        if 'dummygen' in driver_definition and 'enum' in driver_definition['dummygen']:
-            enum = driver_definition['dummygen']['enum']
+        if "dummygen" in driver_definition and "enum" in driver_definition["dummygen"]:
+            enum = driver_definition["dummygen"]["enum"]
 
     pta.set_random_energy_model()
 
-    runs = list(pta.dfs(opt['depth'], with_arguments=True, with_parameters=True, trace_filter=opt['trace-filter'], sleep=opt['sleep']))
+    runs = list(
+        pta.dfs(
+            opt["depth"],
+            with_arguments=True,
+            with_parameters=True,
+            trace_filter=opt["trace-filter"],
+            sleep=opt["sleep"],
+        )
+    )
 
     num_transitions = len(runs)
 
     if len(runs) == 0:
-        print('DFS returned no traces -- perhaps your trace-filter is too restrictive?', file=sys.stderr)
+        print(
+            "DFS returned no traces -- perhaps your trace-filter is too restrictive?",
+            file=sys.stderr,
+        )
         sys.exit(1)
 
     real_energies = list()
     real_durations = list()
     model_energies = list()
     for run in runs:
-        accounting_method = get_simulated_accountingmethod(opt['accounting'])(pta, opt['timer-freq'], opt['timer-type'], opt['timestamp-type'],
-                                                                              opt['power-type'], opt['energy-type'])
-        real_energy, real_duration, _, _ = pta.simulate(run, accounting=accounting_method)
+        accounting_method = get_simulated_accountingmethod(opt["accounting"])(
+            pta,
+            opt["timer-freq"],
+            opt["timer-type"],
+            opt["timestamp-type"],
+            opt["power-type"],
+            opt["energy-type"],
+        )
+        real_energy, real_duration, _, _ = pta.simulate(
+            run, accounting=accounting_method
+        )
         model_energy = accounting_method.get_energy()
         real_energies.append(real_energy)
         real_durations.append(real_duration)
         model_energies.append(model_energy)
 
     measures = regression_measures(np.array(model_energies), np.array(real_energies))
-    print('SMAPE {:.0f}%, MAE {}'.format(measures['smape'], measures['mae']))
+    print("SMAPE {:.0f}%, MAE {}".format(measures["smape"], measures["mae"]))
 
     timer_freqs = [1e3, 2e3, 5e3, 1e4, 2e4, 5e4, 1e5, 2e5, 5e5, 1e6, 2e6, 5e6]
-    timer_types = timestamp_types = power_types = energy_types = 'uint8_t uint16_t uint32_t uint64_t'.split()
+    timer_types = (
+        timestamp_types
+    ) = power_types = energy_types = "uint8_t uint16_t uint32_t uint64_t".split()
 
     def config_weight(timer_freq, timer_type, ts_type, power_type, energy_type):
         base_weight = 0
         for var_type in timer_type, ts_type, power_type, energy_type:
-            if var_type == 'uint8_t':
+            if var_type == "uint8_t":
                 base_weight += 1
-            elif var_type == 'uint16_t':
+            elif var_type == "uint16_t":
                 base_weight += 2
-            elif var_type == 'uint32_t':
+            elif var_type == "uint32_t":
                 base_weight += 4
-            elif var_type == 'uint64_t':
+            elif var_type == "uint64_t":
                 base_weight += 8
         return base_weight
 
     # sys.exit(0)
 
     mean_errors = list()
-    for timer_freq, timer_type, ts_type, power_type, energy_type in itertools.product(timer_freqs, timer_types, timestamp_types, power_types, energy_types):
+    for timer_freq, timer_type, ts_type, power_type, energy_type in itertools.product(
+        timer_freqs, timer_types, timestamp_types, power_types, energy_types
+    ):
         real_energies = list()
         real_durations = list()
         model_energies = list()
         # duration in µs
         # Bei kurzer Dauer (z.B. nur [1e2]) performt auch uint32_t für Energie gut, sonst nicht so (weil overflow)
         for sleep_duration in [1e2, 1e3, 1e4, 1e5, 1e6]:
-            runs = pta.dfs(opt['depth'], with_arguments=True, with_parameters=True, trace_filter=opt['trace-filter'], sleep=sleep_duration)
+            runs = pta.dfs(
+                opt["depth"],
+                with_arguments=True,
+                with_parameters=True,
+                trace_filter=opt["trace-filter"],
+                sleep=sleep_duration,
+            )
             for run in runs:
-                accounting_method = get_simulated_accountingmethod(opt['accounting'])(pta, timer_freq, timer_type, ts_type, power_type, energy_type)
-                real_energy, real_duration, _, _ = pta.simulate(run, accounting=accounting_method)
+                accounting_method = get_simulated_accountingmethod(opt["accounting"])(
+                    pta, timer_freq, timer_type, ts_type, power_type, energy_type
+                )
+                real_energy, real_duration, _, _ = pta.simulate(
+                    run, accounting=accounting_method
+                )
                 model_energy = accounting_method.get_energy()
                 real_energies.append(real_energy)
                 real_durations.append(real_duration)
                 model_energies.append(model_energy)
-        measures = regression_measures(np.array(model_energies), np.array(real_energies))
-        mean_errors.append(((timer_freq, timer_type, ts_type, power_type, energy_type), config_weight(timer_freq, timer_type, ts_type, power_type, energy_type), measures))
+        measures = regression_measures(
+            np.array(model_energies), np.array(real_energies)
+        )
+        mean_errors.append(
+            (
+                (timer_freq, timer_type, ts_type, power_type, energy_type),
+                config_weight(timer_freq, timer_type, ts_type, power_type, energy_type),
+                measures,
+            )
+        )
 
     mean_errors.sort(key=lambda x: x[1])
-    mean_errors.sort(key=lambda x: x[2]['mae'])
+    mean_errors.sort(key=lambda x: x[2]["mae"])
 
     for result in mean_errors:
         config, weight, measures = result
-        print('{}  -> {:.0f}% / {}'.format(
-            config,
-            measures['smape'], measures['mae']))
+        print("{}  -> {:.0f}% / {}".format(config, measures["smape"], measures["mae"]))
 
     sys.exit(0)
diff --git a/bin/eval-outlier-removal.py b/bin/eval-outlier-removal.py
index 3a4aa28..d8b0e9d 100755
--- a/bin/eval-outlier-removal.py
+++ b/bin/eval-outlier-removal.py
@@ -7,63 +7,73 @@ from dfatool.dfatool import PTAModel, RawData, pta_trace_to_aggregate
 
 opts = {}
 
+
 def model_quality_table(result_lists, info_list):
-    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)
+    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)
             for i, results in enumerate(result_lists):
-                results = results['by_name']
+                results = results["by_name"]
                 info = info_list[i]
-                buf += '  |||  '
+                buf += "  |||  "
                 if info == None or info(state_or_tran, key):
                     result = results[state_or_tran][key]
-                    if 'smape' in result:
-                        buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
+                    if "smape" in result:
+                        buf += "{:6.2f}% / {:9.0f}".format(
+                            result["smape"], result["mae"]
+                        )
                     else:
-                        buf += '{:6}    {:9.0f}'.format('', result['mae'])
+                        buf += "{:6}    {:9.0f}".format("", result["mae"])
                 else:
-                    buf += '{:6}----{:9}'.format('', '')
+                    buf += "{:6}----{:9}".format("", "")
             print(buf)
 
+
 def combo_model_quality_table(result_lists, info_list):
-    for state_or_tran in result_lists[0][0]['by_name'].keys():
-        for key in result_lists[0][0]['by_name'][state_or_tran].keys():
+    for state_or_tran in result_lists[0][0]["by_name"].keys():
+        for key in result_lists[0][0]["by_name"][state_or_tran].keys():
             for sub_result_lists in result_lists:
-                buf = '{:20s} {:15s}'.format(state_or_tran, key)
+                buf = "{:20s} {:15s}".format(state_or_tran, key)
                 for i, results in enumerate(sub_result_lists):
-                    results = results['by_name']
+                    results = results["by_name"]
                     info = info_list[i]
-                    buf += '  |||  '
+                    buf += "  |||  "
                     if info == None or info(state_or_tran, key):
                         result = results[state_or_tran][key]
-                        if 'smape' in result:
-                            buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
+                        if "smape" in result:
+                            buf += "{:6.2f}% / {:9.0f}".format(
+                                result["smape"], result["mae"]
+                            )
                         else:
-                            buf += '{:6}    {:9.0f}'.format('', result['mae'])
+                            buf += "{:6}    {:9.0f}".format("", result["mae"])
                     else:
-                        buf += '{:6}----{:9}'.format('', '')
+                        buf += "{:6}----{:9}".format("", "")
                 print(buf)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
 
     ignored_trace_indexes = []
     discard_outliers = None
 
     try:
-        raw_opts, args = getopt.getopt(sys.argv[1:], "",
-            'plot ignored-trace-indexes= discard-outliers='.split(' '))
+        raw_opts, args = getopt.getopt(
+            sys.argv[1:], "", "plot ignored-trace-indexes= discard-outliers=".split(" ")
+        )
 
         for option, parameter in raw_opts:
-            optname = re.sub(r'^--', '', option)
+            optname = re.sub(r"^--", "", option)
             opts[optname] = parameter
 
-            if 'ignored-trace-indexes' in opts:
-                ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(',')))
+            if "ignored-trace-indexes" in opts:
+                ignored_trace_indexes = list(
+                    map(int, opts["ignored-trace-indexes"].split(","))
+                )
                 if 0 in ignored_trace_indexes:
-                    print('[E] arguments to --ignored-trace-indexes start from 1')
+                    print("[E] arguments to --ignored-trace-indexes start from 1")
 
-            if 'discard-outliers' in opts:
-                discard_outliers = float(opts['discard-outliers'])
+            if "discard-outliers" in opts:
+                discard_outliers = float(opts["discard-outliers"])
 
     except getopt.GetoptError as err:
         print(err)
@@ -72,19 +82,29 @@ if __name__ == '__main__':
     raw_data = RawData(args)
 
     preprocessed_data = raw_data.get_preprocessed_data()
-    by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes)
-    m1 = PTAModel(by_name, parameters, arg_count,
-        traces = preprocessed_data,
-        ignore_trace_indexes = ignored_trace_indexes)
-    m2 = PTAModel(by_name, parameters, arg_count,
-        traces = preprocessed_data,
-        ignore_trace_indexes = ignored_trace_indexes,
-        discard_outliers = discard_outliers)
-
-    print('--- simple static model ---')
+    by_name, parameters, arg_count = pta_trace_to_aggregate(
+        preprocessed_data, ignored_trace_indexes
+    )
+    m1 = PTAModel(
+        by_name,
+        parameters,
+        arg_count,
+        traces=preprocessed_data,
+        ignore_trace_indexes=ignored_trace_indexes,
+    )
+    m2 = PTAModel(
+        by_name,
+        parameters,
+        arg_count,
+        traces=preprocessed_data,
+        ignore_trace_indexes=ignored_trace_indexes,
+        discard_outliers=discard_outliers,
+    )
+
+    print("--- simple static model ---")
     static_m1 = m1.get_static()
     static_m2 = m2.get_static()
-    #for state in model.states():
+    # for state in model.states():
     #    print('{:10s}: {:.0f} µW  ({:.2f})'.format(
     #        state,
     #        static_model(state, 'power'),
@@ -94,7 +114,7 @@ if __name__ == '__main__':
     #            '',
     #            param,
     #            model.param_dependence_ratio(state, 'power', param)))
-    #for trans in model.transitions():
+    # for trans in model.transitions():
     #    print('{:10s}: {:.0f} / {:.0f} / {:.0f} pJ  ({:.2f} / {:.2f} / {:.2f})'.format(
     #        trans, static_model(trans, 'energy'),
     #        static_model(trans, 'rel_energy_prev'),
@@ -107,36 +127,84 @@ if __name__ == '__main__':
     static_q2 = m2.assess(static_m2)
     static_q12 = m1.assess(static_m2)
 
-    print('--- LUT ---')
+    print("--- LUT ---")
     lut_m1 = m1.get_param_lut()
     lut_m2 = m2.get_param_lut()
     lut_q1 = m1.assess(lut_m1)
     lut_q2 = m2.assess(lut_m2)
     lut_q12 = m1.assess(lut_m2)
 
-    print('--- param model ---')
+    print("--- param model ---")
     param_m1, param_i1 = m1.get_fitted()
     for state in m1.states():
-        for attribute in ['power']:
+        for attribute in ["power"]:
             if param_i1(state, attribute):
-                print('{:10s}: {}'.format(state, param_i1(state, attribute)['function']._model_str))
-                print('{:10s}  {}'.format('', param_i1(state, attribute)['function']._regression_args))
+                print(
+                    "{:10s}: {}".format(
+                        state, param_i1(state, attribute)["function"]._model_str
+                    )
+                )
+                print(
+                    "{:10s}  {}".format(
+                        "", param_i1(state, attribute)["function"]._regression_args
+                    )
+                )
     for trans in m1.transitions():
-        for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']:
+        for attribute in [
+            "energy",
+            "rel_energy_prev",
+            "rel_energy_next",
+            "duration",
+            "timeout",
+        ]:
             if param_i1(trans, attribute):
-                print('{:10s}: {:10s}: {}'.format(trans, attribute, param_i1(trans, attribute)['function']._model_str))
-                print('{:10s}  {:10s}  {}'.format('', '', param_i1(trans, attribute)['function']._regression_args))
+                print(
+                    "{:10s}: {:10s}: {}".format(
+                        trans,
+                        attribute,
+                        param_i1(trans, attribute)["function"]._model_str,
+                    )
+                )
+                print(
+                    "{:10s}  {:10s}  {}".format(
+                        "", "", param_i1(trans, attribute)["function"]._regression_args
+                    )
+                )
     param_m2, param_i2 = m2.get_fitted()
     for state in m2.states():
-        for attribute in ['power']:
+        for attribute in ["power"]:
             if param_i2(state, attribute):
-                print('{:10s}: {}'.format(state, param_i2(state, attribute)['function']._model_str))
-                print('{:10s}  {}'.format('', param_i2(state, attribute)['function']._regression_args))
+                print(
+                    "{:10s}: {}".format(
+                        state, param_i2(state, attribute)["function"]._model_str
+                    )
+                )
+                print(
+                    "{:10s}  {}".format(
+                        "", param_i2(state, attribute)["function"]._regression_args
+                    )
+                )
     for trans in m2.transitions():
-        for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']:
+        for attribute in [
+            "energy",
+            "rel_energy_prev",
+            "rel_energy_next",
+            "duration",
+            "timeout",
+        ]:
             if param_i2(trans, attribute):
-                print('{:10s}: {:10s}: {}'.format(trans, attribute, param_i2(trans, attribute)['function']._model_str))
-                print('{:10s}  {:10s}  {}'.format('', '', param_i2(trans, attribute)['function']._regression_args))
+                print(
+                    "{:10s}: {:10s}: {}".format(
+                        trans,
+                        attribute,
+                        param_i2(trans, attribute)["function"]._model_str,
+                    )
+                )
+                print(
+                    "{:10s}  {:10s}  {}".format(
+                        "", "", param_i2(trans, attribute)["function"]._regression_args
+                    )
+                )
 
     analytic_q1 = m1.assess(param_m1)
     analytic_q2 = m2.assess(param_m2)
@@ -144,10 +212,13 @@ if __name__ == '__main__':
     model_quality_table([static_q1, analytic_q1, lut_q1], [None, param_i1, None])
     model_quality_table([static_q2, analytic_q2, lut_q2], [None, param_i2, None])
     model_quality_table([static_q12, analytic_q12, lut_q12], [None, param_i2, None])
-    combo_model_quality_table([
-        [static_q1, analytic_q1, lut_q1],
-        [static_q2, analytic_q2, lut_q2],
-        [static_q12, analytic_q12, lut_q12]],
-        [None, param_i1, None])
+    combo_model_quality_table(
+        [
+            [static_q1, analytic_q1, lut_q1],
+            [static_q2, analytic_q2, lut_q2],
+            [static_q12, analytic_q12, lut_q12],
+        ],
+        [None, param_i1, None],
+    )
 
     sys.exit(0)
diff --git a/bin/eval-rel-energy.py b/bin/eval-rel-energy.py
index 6ae35e0..44db226 100755
--- a/bin/eval-rel-energy.py
+++ b/bin/eval-rel-energy.py
@@ -11,15 +11,15 @@ opts = {}
 def get_file_groups(args):
     groups = []
     index_low = 0
-    while ':' in args[index_low:]:
-        index_high = args[index_low:].index(':') + index_low
-        groups.append(args[index_low: index_high])
+    while ":" in args[index_low:]:
+        index_high = args[index_low:].index(":") + index_low
+        groups.append(args[index_low:index_high])
         index_low = index_high + 1
     groups.append(args[index_low:])
     return groups
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     ignored_trace_indexes = []
     discard_outliers = None
@@ -30,36 +30,40 @@ if __name__ == '__main__':
 
     try:
         optspec = (
-            'plot-unparam= plot-param= show-models= show-quality= '
-            'ignored-trace-indexes= discard-outliers= function-override= '
-            'with-safe-functions'
+            "plot-unparam= plot-param= show-models= show-quality= "
+            "ignored-trace-indexes= discard-outliers= function-override= "
+            "with-safe-functions"
         )
-        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)
+            optname = re.sub(r"^--", "", option)
             opts[optname] = parameter
 
-            if 'ignored-trace-indexes' in opts:
-                ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(',')))
+            if "ignored-trace-indexes" in opts:
+                ignored_trace_indexes = list(
+                    map(int, opts["ignored-trace-indexes"].split(","))
+                )
                 if 0 in ignored_trace_indexes:
-                    print('[E] arguments to --ignored-trace-indexes start from 1')
+                    print("[E] arguments to --ignored-trace-indexes start from 1")
 
-            if 'discard-outliers' in opts:
-                discard_outliers = float(opts['discard-outliers'])
+            if "discard-outliers" in opts:
+                discard_outliers = float(opts["discard-outliers"])
 
-            if 'function-override' in opts:
-                for function_desc in opts['function-override'].split(';'):
-                    state_or_tran, attribute, *function_str = function_desc.split(' ')
-                    function_override[(state_or_tran, attribute)] = ' '.join(function_str)
+            if "function-override" in opts:
+                for function_desc in opts["function-override"].split(";"):
+                    state_or_tran, attribute, *function_str = function_desc.split(" ")
+                    function_override[(state_or_tran, attribute)] = " ".join(
+                        function_str
+                    )
 
-            if 'show-models' in opts:
-                show_models = opts['show-models'].split(',')
+            if "show-models" in opts:
+                show_models = opts["show-models"].split(",")
 
-            if 'show-quality' in opts:
-                show_quality = opts['show-quality'].split(',')
+            if "show-quality" in opts:
+                show_quality = opts["show-quality"].split(",")
 
-            if 'with-safe-functions' in opts:
+            if "with-safe-functions" in opts:
                 safe_functions_enabled = True
 
     except getopt.GetoptError as err:
@@ -70,36 +74,50 @@ if __name__ == '__main__':
     score_relative = 0
 
     for file_group in get_file_groups(args):
-        print('')
-        print('{}:'.format(' '.join(file_group)))
+        print("")
+        print("{}:".format(" ".join(file_group)))
         raw_data = RawData(file_group)
 
         preprocessed_data = raw_data.get_preprocessed_data(verbose=False)
-        by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data, ignored_trace_indexes)
-        model = PTAModel(by_name, parameters, arg_count,
-                         traces=preprocessed_data,
-                         ignore_trace_indexes=ignored_trace_indexes,
-                         discard_outliers=discard_outliers,
-                         function_override=function_override,
-                         verbose=False)
+        by_name, parameters, arg_count = pta_trace_to_aggregate(
+            preprocessed_data, ignored_trace_indexes
+        )
+        model = PTAModel(
+            by_name,
+            parameters,
+            arg_count,
+            traces=preprocessed_data,
+            ignore_trace_indexes=ignored_trace_indexes,
+            discard_outliers=discard_outliers,
+            function_override=function_override,
+            verbose=False,
+        )
 
         lut_quality = model.assess(model.get_param_lut())
 
         for trans in model.transitions():
-            absolute_quality = lut_quality['by_name'][trans]['energy']
-            relative_quality = lut_quality['by_name'][trans]['rel_energy_prev']
-            if absolute_quality['mae'] < relative_quality['mae']:
-                best = 'absolute'
+            absolute_quality = lut_quality["by_name"][trans]["energy"]
+            relative_quality = lut_quality["by_name"][trans]["rel_energy_prev"]
+            if absolute_quality["mae"] < relative_quality["mae"]:
+                best = "absolute"
                 score_absolute += 1
             else:
-                best = 'relative'
+                best = "relative"
                 score_relative += 1
 
-            print('{:20s}: {:s}   (diff {:.0f} / {:.2f}%, abs {:.0f} / {:.2f}%, rel {:.0f} / {:.2f}%)'.format(
-                trans, best,
-                abs(absolute_quality['mae'] - relative_quality['mae']),
-                abs(absolute_quality['mae'] - relative_quality['mae']) * 100 / max(absolute_quality['mae'], relative_quality['mae']),
-                absolute_quality['mae'], absolute_quality['smape'],
-                relative_quality['mae'], relative_quality['smape']))
+            print(
+                "{:20s}: {:s}   (diff {:.0f} / {:.2f}%, abs {:.0f} / {:.2f}%, rel {:.0f} / {:.2f}%)".format(
+                    trans,
+                    best,
+                    abs(absolute_quality["mae"] - relative_quality["mae"]),
+                    abs(absolute_quality["mae"] - relative_quality["mae"])
+                    * 100
+                    / max(absolute_quality["mae"], relative_quality["mae"]),
+                    absolute_quality["mae"],
+                    absolute_quality["smape"],
+                    relative_quality["mae"],
+                    relative_quality["smape"],
+                )
+            )
 
     sys.exit(0)
diff --git a/bin/generate-dfa-benchmark.py b/bin/generate-dfa-benchmark.py
index fedd12b..478b221 100755
--- a/bin/generate-dfa-benchmark.py
+++ b/bin/generate-dfa-benchmark.py
@@ -98,22 +98,29 @@ from dfatool.utils import flatten
 opt = dict()
 
 
-def benchmark_from_runs(pta: PTA, runs: list, harness: OnboardTimerHarness, benchmark_id: int = 0, dummy=False, repeat=0) -> io.StringIO:
+def benchmark_from_runs(
+    pta: PTA,
+    runs: list,
+    harness: OnboardTimerHarness,
+    benchmark_id: int = 0,
+    dummy=False,
+    repeat=0,
+) -> io.StringIO:
     outbuf = io.StringIO()
 
     outbuf.write('#include "arch.h"\n')
     if dummy:
         outbuf.write('#include "driver/dummy.h"\n')
-    elif 'includes' in pta.codegen:
-        for include in pta.codegen['includes']:
+    elif "includes" in pta.codegen:
+        for include in pta.codegen["includes"]:
             outbuf.write('#include "{}"\n'.format(include))
     outbuf.write(harness.global_code())
 
-    outbuf.write('int main(void)\n')
-    outbuf.write('{\n')
+    outbuf.write("int main(void)\n")
+    outbuf.write("{\n")
 
-    for driver in ('arch', 'gpio', 'kout'):
-        outbuf.write('{}.setup();\n'.format(driver))
+    for driver in ("arch", "gpio", "kout"):
+        outbuf.write("{}.setup();\n".format(driver))
 
     # There is a race condition between flashing the code and starting the UART log.
     # When starting the log before flashing, output from a previous benchmark may cause bogus data to be added.
@@ -125,37 +132,37 @@ def benchmark_from_runs(pta: PTA, runs: list, harness: OnboardTimerHarness, benc
     # For energytrace, the device is connected to VCC and set up before
     # the initialization delay to -- this puts it into a well-defined state and
     # decreases pre-sync power consumption
-    if 'energytrace' not in opt:
-        if 'mimosa' in opt:
-            outbuf.write('arch.delay_ms(12000);\n')
+    if "energytrace" not in opt:
+        if "mimosa" in opt:
+            outbuf.write("arch.delay_ms(12000);\n")
         else:
-            outbuf.write('arch.delay_ms(2000);\n')
+            outbuf.write("arch.delay_ms(2000);\n")
         # Output some newlines to ensure the parser can determine the start of the first real output line
-        outbuf.write('kout << endl << endl;\n')
+        outbuf.write("kout << endl << endl;\n")
 
-    if 'setup' in pta.codegen:
-        for call in pta.codegen['setup']:
+    if "setup" in pta.codegen:
+        for call in pta.codegen["setup"]:
             outbuf.write(call)
 
-    if 'energytrace' in opt:
-        outbuf.write('for (unsigned char i = 0; i < 10; i++) {\n')
-        outbuf.write('arch.sleep_ms(250);\n}\n')
+    if "energytrace" in opt:
+        outbuf.write("for (unsigned char i = 0; i < 10; i++) {\n")
+        outbuf.write("arch.sleep_ms(250);\n}\n")
         # Output some newlines to ensure the parser can determine the start of the first real output line
-        outbuf.write('kout << endl << endl;\n')
+        outbuf.write("kout << endl << endl;\n")
 
     if repeat:
-        outbuf.write('unsigned char i = 0;\n')
-        outbuf.write('while (i++ < {}) {{\n'.format(repeat))
+        outbuf.write("unsigned char i = 0;\n")
+        outbuf.write("while (i++ < {}) {{\n".format(repeat))
     else:
-        outbuf.write('while (1) {\n')
+        outbuf.write("while (1) {\n")
 
     outbuf.write(harness.start_benchmark())
 
-    class_prefix = ''
-    if 'instance' in opt:
-        class_prefix = '{}.'.format(opt['instance'])
-    elif 'instance' in pta.codegen:
-        class_prefix = '{}.'.format(pta.codegen['instance'])
+    class_prefix = ""
+    if "instance" in opt:
+        class_prefix = "{}.".format(opt["instance"])
+    elif "instance" in pta.codegen:
+        class_prefix = "{}.".format(pta.codegen["instance"])
 
     num_transitions = 0
     num_traces = 0
@@ -167,56 +174,105 @@ def benchmark_from_runs(pta: PTA, runs: list, harness: OnboardTimerHarness, benc
             num_transitions += 1
             harness.append_transition(transition.name, param, arguments)
             harness.append_state(transition.destination.name, parameter.copy())
-            outbuf.write('// {} -> {}\n'.format(transition.origin.name, transition.destination.name))
+            outbuf.write(
+                "// {} -> {}\n".format(
+                    transition.origin.name, transition.destination.name
+                )
+            )
             if transition.is_interrupt:
-                outbuf.write('// wait for {} interrupt\n'.format(transition.name))
-                transition_code = '// TODO add startTransition / stopTransition calls to interrupt routine'
+                outbuf.write("// wait for {} interrupt\n".format(transition.name))
+                transition_code = "// TODO add startTransition / stopTransition calls to interrupt routine"
             else:
-                transition_code = '{}{}({});'.format(class_prefix, transition.name, ', '.join(map(str, arguments)))
-            outbuf.write(harness.pass_transition(pta.get_transition_id(transition), transition_code, transition=transition))
+                transition_code = "{}{}({});".format(
+                    class_prefix, transition.name, ", ".join(map(str, arguments))
+                )
+            outbuf.write(
+                harness.pass_transition(
+                    pta.get_transition_id(transition),
+                    transition_code,
+                    transition=transition,
+                )
+            )
 
             param = parameter
 
-            outbuf.write('// current parameters: {}\n'.format(', '.join(map(lambda kv: '{}={}'.format(*kv), param.items()))))
-
-            if 'delay_after_ms' in transition.codegen:
-                if 'energytrace' in opt:
-                    outbuf.write('arch.sleep_ms({:d}); // {} -- delay mandated by codegen.delay_after_ms\n'.format(transition.codegen['delay_after_ms'], transition.destination.name))
+            outbuf.write(
+                "// current parameters: {}\n".format(
+                    ", ".join(map(lambda kv: "{}={}".format(*kv), param.items()))
+                )
+            )
+
+            if "delay_after_ms" in transition.codegen:
+                if "energytrace" in opt:
+                    outbuf.write(
+                        "arch.sleep_ms({:d}); // {} -- delay mandated by codegen.delay_after_ms\n".format(
+                            transition.codegen["delay_after_ms"],
+                            transition.destination.name,
+                        )
+                    )
                 else:
-                    outbuf.write('arch.delay_ms({:d}); // {} -- delay mandated by codegen.delay_after_ms\n'.format(transition.codegen['delay_after_ms'], transition.destination.name))
-            elif opt['sleep']:
-                if 'energytrace' in opt:
-                    outbuf.write('arch.sleep_ms({:d}); // {}\n'.format(opt['sleep'], transition.destination.name))
+                    outbuf.write(
+                        "arch.delay_ms({:d}); // {} -- delay mandated by codegen.delay_after_ms\n".format(
+                            transition.codegen["delay_after_ms"],
+                            transition.destination.name,
+                        )
+                    )
+            elif opt["sleep"]:
+                if "energytrace" in opt:
+                    outbuf.write(
+                        "arch.sleep_ms({:d}); // {}\n".format(
+                            opt["sleep"], transition.destination.name
+                        )
+                    )
                 else:
-                    outbuf.write('arch.delay_ms({:d}); // {}\n'.format(opt['sleep'], transition.destination.name))
+                    outbuf.write(
+                        "arch.delay_ms({:d}); // {}\n".format(
+                            opt["sleep"], transition.destination.name
+                        )
+                    )
 
         outbuf.write(harness.stop_run(num_traces))
         if dummy:
-            outbuf.write('kout << "[Energy] " << {}getEnergy() << endl;\n'.format(class_prefix))
-        outbuf.write('\n')
+            outbuf.write(
+                'kout << "[Energy] " << {}getEnergy() << endl;\n'.format(class_prefix)
+            )
+        outbuf.write("\n")
         num_traces += 1
 
     outbuf.write(harness.stop_benchmark())
-    outbuf.write('}\n')
+    outbuf.write("}\n")
 
     # Ensure logging can be terminated after the specified number of measurements
     outbuf.write(harness.start_benchmark())
 
-    outbuf.write('while(1) { }\n')
-    outbuf.write('return 0;\n')
-    outbuf.write('}\n')
+    outbuf.write("while(1) { }\n")
+    outbuf.write("return 0;\n")
+    outbuf.write("}\n")
 
     return outbuf
 
 
-def run_benchmark(application_file: str, pta: PTA, runs: list, arch: str, app: str, run_args: list, harness: object, sleep: int = 0, repeat: int = 0, run_offset: int = 0, runs_total: int = 0, dummy=False):
-    if 'mimosa' in opt or 'energytrace' in opt:
+def run_benchmark(
+    application_file: str,
+    pta: PTA,
+    runs: list,
+    arch: str,
+    app: str,
+    run_args: list,
+    harness: object,
+    sleep: int = 0,
+    repeat: int = 0,
+    run_offset: int = 0,
+    runs_total: int = 0,
+    dummy=False,
+):
+    if "mimosa" in opt or "energytrace" in opt:
         outbuf = benchmark_from_runs(pta, runs, harness, dummy=dummy, repeat=1)
     else:
         outbuf = benchmark_from_runs(pta, runs, harness, dummy=dummy, repeat=repeat)
-    with open(application_file, 'w') as f:
+    with open(application_file, "w") as f:
         f.write(outbuf.getvalue())
-        print('[MAKE] building benchmark with {:d} runs'.format(len(runs)))
+        print("[MAKE] building benchmark with {:d} runs".format(len(runs)))
 
     # assume an average of 10ms per transition. Mind the 10s start delay.
     run_timeout = 10 + num_transitions * (sleep + 10) / 1000
@@ -241,23 +297,55 @@ def run_benchmark(application_file: str, pta: PTA, runs: list, arch: str, app: s
     # This has been deliberately taken out of the except clause to avoid nested exception handlers
     # (they lead to pretty interesting tracebacks which are probably more confusing than helpful)
     if needs_split:
-        print('[MAKE] benchmark code is too large, splitting up')
+        print("[MAKE] benchmark code is too large, splitting up")
         mid = len(runs) // 2
         # Previously prepared trace data is useless
         harness.reset()
-        results = run_benchmark(application_file, pta, runs[:mid], arch, app, run_args, harness.copy(), sleep, repeat, run_offset=run_offset, runs_total=runs_total, dummy=dummy)
-        results.extend(run_benchmark(application_file, pta, runs[mid:], arch, app, run_args, harness.copy(), sleep, repeat, run_offset=run_offset + mid, runs_total=runs_total, dummy=dummy))
+        results = run_benchmark(
+            application_file,
+            pta,
+            runs[:mid],
+            arch,
+            app,
+            run_args,
+            harness.copy(),
+            sleep,
+            repeat,
+            run_offset=run_offset,
+            runs_total=runs_total,
+            dummy=dummy,
+        )
+        results.extend(
+            run_benchmark(
+                application_file,
+                pta,
+                runs[mid:],
+                arch,
+                app,
+                run_args,
+                harness.copy(),
+                sleep,
+                repeat,
+                run_offset=run_offset + mid,
+                runs_total=runs_total,
+                dummy=dummy,
+            )
+        )
         return results
 
-    if 'mimosa' in opt or 'energytrace' in opt:
+    if "mimosa" in opt or "energytrace" in opt:
         files = list()
         i = 0
-        while i < opt['repeat']:
+        while i < opt["repeat"]:
             runner.flash(arch, app, run_args)
-            if 'mimosa' in opt:
-                monitor = runner.get_monitor(arch, callback=harness.parser_cb, mimosa=opt['mimosa'])
-            elif 'energytrace' in opt:
-                monitor = runner.get_monitor(arch, callback=harness.parser_cb, energytrace=opt['energytrace'])
+            if "mimosa" in opt:
+                monitor = runner.get_monitor(
+                    arch, callback=harness.parser_cb, mimosa=opt["mimosa"]
+                )
+            elif "energytrace" in opt:
+                monitor = runner.get_monitor(
+                    arch, callback=harness.parser_cb, energytrace=opt["energytrace"]
+                )
 
             sync_error = False
             try:
@@ -266,17 +354,31 @@ def run_benchmark(application_file: str, pta: PTA, runs: list, arch: str, app: s
                     # 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 and not harness.synced and not harness.done:
-                        print('[RUN] has been unsynced for more than 30 seconds, assuming error. Retrying.')
+                    if (
+                        slept > 30
+                        and slept < 40
+                        and not harness.synced
+                        and not harness.done
+                    ):
+                        print(
+                            "[RUN] has been unsynced for more than 30 seconds, assuming error. Retrying."
+                        )
                         sync_error = True
                         break
                     if harness.abort:
-                        print('[RUN] harness encountered an error. Retrying')
+                        print("[RUN] harness encountered an error. Retrying")
                         sync_error = True
                         break
                     time.sleep(5)
                     slept += 5
-                    print('[RUN] {:d}/{:d} ({:.0f}%), current benchmark at {:.0f}%'.format(run_offset, runs_total, run_offset * 100 / runs_total, slept * 100 / run_timeout))
+                    print(
+                        "[RUN] {:d}/{:d} ({:.0f}%), current benchmark at {:.0f}%".format(
+                            run_offset,
+                            runs_total,
+                            run_offset * 100 / runs_total,
+                            slept * 100 / run_timeout,
+                        )
+                    )
             except KeyboardInterrupt:
                 pass
 
@@ -297,8 +399,8 @@ def run_benchmark(application_file: str, pta: PTA, runs: list, arch: str, app: s
         runner.flash(arch, app, run_args)
         monitor = runner.get_monitor(arch, callback=harness.parser_cb)
 
-        if arch == 'posix':
-            print('[RUN] Will run benchmark for {:.0f} seconds'.format(run_timeout))
+        if arch == "posix":
+            print("[RUN] Will run benchmark for {:.0f} seconds".format(run_timeout))
             lines = monitor.run(int(run_timeout))
             return [(runs, harness, lines, list())]
 
@@ -307,7 +409,14 @@ def run_benchmark(application_file: str, pta: PTA, runs: list, arch: str, app: s
             while not harness.done:
                 time.sleep(5)
                 slept += 5
-                print('[RUN] {:d}/{:d} ({:.0f}%), current benchmark at {:.0f}%'.format(run_offset, runs_total, run_offset * 100 / runs_total, slept * 100 / run_timeout))
+                print(
+                    "[RUN] {:d}/{:d} ({:.0f}%), current benchmark at {:.0f}%".format(
+                        run_offset,
+                        runs_total,
+                        run_offset * 100 / runs_total,
+                        slept * 100 / run_timeout,
+                    )
+                )
         except KeyboardInterrupt:
             pass
         monitor.close()
@@ -315,85 +424,91 @@ def run_benchmark(application_file: str, pta: PTA, runs: list, arch: str, app: s
         return [(runs, harness, monitor, list())]
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     try:
         optspec = (
-            'accounting= '
-            'arch= '
-            'app= '
-            'data= '
-            'depth= '
-            'dummy= '
-            'energytrace= '
-            'instance= '
-            'mimosa= '
-            'repeat= '
-            'run= '
-            'sleep= '
-            'shrink '
-            'timing '
-            'timer-pin= '
-            'trace-filter= '
+            "accounting= "
+            "arch= "
+            "app= "
+            "data= "
+            "depth= "
+            "dummy= "
+            "energytrace= "
+            "instance= "
+            "mimosa= "
+            "repeat= "
+            "run= "
+            "sleep= "
+            "shrink "
+            "timing "
+            "timer-pin= "
+            "trace-filter= "
         )
-        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)
+            optname = re.sub(r"^--", "", option)
             opt[optname] = parameter
 
-        if 'app' not in opt:
-            opt['app'] = 'aemr'
+        if "app" not in opt:
+            opt["app"] = "aemr"
 
-        if 'depth' in opt:
-            opt['depth'] = int(opt['depth'])
+        if "depth" in opt:
+            opt["depth"] = int(opt["depth"])
         else:
-            opt['depth'] = 3
+            opt["depth"] = 3
 
-        if 'repeat' in opt:
-            opt['repeat'] = int(opt['repeat'])
+        if "repeat" in opt:
+            opt["repeat"] = int(opt["repeat"])
         else:
-            opt['repeat'] = 0
+            opt["repeat"] = 0
 
-        if 'sleep' in opt:
-            opt['sleep'] = int(opt['sleep'])
+        if "sleep" in opt:
+            opt["sleep"] = int(opt["sleep"])
         else:
-            opt['sleep'] = 0
+            opt["sleep"] = 0
 
-        if 'trace-filter' in opt:
+        if "trace-filter" in opt:
             trace_filter = list()
-            for trace in opt['trace-filter'].split():
-                trace_filter.append(trace.split(','))
-            opt['trace-filter'] = trace_filter
+            for trace in opt["trace-filter"].split():
+                trace_filter.append(trace.split(","))
+            opt["trace-filter"] = trace_filter
         else:
-            opt['trace-filter'] = None
+            opt["trace-filter"] = None
 
-        if 'mimosa' in opt:
-            if opt['mimosa'] == '':
-                opt['mimosa'] = dict()
+        if "mimosa" in opt:
+            if opt["mimosa"] == "":
+                opt["mimosa"] = dict()
             else:
-                opt['mimosa'] = dict(map(lambda x: x.split('='), opt['mimosa'].split(',')))
-            opt.pop('timing', None)
-            if opt['repeat'] == 0:
-                opt['repeat'] = 1
-
-        if 'energytrace' in opt:
-            if opt['energytrace'] == '':
-                opt['energytrace'] = dict()
+                opt["mimosa"] = dict(
+                    map(lambda x: x.split("="), opt["mimosa"].split(","))
+                )
+            opt.pop("timing", None)
+            if opt["repeat"] == 0:
+                opt["repeat"] = 1
+
+        if "energytrace" in opt:
+            if opt["energytrace"] == "":
+                opt["energytrace"] = dict()
             else:
-                opt['energytrace'] = dict(map(lambda x: x.split('='), opt['energytrace'].split(',')))
-            opt.pop('timing', None)
-            if opt['repeat'] == 0:
-                opt['repeat'] = 1
-
-        if 'data' not in opt:
-            opt['data'] = '../data'
-
-        if 'dummy' in opt:
-            if opt['dummy'] == '':
-                opt['dummy'] = dict()
+                opt["energytrace"] = dict(
+                    map(lambda x: x.split("="), opt["energytrace"].split(","))
+                )
+            opt.pop("timing", None)
+            if opt["repeat"] == 0:
+                opt["repeat"] = 1
+
+        if "data" not in opt:
+            opt["data"] = "../data"
+
+        if "dummy" in opt:
+            if opt["dummy"] == "":
+                opt["dummy"] = dict()
             else:
-                opt['dummy'] = dict(map(lambda x: x.split('='), opt['dummy'].split(',')))
+                opt["dummy"] = dict(
+                    map(lambda x: x.split("="), opt["dummy"].split(","))
+                )
 
     except getopt.GetoptError as err:
         print(err)
@@ -404,69 +519,96 @@ if __name__ == '__main__':
     pta = PTA.from_file(modelfile)
     run_flags = None
 
-    if 'shrink' in opt:
+    if "shrink" in opt:
         pta.shrink_argument_values()
 
-    if 'timer-pin' in opt:
-        timer_pin = opt['timer-pin']
+    if "timer-pin" in opt:
+        timer_pin = opt["timer-pin"]
     else:
         timer_pin = None
 
-    if 'dummy' in opt:
+    if "dummy" in opt:
 
         enum = dict()
-        if '.json' not in modelfile:
-            with open(modelfile, 'r') as f:
+        if ".json" not in modelfile:
+            with open(modelfile, "r") as f:
                 driver_definition = yaml.safe_load(f)
-            if 'dummygen' in driver_definition and 'enum' in driver_definition['dummygen']:
-                enum = driver_definition['dummygen']['enum']
-
-        if 'class' in opt['dummy']:
-            class_name = opt['dummy']['class']
+            if (
+                "dummygen" in driver_definition
+                and "enum" in driver_definition["dummygen"]
+            ):
+                enum = driver_definition["dummygen"]["enum"]
+
+        if "class" in opt["dummy"]:
+            class_name = opt["dummy"]["class"]
         else:
-            class_name = driver_definition['codegen']['class']
+            class_name = driver_definition["codegen"]["class"]
 
-        run_flags = ['drivers=dummy']
+        run_flags = ["drivers=dummy"]
 
-        repo = Repo('../multipass/build/repo.acp')
+        repo = Repo("../multipass/build/repo.acp")
 
-        if 'accounting' in opt and 'getEnergy' not in map(lambda x: x.name, pta.transitions):
+        if "accounting" in opt and "getEnergy" not in map(
+            lambda x: x.name, pta.transitions
+        ):
             for state in pta.get_state_names():
-                pta.add_transition(state, state, 'getEnergy')
+                pta.add_transition(state, state, "getEnergy")
 
         pta.set_random_energy_model()
 
-        if 'accounting' in opt:
-            if ',' in opt['accounting']:
-                accounting_settings = opt['accounting'].split(',')
+        if "accounting" in opt:
+            if "," in opt["accounting"]:
+                accounting_settings = opt["accounting"].split(",")
                 accounting_name = accounting_settings[0]
-                accounting_options = dict(map(lambda x: x.split('='), accounting_settings[1:]))
-                accounting_object = get_accountingmethod(accounting_name)(class_name, pta, **accounting_options)
+                accounting_options = dict(
+                    map(lambda x: x.split("="), accounting_settings[1:])
+                )
+                accounting_object = get_accountingmethod(accounting_name)(
+                    class_name, pta, **accounting_options
+                )
             else:
-                accounting_object = get_accountingmethod(opt['accounting'])(class_name, pta)
+                accounting_object = get_accountingmethod(opt["accounting"])(
+                    class_name, pta
+                )
         else:
             accounting_object = None
-        drv = MultipassDriver(class_name, pta, repo.class_by_name[class_name], enum=enum, accounting=accounting_object)
-        with open('../multipass/src/driver/dummy.cc', 'w') as f:
+        drv = MultipassDriver(
+            class_name,
+            pta,
+            repo.class_by_name[class_name],
+            enum=enum,
+            accounting=accounting_object,
+        )
+        with open("../multipass/src/driver/dummy.cc", "w") as f:
             f.write(drv.impl)
-        with open('../multipass/include/driver/dummy.h', 'w') as f:
+        with open("../multipass/include/driver/dummy.h", "w") as f:
             f.write(drv.header)
 
-    if '.json' not in modelfile:
-        with open(modelfile, 'r') as f:
+    if ".json" not in modelfile:
+        with open(modelfile, "r") as f:
             driver_definition = yaml.safe_load(f)
-        if 'codegen' in driver_definition and 'flags' in driver_definition['codegen']:
+        if "codegen" in driver_definition and "flags" in driver_definition["codegen"]:
             if run_flags is None:
-                run_flags = driver_definition['codegen']['flags']
+                run_flags = driver_definition["codegen"]["flags"]
     if run_flags is None:
-        run_flags = opt['run'].split()
-
-    runs = list(pta.dfs(opt['depth'], with_arguments=True, with_parameters=True, trace_filter=opt['trace-filter']))
+        run_flags = opt["run"].split()
+
+    runs = list(
+        pta.dfs(
+            opt["depth"],
+            with_arguments=True,
+            with_parameters=True,
+            trace_filter=opt["trace-filter"],
+        )
+    )
 
     num_transitions = len(runs)
 
     if len(runs) == 0:
-        print('DFS returned no traces -- perhaps your trace-filter is too restrictive?', file=sys.stderr)
+        print(
+            "DFS returned no traces -- perhaps your trace-filter is too restrictive?",
+            file=sys.stderr,
+        )
         sys.exit(1)
 
     need_return_values = False
@@ -479,45 +621,78 @@ if __name__ == '__main__':
     #    # getEnergy() returns energy data. Log it.
     #    need_return_values = True
 
-    if 'mimosa' in opt:
-        harness = TransitionHarness(gpio_pin=timer_pin, pta=pta, log_return_values=need_return_values, repeat=1, post_transition_delay_us=20)
-    elif 'energytrace' in opt:
-        harness = OnboardTimerHarness(gpio_pin=timer_pin, gpio_mode='bar', pta=pta, counter_limits=runner.get_counter_limits_us(opt['arch']), log_return_values=need_return_values, repeat=1)
-    elif 'timing' in opt:
-        harness = OnboardTimerHarness(gpio_pin=timer_pin, pta=pta, counter_limits=runner.get_counter_limits_us(opt['arch']), log_return_values=need_return_values, repeat=opt['repeat'])
+    if "mimosa" in opt:
+        harness = TransitionHarness(
+            gpio_pin=timer_pin,
+            pta=pta,
+            log_return_values=need_return_values,
+            repeat=1,
+            post_transition_delay_us=20,
+        )
+    elif "energytrace" in opt:
+        harness = OnboardTimerHarness(
+            gpio_pin=timer_pin,
+            gpio_mode="bar",
+            pta=pta,
+            counter_limits=runner.get_counter_limits_us(opt["arch"]),
+            log_return_values=need_return_values,
+            repeat=1,
+        )
+    elif "timing" in opt:
+        harness = OnboardTimerHarness(
+            gpio_pin=timer_pin,
+            pta=pta,
+            counter_limits=runner.get_counter_limits_us(opt["arch"]),
+            log_return_values=need_return_values,
+            repeat=opt["repeat"],
+        )
 
     if len(args) > 1:
-        results = run_benchmark(args[1], pta, runs, opt['arch'], opt['app'], run_flags, harness, opt['sleep'], opt['repeat'], runs_total=len(runs), dummy='dummy' in opt)
+        results = run_benchmark(
+            args[1],
+            pta,
+            runs,
+            opt["arch"],
+            opt["app"],
+            run_flags,
+            harness,
+            opt["sleep"],
+            opt["repeat"],
+            runs_total=len(runs),
+            dummy="dummy" in opt,
+        )
         json_out = {
-            'opt': opt,
-            'pta': pta.to_json(),
-            'traces': list(map(lambda x: x[1].traces, results)),
-            'raw_output': list(map(lambda x: x[2].get_lines(), results)),
-            'files': list(map(lambda x: x[3], results)),
-            'configs': list(map(lambda x: x[2].get_config(), results)),
+            "opt": opt,
+            "pta": pta.to_json(),
+            "traces": list(map(lambda x: x[1].traces, results)),
+            "raw_output": list(map(lambda x: x[2].get_lines(), results)),
+            "files": list(map(lambda x: x[3], results)),
+            "configs": list(map(lambda x: x[2].get_config(), results)),
         }
-        extra_files = flatten(json_out['files'])
-        if 'instance' in pta.codegen:
-            output_prefix = opt['data'] + time.strftime('/%Y%m%d-%H%M%S-') + pta.codegen['instance']
+        extra_files = flatten(json_out["files"])
+        if "instance" in pta.codegen:
+            output_prefix = (
+                opt["data"] + time.strftime("/%Y%m%d-%H%M%S-") + pta.codegen["instance"]
+            )
         else:
-            output_prefix = opt['data'] + time.strftime('/%Y%m%d-%H%M%S-ptalog')
+            output_prefix = opt["data"] + time.strftime("/%Y%m%d-%H%M%S-ptalog")
         if len(extra_files):
-            with open('ptalog.json', 'w') as f:
+            with open("ptalog.json", "w") as f:
                 json.dump(json_out, f)
-            with tarfile.open('{}.tar'.format(output_prefix), 'w') as tar:
-                tar.add('ptalog.json')
+            with tarfile.open("{}.tar".format(output_prefix), "w") as tar:
+                tar.add("ptalog.json")
                 for extra_file in extra_files:
                     tar.add(extra_file)
-            print(' --> {}.tar'.format(output_prefix))
-            os.remove('ptalog.json')
+            print(" --> {}.tar".format(output_prefix))
+            os.remove("ptalog.json")
             for extra_file in extra_files:
                 os.remove(extra_file)
         else:
-            with open('{}.json'.format(output_prefix), 'w') as f:
+            with open("{}.json".format(output_prefix), "w") as f:
                 json.dump(json_out, f)
-            print(' --> {}.json'.format(output_prefix))
+            print(" --> {}.json".format(output_prefix))
     else:
-        outbuf = benchmark_from_runs(pta, runs, harness, repeat=opt['repeat'])
+        outbuf = benchmark_from_runs(pta, runs, harness, repeat=opt["repeat"])
         print(outbuf.getvalue())
 
     sys.exit(0)
diff --git a/bin/generate-dummy-class.py b/bin/generate-dummy-class.py
index d86339b..9cfa7fb 100755
--- a/bin/generate-dummy-class.py
+++ b/bin/generate-dummy-class.py
@@ -5,19 +5,19 @@ from dfatool.codegen import MultipassDriver
 from dfatool.automata import PTA
 import yaml
 
-with open('../multipass/model/driver/nrf24l01.dfa', 'r') as f:
+with open("../multipass/model/driver/nrf24l01.dfa", "r") as f:
     driver_definition = yaml.safe_load(f)
     pta = PTA.from_yaml(driver_definition)
-repo = Repo('../multipass/build/repo.acp')
+repo = Repo("../multipass/build/repo.acp")
 
 enum = dict()
 
-if 'dummygen' in driver_definition and 'enum' in driver_definition['dummygen']:
-    enum = driver_definition['dummygen']['enum']
+if "dummygen" in driver_definition and "enum" in driver_definition["dummygen"]:
+    enum = driver_definition["dummygen"]["enum"]
 
-drv = MultipassDriver('Nrf24l01', pta, repo.class_by_name['Nrf24l01'], enum=enum)
+drv = MultipassDriver("Nrf24l01", pta, repo.class_by_name["Nrf24l01"], enum=enum)
 
-with open('../multipass/src/driver/dummy.cc', 'w') as f:
+with open("../multipass/src/driver/dummy.cc", "w") as f:
     f.write(drv.impl)
-with open('../multipass/include/driver/dummy.h', 'w') as f:
+with open("../multipass/include/driver/dummy.h", "w") as f:
     f.write(drv.header)
diff --git a/bin/gptest.py b/bin/gptest.py
index 869328e..82b4575 100755
--- a/bin/gptest.py
+++ b/bin/gptest.py
@@ -2,10 +2,16 @@
 
 import sys
 import numpy as np
-from dfatool.dfatool import PTAModel, RawData, regression_measures, pta_trace_to_aggregate
+from dfatool.dfatool import (
+    PTAModel,
+    RawData,
+    regression_measures,
+    pta_trace_to_aggregate,
+)
 from gplearn.genetic import SymbolicRegressor
 from multiprocessing import Pool
 
+
 def splitidx_srs(length):
     shuffled = np.random.permutation(np.arange(length))
     border = int(length * float(2) / 3)
@@ -13,16 +19,17 @@ def splitidx_srs(length):
     validation = shuffled[border:]
     return (training, validation)
 
+
 def _gp_fit(arg):
     param = arg[0]
     X = arg[1]
     Y = arg[2]
     est_gp = SymbolicRegressor(
-        population_size = param[0],
-        generations = 450,
-        parsimony_coefficient = param[1],
-        function_set = param[2].split(' '),
-        const_range = (-param[3], param[3])
+        population_size=param[0],
+        generations=450,
+        parsimony_coefficient=param[1],
+        function_set=param[2].split(" "),
+        const_range=(-param[3], param[3]),
     )
 
     training, validation = splitidx_srs(len(Y))
@@ -33,22 +40,27 @@ def _gp_fit(arg):
 
     try:
         est_gp.fit(X_train, Y_train)
-        return (param, str(est_gp._program), est_gp._program.raw_fitness_, regression_measures(est_gp.predict(X_validation), Y_validation))
+        return (
+            param,
+            str(est_gp._program),
+            est_gp._program.raw_fitness_,
+            regression_measures(est_gp.predict(X_validation), Y_validation),
+        )
     except Exception as e:
-        return (param, 'Exception: {}'.format(str(e)), 999999999)
+        return (param, "Exception: {}".format(str(e)), 999999999)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     population_size = [100, 500, 1000, 2000, 5000, 10000]
     parsimony_coefficient = [0.1, 0.5, 0.1, 1]
-    function_set = ['add mul', 'add mul sub div', 'add mul sub div sqrt log inv']
+    function_set = ["add mul", "add mul sub div", "add mul sub div sqrt log inv"]
     const_lim = [100000, 50000, 10000, 1000, 500, 10, 1]
     filenames = sys.argv[4:]
     raw_data = RawData(filenames)
 
     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, traces = preprocessed_data)
+    model = PTAModel(by_name, parameters, arg_count, traces=preprocessed_data)
 
     by_param = model.by_param
 
@@ -61,14 +73,12 @@ if __name__ == '__main__':
     X = [[] for i in range(dimension)]
     Y = []
 
-
     for key, val in by_param.items():
         if key[0] == state_or_tran and len(key[1]) == dimension:
             Y.extend(val[model_attribute])
             for i in range(dimension):
                 X[i].extend([float(key[1][i])] * len(val[model_attribute]))
 
-
     X = np.array(X)
     Y = np.array(Y)
 
@@ -85,4 +95,4 @@ if __name__ == '__main__':
         results = pool.map(_gp_fit, paramqueue)
 
     for res in sorted(results, key=lambda r: r[2]):
-        print('{} {:.0f} ({:.0f})\n{}'.format(res[0], res[3]['mae'], res[2], res[1]))
+        print("{} {:.0f} ({:.0f})\n{}".format(res[0], res[3]["mae"], res[2], res[1]))
diff --git a/bin/mim-vs-keysight.py b/bin/mim-vs-keysight.py
index d1ff4e6..c214f2f 100755
--- a/bin/mim-vs-keysight.py
+++ b/bin/mim-vs-keysight.py
@@ -18,6 +18,7 @@ ks = KeysightCSV()
 charges, triggers = mim.load_data(mimfile)
 timestamps, currents = ks.load_data(csvfile)
 
+
 def calfunc330(charge):
     if charge < 140.210488888889:
         return 0
@@ -26,6 +27,7 @@ def calfunc330(charge):
     else:
         return float(charge) * 0.0897304193584184 + -47.2437278033012 + 36.358862
 
+
 def calfunc82(charge):
     if charge < 126.993600:
         return 0
@@ -43,24 +45,25 @@ def calfunc33(charge):
     else:
         return charge * 0.884357 + -112.500777 + 36.358862
 
+
 calfuncs = {
-    33 : calfunc33,
-    82 : calfunc82,
-    330 : calfunc330,
+    33: calfunc33,
+    82: calfunc82,
+    330: calfunc330,
 }
 
 vcalfunc = np.vectorize(calfuncs[int(shunt)], otypes=[np.float64])
 
-#plt.plot(np.arange(0, 1000, 0.01), vcalfunc(np.arange(0, 1000, 0.01)))
-#plt.xlabel('Rohdatenwert')
-#plt.ylabel('Strom [µA]')
-#plt.show()
-#sys.exit(0)
+# plt.plot(np.arange(0, 1000, 0.01), vcalfunc(np.arange(0, 1000, 0.01)))
+# plt.xlabel('Rohdatenwert')
+# plt.ylabel('Strom [µA]')
+# plt.show()
+# sys.exit(0)
 
-mim_x = np.arange(len(charges)-199) * 1e-5
+mim_x = np.arange(len(charges) - 199) * 1e-5
 mim_y = running_mean(mim.charge_to_current_nocal(charges), 200) * 1e-6
 cal_y = running_mean(vcalfunc(charges), 200) * 1e-6
-ks_x = timestamps[:len(timestamps)-9]
+ks_x = timestamps[: len(timestamps) - 9]
 ks_y = running_mean(currents, 10)
 
 # look for synchronization opportunity in first 5 seconds
@@ -97,12 +100,12 @@ plt.plot([mim_x[mim_center]], [mim_y[mim_center]], "yo")
 plt.plot([mim_x[mim_start]], [mim_y[mim_start]], "yo")
 plt.plot([mim_x[mim_end]], [mim_y[mim_end]], "yo")
 #
-mimhandle, = plt.plot(mim_x, mim_y, "r-", label='MIMOSA')
-#calhandle, = plt.plot(mim_x, cal_y, "g-", label='MIMOSA (autocal)')
-kshandle, = plt.plot(ks_x, ks_y, "b-", label='Keysight')
-#plt.legend(handles=[mimhandle, calhandle, kshandle])
-plt.xlabel('Zeit [s]')
-plt.ylabel('Strom [A]')
+(mimhandle,) = plt.plot(mim_x, mim_y, "r-", label="MIMOSA")
+# calhandle, = plt.plot(mim_x, cal_y, "g-", label='MIMOSA (autocal)')
+(kshandle,) = plt.plot(ks_x, ks_y, "b-", label="Keysight")
+# plt.legend(handles=[mimhandle, calhandle, kshandle])
+plt.xlabel("Zeit [s]")
+plt.ylabel("Strom [A]")
 plt.grid(True)
 
 ks_steps_up = []
@@ -112,11 +115,21 @@ mim_steps_down = []
 
 skip = 0
 for i, gradient in enumerate(np.gradient(ks_y, 10000)):
-    if gradient > 0.5e-9 and i - skip > 200 and ks_x[i] < mim_x[mim_center] and ks_x[i] > 5:
+    if (
+        gradient > 0.5e-9
+        and i - skip > 200
+        and ks_x[i] < mim_x[mim_center]
+        and ks_x[i] > 5
+    ):
         plt.plot([ks_x[i]], [ks_y[i]], "go")
         ks_steps_up.append(i)
         skip = i
-    elif gradient < -0.5e-9 and i - skip > 200 and ks_x[i] > mim_x[mim_center] and ks_x[i] < mim_x[mim_end]:
+    elif (
+        gradient < -0.5e-9
+        and i - skip > 200
+        and ks_x[i] > mim_x[mim_center]
+        and ks_x[i] < mim_x[mim_end]
+    ):
         plt.plot([ks_x[i]], [ks_y[i]], "g*")
         ks_steps_down.append(i)
         skip = i
@@ -141,21 +154,31 @@ cal_values = []
 ks_values = []
 
 for i in range(1, len(ks_steps_up)):
-    mim_values.append(np.mean(mim_y[mim_steps_up[i-1]:mim_steps_up[i]]))
-    cal_values.append(np.mean(cal_y[mim_steps_up[i-1]:mim_steps_up[i]]))
-    ks_values.append(np.mean(ks_y[ks_steps_up[i-1]:ks_steps_up[i]]))
-    print("step %d avg %5.3f vs %5.3f vs %5.3f mA" %
-        (i, np.mean(ks_y[ks_steps_up[i-1]:ks_steps_up[i]]) * 1e3,
-            np.mean(mim_y[mim_steps_up[i-1]:mim_steps_up[i]]) * 1e3,
-            np.mean(cal_y[mim_steps_up[i-1]:mim_steps_up[i]]) * 1e3))
+    mim_values.append(np.mean(mim_y[mim_steps_up[i - 1] : mim_steps_up[i]]))
+    cal_values.append(np.mean(cal_y[mim_steps_up[i - 1] : mim_steps_up[i]]))
+    ks_values.append(np.mean(ks_y[ks_steps_up[i - 1] : ks_steps_up[i]]))
+    print(
+        "step %d avg %5.3f vs %5.3f vs %5.3f mA"
+        % (
+            i,
+            np.mean(ks_y[ks_steps_up[i - 1] : ks_steps_up[i]]) * 1e3,
+            np.mean(mim_y[mim_steps_up[i - 1] : mim_steps_up[i]]) * 1e3,
+            np.mean(cal_y[mim_steps_up[i - 1] : mim_steps_up[i]]) * 1e3,
+        )
+    )
 for i in range(1, len(ks_steps_down)):
-    mim_values.append(np.mean(mim_y[mim_steps_down[i-1]:mim_steps_down[i]]))
-    cal_values.append(np.mean(cal_y[mim_steps_down[i-1]:mim_steps_down[i]]))
-    ks_values.append(np.mean(ks_y[ks_steps_down[i-1]:ks_steps_down[i]]))
-    print("step %d avg %5.3f vs %5.3f vs %5.3f mA" %
-        (i, np.mean(ks_y[ks_steps_down[i-1]:ks_steps_down[i]]) * 1e3,
-            np.mean(mim_y[mim_steps_down[i-1]:mim_steps_down[i]]) * 1e3,
-            np.mean(cal_y[mim_steps_down[i-1]:mim_steps_down[i]]) * 1e3))
+    mim_values.append(np.mean(mim_y[mim_steps_down[i - 1] : mim_steps_down[i]]))
+    cal_values.append(np.mean(cal_y[mim_steps_down[i - 1] : mim_steps_down[i]]))
+    ks_values.append(np.mean(ks_y[ks_steps_down[i - 1] : ks_steps_down[i]]))
+    print(
+        "step %d avg %5.3f vs %5.3f vs %5.3f mA"
+        % (
+            i,
+            np.mean(ks_y[ks_steps_down[i - 1] : ks_steps_down[i]]) * 1e3,
+            np.mean(mim_y[mim_steps_down[i - 1] : mim_steps_down[i]]) * 1e3,
+            np.mean(cal_y[mim_steps_down[i - 1] : mim_steps_down[i]]) * 1e3,
+        )
+    )
 
 mim_values = np.array(mim_values)
 cal_values = np.array(cal_values)
@@ -163,60 +186,114 @@ ks_values = np.array(ks_values)
 
 plt.show()
 
-plt.hist(ks_y[ks_steps_up[48]:ks_steps_up[49]] * 1e3, 100, normed=0, facecolor='blue', alpha=0.8)
-plt.xlabel('mA Keysight')
-plt.ylabel('#')
+plt.hist(
+    ks_y[ks_steps_up[48] : ks_steps_up[49]] * 1e3,
+    100,
+    normed=0,
+    facecolor="blue",
+    alpha=0.8,
+)
+plt.xlabel("mA Keysight")
+plt.ylabel("#")
 plt.grid(True)
 plt.show()
-plt.hist(mim_y[mim_steps_up[48]:mim_steps_up[49]] * 1e3, 100, normed=0, facecolor='blue', alpha=0.8)
-plt.xlabel('mA MimosaGUI')
-plt.ylabel('#')
+plt.hist(
+    mim_y[mim_steps_up[48] : mim_steps_up[49]] * 1e3,
+    100,
+    normed=0,
+    facecolor="blue",
+    alpha=0.8,
+)
+plt.xlabel("mA MimosaGUI")
+plt.ylabel("#")
 plt.grid(True)
 plt.show()
 
-mimhandle, = plt.plot(ks_values * 1e3, mim_values * 1e3, "ro", label='Unkalibriert', markersize=4)
-calhandle, = plt.plot(ks_values * 1e3, cal_values * 1e3, "bs", label='Kalibriert', markersize=4)
+(mimhandle,) = plt.plot(
+    ks_values * 1e3, mim_values * 1e3, "ro", label="Unkalibriert", markersize=4
+)
+(calhandle,) = plt.plot(
+    ks_values * 1e3, cal_values * 1e3, "bs", label="Kalibriert", markersize=4
+)
 plt.legend(handles=[mimhandle, calhandle])
-plt.xlabel('mA Keysight')
-plt.ylabel('mA MIMOSA')
+plt.xlabel("mA Keysight")
+plt.ylabel("mA MIMOSA")
 plt.grid(True)
 plt.show()
 
-mimhandle, = plt.plot(ks_values * 1e3, (mim_values - ks_values) * 1e3, "ro", label='Unkalibriert', markersize=4)
-calhandle, = plt.plot(ks_values * 1e3, (cal_values - ks_values) * 1e3, "bs", label='Kalibriert', markersize=4)
+(mimhandle,) = plt.plot(
+    ks_values * 1e3,
+    (mim_values - ks_values) * 1e3,
+    "ro",
+    label="Unkalibriert",
+    markersize=4,
+)
+(calhandle,) = plt.plot(
+    ks_values * 1e3,
+    (cal_values - ks_values) * 1e3,
+    "bs",
+    label="Kalibriert",
+    markersize=4,
+)
 plt.legend(handles=[mimhandle, calhandle])
-plt.xlabel('Sollstrom [mA]')
-plt.ylabel('Messfehler MIMOSA [mA]')
+plt.xlabel("Sollstrom [mA]")
+plt.ylabel("Messfehler MIMOSA [mA]")
 plt.grid(True)
 plt.show()
 
-mimhandle, = plt.plot(ks_values * 1e3, (mim_values - ks_values) * 1e3, "r--", label='Unkalibriert')
-calhandle, = plt.plot(ks_values * 1e3, (cal_values - ks_values) * 1e3, "b-", label='Kalibriert')
+(mimhandle,) = plt.plot(
+    ks_values * 1e3, (mim_values - ks_values) * 1e3, "r--", label="Unkalibriert"
+)
+(calhandle,) = plt.plot(
+    ks_values * 1e3, (cal_values - ks_values) * 1e3, "b-", label="Kalibriert"
+)
 plt.legend(handles=[mimhandle, calhandle])
-plt.xlabel('Sollstrom [mA]')
-plt.ylabel('Messfehler MIMOSA [mA]')
+plt.xlabel("Sollstrom [mA]")
+plt.ylabel("Messfehler MIMOSA [mA]")
 plt.grid(True)
 plt.show()
 
-mimhandle, = plt.plot(ks_values * 1e3, (mim_values - ks_values) / ks_values * 100, "ro", label='Unkalibriert', markersize=4)
-calhandle, = plt.plot(ks_values * 1e3, (cal_values - ks_values) / ks_values * 100, "bs", label='Kalibriert', markersize=4)
+(mimhandle,) = plt.plot(
+    ks_values * 1e3,
+    (mim_values - ks_values) / ks_values * 100,
+    "ro",
+    label="Unkalibriert",
+    markersize=4,
+)
+(calhandle,) = plt.plot(
+    ks_values * 1e3,
+    (cal_values - ks_values) / ks_values * 100,
+    "bs",
+    label="Kalibriert",
+    markersize=4,
+)
 plt.legend(handles=[mimhandle, calhandle])
-plt.xlabel('Sollstrom [mA]')
-plt.ylabel('Messfehler MIMOSA [%]')
+plt.xlabel("Sollstrom [mA]")
+plt.ylabel("Messfehler MIMOSA [%]")
 plt.grid(True)
 plt.show()
 
-mimhandle, = plt.plot(ks_values * 1e3, (mim_values - ks_values) / ks_values * 100, "r--", label='Unkalibriert')
-calhandle, = plt.plot(ks_values * 1e3, (cal_values - ks_values) / ks_values * 100, "b-", label='Kalibriert')
+(mimhandle,) = plt.plot(
+    ks_values * 1e3,
+    (mim_values - ks_values) / ks_values * 100,
+    "r--",
+    label="Unkalibriert",
+)
+(calhandle,) = plt.plot(
+    ks_values * 1e3,
+    (cal_values - ks_values) / ks_values * 100,
+    "b-",
+    label="Kalibriert",
+)
 plt.legend(handles=[mimhandle, calhandle])
-plt.xlabel('Sollstrom [mA]')
-plt.ylabel('Messfehler MIMOSA [%]')
+plt.xlabel("Sollstrom [mA]")
+plt.ylabel("Messfehler MIMOSA [%]")
 plt.grid(True)
 plt.show()
 
-#mimhandle, = plt.plot(mim_x, np.gradient(mim_y, 10000), "r-", label='MIMOSA')
-#kshandle, = plt.plot(ks_x, np.gradient(ks_y, 10000), "b-", label='Keysight')
-#plt.legend(handles=[mimhandle, kshandle])
-#plt.xlabel('Zeit [s]')
-#plt.ylabel('Strom [A]')
-#plt.show()
+# mimhandle, = plt.plot(mim_x, np.gradient(mim_y, 10000), "r-", label='MIMOSA')
+# kshandle, = plt.plot(ks_x, np.gradient(ks_y, 10000), "b-", label='Keysight')
+# plt.legend(handles=[mimhandle, kshandle])
+# plt.xlabel('Zeit [s]')
+# plt.ylabel('Strom [A]')
+# plt.show()
diff --git a/bin/test_corrcoef.py b/bin/test_corrcoef.py
index 835e086..e389d01 100755
--- a/bin/test_corrcoef.py
+++ b/bin/test_corrcoef.py
@@ -9,76 +9,105 @@ from dfatool.dfatool import gplearn_to_function
 
 opts = {}
 
+
 def print_model_quality(results):
     for state_or_tran in results.keys():
         print()
         for key, result in results[state_or_tran].items():
-            if 'smape' in result:
-                print('{:20s} {:15s} {:.2f}% / {:.0f}'.format(
-                    state_or_tran, key, result['smape'], result['mae']))
+            if "smape" in result:
+                print(
+                    "{:20s} {:15s} {:.2f}% / {:.0f}".format(
+                        state_or_tran, key, result["smape"], result["mae"]
+                    )
+                )
             else:
-                print('{:20s} {:15s} {:.0f}'.format(
-                    state_or_tran, key, result['mae']))
+                print("{:20s} {:15s} {:.0f}".format(state_or_tran, key, result["mae"]))
+
 
 def format_quality_measures(result):
-    if 'smape' in result:
-        return '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
+    if "smape" in result:
+        return "{:6.2f}% / {:9.0f}".format(result["smape"], result["mae"])
     else:
-        return '{:6}    {:9.0f}'.format('', result['mae'])
+        return "{:6}    {:9.0f}".format("", result["mae"])
+
 
 def model_quality_table(result_lists, info_list):
-    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)
+    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)
             for i, results in enumerate(result_lists):
                 info = info_list[i]
-                buf += '  |||  '
+                buf += "  |||  "
                 if info == None or info(state_or_tran, key):
-                    result = results['by_name'][state_or_tran][key]
+                    result = results["by_name"][state_or_tran][key]
                     buf += format_quality_measures(result)
                 else:
-                    buf += '{:6}----{:9}'.format('', '')
+                    buf += "{:6}----{:9}".format("", "")
             print(buf)
 
+
 def model_summary_table(result_list):
-    buf = 'transition duration'
+    buf = "transition duration"
     for results in result_list:
         if len(buf):
-            buf += '  |||  '
-        buf += format_quality_measures(results['duration_by_trace'])
+            buf += "  |||  "
+        buf += format_quality_measures(results["duration_by_trace"])
     print(buf)
-    buf = 'total energy       '
+    buf = "total energy       "
     for results in result_list:
         if len(buf):
-            buf += '  |||  '
-        buf += format_quality_measures(results['energy_by_trace'])
+            buf += "  |||  "
+        buf += format_quality_measures(results["energy_by_trace"])
     print(buf)
-    buf = 'transition timeout '
+    buf = "transition timeout "
     for results in result_list:
         if len(buf):
-            buf += '  |||  '
-        buf += format_quality_measures(results['timeout_by_trace'])
+            buf += "  |||  "
+        buf += format_quality_measures(results["timeout_by_trace"])
     print(buf)
 
 
 def print_text_model_data(model, pm, pq, lm, lq, am, ai, aq):
-    print('')
-    print(r'key attribute $1 - \frac{\sigma_X}{...}$')
+    print("")
+    print(r"key attribute $1 - \frac{\sigma_X}{...}$")
     for state_or_tran in model.by_name.keys():
-        for attribute in model.by_name[state_or_tran]['attributes']:
-            print('{} {} {:.8f}'.format(state_or_tran, attribute, model.generic_param_dependence_ratio(state_or_tran, attribute)))
-
-    print('')
-    print(r'key attribute parameter $1 - \frac{...}{...}$')
+        for attribute in model.by_name[state_or_tran]["attributes"]:
+            print(
+                "{} {} {:.8f}".format(
+                    state_or_tran,
+                    attribute,
+                    model.generic_param_dependence_ratio(state_or_tran, attribute),
+                )
+            )
+
+    print("")
+    print(r"key attribute parameter $1 - \frac{...}{...}$")
     for state_or_tran in model.by_name.keys():
-        for attribute in model.by_name[state_or_tran]['attributes']:
+        for attribute in model.by_name[state_or_tran]["attributes"]:
             for param in model.parameters():
-                print('{} {} {} {:.8f}'.format(state_or_tran, attribute, param, model.param_dependence_ratio(state_or_tran, attribute, param)))
+                print(
+                    "{} {} {} {:.8f}".format(
+                        state_or_tran,
+                        attribute,
+                        param,
+                        model.param_dependence_ratio(state_or_tran, attribute, param),
+                    )
+                )
             if state_or_tran in model._num_args:
                 for arg_index in range(model._num_args[state_or_tran]):
-                    print('{} {} {:d} {:.8f}'.format(state_or_tran, attribute, arg_index, model.arg_dependence_ratio(state_or_tran, attribute, arg_index)))
+                    print(
+                        "{} {} {:d} {:.8f}".format(
+                            state_or_tran,
+                            attribute,
+                            arg_index,
+                            model.arg_dependence_ratio(
+                                state_or_tran, attribute, arg_index
+                            ),
+                        )
+                    )
+
 
-if __name__ == '__main__':
+if __name__ == "__main__":
 
     ignored_trace_indexes = None
     discard_outliers = None
@@ -89,36 +118,40 @@ if __name__ == '__main__':
 
     try:
         optspec = (
-            'plot-unparam= plot-param= show-models= show-quality= '
-            'ignored-trace-indexes= discard-outliers= function-override= '
-            'with-safe-functions'
+            "plot-unparam= plot-param= show-models= show-quality= "
+            "ignored-trace-indexes= discard-outliers= function-override= "
+            "with-safe-functions"
         )
-        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)
+            optname = re.sub(r"^--", "", option)
             opts[optname] = parameter
 
-            if 'ignored-trace-indexes' in opts:
-                ignored_trace_indexes = list(map(int, opts['ignored-trace-indexes'].split(',')))
+            if "ignored-trace-indexes" in opts:
+                ignored_trace_indexes = list(
+                    map(int, opts["ignored-trace-indexes"].split(","))
+                )
                 if 0 in ignored_trace_indexes:
-                    print('[E] arguments to --ignored-trace-indexes start from 1')
+                    print("[E] arguments to --ignored-trace-indexes start from 1")
 
-            if 'discard-outliers' in opts:
-                discard_outliers = float(opts['discard-outliers'])
+            if "discard-outliers" in opts:
+                discard_outliers = float(opts["discard-outliers"])
 
-            if 'function-override' in opts:
-                for function_desc in opts['function-override'].split(';'):
-                    state_or_tran, attribute, *function_str = function_desc.split(' ')
-                    function_override[(state_or_tran, attribute)] = ' '.join(function_str)
+            if "function-override" in opts:
+                for function_desc in opts["function-override"].split(";"):
+                    state_or_tran, attribute, *function_str = function_desc.split(" ")
+                    function_override[(state_or_tran, attribute)] = " ".join(
+                        function_str
+                    )
 
-            if 'show-models' in opts:
-                show_models = opts['show-models'].split(',')
+            if "show-models" in opts:
+                show_models = opts["show-models"].split(",")
 
-            if 'show-quality' in opts:
-                show_quality = opts['show-quality'].split(',')
+            if "show-quality" in opts:
+                show_quality = opts["show-quality"].split(",")
 
-            if 'with-safe-functions' in opts:
+            if "with-safe-functions" in opts:
                 safe_functions_enabled = True
 
     except getopt.GetoptError as err:
@@ -131,105 +164,154 @@ if __name__ == '__main__':
     by_name, parameters, arg_count = pta_trace_to_aggregate(preprocessed_data)
 
     ref_model = PTAModel(
-        by_name, parameters, arg_count,
-        traces = preprocessed_data,
-        ignore_trace_indexes = ignored_trace_indexes,
-        discard_outliers = discard_outliers,
-        function_override = function_override,
-        use_corrcoef = False)
+        by_name,
+        parameters,
+        arg_count,
+        traces=preprocessed_data,
+        ignore_trace_indexes=ignored_trace_indexes,
+        discard_outliers=discard_outliers,
+        function_override=function_override,
+        use_corrcoef=False,
+    )
     model = PTAModel(
-        by_name, parameters, arg_count,
-        traces = preprocessed_data,
-        ignore_trace_indexes = ignored_trace_indexes,
-        discard_outliers = discard_outliers,
-        function_override = function_override,
-        use_corrcoef = True)
-
-
-    if 'plot-unparam' in opts:
-        for kv in opts['plot-unparam'].split(';'):
-            state_or_trans, attribute = kv.split(' ')
+        by_name,
+        parameters,
+        arg_count,
+        traces=preprocessed_data,
+        ignore_trace_indexes=ignored_trace_indexes,
+        discard_outliers=discard_outliers,
+        function_override=function_override,
+        use_corrcoef=True,
+    )
+
+    if "plot-unparam" in opts:
+        for kv in opts["plot-unparam"].split(";"):
+            state_or_trans, attribute = kv.split(" ")
             plotter.plot_y(model.by_name[state_or_trans][attribute])
 
     if len(show_models):
-        print('--- simple static model ---')
+        print("--- simple static model ---")
     static_model = model.get_static()
     ref_static_model = ref_model.get_static()
-    if 'static' in show_models or 'all' in show_models:
+    if "static" in show_models or "all" in show_models:
         for state in model.states():
-            print('{:10s}: {:.0f} µW  ({:.2f})'.format(
-                state,
-                static_model(state, 'power'),
-                model.generic_param_dependence_ratio(state, 'power')))
+            print(
+                "{:10s}: {:.0f} µW  ({:.2f})".format(
+                    state,
+                    static_model(state, "power"),
+                    model.generic_param_dependence_ratio(state, "power"),
+                )
+            )
             for param in model.parameters():
-                print('{:10s}  dependence on {:15s}: {:.2f}'.format(
-                    '',
-                    param,
-                    model.param_dependence_ratio(state, 'power', param)))
+                print(
+                    "{:10s}  dependence on {:15s}: {:.2f}".format(
+                        "", param, model.param_dependence_ratio(state, "power", param)
+                    )
+                )
         for trans in model.transitions():
-            print('{:10s}: {:.0f} / {:.0f} / {:.0f} pJ  ({:.2f} / {:.2f} / {:.2f})'.format(
-                trans, static_model(trans, 'energy'),
-                static_model(trans, 'rel_energy_prev'),
-                static_model(trans, 'rel_energy_next'),
-                model.generic_param_dependence_ratio(trans, 'energy'),
-                model.generic_param_dependence_ratio(trans, 'rel_energy_prev'),
-                model.generic_param_dependence_ratio(trans, 'rel_energy_next')))
-            print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration')))
+            print(
+                "{:10s}: {:.0f} / {:.0f} / {:.0f} pJ  ({:.2f} / {:.2f} / {:.2f})".format(
+                    trans,
+                    static_model(trans, "energy"),
+                    static_model(trans, "rel_energy_prev"),
+                    static_model(trans, "rel_energy_next"),
+                    model.generic_param_dependence_ratio(trans, "energy"),
+                    model.generic_param_dependence_ratio(trans, "rel_energy_prev"),
+                    model.generic_param_dependence_ratio(trans, "rel_energy_next"),
+                )
+            )
+            print("{:10s}: {:.0f} µs".format(trans, static_model(trans, "duration")))
     static_quality = model.assess(static_model)
     ref_static_quality = ref_model.assess(ref_static_model)
 
     if len(show_models):
-        print('--- LUT ---')
+        print("--- LUT ---")
     lut_model = model.get_param_lut()
     lut_quality = model.assess(lut_model)
     ref_lut_model = ref_model.get_param_lut()
     ref_lut_quality = ref_model.assess(ref_lut_model)
 
     if len(show_models):
-        print('--- param model ---')
-    param_model, param_info = model.get_fitted(safe_functions_enabled = safe_functions_enabled)
-    ref_param_model, ref_param_info = ref_model.get_fitted(safe_functions_enabled = safe_functions_enabled)
-    print('')
-    print('')
-    print('state_or_trans attribute param stddev_ratio corrcoef')
+        print("--- param model ---")
+    param_model, param_info = model.get_fitted(
+        safe_functions_enabled=safe_functions_enabled
+    )
+    ref_param_model, ref_param_info = ref_model.get_fitted(
+        safe_functions_enabled=safe_functions_enabled
+    )
+    print("")
+    print("")
+    print("state_or_trans attribute param stddev_ratio corrcoef")
     for state in model.states():
         for attribute in model.attributes(state):
             for param in model.parameters():
-                print('{:10s} {:10s} {:10s} {:f} {:f}'.format(state, attribute, param,
-                    ref_model.param_dependence_ratio(state, attribute, param),
-                    model.param_dependence_ratio(state, attribute, param)))
+                print(
+                    "{:10s} {:10s} {:10s} {:f} {:f}".format(
+                        state,
+                        attribute,
+                        param,
+                        ref_model.param_dependence_ratio(state, attribute, param),
+                        model.param_dependence_ratio(state, attribute, param),
+                    )
+                )
     for trans in model.transitions():
         for attribute in model.attributes(trans):
             for param in model.parameters():
-                print('{:10s} {:10s} {:10s} {:f} {:f}'.format(trans, attribute, param,
-                    ref_model.param_dependence_ratio(trans, attribute, param),
-                    model.param_dependence_ratio(trans, attribute, param)))
-    print('')
-    print('')
+                print(
+                    "{:10s} {:10s} {:10s} {:f} {:f}".format(
+                        trans,
+                        attribute,
+                        param,
+                        ref_model.param_dependence_ratio(trans, attribute, param),
+                        model.param_dependence_ratio(trans, attribute, param),
+                    )
+                )
+    print("")
+    print("")
     analytic_quality = model.assess(param_model)
     ref_analytic_quality = ref_model.assess(ref_param_model)
 
-    if 'tex' in show_models or 'tex' in show_quality:
-        print_text_model_data(model, static_model, static_quality, lut_model, lut_quality, param_model, param_info, analytic_quality)
+    if "tex" in show_models or "tex" in show_quality:
+        print_text_model_data(
+            model,
+            static_model,
+            static_quality,
+            lut_model,
+            lut_quality,
+            param_model,
+            param_info,
+            analytic_quality,
+        )
 
-    if 'table' in show_quality or 'all' in show_quality:
-        print('corrcoef:')
-        model_quality_table([static_quality, analytic_quality, lut_quality], [None, param_info, None])
-        print('heuristic:')
-        model_quality_table([ref_static_quality, ref_analytic_quality, ref_lut_quality], [None, ref_param_info, None])
-    if 'summary' in show_quality or 'all' in show_quality:
-        print('corrcoef:')
+    if "table" in show_quality or "all" in show_quality:
+        print("corrcoef:")
+        model_quality_table(
+            [static_quality, analytic_quality, lut_quality], [None, param_info, None]
+        )
+        print("heuristic:")
+        model_quality_table(
+            [ref_static_quality, ref_analytic_quality, ref_lut_quality],
+            [None, ref_param_info, None],
+        )
+    if "summary" in show_quality or "all" in show_quality:
+        print("corrcoef:")
         model_summary_table([static_quality, analytic_quality, lut_quality])
-        print('heuristic:')
+        print("heuristic:")
         model_summary_table([ref_static_quality, ref_analytic_quality, ref_lut_quality])
 
-    if 'plot-param' in opts:
-        for kv in opts['plot-param'].split(';'):
-            state_or_trans, attribute, param_name, *function = kv.split(' ')
+    if "plot-param" in opts:
+        for kv in opts["plot-param"].split(";"):
+            state_or_trans, attribute, param_name, *function = kv.split(" ")
             if len(function):
-                function = gplearn_to_function(' '.join(function))
+                function = gplearn_to_function(" ".join(function))
             else:
                 function = None
-            plotter.plot_param(model, state_or_trans, attribute, model.param_index(param_name), extra_function=function)
+            plotter.plot_param(
+                model,
+                state_or_trans,
+                attribute,
+                model.param_index(param_name),
+                extra_function=function,
+            )
 
     sys.exit(0)
diff --git a/bin/workload.py b/bin/workload.py
index 6199394..19a7378 100755
--- a/bin/workload.py
+++ b/bin/workload.py
@@ -13,30 +13,50 @@ loop_names = set()
 
 
 def simulate_word(timedword):
-    prev_state = 'UNINITIALIZED'
+    prev_state = "UNINITIALIZED"
     prev_param = None
     ret = dict()
     for trace_part in timedword:
-        print('Trace Part {}'.format(trace_part))
+        print("Trace Part {}".format(trace_part))
         if type(trace_part) is TimedWord:
-            result = pta.simulate(trace_part, orig_state=prev_state, orig_param=prev_param)
+            result = pta.simulate(
+                trace_part, orig_state=prev_state, orig_param=prev_param
+            )
         elif type(trace_part) is Workload:
-            result = pta.simulate(trace_part.word, orig_state=prev_state, orig_param=prev_param)
+            result = pta.simulate(
+                trace_part.word, orig_state=prev_state, orig_param=prev_param
+            )
             if prev_state != result.end_state:
-                print('Warning: loop starts in state {}, but terminates in {}'.format(prev_state, result.end_state.name))
+                print(
+                    "Warning: loop starts in state {}, but terminates in {}".format(
+                        prev_state, result.end_state.name
+                    )
+                )
             if prev_param != result.parameters:
-                print('Warning: loop starts with parameters {}, but terminates with {}'.format(prev_param, result.parameters))
+                print(
+                    "Warning: loop starts with parameters {}, but terminates with {}".format(
+                        prev_param, result.parameters
+                    )
+                )
             ret[trace_part.name] = result
             loop_names.add(trace_part.name)
 
-        print('    Duration: ' + human_readable(result.duration, 's'))
+        print("    Duration: " + human_readable(result.duration, "s"))
         if result.duration_mae:
-            print(u'    ± {}  /  {:.0f}%'.format(human_readable(result.duration_mae, 's'), result.duration_mape))
-        print('    Energy: ' + human_readable(result.energy, 'J'))
+            print(
+                u"    ± {}  /  {:.0f}%".format(
+                    human_readable(result.duration_mae, "s"), result.duration_mape
+                )
+            )
+        print("    Energy: " + human_readable(result.energy, "J"))
         if result.energy_mae:
-            print(u'    ± {}  /  {:.0f}%'.format(human_readable(result.energy_mae, 'J'), result.energy_mape))
-        print('    Mean Power: ' + human_readable(result.mean_power, 'W'))
-        print('')
+            print(
+                u"    ± {}  /  {:.0f}%".format(
+                    human_readable(result.energy_mae, "J"), result.energy_mape
+                )
+            )
+        print("    Mean Power: " + human_readable(result.mean_power, "W"))
+        print("")
 
         prev_state = result.end_state
         prev_param = result.parameters
@@ -49,7 +69,7 @@ for i in range(len(args) // 2):
     ptafiles.append(ptafile)
     pta = PTA.from_file(ptafile)
     timedword = TimedSequence(raw_word)
-    print('Input: {}\n'.format(timedword))
+    print("Input: {}\n".format(timedword))
     loops[ptafile] = simulate_word(timedword)
 
 for loop_name in sorted(loop_names):
@@ -59,6 +79,14 @@ for loop_name in sorted(loop_names):
         if loop_name in loops[ptafile]:
             result_set.append(loops[ptafile][loop_name])
             total_power += loops[ptafile][loop_name].mean_power
-    print('{}: total mean power is {}'.format(loop_name, human_readable(total_power, 'W')))
+    print(
+        "{}: total mean power is {}".format(loop_name, human_readable(total_power, "W"))
+    )
     for i, result in enumerate(result_set):
-        print('    {:.0f}% {} (period: {})'.format(result.mean_power * 100 / total_power, ptafiles[i], human_readable(result.duration, 's')))
+        print(
+            "    {:.0f}% {} (period: {})".format(
+                result.mean_power * 100 / total_power,
+                ptafiles[i],
+                human_readable(result.duration, "s"),
+            )
+        )
diff --git a/lib/aspectc.py b/lib/aspectc.py
index 3229057..f4a102c 100644
--- a/lib/aspectc.py
+++ b/lib/aspectc.py
@@ -1,5 +1,6 @@
 import xml.etree.ElementTree as ET
 
+
 class AspectCClass:
     """
     C++ class information provided by the AspectC++ repo.acp
@@ -19,6 +20,7 @@ class AspectCClass:
         for function in self.functions:
             self.function[function.name] = function
 
+
 class AspectCFunction:
     """
     C++ function informationed provided by the AspectC++ repo.acp
@@ -53,16 +55,23 @@ class AspectCFunction:
 
         :param function_node: `xml.etree.ElementTree.Element` node
         """
-        name = function_node.get('name')
-        kind = function_node.get('kind')
-        function_id = function_node.get('id')
+        name = function_node.get("name")
+        kind = function_node.get("kind")
+        function_id = function_node.get("id")
         return_type = None
         argument_types = list()
-        for type_node in function_node.findall('result_type/Type'):
-            return_type = type_node.get('signature')
-        for type_node in function_node.findall('arg_types/Type'):
-            argument_types.append(type_node.get('signature'))
-        return cls(name = name, kind = kind, function_id = function_id, argument_types = argument_types, return_type = return_type)
+        for type_node in function_node.findall("result_type/Type"):
+            return_type = type_node.get("signature")
+        for type_node in function_node.findall("arg_types/Type"):
+            argument_types.append(type_node.get("signature"))
+        return cls(
+            name=name,
+            kind=kind,
+            function_id=function_id,
+            argument_types=argument_types,
+            return_type=return_type,
+        )
+
 
 class Repo:
     """
@@ -85,11 +94,13 @@ class Repo:
 
     def _load_classes(self):
         self.classes = list()
-        for class_node in self.root.findall('root/Namespace[@name="::"]/children/Class'):
-            name = class_node.get('name')
-            class_id = class_node.get('id')
+        for class_node in self.root.findall(
+            'root/Namespace[@name="::"]/children/Class'
+        ):
+            name = class_node.get("name")
+            class_id = class_node.get("id")
             functions = list()
-            for function_node in class_node.findall('children/Function'):
+            for function_node in class_node.findall("children/Function"):
                 function = AspectCFunction.from_function_node(function_node)
                 functions.append(function)
             self.classes.append(AspectCClass(name, class_id, functions))
diff --git a/lib/automata.py b/lib/automata.py
index b7668c5..b3318e0 100755
--- a/lib/automata.py
+++ b/lib/automata.py
@@ -28,7 +28,15 @@ class SimulationResult:
     :param mean_power: mean power during run in W
     """
 
-    def __init__(self, duration: float, energy: float, end_state, parameters, duration_mae: float = None, energy_mae: float = None):
+    def __init__(
+        self,
+        duration: float,
+        energy: float,
+        end_state,
+        parameters,
+        duration_mae: float = None,
+        energy_mae: float = None,
+    ):
         u"""
         Create a new SimulationResult.
 
@@ -77,7 +85,13 @@ class PTAAttribute:
     :param function_error: mean absolute error of function (optional)
     """
 
-    def __init__(self, value: float = 0, function: AnalyticFunction = None, value_error=None, function_error=None):
+    def __init__(
+        self,
+        value: float = 0,
+        function: AnalyticFunction = None,
+        value_error=None,
+        function_error=None,
+    ):
         self.value = value
         self.function = function
         self.value_error = value_error
@@ -85,8 +99,10 @@ class PTAAttribute:
 
     def __repr__(self):
         if self.function is not None:
-            return 'PTAATtribute<{:.0f}, {}>'.format(self.value, self.function._model_str)
-        return 'PTAATtribute<{:.0f}, None>'.format(self.value)
+            return "PTAATtribute<{:.0f}, {}>".format(
+                self.value, self.function._model_str
+            )
+        return "PTAATtribute<{:.0f}, None>".format(self.value)
 
     def eval(self, param_dict=dict(), args=list()):
         """
@@ -108,33 +124,38 @@ class PTAAttribute:
         """
         param_list = _dict_to_list(param_dict)
         if self.function and self.function.is_predictable(param_list):
-            return self.function_error['mae']
-        return self.value_error['mae']
+            return self.function_error["mae"]
+        return self.value_error["mae"]
 
     def to_json(self):
         ret = {
-            'static': self.value,
-            'static_error': self.value_error,
+            "static": self.value,
+            "static_error": self.value_error,
         }
         if self.function:
-            ret['function'] = {
-                'raw': self.function._model_str,
-                'regression_args': list(self.function._regression_args)
+            ret["function"] = {
+                "raw": self.function._model_str,
+                "regression_args": list(self.function._regression_args),
             }
-            ret['function_error'] = self.function_error
+            ret["function_error"] = self.function_error
         return ret
 
     @classmethod
     def from_json(cls, json_input: dict, parameters: dict):
         ret = cls()
-        if 'static' in json_input:
-            ret.value = json_input['static']
-        if 'static_error' in json_input:
-            ret.value_error = json_input['static_error']
-        if 'function' in json_input:
-            ret.function = AnalyticFunction(json_input['function']['raw'], parameters, 0, regression_args=json_input['function']['regression_args'])
-        if 'function_error' in json_input:
-            ret.function_error = json_input['function_error']
+        if "static" in json_input:
+            ret.value = json_input["static"]
+        if "static_error" in json_input:
+            ret.value_error = json_input["static_error"]
+        if "function" in json_input:
+            ret.function = AnalyticFunction(
+                json_input["function"]["raw"],
+                parameters,
+                0,
+                regression_args=json_input["function"]["regression_args"],
+            )
+        if "function_error" in json_input:
+            ret.function_error = json_input["function_error"]
         return ret
 
     @classmethod
@@ -145,16 +166,23 @@ class PTAAttribute:
 
 
 def _json_function_to_analytic_function(base, attribute: str, parameters: list):
-    if attribute in base and 'function' in base[attribute]:
-        base = base[attribute]['function']
-        return AnalyticFunction(base['raw'], parameters, 0, regression_args=base['regression_args'])
+    if attribute in base and "function" in base[attribute]:
+        base = base[attribute]["function"]
+        return AnalyticFunction(
+            base["raw"], parameters, 0, regression_args=base["regression_args"]
+        )
     return None
 
 
 class State:
     """A single PTA state."""
 
-    def __init__(self, name: str, power: PTAAttribute = PTAAttribute(), power_function: AnalyticFunction = None):
+    def __init__(
+        self,
+        name: str,
+        power: PTAAttribute = PTAAttribute(),
+        power_function: AnalyticFunction = None,
+    ):
         u"""
         Create a new PTA state.
 
@@ -173,10 +201,10 @@ class State:
             if type(power_function) is AnalyticFunction:
                 self.power.function = power_function
             else:
-                raise ValueError('power_function must be None or AnalyticFunction')
+                raise ValueError("power_function must be None or AnalyticFunction")
 
     def __repr__(self):
-        return 'State<{:s}, {}>'.format(self.name, self.power)
+        return "State<{:s}, {}>".format(self.name, self.power)
 
     def add_outgoing_transition(self, new_transition: object):
         """Add a new outgoing transition."""
@@ -206,7 +234,11 @@ class State:
         try:
             return self.outgoing_transitions[transition_name]
         except KeyError:
-            raise ValueError('State {} has no outgoing transition called {}'.format(self.name, transition_name)) from None
+            raise ValueError(
+                "State {} has no outgoing transition called {}".format(
+                    self.name, transition_name
+                )
+            ) from None
 
     def has_interrupt_transitions(self) -> bool:
         """Return whether this state has any outgoing interrupt transitions."""
@@ -224,7 +256,9 @@ class State:
         :param parameters: current parameter values
         :returns: Transition object
         """
-        interrupts = filter(lambda x: x.is_interrupt, self.outgoing_transitions.values())
+        interrupts = filter(
+            lambda x: x.is_interrupt, self.outgoing_transitions.values()
+        )
         interrupts = sorted(interrupts, key=lambda x: x.get_timeout(parameters))
         return interrupts[0]
 
@@ -246,15 +280,32 @@ class State:
         # TODO parametergewahrer Trace-Filter, z.B. "setHeaterDuration nur wenn bme680 power mode => FORCED und GAS_ENABLED"
 
         # A '$' entry in trace_filter indicates that the trace should (successfully) terminate here regardless of `depth`.
-        if trace_filter is not None and next(filter(lambda x: x == '$', map(lambda x: x[0], trace_filter)), None) is not None:
+        if (
+            trace_filter is not None
+            and next(
+                filter(lambda x: x == "$", map(lambda x: x[0], trace_filter)), None
+            )
+            is not None
+        ):
             yield []
         # there may be other entries in trace_filter that still yield results.
         if depth == 0:
             for trans in self.outgoing_transitions.values():
-                if trace_filter is not None and len(list(filter(lambda x: x == trans.name, map(lambda x: x[0], trace_filter)))) == 0:
+                if (
+                    trace_filter is not None
+                    and len(
+                        list(
+                            filter(
+                                lambda x: x == trans.name,
+                                map(lambda x: x[0], trace_filter),
+                            )
+                        )
+                    )
+                    == 0
+                ):
                     continue
                 if with_arguments:
-                    if trans.argument_combination == 'cartesian':
+                    if trans.argument_combination == "cartesian":
                         for args in itertools.product(*trans.argument_values):
                             if sleep:
                                 yield [(None, sleep), (trans, args)]
@@ -273,18 +324,35 @@ class State:
                         yield [(trans,)]
         else:
             for trans in self.outgoing_transitions.values():
-                if trace_filter is not None and next(filter(lambda x: x == trans.name, map(lambda x: x[0], trace_filter)), None) is None:
+                if (
+                    trace_filter is not None
+                    and next(
+                        filter(
+                            lambda x: x == trans.name, map(lambda x: x[0], trace_filter)
+                        ),
+                        None,
+                    )
+                    is None
+                ):
                     continue
                 if trace_filter is not None:
-                    new_trace_filter = map(lambda x: x[1:], filter(lambda x: x[0] == trans.name, trace_filter))
+                    new_trace_filter = map(
+                        lambda x: x[1:],
+                        filter(lambda x: x[0] == trans.name, trace_filter),
+                    )
                     new_trace_filter = list(filter(len, new_trace_filter))
                     if len(new_trace_filter) == 0:
                         new_trace_filter = None
                 else:
                     new_trace_filter = None
-                for suffix in trans.destination.dfs(depth - 1, with_arguments=with_arguments, trace_filter=new_trace_filter, sleep=sleep):
+                for suffix in trans.destination.dfs(
+                    depth - 1,
+                    with_arguments=with_arguments,
+                    trace_filter=new_trace_filter,
+                    sleep=sleep,
+                ):
                     if with_arguments:
-                        if trans.argument_combination == 'cartesian':
+                        if trans.argument_combination == "cartesian":
                             for args in itertools.product(*trans.argument_values):
                                 if sleep:
                                     new_suffix = [(None, sleep), (trans, args)]
@@ -314,10 +382,7 @@ class State:
 
     def to_json(self) -> dict:
         """Return JSON encoding of this state object."""
-        ret = {
-            'name': self.name,
-            'power': self.power.to_json()
-        }
+        ret = {"name": self.name, "power": self.power.to_json()}
         return ret
 
 
@@ -345,19 +410,27 @@ class Transition:
     :param codegen: todo
     """
 
-    def __init__(self, orig_state: State, dest_state: State, name: str,
-                 energy: PTAAttribute = PTAAttribute(), energy_function: AnalyticFunction = None,
-                 duration: PTAAttribute = PTAAttribute(), duration_function: AnalyticFunction = None,
-                 timeout: PTAAttribute = PTAAttribute(), timeout_function: AnalyticFunction = None,
-                 is_interrupt: bool = False,
-                 arguments: list = [],
-                 argument_values: list = [],
-                 argument_combination: str = 'cartesian',  # or 'zip'
-                 param_update_function=None,
-                 arg_to_param_map: dict = None,
-                 set_param=None,
-                 return_value_handlers: list = [],
-                 codegen=dict()):
+    def __init__(
+        self,
+        orig_state: State,
+        dest_state: State,
+        name: str,
+        energy: PTAAttribute = PTAAttribute(),
+        energy_function: AnalyticFunction = None,
+        duration: PTAAttribute = PTAAttribute(),
+        duration_function: AnalyticFunction = None,
+        timeout: PTAAttribute = PTAAttribute(),
+        timeout_function: AnalyticFunction = None,
+        is_interrupt: bool = False,
+        arguments: list = [],
+        argument_values: list = [],
+        argument_combination: str = "cartesian",  # or 'zip'
+        param_update_function=None,
+        arg_to_param_map: dict = None,
+        set_param=None,
+        return_value_handlers: list = [],
+        codegen=dict(),
+    ):
         """
         Create a new transition between two PTA states.
 
@@ -400,8 +473,8 @@ class Transition:
                 self.timeout.function = timeout_function
 
         for handler in self.return_value_handlers:
-            if 'formula' in handler:
-                handler['formula'] = NormalizationFunction(handler['formula'])
+            if "formula" in handler:
+                handler["formula"] = NormalizationFunction(handler["formula"])
 
     def get_duration(self, param_dict: dict = {}, args: list = []) -> float:
         u"""
@@ -465,25 +538,25 @@ class Transition:
     def to_json(self) -> dict:
         """Return JSON encoding of this transition object."""
         ret = {
-            'name': self.name,
-            'origin': self.origin.name,
-            'destination': self.destination.name,
-            'is_interrupt': self.is_interrupt,
-            'arguments': self.arguments,
-            'argument_values': self.argument_values,
-            'argument_combination': self.argument_combination,
-            'arg_to_param_map': self.arg_to_param_map,
-            'set_param': self.set_param,
-            'duration': self.duration.to_json(),
-            'energy': self.energy.to_json(),
-            'timeout': self.timeout.to_json()
+            "name": self.name,
+            "origin": self.origin.name,
+            "destination": self.destination.name,
+            "is_interrupt": self.is_interrupt,
+            "arguments": self.arguments,
+            "argument_values": self.argument_values,
+            "argument_combination": self.argument_combination,
+            "arg_to_param_map": self.arg_to_param_map,
+            "set_param": self.set_param,
+            "duration": self.duration.to_json(),
+            "energy": self.energy.to_json(),
+            "timeout": self.timeout.to_json(),
         }
         return ret
 
 
 def _json_get_static(base, attribute: str):
     if attribute in base:
-        return base[attribute]['static']
+        return base[attribute]["static"]
     return 0
 
 
@@ -505,10 +578,15 @@ class PTA:
     :param transitions: list of `Transition` objects
     """
 
-    def __init__(self, state_names: list = [],
-                 accepting_states: list = None,
-                 parameters: list = [], initial_param_values: list = None,
-                 codegen: dict = {}, parameter_normalization: dict = None):
+    def __init__(
+        self,
+        state_names: list = [],
+        accepting_states: list = None,
+        parameters: list = [],
+        initial_param_values: list = None,
+        codegen: dict = {},
+        parameter_normalization: dict = None,
+    ):
         """
         Return a new PTA object.
 
@@ -524,7 +602,9 @@ class PTA:
             `enum`: maps enum descriptors (keys) to parameter values. Note that the mapping is not required to correspond to the driver API.
             `formula`: maps an argument or return value (passed as `param`) to a parameter value. Must be a string describing a valid python lambda function. NumPy is available as `np`.
         """
-        self.state = dict([[state_name, State(state_name)] for state_name in state_names])
+        self.state = dict(
+            [[state_name, State(state_name)] for state_name in state_names]
+        )
         self.accepting_states = accepting_states.copy() if accepting_states else None
         self.parameters = parameters.copy()
         self.parameter_normalization = parameter_normalization
@@ -535,13 +615,15 @@ class PTA:
             self.initial_param_values = [None for x in self.parameters]
         self.transitions = []
 
-        if 'UNINITIALIZED' not in state_names:
-            self.state['UNINITIALIZED'] = State('UNINITIALIZED')
+        if "UNINITIALIZED" not in state_names:
+            self.state["UNINITIALIZED"] = State("UNINITIALIZED")
 
         if self.parameter_normalization:
             for normalization_spec in self.parameter_normalization.values():
-                if 'formula' in normalization_spec:
-                    normalization_spec['formula'] = NormalizationFunction(normalization_spec['formula'])
+                if "formula" in normalization_spec:
+                    normalization_spec["formula"] = NormalizationFunction(
+                        normalization_spec["formula"]
+                    )
 
     def normalize_parameter(self, parameter_name: str, parameter_value) -> float:
         """
@@ -553,11 +635,23 @@ class PTA:
         :param parameter_name: parameter name.
         :param parameter_value: parameter value.
         """
-        if parameter_value is not None and self.parameter_normalization is not None and parameter_name in self.parameter_normalization:
-            if 'enum' in self.parameter_normalization[parameter_name] and parameter_value in self.parameter_normalization[parameter_name]['enum']:
-                return self.parameter_normalization[parameter_name]['enum'][parameter_value]
-            if 'formula' in self.parameter_normalization[parameter_name]:
-                normalization_formula = self.parameter_normalization[parameter_name]['formula']
+        if (
+            parameter_value is not None
+            and self.parameter_normalization is not None
+            and parameter_name in self.parameter_normalization
+        ):
+            if (
+                "enum" in self.parameter_normalization[parameter_name]
+                and parameter_value
+                in self.parameter_normalization[parameter_name]["enum"]
+            ):
+                return self.parameter_normalization[parameter_name]["enum"][
+                    parameter_value
+                ]
+            if "formula" in self.parameter_normalization[parameter_name]:
+                normalization_formula = self.parameter_normalization[parameter_name][
+                    "formula"
+                ]
                 return normalization_formula.eval(parameter_value)
         return parameter_value
 
@@ -580,8 +674,8 @@ class PTA:
     @classmethod
     def from_file(cls, model_file: str):
         """Return PTA loaded from the provided JSON or YAML file."""
-        with open(model_file, 'r') as f:
-            if '.json' in model_file:
+        with open(model_file, "r") as f:
+            if ".json" in model_file:
                 return cls.from_json(json.load(f))
             else:
                 return cls.from_yaml(yaml.safe_load(f))
@@ -593,36 +687,58 @@ class PTA:
 
         Compatible with the to_json method.
         """
-        if 'transition' in json_input:
+        if "transition" in json_input:
             return cls.from_legacy_json(json_input)
 
         kwargs = dict()
-        for key in ('state_names', 'parameters', 'initial_param_values', 'accepting_states'):
+        for key in (
+            "state_names",
+            "parameters",
+            "initial_param_values",
+            "accepting_states",
+        ):
             if key in json_input:
                 kwargs[key] = json_input[key]
         pta = cls(**kwargs)
-        for name, state in json_input['state'].items():
-            pta.add_state(name, power=PTAAttribute.from_json_maybe(state, 'power', pta.parameters))
-        for transition in json_input['transitions']:
+        for name, state in json_input["state"].items():
+            pta.add_state(
+                name, power=PTAAttribute.from_json_maybe(state, "power", pta.parameters)
+            )
+        for transition in json_input["transitions"]:
             kwargs = dict()
-            for key in ['arguments', 'argument_values', 'argument_combination', 'is_interrupt', 'set_param']:
+            for key in [
+                "arguments",
+                "argument_values",
+                "argument_combination",
+                "is_interrupt",
+                "set_param",
+            ]:
                 if key in transition:
                     kwargs[key] = transition[key]
             # arg_to_param_map uses integer indices. This is not supported by JSON
-            if 'arg_to_param_map' in transition:
-                kwargs['arg_to_param_map'] = dict()
-                for arg_index, param_name in transition['arg_to_param_map'].items():
-                    kwargs['arg_to_param_map'][int(arg_index)] = param_name
-            origins = transition['origin']
+            if "arg_to_param_map" in transition:
+                kwargs["arg_to_param_map"] = dict()
+                for arg_index, param_name in transition["arg_to_param_map"].items():
+                    kwargs["arg_to_param_map"][int(arg_index)] = param_name
+            origins = transition["origin"]
             if type(origins) != list:
                 origins = [origins]
             for origin in origins:
-                pta.add_transition(origin, transition['destination'],
-                                   transition['name'],
-                                   duration=PTAAttribute.from_json_maybe(transition, 'duration', pta.parameters),
-                                   energy=PTAAttribute.from_json_maybe(transition, 'energy', pta.parameters),
-                                   timeout=PTAAttribute.from_json_maybe(transition, 'timeout', pta.parameters),
-                                   **kwargs)
+                pta.add_transition(
+                    origin,
+                    transition["destination"],
+                    transition["name"],
+                    duration=PTAAttribute.from_json_maybe(
+                        transition, "duration", pta.parameters
+                    ),
+                    energy=PTAAttribute.from_json_maybe(
+                        transition, "energy", pta.parameters
+                    ),
+                    timeout=PTAAttribute.from_json_maybe(
+                        transition, "timeout", pta.parameters
+                    ),
+                    **kwargs
+                )
 
         return pta
 
@@ -634,37 +750,45 @@ class PTA:
         Compatible with the legacy dfatool/perl format.
         """
         kwargs = {
-            'parameters': list(),
-            'initial_param_values': list(),
+            "parameters": list(),
+            "initial_param_values": list(),
         }
 
-        for param in sorted(json_input['parameter'].keys()):
-            kwargs['parameters'].append(param)
-            kwargs['initial_param_values'].append(json_input['parameter'][param]['default'])
+        for param in sorted(json_input["parameter"].keys()):
+            kwargs["parameters"].append(param)
+            kwargs["initial_param_values"].append(
+                json_input["parameter"][param]["default"]
+            )
 
         pta = cls(**kwargs)
 
-        for name, state in json_input['state'].items():
-            pta.add_state(name, power=PTAAttribute(value=float(state['power']['static'])))
+        for name, state in json_input["state"].items():
+            pta.add_state(
+                name, power=PTAAttribute(value=float(state["power"]["static"]))
+            )
 
-        for trans_name in sorted(json_input['transition'].keys()):
-            transition = json_input['transition'][trans_name]
-            destination = transition['destination']
+        for trans_name in sorted(json_input["transition"].keys()):
+            transition = json_input["transition"][trans_name]
+            destination = transition["destination"]
             arguments = list()
             argument_values = list()
             is_interrupt = False
-            if transition['level'] == 'epilogue':
+            if transition["level"] == "epilogue":
                 is_interrupt = True
             if type(destination) == list:
                 destination = destination[0]
-            for arg in transition['parameters']:
-                arguments.append(arg['name'])
-                argument_values.append(arg['values'])
-            for origin in transition['origins']:
-                pta.add_transition(origin, destination, trans_name,
-                                   arguments=arguments,
-                                   argument_values=argument_values,
-                                   is_interrupt=is_interrupt)
+            for arg in transition["parameters"]:
+                arguments.append(arg["name"])
+                argument_values.append(arg["values"])
+            for origin in transition["origins"]:
+                pta.add_transition(
+                    origin,
+                    destination,
+                    trans_name,
+                    arguments=arguments,
+                    argument_values=argument_values,
+                    is_interrupt=is_interrupt,
+                )
 
         return pta
 
@@ -674,68 +798,79 @@ class PTA:
 
         kwargs = dict()
 
-        if 'parameters' in yaml_input:
-            kwargs['parameters'] = yaml_input['parameters']
+        if "parameters" in yaml_input:
+            kwargs["parameters"] = yaml_input["parameters"]
 
-        if 'initial_param_values' in yaml_input:
-            kwargs['initial_param_values'] = yaml_input['initial_param_values']
+        if "initial_param_values" in yaml_input:
+            kwargs["initial_param_values"] = yaml_input["initial_param_values"]
 
-        if 'states' in yaml_input:
-            kwargs['state_names'] = yaml_input['states']
+        if "states" in yaml_input:
+            kwargs["state_names"] = yaml_input["states"]
         # else: set to UNINITIALIZED by class constructor
 
-        if 'codegen' in yaml_input:
-            kwargs['codegen'] = yaml_input['codegen']
+        if "codegen" in yaml_input:
+            kwargs["codegen"] = yaml_input["codegen"]
 
-        if 'parameter_normalization' in yaml_input:
-            kwargs['parameter_normalization'] = yaml_input['parameter_normalization']
+        if "parameter_normalization" in yaml_input:
+            kwargs["parameter_normalization"] = yaml_input["parameter_normalization"]
 
         pta = cls(**kwargs)
 
-        if 'state' in yaml_input:
-            for state_name, state in yaml_input['state'].items():
-                pta.add_state(state_name, power=PTAAttribute.from_json_maybe(state, 'power', pta.parameters))
+        if "state" in yaml_input:
+            for state_name, state in yaml_input["state"].items():
+                pta.add_state(
+                    state_name,
+                    power=PTAAttribute.from_json_maybe(state, "power", pta.parameters),
+                )
 
-        for trans_name in sorted(yaml_input['transition'].keys()):
+        for trans_name in sorted(yaml_input["transition"].keys()):
             kwargs = dict()
-            transition = yaml_input['transition'][trans_name]
+            transition = yaml_input["transition"][trans_name]
             arguments = list()
             argument_values = list()
             arg_to_param_map = dict()
-            if 'arguments' in transition:
-                for i, argument in enumerate(transition['arguments']):
-                    arguments.append(argument['name'])
-                    argument_values.append(argument['values'])
-                    if 'parameter' in argument:
-                        arg_to_param_map[i] = argument['parameter']
-            if 'argument_combination' in transition:
-                kwargs['argument_combination'] = transition['argument_combination']
-            if 'set_param' in transition:
-                kwargs['set_param'] = transition['set_param']
-            if 'is_interrupt' in transition:
-                kwargs['is_interrupt'] = transition['is_interrupt']
-            if 'return_value' in transition:
-                kwargs['return_value_handlers'] = transition['return_value']
-            if 'codegen' in transition:
-                kwargs['codegen'] = transition['codegen']
-            if 'loop' in transition:
-                for state_name in transition['loop']:
-                    pta.add_transition(state_name, state_name, trans_name,
-                                       arguments=arguments,
-                                       argument_values=argument_values,
-                                       arg_to_param_map=arg_to_param_map,
-                                       **kwargs)
+            if "arguments" in transition:
+                for i, argument in enumerate(transition["arguments"]):
+                    arguments.append(argument["name"])
+                    argument_values.append(argument["values"])
+                    if "parameter" in argument:
+                        arg_to_param_map[i] = argument["parameter"]
+            if "argument_combination" in transition:
+                kwargs["argument_combination"] = transition["argument_combination"]
+            if "set_param" in transition:
+                kwargs["set_param"] = transition["set_param"]
+            if "is_interrupt" in transition:
+                kwargs["is_interrupt"] = transition["is_interrupt"]
+            if "return_value" in transition:
+                kwargs["return_value_handlers"] = transition["return_value"]
+            if "codegen" in transition:
+                kwargs["codegen"] = transition["codegen"]
+            if "loop" in transition:
+                for state_name in transition["loop"]:
+                    pta.add_transition(
+                        state_name,
+                        state_name,
+                        trans_name,
+                        arguments=arguments,
+                        argument_values=argument_values,
+                        arg_to_param_map=arg_to_param_map,
+                        **kwargs
+                    )
             else:
-                if 'src' not in transition:
-                    transition['src'] = ['UNINITIALIZED']
-                if 'dst' not in transition:
-                    transition['dst'] = 'UNINITIALIZED'
-                for origin in transition['src']:
-                    pta.add_transition(origin, transition['dst'], trans_name,
-                                       arguments=arguments,
-                                       argument_values=argument_values,
-                                       arg_to_param_map=arg_to_param_map,
-                                       **kwargs)
+                if "src" not in transition:
+                    transition["src"] = ["UNINITIALIZED"]
+                if "dst" not in transition:
+                    transition["dst"] = "UNINITIALIZED"
+                for origin in transition["src"]:
+                    pta.add_transition(
+                        origin,
+                        transition["dst"],
+                        trans_name,
+                        arguments=arguments,
+                        argument_values=argument_values,
+                        arg_to_param_map=arg_to_param_map,
+                        **kwargs
+                    )
 
         return pta
 
@@ -746,11 +881,13 @@ class PTA:
         Compatible with the from_json method.
         """
         ret = {
-            'parameters': self.parameters,
-            'initial_param_values': self.initial_param_values,
-            'state': dict([[state.name, state.to_json()] for state in self.state.values()]),
-            'transitions': [trans.to_json() for trans in self.transitions],
-            'accepting_states': self.accepting_states,
+            "parameters": self.parameters,
+            "initial_param_values": self.initial_param_values,
+            "state": dict(
+                [[state.name, state.to_json()] for state in self.state.values()]
+            ),
+            "transitions": [trans.to_json() for trans in self.transitions],
+            "accepting_states": self.accepting_states,
         }
         return ret
 
@@ -760,12 +897,19 @@ class PTA:
 
         See the State() documentation for acceptable arguments.
         """
-        if 'power_function' in kwargs and type(kwargs['power_function']) != AnalyticFunction and kwargs['power_function'] is not None:
-            kwargs['power_function'] = AnalyticFunction(kwargs['power_function'],
-                                                        self.parameters, 0)
+        if (
+            "power_function" in kwargs
+            and type(kwargs["power_function"]) != AnalyticFunction
+            and kwargs["power_function"] is not None
+        ):
+            kwargs["power_function"] = AnalyticFunction(
+                kwargs["power_function"], self.parameters, 0
+            )
         self.state[state_name] = State(state_name, **kwargs)
 
-    def add_transition(self, orig_state: str, dest_state: str, function_name: str, **kwargs):
+    def add_transition(
+        self, orig_state: str, dest_state: str, function_name: str, **kwargs
+    ):
         """
         Add function_name as new transition from orig_state to dest_state.
 
@@ -776,8 +920,12 @@ class PTA:
         """
         orig_state = self.state[orig_state]
         dest_state = self.state[dest_state]
-        for key in ('duration_function', 'energy_function', 'timeout_function'):
-            if key in kwargs and kwargs[key] is not None and type(kwargs[key]) != AnalyticFunction:
+        for key in ("duration_function", "energy_function", "timeout_function"):
+            if (
+                key in kwargs
+                and kwargs[key] is not None
+                and type(kwargs[key]) != AnalyticFunction
+            ):
                 kwargs[key] = AnalyticFunction(kwargs[key], self.parameters, 0)
 
         new_transition = Transition(orig_state, dest_state, function_name, **kwargs)
@@ -824,7 +972,12 @@ class PTA:
         return self.get_unique_transitions().index(transition)
 
     def get_initial_param_dict(self):
-        return dict([[self.parameters[i], self.initial_param_values[i]] for i in range(len(self.parameters))])
+        return dict(
+            [
+                [self.parameters[i], self.initial_param_values[i]]
+                for i in range(len(self.parameters))
+            ]
+        )
 
     def set_random_energy_model(self, static_model=True):
         u"""
@@ -841,18 +994,24 @@ class PTA:
     def get_most_expensive_state(self):
         max_state = None
         for state in self.state.values():
-            if state.name != 'UNINITIALIZED' and (max_state is None or state.power.value > max_state.power.value):
+            if state.name != "UNINITIALIZED" and (
+                max_state is None or state.power.value > max_state.power.value
+            ):
                 max_state = state
         return max_state
 
     def get_least_expensive_state(self):
         min_state = None
         for state in self.state.values():
-            if state.name != 'UNINITIALIZED' and (min_state is None or state.power.value < min_state.power.value):
+            if state.name != "UNINITIALIZED" and (
+                min_state is None or state.power.value < min_state.power.value
+            ):
                 min_state = state
         return min_state
 
-    def min_duration_until_energy_overflow(self, energy_granularity=1e-12, max_energy_value=2 ** 32 - 1):
+    def min_duration_until_energy_overflow(
+        self, energy_granularity=1e-12, max_energy_value=2 ** 32 - 1
+    ):
         """
         Return minimum duration (in s) until energy counter overflow during online accounting.
 
@@ -862,7 +1021,9 @@ class PTA:
 
         max_power_state = self.get_most_expensive_state()
         if max_power_state.has_interrupt_transitions():
-            raise RuntimeWarning('state with maximum power consumption has outgoing interrupt transitions, results will be inaccurate')
+            raise RuntimeWarning(
+                "state with maximum power consumption has outgoing interrupt transitions, results will be inaccurate"
+            )
 
         # convert from µW to W
         max_power = max_power_state.power.value * 1e-6
@@ -870,7 +1031,9 @@ class PTA:
         min_duration = max_energy_value * energy_granularity / max_power
         return min_duration
 
-    def max_duration_until_energy_overflow(self, energy_granularity=1e-12, max_energy_value=2 ** 32 - 1):
+    def max_duration_until_energy_overflow(
+        self, energy_granularity=1e-12, max_energy_value=2 ** 32 - 1
+    ):
         """
         Return maximum duration (in s) until energy counter overflow during online accounting.
 
@@ -880,7 +1043,9 @@ class PTA:
 
         min_power_state = self.get_least_expensive_state()
         if min_power_state.has_interrupt_transitions():
-            raise RuntimeWarning('state with maximum power consumption has outgoing interrupt transitions, results will be inaccurate')
+            raise RuntimeWarning(
+                "state with maximum power consumption has outgoing interrupt transitions, results will be inaccurate"
+            )
 
         # convert from µW to W
         min_power = min_power_state.power.value * 1e-6
@@ -904,14 +1069,19 @@ class PTA:
             for i, argument in enumerate(transition.arguments):
                 if len(transition.argument_values[i]) <= 2:
                     continue
-                if transition.argument_combination == 'zip':
+                if transition.argument_combination == "zip":
                     continue
                 values_are_numeric = True
                 for value in transition.argument_values[i]:
-                    if not is_numeric(self.normalize_parameter(transition.arg_to_param_map[i], value)):
+                    if not is_numeric(
+                        self.normalize_parameter(transition.arg_to_param_map[i], value)
+                    ):
                         values_are_numeric = False
                 if values_are_numeric and len(transition.argument_values[i]) > 2:
-                    transition.argument_values[i] = [transition.argument_values[i][0], transition.argument_values[i][-1]]
+                    transition.argument_values[i] = [
+                        transition.argument_values[i][0],
+                        transition.argument_values[i][-1],
+                    ]
 
     def _dfs_with_param(self, generator, param_dict):
         for trace in generator:
@@ -921,13 +1091,23 @@ class PTA:
                 transition, arguments = elem
                 if transition is not None:
                     param = transition.get_params_after_transition(param, arguments)
-                    ret.append((transition, arguments, self.normalize_parameters(param)))
+                    ret.append(
+                        (transition, arguments, self.normalize_parameters(param))
+                    )
                 else:
                     # parameters have already been normalized
                     ret.append((transition, arguments, param))
             yield ret
 
-    def bfs(self, depth: int = 10, orig_state: str = 'UNINITIALIZED', param_dict: dict = None, with_parameters: bool = False, transition_filter=None, state_filter=None):
+    def bfs(
+        self,
+        depth: int = 10,
+        orig_state: str = "UNINITIALIZED",
+        param_dict: dict = None,
+        with_parameters: bool = False,
+        transition_filter=None,
+        state_filter=None,
+    ):
         """
         Return a generator object for breadth-first search of traces starting at orig_state.
 
@@ -968,7 +1148,14 @@ class PTA:
                         yield new_trace
                         state_queue.put((new_trace, transition.destination))
 
-    def dfs(self, depth: int = 10, orig_state: str = 'UNINITIALIZED', param_dict: dict = None, with_parameters: bool = False, **kwargs):
+    def dfs(
+        self,
+        depth: int = 10,
+        orig_state: str = "UNINITIALIZED",
+        param_dict: dict = None,
+        with_parameters: bool = False,
+        **kwargs
+    ):
         """
         Return a generator object for depth-first search starting at orig_state.
 
@@ -994,12 +1181,14 @@ class PTA:
         if with_parameters and not param_dict:
             param_dict = self.get_initial_param_dict()
 
-        if with_parameters and 'with_arguments' not in kwargs:
+        if with_parameters and "with_arguments" not in kwargs:
             raise ValueError("with_parameters = True requires with_arguments = True")
 
         if self.accepting_states:
-            generator = filter(lambda x: x[-1][0].destination.name in self.accepting_states,
-                               self.state[orig_state].dfs(depth, **kwargs))
+            generator = filter(
+                lambda x: x[-1][0].destination.name in self.accepting_states,
+                self.state[orig_state].dfs(depth, **kwargs),
+            )
         else:
             generator = self.state[orig_state].dfs(depth, **kwargs)
 
@@ -1008,7 +1197,13 @@ class PTA:
         else:
             return generator
 
-    def simulate(self, trace: list, orig_state: str = 'UNINITIALIZED', orig_param=None, accounting=None):
+    def simulate(
+        self,
+        trace: list,
+        orig_state: str = "UNINITIALIZED",
+        orig_param=None,
+        accounting=None,
+    ):
         u"""
         Simulate a single run through the PTA and return total energy, duration, final state, and resulting parameters.
 
@@ -1021,10 +1216,10 @@ class PTA:
 
         :returns: SimulationResult with duration in s, total energy in J, end state, and final parameters
         """
-        total_duration = 0.
-        total_duration_mae = 0.
-        total_energy = 0.
-        total_energy_error = 0.
+        total_duration = 0.0
+        total_duration_mae = 0.0
+        total_energy = 0.0
+        total_energy_error = 0.0
         if type(orig_state) is State:
             state = orig_state
         else:
@@ -1032,7 +1227,12 @@ class PTA:
         if orig_param:
             param_dict = orig_param.copy()
         else:
-            param_dict = dict([[self.parameters[i], self.initial_param_values[i]] for i in range(len(self.parameters))])
+            param_dict = dict(
+                [
+                    [self.parameters[i], self.initial_param_values[i]]
+                    for i in range(len(self.parameters))
+                ]
+            )
         for function in trace:
             if isinstance(function[0], Transition):
                 function_name = function[0].name
@@ -1040,11 +1240,13 @@ class PTA:
             else:
                 function_name = function[0]
                 function_args = function[1:]
-            if function_name is None or function_name == '_':
+            if function_name is None or function_name == "_":
                 duration = function_args[0]
                 total_energy += state.get_energy(duration, param_dict)
                 if state.power.value_error is not None:
-                    total_energy_error += (duration * state.power.eval_mae(param_dict, function_args))**2
+                    total_energy_error += (
+                        duration * state.power.eval_mae(param_dict, function_args)
+                    ) ** 2
                 total_duration += duration
                 # assumption: sleep is near-exact and does not contribute to the duration error
                 if accounting is not None:
@@ -1053,15 +1255,21 @@ class PTA:
                 transition = state.get_transition(function_name)
                 total_duration += transition.duration.eval(param_dict, function_args)
                 if transition.duration.value_error is not None:
-                    total_duration_mae += transition.duration.eval_mae(param_dict, function_args)**2
+                    total_duration_mae += (
+                        transition.duration.eval_mae(param_dict, function_args) ** 2
+                    )
                 total_energy += transition.get_energy(param_dict, function_args)
                 if transition.energy.value_error is not None:
-                    total_energy_error += transition.energy.eval_mae(param_dict, function_args)**2
-                param_dict = transition.get_params_after_transition(param_dict, function_args)
+                    total_energy_error += (
+                        transition.energy.eval_mae(param_dict, function_args) ** 2
+                    )
+                param_dict = transition.get_params_after_transition(
+                    param_dict, function_args
+                )
                 state = transition.destination
                 if accounting is not None:
                     accounting.pass_transition(transition)
-                while (state.has_interrupt_transitions()):
+                while state.has_interrupt_transitions():
                     transition = state.get_next_interrupt(param_dict)
                     duration = transition.get_timeout(param_dict)
                     total_duration += duration
@@ -1072,45 +1280,82 @@ class PTA:
                     param_dict = transition.get_params_after_transition(param_dict)
                     state = transition.destination
 
-        return SimulationResult(total_duration, total_energy, state, param_dict, duration_mae=np.sqrt(total_duration_mae), energy_mae=np.sqrt(total_energy_error))
+        return SimulationResult(
+            total_duration,
+            total_energy,
+            state,
+            param_dict,
+            duration_mae=np.sqrt(total_duration_mae),
+            energy_mae=np.sqrt(total_energy_error),
+        )
 
     def update(self, static_model, param_model, static_error=None, analytic_error=None):
         for state in self.state.values():
-            if state.name != 'UNINITIALIZED':
+            if state.name != "UNINITIALIZED":
                 try:
-                    state.power.value = static_model(state.name, 'power')
+                    state.power.value = static_model(state.name, "power")
                     if static_error is not None:
-                        state.power.value_error = static_error[state.name]['power']
-                    if param_model(state.name, 'power'):
-                        state.power.function = param_model(state.name, 'power')['function']
+                        state.power.value_error = static_error[state.name]["power"]
+                    if param_model(state.name, "power"):
+                        state.power.function = param_model(state.name, "power")[
+                            "function"
+                        ]
                         if analytic_error is not None:
-                            state.power.function_error = analytic_error[state.name]['power']
+                            state.power.function_error = analytic_error[state.name][
+                                "power"
+                            ]
                 except KeyError:
-                    print('[W] skipping model update of state {} due to missing data'.format(state.name))
+                    print(
+                        "[W] skipping model update of state {} due to missing data".format(
+                            state.name
+                        )
+                    )
                     pass
         for transition in self.transitions:
             try:
-                transition.duration.value = static_model(transition.name, 'duration')
-                if param_model(transition.name, 'duration'):
-                    transition.duration.function = param_model(transition.name, 'duration')['function']
+                transition.duration.value = static_model(transition.name, "duration")
+                if param_model(transition.name, "duration"):
+                    transition.duration.function = param_model(
+                        transition.name, "duration"
+                    )["function"]
                     if analytic_error is not None:
-                        transition.duration.function_error = analytic_error[transition.name]['duration']
-                transition.energy.value = static_model(transition.name, 'energy')
-                if param_model(transition.name, 'energy'):
-                    transition.energy.function = param_model(transition.name, 'energy')['function']
+                        transition.duration.function_error = analytic_error[
+                            transition.name
+                        ]["duration"]
+                transition.energy.value = static_model(transition.name, "energy")
+                if param_model(transition.name, "energy"):
+                    transition.energy.function = param_model(transition.name, "energy")[
+                        "function"
+                    ]
                     if analytic_error is not None:
-                        transition.energy.function_error = analytic_error[transition.name]['energy']
+                        transition.energy.function_error = analytic_error[
+                            transition.name
+                        ]["energy"]
                 if transition.is_interrupt:
-                    transition.timeout.value = static_model(transition.name, 'timeout')
-                    if param_model(transition.name, 'timeout'):
-                        transition.timeout.function = param_model(transition.name, 'timeout')['function']
+                    transition.timeout.value = static_model(transition.name, "timeout")
+                    if param_model(transition.name, "timeout"):
+                        transition.timeout.function = param_model(
+                            transition.name, "timeout"
+                        )["function"]
                         if analytic_error is not None:
-                            transition.timeout.function_error = analytic_error[transition.name]['timeout']
+                            transition.timeout.function_error = analytic_error[
+                                transition.name
+                            ]["timeout"]
 
                 if static_error is not None:
-                    transition.duration.value_error = static_error[transition.name]['duration']
-                    transition.energy.value_error = static_error[transition.name]['energy']
-                    transition.timeout.value_error = static_error[transition.name]['timeout']
+                    transition.duration.value_error = static_error[transition.name][
+                        "duration"
+                    ]
+                    transition.energy.value_error = static_error[transition.name][
+                        "energy"
+                    ]
+                    transition.timeout.value_error = static_error[transition.name][
+                        "timeout"
+                    ]
             except KeyError:
-                print('[W] skipping model update of transition {} due to missing data'.format(transition.name))
+                print(
+                    "[W] skipping model update of transition {} due to missing data".format(
+                        transition.name
+                    )
+                )
                 pass
diff --git a/lib/codegen.py b/lib/codegen.py
index e0bf45f..62776fd 100644
--- a/lib/codegen.py
+++ b/lib/codegen.py
@@ -60,38 +60,46 @@ class ClassFunction:
         self.body = body
 
     def get_definition(self):
-        return '{} {}({});'.format(self.return_type, self.name, ', '.join(self.arguments))
+        return "{} {}({});".format(
+            self.return_type, self.name, ", ".join(self.arguments)
+        )
 
     def get_implementation(self):
         if self.body is None:
-            return ''
-        return '{} {}::{}({}) {{\n{}}}\n'.format(self.return_type, self.class_name, self.name, ', '.join(self.arguments), self.body)
+            return ""
+        return "{} {}::{}({}) {{\n{}}}\n".format(
+            self.return_type,
+            self.class_name,
+            self.name,
+            ", ".join(self.arguments),
+            self.body,
+        )
 
 
 def get_accountingmethod(method):
     """Return AccountingMethod class for method."""
-    if method == 'static_state_immediate':
+    if method == "static_state_immediate":
         return StaticStateOnlyAccountingImmediateCalculation
-    if method == 'static_state':
+    if method == "static_state":
         return StaticStateOnlyAccounting
-    if method == 'static_statetransition_immediate':
+    if method == "static_statetransition_immediate":
         return StaticAccountingImmediateCalculation
-    if method == 'static_statetransition':
+    if method == "static_statetransition":
         return StaticAccounting
-    raise ValueError('Unknown accounting method: {}'.format(method))
+    raise ValueError("Unknown accounting method: {}".format(method))
 
 
 def get_simulated_accountingmethod(method):
     """Return SimulatedAccountingMethod class for method."""
-    if method == 'static_state_immediate':
+    if method == "static_state_immediate":
         return SimulatedStaticStateOnlyAccountingImmediateCalculation
-    if method == 'static_statetransition_immediate':
+    if method == "static_statetransition_immediate":
         return SimulatedStaticAccountingImmediateCalculation
-    if method == 'static_state':
+    if method == "static_state":
         return SimulatedStaticStateOnlyAccounting
-    if method == 'static_statetransition':
+    if method == "static_statetransition":
         return SimulatedStaticAccounting
-    raise ValueError('Unknown accounting method: {}'.format(method))
+    raise ValueError("Unknown accounting method: {}".format(method))
 
 
 class SimulatedAccountingMethod:
@@ -104,7 +112,18 @@ class SimulatedAccountingMethod:
     * variable size for accounting of durations, power and energy values
     """
 
-    def __init__(self, pta: PTA, timer_freq_hz, timer_type, ts_type, power_type, energy_type, ts_granularity=1e-6, power_granularity=1e-6, energy_granularity=1e-12):
+    def __init__(
+        self,
+        pta: PTA,
+        timer_freq_hz,
+        timer_type,
+        ts_type,
+        power_type,
+        energy_type,
+        ts_granularity=1e-6,
+        power_granularity=1e-6,
+        energy_granularity=1e-12,
+    ):
         """
         Simulate Online Accounting for a given PTA.
 
@@ -121,7 +140,7 @@ class SimulatedAccountingMethod:
         self.ts_class = simulate_int_type(ts_type)
         self.power_class = simulate_int_type(power_type)
         self.energy_class = simulate_int_type(energy_type)
-        self.current_state = pta.state['UNINITIALIZED']
+        self.current_state = pta.state["UNINITIALIZED"]
 
         self.ts_granularity = ts_granularity
         self.power_granularity = power_granularity
@@ -137,7 +156,13 @@ class SimulatedAccountingMethod:
         Does not use Module types and therefore does not consider overflows or data-type limitations"""
         if self.energy_granularity == self.power_granularity * self.ts_granularity:
             return power * time
-        return int(power * self.power_granularity * time * self.ts_granularity / self.energy_granularity)
+        return int(
+            power
+            * self.power_granularity
+            * time
+            * self.ts_granularity
+            / self.energy_granularity
+        )
 
     def _sleep_duration(self, duration_us):
         u"""
@@ -202,11 +227,11 @@ class SimulatedStaticAccountingImmediateCalculation(SimulatedAccountingMethod):
 
     def sleep(self, duration_us):
         time = self._sleep_duration(duration_us)
-        print('sleep duration is {}'.format(time))
+        print("sleep duration is {}".format(time))
         power = int(self.current_state.power.value)
-        print('power is {}'.format(power))
+        print("power is {}".format(power))
         energy = self._energy_from_power_and_time(time, power)
-        print('energy is {}'.format(energy))
+        print("energy is {}".format(energy))
         self.energy += energy
 
     def pass_transition(self, transition: Transition):
@@ -232,7 +257,7 @@ class SimulatedStaticAccounting(SimulatedAccountingMethod):
             self.time_in_state[state_name] = self.ts_class(0)
         self.transition_count = list()
         for transition in pta.transitions:
-            self.transition_count.append(simulate_int_type('uint16_t')(0))
+            self.transition_count.append(simulate_int_type("uint16_t")(0))
 
     def sleep(self, duration_us):
         self.time_in_state[self.current_state.name] += self._sleep_duration(duration_us)
@@ -245,7 +270,9 @@ class SimulatedStaticAccounting(SimulatedAccountingMethod):
         pta = self.pta
         energy = self.energy_class(0)
         for state in pta.state.values():
-            energy += self._energy_from_power_and_time(self.time_in_state[state.name], int(state.power.value))
+            energy += self._energy_from_power_and_time(
+                self.time_in_state[state.name], int(state.power.value)
+            )
         for i, transition in enumerate(pta.transitions):
             energy += self.transition_count[i] * int(transition.energy.value)
         return energy.val
@@ -275,7 +302,9 @@ class SimulatedStaticStateOnlyAccounting(SimulatedAccountingMethod):
         pta = self.pta
         energy = self.energy_class(0)
         for state in pta.state.values():
-            energy += self._energy_from_power_and_time(self.time_in_state[state.name], int(state.power.value))
+            energy += self._energy_from_power_and_time(
+                self.time_in_state[state.name], int(state.power.value)
+            )
         return energy.val
 
 
@@ -290,32 +319,50 @@ class AccountingMethod:
         self.public_functions = list()
 
     def pre_transition_hook(self, transition):
-        return ''
+        return ""
 
     def init_code(self):
-        return ''
+        return ""
 
     def get_includes(self):
         return map(lambda x: '#include "{}"'.format(x), self.include_paths)
 
 
 class StaticStateOnlyAccountingImmediateCalculation(AccountingMethod):
-    def __init__(self, class_name: str, pta: PTA, ts_type='unsigned int', power_type='unsigned int', energy_type='unsigned long'):
+    def __init__(
+        self,
+        class_name: str,
+        pta: PTA,
+        ts_type="unsigned int",
+        power_type="unsigned int",
+        energy_type="unsigned long",
+    ):
         super().__init__(class_name, pta)
         self.ts_type = ts_type
-        self.include_paths.append('driver/uptime.h')
-        self.private_variables.append('unsigned char lastState;')
-        self.private_variables.append('{} lastStateChange;'.format(ts_type))
-        self.private_variables.append('{} totalEnergy;'.format(energy_type))
-        self.private_variables.append(array_template.format(
-            type=power_type,
-            name='state_power',
-            length=len(pta.state),
-            elements=', '.join(map(lambda state_name: '{:.0f}'.format(pta.state[state_name].power), pta.get_state_names()))
-        ))
+        self.include_paths.append("driver/uptime.h")
+        self.private_variables.append("unsigned char lastState;")
+        self.private_variables.append("{} lastStateChange;".format(ts_type))
+        self.private_variables.append("{} totalEnergy;".format(energy_type))
+        self.private_variables.append(
+            array_template.format(
+                type=power_type,
+                name="state_power",
+                length=len(pta.state),
+                elements=", ".join(
+                    map(
+                        lambda state_name: "{:.0f}".format(pta.state[state_name].power),
+                        pta.get_state_names(),
+                    )
+                ),
+            )
+        )
 
         get_energy_function = """return totalEnergy;"""
-        self.public_functions.append(ClassFunction(class_name, energy_type, 'getEnergy', list(), get_energy_function))
+        self.public_functions.append(
+            ClassFunction(
+                class_name, energy_type, "getEnergy", list(), get_energy_function
+            )
+        )
 
     def pre_transition_hook(self, transition):
         return """
@@ -323,30 +370,50 @@ class StaticStateOnlyAccountingImmediateCalculation(AccountingMethod):
         totalEnergy += (now - lastStateChange) * state_power[lastState];
         lastStateChange = now;
         lastState = {};
-        """.format(self.pta.get_state_id(transition.destination))
+        """.format(
+            self.pta.get_state_id(transition.destination)
+        )
 
     def init_code(self):
         return """
         totalEnergy = 0;
         lastStateChange = 0;
         lastState = 0;
-        """.format(num_states=len(self.pta.state))
+        """.format(
+            num_states=len(self.pta.state)
+        )
 
 
 class StaticStateOnlyAccounting(AccountingMethod):
-    def __init__(self, class_name: str, pta: PTA, ts_type='unsigned int', power_type='unsigned int', energy_type='unsigned long'):
+    def __init__(
+        self,
+        class_name: str,
+        pta: PTA,
+        ts_type="unsigned int",
+        power_type="unsigned int",
+        energy_type="unsigned long",
+    ):
         super().__init__(class_name, pta)
         self.ts_type = ts_type
-        self.include_paths.append('driver/uptime.h')
-        self.private_variables.append('unsigned char lastState;')
-        self.private_variables.append('{} lastStateChange;'.format(ts_type))
-        self.private_variables.append(array_template.format(
-            type=power_type,
-            name='state_power',
-            length=len(pta.state),
-            elements=', '.join(map(lambda state_name: '{:.0f}'.format(pta.state[state_name].power), pta.get_state_names()))
-        ))
-        self.private_variables.append('{} timeInState[{}];'.format(ts_type, len(pta.state)))
+        self.include_paths.append("driver/uptime.h")
+        self.private_variables.append("unsigned char lastState;")
+        self.private_variables.append("{} lastStateChange;".format(ts_type))
+        self.private_variables.append(
+            array_template.format(
+                type=power_type,
+                name="state_power",
+                length=len(pta.state),
+                elements=", ".join(
+                    map(
+                        lambda state_name: "{:.0f}".format(pta.state[state_name].power),
+                        pta.get_state_names(),
+                    )
+                ),
+            )
+        )
+        self.private_variables.append(
+            "{} timeInState[{}];".format(ts_type, len(pta.state))
+        )
 
         get_energy_function = """
         {energy_type} total_energy = 0;
@@ -354,8 +421,14 @@ class StaticStateOnlyAccounting(AccountingMethod):
             total_energy += timeInState[i] * state_power[i];
         }}
         return total_energy;
-        """.format(energy_type=energy_type, num_states=len(pta.state))
-        self.public_functions.append(ClassFunction(class_name, energy_type, 'getEnergy', list(), get_energy_function))
+        """.format(
+            energy_type=energy_type, num_states=len(pta.state)
+        )
+        self.public_functions.append(
+            ClassFunction(
+                class_name, energy_type, "getEnergy", list(), get_energy_function
+            )
+        )
 
     def pre_transition_hook(self, transition):
         return """
@@ -363,7 +436,9 @@ class StaticStateOnlyAccounting(AccountingMethod):
         timeInState[lastState] += now - lastStateChange;
         lastStateChange = now;
         lastState = {};
-        """.format(self.pta.get_state_id(transition.destination))
+        """.format(
+            self.pta.get_state_id(transition.destination)
+        )
 
     def init_code(self):
         return """
@@ -372,30 +447,59 @@ class StaticStateOnlyAccounting(AccountingMethod):
         }}
         lastState = 0;
         lastStateChange = 0;
-        """.format(num_states=len(self.pta.state))
+        """.format(
+            num_states=len(self.pta.state)
+        )
 
 
 class StaticAccounting(AccountingMethod):
-    def __init__(self, class_name: str, pta: PTA, ts_type='unsigned int', power_type='unsigned int', energy_type='unsigned long'):
+    def __init__(
+        self,
+        class_name: str,
+        pta: PTA,
+        ts_type="unsigned int",
+        power_type="unsigned int",
+        energy_type="unsigned long",
+    ):
         super().__init__(class_name, pta)
         self.ts_type = ts_type
-        self.include_paths.append('driver/uptime.h')
-        self.private_variables.append('unsigned char lastState;')
-        self.private_variables.append('{} lastStateChange;'.format(ts_type))
-        self.private_variables.append(array_template.format(
-            type=power_type,
-            name='state_power',
-            length=len(pta.state),
-            elements=', '.join(map(lambda state_name: '{:.0f}'.format(pta.state[state_name].power), pta.get_state_names()))
-        ))
-        self.private_variables.append(array_template.format(
-            type=energy_type,
-            name='transition_energy',
-            length=len(pta.get_unique_transitions()),
-            elements=', '.join(map(lambda transition: '{:.0f}'.format(transition.energy), pta.get_unique_transitions()))
-        ))
-        self.private_variables.append('{} timeInState[{}];'.format(ts_type, len(pta.state)))
-        self.private_variables.append('{} transitionCount[{}];'.format('unsigned int', len(pta.get_unique_transitions())))
+        self.include_paths.append("driver/uptime.h")
+        self.private_variables.append("unsigned char lastState;")
+        self.private_variables.append("{} lastStateChange;".format(ts_type))
+        self.private_variables.append(
+            array_template.format(
+                type=power_type,
+                name="state_power",
+                length=len(pta.state),
+                elements=", ".join(
+                    map(
+                        lambda state_name: "{:.0f}".format(pta.state[state_name].power),
+                        pta.get_state_names(),
+                    )
+                ),
+            )
+        )
+        self.private_variables.append(
+            array_template.format(
+                type=energy_type,
+                name="transition_energy",
+                length=len(pta.get_unique_transitions()),
+                elements=", ".join(
+                    map(
+                        lambda transition: "{:.0f}".format(transition.energy),
+                        pta.get_unique_transitions(),
+                    )
+                ),
+            )
+        )
+        self.private_variables.append(
+            "{} timeInState[{}];".format(ts_type, len(pta.state))
+        )
+        self.private_variables.append(
+            "{} transitionCount[{}];".format(
+                "unsigned int", len(pta.get_unique_transitions())
+            )
+        )
 
         get_energy_function = """
         {energy_type} total_energy = 0;
@@ -406,8 +510,16 @@ class StaticAccounting(AccountingMethod):
             total_energy += transitionCount[i] * transition_energy[i];
         }}
         return total_energy;
-        """.format(energy_type=energy_type, num_states=len(pta.state), num_transitions=len(pta.get_unique_transitions()))
-        self.public_functions.append(ClassFunction(class_name, energy_type, 'getEnergy', list(), get_energy_function))
+        """.format(
+            energy_type=energy_type,
+            num_states=len(pta.state),
+            num_transitions=len(pta.get_unique_transitions()),
+        )
+        self.public_functions.append(
+            ClassFunction(
+                class_name, energy_type, "getEnergy", list(), get_energy_function
+            )
+        )
 
     def pre_transition_hook(self, transition):
         return """
@@ -416,7 +528,10 @@ class StaticAccounting(AccountingMethod):
         transitionCount[{}]++;
         lastStateChange = now;
         lastState = {};
-        """.format(self.pta.get_unique_transition_id(transition), self.pta.get_state_id(transition.destination))
+        """.format(
+            self.pta.get_unique_transition_id(transition),
+            self.pta.get_state_id(transition.destination),
+        )
 
     def init_code(self):
         return """
@@ -428,28 +543,53 @@ class StaticAccounting(AccountingMethod):
         }}
         lastState = 0;
         lastStateChange = 0;
-        """.format(num_states=len(self.pta.state), num_transitions=len(self.pta.get_unique_transitions()))
+        """.format(
+            num_states=len(self.pta.state),
+            num_transitions=len(self.pta.get_unique_transitions()),
+        )
 
 
 class StaticAccountingImmediateCalculation(AccountingMethod):
-    def __init__(self, class_name: str, pta: PTA, ts_type='unsigned int', power_type='unsigned int', energy_type='unsigned long'):
+    def __init__(
+        self,
+        class_name: str,
+        pta: PTA,
+        ts_type="unsigned int",
+        power_type="unsigned int",
+        energy_type="unsigned long",
+    ):
         super().__init__(class_name, pta)
         self.ts_type = ts_type
-        self.include_paths.append('driver/uptime.h')
-        self.private_variables.append('unsigned char lastState;')
-        self.private_variables.append('{} lastStateChange;'.format(ts_type))
-        self.private_variables.append('{} totalEnergy;'.format(energy_type))
-        self.private_variables.append(array_template.format(
-            type=power_type,
-            name='state_power',
-            length=len(pta.state),
-            elements=', '.join(map(lambda state_name: '{:.0f}'.format(pta.state[state_name].power), pta.get_state_names()))
-        ))
+        self.include_paths.append("driver/uptime.h")
+        self.private_variables.append("unsigned char lastState;")
+        self.private_variables.append("{} lastStateChange;".format(ts_type))
+        self.private_variables.append("{} totalEnergy;".format(energy_type))
+        self.private_variables.append(
+            array_template.format(
+                type=power_type,
+                name="state_power",
+                length=len(pta.state),
+                elements=", ".join(
+                    map(
+                        lambda state_name: "{:.0f}".format(pta.state[state_name].power),
+                        pta.get_state_names(),
+                    )
+                ),
+            )
+        )
 
         get_energy_function = """
         return totalEnergy;
-        """.format(energy_type=energy_type, num_states=len(pta.state), num_transitions=len(pta.get_unique_transitions()))
-        self.public_functions.append(ClassFunction(class_name, energy_type, 'getEnergy', list(), get_energy_function))
+        """.format(
+            energy_type=energy_type,
+            num_states=len(pta.state),
+            num_transitions=len(pta.get_unique_transitions()),
+        )
+        self.public_functions.append(
+            ClassFunction(
+                class_name, energy_type, "getEnergy", list(), get_energy_function
+            )
+        )
 
     def pre_transition_hook(self, transition):
         return """
@@ -458,21 +598,26 @@ class StaticAccountingImmediateCalculation(AccountingMethod):
         totalEnergy += {};
         lastStateChange = now;
         lastState = {};
-        """.format(transition.energy, self.pta.get_state_id(transition.destination))
+        """.format(
+            transition.energy, self.pta.get_state_id(transition.destination)
+        )
 
     def init_code(self):
         return """
         lastState = 0;
         lastStateChange = 0;
-        """.format(num_states=len(self.pta.state), num_transitions=len(self.pta.get_unique_transitions()))
+        """.format(
+            num_states=len(self.pta.state),
+            num_transitions=len(self.pta.get_unique_transitions()),
+        )
 
 
 class MultipassDriver:
     """Generate C++ header and no-op implementation for a multipass driver based on a DFA model."""
 
     def __init__(self, name, pta, class_info, enum=dict(), accounting=AccountingMethod):
-        self.impl = ''
-        self.header = ''
+        self.impl = ""
+        self.header = ""
         self.name = name
         self.pta = pta
         self.class_info = class_info
@@ -484,35 +629,53 @@ class MultipassDriver:
         private_variables = list()
         public_variables = list()
 
-        public_functions.append(ClassFunction(self.name, '', self.name, list(), accounting.init_code()))
+        public_functions.append(
+            ClassFunction(self.name, "", self.name, list(), accounting.init_code())
+        )
 
         for transition in self.pta.get_unique_transitions():
 
-            if transition.name == 'getEnergy':
+            if transition.name == "getEnergy":
                 continue
 
             # XXX right now we only verify whether both functions have the
             # same number of arguments. This breaks in many overloading cases.
             function_info = self.class_info.function[transition.name]
             for function_candidate in self.class_info.functions:
-                if function_candidate.name == transition.name and len(function_candidate.argument_types) == len(transition.arguments):
+                if function_candidate.name == transition.name and len(
+                    function_candidate.argument_types
+                ) == len(transition.arguments):
                     function_info = function_candidate
 
             function_arguments = list()
 
             for i in range(len(transition.arguments)):
-                function_arguments.append('{} {}'.format(function_info.argument_types[i], transition.arguments[i]))
+                function_arguments.append(
+                    "{} {}".format(
+                        function_info.argument_types[i], transition.arguments[i]
+                    )
+                )
 
             function_body = accounting.pre_transition_hook(transition)
 
-            if function_info.return_type != 'void':
-                function_body += 'return 0;\n'
+            if function_info.return_type != "void":
+                function_body += "return 0;\n"
 
-            public_functions.append(ClassFunction(self.name, function_info.return_type, transition.name, function_arguments, function_body))
+            public_functions.append(
+                ClassFunction(
+                    self.name,
+                    function_info.return_type,
+                    transition.name,
+                    function_arguments,
+                    function_body,
+                )
+            )
 
         enums = list()
         for enum_name in self.enum.keys():
-            enums.append('enum {} {{ {} }};'.format(enum_name, ', '.join(self.enum[enum_name])))
+            enums.append(
+                "enum {} {{ {} }};".format(enum_name, ", ".join(self.enum[enum_name]))
+            )
 
         if accounting:
             includes.extend(accounting.get_includes())
@@ -522,11 +685,21 @@ class MultipassDriver:
             public_variables.extend(accounting.public_variables)
 
         self.header = header_template.format(
-            name=self.name, name_lower=self.name.lower(),
-            includes='\n'.join(includes),
-            private_variables='\n'.join(private_variables),
-            public_variables='\n'.join(public_variables),
-            public_functions='\n'.join(map(lambda x: x.get_definition(), public_functions)),
-            private_functions='',
-            enums='\n'.join(enums))
-        self.impl = implementation_template.format(name=self.name, name_lower=self.name.lower(), functions='\n\n'.join(map(lambda x: x.get_implementation(), public_functions)))
+            name=self.name,
+            name_lower=self.name.lower(),
+            includes="\n".join(includes),
+            private_variables="\n".join(private_variables),
+            public_variables="\n".join(public_variables),
+            public_functions="\n".join(
+                map(lambda x: x.get_definition(), public_functions)
+            ),
+            private_functions="",
+            enums="\n".join(enums),
+        )
+        self.impl = implementation_template.format(
+            name=self.name,
+            name_lower=self.name.lower(),
+            functions="\n\n".join(
+                map(lambda x: x.get_implementation(), public_functions)
+            ),
+        )
diff --git a/lib/cycles_to_energy.py b/lib/cycles_to_energy.py
index 35f9199..a8e88b8 100644
--- a/lib/cycles_to_energy.py
+++ b/lib/cycles_to_energy.py
@@ -5,86 +5,80 @@ Contains classes for some embedded CPUs/MCUs. Given a configuration, each
 class can convert a cycle count to an energy consumption.
 """
 
+
 def get_class(cpu_name):
     """Return model class for cpu_name."""
-    if cpu_name == 'MSP430':
+    if cpu_name == "MSP430":
         return MSP430
-    if cpu_name == 'ATMega168':
+    if cpu_name == "ATMega168":
         return ATMega168
-    if cpu_name == 'ATMega328':
+    if cpu_name == "ATMega328":
         return ATMega328
-    if cpu_name == 'ATTiny88':
+    if cpu_name == "ATTiny88":
         return ATTiny88
-    if cpu_name == 'esp8266':
+    if cpu_name == "esp8266":
         return ESP8266
 
+
 def _param_list_to_dict(device, param_list):
     param_dict = dict()
     for i, parameter in enumerate(sorted(device.parameters.keys())):
         param_dict[parameter] = param_list[i]
     return param_dict
 
+
 class MSP430:
-    name = 'MSP430'
+    name = "MSP430"
     parameters = {
-        'cpu_freq': [1e6, 4e6, 8e6, 12e6, 16e6],
-        'memory' : ['unified', 'fram0', 'fram50', 'fram66', 'fram75', 'fram100', 'ram'],
-        'voltage': [2.2, 3.0],
-    }
-    default_params = {
-        'cpu_freq': 4e6,
-        'memory' : 'unified',
-        'voltage': 3
+        "cpu_freq": [1e6, 4e6, 8e6, 12e6, 16e6],
+        "memory": ["unified", "fram0", "fram50", "fram66", "fram75", "fram100", "ram"],
+        "voltage": [2.2, 3.0],
     }
+    default_params = {"cpu_freq": 4e6, "memory": "unified", "voltage": 3}
 
     current_by_mem = {
-        'unified' : [210,  640, 1220, 1475, 1845],
-        'fram0'   : [370, 1280, 2510, 2080, 2650],
-        'fram50'  : [240,  745, 1440, 1575, 1990],
-        'fram66'  : [200,  560, 1070, 1300, 1620],
-        'fram75'  : [170,  480,  890, 1155, 1420],
-        'fram100' : [110,  235,  420,  640,  730],
-        'ram'     : [130,  320,  585,  890, 1070],
+        "unified": [210, 640, 1220, 1475, 1845],
+        "fram0": [370, 1280, 2510, 2080, 2650],
+        "fram50": [240, 745, 1440, 1575, 1990],
+        "fram66": [200, 560, 1070, 1300, 1620],
+        "fram75": [170, 480, 890, 1155, 1420],
+        "fram100": [110, 235, 420, 640, 730],
+        "ram": [130, 320, 585, 890, 1070],
     }
 
     def get_current(params):
         if type(params) != dict:
             return MSP430.get_current(_param_list_to_dict(MSP430, params))
-        cpu_freq_index = MSP430.parameters['cpu_freq'].index(params['cpu_freq'])
+        cpu_freq_index = MSP430.parameters["cpu_freq"].index(params["cpu_freq"])
 
-        return MSP430.current_by_mem[params['memory']][cpu_freq_index] * 1e-6
+        return MSP430.current_by_mem[params["memory"]][cpu_freq_index] * 1e-6
 
     def get_power(params):
         if type(params) != dict:
             return MSP430.get_energy(_param_list_to_dict(MSP430, params))
 
-        return MSP430.get_current(params) * params['voltage']
+        return MSP430.get_current(params) * params["voltage"]
 
     def get_energy_per_cycle(params):
         if type(params) != dict:
             return MSP430.get_energy_per_cycle(_param_list_to_dict(MSP430, params))
 
-        return MSP430.get_power(params) / params['cpu_freq']
+        return MSP430.get_power(params) / params["cpu_freq"]
+
 
 class ATMega168:
-    name = 'ATMega168'
-    parameters = {
-        'cpu_freq': [1e6, 4e6, 8e6],
-        'voltage': [2, 3, 5]
-    }
-    default_params = {
-        'cpu_freq': 4e6,
-        'voltage': 3
-    }
+    name = "ATMega168"
+    parameters = {"cpu_freq": [1e6, 4e6, 8e6], "voltage": [2, 3, 5]}
+    default_params = {"cpu_freq": 4e6, "voltage": 3}
 
     def get_current(params):
         if type(params) != dict:
             return ATMega168.get_current(_param_list_to_dict(ATMega168, params))
-        if params['cpu_freq'] == 1e6 and params['voltage'] <= 2:
+        if params["cpu_freq"] == 1e6 and params["voltage"] <= 2:
             return 0.5e-3
-        if params['cpu_freq'] == 4e6 and params['voltage'] <= 3:
+        if params["cpu_freq"] == 4e6 and params["voltage"] <= 3:
             return 3.5e-3
-        if params['cpu_freq'] == 8e6 and params['voltage'] <= 5:
+        if params["cpu_freq"] == 8e6 and params["voltage"] <= 5:
             return 12e-3
         return None
 
@@ -92,37 +86,34 @@ class ATMega168:
         if type(params) != dict:
             return ATMega168.get_energy(_param_list_to_dict(ATMega168, params))
 
-        return ATMega168.get_current(params) * params['voltage']
+        return ATMega168.get_current(params) * params["voltage"]
 
     def get_energy_per_cycle(params):
         if type(params) != dict:
-            return ATMega168.get_energy_per_cycle(_param_list_to_dict(ATMega168, params))
+            return ATMega168.get_energy_per_cycle(
+                _param_list_to_dict(ATMega168, params)
+            )
+
+        return ATMega168.get_power(params) / params["cpu_freq"]
 
-        return ATMega168.get_power(params) / params['cpu_freq']
 
 class ATMega328:
-    name = 'ATMega328'
-    parameters = {
-        'cpu_freq': [1e6, 4e6, 8e6],
-        'voltage': [2, 3, 5]
-    }
-    default_params = {
-        'cpu_freq': 4e6,
-        'voltage': 3
-    }
+    name = "ATMega328"
+    parameters = {"cpu_freq": [1e6, 4e6, 8e6], "voltage": [2, 3, 5]}
+    default_params = {"cpu_freq": 4e6, "voltage": 3}
 
     # Source: ATMega328P Datasheet p.316 / table 28.2.4
     def get_current(params):
         if type(params) != dict:
             return ATMega328.get_current(_param_list_to_dict(ATMega328, params))
         # specified for 2V
-        if params['cpu_freq'] == 1e6 and params['voltage'] >= 1.8:
+        if params["cpu_freq"] == 1e6 and params["voltage"] >= 1.8:
             return 0.3e-3
         # specified for 3V
-        if params['cpu_freq'] == 4e6 and params['voltage'] >= 1.8:
+        if params["cpu_freq"] == 4e6 and params["voltage"] >= 1.8:
             return 1.7e-3
         # specified for 5V
-        if params['cpu_freq'] == 8e6 and params['voltage'] >= 2.5:
+        if params["cpu_freq"] == 8e6 and params["voltage"] >= 2.5:
             return 5.2e-3
         return None
 
@@ -130,26 +121,26 @@ class ATMega328:
         if type(params) != dict:
             return ATMega328.get_energy(_param_list_to_dict(ATMega328, params))
 
-        return ATMega328.get_current(params) * params['voltage']
+        return ATMega328.get_current(params) * params["voltage"]
 
     def get_energy_per_cycle(params):
         if type(params) != dict:
-            return ATMega328.get_energy_per_cycle(_param_list_to_dict(ATMega328, params))
+            return ATMega328.get_energy_per_cycle(
+                _param_list_to_dict(ATMega328, params)
+            )
+
+        return ATMega328.get_power(params) / params["cpu_freq"]
 
-        return ATMega328.get_power(params) / params['cpu_freq']
 
 class ESP8266:
     # Source: ESP8266EX Datasheet, table 5-2 (v2017.11) / table 3-4 (v2018.11)
     # Taken at 3.0V
-    name = 'ESP8266'
+    name = "ESP8266"
     parameters = {
-        'cpu_freq': [80e6],
-        'voltage': [2.5, 3.0, 3.3, 3.6] # min / ... / typ / max
-    }
-    default_params = {
-        'cpu_freq': 80e6,
-        'voltage': 3.0
+        "cpu_freq": [80e6],
+        "voltage": [2.5, 3.0, 3.3, 3.6],  # min / ... / typ / max
     }
+    default_params = {"cpu_freq": 80e6, "voltage": 3.0}
 
     def get_current(params):
         if type(params) != dict:
@@ -161,33 +152,27 @@ class ESP8266:
         if type(params) != dict:
             return ESP8266.get_power(_param_list_to_dict(ESP8266, params))
 
-        return ESP8266.get_current(params) * params['voltage']
+        return ESP8266.get_current(params) * params["voltage"]
 
     def get_energy_per_cycle(params):
         if type(params) != dict:
             return ESP8266.get_energy_per_cycle(_param_list_to_dict(ESP8266, params))
 
-        return ESP8266.get_power(params) / params['cpu_freq']
+        return ESP8266.get_power(params) / params["cpu_freq"]
+
 
 class ATTiny88:
-    name = 'ATTiny88'
-    parameters = {
-        'cpu_freq': [1e6, 4e6, 8e6],
-        'voltage': [2, 3, 5]
-    }
-    default_params = {
-        'cpu_freq' : 4e6,
-        'voltage' : 3
-    }
+    name = "ATTiny88"
+    parameters = {"cpu_freq": [1e6, 4e6, 8e6], "voltage": [2, 3, 5]}
+    default_params = {"cpu_freq": 4e6, "voltage": 3}
 
     def get_current(params):
         if type(params) != dict:
             return ATTiny88.get_current(_param_list_to_dict(ATTiny88, params))
-        if params['cpu_freq'] == 1e6 and params['voltage'] <= 2:
+        if params["cpu_freq"] == 1e6 and params["voltage"] <= 2:
             return 0.2e-3
-        if params['cpu_freq'] == 4e6 and params['voltage'] <= 3:
+        if params["cpu_freq"] == 4e6 and params["voltage"] <= 3:
             return 1.4e-3
-        if params['cpu_freq'] == 8e6 and params['voltage'] <= 5:
+        if params["cpu_freq"] == 8e6 and params["voltage"] <= 5:
             return 4.5e-3
         return None
-
diff --git a/lib/data_parameters.py b/lib/data_parameters.py
index 3b7a148..1150b71 100644
--- a/lib/data_parameters.py
+++ b/lib/data_parameters.py
@@ -10,6 +10,7 @@ from . import cycles_to_energy, size_to_radio_energy, utils
 import numpy as np
 import ubjson
 
+
 def _string_value_length(json):
     if type(json) == str:
         return len(json)
@@ -22,6 +23,7 @@ def _string_value_length(json):
 
     return 0
 
+
 # TODO distinguish between int and uint, which is not visible from the
 # data value alone
 def _int_value_length(json):
@@ -40,18 +42,21 @@ def _int_value_length(json):
 
     return 0
 
+
 def _string_key_length(json):
     if type(json) == dict:
         return sum(map(len, json.keys())) + sum(map(_string_key_length, json.values()))
 
     return 0
 
+
 def _num_keys(json):
     if type(json) == dict:
         return len(json.keys()) + sum(map(_num_keys, json.values()))
 
     return 0
 
+
 def _num_of_type(json, wanted_type):
     ret = 0
     if type(json) == wanted_type:
@@ -65,16 +70,17 @@ def _num_of_type(json, wanted_type):
 
     return ret
 
+
 def json_to_param(json):
     """Return numeric parameters describing the structure of JSON data."""
     ret = dict()
 
-    ret['strlen_keys'] = _string_key_length(json)
-    ret['strlen_values'] = _string_value_length(json)
-    ret['bytelen_int'] = _int_value_length(json)
-    ret['num_int'] = _num_of_type(json, int)
-    ret['num_float'] = _num_of_type(json, float)
-    ret['num_str'] = _num_of_type(json, str)
+    ret["strlen_keys"] = _string_key_length(json)
+    ret["strlen_values"] = _string_value_length(json)
+    ret["bytelen_int"] = _int_value_length(json)
+    ret["num_int"] = _num_of_type(json, int)
+    ret["num_float"] = _num_of_type(json, float)
+    ret["num_str"] = _num_of_type(json, str)
 
     return ret
 
@@ -127,16 +133,16 @@ class Protolog:
             # bogus data
             if val > 10_000_000:
                 return np.nan
-        for val in data['nop']:
+        for val in data["nop"]:
             # bogus data
             if val > 10_000_000:
                 return np.nan
         # All measurements in data[key] cover the same instructions, so they
         # should be identical -> it's safe to take the median.
         # However, we leave out the first measurement as it is often bogus.
-        if key == 'nop':
-            return np.median(data['nop'][1:])
-        return max(0, int(np.median(data[key][1:]) - np.median(data['nop'][1:])))
+        if key == "nop":
+            return np.median(data["nop"][1:])
+        return max(0, int(np.median(data[key][1:]) - np.median(data["nop"][1:])))
 
     def _median_callcycles(data):
         ret = dict()
@@ -146,37 +152,44 @@ class Protolog:
 
     idem = lambda x: x
     datamap = [
-        ['bss_nop', 'bss_size_nop', idem],
-        ['bss_ser', 'bss_size_ser', idem],
-        ['bss_serdes', 'bss_size_serdes', idem],
-        ['callcycles_raw', 'callcycles', idem],
-        ['callcycles_median', 'callcycles', _median_callcycles],
+        ["bss_nop", "bss_size_nop", idem],
+        ["bss_ser", "bss_size_ser", idem],
+        ["bss_serdes", "bss_size_serdes", idem],
+        ["callcycles_raw", "callcycles", idem],
+        ["callcycles_median", "callcycles", _median_callcycles],
         # Used to remove nop cycles from callcycles_median
-        ['cycles_nop', 'cycles', lambda x: Protolog._median_cycles(x, 'nop')],
-        ['cycles_ser', 'cycles', lambda x: Protolog._median_cycles(x, 'ser')],
-        ['cycles_des', 'cycles', lambda x: Protolog._median_cycles(x, 'des')],
-        ['cycles_enc', 'cycles', lambda x: Protolog._median_cycles(x, 'enc')],
-        ['cycles_dec', 'cycles', lambda x: Protolog._median_cycles(x, 'dec')],
-        #['cycles_ser_arr', 'cycles', lambda x: np.array(x['ser'][1:]) - np.mean(x['nop'][1:])],
-        #['cycles_des_arr', 'cycles', lambda x: np.array(x['des'][1:]) - np.mean(x['nop'][1:])],
-        #['cycles_enc_arr', 'cycles', lambda x: np.array(x['enc'][1:]) - np.mean(x['nop'][1:])],
-        #['cycles_dec_arr', 'cycles', lambda x: np.array(x['dec'][1:]) - np.mean(x['nop'][1:])],
-        ['data_nop', 'data_size_nop', idem],
-        ['data_ser', 'data_size_ser', idem],
-        ['data_serdes', 'data_size_serdes', idem],
-        ['heap_ser', 'heap_usage_ser', idem],
-        ['heap_des', 'heap_usage_des', idem],
-        ['serialized_size', 'serialized_size', idem],
-        ['stack_alloc_ser', 'stack_online_ser', lambda x: x['allocated']],
-        ['stack_set_ser', 'stack_online_ser', lambda x: x['used']],
-        ['stack_alloc_des', 'stack_online_des', lambda x: x['allocated']],
-        ['stack_set_des', 'stack_online_des', lambda x: x['used']],
-        ['text_nop', 'text_size_nop', idem],
-        ['text_ser', 'text_size_ser', idem],
-        ['text_serdes', 'text_size_serdes', idem],
+        ["cycles_nop", "cycles", lambda x: Protolog._median_cycles(x, "nop")],
+        ["cycles_ser", "cycles", lambda x: Protolog._median_cycles(x, "ser")],
+        ["cycles_des", "cycles", lambda x: Protolog._median_cycles(x, "des")],
+        ["cycles_enc", "cycles", lambda x: Protolog._median_cycles(x, "enc")],
+        ["cycles_dec", "cycles", lambda x: Protolog._median_cycles(x, "dec")],
+        # ['cycles_ser_arr', 'cycles', lambda x: np.array(x['ser'][1:]) - np.mean(x['nop'][1:])],
+        # ['cycles_des_arr', 'cycles', lambda x: np.array(x['des'][1:]) - np.mean(x['nop'][1:])],
+        # ['cycles_enc_arr', 'cycles', lambda x: np.array(x['enc'][1:]) - np.mean(x['nop'][1:])],
+        # ['cycles_dec_arr', 'cycles', lambda x: np.array(x['dec'][1:]) - np.mean(x['nop'][1:])],
+        ["data_nop", "data_size_nop", idem],
+        ["data_ser", "data_size_ser", idem],
+        ["data_serdes", "data_size_serdes", idem],
+        ["heap_ser", "heap_usage_ser", idem],
+        ["heap_des", "heap_usage_des", idem],
+        ["serialized_size", "serialized_size", idem],
+        ["stack_alloc_ser", "stack_online_ser", lambda x: x["allocated"]],
+        ["stack_set_ser", "stack_online_ser", lambda x: x["used"]],
+        ["stack_alloc_des", "stack_online_des", lambda x: x["allocated"]],
+        ["stack_set_des", "stack_online_des", lambda x: x["used"]],
+        ["text_nop", "text_size_nop", idem],
+        ["text_ser", "text_size_ser", idem],
+        ["text_serdes", "text_size_serdes", idem],
     ]
 
-    def __init__(self, logfile, cpu_conf = None, cpu_conf_str = None, radio_conf = None, radio_conf_str = None):
+    def __init__(
+        self,
+        logfile,
+        cpu_conf=None,
+        cpu_conf_str=None,
+        radio_conf=None,
+        radio_conf_str=None,
+    ):
         """
         Load and enrich raw protobench log data.
 
@@ -185,116 +198,177 @@ class Protolog:
         """
         self.cpu = None
         self.radio = None
-        with open(logfile, 'rb') as f:
+        with open(logfile, "rb") as f:
             self.data = ubjson.load(f)
         self.libraries = set()
         self.architectures = set()
         self.aggregate = dict()
 
         for arch_lib in self.data.keys():
-            arch, lib, libopts = arch_lib.split(':')
-            library = lib + ':' + libopts
+            arch, lib, libopts = arch_lib.split(":")
+            library = lib + ":" + libopts
             for benchmark in self.data[arch_lib].keys():
                 for benchmark_item in self.data[arch_lib][benchmark].keys():
                     subv = self.data[arch_lib][benchmark][benchmark_item]
                     for aggregate_label, data_label, getter in Protolog.datamap:
                         try:
-                            self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, aggregate_label, data_label, getter)
+                            self.add_datapoint(
+                                arch,
+                                library,
+                                (benchmark, benchmark_item),
+                                subv,
+                                aggregate_label,
+                                data_label,
+                                getter,
+                            )
                         except KeyError:
                             pass
                         except TypeError as e:
-                            print('TypeError in {} {} {} {}: {} -> {}'.format(
-                                arch_lib, benchmark, benchmark_item, aggregate_label,
-                                subv[data_label]['v'], str(e)))
+                            print(
+                                "TypeError in {} {} {} {}: {} -> {}".format(
+                                    arch_lib,
+                                    benchmark,
+                                    benchmark_item,
+                                    aggregate_label,
+                                    subv[data_label]["v"],
+                                    str(e),
+                                )
+                            )
                             pass
                     try:
-                        codegen = codegen_for_lib(lib, libopts.split(','), subv['data'])
+                        codegen = codegen_for_lib(lib, libopts.split(","), subv["data"])
                         if codegen.max_serialized_bytes != None:
-                            self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, 'buffer_size', data_label, lambda x: codegen.max_serialized_bytes)
+                            self.add_datapoint(
+                                arch,
+                                library,
+                                (benchmark, benchmark_item),
+                                subv,
+                                "buffer_size",
+                                data_label,
+                                lambda x: codegen.max_serialized_bytes,
+                            )
                         else:
-                            self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, 'buffer_size', data_label, lambda x: 0)
+                            self.add_datapoint(
+                                arch,
+                                library,
+                                (benchmark, benchmark_item),
+                                subv,
+                                "buffer_size",
+                                data_label,
+                                lambda x: 0,
+                            )
                     except:
                         # avro's codegen will raise RuntimeError("Unsupported Schema") on unsupported data. Other libraries may just silently ignore it.
-                        self.add_datapoint(arch, library, (benchmark, benchmark_item), subv, 'buffer_size', data_label, lambda x: 0)
-                    #self.aggregate[(benchmark, benchmark_item)][arch][lib][aggregate_label] = getter(value[data_label]['v'])
-
+                        self.add_datapoint(
+                            arch,
+                            library,
+                            (benchmark, benchmark_item),
+                            subv,
+                            "buffer_size",
+                            data_label,
+                            lambda x: 0,
+                        )
+                    # self.aggregate[(benchmark, benchmark_item)][arch][lib][aggregate_label] = getter(value[data_label]['v'])
 
         for key in self.aggregate.keys():
             for arch in self.aggregate[key].keys():
                 for lib, val in self.aggregate[key][arch].items():
                     try:
-                        val['cycles_encser'] = val['cycles_enc'] + val['cycles_ser']
+                        val["cycles_encser"] = val["cycles_enc"] + val["cycles_ser"]
                     except KeyError:
                         pass
                     try:
-                        val['cycles_desdec'] = val['cycles_des'] + val['cycles_dec']
+                        val["cycles_desdec"] = val["cycles_des"] + val["cycles_dec"]
                     except KeyError:
                         pass
                     try:
-                        for line in val['callcycles_median'].keys():
-                            val['callcycles_median'][line] -= val['cycles_nop']
+                        for line in val["callcycles_median"].keys():
+                            val["callcycles_median"][line] -= val["cycles_nop"]
                     except KeyError:
                         pass
                     try:
-                        val['data_serdes_delta'] = val['data_serdes'] - val['data_nop']
+                        val["data_serdes_delta"] = val["data_serdes"] - val["data_nop"]
                     except KeyError:
                         pass
                     try:
-                        val['data_serdes_delta_nobuf'] = val['data_serdes'] - val['data_nop'] - val['buffer_size']
+                        val["data_serdes_delta_nobuf"] = (
+                            val["data_serdes"] - val["data_nop"] - val["buffer_size"]
+                        )
                     except KeyError:
                         pass
                     try:
-                        val['bss_serdes_delta'] = val['bss_serdes'] - val['bss_nop']
+                        val["bss_serdes_delta"] = val["bss_serdes"] - val["bss_nop"]
                     except KeyError:
                         pass
                     try:
-                        val['bss_serdes_delta_nobuf'] = val['bss_serdes'] - val['bss_nop'] - val['buffer_size']
+                        val["bss_serdes_delta_nobuf"] = (
+                            val["bss_serdes"] - val["bss_nop"] - val["buffer_size"]
+                        )
                     except KeyError:
                         pass
                     try:
-                        val['text_serdes_delta'] = val['text_serdes'] - val['text_nop']
+                        val["text_serdes_delta"] = val["text_serdes"] - val["text_nop"]
                     except KeyError:
                         pass
                     try:
-                        val['total_dmem_ser'] = val['stack_alloc_ser']
-                        val['written_dmem_ser'] = val['stack_set_ser']
-                        val['total_dmem_ser'] += val['heap_ser']
-                        val['written_dmem_ser'] += val['heap_ser']
+                        val["total_dmem_ser"] = val["stack_alloc_ser"]
+                        val["written_dmem_ser"] = val["stack_set_ser"]
+                        val["total_dmem_ser"] += val["heap_ser"]
+                        val["written_dmem_ser"] += val["heap_ser"]
                     except KeyError:
                         pass
                     try:
-                        val['total_dmem_des'] = val['stack_alloc_des']
-                        val['written_dmem_des'] = val['stack_set_des']
-                        val['total_dmem_des'] += val['heap_des']
-                        val['written_dmem_des'] += val['heap_des']
+                        val["total_dmem_des"] = val["stack_alloc_des"]
+                        val["written_dmem_des"] = val["stack_set_des"]
+                        val["total_dmem_des"] += val["heap_des"]
+                        val["written_dmem_des"] += val["heap_des"]
                     except KeyError:
                         pass
                     try:
-                        val['total_dmem_serdes'] = max(val['total_dmem_ser'], val['total_dmem_des'])
+                        val["total_dmem_serdes"] = max(
+                            val["total_dmem_ser"], val["total_dmem_des"]
+                        )
                     except KeyError:
                         pass
                     try:
-                        val['text_ser_delta'] = val['text_ser'] - val['text_nop']
-                        val['text_serdes_delta'] = val['text_serdes'] - val['text_nop']
+                        val["text_ser_delta"] = val["text_ser"] - val["text_nop"]
+                        val["text_serdes_delta"] = val["text_serdes"] - val["text_nop"]
                     except KeyError:
                         pass
                     try:
-                        val['bss_ser_delta'] = val['bss_ser'] - val['bss_nop']
-                        val['bss_serdes_delta'] = val['bss_serdes'] - val['bss_nop']
+                        val["bss_ser_delta"] = val["bss_ser"] - val["bss_nop"]
+                        val["bss_serdes_delta"] = val["bss_serdes"] - val["bss_nop"]
                     except KeyError:
                         pass
                     try:
-                        val['data_ser_delta'] = val['data_ser'] - val['data_nop']
-                        val['data_serdes_delta'] = val['data_serdes'] - val['data_nop']
+                        val["data_ser_delta"] = val["data_ser"] - val["data_nop"]
+                        val["data_serdes_delta"] = val["data_serdes"] - val["data_nop"]
                     except KeyError:
                         pass
                     try:
-                        val['allmem_ser'] = val['text_ser'] + val['data_ser'] + val['bss_ser'] + val['total_dmem_ser'] - val['buffer_size']
-                        val['allmem_serdes'] = val['text_serdes'] + val['data_serdes'] + val['bss_serdes'] + val['total_dmem_serdes'] - val['buffer_size']
+                        val["allmem_ser"] = (
+                            val["text_ser"]
+                            + val["data_ser"]
+                            + val["bss_ser"]
+                            + val["total_dmem_ser"]
+                            - val["buffer_size"]
+                        )
+                        val["allmem_serdes"] = (
+                            val["text_serdes"]
+                            + val["data_serdes"]
+                            + val["bss_serdes"]
+                            + val["total_dmem_serdes"]
+                            - val["buffer_size"]
+                        )
                     except KeyError:
                         pass
                     try:
-                        val['smem_serdes'] = val['text_serdes'] + val['data_serdes'] + val['bss_serdes'] - val['buffer_size']
+                        val["smem_serdes"] = (
+                            val["text_serdes"]
+                            + val["data_serdes"]
+                            + val["bss_serdes"]
+                            - val["buffer_size"]
+                        )
                     except KeyError:
                         pass
 
@@ -303,7 +377,7 @@ class Protolog:
 
         if cpu_conf:
             self.cpu_conf = cpu_conf
-            cpu = self.cpu = cycles_to_energy.get_class(cpu_conf['model'])
+            cpu = self.cpu = cycles_to_energy.get_class(cpu_conf["model"])
             for key, value in cpu.default_params.items():
                 if not key in cpu_conf:
                     cpu_conf[key] = value
@@ -312,48 +386,102 @@ class Protolog:
                     for lib, val in self.aggregate[key][arch].items():
                         # All energy data is stored in nanojoules (nJ)
                         try:
-                            val['energy_enc'] = int(val['cycles_enc'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
+                            val["energy_enc"] = int(
+                                val["cycles_enc"]
+                                * cpu.get_power(cpu_conf)
+                                / cpu_conf["cpu_freq"]
+                                * 1e9
+                            )
                         except KeyError:
                             pass
                         except ValueError:
-                            print('cycles_enc is NaN for {} -> {} -> {}'.format(arch, lib, key))
+                            print(
+                                "cycles_enc is NaN for {} -> {} -> {}".format(
+                                    arch, lib, key
+                                )
+                            )
                         try:
-                            val['energy_ser'] = int(val['cycles_ser'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
+                            val["energy_ser"] = int(
+                                val["cycles_ser"]
+                                * cpu.get_power(cpu_conf)
+                                / cpu_conf["cpu_freq"]
+                                * 1e9
+                            )
                         except KeyError:
                             pass
                         except ValueError:
-                            print('cycles_ser is NaN for {} -> {} -> {}'.format(arch, lib, key))
+                            print(
+                                "cycles_ser is NaN for {} -> {} -> {}".format(
+                                    arch, lib, key
+                                )
+                            )
                         try:
-                            val['energy_encser'] = int(val['cycles_encser'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
+                            val["energy_encser"] = int(
+                                val["cycles_encser"]
+                                * cpu.get_power(cpu_conf)
+                                / cpu_conf["cpu_freq"]
+                                * 1e9
+                            )
                         except KeyError:
                             pass
                         except ValueError:
-                            print('cycles_encser is NaN for {} -> {} -> {}'.format(arch, lib, key))
+                            print(
+                                "cycles_encser is NaN for {} -> {} -> {}".format(
+                                    arch, lib, key
+                                )
+                            )
                         try:
-                            val['energy_des'] = int(val['cycles_des'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
+                            val["energy_des"] = int(
+                                val["cycles_des"]
+                                * cpu.get_power(cpu_conf)
+                                / cpu_conf["cpu_freq"]
+                                * 1e9
+                            )
                         except KeyError:
                             pass
                         except ValueError:
-                            print('cycles_des is NaN for {} -> {} -> {}'.format(arch, lib, key))
+                            print(
+                                "cycles_des is NaN for {} -> {} -> {}".format(
+                                    arch, lib, key
+                                )
+                            )
                         try:
-                            val['energy_dec'] = int(val['cycles_dec'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
+                            val["energy_dec"] = int(
+                                val["cycles_dec"]
+                                * cpu.get_power(cpu_conf)
+                                / cpu_conf["cpu_freq"]
+                                * 1e9
+                            )
                         except KeyError:
                             pass
                         except ValueError:
-                            print('cycles_dec is NaN for {} -> {} -> {}'.format(arch, lib, key))
+                            print(
+                                "cycles_dec is NaN for {} -> {} -> {}".format(
+                                    arch, lib, key
+                                )
+                            )
                         try:
-                            val['energy_desdec'] = int(val['cycles_desdec'] * cpu.get_power(cpu_conf) / cpu_conf['cpu_freq'] * 1e9)
+                            val["energy_desdec"] = int(
+                                val["cycles_desdec"]
+                                * cpu.get_power(cpu_conf)
+                                / cpu_conf["cpu_freq"]
+                                * 1e9
+                            )
                         except KeyError:
                             pass
                         except ValueError:
-                            print('cycles_desdec is NaN for {} -> {} -> {}'.format(arch, lib, key))
+                            print(
+                                "cycles_desdec is NaN for {} -> {} -> {}".format(
+                                    arch, lib, key
+                                )
+                            )
 
         if radio_conf_str:
             radio_conf = utils.parse_conf_str(radio_conf_str)
 
         if radio_conf:
             self.radio_conf = radio_conf
-            radio = self.radio = size_to_radio_energy.get_class(radio_conf['model'])
+            radio = self.radio = size_to_radio_energy.get_class(radio_conf["model"])
             for key, value in radio.default_params.items():
                 if not key in radio_conf:
                     radio_conf[key] = value
@@ -361,17 +489,22 @@ class Protolog:
                 for arch in self.aggregate[key].keys():
                     for lib, val in self.aggregate[key][arch].items():
                         try:
-                            radio_conf['txbytes'] = val['serialized_size']
-                            if radio_conf['txbytes'] > 0:
-                                val['energy_tx'] = int(radio.get_energy(radio_conf) * 1e9)
+                            radio_conf["txbytes"] = val["serialized_size"]
+                            if radio_conf["txbytes"] > 0:
+                                val["energy_tx"] = int(
+                                    radio.get_energy(radio_conf) * 1e9
+                                )
                             else:
-                                val['energy_tx'] = 0
-                            val['energy_encsertx'] = val['energy_encser'] + val['energy_tx']
-                            val['energy_desdecrx'] = val['energy_desdec'] + val['energy_tx']
+                                val["energy_tx"] = 0
+                            val["energy_encsertx"] = (
+                                val["energy_encser"] + val["energy_tx"]
+                            )
+                            val["energy_desdecrx"] = (
+                                val["energy_desdec"] + val["energy_tx"]
+                            )
                         except KeyError:
                             pass
 
-
     def add_datapoint(self, arch, lib, key, value, aggregate_label, data_label, getter):
         """
         Set self.aggregate[key][arch][lib][aggregate_Label] = getter(value[data_label]['v']).
@@ -379,7 +512,7 @@ class Protolog:
         Additionally, add lib to self.libraries and arch to self.architectures
         key usually is ('benchmark name', 'sub-benchmark index').
         """
-        if data_label in value and 'v' in value[data_label]:
+        if data_label in value and "v" in value[data_label]:
             self.architectures.add(arch)
             self.libraries.add(lib)
             if not key in self.aggregate:
@@ -388,4 +521,6 @@ class Protolog:
                 self.aggregate[key][arch] = dict()
             if not lib in self.aggregate[key][arch]:
                 self.aggregate[key][arch][lib] = dict()
-            self.aggregate[key][arch][lib][aggregate_label] = getter(value[data_label]['v'])
+            self.aggregate[key][arch][lib][aggregate_label] = getter(
+                value[data_label]["v"]
+            )
diff --git a/lib/dfatool.py b/lib/dfatool.py
index 8fb41a5..56f0f2d 100644
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -15,12 +15,19 @@ 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 (
+    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
 
 try:
     from .pubcode import Code128
     import zbar
+
     zbar_available = True
 except ImportError:
     zbar_available = False
@@ -47,25 +54,25 @@ def gplearn_to_function(function_str: str):
     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.,
-        'sqrt': lambda x: np.sqrt(np.abs(x)),
-        'log': lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 0.,
-        'inv': lambda x: 1. / x if np.abs(x) > 0.001 else 0.,
+        "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:
+        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))
+        arg_list.append("X{:d}".format(i))
 
-    eval_str = 'lambda {}, *whatever: {}'.format(','.join(arg_list), function_str)
+    eval_str = "lambda {}, *whatever: {}".format(",".join(arg_list), function_str)
     print(eval_str)
     return eval(eval_str, eval_globals)
 
@@ -123,32 +130,35 @@ def regression_measures(predicted: np.ndarray, actual: np.ndarray):
     count -- Number of values
     """
     if type(predicted) != np.ndarray:
-        raise ValueError('first arg must be ndarray, is {}'.format(type(predicted)))
+        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)))
+        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),
+        "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
+        measures["mape"] = np.mean(np.abs(deviations / actual)) * 100  # bad measure
     else:
-        measures['mape'] = np.nan
+        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
+        measures["smape"] = (
+            np.mean(np.abs(deviations) / ((np.abs(predicted) + np.abs(actual)) / 2))
+            * 100
+        )
     else:
-        measures['smape'] = np.nan
+        measures["smape"] = np.nan
     # if np.all(rsq_quotient != 0):
     #    measures['rsq'] = (np.sum((actual - mean) * (predicted - mean), dtype=np.float64)**2) / rsq_quotient
 
@@ -177,7 +187,7 @@ class KeysightCSV:
         with open(filename) as f:
             for _ in range(4):
                 next(f)
-            reader = csv.reader(f, delimiter=',')
+            reader = csv.reader(f, delimiter=",")
             for i, row in enumerate(reader):
                 timestamps[i] = float(row[0])
                 currents[i] = float(row[2]) * -1
@@ -266,29 +276,35 @@ class CrossValidator:
             }
         }
         """
-        ret = {
-            'by_name': dict()
-        }
+        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()
+            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 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'])
+            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
 
@@ -296,77 +312,87 @@ class CrossValidator:
         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']
-            }
+            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']
+            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'])
+            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']:
+            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]
+                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()
+            training[name]["param"] = list()
+            validation[name]["param"] = list()
             for idx in training_subset:
-                training[name]['param'].append(self.by_name[name]['param'][idx])
+                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])
+                validation[name]["param"].append(self.by_name[name]["param"][idx])
 
-        training_data = self.model_class(training, self.parameters, self.arg_count, verbose=False)
+        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)
+        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(float(setup['mimosa_voltage']), int(setup['mimosa_shunt']), with_traces=measurement['with_traces'])
+    setup = measurement["setup"]
+    mim = MIMOSA(
+        float(setup["mimosa_voltage"]),
+        int(setup["mimosa_shunt"]),
+        with_traces=measurement["with_traces"],
+    )
     try:
-        charges, triggers = mim.load_data(measurement['content'])
+        charges, triggers = mim.load_data(measurement["content"])
         trigidx = mim.trigger_edges(triggers)
     except EOFError as e:
-        mim.errors.append('MIMOSA logfile error: {}'.format(e))
+        mim.errors.append("MIMOSA logfile error: {}".format(e))
         trigidx = list()
 
     if len(trigidx) == 0:
-        mim.errors.append('MIMOSA log has no triggers')
+        mim.errors.append("MIMOSA log has no triggers")
         return {
-            'fileno': measurement['fileno'],
-            'info': measurement['info'],
-            'has_datasource_error': len(mim.errors) > 0,
-            'datasource_errors': mim.errors,
-            'expected_trace': measurement['expected_trace'],
-            'repeat_id': measurement['repeat_id'],
+            "fileno": measurement["fileno"],
+            "info": measurement["info"],
+            "has_datasource_error": len(mim.errors) > 0,
+            "datasource_errors": mim.errors,
+            "expected_trace": measurement["expected_trace"],
+            "repeat_id": measurement["repeat_id"],
         }
 
-    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)
+    )
     calfunc, caldata = mim.calibration_function(charges, cal_edges)
     vcalfunc = np.vectorize(calfunc, otypes=[np.float64])
 
     processed_data = {
-        'fileno': measurement['fileno'],
-        'info': measurement['info'],
-        'triggers': len(trigidx),
-        'first_trig': trigidx[0] * 10,
-        'calibration': caldata,
-        'energy_trace': mim.analyze_states(charges, trigidx, vcalfunc),
-        'has_datasource_error': len(mim.errors) > 0,
-        'datasource_errors': mim.errors,
+        "fileno": measurement["fileno"],
+        "info": measurement["info"],
+        "triggers": len(trigidx),
+        "first_trig": trigidx[0] * 10,
+        "calibration": caldata,
+        "energy_trace": mim.analyze_states(charges, trigidx, vcalfunc),
+        "has_datasource_error": len(mim.errors) > 0,
+        "datasource_errors": mim.errors,
     }
 
-    for key in ['expected_trace', 'repeat_id']:
+    for key in ["expected_trace", "repeat_id"]:
         if key in measurement:
             processed_data[key] = measurement[key]
 
@@ -374,22 +400,28 @@ def _preprocess_mimosa(measurement):
 
 
 def _preprocess_etlog(measurement):
-    setup = measurement['setup']
-    etlog = EnergyTraceLog(float(setup['voltage']), int(setup['state_duration']), measurement['transition_names'])
+    setup = measurement["setup"]
+    etlog = EnergyTraceLog(
+        float(setup["voltage"]),
+        int(setup["state_duration"]),
+        measurement["transition_names"],
+    )
     try:
-        etlog.load_data(measurement['content'])
-        states_and_transitions = etlog.analyze_states(measurement['expected_trace'], measurement['repeat_id'])
+        etlog.load_data(measurement["content"])
+        states_and_transitions = etlog.analyze_states(
+            measurement["expected_trace"], measurement["repeat_id"]
+        )
     except EOFError as e:
-        etlog.errors.append('EnergyTrace logfile error: {}'.format(e))
+        etlog.errors.append("EnergyTrace logfile error: {}".format(e))
 
     processed_data = {
-        'fileno': measurement['fileno'],
-        'repeat_id': measurement['repeat_id'],
-        'info': measurement['info'],
-        'expected_trace': measurement['expected_trace'],
-        'energy_trace': states_and_transitions,
-        'has_datasource_error': len(etlog.errors) > 0,
-        'datasource_errors': etlog.errors,
+        "fileno": measurement["fileno"],
+        "repeat_id": measurement["repeat_id"],
+        "info": measurement["info"],
+        "expected_trace": measurement["expected_trace"],
+        "energy_trace": states_and_transitions,
+        "has_datasource_error": len(etlog.errors) > 0,
+        "datasource_errors": etlog.errors,
     }
 
     return processed_data
@@ -421,35 +453,40 @@ class TimingData:
         for trace_group in self.traces_by_fileno:
             for trace in trace_group:
                 # TimingHarness logs states, but does not aggregate any data for them at the moment -> throw all states away
-                transitions = list(filter(lambda x: x['isa'] == 'transition', trace['trace']))
-                self.traces.append({
-                    'id': trace['id'],
-                    'trace': transitions,
-                })
+                transitions = list(
+                    filter(lambda x: x["isa"] == "transition", trace["trace"])
+                )
+                self.traces.append(
+                    {"id": trace["id"], "trace": transitions,}
+                )
         for i, trace in enumerate(self.traces):
-            trace['orig_id'] = trace['id']
-            trace['id'] = i
-            for log_entry in trace['trace']:
-                paramkeys = sorted(log_entry['parameter'].keys())
-                if 'param' not in log_entry['offline_aggregates']:
-                    log_entry['offline_aggregates']['param'] = list()
-                if 'duration' in log_entry['offline_aggregates']:
-                    for i in range(len(log_entry['offline_aggregates']['duration'])):
+            trace["orig_id"] = trace["id"]
+            trace["id"] = i
+            for log_entry in trace["trace"]:
+                paramkeys = sorted(log_entry["parameter"].keys())
+                if "param" not in log_entry["offline_aggregates"]:
+                    log_entry["offline_aggregates"]["param"] = list()
+                if "duration" in log_entry["offline_aggregates"]:
+                    for i in range(len(log_entry["offline_aggregates"]["duration"])):
                         paramvalues = list()
                         for paramkey in paramkeys:
-                            if type(log_entry['parameter'][paramkey]) is list:
-                                paramvalues.append(soft_cast_int(log_entry['parameter'][paramkey][i]))
+                            if type(log_entry["parameter"][paramkey]) is list:
+                                paramvalues.append(
+                                    soft_cast_int(log_entry["parameter"][paramkey][i])
+                                )
                             else:
-                                paramvalues.append(soft_cast_int(log_entry['parameter'][paramkey]))
-                        if arg_support_enabled and 'args' in log_entry:
-                            paramvalues.extend(map(soft_cast_int, log_entry['args']))
-                        log_entry['offline_aggregates']['param'].append(paramvalues)
+                                paramvalues.append(
+                                    soft_cast_int(log_entry["parameter"][paramkey])
+                                )
+                        if arg_support_enabled and "args" in log_entry:
+                            paramvalues.extend(map(soft_cast_int, log_entry["args"]))
+                        log_entry["offline_aggregates"]["param"].append(paramvalues)
 
     def _preprocess_0(self):
         for filename in self.filenames:
-            with open(filename, 'r') as f:
+            with open(filename, "r") as f:
                 log_data = json.load(f)
-                self.traces_by_fileno.extend(log_data['traces'])
+                self.traces_by_fileno.extend(log_data["traces"])
         self._concatenate_analyzed_traces()
 
     def get_preprocessed_data(self, verbose=True):
@@ -470,17 +507,25 @@ class TimingData:
 
 def sanity_check_aggregate(aggregate):
     for key in aggregate:
-        if 'param' not in aggregate[key]:
-            raise RuntimeError('aggregate[{}][param] does not exist'.format(key))
-        if 'attributes' not in aggregate[key]:
-            raise RuntimeError('aggregate[{}][attributes] does not exist'.format(key))
-        for attribute in aggregate[key]['attributes']:
+        if "param" not in aggregate[key]:
+            raise RuntimeError("aggregate[{}][param] does not exist".format(key))
+        if "attributes" not in aggregate[key]:
+            raise RuntimeError("aggregate[{}][attributes] does not exist".format(key))
+        for attribute in aggregate[key]["attributes"]:
             if attribute not in aggregate[key]:
-                raise RuntimeError('aggregate[{}][{}] does not exist, even though it is contained in aggregate[{}][attributes]'.format(key, attribute, key))
-            param_len = len(aggregate[key]['param'])
+                raise RuntimeError(
+                    "aggregate[{}][{}] does not exist, even though it is contained in aggregate[{}][attributes]".format(
+                        key, attribute, key
+                    )
+                )
+            param_len = len(aggregate[key]["param"])
             attr_len = len(aggregate[key][attribute])
             if param_len != attr_len:
-                raise RuntimeError('parameter mismatch: len(aggregate[{}][param]) == {} != len(aggregate[{}][{}]) == {}'.format(key, param_len, key, attribute, attr_len))
+                raise RuntimeError(
+                    "parameter mismatch: len(aggregate[{}][param]) == {} != len(aggregate[{}][{}]) == {}".format(
+                        key, param_len, key, attribute, attr_len
+                    )
+                )
 
 
 class RawData:
@@ -559,11 +604,11 @@ class RawData:
 
         with tarfile.open(filenames[0]) as tf:
             for member in tf.getmembers():
-                if member.name == 'ptalog.json' and self.version == 0:
+                if member.name == "ptalog.json" and self.version == 0:
                     self.version = 1
                     # might also be version 2
                     # depends on whether *.etlog exists or not
-                elif '.etlog' in member.name:
+                elif ".etlog" in member.name:
                     self.version = 2
                     break
 
@@ -572,18 +617,18 @@ class RawData:
             self.load_cache()
 
     def set_cache_file(self):
-        cache_key = hashlib.sha256('!'.join(self.filenames).encode()).hexdigest()
-        self.cache_dir = os.path.dirname(self.filenames[0]) + '/cache'
-        self.cache_file = '{}/{}.json'.format(self.cache_dir, cache_key)
+        cache_key = hashlib.sha256("!".join(self.filenames).encode()).hexdigest()
+        self.cache_dir = os.path.dirname(self.filenames[0]) + "/cache"
+        self.cache_file = "{}/{}.json".format(self.cache_dir, cache_key)
 
     def load_cache(self):
         if os.path.exists(self.cache_file):
-            with open(self.cache_file, 'r') as f:
+            with open(self.cache_file, "r") as f:
                 cache_data = json.load(f)
-                self.traces = cache_data['traces']
-                self.preprocessing_stats = cache_data['preprocessing_stats']
-                if 'pta' in cache_data:
-                    self.pta = cache_data['pta']
+                self.traces = cache_data["traces"]
+                self.preprocessing_stats = cache_data["preprocessing_stats"]
+                if "pta" in cache_data:
+                    self.pta = cache_data["pta"]
                 self.preprocessed = True
 
     def save_cache(self):
@@ -593,30 +638,30 @@ class RawData:
             os.mkdir(self.cache_dir)
         except FileExistsError:
             pass
-        with open(self.cache_file, 'w') as f:
+        with open(self.cache_file, "w") as f:
             cache_data = {
-                'traces': self.traces,
-                'preprocessing_stats': self.preprocessing_stats,
-                'pta': self.pta,
+                "traces": self.traces,
+                "preprocessing_stats": self.preprocessing_stats,
+                "pta": self.pta,
             }
             json.dump(cache_data, f)
 
     def _state_is_too_short(self, online, offline, state_duration, next_transition):
         # We cannot control when an interrupt causes a state to be left
-        if next_transition['plan']['level'] == 'epilogue':
+        if next_transition["plan"]["level"] == "epilogue":
             return False
 
         # Note: state_duration is stored as ms, not us
-        return offline['us'] < state_duration * 500
+        return offline["us"] < state_duration * 500
 
     def _state_is_too_long(self, online, offline, state_duration, prev_transition):
         # If the previous state was left by an interrupt, we may have some
         # waiting time left over. So it's okay if the current state is longer
         # than expected.
-        if prev_transition['plan']['level'] == 'epilogue':
+        if prev_transition["plan"]["level"] == "epilogue":
             return False
         # state_duration is stored as ms, not us
-        return offline['us'] > state_duration * 1500
+        return offline["us"] > state_duration * 1500
 
     def _measurement_is_valid_2(self, processed_data):
         """
@@ -642,8 +687,8 @@ class RawData:
         """
 
         # Check for low-level parser errors
-        if processed_data['has_datasource_error']:
-            processed_data['error'] = '; '.join(processed_data['datasource_errors'])
+        if processed_data["has_datasource_error"]:
+            processed_data["error"] = "; ".join(processed_data["datasource_errors"])
             return False
 
         # Note that the low-level parser (EnergyTraceLog) already checks
@@ -680,26 +725,27 @@ class RawData:
             - uW_mean_delta_prev: Differenz zwischen uW_mean und uW_mean des vorherigen Zustands
             - uW_mean_delta_next: Differenz zwischen uW_mean und uW_mean des Folgezustands
         """
-        setup = self.setup_by_fileno[processed_data['fileno']]
-        if 'expected_trace' in processed_data:
-            traces = processed_data['expected_trace']
+        setup = self.setup_by_fileno[processed_data["fileno"]]
+        if "expected_trace" in processed_data:
+            traces = processed_data["expected_trace"]
         else:
-            traces = self.traces_by_fileno[processed_data['fileno']]
-        state_duration = setup['state_duration']
+            traces = self.traces_by_fileno[processed_data["fileno"]]
+        state_duration = setup["state_duration"]
 
         # Check MIMOSA error
-        if processed_data['has_datasource_error']:
-            processed_data['error'] = '; '.join(processed_data['datasource_errors'])
+        if processed_data["has_datasource_error"]:
+            processed_data["error"] = "; ".join(processed_data["datasource_errors"])
             return False
 
         # Check trigger count
         sched_trigger_count = 0
         for run in traces:
-            sched_trigger_count += len(run['trace'])
-        if sched_trigger_count != processed_data['triggers']:
-            processed_data['error'] = 'got {got:d} trigger edges, expected {exp:d}'.format(
-                got=processed_data['triggers'],
-                exp=sched_trigger_count
+            sched_trigger_count += len(run["trace"])
+        if sched_trigger_count != processed_data["triggers"]:
+            processed_data[
+                "error"
+            ] = "got {got:d} trigger edges, expected {exp:d}".format(
+                got=processed_data["triggers"], exp=sched_trigger_count
             )
             return False
         # Check state durations. Very short or long states can indicate a
@@ -707,62 +753,102 @@ class RawData:
         # triggers elsewhere
         online_datapoints = []
         for run_idx, run in enumerate(traces):
-            for trace_part_idx in range(len(run['trace'])):
+            for trace_part_idx in range(len(run["trace"])):
                 online_datapoints.append((run_idx, trace_part_idx))
         for offline_idx, online_ref in enumerate(online_datapoints):
             online_run_idx, online_trace_part_idx = online_ref
-            offline_trace_part = processed_data['energy_trace'][offline_idx]
-            online_trace_part = traces[online_run_idx]['trace'][online_trace_part_idx]
+            offline_trace_part = processed_data["energy_trace"][offline_idx]
+            online_trace_part = traces[online_run_idx]["trace"][online_trace_part_idx]
 
             if self._parameter_names is None:
-                self._parameter_names = sorted(online_trace_part['parameter'].keys())
-
-            if sorted(online_trace_part['parameter'].keys()) != self._parameter_names:
-                processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) has inconsistent parameter set: should be {param_want:s}, is {param_is:s}'.format(
-                    off_idx=offline_idx, on_idx=online_run_idx,
+                self._parameter_names = sorted(online_trace_part["parameter"].keys())
+
+            if sorted(online_trace_part["parameter"].keys()) != self._parameter_names:
+                processed_data[
+                    "error"
+                ] = "Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) has inconsistent parameter set: should be {param_want:s}, is {param_is:s}".format(
+                    off_idx=offline_idx,
+                    on_idx=online_run_idx,
                     on_sub=online_trace_part_idx,
-                    on_name=online_trace_part['name'],
+                    on_name=online_trace_part["name"],
                     param_want=self._parameter_names,
-                    param_is=sorted(online_trace_part['parameter'].keys())
+                    param_is=sorted(online_trace_part["parameter"].keys()),
                 )
 
-            if online_trace_part['isa'] != offline_trace_part['isa']:
-                processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) claims to be {off_isa:s}, but should be {on_isa:s}'.format(
-                    off_idx=offline_idx, on_idx=online_run_idx,
+            if online_trace_part["isa"] != offline_trace_part["isa"]:
+                processed_data[
+                    "error"
+                ] = "Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) claims to be {off_isa:s}, but should be {on_isa:s}".format(
+                    off_idx=offline_idx,
+                    on_idx=online_run_idx,
                     on_sub=online_trace_part_idx,
-                    on_name=online_trace_part['name'],
-                    off_isa=offline_trace_part['isa'],
-                    on_isa=online_trace_part['isa'])
+                    on_name=online_trace_part["name"],
+                    off_isa=offline_trace_part["isa"],
+                    on_isa=online_trace_part["isa"],
+                )
                 return False
 
             # Clipping in UNINITIALIZED (offline_idx == 0) can happen during
             # calibration and is handled by MIMOSA
-            if offline_idx != 0 and offline_trace_part['clip_rate'] != 0 and not self.ignore_clipping:
-                processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) was clipping {clip:f}% of the time'.format(
-                    off_idx=offline_idx, on_idx=online_run_idx,
+            if (
+                offline_idx != 0
+                and offline_trace_part["clip_rate"] != 0
+                and not self.ignore_clipping
+            ):
+                processed_data[
+                    "error"
+                ] = "Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) was clipping {clip:f}% of the time".format(
+                    off_idx=offline_idx,
+                    on_idx=online_run_idx,
                     on_sub=online_trace_part_idx,
-                    on_name=online_trace_part['name'],
-                    clip=offline_trace_part['clip_rate'] * 100,
+                    on_name=online_trace_part["name"],
+                    clip=offline_trace_part["clip_rate"] * 100,
                 )
                 return False
 
-            if online_trace_part['isa'] == 'state' and online_trace_part['name'] != 'UNINITIALIZED' and len(traces[online_run_idx]['trace']) > online_trace_part_idx + 1:
-                online_prev_transition = traces[online_run_idx]['trace'][online_trace_part_idx - 1]
-                online_next_transition = traces[online_run_idx]['trace'][online_trace_part_idx + 1]
+            if (
+                online_trace_part["isa"] == "state"
+                and online_trace_part["name"] != "UNINITIALIZED"
+                and len(traces[online_run_idx]["trace"]) > online_trace_part_idx + 1
+            ):
+                online_prev_transition = traces[online_run_idx]["trace"][
+                    online_trace_part_idx - 1
+                ]
+                online_next_transition = traces[online_run_idx]["trace"][
+                    online_trace_part_idx + 1
+                ]
                 try:
-                    if self._state_is_too_short(online_trace_part, offline_trace_part, state_duration, online_next_transition):
-                        processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) is too short (duration = {dur:d} us)'.format(
-                            off_idx=offline_idx, on_idx=online_run_idx,
+                    if self._state_is_too_short(
+                        online_trace_part,
+                        offline_trace_part,
+                        state_duration,
+                        online_next_transition,
+                    ):
+                        processed_data[
+                            "error"
+                        ] = "Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) is too short (duration = {dur:d} us)".format(
+                            off_idx=offline_idx,
+                            on_idx=online_run_idx,
                             on_sub=online_trace_part_idx,
-                            on_name=online_trace_part['name'],
-                            dur=offline_trace_part['us'])
+                            on_name=online_trace_part["name"],
+                            dur=offline_trace_part["us"],
+                        )
                         return False
-                    if self._state_is_too_long(online_trace_part, offline_trace_part, state_duration, online_prev_transition):
-                        processed_data['error'] = 'Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) is too long (duration = {dur:d} us)'.format(
-                            off_idx=offline_idx, on_idx=online_run_idx,
+                    if self._state_is_too_long(
+                        online_trace_part,
+                        offline_trace_part,
+                        state_duration,
+                        online_prev_transition,
+                    ):
+                        processed_data[
+                            "error"
+                        ] = "Offline #{off_idx:d} (online {on_name:s} @ {on_idx:d}/{on_sub:d}) is too long (duration = {dur:d} us)".format(
+                            off_idx=offline_idx,
+                            on_idx=online_run_idx,
                             on_sub=online_trace_part_idx,
-                            on_name=online_trace_part['name'],
-                            dur=offline_trace_part['us'])
+                            on_name=online_trace_part["name"],
+                            dur=offline_trace_part["us"],
+                        )
                         return False
                 except KeyError:
                     pass
@@ -775,136 +861,169 @@ class RawData:
         # (appends data from measurement['energy_trace'])
         # If measurement['expected_trace'] exists, it is edited in place instead
         online_datapoints = []
-        if 'expected_trace' in measurement:
-            traces = measurement['expected_trace']
-            traces = self.traces_by_fileno[measurement['fileno']]
+        if "expected_trace" in measurement:
+            traces = measurement["expected_trace"]
+            traces = self.traces_by_fileno[measurement["fileno"]]
         else:
-            traces = self.traces_by_fileno[measurement['fileno']]
+            traces = self.traces_by_fileno[measurement["fileno"]]
         for run_idx, run in enumerate(traces):
-            for trace_part_idx in range(len(run['trace'])):
+            for trace_part_idx in range(len(run["trace"])):
                 online_datapoints.append((run_idx, trace_part_idx))
         for offline_idx, online_ref in enumerate(online_datapoints):
             online_run_idx, online_trace_part_idx = online_ref
-            offline_trace_part = measurement['energy_trace'][offline_idx]
-            online_trace_part = traces[online_run_idx]['trace'][online_trace_part_idx]
+            offline_trace_part = measurement["energy_trace"][offline_idx]
+            online_trace_part = traces[online_run_idx]["trace"][online_trace_part_idx]
 
-            if 'offline' not in online_trace_part:
-                online_trace_part['offline'] = [offline_trace_part]
+            if "offline" not in online_trace_part:
+                online_trace_part["offline"] = [offline_trace_part]
             else:
-                online_trace_part['offline'].append(offline_trace_part)
+                online_trace_part["offline"].append(offline_trace_part)
 
-            paramkeys = sorted(online_trace_part['parameter'].keys())
+            paramkeys = sorted(online_trace_part["parameter"].keys())
 
             paramvalues = list()
 
             for paramkey in paramkeys:
-                if type(online_trace_part['parameter'][paramkey]) is list:
-                    paramvalues.append(soft_cast_int(online_trace_part['parameter'][paramkey][measurement['repeat_id']]))
+                if type(online_trace_part["parameter"][paramkey]) is list:
+                    paramvalues.append(
+                        soft_cast_int(
+                            online_trace_part["parameter"][paramkey][
+                                measurement["repeat_id"]
+                            ]
+                        )
+                    )
                 else:
-                    paramvalues.append(soft_cast_int(online_trace_part['parameter'][paramkey]))
+                    paramvalues.append(
+                        soft_cast_int(online_trace_part["parameter"][paramkey])
+                    )
 
             # NB: Unscheduled transitions do not have an 'args' field set.
             # However, they should only be caused by interrupts, and
             # interrupts don't have args anyways.
-            if arg_support_enabled and 'args' in online_trace_part:
-                paramvalues.extend(map(soft_cast_int, online_trace_part['args']))
-
-            if 'offline_aggregates' not in online_trace_part:
-                online_trace_part['offline_attributes'] = ['power', 'duration', 'energy']
-                online_trace_part['offline_aggregates'] = {
-                    'power': [],
-                    'duration': [],
-                    'power_std': [],
-                    'energy': [],
-                    'paramkeys': [],
-                    'param': [],
+            if arg_support_enabled and "args" in online_trace_part:
+                paramvalues.extend(map(soft_cast_int, online_trace_part["args"]))
+
+            if "offline_aggregates" not in online_trace_part:
+                online_trace_part["offline_attributes"] = [
+                    "power",
+                    "duration",
+                    "energy",
+                ]
+                online_trace_part["offline_aggregates"] = {
+                    "power": [],
+                    "duration": [],
+                    "power_std": [],
+                    "energy": [],
+                    "paramkeys": [],
+                    "param": [],
                 }
-                if online_trace_part['isa'] == 'transition':
-                    online_trace_part['offline_attributes'].extend(['rel_energy_prev', 'rel_energy_next', 'timeout'])
-                    online_trace_part['offline_aggregates']['rel_energy_prev'] = []
-                    online_trace_part['offline_aggregates']['rel_energy_next'] = []
-                    online_trace_part['offline_aggregates']['timeout'] = []
+                if online_trace_part["isa"] == "transition":
+                    online_trace_part["offline_attributes"].extend(
+                        ["rel_energy_prev", "rel_energy_next", "timeout"]
+                    )
+                    online_trace_part["offline_aggregates"]["rel_energy_prev"] = []
+                    online_trace_part["offline_aggregates"]["rel_energy_next"] = []
+                    online_trace_part["offline_aggregates"]["timeout"] = []
 
             # Note: All state/transitions are 20us "too long" due to injected
             # active wait states. These are needed to work around MIMOSA's
             # relatively low sample rate of 100 kHz (10us) and removed here.
-            online_trace_part['offline_aggregates']['power'].append(
-                offline_trace_part['uW_mean'])
-            online_trace_part['offline_aggregates']['duration'].append(
-                offline_trace_part['us'] - 20)
-            online_trace_part['offline_aggregates']['power_std'].append(
-                offline_trace_part['uW_std'])
-            online_trace_part['offline_aggregates']['energy'].append(
-                offline_trace_part['uW_mean'] * (offline_trace_part['us'] - 20))
-            online_trace_part['offline_aggregates']['paramkeys'].append(paramkeys)
-            online_trace_part['offline_aggregates']['param'].append(paramvalues)
-            if online_trace_part['isa'] == 'transition':
-                online_trace_part['offline_aggregates']['rel_energy_prev'].append(
-                    offline_trace_part['uW_mean_delta_prev'] * (offline_trace_part['us'] - 20))
-                online_trace_part['offline_aggregates']['rel_energy_next'].append(
-                    offline_trace_part['uW_mean_delta_next'] * (offline_trace_part['us'] - 20))
-                online_trace_part['offline_aggregates']['timeout'].append(
-                    offline_trace_part['timeout'])
+            online_trace_part["offline_aggregates"]["power"].append(
+                offline_trace_part["uW_mean"]
+            )
+            online_trace_part["offline_aggregates"]["duration"].append(
+                offline_trace_part["us"] - 20
+            )
+            online_trace_part["offline_aggregates"]["power_std"].append(
+                offline_trace_part["uW_std"]
+            )
+            online_trace_part["offline_aggregates"]["energy"].append(
+                offline_trace_part["uW_mean"] * (offline_trace_part["us"] - 20)
+            )
+            online_trace_part["offline_aggregates"]["paramkeys"].append(paramkeys)
+            online_trace_part["offline_aggregates"]["param"].append(paramvalues)
+            if online_trace_part["isa"] == "transition":
+                online_trace_part["offline_aggregates"]["rel_energy_prev"].append(
+                    offline_trace_part["uW_mean_delta_prev"]
+                    * (offline_trace_part["us"] - 20)
+                )
+                online_trace_part["offline_aggregates"]["rel_energy_next"].append(
+                    offline_trace_part["uW_mean_delta_next"]
+                    * (offline_trace_part["us"] - 20)
+                )
+                online_trace_part["offline_aggregates"]["timeout"].append(
+                    offline_trace_part["timeout"]
+                )
 
     def _merge_online_and_etlog(self, measurement):
         # Edits self.traces_by_fileno[measurement['fileno']][*]['trace'][*]['offline']
         # and self.traces_by_fileno[measurement['fileno']][*]['trace'][*]['offline_aggregates'] in place
         # (appends data from measurement['energy_trace'])
         online_datapoints = []
-        traces = self.traces_by_fileno[measurement['fileno']]
+        traces = self.traces_by_fileno[measurement["fileno"]]
         for run_idx, run in enumerate(traces):
-            for trace_part_idx in range(len(run['trace'])):
+            for trace_part_idx in range(len(run["trace"])):
                 online_datapoints.append((run_idx, trace_part_idx))
         for offline_idx, online_ref in enumerate(online_datapoints):
             online_run_idx, online_trace_part_idx = online_ref
-            offline_trace_part = measurement['energy_trace'][offline_idx]
-            online_trace_part = traces[online_run_idx]['trace'][online_trace_part_idx]
+            offline_trace_part = measurement["energy_trace"][offline_idx]
+            online_trace_part = traces[online_run_idx]["trace"][online_trace_part_idx]
 
-            if 'offline' not in online_trace_part:
-                online_trace_part['offline'] = [offline_trace_part]
+            if "offline" not in online_trace_part:
+                online_trace_part["offline"] = [offline_trace_part]
             else:
-                online_trace_part['offline'].append(offline_trace_part)
+                online_trace_part["offline"].append(offline_trace_part)
 
-            paramkeys = sorted(online_trace_part['parameter'].keys())
+            paramkeys = sorted(online_trace_part["parameter"].keys())
 
             paramvalues = list()
 
             for paramkey in paramkeys:
-                if type(online_trace_part['parameter'][paramkey]) is list:
-                    paramvalues.append(soft_cast_int(online_trace_part['parameter'][paramkey][measurement['repeat_id']]))
+                if type(online_trace_part["parameter"][paramkey]) is list:
+                    paramvalues.append(
+                        soft_cast_int(
+                            online_trace_part["parameter"][paramkey][
+                                measurement["repeat_id"]
+                            ]
+                        )
+                    )
                 else:
-                    paramvalues.append(soft_cast_int(online_trace_part['parameter'][paramkey]))
+                    paramvalues.append(
+                        soft_cast_int(online_trace_part["parameter"][paramkey])
+                    )
 
             # NB: Unscheduled transitions do not have an 'args' field set.
             # However, they should only be caused by interrupts, and
             # interrupts don't have args anyways.
-            if arg_support_enabled and 'args' in online_trace_part:
-                paramvalues.extend(map(soft_cast_int, online_trace_part['args']))
-
-            if 'offline_aggregates' not in online_trace_part:
-                online_trace_part['offline_aggregates'] = {
-                    'offline_attributes': ['power', 'duration', 'energy'],
-                    'duration': list(),
-                    'power': list(),
-                    'power_std': list(),
-                    'energy': list(),
-                    'paramkeys': list(),
-                    'param': list()
+            if arg_support_enabled and "args" in online_trace_part:
+                paramvalues.extend(map(soft_cast_int, online_trace_part["args"]))
+
+            if "offline_aggregates" not in online_trace_part:
+                online_trace_part["offline_aggregates"] = {
+                    "offline_attributes": ["power", "duration", "energy"],
+                    "duration": list(),
+                    "power": list(),
+                    "power_std": list(),
+                    "energy": list(),
+                    "paramkeys": list(),
+                    "param": list(),
                 }
 
-            offline_aggregates = online_trace_part['offline_aggregates']
+            offline_aggregates = online_trace_part["offline_aggregates"]
 
             # if online_trace_part['isa'] == 'transitions':
             #    online_trace_part['offline_attributes'].extend(['rel_energy_prev', 'rel_energy_next'])
             #    offline_aggregates['rel_energy_prev'] = list()
             #    offline_aggregates['rel_energy_next'] = list()
 
-            offline_aggregates['duration'].append(offline_trace_part['s'] * 1e6)
-            offline_aggregates['power'].append(offline_trace_part['W_mean'] * 1e6)
-            offline_aggregates['power_std'].append(offline_trace_part['W_std'] * 1e6)
-            offline_aggregates['energy'].append(offline_trace_part['W_mean'] * offline_trace_part['s'] * 1e12)
-            offline_aggregates['paramkeys'].append(paramkeys)
-            offline_aggregates['param'].append(paramvalues)
+            offline_aggregates["duration"].append(offline_trace_part["s"] * 1e6)
+            offline_aggregates["power"].append(offline_trace_part["W_mean"] * 1e6)
+            offline_aggregates["power_std"].append(offline_trace_part["W_std"] * 1e6)
+            offline_aggregates["energy"].append(
+                offline_trace_part["W_mean"] * offline_trace_part["s"] * 1e12
+            )
+            offline_aggregates["paramkeys"].append(paramkeys)
+            offline_aggregates["param"].append(paramvalues)
 
             # if online_trace_part['isa'] == 'transition':
             #    offline_aggregates['rel_energy_prev'].append(offline_trace_part['W_mean_delta_prev'] * offline_trace_part['s'] * 1e12)
@@ -922,8 +1041,8 @@ class RawData:
         for trace in list_of_traces:
             trace_output.extend(trace.copy())
         for i, trace in enumerate(trace_output):
-            trace['orig_id'] = trace['id']
-            trace['id'] = i
+            trace["orig_id"] = trace["id"]
+            trace["id"] = i
         return trace_output
 
     def get_preprocessed_data(self, verbose=True):
@@ -1000,25 +1119,29 @@ class RawData:
             if version == 0:
 
                 with tarfile.open(filename) as tf:
-                    self.setup_by_fileno.append(json.load(tf.extractfile('setup.json')))
-                    self.traces_by_fileno.append(json.load(tf.extractfile('src/apps/DriverEval/DriverLog.json')))
+                    self.setup_by_fileno.append(json.load(tf.extractfile("setup.json")))
+                    self.traces_by_fileno.append(
+                        json.load(tf.extractfile("src/apps/DriverEval/DriverLog.json"))
+                    )
                     for member in tf.getmembers():
                         _, extension = os.path.splitext(member.name)
-                        if extension == '.mim':
-                            offline_data.append({
-                                'content': tf.extractfile(member).read(),
-                                'fileno': i,
-                                'info': member,
-                                'setup': self.setup_by_fileno[i],
-                                'with_traces': self.with_traces,
-                            })
+                        if extension == ".mim":
+                            offline_data.append(
+                                {
+                                    "content": tf.extractfile(member).read(),
+                                    "fileno": i,
+                                    "info": member,
+                                    "setup": self.setup_by_fileno[i],
+                                    "with_traces": self.with_traces,
+                                }
+                            )
 
             elif version == 1:
 
                 new_filenames = list()
                 with tarfile.open(filename) as tf:
-                    ptalog = json.load(tf.extractfile(tf.getmember('ptalog.json')))
-                    self.pta = ptalog['pta']
+                    ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json")))
+                    self.pta = ptalog["pta"]
 
                     # Benchmark code may be too large to be executed in a single
                     # run, so benchmarks (a benchmark is basically a list of DFA runs)
@@ -1043,33 +1166,37 @@ class RawData:
                     # ptalog['files'][0][0] is its first iteration/repetition,
                     # ptalog['files'][0][1] the second, etc.
 
-                    for j, traces in enumerate(ptalog['traces']):
-                        new_filenames.append('{}#{}'.format(filename, j))
+                    for j, traces in enumerate(ptalog["traces"]):
+                        new_filenames.append("{}#{}".format(filename, j))
                         self.traces_by_fileno.append(traces)
-                        self.setup_by_fileno.append({
-                            'mimosa_voltage': ptalog['configs'][j]['voltage'],
-                            'mimosa_shunt': ptalog['configs'][j]['shunt'],
-                            'state_duration': ptalog['opt']['sleep'],
-                        })
-                        for repeat_id, mim_file in enumerate(ptalog['files'][j]):
+                        self.setup_by_fileno.append(
+                            {
+                                "mimosa_voltage": ptalog["configs"][j]["voltage"],
+                                "mimosa_shunt": ptalog["configs"][j]["shunt"],
+                                "state_duration": ptalog["opt"]["sleep"],
+                            }
+                        )
+                        for repeat_id, mim_file in enumerate(ptalog["files"][j]):
                             member = tf.getmember(mim_file)
-                            offline_data.append({
-                                'content': tf.extractfile(member).read(),
-                                'fileno': j,
-                                'info': member,
-                                'setup': self.setup_by_fileno[j],
-                                'repeat_id': repeat_id,
-                                'expected_trace': ptalog['traces'][j],
-                                'with_traces': self.with_traces,
-                            })
+                            offline_data.append(
+                                {
+                                    "content": tf.extractfile(member).read(),
+                                    "fileno": j,
+                                    "info": member,
+                                    "setup": self.setup_by_fileno[j],
+                                    "repeat_id": repeat_id,
+                                    "expected_trace": ptalog["traces"][j],
+                                    "with_traces": self.with_traces,
+                                }
+                            )
                 self.filenames = new_filenames
 
             elif version == 2:
 
                 new_filenames = list()
                 with tarfile.open(filename) as tf:
-                    ptalog = json.load(tf.extractfile(tf.getmember('ptalog.json')))
-                    self.pta = ptalog['pta']
+                    ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json")))
+                    self.pta = ptalog["pta"]
 
                     # Benchmark code may be too large to be executed in a single
                     # run, so benchmarks (a benchmark is basically a list of DFA runs)
@@ -1103,32 +1230,45 @@ class RawData:
                     # to an invalid measurement and thus power[b] corresponding
                     # to duration[C]. At the moment, this is harmless, but in the
                     # future it might not be.
-                    if 'offline_aggregates' in ptalog['traces'][0][0]['trace'][0]:
-                        for trace_group in ptalog['traces']:
+                    if "offline_aggregates" in ptalog["traces"][0][0]["trace"][0]:
+                        for trace_group in ptalog["traces"]:
                             for trace in trace_group:
-                                for state_or_transition in trace['trace']:
-                                    offline_aggregates = state_or_transition.pop('offline_aggregates', None)
+                                for state_or_transition in trace["trace"]:
+                                    offline_aggregates = state_or_transition.pop(
+                                        "offline_aggregates", None
+                                    )
                                     if offline_aggregates:
-                                        state_or_transition['online_aggregates'] = offline_aggregates
+                                        state_or_transition[
+                                            "online_aggregates"
+                                        ] = offline_aggregates
 
-                    for j, traces in enumerate(ptalog['traces']):
-                        new_filenames.append('{}#{}'.format(filename, j))
+                    for j, traces in enumerate(ptalog["traces"]):
+                        new_filenames.append("{}#{}".format(filename, j))
                         self.traces_by_fileno.append(traces)
-                        self.setup_by_fileno.append({
-                            'voltage': ptalog['configs'][j]['voltage'],
-                            'state_duration': ptalog['opt']['sleep'],
-                        })
-                        for repeat_id, etlog_file in enumerate(ptalog['files'][j]):
+                        self.setup_by_fileno.append(
+                            {
+                                "voltage": ptalog["configs"][j]["voltage"],
+                                "state_duration": ptalog["opt"]["sleep"],
+                            }
+                        )
+                        for repeat_id, etlog_file in enumerate(ptalog["files"][j]):
                             member = tf.getmember(etlog_file)
-                            offline_data.append({
-                                'content': tf.extractfile(member).read(),
-                                'fileno': j,
-                                'info': member,
-                                'setup': self.setup_by_fileno[j],
-                                'repeat_id': repeat_id,
-                                'expected_trace': ptalog['traces'][j],
-                                'transition_names': list(map(lambda x: x['name'], ptalog['pta']['transitions']))
-                            })
+                            offline_data.append(
+                                {
+                                    "content": tf.extractfile(member).read(),
+                                    "fileno": j,
+                                    "info": member,
+                                    "setup": self.setup_by_fileno[j],
+                                    "repeat_id": repeat_id,
+                                    "expected_trace": ptalog["traces"][j],
+                                    "transition_names": list(
+                                        map(
+                                            lambda x: x["name"],
+                                            ptalog["pta"]["transitions"],
+                                        )
+                                    ),
+                                }
+                            )
                 self.filenames = new_filenames
                 # TODO remove 'offline_aggregates' from pre-parse data and place
                 # it under 'online_aggregates' or similar instead. This way, if
@@ -1145,52 +1285,69 @@ class RawData:
         num_valid = 0
         for measurement in measurements:
 
-            if 'energy_trace' not in measurement:
-                vprint(self.verbose, '[W] Skipping {ar:s}/{m:s}: {e:s}'.format(
-                    ar=self.filenames[measurement['fileno']],
-                    m=measurement['info'].name,
-                    e='; '.join(measurement['datasource_errors'])))
+            if "energy_trace" not in measurement:
+                vprint(
+                    self.verbose,
+                    "[W] Skipping {ar:s}/{m:s}: {e:s}".format(
+                        ar=self.filenames[measurement["fileno"]],
+                        m=measurement["info"].name,
+                        e="; ".join(measurement["datasource_errors"]),
+                    ),
+                )
                 continue
 
             if version == 0:
                 # Strip the last state (it is not part of the scheduled measurement)
-                measurement['energy_trace'].pop()
+                measurement["energy_trace"].pop()
             elif version == 1:
                 # The first online measurement is the UNINITIALIZED state. In v1,
                 # it is not part of the expected PTA trace -> remove it.
-                measurement['energy_trace'].pop(0)
+                measurement["energy_trace"].pop(0)
 
             if version == 0 or version == 1:
                 if self._measurement_is_valid_01(measurement):
                     self._merge_online_and_offline(measurement)
                     num_valid += 1
                 else:
-                    vprint(self.verbose, '[W] Skipping {ar:s}/{m:s}: {e:s}'.format(
-                        ar=self.filenames[measurement['fileno']],
-                        m=measurement['info'].name,
-                        e=measurement['error']))
+                    vprint(
+                        self.verbose,
+                        "[W] 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(
-                        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(
-            num_valid=num_valid,
-            num_total=len(measurements)))
+                    vprint(
+                        self.verbose,
+                        "[W] 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(
+                num_valid=num_valid, num_total=len(measurements)
+            ),
+        )
         if version == 0:
             self.traces = self._concatenate_traces(self.traces_by_fileno)
         elif version == 1:
-            self.traces = self._concatenate_traces(map(lambda x: x['expected_trace'], measurements))
+            self.traces = self._concatenate_traces(
+                map(lambda x: x["expected_trace"], measurements)
+            )
             self.traces = self._concatenate_traces(self.traces_by_fileno)
         elif version == 2:
             self.traces = self._concatenate_traces(self.traces_by_fileno)
         self.preprocessing_stats = {
-            'num_runs': len(measurements),
-            'num_valid': num_valid
+            "num_runs": len(measurements),
+            "num_valid": num_valid,
         }
 
 
@@ -1207,16 +1364,33 @@ class ParallelParamFit:
         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):
+    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]
-        })
+        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):
         """
@@ -1236,13 +1410,17 @@ def _try_fits_parallel(arg):
 
     Must be a global function as it is called from a multiprocessing Pool.
     """
-    return {
-        'key': arg['key'],
-        'result': _try_fits(*arg['args'])
-    }
+    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):
+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.
 
@@ -1281,22 +1459,28 @@ def _try_fits(by_param, state_or_tran, model_attribute, param_index, safe_functi
         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]):
+            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()
-    }
+    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()):
+    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
@@ -1304,10 +1488,14 @@ def _try_fits(by_param, state_or_tran, model_attribute, param_index, safe_functi
 
         # 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))
+        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()):
+        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
@@ -1324,7 +1512,9 @@ def _try_fits(by_param, state_or_tran, model_attribute, param_index, safe_functi
                 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)
+                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():
@@ -1333,38 +1523,37 @@ def _try_fits(by_param, state_or_tran, model_attribute, param_index, safe_functi
                     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
+            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
+            ref_results["median"].append(median_measures["rmsd"])
+            raw_results_by_param[other_parameters]["median"] = median_measures
 
-    if not len(ref_results['mean']):
+    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
-        }
+        return {"best": None, "best_rmsd": np.inf, "results": results}
 
-    for other_parameter_combination, other_parameter_results in raw_results_by_param.items():
+    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']
+                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": 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
@@ -1375,26 +1564,26 @@ def _try_fits(by_param, state_or_tran, model_attribute, param_index, safe_functi
             results[function_name] = {}
             for measure in result.keys():
                 results[function_name][measure] = np.mean(result[measure])
-            rmsd = results[function_name]['rmsd']
+            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
+        "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])
+        if "args" in value:
+            num_args[key] = len(value["args"][0])
     return num_args
 
 
@@ -1413,19 +1602,44 @@ def get_fit_result(results, name, attribute, verbose=False, param_filter: dict =
     """
     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']))
+        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']))
+            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
+                fit_result[result["key"][2]] = this_result
     return fit_result
 
 
@@ -1471,7 +1685,15 @@ class AnalyticModel:
     assess -- calculate model quality
     """
 
-    def __init__(self, by_name, parameters, arg_count=None, function_override=dict(), verbose=True, use_corrcoef=False):
+    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.
 
@@ -1521,19 +1743,29 @@ class AnalyticModel:
         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)
+        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']:
+            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))
+                    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))
+                    vprint(
+                        self.verbose,
+                        "[W] Got no data for {} {}: {}".format(name, key, fpe),
+                    )
         return model
 
     def param_index(self, param_name):
@@ -1596,22 +1828,28 @@ class AnalyticModel:
         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']
+        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 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.enqueue(
+                                name,
+                                attribute,
+                                len(self.parameters) + arg_index,
+                                arg_index,
+                                False,
+                            )
 
         paramfit.fit()
 
@@ -1619,8 +1857,10 @@ class AnalyticModel:
             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)
+            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)]
@@ -1628,25 +1868,27 @@ class AnalyticModel:
                     x.fit(self.by_param, name, attribute)
                     if x.fit_success:
                         param_model[name][attribute] = {
-                            'fit_result': fit_result,
-                            'function': x
+                            "fit_result": fit_result,
+                            "function": x,
                         }
                 elif len(fit_result.keys()):
-                    x = analytic.function_powerset(fit_result, self.parameters, num_args)
+                    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
+                            "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 "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']
+                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]
@@ -1656,8 +1898,8 @@ class AnalyticModel:
                 return param_model[name][key]
             return None
 
-        self.cache['fitted_model_getter'] = model_getter
-        self.cache['fitted_info_getter'] = info_getter
+        self.cache["fitted_model_getter"] = model_getter
+        self.cache["fitted_info_getter"] = info_getter
 
         return model_getter, info_getter
 
@@ -1677,13 +1919,22 @@ class AnalyticModel:
         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])))))
+            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,
+            "by_name": detailed_results,
         }
 
     def to_json(self):
@@ -1695,25 +1946,28 @@ def _add_trace_data_to_aggregate(aggregate, key, element):
     # Only cares about element['isa'], element['offline_aggregates'], and
     # element['plan']['level']
     if key not in aggregate:
-        aggregate[key] = {
-            'isa': element['isa']
-        }
-        for datakey in element['offline_aggregates'].keys():
+        aggregate[key] = {"isa": element["isa"]}
+        for datakey in element["offline_aggregates"].keys():
             aggregate[key][datakey] = []
-        if element['isa'] == 'state':
-            aggregate[key]['attributes'] = ['power']
+        if element["isa"] == "state":
+            aggregate[key]["attributes"] = ["power"]
         else:
             # TODO do not hardcode values
-            aggregate[key]['attributes'] = ['duration', 'energy', 'rel_energy_prev', 'rel_energy_next']
+            aggregate[key]["attributes"] = [
+                "duration",
+                "energy",
+                "rel_energy_prev",
+                "rel_energy_next",
+            ]
             # Uncomment this line if you also want to analyze mean transition power
             # aggrgate[key]['attributes'].append('power')
-            if 'plan' in element and element['plan']['level'] == 'epilogue':
-                aggregate[key]['attributes'].insert(0, 'timeout')
-        attributes = aggregate[key]['attributes'].copy()
+            if "plan" in element and element["plan"]["level"] == "epilogue":
+                aggregate[key]["attributes"].insert(0, "timeout")
+        attributes = aggregate[key]["attributes"].copy()
         for attribute in attributes:
-            if attribute not in element['offline_aggregates']:
-                aggregate[key]['attributes'].remove(attribute)
-    for datakey, dataval in element['offline_aggregates'].items():
+            if attribute not in element["offline_aggregates"]:
+                aggregate[key]["attributes"].remove(attribute)
+    for datakey, dataval in element["offline_aggregates"].items():
         aggregate[key][datakey].extend(dataval)
 
 
@@ -1771,16 +2025,20 @@ def pta_trace_to_aggregate(traces, ignore_trace_indexes=[]):
     """
     arg_count = dict()
     by_name = dict()
-    parameter_names = sorted(traces[0]['trace'][0]['parameter'].keys())
+    parameter_names = sorted(traces[0]["trace"][0]["parameter"].keys())
     for run in traces:
-        if run['id'] not in ignore_trace_indexes:
-            for elem in run['trace']:
-                if elem['isa'] == 'transition' and not elem['name'] in arg_count and 'args' in elem:
-                    arg_count[elem['name']] = len(elem['args'])
-                if elem['name'] != 'UNINITIALIZED':
-                    _add_trace_data_to_aggregate(by_name, elem['name'], elem)
+        if run["id"] not in ignore_trace_indexes:
+            for elem in run["trace"]:
+                if (
+                    elem["isa"] == "transition"
+                    and not elem["name"] in arg_count
+                    and "args" in elem
+                ):
+                    arg_count[elem["name"]] = len(elem["args"])
+                if elem["name"] != "UNINITIALIZED":
+                    _add_trace_data_to_aggregate(by_name, elem["name"], elem)
     for elem in by_name.values():
-        for key in elem['attributes']:
+        for key in elem["attributes"]:
             elem[key] = np.array(elem[key])
     return by_name, parameter_names, arg_count
 
@@ -1817,7 +2075,19 @@ class PTAModel:
     - 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):
+    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.
 
@@ -1854,9 +2124,16 @@ class PTAModel:
         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.stats = ParamStats(
+            self.by_name,
+            self.by_param,
+            self._parameter_names,
+            self._num_args,
+            self._use_corrcoef,
+            verbose=verbose,
+        )
         self.cache = {}
-        np.seterr('raise')
+        np.seterr("raise")
         self._outlier_threshold = discard_outliers
         self.function_override = function_override.copy()
         self.verbose = verbose
@@ -1866,7 +2143,7 @@ class PTAModel:
 
     def _aggregate_to_ndarray(self, aggregate):
         for elem in aggregate.values():
-            for key in elem['attributes']:
+            for key in elem["attributes"]:
                 elem[key] = np.array(elem[key])
 
     # This heuristic is very similar to the "function is not much better than
@@ -1884,13 +2161,16 @@ class PTAModel:
         model = {}
         for name, elem in model_dict.items():
             model[name] = {}
-            for key in elem['attributes']:
+            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))
+                    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))
+                    vprint(
+                        self.verbose,
+                        "[W] Got no data for {} {}: {}".format(name, key, fpe),
+                    )
         return model
 
     def get_static(self, use_mean=False):
@@ -1953,63 +2233,110 @@ class PTAModel:
         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']
+        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()])
+        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']:
+            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':
+                    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)
+                        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':
+            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 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):
+                    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)]
+                    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
+                            "fit_result": fit_results,
+                            "function": x,
                         }
                 elif len(fit_results.keys()):
-                    x = analytic.function_powerset(fit_results, self._parameter_names, num_args)
+                    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
+                            "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 "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']
+                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]
@@ -2019,8 +2346,8 @@ class PTAModel:
                 return param_model[name][key]
             return None
 
-        self.cache['fitted_model_getter'] = model_getter
-        self.cache['fitted_info_getter'] = info_getter
+        self.cache["fitted_model_getter"] = model_getter
+        self.cache["fitted_info_getter"] = info_getter
 
         return model_getter, info_getter
 
@@ -2029,16 +2356,32 @@ class PTAModel:
         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'])
+        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())))
+        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())))
+        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."""
@@ -2050,7 +2393,7 @@ class PTAModel:
         return self._parameter_names
 
     def attributes(self, state_or_trans):
-        return self.by_name[state_or_trans]['attributes']
+        return self.by_name[state_or_trans]["attributes"]
 
     def assess(self, model_function):
         """
@@ -2068,16 +2411,23 @@ class PTAModel:
         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])))))
+            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
-        }
+        return {"by_name": detailed_results}
 
-    def assess_states(self, model_function, model_attribute='power', distribution: dict = None):
+    def assess_states(
+        self, model_function, model_attribute="power", distribution: dict = None
+    ):
         """
         Calculate overall model error assuming equal distribution of states
         """
@@ -2089,7 +2439,9 @@ class PTAModel:
             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')
+            raise ValueError(
+                "distribution must be a probability distribution with sum 1"
+            )
 
         # total_value = None
         # try:
@@ -2097,7 +2449,17 @@ class PTAModel:
         # 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())))
+        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):
@@ -2118,44 +2480,72 @@ class PTAModel:
         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.
-                    real_energy = 0.
-                    model_rel_energy = 0.
-                    model_state_energy = 0.
-                    model_duration = 0.
-                    real_duration = 0.
-                    model_timeout = 0.
-                    real_timeout = 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':
+            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']
+                                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
+                                if isa == "state":
+                                    model_energy += (
+                                        model_function(name, "power", param=param)
+                                        * duration
+                                    )
                                 else:
-                                    model_energy += model_function(name, 'energy', param=param)
+                                    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)
+                                        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)
+                                        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)
+                                    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
@@ -2169,11 +2559,21 @@ class PTAModel:
                     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)),
+            "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)
+            ),
         }
 
 
@@ -2230,17 +2630,19 @@ class EnergyTraceLog:
         """
 
         if not zbar_available:
-            self.errors.append('zbar module is not available. Try "apt install python3-zbar"')
+            self.errors.append(
+                'zbar module is not available. Try "apt install python3-zbar"'
+            )
             return list()
 
-        lines = log_data.decode('ascii').split('\n')
-        data_count = sum(map(lambda x: len(x) > 0 and x[0] != '#', lines))
-        data_lines = filter(lambda x: len(x) > 0 and x[0] != '#', lines)
+        lines = log_data.decode("ascii").split("\n")
+        data_count = sum(map(lambda x: len(x) > 0 and x[0] != "#", lines))
+        data_lines = filter(lambda x: len(x) > 0 and x[0] != "#", lines)
 
         data = np.empty((data_count, 4))
 
         for i, line in enumerate(data_lines):
-            fields = line.split(' ')
+            fields = line.split(" ")
             if len(fields) == 4:
                 timestamp, current, voltage, total_energy = map(int, fields)
             elif len(fields) == 5:
@@ -2252,15 +2654,26 @@ class EnergyTraceLog:
 
         self.interval_start_timestamp = data[:-1, 0] * 1e-6
         self.interval_duration = (data[1:, 0] - data[:-1, 0]) * 1e-6
-        self.interval_power = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / ((data[1:, 0] - data[:-1, 0]) * 1e-6)
+        self.interval_power = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / (
+            (data[1:, 0] - data[:-1, 0]) * 1e-6
+        )
 
         m_duration_us = data[-1, 0] - data[0, 0]
 
         self.sample_rate = data_count / (m_duration_us * 1e-6)
 
-        vprint(self.verbose, 'got {} samples with {} seconds of log data ({} Hz)'.format(data_count, m_duration_us * 1e-6, self.sample_rate))
+        vprint(
+            self.verbose,
+            "got {} samples with {} seconds of log data ({} Hz)".format(
+                data_count, m_duration_us * 1e-6, self.sample_rate
+            ),
+        )
 
-        return self.interval_start_timestamp, self.interval_duration, self.interval_power
+        return (
+            self.interval_start_timestamp,
+            self.interval_duration,
+            self.interval_power,
+        )
 
     def ts_to_index(self, timestamp):
         """
@@ -2279,7 +2692,12 @@ class EnergyTraceLog:
         mid_index = left_index + (right_index - left_index) // 2
 
         # I'm feeling lucky
-        if timestamp > self.interval_start_timestamp[mid_index] and timestamp <= self.interval_start_timestamp[mid_index] + self.interval_duration[mid_index]:
+        if (
+            timestamp > self.interval_start_timestamp[mid_index]
+            and timestamp
+            <= self.interval_start_timestamp[mid_index]
+            + self.interval_duration[mid_index]
+        ):
             return mid_index
 
         if timestamp <= self.interval_start_timestamp[mid_index]:
@@ -2322,16 +2740,29 @@ class EnergyTraceLog:
 
         expected_transitions = list()
         for trace_number, trace in enumerate(traces):
-            for state_or_transition_number, state_or_transition in enumerate(trace['trace']):
-                if state_or_transition['isa'] == 'transition':
+            for state_or_transition_number, state_or_transition in enumerate(
+                trace["trace"]
+            ):
+                if state_or_transition["isa"] == "transition":
                     try:
-                        expected_transitions.append((
-                            state_or_transition['name'],
-                            state_or_transition['online_aggregates']['duration'][offline_index] * 1e-6
-                        ))
+                        expected_transitions.append(
+                            (
+                                state_or_transition["name"],
+                                state_or_transition["online_aggregates"]["duration"][
+                                    offline_index
+                                ]
+                                * 1e-6,
+                            )
+                        )
                     except IndexError:
-                        self.errors.append('Entry #{} ("{}") in trace #{} has no duration entry for offline_index/repeat_id {}'.format(
-                            state_or_transition_number, state_or_transition['name'], trace_number, offline_index))
+                        self.errors.append(
+                            'Entry #{} ("{}") in trace #{} has no duration entry for offline_index/repeat_id {}'.format(
+                                state_or_transition_number,
+                                state_or_transition["name"],
+                                trace_number,
+                                offline_index,
+                            )
+                        )
                         return energy_trace
 
         next_barcode = first_sync
@@ -2342,51 +2773,101 @@ class EnergyTraceLog:
                 print('[!!!] did not find transition "{}"'.format(name))
                 break
             next_barcode = end + self.state_duration + duration
-            vprint(self.verbose, '{} barcode "{}" area: {:0.2f} .. {:0.2f} / {:0.2f} seconds'.format(offline_index, bc, start, stop, end))
+            vprint(
+                self.verbose,
+                '{} 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, '{} estimated transition area: {:0.3f} .. {:0.3f} seconds'.format(offline_index, end, end + duration))
+                vprint(
+                    self.verbose,
+                    '[!!!] mismatch: expected "{}", got "{}"'.format(name, bc),
+                )
+            vprint(
+                self.verbose,
+                "{} estimated transition area: {:0.3f} .. {:0.3f} seconds".format(
+                    offline_index, end, end + duration
+                ),
+            )
 
             transition_start_index = self.ts_to_index(end)
             transition_done_index = self.ts_to_index(end + duration) + 1
             state_start_index = transition_done_index
-            state_done_index = self.ts_to_index(end + duration + self.state_duration) + 1
-
-            vprint(self.verbose, '{} estimated transitionindex: {:0.3f} .. {:0.3f} seconds'.format(offline_index, transition_start_index / self.sample_rate, transition_done_index / self.sample_rate))
+            state_done_index = (
+                self.ts_to_index(end + duration + self.state_duration) + 1
+            )
 
-            energy_trace.append({
-                'isa': 'transition',
-                'W_mean': np.mean(self.interval_power[transition_start_index: transition_done_index]),
-                'W_std': np.std(self.interval_power[transition_start_index: transition_done_index]),
-                's': duration,
-                's_coarse': self.interval_start_timestamp[transition_done_index] - self.interval_start_timestamp[transition_start_index]
+            vprint(
+                self.verbose,
+                "{} estimated transitionindex: {:0.3f} .. {:0.3f} seconds".format(
+                    offline_index,
+                    transition_start_index / self.sample_rate,
+                    transition_done_index / self.sample_rate,
+                ),
+            )
 
-            })
+            energy_trace.append(
+                {
+                    "isa": "transition",
+                    "W_mean": np.mean(
+                        self.interval_power[
+                            transition_start_index:transition_done_index
+                        ]
+                    ),
+                    "W_std": np.std(
+                        self.interval_power[
+                            transition_start_index:transition_done_index
+                        ]
+                    ),
+                    "s": duration,
+                    "s_coarse": self.interval_start_timestamp[transition_done_index]
+                    - self.interval_start_timestamp[transition_start_index],
+                }
+            )
 
             if len(energy_trace) > 1:
-                energy_trace[-1]['W_mean_delta_prev'] = energy_trace[-1]['W_mean'] - energy_trace[-2]['W_mean']
+                energy_trace[-1]["W_mean_delta_prev"] = (
+                    energy_trace[-1]["W_mean"] - energy_trace[-2]["W_mean"]
+                )
 
-            energy_trace.append({
-                'isa': 'state',
-                'W_mean': np.mean(self.interval_power[state_start_index: state_done_index]),
-                'W_std': np.std(self.interval_power[state_start_index: state_done_index]),
-                's': self.state_duration,
-                's_coarse': self.interval_start_timestamp[state_done_index] - self.interval_start_timestamp[state_start_index]
-            })
+            energy_trace.append(
+                {
+                    "isa": "state",
+                    "W_mean": np.mean(
+                        self.interval_power[state_start_index:state_done_index]
+                    ),
+                    "W_std": np.std(
+                        self.interval_power[state_start_index:state_done_index]
+                    ),
+                    "s": self.state_duration,
+                    "s_coarse": self.interval_start_timestamp[state_done_index]
+                    - self.interval_start_timestamp[state_start_index],
+                }
+            )
 
-            energy_trace[-2]['W_mean_delta_next'] = energy_trace[-2]['W_mean'] - energy_trace[-1]['W_mean']
+            energy_trace[-2]["W_mean_delta_next"] = (
+                energy_trace[-2]["W_mean"] - energy_trace[-1]["W_mean"]
+            )
 
         expected_transition_count = len(expected_transitions)
         recovered_transition_ount = len(energy_trace) // 2
 
         if expected_transition_count != recovered_transition_ount:
-            self.errors.append('Expected {:d} transitions, got {:d}'.format(expected_transition_count, recovered_transition_ount))
+            self.errors.append(
+                "Expected {:d} transitions, got {:d}".format(
+                    expected_transition_count, recovered_transition_ount
+                )
+            )
 
         return energy_trace
 
     def find_first_sync(self):
         # LED Power is approx. self.led_power W, use self.led_power/2 W above surrounding median as threshold
-        sync_threshold_power = np.median(self.interval_power[: int(3 * self.sample_rate)]) + self.led_power / 3
+        sync_threshold_power = (
+            np.median(self.interval_power[: int(3 * self.sample_rate)])
+            + self.led_power / 3
+        )
         for i, ts in enumerate(self.interval_start_timestamp):
             if ts > 2 and self.interval_power[i] > sync_threshold_power:
                 return self.interval_start_timestamp[i - 300]
@@ -2410,26 +2891,56 @@ class EnergyTraceLog:
         lookaround = int(0.1 * self.sample_rate)
 
         # LED Power is approx. self.led_power W, use self.led_power/2 W above surrounding median as threshold
-        sync_threshold_power = np.median(self.interval_power[start_position - lookaround: start_position + lookaround]) + self.led_power / 3
+        sync_threshold_power = (
+            np.median(
+                self.interval_power[
+                    start_position - lookaround : start_position + lookaround
+                ]
+            )
+            + self.led_power / 3
+        )
 
-        vprint(self.verbose, 'looking for barcode starting at {:0.2f} s, threshold is {:0.1f} mW'.format(start_ts, sync_threshold_power * 1e3))
+        vprint(
+            self.verbose,
+            "looking for barcode starting at {:0.2f} s, threshold is {:0.1f} mW".format(
+                start_ts, sync_threshold_power * 1e3
+            ),
+        )
 
         sync_area_start = None
         sync_start_ts = None
         sync_area_end = None
         sync_end_ts = None
         for i, ts in enumerate(self.interval_start_timestamp):
-            if sync_area_start is None and ts >= start_ts and self.interval_power[i] > sync_threshold_power:
+            if (
+                sync_area_start is None
+                and ts >= start_ts
+                and self.interval_power[i] > sync_threshold_power
+            ):
                 sync_area_start = i - 300
                 sync_start_ts = ts
-            if sync_area_start is not None and sync_area_end is None and ts > sync_start_ts + self.min_barcode_duration and (ts > sync_start_ts + self.max_barcode_duration or abs(sync_threshold_power - self.interval_power[i]) > self.led_power):
+            if (
+                sync_area_start is not None
+                and sync_area_end is None
+                and ts > sync_start_ts + self.min_barcode_duration
+                and (
+                    ts > sync_start_ts + self.max_barcode_duration
+                    or abs(sync_threshold_power - self.interval_power[i])
+                    > self.led_power
+                )
+            ):
                 sync_area_end = i
                 sync_end_ts = ts
                 break
 
-        barcode_data = self.interval_power[sync_area_start: sync_area_end]
+        barcode_data = self.interval_power[sync_area_start:sync_area_end]
 
-        vprint(self.verbose, 'barcode search area: {:0.2f} .. {:0.2f} seconds ({} samples)'.format(sync_start_ts, sync_end_ts, len(barcode_data)))
+        vprint(
+            self.verbose,
+            "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)
 
@@ -2439,7 +2950,9 @@ class EnergyTraceLog:
         start_ts = self.interval_start_timestamp[sync_area_start + start]
         stop_ts = self.interval_start_timestamp[sync_area_start + stop]
 
-        end_ts = stop_ts + self.module_duration * padding_bits + self.quiet_zone_duration
+        end_ts = (
+            stop_ts + self.module_duration * padding_bits + self.quiet_zone_duration
+        )
 
         # barcode content, barcode start timestamp, barcode stop timestamp, barcode end (stop + padding) timestamp
         return bc, start_ts, stop_ts, end_ts
@@ -2455,7 +2968,9 @@ class EnergyTraceLog:
         # -> Create a black and white (not grayscale) image to avoid this.
         # Unfortunately, this decreases resilience against background noise
         # (e.g. a not-exactly-idle peripheral device or CPU interrupts).
-        image_data = np.around(1 - ((barcode_data - min_power) / (max_power - min_power)))
+        image_data = np.around(
+            1 - ((barcode_data - min_power) / (max_power - min_power))
+        )
         image_data *= 255
 
         # zbar only returns the complete barcode position if it is at least
@@ -2469,12 +2984,12 @@ class EnergyTraceLog:
         # img = Image.frombytes('L', (width, height), image_data).resize((width, 100))
         # img.save('/tmp/test-{}.png'.format(os.getpid()))
 
-        zbimg = zbar.Image(width, height, 'Y800', image_data)
+        zbimg = zbar.Image(width, height, "Y800", image_data)
         scanner = zbar.ImageScanner()
-        scanner.parse_config('enable')
+        scanner.parse_config("enable")
 
         if scanner.scan(zbimg):
-            sym, = zbimg.symbols
+            (sym,) = zbimg.symbols
             content = sym.data
             try:
                 sym_start = sym.location[1][0]
@@ -2482,7 +2997,7 @@ class EnergyTraceLog:
                 sym_start = 0
             sym_end = sym.location[0][0]
 
-            match = re.fullmatch(r'T(\d+)', content)
+            match = re.fullmatch(r"T(\d+)", content)
             if match:
                 content = self.transition_names[int(match.group(1))]
 
@@ -2490,7 +3005,7 @@ class EnergyTraceLog:
             # additional non-barcode padding (encoded as LED off / image white).
             # Calculate the amount of extra bits to determine the offset until
             # the transition starts.
-            padding_bits = len(Code128(sym.data, charset='B').modules) % 8
+            padding_bits = len(Code128(sym.data, charset="B").modules) % 8
 
             # sym_start leaves out the first two bars, but we don't do anything about that here
             # sym_end leaves out the last three bars, each of which is one padding bit long.
@@ -2499,7 +3014,7 @@ class EnergyTraceLog:
 
             return content, sym_start, sym_end, padding_bits
         else:
-            vprint(self.verbose, 'unable to find barcode')
+            vprint(self.verbose, "unable to find barcode")
             return None, None, None, None
 
 
@@ -2555,15 +3070,15 @@ class MIMOSA:
 
         :returns: (numpy array of charges (pJ per 10µs), numpy array of triggers (0/1 int, per 10µs))
         """
-        num_bytes = tf.getmember('/tmp/mimosa//mimosa_scale_1.tmp').size
+        num_bytes = tf.getmember("/tmp/mimosa//mimosa_scale_1.tmp").size
         charges = np.ndarray(shape=(int(num_bytes / 4)), dtype=np.int32)
         triggers = np.ndarray(shape=(int(num_bytes / 4)), dtype=np.int8)
-        with tf.extractfile('/tmp/mimosa//mimosa_scale_1.tmp') as f:
+        with tf.extractfile("/tmp/mimosa//mimosa_scale_1.tmp") as f:
             content = f.read()
-            iterator = struct.iter_unpack('<I', content)
+            iterator = struct.iter_unpack("<I", content)
             i = 0
             for word in iterator:
-                charges[i] = (word[0] >> 4)
+                charges[i] = word[0] >> 4
                 triggers[i] = (word[0] & 0x08) >> 3
                 i += 1
         return charges, triggers
@@ -2616,7 +3131,7 @@ class MIMOSA:
         trigidx = []
 
         if len(triggers) < 1000000:
-            self.errors.append('MIMOSA log is too short')
+            self.errors.append("MIMOSA log is too short")
             return trigidx
 
         prevtrig = triggers[999999]
@@ -2625,13 +3140,17 @@ class MIMOSA:
         # something went wrong and are unable to determine when the first
         # transition starts.
         if prevtrig != 0:
-            self.errors.append('Unable to find start of first transition (log starts with trigger == {} != 0)'.format(prevtrig))
+            self.errors.append(
+                "Unable to find start of first transition (log starts with trigger == {} != 0)".format(
+                    prevtrig
+                )
+            )
 
         # if the last trigger is high (i.e., trigger/buzzer pin is active when the benchmark ends),
         # it terminated in the middle of a transition -- meaning that it was not
         # measured in its entirety.
         if triggers[-1] != 0:
-            self.errors.append('Log ends during a transition'.format(prevtrig))
+            self.errors.append("Log ends during a transition".format(prevtrig))
 
         # the device is reset for MIMOSA calibration in the first 10s and may
         # send bogus interrupts -> bogus triggers
@@ -2663,11 +3182,23 @@ class MIMOSA:
         for i in range(100000, len(currents)):
             if r1idx == 0 and currents[i] > ua_r1 * 0.6:
                 r1idx = i
-            elif r1idx != 0 and r2idx == 0 and i > (r1idx + 180000) and currents[i] < ua_r1 * 0.4:
+            elif (
+                r1idx != 0
+                and r2idx == 0
+                and i > (r1idx + 180000)
+                and currents[i] < ua_r1 * 0.4
+            ):
                 r2idx = i
         # 2s disconnected, 2s r1, 2s r2  with r1 < r2  ->  ua_r1 > ua_r2
         # allow 5ms buffer in both directions to account for bouncing relais contacts
-        return r1idx - 180500, r1idx - 500, r1idx + 500, r2idx - 500, r2idx + 500, r2idx + 180500
+        return (
+            r1idx - 180500,
+            r1idx - 500,
+            r1idx + 500,
+            r2idx - 500,
+            r2idx + 500,
+            r2idx + 180500,
+        )
 
     def calibration_function(self, charges, cal_edges):
         u"""
@@ -2711,7 +3242,7 @@ 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))
+            vprint(self.verbose, "[W] 0 uA == %.f uA during calibration" % (ua_r2))
             b_lower = 0
 
         b_upper = (ua_r1 - ua_r2) / (cal_r1_mean - cal_r2_mean)
@@ -2726,7 +3257,9 @@ class MIMOSA:
                     return 0
                 else:
                     return charge * b_lower + a_lower
+
         else:
+
             def calfunc(charge):
                 if charge < cal_0_mean:
                     return 0
@@ -2736,19 +3269,19 @@ class MIMOSA:
                     return charge * b_upper + a_upper + ua_r2
 
         caldata = {
-            'edges': [x * 10 for x in cal_edges],
-            'offset': cal_0_mean,
-            'offset2': cal_r2_mean,
-            'slope_low': b_lower,
-            'slope_high': b_upper,
-            'add_low': a_lower,
-            'add_high': a_upper,
-            'r0_err_uW': np.mean(self.currents_nocal(chg_r0)) * self.voltage,
-            'r0_std_uW': np.std(self.currents_nocal(chg_r0)) * self.voltage,
-            'r1_err_uW': (np.mean(self.currents_nocal(chg_r1)) - ua_r1) * self.voltage,
-            'r1_std_uW': np.std(self.currents_nocal(chg_r1)) * self.voltage,
-            'r2_err_uW': (np.mean(self.currents_nocal(chg_r2)) - ua_r2) * self.voltage,
-            'r2_std_uW': np.std(self.currents_nocal(chg_r2)) * self.voltage,
+            "edges": [x * 10 for x in cal_edges],
+            "offset": cal_0_mean,
+            "offset2": cal_r2_mean,
+            "slope_low": b_lower,
+            "slope_high": b_upper,
+            "add_low": a_lower,
+            "add_high": a_upper,
+            "r0_err_uW": np.mean(self.currents_nocal(chg_r0)) * self.voltage,
+            "r0_std_uW": np.std(self.currents_nocal(chg_r0)) * self.voltage,
+            "r1_err_uW": (np.mean(self.currents_nocal(chg_r1)) - ua_r1) * self.voltage,
+            "r1_std_uW": np.std(self.currents_nocal(chg_r1)) * self.voltage,
+            "r2_err_uW": (np.mean(self.currents_nocal(chg_r2)) - ua_r2) * self.voltage,
+            "r2_std_uW": np.std(self.currents_nocal(chg_r2)) * self.voltage,
         }
 
         # print("if charge < %f : return 0" % cal_0_mean)
@@ -2843,51 +3376,59 @@ class MIMOSA:
                     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),
-                        })
+                        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,
+                        "threshold": thr,
+                        "states": statelist,
                     }
 
-            isa = 'state'
+            isa = "state"
             if not is_state:
-                isa = 'transition'
+                isa = "transition"
 
             data = {
-                'isa': isa,
-                'clip_rate': np.mean(range_raw == 65535),
-                'raw_mean': np.mean(range_raw),
-                'raw_std': np.std(range_raw),
-                'uW_mean': np.mean(range_ua * self.voltage),
-                'uW_std': np.std(range_ua * self.voltage),
-                'us': (idx - previdx) * 10,
+                "isa": isa,
+                "clip_rate": np.mean(range_raw == 65535),
+                "raw_mean": np.mean(range_raw),
+                "raw_std": np.std(range_raw),
+                "uW_mean": np.mean(range_ua * self.voltage),
+                "uW_std": np.std(range_ua * self.voltage),
+                "us": (idx - previdx) * 10,
             }
 
             if self.with_traces:
-                data['uW'] = range_ua * self.voltage
+                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 "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':
+            if isa == "transition":
                 # subtract average power of previous state
                 # (that is, the state from which this transition originates)
-                data['uW_mean_delta_prev'] = data['uW_mean'] - iterdata[-1]['uW_mean']
+                data["uW_mean_delta_prev"] = data["uW_mean"] - iterdata[-1]["uW_mean"]
                 # placeholder to avoid extra cases in the analysis
-                data['uW_mean_delta_next'] = data['uW_mean']
-                data['timeout'] = iterdata[-1]['us']
+                data["uW_mean_delta_next"] = data["uW_mean"]
+                data["timeout"] = iterdata[-1]["us"]
             elif len(iterdata) > 0:
                 # subtract average power of next state
                 # (the state into which this transition leads)
-                iterdata[-1]['uW_mean_delta_next'] = iterdata[-1]['uW_mean'] - data['uW_mean']
+                iterdata[-1]["uW_mean_delta_next"] = (
+                    iterdata[-1]["uW_mean"] - data["uW_mean"]
+                )
 
             iterdata.append(data)
 
diff --git a/lib/functions.py b/lib/functions.py
index 2451ef6..6d8daa4 100644
--- a/lib/functions.py
+++ b/lib/functions.py
@@ -12,6 +12,7 @@ from .utils import is_numeric, vprint
 
 arg_support_enabled = True
 
+
 def powerset(iterable):
     """
     Return powerset of `iterable` elements.
@@ -19,7 +20,8 @@ def powerset(iterable):
     Example: `powerset([1, 2])` -> `[(), (1), (2), (1, 2)]`
     """
     s = list(iterable)
-    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
+    return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))
+
 
 class ParamFunction:
     """
@@ -82,6 +84,7 @@ class ParamFunction:
         """
         return self._param_function(P, X) - y
 
+
 class NormalizationFunction:
     """
     Wrapper for parameter normalization functions used in YAML PTA/DFA models.
@@ -95,7 +98,7 @@ class NormalizationFunction:
         `param` and return a float.
         """
         self._function_str = function_str
-        self._function = eval('lambda param: ' + function_str)
+        self._function = eval("lambda param: " + function_str)
 
     def eval(self, param_value: float) -> float:
         """
@@ -105,6 +108,7 @@ class NormalizationFunction:
         """
         return self._function(param_value)
 
+
 class AnalyticFunction:
     """
     A multi-dimensional model function, generated from a string, which can be optimized using regression.
@@ -114,7 +118,9 @@ 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, verbose=True, regression_args=None
+    ):
         """
         Create a new AnalyticFunction object from a function string.
 
@@ -143,22 +149,30 @@ class AnalyticFunction:
         self.verbose = verbose
 
         if type(function_str) == str:
-            num_vars_re = re.compile(r'regression_arg\(([0-9]+)\)')
+            num_vars_re = re.compile(r"regression_arg\(([0-9]+)\)")
             num_vars = max(map(int, num_vars_re.findall(function_str))) + 1
             for i in range(len(parameters)):
-                if rawfunction.find('parameter({})'.format(parameters[i])) >= 0:
+                if rawfunction.find("parameter({})".format(parameters[i])) >= 0:
                     self._dependson[i] = True
-                    rawfunction = rawfunction.replace('parameter({})'.format(parameters[i]), 'model_param[{:d}]'.format(i))
+                    rawfunction = rawfunction.replace(
+                        "parameter({})".format(parameters[i]),
+                        "model_param[{:d}]".format(i),
+                    )
             for i in range(0, num_args):
-                if rawfunction.find('function_arg({:d})'.format(i)) >= 0:
+                if rawfunction.find("function_arg({:d})".format(i)) >= 0:
                     self._dependson[len(parameters) + i] = True
-                    rawfunction = rawfunction.replace('function_arg({:d})'.format(i), 'model_param[{:d}]'.format(len(parameters) + i))
+                    rawfunction = rawfunction.replace(
+                        "function_arg({:d})".format(i),
+                        "model_param[{:d}]".format(len(parameters) + i),
+                    )
             for i in range(num_vars):
-                rawfunction = rawfunction.replace('regression_arg({:d})'.format(i), 'reg_param[{:d}]'.format(i))
+                rawfunction = rawfunction.replace(
+                    "regression_arg({:d})".format(i), "reg_param[{:d}]".format(i)
+                )
             self._function_str = rawfunction
-            self._function = eval('lambda reg_param, model_param: ' + rawfunction)
+            self._function = eval("lambda reg_param, model_param: " + rawfunction)
         else:
-            self._function_str = 'raise ValueError'
+            self._function_str = "raise ValueError"
             self._function = function_str
 
         if regression_args:
@@ -217,7 +231,12 @@ 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(state_or_tran, model_attribute, len(key[1]), dimension))
+                vprint(
+                    self.verbose,
+                    "[W] Invalid parameter key length while gathering fit data for {}/{}. is {}, want {}.".format(
+                        state_or_tran, model_attribute, len(key[1]), dimension
+                    ),
+                )
         X = np.array(X)
         Y = np.array(Y)
 
@@ -237,21 +256,40 @@ class AnalyticFunction:
         argument values are present, they must come after parameter values
         in the order of their appearance in the function signature.
         """
-        X, Y, num_valid, num_total = self.get_fit_data(by_param, state_or_tran, model_attribute)
+        X, Y, num_valid, num_total = self.get_fit_data(
+            by_param, state_or_tran, model_attribute
+        )
         if num_valid > 2:
             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)
+                res = optimize.least_squares(
+                    error_function, self._regression_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))
+                vprint(
+                    self.verbose,
+                    "[W] Fit failed for {}/{}: {} (function: {})".format(
+                        state_or_tran, model_attribute, err, self._model_str
+                    ),
+                )
                 return
             if res.status > 0:
                 self._regression_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))
+                vprint(
+                    self.verbose,
+                    "[W] Fit failed for {}/{}: {} (function: {})".format(
+                        state_or_tran, model_attribute, res.message, self._model_str
+                    ),
+                )
         else:
-            vprint(self.verbose, '[W] Insufficient amount of valid parameter keys, cannot fit {}/{}'.format(state_or_tran, model_attribute))
+            vprint(
+                self.verbose,
+                "[W] Insufficient amount of valid parameter keys, cannot fit {}/{}".format(
+                    state_or_tran, model_attribute
+                ),
+            )
 
     def is_predictable(self, param_list):
         """
@@ -268,7 +306,7 @@ class AnalyticFunction:
                 return False
         return True
 
-    def eval(self, param_list, arg_list = []):
+    def eval(self, param_list, arg_list=[]):
         """
         Evaluate model function with specified param/arg values.
 
@@ -280,6 +318,7 @@ class AnalyticFunction:
             return self._function(param_list, arg_list)
         return self._function(self._regression_args, param_list)
 
+
 class analytic:
     """
     Utilities for analytic description of parameter-dependent model attributes and regression analysis.
@@ -292,28 +331,28 @@ class analytic:
     _num0_8 = np.vectorize(lambda x: 8 - bin(int(x)).count("1"))
     _num0_16 = np.vectorize(lambda x: 16 - bin(int(x)).count("1"))
     _num1 = np.vectorize(lambda x: bin(int(x)).count("1"))
-    _safe_log = np.vectorize(lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 1.)
-    _safe_inv = np.vectorize(lambda x: 1 / x if np.abs(x) > 0.001 else 1.)
+    _safe_log = np.vectorize(lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 1.0)
+    _safe_inv = np.vectorize(lambda x: 1 / x if np.abs(x) > 0.001 else 1.0)
     _safe_sqrt = np.vectorize(lambda x: np.sqrt(np.abs(x)))
 
     _function_map = {
-        'linear' : lambda x: x,
-        'logarithmic' : np.log,
-        'logarithmic1' : lambda x: np.log(x + 1),
-        'exponential' : np.exp,
-        'square' : lambda x : x ** 2,
-        'inverse' : lambda x : 1 / x,
-        'sqrt' : lambda x: np.sqrt(np.abs(x)),
-        'num0_8' : _num0_8,
-        'num0_16' : _num0_16,
-        'num1' : _num1,
-        'safe_log' : lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 1.,
-        'safe_inv' : lambda x: 1 / x if np.abs(x) > 0.001 else 1.,
-        'safe_sqrt': lambda x: np.sqrt(np.abs(x)),
+        "linear": lambda x: x,
+        "logarithmic": np.log,
+        "logarithmic1": lambda x: np.log(x + 1),
+        "exponential": np.exp,
+        "square": lambda x: x ** 2,
+        "inverse": lambda x: 1 / x,
+        "sqrt": lambda x: np.sqrt(np.abs(x)),
+        "num0_8": _num0_8,
+        "num0_16": _num0_16,
+        "num1": _num1,
+        "safe_log": lambda x: np.log(np.abs(x)) if np.abs(x) > 0.001 else 1.0,
+        "safe_inv": lambda x: 1 / x if np.abs(x) > 0.001 else 1.0,
+        "safe_sqrt": lambda x: np.sqrt(np.abs(x)),
     }
 
     @staticmethod
-    def functions(safe_functions_enabled = False):
+    def functions(safe_functions_enabled=False):
         """
         Retrieve pre-defined set of regression function candidates.
 
@@ -329,74 +368,87 @@ class analytic:
         variables are expected.
         """
         functions = {
-            'linear' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * model_param,
+            "linear": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * model_param,
                 lambda model_param: True,
-                2
+                2,
             ),
-            'logarithmic' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * np.log(model_param),
+            "logarithmic": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * np.log(model_param),
                 lambda model_param: model_param > 0,
-                2
+                2,
             ),
-            'logarithmic1' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * np.log(model_param + 1),
+            "logarithmic1": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * np.log(model_param + 1),
                 lambda model_param: model_param > -1,
-                2
+                2,
             ),
-            'exponential' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * np.exp(model_param),
+            "exponential": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * np.exp(model_param),
                 lambda model_param: model_param <= 64,
-                2
+                2,
             ),
             #'polynomial' : lambda reg_param, model_param: reg_param[0] + reg_param[1] * model_param + reg_param[2] * model_param ** 2,
-            'square' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * model_param ** 2,
+            "square": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * model_param ** 2,
                 lambda model_param: True,
-                2
+                2,
             ),
-            'inverse' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] / model_param,
+            "inverse": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] / model_param,
                 lambda model_param: model_param != 0,
-                2
+                2,
             ),
-            'sqrt' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * np.sqrt(model_param),
+            "sqrt": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * np.sqrt(model_param),
                 lambda model_param: model_param >= 0,
-                2
+                2,
             ),
-            'num0_8' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._num0_8(model_param),
+            "num0_8": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * analytic._num0_8(model_param),
                 lambda model_param: True,
-                2
+                2,
             ),
-            'num0_16' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._num0_16(model_param),
+            "num0_16": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * analytic._num0_16(model_param),
                 lambda model_param: True,
-                2
+                2,
             ),
-            'num1' : ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._num1(model_param),
+            "num1": ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * analytic._num1(model_param),
                 lambda model_param: True,
-                2
+                2,
             ),
         }
 
         if safe_functions_enabled:
-            functions['safe_log'] = ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._safe_log(model_param),
+            functions["safe_log"] = ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * analytic._safe_log(model_param),
                 lambda model_param: True,
-                2
+                2,
             )
-            functions['safe_inv'] = ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._safe_inv(model_param),
+            functions["safe_inv"] = ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * analytic._safe_inv(model_param),
                 lambda model_param: True,
-                2
+                2,
             )
-            functions['safe_sqrt'] = ParamFunction(
-                lambda reg_param, model_param: reg_param[0] + reg_param[1] * analytic._safe_sqrt(model_param),
+            functions["safe_sqrt"] = ParamFunction(
+                lambda reg_param, model_param: reg_param[0]
+                + reg_param[1] * analytic._safe_sqrt(model_param),
                 lambda model_param: True,
-                2
+                2,
             )
 
         return functions
@@ -404,27 +456,27 @@ class analytic:
     @staticmethod
     def _fmap(reference_type, reference_name, function_type):
         """Map arg/parameter name and best-fit function name to function text suitable for AnalyticFunction."""
-        ref_str = '{}({})'.format(reference_type,reference_name)
-        if function_type == 'linear':
+        ref_str = "{}({})".format(reference_type, reference_name)
+        if function_type == "linear":
             return ref_str
-        if function_type == 'logarithmic':
-            return 'np.log({})'.format(ref_str)
-        if function_type == 'logarithmic1':
-            return 'np.log({} + 1)'.format(ref_str)
-        if function_type == 'exponential':
-            return 'np.exp({})'.format(ref_str)
-        if function_type == 'exponential':
-            return 'np.exp({})'.format(ref_str)
-        if function_type == 'square':
-            return '({})**2'.format(ref_str)
-        if function_type == 'inverse':
-            return '1/({})'.format(ref_str)
-        if function_type == 'sqrt':
-            return 'np.sqrt({})'.format(ref_str)
-        return 'analytic._{}({})'.format(function_type, ref_str)
+        if function_type == "logarithmic":
+            return "np.log({})".format(ref_str)
+        if function_type == "logarithmic1":
+            return "np.log({} + 1)".format(ref_str)
+        if function_type == "exponential":
+            return "np.exp({})".format(ref_str)
+        if function_type == "exponential":
+            return "np.exp({})".format(ref_str)
+        if function_type == "square":
+            return "({})**2".format(ref_str)
+        if function_type == "inverse":
+            return "1/({})".format(ref_str)
+        if function_type == "sqrt":
+            return "np.sqrt({})".format(ref_str)
+        return "analytic._{}({})".format(function_type, ref_str)
 
     @staticmethod
-    def function_powerset(fit_results, parameter_names, num_args = 0):
+    def function_powerset(fit_results, parameter_names, num_args=0):
         """
         Combine per-parameter regression results into a single multi-dimensional function.
 
@@ -443,14 +495,22 @@ class analytic:
         Returns an AnalyticFunction instantce corresponding to the combined
         function.
         """
-        buf = '0'
+        buf = "0"
         arg_idx = 0
         for combination in powerset(fit_results.items()):
-            buf += ' + regression_arg({:d})'.format(arg_idx)
+            buf += " + regression_arg({:d})".format(arg_idx)
             arg_idx += 1
             for function_item in combination:
                 if arg_support_enabled and is_numeric(function_item[0]):
-                    buf += ' * {}'.format(analytic._fmap('function_arg', function_item[0], function_item[1]['best']))
+                    buf += " * {}".format(
+                        analytic._fmap(
+                            "function_arg", function_item[0], function_item[1]["best"]
+                        )
+                    )
                 else:
-                    buf += ' * {}'.format(analytic._fmap('parameter', function_item[0], function_item[1]['best']))
+                    buf += " * {}".format(
+                        analytic._fmap(
+                            "parameter", function_item[0], function_item[1]["best"]
+                        )
+                    )
         return AnalyticFunction(buf, parameter_names, num_args)
diff --git a/lib/harness.py b/lib/harness.py
index 54518e3..3b279c0 100644
--- a/lib/harness.py
+++ b/lib/harness.py
@@ -24,7 +24,16 @@ class TransitionHarness:
           primitive values (-> set by the return value of the current run, not necessarily constan)
         * `args`: function arguments, if isa == 'transition'
     """
-    def __init__(self, gpio_pin = None, gpio_mode = 'around', pta = None, log_return_values = False, repeat = 0, post_transition_delay_us = 0):
+
+    def __init__(
+        self,
+        gpio_pin=None,
+        gpio_mode="around",
+        pta=None,
+        log_return_values=False,
+        repeat=0,
+        post_transition_delay_us=0,
+    ):
         """
         Create a new TransitionHarness
 
@@ -47,7 +56,14 @@ class TransitionHarness:
         self.reset()
 
     def copy(self):
-        new_object = __class__(gpio_pin = self.gpio_pin, gpio_mode = self.gpio_mode, pta = self.pta, log_return_values = self.log_return_values, repeat = self.repeat, post_transition_delay_us = self.post_transition_delay_us)
+        new_object = __class__(
+            gpio_pin=self.gpio_pin,
+            gpio_mode=self.gpio_mode,
+            pta=self.pta,
+            log_return_values=self.log_return_values,
+            repeat=self.repeat,
+            post_transition_delay_us=self.post_transition_delay_us,
+        )
         new_object.traces = self.traces.copy()
         new_object.trace_id = self.trace_id
         return new_object
@@ -62,12 +78,16 @@ class TransitionHarness:
         of the current benchmark iteration. Resets `done` and `synced`,
         """
         for trace in self.traces:
-            for state_or_transition in trace['trace']:
-                if 'return_values' in state_or_transition:
-                    state_or_transition['return_values'] = state_or_transition['return_values'][:undo_from]
-                for param_name in state_or_transition['parameter'].keys():
-                    if type(state_or_transition['parameter'][param_name]) is list:
-                        state_or_transition['parameter'][param_name] = state_or_transition['parameter'][param_name][:undo_from]
+            for state_or_transition in trace["trace"]:
+                if "return_values" in state_or_transition:
+                    state_or_transition["return_values"] = state_or_transition[
+                        "return_values"
+                    ][:undo_from]
+                for param_name in state_or_transition["parameter"].keys():
+                    if type(state_or_transition["parameter"][param_name]) is list:
+                        state_or_transition["parameter"][
+                            param_name
+                        ] = state_or_transition["parameter"][param_name][:undo_from]
 
     def reset(self):
         """
@@ -95,33 +115,32 @@ class TransitionHarness:
 
     def global_code(self):
         """Return global (pre-`main()`) C++ code needed for tracing."""
-        ret = ''
+        ret = ""
         if self.gpio_pin != None:
-            ret += '#define PTALOG_GPIO {}\n'.format(self.gpio_pin)
-            if self.gpio_mode == 'before':
-                ret += '#define PTALOG_GPIO_BEFORE\n'
-            elif self.gpio_mode == 'bar':
-                ret += '#define PTALOG_GPIO_BAR\n'
+            ret += "#define PTALOG_GPIO {}\n".format(self.gpio_pin)
+            if self.gpio_mode == "before":
+                ret += "#define PTALOG_GPIO_BEFORE\n"
+            elif self.gpio_mode == "bar":
+                ret += "#define PTALOG_GPIO_BAR\n"
         if self.log_return_values:
-            ret += '#define PTALOG_WITH_RETURNVALUES\n'
-            ret += 'uint16_t transition_return_value;\n'
+            ret += "#define PTALOG_WITH_RETURNVALUES\n"
+            ret += "uint16_t transition_return_value;\n"
         ret += '#include "object/ptalog.h"\n'
         if self.gpio_pin != None:
-            ret += 'PTALog ptalog({});\n'.format(self.gpio_pin)
+            ret += "PTALog ptalog({});\n".format(self.gpio_pin)
         else:
-            ret += 'PTALog ptalog;\n'
+            ret += "PTALog ptalog;\n"
         return ret
 
-    def start_benchmark(self, benchmark_id = 0):
+    def start_benchmark(self, benchmark_id=0):
         """Return C++ code to signal benchmark start to harness."""
-        return 'ptalog.startBenchmark({:d});\n'.format(benchmark_id)
+        return "ptalog.startBenchmark({:d});\n".format(benchmark_id)
 
     def start_trace(self):
         """Prepare a new trace/run in the internal `.traces` structure."""
-        self.traces.append({
-            'id' : self.trace_id,
-            'trace' : list(),
-        })
+        self.traces.append(
+            {"id": self.trace_id, "trace": list(),}
+        )
         self.trace_id += 1
 
     def append_state(self, state_name, param):
@@ -131,13 +150,11 @@ class TransitionHarness:
         :param state_name: state name
         :param param: parameter dict
         """
-        self.traces[-1]['trace'].append({
-            'name': state_name,
-            'isa': 'state',
-            'parameter': param,
-        })
+        self.traces[-1]["trace"].append(
+            {"name": state_name, "isa": "state", "parameter": param,}
+        )
 
-    def append_transition(self, transition_name, param, args = []):
+    def append_transition(self, transition_name, param, args=[]):
         """
         Append a transition to the current run in the internal `.traces` structure.
 
@@ -145,122 +162,188 @@ class TransitionHarness:
         :param param: parameter dict
         :param args: function arguments (optional)
         """
-        self.traces[-1]['trace'].append({
-            'name': transition_name,
-            'isa': 'transition',
-            'parameter': param,
-            'args' : args,
-        })
+        self.traces[-1]["trace"].append(
+            {
+                "name": transition_name,
+                "isa": "transition",
+                "parameter": param,
+                "args": args,
+            }
+        )
 
     def start_run(self):
         """Return C++ code used to start a new run/trace."""
-        return 'ptalog.reset();\n'
+        return "ptalog.reset();\n"
 
     def _pass_transition_call(self, transition_id):
-        if self.gpio_mode == 'bar':
-            barcode_bits = Code128('T{}'.format(transition_id), charset='B').modules
+        if self.gpio_mode == "bar":
+            barcode_bits = Code128("T{}".format(transition_id), charset="B").modules
             if len(barcode_bits) % 8 != 0:
                 barcode_bits.extend([1] * (8 - (len(barcode_bits) % 8)))
-            barcode_bytes = [255 - int("".join(map(str, reversed(barcode_bits[i:i+8]))), 2) for i in range(0, len(barcode_bits), 8)]
-            inline_array = "".join(map(lambda s: '\\x{:02x}'.format(s), barcode_bytes))
-            return 'ptalog.startTransition("{}", {});\n'.format(inline_array, len(barcode_bytes))
+            barcode_bytes = [
+                255 - int("".join(map(str, reversed(barcode_bits[i : i + 8]))), 2)
+                for i in range(0, len(barcode_bits), 8)
+            ]
+            inline_array = "".join(map(lambda s: "\\x{:02x}".format(s), barcode_bytes))
+            return 'ptalog.startTransition("{}", {});\n'.format(
+                inline_array, len(barcode_bytes)
+            )
         else:
-            return 'ptalog.startTransition();\n'
+            return "ptalog.startTransition();\n"
 
-    def pass_transition(self, transition_id, transition_code, transition: object = None):
+    def pass_transition(
+        self, transition_id, transition_code, transition: object = None
+    ):
         """
         Return C++ code used to pass a transition, including the corresponding function call.
 
         Tracks which transition has been executed and optionally its return value. May also inject a delay, if
         `post_transition_delay_us` is set.
         """
-        ret = 'ptalog.passTransition({:d});\n'.format(transition_id)
+        ret = "ptalog.passTransition({:d});\n".format(transition_id)
         ret += self._pass_transition_call(transition_id)
-        if self.log_return_values and transition and len(transition.return_value_handlers):
-            ret += 'transition_return_value = {}\n'.format(transition_code)
-            ret += 'ptalog.logReturn(transition_return_value);\n'
+        if (
+            self.log_return_values
+            and transition
+            and len(transition.return_value_handlers)
+        ):
+            ret += "transition_return_value = {}\n".format(transition_code)
+            ret += "ptalog.logReturn(transition_return_value);\n"
         else:
-            ret += '{}\n'.format(transition_code)
+            ret += "{}\n".format(transition_code)
         if self.post_transition_delay_us:
-            ret += 'arch.delay_us({});\n'.format(self.post_transition_delay_us)
-        ret += 'ptalog.stopTransition();\n'
+            ret += "arch.delay_us({});\n".format(self.post_transition_delay_us)
+        ret += "ptalog.stopTransition();\n"
         return ret
 
-    def stop_run(self, num_traces = 0):
-        return 'ptalog.dump({:d});\n'.format(num_traces)
+    def stop_run(self, num_traces=0):
+        return "ptalog.dump({:d});\n".format(num_traces)
 
     def stop_benchmark(self):
-        return 'ptalog.stopBenchmark();\n'
+        return "ptalog.stopBenchmark();\n"
 
-    def _append_nondeterministic_parameter_value(self, log_data_target, parameter_name, parameter_value):
-        if log_data_target['parameter'][parameter_name] is None:
-            log_data_target['parameter'][parameter_name] = list()
-        log_data_target['parameter'][parameter_name].append(parameter_value)
+    def _append_nondeterministic_parameter_value(
+        self, log_data_target, parameter_name, parameter_value
+    ):
+        if log_data_target["parameter"][parameter_name] is None:
+            log_data_target["parameter"][parameter_name] = list()
+        log_data_target["parameter"][parameter_name].append(parameter_value)
 
     def parser_cb(self, line):
-        #print('[HARNESS] got line {}'.format(line))
-        if re.match(r'\[PTA\] benchmark stop', line):
+        # print('[HARNESS] got line {}'.format(line))
+        if re.match(r"\[PTA\] benchmark stop", line):
             self.repetitions += 1
             self.synced = False
             if self.repeat > 0 and self.repetitions == self.repeat:
                 self.done = True
-                print('[HARNESS] done')
+                print("[HARNESS] done")
                 return
-        if re.match(r'\[PTA\] benchmark start, id=(\S+)', line):
+        if re.match(r"\[PTA\] benchmark start, id=(\S+)", line):
             self.synced = True
-            print('[HARNESS] synced, {}/{}'.format(self.repetitions + 1, self.repeat))
+            print("[HARNESS] synced, {}/{}".format(self.repetitions + 1, self.repeat))
         if self.synced:
-            res = re.match(r'\[PTA\] trace=(\S+) count=(\S+)', line)
+            res = re.match(r"\[PTA\] trace=(\S+) count=(\S+)", line)
             if res:
                 self.trace_id = int(res.group(1))
                 self.trace_length = int(res.group(2))
                 self.current_transition_in_trace = 0
             if self.log_return_values:
-                res = re.match(r'\[PTA\] transition=(\S+) return=(\S+)', line)
+                res = re.match(r"\[PTA\] transition=(\S+) return=(\S+)", line)
             else:
-                res = re.match(r'\[PTA\] transition=(\S+)', line)
+                res = re.match(r"\[PTA\] transition=(\S+)", line)
             if res:
                 transition_id = int(res.group(1))
                 # self.traces contains transitions and states, UART output only contains transitions -> use index * 2
                 try:
-                    log_data_target = self.traces[self.trace_id]['trace'][self.current_transition_in_trace * 2]
+                    log_data_target = self.traces[self.trace_id]["trace"][
+                        self.current_transition_in_trace * 2
+                    ]
                 except IndexError:
                     transition_name = None
                     if self.pta:
                         transition_name = self.pta.transitions[transition_id].name
-                    print('[HARNESS] benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}, name {}) is out of bounds'.format(0, self.trace_id, self.current_transition_in_trace, transition_id, transition_name))
-                    print('          Offending line: {}'.format(line))
+                    print(
+                        "[HARNESS] benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}, name {}) is out of bounds".format(
+                            0,
+                            self.trace_id,
+                            self.current_transition_in_trace,
+                            transition_id,
+                            transition_name,
+                        )
+                    )
+                    print("          Offending line: {}".format(line))
                     return
-                if log_data_target['isa'] != 'transition':
+                if log_data_target["isa"] != "transition":
                     self.abort = True
-                    raise RuntimeError('Log mismatch: Expected transition, got {:s}'.format(log_data_target['isa']))
+                    raise RuntimeError(
+                        "Log mismatch: Expected transition, got {:s}".format(
+                            log_data_target["isa"]
+                        )
+                    )
                 if self.pta:
                     transition = self.pta.transitions[transition_id]
-                    if transition.name != log_data_target['name']:
+                    if transition.name != log_data_target["name"]:
                         self.abort = True
-                        raise RuntimeError('Log mismatch: Expected transition {:s}, got transition {:s} -- may have been caused by preceding malformed UART output'.format(log_data_target['name'], transition.name))
+                        raise RuntimeError(
+                            "Log mismatch: Expected transition {:s}, got transition {:s} -- may have been caused by preceding malformed UART output".format(
+                                log_data_target["name"], transition.name
+                            )
+                        )
                     if self.log_return_values and len(transition.return_value_handlers):
                         for handler in transition.return_value_handlers:
-                            if 'parameter' in handler:
+                            if "parameter" in handler:
                                 parameter_value = return_value = int(res.group(2))
 
-                                if 'return_values' not in log_data_target:
-                                    log_data_target['return_values'] = list()
-                                log_data_target['return_values'].append(return_value)
-
-                                if 'formula' in handler:
-                                    parameter_value = handler['formula'].eval(return_value)
-
-                                self._append_nondeterministic_parameter_value(log_data_target, handler['parameter'], parameter_value)
-                                for following_log_data_target in self.traces[self.trace_id]['trace'][(self.current_transition_in_trace * 2 + 1) :]:
-                                    self._append_nondeterministic_parameter_value(following_log_data_target, handler['parameter'], parameter_value)
-                                if 'apply_from' in handler and any(map(lambda x: x['name'] == handler['apply_from'], self.traces[self.trace_id]['trace'][: (self.current_transition_in_trace * 2 + 1)])):
-                                    for preceding_log_data_target in reversed(self.traces[self.trace_id]['trace'][: (self.current_transition_in_trace * 2)]):
-                                        self._append_nondeterministic_parameter_value(preceding_log_data_target, handler['parameter'], parameter_value)
-                                        if preceding_log_data_target['name'] == handler['apply_from']:
+                                if "return_values" not in log_data_target:
+                                    log_data_target["return_values"] = list()
+                                log_data_target["return_values"].append(return_value)
+
+                                if "formula" in handler:
+                                    parameter_value = handler["formula"].eval(
+                                        return_value
+                                    )
+
+                                self._append_nondeterministic_parameter_value(
+                                    log_data_target,
+                                    handler["parameter"],
+                                    parameter_value,
+                                )
+                                for following_log_data_target in self.traces[
+                                    self.trace_id
+                                ]["trace"][
+                                    (self.current_transition_in_trace * 2 + 1) :
+                                ]:
+                                    self._append_nondeterministic_parameter_value(
+                                        following_log_data_target,
+                                        handler["parameter"],
+                                        parameter_value,
+                                    )
+                                if "apply_from" in handler and any(
+                                    map(
+                                        lambda x: x["name"] == handler["apply_from"],
+                                        self.traces[self.trace_id]["trace"][
+                                            : (self.current_transition_in_trace * 2 + 1)
+                                        ],
+                                    )
+                                ):
+                                    for preceding_log_data_target in reversed(
+                                        self.traces[self.trace_id]["trace"][
+                                            : (self.current_transition_in_trace * 2)
+                                        ]
+                                    ):
+                                        self._append_nondeterministic_parameter_value(
+                                            preceding_log_data_target,
+                                            handler["parameter"],
+                                            parameter_value,
+                                        )
+                                        if (
+                                            preceding_log_data_target["name"]
+                                            == handler["apply_from"]
+                                        ):
                                             break
                 self.current_transition_in_trace += 1
 
+
 class OnboardTimerHarness(TransitionHarness):
     """TODO
 
@@ -271,13 +354,25 @@ class OnboardTimerHarness(TransitionHarness):
         benchmark iteration.
         I.e. `.traces[*]['trace'][*]['offline_aggregates']['duration'] = [..., ...]`
     """
+
     def __init__(self, counter_limits, **kwargs):
         super().__init__(**kwargs)
         self.trace_length = 0
-        self.one_cycle_in_us, self.one_overflow_in_us, self.counter_max_overflow = counter_limits
+        (
+            self.one_cycle_in_us,
+            self.one_overflow_in_us,
+            self.counter_max_overflow,
+        ) = counter_limits
 
     def copy(self):
-        new_harness = __class__((self.one_cycle_in_us, self.one_overflow_in_us, self.counter_max_overflow), gpio_pin = self.gpio_pin, gpio_mode = self.gpio_mode, pta = self.pta, log_return_values = self.log_return_values, repeat = self.repeat)
+        new_harness = __class__(
+            (self.one_cycle_in_us, self.one_overflow_in_us, self.counter_max_overflow),
+            gpio_pin=self.gpio_pin,
+            gpio_mode=self.gpio_mode,
+            pta=self.pta,
+            log_return_values=self.log_return_values,
+            repeat=self.repeat,
+        )
         new_harness.traces = self.traces.copy()
         new_harness.trace_id = self.trace_id
         return new_harness
@@ -293,123 +388,215 @@ class OnboardTimerHarness(TransitionHarness):
         """
         super().undo(undo_from)
         for trace in self.traces:
-            for state_or_transition in trace['trace']:
-                if 'offline_aggregates' in state_or_transition:
-                    state_or_transition['offline_aggregates']['duration'] = state_or_transition['offline_aggregates']['duration'][:undo_from]
+            for state_or_transition in trace["trace"]:
+                if "offline_aggregates" in state_or_transition:
+                    state_or_transition["offline_aggregates"][
+                        "duration"
+                    ] = state_or_transition["offline_aggregates"]["duration"][
+                        :undo_from
+                    ]
 
     def global_code(self):
         ret = '#include "driver/counter.h"\n'
-        ret += '#define PTALOG_TIMING\n'
+        ret += "#define PTALOG_TIMING\n"
         ret += super().global_code()
         return ret
 
-    def start_benchmark(self, benchmark_id = 0):
-        ret = 'counter.start();\n'
-        ret += 'counter.stop();\n'
-        ret += 'ptalog.passNop(counter);\n'
+    def start_benchmark(self, benchmark_id=0):
+        ret = "counter.start();\n"
+        ret += "counter.stop();\n"
+        ret += "ptalog.passNop(counter);\n"
         ret += super().start_benchmark(benchmark_id)
         return ret
 
-    def pass_transition(self, transition_id, transition_code, transition: object = None):
-        ret = 'ptalog.passTransition({:d});\n'.format(transition_id)
+    def pass_transition(
+        self, transition_id, transition_code, transition: object = None
+    ):
+        ret = "ptalog.passTransition({:d});\n".format(transition_id)
         ret += self._pass_transition_call(transition_id)
-        ret += 'counter.start();\n'
-        if self.log_return_values and transition and len(transition.return_value_handlers):
-            ret += 'transition_return_value = {}\n'.format(transition_code)
+        ret += "counter.start();\n"
+        if (
+            self.log_return_values
+            and transition
+            and len(transition.return_value_handlers)
+        ):
+            ret += "transition_return_value = {}\n".format(transition_code)
         else:
-            ret += '{}\n'.format(transition_code)
-        ret += 'counter.stop();\n'
-        if self.log_return_values and transition and len(transition.return_value_handlers):
-            ret += 'ptalog.logReturn(transition_return_value);\n'
-        ret += 'ptalog.stopTransition(counter);\n'
+            ret += "{}\n".format(transition_code)
+        ret += "counter.stop();\n"
+        if (
+            self.log_return_values
+            and transition
+            and len(transition.return_value_handlers)
+        ):
+            ret += "ptalog.logReturn(transition_return_value);\n"
+        ret += "ptalog.stopTransition(counter);\n"
         return ret
 
-    def _append_nondeterministic_parameter_value(self, log_data_target, parameter_name, parameter_value):
-        if log_data_target['parameter'][parameter_name] is None:
-            log_data_target['parameter'][parameter_name] = list()
-        log_data_target['parameter'][parameter_name].append(parameter_value)
+    def _append_nondeterministic_parameter_value(
+        self, log_data_target, parameter_name, parameter_value
+    ):
+        if log_data_target["parameter"][parameter_name] is None:
+            log_data_target["parameter"][parameter_name] = list()
+        log_data_target["parameter"][parameter_name].append(parameter_value)
 
     def parser_cb(self, line):
         # print('[HARNESS] got line {}'.format(line))
-        res = re.match(r'\[PTA\] nop=(\S+)/(\S+)', line)
+        res = re.match(r"\[PTA\] nop=(\S+)/(\S+)", line)
         if res:
             self.nop_cycles = int(res.group(1))
             if int(res.group(2)):
-                raise RuntimeError('Counter overflow ({:d}/{:d}) during NOP test, wtf?!'.format(res.group(1), res.group(2)))
-        if re.match(r'\[PTA\] benchmark stop', line):
+                raise RuntimeError(
+                    "Counter overflow ({:d}/{:d}) during NOP test, wtf?!".format(
+                        res.group(1), res.group(2)
+                    )
+                )
+        if re.match(r"\[PTA\] benchmark stop", line):
             self.repetitions += 1
             self.synced = False
             if self.repeat > 0 and self.repetitions == self.repeat:
                 self.done = True
-                print('[HARNESS] done')
+                print("[HARNESS] done")
                 return
         # May be repeated, e.g. if the device is reset shortly after start by
         # EnergyTrace.
-        if re.match(r'\[PTA\] benchmark start, id=(\S+)', line):
+        if re.match(r"\[PTA\] benchmark start, id=(\S+)", line):
             self.synced = True
-            print('[HARNESS] synced, {}/{}'.format(self.repetitions + 1, self.repeat))
+            print("[HARNESS] synced, {}/{}".format(self.repetitions + 1, self.repeat))
         if self.synced:
-            res = re.match(r'\[PTA\] trace=(\S+) count=(\S+)', line)
+            res = re.match(r"\[PTA\] trace=(\S+) count=(\S+)", line)
             if res:
                 self.trace_id = int(res.group(1))
                 self.trace_length = int(res.group(2))
                 self.current_transition_in_trace = 0
             if self.log_return_values:
-                res = re.match(r'\[PTA\] transition=(\S+) cycles=(\S+)/(\S+) return=(\S+)', line)
+                res = re.match(
+                    r"\[PTA\] transition=(\S+) cycles=(\S+)/(\S+) return=(\S+)", line
+                )
             else:
-                res = re.match(r'\[PTA\] transition=(\S+) cycles=(\S+)/(\S+)', line)
+                res = re.match(r"\[PTA\] transition=(\S+) cycles=(\S+)/(\S+)", line)
             if res:
                 transition_id = int(res.group(1))
                 cycles = int(res.group(2))
                 overflow = int(res.group(3))
                 if overflow >= self.counter_max_overflow:
                     self.abort = True
-                    raise RuntimeError('Counter overflow ({:d}/{:d}) in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d})'.format(cycles, overflow, 0, self.trace_id, self.current_transition_in_trace, transition_id))
-                duration_us = cycles * self.one_cycle_in_us + overflow * self.one_overflow_in_us - self.nop_cycles * self.one_cycle_in_us
+                    raise RuntimeError(
+                        "Counter overflow ({:d}/{:d}) in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d})".format(
+                            cycles,
+                            overflow,
+                            0,
+                            self.trace_id,
+                            self.current_transition_in_trace,
+                            transition_id,
+                        )
+                    )
+                duration_us = (
+                    cycles * self.one_cycle_in_us
+                    + overflow * self.one_overflow_in_us
+                    - self.nop_cycles * self.one_cycle_in_us
+                )
                 if duration_us < 0:
                     duration_us = 0
                 # self.traces contains transitions and states, UART output only contains transitions -> use index * 2
                 try:
-                    log_data_target = self.traces[self.trace_id]['trace'][self.current_transition_in_trace * 2]
+                    log_data_target = self.traces[self.trace_id]["trace"][
+                        self.current_transition_in_trace * 2
+                    ]
                 except IndexError:
                     transition_name = None
                     if self.pta:
                         transition_name = self.pta.transitions[transition_id].name
-                    print('[HARNESS] benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}, name {}) is out of bounds'.format(0, self.trace_id, self.current_transition_in_trace, transition_id, transition_name))
-                    print('          Offending line: {}'.format(line))
+                    print(
+                        "[HARNESS] benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}, name {}) is out of bounds".format(
+                            0,
+                            self.trace_id,
+                            self.current_transition_in_trace,
+                            transition_id,
+                            transition_name,
+                        )
+                    )
+                    print("          Offending line: {}".format(line))
                     return
-                if log_data_target['isa'] != 'transition':
+                if log_data_target["isa"] != "transition":
                     self.abort = True
-                    raise RuntimeError('Log mismatch in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}): Expected transition, got {:s}'.format(0,
-                        self.trace_id, self.current_transition_in_trace, transition_id, log_data_target['isa']))
+                    raise RuntimeError(
+                        "Log mismatch in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}): Expected transition, got {:s}".format(
+                            0,
+                            self.trace_id,
+                            self.current_transition_in_trace,
+                            transition_id,
+                            log_data_target["isa"],
+                        )
+                    )
                 if self.pta:
                     transition = self.pta.transitions[transition_id]
-                    if transition.name != log_data_target['name']:
+                    if transition.name != log_data_target["name"]:
                         self.abort = True
-                        raise RuntimeError('Log mismatch in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}): Expected transition {:s}, got transition {:s} -- may have been caused by preceding maformed UART output'.format(0, self.trace_id, self.current_transition_in_trace, transition_id, log_data_target['name'], transition.name, line))
+                        raise RuntimeError(
+                            "Log mismatch in benchmark id={:d} trace={:d}: transition #{:d} (ID {:d}): Expected transition {:s}, got transition {:s} -- may have been caused by preceding maformed UART output".format(
+                                0,
+                                self.trace_id,
+                                self.current_transition_in_trace,
+                                transition_id,
+                                log_data_target["name"],
+                                transition.name,
+                                line,
+                            )
+                        )
                     if self.log_return_values and len(transition.return_value_handlers):
                         for handler in transition.return_value_handlers:
-                            if 'parameter' in handler:
+                            if "parameter" in handler:
                                 parameter_value = return_value = int(res.group(4))
 
-                                if 'return_values' not in log_data_target:
-                                    log_data_target['return_values'] = list()
-                                log_data_target['return_values'].append(return_value)
-
-                                if 'formula' in handler:
-                                    parameter_value = handler['formula'].eval(return_value)
-
-                                self._append_nondeterministic_parameter_value(log_data_target, handler['parameter'], parameter_value)
-                                for following_log_data_target in self.traces[self.trace_id]['trace'][(self.current_transition_in_trace * 2 + 1) :]:
-                                    self._append_nondeterministic_parameter_value(following_log_data_target, handler['parameter'], parameter_value)
-                                if 'apply_from' in handler and any(map(lambda x: x['name'] == handler['apply_from'], self.traces[self.trace_id]['trace'][: (self.current_transition_in_trace * 2 + 1)])):
-                                    for preceding_log_data_target in reversed(self.traces[self.trace_id]['trace'][: (self.current_transition_in_trace * 2)]):
-                                        self._append_nondeterministic_parameter_value(preceding_log_data_target, handler['parameter'], parameter_value)
-                                        if preceding_log_data_target['name'] == handler['apply_from']:
+                                if "return_values" not in log_data_target:
+                                    log_data_target["return_values"] = list()
+                                log_data_target["return_values"].append(return_value)
+
+                                if "formula" in handler:
+                                    parameter_value = handler["formula"].eval(
+                                        return_value
+                                    )
+
+                                self._append_nondeterministic_parameter_value(
+                                    log_data_target,
+                                    handler["parameter"],
+                                    parameter_value,
+                                )
+                                for following_log_data_target in self.traces[
+                                    self.trace_id
+                                ]["trace"][
+                                    (self.current_transition_in_trace * 2 + 1) :
+                                ]:
+                                    self._append_nondeterministic_parameter_value(
+                                        following_log_data_target,
+                                        handler["parameter"],
+                                        parameter_value,
+                                    )
+                                if "apply_from" in handler and any(
+                                    map(
+                                        lambda x: x["name"] == handler["apply_from"],
+                                        self.traces[self.trace_id]["trace"][
+                                            : (self.current_transition_in_trace * 2 + 1)
+                                        ],
+                                    )
+                                ):
+                                    for preceding_log_data_target in reversed(
+                                        self.traces[self.trace_id]["trace"][
+                                            : (self.current_transition_in_trace * 2)
+                                        ]
+                                    ):
+                                        self._append_nondeterministic_parameter_value(
+                                            preceding_log_data_target,
+                                            handler["parameter"],
+                                            parameter_value,
+                                        )
+                                        if (
+                                            preceding_log_data_target["name"]
+                                            == handler["apply_from"]
+                                        ):
                                             break
-                if 'offline_aggregates' not in log_data_target:
-                    log_data_target['offline_aggregates'] = {
-                        'duration' : list()
-                    }
-                log_data_target['offline_aggregates']['duration'].append(duration_us)
+                if "offline_aggregates" not in log_data_target:
+                    log_data_target["offline_aggregates"] = {"duration": list()}
+                log_data_target["offline_aggregates"]["duration"].append(duration_us)
                 self.current_transition_in_trace += 1
diff --git a/lib/ipython_energymodel_prelude.py b/lib/ipython_energymodel_prelude.py
index 6777b17..0457838 100755
--- a/lib/ipython_energymodel_prelude.py
+++ b/lib/ipython_energymodel_prelude.py
@@ -5,9 +5,11 @@ from dfatool import PTAModel, RawData, soft_cast_int
 
 ignored_trace_indexes = None
 
-files = '../data/20170125_125433_cc1200.tar ../data/20170125_142420_cc1200.tar ../data/20170125_144957_cc1200.tar ../data/20170125_151149_cc1200.tar ../data/20170125_151824_cc1200.tar ../data/20170125_154019_cc1200.tar'.split(' ')
-#files = '../data/20170116_124500_LM75x.tar ../data/20170116_131306_LM75x.tar'.split(' ')
+files = "../data/20170125_125433_cc1200.tar ../data/20170125_142420_cc1200.tar ../data/20170125_144957_cc1200.tar ../data/20170125_151149_cc1200.tar ../data/20170125_151824_cc1200.tar ../data/20170125_154019_cc1200.tar".split(
+    " "
+)
+# files = '../data/20170116_124500_LM75x.tar ../data/20170116_131306_LM75x.tar'.split(' ')
 
 raw_data = RawData(files)
 preprocessed_data = raw_data.get_preprocessed_data()
-model = PTAModel(preprocessed_data, ignore_trace_indexes = ignored_trace_indexes)
+model = PTAModel(preprocessed_data, ignore_trace_indexes=ignored_trace_indexes)
diff --git a/lib/keysightdlog.py b/lib/keysightdlog.py
index 0cf8da1..89264b9 100755
--- a/lib/keysightdlog.py
+++ b/lib/keysightdlog.py
@@ -8,69 +8,74 @@ import struct
 import sys
 import xml.etree.ElementTree as ET
 
+
 def plot_y(Y, **kwargs):
     plot_xy(np.arange(len(Y)), Y, **kwargs)
 
-def plot_xy(X, Y, xlabel = None, ylabel = None, title = None, output = None):
-    fig, ax1 = plt.subplots(figsize=(10,6))
+
+def plot_xy(X, Y, xlabel=None, ylabel=None, title=None, output=None):
+    fig, ax1 = plt.subplots(figsize=(10, 6))
     if title != None:
         fig.canvas.set_window_title(title)
     if xlabel != None:
         ax1.set_xlabel(xlabel)
     if ylabel != None:
         ax1.set_ylabel(ylabel)
-    plt.subplots_adjust(left = 0.1, bottom = 0.1, right = 0.99, top = 0.99)
+    plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99)
     plt.plot(X, Y, "bo", markersize=2)
     if output:
         plt.savefig(output)
-        with open('{}.txt'.format(output), 'w') as f:
-            print('X Y', file=f)
+        with open("{}.txt".format(output), "w") as f:
+            print("X Y", file=f)
             for i in range(len(X)):
-                print('{} {}'.format(X[i], Y[i]), file=f)
+                print("{} {}".format(X[i], Y[i]), file=f)
     else:
         plt.show()
 
+
 filename = sys.argv[1]
 
-with open(filename, 'rb') as logfile:
+with open(filename, "rb") as logfile:
     lines = []
-    line = ''
+    line = ""
 
-    if '.xz' in filename:
+    if ".xz" in filename:
         f = lzma.open(logfile)
     else:
         f = logfile
 
-    while line != '</dlog>\n':
+    while line != "</dlog>\n":
         line = f.readline().decode()
         lines.append(line)
-    xml_header = ''.join(lines)
+    xml_header = "".join(lines)
     raw_header = f.read(8)
     data_offset = f.tell()
     raw_data = f.read()
 
-    xml_header = xml_header.replace('1ua>', 'X1ua>')
-    xml_header = xml_header.replace('2ua>', 'X2ua>')
+    xml_header = xml_header.replace("1ua>", "X1ua>")
+    xml_header = xml_header.replace("2ua>", "X2ua>")
     dlog = ET.fromstring(xml_header)
     channels = []
-    for channel in dlog.findall('channel'):
-        channel_id = int(channel.get('id'))
-        sense_curr = channel.find('sense_curr').text
-        sense_volt = channel.find('sense_volt').text
-        model = channel.find('ident').find('model').text
-        if sense_volt == '1':
-            channels.append((channel_id, model, 'V'))
-        if sense_curr == '1':
-            channels.append((channel_id, model, 'A'))
+    for channel in dlog.findall("channel"):
+        channel_id = int(channel.get("id"))
+        sense_curr = channel.find("sense_curr").text
+        sense_volt = channel.find("sense_volt").text
+        model = channel.find("ident").find("model").text
+        if sense_volt == "1":
+            channels.append((channel_id, model, "V"))
+        if sense_curr == "1":
+            channels.append((channel_id, model, "A"))
 
     num_channels = len(channels)
-    duration = int(dlog.find('frame').find('time').text)
-    interval = float(dlog.find('frame').find('tint').text)
+    duration = int(dlog.find("frame").find("time").text)
+    interval = float(dlog.find("frame").find("tint").text)
     real_duration = interval * int(len(raw_data) / (4 * num_channels))
 
-    data = np.ndarray(shape=(num_channels, int(len(raw_data) / (4 * num_channels))), dtype=np.float32)
+    data = np.ndarray(
+        shape=(num_channels, int(len(raw_data) / (4 * num_channels))), dtype=np.float32
+    )
 
-    iterator = struct.iter_unpack('>f', raw_data)
+    iterator = struct.iter_unpack(">f", raw_data)
     channel_offset = 0
     measurement_offset = 0
     for value in iterator:
@@ -82,34 +87,59 @@ with open(filename, 'rb') as logfile:
             channel_offset += 1
 
 if int(real_duration) != duration:
-    print('Measurement duration: {:f} of {:d} seconds at {:f} µs per sample'.format(
-        real_duration, duration, interval * 1000000))
+    print(
+        "Measurement duration: {:f} of {:d} seconds at {:f} µs per sample".format(
+            real_duration, duration, interval * 1000000
+        )
+    )
 else:
-    print('Measurement duration: {:d} seconds at {:f} µs per sample'.format(
-        duration, interval * 1000000))
+    print(
+        "Measurement duration: {:d} seconds at {:f} µs per sample".format(
+            duration, interval * 1000000
+        )
+    )
 
 for i, channel in enumerate(channels):
     channel_id, channel_model, channel_type = channel
-    print('channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} {:s}'.format(
-        channel_id, channel_model, np.min(data[i]), np.max(data[i]), np.mean(data[i]),
-        channel_type))
+    print(
+        "channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} {:s}".format(
+            channel_id,
+            channel_model,
+            np.min(data[i]),
+            np.max(data[i]),
+            np.mean(data[i]),
+            channel_type,
+        )
+    )
 
-    if i > 0 and channel_type == 'A' and channels[i-1][2] == 'V' and channel_id == channels[i-1][0]:
-        power = data[i-1] * data[i]
+    if (
+        i > 0
+        and channel_type == "A"
+        and channels[i - 1][2] == "V"
+        and channel_id == channels[i - 1][0]
+    ):
+        power = data[i - 1] * data[i]
         power = 3.6 * data[i]
-        print('channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} W'.format(
-            channel_id, channel_model, np.min(power), np.max(power), np.mean(power)))
+        print(
+            "channel {:d} ({:s}): min {:f}, max {:f}, mean {:f} W".format(
+                channel_id, channel_model, np.min(power), np.max(power), np.mean(power)
+            )
+        )
         min_power = np.min(power)
         max_power = np.max(power)
         power_border = np.mean([min_power, max_power])
         low_power = power[power < power_border]
         high_power = power[power >= power_border]
         plot_y(power)
-        print('    avg low / high power (delta): {:f} / {:f} ({:f}) W'.format(
-            np.mean(low_power), np.mean(high_power),
-            np.mean(high_power) - np.mean(low_power)))
-        #plot_y(low_power)
-        #plot_y(high_power)
+        print(
+            "    avg low / high power (delta): {:f} / {:f} ({:f}) W".format(
+                np.mean(low_power),
+                np.mean(high_power),
+                np.mean(high_power) - np.mean(low_power),
+            )
+        )
+        # plot_y(low_power)
+        # plot_y(high_power)
         high_power_durations = []
         current_high_power_duration = 0
         for is_hpe in power >= power_border:
@@ -119,12 +149,16 @@ for i, channel in enumerate(channels):
                 if current_high_power_duration > 0:
                     high_power_durations.append(current_high_power_duration)
                 current_high_power_duration = 0
-        print('    avg high-power duration: {:f} µs'.format(np.mean(high_power_durations) * 1000000))
-
-#print(xml_header)
-#print(raw_header)
-#print(channels)
-#print(data)
-#print(np.mean(data[0]))
-#print(np.mean(data[1]))
-#print(np.mean(data[0] * data[1]))
+        print(
+            "    avg high-power duration: {:f} µs".format(
+                np.mean(high_power_durations) * 1000000
+            )
+        )
+
+# print(xml_header)
+# print(raw_header)
+# print(channels)
+# print(data)
+# print(np.mean(data[0]))
+# print(np.mean(data[1]))
+# print(np.mean(data[0] * data[1]))
diff --git a/lib/lex.py b/lib/lex.py
index b18fa2b..7bb3760 100644
--- a/lib/lex.py
+++ b/lib/lex.py
@@ -3,34 +3,44 @@ from .sly import Lexer, Parser
 
 class TimedWordLexer(Lexer):
     tokens = {LPAREN, RPAREN, IDENTIFIER, NUMBER, ARGSEP, FUNCTIONSEP}
-    ignore = ' \t'
+    ignore = " \t"
 
-    LPAREN = r'\('
-    RPAREN = r'\)'
-    IDENTIFIER = r'[a-zA-Z_][a-zA-Z0-9_]*'
-    NUMBER = r'[0-9e.]+'
-    ARGSEP = r','
-    FUNCTIONSEP = r';'
+    LPAREN = r"\("
+    RPAREN = r"\)"
+    IDENTIFIER = r"[a-zA-Z_][a-zA-Z0-9_]*"
+    NUMBER = r"[0-9e.]+"
+    ARGSEP = r","
+    FUNCTIONSEP = r";"
 
 
 class TimedSequenceLexer(Lexer):
-    tokens = {LPAREN, RPAREN, LBRACE, RBRACE, CYCLE, IDENTIFIER, NUMBER, ARGSEP, FUNCTIONSEP}
-    ignore = ' \t'
-
-    LPAREN = r'\('
-    RPAREN = r'\)'
-    LBRACE = r'\{'
-    RBRACE = r'\}'
-    CYCLE = r'cycle'
-    IDENTIFIER = r'[a-zA-Z_][a-zA-Z0-9_]*'
-    NUMBER = r'[0-9e.]+'
-    ARGSEP = r','
-    FUNCTIONSEP = r';'
+    tokens = {
+        LPAREN,
+        RPAREN,
+        LBRACE,
+        RBRACE,
+        CYCLE,
+        IDENTIFIER,
+        NUMBER,
+        ARGSEP,
+        FUNCTIONSEP,
+    }
+    ignore = " \t"
+
+    LPAREN = r"\("
+    RPAREN = r"\)"
+    LBRACE = r"\{"
+    RBRACE = r"\}"
+    CYCLE = r"cycle"
+    IDENTIFIER = r"[a-zA-Z_][a-zA-Z0-9_]*"
+    NUMBER = r"[0-9e.]+"
+    ARGSEP = r","
+    FUNCTIONSEP = r";"
 
     def error(self, t):
         print("Illegal character '%s'" % t.value[0])
-        if t.value[0] == '{' and t.value.find('}'):
-            self.index += 1 + t.value.find('}')
+        if t.value[0] == "{" and t.value.find("}"):
+            self.index += 1 + t.value.find("}")
         else:
             self.index += 1
 
@@ -38,39 +48,39 @@ class TimedSequenceLexer(Lexer):
 class TimedWordParser(Parser):
     tokens = TimedWordLexer.tokens
 
-    @_('timedSymbol FUNCTIONSEP timedWord')
+    @_("timedSymbol FUNCTIONSEP timedWord")
     def timedWord(self, p):
         ret = [p.timedSymbol]
         ret.extend(p.timedWord)
         return ret
 
-    @_('timedSymbol FUNCTIONSEP', 'timedSymbol')
+    @_("timedSymbol FUNCTIONSEP", "timedSymbol")
     def timedWord(self, p):
         return [p.timedSymbol]
 
-    @_('IDENTIFIER', 'IDENTIFIER LPAREN RPAREN')
+    @_("IDENTIFIER", "IDENTIFIER LPAREN RPAREN")
     def timedSymbol(self, p):
         return (p.IDENTIFIER,)
 
-    @_('IDENTIFIER LPAREN args RPAREN')
+    @_("IDENTIFIER LPAREN args RPAREN")
     def timedSymbol(self, p):
         return (p.IDENTIFIER, *p.args)
 
-    @_('arg ARGSEP args')
+    @_("arg ARGSEP args")
     def args(self, p):
         ret = [p.arg]
         ret.extend(p.args)
         return ret
 
-    @_('arg')
+    @_("arg")
     def args(self, p):
         return [p.arg]
 
-    @_('NUMBER')
+    @_("NUMBER")
     def arg(self, p):
         return float(p.NUMBER)
 
-    @_('IDENTIFIER')
+    @_("IDENTIFIER")
     def arg(self, p):
         return p.IDENTIFIER
 
@@ -78,66 +88,66 @@ class TimedWordParser(Parser):
 class TimedSequenceParser(Parser):
     tokens = TimedSequenceLexer.tokens
 
-    @_('timedSequenceL', 'timedSequenceW')
+    @_("timedSequenceL", "timedSequenceW")
     def timedSequence(self, p):
         return p[0]
 
-    @_('loop')
+    @_("loop")
     def timedSequenceL(self, p):
         return [p.loop]
 
-    @_('loop timedSequenceW')
+    @_("loop timedSequenceW")
     def timedSequenceL(self, p):
         ret = [p.loop]
         ret.extend(p.timedSequenceW)
         return ret
 
-    @_('timedWord')
+    @_("timedWord")
     def timedSequenceW(self, p):
         return [p.timedWord]
 
-    @_('timedWord timedSequenceL')
+    @_("timedWord timedSequenceL")
     def timedSequenceW(self, p):
         ret = [p.timedWord]
         ret.extend(p.timedSequenceL)
         return ret
 
-    @_('timedSymbol FUNCTIONSEP timedWord')
+    @_("timedSymbol FUNCTIONSEP timedWord")
     def timedWord(self, p):
         p.timedWord.word.insert(0, p.timedSymbol)
         return p.timedWord
 
-    @_('timedSymbol FUNCTIONSEP')
+    @_("timedSymbol FUNCTIONSEP")
     def timedWord(self, p):
         return TimedWord(word=[p.timedSymbol])
 
-    @_('CYCLE LPAREN IDENTIFIER RPAREN LBRACE timedWord RBRACE')
+    @_("CYCLE LPAREN IDENTIFIER RPAREN LBRACE timedWord RBRACE")
     def loop(self, p):
         return Workload(p.IDENTIFIER, p.timedWord)
 
-    @_('IDENTIFIER', 'IDENTIFIER LPAREN RPAREN')
+    @_("IDENTIFIER", "IDENTIFIER LPAREN RPAREN")
     def timedSymbol(self, p):
         return (p.IDENTIFIER,)
 
-    @_('IDENTIFIER LPAREN args RPAREN')
+    @_("IDENTIFIER LPAREN args RPAREN")
     def timedSymbol(self, p):
         return (p.IDENTIFIER, *p.args)
 
-    @_('arg ARGSEP args')
+    @_("arg ARGSEP args")
     def args(self, p):
         ret = [p.arg]
         ret.extend(p.args)
         return ret
 
-    @_('arg')
+    @_("arg")
     def args(self, p):
         return [p.arg]
 
-    @_('NUMBER')
+    @_("NUMBER")
     def arg(self, p):
         return float(p.NUMBER)
 
-    @_('IDENTIFIER')
+    @_("IDENTIFIER")
     def arg(self, p):
         return p.IDENTIFIER
 
@@ -165,8 +175,8 @@ class TimedWord:
     def __repr__(self):
         ret = list()
         for symbol in self.word:
-            ret.append('{}({})'.format(symbol[0], ', '.join(map(str, symbol[1:]))))
-        return 'TimedWord<"{}">'.format('; '.join(ret))
+            ret.append("{}({})".format(symbol[0], ", ".join(map(str, symbol[1:]))))
+        return 'TimedWord<"{}">'.format("; ".join(ret))
 
 
 class Workload:
@@ -196,5 +206,5 @@ class TimedSequence:
     def __repr__(self):
         ret = list()
         for symbol in self.seq:
-            ret.append('{}'.format(symbol))
-        return 'TimedSequence(seq=[{}])'.format(', '.join(ret))
+            ret.append("{}".format(symbol))
+        return "TimedSequence(seq=[{}])".format(", ".join(ret))
diff --git a/lib/modular_arithmetic.py b/lib/modular_arithmetic.py
index 0a69b79..c5ed1aa 100644
--- a/lib/modular_arithmetic.py
+++ b/lib/modular_arithmetic.py
@@ -3,6 +3,7 @@
 import operator
 import functools
 
+
 @functools.total_ordering
 class Mod:
     """A class for modular arithmetic, useful to simulate behaviour of uint8 and other limited data types.
@@ -14,20 +15,21 @@ class Mod:
     :param val: stored integer value
     Param mod: modulus
     """
-    __slots__ = ['val','mod']
+
+    __slots__ = ["val", "mod"]
 
     def __init__(self, val, mod):
         if isinstance(val, Mod):
             val = val.val
         if not isinstance(val, int):
-            raise ValueError('Value must be integer')
-        if not isinstance(mod, int) or mod<=0:
-            raise ValueError('Modulo must be positive integer')
+            raise ValueError("Value must be integer")
+        if not isinstance(mod, int) or mod <= 0:
+            raise ValueError("Modulo must be positive integer")
         self.val = val % mod
         self.mod = mod
 
     def __repr__(self):
-        return 'Mod({}, {})'.format(self.val, self.mod)
+        return "Mod({}, {})".format(self.val, self.mod)
 
     def __int__(self):
         return self.val
@@ -50,7 +52,7 @@ class Mod:
 
     def _check_operand(self, other):
         if not isinstance(other, (int, Mod)):
-            raise TypeError('Only integer and Mod operands are supported')
+            raise TypeError("Only integer and Mod operands are supported")
 
     def __pow__(self, other):
         self._check_operand(other)
@@ -61,32 +63,46 @@ class Mod:
         return Mod(self.mod - self.val, self.mod)
 
     def __pos__(self):
-        return self # The unary plus operator does nothing.
+        return self  # The unary plus operator does nothing.
 
     def __abs__(self):
-        return self # The value is always kept non-negative, so the abs function should do nothing.
+        return self  # The value is always kept non-negative, so the abs function should do nothing.
+
 
 # Helper functions to build common operands based on a template.
 # They need to be implemented as functions for the closures to work properly.
 def _make_op(opname):
-    op_fun = getattr(operator, opname)  # Fetch the operator by name from the operator module
+    op_fun = getattr(
+        operator, opname
+    )  # Fetch the operator by name from the operator module
+
     def op(self, other):
         self._check_operand(other)
         return Mod(op_fun(self.val, int(other)) % self.mod, self.mod)
+
     return op
 
+
 def _make_reflected_op(opname):
     op_fun = getattr(operator, opname)
+
     def op(self, other):
         self._check_operand(other)
         return Mod(op_fun(int(other), self.val) % self.mod, self.mod)
+
     return op
 
+
 # Build the actual operator overload methods based on the template.
-for opname, reflected_opname in [('__add__', '__radd__'), ('__sub__', '__rsub__'), ('__mul__', '__rmul__')]:
+for opname, reflected_opname in [
+    ("__add__", "__radd__"),
+    ("__sub__", "__rsub__"),
+    ("__mul__", "__rmul__"),
+]:
     setattr(Mod, opname, _make_op(opname))
     setattr(Mod, reflected_opname, _make_reflected_op(opname))
 
+
 class Uint8(Mod):
     __slots__ = []
 
@@ -94,7 +110,8 @@ class Uint8(Mod):
         super().__init__(val, 256)
 
     def __repr__(self):
-        return 'Uint8({})'.format(self.val)
+        return "Uint8({})".format(self.val)
+
 
 class Uint16(Mod):
     __slots__ = []
@@ -103,7 +120,8 @@ class Uint16(Mod):
         super().__init__(val, 65536)
 
     def __repr__(self):
-        return 'Uint16({})'.format(self.val)
+        return "Uint16({})".format(self.val)
+
 
 class Uint32(Mod):
     __slots__ = []
@@ -112,7 +130,8 @@ class Uint32(Mod):
         super().__init__(val, 4294967296)
 
     def __repr__(self):
-        return 'Uint32({})'.format(self.val)
+        return "Uint32({})".format(self.val)
+
 
 class Uint64(Mod):
     __slots__ = []
@@ -121,7 +140,7 @@ class Uint64(Mod):
         super().__init__(val, 18446744073709551616)
 
     def __repr__(self):
-        return 'Uint64({})'.format(self.val)
+        return "Uint64({})".format(self.val)
 
 
 def simulate_int_type(int_type: str) -> Mod:
@@ -131,12 +150,12 @@ def simulate_int_type(int_type: str) -> Mod:
     :param int_type: uint8_t / uint16_t / uint32_t / uint64_t
     :returns: `Mod` subclass, e.g. Uint8
     """
-    if int_type == 'uint8_t':
+    if int_type == "uint8_t":
         return Uint8
-    if int_type == 'uint16_t':
+    if int_type == "uint16_t":
         return Uint16
-    if int_type == 'uint32_t':
+    if int_type == "uint32_t":
         return Uint32
-    if int_type == 'uint64_t':
+    if int_type == "uint64_t":
         return Uint64
-    raise ValueError('unsupported integer type: {}'.format(int_type))
+    raise ValueError("unsupported integer type: {}".format(int_type))
diff --git a/lib/parameters.py b/lib/parameters.py
index 41e312a..8b562b6 100644
--- a/lib/parameters.py
+++ b/lib/parameters.py
@@ -21,8 +21,10 @@ def distinct_param_values(by_name, state_or_tran):
     write() or similar has not been called yet. Other parameters should always
     be initialized when leaving UNINITIALIZED.
     """
-    distinct_values = [OrderedDict() for i in range(len(by_name[state_or_tran]['param'][0]))]
-    for param_tuple in by_name[state_or_tran]['param']:
+    distinct_values = [
+        OrderedDict() for i in range(len(by_name[state_or_tran]["param"][0]))
+    ]
+    for param_tuple in by_name[state_or_tran]["param"]:
         for i in range(len(param_tuple)):
             distinct_values[i][param_tuple[i]] = True
 
@@ -30,8 +32,9 @@ def distinct_param_values(by_name, state_or_tran):
     distinct_values = list(map(lambda x: list(x.keys()), distinct_values))
     return distinct_values
 
+
 def _depends_on_param(corr_param, std_param, std_lut):
-    #if self.use_corrcoef:
+    # if self.use_corrcoef:
     if False:
         return corr_param > 0.1
     elif std_param == 0:
@@ -40,6 +43,7 @@ def _depends_on_param(corr_param, std_param, std_lut):
         return False
     return std_lut / std_param < 0.5
 
+
 def _reduce_param_matrix(matrix: np.ndarray, parameter_names: list) -> list:
     """
     :param matrix: parameter dependence matrix, M[(...)] == 1 iff (model attribute) is influenced by (parameter) for other parameter value indxe == (...)
@@ -53,7 +57,7 @@ def _reduce_param_matrix(matrix: np.ndarray, parameter_names: list) -> list:
     # Diese Abbruchbedingung scheint noch nicht so schlau zu sein...
     # Mit wird zu viel rausgefiltert (z.B. auto_ack! -> max_retry_count in "bin/analyze-timing.py ../data/20190815_122531_nRF24_no-rx.json" nicht erkannt)
     # Ohne wird zu wenig rausgefiltert (auch ganz viele Abhängigkeiten erkannt, bei denen eine Parameter-Abhängigketi immer unabhängig vom Wert der anderen Parameter besteht)
-    #if not is_power_of_two(np.count_nonzero(matrix)):
+    # if not is_power_of_two(np.count_nonzero(matrix)):
     #    # cannot be reliably reduced to a list of parameters
     #    return list()
 
@@ -65,20 +69,23 @@ def _reduce_param_matrix(matrix: np.ndarray, parameter_names: list) -> list:
         return influential_parameters
 
     for axis in range(matrix.ndim):
-        candidate = _reduce_param_matrix(np.all(matrix, axis=axis), remove_index_from_tuple(parameter_names, axis))
+        candidate = _reduce_param_matrix(
+            np.all(matrix, axis=axis), remove_index_from_tuple(parameter_names, axis)
+        )
         if len(candidate):
             return candidate
 
     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. if x == 0 else 1 - x/y)
+    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')
+    err_mode = np.seterr("ignore")
     dep_by_value = ratio_by_value > 0.5
     np.seterr(**err_mode)
 
@@ -86,7 +93,10 @@ def _codependent_parameters(param, lut_by_param_values, std_by_param_values):
     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, verbose=False
+):
     u"""
     Calculate standard deviations for a static model where all parameters but `param_index` are constant.
 
@@ -130,7 +140,10 @@ def _std_by_param(by_param, all_param_values, state_or_tran, attribute, param_in
         param_partition = list()
         std_list = list()
         for k, v in by_param.items():
-            if k[0] == state_or_tran and (*k[1][:param_index], *k[1][param_index+1:]) == param_value:
+            if (
+                k[0] == state_or_tran
+                and (*k[1][:param_index], *k[1][param_index + 1 :]) == param_value
+            ):
                 param_partition.extend(v[attribute])
                 std_list.append(np.std(v[attribute]))
 
@@ -143,17 +156,26 @@ def _std_by_param(by_param, all_param_values, state_or_tran, attribute, param_in
             lut_matrix[matrix_index] = np.mean(std_list)
         # This can (and will) happen in normal operation, e.g. when a transition's
         # arguments are combined using 'zip' rather than 'cartesian'.
-        #elif len(param_partition) == 1:
+        # elif len(param_partition) == 1:
         #    vprint(verbose, '[W] parameter value partition for {} contains only one element -- skipping'.format(param_value))
-        #else:
+        # else:
         #    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))
-        print('stddev_matrix = {}'.format(stddev_matrix))
-        return stddev_matrix, 0.
+        print(
+            "[W] {}/{} parameter #{} has no data partitions -- how did this even happen?".format(
+                state_or_tran, attribute, param_index
+            )
+        )
+        print("stddev_matrix = {}".format(stddev_matrix))
+        return stddev_matrix, 0.0
+
+    return (
+        stddev_matrix,
+        np.nanmean(stddev_matrix),
+        lut_matrix,
+    )  # np.mean([np.std(partition) for partition in partitions])
 
-    return stddev_matrix, np.nanmean(stddev_matrix), lut_matrix #np.mean([np.std(partition) for partition in partitions])
 
 def _corr_by_param(by_name, state_or_trans, attribute, param_index):
     """
@@ -169,22 +191,46 @@ def _corr_by_param(by_name, state_or_trans, attribute, param_index):
     :param param_index: index of parameter in `by_name[state_or_trans]['param']`
     """
     if _all_params_are_numeric(by_name[state_or_trans], param_index):
-        param_values = np.array(list((map(lambda x: x[param_index], by_name[state_or_trans]['param']))))
+        param_values = np.array(
+            list((map(lambda x: x[param_index], by_name[state_or_trans]["param"])))
+        )
         try:
             return np.corrcoef(by_name[state_or_trans][attribute], param_values)[0, 1]
         except FloatingPointError:
             # Typically happens when all parameter values are identical.
             # Building a correlation coefficient is pointless in this case
             # -> assume no correlation
-            return 0.
+            return 0.0
         except ValueError:
-            print('[!] Exception 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(state_or_trans, attribute, by_name[state_or_trans][attribute], param_values))
+            print(
+                "[!] Exception 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(
+                    state_or_trans,
+                    attribute,
+                    by_name[state_or_trans][attribute],
+                    param_values,
+                )
+            )
             raise
     else:
-        return 0.
-
-def _compute_param_statistics(by_name, by_param, parameter_names, arg_count, state_or_trans, attribute, distinct_values, distinct_values_by_param_index, verbose = False):
+        return 0.0
+
+
+def _compute_param_statistics(
+    by_name,
+    by_param,
+    parameter_names,
+    arg_count,
+    state_or_trans,
+    attribute,
+    distinct_values,
+    distinct_values_by_param_index,
+    verbose=False,
+):
     """
     Compute standard deviation and correlation coefficient for various data partitions.
 
@@ -223,87 +269,140 @@ def _compute_param_statistics(by_name, by_param, parameter_names, arg_count, sta
         Only set if state_or_trans appears in arg_count, empty dict otherwise.
     """
     ret = {
-        'std_static' : np.std(by_name[state_or_trans][attribute]),
-        'std_param_lut' : np.mean([np.std(by_param[x][attribute]) for x in by_param.keys() if x[0] == state_or_trans]),
-        'std_by_param' : {},
-        'std_by_param_values' : {},
-        'lut_by_param_values' : {},
-        'std_by_arg' : [],
-        'std_by_arg_values' : [],
-        'lut_by_arg_values' : [],
-        'corr_by_param' : {},
-        'corr_by_arg' : [],
-        'depends_on_param' : {},
-        'depends_on_arg' : [],
-        'param_data' : {},
+        "std_static": np.std(by_name[state_or_trans][attribute]),
+        "std_param_lut": np.mean(
+            [
+                np.std(by_param[x][attribute])
+                for x in by_param.keys()
+                if x[0] == state_or_trans
+            ]
+        ),
+        "std_by_param": {},
+        "std_by_param_values": {},
+        "lut_by_param_values": {},
+        "std_by_arg": [],
+        "std_by_arg_values": [],
+        "lut_by_arg_values": [],
+        "corr_by_param": {},
+        "corr_by_arg": [],
+        "depends_on_param": {},
+        "depends_on_arg": [],
+        "param_data": {},
     }
 
-    np.seterr('raise')
+    np.seterr("raise")
 
     for param_idx, param in enumerate(parameter_names):
-        std_matrix, mean_std, lut_matrix = _std_by_param(by_param, distinct_values_by_param_index, state_or_trans, attribute, param_idx, verbose)
-        ret['std_by_param'][param] = mean_std
-        ret['std_by_param_values'][param] = std_matrix
-        ret['lut_by_param_values'][param] = lut_matrix
-        ret['corr_by_param'][param] = _corr_by_param(by_name, state_or_trans, attribute, param_idx)
-
-        ret['depends_on_param'][param] = _depends_on_param(ret['corr_by_param'][param], ret['std_by_param'][param], 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()
+        std_matrix, mean_std, lut_matrix = _std_by_param(
+            by_param,
+            distinct_values_by_param_index,
+            state_or_trans,
+            attribute,
+            param_idx,
+            verbose,
+        )
+        ret["std_by_param"][param] = mean_std
+        ret["std_by_param_values"][param] = std_matrix
+        ret["lut_by_param_values"][param] = lut_matrix
+        ret["corr_by_param"][param] = _corr_by_param(
+            by_name, state_or_trans, attribute, param_idx
+        )
+
+        ret["depends_on_param"][param] = _depends_on_param(
+            ret["corr_by_param"][param],
+            ret["std_by_param"][param],
+            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']:
+            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_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)
+                    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(by_param, distinct_values_by_param_index, 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)
-            ret['lut_by_arg_values'].append(lut_matrix)
-            ret['corr_by_arg'].append(_corr_by_param(by_name, state_or_trans, attribute, len(parameter_names) + arg_index))
+            std_matrix, mean_std, lut_matrix = _std_by_param(
+                by_param,
+                distinct_values_by_param_index,
+                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)
+            ret["lut_by_arg_values"].append(lut_matrix)
+            ret["corr_by_arg"].append(
+                _corr_by_param(
+                    by_name, state_or_trans, attribute, len(parameter_names) + arg_index
+                )
+            )
 
             if False:
-                ret['depends_on_arg'].append(ret['corr_by_arg'][arg_index] > 0.1)
-            elif ret['std_by_arg'][arg_index] == 0:
+                ret["depends_on_arg"].append(ret["corr_by_arg"][arg_index] > 0.1)
+            elif ret["std_by_arg"][arg_index] == 0:
                 # In general, std_param_lut < std_by_arg. So, if std_by_arg == 0, std_param_lut == 0 follows.
                 # This means that the variation of arg does not affect the model quality -> no influence
-                ret['depends_on_arg'].append(False)
+                ret["depends_on_arg"].append(False)
             else:
-                ret['depends_on_arg'].append(ret['std_param_lut'] / ret['std_by_arg'][arg_index] < 0.5)
+                ret["depends_on_arg"].append(
+                    ret["std_param_lut"] / ret["std_by_arg"][arg_index] < 0.5
+                )
 
     return ret
 
+
 def _compute_param_statistics_parallel(arg):
-    return {
-        'key' : arg['key'],
-        'result': _compute_param_statistics(*arg['args'])
-    }
+    return {"key": arg["key"], "result": _compute_param_statistics(*arg["args"])}
+
 
 def _all_params_are_numeric(data, param_idx):
     """Check if all `data['param'][*][param_idx]` elements are numeric, as reported by `utils.is_numeric`."""
-    param_values = list(map(lambda x: x[param_idx], data['param']))
+    param_values = list(map(lambda x: x[param_idx], data["param"]))
     if len(list(filter(is_numeric, param_values))) == len(param_values):
         return True
     return False
 
-def prune_dependent_parameters(by_name, parameter_names, correlation_threshold = 0.5):
+
+def prune_dependent_parameters(by_name, parameter_names, correlation_threshold=0.5):
     """
     Remove dependent parameters from aggregate.
 
@@ -320,15 +419,17 @@ def prune_dependent_parameters(by_name, parameter_names, correlation_threshold =
     """
 
     parameter_indices_to_remove = list()
-    for parameter_combination in itertools.product(range(len(parameter_names)), range(len(parameter_names))):
+    for parameter_combination in itertools.product(
+        range(len(parameter_names)), range(len(parameter_names))
+    ):
         index_1, index_2 = parameter_combination
         if index_1 >= index_2:
             continue
-        parameter_values = [list(), list()] # both parameters have a value
-        parameter_values_1 = list() # parameter 1 has a value
-        parameter_values_2 = list() # parameter 2 has a value
+        parameter_values = [list(), list()]  # both parameters have a value
+        parameter_values_1 = list()  # parameter 1 has a value
+        parameter_values_2 = list()  # parameter 2 has a value
         for name in by_name:
-            for measurement in by_name[name]['param']:
+            for measurement in by_name[name]["param"]:
                 value_1 = measurement[index_1]
                 value_2 = measurement[index_2]
                 if is_numeric(value_1):
@@ -342,16 +443,30 @@ def prune_dependent_parameters(by_name, parameter_names, correlation_threshold =
             # Calculating the correlation coefficient only makes sense when neither value is constant
             if np.std(parameter_values_1) != 0 and np.std(parameter_values_2) != 0:
                 correlation = np.corrcoef(parameter_values)[0][1]
-                if correlation != np.nan and np.abs(correlation) > correlation_threshold:
-                    print('[!] Parameters {} <-> {} are correlated with coefficcient {}'.format(parameter_names[index_1], parameter_names[index_2], correlation))
+                if (
+                    correlation != np.nan
+                    and np.abs(correlation) > correlation_threshold
+                ):
+                    print(
+                        "[!] Parameters {} <-> {} are correlated with coefficcient {}".format(
+                            parameter_names[index_1],
+                            parameter_names[index_2],
+                            correlation,
+                        )
+                    )
                     if len(parameter_values_1) < len(parameter_values_2):
                         index_to_remove = index_1
                     else:
                         index_to_remove = index_2
-                    print('    Removing parameter {}'.format(parameter_names[index_to_remove]))
+                    print(
+                        "    Removing parameter {}".format(
+                            parameter_names[index_to_remove]
+                        )
+                    )
                     parameter_indices_to_remove.append(index_to_remove)
     remove_parameters_by_indices(by_name, parameter_names, parameter_indices_to_remove)
 
+
 def remove_parameters_by_indices(by_name, parameter_names, parameter_indices_to_remove):
     """
     Remove parameters listed in `parameter_indices` from aggregate `by_name` and `parameter_names`.
@@ -365,12 +480,13 @@ def remove_parameters_by_indices(by_name, parameter_names, parameter_indices_to_
     """
 
     # Start removal from the end of the list to avoid renumbering of list elemenets
-    for parameter_index in sorted(parameter_indices_to_remove, reverse = True):
+    for parameter_index in sorted(parameter_indices_to_remove, reverse=True):
         for name in by_name:
-            for measurement in by_name[name]['param']:
+            for measurement in by_name[name]["param"]:
                 measurement.pop(parameter_index)
         parameter_names.pop(parameter_index)
 
+
 class ParamStats:
     """
     :param stats: `stats[state_or_tran][attribute]` = std_static, std_param_lut, ... (see `compute_param_statistics`)
@@ -378,7 +494,15 @@ class ParamStats:
     :param distinct_values_by_param_index: `distinct_values[state_or_tran][i]` = [distinct values in aggregate]
     """
 
-    def __init__(self, by_name, by_param, parameter_names, arg_count, use_corrcoef = False, verbose = False):
+    def __init__(
+        self,
+        by_name,
+        by_param,
+        parameter_names,
+        arg_count,
+        use_corrcoef=False,
+        verbose=False,
+    ):
         """
         Compute standard deviation and correlation coefficient on parameterized data partitions.
 
@@ -411,24 +535,40 @@ class ParamStats:
 
         for state_or_tran in by_name.keys():
             self.stats[state_or_tran] = dict()
-            self.distinct_values_by_param_index[state_or_tran] = distinct_param_values(by_name, state_or_tran)
+            self.distinct_values_by_param_index[state_or_tran] = distinct_param_values(
+                by_name, state_or_tran
+            )
             self.distinct_values[state_or_tran] = dict()
             for i, param in enumerate(parameter_names):
-                self.distinct_values[state_or_tran][param] = self.distinct_values_by_param_index[state_or_tran][i]
-            for attribute in by_name[state_or_tran]['attributes']:
-                stats_queue.append({
-                    'key': [state_or_tran, attribute],
-                    'args': [by_name, by_param, parameter_names, arg_count, state_or_tran, attribute, self.distinct_values[state_or_tran], self.distinct_values_by_param_index[state_or_tran], verbose],
-                })
+                self.distinct_values[state_or_tran][
+                    param
+                ] = self.distinct_values_by_param_index[state_or_tran][i]
+            for attribute in by_name[state_or_tran]["attributes"]:
+                stats_queue.append(
+                    {
+                        "key": [state_or_tran, attribute],
+                        "args": [
+                            by_name,
+                            by_param,
+                            parameter_names,
+                            arg_count,
+                            state_or_tran,
+                            attribute,
+                            self.distinct_values[state_or_tran],
+                            self.distinct_values_by_param_index[state_or_tran],
+                            verbose,
+                        ],
+                    }
+                )
 
         with Pool() as pool:
             stats_results = pool.map(_compute_param_statistics_parallel, stats_queue)
 
         for stats in stats_results:
-            state_or_tran, attribute = stats['key']
-            self.stats[state_or_tran][attribute] = stats['result']
+            state_or_tran, attribute = stats["key"]
+            self.stats[state_or_tran][attribute] = stats["result"]
 
-    def can_be_fitted(self, state_or_tran = None) -> bool:
+    def can_be_fitted(self, state_or_tran=None) -> bool:
         """
         Return whether a sufficient amount of distinct numeric parameter values is available, allowing a parameter-aware model to be generated.
 
@@ -441,8 +581,27 @@ class ParamStats:
 
         for key in keys:
             for param in self._parameter_names:
-                if len(list(filter(lambda n: is_numeric(n), self.distinct_values[key][param]))) > 2:
-                    print(key, param, list(filter(lambda n: is_numeric(n), self.distinct_values[key][param])))
+                if (
+                    len(
+                        list(
+                            filter(
+                                lambda n: is_numeric(n),
+                                self.distinct_values[key][param],
+                            )
+                        )
+                    )
+                    > 2
+                ):
+                    print(
+                        key,
+                        param,
+                        list(
+                            filter(
+                                lambda n: is_numeric(n),
+                                self.distinct_values[key][param],
+                            )
+                        ),
+                    )
                     return True
         return False
 
@@ -456,7 +615,9 @@ class ParamStats:
         # TODO
         pass
 
-    def has_codependent_parameters(self, state_or_tran: str, attribute: str, param: str) -> bool:
+    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.
 
@@ -468,7 +629,9 @@ class ParamStats:
             return True
         return False
 
-    def codependent_parameters(self, state_or_tran: str, attribute: str, param: str) -> list:
+    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.
 
@@ -476,12 +639,15 @@ class ParamStats:
         :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']
+        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:
+    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
 
@@ -490,11 +656,14 @@ class ParamStats:
         """
         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))
+            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')
+                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
@@ -508,14 +677,21 @@ class ParamStats:
         """
         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:
+            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):
+                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:
+    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`.
 
@@ -525,11 +701,15 @@ class ParamStats:
         :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']
+        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'):
+    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`.
 
@@ -538,16 +718,21 @@ class ParamStats:
         :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']
+        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'):
+                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.
@@ -559,9 +744,9 @@ class ParamStats:
         if self.use_corrcoef:
             # not supported
             raise ValueError
-        if statistics['std_static'] == 0:
+        if statistics["std_static"] == 0:
             return 0
-        return statistics['std_param_lut'] / statistics['std_static']
+        return statistics["std_param_lut"] / statistics["std_static"]
 
     def generic_param_dependence_ratio(self, state_or_trans, attribute):
         """
@@ -572,7 +757,9 @@ class ParamStats:
         """
         return 1 - self._generic_param_independence_ratio(state_or_trans, attribute)
 
-    def _param_independence_ratio(self, state_or_trans: str, attribute: str, param: str) -> float:
+    def _param_independence_ratio(
+        self, state_or_trans: str, attribute: str, param: str
+    ) -> float:
         """
         Return the heuristic ratio of parameter independence for state_or_trans, attribute, and param.
 
@@ -580,17 +767,19 @@ class ParamStats:
         """
         statistics = self.stats[state_or_trans][attribute]
         if self.use_corrcoef:
-            return 1 - np.abs(statistics['corr_by_param'][param])
-        if statistics['std_by_param'][param] == 0:
-            if statistics['std_param_lut'] != 0:
+            return 1 - np.abs(statistics["corr_by_param"][param])
+        if statistics["std_by_param"][param] == 0:
+            if statistics["std_param_lut"] != 0:
                 raise RuntimeError("wat")
             # In general, std_param_lut < std_by_param. So, if std_by_param == 0, std_param_lut == 0 follows.
             # This means that the variation of param does not affect the model quality -> no influence, return 1
-            return 1.
+            return 1.0
 
-        return statistics['std_param_lut'] / statistics['std_by_param'][param]
+        return statistics["std_param_lut"] / statistics["std_by_param"][param]
 
-    def param_dependence_ratio(self, state_or_trans: str, attribute: str, param: str) -> float:
+    def param_dependence_ratio(
+        self, state_or_trans: str, attribute: str, param: str
+    ) -> float:
         """
         Return the heuristic ratio of parameter dependence for state_or_trans, attribute, and param.
 
@@ -607,16 +796,18 @@ class ParamStats:
     def _arg_independence_ratio(self, state_or_trans, attribute, arg_index):
         statistics = self.stats[state_or_trans][attribute]
         if self.use_corrcoef:
-            return 1 - np.abs(statistics['corr_by_arg'][arg_index])
-        if statistics['std_by_arg'][arg_index] == 0:
-            if statistics['std_param_lut'] != 0:
+            return 1 - np.abs(statistics["corr_by_arg"][arg_index])
+        if statistics["std_by_arg"][arg_index] == 0:
+            if statistics["std_param_lut"] != 0:
                 raise RuntimeError("wat")
             # In general, std_param_lut < std_by_arg. So, if std_by_arg == 0, std_param_lut == 0 follows.
             # This means that the variation of arg does not affect the model quality -> no influence, return 1
             return 1
-        return statistics['std_param_lut'] / statistics['std_by_arg'][arg_index]
+        return statistics["std_param_lut"] / statistics["std_by_arg"][arg_index]
 
-    def arg_dependence_ratio(self, state_or_trans: str, attribute: str, arg_index: int) -> float:
+    def arg_dependence_ratio(
+        self, state_or_trans: str, attribute: str, arg_index: int
+    ) -> float:
         return 1 - self._arg_independence_ratio(state_or_trans, attribute, arg_index)
 
     # This heuristic is very similar to the "function is not much better than
@@ -625,10 +816,9 @@ class ParamStats:
     # --df, 2018-04-18
     def depends_on_param(self, state_or_trans, attribute, param):
         """Return whether attribute of state_or_trans depens on param."""
-        return self.stats[state_or_trans][attribute]['depends_on_param'][param]
+        return self.stats[state_or_trans][attribute]["depends_on_param"][param]
 
     # See notes on depends_on_param
     def depends_on_arg(self, state_or_trans, attribute, arg_index):
         """Return whether attribute of state_or_trans depens on arg_index."""
-        return self.stats[state_or_trans][attribute]['depends_on_arg'][arg_index]
-
+        return self.stats[state_or_trans][attribute]["depends_on_arg"][arg_index]
diff --git a/lib/plotter.py b/lib/plotter.py
index deed93a..16c0145 100755
--- a/lib/plotter.py
+++ b/lib/plotter.py
@@ -8,75 +8,89 @@ import re
 
 def is_state(aggregate, name):
     """Return true if name is a state and not UNINITIALIZED."""
-    return aggregate[name]['isa'] == 'state' and name != 'UNINITIALIZED'
+    return aggregate[name]["isa"] == "state" and name != "UNINITIALIZED"
 
 
 def plot_states(model, aggregate):
     keys = [key for key in sorted(aggregate.keys()) if is_state(aggregate, key)]
-    data = [aggregate[key]['means'] for key in keys]
-    mdata = [int(model['state'][key]['power']['static']) for key in keys]
-    boxplot(keys, data, 'Zustand', 'µW', modeldata=mdata)
+    data = [aggregate[key]["means"] for key in keys]
+    mdata = [int(model["state"][key]["power"]["static"]) for key in keys]
+    boxplot(keys, data, "Zustand", "µW", modeldata=mdata)
 
 
 def plot_transitions(model, aggregate):
-    keys = [key for key in sorted(aggregate.keys()) if aggregate[key]['isa'] == 'transition']
-    data = [aggregate[key]['rel_energies'] for key in keys]
-    mdata = [int(model['transition'][key]['rel_energy']['static']) for key in keys]
-    boxplot(keys, data, 'Transition', 'pJ (rel)', modeldata=mdata)
-    data = [aggregate[key]['energies'] for key in keys]
-    mdata = [int(model['transition'][key]['energy']['static']) for key in keys]
-    boxplot(keys, data, 'Transition', 'pJ', modeldata=mdata)
+    keys = [
+        key for key in sorted(aggregate.keys()) if aggregate[key]["isa"] == "transition"
+    ]
+    data = [aggregate[key]["rel_energies"] for key in keys]
+    mdata = [int(model["transition"][key]["rel_energy"]["static"]) for key in keys]
+    boxplot(keys, data, "Transition", "pJ (rel)", modeldata=mdata)
+    data = [aggregate[key]["energies"] for key in keys]
+    mdata = [int(model["transition"][key]["energy"]["static"]) for key in keys]
+    boxplot(keys, data, "Transition", "pJ", modeldata=mdata)
 
 
 def plot_states_duration(model, aggregate):
     keys = [key for key in sorted(aggregate.keys()) if is_state(aggregate, key)]
-    data = [aggregate[key]['durations'] for key in keys]
-    boxplot(keys, data, 'Zustand', 'µs')
+    data = [aggregate[key]["durations"] for key in keys]
+    boxplot(keys, data, "Zustand", "µs")
 
 
 def plot_transitions_duration(model, aggregate):
-    keys = [key for key in sorted(aggregate.keys()) if aggregate[key]['isa'] == 'transition']
-    data = [aggregate[key]['durations'] for key in keys]
-    boxplot(keys, data, 'Transition', 'µs')
+    keys = [
+        key for key in sorted(aggregate.keys()) if aggregate[key]["isa"] == "transition"
+    ]
+    data = [aggregate[key]["durations"] for key in keys]
+    boxplot(keys, data, "Transition", "µs")
 
 
 def plot_transitions_timeout(model, aggregate):
-    keys = [key for key in sorted(aggregate.keys()) if aggregate[key]['isa'] == 'transition']
-    data = [aggregate[key]['timeouts'] for key in keys]
-    boxplot(keys, data, 'Timeout', 'µs')
+    keys = [
+        key for key in sorted(aggregate.keys()) if aggregate[key]["isa"] == "transition"
+    ]
+    data = [aggregate[key]["timeouts"] for key in keys]
+    boxplot(keys, data, "Timeout", "µs")
 
 
 def plot_states_clips(model, aggregate):
     keys = [key for key in sorted(aggregate.keys()) if is_state(aggregate, key)]
-    data = [np.array([100]) * aggregate[key]['clip_rate'] for key in keys]
-    boxplot(keys, data, 'Zustand', '% Clipping')
+    data = [np.array([100]) * aggregate[key]["clip_rate"] for key in keys]
+    boxplot(keys, data, "Zustand", "% Clipping")
 
 
 def plot_transitions_clips(model, aggregate):
-    keys = [key for key in sorted(aggregate.keys()) if aggregate[key]['isa'] == 'transition']
-    data = [np.array([100]) * aggregate[key]['clip_rate'] for key in keys]
-    boxplot(keys, data, 'Transition', '% Clipping')
+    keys = [
+        key for key in sorted(aggregate.keys()) if aggregate[key]["isa"] == "transition"
+    ]
+    data = [np.array([100]) * aggregate[key]["clip_rate"] for key in keys]
+    boxplot(keys, data, "Transition", "% Clipping")
 
 
 def plot_substate_thresholds(model, aggregate):
     keys = [key for key in sorted(aggregate.keys()) if is_state(aggregate, key)]
-    data = [aggregate[key]['sub_thresholds'] for key in keys]
-    boxplot(keys, data, 'Zustand', 'substate threshold (mW/dmW)')
+    data = [aggregate[key]["sub_thresholds"] for key in keys]
+    boxplot(keys, data, "Zustand", "substate threshold (mW/dmW)")
 
 
 def plot_histogram(data):
-    n, bins, patches = plt.hist(data, 1000, normed=1, facecolor='green', alpha=0.75)
+    n, bins, patches = plt.hist(data, 1000, normed=1, facecolor="green", alpha=0.75)
     plt.show()
 
 
 def plot_states_param(model, aggregate):
-    keys = [key for key in sorted(aggregate.keys()) if aggregate[key]['isa'] == 'state' and key[0] != 'UNINITIALIZED']
-    data = [aggregate[key]['means'] for key in keys]
-    mdata = [int(model['state'][key[0]]['power']['static']) for key in keys]
-    boxplot(keys, data, 'Transition', 'µW', modeldata=mdata)
-
-
-def plot_attribute(aggregate, attribute, attribute_unit='', key_filter=lambda x: True, **kwargs):
+    keys = [
+        key
+        for key in sorted(aggregate.keys())
+        if aggregate[key]["isa"] == "state" and key[0] != "UNINITIALIZED"
+    ]
+    data = [aggregate[key]["means"] for key in keys]
+    mdata = [int(model["state"][key[0]]["power"]["static"]) for key in keys]
+    boxplot(keys, data, "Transition", "µW", modeldata=mdata)
+
+
+def plot_attribute(
+    aggregate, attribute, attribute_unit="", key_filter=lambda x: True, **kwargs
+):
     """
     Boxplot measurements of a single attribute according to the partitioning provided by aggregate.
 
@@ -94,13 +108,17 @@ def plot_attribute(aggregate, attribute, attribute_unit='', key_filter=lambda x:
 
 
 def plot_substate_thresholds_p(model, aggregate):
-    keys = [key for key in sorted(aggregate.keys()) if aggregate[key]['isa'] == 'state' and key[0] != 'UNINITIALIZED']
-    data = [aggregate[key]['sub_thresholds'] for key in keys]
-    boxplot(keys, data, 'Zustand', '% Clipping')
+    keys = [
+        key
+        for key in sorted(aggregate.keys())
+        if aggregate[key]["isa"] == "state" and key[0] != "UNINITIALIZED"
+    ]
+    data = [aggregate[key]["sub_thresholds"] for key in keys]
+    boxplot(keys, data, "Zustand", "% Clipping")
 
 
 def plot_y(Y, **kwargs):
-    if 'family' in kwargs and kwargs['family']:
+    if "family" in kwargs and kwargs["family"]:
         plot_xy(None, Y, **kwargs)
     else:
         plot_xy(np.arange(len(Y)), Y, **kwargs)
@@ -116,26 +134,39 @@ def plot_xy(X, Y, xlabel=None, ylabel=None, title=None, output=None, family=Fals
         ax1.set_ylabel(ylabel)
     plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99)
     if family:
-        cm = plt.get_cmap('brg', len(Y))
+        cm = plt.get_cmap("brg", len(Y))
         for i, YY in enumerate(Y):
             plt.plot(np.arange(len(YY)), YY, "-", markersize=2, color=cm(i))
     else:
         plt.plot(X, Y, "bo", markersize=2)
     if output:
         plt.savefig(output)
-        with open('{}.txt'.format(output), 'w') as f:
-            print('X Y', file=f)
+        with open("{}.txt".format(output), "w") as f:
+            print("X Y", file=f)
             for i in range(len(X)):
-                print('{} {}'.format(X[i], Y[i]), file=f)
+                print("{} {}".format(X[i], Y[i]), file=f)
     else:
         plt.show()
 
 
 def _param_slice_eq(a, b, index):
-    return (*a[1][:index], *a[1][index + 1:]) == (*b[1][:index], *b[1][index + 1:]) and a[0] == b[0]
-
-
-def plot_param(model, state_or_trans, attribute, param_idx, xlabel=None, ylabel=None, title=None, extra_function=None, output=None):
+    return (*a[1][:index], *a[1][index + 1 :]) == (
+        *b[1][:index],
+        *b[1][index + 1 :],
+    ) and a[0] == b[0]
+
+
+def plot_param(
+    model,
+    state_or_trans,
+    attribute,
+    param_idx,
+    xlabel=None,
+    ylabel=None,
+    title=None,
+    extra_function=None,
+    output=None,
+):
     fig, ax1 = plt.subplots(figsize=(10, 6))
     if title is not None:
         fig.canvas.set_window_title(title)
@@ -147,8 +178,12 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel=None, ylabel=
 
     param_name = model.param_name(param_idx)
 
-    function_filename = 'plot_param_{}_{}_{}.txt'.format(state_or_trans, attribute, param_name)
-    data_filename_base = 'measurements_{}_{}_{}'.format(state_or_trans, attribute, param_name)
+    function_filename = "plot_param_{}_{}_{}.txt".format(
+        state_or_trans, attribute, param_name
+    )
+    data_filename_base = "measurements_{}_{}_{}".format(
+        state_or_trans, attribute, param_name
+    )
 
     param_model, param_info = model.get_fitted()
 
@@ -156,16 +191,18 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel=None, ylabel=
 
     XX = []
 
-    legend_sanitizer = re.compile(r'[^0-9a-zA-Z]+')
+    legend_sanitizer = re.compile(r"[^0-9a-zA-Z]+")
 
     for k, v in model.by_param.items():
         if k[0] == state_or_trans:
-            other_param_key = (*k[1][:param_idx], *k[1][param_idx + 1:])
+            other_param_key = (*k[1][:param_idx], *k[1][param_idx + 1 :])
             if other_param_key not in by_other_param:
-                by_other_param[other_param_key] = {'X': [], 'Y': []}
-            by_other_param[other_param_key]['X'].extend([float(k[1][param_idx])] * len(v[attribute]))
-            by_other_param[other_param_key]['Y'].extend(v[attribute])
-            XX.extend(by_other_param[other_param_key]['X'])
+                by_other_param[other_param_key] = {"X": [], "Y": []}
+            by_other_param[other_param_key]["X"].extend(
+                [float(k[1][param_idx])] * len(v[attribute])
+            )
+            by_other_param[other_param_key]["Y"].extend(v[attribute])
+            XX.extend(by_other_param[other_param_key]["X"])
 
     XX = np.array(XX)
     x_range = int((XX.max() - XX.min()) * 10)
@@ -175,22 +212,22 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel=None, ylabel=
     YY2 = []
     YY2_legend = []
 
-    cm = plt.get_cmap('brg', len(by_other_param))
+    cm = plt.get_cmap("brg", len(by_other_param))
     for i, k in sorted(enumerate(by_other_param), key=lambda x: x[1]):
         v = by_other_param[k]
-        v['X'] = np.array(v['X'])
-        v['Y'] = np.array(v['Y'])
-        plt.plot(v['X'], v['Y'], "ro", color=cm(i), markersize=3)
-        YY2_legend.append(legend_sanitizer.sub('_', 'X_{}'.format(k)))
-        YY2.append(v['X'])
-        YY2_legend.append(legend_sanitizer.sub('_', 'Y_{}'.format(k)))
-        YY2.append(v['Y'])
-
-        sanitized_k = legend_sanitizer.sub('_', str(k))
-        with open('{}_{}.txt'.format(data_filename_base, sanitized_k), 'w') as f:
-            print('X Y', file=f)
-            for i in range(len(v['X'])):
-                print('{} {}'.format(v['X'][i], v['Y'][i]), file=f)
+        v["X"] = np.array(v["X"])
+        v["Y"] = np.array(v["Y"])
+        plt.plot(v["X"], v["Y"], "ro", color=cm(i), markersize=3)
+        YY2_legend.append(legend_sanitizer.sub("_", "X_{}".format(k)))
+        YY2.append(v["X"])
+        YY2_legend.append(legend_sanitizer.sub("_", "Y_{}".format(k)))
+        YY2.append(v["Y"])
+
+        sanitized_k = legend_sanitizer.sub("_", str(k))
+        with open("{}_{}.txt".format(data_filename_base, sanitized_k), "w") as f:
+            print("X Y", file=f)
+            for i in range(len(v["X"])):
+                print("{} {}".format(v["X"][i], v["Y"][i]), file=f)
 
         # x_range = int((v['X'].max() - v['X'].min()) * 10)
         # xsp = np.linspace(v['X'].min(), v['X'].max(), x_range)
@@ -201,21 +238,21 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel=None, ylabel=
                 ysp.append(param_model(state_or_trans, attribute, param=xarg))
             plt.plot(xsp, ysp, "r-", color=cm(i), linewidth=0.5)
             YY.append(ysp)
-            YY_legend.append(legend_sanitizer.sub('_', 'regr_{}'.format(k)))
+            YY_legend.append(legend_sanitizer.sub("_", "regr_{}".format(k)))
         if extra_function is not None:
             ysp = []
-            with np.errstate(divide='ignore', invalid='ignore'):
+            with np.errstate(divide="ignore", invalid="ignore"):
                 for x in xsp:
                     xarg = [*k[:param_idx], x, *k[param_idx:]]
                     ysp.append(extra_function(*xarg))
             plt.plot(xsp, ysp, "r--", color=cm(i), linewidth=1, dashes=(3, 3))
             YY.append(ysp)
-            YY_legend.append(legend_sanitizer.sub('_', 'symb_{}'.format(k)))
+            YY_legend.append(legend_sanitizer.sub("_", "symb_{}".format(k)))
 
-    with open(function_filename, 'w') as f:
-        print(' '.join(YY_legend), file=f)
+    with open(function_filename, "w") as f:
+        print(" ".join(YY_legend), file=f)
         for elem in np.array(YY).T:
-            print(' '.join(map(str, elem)), file=f)
+            print(" ".join(map(str, elem)), file=f)
 
     print(data_filename_base, function_filename)
     if output:
@@ -224,7 +261,19 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel=None, ylabel=
         plt.show()
 
 
-def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X, Y, xaxis=None, yaxis=None):
+def plot_param_fit(
+    function,
+    name,
+    fitfunc,
+    funp,
+    parameters,
+    datatype,
+    index,
+    X,
+    Y,
+    xaxis=None,
+    yaxis=None,
+):
     fig, ax1 = plt.subplots(figsize=(10, 6))
     fig.canvas.set_window_title("fit %s" % (function))
     plt.subplots_adjust(left=0.14, right=0.99, top=0.99, bottom=0.14)
@@ -244,10 +293,10 @@ def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X
     if yaxis is not None:
         ax1.set_ylabel(yaxis)
     else:
-        ax1.set_ylabel('%s %s' % (name, datatype))
+        ax1.set_ylabel("%s %s" % (name, datatype))
 
-    otherparams = list(set(itertools.product(*X[:index], *X[index + 1:])))
-    cm = plt.get_cmap('brg', len(otherparams))
+    otherparams = list(set(itertools.product(*X[:index], *X[index + 1 :])))
+    cm = plt.get_cmap("brg", len(otherparams))
     for i in range(len(otherparams)):
         elem = otherparams[i]
         color = cm(i)
@@ -268,18 +317,17 @@ def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X
     plt.show()
 
 
-def boxplot(ticks, measurements, xlabel='', ylabel='', modeldata=None, output=None):
+def boxplot(ticks, measurements, xlabel="", ylabel="", modeldata=None, output=None):
     fig, ax1 = plt.subplots(figsize=(10, 6))
-    fig.canvas.set_window_title('DriverEval')
+    fig.canvas.set_window_title("DriverEval")
     plt.subplots_adjust(left=0.1, right=0.95, top=0.95, bottom=0.1)
 
-    bp = plt.boxplot(measurements, notch=0, sym='+', vert=1, whis=1.5)
-    plt.setp(bp['boxes'], color='black')
-    plt.setp(bp['whiskers'], color='black')
-    plt.setp(bp['fliers'], color='red', marker='+')
+    bp = plt.boxplot(measurements, notch=0, sym="+", vert=1, whis=1.5)
+    plt.setp(bp["boxes"], color="black")
+    plt.setp(bp["whiskers"], color="black")
+    plt.setp(bp["fliers"], color="red", marker="+")
 
-    ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
-                   alpha=0.5)
+    ax1.yaxis.grid(True, linestyle="-", which="major", color="lightgrey", alpha=0.5)
 
     ax1.set_axisbelow(True)
     # ax1.set_title('DriverEval')
@@ -294,7 +342,7 @@ def boxplot(ticks, measurements, xlabel='', ylabel='', modeldata=None, output=No
     # boxColors = ['darkkhaki', 'royalblue']
     medians = list(range(numBoxes))
     for i in range(numBoxes):
-        box = bp['boxes'][i]
+        box = bp["boxes"][i]
         boxX = []
         boxY = []
         for j in range(5):
@@ -306,21 +354,31 @@ def boxplot(ticks, measurements, xlabel='', ylabel='', modeldata=None, output=No
         # boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
         # ax1.add_patch(boxPolygon)
         # Now draw the median lines back over what we just filled in
-        med = bp['medians'][i]
+        med = bp["medians"][i]
         medianX = []
         medianY = []
         for j in range(2):
             medianX.append(med.get_xdata()[j])
             medianY.append(med.get_ydata()[j])
-            plt.plot(medianX, medianY, 'k')
+            plt.plot(medianX, medianY, "k")
             medians[i] = medianY[0]
         # Finally, overplot the sample averages, with horizontal alignment
         # in the center of each box
-        plt.plot([np.average(med.get_xdata())], [np.average(measurements[i])],
-                 color='w', marker='*', markeredgecolor='k')
+        plt.plot(
+            [np.average(med.get_xdata())],
+            [np.average(measurements[i])],
+            color="w",
+            marker="*",
+            markeredgecolor="k",
+        )
         if modeldata:
-            plt.plot([np.average(med.get_xdata())], [modeldata[i]],
-                     color='w', marker='o', markeredgecolor='k')
+            plt.plot(
+                [np.average(med.get_xdata())],
+                [modeldata[i]],
+                color="w",
+                marker="o",
+                markeredgecolor="k",
+            )
 
     pos = np.arange(numBoxes) + 1
     upperLabels = [str(np.round(s, 2)) for s in medians]
@@ -330,16 +388,21 @@ def boxplot(ticks, measurements, xlabel='', ylabel='', modeldata=None, output=No
         y0, y1 = ax1.get_ylim()
         textpos = y0 + (y1 - y0) * 0.97
         # ypos = ax1.get_ylim()[0]
-        ax1.text(pos[tick], textpos, upperLabels[tick],
-                 horizontalalignment='center', size='small',
-                 color='royalblue')
+        ax1.text(
+            pos[tick],
+            textpos,
+            upperLabels[tick],
+            horizontalalignment="center",
+            size="small",
+            color="royalblue",
+        )
 
     if output:
         plt.savefig(output)
-        with open('{}.txt'.format(output), 'w') as f:
-            print('X Y', file=f)
+        with open("{}.txt".format(output), "w") as f:
+            print("X Y", file=f)
             for i, data in enumerate(measurements):
                 for value in data:
-                    print('{} {}'.format(ticks[i], value), file=f)
+                    print("{} {}".format(ticks[i], value), file=f)
     else:
         plt.show()
diff --git a/lib/protocol_benchmarks.py b/lib/protocol_benchmarks.py
index e82af67..b42e821 100755
--- a/lib/protocol_benchmarks.py
+++ b/lib/protocol_benchmarks.py
@@ -18,40 +18,41 @@ import re
 import time
 from filelock import FileLock
 
+
 class DummyProtocol:
     def __init__(self):
         self.max_serialized_bytes = None
-        self.enc_buf = ''
-        self.dec_buf = ''
-        self.dec_buf0 = ''
-        self.dec_buf1 = ''
-        self.dec_buf2 = ''
+        self.enc_buf = ""
+        self.dec_buf = ""
+        self.dec_buf0 = ""
+        self.dec_buf1 = ""
+        self.dec_buf2 = ""
         self.dec_index = 0
         self.transition_map = dict()
 
-    def assign_and_kout(self, signature, assignment, transition_args = None):
+    def assign_and_kout(self, signature, assignment, transition_args=None):
         self.new_var(signature)
-        self.assign_var(assignment, transition_args = transition_args)
+        self.assign_var(assignment, transition_args=transition_args)
         self.kout_var()
 
     def new_var(self, signature):
         self.dec_index += 1
-        self.dec_buf0 += '{} dec_{:d};\n'.format(signature, self.dec_index)
+        self.dec_buf0 += "{} dec_{:d};\n".format(signature, self.dec_index)
 
-    def assign_var(self, assignment, transition_args = None):
-        snippet = 'dec_{:d} = {};\n'.format(self.dec_index, assignment)
+    def assign_var(self, assignment, transition_args=None):
+        snippet = "dec_{:d} = {};\n".format(self.dec_index, assignment)
         self.dec_buf1 += snippet
         if transition_args:
             self.add_transition(snippet, transition_args)
 
     def get_var(self):
-        return 'dec_{:d}'.format(self.dec_index)
+        return "dec_{:d}".format(self.dec_index)
 
     def kout_var(self):
-        self.dec_buf2 += 'kout << dec_{:d};\n'.format(self.dec_index)
+        self.dec_buf2 += "kout << dec_{:d};\n".format(self.dec_index)
 
     def note_unsupported(self, value):
-        note = '// value {} has unsupported type {}\n'.format(value, type(value))
+        note = "// value {} has unsupported type {}\n".format(value, type(value))
         self.enc_buf += note
         self.dec_buf += note
         self.dec_buf1 += note
@@ -60,31 +61,31 @@ class DummyProtocol:
         return True
 
     def get_encode(self):
-        return ''
+        return ""
 
     def get_buffer_declaration(self):
-        return ''
+        return ""
 
     def get_buffer_name(self):
         return '"none"'
 
     def get_serialize(self):
-        return ''
+        return ""
 
     def get_deserialize(self):
-        return ''
+        return ""
 
     def get_decode_and_output(self):
-        return ''
+        return ""
 
     def get_decode_vars(self):
-        return ''
+        return ""
 
     def get_decode(self):
-        return ''
+        return ""
 
     def get_decode_output(self):
-        return ''
+        return ""
 
     def get_extra_files(self):
         return dict()
@@ -101,30 +102,34 @@ class DummyProtocol:
             return self.transition_map[code_snippet]
         return list()
 
-class Avro(DummyProtocol):
 
-    def __init__(self, data, strip_schema = False):
+class Avro(DummyProtocol):
+    def __init__(self, data, strip_schema=False):
         super().__init__()
         self.data = data
         self.strip_schema = strip_schema
         self.schema = {
-            'namespace' : 'benchmark.avro',
-            'type' : 'record',
-            'name' : 'Benchmark',
-            'fields' : []
+            "namespace": "benchmark.avro",
+            "type": "record",
+            "name": "Benchmark",
+            "fields": [],
         }
         for key, value in data.items():
-            self.add_to_dict(self.schema['fields'], key, value)
+            self.add_to_dict(self.schema["fields"], key, value)
         buf = io.BytesIO()
         try:
-            writer = avro.datafile.DataFileWriter(buf, avro.io.DatumWriter(), avro.schema.Parse(json.dumps(self.schema)))
+            writer = avro.datafile.DataFileWriter(
+                buf, avro.io.DatumWriter(), avro.schema.Parse(json.dumps(self.schema))
+            )
             writer.append(data)
             writer.flush()
         except avro.schema.SchemaParseException:
-            raise RuntimeError('Unsupported schema') from None
+            raise RuntimeError("Unsupported schema") from None
         self.serialized_data = buf.getvalue()
         if strip_schema:
-            self.serialized_data = self.serialized_data[self.serialized_data.find(b'}\x00')+2 : ]
+            self.serialized_data = self.serialized_data[
+                self.serialized_data.find(b"}\x00") + 2 :
+            ]
             # strip leading 16-byte sync marker
             self.serialized_data = self.serialized_data[16:]
             # strip trailing 16-byte sync marker
@@ -138,29 +143,30 @@ class Avro(DummyProtocol):
 
     def type_to_type_name(self, type_type):
         if type_type == int:
-            return 'int'
+            return "int"
         if type_type == float:
-            return 'float'
+            return "float"
         if type_type == str:
-            return 'string'
+            return "string"
         if type_type == list:
-            return 'array'
+            return "array"
         if type_type == dict:
-            return 'record'
+            return "record"
 
     def add_to_dict(self, fields, key, value):
-        new_field = {
-            'name' : key,
-            'type' : self.type_to_type_name(type(value))
-        }
-        if new_field['type'] == 'array':
-            new_field['type'] = {'type' : 'array', 'items' : self.type_to_type_name(type(value[0]))}
-        if new_field['type'] == 'record':
-            new_field['type'] = {'type' : 'record', 'name': key, 'fields' : []}
+        new_field = {"name": key, "type": self.type_to_type_name(type(value))}
+        if new_field["type"] == "array":
+            new_field["type"] = {
+                "type": "array",
+                "items": self.type_to_type_name(type(value[0])),
+            }
+        if new_field["type"] == "record":
+            new_field["type"] = {"type": "record", "name": key, "fields": []}
             for key, value in value.items():
-                self.add_to_dict(new_field['type']['fields'], key, value)
+                self.add_to_dict(new_field["type"]["fields"], key, value)
         fields.append(new_field)
 
+
 class Thrift(DummyProtocol):
 
     class_index = 1
@@ -169,10 +175,10 @@ class Thrift(DummyProtocol):
         super().__init__()
         self.data = data
         self._field_id = 1
-        self.proto_buf = ''
+        self.proto_buf = ""
         self.proto_from_json(data)
 
-        with open('/tmp/test.thrift', 'w') as f:
+        with open("/tmp/test.thrift", "w") as f:
             f.write(self.proto_buf)
 
         membuf = TCyMemoryBuffer()
@@ -180,7 +186,9 @@ class Thrift(DummyProtocol):
         # TODO irgendwo bleibt state übrig -> nur das bei allerersten
         # Aufruf geladene Protokoll wird berücksichtigt, dazu nicht passende
         # Daten werden nicht serialisiert
-        test_thrift = thriftpy.load('/tmp/test.thrift', module_name='test{:d}_thrift'.format(Thrift.class_index))
+        test_thrift = thriftpy.load(
+            "/tmp/test.thrift", module_name="test{:d}_thrift".format(Thrift.class_index)
+        )
         Thrift.class_index += 1
         benchmark = test_thrift.Benchmark()
 
@@ -190,7 +198,7 @@ class Thrift(DummyProtocol):
         try:
             proto.write_struct(benchmark)
         except thriftpy.thrift.TDecodeException:
-            raise RuntimeError('Unsupported data layout') from None
+            raise RuntimeError("Unsupported data layout") from None
         membuf.flush()
         self.serialized_data = membuf.getvalue()
 
@@ -203,31 +211,31 @@ class Thrift(DummyProtocol):
     def type_to_type_name(self, value):
         type_type = type(value)
         if type_type == int:
-            return 'i32'
+            return "i32"
         if type_type == float:
-            return 'double'
+            return "double"
         if type_type == str:
-            return 'string'
+            return "string"
         if type_type == list:
-            return 'list<{}>'.format(self.type_to_type_name(value[0]))
+            return "list<{}>".format(self.type_to_type_name(value[0]))
         if type_type == dict:
             sub_value = list(value.values())[0]
-            return 'map<{},{}>'.format('string', self.type_to_type_name(sub_value))
+            return "map<{},{}>".format("string", self.type_to_type_name(sub_value))
 
     def add_to_dict(self, key, value):
         key_type = self.type_to_type_name(value)
-        self.proto_buf += '{:d}: {} {};\n'.format(self._field_id, key_type, key)
+        self.proto_buf += "{:d}: {} {};\n".format(self._field_id, key_type, key)
         self._field_id += 1
 
     def proto_from_json(self, data):
-        self.proto_buf += 'struct Benchmark {\n'
+        self.proto_buf += "struct Benchmark {\n"
         for key, value in data.items():
             self.add_to_dict(key, value)
-        self.proto_buf += '}\n'
+        self.proto_buf += "}\n"
 
-class ArduinoJSON(DummyProtocol):
 
-    def __init__(self, data, bufsize = 255, int_type = 'uint16_t', float_type = 'float'):
+class ArduinoJSON(DummyProtocol):
+    def __init__(self, data, bufsize=255, int_type="uint16_t", float_type="float"):
         super().__init__()
         self.data = data
         self.max_serialized_bytes = self.get_serialized_length() + 2
@@ -235,9 +243,12 @@ class ArduinoJSON(DummyProtocol):
         self.bufsize = bufsize
         self.int_type = int_type
         self.float_type = float_type
-        self.enc_buf += self.add_transition('ArduinoJson::StaticJsonBuffer<{:d}> jsonBuffer;\n'.format(bufsize), [bufsize])
-        self.enc_buf += 'ArduinoJson::JsonObject& root = jsonBuffer.createObject();\n'
-        self.from_json(data, 'root')
+        self.enc_buf += self.add_transition(
+            "ArduinoJson::StaticJsonBuffer<{:d}> jsonBuffer;\n".format(bufsize),
+            [bufsize],
+        )
+        self.enc_buf += "ArduinoJson::JsonObject& root = jsonBuffer.createObject();\n"
+        self.from_json(data, "root")
 
     def get_serialized_length(self):
         return len(json.dumps(self.data))
@@ -249,24 +260,33 @@ class ArduinoJSON(DummyProtocol):
         return self.enc_buf
 
     def get_buffer_declaration(self):
-        return 'char buf[{:d}];\n'.format(self.max_serialized_bytes)
+        return "char buf[{:d}];\n".format(self.max_serialized_bytes)
 
     def get_buffer_name(self):
-        return 'buf'
+        return "buf"
 
     def get_length_var(self):
-        return 'serialized_size'
+        return "serialized_size"
 
     def get_serialize(self):
-        return self.add_transition('uint16_t serialized_size = root.printTo(buf);\n', [self.max_serialized_bytes])
+        return self.add_transition(
+            "uint16_t serialized_size = root.printTo(buf);\n",
+            [self.max_serialized_bytes],
+        )
 
     def get_deserialize(self):
-        ret = self.add_transition('ArduinoJson::StaticJsonBuffer<{:d}> jsonBuffer;\n'.format(self.bufsize), [self.bufsize])
-        ret += self.add_transition('ArduinoJson::JsonObject& root = jsonBuffer.parseObject(buf);\n', [self.max_serialized_bytes])
+        ret = self.add_transition(
+            "ArduinoJson::StaticJsonBuffer<{:d}> jsonBuffer;\n".format(self.bufsize),
+            [self.bufsize],
+        )
+        ret += self.add_transition(
+            "ArduinoJson::JsonObject& root = jsonBuffer.parseObject(buf);\n",
+            [self.max_serialized_bytes],
+        )
         return ret
 
     def get_decode_and_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf + 'kout << endl;\n';
+        return 'kout << dec << "dec:";\n' + self.dec_buf + "kout << endl;\n"
 
     def get_decode_vars(self):
         return self.dec_buf0
@@ -275,93 +295,158 @@ class ArduinoJSON(DummyProtocol):
         return self.dec_buf1
 
     def get_decode_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf2 + 'kout << endl;\n';
+        return 'kout << dec << "dec:";\n' + self.dec_buf2 + "kout << endl;\n"
 
     def add_to_list(self, enc_node, dec_node, offset, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
-                self.enc_buf += '{}.add({});\n'.format(enc_node, value[1:])
-                self.dec_buf += 'kout << {}[{:d}].as<{}>();\n'.format(dec_node, offset, self.int_type)
-                self.assign_and_kout(self.int_type, '{}[{:d}].as<{}>()'.format(dec_node, offset, self.int_type))
+            if len(value) and value[0] == "$":
+                self.enc_buf += "{}.add({});\n".format(enc_node, value[1:])
+                self.dec_buf += "kout << {}[{:d}].as<{}>();\n".format(
+                    dec_node, offset, self.int_type
+                )
+                self.assign_and_kout(
+                    self.int_type,
+                    "{}[{:d}].as<{}>()".format(dec_node, offset, self.int_type),
+                )
             else:
-                self.enc_buf += self.add_transition('{}.add("{}");\n'.format(enc_node, value), [len(value)])
-                self.dec_buf += 'kout << {}[{:d}].as<const char *>();\n'.format(dec_node, offset)
-                self.assign_and_kout('char const*', '{}[{:d}].as<char const *>()'.format(dec_node, offset), transition_args = [len(value)])
+                self.enc_buf += self.add_transition(
+                    '{}.add("{}");\n'.format(enc_node, value), [len(value)]
+                )
+                self.dec_buf += "kout << {}[{:d}].as<const char *>();\n".format(
+                    dec_node, offset
+                )
+                self.assign_and_kout(
+                    "char const*",
+                    "{}[{:d}].as<char const *>()".format(dec_node, offset),
+                    transition_args=[len(value)],
+                )
 
         elif type(value) == list:
-            child = enc_node + 'l'
+            child = enc_node + "l"
             while child in self.children:
-                child += '_'
-            self.enc_buf += 'ArduinoJson::JsonArray& {} = {}.createNestedArray();\n'.format(
-                child, enc_node)
+                child += "_"
+            self.enc_buf += "ArduinoJson::JsonArray& {} = {}.createNestedArray();\n".format(
+                child, enc_node
+            )
             self.children.add(child)
             self.from_json(value, child)
 
         elif type(value) == dict:
-            child = enc_node + 'o'
+            child = enc_node + "o"
             while child in self.children:
-                child += '_'
-            self.enc_buf += 'ArduinoJson::JsonObject& {} = {}.createNestedObject();\n'.format(
-                child, enc_node)
+                child += "_"
+            self.enc_buf += "ArduinoJson::JsonObject& {} = {}.createNestedObject();\n".format(
+                child, enc_node
+            )
             self.children.add(child)
             self.from_json(value, child)
 
         elif type(value) == float:
-            self.enc_buf += '{}.add({});\n'.format(enc_node, value)
-            self.dec_buf += 'kout << {}[{:d}].as<{}>();\n'.format(dec_node, offset, self.float_type)
-            self.assign_and_kout(self.float_type, '{}[{:d}].as<{}>()'.format(dec_node, offset, self.float_type))
+            self.enc_buf += "{}.add({});\n".format(enc_node, value)
+            self.dec_buf += "kout << {}[{:d}].as<{}>();\n".format(
+                dec_node, offset, self.float_type
+            )
+            self.assign_and_kout(
+                self.float_type,
+                "{}[{:d}].as<{}>()".format(dec_node, offset, self.float_type),
+            )
 
         elif type(value) == int:
-            self.enc_buf += '{}.add({});\n'.format(enc_node, value)
-            self.dec_buf += 'kout << {}[{:d}].as<{}>();\n'.format(dec_node, offset, self.int_type)
-            self.assign_and_kout(self.int_type, '{}[{:d}].as<{}>()'.format(dec_node, offset, self.int_type))
+            self.enc_buf += "{}.add({});\n".format(enc_node, value)
+            self.dec_buf += "kout << {}[{:d}].as<{}>();\n".format(
+                dec_node, offset, self.int_type
+            )
+            self.assign_and_kout(
+                self.int_type,
+                "{}[{:d}].as<{}>()".format(dec_node, offset, self.int_type),
+            )
 
         else:
             self.note_unsupported(value)
 
     def add_to_dict(self, enc_node, dec_node, key, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
-                self.enc_buf += self.add_transition('{}["{}"] = {};\n'.format(enc_node, key, value[1:]), [len(key)])
-                self.dec_buf += 'kout << {}["{}"].as<{}>();\n'.format(dec_node, key, self.int_type)
-                self.assign_and_kout(self.int_type, '{}["{}"].as<{}>()'.format(dec_node, key, self.int_type))
+            if len(value) and value[0] == "$":
+                self.enc_buf += self.add_transition(
+                    '{}["{}"] = {};\n'.format(enc_node, key, value[1:]), [len(key)]
+                )
+                self.dec_buf += 'kout << {}["{}"].as<{}>();\n'.format(
+                    dec_node, key, self.int_type
+                )
+                self.assign_and_kout(
+                    self.int_type,
+                    '{}["{}"].as<{}>()'.format(dec_node, key, self.int_type),
+                )
             else:
-                self.enc_buf += self.add_transition('{}["{}"] = "{}";\n'.format(enc_node, key, value), [len(key), len(value)])
-                self.dec_buf += 'kout << {}["{}"].as<const char *>();\n'.format(dec_node, key)
-                self.assign_and_kout('char const*', '{}["{}"].as<const char *>()'.format(dec_node, key), transition_args = [len(key), len(value)])
+                self.enc_buf += self.add_transition(
+                    '{}["{}"] = "{}";\n'.format(enc_node, key, value),
+                    [len(key), len(value)],
+                )
+                self.dec_buf += 'kout << {}["{}"].as<const char *>();\n'.format(
+                    dec_node, key
+                )
+                self.assign_and_kout(
+                    "char const*",
+                    '{}["{}"].as<const char *>()'.format(dec_node, key),
+                    transition_args=[len(key), len(value)],
+                )
 
         elif type(value) == list:
-            child = enc_node + 'l'
+            child = enc_node + "l"
             while child in self.children:
-                child += '_'
-            self.enc_buf += self.add_transition('ArduinoJson::JsonArray& {} = {}.createNestedArray("{}");\n'.format(
-                child, enc_node, key), [len(key)])
+                child += "_"
+            self.enc_buf += self.add_transition(
+                'ArduinoJson::JsonArray& {} = {}.createNestedArray("{}");\n'.format(
+                    child, enc_node, key
+                ),
+                [len(key)],
+            )
             self.children.add(child)
             self.from_json(value, child, '{}["{}"]'.format(dec_node, key))
 
         elif type(value) == dict:
-            child = enc_node + 'o'
+            child = enc_node + "o"
             while child in self.children:
-                child += '_'
-            self.enc_buf += self.add_transition('ArduinoJson::JsonObject& {} = {}.createNestedObject("{}");\n'.format(
-                child, enc_node, key), [len(key)])
+                child += "_"
+            self.enc_buf += self.add_transition(
+                'ArduinoJson::JsonObject& {} = {}.createNestedObject("{}");\n'.format(
+                    child, enc_node, key
+                ),
+                [len(key)],
+            )
             self.children.add(child)
             self.from_json(value, child, '{}["{}"]'.format(dec_node, key))
 
         elif type(value) == float:
-            self.enc_buf += self.add_transition('{}["{}"] = {};\n'.format(enc_node, key, value), [len(key)])
-            self.dec_buf += 'kout << {}["{}"].as<{}>();\n'.format(dec_node, key, self.float_type)
-            self.assign_and_kout(self.float_type, '{}["{}"].as<{}>()'.format(dec_node, key, self.float_type), transition_args = [len(key)])
+            self.enc_buf += self.add_transition(
+                '{}["{}"] = {};\n'.format(enc_node, key, value), [len(key)]
+            )
+            self.dec_buf += 'kout << {}["{}"].as<{}>();\n'.format(
+                dec_node, key, self.float_type
+            )
+            self.assign_and_kout(
+                self.float_type,
+                '{}["{}"].as<{}>()'.format(dec_node, key, self.float_type),
+                transition_args=[len(key)],
+            )
 
         elif type(value) == int:
-            self.enc_buf += self.add_transition('{}["{}"] = {};\n'.format(enc_node, key, value), [len(key)])
-            self.dec_buf += 'kout << {}["{}"].as<{}>();\n'.format(dec_node, key, self.int_type)
-            self.assign_and_kout(self.int_type, '{}["{}"].as<{}>()'.format(dec_node, key, self.int_type), transition_args = [len(key)])
+            self.enc_buf += self.add_transition(
+                '{}["{}"] = {};\n'.format(enc_node, key, value), [len(key)]
+            )
+            self.dec_buf += 'kout << {}["{}"].as<{}>();\n'.format(
+                dec_node, key, self.int_type
+            )
+            self.assign_and_kout(
+                self.int_type,
+                '{}["{}"].as<{}>()'.format(dec_node, key, self.int_type),
+                transition_args=[len(key)],
+            )
 
         else:
             self.note_unsupported(value)
 
-    def from_json(self, data, enc_node = 'root', dec_node = 'root'):
+    def from_json(self, data, enc_node="root", dec_node="root"):
         if type(data) == dict:
             for key in sorted(data.keys()):
                 self.add_to_dict(enc_node, dec_node, key, data[key])
@@ -371,8 +456,16 @@ class ArduinoJSON(DummyProtocol):
 
 
 class CapnProtoC(DummyProtocol):
-
-    def __init__(self, data, max_serialized_bytes = 128, packed = False, trail = ['benchmark'], int_type = 'uint16_t', float_type = 'float', dec_index = 0):
+    def __init__(
+        self,
+        data,
+        max_serialized_bytes=128,
+        packed=False,
+        trail=["benchmark"],
+        int_type="uint16_t",
+        float_type="float",
+        dec_index=0,
+    ):
         super().__init__()
         self.data = data
         self.max_serialized_bytes = max_serialized_bytes
@@ -384,169 +477,190 @@ class CapnProtoC(DummyProtocol):
         self.float_type = float_type
         self.proto_float_type = self.float_type_to_proto_type(float_type)
         self.dec_index = dec_index
-        self.trail_name = '_'.join(map(lambda x: x.capitalize(), trail))
-        self.proto_buf = ''
-        self.enc_buf += 'struct {} {};\n'.format(self.trail_name, self.name)
-        self.cc_tail = ''
+        self.trail_name = "_".join(map(lambda x: x.capitalize(), trail))
+        self.proto_buf = ""
+        self.enc_buf += "struct {} {};\n".format(self.trail_name, self.name)
+        self.cc_tail = ""
         self.key_counter = 0
         self.from_json(data)
 
     def int_type_to_proto_type(self, int_type):
-        sign = ''
-        if int_type[0] == 'u':
-            sign = 'U'
-        if '8' in int_type:
+        sign = ""
+        if int_type[0] == "u":
+            sign = "U"
+        if "8" in int_type:
             self.int_bits = 8
-            return sign + 'Int8'
-        if '16' in int_type:
+            return sign + "Int8"
+        if "16" in int_type:
             self.int_bits = 16
-            return sign + 'Int16'
-        if '32' in int_type:
+            return sign + "Int16"
+        if "32" in int_type:
             self.int_bits = 32
-            return sign + 'Int32'
+            return sign + "Int32"
         self.int_bits = 64
-        return sign + 'Int64'
+        return sign + "Int64"
 
     def float_type_to_proto_type(self, float_type):
-        if float_type == 'float':
+        if float_type == "float":
             self.float_bits = 32
-            return 'Float32'
+            return "Float32"
         self.float_bits = 64
-        return 'Float64'
+        return "Float64"
 
     def is_ascii(self):
         return False
 
     def get_proto(self):
-        return '@0xad5b236043de2389;\n\n' + self.proto_buf
+        return "@0xad5b236043de2389;\n\n" + self.proto_buf
 
     def get_extra_files(self):
-        return {
-            'capnp_c_bench.capnp' : self.get_proto()
-        }
+        return {"capnp_c_bench.capnp": self.get_proto()}
 
     def get_buffer_declaration(self):
-        ret = 'uint8_t buf[{:d}];\n'.format(self.max_serialized_bytes)
-        ret += 'uint16_t serialized_size;\n'
+        ret = "uint8_t buf[{:d}];\n".format(self.max_serialized_bytes)
+        ret += "uint16_t serialized_size;\n"
         return ret
 
     def get_buffer_name(self):
-        return 'buf'
+        return "buf"
 
     def get_encode(self):
-        ret = 'struct capn c;\n'
-        ret += 'capn_init_malloc(&c);\n'
-        ret += 'capn_ptr cr = capn_root(&c);\n'
-        ret += 'struct capn_segment *cs = cr.seg;\n\n'
-        ret += '{}_ptr {}_ptr = new_{}(cs);\n'.format(
-            self.trail_name, self.name, self.trail_name)
+        ret = "struct capn c;\n"
+        ret += "capn_init_malloc(&c);\n"
+        ret += "capn_ptr cr = capn_root(&c);\n"
+        ret += "struct capn_segment *cs = cr.seg;\n\n"
+        ret += "{}_ptr {}_ptr = new_{}(cs);\n".format(
+            self.trail_name, self.name, self.trail_name
+        )
 
-        tail = 'write_{}(&{}, {}_ptr);\n'.format(
-            self.trail_name, self.name, self.name)
-        tail += 'capn_setp(cr, 0, {}_ptr.p);\n'.format(self.name)
+        tail = "write_{}(&{}, {}_ptr);\n".format(self.trail_name, self.name, self.name)
+        tail += "capn_setp(cr, 0, {}_ptr.p);\n".format(self.name)
 
         return ret + self.enc_buf + self.cc_tail + tail
 
     def get_serialize(self):
-        ret = 'serialized_size = capn_write_mem(&c, buf, sizeof(buf), {:d});\n'.format(self.packed)
-        ret += 'capn_free(&c);\n'
+        ret = "serialized_size = capn_write_mem(&c, buf, sizeof(buf), {:d});\n".format(
+            self.packed
+        )
+        ret += "capn_free(&c);\n"
         return ret
 
     def get_deserialize(self):
-        ret = 'struct capn c;\n'
-        ret += 'capn_init_mem(&c, buf, serialized_size, 0);\n'
+        ret = "struct capn c;\n"
+        ret += "capn_init_mem(&c, buf, serialized_size, 0);\n"
         return ret
 
     def get_decode_and_output(self):
-        ret = '{}_ptr {}_ptr;\n'.format(self.trail_name, self.name)
-        ret += '{}_ptr.p = capn_getp(capn_root(&c), 0, 1);\n'.format(self.name)
-        ret += 'struct {} {};\n'.format(self.trail_name, self.name)
+        ret = "{}_ptr {}_ptr;\n".format(self.trail_name, self.name)
+        ret += "{}_ptr.p = capn_getp(capn_root(&c), 0, 1);\n".format(self.name)
+        ret += "struct {} {};\n".format(self.trail_name, self.name)
         ret += 'kout << dec << "dec:";\n'
         ret += self.dec_buf
-        ret += 'kout << endl;\n'
-        ret += 'capn_free(&c);\n'
+        ret += "kout << endl;\n"
+        ret += "capn_free(&c);\n"
         return ret
 
     def get_decode_vars(self):
         return self.dec_buf0
 
     def get_decode(self):
-        ret = '{}_ptr {}_ptr;\n'.format(self.trail_name, self.name)
-        ret += '{}_ptr.p = capn_getp(capn_root(&c), 0, 1);\n'.format(self.name)
-        ret += 'struct {} {};\n'.format(self.trail_name, self.name)
+        ret = "{}_ptr {}_ptr;\n".format(self.trail_name, self.name)
+        ret += "{}_ptr.p = capn_getp(capn_root(&c), 0, 1);\n".format(self.name)
+        ret += "struct {} {};\n".format(self.trail_name, self.name)
         ret += self.dec_buf1
-        ret += 'capn_free(&c);\n'
+        ret += "capn_free(&c);\n"
         return ret
 
     def get_decode_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf2 + 'kout << endl;\n';
+        return 'kout << dec << "dec:";\n' + self.dec_buf2 + "kout << endl;\n"
 
     def get_length_var(self):
-        return 'serialized_size'
+        return "serialized_size"
 
     def add_field(self, fieldtype, key, value):
-        extra = ''
+        extra = ""
         texttype = self.proto_int_type
 
         if fieldtype == str:
-            texttype = 'Text'
+            texttype = "Text"
         elif fieldtype == float:
             texttype = self.proto_float_type
         elif fieldtype == dict:
             texttype = key.capitalize()
 
         if type(value) == list:
-            texttype = 'List({})'.format(texttype)
+            texttype = "List({})".format(texttype)
 
-        self.proto_buf += '{} @{:d} :{};\n'.format(
-            key, self.key_counter, texttype)
+        self.proto_buf += "{} @{:d} :{};\n".format(key, self.key_counter, texttype)
         self.key_counter += 1
 
         if fieldtype == str:
-            self.enc_buf += 'capn_text {}_text;\n'.format(key)
-            self.enc_buf += '{}_text.len = {:d};\n'.format(key, len(value))
+            self.enc_buf += "capn_text {}_text;\n".format(key)
+            self.enc_buf += "{}_text.len = {:d};\n".format(key, len(value))
             self.enc_buf += '{}_text.str = "{}";\n'.format(key, value)
-            self.enc_buf += '{}_text.seg = NULL;\n'.format(key)
-            self.enc_buf += '{}.{} = {}_text;\n\n'.format(self.name, key, key)
-            self.dec_buf += 'kout << {}.{}.str;\n'.format(self.name, key)
-            self.assign_and_kout('char const *', '{}.{}.str'.format(self.name, key))
+            self.enc_buf += "{}_text.seg = NULL;\n".format(key)
+            self.enc_buf += "{}.{} = {}_text;\n\n".format(self.name, key, key)
+            self.dec_buf += "kout << {}.{}.str;\n".format(self.name, key)
+            self.assign_and_kout("char const *", "{}.{}.str".format(self.name, key))
         elif fieldtype == dict:
-            pass # content is handled recursively in add_to_dict
+            pass  # content is handled recursively in add_to_dict
         elif type(value) == list:
             if type(value[0]) == float:
-                self.enc_buf += self.add_transition('{}.{} = capn_new_list{:d}(cs, {:d});\n'.format(
-                    self.name, key, self.float_bits, len(value)), [len(value)])
+                self.enc_buf += self.add_transition(
+                    "{}.{} = capn_new_list{:d}(cs, {:d});\n".format(
+                        self.name, key, self.float_bits, len(value)
+                    ),
+                    [len(value)],
+                )
                 for i, elem in enumerate(value):
-                    self.enc_buf += 'capn_set{:d}({}.{}, {:d}, capn_from_f{:d}({:f}));\n'.format(
-                        self.float_bits, self.name, key, i, self.float_bits, elem)
-                    self.dec_buf += 'kout << capn_to_f{:d}(capn_get{:d}({}.{}, {:d}));\n'.format(
-                        self.float_bits, self.float_bits, self.name, key, i)
-                    self.assign_and_kout(self.float_type, 'capn_to_f{:d}(capn_get{:d}({}.{}, {:d}))'.format(self.float_bits, self.float_bits, self.name, key, i))
+                    self.enc_buf += "capn_set{:d}({}.{}, {:d}, capn_from_f{:d}({:f}));\n".format(
+                        self.float_bits, self.name, key, i, self.float_bits, elem
+                    )
+                    self.dec_buf += "kout << capn_to_f{:d}(capn_get{:d}({}.{}, {:d}));\n".format(
+                        self.float_bits, self.float_bits, self.name, key, i
+                    )
+                    self.assign_and_kout(
+                        self.float_type,
+                        "capn_to_f{:d}(capn_get{:d}({}.{}, {:d}))".format(
+                            self.float_bits, self.float_bits, self.name, key, i
+                        ),
+                    )
             else:
-                self.enc_buf += self.add_transition('{}.{} = capn_new_list{:d}(cs, {:d});\n'.format(
-                    self.name, key, self.int_bits, len(value)), [len(value)])
+                self.enc_buf += self.add_transition(
+                    "{}.{} = capn_new_list{:d}(cs, {:d});\n".format(
+                        self.name, key, self.int_bits, len(value)
+                    ),
+                    [len(value)],
+                )
                 for i, elem in enumerate(value):
-                    self.enc_buf += 'capn_set{:d}({}.{}, {:d}, {:d});\n'.format(
-                        self.int_bits, self.name, key, i, elem)
-                    self.dec_buf += 'kout << capn_get{:d}({}.{}, {:d});\n'.format(
-                        self.int_bits, self.name, key, i)
-                    self.assign_and_kout(self.int_type, 'capn_get{:d}({}.{}, {:d})'.format(self.int_bits, self.name, key, i))
+                    self.enc_buf += "capn_set{:d}({}.{}, {:d}, {:d});\n".format(
+                        self.int_bits, self.name, key, i, elem
+                    )
+                    self.dec_buf += "kout << capn_get{:d}({}.{}, {:d});\n".format(
+                        self.int_bits, self.name, key, i
+                    )
+                    self.assign_and_kout(
+                        self.int_type,
+                        "capn_get{:d}({}.{}, {:d})".format(
+                            self.int_bits, self.name, key, i
+                        ),
+                    )
         elif fieldtype == float:
-            self.enc_buf += '{}.{} = {};\n\n'.format(self.name, key, value)
-            self.dec_buf += 'kout << {}.{};\n'.format(self.name, key)
-            self.assign_and_kout(self.float_type, '{}.{}'.format(self.name, key))
+            self.enc_buf += "{}.{} = {};\n\n".format(self.name, key, value)
+            self.dec_buf += "kout << {}.{};\n".format(self.name, key)
+            self.assign_and_kout(self.float_type, "{}.{}".format(self.name, key))
 
         elif fieldtype == int:
-            self.enc_buf += '{}.{} = {};\n\n'.format(self.name, key, value)
-            self.dec_buf += 'kout << {}.{};\n'.format(self.name, key)
-            self.assign_and_kout(self.int_type, '{}.{}'.format(self.name, key))
+            self.enc_buf += "{}.{} = {};\n\n".format(self.name, key, value)
+            self.dec_buf += "kout << {}.{};\n".format(self.name, key)
+            self.assign_and_kout(self.int_type, "{}.{}".format(self.name, key))
 
         else:
             self.note_unsupported(value)
 
     def add_to_dict(self, key, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
+            if len(value) and value[0] == "$":
                 self.add_field(int, key, value[1:])
             else:
                 self.add_field(str, key, value)
@@ -555,19 +669,35 @@ class CapnProtoC(DummyProtocol):
         elif type(value) == dict:
             trail = list(self.trail)
             trail.append(key)
-            nested = CapnProtoC(value, trail = trail, int_type = self.int_type, float_type = self.float_type, dec_index = self.dec_index)
+            nested = CapnProtoC(
+                value,
+                trail=trail,
+                int_type=self.int_type,
+                float_type=self.float_type,
+                dec_index=self.dec_index,
+            )
             self.add_field(dict, key, value)
-            self.enc_buf += '{}.{} = new_{}_{}(cs);\n'.format(
-                self.name, key, self.trail_name, key.capitalize())
+            self.enc_buf += "{}.{} = new_{}_{}(cs);\n".format(
+                self.name, key, self.trail_name, key.capitalize()
+            )
             self.enc_buf += nested.enc_buf
-            self.enc_buf += 'write_{}_{}(&{}, {}.{});\n'.format(
-                self.trail_name, key.capitalize(), key, self.name, key)
-            self.dec_buf += 'struct {}_{} {};\n'.format(self.trail_name, key.capitalize(), key)
-            self.dec_buf += 'read_{}_{}(&{}, {}.{});\n'.format(self.trail_name, key.capitalize(), key, self.name, key)
+            self.enc_buf += "write_{}_{}(&{}, {}.{});\n".format(
+                self.trail_name, key.capitalize(), key, self.name, key
+            )
+            self.dec_buf += "struct {}_{} {};\n".format(
+                self.trail_name, key.capitalize(), key
+            )
+            self.dec_buf += "read_{}_{}(&{}, {}.{});\n".format(
+                self.trail_name, key.capitalize(), key, self.name, key
+            )
             self.dec_buf += nested.dec_buf
             self.dec_buf0 += nested.dec_buf0
-            self.dec_buf1 += 'struct {}_{} {};\n'.format(self.trail_name, key.capitalize(), key)
-            self.dec_buf1 += 'read_{}_{}(&{}, {}.{});\n'.format(self.trail_name, key.capitalize(), key, self.name, key)
+            self.dec_buf1 += "struct {}_{} {};\n".format(
+                self.trail_name, key.capitalize(), key
+            )
+            self.dec_buf1 += "read_{}_{}(&{}, {}.{});\n".format(
+                self.trail_name, key.capitalize(), key, self.name, key
+            )
             self.dec_buf1 += nested.dec_buf1
             self.dec_buf2 += nested.dec_buf2
             self.dec_index = nested.dec_index
@@ -576,19 +706,19 @@ class CapnProtoC(DummyProtocol):
             self.add_field(type(value), key, value)
 
     def from_json(self, data):
-        self.proto_buf += 'struct {} {{\n'.format(self.name.capitalize())
+        self.proto_buf += "struct {} {{\n".format(self.name.capitalize())
         if type(data) == dict:
             for key in sorted(data.keys()):
                 self.add_to_dict(key, data[key])
-        self.proto_buf += '}\n'
+        self.proto_buf += "}\n"
 
-class ManualJSON(DummyProtocol):
 
+class ManualJSON(DummyProtocol):
     def __init__(self, data):
         super().__init__()
         self.data = data
         self.max_serialized_bytes = self.get_serialized_length() + 2
-        self.buf = 'BufferOutput<> bout(buf);\n'
+        self.buf = "BufferOutput<> bout(buf);\n"
         self.buf += 'bout << "{";\n'
         self.from_json(data)
         self.buf += 'bout << "}";\n'
@@ -603,23 +733,25 @@ class ManualJSON(DummyProtocol):
         return True
 
     def get_buffer_declaration(self):
-        return 'char buf[{:d}];\n'.format(self.max_serialized_bytes);
+        return "char buf[{:d}];\n".format(self.max_serialized_bytes)
 
     def get_buffer_name(self):
-        return 'buf'
+        return "buf"
 
     def get_encode(self):
         return self.buf
 
     def get_length_var(self):
-        return 'bout.size()'
+        return "bout.size()"
 
     def add_to_list(self, value, is_last):
         if type(value) == str:
-            if len(value) and value[0] == '$':
-                self.buf += 'bout << dec << {}'.format(value[1:])
+            if len(value) and value[0] == "$":
+                self.buf += "bout << dec << {}".format(value[1:])
             else:
-                self.buf += self.add_transition('bout << "\\"{}\\""'.format(value), [len(value)])
+                self.buf += self.add_transition(
+                    'bout << "\\"{}\\""'.format(value), [len(value)]
+                )
 
         elif type(value) == list:
             self.buf += 'bout << "[";\n'
@@ -632,36 +764,48 @@ class ManualJSON(DummyProtocol):
             self.buf += 'bout << "}"'
 
         else:
-            self.buf += 'bout << {}'.format(value)
+            self.buf += "bout << {}".format(value)
 
         if is_last:
-            self.buf += ';\n';
+            self.buf += ";\n"
         else:
             self.buf += ' << ",";\n'
 
     def add_to_dict(self, key, value, is_last):
         if type(value) == str:
-            if len(value) and value[0] == '$':
-                self.buf += self.add_transition('bout << "\\"{}\\":" << dec << {}'.format(key, value[1:]), [len(key)])
+            if len(value) and value[0] == "$":
+                self.buf += self.add_transition(
+                    'bout << "\\"{}\\":" << dec << {}'.format(key, value[1:]),
+                    [len(key)],
+                )
             else:
-                self.buf += self.add_transition('bout << "\\"{}\\":\\"{}\\""'.format(key, value), [len(key), len(value)])
+                self.buf += self.add_transition(
+                    'bout << "\\"{}\\":\\"{}\\""'.format(key, value),
+                    [len(key), len(value)],
+                )
 
         elif type(value) == list:
-            self.buf += self.add_transition('bout << "\\"{}\\":[";\n'.format(key), [len(key)])
+            self.buf += self.add_transition(
+                'bout << "\\"{}\\":[";\n'.format(key), [len(key)]
+            )
             self.from_json(value)
             self.buf += 'bout << "]"'
 
         elif type(value) == dict:
             # '{{' is an escaped '{' character
-            self.buf += self.add_transition('bout << "\\"{}\\":{{";\n'.format(key), [len(key)])
+            self.buf += self.add_transition(
+                'bout << "\\"{}\\":{{";\n'.format(key), [len(key)]
+            )
             self.from_json(value)
             self.buf += 'bout << "}"'
 
         else:
-            self.buf += self.add_transition('bout << "\\"{}\\":" << {}'.format(key, value), [len(key)])
+            self.buf += self.add_transition(
+                'bout << "\\"{}\\":" << {}'.format(key, value), [len(key)]
+            )
 
         if is_last:
-            self.buf += ';\n'
+            self.buf += ";\n"
         else:
             self.buf += ' << ",";\n'
 
@@ -674,74 +818,74 @@ class ManualJSON(DummyProtocol):
             for i, elem in enumerate(data):
                 self.add_to_list(elem, i == len(data) - 1)
 
-class ModernJSON(DummyProtocol):
 
-    def __init__(self, data, output_format = 'json'):
+class ModernJSON(DummyProtocol):
+    def __init__(self, data, output_format="json"):
         super().__init__()
         self.data = data
         self.output_format = output_format
-        self.buf = 'nlohmann::json js;\n'
+        self.buf = "nlohmann::json js;\n"
         self.from_json(data)
 
     def is_ascii(self):
-        if self.output_format == 'json':
+        if self.output_format == "json":
             return True
         return False
 
     def get_buffer_name(self):
-        return 'out'
+        return "out"
 
     def get_encode(self):
         return self.buf
 
     def get_serialize(self):
-        if self.output_format == 'json':
-            return 'std::string out = js.dump();\n'
-        elif self.output_format == 'bson':
-            return 'std::vector<std::uint8_t> out = nlohmann::json::to_bson(js);\n'
-        elif self.output_format == 'cbor':
-            return 'std::vector<std::uint8_t> out = nlohmann::json::to_cbor(js);\n'
-        elif self.output_format == 'msgpack':
-            return 'std::vector<std::uint8_t> out = nlohmann::json::to_msgpack(js);\n'
-        elif self.output_format == 'ubjson':
-            return 'std::vector<std::uint8_t> out = nlohmann::json::to_ubjson(js);\n'
+        if self.output_format == "json":
+            return "std::string out = js.dump();\n"
+        elif self.output_format == "bson":
+            return "std::vector<std::uint8_t> out = nlohmann::json::to_bson(js);\n"
+        elif self.output_format == "cbor":
+            return "std::vector<std::uint8_t> out = nlohmann::json::to_cbor(js);\n"
+        elif self.output_format == "msgpack":
+            return "std::vector<std::uint8_t> out = nlohmann::json::to_msgpack(js);\n"
+        elif self.output_format == "ubjson":
+            return "std::vector<std::uint8_t> out = nlohmann::json::to_ubjson(js);\n"
         else:
-            raise ValueError('invalid output format {}'.format(self.output_format))
+            raise ValueError("invalid output format {}".format(self.output_format))
 
     def get_serialized_length(self):
-        if self.output_format == 'json':
+        if self.output_format == "json":
             return len(json.dumps(self.data))
-        elif self.output_format == 'bson':
+        elif self.output_format == "bson":
             return len(bson.BSON.encode(self.data))
-        elif self.output_format == 'cbor':
+        elif self.output_format == "cbor":
             return len(cbor.dumps(self.data))
-        elif self.output_format == 'msgpack':
+        elif self.output_format == "msgpack":
             return len(msgpack.dumps(self.data))
-        elif self.output_format == 'ubjson':
+        elif self.output_format == "ubjson":
             return len(ubjson.dumpb(self.data))
         else:
-            raise ValueError('invalid output format {}'.format(self.output_format))
+            raise ValueError("invalid output format {}".format(self.output_format))
 
     def can_get_serialized_length(self):
         return True
 
     def get_length_var(self):
-        return 'out.size()'
+        return "out.size()"
 
     def add_to_list(self, prefix, index, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
+            if len(value) and value[0] == "$":
                 self.buf += value[1:]
-                self.buf += '{}[{:d}] = {};\n'.format(prefix, index, value[1:])
+                self.buf += "{}[{:d}] = {};\n".format(prefix, index, value[1:])
             else:
                 self.buf += '{}[{:d}] = "{}";\n'.format(prefix, index, value)
 
         else:
-            self.buf += '{}[{:d}] = {};\n'.format(prefix, index, value)
+            self.buf += "{}[{:d}] = {};\n".format(prefix, index, value)
 
     def add_to_dict(self, prefix, key, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
+            if len(value) and value[0] == "$":
                 self.buf += '{}["{}"] = {};\n'.format(prefix, key, value[1:])
             else:
                 self.buf += '{}["{}"] = "{}";\n'.format(prefix, key, value)
@@ -755,7 +899,7 @@ class ModernJSON(DummyProtocol):
         else:
             self.buf += '{}["{}"] = {};\n'.format(prefix, key, value)
 
-    def from_json(self, data, prefix = 'js'):
+    def from_json(self, data, prefix="js"):
         if type(data) == dict:
             for key in sorted(data.keys()):
                 self.add_to_dict(prefix, key, data[key])
@@ -763,17 +907,20 @@ class ModernJSON(DummyProtocol):
             for i, elem in enumerate(data):
                 self.add_to_list(prefix, i, elem)
 
-class MPack(DummyProtocol):
 
-    def __init__(self, data, int_type = 'uint16_t', float_type = 'float'):
+class MPack(DummyProtocol):
+    def __init__(self, data, int_type="uint16_t", float_type="float"):
         super().__init__()
         self.data = data
         self.max_serialized_bytes = self.get_serialized_length() + 2
         self.int_type = int_type
         self.float_type = float_type
-        self.enc_buf += 'mpack_writer_t writer;\n'
-        self.enc_buf += self.add_transition('mpack_writer_init(&writer, buf, sizeof(buf));\n', [self.max_serialized_bytes])
-        self.dec_buf0 += 'char strbuf[16];\n'
+        self.enc_buf += "mpack_writer_t writer;\n"
+        self.enc_buf += self.add_transition(
+            "mpack_writer_init(&writer, buf, sizeof(buf));\n",
+            [self.max_serialized_bytes],
+        )
+        self.dec_buf0 += "char strbuf[16];\n"
         self.from_json(data)
 
     def get_serialized_length(self):
@@ -786,34 +933,37 @@ class MPack(DummyProtocol):
         return False
 
     def get_buffer_declaration(self):
-        ret = 'char buf[{:d}];\n'.format(self.max_serialized_bytes)
-        ret += 'uint16_t serialized_size;\n'
+        ret = "char buf[{:d}];\n".format(self.max_serialized_bytes)
+        ret += "uint16_t serialized_size;\n"
         return ret
 
     def get_buffer_name(self):
-        return 'buf'
+        return "buf"
 
     def get_encode(self):
         return self.enc_buf
 
     def get_serialize(self):
-        ret = 'serialized_size = mpack_writer_buffer_used(&writer);\n'
+        ret = "serialized_size = mpack_writer_buffer_used(&writer);\n"
         # OptionalTimingAnalysis and other wrappers only wrap lines ending with a ;
         # We therefore deliberately do not use proper if { ... } syntax here
         # to make sure that these two statements are timed as well.
-        ret += 'if (mpack_writer_destroy(&writer) != mpack_ok) '
+        ret += "if (mpack_writer_destroy(&writer) != mpack_ok) "
         ret += 'kout << "Encoding failed" << endl;\n'
         return ret
 
     def get_deserialize(self):
-        ret = 'mpack_reader_t reader;\n'
-        ret += self.add_transition('mpack_reader_init_data(&reader, buf, serialized_size);\n', [self.max_serialized_bytes])
+        ret = "mpack_reader_t reader;\n"
+        ret += self.add_transition(
+            "mpack_reader_init_data(&reader, buf, serialized_size);\n",
+            [self.max_serialized_bytes],
+        )
         return ret
 
     def get_decode_and_output(self):
         ret = 'kout << dec << "dec:";\n'
-        ret += 'char strbuf[16];\n'
-        return ret + self.dec_buf + 'kout << endl;\n'
+        ret += "char strbuf[16];\n"
+        return ret + self.dec_buf + "kout << endl;\n"
 
     def get_decode_vars(self):
         return self.dec_buf0
@@ -822,65 +972,99 @@ class MPack(DummyProtocol):
         return self.dec_buf1
 
     def get_decode_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf2 + 'kout << endl;\n';
+        return 'kout << dec << "dec:";\n' + self.dec_buf2 + "kout << endl;\n"
 
     def get_length_var(self):
-        return 'serialized_size'
+        return "serialized_size"
 
     def add_value(self, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
-                self.enc_buf += 'mpack_write(&writer, {});\n'.format(value[1:])
-                self.dec_buf += 'kout << mpack_expect_uint(&reader);\n'
-                self.assign_and_kout(self.int_type, 'mpack_expect_uint(&reader)')
+            if len(value) and value[0] == "$":
+                self.enc_buf += "mpack_write(&writer, {});\n".format(value[1:])
+                self.dec_buf += "kout << mpack_expect_uint(&reader);\n"
+                self.assign_and_kout(self.int_type, "mpack_expect_uint(&reader)")
             else:
-                self.enc_buf += self.add_transition('mpack_write_cstr_or_nil(&writer, "{}");\n'.format(value), [len(value)])
-                self.dec_buf += 'mpack_expect_cstr(&reader, strbuf, sizeof(strbuf));\n'
-                self.dec_buf += 'kout << strbuf;\n'
-                self.dec_buf1 += self.add_transition('mpack_expect_cstr(&reader, strbuf, sizeof(strbuf));\n', [len(value)])
-                self.dec_buf2 += 'kout << strbuf;\n'
+                self.enc_buf += self.add_transition(
+                    'mpack_write_cstr_or_nil(&writer, "{}");\n'.format(value),
+                    [len(value)],
+                )
+                self.dec_buf += "mpack_expect_cstr(&reader, strbuf, sizeof(strbuf));\n"
+                self.dec_buf += "kout << strbuf;\n"
+                self.dec_buf1 += self.add_transition(
+                    "mpack_expect_cstr(&reader, strbuf, sizeof(strbuf));\n",
+                    [len(value)],
+                )
+                self.dec_buf2 += "kout << strbuf;\n"
         elif type(value) == list:
             self.from_json(value)
         elif type(value) == dict:
             self.from_json(value)
         elif type(value) == int:
-            self.enc_buf += 'mpack_write(&writer, ({}){:d});\n'.format(self.int_type, value)
-            self.dec_buf += 'kout << mpack_expect_uint(&reader);\n'
-            self.assign_and_kout(self.int_type, 'mpack_expect_uint(&reader)')
+            self.enc_buf += "mpack_write(&writer, ({}){:d});\n".format(
+                self.int_type, value
+            )
+            self.dec_buf += "kout << mpack_expect_uint(&reader);\n"
+            self.assign_and_kout(self.int_type, "mpack_expect_uint(&reader)")
         elif type(value) == float:
-            self.enc_buf += 'mpack_write(&writer, ({}){:f});\n'.format(self.float_type, value)
-            self.dec_buf += 'kout << mpack_expect_float(&reader);\n'
-            self.assign_and_kout(self.float_type, 'mpack_expect_float(&reader)')
+            self.enc_buf += "mpack_write(&writer, ({}){:f});\n".format(
+                self.float_type, value
+            )
+            self.dec_buf += "kout << mpack_expect_float(&reader);\n"
+            self.assign_and_kout(self.float_type, "mpack_expect_float(&reader)")
         else:
             self.note_unsupported(value)
 
     def from_json(self, data):
         if type(data) == dict:
-            self.enc_buf += self.add_transition('mpack_start_map(&writer, {:d});\n'.format(len(data)), [len(data)])
-            self.dec_buf += 'mpack_expect_map_max(&reader, {:d});\n'.format(len(data))
-            self.dec_buf1 += self.add_transition('mpack_expect_map_max(&reader, {:d});\n'.format(len(data)), [len(data)])
+            self.enc_buf += self.add_transition(
+                "mpack_start_map(&writer, {:d});\n".format(len(data)), [len(data)]
+            )
+            self.dec_buf += "mpack_expect_map_max(&reader, {:d});\n".format(len(data))
+            self.dec_buf1 += self.add_transition(
+                "mpack_expect_map_max(&reader, {:d});\n".format(len(data)), [len(data)]
+            )
             for key in sorted(data.keys()):
-                self.enc_buf += self.add_transition('mpack_write_cstr(&writer, "{}");\n'.format(key), [len(key)])
+                self.enc_buf += self.add_transition(
+                    'mpack_write_cstr(&writer, "{}");\n'.format(key), [len(key)]
+                )
                 self.dec_buf += 'mpack_expect_cstr_match(&reader, "{}");\n'.format(key)
-                self.dec_buf1 += self.add_transition('mpack_expect_cstr_match(&reader, "{}");\n'.format(key), [len(key)])
+                self.dec_buf1 += self.add_transition(
+                    'mpack_expect_cstr_match(&reader, "{}");\n'.format(key), [len(key)]
+                )
                 self.add_value(data[key])
-            self.enc_buf += 'mpack_finish_map(&writer);\n'
-            self.dec_buf += 'mpack_done_map(&reader);\n'
-            self.dec_buf1 += 'mpack_done_map(&reader);\n'
+            self.enc_buf += "mpack_finish_map(&writer);\n"
+            self.dec_buf += "mpack_done_map(&reader);\n"
+            self.dec_buf1 += "mpack_done_map(&reader);\n"
         if type(data) == list:
-            self.enc_buf += self.add_transition('mpack_start_array(&writer, {:d});\n'.format(len(data)), [len(data)])
-            self.dec_buf += 'mpack_expect_array_max(&reader, {:d});\n'.format(len(data))
-            self.dec_buf1 += self.add_transition('mpack_expect_array_max(&reader, {:d});\n'.format(len(data)), [len(data)])
+            self.enc_buf += self.add_transition(
+                "mpack_start_array(&writer, {:d});\n".format(len(data)), [len(data)]
+            )
+            self.dec_buf += "mpack_expect_array_max(&reader, {:d});\n".format(len(data))
+            self.dec_buf1 += self.add_transition(
+                "mpack_expect_array_max(&reader, {:d});\n".format(len(data)),
+                [len(data)],
+            )
             for elem in data:
-                self.add_value(elem);
-            self.enc_buf += 'mpack_finish_array(&writer);\n'
-            self.dec_buf += 'mpack_done_array(&reader);\n'
-            self.dec_buf1 += 'mpack_done_array(&reader);\n'
+                self.add_value(elem)
+            self.enc_buf += "mpack_finish_array(&writer);\n"
+            self.dec_buf += "mpack_done_array(&reader);\n"
+            self.dec_buf1 += "mpack_done_array(&reader);\n"
 
-class NanoPB(DummyProtocol):
 
-    def __init__(self, data, max_serialized_bytes = 256, cardinality = 'required', use_maps = False, max_string_length = None, cc_prefix = '', name = 'Benchmark',
-        int_type = 'uint16_t', float_type = 'float', dec_index = 0):
+class NanoPB(DummyProtocol):
+    def __init__(
+        self,
+        data,
+        max_serialized_bytes=256,
+        cardinality="required",
+        use_maps=False,
+        max_string_length=None,
+        cc_prefix="",
+        name="Benchmark",
+        int_type="uint16_t",
+        float_type="float",
+        dec_index=0,
+    ):
         super().__init__()
         self.data = data
         self.max_serialized_bytes = max_serialized_bytes
@@ -895,60 +1079,62 @@ class NanoPB(DummyProtocol):
         self.proto_float_type = self.float_type_to_proto_type(float_type)
         self.dec_index = dec_index
         self.fieldnum = 1
-        self.proto_head = 'syntax = "proto2";\nimport "src/app/prototest/nanopb.proto";\n\n'
-        self.proto_fields = ''
-        self.proto_options = ''
+        self.proto_head = (
+            'syntax = "proto2";\nimport "src/app/prototest/nanopb.proto";\n\n'
+        )
+        self.proto_fields = ""
+        self.proto_options = ""
         self.sub_protos = []
-        self.cc_encoders = ''
+        self.cc_encoders = ""
         self.from_json(data)
 
     def is_ascii(self):
         return False
 
     def int_type_to_proto_type(self, int_type):
-        sign = 'u'
-        if int_type[0] != 'u':
-            sign = ''
-        if '64' in int_type:
+        sign = "u"
+        if int_type[0] != "u":
+            sign = ""
+        if "64" in int_type:
             self.int_bits = 64
-            return sign + 'int64'
+            return sign + "int64"
         # Protocol Buffers only have 32 and 64 bit integers, so we default to 32
         self.int_bits = 32
-        return sign + 'int32'
+        return sign + "int32"
 
     def float_type_to_proto_type(self, float_type):
-        if float_type == 'float':
+        if float_type == "float":
             self.float_bits = 32
         else:
             self.float_bits = 64
         return float_type
 
     def get_buffer_declaration(self):
-        ret = 'uint8_t buf[{:d}];\n'.format(self.max_serialized_bytes)
-        ret += 'uint16_t serialized_size;\n'
+        ret = "uint8_t buf[{:d}];\n".format(self.max_serialized_bytes)
+        ret += "uint16_t serialized_size;\n"
         return ret + self.get_cc_functions()
 
     def get_buffer_name(self):
-        return 'buf'
+        return "buf"
 
     def get_serialize(self):
-        ret = 'pb_ostream_t stream = pb_ostream_from_buffer(buf, sizeof(buf));\n'
-        ret += 'pb_encode(&stream, Benchmark_fields, &msg);\n'
-        ret += 'serialized_size = stream.bytes_written;\n'
+        ret = "pb_ostream_t stream = pb_ostream_from_buffer(buf, sizeof(buf));\n"
+        ret += "pb_encode(&stream, Benchmark_fields, &msg);\n"
+        ret += "serialized_size = stream.bytes_written;\n"
         return ret
 
     def get_deserialize(self):
-        ret = 'Benchmark msg = Benchmark_init_zero;\n'
-        ret += 'pb_istream_t stream = pb_istream_from_buffer(buf, serialized_size);\n'
+        ret = "Benchmark msg = Benchmark_init_zero;\n"
+        ret += "pb_istream_t stream = pb_istream_from_buffer(buf, serialized_size);\n"
         # OptionalTimingAnalysis and other wrappers only wrap lines ending with a ;
         # We therefore deliberately do not use proper if { ... } syntax here
         # to make sure that these two statements are timed as well.
-        ret += 'if (pb_decode(&stream, Benchmark_fields, &msg) == false) '
+        ret += "if (pb_decode(&stream, Benchmark_fields, &msg) == false) "
         ret += 'kout << "deserialized failed" << endl;\n'
         return ret
 
     def get_decode_and_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf + 'kout << endl;\n'
+        return 'kout << dec << "dec:";\n' + self.dec_buf + "kout << endl;\n"
 
     def get_decode_vars(self):
         return self.dec_buf0
@@ -957,100 +1143,133 @@ class NanoPB(DummyProtocol):
         return self.dec_buf1
 
     def get_decode_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf2 + 'kout << endl;\n';
+        return 'kout << dec << "dec:";\n' + self.dec_buf2 + "kout << endl;\n"
 
     def get_length_var(self):
-        return 'serialized_size'
+        return "serialized_size"
 
     def add_field(self, cardinality, fieldtype, key, value):
-        extra = ''
+        extra = ""
         texttype = self.proto_int_type
         dectype = self.int_type
         if fieldtype == str:
-            texttype = 'string'
+            texttype = "string"
         elif fieldtype == float:
             texttype = self.proto_float_type
             dectype = self.float_type
         elif fieldtype == dict:
             texttype = key.capitalize()
         if type(value) == list:
-            extra = '[(nanopb).max_count = {:d}]'.format(len(value))
-            self.enc_buf += 'msg.{}{}_count = {:d};\n'.format(self.cc_prefix, key, len(value))
-        self.proto_fields += '{} {} {} = {:d} {};\n'.format(
-            cardinality, texttype, key, self.fieldnum, extra)
+            extra = "[(nanopb).max_count = {:d}]".format(len(value))
+            self.enc_buf += "msg.{}{}_count = {:d};\n".format(
+                self.cc_prefix, key, len(value)
+            )
+        self.proto_fields += "{} {} {} = {:d} {};\n".format(
+            cardinality, texttype, key, self.fieldnum, extra
+        )
         self.fieldnum += 1
         if fieldtype == str:
-            if cardinality == 'optional':
-                self.enc_buf += 'msg.{}has_{} = true;\n'.format(self.cc_prefix, key)
+            if cardinality == "optional":
+                self.enc_buf += "msg.{}has_{} = true;\n".format(self.cc_prefix, key)
             if self.max_strlen:
-                self.proto_options += '{}.{} max_size:{:d}\n'.format(self.name, key, self.max_strlen)
+                self.proto_options += "{}.{} max_size:{:d}\n".format(
+                    self.name, key, self.max_strlen
+                )
                 i = -1
                 for i, character in enumerate(value):
-                    self.enc_buf += '''msg.{}{}[{:d}] = '{}';\n'''.format(self.cc_prefix, key, i, character)
-                self.enc_buf += 'msg.{}{}[{:d}] = 0;\n'.format(self.cc_prefix, key, i+1)
-                self.dec_buf += 'kout << msg.{}{};\n'.format(self.cc_prefix, key)
-                self.assign_and_kout('char *', 'msg.{}{}'.format(self.cc_prefix, key))
+                    self.enc_buf += """msg.{}{}[{:d}] = '{}';\n""".format(
+                        self.cc_prefix, key, i, character
+                    )
+                self.enc_buf += "msg.{}{}[{:d}] = 0;\n".format(
+                    self.cc_prefix, key, i + 1
+                )
+                self.dec_buf += "kout << msg.{}{};\n".format(self.cc_prefix, key)
+                self.assign_and_kout("char *", "msg.{}{}".format(self.cc_prefix, key))
             else:
-                self.cc_encoders += 'bool encode_{}(pb_ostream_t *stream, const pb_field_t *field, void * const *arg)\n'.format(key)
-                self.cc_encoders += '{\n'
-                self.cc_encoders += 'if (!pb_encode_tag_for_field(stream, field)) return false;\n'
-                self.cc_encoders += 'return pb_encode_string(stream, (uint8_t*)"{}", {:d});\n'.format(value, len(value))
-                self.cc_encoders += '}\n'
-                self.enc_buf += 'msg.{}{}.funcs.encode = encode_{};\n'.format(self.cc_prefix, key, key)
-                self.dec_buf += '// TODO decode string {}{} via callback\n'.format(self.cc_prefix, key)
-                self.dec_buf1 += '// TODO decode string {}{} via callback\n'.format(self.cc_prefix, key)
+                self.cc_encoders += "bool encode_{}(pb_ostream_t *stream, const pb_field_t *field, void * const *arg)\n".format(
+                    key
+                )
+                self.cc_encoders += "{\n"
+                self.cc_encoders += (
+                    "if (!pb_encode_tag_for_field(stream, field)) return false;\n"
+                )
+                self.cc_encoders += 'return pb_encode_string(stream, (uint8_t*)"{}", {:d});\n'.format(
+                    value, len(value)
+                )
+                self.cc_encoders += "}\n"
+                self.enc_buf += "msg.{}{}.funcs.encode = encode_{};\n".format(
+                    self.cc_prefix, key, key
+                )
+                self.dec_buf += "// TODO decode string {}{} via callback\n".format(
+                    self.cc_prefix, key
+                )
+                self.dec_buf1 += "// TODO decode string {}{} via callback\n".format(
+                    self.cc_prefix, key
+                )
         elif fieldtype == dict:
-            if cardinality == 'optional':
-                self.enc_buf += 'msg.{}has_{} = true;\n'.format(self.cc_prefix, key)
+            if cardinality == "optional":
+                self.enc_buf += "msg.{}has_{} = true;\n".format(self.cc_prefix, key)
             # The rest is handled recursively in add_to_dict
         elif type(value) == list:
             for i, elem in enumerate(value):
-                self.enc_buf += 'msg.{}{}[{:d}] = {};\n'.format(self.cc_prefix, key, i, elem)
-                self.dec_buf += 'kout << msg.{}{}[{:d}];\n'.format(self.cc_prefix, key, i)
+                self.enc_buf += "msg.{}{}[{:d}] = {};\n".format(
+                    self.cc_prefix, key, i, elem
+                )
+                self.dec_buf += "kout << msg.{}{}[{:d}];\n".format(
+                    self.cc_prefix, key, i
+                )
                 if fieldtype == float:
-                    self.assign_and_kout(self.float_type, 'msg.{}{}[{:d}]'.format(self.cc_prefix, key, i))
+                    self.assign_and_kout(
+                        self.float_type, "msg.{}{}[{:d}]".format(self.cc_prefix, key, i)
+                    )
                 elif fieldtype == int:
-                    self.assign_and_kout(self.int_type, 'msg.{}{}[{:d}]'.format(self.cc_prefix, key, i))
+                    self.assign_and_kout(
+                        self.int_type, "msg.{}{}[{:d}]".format(self.cc_prefix, key, i)
+                    )
         elif fieldtype == int:
-            if cardinality == 'optional':
-                self.enc_buf += 'msg.{}has_{} = true;\n'.format(self.cc_prefix, key)
-            self.enc_buf += 'msg.{}{} = {};\n'.format(self.cc_prefix, key, value)
-            self.dec_buf += 'kout << msg.{}{};\n'.format(self.cc_prefix, key)
-            self.assign_and_kout(self.int_type, 'msg.{}{}'.format(self.cc_prefix, key))
+            if cardinality == "optional":
+                self.enc_buf += "msg.{}has_{} = true;\n".format(self.cc_prefix, key)
+            self.enc_buf += "msg.{}{} = {};\n".format(self.cc_prefix, key, value)
+            self.dec_buf += "kout << msg.{}{};\n".format(self.cc_prefix, key)
+            self.assign_and_kout(self.int_type, "msg.{}{}".format(self.cc_prefix, key))
         elif fieldtype == float:
-            if cardinality == 'optional':
-                self.enc_buf += 'msg.{}has_{} = true;\n'.format(self.cc_prefix, key)
-            self.enc_buf += 'msg.{}{} = {};\n'.format(self.cc_prefix, key, value)
-            self.dec_buf += 'kout << msg.{}{};\n'.format(self.cc_prefix, key)
-            self.assign_and_kout(self.float_type, 'msg.{}{}'.format(self.cc_prefix, key))
+            if cardinality == "optional":
+                self.enc_buf += "msg.{}has_{} = true;\n".format(self.cc_prefix, key)
+            self.enc_buf += "msg.{}{} = {};\n".format(self.cc_prefix, key, value)
+            self.dec_buf += "kout << msg.{}{};\n".format(self.cc_prefix, key)
+            self.assign_and_kout(
+                self.float_type, "msg.{}{}".format(self.cc_prefix, key)
+            )
         elif fieldtype == dict:
-            if cardinality == 'optional':
-                self.enc_buf += 'msg.{}has_{} = true;\n'.format(self.cc_prefix, key)
-            self.enc_buf += 'msg.{}{} = {};\n'.format(self.cc_prefix, key, value)
-            self.dec_buf += 'kout << msg.{}{};\n'.format(self.cc_prefix, key)
-            self.assign_and_kout(self.float_type, 'msg.{}{}'.format(self.cc_prefix, key))
+            if cardinality == "optional":
+                self.enc_buf += "msg.{}has_{} = true;\n".format(self.cc_prefix, key)
+            self.enc_buf += "msg.{}{} = {};\n".format(self.cc_prefix, key, value)
+            self.dec_buf += "kout << msg.{}{};\n".format(self.cc_prefix, key)
+            self.assign_and_kout(
+                self.float_type, "msg.{}{}".format(self.cc_prefix, key)
+            )
         else:
             self.note_unsupported(value)
 
     def get_proto(self):
-        return self.proto_head + '\n\n'.join(self.get_message_definitions('Benchmark'))
+        return self.proto_head + "\n\n".join(self.get_message_definitions("Benchmark"))
 
     def get_proto_options(self):
         return self.proto_options
 
     def get_extra_files(self):
         return {
-            'nanopbbench.proto' : self.get_proto(),
-            'nanopbbench.options' : self.get_proto_options()
+            "nanopbbench.proto": self.get_proto(),
+            "nanopbbench.options": self.get_proto_options(),
         }
 
     def get_message_definitions(self, msgname):
         ret = list(self.sub_protos)
-        ret.append('message {} {{\n'.format(msgname) + self.proto_fields + '}\n')
+        ret.append("message {} {{\n".format(msgname) + self.proto_fields + "}\n")
         return ret
 
     def get_encode(self):
-        ret = 'Benchmark msg = Benchmark_init_zero;\n'
+        ret = "Benchmark msg = Benchmark_init_zero;\n"
         return ret + self.enc_buf
 
     def get_cc_functions(self):
@@ -1058,19 +1277,27 @@ class NanoPB(DummyProtocol):
 
     def add_to_dict(self, key, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
+            if len(value) and value[0] == "$":
                 self.add_field(self.cardinality, int, key, value[1:])
             else:
                 self.add_field(self.cardinality, str, key, value)
         elif type(value) == list:
-            self.add_field('repeated', type(value[0]), key, value)
+            self.add_field("repeated", type(value[0]), key, value)
         elif type(value) == dict:
             nested_proto = NanoPB(
-                value, max_string_length = self.max_strlen, cardinality = self.cardinality, use_maps = self.use_maps, cc_prefix =
-                '{}{}.'.format(self.cc_prefix, key), name = key.capitalize(),
-                int_type = self.int_type, float_type = self.float_type,
-                dec_index = self.dec_index)
-            self.sub_protos.extend(nested_proto.get_message_definitions(key.capitalize()))
+                value,
+                max_string_length=self.max_strlen,
+                cardinality=self.cardinality,
+                use_maps=self.use_maps,
+                cc_prefix="{}{}.".format(self.cc_prefix, key),
+                name=key.capitalize(),
+                int_type=self.int_type,
+                float_type=self.float_type,
+                dec_index=self.dec_index,
+            )
+            self.sub_protos.extend(
+                nested_proto.get_message_definitions(key.capitalize())
+            )
             self.proto_options += nested_proto.proto_options
             self.cc_encoders += nested_proto.cc_encoders
             self.add_field(self.cardinality, dict, key, value)
@@ -1089,19 +1316,21 @@ class NanoPB(DummyProtocol):
                 self.add_to_dict(key, data[key])
 
 
-
 class UBJ(DummyProtocol):
-
-    def __init__(self, data, max_serialized_bytes = 255, int_type = 'uint16_t', float_type = 'float'):
+    def __init__(
+        self, data, max_serialized_bytes=255, int_type="uint16_t", float_type="float"
+    ):
         super().__init__()
         self.data = data
         self.max_serialized_bytes = self.get_serialized_length() + 2
         self.int_type = int_type
         self.float_type = self.parse_float_type(float_type)
-        self.enc_buf += 'ubjw_context_t* ctx = ubjw_open_memory(buf, buf + sizeof(buf));\n'
-        self.enc_buf += 'ubjw_begin_object(ctx, UBJ_MIXED, 0);\n'
-        self.from_json('root', data)
-        self.enc_buf += 'ubjw_end(ctx);\n'
+        self.enc_buf += (
+            "ubjw_context_t* ctx = ubjw_open_memory(buf, buf + sizeof(buf));\n"
+        )
+        self.enc_buf += "ubjw_begin_object(ctx, UBJ_MIXED, 0);\n"
+        self.from_json("root", data)
+        self.enc_buf += "ubjw_end(ctx);\n"
 
     def get_serialized_length(self):
         return len(ubjson.dumpb(self.data))
@@ -1113,41 +1342,41 @@ class UBJ(DummyProtocol):
         return False
 
     def parse_float_type(self, float_type):
-        if float_type == 'float':
+        if float_type == "float":
             self.float_bits = 32
         else:
             self.float_bits = 64
         return float_type
 
     def get_buffer_declaration(self):
-        ret = 'uint8_t buf[{:d}];\n'.format(self.max_serialized_bytes)
-        ret += 'uint16_t serialized_size;\n'
+        ret = "uint8_t buf[{:d}];\n".format(self.max_serialized_bytes)
+        ret += "uint16_t serialized_size;\n"
         return ret
 
     def get_buffer_name(self):
-        return 'buf'
+        return "buf"
 
     def get_length_var(self):
-        return 'serialized_size'
+        return "serialized_size"
 
     def get_serialize(self):
-        return 'serialized_size = ubjw_close_context(ctx);\n'
+        return "serialized_size = ubjw_close_context(ctx);\n"
 
     def get_encode(self):
         return self.enc_buf
 
     def get_deserialize(self):
-        ret = 'ubjr_context_t* ctx = ubjr_open_memory(buf, buf + serialized_size);\n'
-        ret += 'ubjr_dynamic_t dynamic_root = ubjr_read_dynamic(ctx);\n'
-        ret += 'ubjr_dynamic_t* root_values = (ubjr_dynamic_t*)dynamic_root.container_object.values;\n'
+        ret = "ubjr_context_t* ctx = ubjr_open_memory(buf, buf + serialized_size);\n"
+        ret += "ubjr_dynamic_t dynamic_root = ubjr_read_dynamic(ctx);\n"
+        ret += "ubjr_dynamic_t* root_values = (ubjr_dynamic_t*)dynamic_root.container_object.values;\n"
         return ret
 
     def get_decode_and_output(self):
         ret = 'kout << dec << "dec:";\n'
         ret += self.dec_buf
-        ret += 'kout << endl;\n'
-        ret += 'ubjr_cleanup_dynamic(&dynamic_root);\n' # This causes the data (including all strings) to be free'd
-        ret += 'ubjr_close_context(ctx);\n'
+        ret += "kout << endl;\n"
+        ret += "ubjr_cleanup_dynamic(&dynamic_root);\n"  # This causes the data (including all strings) to be free'd
+        ret += "ubjr_close_context(ctx);\n"
         return ret
 
     def get_decode_vars(self):
@@ -1157,90 +1386,144 @@ class UBJ(DummyProtocol):
         return self.dec_buf1
 
     def get_decode_output(self):
-        ret =  'kout << dec << "dec:";\n' + self.dec_buf2 + 'kout << endl;\n'
-        ret += 'ubjr_cleanup_dynamic(&dynamic_root);\n'
-        ret += 'ubjr_close_context(ctx);\n'
+        ret = 'kout << dec << "dec:";\n' + self.dec_buf2 + "kout << endl;\n"
+        ret += "ubjr_cleanup_dynamic(&dynamic_root);\n"
+        ret += "ubjr_close_context(ctx);\n"
         return ret
 
     def add_to_list(self, root, index, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
-                self.enc_buf += 'ubjw_write_integer(ctx, {});\n'.format(value[1:])
-                self.dec_buf += 'kout << {}_values[{:d}].integer;\n'.format(root, index)
-                self.assign_and_kout(self.int_type, '{}_values[{:d}].integer'.format(root, index))
+            if len(value) and value[0] == "$":
+                self.enc_buf += "ubjw_write_integer(ctx, {});\n".format(value[1:])
+                self.dec_buf += "kout << {}_values[{:d}].integer;\n".format(root, index)
+                self.assign_and_kout(
+                    self.int_type, "{}_values[{:d}].integer".format(root, index)
+                )
             else:
-                self.enc_buf += self.add_transition('ubjw_write_string(ctx, "{}");\n'.format(value), [len(value)])
-                self.dec_buf += 'kout << {}_values[{:d}].string;\n'.format(root, index)
-                self.assign_and_kout('char *', '{}_values[{:d}].string'.format(root, index))
+                self.enc_buf += self.add_transition(
+                    'ubjw_write_string(ctx, "{}");\n'.format(value), [len(value)]
+                )
+                self.dec_buf += "kout << {}_values[{:d}].string;\n".format(root, index)
+                self.assign_and_kout(
+                    "char *", "{}_values[{:d}].string".format(root, index)
+                )
 
         elif type(value) == list:
-            self.enc_buf += 'ubjw_begin_array(ctx, UBJ_MIXED, 0);\n'
-            self.dec_buf += '// decoding nested lists is not supported\n'
-            self.dec_buf1 += '// decoding nested lists is not supported\n'
+            self.enc_buf += "ubjw_begin_array(ctx, UBJ_MIXED, 0);\n"
+            self.dec_buf += "// decoding nested lists is not supported\n"
+            self.dec_buf1 += "// decoding nested lists is not supported\n"
             self.from_json(root, value)
-            self.enc_buf += 'ubjw_end(ctx);\n'
+            self.enc_buf += "ubjw_end(ctx);\n"
 
         elif type(value) == dict:
-            self.enc_buf += 'ubjw_begin_object(ctx, UBJ_MIXED, 0);\n'
-            self.dec_buf += '// decoding objects in lists is not supported\n'
-            self.dec_buf1 += '// decoding objects in lists is not supported\n'
+            self.enc_buf += "ubjw_begin_object(ctx, UBJ_MIXED, 0);\n"
+            self.dec_buf += "// decoding objects in lists is not supported\n"
+            self.dec_buf1 += "// decoding objects in lists is not supported\n"
             self.from_json(root, value)
-            self.enc_buf += 'ubjw_end(ctx);\n'
+            self.enc_buf += "ubjw_end(ctx);\n"
 
         elif type(value) == float:
-            self.enc_buf += 'ubjw_write_float{:d}(ctx, {});\n'.format(self.float_bits, value)
-            self.dec_buf += 'kout << {}_values[{:d}].real;\n'.format(root, index)
-            self.assign_and_kout(self.float_type, '{}_values[{:d}].real'.format(root, index))
+            self.enc_buf += "ubjw_write_float{:d}(ctx, {});\n".format(
+                self.float_bits, value
+            )
+            self.dec_buf += "kout << {}_values[{:d}].real;\n".format(root, index)
+            self.assign_and_kout(
+                self.float_type, "{}_values[{:d}].real".format(root, index)
+            )
 
         elif type(value) == int:
-            self.enc_buf += 'ubjw_write_integer(ctx, {});\n'.format(value)
-            self.dec_buf += 'kout << {}_values[{:d}].integer;\n'.format(root, index)
-            self.assign_and_kout(self.int_type, '{}_values[{:d}].integer'.format(root, index))
+            self.enc_buf += "ubjw_write_integer(ctx, {});\n".format(value)
+            self.dec_buf += "kout << {}_values[{:d}].integer;\n".format(root, index)
+            self.assign_and_kout(
+                self.int_type, "{}_values[{:d}].integer".format(root, index)
+            )
 
         else:
-            raise TypeError('Cannot handle {} of type {}'.format(value, type(value)))
+            raise TypeError("Cannot handle {} of type {}".format(value, type(value)))
 
     def add_to_dict(self, root, index, key, value):
         if type(value) == str:
-            if len(value) and value[0] == '$':
-                self.enc_buf += self.add_transition('ubjw_write_key(ctx, "{}"); ubjw_write_integer(ctx, {});\n'.format(key, value[1:]), [len(key)])
-                self.dec_buf += 'kout << {}_values[{:d}].integer;\n'.format(root, index)
-                self.assign_and_kout(self.int_type, '{}_values[{:d}].integer'.format(root, index))
+            if len(value) and value[0] == "$":
+                self.enc_buf += self.add_transition(
+                    'ubjw_write_key(ctx, "{}"); ubjw_write_integer(ctx, {});\n'.format(
+                        key, value[1:]
+                    ),
+                    [len(key)],
+                )
+                self.dec_buf += "kout << {}_values[{:d}].integer;\n".format(root, index)
+                self.assign_and_kout(
+                    self.int_type, "{}_values[{:d}].integer".format(root, index)
+                )
             else:
-                self.enc_buf += self.add_transition('ubjw_write_key(ctx, "{}"); ubjw_write_string(ctx, "{}");\n'.format(key, value), [len(key), len(value)])
-                self.dec_buf += 'kout << {}_values[{:d}].string;\n'.format(root, index)
-                self.assign_and_kout('char *', '{}_values[{:d}].string'.format(root, index))
+                self.enc_buf += self.add_transition(
+                    'ubjw_write_key(ctx, "{}"); ubjw_write_string(ctx, "{}");\n'.format(
+                        key, value
+                    ),
+                    [len(key), len(value)],
+                )
+                self.dec_buf += "kout << {}_values[{:d}].string;\n".format(root, index)
+                self.assign_and_kout(
+                    "char *", "{}_values[{:d}].string".format(root, index)
+                )
 
         elif type(value) == list:
-            self.enc_buf += self.add_transition('ubjw_write_key(ctx, "{}"); ubjw_begin_array(ctx, UBJ_MIXED, 0);\n'.format(key), [len(key)])
-            self.dec_buf += 'ubjr_dynamic_t *{}_values = (ubjr_dynamic_t*){}_values[{:d}].container_array.values;\n'.format(
-                key, root, index)
-            self.dec_buf1 += 'ubjr_dynamic_t *{}_values = (ubjr_dynamic_t*){}_values[{:d}].container_array.values;\n'.format(
-                key, root, index)
+            self.enc_buf += self.add_transition(
+                'ubjw_write_key(ctx, "{}"); ubjw_begin_array(ctx, UBJ_MIXED, 0);\n'.format(
+                    key
+                ),
+                [len(key)],
+            )
+            self.dec_buf += "ubjr_dynamic_t *{}_values = (ubjr_dynamic_t*){}_values[{:d}].container_array.values;\n".format(
+                key, root, index
+            )
+            self.dec_buf1 += "ubjr_dynamic_t *{}_values = (ubjr_dynamic_t*){}_values[{:d}].container_array.values;\n".format(
+                key, root, index
+            )
             self.from_json(key, value)
-            self.enc_buf += 'ubjw_end(ctx);\n'
+            self.enc_buf += "ubjw_end(ctx);\n"
 
         elif type(value) == dict:
-            self.enc_buf += self.add_transition('ubjw_write_key(ctx, "{}"); ubjw_begin_object(ctx, UBJ_MIXED, 0);\n'.format(key), [len(key)])
-            self.dec_buf += 'ubjr_dynamic_t *{}_values = (ubjr_dynamic_t*){}_values[{:d}].container_object.values;\n'.format(
-                key, root, index)
-            self.dec_buf1 += 'ubjr_dynamic_t *{}_values = (ubjr_dynamic_t*){}_values[{:d}].container_object.values;\n'.format(
-                key, root, index)
+            self.enc_buf += self.add_transition(
+                'ubjw_write_key(ctx, "{}"); ubjw_begin_object(ctx, UBJ_MIXED, 0);\n'.format(
+                    key
+                ),
+                [len(key)],
+            )
+            self.dec_buf += "ubjr_dynamic_t *{}_values = (ubjr_dynamic_t*){}_values[{:d}].container_object.values;\n".format(
+                key, root, index
+            )
+            self.dec_buf1 += "ubjr_dynamic_t *{}_values = (ubjr_dynamic_t*){}_values[{:d}].container_object.values;\n".format(
+                key, root, index
+            )
             self.from_json(key, value)
-            self.enc_buf += 'ubjw_end(ctx);\n'
+            self.enc_buf += "ubjw_end(ctx);\n"
 
         elif type(value) == float:
-            self.enc_buf += self.add_transition('ubjw_write_key(ctx, "{}"); ubjw_write_float{:d}(ctx, {});\n'.format(key, self.float_bits, value), [len(key)])
-            self.dec_buf += 'kout << {}_values[{:d}].real;\n'.format(root, index)
-            self.assign_and_kout(self.float_type, '{}_values[{:d}].real'.format(root, index))
+            self.enc_buf += self.add_transition(
+                'ubjw_write_key(ctx, "{}"); ubjw_write_float{:d}(ctx, {});\n'.format(
+                    key, self.float_bits, value
+                ),
+                [len(key)],
+            )
+            self.dec_buf += "kout << {}_values[{:d}].real;\n".format(root, index)
+            self.assign_and_kout(
+                self.float_type, "{}_values[{:d}].real".format(root, index)
+            )
 
         elif type(value) == int:
-            self.enc_buf += self.add_transition('ubjw_write_key(ctx, "{}"); ubjw_write_integer(ctx, {});\n'.format(key, value), [len(key)])
-            self.dec_buf += 'kout << {}_values[{:d}].integer;\n'.format(root, index)
-            self.assign_and_kout(self.int_type, '{}_values[{:d}].integer'.format(root, index))
+            self.enc_buf += self.add_transition(
+                'ubjw_write_key(ctx, "{}"); ubjw_write_integer(ctx, {});\n'.format(
+                    key, value
+                ),
+                [len(key)],
+            )
+            self.dec_buf += "kout << {}_values[{:d}].integer;\n".format(root, index)
+            self.assign_and_kout(
+                self.int_type, "{}_values[{:d}].integer".format(root, index)
+            )
 
         else:
-            raise TypeError('Cannot handle {} of type {}'.format(value, type(value)))
+            raise TypeError("Cannot handle {} of type {}".format(value, type(value)))
 
     def from_json(self, root, data):
         if type(data) == dict:
@@ -1253,63 +1536,64 @@ class UBJ(DummyProtocol):
 
 
 class XDR(DummyProtocol):
-
-    def __init__(self, data, max_serialized_bytes = 256, int_type = 'uint16_t', float_type = 'float'):
+    def __init__(
+        self, data, max_serialized_bytes=256, int_type="uint16_t", float_type="float"
+    ):
         super().__init__()
         self.data = data
         self.max_serialized_bytes = 256
         self.enc_int_type = int_type
         self.dec_int_type = self.parse_int_type(int_type)
         self.float_type = self.parse_float_type(float_type)
-        self.enc_buf += 'XDRWriter xdrstream(buf);\n'
-        self.dec_buf += 'XDRReader xdrinput(buf);\n'
-        self.dec_buf0 += 'XDRReader xdrinput(buf);\n'
+        self.enc_buf += "XDRWriter xdrstream(buf);\n"
+        self.dec_buf += "XDRReader xdrinput(buf);\n"
+        self.dec_buf0 += "XDRReader xdrinput(buf);\n"
         # By default, XDR does not even include a version / protocol specifier.
         # This seems rather impractical -> emulate that here.
-        #self.enc_buf += 'xdrstream << (uint32_t)22075;\n'
-        self.dec_buf += 'char strbuf[16];\n'
-        #self.dec_buf += 'xdrinput.get_uint32();\n'
-        self.dec_buf0 += 'char strbuf[16];\n'
-        #self.dec_buf0 += 'xdrinput.get_uint32();\n'
+        # self.enc_buf += 'xdrstream << (uint32_t)22075;\n'
+        self.dec_buf += "char strbuf[16];\n"
+        # self.dec_buf += 'xdrinput.get_uint32();\n'
+        self.dec_buf0 += "char strbuf[16];\n"
+        # self.dec_buf0 += 'xdrinput.get_uint32();\n'
         self.from_json(data)
 
     def is_ascii(self):
         return False
 
     def parse_int_type(self, int_type):
-        sign = ''
-        if int_type[0] == 'u':
-            sign = 'u'
-        if '64' in int_type:
+        sign = ""
+        if int_type[0] == "u":
+            sign = "u"
+        if "64" in int_type:
             self.int_bits = 64
-            return sign + 'int64'
+            return sign + "int64"
         else:
             self.int_bits = 32
-            return sign + 'int32'
+            return sign + "int32"
 
     def parse_float_type(self, float_type):
-        if float_type == 'float':
+        if float_type == "float":
             self.float_bits = 32
         else:
             self.float_bits = 64
         return float_type
 
     def get_buffer_declaration(self):
-        ret = 'uint16_t serialized_size;\n'
-        ret += 'char buf[{:d}];\n'.format(self.max_serialized_bytes)
+        ret = "uint16_t serialized_size;\n"
+        ret += "char buf[{:d}];\n".format(self.max_serialized_bytes)
         return ret
 
     def get_buffer_name(self):
-        return 'buf'
+        return "buf"
 
     def get_length_var(self):
-        return 'xdrstream.size()'
+        return "xdrstream.size()"
 
     def get_encode(self):
         return self.enc_buf
 
     def get_decode_and_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf + 'kout << endl;\n'
+        return 'kout << dec << "dec:";\n' + self.dec_buf + "kout << endl;\n"
 
     def get_decode_vars(self):
         return self.dec_buf0
@@ -1318,115 +1602,129 @@ class XDR(DummyProtocol):
         return self.dec_buf1
 
     def get_decode_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf2 + 'kout << endl;\n'
+        return 'kout << dec << "dec:";\n' + self.dec_buf2 + "kout << endl;\n"
 
     def from_json(self, data):
         if type(data) == dict:
             for key in sorted(data.keys()):
                 self.from_json(data[key])
         elif type(data) == list:
-            self.enc_buf += 'xdrstream.setNextArrayLen({});\n'.format(len(data))
-            self.enc_buf += 'xdrstream.setVariableLength();\n'
-            self.enc_buf += 'xdrstream.startList();\n'
-            self.dec_buf += 'xdrinput.get_uint32();\n'
-            self.dec_buf1 += 'xdrinput.get_uint32();\n'
+            self.enc_buf += "xdrstream.setNextArrayLen({});\n".format(len(data))
+            self.enc_buf += "xdrstream.setVariableLength();\n"
+            self.enc_buf += "xdrstream.startList();\n"
+            self.dec_buf += "xdrinput.get_uint32();\n"
+            self.dec_buf1 += "xdrinput.get_uint32();\n"
             for elem in data:
                 self.from_json(elem)
         elif type(data) == str:
-            if len(data) and data[0] == '$':
-                self.enc_buf += 'xdrstream.put(({}){});\n'.format(self.enc_int_type, data[1:])
-                self.dec_buf += 'kout << xdrinput.get_{}();\n'.format(self.dec_int_type)
-                self.dec_buf0 += '{} dec_{};\n'.format(self.enc_int_type, self.dec_index)
-                self.dec_buf1 += 'dec_{} = xdrinput.get_{}();;\n'.format(self.dec_index, self.dec_int_type)
-                self.dec_buf2 += 'kout << dec_{};\n'.format(self.dec_index)
+            if len(data) and data[0] == "$":
+                self.enc_buf += "xdrstream.put(({}){});\n".format(
+                    self.enc_int_type, data[1:]
+                )
+                self.dec_buf += "kout << xdrinput.get_{}();\n".format(self.dec_int_type)
+                self.dec_buf0 += "{} dec_{};\n".format(
+                    self.enc_int_type, self.dec_index
+                )
+                self.dec_buf1 += "dec_{} = xdrinput.get_{}();;\n".format(
+                    self.dec_index, self.dec_int_type
+                )
+                self.dec_buf2 += "kout << dec_{};\n".format(self.dec_index)
             else:
                 # Kodierte Strings haben nicht immer ein Nullbyte am Ende
-                self.enc_buf += 'xdrstream.setNextArrayLen({});\n'.format(len(data))
-                self.enc_buf += 'xdrstream.setVariableLength();\n'
-                self.enc_buf += self.add_transition('xdrstream.put("{}");\n'.format(data), [len(data)])
-                self.dec_buf += 'xdrinput.get_string(strbuf);\n'
-                self.dec_buf += 'kout << strbuf;\n'
-                self.dec_buf1 += 'xdrinput.get_string(strbuf);\n'
-                self.dec_buf2 += 'kout << strbuf;\n'
+                self.enc_buf += "xdrstream.setNextArrayLen({});\n".format(len(data))
+                self.enc_buf += "xdrstream.setVariableLength();\n"
+                self.enc_buf += self.add_transition(
+                    'xdrstream.put("{}");\n'.format(data), [len(data)]
+                )
+                self.dec_buf += "xdrinput.get_string(strbuf);\n"
+                self.dec_buf += "kout << strbuf;\n"
+                self.dec_buf1 += "xdrinput.get_string(strbuf);\n"
+                self.dec_buf2 += "kout << strbuf;\n"
         elif type(data) == float:
-            self.enc_buf += 'xdrstream.put(({}){});\n'.format(self.float_type, data)
-            self.dec_buf += 'kout << xdrinput.get_{}();\n'.format(self.float_type)
-            self.dec_buf0 += '{} dec_{};\n'.format(self.float_type, self.dec_index)
-            self.dec_buf1 += 'dec_{} = xdrinput.get_{}();\n'.format(self.dec_index, self.float_type)
-            self.dec_buf2 += 'kout << dec_{};\n'.format(self.dec_index)
+            self.enc_buf += "xdrstream.put(({}){});\n".format(self.float_type, data)
+            self.dec_buf += "kout << xdrinput.get_{}();\n".format(self.float_type)
+            self.dec_buf0 += "{} dec_{};\n".format(self.float_type, self.dec_index)
+            self.dec_buf1 += "dec_{} = xdrinput.get_{}();\n".format(
+                self.dec_index, self.float_type
+            )
+            self.dec_buf2 += "kout << dec_{};\n".format(self.dec_index)
         elif type(data) == int:
-            self.enc_buf += 'xdrstream.put(({}){});\n'.format(self.enc_int_type, data)
-            self.dec_buf += 'kout << xdrinput.get_{}();\n'.format(self.dec_int_type)
-            self.dec_buf0 += '{} dec_{};\n'.format(self.enc_int_type, self.dec_index)
-            self.dec_buf1 += 'dec_{} = xdrinput.get_{}();\n'.format(self.dec_index, self.dec_int_type)
-            self.dec_buf2 += 'kout << dec_{};\n'.format(self.dec_index)
+            self.enc_buf += "xdrstream.put(({}){});\n".format(self.enc_int_type, data)
+            self.dec_buf += "kout << xdrinput.get_{}();\n".format(self.dec_int_type)
+            self.dec_buf0 += "{} dec_{};\n".format(self.enc_int_type, self.dec_index)
+            self.dec_buf1 += "dec_{} = xdrinput.get_{}();\n".format(
+                self.dec_index, self.dec_int_type
+            )
+            self.dec_buf2 += "kout << dec_{};\n".format(self.dec_index)
         else:
-            self.enc_buf += 'xdrstream.put({});\n'.format(data)
-            self.dec_buf += '// unsupported type {} of {}\n'.format(type(data), data)
-            self.dec_buf1 += '// unsupported type {} of {}\n'.format(type(data), data)
+            self.enc_buf += "xdrstream.put({});\n".format(data)
+            self.dec_buf += "// unsupported type {} of {}\n".format(type(data), data)
+            self.dec_buf1 += "// unsupported type {} of {}\n".format(type(data), data)
         self.dec_index += 1
 
-class XDR16(DummyProtocol):
 
-    def __init__(self, data, max_serialized_bytes = 256, int_type = 'uint16_t', float_type = 'float'):
+class XDR16(DummyProtocol):
+    def __init__(
+        self, data, max_serialized_bytes=256, int_type="uint16_t", float_type="float"
+    ):
         super().__init__()
         self.data = data
         self.max_serialized_bytes = 256
         self.enc_int_type = int_type
         self.dec_int_type = self.parse_int_type(int_type)
         self.float_type = self.parse_float_type(float_type)
-        self.enc_buf += 'XDRWriter xdrstream(buf);\n'
-        self.dec_buf += 'XDRReader xdrinput(buf);\n'
-        self.dec_buf0 += 'XDRReader xdrinput(buf);\n'
+        self.enc_buf += "XDRWriter xdrstream(buf);\n"
+        self.dec_buf += "XDRReader xdrinput(buf);\n"
+        self.dec_buf0 += "XDRReader xdrinput(buf);\n"
         # By default, XDR does not even include a version / protocol specifier.
         # This seems rather impractical -> emulate that here.
-        #self.enc_buf += 'xdrstream << (uint32_t)22075;\n'
-        self.dec_buf += 'char strbuf[16];\n'
-        #self.dec_buf += 'xdrinput.get_uint32();\n'
-        self.dec_buf0 += 'char strbuf[16];\n'
-        #self.dec_buf0 += 'xdrinput.get_uint32();\n'
+        # self.enc_buf += 'xdrstream << (uint32_t)22075;\n'
+        self.dec_buf += "char strbuf[16];\n"
+        # self.dec_buf += 'xdrinput.get_uint32();\n'
+        self.dec_buf0 += "char strbuf[16];\n"
+        # self.dec_buf0 += 'xdrinput.get_uint32();\n'
         self.from_json(data)
 
     def is_ascii(self):
         return False
 
     def parse_int_type(self, int_type):
-        sign = ''
-        if int_type[0] == 'u':
-            sign = 'u'
-        if '64' in int_type:
+        sign = ""
+        if int_type[0] == "u":
+            sign = "u"
+        if "64" in int_type:
             self.int_bits = 64
-            return sign + 'int64'
-        if '32' in int_type:
+            return sign + "int64"
+        if "32" in int_type:
             self.int_bits = 32
-            return sign + 'int32'
+            return sign + "int32"
         else:
             self.int_bits = 16
-            return sign + 'int16'
+            return sign + "int16"
 
     def parse_float_type(self, float_type):
-        if float_type == 'float':
+        if float_type == "float":
             self.float_bits = 32
         else:
             self.float_bits = 64
         return float_type
 
     def get_buffer_declaration(self):
-        ret = 'uint16_t serialized_size;\n'
-        ret += 'char buf[{:d}];\n'.format(self.max_serialized_bytes)
+        ret = "uint16_t serialized_size;\n"
+        ret += "char buf[{:d}];\n".format(self.max_serialized_bytes)
         return ret
 
     def get_buffer_name(self):
-        return 'buf'
+        return "buf"
 
     def get_length_var(self):
-        return 'xdrstream.size()'
+        return "xdrstream.size()"
 
     def get_encode(self):
         return self.enc_buf
 
     def get_decode_and_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf + 'kout << endl;\n'
+        return 'kout << dec << "dec:";\n' + self.dec_buf + "kout << endl;\n"
 
     def get_decode_vars(self):
         return self.dec_buf0
@@ -1435,76 +1733,99 @@ class XDR16(DummyProtocol):
         return self.dec_buf1
 
     def get_decode_output(self):
-        return 'kout << dec << "dec:";\n' + self.dec_buf2 + 'kout << endl;\n';
+        return 'kout << dec << "dec:";\n' + self.dec_buf2 + "kout << endl;\n"
 
     def from_json(self, data):
         if type(data) == dict:
             for key in sorted(data.keys()):
                 self.from_json(data[key])
         elif type(data) == list:
-            self.enc_buf += 'xdrstream.setNextArrayLen({});\n'.format(len(data))
-            self.enc_buf += 'xdrstream.setVariableLength();\n'
-            self.enc_buf += 'xdrstream.startList();\n'
-            self.dec_buf += 'xdrinput.get_uint16();\n'
-            self.dec_buf1 += 'xdrinput.get_uint16();\n'
+            self.enc_buf += "xdrstream.setNextArrayLen({});\n".format(len(data))
+            self.enc_buf += "xdrstream.setVariableLength();\n"
+            self.enc_buf += "xdrstream.startList();\n"
+            self.dec_buf += "xdrinput.get_uint16();\n"
+            self.dec_buf1 += "xdrinput.get_uint16();\n"
             for elem in data:
                 self.from_json(elem)
         elif type(data) == str:
-            if len(data) and data[0] == '$':
-                self.enc_buf += 'xdrstream.put(({}){});\n'.format(self.enc_int_type, data[1:])
-                self.dec_buf += 'kout << xdrinput.get_{}();\n'.format(self.dec_int_type)
-                self.dec_buf0 += '{} dec_{};\n'.format(self.enc_int_type, self.dec_index)
-                self.dec_buf1 += 'dec_{} = xdrinput.get_{}();;\n'.format(self.dec_index, self.dec_int_type)
-                self.dec_buf2 += 'kout << dec_{};\n'.format(self.dec_index)
+            if len(data) and data[0] == "$":
+                self.enc_buf += "xdrstream.put(({}){});\n".format(
+                    self.enc_int_type, data[1:]
+                )
+                self.dec_buf += "kout << xdrinput.get_{}();\n".format(self.dec_int_type)
+                self.dec_buf0 += "{} dec_{};\n".format(
+                    self.enc_int_type, self.dec_index
+                )
+                self.dec_buf1 += "dec_{} = xdrinput.get_{}();;\n".format(
+                    self.dec_index, self.dec_int_type
+                )
+                self.dec_buf2 += "kout << dec_{};\n".format(self.dec_index)
             else:
                 # Kodierte Strings haben nicht immer ein Nullbyte am Ende
-                self.enc_buf += 'xdrstream.setNextArrayLen({});\n'.format(len(data))
-                self.enc_buf += 'xdrstream.setVariableLength();\n'
-                self.enc_buf += self.add_transition('xdrstream.put("{}");\n'.format(data), [len(data)])
-                self.dec_buf += 'xdrinput.get_string(strbuf);\n'
-                self.dec_buf += 'kout << strbuf;\n'
-                self.dec_buf1 += 'xdrinput.get_string(strbuf);\n'
-                self.dec_buf2 += 'kout << strbuf;\n'
+                self.enc_buf += "xdrstream.setNextArrayLen({});\n".format(len(data))
+                self.enc_buf += "xdrstream.setVariableLength();\n"
+                self.enc_buf += self.add_transition(
+                    'xdrstream.put("{}");\n'.format(data), [len(data)]
+                )
+                self.dec_buf += "xdrinput.get_string(strbuf);\n"
+                self.dec_buf += "kout << strbuf;\n"
+                self.dec_buf1 += "xdrinput.get_string(strbuf);\n"
+                self.dec_buf2 += "kout << strbuf;\n"
         elif type(data) == float:
-            self.enc_buf += 'xdrstream.put(({}){});\n'.format(self.float_type, data)
-            self.dec_buf += 'kout << xdrinput.get_{}();\n'.format(self.float_type)
-            self.dec_buf0 += '{} dec_{};\n'.format(self.float_type, self.dec_index)
-            self.dec_buf1 += 'dec_{} = xdrinput.get_{}();\n'.format(self.dec_index, self.float_type)
-            self.dec_buf2 += 'kout << dec_{};\n'.format(self.dec_index)
+            self.enc_buf += "xdrstream.put(({}){});\n".format(self.float_type, data)
+            self.dec_buf += "kout << xdrinput.get_{}();\n".format(self.float_type)
+            self.dec_buf0 += "{} dec_{};\n".format(self.float_type, self.dec_index)
+            self.dec_buf1 += "dec_{} = xdrinput.get_{}();\n".format(
+                self.dec_index, self.float_type
+            )
+            self.dec_buf2 += "kout << dec_{};\n".format(self.dec_index)
         elif type(data) == int:
-            self.enc_buf += 'xdrstream.put(({}){});\n'.format(self.enc_int_type, data)
-            self.dec_buf += 'kout << xdrinput.get_{}();\n'.format(self.dec_int_type)
-            self.dec_buf0 += '{} dec_{};\n'.format(self.enc_int_type, self.dec_index)
-            self.dec_buf1 += 'dec_{} = xdrinput.get_{}();\n'.format(self.dec_index, self.dec_int_type)
-            self.dec_buf2 += 'kout << dec_{};\n'.format(self.dec_index)
+            self.enc_buf += "xdrstream.put(({}){});\n".format(self.enc_int_type, data)
+            self.dec_buf += "kout << xdrinput.get_{}();\n".format(self.dec_int_type)
+            self.dec_buf0 += "{} dec_{};\n".format(self.enc_int_type, self.dec_index)
+            self.dec_buf1 += "dec_{} = xdrinput.get_{}();\n".format(
+                self.dec_index, self.dec_int_type
+            )
+            self.dec_buf2 += "kout << dec_{};\n".format(self.dec_index)
         else:
-            self.enc_buf += 'xdrstream.put({});\n'.format(data)
-            self.dec_buf += '// unsupported type {} of {}\n'.format(type(data), data)
-            self.dec_buf1 += '// unsupported type {} of {}\n'.format(type(data), data)
-        self.dec_index += 1;
+            self.enc_buf += "xdrstream.put({});\n".format(data)
+            self.dec_buf += "// unsupported type {} of {}\n".format(type(data), data)
+            self.dec_buf1 += "// unsupported type {} of {}\n".format(type(data), data)
+        self.dec_index += 1
 
-class Benchmark:
 
+class Benchmark:
     def __init__(self, logfile):
         self.atomic = True
         self.logfile = logfile
 
     def __enter__(self):
         self.atomic = False
-        with FileLock(self.logfile + '.lock'):
+        with FileLock(self.logfile + ".lock"):
             if os.path.exists(self.logfile):
-                with open(self.logfile, 'rb') as f:
+                with open(self.logfile, "rb") as f:
                     self.data = ubjson.load(f)
             else:
                 self.data = {}
         return self
 
     def __exit__(self, exc_type, exc_value, exc_traceback):
-        with FileLock(self.logfile + '.lock'):
-            with open(self.logfile, 'wb') as f:
+        with FileLock(self.logfile + ".lock"):
+            with open(self.logfile, "wb") as f:
                 ubjson.dump(self.data, f)
 
-    def _add_log_entry(self, benchmark_data, arch, libkey, bench_name, bench_index, data, key, value, error):
+    def _add_log_entry(
+        self,
+        benchmark_data,
+        arch,
+        libkey,
+        bench_name,
+        bench_index,
+        data,
+        key,
+        value,
+        error,
+    ):
         if not libkey in benchmark_data:
             benchmark_data[libkey] = dict()
         if not bench_name in benchmark_data[libkey]:
@@ -1514,94 +1835,127 @@ class Benchmark:
         this_result = benchmark_data[libkey][bench_name][bench_index]
         # data is unset for log(...) calls from postprocessing
         if data != None:
-            this_result['data'] = data
+            this_result["data"] = data
         if value != None:
-            this_result[key] = {
-                'v' : value,
-                'ts' : int(time.time())
-            }
-            print('{} {} {} ({}) :: {} -> {}'.format(
-                libkey, bench_name, bench_index, data, key, value))
+            this_result[key] = {"v": value, "ts": int(time.time())}
+            print(
+                "{} {} {} ({}) :: {} -> {}".format(
+                    libkey, bench_name, bench_index, data, key, value
+                )
+            )
         else:
-            this_result[key] = {
-                'e' : error,
-                'ts' : int(time.time())
-            }
-            print('{} {} {} ({}) :: {} -> [E] {}'.format(
-                libkey, bench_name, bench_index, data, key, error[:500]))
-
-    def log(self, arch, library, library_options, bench_name, bench_index, data, key, value = None, error = None):
+            this_result[key] = {"e": error, "ts": int(time.time())}
+            print(
+                "{} {} {} ({}) :: {} -> [E] {}".format(
+                    libkey, bench_name, bench_index, data, key, error[:500]
+                )
+            )
+
+    def log(
+        self,
+        arch,
+        library,
+        library_options,
+        bench_name,
+        bench_index,
+        data,
+        key,
+        value=None,
+        error=None,
+    ):
         if not library_options:
             library_options = []
-        libkey = '{}:{}:{}'.format(arch, library, ','.join(library_options))
+        libkey = "{}:{}:{}".format(arch, library, ",".join(library_options))
         # JSON does not differentiate between int and str keys -> always use
         # str(bench_index)
         bench_index = str(bench_index)
         if self.atomic:
-            with FileLock(self.logfile + '.lock'):
+            with FileLock(self.logfile + ".lock"):
                 if os.path.exists(self.logfile):
-                    with open(self.logfile, 'rb') as f:
+                    with open(self.logfile, "rb") as f:
                         benchmark_data = ubjson.load(f)
                 else:
                     benchmark_data = {}
-                self._add_log_entry(benchmark_data, arch, libkey, bench_name, bench_index, data, key, value, error)
-                with open(self.logfile, 'wb') as f:
+                self._add_log_entry(
+                    benchmark_data,
+                    arch,
+                    libkey,
+                    bench_name,
+                    bench_index,
+                    data,
+                    key,
+                    value,
+                    error,
+                )
+                with open(self.logfile, "wb") as f:
                     ubjson.dump(benchmark_data, f)
         else:
-            self._add_log_entry(self.data, arch, libkey, bench_name, bench_index, data, key, value, error)
+            self._add_log_entry(
+                self.data,
+                arch,
+                libkey,
+                bench_name,
+                bench_index,
+                data,
+                key,
+                value,
+                error,
+            )
 
     def get_snapshot(self):
-        with FileLock(self.logfile + '.lock'):
+        with FileLock(self.logfile + ".lock"):
             if os.path.exists(self.logfile):
-                with open(self.logfile, 'rb') as f:
+                with open(self.logfile, "rb") as f:
                     benchmark_data = ubjson.load(f)
             else:
                 benchmark_data = {}
         return benchmark_data
 
+
 def codegen_for_lib(library, library_options, data):
-    if library == 'arduinojson':
-        return ArduinoJSON(data, bufsize = 512)
+    if library == "arduinojson":
+        return ArduinoJSON(data, bufsize=512)
 
-    if library == 'avro':
+    if library == "avro":
         strip_schema = bool(int(library_options[0]))
-        return Avro(data, strip_schema = strip_schema)
+        return Avro(data, strip_schema=strip_schema)
 
-    if library == 'capnproto_c':
+    if library == "capnproto_c":
         packed = bool(int(library_options[0]))
-        return CapnProtoC(data, packed = packed)
+        return CapnProtoC(data, packed=packed)
 
-    if library == 'manualjson':
+    if library == "manualjson":
         return ManualJSON(data)
 
-    if library == 'modernjson':
-        dataformat, = library_options
+    if library == "modernjson":
+        (dataformat,) = library_options
         return ModernJSON(data, dataformat)
 
-    if library == 'mpack':
+    if library == "mpack":
         return MPack(data)
 
-    if library == 'nanopb':
+    if library == "nanopb":
         cardinality, strbuf = library_options
-        if not len(strbuf) or strbuf == '0':
+        if not len(strbuf) or strbuf == "0":
             strbuf = None
         else:
             strbuf = int(strbuf)
-        return NanoPB(data, cardinality = cardinality, max_string_length = strbuf)
+        return NanoPB(data, cardinality=cardinality, max_string_length=strbuf)
 
-    if library == 'thrift':
+    if library == "thrift":
         return Thrift(data)
 
-    if library == 'ubjson':
+    if library == "ubjson":
         return UBJ(data)
 
-    if library == 'xdr':
+    if library == "xdr":
         return XDR(data)
 
-    if library == 'xdr16':
+    if library == "xdr16":
         return XDR16(data)
 
-    raise ValueError('Unsupported library: {}'.format(library))
+    raise ValueError("Unsupported library: {}".format(library))
+
 
 def shorten_call(snippet, lib):
     """
@@ -1612,54 +1966,78 @@ def shorten_call(snippet, lib):
     """
     # The following adjustments are protobench-specific
     # "xdrstream << (uint16_t)123" -> "xdrstream << (uint16_t"
-    if 'xdrstream << (' in snippet:
-        snippet = snippet.split(')')[0]
+    if "xdrstream << (" in snippet:
+        snippet = snippet.split(")")[0]
     # "xdrstream << variable << ..." -> "xdrstream << variable"
-    elif 'xdrstream << variable' in snippet:
-        snippet = '<<'.join(snippet.split('<<')[0:2])
-    elif 'xdrstream.setNextArrayLen(' in snippet:
-        snippet = 'xdrstream.setNextArrayLen'
-    elif 'ubjw' in snippet:
-        snippet = re.sub('ubjw_write_key\(ctx, [^)]+\)', 'ubjw_write_key(ctx, ?)', snippet)
-        snippet = re.sub('ubjw_write_([^(]+)\(ctx, [^)]+\)', 'ubjw_write_\\1(ctx, ?)', snippet)
+    elif "xdrstream << variable" in snippet:
+        snippet = "<<".join(snippet.split("<<")[0:2])
+    elif "xdrstream.setNextArrayLen(" in snippet:
+        snippet = "xdrstream.setNextArrayLen"
+    elif "ubjw" in snippet:
+        snippet = re.sub(
+            "ubjw_write_key\(ctx, [^)]+\)", "ubjw_write_key(ctx, ?)", snippet
+        )
+        snippet = re.sub(
+            "ubjw_write_([^(]+)\(ctx, [^)]+\)", "ubjw_write_\\1(ctx, ?)", snippet
+        )
     # mpack_write(&writer, (type)value) -> mpack_write(&writer, (type
-    elif 'mpack_write(' in snippet:
-        snippet = snippet.split(')')[0]
+    elif "mpack_write(" in snippet:
+        snippet = snippet.split(")")[0]
     # mpack_write_cstr(&writer, "foo") -> mpack_write_cstr(&writer,
     # same for mpack_write_cstr_or_nil
-    elif 'mpack_write_cstr' in snippet:
+    elif "mpack_write_cstr" in snippet:
         snippet = snippet.split('"')[0]
     # mpack_start_map(&writer, x) -> mpack_start_map(&writer
     # mpack_start_array(&writer, x) -> mpack_start_array(&writer
-    elif 'mpack_start_' in snippet:
-        snippet = snippet.split(',')[0]
-    #elif 'bout <<' in snippet:
+    elif "mpack_start_" in snippet:
+        snippet = snippet.split(",")[0]
+    # elif 'bout <<' in snippet:
     #    if '\\":\\"' in snippet:
     #        snippet = 'bout << key:str'
     #    elif 'bout << "\\"' in snippet:
     #        snippet = 'bout << key'
     #    else:
     #        snippet = 'bout << other'
-    elif 'msg.' in snippet:
-        snippet = re.sub('msg.(?:[^[]+)(?:\[.*?\])? = .*', 'msg.? = ?', snippet)
-    elif lib == 'arduinojson:':
-        snippet = re.sub('ArduinoJson::JsonObject& [^ ]+ = [^.]+.createNestedObject\([^")]*\);', 'ArduinoJson::JsonObject& ? = ?.createNestedObject();', snippet)
-        snippet = re.sub('ArduinoJson::JsonObject& [^ ]+ = [^.]+.createNestedObject\("[^")]*"\);', 'ArduinoJson::JsonObject& ? = ?.createNestedObject(?);', snippet)
-        snippet = re.sub('ArduinoJson::JsonArray& [^ ]+ = [^.]+.createNestedArray\([^")]*\);', 'ArduinoJson::JsonArray& ? = ?.createNestedArray();', snippet)
-        snippet = re.sub('ArduinoJson::JsonArray& [^ ]+ = [^.]+.createNestedArray\("[^")]*"\);', 'ArduinoJson::JsonArray& ? = ?.createNestedArray(?);', snippet)
+    elif "msg." in snippet:
+        snippet = re.sub("msg.(?:[^[]+)(?:\[.*?\])? = .*", "msg.? = ?", snippet)
+    elif lib == "arduinojson:":
+        snippet = re.sub(
+            'ArduinoJson::JsonObject& [^ ]+ = [^.]+.createNestedObject\([^")]*\);',
+            "ArduinoJson::JsonObject& ? = ?.createNestedObject();",
+            snippet,
+        )
+        snippet = re.sub(
+            'ArduinoJson::JsonObject& [^ ]+ = [^.]+.createNestedObject\("[^")]*"\);',
+            "ArduinoJson::JsonObject& ? = ?.createNestedObject(?);",
+            snippet,
+        )
+        snippet = re.sub(
+            'ArduinoJson::JsonArray& [^ ]+ = [^.]+.createNestedArray\([^")]*\);',
+            "ArduinoJson::JsonArray& ? = ?.createNestedArray();",
+            snippet,
+        )
+        snippet = re.sub(
+            'ArduinoJson::JsonArray& [^ ]+ = [^.]+.createNestedArray\("[^")]*"\);',
+            "ArduinoJson::JsonArray& ? = ?.createNestedArray(?);",
+            snippet,
+        )
         snippet = re.sub('root[^[]*\["[^"]*"\] = [^";]+', 'root?["?"] = ?', snippet)
         snippet = re.sub('root[^[]*\["[^"]*"\] = "[^"]+"', 'root?["?"] = "?"', snippet)
-        snippet = re.sub('rootl.add\([^)]*\)', 'rootl.add(?)', snippet)
+        snippet = re.sub("rootl.add\([^)]*\)", "rootl.add(?)", snippet)
 
-    snippet = re.sub('^dec_[^ ]*', 'dec_?', snippet)
-    if lib == 'arduinojson:':
-        snippet = re.sub('root[^[]*\[[^]"]+\]\.as', 'root[?].as', snippet)
+    snippet = re.sub("^dec_[^ ]*", "dec_?", snippet)
+    if lib == "arduinojson:":
+        snippet = re.sub('root[^[]*\[[^]"]+\]\.as', "root[?].as", snippet)
         snippet = re.sub('root[^[]*\["[^]]+"\]\.as', 'root["?"].as', snippet)
-    elif 'nanopb:' in lib:
-        snippet = re.sub('= msg\.[^;]+;', '= msg.?;', snippet)
-    elif lib == 'mpack:':
-        snippet = re.sub('mpack_expect_([^_]+)_max\(&reader, [^)]+\)', 'mpack_expect_\\1_max(&reader, ?)', snippet)
-    elif lib == 'ubjson:':
-        snippet = re.sub('[^_ ]+_values[^.]+\.', '?_values[?].', snippet)
+    elif "nanopb:" in lib:
+        snippet = re.sub("= msg\.[^;]+;", "= msg.?;", snippet)
+    elif lib == "mpack:":
+        snippet = re.sub(
+            "mpack_expect_([^_]+)_max\(&reader, [^)]+\)",
+            "mpack_expect_\\1_max(&reader, ?)",
+            snippet,
+        )
+    elif lib == "ubjson:":
+        snippet = re.sub("[^_ ]+_values[^.]+\.", "?_values[?].", snippet)
 
     return snippet
diff --git a/lib/pubcode/__init__.py b/lib/pubcode/__init__.py
index 30c8490..3007bdf 100644
--- a/lib/pubcode/__init__.py
+++ b/lib/pubcode/__init__.py
@@ -1,6 +1,6 @@
 """A simple module for creating barcodes.
 
 """
-__version__ = '1.1.0'
+__version__ = "1.1.0"
 
 from .code128 import Code128
diff --git a/lib/pubcode/code128.py b/lib/pubcode/code128.py
index 1c37f37..4fd7aed 100644
--- a/lib/pubcode/code128.py
+++ b/lib/pubcode/code128.py
@@ -5,6 +5,7 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 from builtins import *  # Use Python3-like builtins for Python2.
 import base64
 import io
+
 try:
     from PIL import Image
 except ImportError:
@@ -34,74 +35,189 @@ class Code128(object):
     # List of bar and space weights, indexed by symbol character values (0-105), and the STOP character (106).
     # The first weights is a bar and then it alternates.
     _val2bars = [
-        '212222', '222122', '222221', '121223', '121322', '131222', '122213', '122312', '132212', '221213',
-        '221312', '231212', '112232', '122132', '122231', '113222', '123122', '123221', '223211', '221132',
-        '221231', '213212', '223112', '312131', '311222', '321122', '321221', '312212', '322112', '322211',
-        '212123', '212321', '232121', '111323', '131123', '131321', '112313', '132113', '132311', '211313',
-        '231113', '231311', '112133', '112331', '132131', '113123', '113321', '133121', '313121', '211331',
-        '231131', '213113', '213311', '213131', '311123', '311321', '331121', '312113', '312311', '332111',
-        '314111', '221411', '431111', '111224', '111422', '121124', '121421', '141122', '141221', '112214',
-        '112412', '122114', '122411', '142112', '142211', '241211', '221114', '413111', '241112', '134111',
-        '111242', '121142', '121241', '114212', '124112', '124211', '411212', '421112', '421211', '212141',
-        '214121', '412121', '111143', '111341', '131141', '114113', '114311', '411113', '411311', '113141',
-        '114131', '311141', '411131', '211412', '211214', '211232', '2331112'
+        "212222",
+        "222122",
+        "222221",
+        "121223",
+        "121322",
+        "131222",
+        "122213",
+        "122312",
+        "132212",
+        "221213",
+        "221312",
+        "231212",
+        "112232",
+        "122132",
+        "122231",
+        "113222",
+        "123122",
+        "123221",
+        "223211",
+        "221132",
+        "221231",
+        "213212",
+        "223112",
+        "312131",
+        "311222",
+        "321122",
+        "321221",
+        "312212",
+        "322112",
+        "322211",
+        "212123",
+        "212321",
+        "232121",
+        "111323",
+        "131123",
+        "131321",
+        "112313",
+        "132113",
+        "132311",
+        "211313",
+        "231113",
+        "231311",
+        "112133",
+        "112331",
+        "132131",
+        "113123",
+        "113321",
+        "133121",
+        "313121",
+        "211331",
+        "231131",
+        "213113",
+        "213311",
+        "213131",
+        "311123",
+        "311321",
+        "331121",
+        "312113",
+        "312311",
+        "332111",
+        "314111",
+        "221411",
+        "431111",
+        "111224",
+        "111422",
+        "121124",
+        "121421",
+        "141122",
+        "141221",
+        "112214",
+        "112412",
+        "122114",
+        "122411",
+        "142112",
+        "142211",
+        "241211",
+        "221114",
+        "413111",
+        "241112",
+        "134111",
+        "111242",
+        "121142",
+        "121241",
+        "114212",
+        "124112",
+        "124211",
+        "411212",
+        "421112",
+        "421211",
+        "212141",
+        "214121",
+        "412121",
+        "111143",
+        "111341",
+        "131141",
+        "114113",
+        "114311",
+        "411113",
+        "411311",
+        "113141",
+        "114131",
+        "311141",
+        "411131",
+        "211412",
+        "211214",
+        "211232",
+        "2331112",
     ]
 
     class Special(object):
         """These are special characters used by the Code128 encoding."""
-        START_A = '[Start Code A]'
-        START_B = '[Start Code B]'
-        START_C = '[Start Code C]'
-        CODE_A = '[Code A]'
-        CODE_B = '[Code B]'
-        CODE_C = '[Code C]'
-        SHIFT_A = '[Shift A]'
-        SHIFT_B = '[Shift B]'
-        FNC_1 = '[FNC 1]'
-        FNC_2 = '[FNC 2]'
-        FNC_3 = '[FNC 3]'
-        FNC_4 = '[FNC 4]'
-        STOP = '[Stop]'
-
-    _start_codes = {'A': Special.START_A, 'B': Special.START_B, 'C': Special.START_C}
-    _char_codes = {'A': Special.CODE_A, 'B': Special.CODE_B, 'C': Special.CODE_C}
+
+        START_A = "[Start Code A]"
+        START_B = "[Start Code B]"
+        START_C = "[Start Code C]"
+        CODE_A = "[Code A]"
+        CODE_B = "[Code B]"
+        CODE_C = "[Code C]"
+        SHIFT_A = "[Shift A]"
+        SHIFT_B = "[Shift B]"
+        FNC_1 = "[FNC 1]"
+        FNC_2 = "[FNC 2]"
+        FNC_3 = "[FNC 3]"
+        FNC_4 = "[FNC 4]"
+        STOP = "[Stop]"
+
+    _start_codes = {"A": Special.START_A, "B": Special.START_B, "C": Special.START_C}
+    _char_codes = {"A": Special.CODE_A, "B": Special.CODE_B, "C": Special.CODE_C}
 
     # Lists mapping symbol values to characters in each character set. This defines the alphabet and Code128._sym2val
     # is derived from this structure.
     _val2sym = {
         # Code Set A includes ordinals 0 through 95 and 7 special characters. The ordinals include digits,
         # upper case characters, punctuation and control characters.
-        'A':
-            [chr(x) for x in range(32, 95 + 1)] +
-            [chr(x) for x in range(0, 31 + 1)] +
-            [
-                Special.FNC_3, Special.FNC_2, Special.SHIFT_B, Special.CODE_C,
-                Special.CODE_B, Special.FNC_4, Special.FNC_1,
-                Special.START_A, Special.START_B, Special.START_C, Special.STOP
-            ],
+        "A": [chr(x) for x in range(32, 95 + 1)]
+        + [chr(x) for x in range(0, 31 + 1)]
+        + [
+            Special.FNC_3,
+            Special.FNC_2,
+            Special.SHIFT_B,
+            Special.CODE_C,
+            Special.CODE_B,
+            Special.FNC_4,
+            Special.FNC_1,
+            Special.START_A,
+            Special.START_B,
+            Special.START_C,
+            Special.STOP,
+        ],
         # Code Set B includes ordinals 32 through 127 and 7 special characters. The ordinals include digits,
         # upper and lover case characters and punctuation.
-        'B':
-            [chr(x) for x in range(32, 127 + 1)] +
-            [
-                Special.FNC_3, Special.FNC_2, Special.SHIFT_A, Special.CODE_C,
-                Special.FNC_4, Special.CODE_A, Special.FNC_1,
-                Special.START_A, Special.START_B, Special.START_C, Special.STOP
-            ],
+        "B": [chr(x) for x in range(32, 127 + 1)]
+        + [
+            Special.FNC_3,
+            Special.FNC_2,
+            Special.SHIFT_A,
+            Special.CODE_C,
+            Special.FNC_4,
+            Special.CODE_A,
+            Special.FNC_1,
+            Special.START_A,
+            Special.START_B,
+            Special.START_C,
+            Special.STOP,
+        ],
         # Code Set C includes all pairs of 2 digits and 3 special characters.
-        'C':
-            ['%02d' % (x,) for x in range(0, 99 + 1)] +
-            [
-                Special.CODE_B, Special.CODE_A, Special.FNC_1,
-                Special.START_A, Special.START_B, Special.START_C, Special.STOP
-            ],
+        "C": ["%02d" % (x,) for x in range(0, 99 + 1)]
+        + [
+            Special.CODE_B,
+            Special.CODE_A,
+            Special.FNC_1,
+            Special.START_A,
+            Special.START_B,
+            Special.START_C,
+            Special.STOP,
+        ],
     }
 
     # Dicts mapping characters to symbol values in each character set.
     _sym2val = {
-        'A': {char: val for val, char in enumerate(_val2sym['A'])},
-        'B': {char: val for val, char in enumerate(_val2sym['B'])},
-        'C': {char: val for val, char in enumerate(_val2sym['C'])},
+        "A": {char: val for val, char in enumerate(_val2sym["A"])},
+        "B": {char: val for val, char in enumerate(_val2sym["B"])},
+        "C": {char: val for val, char in enumerate(_val2sym["C"])},
     }
 
     # How large the quiet zone is on either side of the barcode, when quiet zone is used.
@@ -121,13 +237,13 @@ class Code128(object):
         """
         self._validate_charset(data, charset)
 
-        if charset in ('A', 'B'):
+        if charset in ("A", "B"):
             charset *= len(data)
-        elif charset in ('C',):
-            charset *= (len(data) // 2)
+        elif charset in ("C",):
+            charset *= len(data) // 2
             if len(data) % 2 == 1:
                 # If there are an odd number of characters for charset C, encode the last character with charset B.
-                charset += 'B'
+                charset += "B"
 
         self.data = data
         self.symbol_values = self._encode(data, charset)
@@ -148,13 +264,13 @@ class Code128(object):
         if len(charset) > 1:
             charset_data_length = 0
             for symbol_charset in charset:
-                if symbol_charset not in ('A', 'B', 'C'):
+                if symbol_charset not in ("A", "B", "C"):
                     raise Code128.CharsetError
-                charset_data_length += 2 if symbol_charset is 'C' else 1
+                charset_data_length += 2 if symbol_charset is "C" else 1
             if charset_data_length != len(data):
                 raise Code128.CharsetLengthError
         elif len(charset) == 1:
-            if charset not in ('A', 'B', 'C'):
+            if charset not in ("A", "B", "C"):
                 raise Code128.CharsetError
         elif charset is not None:
             raise Code128.CharsetError
@@ -182,10 +298,12 @@ class Code128(object):
             if charset is not prev_charset:
                 # Handle a special case of there being a single A in middle of two B's or the other way around, where
                 # using a single shift character is more efficient than using two character set switches.
-                next_charset = charsets[symbol_num + 1] if symbol_num + 1 < len(charsets) else None
-                if charset == 'A' and prev_charset == next_charset == 'B':
+                next_charset = (
+                    charsets[symbol_num + 1] if symbol_num + 1 < len(charsets) else None
+                )
+                if charset == "A" and prev_charset == next_charset == "B":
                     result.append(cls._sym2val[prev_charset][cls.Special.SHIFT_A])
-                elif charset == 'B' and prev_charset == next_charset == 'A':
+                elif charset == "B" and prev_charset == next_charset == "A":
                     result.append(cls._sym2val[prev_charset][cls.Special.SHIFT_B])
                 else:
                     # This is the normal case.
@@ -193,7 +311,7 @@ class Code128(object):
                     result.append(cls._sym2val[prev_charset][charset_symbol])
                     prev_charset = charset
 
-            nxt = cur + (2 if charset == 'C' else 1)
+            nxt = cur + (2 if charset == "C" else 1)
             symbol = data[cur:nxt]
             cur = nxt
             result.append(cls._sym2val[charset][symbol])
@@ -206,9 +324,10 @@ class Code128(object):
     @property
     def symbols(self):
         """List of the coded symbols as strings, with special characters included."""
+
         def _iter_symbols(symbol_values):
             # The initial charset doesn't matter, as the start codes have the same symbol values in all charsets.
-            charset = 'A'
+            charset = "A"
 
             shift_charset = None
             for symbol_value in symbol_values:
@@ -219,15 +338,15 @@ class Code128(object):
                     symbol = self._val2sym[charset][symbol_value]
 
                 if symbol in (self.Special.START_A, self.Special.CODE_A):
-                    charset = 'A'
+                    charset = "A"
                 elif symbol in (self.Special.START_B, self.Special.CODE_B):
-                    charset = 'B'
+                    charset = "B"
                 elif symbol in (self.Special.START_C, self.Special.CODE_C):
-                    charset = 'C'
+                    charset = "C"
                 elif symbol in (self.Special.SHIFT_A,):
-                    shift_charset = 'A'
+                    shift_charset = "A"
                 elif symbol in (self.Special.SHIFT_B,):
-                    shift_charset = 'B'
+                    shift_charset = "B"
 
                 yield symbol
 
@@ -243,7 +362,7 @@ class Code128(object):
 
         :rtype: string
         """
-        return ''.join(map((lambda val: self._val2bars[val]), self.symbol_values))
+        return "".join(map((lambda val: self._val2bars[val]), self.symbol_values))
 
     @property
     def modules(self):
@@ -255,6 +374,7 @@ class Code128(object):
 
         :rtype: list[int]
         """
+
         def _iterate_modules(bars):
             is_bar = True
             for char in map(int, bars):
@@ -288,7 +408,9 @@ class Code128(object):
         :return: A monochromatic image containing the barcode as black bars on white background.
         """
         if Image is None:
-            raise Code128.MissingDependencyError("PIL module is required to use image method.")
+            raise Code128.MissingDependencyError(
+                "PIL module is required to use image method."
+            )
 
         modules = list(self.modules)
         if add_quiet_zone:
@@ -296,7 +418,7 @@ class Code128(object):
             modules = [1] * self.quiet_zone + modules + [1] * self.quiet_zone
         width = len(modules)
 
-        img = Image.new(mode='1', size=(width, 1))
+        img = Image.new(mode="1", size=(width, 1))
         img.putdata(modules)
 
         if height == 1 and module_width == 1:
@@ -305,7 +427,7 @@ class Code128(object):
             new_size = (width * module_width, height)
             return img.resize(new_size, resample=Image.NEAREST)
 
-    def data_url(self, image_format='png', add_quiet_zone=True):
+    def data_url(self, image_format="png", add_quiet_zone=True):
         """Get a data URL representing the barcode.
 
         >>> barcode = Code128('Hello!', charset='B')
@@ -327,22 +449,21 @@ class Code128(object):
         # Using BMP can often result in smaller data URLs than PNG, but it isn't as widely supported by browsers as PNG.
         # GIFs result in data URLs 10 times bigger than PNG or BMP, possibly due to lack of support for monochrome GIFs
         # in Pillow, so they shouldn't be used.
-        if image_format == 'png':
+        if image_format == "png":
             # Unfortunately there is no way to avoid adding the zlib headers.
             # Using compress_level=0 sometimes results in a slightly bigger data size (by a few bytes), but there
             # doesn't appear to be a difference between levels 9 and 1, so let's just use 1.
-            pil_image.save(memory_file, format='png', compress_level=1)
-        elif image_format == 'bmp':
-            pil_image.save(memory_file, format='bmp')
+            pil_image.save(memory_file, format="png", compress_level=1)
+        elif image_format == "bmp":
+            pil_image.save(memory_file, format="bmp")
         else:
-            raise Code128.UnknownFormatError('Only png and bmp are supported.')
+            raise Code128.UnknownFormatError("Only png and bmp are supported.")
 
         # Encode the data in the BytesIO object and convert the result into unicode.
-        base64_image = base64.b64encode(memory_file.getvalue()).decode('ascii')
+        base64_image = base64.b64encode(memory_file.getvalue()).decode("ascii")
 
-        data_url = 'data:image/{format};base64,{base64_data}'.format(
-            format=image_format,
-            base64_data=base64_image
+        data_url = "data:image/{format};base64,{base64_data}".format(
+            format=image_format, base64_data=base64_image
         )
 
         return data_url
diff --git a/lib/runner.py b/lib/runner.py
index 85df2b6..16f0a29 100644
--- a/lib/runner.py
+++ b/lib/runner.py
@@ -30,7 +30,7 @@ class SerialReader(serial.threaded.Protocol):
     def __init__(self, callback=None):
         """Create a new SerialReader object."""
         self.callback = callback
-        self.recv_buf = ''
+        self.recv_buf = ""
         self.lines = []
 
     def __call__(self):
@@ -39,13 +39,13 @@ class SerialReader(serial.threaded.Protocol):
     def data_received(self, data):
         """Append newly received serial data to the line buffer."""
         try:
-            str_data = data.decode('UTF-8')
+            str_data = data.decode("UTF-8")
             self.recv_buf += str_data
 
             # We may get anything between \r\n, \n\r and simple \n newlines.
             # We assume that \n is always present and use str.strip to remove leading/trailing \r symbols
             # Note: Do not call str.strip on lines[-1]! Otherwise, lines may be mangled
-            lines = self.recv_buf.split('\n')
+            lines = self.recv_buf.split("\n")
             if len(lines) > 1:
                 self.lines.extend(map(str.strip, lines[:-1]))
                 self.recv_buf = lines[-1]
@@ -94,14 +94,16 @@ class SerialMonitor:
         """
         self.ser = serial.serial_for_url(port, do_not_open=True)
         self.ser.baudrate = baud
-        self.ser.parity = 'N'
+        self.ser.parity = "N"
         self.ser.rtscts = False
         self.ser.xonxoff = False
 
         try:
             self.ser.open()
         except serial.SerialException as e:
-            sys.stderr.write('Could not open serial port {}: {}\n'.format(self.ser.name, e))
+            sys.stderr.write(
+                "Could not open serial port {}: {}\n".format(self.ser.name, e)
+            )
             sys.exit(1)
 
         self.reader = SerialReader(callback=callback)
@@ -131,6 +133,7 @@ class SerialMonitor:
         self.worker.stop()
         self.ser.close()
 
+
 # TODO Optionale Kalibrierung mit bekannten Widerständen an GPIOs am Anfang
 # TODO Sync per LED? -> Vor und ggf nach jeder Transition kurz pulsen
 # TODO Für Verbraucher mit wenig Energiebedarf: Versorgung direkt per GPIO
@@ -143,14 +146,14 @@ class EnergyTraceMonitor(SerialMonitor):
     def __init__(self, port: str, baud: int, callback=None, voltage=3.3):
         super().__init__(port=port, baud=baud, callback=callback)
         self._voltage = voltage
-        self._output = time.strftime('%Y%m%d-%H%M%S.etlog')
+        self._output = time.strftime("%Y%m%d-%H%M%S.etlog")
         self._start_energytrace()
 
     def _start_energytrace(self):
-        cmd = ['msp430-etv', '--save', self._output, '0']
-        self._logger = subprocess.Popen(cmd,
-                                        stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-                                        universal_newlines=True)
+        cmd = ["msp430-etv", "--save", self._output, "0"]
+        self._logger = subprocess.Popen(
+            cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True
+        )
 
     def close(self):
         super().close()
@@ -162,14 +165,16 @@ class EnergyTraceMonitor(SerialMonitor):
 
     def get_config(self) -> dict:
         return {
-            'voltage': self._voltage,
+            "voltage": self._voltage,
         }
 
 
 class MIMOSAMonitor(SerialMonitor):
     """MIMOSAMonitor captures serial output and MIMOSA energy data for a specific amount of time."""
 
-    def __init__(self, port: str, baud: int, callback=None, offset=130, shunt=330, voltage=3.3):
+    def __init__(
+        self, port: str, baud: int, callback=None, offset=130, shunt=330, voltage=3.3
+    ):
         super().__init__(port=port, baud=baud, callback=callback)
         self._offset = offset
         self._shunt = shunt
@@ -177,39 +182,41 @@ class MIMOSAMonitor(SerialMonitor):
         self._start_mimosa()
 
     def _mimosactl(self, subcommand):
-        cmd = ['mimosactl']
+        cmd = ["mimosactl"]
         cmd.append(subcommand)
         res = subprocess.run(cmd)
         if res.returncode != 0:
             res = subprocess.run(cmd)
             if res.returncode != 0:
-                raise RuntimeError('{} returned {}'.format(' '.join(cmd), res.returncode))
+                raise RuntimeError(
+                    "{} returned {}".format(" ".join(cmd), res.returncode)
+                )
 
     def _mimosacmd(self, opts):
-        cmd = ['MimosaCMD']
+        cmd = ["MimosaCMD"]
         cmd.extend(opts)
         res = subprocess.run(cmd)
         if res.returncode != 0:
-            raise RuntimeError('{} returned {}'.format(' '.join(cmd), res.returncode))
+            raise RuntimeError("{} returned {}".format(" ".join(cmd), res.returncode))
 
     def _start_mimosa(self):
-        self._mimosactl('disconnect')
-        self._mimosacmd(['--start'])
-        self._mimosacmd(['--parameter', 'offset', str(self._offset)])
-        self._mimosacmd(['--parameter', 'shunt', str(self._shunt)])
-        self._mimosacmd(['--parameter', 'voltage', str(self._voltage)])
-        self._mimosacmd(['--mimosa-start'])
+        self._mimosactl("disconnect")
+        self._mimosacmd(["--start"])
+        self._mimosacmd(["--parameter", "offset", str(self._offset)])
+        self._mimosacmd(["--parameter", "shunt", str(self._shunt)])
+        self._mimosacmd(["--parameter", "voltage", str(self._voltage)])
+        self._mimosacmd(["--mimosa-start"])
         time.sleep(2)
-        self._mimosactl('1k')  # 987 ohm
+        self._mimosactl("1k")  # 987 ohm
         time.sleep(2)
-        self._mimosactl('100k')  # 99.3 kohm
+        self._mimosactl("100k")  # 99.3 kohm
         time.sleep(2)
-        self._mimosactl('connect')
+        self._mimosactl("connect")
 
     def _stop_mimosa(self):
         # Make sure the MIMOSA daemon has gathered all needed data
         time.sleep(2)
-        self._mimosacmd(['--mimosa-stop'])
+        self._mimosacmd(["--mimosa-stop"])
         mtime_changed = True
         mim_file = None
         time.sleep(1)
@@ -218,7 +225,7 @@ class MIMOSAMonitor(SerialMonitor):
         # files lying around in the directory will not confuse our
         # heuristic.
         for filename in sorted(os.listdir(), reverse=True):
-            if re.search(r'[.]mim$', filename):
+            if re.search(r"[.]mim$", filename):
                 mim_file = filename
                 break
         while mtime_changed:
@@ -226,7 +233,7 @@ class MIMOSAMonitor(SerialMonitor):
             if time.time() - os.stat(mim_file).st_mtime < 3:
                 mtime_changed = True
             time.sleep(1)
-        self._mimosacmd(['--stop'])
+        self._mimosacmd(["--stop"])
         return mim_file
 
     def close(self):
@@ -238,9 +245,9 @@ class MIMOSAMonitor(SerialMonitor):
 
     def get_config(self) -> dict:
         return {
-            'offset': self._offset,
-            'shunt': self._shunt,
-            'voltage': self._voltage,
+            "offset": self._offset,
+            "shunt": self._shunt,
+            "voltage": self._voltage,
         }
 
 
@@ -263,14 +270,17 @@ class ShellMonitor:
         stderr and return status are discarded at the moment.
         """
         if type(timeout) != int:
-            raise ValueError('timeout argument must be int')
-        res = subprocess.run(['timeout', '{:d}s'.format(timeout), self.script],
-                             stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-                             universal_newlines=True)
+            raise ValueError("timeout argument must be int")
+        res = subprocess.run(
+            ["timeout", "{:d}s".format(timeout), self.script],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            universal_newlines=True,
+        )
         if self.callback:
-            for line in res.stdout.split('\n'):
+            for line in res.stdout.split("\n"):
                 self.callback(line)
-        return res.stdout.split('\n')
+        return res.stdout.split("\n")
 
     def monitor(self):
         raise NotImplementedError
@@ -285,28 +295,35 @@ class ShellMonitor:
 
 
 def build(arch, app, opts=[]):
-    command = ['make', 'arch={}'.format(arch), 'app={}'.format(app), 'clean']
+    command = ["make", "arch={}".format(arch), "app={}".format(app), "clean"]
     command.extend(opts)
-    res = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-                         universal_newlines=True)
+    res = subprocess.run(
+        command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True
+    )
     if res.returncode != 0:
-        raise RuntimeError('Build failure, executing {}:\n'.format(command) + res.stderr)
-    command = ['make', '-B', 'arch={}'.format(arch), 'app={}'.format(app)]
+        raise RuntimeError(
+            "Build failure, executing {}:\n".format(command) + res.stderr
+        )
+    command = ["make", "-B", "arch={}".format(arch), "app={}".format(app)]
     command.extend(opts)
-    res = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-                         universal_newlines=True)
+    res = subprocess.run(
+        command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True
+    )
     if res.returncode != 0:
-        raise RuntimeError('Build failure, executing {}:\n '.format(command) + res.stderr)
+        raise RuntimeError(
+            "Build failure, executing {}:\n ".format(command) + res.stderr
+        )
     return command
 
 
 def flash(arch, app, opts=[]):
-    command = ['make', 'arch={}'.format(arch), 'app={}'.format(app), 'program']
+    command = ["make", "arch={}".format(arch), "app={}".format(app), "program"]
     command.extend(opts)
-    res = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-                         universal_newlines=True)
+    res = subprocess.run(
+        command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True
+    )
     if res.returncode != 0:
-        raise RuntimeError('Flash failure')
+        raise RuntimeError("Flash failure")
     return command
 
 
@@ -316,13 +333,14 @@ def get_info(arch, opts: list = []) -> list:
 
     Returns a list.
     """
-    command = ['make', 'arch={}'.format(arch), 'info']
+    command = ["make", "arch={}".format(arch), "info"]
     command.extend(opts)
-    res = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
-                         universal_newlines=True)
+    res = subprocess.run(
+        command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True
+    )
     if res.returncode != 0:
-        raise RuntimeError('make info Failure')
-    return res.stdout.split('\n')
+        raise RuntimeError("make info Failure")
+    return res.stdout.split("\n")
 
 
 def get_monitor(arch: str, **kwargs) -> object:
@@ -336,32 +354,32 @@ def get_monitor(arch: str, **kwargs) -> object:
     :param mimosa: `MIMOSAMonitor` options. Returns a MIMOSA monitor if not None.
     """
     for line in get_info(arch):
-        if 'Monitor:' in line:
-            _, port, arg = line.split(' ')
-            if port == 'run':
+        if "Monitor:" in line:
+            _, port, arg = line.split(" ")
+            if port == "run":
                 return ShellMonitor(arg, **kwargs)
-            elif 'mimosa' in kwargs and kwargs['mimosa'] is not None:
-                mimosa_kwargs = kwargs.pop('mimosa')
+            elif "mimosa" in kwargs and kwargs["mimosa"] is not None:
+                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')
+            elif "energytrace" in kwargs and kwargs["energytrace"] is not None:
+                energytrace_kwargs = kwargs.pop("energytrace")
                 return EnergyTraceMonitor(port, arg, **energytrace_kwargs, **kwargs)
             else:
-                kwargs.pop('energytrace', None)
-                kwargs.pop('mimosa', None)
+                kwargs.pop("energytrace", None)
+                kwargs.pop("mimosa", None)
                 return SerialMonitor(port, arg, **kwargs)
-    raise RuntimeError('Monitor failure')
+    raise RuntimeError("Monitor failure")
 
 
 def get_counter_limits(arch: str) -> tuple:
     """Return multipass max counter and max overflow value for arch."""
     for line in get_info(arch):
-        match = re.match('Counter Overflow: ([^/]*)/(.*)', line)
+        match = re.match("Counter Overflow: ([^/]*)/(.*)", line)
         if match:
             overflow_value = int(match.group(1))
             max_overflow = int(match.group(2))
             return overflow_value, max_overflow
-    raise RuntimeError('Did not find Counter Overflow limits')
+    raise RuntimeError("Did not find Counter Overflow limits")
 
 
 def get_counter_limits_us(arch: str) -> tuple:
@@ -370,13 +388,13 @@ def get_counter_limits_us(arch: str) -> tuple:
     overflow_value = 0
     max_overflow = 0
     for line in get_info(arch):
-        match = re.match(r'CPU\s+Freq:\s+(.*)\s+Hz', line)
+        match = re.match(r"CPU\s+Freq:\s+(.*)\s+Hz", line)
         if match:
             cpu_freq = int(match.group(1))
-        match = re.match(r'Counter Overflow:\s+([^/]*)/(.*)', line)
+        match = re.match(r"Counter Overflow:\s+([^/]*)/(.*)", line)
         if match:
             overflow_value = int(match.group(1))
             max_overflow = int(match.group(2))
     if cpu_freq and overflow_value:
         return 1000000 / cpu_freq, overflow_value * 1000000 / cpu_freq, max_overflow
-    raise RuntimeError('Did not find Counter Overflow limits')
+    raise RuntimeError("Did not find Counter Overflow limits")
diff --git a/lib/size_to_radio_energy.py b/lib/size_to_radio_energy.py
index a3cf7c5..10de1a3 100644
--- a/lib/size_to_radio_energy.py
+++ b/lib/size_to_radio_energy.py
@@ -7,38 +7,42 @@ class can convert a cycle count to an energy consumption.
 
 import numpy as np
 
+
 def get_class(radio_name: str):
     """Return model class for radio_name."""
-    if radio_name == 'CC1200tx':
+    if radio_name == "CC1200tx":
         return CC1200tx
-    if radio_name == 'CC1200rx':
+    if radio_name == "CC1200rx":
         return CC1200rx
-    if radio_name == 'NRF24L01tx':
+    if radio_name == "NRF24L01tx":
         return NRF24L01tx
-    if radio_name == 'NRF24L01dtx':
+    if radio_name == "NRF24L01dtx":
         return NRF24L01dtx
-    if radio_name == 'esp8266dtx':
+    if radio_name == "esp8266dtx":
         return ESP8266dtx
-    if radio_name == 'esp8266drx':
+    if radio_name == "esp8266drx":
         return ESP8266drx
 
+
 def _param_list_to_dict(device, param_list):
     param_dict = dict()
     for i, parameter in enumerate(sorted(device.parameters.keys())):
         param_dict[parameter] = param_list[i]
     return param_dict
 
+
 class CC1200tx:
     """CC1200 TX energy based on aemr measurements."""
-    name = 'CC1200tx'
+
+    name = "CC1200tx"
     parameters = {
-        'symbolrate' : [6, 12, 25, 50, 100, 200, 250], # ksps
-        'txbytes' : [],
-        'txpower' : [10, 20, 30, 40, 47], # dBm = f(txpower)
+        "symbolrate": [6, 12, 25, 50, 100, 200, 250],  # ksps
+        "txbytes": [],
+        "txpower": [10, 20, 30, 40, 47],  # dBm = f(txpower)
     }
     default_params = {
-        'symbolrate' : 100,
-        'txpower' : 47,
+        "symbolrate": 100,
+        "txpower": 47,
     }
 
     @staticmethod
@@ -48,106 +52,129 @@ class CC1200tx:
 
         # Mittlere TX-Leistung, gefitted von AEMR
         # Messdaten erhoben bei 3.6V
-        power  = 8.18053941e+04
-        power -= 1.24208376e+03 * np.sqrt(params['symbolrate'])
-        power -= 5.73742779e+02 * np.log(params['txbytes'])
-        power += 1.76945886e+01 * (params['txpower'])**2
-        power += 2.33469617e+02 * np.sqrt(params['symbolrate']) * np.log(params['txbytes'])
-        power -= 6.99137635e-01 * np.sqrt(params['symbolrate']) * (params['txpower'])**2
-        power -= 3.31365158e-01 * np.log(params['txbytes']) * (params['txpower'])**2
-        power += 1.32784945e-01 * np.sqrt(params['symbolrate']) * np.log(params['txbytes']) * (params['txpower'])**2
+        power = 8.18053941e04
+        power -= 1.24208376e03 * np.sqrt(params["symbolrate"])
+        power -= 5.73742779e02 * np.log(params["txbytes"])
+        power += 1.76945886e01 * (params["txpower"]) ** 2
+        power += (
+            2.33469617e02 * np.sqrt(params["symbolrate"]) * np.log(params["txbytes"])
+        )
+        power -= (
+            6.99137635e-01 * np.sqrt(params["symbolrate"]) * (params["txpower"]) ** 2
+        )
+        power -= 3.31365158e-01 * np.log(params["txbytes"]) * (params["txpower"]) ** 2
+        power += (
+            1.32784945e-01
+            * np.sqrt(params["symbolrate"])
+            * np.log(params["txbytes"])
+            * (params["txpower"]) ** 2
+        )
 
         # txDone-Timeout, gefitted von AEMR
-        duration  = 3.65513500e+02
-        duration += 8.01016526e+04 * 1/(params['symbolrate'])
-        duration -= 7.06364515e-03 * params['txbytes']
-        duration += 8.00029860e+03 * 1/(params['symbolrate']) * params['txbytes']
+        duration = 3.65513500e02
+        duration += 8.01016526e04 * 1 / (params["symbolrate"])
+        duration -= 7.06364515e-03 * params["txbytes"]
+        duration += 8.00029860e03 * 1 / (params["symbolrate"]) * params["txbytes"]
 
         # TX-Energie, gefitted von AEMR
         # Achtung: Energy ist in µJ, nicht (wie in AEMR-Transitionsmodellen üblich) in pJ
         # Messdaten erhoben bei 3.6V
 
-        energy  = 1.74383259e+01
-        energy += 6.29922138e+03 * 1/(params['symbolrate'])
-        energy += 1.13307135e-02 * params['txbytes']
-        energy -= 1.28121377e-04 * (params['txpower'])**2
-        energy += 6.29080184e+02 * 1/(params['symbolrate']) * params['txbytes']
-        energy += 1.25647926e+00 * 1/(params['symbolrate']) * (params['txpower'])**2
-        energy += 1.31996202e-05 * params['txbytes'] * (params['txpower'])**2
-        energy += 1.25676966e-01 * 1/(params['symbolrate']) * params['txbytes'] * (params['txpower'])**2
+        energy = 1.74383259e01
+        energy += 6.29922138e03 * 1 / (params["symbolrate"])
+        energy += 1.13307135e-02 * params["txbytes"]
+        energy -= 1.28121377e-04 * (params["txpower"]) ** 2
+        energy += 6.29080184e02 * 1 / (params["symbolrate"]) * params["txbytes"]
+        energy += 1.25647926e00 * 1 / (params["symbolrate"]) * (params["txpower"]) ** 2
+        energy += 1.31996202e-05 * params["txbytes"] * (params["txpower"]) ** 2
+        energy += (
+            1.25676966e-01
+            * 1
+            / (params["symbolrate"])
+            * params["txbytes"]
+            * (params["txpower"]) ** 2
+        )
 
         return energy * 1e-6
 
     @staticmethod
     def get_energy_per_byte(params):
-        A  = 8.18053941e+04
-        A -= 1.24208376e+03 * np.sqrt(params['symbolrate'])
-        A += 1.76945886e+01 * (params['txpower'])**2
-        A -= 6.99137635e-01 * np.sqrt(params['symbolrate']) * (params['txpower'])**2
-        B  = -5.73742779e+02
-        B += 2.33469617e+02 * np.sqrt(params['symbolrate'])
-        B -= 3.31365158e-01 * (params['txpower'])**2
-        B += 1.32784945e-01 * np.sqrt(params['symbolrate']) * (params['txpower'])**2
-        C  = 3.65513500e+02
-        C += 8.01016526e+04 * 1/(params['symbolrate'])
-        D  = -7.06364515e-03
-        D += 8.00029860e+03 * 1/(params['symbolrate'])
-
-        x = params['txbytes']
+        A = 8.18053941e04
+        A -= 1.24208376e03 * np.sqrt(params["symbolrate"])
+        A += 1.76945886e01 * (params["txpower"]) ** 2
+        A -= 6.99137635e-01 * np.sqrt(params["symbolrate"]) * (params["txpower"]) ** 2
+        B = -5.73742779e02
+        B += 2.33469617e02 * np.sqrt(params["symbolrate"])
+        B -= 3.31365158e-01 * (params["txpower"]) ** 2
+        B += 1.32784945e-01 * np.sqrt(params["symbolrate"]) * (params["txpower"]) ** 2
+        C = 3.65513500e02
+        C += 8.01016526e04 * 1 / (params["symbolrate"])
+        D = -7.06364515e-03
+        D += 8.00029860e03 * 1 / (params["symbolrate"])
+
+        x = params["txbytes"]
 
         # in pJ
-        de_dx = A * D + B * C * 1/x + B * D * (np.log(x) + 1)
+        de_dx = A * D + B * C * 1 / x + B * D * (np.log(x) + 1)
 
         # in µJ
-        de_dx  = 1.13307135e-02
-        de_dx += 6.29080184e+02 * 1/(params['symbolrate'])
-        de_dx += 1.31996202e-05 * (params['txpower'])**2
-        de_dx += 1.25676966e-01 * 1/(params['symbolrate']) * (params['txpower'])**2
+        de_dx = 1.13307135e-02
+        de_dx += 6.29080184e02 * 1 / (params["symbolrate"])
+        de_dx += 1.31996202e-05 * (params["txpower"]) ** 2
+        de_dx += 1.25676966e-01 * 1 / (params["symbolrate"]) * (params["txpower"]) ** 2
 
-        #de_dx = (B * 1/x) * (C + D * x) + (A + B * np.log(x)) * D
+        # de_dx = (B * 1/x) * (C + D * x) + (A + B * np.log(x)) * D
 
         return de_dx * 1e-6
 
+
 class CC1200rx:
     """CC1200 RX energy based on aemr measurements."""
-    name = 'CC1200rx'
+
+    name = "CC1200rx"
     parameters = {
-        'symbolrate' : [6, 12, 25, 50, 100, 200, 250], # ksps
-        'txbytes' : [],
-        'txpower' : [10, 20, 30, 40, 47], # dBm = f(txpower)
+        "symbolrate": [6, 12, 25, 50, 100, 200, 250],  # ksps
+        "txbytes": [],
+        "txpower": [10, 20, 30, 40, 47],  # dBm = f(txpower)
     }
     default_params = {
-        'symbolrate' : 100,
-        'txpower' : 47,
+        "symbolrate": 100,
+        "txpower": 47,
     }
 
     @staticmethod
     def get_energy(params):
         # TODO
-        return params['txbytes'] * CC1200rx.get_energy_per_byte(params)
+        return params["txbytes"] * CC1200rx.get_energy_per_byte(params)
 
     @staticmethod
     def get_energy_per_byte(params):
-        #RX        : 0 + regression_arg(0) + regression_arg(1) * np.log(parameter(symbolrate) + 1)
+        # RX        : 0 + regression_arg(0) + regression_arg(1) * np.log(parameter(symbolrate) + 1)
         # [84414.91636169   205.63323036]
 
-        de_dx = (84414.91636169 + 205.63323036 * np.log(params['symbolrate'] + 1)) * 8000 / params['symbolrate']
+        de_dx = (
+            (84414.91636169 + 205.63323036 * np.log(params["symbolrate"] + 1))
+            * 8000
+            / params["symbolrate"]
+        )
 
         return de_dx * 1e-12
 
+
 class NRF24L01rx:
     """NRF24L01+ RX energy based on aemr measurements (using variable packet size)"""
-    name = 'NRF24L01'
+
+    name = "NRF24L01"
     parameters = {
-        'datarate' : [250, 1000, 2000], # kbps
-        'txbytes' : [],
-        'txpower' : [-18, -12, -6, 0], # dBm
-        'voltage' : [1.9, 3.6],
+        "datarate": [250, 1000, 2000],  # kbps
+        "txbytes": [],
+        "txpower": [-18, -12, -6, 0],  # dBm
+        "voltage": [1.9, 3.6],
     }
     default_params = {
-        'datarate' : 1000,
-        'txpower' : -6,
-        'voltage' : 3,
+        "datarate": 1000,
+        "txpower": -6,
+        "voltage": 3,
     }
 
     @staticmethod
@@ -155,35 +182,40 @@ class NRF24L01rx:
         # RX        : 0 + regression_arg(0) + regression_arg(1) * np.sqrt(parameter(datarate))
         #     [48530.73235537   117.25274402]
 
-        de_dx = (48530.73235537 + 117.25274402 * np.sqrt(params['datarate'])) * 8000 / params['datarate']
+        de_dx = (
+            (48530.73235537 + 117.25274402 * np.sqrt(params["datarate"]))
+            * 8000
+            / params["datarate"]
+        )
 
         return de_dx * 1e-12
 
+
 # PYTHONPATH=lib bin/analyze-archive.py --show-model=all --show-quality=table ../data/*_RF24_no_retries.tar
 class NRF24L01tx:
     """NRF24L01+ TX(*) energy based on aemr measurements (32B fixed packet size, (*)ack-await, no retries)."""
-    name = 'NRF24L01'
+
+    name = "NRF24L01"
     parameters = {
-        'datarate' : [250, 1000, 2000], # kbps
-        'txbytes' : [],
-        'txpower' : [-18, -12, -6, 0], # dBm
-        'voltage' : [1.9, 3.6],
+        "datarate": [250, 1000, 2000],  # kbps
+        "txbytes": [],
+        "txpower": [-18, -12, -6, 0],  # dBm
+        "voltage": [1.9, 3.6],
     }
     default_params = {
-        'datarate' : 1000,
-        'txpower' : -6,
-        'voltage' : 3,
+        "datarate": 1000,
+        "txpower": -6,
+        "voltage": 3,
     }
 
-# AEMR:
-# TX power / energy:
-#TX        : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * (19.47+parameter(txpower))**2 + regression_arg(3) * 1/(parameter(datarate)) * (19.47+parameter(txpower))**2
-#            [6.30323056e+03 2.59889924e+06 7.82186268e+00 8.69746093e+03]
-#TX        : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * (19.47+parameter(txpower))**2 + regression_arg(3) * 1/(parameter(datarate)) * (19.47+parameter(txpower))**2
-#            [7.67932887e+00 1.02969455e+04 4.55116475e-03 2.99786534e+01]
-#epilogue  : timeout   : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))
-#            [  1624.06589147 332251.93798766]
-
+    # AEMR:
+    # TX power / energy:
+    # TX        : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * (19.47+parameter(txpower))**2 + regression_arg(3) * 1/(parameter(datarate)) * (19.47+parameter(txpower))**2
+    #            [6.30323056e+03 2.59889924e+06 7.82186268e+00 8.69746093e+03]
+    # TX        : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate)) + regression_arg(2) * (19.47+parameter(txpower))**2 + regression_arg(3) * 1/(parameter(datarate)) * (19.47+parameter(txpower))**2
+    #            [7.67932887e+00 1.02969455e+04 4.55116475e-03 2.99786534e+01]
+    # epilogue  : timeout   : 0 + regression_arg(0) + regression_arg(1) * 1/(parameter(datarate))
+    #            [  1624.06589147 332251.93798766]
 
     @staticmethod
     def get_energy(params):
@@ -192,31 +224,37 @@ class NRF24L01tx:
 
         # TX-Leistung, gefitted von AEMR
         # Messdaten erhoben bei 3.6V
-        power = 6.30323056e+03
-        power += 2.59889924e+06 * 1/params['datarate']
-        power += 7.82186268e+00 * (19.47+params['txpower'])**2
-        power += 8.69746093e+03 * 1/params['datarate'] *  (19.47+params['txpower'])**2
+        power = 6.30323056e03
+        power += 2.59889924e06 * 1 / params["datarate"]
+        power += 7.82186268e00 * (19.47 + params["txpower"]) ** 2
+        power += (
+            8.69746093e03 * 1 / params["datarate"] * (19.47 + params["txpower"]) ** 2
+        )
 
         # TX-Dauer, gefitted von AEMR
         duration = 1624.06589147
-        duration += 332251.93798766 * 1/params['datarate']
+        duration += 332251.93798766 * 1 / params["datarate"]
 
         # TX-Energie, gefitted von AEMR
         # Achtung: Energy ist in µJ, nicht (wie in AEMR-Transitionsmodellen üblich) in pJ
         # Messdaten erhoben bei 3.6V
-        energy = 7.67932887e+00
-        energy += 1.02969455e+04 * 1/params['datarate']
-        energy += 4.55116475e-03 * (19.47+params['txpower'])**2
-        energy += 2.99786534e+01 * 1/params['datarate'] * (19.47+params['txpower'])**2
+        energy = 7.67932887e00
+        energy += 1.02969455e04 * 1 / params["datarate"]
+        energy += 4.55116475e-03 * (19.47 + params["txpower"]) ** 2
+        energy += (
+            2.99786534e01 * 1 / params["datarate"] * (19.47 + params["txpower"]) ** 2
+        )
 
-        energy = power * 1e-6 * duration * 1e-6 * np.ceil(params['txbytes'] / 32)
+        energy = power * 1e-6 * duration * 1e-6 * np.ceil(params["txbytes"] / 32)
 
         return energy
 
     @staticmethod
     def get_energy_per_byte(params):
         if type(params) != dict:
-            return NRF24L01tx.get_energy_per_byte(_param_list_to_dict(NRF24L01tx, params))
+            return NRF24L01tx.get_energy_per_byte(
+                _param_list_to_dict(NRF24L01tx, params)
+            )
 
         # in µJ
         de_dx = 0
@@ -224,17 +262,18 @@ class NRF24L01tx:
 
 class NRF24L01dtx:
     """nRF24L01+ TX energy based on datasheet values (probably unerestimated)"""
-    name = 'NRF24L01'
+
+    name = "NRF24L01"
     parameters = {
-        'datarate' : [250, 1000, 2000], # kbps
-        'txbytes' : [],
-        'txpower' : [-18, -12, -6, 0], # dBm
-        'voltage' : [1.9, 3.6],
+        "datarate": [250, 1000, 2000],  # kbps
+        "txbytes": [],
+        "txpower": [-18, -12, -6, 0],  # dBm
+        "voltage": [1.9, 3.6],
     }
     default_params = {
-        'datarate' : 1000,
-        'txpower' : -6,
-        'voltage' : 3,
+        "datarate": 1000,
+        "txpower": -6,
+        "voltage": 3,
     }
 
     # 130 us RX settling: 8.9 mE
@@ -248,35 +287,37 @@ class NRF24L01dtx:
         header_bytes = 7
 
         # TX settling: 130 us @ 8 mA
-        energy = 8e-3 * params['voltage'] * 130e-6
+        energy = 8e-3 * params["voltage"] * 130e-6
 
-        if params['txpower'] == -18:
+        if params["txpower"] == -18:
             current = 7e-3
-        elif params['txpower'] == -12:
+        elif params["txpower"] == -12:
             current = 7.5e-3
-        elif params['txpower'] == -6:
+        elif params["txpower"] == -6:
             current = 9e-3
-        elif params['txpower'] == 0:
+        elif params["txpower"] == 0:
             current = 11.3e-3
 
-        energy += current * params['voltage'] * ((header_bytes + params['txbytes']) * 8 / (params['datarate'] * 1e3))
+        energy += (
+            current
+            * params["voltage"]
+            * ((header_bytes + params["txbytes"]) * 8 / (params["datarate"] * 1e3))
+        )
 
         return energy
 
+
 class ESP8266dtx:
     """esp8266 TX energy based on (hardly documented) datasheet values"""
-    name = 'esp8266'
+
+    name = "esp8266"
     parameters = {
-        'voltage' : [2.5, 3.0, 3.3, 3.6],
-        'txbytes' : [],
-        'bitrate' : [65e6],
-        'tx_current' : [120e-3],
-    }
-    default_params = {
-        'voltage' : 3,
-        'bitrate' : 65e6,
-        'tx_current' : 120e-3
+        "voltage": [2.5, 3.0, 3.3, 3.6],
+        "txbytes": [],
+        "bitrate": [65e6],
+        "tx_current": [120e-3],
     }
+    default_params = {"voltage": 3, "bitrate": 65e6, "tx_current": 120e-3}
 
     @staticmethod
     def get_energy(params):
@@ -286,26 +327,26 @@ class ESP8266dtx:
     @staticmethod
     def get_energy_per_byte(params):
         if type(params) != dict:
-            return ESP8266dtx.get_energy_per_byte(_param_list_to_dict(ESP8266dtx, params))
+            return ESP8266dtx.get_energy_per_byte(
+                _param_list_to_dict(ESP8266dtx, params)
+            )
 
         # TX in 802.11n MCS7 -> 64QAM, 65/72.2 Mbit/s @ 20MHz channel, 135/150 Mbit/s @ 40MHz
         # -> Value for 65 Mbit/s @ 20MHz channel
-        return params['tx_current'] * params['voltage'] / params['bitrate']
+        return params["tx_current"] * params["voltage"] / params["bitrate"]
+
 
 class ESP8266drx:
     """esp8266 RX energy based on (hardly documented) datasheet values"""
-    name = 'esp8266'
+
+    name = "esp8266"
     parameters = {
-        'voltage' : [2.5, 3.0, 3.3, 3.6],
-        'txbytes' : [],
-        'bitrate' : [65e6],
-        'rx_current' : [56e-3],
-    }
-    default_params = {
-        'voltage' : 3,
-        'bitrate' : 65e6,
-        'rx_current' : 56e-3
+        "voltage": [2.5, 3.0, 3.3, 3.6],
+        "txbytes": [],
+        "bitrate": [65e6],
+        "rx_current": [56e-3],
     }
+    default_params = {"voltage": 3, "bitrate": 65e6, "rx_current": 56e-3}
 
     @staticmethod
     def get_energy(params):
@@ -315,8 +356,10 @@ class ESP8266drx:
     @staticmethod
     def get_energy_per_byte(params):
         if type(params) != dict:
-            return ESP8266drx.get_energy_per_byte(_param_list_to_dict(ESP8266drx, params))
+            return ESP8266drx.get_energy_per_byte(
+                _param_list_to_dict(ESP8266drx, params)
+            )
 
         # TX in 802.11n MCS7 -> 64QAM, 65/72.2 Mbit/s @ 20MHz channel, 135/150 Mbit/s @ 40MHz
         # -> Value for 65 Mbit/s @ 20MHz channel
-        return params['rx_current'] * params['voltage'] / params['bitrate']
+        return params["rx_current"] * params["voltage"] / params["bitrate"]
diff --git a/lib/sly/__init__.py b/lib/sly/__init__.py
index 3c1e708..3a2d92e 100644
--- a/lib/sly/__init__.py
+++ b/lib/sly/__init__.py
@@ -1,6 +1,5 @@
-
 from .lex import *
 from .yacc import *
 
 __version__ = "0.4"
-__all__ = [ *lex.__all__, *yacc.__all__ ]
+__all__ = [*lex.__all__, *yacc.__all__]
diff --git a/lib/sly/ast.py b/lib/sly/ast.py
index 7b79ac5..05802bd 100644
--- a/lib/sly/ast.py
+++ b/lib/sly/ast.py
@@ -1,25 +1,24 @@
 # sly/ast.py
 import sys
 
+
 class AST(object):
-    
     @classmethod
     def __init_subclass__(cls, **kwargs):
         mod = sys.modules[cls.__module__]
-        if not hasattr(cls, '__annotations__'):
+        if not hasattr(cls, "__annotations__"):
             return
 
         hints = list(cls.__annotations__.items())
 
         def __init__(self, *args, **kwargs):
             if len(hints) != len(args):
-                raise TypeError(f'Expected {len(hints)} arguments')
+                raise TypeError(f"Expected {len(hints)} arguments")
             for arg, (name, val) in zip(args, hints):
                 if isinstance(val, str):
                     val = getattr(mod, val)
                 if not isinstance(arg, val):
-                    raise TypeError(f'{name} argument must be {val}')
+                    raise TypeError(f"{name} argument must be {val}")
                 setattr(self, name, arg)
 
         cls.__init__ = __init__
-
diff --git a/lib/sly/docparse.py b/lib/sly/docparse.py
index d5a83ce..0f35c97 100644
--- a/lib/sly/docparse.py
+++ b/lib/sly/docparse.py
@@ -2,7 +2,8 @@
 #
 # Support doc-string parsing classes
 
-__all__ = [ 'DocParseMeta' ]
+__all__ = ["DocParseMeta"]
+
 
 class DocParseMeta(type):
     '''
@@ -44,17 +45,17 @@ class DocParseMeta(type):
 
     @staticmethod
     def __new__(meta, clsname, bases, clsdict):
-        if '__doc__' in clsdict:
+        if "__doc__" in clsdict:
             lexer = meta.lexer()
             parser = meta.parser()
             lexer.cls_name = parser.cls_name = clsname
-            lexer.cls_qualname = parser.cls_qualname = clsdict['__qualname__']
-            lexer.cls_module = parser.cls_module = clsdict['__module__']
-            parsedict = parser.parse(lexer.tokenize(clsdict['__doc__']))
-            assert isinstance(parsedict, dict), 'Parser must return a dictionary'
+            lexer.cls_qualname = parser.cls_qualname = clsdict["__qualname__"]
+            lexer.cls_module = parser.cls_module = clsdict["__module__"]
+            parsedict = parser.parse(lexer.tokenize(clsdict["__doc__"]))
+            assert isinstance(parsedict, dict), "Parser must return a dictionary"
             clsdict.update(parsedict)
         return super().__new__(meta, clsname, bases, clsdict)
 
     @classmethod
     def __init_subclass__(cls):
-        assert hasattr(cls, 'parser') and hasattr(cls, 'lexer')
+        assert hasattr(cls, "parser") and hasattr(cls, "lexer")
diff --git a/lib/sly/lex.py b/lib/sly/lex.py
index 246dd9e..0ab0160 100644
--- a/lib/sly/lex.py
+++ b/lib/sly/lex.py
@@ -31,51 +31,63 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 # -----------------------------------------------------------------------------
 
-__all__ = ['Lexer', 'LexerStateChange']
+__all__ = ["Lexer", "LexerStateChange"]
 
 import re
 import copy
 
+
 class LexError(Exception):
-    '''
+    """
     Exception raised if an invalid character is encountered and no default
     error handler function is defined.  The .text attribute of the exception
     contains all remaining untokenized text. The .error_index is the index
     location of the error.
-    '''
+    """
+
     def __init__(self, message, text, error_index):
         self.args = (message,)
         self.text = text
         self.error_index = error_index
 
+
 class PatternError(Exception):
-    '''
+    """
     Exception raised if there's some kind of problem with the specified
     regex patterns in the lexer.
-    '''
+    """
+
     pass
 
+
 class LexerBuildError(Exception):
-    '''
+    """
     Exception raised if there's some sort of problem building the lexer.
-    '''
+    """
+
     pass
 
+
 class LexerStateChange(Exception):
-    '''
+    """
     Exception raised to force a lexing state change
-    '''
+    """
+
     def __init__(self, newstate, tok=None):
         self.newstate = newstate
         self.tok = tok
 
+
 class Token(object):
-    '''
+    """
     Representation of a single token.
-    '''
-    __slots__ = ('type', 'value', 'lineno', 'index')
+    """
+
+    __slots__ = ("type", "value", "lineno", "index")
+
     def __repr__(self):
-        return f'Token(type={self.type!r}, value={self.value!r}, lineno={self.lineno}, index={self.index})'
+        return f"Token(type={self.type!r}, value={self.value!r}, lineno={self.lineno}, index={self.index})"
+
 
 class TokenStr(str):
     @staticmethod
@@ -95,35 +107,38 @@ class TokenStr(str):
         if self.remap is not None:
             self.remap[self.key, key] = self.key
 
+
 class _Before:
     def __init__(self, tok, pattern):
         self.tok = tok
         self.pattern = pattern
 
+
 class LexerMetaDict(dict):
-    '''
+    """
     Special dictionary that prohibits duplicate definitions in lexer specifications.
-    '''
+    """
+
     def __init__(self):
-        self.before = { }
-        self.delete = [ ]
-        self.remap = { }
+        self.before = {}
+        self.delete = []
+        self.remap = {}
 
     def __setitem__(self, key, value):
         if isinstance(value, str):
             value = TokenStr(value, key, self.remap)
-            
+
         if isinstance(value, _Before):
             self.before[key] = value.tok
             value = TokenStr(value.pattern, key, self.remap)
-            
+
         if key in self and not isinstance(value, property):
             prior = self[key]
             if isinstance(prior, str):
                 if callable(value):
                     value.pattern = prior
                 else:
-                    raise AttributeError(f'Name {key} redefined')
+                    raise AttributeError(f"Name {key} redefined")
 
         super().__setitem__(key, value)
 
@@ -135,41 +150,47 @@ class LexerMetaDict(dict):
             return super().__delitem__(key)
 
     def __getitem__(self, key):
-        if key not in self and key.split('ignore_')[-1].isupper() and key[:1] != '_':
+        if key not in self and key.split("ignore_")[-1].isupper() and key[:1] != "_":
             return TokenStr(key, key, self.remap)
         else:
             return super().__getitem__(key)
 
+
 class LexerMeta(type):
-    '''
+    """
     Metaclass for collecting lexing rules
-    '''
+    """
+
     @classmethod
     def __prepare__(meta, name, bases):
         d = LexerMetaDict()
 
         def _(pattern, *extra):
             patterns = [pattern, *extra]
+
             def decorate(func):
-                pattern = '|'.join(f'({pat})' for pat in patterns )
-                if hasattr(func, 'pattern'):
-                    func.pattern = pattern + '|' + func.pattern
+                pattern = "|".join(f"({pat})" for pat in patterns)
+                if hasattr(func, "pattern"):
+                    func.pattern = pattern + "|" + func.pattern
                 else:
                     func.pattern = pattern
                 return func
+
             return decorate
 
-        d['_'] = _
-        d['before'] = _Before
+        d["_"] = _
+        d["before"] = _Before
         return d
 
     def __new__(meta, clsname, bases, attributes):
-        del attributes['_']
-        del attributes['before']
+        del attributes["_"]
+        del attributes["before"]
 
         # Create attributes for use in the actual class body
-        cls_attributes = { str(key): str(val) if isinstance(val, TokenStr) else val
-                           for key, val in attributes.items() }
+        cls_attributes = {
+            str(key): str(val) if isinstance(val, TokenStr) else val
+            for key, val in attributes.items()
+        }
         cls = super().__new__(meta, clsname, bases, cls_attributes)
 
         # Attach various metadata to the class
@@ -180,11 +201,12 @@ class LexerMeta(type):
         cls._build()
         return cls
 
+
 class Lexer(metaclass=LexerMeta):
     # These attributes may be defined in subclasses
     tokens = set()
     literals = set()
-    ignore = ''
+    ignore = ""
     reflags = 0
     regex_module = re
 
@@ -214,7 +236,7 @@ class Lexer(metaclass=LexerMeta):
         #     Such functions can be created with the @_ decorator or by defining
         #     function with the same name as a previously defined string.
         #
-        # This function is responsible for keeping rules in order. 
+        # This function is responsible for keeping rules in order.
 
         # Collect all previous rules from base classes
         rules = []
@@ -222,15 +244,21 @@ class Lexer(metaclass=LexerMeta):
         for base in cls.__bases__:
             if isinstance(base, LexerMeta):
                 rules.extend(base._rules)
-                
+
         # Dictionary of previous rules
         existing = dict(rules)
 
         for key, value in cls._attributes.items():
-            if (key in cls._token_names) or key.startswith('ignore_') or hasattr(value, 'pattern'):
-                if callable(value) and not hasattr(value, 'pattern'):
-                    raise LexerBuildError(f"function {value} doesn't have a regex pattern")
-                
+            if (
+                (key in cls._token_names)
+                or key.startswith("ignore_")
+                or hasattr(value, "pattern")
+            ):
+                if callable(value) and not hasattr(value, "pattern"):
+                    raise LexerBuildError(
+                        f"function {value} doesn't have a regex pattern"
+                    )
+
                 if key in existing:
                     # The definition matches something that already existed in the base class.
                     # We replace it, but keep the original ordering
@@ -252,21 +280,27 @@ class Lexer(metaclass=LexerMeta):
                     rules.append((key, value))
                     existing[key] = value
 
-            elif isinstance(value, str) and not key.startswith('_') and key not in {'ignore', 'literals'}:
-                raise LexerBuildError(f'{key} does not match a name in tokens')
+            elif (
+                isinstance(value, str)
+                and not key.startswith("_")
+                and key not in {"ignore", "literals"}
+            ):
+                raise LexerBuildError(f"{key} does not match a name in tokens")
 
         # Apply deletion rules
-        rules = [ (key, value) for key, value in rules if key not in cls._delete ]
+        rules = [(key, value) for key, value in rules if key not in cls._delete]
         cls._rules = rules
 
     @classmethod
     def _build(cls):
-        '''
+        """
         Build the lexer object from the collected tokens and regular expressions.
         Validate the rules to make sure they look sane.
-        '''
-        if 'tokens' not in vars(cls):
-            raise LexerBuildError(f'{cls.__qualname__} class does not define a tokens attribute')
+        """
+        if "tokens" not in vars(cls):
+            raise LexerBuildError(
+                f"{cls.__qualname__} class does not define a tokens attribute"
+            )
 
         # Pull definitions created for any parent classes
         cls._token_names = cls._token_names | set(cls.tokens)
@@ -282,17 +316,17 @@ class Lexer(metaclass=LexerMeta):
         remapped_toks = set()
         for d in cls._remapping.values():
             remapped_toks.update(d.values())
-            
+
         undefined = remapped_toks - set(cls._token_names)
         if undefined:
-            missing = ', '.join(undefined)
-            raise LexerBuildError(f'{missing} not included in token(s)')
+            missing = ", ".join(undefined)
+            raise LexerBuildError(f"{missing} not included in token(s)")
 
         cls._collect_rules()
 
         parts = []
         for tokname, value in cls._rules:
-            if tokname.startswith('ignore_'):
+            if tokname.startswith("ignore_"):
                 tokname = tokname[7:]
                 cls._ignored_tokens.add(tokname)
 
@@ -301,20 +335,20 @@ class Lexer(metaclass=LexerMeta):
 
             elif callable(value):
                 cls._token_funcs[tokname] = value
-                pattern = getattr(value, 'pattern')
+                pattern = getattr(value, "pattern")
 
             # Form the regular expression component
-            part = f'(?P<{tokname}>{pattern})'
+            part = f"(?P<{tokname}>{pattern})"
 
             # Make sure the individual regex compiles properly
             try:
                 cpat = cls.regex_module.compile(part, cls.reflags)
             except Exception as e:
-                raise PatternError(f'Invalid regex for token {tokname}') from e
+                raise PatternError(f"Invalid regex for token {tokname}") from e
 
             # Verify that the pattern doesn't match the empty string
-            if cpat.match(''):
-                raise PatternError(f'Regex for token {tokname} matches empty input')
+            if cpat.match(""):
+                raise PatternError(f"Regex for token {tokname} matches empty input")
 
             parts.append(part)
 
@@ -322,43 +356,45 @@ class Lexer(metaclass=LexerMeta):
             return
 
         # Form the master regular expression
-        #previous = ('|' + cls._master_re.pattern) if cls._master_re else ''
+        # previous = ('|' + cls._master_re.pattern) if cls._master_re else ''
         # cls._master_re = cls.regex_module.compile('|'.join(parts) + previous, cls.reflags)
-        cls._master_re = cls.regex_module.compile('|'.join(parts), cls.reflags)
+        cls._master_re = cls.regex_module.compile("|".join(parts), cls.reflags)
 
         # Verify that that ignore and literals specifiers match the input type
         if not isinstance(cls.ignore, str):
-            raise LexerBuildError('ignore specifier must be a string')
+            raise LexerBuildError("ignore specifier must be a string")
 
         if not all(isinstance(lit, str) for lit in cls.literals):
-            raise LexerBuildError('literals must be specified as strings')
+            raise LexerBuildError("literals must be specified as strings")
 
     def begin(self, cls):
-        '''
+        """
         Begin a new lexer state
-        '''
+        """
         assert isinstance(cls, LexerMeta), "state must be a subclass of Lexer"
         if self.__set_state:
             self.__set_state(cls)
         self.__class__ = cls
 
     def push_state(self, cls):
-        '''
+        """
         Push a new lexer state onto the stack
-        '''
+        """
         if self.__state_stack is None:
             self.__state_stack = []
         self.__state_stack.append(type(self))
         self.begin(cls)
 
     def pop_state(self):
-        '''
+        """
         Pop a lexer state from the stack
-        '''
+        """
         self.begin(self.__state_stack.pop())
 
     def tokenize(self, text, lineno=1, index=0):
-        _ignored_tokens = _master_re = _ignore = _token_funcs = _literals = _remapping = None
+        _ignored_tokens = (
+            _master_re
+        ) = _ignore = _token_funcs = _literals = _remapping = None
 
         def _set_state(cls):
             nonlocal _ignored_tokens, _master_re, _ignore, _token_funcs, _literals, _remapping
@@ -419,7 +455,7 @@ class Lexer(metaclass=LexerMeta):
                         # A lexing error
                         self.index = index
                         self.lineno = lineno
-                        tok.type = 'ERROR'
+                        tok.type = "ERROR"
                         tok.value = text[index:]
                         tok = self.error(tok)
                         if tok is not None:
@@ -436,4 +472,8 @@ class Lexer(metaclass=LexerMeta):
 
     # Default implementations of the error handler. May be changed in subclasses
     def error(self, t):
-        raise LexError(f'Illegal character {t.value[0]!r} at index {self.index}', t.value, self.index)
+        raise LexError(
+            f"Illegal character {t.value[0]!r} at index {self.index}",
+            t.value,
+            self.index,
+        )
diff --git a/lib/sly/yacc.py b/lib/sly/yacc.py
index c30f13c..167168d 100644
--- a/lib/sly/yacc.py
+++ b/lib/sly/yacc.py
@@ -35,22 +35,25 @@ import sys
 import inspect
 from collections import OrderedDict, defaultdict
 
-__all__        = [ 'Parser' ]
+__all__ = ["Parser"]
+
 
 class YaccError(Exception):
-    '''
+    """
     Exception raised for yacc-related build errors.
-    '''
+    """
+
     pass
 
-#-----------------------------------------------------------------------------
+
+# -----------------------------------------------------------------------------
 #                     === User configurable parameters ===
 #
-# Change these to modify the default behavior of yacc (if you wish).  
+# Change these to modify the default behavior of yacc (if you wish).
 # Move these parameters to the Yacc class itself.
-#-----------------------------------------------------------------------------
+# -----------------------------------------------------------------------------
 
-ERROR_COUNT = 3                # Number of symbols that must be shifted to leave recovery mode
+ERROR_COUNT = 3  # Number of symbols that must be shifted to leave recovery mode
 MAXINT = sys.maxsize
 
 # This object is a stand-in for a logging object created by the
@@ -59,20 +62,21 @@ MAXINT = sys.maxsize
 # information, they can create their own logging object and pass
 # it into SLY.
 
+
 class SlyLogger(object):
     def __init__(self, f):
         self.f = f
 
     def debug(self, msg, *args, **kwargs):
-        self.f.write((msg % args) + '\n')
+        self.f.write((msg % args) + "\n")
 
     info = debug
 
     def warning(self, msg, *args, **kwargs):
-        self.f.write('WARNING: ' + (msg % args) + '\n')
+        self.f.write("WARNING: " + (msg % args) + "\n")
 
     def error(self, msg, *args, **kwargs):
-        self.f.write('ERROR: ' + (msg % args) + '\n')
+        self.f.write("ERROR: " + (msg % args) + "\n")
 
     critical = debug
 
@@ -86,6 +90,7 @@ class SlyLogger(object):
 #        .index      = Starting lex position
 # ----------------------------------------------------------------------
 
+
 class YaccSymbol:
     def __str__(self):
         return self.type
@@ -93,19 +98,22 @@ class YaccSymbol:
     def __repr__(self):
         return str(self)
 
+
 # ----------------------------------------------------------------------
 # This class is a wrapper around the objects actually passed to each
 # grammar rule.   Index lookup and assignment actually assign the
 # .value attribute of the underlying YaccSymbol object.
 # The lineno() method returns the line number of a given
-# item (or 0 if not defined).   
+# item (or 0 if not defined).
 # ----------------------------------------------------------------------
 
+
 class YaccProduction:
-    __slots__ = ('_slice', '_namemap', '_stack')
+    __slots__ = ("_slice", "_namemap", "_stack")
+
     def __init__(self, s, stack=None):
         self._slice = s
-        self._namemap = { }
+        self._namemap = {}
         self._stack = stack
 
     def __getitem__(self, n):
@@ -128,34 +136,35 @@ class YaccProduction:
         for tok in self._slice:
             if isinstance(tok, YaccSymbol):
                 continue
-            lineno = getattr(tok, 'lineno', None)
+            lineno = getattr(tok, "lineno", None)
             if lineno:
                 return lineno
-        raise AttributeError('No line number found')
+        raise AttributeError("No line number found")
 
     @property
     def index(self):
         for tok in self._slice:
             if isinstance(tok, YaccSymbol):
                 continue
-            index = getattr(tok, 'index', None)
+            index = getattr(tok, "index", None)
             if index is not None:
                 return index
-        raise AttributeError('No index attribute found')
+        raise AttributeError("No index attribute found")
 
     def __getattr__(self, name):
         if name in self._namemap:
             return self._slice[self._namemap[name]].value
         else:
-            nameset = '{' + ', '.join(self._namemap) + '}'
-            raise AttributeError(f'No symbol {name}. Must be one of {nameset}.')
+            nameset = "{" + ", ".join(self._namemap) + "}"
+            raise AttributeError(f"No symbol {name}. Must be one of {nameset}.")
 
     def __setattr__(self, name, value):
-        if name[:1] == '_':
+        if name[:1] == "_":
             super().__setattr__(name, value)
         else:
             raise AttributeError(f"Can't reassign the value of attribute {name!r}")
 
+
 # -----------------------------------------------------------------------------
 #                          === Grammar Representation ===
 #
@@ -187,19 +196,23 @@ class YaccProduction:
 #       usyms     - Set of unique symbols found in the production
 # -----------------------------------------------------------------------------
 
+
 class Production(object):
     reduced = 0
-    def __init__(self, number, name, prod, precedence=('right', 0), func=None, file='', line=0):
-        self.name     = name
-        self.prod     = tuple(prod)
-        self.number   = number
-        self.func     = func
-        self.file     = file
-        self.line     = line
-        self.prec     = precedence
-        
+
+    def __init__(
+        self, number, name, prod, precedence=("right", 0), func=None, file="", line=0
+    ):
+        self.name = name
+        self.prod = tuple(prod)
+        self.number = number
+        self.func = func
+        self.file = file
+        self.line = line
+        self.prec = precedence
+
         # Internal settings used during table construction
-        self.len  = len(self.prod)   # Length of the production
+        self.len = len(self.prod)  # Length of the production
 
         # Create a list of unique production symbols used in the production
         self.usyms = []
@@ -216,33 +229,33 @@ class Production(object):
                 m[key] = indices[0]
             else:
                 for n, index in enumerate(indices):
-                    m[key+str(n)] = index
+                    m[key + str(n)] = index
 
         self.namemap = m
-                
+
         # List of all LR items for the production
         self.lr_items = []
         self.lr_next = None
 
     def __str__(self):
         if self.prod:
-            s = '%s -> %s' % (self.name, ' '.join(self.prod))
+            s = "%s -> %s" % (self.name, " ".join(self.prod))
         else:
-            s = f'{self.name} -> <empty>'
+            s = f"{self.name} -> <empty>"
 
         if self.prec[1]:
-            s += '  [precedence=%s, level=%d]' % self.prec
+            s += "  [precedence=%s, level=%d]" % self.prec
 
         return s
 
     def __repr__(self):
-        return f'Production({self})'
+        return f"Production({self})"
 
     def __len__(self):
         return len(self.prod)
 
     def __nonzero__(self):
-        raise RuntimeError('Used')
+        raise RuntimeError("Used")
         return 1
 
     def __getitem__(self, index):
@@ -255,15 +268,16 @@ class Production(object):
         p = LRItem(self, n)
         # Precompute the list of productions immediately following.
         try:
-            p.lr_after = Prodnames[p.prod[n+1]]
+            p.lr_after = Prodnames[p.prod[n + 1]]
         except (IndexError, KeyError):
             p.lr_after = []
         try:
-            p.lr_before = p.prod[n-1]
+            p.lr_before = p.prod[n - 1]
         except IndexError:
             p.lr_before = None
         return p
 
+
 # -----------------------------------------------------------------------------
 # class LRItem
 #
@@ -288,27 +302,29 @@ class Production(object):
 #       lr_before   - Grammar symbol immediately before
 # -----------------------------------------------------------------------------
 
+
 class LRItem(object):
     def __init__(self, p, n):
-        self.name       = p.name
-        self.prod       = list(p.prod)
-        self.number     = p.number
-        self.lr_index   = n
+        self.name = p.name
+        self.prod = list(p.prod)
+        self.number = p.number
+        self.lr_index = n
         self.lookaheads = {}
-        self.prod.insert(n, '.')
-        self.prod       = tuple(self.prod)
-        self.len        = len(self.prod)
-        self.usyms      = p.usyms
+        self.prod.insert(n, ".")
+        self.prod = tuple(self.prod)
+        self.len = len(self.prod)
+        self.usyms = p.usyms
 
     def __str__(self):
         if self.prod:
-            s = '%s -> %s' % (self.name, ' '.join(self.prod))
+            s = "%s -> %s" % (self.name, " ".join(self.prod))
         else:
-            s = f'{self.name} -> <empty>'
+            s = f"{self.name} -> <empty>"
         return s
 
     def __repr__(self):
-        return f'LRItem({self})'
+        return f"LRItem({self})"
+
 
 # -----------------------------------------------------------------------------
 # rightmost_terminal()
@@ -323,6 +339,7 @@ def rightmost_terminal(symbols, terminals):
         i -= 1
     return None
 
+
 # -----------------------------------------------------------------------------
 #                           === GRAMMAR CLASS ===
 #
@@ -331,45 +348,52 @@ def rightmost_terminal(symbols, terminals):
 # This data is used for critical parts of the table generation process later.
 # -----------------------------------------------------------------------------
 
+
 class GrammarError(YaccError):
     pass
 
+
 class Grammar(object):
     def __init__(self, terminals):
-        self.Productions  = [None]  # A list of all of the productions.  The first
-                                    # entry is always reserved for the purpose of
-                                    # building an augmented grammar
+        self.Productions = [None]  # A list of all of the productions.  The first
+        # entry is always reserved for the purpose of
+        # building an augmented grammar
 
-        self.Prodnames    = {}      # A dictionary mapping the names of nonterminals to a list of all
-                                    # productions of that nonterminal.
+        self.Prodnames = (
+            {}
+        )  # A dictionary mapping the names of nonterminals to a list of all
+        # productions of that nonterminal.
 
-        self.Prodmap      = {}      # A dictionary that is only used to detect duplicate
-                                    # productions.
+        self.Prodmap = {}  # A dictionary that is only used to detect duplicate
+        # productions.
 
-        self.Terminals    = {}      # A dictionary mapping the names of terminal symbols to a
-                                    # list of the rules where they are used.
+        self.Terminals = {}  # A dictionary mapping the names of terminal symbols to a
+        # list of the rules where they are used.
 
         for term in terminals:
             self.Terminals[term] = []
 
-        self.Terminals['error'] = []
-
-        self.Nonterminals = {}      # A dictionary mapping names of nonterminals to a list
-                                    # of rule numbers where they are used.
+        self.Terminals["error"] = []
 
-        self.First        = {}      # A dictionary of precomputed FIRST(x) symbols
+        self.Nonterminals = {}  # A dictionary mapping names of nonterminals to a list
+        # of rule numbers where they are used.
 
-        self.Follow       = {}      # A dictionary of precomputed FOLLOW(x) symbols
+        self.First = {}  # A dictionary of precomputed FIRST(x) symbols
 
-        self.Precedence   = {}      # Precedence rules for each terminal. Contains tuples of the
-                                    # form ('right',level) or ('nonassoc', level) or ('left',level)
+        self.Follow = {}  # A dictionary of precomputed FOLLOW(x) symbols
 
-        self.UsedPrecedence = set() # Precedence rules that were actually used by the grammer.
-                                    # This is only used to provide error checking and to generate
-                                    # a warning about unused precedence rules.
+        self.Precedence = (
+            {}
+        )  # Precedence rules for each terminal. Contains tuples of the
+        # form ('right',level) or ('nonassoc', level) or ('left',level)
 
-        self.Start = None           # Starting symbol for the grammar
+        self.UsedPrecedence = (
+            set()
+        )  # Precedence rules that were actually used by the grammer.
+        # This is only used to provide error checking and to generate
+        # a warning about unused precedence rules.
 
+        self.Start = None  # Starting symbol for the grammar
 
     def __len__(self):
         return len(self.Productions)
@@ -386,11 +410,15 @@ class Grammar(object):
     # -----------------------------------------------------------------------------
 
     def set_precedence(self, term, assoc, level):
-        assert self.Productions == [None], 'Must call set_precedence() before add_production()'
+        assert self.Productions == [
+            None
+        ], "Must call set_precedence() before add_production()"
         if term in self.Precedence:
-            raise GrammarError(f'Precedence already specified for terminal {term!r}')
-        if assoc not in ['left', 'right', 'nonassoc']:
-            raise GrammarError(f"Associativity of {term!r} must be one of 'left','right', or 'nonassoc'")
+            raise GrammarError(f"Precedence already specified for terminal {term!r}")
+        if assoc not in ["left", "right", "nonassoc"]:
+            raise GrammarError(
+                f"Associativity of {term!r} must be one of 'left','right', or 'nonassoc'"
+            )
         self.Precedence[term] = (assoc, level)
 
     # -----------------------------------------------------------------------------
@@ -410,51 +438,65 @@ class Grammar(object):
     # are valid and that %prec is used correctly.
     # -----------------------------------------------------------------------------
 
-    def add_production(self, prodname, syms, func=None, file='', line=0):
+    def add_production(self, prodname, syms, func=None, file="", line=0):
 
         if prodname in self.Terminals:
-            raise GrammarError(f'{file}:{line}: Illegal rule name {prodname!r}. Already defined as a token')
-        if prodname == 'error':
-            raise GrammarError(f'{file}:{line}: Illegal rule name {prodname!r}. error is a reserved word')
+            raise GrammarError(
+                f"{file}:{line}: Illegal rule name {prodname!r}. Already defined as a token"
+            )
+        if prodname == "error":
+            raise GrammarError(
+                f"{file}:{line}: Illegal rule name {prodname!r}. error is a reserved word"
+            )
 
         # Look for literal tokens
         for n, s in enumerate(syms):
             if s[0] in "'\"" and s[0] == s[-1]:
                 c = s[1:-1]
-                if (len(c) != 1):
-                    raise GrammarError(f'{file}:{line}: Literal token {s} in rule {prodname!r} may only be a single character')
+                if len(c) != 1:
+                    raise GrammarError(
+                        f"{file}:{line}: Literal token {s} in rule {prodname!r} may only be a single character"
+                    )
                 if c not in self.Terminals:
                     self.Terminals[c] = []
                 syms[n] = c
                 continue
 
         # Determine the precedence level
-        if '%prec' in syms:
-            if syms[-1] == '%prec':
-                raise GrammarError(f'{file}:{line}: Syntax error. Nothing follows %%prec')
-            if syms[-2] != '%prec':
-                raise GrammarError(f'{file}:{line}: Syntax error. %prec can only appear at the end of a grammar rule')
+        if "%prec" in syms:
+            if syms[-1] == "%prec":
+                raise GrammarError(
+                    f"{file}:{line}: Syntax error. Nothing follows %%prec"
+                )
+            if syms[-2] != "%prec":
+                raise GrammarError(
+                    f"{file}:{line}: Syntax error. %prec can only appear at the end of a grammar rule"
+                )
             precname = syms[-1]
             prodprec = self.Precedence.get(precname)
             if not prodprec:
-                raise GrammarError(f'{file}:{line}: Nothing known about the precedence of {precname!r}')
+                raise GrammarError(
+                    f"{file}:{line}: Nothing known about the precedence of {precname!r}"
+                )
             else:
                 self.UsedPrecedence.add(precname)
-            del syms[-2:]     # Drop %prec from the rule
+            del syms[-2:]  # Drop %prec from the rule
         else:
             # If no %prec, precedence is determined by the rightmost terminal symbol
             precname = rightmost_terminal(syms, self.Terminals)
-            prodprec = self.Precedence.get(precname, ('right', 0))
+            prodprec = self.Precedence.get(precname, ("right", 0))
 
         # See if the rule is already in the rulemap
-        map = '%s -> %s' % (prodname, syms)
+        map = "%s -> %s" % (prodname, syms)
         if map in self.Prodmap:
             m = self.Prodmap[map]
-            raise GrammarError(f'{file}:{line}: Duplicate rule {m}. ' +
-                               f'Previous definition at {m.file}:{m.line}')
+            raise GrammarError(
+                f"{file}:{line}: Duplicate rule {m}. "
+                + f"Previous definition at {m.file}:{m.line}"
+            )
 
         # From this point on, everything is valid.  Create a new Production instance
-        pnumber  = len(self.Productions)
+        pnumber = len(self.Productions)
         if prodname not in self.Nonterminals:
             self.Nonterminals[prodname] = []
 
@@ -493,7 +535,7 @@ class Grammar(object):
             start = self.Productions[1].name
 
         if start not in self.Nonterminals:
-            raise GrammarError(f'start symbol {start} undefined')
+            raise GrammarError(f"start symbol {start} undefined")
         self.Productions[0] = Production(0, "S'", [start])
         self.Nonterminals[start].append(0)
         self.Start = start
@@ -535,7 +577,7 @@ class Grammar(object):
         for t in self.Terminals:
             terminates[t] = True
 
-        terminates['$end'] = True
+        terminates["$end"] = True
 
         # Nonterminals:
 
@@ -576,7 +618,7 @@ class Grammar(object):
         infinite = []
         for (s, term) in terminates.items():
             if not term:
-                if s not in self.Prodnames and s not in self.Terminals and s != 'error':
+                if s not in self.Prodnames and s not in self.Terminals and s != "error":
                     # s is used-but-not-defined, and we've already warned of that,
                     # so it would be overkill to say that it's also non-terminating.
                     pass
@@ -599,7 +641,7 @@ class Grammar(object):
                 continue
 
             for s in p.prod:
-                if s not in self.Prodnames and s not in self.Terminals and s != 'error':
+                if s not in self.Prodnames and s not in self.Terminals and s != "error":
                     result.append((s, p))
         return result
 
@@ -612,7 +654,7 @@ class Grammar(object):
     def unused_terminals(self):
         unused_tok = []
         for s, v in self.Terminals.items():
-            if s != 'error' and not v:
+            if s != "error" and not v:
                 unused_tok.append(s)
 
         return unused_tok
@@ -666,7 +708,7 @@ class Grammar(object):
 
             # Add all the non-<empty> symbols of First[x] to the result.
             for f in self.First[x]:
-                if f == '<empty>':
+                if f == "<empty>":
                     x_produces_empty = True
                 else:
                     if f not in result:
@@ -683,7 +725,7 @@ class Grammar(object):
             # There was no 'break' from the loop,
             # so x_produces_empty was true for all x in beta,
             # so beta produces empty as well.
-            result.append('<empty>')
+            result.append("<empty>")
 
         return result
 
@@ -700,7 +742,7 @@ class Grammar(object):
         for t in self.Terminals:
             self.First[t] = [t]
 
-        self.First['$end'] = ['$end']
+        self.First["$end"] = ["$end"]
 
         # Nonterminals:
 
@@ -745,7 +787,7 @@ class Grammar(object):
         if not start:
             start = self.Productions[1].name
 
-        self.Follow[start] = ['$end']
+        self.Follow[start] = ["$end"]
 
         while True:
             didadd = False
@@ -754,15 +796,15 @@ class Grammar(object):
                 for i, B in enumerate(p.prod):
                     if B in self.Nonterminals:
                         # Okay. We got a non-terminal in a production
-                        fst = self._first(p.prod[i+1:])
+                        fst = self._first(p.prod[i + 1 :])
                         hasempty = False
                         for f in fst:
-                            if f != '<empty>' and f not in self.Follow[B]:
+                            if f != "<empty>" and f not in self.Follow[B]:
                                 self.Follow[B].append(f)
                                 didadd = True
-                            if f == '<empty>':
+                            if f == "<empty>":
                                 hasempty = True
-                        if hasempty or i == (len(p.prod)-1):
+                        if hasempty or i == (len(p.prod) - 1):
                             # Add elements of follow(a) to follow(b)
                             for f in self.Follow[p.name]:
                                 if f not in self.Follow[B]:
@@ -772,7 +814,6 @@ class Grammar(object):
                 break
         return self.Follow
 
-
     # -----------------------------------------------------------------------------
     # build_lritems()
     #
@@ -800,11 +841,11 @@ class Grammar(object):
                     lri = LRItem(p, i)
                     # Precompute the list of productions immediately following
                     try:
-                        lri.lr_after = self.Prodnames[lri.prod[i+1]]
+                        lri.lr_after = self.Prodnames[lri.prod[i + 1]]
                     except (IndexError, KeyError):
                         lri.lr_after = []
                     try:
-                        lri.lr_before = lri.prod[i-1]
+                        lri.lr_before = lri.prod[i - 1]
                     except IndexError:
                         lri.lr_before = None
 
@@ -816,33 +857,38 @@ class Grammar(object):
                 i += 1
             p.lr_items = lr_items
 
-
     # ----------------------------------------------------------------------
     # Debugging output.  Printing the grammar will produce a detailed
     # description along with some diagnostics.
     # ----------------------------------------------------------------------
     def __str__(self):
         out = []
-        out.append('Grammar:\n')
+        out.append("Grammar:\n")
         for n, p in enumerate(self.Productions):
-            out.append(f'Rule {n:<5d} {p}')
-        
+            out.append(f"Rule {n:<5d} {p}")
+
         unused_terminals = self.unused_terminals()
         if unused_terminals:
-            out.append('\nUnused terminals:\n')
+            out.append("\nUnused terminals:\n")
             for term in unused_terminals:
-                out.append(f'    {term}')
+                out.append(f"    {term}")
 
-        out.append('\nTerminals, with rules where they appear:\n')
+        out.append("\nTerminals, with rules where they appear:\n")
         for term in sorted(self.Terminals):
-            out.append('%-20s : %s' % (term, ' '.join(str(s) for s in self.Terminals[term])))
+            out.append(
+                "%-20s : %s" % (term, " ".join(str(s) for s in self.Terminals[term]))
+            )
 
-        out.append('\nNonterminals, with rules where they appear:\n')
+        out.append("\nNonterminals, with rules where they appear:\n")
         for nonterm in sorted(self.Nonterminals):
-            out.append('%-20s : %s' % (nonterm, ' '.join(str(s) for s in self.Nonterminals[nonterm])))
+            out.append(
+                "%-20s : %s"
+                % (nonterm, " ".join(str(s) for s in self.Nonterminals[nonterm]))
+            )
+
+        out.append("")
+        return "\n".join(out)
 
-        out.append('')
-        return '\n'.join(out)
 
 # -----------------------------------------------------------------------------
 #                           === LR Generator ===
@@ -868,6 +914,7 @@ class Grammar(object):
 #          FP   - Set-valued function
 # ------------------------------------------------------------------------------
 
+
 def digraph(X, R, FP):
     N = {}
     for x in X:
@@ -879,13 +926,14 @@ def digraph(X, R, FP):
             traverse(x, N, stack, F, X, R, FP)
     return F
 
+
 def traverse(x, N, stack, F, X, R, FP):
     stack.append(x)
     d = len(stack)
     N[x] = d
-    F[x] = FP(x)             # F(X) <- F'(x)
+    F[x] = FP(x)  # F(X) <- F'(x)
 
-    rel = R(x)               # Get y's related to x
+    rel = R(x)  # Get y's related to x
     for y in rel:
         if N[y] == 0:
             traverse(y, N, stack, F, X, R, FP)
@@ -902,9 +950,11 @@ def traverse(x, N, stack, F, X, R, FP):
             F[stack[-1]] = F[x]
             element = stack.pop()
 
+
 class LALRError(YaccError):
     pass
 
+
 # -----------------------------------------------------------------------------
 #                             == LRGeneratedTable ==
 #
@@ -912,26 +962,27 @@ class LALRError(YaccError):
 # public methods except for write()
 # -----------------------------------------------------------------------------
 
+
 class LRTable(object):
     def __init__(self, grammar):
         self.grammar = grammar
 
         # Internal attributes
-        self.lr_action     = {}        # Action table
-        self.lr_goto       = {}        # Goto table
-        self.lr_productions  = grammar.Productions    # Copy of grammar Production array
-        self.lr_goto_cache = {}        # Cache of computed gotos
-        self.lr0_cidhash   = {}        # Cache of closures
-        self._add_count    = 0         # Internal counter used to detect cycles
+        self.lr_action = {}  # Action table
+        self.lr_goto = {}  # Goto table
+        self.lr_productions = grammar.Productions  # Copy of grammar Production array
+        self.lr_goto_cache = {}  # Cache of computed gotos
+        self.lr0_cidhash = {}  # Cache of closures
+        self._add_count = 0  # Internal counter used to detect cycles
 
         # Diagonistic information filled in by the table generator
         self.state_descriptions = OrderedDict()
-        self.sr_conflict   = 0
-        self.rr_conflict   = 0
-        self.conflicts     = []        # List of conflicts
+        self.sr_conflict = 0
+        self.rr_conflict = 0
+        self.conflicts = []  # List of conflicts
 
-        self.sr_conflicts  = []
-        self.rr_conflicts  = []
+        self.sr_conflicts = []
+        self.rr_conflicts = []
 
         # Build the tables
         self.grammar.build_lritems()
@@ -964,7 +1015,7 @@ class LRTable(object):
             didadd = False
             for j in J:
                 for x in j.lr_after:
-                    if getattr(x, 'lr0_added', 0) == self._add_count:
+                    if getattr(x, "lr0_added", 0) == self._add_count:
                         continue
                     # Add B --> .G to J
                     J.append(x.lr_next)
@@ -1004,13 +1055,13 @@ class LRTable(object):
                     s[id(n)] = s1
                 gs.append(n)
                 s = s1
-        g = s.get('$end')
+        g = s.get("$end")
         if not g:
             if gs:
                 g = self.lr0_closure(gs)
-                s['$end'] = g
+                s["$end"] = g
             else:
-                s['$end'] = gs
+                s["$end"] = gs
         self.lr_goto_cache[(id(I), x)] = g
         return g
 
@@ -1105,7 +1156,7 @@ class LRTable(object):
         for stateno, state in enumerate(C):
             for p in state:
                 if p.lr_index < p.len - 1:
-                    t = (stateno, p.prod[p.lr_index+1])
+                    t = (stateno, p.prod[p.lr_index + 1])
                     if t[1] in self.grammar.Nonterminals:
                         if t not in trans:
                             trans.append(t)
@@ -1128,14 +1179,14 @@ class LRTable(object):
         g = self.lr0_goto(C[state], N)
         for p in g:
             if p.lr_index < p.len - 1:
-                a = p.prod[p.lr_index+1]
+                a = p.prod[p.lr_index + 1]
                 if a in self.grammar.Terminals:
                     if a not in terms:
                         terms.append(a)
 
         # This extra bit is to handle the start state
         if state == 0 and N == self.grammar.Productions[0].prod[0]:
-            terms.append('$end')
+            terms.append("$end")
 
         return terms
 
@@ -1189,8 +1240,8 @@ class LRTable(object):
     # -----------------------------------------------------------------------------
 
     def compute_lookback_includes(self, C, trans, nullable):
-        lookdict = {}          # Dictionary of lookback relations
-        includedict = {}       # Dictionary of include relations
+        lookdict = {}  # Dictionary of lookback relations
+        includedict = {}  # Dictionary of include relations
 
         # Make a dictionary of non-terminal transitions
         dtrans = {}
@@ -1223,7 +1274,7 @@ class LRTable(object):
                         li = lr_index + 1
                         while li < p.len:
                             if p.prod[li] in self.grammar.Terminals:
-                                break      # No forget it
+                                break  # No forget it
                             if p.prod[li] not in nullable:
                                 break
                             li = li + 1
@@ -1231,8 +1282,8 @@ class LRTable(object):
                             # Appears to be a relation between (j,t) and (state,N)
                             includes.append((j, t))
 
-                    g = self.lr0_goto(C[j], t)               # Go to next set
-                    j = self.lr0_cidhash.get(id(g), -1)      # Go to next state
+                    g = self.lr0_goto(C[j], t)  # Go to next set
+                    j = self.lr0_cidhash.get(id(g), -1)  # Go to next state
 
                 # When we get here, j is the final state, now we have to locate the production
                 for r in C[j]:
@@ -1243,7 +1294,7 @@ class LRTable(object):
                     i = 0
                     # This look is comparing a production ". A B C" with "A B C ."
                     while i < r.lr_index:
-                        if r.prod[i] != p.prod[i+1]:
+                        if r.prod[i] != p.prod[i + 1]:
                             break
                         i = i + 1
                     else:
@@ -1270,7 +1321,7 @@ class LRTable(object):
 
     def compute_read_sets(self, C, ntrans, nullable):
         FP = lambda x: self.dr_relation(C, x, nullable)
-        R =  lambda x: self.reads_relation(C, x, nullable)
+        R = lambda x: self.reads_relation(C, x, nullable)
         F = digraph(ntrans, R, FP)
         return F
 
@@ -1292,7 +1343,7 @@ class LRTable(object):
 
     def compute_follow_sets(self, ntrans, readsets, inclsets):
         FP = lambda x: readsets[x]
-        R  = lambda x: inclsets.get(x, [])
+        R = lambda x: inclsets.get(x, [])
         F = digraph(ntrans, R, FP)
         return F
 
@@ -1352,11 +1403,11 @@ class LRTable(object):
     # -----------------------------------------------------------------------------
     def lr_parse_table(self):
         Productions = self.grammar.Productions
-        Precedence  = self.grammar.Precedence
-        goto   = self.lr_goto         # Goto array
-        action = self.lr_action       # Action array
+        Precedence = self.grammar.Precedence
+        goto = self.lr_goto  # Goto array
+        action = self.lr_action  # Action array
 
-        actionp = {}                  # Action production array (temporary)
+        actionp = {}  # Action production array (temporary)
 
         # Step 1: Construct C = { I0, I1, ... IN}, collection of LR(0) items
         # This determines the number of states
@@ -1368,129 +1419,149 @@ class LRTable(object):
         for st, I in enumerate(C):
             descrip = []
             # Loop over each production in I
-            actlist = []              # List of actions
-            st_action  = {}
+            actlist = []  # List of actions
+            st_action = {}
             st_actionp = {}
-            st_goto    = {}
+            st_goto = {}
 
-            descrip.append(f'\nstate {st}\n')
+            descrip.append(f"\nstate {st}\n")
             for p in I:
-                descrip.append(f'    ({p.number}) {p}')
+                descrip.append(f"    ({p.number}) {p}")
 
             for p in I:
-                    if p.len == p.lr_index + 1:
-                        if p.name == "S'":
-                            # Start symbol. Accept!
-                            st_action['$end'] = 0
-                            st_actionp['$end'] = p
-                        else:
-                            # We are at the end of a production.  Reduce!
-                            laheads = p.lookaheads[st]
-                            for a in laheads:
-                                actlist.append((a, p, f'reduce using rule {p.number} ({p})'))
-                                r = st_action.get(a)
-                                if r is not None:
-                                    # Have a shift/reduce or reduce/reduce conflict
-                                    if r > 0:
-                                        # Need to decide on shift or reduce here
-                                        # By default we favor shifting. Need to add
-                                        # some precedence rules here.
-
-                                        # Shift precedence comes from the token
-                                        sprec, slevel = Precedence.get(a, ('right', 0))
-
-                                        # Reduce precedence comes from rule being reduced (p)
-                                        rprec, rlevel = Productions[p.number].prec
-
-                                        if (slevel < rlevel) or ((slevel == rlevel) and (rprec == 'left')):
-                                            # We really need to reduce here.
-                                            st_action[a] = -p.number
-                                            st_actionp[a] = p
-                                            if not slevel and not rlevel:
-                                                descrip.append(f'  ! shift/reduce conflict for {a} resolved as reduce')
-                                                self.sr_conflicts.append((st, a, 'reduce'))
-                                            Productions[p.number].reduced += 1
-                                        elif (slevel == rlevel) and (rprec == 'nonassoc'):
-                                            st_action[a] = None
-                                        else:
-                                            # Hmmm. Guess we'll keep the shift
-                                            if not rlevel:
-                                                descrip.append(f'  ! shift/reduce conflict for {a} resolved as shift')
-                                                self.sr_conflicts.append((st, a, 'shift'))
-                                    elif r <= 0:
-                                        # Reduce/reduce conflict.   In this case, we favor the rule
-                                        # that was defined first in the grammar file
-                                        oldp = Productions[-r]
-                                        pp = Productions[p.number]
-                                        if oldp.line > pp.line:
-                                            st_action[a] = -p.number
-                                            st_actionp[a] = p
-                                            chosenp, rejectp = pp, oldp
-                                            Productions[p.number].reduced += 1
-                                            Productions[oldp.number].reduced -= 1
-                                        else:
-                                            chosenp, rejectp = oldp, pp
-                                        self.rr_conflicts.append((st, chosenp, rejectp))
-                                        descrip.append('  ! reduce/reduce conflict for %s resolved using rule %d (%s)' % 
-                                                       (a, st_actionp[a].number, st_actionp[a]))
+                if p.len == p.lr_index + 1:
+                    if p.name == "S'":
+                        # Start symbol. Accept!
+                        st_action["$end"] = 0
+                        st_actionp["$end"] = p
+                    else:
+                        # We are at the end of a production.  Reduce!
+                        laheads = p.lookaheads[st]
+                        for a in laheads:
+                            actlist.append(
+                                (a, p, f"reduce using rule {p.number} ({p})")
+                            )
+                            r = st_action.get(a)
+                            if r is not None:
+                                # Have a shift/reduce or reduce/reduce conflict
+                                if r > 0:
+                                    # Need to decide on shift or reduce here
+                                    # By default we favor shifting. Need to add
+                                    # some precedence rules here.
+
+                                    # Shift precedence comes from the token
+                                    sprec, slevel = Precedence.get(a, ("right", 0))
+
+                                    # Reduce precedence comes from rule being reduced (p)
+                                    rprec, rlevel = Productions[p.number].prec
+
+                                    if (slevel < rlevel) or (
+                                        (slevel == rlevel) and (rprec == "left")
+                                    ):
+                                        # We really need to reduce here.
+                                        st_action[a] = -p.number
+                                        st_actionp[a] = p
+                                        if not slevel and not rlevel:
+                                            descrip.append(
+                                                f"  ! shift/reduce conflict for {a} resolved as reduce"
+                                            )
+                                            self.sr_conflicts.append((st, a, "reduce"))
+                                        Productions[p.number].reduced += 1
+                                    elif (slevel == rlevel) and (rprec == "nonassoc"):
+                                        st_action[a] = None
+                                    else:
+                                        # Hmmm. Guess we'll keep the shift
+                                        if not rlevel:
+                                            descrip.append(
+                                                f"  ! shift/reduce conflict for {a} resolved as shift"
+                                            )
+                                            self.sr_conflicts.append((st, a, "shift"))
+                                elif r <= 0:
+                                    # Reduce/reduce conflict.   In this case, we favor the rule
+                                    # that was defined first in the grammar file
+                                    oldp = Productions[-r]
+                                    pp = Productions[p.number]
+                                    if oldp.line > pp.line:
+                                        st_action[a] = -p.number
+                                        st_actionp[a] = p
+                                        chosenp, rejectp = pp, oldp
+                                        Productions[p.number].reduced += 1
+                                        Productions[oldp.number].reduced -= 1
                                     else:
-                                        raise LALRError(f'Unknown conflict in state {st}')
+                                        chosenp, rejectp = oldp, pp
+                                    self.rr_conflicts.append((st, chosenp, rejectp))
+                                    descrip.append(
+                                        "  ! reduce/reduce conflict for %s resolved using rule %d (%s)"
+                                        % (a, st_actionp[a].number, st_actionp[a])
+                                    )
                                 else:
-                                    st_action[a] = -p.number
-                                    st_actionp[a] = p
-                                    Productions[p.number].reduced += 1
-                    else:
-                        i = p.lr_index
-                        a = p.prod[i+1]       # Get symbol right after the "."
-                        if a in self.grammar.Terminals:
-                            g = self.lr0_goto(I, a)
-                            j = self.lr0_cidhash.get(id(g), -1)
-                            if j >= 0:
-                                # We are in a shift state
-                                actlist.append((a, p, f'shift and go to state {j}'))
-                                r = st_action.get(a)
-                                if r is not None:
-                                    # Whoa have a shift/reduce or shift/shift conflict
-                                    if r > 0:
-                                        if r != j:
-                                            raise LALRError(f'Shift/shift conflict in state {st}')
-                                    elif r <= 0:
-                                        # Do a precedence check.
-                                        #   -  if precedence of reduce rule is higher, we reduce.
-                                        #   -  if precedence of reduce is same and left assoc, we reduce.
-                                        #   -  otherwise we shift
-                                        rprec, rlevel = Productions[st_actionp[a].number].prec
-                                        sprec, slevel = Precedence.get(a, ('right', 0))
-                                        if (slevel > rlevel) or ((slevel == rlevel) and (rprec == 'right')):
-                                            # We decide to shift here... highest precedence to shift
-                                            Productions[st_actionp[a].number].reduced -= 1
-                                            st_action[a] = j
-                                            st_actionp[a] = p
-                                            if not rlevel:
-                                                descrip.append(f'  ! shift/reduce conflict for {a} resolved as shift')
-                                                self.sr_conflicts.append((st, a, 'shift'))
-                                        elif (slevel == rlevel) and (rprec == 'nonassoc'):
-                                            st_action[a] = None
-                                        else:
-                                            # Hmmm. Guess we'll keep the reduce
-                                            if not slevel and not rlevel:
-                                                descrip.append(f'  ! shift/reduce conflict for {a} resolved as reduce')
-                                                self.sr_conflicts.append((st, a, 'reduce'))
-
+                                    raise LALRError(f"Unknown conflict in state {st}")
+                            else:
+                                st_action[a] = -p.number
+                                st_actionp[a] = p
+                                Productions[p.number].reduced += 1
+                else:
+                    i = p.lr_index
+                    a = p.prod[i + 1]  # Get symbol right after the "."
+                    if a in self.grammar.Terminals:
+                        g = self.lr0_goto(I, a)
+                        j = self.lr0_cidhash.get(id(g), -1)
+                        if j >= 0:
+                            # We are in a shift state
+                            actlist.append((a, p, f"shift and go to state {j}"))
+                            r = st_action.get(a)
+                            if r is not None:
+                                # Whoa have a shift/reduce or shift/shift conflict
+                                if r > 0:
+                                    if r != j:
+                                        raise LALRError(
+                                            f"Shift/shift conflict in state {st}"
+                                        )
+                                elif r <= 0:
+                                    # Do a precedence check.
+                                    #   -  if precedence of reduce rule is higher, we reduce.
+                                    #   -  if precedence of reduce is same and left assoc, we reduce.
+                                    #   -  otherwise we shift
+                                    rprec, rlevel = Productions[
+                                        st_actionp[a].number
+                                    ].prec
+                                    sprec, slevel = Precedence.get(a, ("right", 0))
+                                    if (slevel > rlevel) or (
+                                        (slevel == rlevel) and (rprec == "right")
+                                    ):
+                                        # We decide to shift here... highest precedence to shift
+                                        Productions[st_actionp[a].number].reduced -= 1
+                                        st_action[a] = j
+                                        st_actionp[a] = p
+                                        if not rlevel:
+                                            descrip.append(
+                                                f"  ! shift/reduce conflict for {a} resolved as shift"
+                                            )
+                                            self.sr_conflicts.append((st, a, "shift"))
+                                    elif (slevel == rlevel) and (rprec == "nonassoc"):
+                                        st_action[a] = None
                                     else:
-                                        raise LALRError(f'Unknown conflict in state {st}')
+                                        # Hmmm. Guess we'll keep the reduce
+                                        if not slevel and not rlevel:
+                                            descrip.append(
+                                                f"  ! shift/reduce conflict for {a} resolved as reduce"
+                                            )
+                                            self.sr_conflicts.append((st, a, "reduce"))
+
                                 else:
-                                    st_action[a] = j
-                                    st_actionp[a] = p
+                                    raise LALRError(f"Unknown conflict in state {st}")
+                            else:
+                                st_action[a] = j
+                                st_actionp[a] = p
 
             # Print the actions associated with each terminal
             _actprint = {}
             for a, p, m in actlist:
                 if a in st_action:
                     if p is st_actionp[a]:
-                        descrip.append(f'    {a:<15s} {m}')
+                        descrip.append(f"    {a:<15s} {m}")
                         _actprint[(a, m)] = 1
-            descrip.append('')
+            descrip.append("")
 
             # Construct the goto table for this state
             nkeys = {}
@@ -1503,12 +1574,12 @@ class LRTable(object):
                 j = self.lr0_cidhash.get(id(g), -1)
                 if j >= 0:
                     st_goto[n] = j
-                    descrip.append(f'    {n:<30s} shift and go to state {j}')
+                    descrip.append(f"    {n:<30s} shift and go to state {j}")
 
             action[st] = st_action
             actionp[st] = st_actionp
             goto[st] = st_goto
-            self.state_descriptions[st] = '\n'.join(descrip)
+            self.state_descriptions[st] = "\n".join(descrip)
 
     # ----------------------------------------------------------------------
     # Debugging output.   Printing the LRTable object will produce a listing
@@ -1518,28 +1589,33 @@ class LRTable(object):
         out = []
         for descrip in self.state_descriptions.values():
             out.append(descrip)
-            
+
         if self.sr_conflicts or self.rr_conflicts:
-            out.append('\nConflicts:\n')
+            out.append("\nConflicts:\n")
 
             for state, tok, resolution in self.sr_conflicts:
-                out.append(f'shift/reduce conflict for {tok} in state {state} resolved as {resolution}')
+                out.append(
+                    f"shift/reduce conflict for {tok} in state {state} resolved as {resolution}"
+                )
 
             already_reported = set()
             for state, rule, rejected in self.rr_conflicts:
                 if (state, id(rule), id(rejected)) in already_reported:
                     continue
-                out.append(f'reduce/reduce conflict in state {state} resolved using rule {rule}')
-                out.append(f'rejected rule ({rejected}) in state {state}')
+                out.append(
+                    f"reduce/reduce conflict in state {state} resolved using rule {rule}"
+                )
+                out.append(f"rejected rule ({rejected}) in state {state}")
                 already_reported.add((state, id(rule), id(rejected)))
 
             warned_never = set()
             for state, rule, rejected in self.rr_conflicts:
                 if not rejected.reduced and (rejected not in warned_never):
-                    out.append(f'Rule ({rejected}) is never reduced')
+                    out.append(f"Rule ({rejected}) is never reduced")
                     warned_never.add(rejected)
 
-        return '\n'.join(out)
+        return "\n".join(out)
+
 
 # Collect grammar rules from a function
 def _collect_grammar_rules(func):
@@ -1549,70 +1625,80 @@ def _collect_grammar_rules(func):
         unwrapped = inspect.unwrap(func)
         filename = unwrapped.__code__.co_filename
         lineno = unwrapped.__code__.co_firstlineno
-        for rule, lineno in zip(func.rules, range(lineno+len(func.rules)-1, 0, -1)):
+        for rule, lineno in zip(func.rules, range(lineno + len(func.rules) - 1, 0, -1)):
             syms = rule.split()
-            if syms[1:2] == [':'] or syms[1:2] == ['::=']:
+            if syms[1:2] == [":"] or syms[1:2] == ["::="]:
                 grammar.append((func, filename, lineno, syms[0], syms[2:]))
             else:
                 grammar.append((func, filename, lineno, prodname, syms))
-        func = getattr(func, 'next_func', None)
+        func = getattr(func, "next_func", None)
 
     return grammar
 
+
 class ParserMetaDict(dict):
-    '''
+    """
     Dictionary that allows decorated grammar rule functions to be overloaded
-    '''
+    """
+
     def __setitem__(self, key, value):
-        if key in self and callable(value) and hasattr(value, 'rules'):
+        if key in self and callable(value) and hasattr(value, "rules"):
             value.next_func = self[key]
-            if not hasattr(value.next_func, 'rules'):
-                raise GrammarError(f'Redefinition of {key}. Perhaps an earlier {key} is missing @_')
+            if not hasattr(value.next_func, "rules"):
+                raise GrammarError(
+                    f"Redefinition of {key}. Perhaps an earlier {key} is missing @_"
+                )
         super().__setitem__(key, value)
-    
+
     def __getitem__(self, key):
-        if key not in self and key.isupper() and key[:1] != '_':
+        if key not in self and key.isupper() and key[:1] != "_":
             return key.upper()
         else:
             return super().__getitem__(key)
 
+
 class ParserMeta(type):
     @classmethod
     def __prepare__(meta, *args, **kwargs):
         d = ParserMetaDict()
+
         def _(rule, *extra):
             rules = [rule, *extra]
+
             def decorate(func):
-                func.rules = [ *getattr(func, 'rules', []), *rules[::-1] ]
+                func.rules = [*getattr(func, "rules", []), *rules[::-1]]
                 return func
+
             return decorate
-        d['_'] = _
+
+        d["_"] = _
         return d
 
     def __new__(meta, clsname, bases, attributes):
-        del attributes['_']
+        del attributes["_"]
         cls = super().__new__(meta, clsname, bases, attributes)
         cls._build(list(attributes.items()))
         return cls
 
+
 class Parser(metaclass=ParserMeta):
     # Logging object where debugging/diagnostic messages are sent
-    log = SlyLogger(sys.stderr)     
+    log = SlyLogger(sys.stderr)
 
     # Debugging filename where parsetab.out data can be written
     debugfile = None
 
     @classmethod
     def __validate_tokens(cls):
-        if not hasattr(cls, 'tokens'):
-            cls.log.error('No token list is defined')
+        if not hasattr(cls, "tokens"):
+            cls.log.error("No token list is defined")
             return False
 
         if not cls.tokens:
-            cls.log.error('tokens is empty')
+            cls.log.error("tokens is empty")
             return False
 
-        if 'error' in cls.tokens:
+        if "error" in cls.tokens:
             cls.log.error("Illegal token name 'error'. Is a reserved word")
             return False
 
@@ -1620,28 +1706,32 @@ class Parser(metaclass=ParserMeta):
 
     @classmethod
     def __validate_precedence(cls):
-        if not hasattr(cls, 'precedence'):
+        if not hasattr(cls, "precedence"):
             cls.__preclist = []
             return True
 
         preclist = []
         if not isinstance(cls.precedence, (list, tuple)):
-            cls.log.error('precedence must be a list or tuple')
+            cls.log.error("precedence must be a list or tuple")
             return False
 
         for level, p in enumerate(cls.precedence, start=1):
             if not isinstance(p, (list, tuple)):
-                cls.log.error(f'Bad precedence table entry {p!r}. Must be a list or tuple')
+                cls.log.error(
+                    f"Bad precedence table entry {p!r}. Must be a list or tuple"
+                )
                 return False
 
             if len(p) < 2:
-                cls.log.error(f'Malformed precedence entry {p!r}. Must be (assoc, term, ..., term)')
+                cls.log.error(
+                    f"Malformed precedence entry {p!r}. Must be (assoc, term, ..., term)"
+                )
                 return False
 
             if not all(isinstance(term, str) for term in p):
-                cls.log.error('precedence items must be strings')
+                cls.log.error("precedence items must be strings")
                 return False
-            
+
             assoc = p[0]
             preclist.extend((term, assoc, level) for term in p[1:])
 
@@ -1650,9 +1740,9 @@ class Parser(metaclass=ParserMeta):
 
     @classmethod
     def __validate_specification(cls):
-        '''
+        """
         Validate various parts of the grammar specification
-        '''
+        """
         if not cls.__validate_tokens():
             return False
         if not cls.__validate_precedence():
@@ -1661,14 +1751,14 @@ class Parser(metaclass=ParserMeta):
 
     @classmethod
     def __build_grammar(cls, rules):
-        '''
+        """
         Build the grammar from the grammar rules
-        '''
+        """
         grammar_rules = []
-        errors = ''
+        errors = ""
         # Check for non-empty symbols
         if not rules:
-            raise YaccError('No grammar rules are defined')
+            raise YaccError("No grammar rules are defined")
 
         grammar = Grammar(cls.tokens)
 
@@ -1677,95 +1767,110 @@ class Parser(metaclass=ParserMeta):
             try:
                 grammar.set_precedence(term, assoc, level)
             except GrammarError as e:
-                errors += f'{e}\n'
+                errors += f"{e}\n"
 
         for name, func in rules:
             try:
                 parsed_rule = _collect_grammar_rules(func)
                 for pfunc, rulefile, ruleline, prodname, syms in parsed_rule:
                     try:
-                        grammar.add_production(prodname, syms, pfunc, rulefile, ruleline)
+                        grammar.add_production(
+                            prodname, syms, pfunc, rulefile, ruleline
+                        )
                     except GrammarError as e:
-                        errors += f'{e}\n'
+                        errors += f"{e}\n"
             except SyntaxError as e:
-                errors += f'{e}\n'
+                errors += f"{e}\n"
         try:
-            grammar.set_start(getattr(cls, 'start', None))
+            grammar.set_start(getattr(cls, "start", None))
         except GrammarError as e:
-            errors += f'{e}\n'
+            errors += f"{e}\n"
 
         undefined_symbols = grammar.undefined_symbols()
         for sym, prod in undefined_symbols:
-            errors += '%s:%d: Symbol %r used, but not defined as a token or a rule\n' % (prod.file, prod.line, sym)
+            errors += (
+                "%s:%d: Symbol %r used, but not defined as a token or a rule\n"
+                % (prod.file, prod.line, sym)
+            )
 
         unused_terminals = grammar.unused_terminals()
         if unused_terminals:
-            unused_str = '{' + ','.join(unused_terminals) + '}'
-            cls.log.warning(f'Token{"(s)" if len(unused_terminals) >1 else ""} {unused_str} defined, but not used')
+            unused_str = "{" + ",".join(unused_terminals) + "}"
+            cls.log.warning(
+                f'Token{"(s)" if len(unused_terminals) >1 else ""} {unused_str} defined, but not used'
+            )
 
         unused_rules = grammar.unused_rules()
         for prod in unused_rules:
-            cls.log.warning('%s:%d: Rule %r defined, but not used', prod.file, prod.line, prod.name)
+            cls.log.warning(
+                "%s:%d: Rule %r defined, but not used", prod.file, prod.line, prod.name
+            )
 
         if len(unused_terminals) == 1:
-            cls.log.warning('There is 1 unused token')
+            cls.log.warning("There is 1 unused token")
         if len(unused_terminals) > 1:
-            cls.log.warning('There are %d unused tokens', len(unused_terminals))
+            cls.log.warning("There are %d unused tokens", len(unused_terminals))
 
         if len(unused_rules) == 1:
-            cls.log.warning('There is 1 unused rule')
+            cls.log.warning("There is 1 unused rule")
         if len(unused_rules) > 1:
-            cls.log.warning('There are %d unused rules', len(unused_rules))
+            cls.log.warning("There are %d unused rules", len(unused_rules))
 
         unreachable = grammar.find_unreachable()
         for u in unreachable:
-           cls.log.warning('Symbol %r is unreachable', u)
+            cls.log.warning("Symbol %r is unreachable", u)
 
         if len(undefined_symbols) == 0:
             infinite = grammar.infinite_cycles()
             for inf in infinite:
-                errors += 'Infinite recursion detected for symbol %r\n' % inf
+                errors += "Infinite recursion detected for symbol %r\n" % inf
 
         unused_prec = grammar.unused_precedence()
         for term, assoc in unused_prec:
-            errors += 'Precedence rule %r defined for unknown symbol %r\n' % (assoc, term)
+            errors += "Precedence rule %r defined for unknown symbol %r\n" % (
+                assoc,
+                term,
+            )
 
         cls._grammar = grammar
         if errors:
-            raise YaccError('Unable to build grammar.\n'+errors)
+            raise YaccError("Unable to build grammar.\n" + errors)
 
     @classmethod
     def __build_lrtables(cls):
-        '''
+        """
         Build the LR Parsing tables from the grammar
-        '''
+        """
         lrtable = LRTable(cls._grammar)
         num_sr = len(lrtable.sr_conflicts)
 
         # Report shift/reduce and reduce/reduce conflicts
-        if num_sr != getattr(cls, 'expected_shift_reduce', None):
+        if num_sr != getattr(cls, "expected_shift_reduce", None):
             if num_sr == 1:
-                cls.log.warning('1 shift/reduce conflict')
+                cls.log.warning("1 shift/reduce conflict")
             elif num_sr > 1:
-                cls.log.warning('%d shift/reduce conflicts', num_sr)
+                cls.log.warning("%d shift/reduce conflicts", num_sr)
 
         num_rr = len(lrtable.rr_conflicts)
-        if num_rr != getattr(cls, 'expected_reduce_reduce', None):
+        if num_rr != getattr(cls, "expected_reduce_reduce", None):
             if num_rr == 1:
-                cls.log.warning('1 reduce/reduce conflict')
+                cls.log.warning("1 reduce/reduce conflict")
             elif num_rr > 1:
-                cls.log.warning('%d reduce/reduce conflicts', num_rr)
+                cls.log.warning("%d reduce/reduce conflicts", num_rr)
 
         cls._lrtable = lrtable
         return True
 
     @classmethod
     def __collect_rules(cls, definitions):
-        '''
+        """
         Collect all of the tagged grammar rules
-        '''
-        rules = [ (name, value) for name, value in definitions
-                  if callable(value) and hasattr(value, 'rules') ]
+        """
+        rules = [
+            (name, value)
+            for name, value in definitions
+            if callable(value) and hasattr(value, "rules")
+        ]
         return rules
 
     # ----------------------------------------------------------------------
@@ -1775,7 +1880,7 @@ class Parser(metaclass=ParserMeta):
     # ----------------------------------------------------------------------
     @classmethod
     def _build(cls, definitions):
-        if vars(cls).get('_build', False):
+        if vars(cls).get("_build", False):
             return
 
         # Collect all of the grammar rules from the class definition
@@ -1783,77 +1888,89 @@ class Parser(metaclass=ParserMeta):
 
         # Validate other parts of the grammar specification
         if not cls.__validate_specification():
-            raise YaccError('Invalid parser specification')
+            raise YaccError("Invalid parser specification")
 
         # Build the underlying grammar object
         cls.__build_grammar(rules)
 
         # Build the LR tables
         if not cls.__build_lrtables():
-            raise YaccError('Can\'t build parsing tables')
+            raise YaccError("Can't build parsing tables")
 
         if cls.debugfile:
-            with open(cls.debugfile, 'w') as f:
+            with open(cls.debugfile, "w") as f:
                 f.write(str(cls._grammar))
-                f.write('\n')
+                f.write("\n")
                 f.write(str(cls._lrtable))
-            cls.log.info('Parser debugging for %s written to %s', cls.__qualname__, cls.debugfile)
+            cls.log.info(
+                "Parser debugging for %s written to %s", cls.__qualname__, cls.debugfile
+            )
 
     # ----------------------------------------------------------------------
     # Parsing Support.  This is the parsing runtime that users use to
     # ----------------------------------------------------------------------
     def error(self, token):
-        '''
+        """
         Default error handling function.  This may be subclassed.
-        '''
+        """
         if token:
-            lineno = getattr(token, 'lineno', 0)
+            lineno = getattr(token, "lineno", 0)
             if lineno:
-                sys.stderr.write(f'sly: Syntax error at line {lineno}, token={token.type}\n')
+                sys.stderr.write(
+                    f"sly: Syntax error at line {lineno}, token={token.type}\n"
+                )
             else:
-                sys.stderr.write(f'sly: Syntax error, token={token.type}')
+                sys.stderr.write(f"sly: Syntax error, token={token.type}")
         else:
-            sys.stderr.write('sly: Parse error in input. EOF\n')
- 
+            sys.stderr.write("sly: Parse error in input. EOF\n")
+
     def errok(self):
-        '''
+        """
         Clear the error status
-        '''
+        """
         self.errorok = True
 
     def restart(self):
-        '''
+        """
         Force the parser to restart from a fresh state. Clears the statestack
-        '''
+        """
         del self.statestack[:]
         del self.symstack[:]
         sym = YaccSymbol()
-        sym.type = '$end'
+        sym.type = "$end"
         self.symstack.append(sym)
         self.statestack.append(0)
         self.state = 0
 
     def parse(self, tokens):
-        '''
+        """
         Parse the given input tokens.
-        '''
-        lookahead = None                                  # Current lookahead symbol
-        lookaheadstack = []                               # Stack of lookahead symbols
-        actions = self._lrtable.lr_action                 # Local reference to action table (to avoid lookup on self.)
-        goto    = self._lrtable.lr_goto                   # Local reference to goto table (to avoid lookup on self.)
-        prod    = self._grammar.Productions               # Local reference to production list (to avoid lookup on self.)
-        defaulted_states = self._lrtable.defaulted_states # Local reference to defaulted states
-        pslice  = YaccProduction(None)                    # Production object passed to grammar rules
-        errorcount = 0                                    # Used during error recovery
+        """
+        lookahead = None  # Current lookahead symbol
+        lookaheadstack = []  # Stack of lookahead symbols
+        actions = (
+            self._lrtable.lr_action
+        )  # Local reference to action table (to avoid lookup on self.)
+        goto = (
+            self._lrtable.lr_goto
+        )  # Local reference to goto table (to avoid lookup on self.)
+        prod = (
+            self._grammar.Productions
+        )  # Local reference to production list (to avoid lookup on self.)
+        defaulted_states = (
+            self._lrtable.defaulted_states
+        )  # Local reference to defaulted states
+        pslice = YaccProduction(None)  # Production object passed to grammar rules
+        errorcount = 0  # Used during error recovery
 
         # Set up the state and symbol stacks
         self.tokens = tokens
-        self.statestack = statestack = []                 # Stack of parsing states
-        self.symstack = symstack = []                     # Stack of grammar symbols
-        pslice._stack = symstack                           # Associate the stack with the production
+        self.statestack = statestack = []  # Stack of parsing states
+        self.symstack = symstack = []  # Stack of grammar symbols
+        pslice._stack = symstack  # Associate the stack with the production
         self.restart()
 
-        errtoken   = None                                 # Err token
+        errtoken = None  # Err token
         while True:
             # Get the next symbol on the input.  If a lookahead symbol
             # is already set, we just use that. Otherwise, we'll pull
@@ -1866,7 +1983,7 @@ class Parser(metaclass=ParserMeta):
                         lookahead = lookaheadstack.pop()
                     if not lookahead:
                         lookahead = YaccSymbol()
-                        lookahead.type = '$end'
+                        lookahead.type = "$end"
 
                 # Check the action table
                 ltype = lookahead.type
@@ -1892,14 +2009,14 @@ class Parser(metaclass=ParserMeta):
                     # reduce a symbol on the stack, emit a production
                     self.production = p = prod[-t]
                     pname = p.name
-                    plen  = p.len
+                    plen = p.len
                     pslice._namemap = p.namemap
 
                     # Call the production function
                     pslice._slice = symstack[-plen:] if plen else []
 
                     sym = YaccSymbol()
-                    sym.type = pname       
+                    sym.type = pname
                     value = p.func(self, pslice)
                     if value is pslice:
                         value = (pname, *(s.value for s in pslice._slice))
@@ -1915,7 +2032,7 @@ class Parser(metaclass=ParserMeta):
 
                 if t == 0:
                     n = symstack[-1]
-                    result = getattr(n, 'value', None)
+                    result = getattr(n, "value", None)
                     return result
 
             if t is None:
@@ -1932,8 +2049,8 @@ class Parser(metaclass=ParserMeta):
                 if errorcount == 0 or self.errorok:
                     errorcount = ERROR_COUNT
                     self.errorok = False
-                    if lookahead.type == '$end':
-                        errtoken = None               # End of file!
+                    if lookahead.type == "$end":
+                        errtoken = None  # End of file!
                     else:
                         errtoken = lookahead
 
@@ -1957,7 +2074,7 @@ class Parser(metaclass=ParserMeta):
                 # entire parse has been rolled back and we're completely hosed.   The token is
                 # discarded and we just keep going.
 
-                if len(statestack) <= 1 and lookahead.type != '$end':
+                if len(statestack) <= 1 and lookahead.type != "$end":
                     lookahead = None
                     self.state = 0
                     # Nuke the lookahead stack
@@ -1968,13 +2085,13 @@ class Parser(metaclass=ParserMeta):
                 # at the end of the file. nuke the top entry and generate an error token
 
                 # Start nuking entries on the stack
-                if lookahead.type == '$end':
+                if lookahead.type == "$end":
                     # Whoa. We're really hosed here. Bail out
                     return
 
-                if lookahead.type != 'error':
+                if lookahead.type != "error":
                     sym = symstack[-1]
-                    if sym.type == 'error':
+                    if sym.type == "error":
                         # Hmmm. Error is on top of stack, we'll just nuke input
                         # symbol and continue
                         lookahead = None
@@ -1982,11 +2099,11 @@ class Parser(metaclass=ParserMeta):
 
                     # Create the error symbol for the first time and make it the new lookahead symbol
                     t = YaccSymbol()
-                    t.type = 'error'
+                    t.type = "error"
 
-                    if hasattr(lookahead, 'lineno'):
+                    if hasattr(lookahead, "lineno"):
                         t.lineno = lookahead.lineno
-                    if hasattr(lookahead, 'index'):
+                    if hasattr(lookahead, "index"):
                         t.index = lookahead.index
                     t.value = lookahead
                     lookaheadstack.append(lookahead)
@@ -1998,4 +2115,4 @@ class Parser(metaclass=ParserMeta):
                 continue
 
             # Call an error function here
-            raise RuntimeError('sly: internal parser error!!!\n')
+            raise RuntimeError("sly: internal parser error!!!\n")
diff --git a/lib/utils.py b/lib/utils.py
index 26a591e..91dded0 100644
--- a/lib/utils.py
+++ b/lib/utils.py
@@ -21,15 +21,25 @@ def running_mean(x: np.ndarray, N: int) -> np.ndarray:
     :param x: 1-Dimensional NumPy array
     :param N: how many items to average
     """
+    # FIXME np.insert(x, 0, [x[0] for i in range(N/2)])
+    # FIXME np.insert(x, -1, [x[-1] for i in range(N/2)])
+    # (dabei ungerade N beachten)
     cumsum = np.cumsum(np.insert(x, 0, 0))
     return (cumsum[N:] - cumsum[:-N]) / N
 
 
 def human_readable(value, unit):
-    for prefix, factor in (('p', 1e-12), ('n', 1e-9), (u'µ', 1e-6), ('m', 1e-3), ('', 1), ('k', 1e3)):
+    for prefix, factor in (
+        ("p", 1e-12),
+        ("n", 1e-9),
+        (u"µ", 1e-6),
+        ("m", 1e-3),
+        ("", 1),
+        ("k", 1e3),
+    ):
         if value < 1e3 * factor:
-            return '{:.2f} {}{}'.format(value * (1 / factor), prefix, unit)
-    return '{:.2f} {}'.format(value, unit)
+            return "{:.2f} {}{}".format(value * (1 / factor), prefix, unit)
+    return "{:.2f} {}".format(value, unit)
 
 
 def is_numeric(n):
@@ -65,7 +75,7 @@ def soft_cast_int(n):
     If `n` is empty, returns None.
     If `n` is not numeric, it is left unchanged.
     """
-    if n is None or n == '':
+    if n is None or n == "":
         return None
     try:
         return int(n)
@@ -80,7 +90,7 @@ def soft_cast_float(n):
     If `n` is empty, returns None.
     If `n` is not numeric, it is left unchanged.
     """
-    if n is None or n == '':
+    if n is None or n == "":
         return None
     try:
         return float(n)
@@ -104,8 +114,8 @@ def parse_conf_str(conf_str):
     Values are casted to float if possible and kept as-is otherwise.
     """
     conf_dict = dict()
-    for option in conf_str.split(','):
-        key, value = option.split('=')
+    for option in conf_str.split(","):
+        key, value = option.split("=")
         conf_dict[key] = soft_cast_float(value)
     return conf_dict
 
@@ -118,7 +128,7 @@ def remove_index_from_tuple(parameters, index):
     :param index: index of element which is to be removed
     :returns: parameters tuple without the element at index
     """
-    return (*parameters[:index], *parameters[index + 1:])
+    return (*parameters[:index], *parameters[index + 1 :])
 
 
 def param_slice_eq(a, b, index):
@@ -137,7 +147,9 @@ def param_slice_eq(a, b, index):
     ('foo', [1, 4]), ('foo', [2, 4]), 1 -> False
 
     """
-    if (*a[1][:index], *a[1][index + 1:]) == (*b[1][:index], *b[1][index + 1:]) and a[0] == b[0]:
+    if (*a[1][:index], *a[1][index + 1 :]) == (*b[1][:index], *b[1][index + 1 :]) and a[
+        0
+    ] == b[0]:
         return True
     return False
 
@@ -164,20 +176,20 @@ def by_name_to_by_param(by_name: dict):
     """
     by_param = dict()
     for name in by_name.keys():
-        for i, parameters in enumerate(by_name[name]['param']):
+        for i, parameters in enumerate(by_name[name]["param"]):
             param_key = (name, tuple(parameters))
             if param_key not in by_param:
                 by_param[param_key] = dict()
                 for key in by_name[name].keys():
                     by_param[param_key][key] = list()
-                by_param[param_key]['attributes'] = by_name[name]['attributes']
+                by_param[param_key]["attributes"] = by_name[name]["attributes"]
                 # special case for PTA models
-                if 'isa' in by_name[name]:
-                    by_param[param_key]['isa'] = by_name[name]['isa']
-            for attribute in by_name[name]['attributes']:
+                if "isa" in by_name[name]:
+                    by_param[param_key]["isa"] = by_name[name]["isa"]
+            for attribute in by_name[name]["attributes"]:
                 by_param[param_key][attribute].append(by_name[name][attribute][i])
             # Required for match_parameter_valuse in _try_fits
-            by_param[param_key]['param'].append(by_name[name]['param'][i])
+            by_param[param_key]["param"].append(by_name[name]["param"][i])
     return by_param
 
 
@@ -197,14 +209,26 @@ def filter_aggregate_by_param(aggregate, parameters, parameter_filter):
         param_value = soft_cast_int(param_name_and_value[1])
         names_to_remove = set()
         for name in aggregate.keys():
-            indices_to_keep = list(map(lambda x: x[param_index] == param_value, aggregate[name]['param']))
-            aggregate[name]['param'] = list(map(lambda iv: iv[1], filter(lambda iv: indices_to_keep[iv[0]], enumerate(aggregate[name]['param']))))
+            indices_to_keep = list(
+                map(lambda x: x[param_index] == param_value, aggregate[name]["param"])
+            )
+            aggregate[name]["param"] = list(
+                map(
+                    lambda iv: iv[1],
+                    filter(
+                        lambda iv: indices_to_keep[iv[0]],
+                        enumerate(aggregate[name]["param"]),
+                    ),
+                )
+            )
             if len(indices_to_keep) == 0:
-                print('??? {}->{}'.format(parameter_filter, name))
+                print("??? {}->{}".format(parameter_filter, name))
                 names_to_remove.add(name)
             else:
-                for attribute in aggregate[name]['attributes']:
-                    aggregate[name][attribute] = aggregate[name][attribute][indices_to_keep]
+                for attribute in aggregate[name]["attributes"]:
+                    aggregate[name][attribute] = aggregate[name][attribute][
+                        indices_to_keep
+                    ]
                     if len(aggregate[name][attribute]) == 0:
                         names_to_remove.add(name)
         for name in names_to_remove:
@@ -218,25 +242,25 @@ class OptionalTimingAnalysis:
         self.index = 1
 
     def get_header(self):
-        ret = ''
+        ret = ""
         if self.enabled:
-            ret += '#define TIMEIT(index, functioncall) '
-            ret += 'counter.start(); '
-            ret += 'functioncall; '
-            ret += 'counter.stop();'
+            ret += "#define TIMEIT(index, functioncall) "
+            ret += "counter.start(); "
+            ret += "functioncall; "
+            ret += "counter.stop();"
             ret += 'kout << endl << index << " :: " << counter.value << "/" << counter.overflow << endl;\n'
         return ret
 
     def wrap_codeblock(self, codeblock):
         if not self.enabled:
             return codeblock
-        lines = codeblock.split('\n')
+        lines = codeblock.split("\n")
         ret = list()
         for line in lines:
-            if re.fullmatch('.+;', line):
-                ret.append('TIMEIT( {:d}, {} )'.format(self.index, line))
+            if re.fullmatch(".+;", line):
+                ret.append("TIMEIT( {:d}, {} )".format(self.index, line))
                 self.wrapped_lines.append(line)
                 self.index += 1
             else:
                 ret.append(line)
-        return '\n'.join(ret)
+        return "\n".join(ret)