split up rel_energy inte relative energy to previous state and to next state

5 files changed, 190 insertions, 94 deletions

diff --git a/bin/merge.py b/bin/merge.py
index 1ff7e74..b0d7d22 100755
--- a/bin/merge.py
+++ b/bin/merge.py
@@ -9,6 +9,7 @@ import sys
 import plotter
 from copy import deepcopy
 from dfatool import aggregate_measures, regression_measures, is_numeric, powerset
+from dfatool import append_if_set, mean_or_none
 from matplotlib.patches import Polygon
 from scipy import optimize
 
@@ -42,17 +43,21 @@ def mimosa_data(elem):
     substate_thresholds = []
     substate_data = []
     timeouts = []
-    rel_energies = []
+    rel_energies_prev = []
+    rel_energies_next = []
     if 'timeout' in elem['offline'][0]:
         timeouts = [x['timeout'] for x in elem['offline']]
-    if 'uW_mean_delta' in elem['offline'][0]:
-        rel_energies = [x['uW_mean_delta'] * (x['us'] - 20) for x in elem['offline']]
+    if 'uW_mean_delta_prev' in elem['offline'][0]:
+        rel_energies_prev = [x['uW_mean_delta_prev'] * (x['us'] - 20) for x in elem['offline']]
+    if 'uW_mean_delta_next' in elem['offline'][0]:
+        rel_energies_next = [x['uW_mean_delta_next'] * (x['us'] - 20) for x in elem['offline']]
     for x in elem['offline']:
         if 'substates' in x:
             substate_thresholds.append(x['substates']['threshold'])
             substate_data.append(x['substates']['states'])
 
-    return means, stds, durations, energies, rel_energies, clips, timeouts, substate_thresholds
+    return (means, stds, durations, energies, rel_energies_prev,
+        rel_energies_next, clips, timeouts, substate_thresholds)
 
 def online_data(elem):
     means = [int(x['power']) for x in elem['online']]
@@ -254,8 +259,8 @@ def xv_assess_function(name, funbase, what, validation, mae, smape):
             mae[name] = []
         if not name in smape:
             smape[name] = []
-        mae[name].append(goodness['mae'])
-        smape[name].append(goodness['smape'])
+        append_if_set(mae, goodness, 'mae')
+        append_if_set(smape, goodness, 'smape')
 
 def xv2_assess_function(name, funbase, what, validation, mae, smape, rmsd):
     goodness = assess_function(funbase, name, what, parameters, validation)
@@ -311,15 +316,19 @@ def fake_add_data_to_aggregate(aggregate, key, isa, database, idx):
     timeout_val = []
     if len(database['timeouts']):
         timeout_val = [database['timeouts'][idx]]
-    rel_energy_val = []
-    if len(database['rel_energies']):
-        rel_energy_val = [database['rel_energies'][idx]]
+    rel_energy_p_val = []
+    if len(database['rel_energies_prev']):
+        rel_energy_p_val = [database['rel_energies_prev'][idx]]
+    rel_energy_n_val = []
+    if len(database['rel_energies_next']):
+        rel_energy_n_val = [database['rel_energies_next'][idx]]
     add_data_to_aggregate(aggregate, key, isa, {
         'means' : [database['means'][idx]],
         'stds' : [database['stds'][idx]],
         'durations' : [database['durations'][idx]],
         'energies' : [database['energies'][idx]],
-        'rel_energies' : rel_energy_val,
+        'rel_energies_prev' : rel_energy_p_val,
+        'rel_energies_next' : rel_energy_n_val,
         'clip_rate' : [database['clip_rate'][idx]],
         'timeouts' : timeout_val,
     })
@@ -377,7 +386,7 @@ def mean_std_by_trace_part(data, transitions, name, what):
 
 
 def load_run_elem(index, element, trace, by_name, by_param, by_trace):
-    means, stds, durations, energies, rel_energies, clips, timeouts, sub_thresholds = mimosa_data(element)
+    means, stds, durations, energies, rel_energies_prev, rel_energies_next, clips, timeouts, sub_thresholds = mimosa_data(element)
 
     online_means = []
     online_durations = []
@@ -394,7 +403,8 @@ def load_run_elem(index, element, trace, by_name, by_param, by_trace):
         'stds' : stds,
         'durations' : durations,
         'energies' : energies,
-        'rel_energies' : rel_energies,
+        'rel_energies_prev' : rel_energies_prev,
+        'rel_energies_next' : rel_energies_next,
         'clip_rate' : clips,
         'timeouts' : timeouts,
         'sub_thresholds' : sub_thresholds,
@@ -407,7 +417,8 @@ def load_run_elem(index, element, trace, by_name, by_param, by_trace):
         'stds' : stds,
         'durations' : durations,
         'energies' : energies,
-        'rel_energies' : rel_energies,
+        'rel_energies_prev' : rel_energies_prev,
+        'rel_energies_next' : rel_energies_next,
         'clip_rate' : clips,
         'timeouts' : timeouts,
         'sub_thresholds' : sub_thresholds,
@@ -419,7 +430,8 @@ def load_run_elem(index, element, trace, by_name, by_param, by_trace):
         'stds' : stds,
         'durations' : durations,
         'energies' : energies,
-        'rel_energies' : rel_energies,
+        'rel_energies_prev' : rel_energies_prev,
+        'rel_energies_next' : rel_energies_next,
         'clip_rate' : clips,
         'timeouts' : timeouts,
         'sub_thresholds' : sub_thresholds,
@@ -487,16 +499,14 @@ def param_measures(name, paramdata, key, fun):
             # Mean ist besseres für SSR. Da least_squares SSR optimiert
             # nutzen wir hier auch Mean.
             goodness = aggregate_measures(fun(pval[key]), pval[key])
-            mae.append(goodness['mae'])
-            rmsd.append(goodness['rmsd'])
-            if 'smape' in goodness:
-                smape.append(goodness['smape'])
+            append_if_set(mae, goodness, 'mae')
+            append_if_set(rmsd, goodness, 'rmsd')
+            append_if_set(smape, goodness, 'smape')
     ret = {
-        'mae' : np.mean(mae),
-        'rmsd' : np.mean(rmsd)
+        'mae' : mean_or_none(mae),
+        'rmsd' : mean_or_none(rmsd),
+        'smape' : mean_or_none(smape)
     }
-    if len(smape):
-        ret['smape'] = np.mean(smape)
 
     return ret
 
@@ -548,10 +558,9 @@ def val_run(aggdata, split_fun, count):
         validation = aggdata[pairs[i][1]]
         median = np.median(training)
         goodness = aggregate_measures(median, validation)
-        mae.append(goodness['mae'])
-        rmsd.append(goodness['rmsd'])
-        if 'smape' in goodness:
-            smape.append(goodness['smape'])
+        append_if_set(mae, goodness, 'mae')
+        append_if_set(rmsd, goodness, 'rmsd')
+        append_if_set(smape, goodness, 'smape')
 
     mae_mean = np.mean(mae)
     rmsd_mean = np.mean(rmsd)
@@ -628,7 +637,8 @@ def crossvalidate(by_name, by_param, by_trace, model, parameters):
         isa = by_name[name]['isa']
         by_name[name]['means'] = np.array(by_name[name]['means'])
         by_name[name]['energies'] = np.array(by_name[name]['energies'])
-        by_name[name]['rel_energies'] = np.array(by_name[name]['rel_energies'])
+        by_name[name]['rel_energies_prev'] = np.array(by_name[name]['rel_energies_prev'])
+        by_name[name]['rel_energies_next'] = np.array(by_name[name]['rel_energies_next'])
         by_name[name]['durations'] = np.array(by_name[name]['durations'])
 
         if isa == 'state':
@@ -641,10 +651,14 @@ def crossvalidate(by_name, by_param, by_trace, model, parameters):
             print('%16s,   static     energy,             Monte Carlo: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
             mae_mean, smape_mean, rms_mean = val_run(by_name[name]['energies'], splitidx_kfold, 10)
             print('%16s,   static     energy,             10-fold sys: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies'], splitidx_srs, 200)
-            print('%16s,   static rel_energy,             Monte Carlo: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
-            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies'], splitidx_kfold, 10)
-            print('%16s,   static rel_energy,             10-fold sys: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
+            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_prev'], splitidx_srs, 200)
+            print('%16s,   static rel_energy_p,             Monte Carlo: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
+            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_prev'], splitidx_kfold, 10)
+            print('%16s,   static rel_energy_p,             10-fold sys: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
+            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_next'], splitidx_srs, 200)
+            print('%16s,   static rel_energy_n,             Monte Carlo: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
+            mae_mean, smape_mean, rms_mean = val_run(by_name[name]['rel_energies_next'], splitidx_kfold, 10)
+            print('%16s,   static rel_energy_n,             10-fold sys: MAE %8.f pJ,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
             mae_mean, smape_mean, rms_mean = val_run(by_name[name]['durations'], splitidx_srs, 200)
             print('%16s,   static   duration,             Monte Carlo: MAE %8.f µs,  SMAPE %6.2f%%,  RMS %d' % (name, mae_mean, smape_mean, rms_mean))
             mae_mean, smape_mean, rms_mean = val_run(by_name[name]['durations'], splitidx_kfold, 10)
@@ -701,11 +715,16 @@ def crossvalidate(by_name, by_param, by_trace, model, parameters):
                 val_run_funs(by_name, by_trace, name, 'energies', 'energy', 'user', 'pJ')
             if 'estimate' in model[isa][name]['energy']['function']:
                 val_run_funs(by_name, by_trace, name, 'energies', 'energy', 'estimate', 'pJ')
-        if 'rel_energy' in model[isa][name] and 'function' in model[isa][name]['rel_energy']:
-            if 'user' in model[isa][name]['rel_energy']['function']:
-                val_run_funs(by_name, by_trace, name, 'rel_energies', 'rel_energy', 'user', 'pJ')
-            if 'estimate' in model[isa][name]['rel_energy']['function']:
-                val_run_funs(by_name, by_trace, name, 'rel_energies', 'rel_energy', 'estimate', 'pJ')
+        if 'rel_energy_prev' in model[isa][name] and 'function' in model[isa][name]['rel_energy_prev']:
+            if 'user' in model[isa][name]['rel_energy_prev']['function']:
+                val_run_funs(by_name, by_trace, name, 'rel_energies_prev', 'rel_energy_prev', 'user', 'pJ')
+            if 'estimate' in model[isa][name]['rel_energy_prev']['function']:
+                val_run_funs(by_name, by_trace, name, 'rel_energies_prev', 'rel_energy_prev', 'estimate', 'pJ')
+        if 'rel_energy_next' in model[isa][name] and 'function' in model[isa][name]['rel_energy_next']:
+            if 'user' in model[isa][name]['rel_energy_next']['function']:
+                val_run_funs(by_name, by_trace, name, 'rel_energies_next', 'rel_energy_next', 'user', 'pJ')
+            if 'estimate' in model[isa][name]['rel_energy_next']['function']:
+                val_run_funs(by_name, by_trace, name, 'rel_energies_next', 'rel_energy_next', 'estimate', 'pJ')
 
     return
     for i, param in enumerate(parameters):
@@ -819,11 +838,18 @@ def validate(by_name, by_param, parameters):
                     'std_inner' : np.std(val['energies']),
                     'function' : {},
                 },
-                'rel_energy' : {
-                    'goodness' : aggregate_measures(model['rel_energy']['static'], val['rel_energies']),
-                    'median' : np.median(val['rel_energies']),
-                    'mean'   : np.mean(val['rel_energies']),
-                    'std_inner' : np.std(val['rel_energies']),
+                'rel_energy_prev' : {
+                    'goodness' : aggregate_measures(model['rel_energy_prev']['static'], val['rel_energies_prev']),
+                    'median' : np.median(val['rel_energies_prev']),
+                    'mean'   : np.mean(val['rel_energies_prev']),
+                    'std_inner' : np.std(val['rel_energies_prev']),
+                    'function' : {},
+                },
+                'rel_energy_next' : {
+                    'goodness' : aggregate_measures(model['rel_energy_next']['static'], val['rel_energies_next']),
+                    'median' : np.median(val['rel_energies_next']),
+                    'mean'   : np.mean(val['rel_energies_next']),
+                    'std_inner' : np.std(val['rel_energies_next']),
                     'function' : {},
                 },
                 'clip' : {
@@ -874,7 +900,8 @@ def analyze(by_name, by_param, by_trace, parameters):
         aggval['power']['std_outer'] = np.mean(val['stds'])
 
         if isa == 'transition':
-            aggval['rel_energy'] = keydata(name, val, by_param, by_trace, 'rel_energies')
+            aggval['rel_energy_prev'] = keydata(name, val, by_param, by_trace, 'rel_energies_prev')
+            aggval['rel_energy_next'] = keydata(name, val, by_param, by_trace, 'rel_energies_next')
 
         if isa == 'transition' and 'function' in data['model']['transition'][name]['timeout']:
             aggval['timeout'] = keydata(name, val, by_param, by_trace, 'timeouts')
@@ -898,10 +925,14 @@ def analyze(by_name, by_param, by_trace, parameters):
                         by_param, allvalues, name, 'energies', i)
                     if aggval['energy']['std_by_param'][param] > 0 and aggval['energy']['std_param'] / aggval['energy']['std_by_param'][param] < 0.6:
                         aggval['energy']['fit_guess'][param] = try_fits(name, 'energies', i, by_param)
-                    aggval['rel_energy']['std_by_param'][param] = mean_std_by_param(
-                        by_param, allvalues, name, 'rel_energies', i)
-                    if aggval['rel_energy']['std_by_param'][param] > 0 and aggval['rel_energy']['std_param'] / aggval['rel_energy']['std_by_param'][param] < 0.6:
-                        aggval['rel_energy']['fit_guess'][param] = try_fits(name, 'rel_energies', i, by_param)
+                    aggval['rel_energy_prev']['std_by_param'][param] = mean_std_by_param(
+                        by_param, allvalues, name, 'rel_energies_prev', i)
+                    if aggval['rel_energy_prev']['std_by_param'][param] > 0 and aggval['rel_energy_prev']['std_param'] / aggval['rel_energy_prev']['std_by_param'][param] < 0.6:
+                        aggval['rel_energy_prev']['fit_guess'][param] = try_fits(name, 'rel_energies_prev', i, by_param)
+                    aggval['rel_energy_next']['std_by_param'][param] = mean_std_by_param(
+                        by_param, allvalues, name, 'rel_energies_next', i)
+                    if aggval['rel_energy_next']['std_by_param'][param] > 0 and aggval['rel_energy_next']['std_param'] / aggval['rel_energy_next']['std_by_param'][param] < 0.6:
+                        aggval['rel_energy_next']['fit_guess'][param] = try_fits(name, 'rel_energies_next', i, by_param)
                 if isa == 'transition' and 'function' in data['model']['transition'][name]['timeout']:
                     aggval['timeout']['std_by_param'][param] = mean_std_by_param(
                         by_param, allvalues, name, 'timeouts', i)
@@ -926,7 +957,9 @@ def analyze(by_name, by_param, by_trace, parameters):
                 'estimated %s duration [µs]' % name)
             fguess_to_function(name, 'energies', aggval['energy'], parameters, by_param,
                 'estimated %s energy [pJ]' % name)
-            fguess_to_function(name, 'rel_energies', aggval['rel_energy'], parameters, by_param,
+            fguess_to_function(name, 'rel_energies_prev', aggval['rel_energy_prev'], parameters, by_param,
+                'estimated relative %s energy [pJ]' % name)
+            fguess_to_function(name, 'rel_energies_next', aggval['rel_energy_next'], parameters, by_param,
                 'estimated relative %s energy [pJ]' % name)
             if 'function' in model['duration'] and 'user' in model['duration']['function']:
                 aggval['duration']['function']['user'] = {
@@ -944,14 +977,22 @@ def analyze(by_name, by_param, by_trace, parameters):
                 fit_function(
                     aggval['energy']['function']['user'], name, 'energies', parameters, by_param,
                     yaxis='%s energy [pJ]' % name)
-            if 'function' in model['rel_energy'] and 'user' in model['rel_energy']['function']:
-                aggval['rel_energy']['function']['user'] = {
-                    'raw' : model['rel_energy']['function']['user']['raw'],
-                    'params' : model['rel_energy']['function']['user']['params'],
+            if 'function' in model['rel_energy_prev'] and 'user' in model['rel_energy_prev']['function']:
+                aggval['rel_energy_prev']['function']['user'] = {
+                    'raw' : model['rel_energy_prev']['function']['user']['raw'],
+                    'params' : model['rel_energy_prev']['function']['user']['params'],
+                }
+                fit_function(
+                    aggval['rel_energy_prev']['function']['user'], name, 'rel_energies_prev', parameters, by_param,
+                    yaxis='%s rel_energy_prev [pJ]' % name)
+            if 'function' in model['rel_energy_next'] and 'user' in model['rel_energy_next']['function']:
+                aggval['rel_energy_next']['function']['user'] = {
+                    'raw' : model['rel_energy_next']['function']['user']['raw'],
+                    'params' : model['rel_energy_next']['function']['user']['params'],
                 }
                 fit_function(
-                    aggval['rel_energy']['function']['user'], name, 'rel_energies', parameters, by_param,
-                    yaxis='%s rel_energy [pJ]' % name)
+                    aggval['rel_energy_next']['function']['user'], name, 'rel_energies_next', parameters, by_param,
+                    yaxis='%s rel_energy_next [pJ]' % name)
             if 'function' in model['timeout'] and 'user' in model['timeout']['function']:
                 fguess_to_function(name, 'timeouts', aggval['timeout'], parameters, by_param,
                     'estimated %s timeout [µs]' % name)
diff --git a/lib/Kratos/DFADriver.pm b/lib/Kratos/DFADriver.pm
index a3473f7..9801d99 100644
--- a/lib/Kratos/DFADriver.pm
+++ b/lib/Kratos/DFADriver.pm
@@ -469,9 +469,15 @@ sub assess_model {
 		$self->printf_aggr( $transition, 'energy', 'pJ' );
 		$self->printf_parameterized( $transition, 'energy' );
 		$self->printf_fit( $transition, 'energy', 'pJ' );
-		$self->printf_aggr( $transition, 'rel_energy', 'pJ' );
-		$self->printf_parameterized( $transition, 'rel_energy' );
-		$self->printf_fit( $transition, 'rel_energy', 'pJ' );
+		$self->printf_aggr( $transition, 'rel_energy_prev', 'pJ' );
+		$self->printf_parameterized( $transition, 'rel_energy_prev' );
+		$self->printf_fit( $transition, 'rel_energy_prev', 'pJ' );
+
+		if ( exists $transition->{rel_energy_next}{median} ) {
+			$self->printf_aggr( $transition, 'rel_energy_next', 'pJ' );
+			$self->printf_parameterized( $transition, 'rel_energy_next' );
+			$self->printf_fit( $transition, 'rel_energy_next', 'pJ' );
+		}
 
 		if ( exists $transition->{timeout}{median} ) {
 			$self->printf_aggr( $transition, 'timeout', 'µs' );
@@ -506,12 +512,14 @@ sub assess_model_tex {
 		printf("\n%20s", $name);
 
 		$self->printf_aggr_tex( $transition, 'energy', '\uJ', 1e6 );
-		$self->printf_aggr_tex( $transition, 'rel_energy', '\uJ', 1e6 );
+		$self->printf_aggr_tex( $transition, 'rel_energy_prev', '\uJ', 1e6 );
+		$self->printf_aggr_tex( $transition, 'rel_energy_next', '\uJ', 1e6 );
 		$self->printf_aggr_tex( $transition, 'duration', 'ms', 1e3 );
 		$self->printf_count_tex( $transition, 'energy' );
 		print " \\\\";
 		$self->printf_eval_tex( $transition, 'energy', '\uJ', 1e6 );
-		$self->printf_eval_tex( $transition, 'rel_energy', '\uJ', 1e6 );
+		$self->printf_eval_tex( $transition, 'rel_energy_prev', '\uJ', 1e6 );
+		$self->printf_eval_tex( $transition, 'rel_energy_next', '\uJ', 1e6 );
 		$self->printf_eval_tex( $transition, 'duration', 'ms', 1e3 );
 		$self->printf_count_tex;
 		print " \\\\";
@@ -548,11 +556,14 @@ sub assess_validation {
 			$self->model->get_transition_by_name($name)->{energy}{static},
 			$transition, 'energy', 'pJ' );
 		$self->printf_goodness(
-			$self->model->get_transition_by_name($name)->{rel_energy}{static},
-			$transition, 'rel_energy', 'pJ' );
+			$self->model->get_transition_by_name($name)->{rel_energy_prev}{static},
+			$transition, 'rel_energy_prev', 'pJ' );
+		if ( exists $transition->{rel_energy_next}{median} ) {
+			$self->printf_goodness(
+				$self->model->get_transition_by_name($name)->{rel_energy_next}{static},
+				$transition, 'rel_energy_next', 'pJ' );
+		}
 		if ( exists $transition->{timeout}{median} ) {
-
-			#$self->printf_goodness('?', $transition, 'timeout', 'µs');
 			$self->printf_fit( $transition, 'timeout', 'µs' );
 		}
 	}
@@ -578,9 +589,10 @@ sub update_model {
 			$name,
 			$transition->{duration}{median},
 			$transition->{energy}{median},
-			$transition->{rel_energy}{median}
+			$transition->{rel_energy_prev}{median},
+			$transition->{rel_energy_next}{median}
 		);
-		for my $key (qw(duration energy rel_energy timeout)) {
+		for my $key (qw(duration energy rel_energy_prev rel_energy_next timeout)) {
 			for my $fname ( keys %{ $transition->{$key}{function} } ) {
 				$self->model->set_transition_params(
 					$name, $key, $fname,
@@ -698,7 +710,7 @@ EOF
 
 		advice ${adv_type}("% ${class_name}::$transition->{name}(...)") ${ignore_nested} : after() {
 			tjp->target()->passTransition(${class_name}::statepower[tjp->target()->state],
-			$transition->{rel_energy}{static}, $transition->{id},
+			$transition->{rel_energy_prev}{static}, $transition->{id},
 			${dest_state_id});
 		};
 
@@ -709,7 +721,7 @@ EOF
 
 		advice execution("% ${class_name}::$transition->{name}(...)") : after() {
 			tjp->target()->passTransition(${class_name}::statepower[tjp->target()->state],
-			$transition->{rel_energy}{static}, $transition->{id},
+			$transition->{rel_energy_prev}{static}, $transition->{id},
 			${dest_state_id});
 		};
 
diff --git a/lib/Kratos/DFADriver/Model.pm b/lib/Kratos/DFADriver/Model.pm
index 67fb318..bf57c49 100644
--- a/lib/Kratos/DFADriver/Model.pm
+++ b/lib/Kratos/DFADriver/Model.pm
@@ -117,17 +117,18 @@ sub parse_xml {
 		}
 
 		my $transition = {
-			name        => $transition_node->getAttribute('name'),
-			duration    => { static => 0+($transition_node->getAttribute('duration') // 0) },
-			energy      => { static => 0+($transition_node->getAttribute('energy') // 0) },
-			rel_energy  => { static => 0+($transition_node->getAttribute('rel_energy') // 0) },
-			parameters  => [@parameters],
-			origins     => [@source_states],
-			destination => $dst_node->textContent,
-			level       => $level_node->textContent,
-			id          => $transition_index,
-			affects     => {%affects},
-			node        => $transition_node,
+			name             => $transition_node->getAttribute('name'),
+			duration         => { static => 0+($transition_node->getAttribute('duration') // 0) },
+			energy           => { static => 0+($transition_node->getAttribute('energy') // 0) },
+			rel_energy_prev  => { static => 0+($transition_node->getAttribute('rel_energy_prev') // 0) },
+			rel_energy_next  => { static => 0+($transition_node->getAttribute('rel_energy_next') // 0) },
+			parameters       => [@parameters],
+			origins          => [@source_states],
+			destination      => $dst_node->textContent,
+			level            => $level_node->textContent,
+			id               => $transition_index,
+			affects          => {%affects},
+			node             => $transition_node,
 		};
 
 		for my $fun_node ( $transition_node->findnodes('./timeoutfunction/*') )
@@ -184,18 +185,36 @@ sub parse_xml {
 			}
 		}
 
-		for my $fun_node ( $transition_node->findnodes('./rel_energyfunction/*') )
+		for my $fun_node ( $transition_node->findnodes('./rel_energy_prevfunction/*') )
 		{
 			my $name     = $fun_node->nodeName;
 			my $function = $fun_node->textContent;
 			$function =~ s{^ \n* \s* }{}x;
 			$function =~ s{\s* \n* $}{}x;
-			$transition->{rel_energy}{function}{$name}{raw}  = $function;
-			$transition->{rel_energy}{function}{$name}{node} = $fun_node;
+			$transition->{rel_energy_prev}{function}{$name}{raw}  = $function;
+			$transition->{rel_energy_prev}{function}{$name}{node} = $fun_node;
 			my $attrindex = 0;
 			while ( $fun_node->hasAttribute("param${attrindex}") ) {
 				push(
-					@{ $transition->{rel_energy}{function}{$name}{params} },
+					@{ $transition->{rel_energy_prev}{function}{$name}{params} },
+					$fun_node->getAttribute("param${attrindex}")
+				);
+				$attrindex++;
+			}
+		}
+
+		for my $fun_node ( $transition_node->findnodes('./rel_energy_nextfunction/*') )
+		{
+			my $name     = $fun_node->nodeName;
+			my $function = $fun_node->textContent;
+			$function =~ s{^ \n* \s* }{}x;
+			$function =~ s{\s* \n* $}{}x;
+			$transition->{rel_energy_next}{function}{$name}{raw}  = $function;
+			$transition->{rel_energy_next}{function}{$name}{node} = $fun_node;
+			my $attrindex = 0;
+			while ( $fun_node->hasAttribute("param${attrindex}") ) {
+				push(
+					@{ $transition->{rel_energy_next}{function}{$name}{params} },
 					$fun_node->getAttribute("param${attrindex}")
 				);
 				$attrindex++;
@@ -249,7 +268,8 @@ sub reset {
 	for my $transition (@{$self->{transitions}}) {
 		$transition->{node}->removeAttribute('duration');
 		$transition->{node}->removeAttribute('energy');
-		$transition->{node}->removeAttribute('rel_energy');
+		$transition->{node}->removeAttribute('rel_energy_prev');
+		$transition->{node}->removeAttribute('rel_energy_next');
 		for my $list_node (@{$transition->{node}->findnodes('./timeoutfunction')}) {
 			for my $fun_name (keys %{$transition->{timeout}{function}}) {
 				my $fun_node = $transition->{timeout}{function}{$fun_name}{node};
@@ -366,7 +386,7 @@ sub set_transition_params {
 }
 
 sub set_transition_data {
-	my ( $self, $transition_name, $duration, $energy, $rel_energy ) = @_;
+	my ( $self, $transition_name, $duration, $energy, $rel_energy_prev, $rel_energy_next ) = @_;
 
 	my $transition = $self->get_transition_by_name($transition_name);
 	$duration = sprintf( '%.f', $duration );
@@ -383,17 +403,23 @@ sub set_transition_data {
 	$transition->{energy}{static} = $energy;
 	$transition->{node}->setAttribute( 'energy', $energy );
 
-	if (defined $rel_energy) {
-		$rel_energy = sprintf('%.f', $rel_energy);
-		printf( ", relative energy %d -> %d pJ\n",
-			$transition->{rel_energy}{static}, $rel_energy );
+	if (defined $rel_energy_prev) {
+		$rel_energy_prev = sprintf('%.f', $rel_energy_prev);
+		printf( ", relative_prev energy %d -> %d pJ",
+			$transition->{rel_energy_prev}{static}, $rel_energy_prev );
 
-		$transition->{rel_energy}{static} = $rel_energy;
-		$transition->{node}->setAttribute( 'rel_energy', $rel_energy );
+		$transition->{rel_energy_prev}{static} = $rel_energy_prev;
+		$transition->{node}->setAttribute( 'rel_energy_prev', $rel_energy_prev );
 	}
-	else {
-		print("\n");
+	if (defined $rel_energy_next) {
+		$rel_energy_next = sprintf('%.f', $rel_energy_next);
+		printf( ", relative_next energy %d -> %d pJ",
+			$transition->{rel_energy_next}{static}, $rel_energy_next );
+
+		$transition->{rel_energy_next}{static} = $rel_energy_next;
+		$transition->{node}->setAttribute( 'rel_energy_next', $rel_energy_next );
 	}
+	print("\n");
 }
 
 sub save {
@@ -530,7 +556,7 @@ sub TO_JSON {
 	}
 	for my $val ( values %transition_copy ) {
 		delete $val->{node};
-		for my $key (qw(duration energy rel_energy timeout)) {
+		for my $key (qw(duration energy rel_energy_prev rel_energy_next timeout)) {
 			if ( exists $val->{$key}{function} ) {
 				$val->{$key} = { %{ $val->{$key} } };
 				$val->{$key}{function} = { %{ $val->{$key}{function} } };
diff --git a/lib/MIMOSA/Log.pm b/lib/MIMOSA/Log.pm
index 4f7c6a2..8574132 100644
--- a/lib/MIMOSA/Log.pm
+++ b/lib/MIMOSA/Log.pm
@@ -14,7 +14,7 @@ use List::Util qw(sum);
 #use Statistics::Basic::StdDev;
 
 our $VERSION = '0.00';
-my $CACHE_VERSION = 5;
+my $CACHE_VERSION = 6;
 
 sub new {
 	my ( $class, %opt ) = @_;
diff --git a/lib/dfatool.py b/lib/dfatool.py
index 8a07b50..994f8a2 100755
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -21,12 +21,23 @@ def is_numeric(n):
     except ValueError:
         return False
 
+def append_if_set(aggregate, data, key):
+    if key in data:
+        aggregate.append(data[key])
+
+def mean_or_none(arr):
+    if len(arr):
+        return np.mean(arr)
+    return -1
+
 def aggregate_measures(aggregate, actual):
     aggregate_array = np.array([aggregate] * len(actual))
     return regression_measures(aggregate_array, np.array(actual))
 
 def regression_measures(predicted, actual):
     deviations = predicted - actual
+    if len(deviations) == 0:
+        return {}
     measures = {
         'mae' : np.mean(np.abs(deviations), dtype=np.float64),
         'msd' : np.mean(deviations**2, dtype=np.float64),
@@ -281,8 +292,14 @@ class MIMOSA:
             if isa == 'transition':
                 # subtract average power of previous state
                 # (that is, the state from which this transition originates)
-                data['uW_mean_delta'] = 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']
+            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.append(data)