diff options
Diffstat (limited to 'bin')
| -rwxr-xr-x | bin/merge.py | 64 |
1 files changed, 17 insertions, 47 deletions
diff --git a/bin/merge.py b/bin/merge.py index 3cbdf6c..67c9cc9 100755 --- a/bin/merge.py +++ b/bin/merge.py @@ -858,6 +858,16 @@ def analyze_by_param(aggval, by_param, allvalues, name, key1, key2, param, param if aggval[key1]['std_by_param'][param] > 0 and aggval[key1]['std_param'] / aggval[key1]['std_by_param'][param] < 0.6: aggval[key1]['fit_guess'][param] = try_fits(name, key2, param_idx, by_param) +def maybe_fit_function(aggval, model, by_param, parameters, name, key1, key2, unit): + if 'function' in model[key1] and 'user' in model[key1]['function']: + aggval[key1]['function']['user'] = { + 'raw' : model[key1]['function']['user']['raw'], + 'params' : model[key1]['function']['user']['params'], + } + fit_function( + aggval[key1]['function']['user'], name, key2, parameters, by_param, + yaxis='%s %s [%s]' % (name, key1, unit)) + def analyze(by_name, by_param, by_trace, parameters): aggdata = { 'state' : {}, @@ -907,14 +917,7 @@ def analyze(by_name, by_param, by_trace, parameters): if isa == 'state': fguess_to_function(name, 'means', aggval['power'], parameters, by_param, 'estimated %s power [µW]' % name) - if 'function' in model['power'] and 'user' in model['power']['function']: - aggval['power']['function']['user'] = { - 'raw' : model['power']['function']['user']['raw'], - 'params' : model['power']['function']['user']['params'], - } - fit_function( - aggval['power']['function']['user'], name, 'means', parameters, by_param, - yaxis='%s power [µW]' % name) + maybe_fit_function(aggval, model, by_param, parameters, name, 'power', 'means', 'µW') if aggval['power']['std_param'] > 0 and aggval['power']['std_trace'] / aggval['power']['std_param'] < 0.5: aggval['power']['std_by_trace'] = mean_std_by_trace_part(by_trace, transition_names, name, 'means') else: @@ -926,48 +929,15 @@ def analyze(by_name, by_param, by_trace, parameters): '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'] = { - 'raw' : model['duration']['function']['user']['raw'], - 'params' : model['duration']['function']['user']['params'], - } - fit_function( - aggval['duration']['function']['user'], name, 'durations', parameters, by_param, - yaxis='%s duration [µs]' % name) - if 'function' in model['energy'] and 'user' in model['energy']['function']: - aggval['energy']['function']['user'] = { - 'raw' : model['energy']['function']['user']['raw'], - 'params' : model['energy']['function']['user']['params'], - } - fit_function( - aggval['energy']['function']['user'], name, 'energies', parameters, by_param, - yaxis='%s energy [pJ]' % name) - 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_next']['function']['user'], name, 'rel_energies_next', parameters, by_param, - yaxis='%s rel_energy_next [pJ]' % name) + maybe_fit_function(aggval, model, by_param, parameters, name, 'duration', 'durations', 'µs') + maybe_fit_function(aggval, model, by_param, parameters, name, 'energy', 'energies', 'pJ') + maybe_fit_function(aggval, model, by_param, parameters, name, 'rel_energy_prev', 'rel_energies_prev', 'pJ') + maybe_fit_function(aggval, model, by_param, parameters, name, 'rel_energy_next', 'rel_energies_next', 'pJ') 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) - aggval['timeout']['function']['user'] = { - 'raw' : model['timeout']['function']['user']['raw'], - 'params' : model['timeout']['function']['user']['params'], - } - fit_function( - aggval['timeout']['function']['user'], name, 'timeouts', parameters, - by_param, yaxis='%s timeout [µs]' % name) + maybe_fit_function(aggval, model, by_param, parameters, name, 'timeout', 'timeouts', 'µs') + if 'function' in model['timeout'] and 'user' in model['timeout']['function']: if aggval['timeout']['std_param'] > 0 and aggval['timeout']['std_trace'] / aggval['timeout']['std_param'] < 0.5: aggval['timeout']['std_by_trace'] = mean_std_by_trace_part(by_trace, transition_names, name, 'timeouts') |