diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/plotter.py | 32 |
1 files changed, 16 insertions, 16 deletions
diff --git a/lib/plotter.py b/lib/plotter.py index 2398c12..8a28f4c 100755 --- a/lib/plotter.py +++ b/lib/plotter.py @@ -22,46 +22,46 @@ 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, mdata, None, data, 'Zustand', 'µW') + 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, mdata, None, data, 'Transition', 'pJ (rel)') + 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, mdata, None, data, 'Transition', 'pJ') + 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, None, None, data, 'Zustand', 'µs') + 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, None, None, data, 'Transition', 'µs') + 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, None, None, data, 'Timeout', 'µs') + 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, None, None, data, 'Zustand', '% Clipping') + 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, None, None, data, 'Transition', '% Clipping') + 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, None, None, data, 'Zustand', 'substate threshold (mW/dmW)') + 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) @@ -71,7 +71,7 @@ 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, mdata, None, data, 'Transition', 'µW') + boxplot(keys, data, 'Transition', 'µW', modeldata = mdata) def plot_attribute(aggregate, attribute, attribute_unit = '', key_filter = lambda x: True): """ @@ -87,12 +87,12 @@ def plot_attribute(aggregate, attribute, attribute_unit = '', key_filter = lambd """ keys = list(filter(key_filter, sorted(aggregate.keys()))) data = [aggregate[key][attribute] for key in keys] - boxplot(keys, None, None, data, attribute, attribute_unit) + boxplot(keys, data, attribute, attribute_unit) 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, None, None, data, 'Zustand', '% Clipping') + boxplot(keys, data, 'Zustand', '% Clipping') def plot_y(Y, **kwargs): plot_xy(np.arange(len(Y)), Y, **kwargs) @@ -252,12 +252,12 @@ def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X plt.show() -def boxplot(ticks, modeldata, onlinedata, mimosadata, xlabel, ylabel): +def boxplot(ticks, measurements, xlabel = '', ylabel = '', modeldata = None): fig, ax1 = plt.subplots(figsize=(10,6)) fig.canvas.set_window_title('DriverEval') plt.subplots_adjust(left=0.1, right=0.95, top=0.95, bottom=0.1) - bp = plt.boxplot(mimosadata, notch=0, sym='+', vert=1, whis=1.5) + 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='+') @@ -270,7 +270,7 @@ def boxplot(ticks, modeldata, onlinedata, mimosadata, xlabel, ylabel): ax1.set_xlabel(xlabel) ax1.set_ylabel(ylabel) - numBoxes = len(mimosadata) + numBoxes = len(measurements) xtickNames = plt.setp(ax1, xticklabels=ticks) plt.setp(xtickNames, rotation=0, fontsize=10) @@ -300,7 +300,7 @@ def boxplot(ticks, modeldata, onlinedata, mimosadata, xlabel, ylabel): 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(mimosadata[i])], + 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]], |