diff options
Diffstat (limited to 'lib/plotter.py')
| -rwxr-xr-x | lib/plotter.py | 261 |
1 files changed, 162 insertions, 99 deletions
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() |