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#!/usr/bin/env python3
import itertools
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
def flatten(somelist):
return [item for sublist in somelist for item in sublist]
def is_state(aggregate, name):
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, mdata, None, data, 'Zustand', 'µW')
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)')
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')
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')
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')
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')
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')
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')
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)')
def plot_histogram(data):
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, mdata, None, data, 'Transition', 'µW')
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')
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)
x_range = X[index].max() - X[index].min() + 1
if x_range > 100 and x_range < 500:
xsp = np.linspace(X[index].min(), X[index].max(), x_range)
else:
xsp = np.linspace(X[index].min(), X[index].max(), 100)
x_range = 100
if xaxis != None:
ax1.set_xlabel(xaxis)
else:
ax1.set_xlabel(parameters[index])
if yaxis != None:
ax1.set_ylabel(yaxis)
else:
ax1.set_ylabel('%s %s' % (name, datatype))
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)
tt = np.full((len(X[index])), True, dtype=bool)
for k in range(len(parameters)):
if k < index:
tt &= X[k] == elem[k]
elif k > index:
tt &= X[k] == elem[k-1]
plt.plot(X[index][tt], Y[tt], "rx", color=color)
xarg = [np.array([x] * x_range) for x in elem[:index]]
xarg.append(xsp)
xarg.extend([np.array([x] * x_range) for x in elem[index:]])
plt.plot(xsp, fitfunc(funp, xarg), "r-", color=color)
plt.show()
def boxplot(ticks, modeldata, onlinedata, mimosadata, xlabel, ylabel):
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)
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.set_axisbelow(True)
#ax1.set_title('DriverEval')
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
numBoxes = len(mimosadata)
xtickNames = plt.setp(ax1, xticklabels=ticks)
plt.setp(xtickNames, rotation=0, fontsize=10)
boxColors = ['darkkhaki', 'royalblue']
medians = list(range(numBoxes))
for i in range(numBoxes):
box = bp['boxes'][i]
boxX = []
boxY = []
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
boxCoords = list(zip(boxX, boxY))
# Alternate between Dark Khaki and Royal Blue
k = i % 2
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]
medianX = []
medianY = []
for j in range(2):
medianX.append(med.get_xdata()[j])
medianY.append(med.get_ydata()[j])
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(mimosadata[i])],
color='w', marker='*', markeredgecolor='k')
if modeldata:
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]
weights = ['bold', 'semibold']
for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
k = tick % 2
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')
plt.show()
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