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-rwxr-xr-xlib/plotter.py113
1 files changed, 64 insertions, 49 deletions
diff --git a/lib/plotter.py b/lib/plotter.py
index 30b5b82..b9d5c3e 100755
--- a/lib/plotter.py
+++ b/lib/plotter.py
@@ -4,69 +4,79 @@ import itertools
import numpy as np
import matplotlib.pyplot as plt
import re
-from matplotlib.patches import Polygon
-from utils import flatten
+
def is_state(aggregate, name):
"""Return true if name is a state and not 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)
+ 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)
+ 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)
+ 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')
+
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')
+
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')
+
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')
+
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')
+
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)')
+
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, data, 'Transition', 'µW', modeldata = mdata)
+ boxplot(keys, data, 'Transition', 'µW', modeldata=mdata)
-def plot_attribute(aggregate, attribute, attribute_unit = '', key_filter = lambda x: True, **kwargs):
+
+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.
@@ -82,23 +92,26 @@ def plot_attribute(aggregate, attribute, attribute_unit = '', key_filter = lambd
data = [aggregate[key][attribute] for key in keys]
boxplot(keys, data, attribute, attribute_unit, **kwargs)
+
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')
+
def plot_y(Y, **kwargs):
plot_xy(np.arange(len(Y)), Y, **kwargs)
-def plot_xy(X, Y, xlabel = None, ylabel = None, title = None, output = None):
- fig, ax1 = plt.subplots(figsize=(10,6))
- if title != None:
+
+def plot_xy(X, Y, xlabel=None, ylabel=None, title=None, output=None):
+ fig, ax1 = plt.subplots(figsize=(10, 6))
+ if title is not None:
fig.canvas.set_window_title(title)
- if xlabel != None:
+ if xlabel is not None:
ax1.set_xlabel(xlabel)
- if ylabel != None:
+ if ylabel is not None:
ax1.set_ylabel(ylabel)
- plt.subplots_adjust(left = 0.1, bottom = 0.1, right = 0.99, top = 0.99)
+ plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99)
plt.plot(X, Y, "bo", markersize=2)
if output:
plt.savefig(output)
@@ -109,18 +122,20 @@ def plot_xy(X, Y, xlabel = None, ylabel = None, title = None, output = None):
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]
+ 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 != None:
+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)
- if xlabel != None:
+ if xlabel is not None:
ax1.set_xlabel(xlabel)
- if ylabel != None:
+ if ylabel is not None:
ax1.set_ylabel(ylabel)
- plt.subplots_adjust(left = 0.1, bottom = 0.1, right = 0.99, top = 0.99)
+ plt.subplots_adjust(left=0.1, bottom=0.1, right=0.99, top=0.99)
param_name = model.param_name(param_idx)
@@ -137,8 +152,8 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabe
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:])
- if not other_param_key in by_other_param:
+ 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])
@@ -153,7 +168,7 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabe
YY2_legend = []
cm = plt.get_cmap('brg', len(by_other_param))
- for i, k in sorted(enumerate(by_other_param), key = lambda x: x[1]):
+ 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'])
@@ -169,17 +184,17 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabe
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)
+ # x_range = int((v['X'].max() - v['X'].min()) * 10)
+ # xsp = np.linspace(v['X'].min(), v['X'].max(), x_range)
if param_model:
ysp = []
for x in xsp:
xarg = [*k[:param_idx], x, *k[param_idx:]]
- ysp.append(param_model(state_or_trans, attribute, param = xarg))
+ 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)))
- if extra_function != None:
+ if extra_function is not None:
ysp = []
with np.errstate(divide='ignore', invalid='ignore'):
for x in xsp:
@@ -202,7 +217,7 @@ def plot_param(model, state_or_trans, attribute, param_idx, xlabel = None, ylabe
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, 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)
@@ -214,16 +229,16 @@ def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X
xsp = np.linspace(X[index].min(), X[index].max(), 100)
x_range = 100
- if xaxis != None:
+ if xaxis is not None:
ax1.set_xlabel(xaxis)
else:
ax1.set_xlabel(parameters[index])
- if yaxis != None:
+ if yaxis is not None:
ax1.set_ylabel(yaxis)
else:
ax1.set_ylabel('%s %s' % (name, datatype))
- otherparams = list(set(itertools.product(*X[:index], *X[index+1:])))
+ 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]
@@ -234,7 +249,7 @@ def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X
if k < index:
tt &= X[k] == elem[k]
elif k > index:
- tt &= X[k] == elem[k-1]
+ tt &= X[k] == elem[k - 1]
plt.plot(X[index][tt], Y[tt], "rx", color=color)
@@ -245,8 +260,8 @@ def plot_param_fit(function, name, fitfunc, funp, parameters, datatype, index, X
plt.show()
-def boxplot(ticks, measurements, xlabel = '', ylabel = '', modeldata = None, output = None):
- fig, ax1 = plt.subplots(figsize=(10,6))
+def boxplot(ticks, measurements, xlabel='', ylabel='', modeldata=None, output=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)
@@ -256,10 +271,10 @@ def boxplot(ticks, measurements, xlabel = '', ylabel = '', modeldata = None, out
plt.setp(bp['fliers'], color='red', marker='+')
ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
- alpha=0.5)
+ alpha=0.5)
ax1.set_axisbelow(True)
- #ax1.set_title('DriverEval')
+ # ax1.set_title('DriverEval')
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel)
@@ -268,7 +283,7 @@ def boxplot(ticks, measurements, xlabel = '', ylabel = '', modeldata = None, out
xtickNames = plt.setp(ax1, xticklabels=ticks)
plt.setp(xtickNames, rotation=0, fontsize=10)
- boxColors = ['darkkhaki', 'royalblue']
+ # boxColors = ['darkkhaki', 'royalblue']
medians = list(range(numBoxes))
for i in range(numBoxes):
box = bp['boxes'][i]
@@ -277,11 +292,11 @@ def boxplot(ticks, measurements, xlabel = '', ylabel = '', modeldata = None, out
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
- boxCoords = list(zip(boxX, boxY))
+ # 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)
+ # 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 = []
@@ -294,22 +309,22 @@ def boxplot(ticks, measurements, xlabel = '', ylabel = '', modeldata = None, out
# 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')
+ color='w', marker='*', markeredgecolor='k')
if modeldata:
plt.plot([np.average(med.get_xdata())], [modeldata[i]],
- color='w', marker='o', markeredgecolor='k')
+ color='w', marker='o', markeredgecolor='k')
pos = np.arange(numBoxes) + 1
upperLabels = [str(np.round(s, 2)) for s in medians]
- weights = ['bold', 'semibold']
+ # weights = ['bold', 'semibold']
for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
- k = tick % 2
+ # k = tick % 2
y0, y1 = ax1.get_ylim()
- textpos = y0 + (y1 - y0)*0.97
- ypos = ax1.get_ylim()[0]
+ textpos = y0 + (y1 - y0) * 0.97
+ # ypos = ax1.get_ylim()[0]
ax1.text(pos[tick], textpos, upperLabels[tick],
- horizontalalignment='center', size='small',
- color='royalblue')
+ horizontalalignment='center', size='small',
+ color='royalblue')
if output:
plt.savefig(output)
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-29 08:19:05 +0000