# curve.append(laplace.pdf(i, loc=location, scale=b))
# print(i, ",", curve[-1])
return curve_x, curve_y
if __name__ == '__main__':
# Experiment config
period1 = 33.33
period2 = 100
j = 16
delta = 190
epsilon = 100
PlotUtility.config_plot(plt)
plt.rcParams['figure.figsize'] = 6, 4
fig, ax = plt.subplots()
curve1_x, curve1_y = get_epsilon_scheduler_noise_distribution_curve(
location=period1, epsilon=epsilon, j=j, delta=delta)
curve2_x, curve2_y = get_epsilon_scheduler_noise_distribution_curve(
location=period2, epsilon=epsilon, j=j, delta=delta)
ylim = max(max(curve1_y), max(curve2_y)) * 1.1
ax.plot(curve1_x,
curve1_y,
color=PlotUtility.palletForMany[0],
linestyle=PlotUtility.line_style_list[0],
linewidth=2,
label='$33.3ms$')
def plot_peak_count(show_plot, in_csv_file_list, out_plot_file_list,
label_list):
''' general plot configurations '''
PlotUtility.config_plot(plt)
# plt.rcParams['figure.figsize'] = 8,5.5
plt.rcParams['figure.figsize'] = 6.5, 5
plot_labels = [] if label_list is None else label_list
''' Data '''
x_list = []
y_list = []
y_mean_list = []
y_error_list = []
x_mean = []
first = True
for csv_file in in_csv_file_list:
data_dict = np.genfromtxt(csv_file,
delimiter=',',
unpack=True,
names=True)
if first:
first = False
base_context_switches = data_dict['Context_Switches']
continue
# if first:
# x = data_dict['Utilization']
# first = False
this_x = data_dict['Utilization']
x_list.append(this_x)
this_y = data_dict['Context_Switches'] / base_context_switches
y_list.append(this_y)
x_mean = []
y_mean = []
y_error = []
for u in range(10):
util = u / 10
matched_values = np.array([])
for i in range(len(this_y)):
if util <= this_x[i] < (util + 0.1):
matched_values = np.append(matched_values, this_y[i])
x_mean.append(u / 10 + 0.05)
y_mean.append(matched_values.mean())
y_error.append(matched_values.std())
y_mean_list.append(y_mean)
y_error_list.append(y_error)
# plot general config
# plt.title('Interesting Graph\nCheck it out')
# plt.legend()
# plt.grid(True, 'major', 'both', color='0.8', linestyle='--', linewidth=1)
# x axis config
plt.xlabel('Task Set Utilization')
plt.xlim(0, 1)
plt.xticks([i / 10 for i in range(0, 11, 1)])
# y axis config
plt.ylabel(
'Ratio of the Number of Context Switches\nby the Corresponding Schedulers\nto that by Vanilla EDF'
)
# ylimMax = (max(y)/5+1)*5
# ylimMax = math.ceil(max(y))
# plt.ylim([0.8, ylimMax])
# plt.yticks([i*0.5 for i in range(2, (int(ylimMax))*2, 1)])
# plt.ylim(0, 1.1)
# plt.yticks([i/10 for i in range(1, 11, 1)])
# plt.grid(True, 'major', 'y', color='0.8', linestyle='--', linewidth=1)
# plt.grid(True, 'major', 'x', color='0.8', linestyle='--', linewidth=1)
# minor_ticks = [tmp / 10 for tmp in range(0, 11)]
# plt.axes().set_yticks(minor_ticks, minor=True)
# data points
markerSize = 30
for i in range(len(y_list)):
style_idx = i + 1
y = y_list[i]
x = x_list[i]
y_mean = y_mean_list[i]
y_error = y_error_list[i]
plt.scatter(x,
y,
marker=PlotUtility.pattern_list[style_idx],
facecolors=PlotUtility.palletForMany[style_idx],
color=PlotUtility.palletForMany[style_idx],
alpha=0.5,
s=[markerSize for i in range(len(y))],
label=plot_labels[i])
plt.errorbar(x_mean,
y_mean,
y_error,
color="black",
marker=PlotUtility.pattern_list[style_idx],
linewidth=1)
# Post plot configurations
legend = plt.legend(shadow=False)
legend.get_frame().set_edgecolor('grey')
''' Save the plot to files with the specified format '''
for outFileName in out_plot_file_list:
print("Exporting the plot ...")
# if outFileName == "png" or outFileName == "pdf":
# outFileName = "{}.{}".format(inFileName.split('.')[0], outFileName)
outputFormat = outFileName.split('.')[-1]
if outputFormat == "pdf" or outputFormat == "png" or True:
print('Saving the plot to "{}" ...'.format(outFileName), end=" ")
plt.savefig(outFileName, pad_inches=0.02, bbox_inches='tight')
print("Done")
if show_plot:
plt.show()
def plot_single_dft_of_stft(show_plot, in_csv_file_list, out_plot_file_list, label_list):
### general plot configurations ###
PlotUtility.config_plot(plt)
plt.rcParams['figure.figsize'] = 7, 2.5
# Load .rtstft json
print("Parsing the json file ...", end =" ")
try:
jsonRoot = json.load(open(in_csv_file_list[0], 'r'))
print("Done")
except:
print("Failed to load the rtstft file: {}".format(in_csv_file_list[0]))
exit()
# Load task set
print("Loading the task set ...", end =" ")
taskSet = LogLoader.loadSingleTaskSetFromJsonObject(jsonRoot)
print("Done")
print(taskSet.toString())
# Time unit
nsPerTick = jsonRoot['data']['tickUnitInNs']
tickToMsMultiplier = nsPerTick/1000_000_000
# How many time bins (x-axis size)?
lenTimeBins = len(jsonRoot['data']['unevenSpectrum'])
dftIndx = lenTimeBins-1
# Extract time bin labels
timeBinLabels = []
spectrumData = {}
xFreq = jsonRoot['data']['unevenSpectrum'][dftIndx]['frequencies']
# idx_of_freq_upper_limit = findIndexOfFrequency(jsonRoot['data']['unevenSpectrum'][i]['frequencies'],freq_upper_limit)
# true_top_freq = jsonRoot['data']['unevenSpectrum'][i]['frequencies'][idx_of_freq_upper_limit]
# print(true_top_freq, idx_of_freq_upper_limit)
# axx = ax.inset_axes([i * heatmapWidth, 0, heatmapWidth, true_top_freq / freq_upper_limit])
df = pd.DataFrame(jsonRoot['data']['unevenSpectrum'][dftIndx]['magnitudes'])
df_max_min_range = 1 if (df.max() - df.min()).max() == 0 else (df.max() - df.min()) # in case if divisor is zero
yMag = (df - df.min()) / (df_max_min_range) # min-max normalization
# yMag = df
# sns.heatmap(df, cmap='Blues', cbar_kws={'label': 'Amplitude', 'aspect': 0.01}, ax=axx, cbar_ax=ax_cbar)
# axx.tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
# axx.set_ylim(-1, idx_of_freq_upper_limit + 1)
ax = plt.subplot(111)
# X-axis Settings
plt.xlabel('Frequency (Hz)')
# Set X-axis limit based on the largest frequency of all tasks
X_AXIS_FREQ_LIMIT = 100*(int(taskSet.getLargestFreq()/100)+1)
X_AXIS_TICK_INTERVAL = 10
#ax.set_xticks(major_ticks)
ax.xaxis.set_major_locator(ticker.MultipleLocator(X_AXIS_TICK_INTERVAL))
ax.set_xlim(0, X_AXIS_FREQ_LIMIT)
# Y-axis Settings
ax.set_ylabel(ylabel='Magnitude \n(Normalized)')
yaxis_limit = 1 # normalized # maxAmplitude if not normalized
ax.set_ylim([0, yaxis_limit])
ax.yaxis.labelpad = 3
ax.xaxis.labelpad = 3
# get X and Y axis values (trimmed to the desired display range) to be displayed
# X
xFreq_limitIndx = findIndexOfFrequency(xFreq, X_AXIS_FREQ_LIMIT)
displayedFreq = xFreq[:xFreq_limitIndx]
# Y
maxAmplitude = max(yMag[:xFreq_limitIndx])
# displayedAmplitude = [amp / maxAmplitude for amp in yMag[:xFreq_limitIndx]] # Normalized
displayedAmplitude = yMag[:xFreq_limitIndx]
# sns.heatmap(yMag, cmap='Blues', cbar_kws={'label': 'Amplitude'}, ax=ax)
plt.plot(displayedFreq, displayedAmplitude, color='navy', alpha=0.75, linewidth=1.5)
plt.grid(linestyle=':')
'''Add ground truth lines'''
if taskSet is not None:
for task in taskSet.tasks:
plt.plot([task.frequency, task.frequency], [0, yaxis_limit], color='maroon', alpha=0.8, linestyle='--', linewidth=1.5)
ax.arrow(task.frequency, 1, 0, -0.05, head_width=3, head_length=0.03, color='maroon')
''' Save the plot to files with the specified format '''
for outFileName in out_plot_file_list:
print("Exporting the plot ...")
# if outFileName == "png" or outFileName == "pdf":
# outFileName = "{}.{}".format(inFileName.split('.')[0], outFileName)
outputFormat = outFileName.split('.')[-1]
if outputFormat == "pdf" or outputFormat == "png" or True:
print('Saving the plot to "{}" ...'.format(outFileName), end=" ")
plt.savefig(outFileName, pad_inches=0.02, bbox_inches='tight')
print("Done")
if show_plot:
plt.show()
print("Exporting the plot ...")
# if outFileName == "png" or outFileName == "pdf":
# outFileName = "{}.{}".format(inFileName.split('.')[0], outFileName)
outputFormat = outFileName.split('.')[-1]
if outputFormat == "pdf" or outputFormat == "png" or True:
print('Saving the plot to "{}" ...'.format(outFileName), end=" ")
plt.savefig(outFileName, pad_inches=0.02, bbox_inches='tight')
print("Done")
if show_plot:
plt.show()
if __name__ == '__main__':
# out_plot_file_list = []
# csv_file_list = [
# '/Users/jjs/Documents/myProject/RTS-Schedule-Simulator/experiments/test_cases/laplace_scheduler/dft_variance/edf-d20000_dftDuration.csv',
# '/Users/jjs/Documents/myProject/RTS-Schedule-Simulator/experiments/test_cases/laplace_scheduler/dft_variance/laplace-e1000-d20000_dftDuration.csv',
# '/Users/jjs/Documents/myProject/RTS-Schedule-Simulator/experiments/test_cases/laplace_scheduler/dft_variance/laplace-e10-d20000_dftDuration.csv',
# # '/Users/jjs/Documents/myProject/RTS-Schedule-Simulator/experiments/test_cases/laplace_scheduler/dft_variance/laplace-e1-d20000_dftDuration.csv'
# ]
# label_list = ["EDF", "$\epsilon-Scheduler=1000$", "$\epsilon-Scheduler=10$"]
# show_plot = True
show_plot, csv_file_list, out_plot_file_list, label_list = PlotUtility.parse_arguments(
)
plot_peak_count(show_plot, csv_file_list, out_plot_file_list, label_list)
def plot_noise_range(show_plot, in_csv_file_list, out_plot_file_list, label_list):
percentile = 0.95
delta = 190 # 10 ms
j_list = [1, 10, 50, 100]
# Build epsilon list
epsilon_list = []
# [0.01, 0.1)
epsilon_list.extend([i/100 for i in range(1, 10)])
# [0.1, 1)
epsilon_list.extend([i/10 for i in range(1, 10)])
# [1, 1000]
epsilon_list.extend([i for i in range(1, 1000)])
# epsilon_list.extned([pow(10, i) for i in range(-2, 5, 1)])
noise_level_list = []
for i in range(len(j_list)):
noise_level_list.append([compute_noise_level_percentile(epsilon, j_list[i], delta, percentile) for epsilon in epsilon_list])
### general plot configurations ###
PlotUtility.config_plot(plt)
plt.rcParams['figure.figsize'] = 5.5,4.5
fig, ax = plt.subplots()
# ax.set_xscale("log")
plt.xscale('log')
plt.yscale('log')
for i in range(len(j_list)):
plt.plot(epsilon_list, noise_level_list[i], label=j_list[i], linewidth=2, linestyle=PlotUtility.line_style_list[i])
# Post plot configurations
# Legend
legend = plt.legend(shadow=True, loc='lower left', title='$J_i$')
legend.get_frame().set_edgecolor('grey')
# Axis Titles
plt.ylabel('Noise Scale at '+str(100*percentile)[:-2]+'$^{th}$ Percentile ($ms$)')
plt.xlabel('$\epsilon_i$')
plt.xlim(0, max(epsilon_list)+1)
plt.ylim(0, 100000)
# plt.ylim(bottom=0)
# Grid
plt.grid(True, 'major', 'y', color='0.8', linestyle='--', linewidth=1)
plt.grid(True, 'major', 'x', color='0.8', linestyle='--', linewidth=1)
title = "$\Delta\eta_i={}$".format(delta)
ax.text(0.98, 0.98, title, transform=ax.transAxes, fontsize=14,
verticalalignment='top', horizontalalignment='right')
''' Save the plot to files with the specified format '''
for outFileName in out_plot_file_list:
print("Exporting the plot ...")
# if outFileName == "png" or outFileName == "pdf":
# outFileName = "{}.{}".format(inFileName.split('.')[0], outFileName)
outputFormat = outFileName.split('.')[-1]
if outputFormat == "pdf" or outputFormat == "png" or True:
print('Saving the plot to "{}" ...'.format(outFileName), end=" ")
plt.savefig(outFileName, pad_inches=0.02, bbox_inches='tight')
print("Done")
if show_plot:
plt.show()
def plot_peak_count(show_plot, in_csv_file_list, out_plot_file_list,
label_list):
''' general plot configurations '''
PlotUtility.config_plot(plt)
# plt.rcParams['figure.figsize'] = 8,5.5
plt.rcParams['figure.figsize'] = 6.5, 5.5
AX = gridspec.GridSpec(6, 1)
AX.update(wspace=0.3, hspace=0.15)
ax_top = plt.subplot(AX[0, 0])
ax = plt.subplot(AX[1:, 0])
plot_labels = [] if label_list is None else label_list
''' Data '''
y_list = []
y_mean_list = []
y_error_list = []
x_mean = []
first = True
for csv_file in in_csv_file_list:
data_dict = np.genfromtxt(csv_file,
delimiter=',',
unpack=True,
names=True)
if first:
x = data_dict['Utilization']
first = False
this_y = data_dict['ZScore_Based_Peak_Count']
y_list.append(this_y)
x_mean = []
y_mean = []
y_error = []
for u in range(10):
util = u / 10
matched_values = np.array([])
for i in range(len(this_y)):
if x[i] >= util and x[i] < (util + 0.1):
matched_values = np.append(matched_values, this_y[i])
x_mean.append(u / 10 + 0.05)
y_mean.append(matched_values.mean())
y_error.append(matched_values.std())
y_mean_list.append(y_mean)
y_error_list.append(y_error)
''' Deadline Miss Ratio (only for the last CSV) '''
data_dict = np.genfromtxt(in_csv_file_list[-1],
delimiter=',',
unpack=True,
names=True)
x_deadline_miss_ratio = data_dict['Utilization']
y_deadline_miss_ratio = data_dict['Deadline_Miss_Ratio']
tasksets_with_deadline_misses_count = []
for i in range(10):
tasksets_with_deadline_misses_count.append(0)
for j in range(len(x_deadline_miss_ratio)):
if i / 10 <= x_deadline_miss_ratio[j] < (i + 1) / 10:
if y_deadline_miss_ratio[j] > 0.0:
tasksets_with_deadline_misses_count[i] += 1
# print(tasksets_with_deadline_misses_count[-1])
# plot general config
# plt.title('Interesting Graph\nCheck it out')
# plt.legend()
# plt.grid(True, 'major', 'both', color='0.8', linestyle='--', linewidth=1)
# x axis config
plt.xlabel('Task Set Utilization')
plt.xlim(0, 1)
plt.xticks([i / 10 for i in range(0, 11, 1)])
# y axis config
plt.ylabel('Outstanding Peak Count')
# ylimMax = (max(y)/5+1)*5
# ylimMax = math.ceil(max(y))
# plt.ylim([0.8, ylimMax])
# plt.yticks([i*0.5 for i in range(2, (int(ylimMax))*2, 1)])
# plt.ylim(0, 1.1)
# plt.yticks([i/10 for i in range(1, 11, 1)])
# plt.grid(True, 'major', 'y', color='0.8', linestyle='--', linewidth=1)
# plt.grid(True, 'major', 'x', color='0.8', linestyle='--', linewidth=1)
# minor_ticks = [tmp / 10 for tmp in range(0, 11)]
# plt.axes().set_yticks(minor_ticks, minor=True)
# data points
markerSize = 30
for i in range(len(y_list)):
y = y_list[i]
y_mean = y_mean_list[i]
y_error = y_error_list[i]
ax.scatter(x,
y,
marker=PlotUtility.pattern_list[i],
facecolors=PlotUtility.palletForMany[i],
color=PlotUtility.palletForMany[i],
alpha=0.5,
s=[markerSize for i in range(len(y))],
label=plot_labels[i])
ax.errorbar(x_mean,
y_mean,
y_error,
color="black",
marker=PlotUtility.pattern_list[i],
linewidth=1)
ax_top.set_xlim(-0.5, 9.5)
# ax_top_ytop = math.ceil(max(y_deadline_miss_ratio)/0.1)*0.1
ax_top_ytop = math.ceil(max(tasksets_with_deadline_misses_count) / 50) * 50
# # ax_top.set_ylim(0, max(y_deadline_miss_ratio))
ax_top.set_ylim(0, ax_top_ytop)
# ax_top.get_yaxis().set_major_locator(ticker.LinearLocator(numticks=(ax_top_ytop/0.1)+1))
ax_top.get_yaxis().set_major_locator(
ticker.LinearLocator(numticks=(ax_top_ytop / 50) + 1))
ax_top.yaxis.tick_right()
ax_top.tick_params(top=False,
bottom=False,
left=False,
right=True,
labelleft=False,
labelbottom=False,
labeltop=False,
labelright=False,
direction='in')
ax_top.spines["left"].set_visible(False)
ax_top.spines["top"].set_visible(False)
# ax_top.scatter(x_deadline_miss_ratio, y_deadline_miss_ratio, marker='o', facecolors='grey', color='grey', alpha=0.5, s=[5 for i in range(len(y_deadline_miss_ratio))],)
# ax_top.bar(range(10), tasksets_with_deadline_misses_count)
ax_top.plot(range(10),
tasksets_with_deadline_misses_count,
marker=PlotUtility.pattern_list[len(csv_file_list) - 1],
markersize=10,
color='black')
ax_top.text(
0,
1.1,
"The Number of Task Sets\nthat Have Deadline Misses\nin {}".format(
label_list[-1]),
transform=ax_top.transAxes,
fontsize=14,
verticalalignment='top',
horizontalalignment='left')
for i in range(10):
if tasksets_with_deadline_misses_count[i] > 0:
ax_top.text(i,
tasksets_with_deadline_misses_count[i] + 10,
'$\\frac{' +
str(tasksets_with_deadline_misses_count[i]) +
'}{600}$',
fontsize=12,
ha='center',
va='bottom')
# Post plot configurations
legend = plt.legend(shadow=False, loc='upper right')
legend.get_frame().set_edgecolor('grey')
''' Save the plot to files with the specified format '''
for outFileName in out_plot_file_list:
print("Exporting the plot ...")
# if outFileName == "png" or outFileName == "pdf":
# outFileName = "{}.{}".format(inFileName.split('.')[0], outFileName)
outputFormat = outFileName.split('.')[-1]
if outputFormat == "pdf" or outputFormat == "png" or True:
print('Saving the plot to "{}" ...'.format(outFileName), end=" ")
plt.savefig(outFileName, pad_inches=0.02, bbox_inches='tight')
print("Done")
if show_plot:
plt.show()
def plot_execution_range_ratio(show_plot, in_csv_file_list, out_plot_file_list,
label_list):
''' general plot configurations '''
PlotUtility.config_plot(plt)
plt.rcParams['figure.figsize'] = 8, 5.5
# change font to Arial
plt.rcParams["font.family"] = "Arial"
plt.rcParams['font.size'] = 22
plt.rcParams['legend.fontsize'] = 13
plt.rcParams['axes.titlesize'] = 15
plt.rcParams['ytick.labelsize'] = 20
plt.rcParams['xtick.labelsize'] = 20
plt.rcParams["legend.edgecolor"] = 'black'
plot_labels = [] if label_list is None else label_list
''' Data '''
data_dict1 = np.genfromtxt(in_csv_file_list[0],
delimiter=',',
unpack=True,
names=True)
data_dict2 = np.genfromtxt(in_csv_file_list[1],
delimiter=',',
unpack=True,
names=True)
x = data_dict1['Utilization']
y1 = data_dict1['Mean_Execution_Range_Ratio_To_Period']
y2 = data_dict2['Mean_Execution_Range_Ratio_To_Period']
# plot general config
# plt.title('Interesting Graph\nCheck it out')
# plt.legend()
# plt.grid(True, 'major', 'both', color='0.8', linestyle='--', linewidth=1)
# x axis config
plt.xlabel('Task Set Utilization')
plt.xlim(0, 1)
plt.xticks([i / 10 for i in range(0, 11, 1)])
# y axis config
plt.ylabel('Mean Ratio of Task \n Execution Range to Period')
# ylimMax = (max(y)/5+1)*5
# plt.ylim([0, ylimMax])
# plt.yticks([i for i in range(0, int(ylimMax)+1, 2)])
plt.ylim(0, 1.1)
plt.yticks([i / 10 for i in range(1, 11, 1)])
# plt.grid(True, 'major', 'y', color='0.8', linestyle='--', linewidth=1)
# plt.grid(True, 'major', 'x', color='0.8', linestyle='--', linewidth=1)
# minor_ticks = [tmp / 10 for tmp in range(0, 11)]
# plt.axes().set_yticks(minor_ticks, minor=True)
# data points
markerSize = 30
plt.scatter(x,
y1,
marker='+',
color='grey',
alpha=0.5,
s=[markerSize for i in range(len(y1))],
label=plot_labels[0])
plt.scatter(x,
y2,
marker='x',
color='DodgerBlue',
alpha=0.5,
s=[markerSize for i in range(len(y2))],
label=plot_labels[1])
# plt.scatter(x, y1, marker='o', facecolors='none', color='grey', alpha=0.5, s=[markerSize for i in range(len(y1))], label=plot_labels[0])
# plt.scatter(x, y2, marker='o', facecolors='none', color='DodgerBlue', alpha=0.5, s=[markerSize for i in range(len(y2))], label=plot_labels[1])
# Post plot configurations
legend = plt.legend(shadow=False, loc='upper right')
legend.get_frame().set_edgecolor('grey')
''' Save the plot to files with the specified format '''
for outFileName in out_plot_file_list:
print("Exporting the plot ...")
# if outFileName == "png" or outFileName == "pdf":
# outFileName = "{}.{}".format(inFileName.split('.')[0], outFileName)
outputFormat = outFileName.split('.')[-1]
if outputFormat == "pdf" or outputFormat == "png" or True:
print('Saving the plot to "{}" ...'.format(outFileName), end=" ")
plt.savefig(outFileName, pad_inches=0.02, bbox_inches='tight')
print("Done")
if show_plot:
plt.show()
def plot_context_switch_increase_ratio(show_plot, in_csv_file_list,
out_plot_file_list, label_list):
''' general plot configurations '''
PlotUtility.config_plot(plt)
plt.rcParams['figure.figsize'] = 8, 5.5
# change font to Arial
plt.rcParams["font.family"] = "Arial"
plt.rcParams['font.size'] = 22
plt.rcParams['legend.fontsize'] = 13
plt.rcParams['axes.titlesize'] = 15
plt.rcParams['ytick.labelsize'] = 20
plt.rcParams['xtick.labelsize'] = 20
plt.rcParams["legend.edgecolor"] = 'black'
plot_labels = [] if label_list is None else label_list
''' Data '''
data_dict1 = np.genfromtxt(in_csv_file_list[0],
delimiter=',',
unpack=True,
names=True)
data_dict2 = np.genfromtxt(in_csv_file_list[1],
delimiter=',',
unpack=True,
names=True)
x = data_dict1['Utilization']
y = data_dict2['Context_Switches'] / data_dict1[
'Context_Switches'] # context switch ratio
x_mean = []
y_mean = []
y_error = []
for u in range(10):
util = u / 10
matched_values = np.array([])
for i in range(len(y)):
if x[i] >= util and x[i] < (util + 0.1):
matched_values = np.append(matched_values, y[i])
x_mean.append(u / 10 + 0.05)
y_mean.append(matched_values.mean())
y_error.append(matched_values.std())
# plot general config
# plt.title('Interesting Graph\nCheck it out')
# plt.legend()
# plt.grid(True, 'major', 'both', color='0.8', linestyle='--', linewidth=1)
# x axis config
plt.xlabel('Task Set Utilization')
plt.xlim(0, 1)
plt.xticks([i / 10 for i in range(0, 11, 1)])
# y axis config
plt.ylabel('Context Switch Ratio \n (TaskShuffler/RM)')
# ylimMax = (max(y)/5+1)*5
ylimMax = math.ceil(max(y))
plt.ylim([0.8, ylimMax])
plt.yticks([i * 0.5 for i in range(2, (int(ylimMax)) * 2, 1)])
# plt.ylim(0, 1.1)
# plt.yticks([i/10 for i in range(1, 11, 1)])
# plt.grid(True, 'major', 'y', color='0.8', linestyle='--', linewidth=1)
# plt.grid(True, 'major', 'x', color='0.8', linestyle='--', linewidth=1)
# minor_ticks = [tmp / 10 for tmp in range(0, 11)]
# plt.axes().set_yticks(minor_ticks, minor=True)
# data points
markerSize = 30
plt.scatter(x,
y,
marker='o',
facecolors='none',
color='DodgerBlue',
alpha=0.7,
s=[markerSize for i in range(len(y))])
plt.errorbar(x_mean, y_mean, y_error, color="black", marker='o')
# Post plot configurations
# legend = plt.legend(shadow=False, loc='upper right')
# legend.get_frame().set_edgecolor('grey')
''' Save the plot to files with the specified format '''
for outFileName in out_plot_file_list:
print("Exporting the plot ...")
# if outFileName == "png" or outFileName == "pdf":
# outFileName = "{}.{}".format(inFileName.split('.')[0], outFileName)
outputFormat = outFileName.split('.')[-1]
if outputFormat == "pdf" or outputFormat == "png" or True:
print('Saving the plot to "{}" ...'.format(outFileName), end=" ")
plt.savefig(outFileName, pad_inches=0.02, bbox_inches='tight')
print("Done")
if show_plot:
plt.show()