def process_3():
for item in files:
issue_data = read.get_issues(item)
users = [
max(issue_data[issue], key=lambda item: item['when'])["user"]
for issue in issue_data.keys()
]
users = Counter(users)
graph.pie_chart(users, "Project " + item)
def process_1():
issues = {}
for item in files:
time_diff = []
issue_data = read.get_issues(item)
for issue in issue_data.keys():
single_list = [x["when"] for x in issue_data[issue]]
t = (max(single_list) - min(single_list)) / (60*60*24)
if t == 0:
continue
time_diff.append(t)
issues[item] = time_diff
data = {"Less than a day": sum([1 for x in time_diff if x>=0 and x<1]),
"2 to 5 days": sum([1 for x in time_diff if x>=1 and x<5]),
"5 to 10 days": sum([1 for x in time_diff if x>=5 and x<10]),
"10 days": sum([1 for x in time_diff if x>=10])
}
graph.pie_chart(data, "Issue open time of project "+item)
graph.multiple_lines([issues[x] for x in issues.keys()])
def process_1():
issues = {}
for item in files:
time_diff = []
issue_data = read.get_issues(item)
for issue in issue_data.keys():
single_list = [x["when"] for x in issue_data[issue]]
t = (max(single_list) - min(single_list)) / (60 * 60 * 24)
if t == 0:
continue
time_diff.append(t)
issues[item] = time_diff
data = {
"Less than a day": sum([1 for x in time_diff if x >= 0 and x < 1]),
"2 to 5 days": sum([1 for x in time_diff if x >= 1 and x < 5]),
"5 to 10 days": sum([1 for x in time_diff if x >= 5 and x < 10]),
"10 days": sum([1 for x in time_diff if x >= 10])
}
graph.pie_chart(data, "Issue open time of project " + item)
graph.multiple_lines([issues[x] for x in issues.keys()])
def process_2():
neg_full = {}
for item in files:
milestones = read.get_milestones(item)
milestones = {x["name"]:x["closed_at"] for x in milestones if x["closed_at"] }
issue_data = read.get_issues(item)
issue_diffs = []
for issue in issue_data.keys():
ist = max(issue_data[issue], key=lambda item:item['when'])
if ist["milestone"] in milestones.keys():
issue_diffs.append((milestones[ist["milestone"]] - ist["when"]) / (60*60))
pos_diff = [x for x in issue_diffs if x>0]
data = {"Less than an hour": sum([1 for x in pos_diff if x>=0 and x<1]),
"2 to 5 hours": sum([1 for x in pos_diff if x>=1 and x<5]),
"5 to 10 hours": sum([1 for x in pos_diff if x>=5 and x<10]),
"10 hours": sum([1 for x in pos_diff if x>=10])
}
pos_diff_per = len(pos_diff)*100/len(issue_diffs)
graph.pie_chart(data, str(pos_diff_per)+"\% for project "+item)
neg_diff = [-x for x in issue_diffs if x<0]
data = {"Less than an hour": sum([1 for x in neg_diff if x>=0 and x<1]),
"2 to 5 hours": sum([1 for x in neg_diff if x>=1 and x<5]),
"5 to 10 hours": sum([1 for x in neg_diff if x>=5 and x<10]),
"10 hours": sum([1 for x in neg_diff if x>=10])
}
neg_diff_per = len(neg_diff)*100/len(issue_diffs)
graph.pie_chart(data, str(neg_diff_per)+"\% for project "+item)
neg_full["Project "+item] = neg_diff_per
graph.pie_chart(neg_full, "Closed after milestone")
def process_2():
neg_full = {}
for item in files:
milestones = read.get_milestones(item)
milestones = {
x["name"]: x["closed_at"]
for x in milestones if x["closed_at"]
}
issue_data = read.get_issues(item)
issue_diffs = []
for issue in issue_data.keys():
ist = max(issue_data[issue], key=lambda item: item['when'])
if ist["milestone"] in milestones.keys():
issue_diffs.append(
(milestones[ist["milestone"]] - ist["when"]) / (60 * 60))
pos_diff = [x for x in issue_diffs if x > 0]
data = {
"Less than an hour":
sum([1 for x in pos_diff if x >= 0 and x < 1]),
"2 to 5 hours": sum([1 for x in pos_diff if x >= 1 and x < 5]),
"5 to 10 hours": sum([1 for x in pos_diff if x >= 5 and x < 10]),
"10 hours": sum([1 for x in pos_diff if x >= 10])
}
pos_diff_per = len(pos_diff) * 100 / len(issue_diffs)
graph.pie_chart(data, str(pos_diff_per) + "\% for project " + item)
neg_diff = [-x for x in issue_diffs if x < 0]
data = {
"Less than an hour":
sum([1 for x in neg_diff if x >= 0 and x < 1]),
"2 to 5 hours": sum([1 for x in neg_diff if x >= 1 and x < 5]),
"5 to 10 hours": sum([1 for x in neg_diff if x >= 5 and x < 10]),
"10 hours": sum([1 for x in neg_diff if x >= 10])
}
neg_diff_per = len(neg_diff) * 100 / len(issue_diffs)
graph.pie_chart(data, str(neg_diff_per) + "\% for project " + item)
neg_full["Project " + item] = neg_diff_per
graph.pie_chart(neg_full, "Closed after milestone")
def process_7():
for item in files:
commits = read.get_commits(item)
users = [x["user"] for x in commits]
users = Counter(users)
graph.pie_chart(users, "Project " + item)
def process_5(): for item in files: comments = read.get_comments(item) users = [x["user"] for x in comments] users = Counter(users) graph.pie_chart(users, "Project "+item)
def process_3(): for item in files: issue_data = read.get_issues(item) users = [max(issue_data[issue], key=lambda item:item['when'])["user"] for issue in issue_data.keys()] users = Counter(users) graph.pie_chart(users, "Project "+item)
def evaluate(user_func, *args, pdf=False, png = False, timeseries=False, powerLoss=0.8, energyOutput=False, \
locations=["Mongolia", "Iceland", "Switzerland"], year="2016", printToScreen = True):
""" Calculates effective emissions of the function
Parameters:
user_func: user's function + associated args
pdf (bool): whether a PDF report should be generated
powerLoss (float): PSU efficiency rating
energyOutput (bool): return value also includes information about energy usage, not just function's return
locations (list of strings): list of locations to be compared
year (str): year of dataset to be used
printToScreen (bool): get information in the command line
"""
try:
utils.setGlobal(printToScreen)
if (utils.valid_cpu() or utils.valid_gpu()):
result, return_value, watt_averages, files, total_time, time_baseline, reading_baseline_wattage, time_process, reading_process_wattage = energy(user_func, *args, powerLoss = powerLoss, year = year, \
printToScreen = printToScreen, timeseries = timeseries)
location, default_location, comparison_values, default_emissions = get_comparison_data(result, locations, year, printToScreen)
breakdown = energy_mix(location, year = year)
emission, state_emission = emissions(result, breakdown, location, year, printToScreen)
if printToScreen:
utils.log("Assumed Carbon Equivalencies")
if printToScreen:
utils.log("Process Energy", result)
func_info = [user_func.__name__, *args]
kwh_and_emissions = [result, emission, state_emission]
if pdf:
#pass
report.generate(location, watt_averages, breakdown, kwh_and_emissions, \
func_info, comparison_values, default_emissions, default_location)
if png:
# generate energy mix pie chart
energy_dict = {"Coal" : breakdown[0], "Petroleum" : breakdown[1], "Natural Gas" : breakdown[2], "Low Carbon" : breakdown[3]}
figtitle = "Location: " + location
location_split = location.split()
filename = location_split[0]
for i in range(1, len(location_split)):
filename += "_" + location_split[i]
filename += ".png"
if locate.in_US(location):
energy_dict["Oil"] = energy_dict.pop("Petroleum")
figtitle = figtitle + ", USA"
graph.pie_chart(energy_dict, figtitle, filename)
# generate emissions comparison bar charts
png_bar_chart(location, emission, default_emissions)
if timeseries:
graph.timeseries(time_baseline, reading_baseline_wattage, "Baseline Wattage Timeseries")
graph.timeseries(time_process, reading_process_wattage, "Process Wattage Timeseries")
if energyOutput:
return (total_time, result, return_value)
else:
return return_value
else:
utils.log("The energy-usage package only works on Linux kernels "
"with Intel processors that support the RAPL interface and/or machines with"
" an Nvidia GPU. Please try again on a different machine.")
except Exception as e:
print("\n" + str(e))
