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Fix the Joule to kWh conversion issue #16

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Fix the Joule to kWh conversion issue #16

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18 changes: 9 additions & 9 deletions grapher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -149,17 +149,17 @@ def mean_confidence_interval(data, confidence=0.90):
stats = all_stats[model]
# Major metrics
if ylabel == 'Total Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([])
Data[ylabel][model], CI[ylabel][model] = np.sum(d), 0
if ylabel == 'Average Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([])
d2 = np.array([i['numdestroyed'] for i in stats.metrics]) if stats else np.array([1])
Data[ylabel][model], CI[ylabel][model] = np.sum(d)/np.sum(d2), 0
if ylabel == 'Interval Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([0])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([0])
Data[ylabel][model], CI[ylabel][model] = np.mean(d), mean_confidence_interval(d)
if ylabel == 'Average Interval Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([0])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([0])
d2 = np.array([i['numdestroyed'] for i in stats.metrics]) if stats else np.array([1])
Data[ylabel][model], CI[ylabel][model] = np.mean(d[d2>0]/d2[d2>0]), mean_confidence_interval(d[d2>0]/d2[d2>0])
if ylabel == 'Number of completed tasks':
Expand Down Expand Up @@ -366,14 +366,14 @@ def mean_confidence_interval(data, confidence=0.90):
stats = all_stats[model]
# Major metrics
if ylabel == 'Average Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([])
d2 = np.array([i['numdestroyed'] for i in stats.metrics]) if stats else np.array([1])
Data[ylabel][model], CI[ylabel][model] = d[d2>0]/d2[d2>0], 0
if ylabel == 'Interval Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([0])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([0])
Data[ylabel][model], CI[ylabel][model] = d, mean_confidence_interval(d)
if ylabel == 'Average Interval Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([0])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([0])
d2 = np.array([i['numdestroyed'] for i in stats.metrics]) if stats else np.array([1])
Data[ylabel][model], CI[ylabel][model] = d[d2>0]/d2[d2>0], mean_confidence_interval(d[d2>0]/d2[d2>0])
if ylabel == 'Number of completed tasks per interval':
Expand Down Expand Up @@ -494,10 +494,10 @@ def mean_confidence_interval(data, confidence=0.90):
stats = all_stats[model]
# Major metrics
if ylabel == 'Interval Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([0])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([0])
Data[ylabel][model], CI[ylabel][model] = d, mean_confidence_interval(d)
if ylabel == 'Average Interval Energy (Kilowatt-hr)':
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000 if stats else np.array([0])
d = np.array([i['energytotalinterval'] for i in stats.metrics])/1000/3600 if stats else np.array([0])
d2 = np.array([i['numdestroyed'] for i in stats.metrics]) if stats else np.array([1])
Data[ylabel][model], CI[ylabel][model] = d/(d2+0.001), mean_confidence_interval(d/(d2+0.001))
if ylabel == 'Number of completed tasks':
Expand Down