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Report: Compare MRR data
[csit.git]
/
resources
/
tools
/
presentation
/
generator_CPTA.py
diff --git
a/resources/tools/presentation/generator_CPTA.py
b/resources/tools/presentation/generator_CPTA.py
index
3a8ea93
..
066bfbd
100644
(file)
--- a/
resources/tools/presentation/generator_CPTA.py
+++ b/
resources/tools/presentation/generator_CPTA.py
@@
-164,19
+164,21
@@
def _evaluate_results(in_data, trimmed_data, window=10):
if len(in_data) > 2:
win_size = in_data.size if in_data.size < window else window
if len(in_data) > 2:
win_size = in_data.size if in_data.size < window else window
- results = [0.0, ]
* win_size
+ results = [0.0, ]
median = in_data.rolling(window=win_size).median()
stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std()
m_vals = median.values
s_vals = stdev_t.values
d_vals = in_data.values
median = in_data.rolling(window=win_size).median()
stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std()
m_vals = median.values
s_vals = stdev_t.values
d_vals = in_data.values
- for day in range(win_size, in_data.size):
- if np.isnan(m_vals[day - 1]) or np.isnan(s_vals[day - 1]):
+ for day in range(1, in_data.size):
+ if np.isnan(m_vals[day]) \
+ or np.isnan(s_vals[day]) \
+ or np.isnan(d_vals[day]):
results.append(0.0)
results.append(0.0)
- elif d_vals[day] < (m_vals[day
- 1] - 3 * s_vals[day - 1
]):
+ elif d_vals[day] < (m_vals[day
] - 3 * s_vals[day
]):
results.append(0.33)
results.append(0.33)
- elif (m_vals[day
- 1] - 3 * s_vals[day - 1
]) <= d_vals[day] <= \
- (m_vals[day
- 1] + 3 * s_vals[day - 1
]):
+ elif (m_vals[day
] - 3 * s_vals[day
]) <= d_vals[day] <= \
+ (m_vals[day
] + 3 * s_vals[day
]):
results.append(0.66)
else:
results.append(1.0)
results.append(0.66)
else:
results.append(1.0)
@@
-244,7
+246,7
@@
def _generate_trending_traces(in_data, build_info, period, moving_win_size=10,
data_y = [val for val in in_data.values()]
data_pd = pd.Series(data_y, index=data_x)
data_y = [val for val in in_data.values()]
data_pd = pd.Series(data_y, index=data_x)
- t_data, outliers = find_outliers(data_pd)
+ t_data, outliers = find_outliers(data_pd
, outlier_const=1.5
)
results = _evaluate_results(data_pd, t_data, window=moving_win_size)
results = _evaluate_results(data_pd, t_data, window=moving_win_size)