def test_bin_by_semester(self):
dates = [
parser.parse('2015-10-31 23:59:59'),
parser.parse('2015-11-01 0:00:00'),
parser.parse('2015-12-31 23:59:59'),
parser.parse('2016-01-01 0:00:00'),
parser.parse('2016-03-23 13:00:00'),
parser.parse('2016-04-30 23:59:59'),
parser.parse('2016-05-01 0:00:00'),
parser.parse('2016-08-23 04:05:06'),
parser.parse('2016-10-31 23:59:59'),
parser.parse('2016-11-01 0:00:00'),
parser.parse('2023-05-01 0:00:00')
]
df = pd.DataFrame(
dict(dates=dates,
a=[i for i in range(0, len(dates))],
b=[2 * i for i in range(0, len(dates))]))
# default aggregation function
binned = bin_by_semester(df,
date_column='dates',
semester_column='semesters')
self.assertEqual(5, len(binned['semesters']))
self.assertEqual('2015-1', binned['semesters'].iloc[0])
self.assertEqual('2015-2', binned['semesters'].iloc[1])
self.assertEqual('2016-1', binned['semesters'].iloc[2])
self.assertEqual('2016-2', binned['semesters'].iloc[3])
self.assertEqual('2023-1', binned['semesters'].iloc[4])
self.assertEqual([0, 15, 21, 9, 10], binned['a'].values.tolist())
self.assertEqual([0, 30, 42, 18, 20], binned['b'].values.tolist())
# np.sum as aggregation function
binned = bin_by_semester(df,
agg_func=np.sum,
date_column='dates',
semester_column='semesters')
self.assertEqual(5, len(binned['semesters']))
self.assertEqual('2015-1', binned['semesters'].iloc[0])
self.assertEqual('2015-2', binned['semesters'].iloc[1])
self.assertEqual('2016-1', binned['semesters'].iloc[2])
self.assertEqual('2016-2', binned['semesters'].iloc[3])
self.assertEqual('2023-1', binned['semesters'].iloc[4])
self.assertEqual([0, 15, 21, 9, 10], binned['a'].values.tolist())
self.assertEqual([0, 30, 42, 18, 20], binned['b'].values.tolist())
# np.mean aggregation function
binned = bin_by_semester(df,
agg_func=np.mean,
date_column='dates',
semester_column='semesters')
self.assertEqual(5, len(binned['semesters']))
self.assertEqual('2015-1', binned['semesters'].iloc[0])
self.assertEqual('2015-2', binned['semesters'].iloc[1])
self.assertEqual('2016-1', binned['semesters'].iloc[2])
self.assertEqual('2016-2', binned['semesters'].iloc[3])
self.assertEqual('2023-1', binned['semesters'].iloc[4])
self.assertEqual([0, 3, 7, 9, 10], binned['a'].values.tolist())
self.assertEqual([0, 6, 14, 18, 20], binned['b'].values.tolist())
def test_bin_by_semester(self):
dates = [parser.parse('2015-10-31 23:59:59'),
parser.parse('2015-11-01 0:00:00'),
parser.parse('2015-12-31 23:59:59'),
parser.parse('2016-01-01 0:00:00'),
parser.parse('2016-03-23 13:00:00'),
parser.parse('2016-04-30 23:59:59'),
parser.parse('2016-05-01 0:00:00'),
parser.parse('2016-08-23 04:05:06'),
parser.parse('2016-10-31 23:59:59'),
parser.parse('2016-11-01 0:00:00'),
parser.parse('2023-05-01 0:00:00')]
df = pd.DataFrame(dict(dates=dates,
a=[i for i in range(0, len(dates))],
b=[2 * i for i in range(0, len(dates))]))
# default aggregation function
binned = bin_by_semester(df, date_column='dates', semester_column='semesters')
self.assertEqual(5, len(binned['semesters']))
self.assertEqual('2015-1', binned['semesters'].iloc[0])
self.assertEqual('2015-2', binned['semesters'].iloc[1])
self.assertEqual('2016-1', binned['semesters'].iloc[2])
self.assertEqual('2016-2', binned['semesters'].iloc[3])
self.assertEqual('2023-1', binned['semesters'].iloc[4])
self.assertEqual([0, 15, 21, 9, 10], binned['a'].values.tolist())
self.assertEqual([0, 30, 42, 18, 20], binned['b'].values.tolist())
# np.sum as aggregation function
binned = bin_by_semester(df, agg_func=np.sum, date_column='dates', semester_column='semesters')
self.assertEqual(5, len(binned['semesters']))
self.assertEqual('2015-1', binned['semesters'].iloc[0])
self.assertEqual('2015-2', binned['semesters'].iloc[1])
self.assertEqual('2016-1', binned['semesters'].iloc[2])
self.assertEqual('2016-2', binned['semesters'].iloc[3])
self.assertEqual('2023-1', binned['semesters'].iloc[4])
self.assertEqual([0, 15, 21, 9, 10], binned['a'].values.tolist())
self.assertEqual([0, 30, 42, 18, 20], binned['b'].values.tolist())
# np.mean aggregation function
binned = bin_by_semester(df, agg_func=np.mean, date_column='dates', semester_column='semesters')
self.assertEqual(5, len(binned['semesters']))
self.assertEqual('2015-1', binned['semesters'].iloc[0])
self.assertEqual('2015-2', binned['semesters'].iloc[1])
self.assertEqual('2016-1', binned['semesters'].iloc[2])
self.assertEqual('2016-2', binned['semesters'].iloc[3])
self.assertEqual('2023-1', binned['semesters'].iloc[4])
self.assertEqual([0, 3, 7, 9, 10], binned['a'].values.tolist())
self.assertEqual([0, 6, 14, 18, 20], binned['b'].values.tolist())
def semester_to_date_plot(self):
"""Dial plot displaying the shutter open efficiency for the semester in which `self.date` lies.
All dates in the semester up to but excluding `self.date` are included when calculating the shutter open
efficiency.
Returns:
--------
app.plot.plot.DialPlot
Plot of the shutter open efficiency for the semester to date.
"""
sem = semester(self.date)
binned_df = bin_by_semester(df=self.df,
cutoff_date=self.date,
date_column='Date',
semester_column='Semester')
current_semester = binned_df[binned_df.Semester == sem]
if len(current_semester):
shutter_open_efficiency = self._shutter_open_efficiency(
current_semester.ShutterOpenTime[0],
current_semester.ScienceTime[0])
else:
shutter_open_efficiency = 0
dial_color_func = neutral_color_func
return DialPlot(
values=[shutter_open_efficiency],
label_values=range(0, 101, 10),
dial_color_func=dial_color_func,
display_values=['{:.1f}%'.format(shutter_open_efficiency)],
**self.kwargs)
def semester_to_date_plot(self):
"""Dial plot displaying the operation efficiency for the semester in which `self.date` lies.
All dates in the semester up to but excluding `self.date` are included when calculating the operation
efficiency.
Returns:
--------
app.plot.plot.DialPlot
Plot of the operation efficiency for the semester to date.
"""
sem = semester(self.date)
binned_df = bin_by_semester(df=self.df, cutoff_date=self.date, date_column='Date', semester_column='Semester')
current_semester = binned_df[binned_df.Semester == sem]
if len(current_semester):
operation_efficiency = self._observation_efficiency(current_semester.ObsTime[0],
current_semester.ScienceTime[0])
else:
operation_efficiency = 0
dial_color_func = good_mediocre_bad_color_func(good_limit=90, bad_limit=80)
required_operation_efficiency = required_for_semester_average(date=self.date,
average=operation_efficiency,
target_average=90)
return DialPlot(values=[operation_efficiency, required_operation_efficiency],
label_values=range(0, 101, 10),
dial_color_func=dial_color_func,
display_values=['{:.1f}%'.format(operation_efficiency)],
**self.kwargs)
def semester_to_date_plot(self):
"""Dial plot displaying the weather downtime for the semester in which `self.date` lies.
All dates in the semester up to but excluding `self.date` are included when calculating the shutter open
efficiency.
The weather downtime is displayed as a percentage relative to the night length. A second hand shows the average
percentage which must be achieved in the remaining time of the semester in order to achieve the target
percentage.
In addition its absolute value is displayed in hours.
Returns:
--------
app.plot.plot.DialPlot
Plot of the shutter open efficiency for the semester to date.
"""
sem = semester(self.date)
binned_df = bin_by_semester(df=self.df,
cutoff_date=self.date,
date_column='Date',
semester_column='Semester')
current_semester = binned_df[binned_df.Semester == sem]
target_percentage = 55
if len(current_semester):
weather_downtime_percentage = 100 * current_semester.TimeLostToWeather[
0] / current_semester.NightLength[0]
required_percentage = required_for_semester_average(
self.date, weather_downtime_percentage, target_percentage)
weather_downtime = current_semester.TimeLostToWeather[0] / 3600
else:
weather_downtime_percentage = 0
required_percentage = target_percentage
weather_downtime = 0
dial_color_func = good_mediocre_bad_color_func(good_limit=49,
bad_limit=40)
return DialPlot(
values=[weather_downtime_percentage, required_percentage],
label_values=[v for v in range(0, 41, 10)] + [45] +
[v for v in range(50, 101, 10)],
dial_color_func=dial_color_func,
display_values=[
'{:.1f}%'.format(weather_downtime_percentage),
'{:.1f}h'.format(weather_downtime)
],
**self.kwargs)
def semester_to_date_plot(self):
"""Dial plot displaying the telescope downtime for the semester in which `self.date` lies.
All dates in the semester up to but excluding `self.date` are included when calculating the shutter open
efficiency.
The telescope downtime is displayed as a percentage relative to the night length. A second hand shows the
average percentage which must be achieved in the remaining time of the semester in order to achieve the target
percentage.
In addition its absolute value is displayed in hours.
Returns:
--------
app.plot.plot.DialPlot
Plot of the shutter open efficiency for the semester to date.
"""
sem = semester(self.date)
binned_df = bin_by_semester(df=self.df, cutoff_date=self.date, date_column='Date', semester_column='Semester')
current_semester = binned_df[binned_df.Semester == sem]
target_percentage = 3
if len(current_semester):
telescope_downtime_percentage =\
100 * current_semester.TimeLostToProblems[0] / current_semester.NightLength[0]
required_percentage = required_for_semester_average(self.date,
telescope_downtime_percentage,
target_percentage)
telescope_downtime = current_semester.TimeLostToProblems[0] / 3600
else:
telescope_downtime_percentage = 0
required_percentage = target_percentage
telescope_downtime = 0
dial_color_func = good_mediocre_bad_color_func(good_limit=3, bad_limit=6)
return DialPlot(values=[telescope_downtime_percentage, required_percentage],
label_values=range(0, 16, 1),
dial_color_func=dial_color_func,
display_values=['{:.1f}%'.format(telescope_downtime_percentage),
'{:.1f}h'.format(telescope_downtime)],
**self.kwargs)
def semester_to_date_plot(self):
"""Dial plot displaying the operation efficiency for the semester in which `self.date` lies.
All dates in the semester up to but excluding `self.date` are included when calculating the operation
efficiency.
Returns:
--------
app.plot.plot.DialPlot
Plot of the operation efficiency for the semester to date.
"""
sem = semester(self.date)
binned_df = bin_by_semester(df=self.df,
cutoff_date=self.date,
date_column='Date',
semester_column='Semester')
current_semester = binned_df[binned_df.Semester == sem]
if len(current_semester):
operation_efficiency = self._observation_efficiency(
current_semester.ObsTime[0], current_semester.ScienceTime[0])
else:
operation_efficiency = 0
dial_color_func = good_mediocre_bad_color_func(good_limit=90,
bad_limit=80)
required_operation_efficiency = required_for_semester_average(
date=self.date, average=operation_efficiency, target_average=90)
return DialPlot(
values=[operation_efficiency, required_operation_efficiency],
label_values=range(0, 101, 10),
dial_color_func=dial_color_func,
display_values=['{:.1f}%'.format(operation_efficiency)],
**self.kwargs)