def __init__(self, date, **kwargs):
self.date = date
start = self.date - datetime.timedelta(days=300)
end = self.date + datetime.timedelta(days=150)
self.kwargs = kwargs
queries = DateRangeQueries(start, end, db.engine)
df_obs_time = queries.observation_time()
df_time_breakdown = queries.time_breakdown()
self.df = pd.merge(df_obs_time, df_time_breakdown, on=['Date'], how='outer')
# ignore values with no science time
self.df = self.df[self.df.ScienceTime > 0.0001]
# avoid NaN issues later on
self.df.fillna(value=0, inplace=True)
def __init__(self, date, **kwargs):
self.date = date
start = self.date - datetime.timedelta(days=300)
end = self.date + datetime.timedelta(days=150)
self.kwargs = kwargs
queries = DateRangeQueries(start, end, db.engine)
df_shutter_open_time = queries.shutter_open_time()
df_time_breakdown = queries.time_breakdown()
self.df = pd.merge(df_shutter_open_time,
df_time_breakdown,
on=['Date'],
how='outer')
# ignore values with no science time
self.df = self.df[self.df.ScienceTime > 0.0001]
# avoid NaN issues later on
self.df.fillna(value=0, inplace=True)
class TelescopeDowntimePlots:
"""Plots displaying the weather downtime.
Params:
-------
date : datetime.date
The date for which to generate the plots.
**kwargs: keyword arguments
Additional keyword arguments are passed on to the function or constructor creating a plot.
"""
def __init__(self, date, **kwargs):
self.date = date
start = self.date - datetime.timedelta(days=300)
end = self.date + datetime.timedelta(days=150)
self.kwargs = kwargs
self.df = DateRangeQueries(start, end, db.engine)
self.df = self.df.time_breakdown()[[
'Date', 'NightLength', 'TimeLostToProblems'
]]
def last_night_plot(self):
"""Dial plot displaying the weather downtime for the date preceding `self.date`.
The weather downtime is displayed as a percentage relative to the night length. In addition its absolute value
is displayed in minutes.
Returns:
--------
app.plot.plot.DialPlot
Plot displaying the weather downtime for last night.
"""
weather_downtime = value_last_night(df=self.df,
date=self.date,
date_column='Date',
value_column='TimeLostToProblems')
night_length = value_last_night(df=self.df,
date=self.date,
date_column='Date',
value_column='NightLength')
weather_downtime_percentage = 100 * weather_downtime / night_length
dial_color_func = good_mediocre_bad_color_func(good_limit=3,
bad_limit=6)
return DialPlot(values=[weather_downtime_percentage],
label_values=[0, 3, 6] +
[v for v in range(10, 101, 10)],
dial_color_func=dial_color_func,
display_values=[
'{:.1f}%'.format(weather_downtime_percentage),
'{:d}m'.format(int(weather_downtime / 60))
],
**self.kwargs)
def week_to_date_plot(self):
"""Dial plot displaying the weather downtime for the seven days leading up to but excluding `self.date`.
The weather downtime is displayed as a percentage relative to the night length. In addition its absolute value
is displayed in minutes.
Returns:
--------
app.plot.plot.DialPlot
Plot displaying the weather downtime for the last seven days.
"""
weather_downtime = value_last_week(df=self.df,
date=self.date,
date_column='Date',
value_column='TimeLostToProblems')
night_length = value_last_week(df=self.df,
date=self.date,
date_column='Date',
value_column='NightLength')
weather_downtime_percentage = 100 * weather_downtime / night_length
dial_color_func = good_mediocre_bad_color_func(good_limit=3,
bad_limit=6)
return DialPlot(values=[weather_downtime_percentage],
label_values=[0, 3, 6] +
[v for v in range(10, 101, 10)],
dial_color_func=dial_color_func,
display_values=[
'{:.1f}%'.format(weather_downtime_percentage),
'{:d}m'.format(int(weather_downtime / 60))
],
**self.kwargs)
def daily_plot(self, days):
"""Bar plot displaying the number of block visits per day.
The number of block visits is shown for the `days` days leading to but excluding `self.date`. A day here refers
to the time from noon to noon. For example, 22 May 2016 refers to the period from 22 May 2016, 12:00 to 23 May
2016, 12:00.
Params:
-------
days : int
Number of days.
Returns:
--------
app.plot.plot.TimeBarPlot
Plot of number of block visits as a function of the day.
"""
start_date, end_date = day_range(self.date, days)
trend_func = functools.partial(day_running_average,
ignore_missing_values=False)
df = self.df.copy()
df['TimeLostToProblemsPercentage'] = 100 * np.divide(
df.TimeLostToProblems, df.NightLength)
df['TimeLostToProblems'] /= 60
return daily_bar_plot(
df=df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column=['TimeLostToProblems', 'TimeLostToProblemsPercentage'],
y_range=[Range1d(start=0, end=250),
Range1d(start=0, end=40)],
y_formatters=[
PrintfTickFormatter(format='%.0fm'),
PrintfTickFormatter(format='%.0f%%')
],
trend_func=trend_func,
**self.kwargs)
def monthly_plot(self, months):
"""Bar plot displaying the shutter open efficiency per momth.
The operation efficiency is shown for the `months` months leading to but excluding the month containing
`self.date`. A month here refers start at noon of the first of the month. For example, May 2016 refers to the
period from 1 May 2016, 12:00 to 1 June 2016, 12:00.
Params:
-------
months : int
Number of months.
Returns:
--------
app.plot.plot.TimeBarPlot
Plot of shutter open efficiency as a function of the month.
"""
start_date, end_date = month_range(self.date, months)
trend_func = functools.partial(month_running_average,
ignore_missing_values=False)
def post_binning_func(df):
df['TimeLostToProblemsPercentage'] = 100 * np.divide(
df.TimeLostToProblems, df.NightLength)
df.TimeLostToProblems /= 3600
return monthly_bar_plot(
df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
month_column='Month',
y_column=['TimeLostToProblems', 'TimeLostToProblemsPercentage'],
y_range=[Range1d(start=0, end=60),
Range1d(start=0, end=20)],
y_formatters=[
PrintfTickFormatter(format='%.0fh'),
PrintfTickFormatter(format='%.0f%%')
],
trend_func=trend_func,
post_binning_func=post_binning_func,
**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)
class TelescopeDowntimePlots:
"""Plots displaying the weather downtime.
Params:
-------
date : datetime.date
The date for which to generate the plots.
**kwargs: keyword arguments
Additional keyword arguments are passed on to the function or constructor creating a plot.
"""
def __init__(self, date, **kwargs):
self.date = date
start = self.date - datetime.timedelta(days=300)
end = self.date + datetime.timedelta(days=150)
self.kwargs = kwargs
self.df = DateRangeQueries(start, end, db.engine)
self.df = self.df.time_breakdown()[['Date', 'NightLength', 'TimeLostToProblems']]
def last_night_plot(self):
"""Dial plot displaying the weather downtime for the date preceding `self.date`.
The weather downtime is displayed as a percentage relative to the night length. In addition its absolute value
is displayed in minutes.
Returns:
--------
app.plot.plot.DialPlot
Plot displaying the weather downtime for last night.
"""
weather_downtime = value_last_night(df=self.df,
date=self.date,
date_column='Date',
value_column='TimeLostToProblems')
night_length = value_last_night(df=self.df, date=self.date, date_column='Date', value_column='NightLength')
weather_downtime_percentage = 100 * weather_downtime / night_length
dial_color_func = good_mediocre_bad_color_func(good_limit=3, bad_limit=6)
return DialPlot(values=[weather_downtime_percentage],
label_values=[0, 3, 6] + [v for v in range(10, 101, 10)],
dial_color_func=dial_color_func,
display_values=['{:.1f}%'.format(weather_downtime_percentage),
'{:d}m'.format(int(weather_downtime / 60))],
**self.kwargs)
def week_to_date_plot(self):
"""Dial plot displaying the weather downtime for the seven days leading up to but excluding `self.date`.
The weather downtime is displayed as a percentage relative to the night length. In addition its absolute value
is displayed in minutes.
Returns:
--------
app.plot.plot.DialPlot
Plot displaying the weather downtime for the last seven days.
"""
weather_downtime = value_last_week(df=self.df,
date=self.date,
date_column='Date',
value_column='TimeLostToProblems')
night_length = value_last_week(df=self.df, date=self.date, date_column='Date', value_column='NightLength')
weather_downtime_percentage = 100 * weather_downtime / night_length
dial_color_func = good_mediocre_bad_color_func(good_limit=3, bad_limit=6)
return DialPlot(values=[weather_downtime_percentage],
label_values=[0, 3, 6] + [v for v in range(10, 101, 10)],
dial_color_func=dial_color_func,
display_values=['{:.1f}%'.format(weather_downtime_percentage),
'{:d}m'.format(int(weather_downtime / 60))],
**self.kwargs)
def daily_plot(self, days):
"""Bar plot displaying the number of block visits per day.
The number of block visits is shown for the `days` days leading to but excluding `self.date`. A day here refers
to the time from noon to noon. For example, 22 May 2016 refers to the period from 22 May 2016, 12:00 to 23 May
2016, 12:00.
Params:
-------
days : int
Number of days.
Returns:
--------
app.plot.plot.TimeBarPlot
Plot of number of block visits as a function of the day.
"""
start_date, end_date = day_range(self.date, days)
trend_func = functools.partial(day_running_average, ignore_missing_values=False)
df = self.df.copy()
df['TimeLostToProblemsPercentage'] = 100 * np.divide(df.TimeLostToProblems, df.NightLength)
df['TimeLostToProblems'] /= 60
return daily_bar_plot(df=df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column=['TimeLostToProblems', 'TimeLostToProblemsPercentage'],
y_range=[Range1d(start=0, end=250), Range1d(start=0, end=40)],
y_formatters=[PrintfTickFormatter(format='%.0fm'), PrintfTickFormatter(format='%.0f%%')],
trend_func=trend_func,
**self.kwargs)
def monthly_plot(self, months):
"""Bar plot displaying the shutter open efficiency per momth.
The operation efficiency is shown for the `months` months leading to but excluding the month containing
`self.date`. A month here refers start at noon of the first of the month. For example, May 2016 refers to the
period from 1 May 2016, 12:00 to 1 June 2016, 12:00.
Params:
-------
months : int
Number of months.
Returns:
--------
app.plot.plot.TimeBarPlot
Plot of shutter open efficiency as a function of the month.
"""
start_date, end_date = month_range(self.date, months)
trend_func = functools.partial(month_running_average, ignore_missing_values=False)
def post_binning_func(df):
df['TimeLostToProblemsPercentage'] = 100 * np.divide(df.TimeLostToProblems, df.NightLength)
df.TimeLostToProblems /= 3600
return monthly_bar_plot(df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
month_column='Month',
y_column=['TimeLostToProblems', 'TimeLostToProblemsPercentage'],
y_range=[Range1d(start=0, end=60), Range1d(start=0, end=20)],
y_formatters=[PrintfTickFormatter(format='%.0fh'),
PrintfTickFormatter(format='%.0f%%')],
trend_func=trend_func,
post_binning_func=post_binning_func,
**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)