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 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)
return daily_bar_plot(df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column='BlockCount',
y_range=Range1d(start=0, end=30),
trend_func=trend_func,
**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)
return daily_bar_plot(df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column='BlockCount',
y_range=Range1d(start=0, end=30),
trend_func=trend_func,
**self.kwargs)
def daily_plot(self, days):
"""Bar plot displaying the operation efficiency per day.
The operation efficiency 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 operation efficiency as a function of the day.
"""
df = self.df.copy()
df['OperationEfficiency'] = 100 * np.divide(df.ObsTime, df.ScienceTime)
start_date, end_date = day_range(self.date, days)
trend_func = functools.partial(day_running_average,
ignore_missing_values=True)
return daily_bar_plot(
df=df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column='OperationEfficiency',
y_range=Range1d(start=0, end=140),
trend_func=trend_func,
y_formatters=[PrintfTickFormatter(format='%d%%')],
**self.kwargs)
def daily_plot(self, days):
"""Bar plot displaying the operation efficiency per day.
The operation efficiency 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 operation efficiency as a function of the day.
"""
df = self.df.copy()
df['OperationEfficiency'] = 100 * np.divide(df.ObsTime, df.ScienceTime)
start_date, end_date = day_range(self.date, days)
trend_func = functools.partial(day_running_average, ignore_missing_values=True)
return daily_bar_plot(df=df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column='OperationEfficiency',
y_range=Range1d(start=0, end=140),
trend_func=trend_func,
y_formatters=[PrintfTickFormatter(format='%d%%')],
**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['EngineeringTimePercentage'] = 100 * np.divide(df.EngineeringTime, df.NightLength)
df['EngineeringTime'] /= 60
return daily_bar_plot(df=df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column=['EngineeringTime', 'EngineeringTimePercentage'],
y_range=[Range1d(start=0, end=200), Range1d(start=0, end=30)],
y_formatters=[PrintfTickFormatter(format='%.0fm'), PrintfTickFormatter(format='%.0f%%')],
trend_func=trend_func,
**self.kwargs)