def monthly_plot(self, months):
"""Bar plot displaying the number of block visits per momth.
The number of block visits 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 number of block visits 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)
return monthly_bar_plot(df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
month_column='Month',
y_column='BlockCount',
y_range=Range1d(start=0, end=300),
trend_func=trend_func,
**self.kwargs)
def monthly_plot(self, months):
"""Bar plot displaying the number of block visits per momth.
The number of block visits 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 number of block visits 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)
return monthly_bar_plot(df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
month_column='Month',
y_column='BlockCount',
y_range=Range1d(start=0, end=300),
trend_func=trend_func,
**self.kwargs)
def monthly_plot(self, months):
"""Bar plot displaying the operation 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 operation 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['OperationEfficiency'] = 100 * np.divide(df.ObsTime, df.ScienceTime)
return monthly_bar_plot(df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
month_column='Month',
y_column='OperationEfficiency',
y_range=Range1d(start=0, end=120),
trend_func=trend_func,
post_binning_func=post_binning_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 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 monthly_plot(self, months):
"""Bar plot displaying the operation 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 operation 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['OperationEfficiency'] = 100 * np.divide(
df.ObsTime, df.ScienceTime)
return monthly_bar_plot(df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
month_column='Month',
y_column='OperationEfficiency',
y_range=Range1d(start=0, end=120),
trend_func=trend_func,
post_binning_func=post_binning_func,
**self.kwargs)