def test_align_index(dobj, removal):
index = pd.date_range(start='now', freq='5min',
periods=20, name='timestamp')
data = dobj(index=index, dtype=float)
data = data.drop(index[removal])
out = utils.align_index(data, pd.Timedelta('5min'))
assert_index_equal(out.index, index)
def test_align_index_new_length():
index = pd.date_range(start='now', freq='5min',
periods=20, name='timestamp')
data = pd.Series(index=index, dtype=float)
out = utils.align_index(data, pd.Timedelta('1min'))
nindex = pd.date_range(start=index[0], end=index[-1], freq='1min',
name='timestamp')
assert_index_equal(out.index, nindex)
def generate_observation_figure(observation, data, limit=pd.Timedelta('3d')):
"""
Creates a bokeh figure from API responses for an observation
Parameters
----------
observation : datamodel.Observation
The Observation that is being plotted
data : pandas.DataFrame
The observation data to be plotted with datetime index
and ('value', 'quality_flag') columns
limit : pandas.Timedelta or None
The time limit from the last datapoint to plot. If None, all
data is plotted.
Returns
-------
None
When the data is empty
script, div : str
When return_components = True, return the <script> and <div>
components for the Bokeh plot.
bokeh components from gridplot
When return_components = False
"""
logger.info('Starting observation forecast generation...')
if len(data.index) == 0:
return None
data = plot_utils.align_index(data, observation.interval_length, limit)
quality_flag = data.pop('quality_flag').dropna().astype(int)
bool_flags = quality_mapping.convert_mask_into_dataframe(quality_flag)
active_flags = quality_mapping.convert_flag_frame_to_strings(bool_flags)
active_flags.name = 'active_flags'
flags = bool_flags.mask(~bool_flags).reindex(data.index) # add missing
flags['MISSING'] = pd.Series(1.0, index=data.index)[pd.isna(data['value'])]
# need to fill as line needs more than a single point to show up
if observation.interval_label == 'ending':
flags.bfill(axis=0, limit=1, inplace=True)
else:
# for interval beginning and instantaneous
flags.ffill(axis=0, limit=1, inplace=True)
cds = ColumnDataSource(pd.concat([data, flags, active_flags], axis=1))
figs = [
make_basic_timeseries(cds, observation.name, observation.variable,
observation.interval_label, PLOT_WIDTH)
]
figs.extend(make_quality_bars(cds, PLOT_WIDTH, figs[0].x_range))
layout = _make_layout(figs)
logger.info('Figure generated succesfully')
return layout
def test_align_index_limit():
index = pd.date_range(start='now',
freq='5min',
periods=20,
name='timestamp')
data = pd.Series(index=index)
out = utils.align_index(data,
pd.Timedelta('5min'),
limit=pd.Timedelta('60min'))
nindex = pd.date_range(start=index[-13],
end=index[-1],
freq='5min',
name='timestamp')
assert_index_equal(out.index, nindex)
def generate_forecast_figure(forecast, data, limit=None):
"""
Creates a bokeh timeseries figure for forcast data
Parameters
----------
forecast : datamodel.Forecast
The Forecast that is being plotted
data : pandas.Series
The forecast data with a datetime index to be plotted
limit : pandas.Timedelta or None
The time limit from the last datapoint to plot. If None, all
data is plotted.
Returns
-------
None
When the data is empty
script, div : str
When return_components = True, return the <script> and <div>
components for the Bokeh plot.
bokeh components from gridplot
When return_components = False
"""
logger.info('Starting forecast figure generation...')
if len(data.index) == 0:
return None
data = plot_utils.align_index(data, forecast.interval_length, limit)
cds = ColumnDataSource(data.reset_index())
fig = make_basic_timeseries(cds, forecast.name, forecast.variable,
forecast.interval_label, PLOT_WIDTH)
layout = _make_layout([fig])
logger.info('Figure generated succesfully')
return layout