def compute_predictions(session):
"""compute and cache predictions for all runs
Args:
session: (Session) database connection
Returns:
True: if observations were successfully retrieved and inserted
False: otherwise
"""
try:
arima = Arima(session)
repo = Repository(session)
runs = repo.get_all_runs_as_list()
for run in runs:
try:
predictions = arima.arima_model(run.run_id)
to_add = [
Prediction(run_id=run.run_id,
timestamp=pd.to_datetime(d),
fr_lb=round(float(p), 1),
fr=round(float(p), 1),
fr_ub=round(float(p), 1)) for p, d in
zip(predictions.values, predictions.index.values)
]
repo.clear_predictions(run.run_id)
repo.put_predictions(to_add)
log(f'predictions for {run.run_id}-{run.run_name} added to db')
except SQLAlchemyError as e:
log(f'{run.run_id}-{run.run_name} failed - {[str(a) for a in e.args]}'
)
session.rollback()
except Exception as e:
log(f'predictions for {run.run_id}-{run.run_name} failed - {[str(a) for a in e.args]}'
)
return True
except Exception as e:
log(f'failed to compute daily predictions - {str(e.args)}')
return False
from riverrunner import settings
# IP address for running application
HOST_IP = '192.168.80.13'
# enable for application debugging features
DEBUG = False
# mapping from river's predicted status to a color code
COLOR_MAP = dict(unknown='#41434C',
optimal='#4254CC',
fair='#8F8A18',
not_recommended='#A63617')
repo = Repository()
runs = repo.get_all_runs_as_list()
runs = [run for run in runs if run.todays_runability != -2]
options = [r.select_option for r in runs]
options.sort(key=lambda r: r['label'])
# create a new Dash app adding custom fonts and CSS
app = dash.Dash()
font_url = 'https://fonts.googleapis.com/css?family=Montserrat|Permanent+Marker'
app.css.append_css({'external_url': font_url})
def color_scale(x):
"""prediction binning
method bins river predictions into discrete categories for color coding