def calculate(process_name, tasks, results):
print('[%s] evaluation routine starts' % process_name)
while True:
new_value = tasks.get()
if new_value == "None":
print('[%s] evaluation routine quits' % process_name)
# Indicate finished
results.put(-1)
break
else:
# Initialise icestupa object
location = new_value
icestupa = Icestupa(location)
# Derive all the input parameters
icestupa.derive_parameters()
# icestupa.read_input()
# Generate results
icestupa.melt_freeze()
# Summarise and save model results
icestupa.save()
# Read Output
# icestupa.read_output()
# Create figures for web interface
icestupa.summary_figures()
# Compute result and mimic a long-running task
compute = icestupa.df.iceV.max()
# Output which process received the value
print('[%s] received value: %s' % (process_name, new_value))
print('[%s] calculated max ice volume: %.1f' %
(process_name, compute))
# Add result to the queue
results.put(compute)
return
answers = dict(
# location="Schwarzsee 2019",
location="Guttannen 2021",
# location="Gangles 2021",
run="yes",
# run="no",
)
# Initialise icestupa object
# icestupa = Icestupa(answers["location"], params='best')
icestupa = Icestupa(answers["location"])
if answers["run"] == "yes":
# Derive all the input parameters
icestupa.derive_parameters()
# Generate results
icestupa.melt_freeze(test=True)
# icestupa.melt_freeze()
# Summarise and save model results
icestupa.save()
# Create figures for web interface
icestupa.summary_figures()
else:
# Use output parameters from cache
icestupa.read_output()
icestupa.summary_figures()