In this example, I want to give the Meteorology component a constant scalar precipitation value and see whether it produces output when the model state is updated.
Start with an import and some magic:
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%matplotlib inline
import numpy as np
Import the Meteorology component and create an instance:
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from cmt.components import Meteorology
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m = Meteorology()
Locate the cfg file:
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cfg_file = './input/meteorology.cfg'
Initialize the component with the cfg file:
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m.initialize(cfg_file)
Note that I set P (the precipitation rate) to 20.0 mm/hr in the cfg file. Why does Meteorology now report a different value?
Aha! On line 552 of met_base.py, the constant mmph_to_mps is defined:
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mmph_to_mps = (np.float64(1) / np.float64(3600000))
And on line 764 of met_base.py it's applied the input value of P:
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print 20.0 * mmph_to_mps
So it looks like the [mmph] label in the initialize output should be [m/s]. A typo.
However, if I call get_value at this point, the model precip values are still zeroed out:
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precip = m.get_value('atmosphere_water__precipitation_leq-volume_flux') # `P` internally
print precip.shape
print precip.min(), precip.max(), precip.mean()
Maybe this will change after the first update?
Show the model start, current, and stop times:
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print m.get_start_time(), m.get_current_time(), m.get_end_time()
Get the model time step:
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time_step = m.get_value('model__time_step') # `dt` internally
time_step = time_step.max()
print time_step
Advance the model by one time step:
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m.update(time_step)
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print m.get_start_time(), m.get_current_time(), m.get_end_time()
Note that it hasn't precipitated:
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precip = m.get_value('atmosphere_water__precipitation_leq-volume_flux')
print precip.min(), precip.max(), precip.mean()
But this might be expected, since the initial value retrieved by get_value was also zero.
Try manually setting the precip rate variable to a non-zero value:
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m.set_value('atmosphere_water__precipitation_leq-volume_flux', 15.0)
precip = m.get_value('atmosphere_water__precipitation_leq-volume_flux')
print precip.shape
print precip.min(), precip.max(), precip.mean()
Advance the model by another time step:
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m.update(2*time_step)
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