Open hunjhunj opened 4 years ago
@hunjhunj I can look into adding this capability. It should just be a matter of updating the rafem metadata in the pymt-lab/pymt_rafem. To see which input file values you can change through the setup method, you can look at the parameters attribute or help(rafem)
. Here is how you might do it with parameters,
>>> from pprint import pprint
>>> from pymt.models import Rafem
>>> rafem = Rafem()
>>> pprint(dict(rafem.parameters)) # input parameter names and default values
{'channel_depth': 1.0,
'channel_discharge': 10.0,
'channel_width': 10.0,
'dx': 0.1,
'dy': 0.1,
'initial_slope': 0.001,
'n_cols': 120,
'n_rows': 100,
'sea_level_initial': 0.0,
'sea_level_rise_rate': 0.0,
'sediment_bed_concentration': 1.0,
'sediment_specific_gravity': 2.65,
'subsidence_rate': 0.0,
'time_step': 0.05,
'upstream_elevation': 5.0,
'wetland_distance': 0.0,
'wetland_elevation': 0.0}
To add rand_seed to this list, you would have to update the parameters.yaml and input.yaml in pymt-lab/pymt_rafem.
@hunjhunj I can look into adding this capability. It should just be a matter of updating the rafem metadata in the _pymt-lab/pymtrafem. To see which input file values you can change through the setup method, you can look at the parameters attribute or
help(rafem)
. Here is how you might do it with parameters,>>> from pprint import pprint >>> from pymt.models import Rafem >>> rafem = Rafem() >>> pprint(dict(rafem.parameters)) # input parameter names and default values {'channel_depth': 1.0, 'channel_discharge': 10.0, 'channel_width': 10.0, 'dx': 0.1, 'dy': 0.1, 'initial_slope': 0.001, 'n_cols': 120, 'n_rows': 100, 'sea_level_initial': 0.0, 'sea_level_rise_rate': 0.0, 'sediment_bed_concentration': 1.0, 'sediment_specific_gravity': 2.65, 'subsidence_rate': 0.0, 'time_step': 0.05, 'upstream_elevation': 5.0, 'wetland_distance': 0.0, 'wetland_elevation': 0.0}
To add _randseed to this list, you would have to update the parameters.yaml and input.yaml in _pymt-lab/pymtrafem.
Thank you!
Hi,
I am using the coupled CEM-RAFEM model and updates seed number in the run_model.py file as the screen shot below:
However, the rand_seed number in the input.yaml from the output of the experiment was not updated (as the figure below):
Would you like to look into it? Thank you for your help!