nemo rs

remap_nemo_rs/readme.md

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+# Redisplaying restart files for "tricking" NEMO into starting from a custom restart
+
+At initialization, NEMO either starts from:
+
+  - rest, with temperatures and salinities taken from `namtsd` in `namelist_cfg`, and ice fields potentially initialized in `namelist_ice_cfg`:
+  
+  - a full restart file, which is a snapshot of a run's RAM at a given time. NEMO restarts are specific to the domain decomposition of your setting: configuration, number of cores, potential use of "land suppression" (check `nammpp` in NEMO namelist).
+
+Hence, for starting NEMO from a "custom" restart file (e.g. spin up), you'll need to redistribute the restart according to the domain decomposition you want to run with. This is what this tool does. It requires:
+
+  - A set of processor-specific NEMO restart files defined with a certain domain decomposition, that you'll want to run at (2*n_proc, n_proc for oce and n_proc for ice). You can get this by doing a dummy run with the number of processors you'll eventually want.
+  
+  - A pair of rebuilt (processor-wise) NEMO restart files, which you'll want to get the actual restart data from. Check the `REBUILD_NEMO` tool for rebuilding NEMO outputs.
+
+# Step 1: on the ELIC servers
+
+Prepare the restart files with the help `redisplay_restart.py` (the code is commented), and the ingredients above. Upload the resulting restart file sets to the cluster you want to run on.
+
+
+# Step 2: on the cluster where NEMO is running
+
+  
+In `experiment.cfg`, set:
+
+```
+    special_rs_nemo=true
+    special_rs_nemo_stencil="<path_to_restart_files>"
+```
+Coral expects restart files to be named `<path_to_restart_files><oce,ice>_XXXX.nc`, where `XXXX` are the 4 digits distinguishing processor numbers.
+
+In `build_namelist.cfg.sh`, set `ln_rstart = .TRUE.`, and `nn_rstctl = 0`.
+  
+Build and submit.
+	  
+
+

remap_nemo_rs/redisplay_restart.py

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+import numpy as np
+from netCDF4 import Dataset
+import os
+import sys
+
+# Inputs:
+# a) a set of processor-specific NEMO restart files defined with a certain domain decomposition, that you'll want to run at (2*n_proc, n_proc for oce and n_proc for ice). You can get this by doing a dummy run with the number of processors you'll eventually want.
+# b) a pair of rebuilt (processor-wise) NEMO restart files, which you'll want to get the actual restart data from
+
+# Outputs:
+# a set of processor-specific NEMO restart files, using the a) domain decomposition, with the data coming from b)
+
+
+# input a). The script expects the files to be <geom_in_stencil><oce,ice>_XXXX.nc, where XXXX are four numbers.
+geom_in_stencil = "/afast/pelletie/nemo_rs/geom_in/mpp_209_fET171-h40/so025_paraso_temoin_00000096_restart_"
+# Number of processors for destination geom (geom_in_stencil)
+n_newdisp = 209
+
+# input b) The script expects the files to be <rebuilt_rs_stencil><oce,ice>.nc
+rebuilt_rs_stencil = "/afast/pelletie/nemo_rs/rs_data/h40/org/so025_fET171_spin_h40_00035040_restart_"
+## WARNING: be sure that your b) files contain all the fields you'll need.
+##          Some NEMO options (melt ponds, iscpl) create/require new restart fields.
+
+# Output files. The script will write <output_stencil><oce,ice>_XXXX.nc
+output_stencil = "/afast/pelletie/nemo_rs/rs_data/h40/redisp/mpp_209_fET171-h40/so025_fET171_spin_h40_00035040_remap209-mpp_restart_"
+
+nz = 75
+media = ['ice', 'oce']
+
+for medium in media:
+
+    big_rs = Dataset(rebuilt_rs_stencil+medium+'.nc', mode='r')
+        
+    for m in range(0,n_newdisp):
+
+        geom_nc = Dataset(geom_in_stencil+medium+'_%04d'%m+'.nc', mode='r')
+
+        nx_nd, ny_nd = geom_nc.DOMAIN_size_local
+        sx_beg, sy_beg = geom_nc.DOMAIN_position_first - 1
+        sx_end, sy_end = geom_nc.DOMAIN_position_last
+        nx_nd, ny_nd = geom_nc.DOMAIN_size_local
+
+        if( sx_end - sx_beg != nx_nd or sy_end - sy_beg != ny_nd):
+            print('error nx, beg end')
+            sys.exit()
+        
+        fmt = "%04d"%m
+        outfile = output_stencil+medium+'_'+fmt+'.nc'
+
+        out_nc = Dataset(outfile, mode='w')
+
+        out_nc.createDimension('x', nx_nd)
+        out_nc.createDimension('y', ny_nd)
+        out_nc.createDimension('z', nz)
+        out_nc.createDimension('t', 0)
+
+        print(sx_beg,sx_end,sy_beg,sy_end)
+        for name, variable in big_rs.variables.items():
+            varout = out_nc.createVariable(name, variable.datatype, variable.dimensions)
+            len_tmp = len(variable.dimensions)
+            
+            if( 'x' in variable.dimensions):
+                if(len_tmp == 2):
+                    varout[:] = big_rs.variables[name][sy_beg:sy_end,sx_beg:sx_end]
+                elif(len_tmp == 3):
+                    varout[:] = big_rs.variables[name][:,sy_beg:sy_end,sx_beg:sx_end]
+                elif(len_tmp == 4):
+                    varout[:] = big_rs.variables[name][:,:,sy_beg:sy_end,sx_beg:sx_end]
+                    
+            else:
+                varin = big_rs.variables[name][:]
+                if(len_tmp > 0 ):
+                    varout[:] = varin[:]
+                else:
+                    varout[:] = varin
+
+        out_nc.setncatts(geom_nc.__dict__)
+                
+        out_nc.close()
+        print('generated '+outfile)
+
+        geom_nc.close()
+        
+    big_rs.close()
+