my_runs = basic_runner(\
nproc = 1,\
# Set the directory
directory = 'MMS',\
# Set the time domain
nout = 1,\
timestep = 1,\
# Set mms to true
mms = True,\
# Set the spatial domain
nx = [4, 8, 16],\
ny = [4, 8, 16],\
nz = [4, 8, 16],\
# Additional (put here to illustrate the sorting)
series_add = [('cst','D_par',[1,2]), ('cst','D_perp',[0.5,1])],\
# Since we would like to do a MMS test, we would like to run
# the runs in a particular order. In this example, we would
# like to run all the possible spatial variables before
# doing the test. Hence we would like the spatial domain
# option to be the fastest varying.
# Since we have put post_process_after_every_run = False in
# the run function below, the processing function being
# called when all possibilities of the fastest variable has
# been run.
sort_by = 'spatial_domain'\
# Some additional sorting examples:
#
# This returns an error, stating the sorting possibilities
# (which will depend on the member data of this object)
# sort_by = 'uncomment_me'\
#
# In this example cst:D_par will be the fastest varying
# variable, followed by the spatial_domain. The post
# processing function will be called when all possibilities
# of these variables has been run
# sort_by = ['cst:D_par', 'spatial_domain']\
)
# With a few exceptions: All variables in the constructor can be given
# as an iterable When execute_runs is called, all combinations of the
# member data is going to be runned
my_runs = basic_runner(\
# nx, ny and nz must be of the same size as they constitute
# one "part" of the combination (i.e. there will be no
# internal combination between the elements in nx, ny and
# nz)
nx = (9, 18),\
ny = (6, 12),\
nz = (8, 16),\
# nout and timestep must be of the same dimension for the
# same reason as mention above
nout = (10, 11, 12),\
timestep = (0.01, 0.01, 0.01),\
# The differencing option
ddz_second = ('FFT','C2'),\
# Additional options
additional = [('cst','D_perp',[1, 2])]\
)
# Execute all the runs
# 2 runs for each combination of nx, ny, nz
#!/usr/bin/env python """Driver which runs 3d_diffusion with the options given in BOUT.inp""" from bout_runners.bout_runners import basic_runner # Create the instance my_runs = basic_runner() # Do the run my_runs.execute_runs()
from bout_runners.bout_runners import basic_runner
my_instance = basic_runner(\
nproc = 1,\
directory = 'data',\
solver = ['cvode', 'ida'],\
grid_file = ['conduct_grid.nc', 'lol.nc'],\
ddx_first = ['C2', 'C4'],\
ddx_second = 'W3',\
ddx_upwind = ['U1', 'W3'],\
ddx_flux = ['SPLIT', 'NND'],\
ddy_first = 'C2',\
ddy_second = ['C4', 'W3'],\
ddy_upwind = 'W3',\
ddy_flux = 'NND',\
ddz_first = 'FFT',\
ddz_second = ['FFT', 'C2'],\
#ddz_upwind = 'U4',\
ddz_flux = 'SPLIT',\
nout = [0, 1],\
timestep = [1, 2],\
MXG = 1,\
MYG = 1,\
# additional = ('test1','ok1',1),\
additional = [('test1','ok1',[1, 2]), ('test2','ok2',1)],\
restart = 'overwrite',\
cpy_source = True)
my_instance.run()
my_runs = basic_runner(\
nproc = 1,\
# Set the directory
directory = 'MMS',\
# Set the time domain
nout = 1,\
timestep = 1,\
# Set mms to true
mms = True,\
# Set the spatial domain
nx = [4, 8, 16],\
ny = [4, 8, 16],\
nz = [4, 8, 16],\
# Additional (put here to illustrate the sorting)
series_add = [('cst','D_par',[1,2]), ('cst','D_perp',[0.5,1])],\
# Since we would like to do a MMS test, we would like to run
# the runs in a particular order. In this example, we would
# like to run all the possible spatial variables before
# doing the test. Hence we would like the spatial domain
# option to be the fastest varying.
# Since we have put post_process_after_every_run = False in
# the run function below, the processing function being
# called when all possibilities of the fastest variable has
# been run.
sort_by = 'spatial_domain'\
# Some additional sorting examples:
#
# This returns an error, stating the sorting possibilities
# (which will depend on the member data of this object)
# sort_by = 'uncomment_me'\
#
# In this example cst:D_par will be the fastest varying
# variable, followed by the spatial_domain. The post
# processing function will be called when all possibilities
# of these variables has been run
# sort_by = ['cst:D_par', 'spatial_domain']\
)
#!/usr/bin/env python
"""Driver which runs 3D_diffusion using grid files."""
from bout_runners.bout_runners import basic_runner
from grid_generator import generate_grid
# Generate a grid
generate_grid(file_name = "3D_diffusion_grid",\
inp_path = "data")
my_runs = basic_runner(\
grid_file = "3D_diffusion_grid.nc",\
# Copy the grid file
cpy_grid = True,\
# Set the flag in 3D_diffusion that a grid file will be
# used
additional = ('flags', 'use_grid', 'true')\
)
my_runs.execute_runs()
#!/usr/bin/env python
"""Driver which runs 3d_diffusion, and calls the function show_the_data when done"""
from post_processing_show_the_data import show_the_data
from bout_runners.bout_runners import basic_runner
my_runs = basic_runner()
# Put this in the post-processing function
my_runs.execute_runs(\
post_processing_function = show_the_data,\
# This function will be called every time after
# performing a run
post_process_after_every_run = True,\
# Below are the kwargs arguments being passed to
# show_the_data
t = slice(0,None),\
x = 1,\
y = slice(0,None),\
z = slice(0,None)\
)
my_runs = basic_runner(
# Number of processors
nproc=2,
# Directory of the inp file
directory="data",
# Set the solver option
solver="rk4",
mms=False,
atol=1.0e-8,
rtol=1.0e-8,
mxstep=10000000,
# Spatial domain option
nx=19,
ny=17,
nz=16,
# These can be set if needed
zperiod=None,
zmin=None,
zmax=None,
# These are not set here, but the code handles them
# internally
dx=None,
dy=None,
dz=None,
# The same as in BOUT.inp
# (Setting them to a different value doesn't make much sense)
MXG=1,
MYG=1,
# These can also be set
ixseps1=None,
ixseps2=None,
jyseps1_1=None,
jyseps1_2=None,
jyseps2_1=None,
jyseps2_2=None,
symGlobX=None,
symGlobY=None,
# The differencing option
ddx_first="C2",
ddx_second="C2",
ddx_upwind="U1",
ddx_flux="SPLIT",
ddy_first="C2",
ddy_second="C2",
ddy_upwind="U1",
ddy_flux="SPLIT",
ddz_first="FFT",
ddz_second="FFT",
ddz_upwind="U4",
ddz_flux="SPLIT",
# Temporal domain option
nout=11,
timestep=0.02,
# Additional options
# (An example ofadditional options run in series is found in
# 6a-run_with_MMS_post_processing_specify_numbers.py)
# tuple[0] - section name
# tuple[1] - variable name for the section
# tuple[2] - value of the variable name in the section
additional=[("cst", "D_perp", 5), ("cst", "D_par", 0.5)],
# Can set this to overwrite or append
restart=None,
# Will copy the source file
cpy_source=True,
# Will remake the file
make=True,
# Code will return an error if False, due to the mismatch
# between nx, ny and nproc
allow_size_modification=True,
)
my_runs = basic_runner(\
# Number of processors
nproc = 2,\
# Directory of the inp file
directory = 'data',\
# Set the solver option
solver = 'rk4',\
mms = False,\
atol = 1.0e-8,\
rtol = 1.0e-8,\
mxstep = 10000000,\
# Spatial domain option
nx = 19,\
ny = 17,\
nz = 16,\
# These can be set if needed
zperiod = None,\
zmin = None,\
zmax = None,\
# These are not set here, but the code handles them
# internally
dx = None,\
dy = None,\
dz = None,\
# The same as in BOUT.inp
# (Setting them to a different value doesn't make much sense)
MXG = 1,\
MYG = 1,\
# These can also be set
ixseps1 = None,\
ixseps2 = None,\
jyseps1_1 = None,\
jyseps1_2 = None,\
jyseps2_1 = None,\
jyseps2_2 = None,\
symGlobX = None,\
symGlobY = None,\
# The differencing option
ddx_first = 'C2',\
ddx_second = 'C2',\
ddx_upwind = 'U1',\
ddx_flux = 'SPLIT',\
ddy_first = 'C2',\
ddy_second = 'C2',\
ddy_upwind = 'U1',\
ddy_flux = 'SPLIT',\
ddz_first = 'FFT',\
ddz_second = 'FFT',\
ddz_upwind = 'U4',\
ddz_flux = 'SPLIT',\
# Temporal domain option
nout = 11,\
timestep = 0.02,\
# Additional options
# (An example ofadditional options run in series is found in
# 6a-run_with_MMS_post_processing_specify_numbers.py)
# tuple[0] - section name
# tuple[1] - variable name for the section
# tuple[2] - value of the variable name in the section
additional = [('cst','D_perp',5), ('cst', 'D_par', 0.5)],\
# Can set this to overwrite or append
restart = None,\
# Will copy the source file
cpy_source = True,\
# Will remake the file
make = True,\
# Code will return an error if False, due to the mismatch
# between nx, ny and nproc
allow_size_modification = True)
#!/usr/bin/env python
"""Driver which resizes the grid after restart"""
from post_processing_show_the_data import show_the_data
from bout_runners.bout_runners import basic_runner
# Initial run
# ===========================================================================
init_run = basic_runner(nz=8)
dmp_folder, _ =\
init_run.execute_runs(\
post_processing_function = show_the_data,\
# This function will be called every time after
# performing a run
post_process_after_every_run = True,\
# Below are the kwargs arguments being passed to
# show_the_data
t = slice(0,None),\
x = 1,\
y = slice(0,None),\
z = slice(0,None)\
)
# ===========================================================================
# Restart the run after resizing the grid
# ===========================================================================
#!/usr/bin/env python
"""Driver which runs 3D_diffusion using grid files."""
from bout_runners.bout_runners import basic_runner
from grid_generator import generate_grid
# Generate a grid
generate_grid(file_name = "3D_diffusion_grid",\
inp_path = "data")
my_runs = basic_runner(\
grid_file = "3D_diffusion_grid.nc",\
# Copy the grid file
cpy_grid = True,\
# Set the flag in 3D_diffusion that a grid file will be
# used
additional = ('flags', 'use_grid', 'true')\
)
my_runs.execute_runs()
the input. """
from bout_runners.bout_runners import basic_runner
# With a few exceptions: All variables in the constructor can be given
# as an iterable When execute_runs is called, all combinations of the
# member data is going to be runned
my_runs = basic_runner(\
# nx, ny and nz must be of the same size as they constitute
# one "part" of the combination (i.e. there will be no
# internal combination between the elements in nx, ny and
# nz)
nx = (9, 18),\
ny = (6, 12),\
nz = (8, 16),\
# nout and timestep must be of the same dimension for the
# same reason as mention above
nout = (10, 11, 12),\
timestep = (0.01, 0.01, 0.01),\
# The differencing option
ddz_second = ('FFT','C2'),\
# Additional options
additional = [('cst','D_perp',[1, 2])]\
)
# Execute all the runs
# 2 runs for each combination of nx, ny, nz
# 3 runs for each combination of nout and timestep
# 2 runs for each combination in ddz_second
# 2 runs for each combination of cst:const:value
# In total: 24 runs
grid_files.append(file_name + '.nc')
my_runs = basic_runner(\
nproc = 1,\
# Set the directory
directory = 'MMS',\
# Set the time domain
nout = 1,\
timestep = 1,\
# Set mms to true
mms = True,\
# Set the spatial domain
grid_file = grid_files,\
# Set the flag in 3D_diffusion that a grid file will be
# used
additional = ('flags','use_grid','true'),\
# Copy the grid file
cpy_grid = True,\
# Sort the runs by the spatial domain
sort_by = 'grid_file'
)
# Put this in the post-processing function
value_start = hits[0] + len(scan_parameter) + 1
# Here we assume that the value is not separated by an
# underscore
values[scan_parameter] = dmp_folder[value_start:].split("_")[0]
# Insert the values
restart_from = restart_template.format(values)
return restart_from
#}}}
# Initial runs
# ===========================================================================
init_run = basic_runner(additional=scan)
dmp_folder, _ =\
init_run.execute_runs(\
post_processing_function = show_the_data,\
# This function will be called every time after
# performing a run
post_process_after_every_run = True,\
# Below are the kwargs arguments being passed to
# show_the_data
t = slice(0,None),\
x = 1,\
# Choose the first hit to get the value from (again we assume
# that the value does not contain a _)
value_start = hits[0] + len(scan_parameter) + 1
# Here we assume that the value is not separated by an
# underscore
values[scan_parameter] = dmp_folder[value_start:].split("_")[0]
# Insert the values
restart_from = restart_template.format(values)
return restart_from
#}}}
# Initial runs
# ===========================================================================
init_run = basic_runner(additional = scan)
dmp_folder, _ =\
init_run.execute_runs(\
post_processing_function = show_the_data,\
# This function will be called every time after
# performing a run
post_process_after_every_run = True,\
# Below are the kwargs arguments being passed to
# show_the_data
t = slice(0,None),\
x = 1,\
y = slice(0,None),\
z = slice(0,None)\
)
#!/usr/bin/env python
"""Driver which resizes the grid after restart"""
from post_processing_show_the_data import show_the_data
from bout_runners.bout_runners import basic_runner
# Initial run
# ===========================================================================
init_run = basic_runner(nz = 8)
dmp_folder, _ =\
init_run.execute_runs(\
post_processing_function = show_the_data,\
# This function will be called every time after
# performing a run
post_process_after_every_run = True,\
# Below are the kwargs arguments being passed to
# show_the_data
t = slice(0,None),\
x = 1,\
y = slice(0,None),\
z = slice(0,None)\
)
# ===========================================================================
# Restart the run after resizing the grid
# ===========================================================================
restart_run = basic_runner(restart = "overwrite" ,\
restart_from = dmp_folder[0],\
# Additional options
remove_old = False
directory = "gaussianWSinAndParabola"
make = False
nproc = 4
# =============================================================================
# Create the runner
# =============================================================================
my_runs = basic_runner(\
directory = directory ,\
nproc = nproc ,\
# Spatial domain
nx = nx,\
# Copy the source file
cpy_source = True ,\
make = make ,\
# Sort the runs by the spatial domain
sort_by = 'spatial_domain'
)
# =============================================================================
# Perform the run
# =============================================================================
my_runs.execute_runs(\
remove_old = remove_old,\
# Set the proper directory
post_processing_function = perform_MES_test,\
nz = grid_number,\
inp_path = 'MMS' ,\
file_name = file_name)
# Append the grid_files list
grid_files.append(file_name + '.nc')
my_runs = basic_runner(\
nproc = 1,\
# Set the directory
directory = 'MMS',\
# Set the time domain
nout = 1,\
timestep = 1,\
# Set mms to true
mms = True,\
# Set the spatial domain
grid_file = grid_files,\
# Set the flag in 3D_diffusion that a grid file will be
# used
additional = ('flags','use_grid','true'),\
# Copy the grid file
cpy_grid = True,\
# Sort the runs by the spatial domain
sort_by = 'grid_file'
)
# Put this in the post-processing function
my_runs.execute_runs(\
post_processing_function = perform_MMS_test,\
# As we need several runs in order to perform the
# MMS test, this needs to be false
