def test_working_dir():
cwd = Path(os.getcwd()).abspath()
p = project.Project(name='')
try:
assert p.name
assert str(cwd) != str(p.dirname)
assert (cwd / p.name).exists()
assert (cwd / p.name).isdir()
for d in ('input', 'output'):
assert (p.dirname / d).exists()
assert (p.dirname / d).isdir()
assert (p.dirname / 'which_output_files.csv').exists()
p.deactivate()
whereIam = str(Path(os.getcwd()).abspath())
assert str(cwd) == whereIam, str(cwd) + ' / ' + whereIam
p.activate()
except:
raise
finally:
p.remove(force=True)
assert (cwd / p.name).exists() is False
p = project.Project()
p.deactivate()
pp = project.Project(name=p.name)
try:
assert str(pp.dirname) == str(p.dirname)
except:
raise
finally:
pp.remove(force=True)
def test_shift_in_light():
#assert : the difference between the light intercepted by one plant on
# one day and the light intercepted by that same plant the next day
# (PAR_per_axes.csv output file ; value for Sum_PAR) is always less
# than 1000% (or x10)
p = project.Project(name='shift_in_light')
try:
# run the reference simulation
lsys, lstring = p.run_parameters('sim_scheme_test.csv')
PAR_per_axes_dico = lsys.context().locals()['PAR_per_axes_dico']
df = pandas.DataFrame(PAR_per_axes_dico)
df['relative_Inc_PAR'] = df['Inc_PAR'] / df['Inc_PAR'].mean()
df['relative_Sum_PAR'] = df['Sum_PAR'] / df['relative_Inc_PAR']
def _max_variation(x):
variation = (x.relative_Sum_PAR.diff().abs() /
x.relative_Sum_PAR)[1:] * 100
return variation.max()
# test max variation per plante
relative_variation = df.groupby([
'Num_plante', 'Elapsed_time'
]).agg('sum').reset_index().groupby('Num_plante').apply(_max_variation)
assert all(relative_variation < 50)
except:
raise
finally:
p.remove(force=True)
def projecion_screen_tuning():
p = project.Project(name='projection_screen_tuning')
# run the reference simulation
lsys, lstring = p.run_parameters('sim_scheme_test.csv')
caribu_recorder = lsys.context().locals()['caribu_recorder']
df = pandas.DataFrame(caribu_recorder.records_data())
p.remove(force=True)
def test_unedfined_Ln_final():
p = project.Project()
try:
param = p.csv_parameters('sim_scheme_test_undefined_ln_final.csv')[0]
lsys, lstring = p.run(**param)
assert lstring
except:
raise
finally:
p.remove(force=True)
def test_bug_Ln_final():
p = project.Project()
try:
param = p.csv_parameters('sim_scheme_test.csv')[0]
param.update(dict(Ln_final_Maxwell=20.3, nbj=120))
lsys, lstring = p.run(**param)
assert lstring
except:
raise
finally:
p.remove(force=True)
def test_direct_run():
p = project.Project()
lsys, lstring = p.run(nb_plt_utiles=1,
dist_border_x=0,
dist_border_y=0,
nbj=30,
beginning_CARIBU=290)
s = lsys.sceneInterpretation(lstring)
p.remove(force=True)
assert len(lstring) > 10
assert len(s) > 0
def test_modified_parameters():
reset_call_dir()
p = project.Project()
try:
param = p.csv_parameters('sim_scheme_fast_test.csv')[0]
param.update(dict(nbj=40))
lsys, lstring = p.run(**param)
assert lstring
assert p.combi_params.nbj[0] == 40
except:
raise
finally:
p.remove(force=True)
def shift_in_light_bug():
"""Inspect the shift in light bug """
p = project.Project(name='shift_in_light')
params = p.csv_parameters('sim_scheme_test.csv')[0]
params.update(dict(nbj=225))
lsys, lstring = p.run(**params)
res_sky = get_res_sky(lsys, lstring)
dd = pandas.DataFrame(lsys.context().locals()['PAR_per_organ'])
dfag = dd.groupby(['Num_plante', 'Num_talle']).agg({
'Organ_surface': 'sum',
'tiller_surface': 'mean'
})
dfag.tiller_surface / dfag.Organ_surface
def test_which_outputs():
p = project.Project()
try:
output = p.which_outputs
assert output
assert output['Apex']
p.which_outputs = {'Apex': 0}
new_output = p.which_outputs
assert not new_output['Apex']
assert len(new_output) == len(output) # all outputs flag are required
except:
raise
finally:
p.remove(force=True)
def test_which_parameters():
reset_call_dir()
p = project.Project()
try:
lsys, lstring = p.run_parameters('sim_scheme_fast_test2.csv', which=0)
assert lstring
assert len(p.combi_params) == 1
lsys, lstring = p.run_parameters('sim_scheme_fast_test2.csv',
which=[1, 2])
assert lstring
assert len(p.combi_params) == 3
except:
raise
finally:
p.remove(force=True)
def test_same_result(debug=False):
#assert : verify if the same wheat field have the same results for the same chosen parameters.
p = project.Project(name='same') # Create the simulation directory
try:
directory = project.walter_data()
params = p.csv_parameters(str(directory/'sim_scheme_ref.csv'))[0] # recovery of parameters
p.run(**params)
_compare_list_of_files(p)
_compare_file_content(p)
except:
raise
finally:
if debug:
return p
else:
p.remove(force=True)
def change_reference():
p = project.Project(name='same') # Create the simulation directory
directory = project.walter_data()
params = p.csv_parameters(str(directory/'sim_scheme_ref.csv'))[0] # recovery of parameters
p.run(**params)
reference_directory = get_data_dir() + '/ref_output' # Reference folder
if os.path.isdir(reference_directory):
shutil.rmtree(reference_directory)
result_directory = str(p.output_path()) + '/'
list_of_result = os.listdir(result_directory)
os.mkdir(Path(project.walter_data()/'ref_output'))
for i in list_of_result:
shutil.move(result_directory+i, reference_directory+'/'+i)
print(result_directory+i, "has being moved into reference directory ")
p.remove(force=True)
def test_run_parameters():
reset_call_dir()
p = project.Project()
try:
lsys, lstring = p.run_parameters('sim_scheme_fast_test.csv')
s = lsys.sceneInterpretation(lstring)
assert len(lstring) > 10
assert len(s) > 0
lsys, lstring = p.run_parameters('sim_scheme_fast_test2.csv')
s = lsys.sceneInterpretation(lstring)
assert len(lstring) > 10
assert len(s) > 0
assert len(p.combi_params) == 3
except:
raise
finally:
p.remove(force=True)
def test_infinite():
p = project.Project(name='infinite_canopy')
try:
params = p.csv_parameters('sim_scheme_test.csv')[0]
params.update(
dict(nb_plt_utiles=1,
dist_border_x=0,
dist_border_y=0,
nbj=53,
infinity_CARIBU=1,
beginning_CARIBU=290))
lsys, lstring = p.run(**params)
crop_scheme = lsys.context().locals()['crop_scheme']
pattern = scene_pattern(crop_scheme)
assert pattern[0] > 1
except:
raise
finally:
p.remove(force=True)
def test_check_light_balance():
"""test if total par intercepted is above or below incident par"""
p = project.Project(name='light_balance')
try:
params = p.csv_parameters('sim_scheme_test.csv')[0]
params.update(dict(write_debug_PAR=True, infinity_CARIBU=1))
lsys, lstring = p.run(**params)
crop_scheme = lsys.context().locals()['crop_scheme']
# do we really need debug par dico df ? (simulation time is extremly long !)
df = pandas.DataFrame(lsys.context().locals()['Debug_PAR_dico_df'])
control = df.groupby('Elapsed_time').agg({
'Organ_PAR': 'sum',
'Inc_PAR': 'mean'
})
balance = control.Organ_PAR / control.Inc_PAR / crop_scheme[
'surface_sol']
assert all(balance <= 1)
except:
raise
finally:
p.remove(force=True)
def test_combi_params():
p = project.Project()
try:
assert len(p.combi_params) == 0
p.run(dry_run=True)
combi = p.combi_params
assert len(combi) == 1
assert combi.ID[0] == 'walter_defaults'
p.run(nbj=30, dry_run=True)
combi = p.combi_params
assert len(combi) == 2
assert combi.ID[0] == 'walter_defaults'
assert combi.nbj[1] == 30
# repeat run: same id
p.run(nbj=30, dry_run=True)
combi = p.combi_params
assert len(combi) == 2
assert combi.ID[0] == 'walter_defaults'
assert combi.nbj[1] == 30
path = 'sim_scheme_test.csv'
p.run_parameters(path, dry_run=True)
combi = p.combi_params
assert len(combi) == 3
assert len(combi.columns) == 27
# repeat
p.run_parameters(path, dry_run=True)
combi = p.combi_params
assert len(combi) == 3
assert len(combi.columns) == 27
except:
raise
finally:
p.remove(force=True)
def test_zero_light():
#assert : all tillers receive light (value > 0 for Sum_PAR in the PAR_per_axes.csv output file)
p = project.Project(name='zero_light') #Create Folder
try:
params = p.csv_parameters('sim_scheme_test.csv')[
0] #Recover list of parameters
params.update(dict(nb_plt_temp=50, nb_rang=10,
nbj=156)) #Add new parameters
lsys, lstring = p.run(**params)
PAR_per_axes_dico = lsys.context().locals()['PAR_per_axes_dico']
df = pandas.DataFrame(PAR_per_axes_dico)
PAR = df.groupby('Num_plante').agg('sum')['Sum_PAR'].values
assert all(PAR > 0)
outdir = p.output_path()
dfout = pandas.read_csv(outdir / 'PAR_per_axes.csv', sep='\t')
PARout = df.groupby('Num_plante').agg('sum')['Sum_PAR'].values
assert all(PARout > 0)
except:
raise
finally:
p.remove(force=True)
def main():
parser = walter_parser()
args = parser.parse_args()
if args.p == '.': # check '.' is walter-like (in case user has forgotten -p)
if not project.check_cwd():
answer = raw_input(
"Current directory doesn't look like a walter project dir, Continue ? [Y/N]"
)
if answer != 'Y':
return
prj = project.Project(args.p)
# TODO: add a flag in the project to know if the project has been generated, modified or not.
if prj.dirname.exists():
print('Use Project %s located at %s' % (prj.name, prj.dirname))
else:
print('Project %s has been generated at %s' % (prj.name, prj.dirname))
if args.i: # -i has been set
sim_scheme = args.i
if sim_scheme == 'walter_default': # walter command called with dry -i args
prj.run(dry_run=args.dry_run)
else:
param_list = prj.csv_parameters(sim_scheme)
if len(param_list) == 1:
prj.run(dry_run=args.dry_run, **(param_list[0]))
else:
print('Multiple processes')
print('generate ids')
prj.run_parameters(sim_scheme, dry_run=True)
print('run simulations')
tmp = prj.dirname / 'tmp'
if not tmp.exists():
tmp.mkdir()
pids = []
procs = {}
active_procs = []
for i, pdict in enumerate(param_list):
############################################# temporary fix #############################################################################################
while len(
active_procs
) > 2: # As long as there are 3 active_procs, test if one ends : temporary fix to avoid running too many processes at the same time
active_procs = [
proc for proc in active_procs
if proc.poll() == None
]
#time.sleep(300) # To avoid testing for finished processes too often, wait 5 minutes between loops
df = pd.DataFrame.from_dict(data=[pdict], orient='columns')
scheme_name = str(tmp / 'sim_scheme_%d.csv' % (i + 1))
df.to_csv(path_or_buf=scheme_name, sep='\t', index=False)
prj.activate()
pid = Popen(["walter", "-i", scheme_name])
pids.append(pid)
procs[scheme_name] = pid
active_procs.append(pid)
############################################# temporary fix #############################################################################################
# Test caribuRunError re-launching : temporary fix until CaribuRunErrors are solved
while len(procs) > 0: # While there are processes to test
for scheme in procs.keys(
): #Not using iteritems because you cannot change the size of a dictionary while iterating on it
if procs[scheme].poll(
) != None: # If the proces is finished
procs.pop(scheme) # Remove this proc from procs
param_list_dict = prj.csv_parameters(scheme)
sim_id = prj.get_id(
param_list_dict[0]) # Get the ID
if os.path.exists(
prj.dirname + "/output/" + sim_id +
"/error_caribu.txt"
): # Check if the file error_caribu.txt has been generated
shutil.rmtree(
prj.dirname + "/output/" +
sim_id) # Supress the output directory
ex_rep = param_list_dict[0][
"rep"] # Get the rep (random seed) used for the simulation
param_list_dict[0].update(
rep=ex_rep + 1
) # Update the sim_scheme with a new seed to re-launch the simulation
df = pd.DataFrame.from_dict(
data=param_list_dict, orient='columns')
df.to_csv(
path_or_buf=scheme, sep='\t', index=False
) # Create the csv file sim_scheme to launch the simulation
p = Popen(["walter", "-i",
scheme]) # Launch the simulation
prj.itable[sim_id] = param_list_dict[
0] # updating combi_param
prj.update_itable()
pids.append(
p
) # Add the new process to the list of processes for futher testing
procs[
scheme] = p # Add the new process to the dict of processes
#time.sleep(120) # To avoid testing for finished processes too often, wait 2 minutes between loops
tmp.rmtree()