x=string.split('-')
return (int(x[0]),int(x[1]))
else:
return int(string)
parser = argparse.ArgumentParser(
description="Plots for Pressure Swing Adsorption Oxygen Concentration simulation",
epilog='''
''')
parser.add_argument("files", nargs='+',type=str, help="pickle files to plot", default=None)
parser.add_argument('-c', '--cycles', type=parseIntRange, help="cycle or range of cycles to plot, .e.g. 4 or 0-2", default=None)
parser.add_argument('-n', '--nopause', action='store_true', help="do not pause after generating plots")
options = parser.parse_args()
print('git revision:{}'.format(util.get_git_commit()))
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
for file in options.files:
with open(file, 'rb') as fp:
data=pickle.load(fp)
print('loaded {}'.format(file))
print('cyles {}'.format(options.cycles))
head,tail=os.path.split(file)
print('head={} tail={}'.format(head,tail))
#take the part up to the dash in tail, and add something
def simulate(override_param, outdir=None,params_file='params',verbose=False):
#params to override the defaults from params.py
#plots: create output plots
#pause: if creating plots, pause when they are displayed on screen for <Enter>
#returns metrics of simulation as a dict, and the full params used
#writes a log file (and optional plots) into outdir. use outdir=None for
#no log file and instead direct all outout to stdout. plots will go into
#current directory in that case.
#params is the file to import (default is params)
#roi is (t1,t2) time range for the spatial plots
#returns out_place: directory+logcode prefix that it used for log file
global gverbose
gverbose=verbose
params=importlib.import_module(params_file)
np.seterr(all='warn')
global param
global norm
global dg
global count
global tstart
#we will alter print statement for this module to go into the log file
global print
if outdir is None:
dbg=mylog.Log(outdir=None,descr='psa')
OUTDIR='./'
else:
OUTDIR=outdir # for log files and png files
dbg=mylog.Log(outdir=OUTDIR,descr='psa')
#change the print function in the module to print to the log file
old_print=print
print=dbg.print
param=params.create_param(override_param, dbg.print)
norm=create_norm()
print('simulation params: time 0 to {}, step {}'.format(param.end_time,param.tstep))
print('step time might be increased if long cycle time')
print('normalization velocity {:8.3f} m/s'.format(param.norm_v0))
print('normalization of time norm.t0 {:8.3f} sec'.format(norm.t0))
print('starting with params import file: {}'.format(params))
state1=init(param,norm)
state2=init(param,norm)
prod=AttrDict()
prod.Pprod=1e5/norm.P0 # 1 atm to start
prod.yprod=param.feed_O2 # fract of O2 in product tank
#create dimensionless groups for the constants needed in the simulation
dg=create_dgroups(norm,param,state1)
np.set_printoptions(precision=5, edgeitems=6, linewidth=95, suppress=False)
pp=pprint.PrettyPrinter(indent=2)
print('git revision:{}'.format(util.get_git_commit()))
print('log code: {}'.format(dbg.log_code))
sys.stdout.write('log file prefix code: {}\n'.format(dbg.log_code))
print('version:{}'.format(vinfo))
print('plt {}'.format(matplotlib.__version__))
#print out the parameters and numbers for the record
print('dimensionless groups:\n',pp.pformat(dg.__dict__))
print('parameters:\n',pp.pformat(param.__dict__))
#create directory and log_code to prepend to out png files
util.safe_mkdir(OUTDIR)
out_place=os.path.join(OUTDIR,dbg.log_code)
#write to screen, not log file
sys.stdout.write('out_place={}\n'.format(out_place))
#output is to 1 ATM pressure (abs)
x01=np.hstack((state1.P,state1.T,state1.Tw,state1.xA,state1.xB,state1.yA,state1.yB))
x02=np.hstack((state2.P,state2.T,state2.Tw,state2.xA,state2.xB,state2.yA,state2.yB))
x03=np.array([prod.Pprod, prod.yprod])
print(x01.shape,x02.shape,x03.shape)
x0=np.hstack((x01,x02,x03))
tstart=time.time()
#time normalized by norm.t0
#Vectorized means that myfun can take a matrix of values to evaluate dx/dt at,
#not that x0 is a matrix
compute_alpha(verbose=True) # just print out the values for the log file
#we add a small number because we want to get full cycle at end
#also, increase tstep is a long cycle time:
param.tstep=max(param.tstep, param.cycle_time/100)
end_simulation=param.end_time+1e-6
t_span=(0,end_simulation)
#we want to include the end_simulation time
ev=np.arange(0,end_simulation,param.tstep)
print('t_span {}'.format(t_span))
#print('t_eval {}'.format(ev))
sys.stdout.write('Calling ODE solver\n')
bunch=scipy.integrate.solve_ivp(myfun, t_span, x0, vectorized=False, t_eval=ev,
method='BDF')
### method='Radau')
if not bunch.success:
print(bunch.message)
sys.stdout.write(bunch.message)
return None
print('evaluated at {} places'.format(bunch.t.size))
print('num eval of RHS {}'.format(bunch.nfev))
print('num eval of Jacobian {}'.format(bunch.njev))
print('total number of elements in data is {}'.format(bunch.y.size))
print('number of elements > 2 = {}'.format(np.sum(bunch.y>2)))
print('number of elements > 1 = {}'.format(np.sum(bunch.y>1)))
print('number of elements < 0 = {}'.format(np.sum(bunch.y<0)))
#t will be array of time points (t_points)
#y will be array of (n x t_points) values
N=param.N
#take the output data from ODE solver and place in data.<name> for plotting
data=AttrDict()
data.data1,data.data2,data.datap=data_to_state(bunch.y)
data.t=bunch.t
print('bunch.t',data.t)
data.param=param
print_state_ranges(data.data1)
print_state_ranges(data.data2)
#compute xAs at each point
data.data1.xAs,data.data1.xBs=adsorp_equilibrium(data.data1.yA,data.data1.yB,data.data1.P)
data.data2.xAs,data.data2.xBs=adsorp_equilibrium(data.data2.yA,data.data2.yB,data.data2.P)
#compute velocity at each point
data.data1.V=compute_vel(data.data1)
data.data2.V=compute_vel(data.data2)
#Output key statistics in log file with keywords
#get indexes for full cycle
print('CYCLE {:8.2f}'.format(param.cycle_time))
print('VENT {:8.2f}'.format(param.vent_time))
#indexes to select over - the last 2 cycles for container 1
r=np.argmax(bunch.t>(max(bunch.t)-2*param.cycle_time))
ret=AttrDict()
ret.time=datetime.datetime.now().replace(microsecond=0).isoformat()
ret.end_time=end_simulation
ret.cycle_time=param.cycle_time
ret.vent_time=param.vent_time
ret.norm_t0=norm.t0
ret.real_cycle_time=param.cycle_time*norm.t0
ret.real_vent_time=param.vent_time*norm.t0
#these are all min,max,mean for the last cycle of container 1
#for container 1, we calculate parameters for all the odd cycles and store in
# a list. To get last one, use index [-1]
idx=0
ret.container_y=[]
ret.prod_pressure=[]
ret.prod_y=[]
ret.prod_flow_rate=[]
while True:
start=param.cycle_time*idx
stop=param.cycle_time*(idx+1)
if stop > max(bunch.t):
break
#create a mask of the times for this odd cycle (starting with 1st one)
mask=np.logical_and(start <= bunch.t, bunch.t <= stop)
ret.container_y.append([np.min(data.data1.yA[-1,mask]),
np.max(data.data1.yA[-1,mask]),
np.mean(data.data1.yA[-1,mask])])
ret.prod_pressure.append([np.min(data.datap.Pprod[0,mask]),
np.max(data.datap.Pprod[0,mask]),
np.mean(data.datap.Pprod[0,mask])])
ret.prod_y.append([np.min(data.datap.yprod[0,mask]),
np.max(data.datap.yprod[0,mask]),
np.mean(data.datap.yprod[0,mask])])
#compute product flow in LPM
ret.prod_flow_rate.append([product_flow_rate(np.min(data.datap.Pprod[0,mask])),
product_flow_rate(np.max(data.datap.Pprod[0,mask])),
product_flow_rate(np.mean(data.datap.Pprod[0,mask]))])
idx+=2
ret.logcode=dbg.log_code
print('results:\n',pp.pformat(ret))
print('These are repr() outputs for programmatic reading')
print('PARAM {}'.format(repr(param)))
print('RESULT {}'.format(repr(ret)))
data.ret=ret
#save to pickle file
pickle_name='{}-data.pickle'.format(out_place)
with open(pickle_name, 'wb') as fp:
pickle.dump(data,fp)
#close the log file because might be called again before exiting and will
#want a new log file for that simulation
dbg.close()
#restore print function
print=old_print
return data, ret, param, pickle_name, out_place