def minimize(pmt, q, ID):
stop = 0
ps = make_ps()
ps[0] = [
0.19067284, 0.13088517, 0.09616244, 0.16611313, 0.14309298, 0.24170699,
0.51105228, 0.21396462, 0.7625556, 0.57751496, 1.19543109, 0.7949252,
28.36920857, 0.1
]
PS = np.zeros((1000, np.shape(ps)[1]))
ls = []
while not stop:
stop = 1
count = 0
if pmt >= 0:
ps[:, pmt] = q
l = np.zeros(np.shape(ps)[0])
for i in range(len(l)):
l[i] = L(ps[i], pmt, q, PS, ls, ID)
while len(ls) < 550:
count += 1
h = 0.5
a = 1
g = 1
s = 0.5
ind = np.argsort(l)
m = np.mean(ps[ind[:-1]], axis=0)
r = m + a * (m - ps[ind[-1]])
lr = L(r, pmt, q, PS, ls, ID)
if l[ind[0]] < lr and lr < l[ind[-2]]:
ps[ind[-1]] = r
l[ind[-1]] = lr
elif lr < l[ind[0]]:
e = m + g * (r - m)
le = L(e, pmt, q, PS, ls, ID)
if le < lr:
ps[ind[-1]] = e
l[ind[-1]] = le
else:
ps[ind[-1]] = r
l[ind[-1]] = lr
else:
c = m + h * (ps[ind[-1]] - m)
lc = L(c, pmt, q, PS, ls, ID)
if lc < l[ind[-1]]:
ps[ind[-1]] = c
l[ind[-1]] = lc
else:
for i in ind[1:]:
ps[i] = ps[ind[0]] + s * (ps[i] - ps[ind[0]])
l[i] = L(ps[i], pmt, q, PS, ls, ID)
def minimize(pmt, q, ID):
stop=0
ps=make_ps()
ps[:,0]=0
PS=np.zeros((2000, np.shape(ps)[1]))
ls=[]
while not stop:
stop=1
count=0
# if q>=0:
# ps[:,1]=q
# if ID==1:
# ps[:,0]=1
l=np.zeros(np.shape(ps)[0])
for i in range(len(l)):
l[i]=L(ps[i], pmt, q, PS, ls, ID)
while len(ls)<1750:
count+=1
h=0.5
a=1
g=1
s=0.5
ind=np.argsort(l, axis=0)
m=np.mean(ps[ind[:-1]], axis=0)
r=m+a*(m-ps[ind[-1]])
lr=L(r, pmt, q, PS, ls, ID)
if l[ind[0]]<lr and lr<l[ind[-2]]:
ps[ind[-1]]=r
l[ind[-1]]=lr
elif lr<l[ind[0]]:
e=m+g*(r-m)
le=L(e, pmt, q, PS, ls, ID)
if le<lr:
ps[ind[-1]]=e
l[ind[-1]]=le
else:
ps[ind[-1]]=r
l[ind[-1]]=lr
else:
c=m+h*(ps[ind[-1]]-m)
lc=L(c, pmt, q, PS, ls, ID)
if lc<l[ind[-1]]:
ps[ind[-1]]=c
l[ind[-1]]=lc
else:
for i in ind[1:]:
ps[i]=ps[ind[0]]+s*(ps[i]-ps[ind[0]])
l[i]=L(ps[i], pmt, q, PS, ls, ID)
def minimize(pmt, q, ID):
stop = 0
ps = make_ps()
# ps[0]=[0.15*160/120, 0.2*105/175, 0.15*95/140, 0.2*130/170, 0.15*125/145, 0.2, 30, np.sqrt(1000*662/30)]
PS = np.zeros((1000, np.shape(ps)[1]))
ls = []
while not stop:
stop = 1
count = 0
if pmt >= 0:
ps[:, pmt] = q
l = np.zeros(np.shape(ps)[0])
for i in range(len(l)):
l[i] = L(ps[i], pmt, q, PS, ls, ID)
while len(ls) < 550:
count += 1
h = 0.5
a = 1
g = 1
s = 0.5
ind = np.argsort(l)
m = np.mean(ps[ind[:-1]], axis=0)
r = m + a * (m - ps[ind[-1]])
lr = L(r, pmt, q, PS, ls, ID)
if l[ind[0]] < lr and lr < l[ind[-2]]:
ps[ind[-1]] = r
l[ind[-1]] = lr
elif lr < l[ind[0]]:
e = m + g * (r - m)
le = L(e, pmt, q, PS, ls, ID)
if le < lr:
ps[ind[-1]] = e
l[ind[-1]] = le
else:
ps[ind[-1]] = r
l[ind[-1]] = lr
else:
c = m + h * (ps[ind[-1]] - m)
lc = L(c, pmt, q, PS, ls, ID)
if lc < l[ind[-1]]:
ps[ind[-1]] = c
l[ind[-1]] = lc
else:
for i in ind[1:]:
ps[i] = ps[ind[0]] + s * (ps[i] - ps[ind[0]])
l[i] = L(ps[i], pmt, q, PS, ls, ID)
def minimize(rec, param, ind, param_name):
ls = []
PS = np.zeros((100, 6))
stop = 0
count = 0
ps = make_ps(rec_to_p(rec))
ps[:, ind] = param
l = np.zeros(np.shape(ps)[0])
for i in range(len(l)):
print('Init minimizing', i, 'out of', len(l))
l[i] = L(ps[i], param, ind, param_name, PS, ls)
while len(ls) < np.shape(PS)[0]:
print(param_name, '=', param, count)
count += 1
h = 0.5
a = 1
g = 1
s = 0.5
ind = np.argsort(l)
m = np.mean(ps[ind[:-1]], axis=0)
r = m + a * (m - ps[ind[-1]])
lr = L(r, param, ind, param_name, PS, ls)
if l[ind[0]] < lr and lr < l[ind[-2]]:
ps[ind[-1]] = r
l[ind[-1]] = lr
elif lr < l[ind[0]]:
e = m + g * (r - m)
le = L(e, param, ind, param_name, PS, ls)
if le < lr:
ps[ind[-1]] = e
l[ind[-1]] = le
else:
ps[ind[-1]] = r
l[ind[-1]] = lr
else:
c = m + h * (ps[ind[-1]] - m)
lc = L(c, param, ind, param_name, PS, ls)
if lc < l[ind[-1]]:
ps[ind[-1]] = c
l[ind[-1]] = lc
else:
for i in ind[1:]:
ps[i] = ps[ind[0]] + s * (ps[i] - ps[ind[0]])
l[i] = L(ps[i], param, ind, param_name, PS, ls)
def minimize():
stop = 0
while not stop:
count = 0
# ps=make_ps(rec_to_p(rec))
ps = make_ps()
l = np.zeros(np.shape(ps)[0])
for i in range(len(l)):
l[i] = L(ps[i])
while count < 1000:
count += 1
h = 0.5
a = 1
g = 1
s = 0.5
ind = np.argsort(l)
m = np.mean(ps[ind[:-1]], axis=0)
r = m + a * (m - ps[ind[-1]])
lr = L(r)
if l[ind[0]] < lr and lr < l[ind[-2]]:
ps[ind[-1]] = r
l[ind[-1]] = lr
elif lr < l[ind[0]]:
e = m + g * (r - m)
le = L(e)
if le < lr:
ps[ind[-1]] = e
l[ind[-1]] = le
else:
ps[ind[-1]] = r
l[ind[-1]] = lr
else:
c = m + h * (ps[ind[-1]] - m)
lc = L(c)
if lc < l[ind[-1]]:
ps[ind[-1]] = c
l[ind[-1]] = lc
else:
for i in ind[1:]:
ps[i] = ps[ind[0]] + s * (ps[i] - ps[ind[0]])
l[i] = L(ps[i])
def thetal(T, p, wv, wl):
"""
thetal(T,p,wv,wl)
Calculates the liquid water potential temperature of a parcel.
Parameters
- - - - - -
T : float
Temperature (K).
p : float
Pressure (Pa)
wv : float
Vapour mixing ratio (kg/kg).
wl : float
Liquid water potential temperature (kg/kg).
Returns
- - - -
thetalOut : float
Liquid water potential temperature (K).
References
- - - - - -
Emanuel 4.5.15 p. 121
Examples
- - - - -
>>> thetal(300., 8.e4, 0.03, 0.01)
298.21374486200278
"""
c = constants()
Lval = L(T)
wt = wv + wl
chi = (c.Rd + wt * c.Rv) / (c.cpd + wt * c.cpv)
gamma = wt * c.Rv / (c.cpd + wt * c.cpv)
thetaVal = T * (c.p0 / p)**chi
term1 = (1 - wl / (1. + wt)) * (1 - wl / (c.eps + wt))**(chi - 1)
term2 = (1 - wl / wt)**(-1. * gamma)
cp = c.cpd + wt * c.cpv
thetalOut = thetaVal * term1 * term2 \
* np.exp(-1. * Lval * wl/(cp*T))
return thetalOut
format(name=token_name,
text="'" + text +
"'" if token_name != "FLOATING" else text,
st_line=tok_start_line,
st_col=tok_start_column,
end_line=tok_end_line,
end_col=tok_end_column),
end="\t")
print()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('file', help='L-program file name')
args = parser.parse_args()
file_stream = FileStream(args.file)
lexer = Lexer(file_stream)
class MyErrorListener:
def syntaxError(self, recognizer, offendingSymbol, line,
charPositionInLine, msg, exc):
print('Syntax error at ({line}:{char}): {msg}'.format(
line=line - 1, char=charPositionInLine, msg=msg))
sys.exit(1)
lexer.removeErrorListeners()
lexer.addErrorListener(MyErrorListener())
output(lexer)