def conditionError(inverse,surfaces,neighbors,distances,fname=False,disp=-1,params={},savepath=False,title=False,show=True):
conditions = []
levels = []
errors = []
fram = []
print(params)
params.setdefault("upperbound",1000)
params.setdefault("reflevel",1000)
if savepath:
try:
os.makedirs(savepath)
except FileExistsError as e:
print(e)
for lowlevel in range(params["upperbound"]+200,2600,200):
params.update({"lowerbound":lowlevel})
print(params)
out = inverttools.invert(inverse,surfaces,neighbors,distances,params=params)
inv = out["surfaces"]
columndict = out["coldict"]
svds = out["svddecomp"]
A = out["matrixsetup"]
e = out["errors"]
meta = out["metadata"]
if inv:
s = np.diag((1.0/svds[1]))
condition = s[0]/s[-1]
conditions.append(condition)
errors.append(np.sum(np.abs(e[1])))
levels.append(lowlevel)
i,o = graph.transportLine(inv,(-24.71,83.60),(27.496,80.06),2400,False,show=False)
fram.append(abs(abs(i)-abs(o)))
inv = nstools.streamFuncToUV(inv,neighbors,distances)
#fram.append(abs(distanceFromKnown(inv)))
if lowlevel ==disp:
coupleinvert = nstools.streamFuncToUV(inv,neighbors,distances)
#graph.transportLine(coupleinvert,(-24.71,83.60),(27.496,80.06),2400,True)
graph.graphVectorField(inv,"uabs","vabs","z")
fig, (ax1,ax3) = plt.subplots(2,1)
ax1.scatter(levels,errors,label="error")
ax1.set_ylabel("R")
ax2 = ax1.twinx()
ax2.scatter(levels,conditions,color="red",label = "matrix condition")
ax2.set_ylabel("Matrix Condition")
ax1.set_xlabel("Lowest Neutral Surface")
if not title:
fig.suptitle(params["reflevel"])
else:
fig.suptitle(title)
ax3.scatter(levels,fram)
ax3.set_ylabel("Net transport through Fram Strait in Sv")
ax3.set_xlabel("Lowest Neutral Surface")
plt.legend()
fig.set_size_inches(16.5,12)
if savepath and not fname:
plt.savefig(savepath+"ns"+str(params["reflevel"])+".png")
if savepath and fname:
plt.savefig(savepath+str(fname)+".png")
if show:
plt.show()
plt.close()
def sensitivityRunCompress(infilename, outfilename):
with open(infilename, 'rb') as infile:
[out, neighbors, distances] = pickle.load(infile)
inv = out["surfaces"]
inv = nstools.addOldUnits(inv)
inv = nstools.twoCReference(inv)
inv = nstools.streamFuncToUV(inv, neighbors, distances)
inv = nstools.externalReference(inv, "data/yomaha_1000.nc")
surfaces = surfaceCompress(inv)
errors = out["errors"]
metadata = out["metadata"]
with open(outfilename, 'wb') as outfile:
pickle.dump([surfaces, errors, metadata], outfile)
def decayScaleSensitivity(originalsurfaces,neighbors,distances):
f = open("h0log.txt", "w")
flat_kvs={}
for H_0 in range(500,3000,500):
surfaces = copy.deepcopy(originalsurfaces)
surfaces = nstools.addParametersToSurfaces(brasil,surfaces,neighbors,distances,H_0=H_0)
# graph.NSGAMCompare(preinterpsurfaces,surfaces,-30,-180,180)
# print(surfaces.keys())
params = {"reflevel":int(2200),"upperbound":1000,"lowerbound":4200,\
"mixs":{"kvo":True,"kvb":True,"kh":True},"debug":False,\
"3point":True,"edgeguard":True,"H_0":H_0}
# Conditions
# All mixing: 201235
# No mixing: 147
# Kv0 only: 147
# KvH and Kv0 only: 148
# KvH and Kv0 only with out edgeguard (tm): 489
out= inverttools.invert("coupled",surfaces,neighbors,distances,params=params)
inv = out["surfaces"]
condition = int(out["metadata"]["condition"])
err = int(out["metadata"]["error"])
inv = nstools.streamFuncToUV(inv,neighbors,distances)
print("#"*10+str(H_0),file=f)
print(out["metadata"],file=f)
#result = nstools.transportDiagnostics(inv)
#print(result,file=f)
kvs = []
for l in inv.keys():
kvs += list(inv[l]["data"]["kv"])
kvs = np.asarray(kvs).flatten()
flat_kvs[str(H_0)+"|"+str(condition)+"|"+str(err)] = kvs
print("kv: ,"+str(np.nanmean(kvs)),file=f)
print("% negative: ,"+str(np.sum(kvs<0)/np.sum(~np.isnan(kvs))),file=f)
del out
del inv
del surfaces
fig, (ax1,ax2) = plt.subplots(1,2)
bins=range(-10,0)
for k in flat_kvs.keys():
ax1.hist(np.log10(np.abs(flat_kvs[k][flat_kvs[k]>0])),bins,histtype='step',label=k)
ax2.hist(np.log10(np.abs(flat_kvs[k][flat_kvs[k]<0])),bins,histtype='step',label=k)
plt.legend()
plt.show()
f.close()
def weightingSensitivity(originalsurfaces,neighbors,distances):
flat_kvs={}
originalsurfaces = nstools.addParametersToSurfaces(brasil,originalsurfaces,neighbors,distances,H_0=500)
for o in [-1,0,1]:
for b in [-1,0,1]:
for h in [0]:
surfaces = copy.deepcopy(originalsurfaces)
# graph.NSGAMCompare(preinterpsurfaces,surfaces,-30,-180,180)
# print(surfaces.keys())
params = {"reflevel":int(2062),"upperbound":1000,"lowerbound":4200,\
"scalecoeffs":{"Ar":0.05,"kvo":5*10**(-6+o),"kvb":5*10**(-5+b),"kh":5*10**(2+h)},\
"mixs":{"kvo":True,"kvb":True,"kh":True},"debug":False,\
"3point":True,"edgeguard":True,"H_0":500}
# Conditions
# All mixing: 201235
# No mixing: 147
# Kv0 only: 147
# KvH and Kv0 only: 148
# KvH and Kv0 only with out edgeguard (tm): 489
out= inverttools.invert("coupled",surfaces,neighbors,distances,params=params)
inv = out["surfaces"]
condition = out["metadata"]["condition"]
inv = nstools.streamFuncToUV(inv,neighbors,distances)
result = nstools.transportDiagnostics(inv)
print(result)
kvs = []
for l in inv.keys():
kvs += list(inv[l]["data"]["kv"])
kvs = np.asarray(kvs).flatten()
flat_kvs[str(o)+str(b)+str(h)+"|"+str(int(condition))] = kvs
del out
del inv
del surfaces
fig, (ax1,ax2) = plt.subplots(1,2)
bins=range(-10,0)
for k in flat_kvs.keys():
ax1.hist(np.log10(np.abs(flat_kvs[k][flat_kvs[k]>0])),bins,histtype='step',label=k)
ax2.hist(np.log10(np.abs(flat_kvs[k][flat_kvs[k]<0])),bins,histtype='step',label=k)
plt.legend()
plt.show()
f.close()
reflevels = surfaces.keys()
for k in reflevels:
if int(k) > 1200 and int(k) < 4000:
with open('data/run0/withparams.pickle', 'rb') as outfile:
[surfaces, neighbors, distances] = pickle.load(outfile)
params = {"reflevel":int(k),"upperbound":1000,"lowerbound":4000,\
"mixs":{"kvo":True,"kvb":True,"kh":True},"debug":False,\
"3point":True,"edgeguard":True,"H_0":1000}
out = inverttools.invert("coupled",
surfaces,
neighbors,
distances,
params=params)
inv = out["surfaces"]
inv = nstools.streamFuncToUV(inv, neighbors, distances)
with open('sens/reflevel/{}.pickle'.format(k), 'wb') as outfile:
pickle.dump([out, neighbors, distances], outfile)
if switch["H_0"]:
######################
###### H_0 Testing
######################
for H_0 in [200, 500, 1000, 1500, 2000]:
with open('data/run0/interpedbrasil.pickle', 'rb') as outfile:
[surfaces, neighbors, distances] = pickle.load(outfile)
surfaces, neighbors, distances = nstools.addParametersToSurfaces(
brasil, surfaces, neighbors, distances, H_0=H_0)
params = {"reflevel":2000,"upperbound":1000,"lowerbound":4000,\
"mixs":{"kvo":True,"kvb":True,"kh":True},"debug":False,\