def prepare_data(uko, vke, upar, uper, minimal, cline_s, minimaly):
along = np.zeros((uko.shape[0], 400)) # take only 100 across points
across = np.zeros((uko.shape[0], 400))
data1 = np.zeros((uko.shape[0], 400))
data2 = np.zeros((uko.shape[0], 400))
data3 = np.zeros((uko.shape[0], 400))
data4 = np.zeros((uko.shape[0], 400))
arc = coasti.arc_length(cline_s)
for y in range(10, uko.shape[0] - 10):
i = 0
for x in range(5, uko.shape[1] - 5):
if minimal[y, x] >= -1 and minimal[
y, x] < 6 and i < 400 and uko.mask[y, x] == False:
a = arc[minimaly[y, x]]
along[y, i] = a[0]
across[y, i] = minimal[y, x]
data1[y, i] = upar[y, x]
data2[y, i] = uper[y, x]
data3[y, i] = uko[y, x]
data4[y, i] = vke[y, x]
i = i + 1
tmp = data1[10, :]
index, value = min(enumerate(tmp), key=operator.itemgetter(1))
ref = across[10, index]
acr = across.copy()
for y in range(across.shape[0]):
tmp = data1[y, :]
index, value = min(enumerate(tmp), key=operator.itemgetter(1))
diff = across[y, index] - ref
acr[y, :] = across[y, :] - diff
data1[data1 == 0] = np.nan
data2[data2 == 0] = np.nan
data3[data3 == 0] = np.nan
data4[data4 == 0] = np.nan
#along[along == 0] = np.nan
#across[across == 0] = np.nan
return [across, acr, along, data1, data2, data3, data4]
def preparation(uko, upar, minimal, cline_s, minimaly, rho):
arc = coasti.arc_length(cline_s)
along = np.zeros((minimal.shape[0], 1000)) # take only 400 across points
across = np.zeros((minimal.shape[0], 1000))
raw = Args()
full = Args()
raw_rho = np.zeros((minimal.shape[0], 1000))
raw_upar = np.zeros((minimal.shape[0], 1000))
full_rho = np.zeros((minimal.shape[0], 1000))
full_upar = np.zeros((minimal.shape[0], 1000))
for y in range(len(arc)):
along[y, :] = arc[y]
for x in range(1000):
across[:, x] = -1 + x * 0.01
mini = np.around(minimal, 2)
for y in range(len(arc)):
for x in range(minimal.shape[1]):
if uko.mask[y, x] == False and mini[y, x] < 10 and mini[
y, x] > -2 and mini[y, x] != 0:
a = across[y, :] - mini[y, x]
tmp = np.where(abs(a) < 0.005)
tmp = tmp[0]
if len(tmp) != 0:
tmp = tmp[0]
#print tmp
tmp2 = np.int16(minimaly[y, x])
raw_upar[tmp2, tmp] = upar[y, x]
raw_rho[tmp2, tmp] = rho[y, x]
else:
print("no hit")
data_raw_upar = raw_upar.copy()
data_raw_rho = raw_rho.copy()
for y in range(len(arc)):
c1 = np.zeros((raw_upar.shape[1]))
c1[:] = raw_upar[y, :]
c2 = np.zeros((raw_rho.shape[1]))
c2[:] = raw_rho[y, :]
np.reshape(c1, len(c1))
bad_indexes = (c1 == 0)
c1[bad_indexes] = np.nan
c2[bad_indexes] = np.nan
good_indexes = np.logical_not(bad_indexes)
good_data1 = c1[good_indexes].copy()
good_data2 = c2[good_indexes].copy()
if good_data1.size:
interpolated1 = np.interp(bad_indexes.nonzero()[0],
good_indexes.nonzero()[0], good_data1)
c1[bad_indexes] = interpolated1
interpolated2 = np.interp(bad_indexes.nonzero()[0],
good_indexes.nonzero()[0], good_data2)
c2[bad_indexes] = interpolated2
full_upar[y, :] = c1.copy()
full_rho[y, :] = c2.copy()
tmp = np.nonzero(data_raw_upar[y, :])
tmp = tmp[0]
if tmp.size:
tmp1 = min(tmp)
full_upar[y, 0:tmp1 - 1] = 0.
tmp2 = max(tmp)
full_upar[y, tmp2 + 1:full_upar.shape[1]] = 0.
tmp = np.nonzero(data_raw_rho[y, :])
tmp = tmp[0]
if tmp.size:
tmp1 = min(tmp)
full_rho[y, 0:tmp1 - 1] = np.nan
tmp2 = max(tmp)
full_rho[y, tmp2 + 1:full_rho.shape[1]] = np.nan
tmp = data_raw_upar[10, :]
index, value = min(enumerate(tmp), key=operator.itemgetter(1))
ref = across[10, index]
acr = across.copy()
for y in range(across.shape[0]):
tmp = data_raw_upar[y, :]
index, value = min(enumerate(tmp), key=operator.itemgetter(1))
diff = across[y, index] - ref
acr[y, :] = across[y, :] - diff
#acr=data_raw.rho - full.rho
#acr=0
return [along, across, acr, raw_upar, full_upar, raw_rho, full_rho]
def prepare_data(inn,gulf,cline_s):
arc = coasti.arc_length(cline_s)
# New coordiantes
along = np.zeros((gulf.minimal.shape[0],1000))
across = np.zeros((gulf.minimal.shape[0],1000))
# Variables that carry the new coordiantes
raw = Args()
full = Args()
raw.upar = np.zeros((gulf.minimal.shape[0],1000))
raw.rho = np.zeros((gulf.minimal.shape[0],1000))
raw.po = np.zeros((gulf.minimal.shape[0],1000))
full.upar = np.zeros((gulf.minimal.shape[0],1000))
full.rho = np.zeros((gulf.minimal.shape[0],1000))
full.po = np.zeros((gulf.minimal.shape[0],1000))
for y in range(len(arc)):
along[y,:] = arc[y]
for x in range(1000):
across[:,x] = -1+x*0.01
mini = np.around(gulf.minimal,2)
#j=0
for y in range(len(arc)):
for x in range(gulf.minimal.shape[1]):
if inn.uko.mask[y,x] == False and mini[y,x] < 10 and mini[y,x] > -2 and mini[y,x] != 0:
a = across[y,:]-mini[y,x]
tmp = np.where(abs(a)<0.005)
tmp = tmp[0]
if len(tmp) != 0:
tmp = tmp[0]
#print tmp
tmp2 = np.int16(gulf.minimaly[y,x])
if raw.upar[tmp2,tmp] == 0:
raw.upar[tmp2,tmp] = gulf.upar[y,x]
raw.rho[tmp2,tmp] = inn.rho[y,x]
raw.po[tmp2,tmp] = inn.po[y,x]
#else:
#print("no hit")
#print("doppelt", j)
data_raw_upar = raw.upar.copy()
data_raw_rho = raw.rho.copy()
for y in range(len(arc)):
c1 = np.zeros((raw.upar.shape[1]))
c1[:] = raw.upar[y,:]
c2 = np.zeros((raw.rho.shape[1]))
c2[:] = raw.rho[y,:]
c3 = np.zeros((raw.po.shape[1]))
c3[:] = raw.po[y,:]
np.reshape(c1, len(c1))
bad_indexes = (c1==0)
c1[bad_indexes] = np.nan
c2[bad_indexes] = np.nan
c3[bad_indexes] = np.nan
good_indexes = np.logical_not(bad_indexes)
good_data1 = c1[good_indexes].copy()
good_data2 = c2[good_indexes].copy()
good_data3 = c3[good_indexes].copy()
if good_data1.size:
interpolated1 = np.interp(bad_indexes.nonzero()[0], good_indexes.nonzero()[0], good_data1)
c1[bad_indexes] = interpolated1
interpolated2 = np.interp(bad_indexes.nonzero()[0], good_indexes.nonzero()[0], good_data2)
c2[bad_indexes] = interpolated2
interpolated3 = np.interp(bad_indexes.nonzero()[0], good_indexes.nonzero()[0], good_data3)
c3[bad_indexes] = interpolated3
full.upar[y,:] = c1.copy()
full.rho[y,:] = c2.copy()
full.po[y,:] = c3.copy()
tmp = np.nonzero(data_raw_upar[y,:])
tmp = tmp[0]
if tmp.size:
tmp1 = min(tmp)
full.upar[y,0:tmp1-1] = 0.
tmp2 = max(tmp)
full.upar[y,tmp2+1:full.upar.shape[1]] = 0.
tmp = np.nonzero(data_raw_rho[y,:])
tmp = tmp[0]
if tmp.size:
tmp1 = min(tmp)
full.rho[y,0:tmp1-1] = np.nan
full.po[y,0:tmp1-1] = np.nan
tmp2 = max(tmp)
full.rho[y,tmp2+1:full.rho.shape[1]] = np.nan
full.po[y,tmp2+1:full.po.shape[1]] = np.nan
tmp = data_raw_upar[10,:]
index, value = min(enumerate(tmp), key=operator.itemgetter(1))
ref = across[10,index]
acr = across.copy()
ind = 0
for y in range(across.shape[0]):
tmp = data_raw_upar[y,:]
index, value = min(enumerate(tmp), key=operator.itemgetter(1))
diff = across[y,index] - ref
acr[y,:] = across[y,:] -diff
ind = ind + index
ind = ind /(across.shape[0])
raw.ind = np.int16(ind)
return [along,across,acr,raw,full]