def pr_sowfa(dir, trs_para, varD):
""" extract horizontal average of velocity at various times and heights """
# coordinate transmation
O = trs_para[0]
alpha = trs_para[1]
fr = open(dir + '/data/' + 'aveData', 'rb')
aveData = pickle.load(fr)
fr.close()
zSeq = aveData['H']
zNum = zSeq.size
tSeq = aveData['time']
tNum = tSeq.size
tDelta = tSeq[1] - tSeq[0]
uSeq = aveData['U_mean']
vSeq = aveData['V_mean']
wSeq = aveData['W_mean']
varSeq = np.zeros((tNum, zNum))
for zInd in range(zNum):
tmp = np.concatenate(
(uSeq[:, zInd].reshape(tNum, 1), vSeq[:, zInd].reshape(tNum, 1)),
axis=1)
tmp = np.concatenate((tmp, wSeq[:, zInd].reshape(tNum, 1)), axis=1)
tmp_ = funcs.trs(tmp, O, alpha)
varSeq[:, zInd] = tmp_[:, varD]
return tSeq, zSeq, varSeq
def getData_sowfa(dir, prbg, trs_para, var, varD):
""" extract velocity data of specified probe groups """
# coordinate transmation
O = trs_para[0]
alpha = trs_para[1]
# read data
readDir = dir + '/data/'
readName = prbg
fr = open(readDir + readName, 'rb')
data_org = pickle.load(fr)
fr.close()
coors = data_org['coors']
# coordinate transformation
prbNum = coors.shape[0]
for p in range(prbNum):
tmp = data_org[var][p]
data_org[var][p] = funcs.trs(tmp, O, alpha)
xSeq = np.array(data_org['info'][2])
ySeq = np.array(data_org['info'][3])
zSeq = np.array(data_org['info'][4])
xNum = xSeq.size
yNum = ySeq.size
zNum = zSeq.size
varSeq = data_org[var][:, :, varD]
tSeq = data_org['time']
tNum = tSeq.size
return tSeq, xSeq, ySeq, zSeq, varSeq, coors
def getSliceData_Ny_sowfa(dir, jobName, slice, var, varD, trs_para, tInd, xInd,
zInd):
""" extract velocity data of specified slice_Nz """
readDir = dir + '/data/'
readName = slice
fr = open(readDir + readName, 'rb')
data = pickle.load(fr)
fr.close()
slc = sdc.Slice(data, 1)
del data # 2 means z-axis
tSeq = slc.data['time']
tSeq = tSeq[tInd[0]:tInd[1]]
Y = slc.N_location # height of this plane
O, alpha = trs_para[0], trs_para[1]
varSeq = []
for t_ind in range(tInd[0], tInd[1]):
print('processing: ' + str(t_ind) + ' ...')
tmp = slc.data[var][t_ind]
tmp = funcs.trs(tmp, O, alpha)
tmp1 = slc.meshITP_Ny(xInd, zInd, tmp[:, varD], 0, method_='linear')
xSeq, zSeq, varSeq_ = tmp1[0], tmp1[1], tmp1[2]
varSeq.append(varSeq_)
varSeq = np.array(varSeq)
return tSeq, xSeq, zSeq, Y, varSeq
def uwflux_sowfa(ppDir, trs_para, varD):
O = trs_para[0]
alpha = trs_para[1]
# ave_itv = t_para[0]
# tplot = t_para[1]
fr = open(ppDir + '/data/' + 'aveData', 'rb')
aveData = pickle.load(fr)
fr.close()
zSeq = aveData['H']
zNum = zSeq.size
tSeq = aveData['time']
tNum = tSeq.size
tDelta = tSeq[1] - tSeq[0]
uwSeq = aveData['uw_mean']
vwSeq = aveData['vw_mean']
wwSeq = aveData['ww_mean']
R13Seq = aveData['R13_mean']
R23Seq = aveData['R23_mean']
R33Seq = aveData['R33_mean']
rsvSeq = np.zeros((tNum, zNum))
sgsSeq = np.zeros((tNum, zNum))
for zInd in range(zNum):
tmp = np.concatenate(
(uwSeq[:, zInd].reshape(tNum, 1), vwSeq[:, zInd].reshape(tNum, 1)),
axis=1)
tmp = np.concatenate((tmp, wwSeq[:, zInd].reshape(tNum, 1)), axis=1)
tmp_ = funcs.trs(tmp, O, alpha)
rsvSeq[:, zInd] = tmp_[:, varD]
tmp = np.concatenate(
(R13Seq[:, zInd].reshape(tNum, 1), R23Seq[:, zInd].reshape(
tNum, 1)),
axis=1)
tmp = np.concatenate((tmp, R33Seq[:, zInd].reshape(tNum, 1)), axis=1)
tmp_ = funcs.trs(tmp, O, alpha)
sgsSeq[:, zInd] = tmp_[:, varD]
totSeq = rsvSeq + sgsSeq
return tSeq, zSeq, rsvSeq, sgsSeq, totSeq
def PSD_data_sowfa(jobDir, prbg, trs_para, var, varD):
"""
Extract velocity data of specified probe groups
INPUT
jobDir: job directory, e.g. '/scratch/sowfadata/pp/tutorials/example_nbl'
prbg: name of the probe group, e.g. 'prbg0'
trs_para: coordinate transform parameters, e.g. ((0,0,0),30.0), the origin and the conterclockwise rotation degree
var: name of the target variable, e.g. 'U'
varD: the dimension of which the target vector variable will be outputted
OUTPUT
tSeq: 1D array of times
xSeq: 1D array of x positions
ySeq: 1D array of y positions
zSeq: 1D array of heights
varSeq: 2D array of the target variable, 1st dim point ID, 2nd dim time
coors: 2D array of coordinates of the probes, 1st dim point ID, 2nd dim x, y, z
"""
# coordinate transmation
O = trs_para[0]
alpha = trs_para[1]
# read data
readDir = jobDir + '/data/'
readName = prbg
fr = open(readDir + readName, 'rb')
data_org = pickle.load(fr)
fr.close()
coors = data_org['coors']
# coordinate transformation
prbNum = coors.shape[0]
for p in range(prbNum):
tmp = data_org[var][p]
data_org[var][p] = funcs.trs(tmp, O, alpha)
xSeq = np.array(data_org['info'][2])
ySeq = np.array(data_org['info'][3])
zSeq = np.array(data_org['info'][4])
xNum = xSeq.size
yNum = ySeq.size
zNum = zSeq.size
varSeq = data_org[var][:, :, varD]
tSeq = data_org['time']
tNum = tSeq.size
return tSeq, xSeq, ySeq, zSeq, varSeq, coors
def getSliceData_Nz_sowfa(jobDir, slice, var, varD, trs_para, tInd, xcoor,
ycoor):
"""
Extract data from specified slice_Nz
INPUT
jobDir: job directory, e.g. '/scratch/sowfadata/pp/tutorials/example_nbl'
slice: the name of the slice, e.g. 'Nz2'
var: name of the target variable, e.g. 'U'
varD: the dimension of which the target vector variable will be outputted
trs_para: coordinate transform parameters, e.g. ((0,0,0),30.0), the origin and the conterclockwise rotation degree
tInd: a tuple containing the start time index and end time index of the output data (not including the end tInd)
xcoor: a tuple containing the start x, end x and x.size of the output data
ycoor: a tuple containing the start y, end y and y.size of the output data
OUTPUT
tSeq: 1D array of times
ySeq: 1D array of y positions
xSeq: 1D array of x positions
H: the height of the slice
varSeq: 3D array of the target variable, 1st dim time, 2nd dim y, 3rd x
"""
readDir = jobDir + '/data/'
readName = slice
fr = open(readDir + readName, 'rb')
data = pickle.load(fr)
fr.close()
slc = sdc.Slice(data, 2)
del data # 2 means z-axis
tSeq = slc.data['time']
tSeq = tSeq[tInd[0]:tInd[1]]
H = slc.N_location # height of this plane
O, alpha = trs_para[0], trs_para[1]
varSeq = []
for t_ind in range(tInd[0], tInd[1]):
print('processing: ' + str(t_ind) + ' ...')
tmp = slc.data[var][t_ind]
tmp = funcs.trs(tmp, O, alpha)
tmp1 = slc.meshITP_Nz(xcoor, ycoor, tmp[:, varD], method_='linear')
xSeq, ySeq, varSeq_ = tmp1[0], tmp1[1], tmp1[2]
varSeq.append(varSeq_)
varSeq = np.array(varSeq)
return tSeq, xSeq, ySeq, H, varSeq
def velo_pr_sowfa(jobDir, trs_para, varD):
"""
Extract horizontal average of velocity at various times and heights based on aveData from SOWFA
INPUT
jobDir: job directory, e.g. '/scratch/sowfadata/pp/tutorials/example_nbl'
trs_para: coordinate transform parameters, e.g. ((0,0,0),30.0), the origin and the conterclockwise rotation degree
varD: the dimension of which the target vector variable will be outputted
OUTPUT:
tSeq: 1D array of times
zSeq: 1D array of heights
varSeq: 2D array of time and horizontal average variables, 1st dim time, 2nd dim height
"""
# coordinate transmation
O = trs_para[0]
alpha = trs_para[1]
fr = open(jobDir + '/data/' + 'aveData', 'rb')
aveData = pickle.load(fr)
fr.close()
zSeq = aveData['H']
zNum = zSeq.size
tSeq = aveData['time']
tNum = tSeq.size
tDelta = tSeq[1] - tSeq[0]
uSeq = aveData['U_mean']
vSeq = aveData['V_mean']
wSeq = aveData['W_mean']
varSeq = np.zeros((tNum, zNum))
for zInd in range(zNum):
tmp = np.concatenate(
(uSeq[:, zInd].reshape(tNum, 1), vSeq[:, zInd].reshape(tNum, 1)),
axis=1)
tmp = np.concatenate((tmp, wSeq[:, zInd].reshape(tNum, 1)), axis=1)
tmp_ = funcs.trs(tmp, O, alpha)
varSeq[:, zInd] = tmp_[:, varD]
return tSeq, zSeq, varSeq
plotDataList = []
HList = []
for slice in sliceList:
readDir = ppDir + '/data/'
readName = slice
fr = open(readDir + readName, 'rb')
data_org = pickle.load(fr)
fr.close()
slc = sdc.Slice(data_org, 2)
H = slc.N_location # height of this plane
HList.append(H)
tmp = slc.data[var][tInd][:]
tmp = funcs.trs(tmp, O, alpha)
slcData = slc.meshITP_Nz((0, 2000, 100), (0, 2000, 100),
tmp[:, varD],
method_='linear')
ySeq = slcData[1]
yNum = slcData[1].size
xNum = slcData[0].size
PSD_list = []
for xInd in range(xNum):
# detrend by deg_ order plynomial fit
deg_ = 1
polyFunc = np.poly1d(np.polyfit(ySeq, slcData[2][xInd], deg=deg_))
tmp = slcData[2][xInd] - polyFunc(ySeq)
tmp = tmp - tmp.mean()
plotDataList = []
HList = []
for slice in sliceList:
readDir = ppDir + '/data/'
readName = slice
fr = open(readDir + readName, 'rb')
data_org = pickle.load(fr)
fr.close()
slc = sdc.Slice(data_org, 2)
H = slc.N_location # height of this plane
HList.append(H)
var_av = slc.get_ave(var)
var_av = funcs.trs(var_av, O, alpha)
xcoor, ycoor, u_seq = slc.meshITP_Nz((0, 2000, 100), (0, 2000, 100),
var_av[:, varD],
method_='linear')
plotData = (xcoor, ycoor, u_seq)
plotDataList.append(plotData)
### group plot
rNum, cNum = (4, 2)
fig, axs = plt.subplots(rNum, cNum, constrained_layout=True)
fig.set_figwidth(8)
fig.set_figheight(12)
uwSeq = aveData['uw_mean']
vwSeq = aveData['vw_mean']
wwSeq = aveData['ww_mean']
R13Seq = aveData['R13_mean']
R23Seq = aveData['R23_mean']
R33Seq = aveData['R33_mean']
rsvSeq = np.zeros((tNum, zNum))
sgsSeq = np.zeros((tNum, zNum))
for zInd in range(zNum):
tmp = np.concatenate(
(uwSeq[:, zInd].reshape(tNum, 1), vwSeq[:, zInd].reshape(tNum, 1)),
axis=1)
tmp = np.concatenate((tmp, wwSeq[:, zInd].reshape(tNum, 1)), axis=1)
tmp_ = funcs.trs(tmp, O, alpha)
rsvSeq[:, zInd] = tmp_[:, varD]
tmp = np.concatenate(
(R13Seq[:, zInd].reshape(tNum, 1), R23Seq[:, zInd].reshape(tNum, 1)),
axis=1)
tmp = np.concatenate((tmp, R33Seq[:, zInd].reshape(tNum, 1)), axis=1)
tmp_ = funcs.trs(tmp, O, alpha)
sgsSeq[:, zInd] = tmp_[:, varD]
### plot
ave_itv = 3600.0 # by default, the averaging interval is 3600s
tplot = 72000.0
rsvplot = np.zeros(zNum)
sgsplot = np.zeros(zNum)
fr = open(readDir + readName, 'rb')
data_org = pickle.load(fr)
fr.close()
slc = sdc.Slice(data_org, 2)
H = slc.N_location # height of this plane
HList.append(H)
pNum = slc.data['pNo'].size
autocorr_list = []
for pInd in range(pNum):
print(pInd)
# coordinate transformation
U_ = slc.data[var][:, pInd]
U_ = funcs.trs(U_, O, alpha)
# interpolate
vSeq = U_[:, varD]
f = interp1d(tSeq, vSeq, fill_value='extrapolate')
v_seq = f(t_seq)
# detrend
v_seq = funcs.detrend(t_seq, v_seq)
tau_seq, tmp = funcs.corr(t_seq, v_seq, v_seq)
autocorr_list.append(tmp)
autocorr_seq = np.average(np.array(autocorr_list), axis=0)
plotDataList.append((tau_seq, autocorr_seq))
zNum = len(HList)
# plot
fig, ax = plt.subplots(figsize=(6, 6))
xNum = int((xMax - xMin) / dx + 1)
x = np.linspace(xMin, xMax, xNum)
f = open("/scratch/ppcode/sowfa/tmp/probesNx", "w")
for zInd in range(zNum):
z = zList[zInd]
coors = np.zeros((xNum, 3))
for i in range(xNum):
coors[i][0], coors[i][1], coors[i][2] = x[i], y, z
# probes's coordinate transmation
O = ((xMin + xMax) / 2, y, z)
coors_ = funcs.trs(coors, O, alpha)
for r in range(xNum):
coors_[r] += O
f.write('\n')
f.write(' ' + 'probeNx' + str(zInd) + '\n')
f.write(' {' + '\n')
f.write(' type probes;' + '\n')
f.write(' functionObjectLibs ("libsampling.so");' + '\n')
f.write(' outputControl runTime;' + '\n')
f.write(' writeInterval 0.1;' + '\n')
f.write(' timeStart 0.0;' + '\n')
f.write(' fields' + '\n')
f.write(' (' + '\n')
f.write(' U' + '\n')
f.write(' );' + '\n')
""" wind direction profile of stationary flow (sowfa vs palm) """
fig, ax = plt.subplots(figsi""" animation for deepwind_gs10 (SOWFA) """
prjDir = '/scratch/sowfadata/JOBS'
prjName = 'deepwind'
jobName = 'gs10'
ppDir = '/scratch/sowfadata/pp/' + prjName + '/' + jobName
tSeq, xSeq, ySeq, H, varSeq = getSliceData_Nz_sowfa(ppDir, jobName, 'Nz0', 'U', 0, ((0,0,0),30), (0,150), (0,2560,256), (0,2560,256))
t0 = tSeq[0]
vMin, vMax, vDelta = (-2, 2, 0.4)
cbreso = 100 # resolution of colorbar
levels = np.linspace(vMin, vMax, cbreso + 1)
# transform coors of prbg
prbg0 = np.vstack((780 + 20*np.arange(0,51), np.array([1280 for i in range(51)]), np.array([0 for i in range(51)])))
prbg0 = funcs.trs(prbg0.T, (1280,1280,0), -30); prbg0[:,0] += 1280; prbg0[:,1] += 1280;
prbg1 = np.vstack((np.array([1280 for i in range(51)]), 780 + 20*np.arange(0,51), np.array([0 for i in range(51)])))
prbg1 = funcs.trs(prbg1.T, (1280,1280,0), -30); prbg1[:,0] += 1280; prbg1[:,1] += 1280;
for tInd in range(0,150,100):
fig, axs = plt.subplots(figsize=(8,8), constrained_layout=False)
x_ = xSeq
y_ = ySeq
v_ = varSeq[tInd]
v_ -= v_.mean()
v_[np.where(v_ < vMin)] = vMin
v_[np.where(v_ > vMax)] = vMax
CS = axs.contourf(x_, y_, v_, cbreso, levels=levels, cmap='jet', vmin=vMin, vmax=vMax)
plt.scatter(prbg0[:,0], prbg0[:,1], 1, marker='o', color='k')
plt.scatter(prbg1[:,0], prbg1[:,1], 1, marker='o', color='k')
readName = slice
fr = open(readDir + readName, 'rb')
data_org = pickle.load(fr)
fr.close()
slc = sdc.Slice(data_org, 2)
H = slc.N_location # height of this plane
tIndList = list(range(0, 600, 10))
tNum = len(tIndList)
for tInd in tIndList:
print(tInd)
tmp = slc.data[var][tInd]
tmp = funcs.trs(tmp, O, alpha) # coordinate transformation
plotData = slc.meshITP_Nz((0, 2000, 100), (0, 2000, 100),
slc.data[var][tInd][:, varD],
method_='linear')
plotDataList.append(plotData)
vMin, vMax, vDelta = (-1.8, 1.8, 0.4)
cbreso = 100 # resolution of colorbar
levels = np.linspace(vMin, vMax, cbreso + 1)
for tIndInd in range(tNum):
fig, axs = plt.subplots(figsize=(8, 8), constrained_layout=True)
x_ = plotDataList[tIndInd][0]
y_ = plotDataList[tIndInd][1]
v_ = plotDataList[tIndInd][2]
ppDir = '/scratch/sowfadata/pp/' + prjName + '/' + jobName
tSeq, xSeq, ySeq, H, varSeq = getSliceData_Nz_sowfa(ppDir, jobName, 'Nz2', 'U',
0, ((0, 0, 0), 30),
(0, 30), (680, 1880, 240),
(680, 1880, 240))
t0 = tSeq[0]
vMin, vMax, vDelta = (-2, 2, 0.4)
cbreso = 100 # resolution of colorbar
levels = np.linspace(vMin, vMax, cbreso + 1)
# transform coors of prbg
prbg0 = np.vstack(
(780 + 20 * np.arange(0, 51), np.array([1280 for i in range(51)]),
np.array([0 for i in range(51)])))
prbg0 = funcs.trs(prbg0.T, (1280, 1280, 0), -30)
prbg0[:, 0] += 1280
prbg0[:, 1] += 1280
prbg1 = np.vstack(
(np.array([1280 for i in range(51)]), 780 + 20 * np.arange(0, 51),
np.array([0 for i in range(51)])))
prbg1 = funcs.trs(prbg1.T, (1280, 1280, 0), -30)
prbg1[:, 0] += 1280
prbg1[:, 1] += 1280
for tInd in range(0, 30, 1):
fig, axs = plt.subplots(figsize=(4.5, 3.8), constrained_layout=False)
x_ = xSeq
y_ = ySeq
v_ = varSeq[tInd]
v_ -= v_.mean()
varName = 'Su'
varUnit = r'$\mathrm{m^2/s}$'
varName_save = 'Su'
# read data
readDir = ppDir + '/data/'
readName = prbg
fr = open(readDir + readName, 'rb')
data_org = pickle.load(fr)
fr.close()
# coordinate transformation
prbNum = data_org['coors'].shape[0]
for p in range(prbNum):
tmp = data_org[var][p]
data_org[var][p] = funcs.trs(tmp, O, alpha)
# choose the probegroup to be used in plotting
xSeq = np.array(data_org['info'][2])
ySeq = np.array(data_org['info'][3])
zSeq = np.array(data_org['info'][4])
xNum = xSeq.size
yNum = ySeq.size
zNum = zSeq.size
t_start = 432000.0
t_end = 434400.0
t_delta = 0.1
fs = 1 / t_delta # sampling frequency
t_num = int((t_end - t_start) / t_delta + 1)
t_seq = np.linspace(t_start, t_end, t_num)
K = 1
else:
K = len(zList)
''' save (I,J,K) into a binary file with pickle '''
f = open(jobDir + '/data/' + prbgName + '_info', 'wb')
pickle.dump([O, alpha, xList, yList, zList], f)
f.close()
coorList = []
for k in range(K):
for j in range(J):
for i in range(I):
coorList.append((xList[i], yList[j], zList[k]))
coors = np.array(coorList)
coors = funcs.trs(coors, O, alpha)
for p in range(I * J * K):
coors[p] += O
f = open(jobDir + '/data/' + prbgName + '_dict', "w")
f.write('\n')
f.write(' ' + prbgName + '\n')
f.write(' {' + '\n')
f.write(' type probes;' + '\n')
f.write(' functionObjectLibs ("libsampling.so");' + '\n')
f.write(' interpolationScheme cellPointFace;' + '\n')
f.write(' outputControl runTime;' + '\n')
f.write(' writeInterval 0.5;' + '\n')
f.write(' timeStart 0.0;' + '\n')
f.write(' fields' + '\n')
f.write(' (' + '\n')
