#fname = 'ThawData_Yr10_All.csv'
#with open(fname, 'wb') as csvFile:
# writer = csv.writer(csvFile, delimiter=',')
for i in range(len(runPrefixList)):
y = np.nan * np.ones([nruns, len(trange)], 'd')
for j in range(nruns):
directory = runPrefixList[i] + '_' + runDate + '.' + str(j + 1)
isDir = os.path.isdir(os.getcwd() + '/' + directory)
if not isDir:
continue
keys, times, dat = parse_ats.readATS(directory,
"visdump_data.h5",
timeunits='yr')
col_dat = transect_data.transect_data(['saturation_ice'],
keys=np.s_[trange],
directory=directory)
times_subset = times[trange]
#col_dat structure:
#col 1 = variable
#col 2 = time
#col 3 = x position
#col 4 = z position
z_surf = col_dat[1, 0, :, -1] + (
col_dat[1, 0, :, -1] - col_dat[1, 0, :, -2]
) / 2. # average of the uppermost and second-to-uppermost rows, shifted up to the top row
z_bott = col_dat[1, 0, :, 0] - (
col_dat[1, 0, :, 1] - col_dat[1, 0, :, 0]
) / 2. # average of the bottom and second-to-bottom rows, shifted up to the bottom row
for k in range(len(trange)):
where_unsat = np.where(col_dat[2, k, xPos, :] == 1)[0]
## Testing the datetime functionality
dt = datetime.fromordinal(733828)
dt
dt.strftime('%Y%m%d')
directory = "./hillslope-test-allMinColumn/"
keys, times, dat = parse_ats.readATS(directory, "visdump_data.h5", timeunits='yr')
## SELECT TIME INTERVAL TO PULL AND PLOT:
# Visdumps are spit out every 30 days. Index 0 is January of year 1; 12 is January year 2; etc...
ind_start = 0
ind_end = -1 # if you want it to run to the end of the list, type '-1' for the last item on the list.
ind_int = 90 # there is one checkpoint file every 30 days
col_dat = transect_data.transect_data(['saturation_gas','saturation_ice'], keys=np.s_[ind_start:ind_end:ind_int], directory=directory)
times_subset = times[ind_start:ind_end:ind_int]
print times_subset
nvar, nt, nx, nz = col_dat.shape
print nt
n = 12 # length of a year in python indeces
m = (nt)/(n) # determine how many years we're viewing, adding 1 because python indexes at 0
print m,n
# z_surf and z_bott is extrapolated based on dz, and is not necessarily exact if
# dz varies in the top (respectively bottom) two cells
z_surf = col_dat[1,0,:,-1] + (col_dat[1,0,:,-1] - col_dat[1,0,:,-2])/2. # average of the uppermost and second-to-uppermost rows, shifted up to the top row
z_bott = col_dat[1,0,:,0] - (col_dat[1,0,:,1] - col_dat[1,0,:,0])/2. # average of the bottom and second-to-bottom rows, shifted up to the bottom row
def water_table2D(dirname, datum=None, location='center', v86=False, patm=101325.):
prefix = "" if v86 else "surface-"
# get the ponded depth
keys,times,dats = parse_ats.readATS(dirname, "visdump_surface_data.h5")
pd = parse_ats.get2DSurfaceData(keys, dats, prefix+"ponded_depth")
if location == 'center':
center = len(dats[prefix+"elevation.cell.0"][keys[0]])
elif location == 'trough':
center = 1
print ('Location: ', location, center)
elev_cell = dats[prefix+"elevation.cell.0"][keys[0]][center-1]
dats.close()
print ('Datum (surface elevation)', elev_cell)
if datum is None:
datum = elev_cell
datum_offset = elev_cell - datum
# get the columnar pressure
water_table = np.zeros((len(keys),),'d')
thaw_depth = np.zeros((len(keys),),'d')
col_dat = transect_data.transect_data(['pressure', 'temperature'], directory=dirname)
vars = 2
nvar, cycles, xnum, znum = col_dat.shape
#print xnum
xnum = center
#print xnum
for k in range(col_dat.shape[1]):
if pd[k, xnum-1] > 0:
water_table[k] = pd[k, xnum-1] + datum_offset
#wt_surf[k] = pd[k, xnum-1] + datum_offset
#print 'surface:: ',k, pd[k, xnum-1]
else:
#print 'subsurface: ', k, pd[k, xnum-1]
wt_index = np.where(col_dat[vars ,k,xnum-1, :] >= patm)[0] #var 1
temp_index = np.where(col_dat[vars+1 ,k,xnum-1, :] >= 273.25)[0] ##
temp_data = col_dat[vars + 1,k,xnum-1, :] #var 2
if len(wt_index) is 0:
wt_index = 0
else:
wt_index = wt_index[-1]
if (len(temp_index) is 0):
temp_index =0
else:
temp_index = temp_index[0]
if (temp_data[wt_index] > 273.25):
water_table[k] = col_dat[1,k,xnum-1, wt_index] - datum
temp_index = np.where(col_dat[vars+1 ,k,xnum-1, :] >= 273.2)[0] ##
#print col_dat[vars+1 ,k,xnum-1, :], round(times[k]*365.25,2), k
if len(temp_index) > 0:
#print 'ALT: ', len(temp_index), temp_index[0], col_dat[vars+1 ,k,xnum-1, temp_index[0]]
thaw_depth[k] = col_dat[1,k,xnum-1, temp_index[0]] - datum
return times, water_table, thaw_depth, elev_cell
def water_table(dirname, datum=None, v86=False, patm=101325.):
prefix = "" if v86 else "surface-"
# get the ponded depth
keys,times,dats = parse_ats.readATS(dirname, "visdump_surface_data.h5")
pd = parse_ats.get2DSurfaceData(keys, dats, prefix+"ponded_depth")
center = len(dats[prefix+"elevation.cell.0"][keys[0]])
print ('Center location: ', center)
elev_surf = dats[prefix+"elevation.cell.0"][keys[0]][center-1]
dats.close()
print ('Datum (surface elevation)', elev_surf)
if datum is None:
datum = elev_surf
datum_offset = elev_surf - datum
# get the columnar pressure
wt = np.zeros((len(keys),),'d')
wt_ss = np.zeros((len(keys),),'d')
wt_surf = np.zeros((len(keys),),'d')
thaw_depth = np.zeros((len(keys),),'d')
wt_bottom = np.zeros((len(keys),),'d')
col_dat = transect_data.transect_data(['pressure', 'temperature'], directory=dirname)
vars = 2
nvar, cycles, xnum, znum = col_dat.shape
for k in range(col_dat.shape[1]):
if pd[k, xnum-1] > 0:
wt[k] = pd[k, xnum-1] + datum_offset
wt_surf[k] = pd[k, xnum-1] + datum_offset
else:
wt_index = np.where(col_dat[vars ,k,xnum-1, :] >= patm)[0] #var 1
temp_index = np.where(col_dat[vars+1 ,k,xnum-1, :] >= 273.25)[0] ##
temp_data = col_dat[vars + 1,k,xnum-1, :] #var 2
wt_index0 = wt_index
if len(wt_index) is 0:
wt_index = 0
wt_index0 = 0
else:
wt_index = wt_index[-1]
if (len(wt_index0) > 2):
wt_index0 = wt_index0[-2]
if (len(temp_index) is 0):
temp_index =0
else:
temp_index = temp_index[0]
if (temp_data[wt_index] > 273.25):
wt[k] = col_dat[1,k,xnum-1, wt_index] - datum
else:
wt_ss[k] = col_dat[1,k,xnum-1, temp_index] - datum
temp_index = np.where(col_dat[vars+1 ,k,xnum-1, :] >= 273.25)[0] ##
if len(temp_index) > 0:
thaw_depth[k] = col_dat[1,k,xnum-1, temp_index[0]] - datum
return times, wt, wt_ss, wt_bottom, thaw_depth
fig, axes = plt.subplots(4,2,sharex=True)
#fname = 'ThawData_Yr10_All.csv'
#with open(fname, 'wb') as csvFile:
# writer = csv.writer(csvFile, delimiter=',')
for i in range(len(runPrefixList)):
y = np.nan*np.ones([nruns,len(trange)],'d')
for j in range(nruns):
directory = runPrefixList[i] + '_' + runDate + '.' + str(j+1)
isDir = os.path.isdir(os.getcwd() + '/' + directory)
if not isDir:
continue
keys, times, dat = parse_ats.readATS(directory, "visdump_data.h5", timeunits='yr')
col_dat = transect_data.transect_data(['saturation_gas','porosity','cell_volume',], keys=np.s_[trange], directory=directory)
times_subset = times[trange]
#col_dat structure:
#col 1 = variable
#col 2 = time
#col 3 = x position
#col 4 = z position
z_surf = col_dat[1,0,:,-1] + (col_dat[1,0,:,-1] - col_dat[1,0,:,-2])/2. # average of the uppermost and second-to-uppermost rows, shifted up to the top row
z_bott = col_dat[1,0,:,0] - (col_dat[1,0,:,1] - col_dat[1,0,:,0])/2. # average of the bottom and second-to-bottom rows, shifted up to the bottom row
yTemp = col_dat[2,:,:,:]*col_dat[3,:,:,:]*col_dat[4,:,:,:] # calculate the empty pore volume of each celll (gas saturation*porosity*cell volume at all locations)
zSum = np.sum(yTemp,2) # sum the 2nd dimension of the yTemp matrix to get the total empty pore volume in EACH COLUMN with depth
xSum = np.sum(zSum,1) # sum the 1st dimension of the zSum matrix to get the total empty pore volume in EACH DOMAIN at every point in time
y[j,:] = xSum
if(j<8):
cVal = 'k'
elif(j<16):
