def test_afile_writeread_nomask(self):
idm = random.randrange(10, 5000)
jdm = random.randrange(10, 5000)
#print "Creating %dX%d random arrays"%(idm,jdm)
scale = 1e4
wfld1 = numpy.random.rand(jdm, idm) * scale
wfld2 = numpy.random.rand(jdm, idm) * scale
wfld3 = numpy.random.rand(jdm, idm) * scale
afile = abfile.AFile(idm, jdm, "test.a", "w")
afile.writerecord(wfld1, None, record=None)
afile.writerecord(wfld2, None, record=None)
afile.writerecord(wfld3, None, record=None)
afile.close()
afile = abfile.AFile(idm, jdm, "test.a", "r")
rfld1 = afile.read_record(record=0)
rfld2 = afile.read_record(record=1)
rfld3 = afile.read_record(record=2)
afile.close()
maxdiff1 = numpy.abs(numpy.amax(rfld1 - wfld1)) / scale
maxdiff2 = numpy.abs(numpy.amax(rfld2 - wfld2)) / scale
maxdiff3 = numpy.abs(numpy.amax(rfld3 - wfld3)) / scale
#print maxdiff1,maxdiff2,maxdiff3
if all([elem < 1e-7 for elem in [maxdiff1, maxdiff2, maxdiff3]]):
#print "afile io test passed for %d x %d array " %(idm,jdm)
pass
else:
self.fail(
"AFile IO failed. MAx diff between read/written: %14.7g" %
max([maxdiff1, maxdiff2, maxdiff3]))
ch.setLevel(_loglevel)
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.propagate = False
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='')
parser.add_argument('idm', type=int, help='Grid dimension 1st index []')
parser.add_argument('jdm', type=int, help='Grid dimension 2nd index []')
parser.add_argument('filename', help="")
parser.add_argument('records', nargs="+", type=int)
args = parser.parse_args()
#afile = modeltools.hycom.io.AFile(args.idm,args.jdm,args.filename,"r")
afile = abfile.AFile(args.idm, args.jdm, args.filename, "r")
cmap = matplotlib.pyplot.get_cmap("jet")
#cmap=matplotlib.pyplot.get_cmap("YlOrRd")
for record in args.records:
fld = afile.read_record(record)
figure = matplotlib.pyplot.figure(figsize=(8, 8))
ax = figure.add_subplot(111)
#P=ax.pcolormesh(fld)
#P=ax.pcolormesh(fld[2200:2800,3500:4500],cmap=cmap)
P = ax.pcolormesh(fld, cmap=cmap)
ax.figure.colorbar(P)
#figure.colorbar(P,norm=matplotlib.colors.LogNorm(vmin=w5.min(), vmax=w5.max()))
def main(rmuwidth,exptid="") :
#fh = Dataset('mld_dr003_l3.nc', mode='r')
#fh_lons = fh.variables['lon'][:]
#fh_lats = fh.variables['lat'][:]
#fh_time = fh.variables['time'][:]
#fh_mld_clim = fh.variables['mld_dr003_rmoutliers_smth_okrg'][:]
#fh.close()
grdpath='./'
#gfile = abfile.ABFileGrid(jip+"regional.grid","r")
gfile = abfile.ABFileGrid("regional.grid","r")
idm=gfile.idm
jdm=gfile.jdm
#gfile = abfile.ABFileGrid("regional.grid","r")
plon=gfile.read_field("plon")
plat=gfile.read_field("plat")
gfile.close()
dpfile = abfile.ABFileBathy("regional.depth","r",idm=gfile.idm,jdm=gfile.jdm)
depth=dpfile.read_field("depth")
dpfile.close()
# read from nc file
#wdth=40
wdth=rmuwidth
mnn=0.02
mxx=0.125
thkdf4_array=numpy.zeros(depth.shape)
thkdf4_array[:,:]=mnn
ddd_mnn_mxx=np.linspace(mnn, mxx, num=wdth)
ddd_mxx_mnn=numpy.flip(ddd_mnn_mxx[:])
#print("ddd_E=",ddd_mnn_mxx[:])
##AA thkdf4_array[:,:]=0.015
##AA ddd=[(0.015+ii*0.0055) for ii in range(20)]
#print "dd=",ddd[:]
print("thkdf4_array.shape=",thkdf4_array.shape)
for jjj in range(thkdf4_array.shape[0]) :
thkdf4_array[jjj, 0:wdth] =ddd_mxx_mnn[:]
thkdf4_array[jjj,-1*wdth:]=ddd_mnn_mxx[:]
for iii in range(thkdf4_array.shape[1]):
thkdf4_array[0:wdth ,iii]=ddd_mxx_mnn[:]
thkdf4_array[-1*wdth:,iii]=ddd_mnn_mxx[:]
#
#fix lower left corner
for iii in range(wdth):
for jjj in range(iii,wdth):
thkdf4_array[jjj,iii]=ddd_mxx_mnn[iii]
#print(thkdf4_array[jjj,iii])
#fix upper left corner
for iii in range(wdth):
for jjj in range(jdm-iii-1,jdm-wdth-1,-1):
thkdf4_array[jjj,iii]=ddd_mxx_mnn[iii]
#fix lower right corner
LL = range(idm-wdth,idm)
LL_r=LL[::-1]
for iii in LL_r[:]:
for jjj in range(idm-iii-1,wdth):
#print("jjj,iii=",jjj,iii)
thkdf4_array[jjj,iii]=ddd_mxx_mnn[idm-iii-1]
#fix upper right corner
for iii in LL_r[:]:
for jjj in range(jdm-1-(idm-iii-1),jdm-1-wdth,-1):
thkdf4_array[jjj,iii]=ddd_mxx_mnn[idm-iii-1]
#print '-----------------'
#write thkdf4 to ab file
af = abfile.AFile(plon.shape[1],plon.shape[0],"thkdf4.a","w")
hmin,hmax = af.writerecord(thkdf4_array,None,record=0)
af.close()
print("thkdf4: range = %14.6e%14.6e\n"%(hmin,hmax))
bf = open("thkdf4.b","w")
bf.write("thkdf4: range = %14.6e%14.6e\n"%(hmin,hmax))
bf.close()
#print '-----------------'
#write veldf4 to ab file
af1 = abfile.AFile(plon.shape[1],plon.shape[0],"veldf4.a","w")
hmin,hmax = af1.writerecord(thkdf4_array,None,record=0)
af1.close()
print("veldf4: range = %14.6e%14.6e\n"%(hmin,hmax))
bf1 = open("veldf4.b","w")
bf1.write("veldf4: range = %14.6e%14.6e\n"%(hmin,hmax))
bf1.close()
#print '-----------------'
print("write to NC file------")
ncfilename='thkdf4.nc'
rootgrp = Dataset(ncfilename, "w", format="NETCDF4")
logger.info("output to ncfile in %s"%ncfilename)
#dimension
lat = rootgrp.createDimension("lat", gfile.jdm)
lon = rootgrp.createDimension("lon", gfile.idm)
#variable
thkdf4_v = rootgrp.createVariable("thkdf4","f4",("lat","lon",))
print('thkdf4_v.shape=',thkdf4_v.shape)
thkdf4_v[:,:]=thkdf4_array[:,:]
rootgrp.close()
def main(region,experiment,tracer,month,cmap,clim,workdir):
user = getpass.getuser()
if region == 'TP0' or region == 'TP2' or region == 'TP5' or region == 'NAT' :
version = 'new'
elif region == 'TP4' or region == 'NA2':
version = 'old'
else :
print('wrong or not implemented domain is provided')
quit()
if tracer == "nitrate" or tracer == 'phosphate' or \
tracer == 'silicate' or tracer == "volume" :
pass
else :
print('tracer name not correct')
print('choose: nitrate, phosphate, silicate, volume')
quit()
units = "(mgC m$^{-2}$ s$^{-1}$), nutrient converted to C"
if version == "new":
if tracer == "nitrate":
name = "ECO_no3"
elif tracer == "phosphate":
name = "ECO_pho"
elif tracer == "silicate":
name = "ECO_sil"
else:
name = "rivers"
units = "(m s$^{-1}$)"
if version == "old":
if tracer == "nitrate":
name = "rivnitr"
elif tracer == "phosphate":
name = "rivphos"
elif tracer == "silicate":
name = "rivsili"
else:
name = "rivers"
units = "(m s$^{-1}$)"
if region == "TP0":
region = "TP0a1.00"
if region == "TP2":
region = "TP2a0.10"
if region == "TP4":
region = "TP4a0.12"
if region == "TP5":
region = "TP5a0.06"
if region == "NAT":
region = "NATa1.00"
if region == "NA2":
region = "NA2a0.80"
abgrid = abfile.ABFileGrid(workdir + user + "/" + \
region + "/topo/regional.grid","r")
plon=abgrid.read_field("plon")
plat=abgrid.read_field("plat")
jdm,idm=plon.shape
abriver = abfile.AFile(idm,jdm,workdir + user + "/" + \
region + "/force/rivers/" + experiment + \
"/" + name + ".a","r")
river = abriver.read_record(int(month)-1)
abriver.close()
if version == "old":
if tracer == "nitrate":
river = river * 12.01 * 6.625 / 14.007
elif tracer == "phosphate":
river = river * 12.01 * 106. / 30.97
elif tracer == "silicate":
river = river * 12.01 * 106. / 28.09
abdepth = abfile.ABFileBathy(workdir + user + "/" + \
region + "/force/rivers/SCRATCH/regional.depth.b","r",idm=idm,jdm=jdm)
depthm=abdepth.read_field("depth")
river = np.ma.masked_where(depthm.mask,river)
# now plot
fig=plt.figure(figsize=(6,5),facecolor='w')
ax = fig.add_subplot(1,1,1)
ax.set_position([0.01,0.02,0.865,0.96])
cmap = plt.get_cmap(cmap)
ax.set_facecolor('xkcd:gray')
pmesh = plt.pcolormesh(river,cmap=cmap)
cb=ax.figure.colorbar(pmesh)
if clim is not None : pmesh.set_clim(clim)
plt.text(0.015,1.05, "%s %s %s %s" %("river", tracer, "flux month:",\
month.zfill(2)),transform=ax.transAxes,fontsize=13)
plt.text(0.015,1.005, units,transform=ax.transAxes,fontsize=8)
# save figure
fig.canvas.print_figure(workdir + user + "/" + \
region + "/force/rivers/" + experiment + \
"/" + name + ".png",dpi=180)
def main():
# Read plon,plat
gfile = abfile.ABFileGrid("regional.grid", "r")
plon = gfile.read_field("plon")
plat = gfile.read_field("plat")
gfile.close()
# Read input bathymetry
bfile = abfile.ABFileBathy("regional.depth",
"r",
idm=gfile.idm,
jdm=gfile.jdm,
mask=True)
in_depth_m = bfile.read_field("depth")
bfile.close()
#in_depth=numpy.ma.filled(in_depth_m,bathy_threshold)
# Starting point (State 1 for Atlantic)
kapref = numpy.ones(plat.shape) * 1.0
print kapref.min(), kapref.max()
# Find regions north of northern limit. Assumes
for segment in range(len(northern_limit_longitudes)):
ind1 = segment
ind2 = (segment + 1) % len(northern_limit_longitudes)
lo1 = northern_limit_longitudes[ind1]
la1 = northern_limit_latitudes[ind1]
lo2 = northern_limit_longitudes[ind2]
la2 = northern_limit_latitudes[ind2]
tmp1 = numpy.mod(plon + 360 - lo1, 360.)
tmp2 = numpy.mod(lo2 + 360 - lo1, 360.)
J = tmp1 <= tmp2
#print numpy.count_nonzero(J)
# Linear weights and latitude in selected points
w2 = tmp1 / tmp2
w1 = 1. - w2
la = la2 * w2 + la1 * w1
kapref[J] = numpy.where(plat[J] > la[J], 2.0, kapref[J])
import scipy.ndimage
kapref = scipy.ndimage.gaussian_filter(kapref, sigma=20)
#print in_depth_m.min(),type(in_depth_m)
kaprefplot = numpy.ma.masked_where(in_depth_m.mask, kapref)
figure = matplotlib.pyplot.figure()
ax = figure.add_subplot(111)
P = ax.pcolormesh(kaprefplot)
figure.colorbar(P, ax=ax)
figure.canvas.print_figure("kapref.png")
af = abfile.AFile(plon.shape[1], plon.shape[0], "tbaric.a", "w")
hmin, hmax = af.writerecord(kapref, None, record=0)
af.close()
bf = open("tbaric.b", "w")
bf.write("tbaric.b\n")
bf.write("\n")
bf.write("\n")
bf.write("\n")
bf.write("i/jdm = %5d %5d\n" % (plon.shape[1], plon.shape[0]))
bf.write("tbaric: range = %14.6e%14.6e\n" % (hmin, hmax))
bf.close()
plat=gfile.read_field("plat")
#
#
#read lons from rivers.nc form A-Ehype
#file_AEhype_river_nc="./SCRATCH_Ahype/rivers.nc"
#fh_ahype = Dataset(file_AEhype_river_nc, mode='r')
lons=plon
lats=plat
#lons = fh_ahype.variables['modlon'][:]
#lats = fh_ahype.variables['modlat'][:]
#fh_ahype.close()
#----------------- read from ab file
#---- ERAI TRIP
#file_TRIP_river="./SCRATCH_ERAI-TRIP/rivers.a"
file_TRIP_river=args.Trip_river_afile
afile_trip = abfile.AFile(800,760,file_TRIP_river,"r")
# compute monthly flux:
#tripafil_discharge_month = np.NaN * np.ones([12,])
Greenland_trip_discharge_month = np.NaN * np.ones([12,])
Greenland_trip_riverflux=np.zeros((12,760,800))
Green_trip_total_riverflux=np.zeros(lons.shape)
countr=0
for record in range(12) :
trp_fld = afile_trip.read_record(record)
Gr_trp_fld=greenlandmask(trp_fld,lats,lons)
Greenland_trip_riverflux[record,:,:]=Gr_trp_fld
## convert from m^3/s km^3/year
Gr_trp_fld=Gr_trp_fld*(365*86400.)*1e-9/12
Greenland_trip_discharge_month[record] = np.nansum(Gr_trp_fld*scpx*scpy)
#uns_discharge_month[record] = np.nansum(uns_riverflux[record,:,:]*scpx*scpy)
Green_trip_total_riverflux=Green_trip_total_riverflux+Gr_trp_fld*scpx*scpy
def main(path):
print(path)
# coordinates for the river mouth based on the grid location on TP5 domain
river_lat = 66.37281
river_lon = 71.15888
# coordinates for the original river discharge effective area
#(lat,lon)_1 = 71.496376,72.00326
#(lat,lon)_2 = 70.26682,82.36286
#(lat,lon)_3 = 62.861946,67.83625
#(lat,lon)_4 = 62.23391,75.26198
abgrid = abfile.ABFileGrid(path + "../../../topo/regional.grid", "r")
plon = abgrid.read_field("plon")
plat = abgrid.read_field("plat")
jdm, idm = plon.shape
abdepth = abfile.ABFileBathy(path + "../../../topo/regional.depth.b",
"r",
idm=idm,
jdm=jdm)
depthm = abdepth.read_field("depth")
#
cooINDEX = abs(plat - river_lat) + abs(plon - river_lon)
la, lo = np.unravel_index(cooINDEX.argmin(), cooINDEX.shape)
#
cooINDEX = abs(plat - 71.496376) + abs(plon - 72.00326)
la1, lo1 = np.unravel_index(cooINDEX.argmin(), cooINDEX.shape)
cooINDEX = abs(plat - 70.26682) + abs(plon - 82.36286)
la2, lo2 = np.unravel_index(cooINDEX.argmin(), cooINDEX.shape)
cooINDEX = abs(plat - 62.861946) + abs(plon - 67.83625)
la3, lo3 = np.unravel_index(cooINDEX.argmin(), cooINDEX.shape)
cooINDEX = abs(plat - 62.23391) + abs(plon - 75.26198)
la4, lo4 = np.unravel_index(cooINDEX.argmin(), cooINDEX.shape)
dist = np.zeros((plon.shape))
for j in range(plon.shape[0] - 1):
for i in range(plon.shape[1] - 1):
dist[j, i] = distance_on_unit_sphere(plat[j, i], plon[j, i],
plat[la, lo], plon[la, lo])
dist = np.ma.masked_where(depthm < 0., dist)
dist = np.ma.masked_where(plat >= 74., dist)
dist = np.ma.masked_where(plon <= 70., dist)
dist = np.ma.masked_where(plon >= 90., dist)
dist = dist / dist.max()
dist = np.abs(dist - 1.)
totaldist = np.sum(dist)
# now modify rivers for 3 nutrients
for varib in ['no3', 'pho', 'sil']:
if varib == 'no3': longname = 'nitrate'
if varib == 'pho': longname = 'phosphate'
if varib == 'no3': longname = 'silicate'
outfile=abfile.ABFileRiver(path + "ECO_"+varib+"_new.a","w",idm=idm,jdm=jdm,\
cline1='River '+longname+' fluxes (Ob River dispersed out the bay)',\
cline2='mgC m-2 s-1')
outfile.write_header()
abriver = abfile.AFile(idm, jdm, path + "ECO_" + varib + ".a", "r")
for month in range(12):
river = abriver.read_record(month)
river_modified = abriver.read_record(month)
river = np.ma.masked_where(depthm < 0., river)
river_modified = np.ma.masked_where(depthm < 0., river_modified)
unit = np.sum(river[la1:la2, lo2:lo4]) / totaldist
final = unit * dist
river_modified[la1:la2,
lo2:lo4][~river_modified[la1:la2,
lo2:lo4].mask] = 0.
river_modified[
~final.mask] = river_modified[~final.mask] + final[~final.mask]
print("original total mass: " + str(np.sum(river)) +
" modified total mass: " + str(np.sum(river_modified)))
outfile.write_field(river_modified, None, "river " + longname,
month + 1)
abriver.close()
outfile.close()
origAfile = path + "ECO_" + varib + ".a"
origBfile = path + "ECO_" + varib + ".b"
oldAfile = path + "ECO_" + varib + "_orig.a"
oldBfile = path + "ECO_" + varib + "_orig.b"
newAfile = path + "ECO_" + varib + "_new.a"
newBfile = path + "ECO_" + varib + "_new.b"
os.rename(origAfile, oldAfile)
os.rename(origBfile, oldBfile)
os.rename(newAfile, origAfile)
os.rename(newBfile, origBfile)