def _create_df_with_sats(time1, time2, ts, satname, satnames, **kw):
"""
Create a DataFrame with one TS per `satellite`.
time1, time2 : integer representation of time: YYYYMMDD
Note: i,j = y,x <<<<< important!
"""
dtime1 = ap.num2date(time1)
dtime2 = ap.num2date(time2)
dtime_range = full_dtime_range(dtime1, dtime2, **kw)
#dtime_range = full_dtime_range(dtime1.min(), dtime2.max(), **kw)
df = pn.DataFrame(index=dtime_range, columns=satnames) # empty DF
# fill df with 1 `satname` TS at a time
if not np.alltrue(np.isnan(ts)):
for sat in satnames:
ind, = np.where(sat == satname)
df[sat] = pn.Series(ts[ind], index=dtime2[ind])
return df
def create_df_with_ts_by_first(time1, time2, ts, **kw):
"""
Create a DataFrame with all the different time1-TS.
time1, time2 : integer representation of time: YYYYMMDD
ts : time series for 1-grid-cell/1-sat/all-times
"""
dtime1 = ap.num2date(time1)
dtime2 = ap.num2date(time2)
dtime_range = full_dtime_range(dtime1, dtime2, **kw)
df = pn.DataFrame(index=dtime_range, columns=dtime_range) # empty DF
# fill df with 1 `reftime-TS` at a time
if not np.alltrue(np.isnan(ts)):
for dt_ref in dtime_range:
ind, = np.where(dtime1 == dt_ref)
df[dt_ref] = pn.Series(ts[ind], index=dtime2[ind])
df[dt_ref][dt_ref] = np.nan # diagonal
'''
if not np.alltrue(df[dt_ref].isnull().values):
df[dt_ref][dt_ref] = 0.
'''
return df
def create_df_with_sats(time, ts, satname, satnames, **kw):
"""
Create a DataFrame with one TS per `satellite`.
time1, time2 : integer representation of time: YYYYMMDD
Note1: i,j = y,x <<<<< important!
Note2: `satenames` is needed to preserve the order.
"""
dtime = ap.num2date(time)
dtime_range = full_dtime_range2(dtime)
df = pn.DataFrame(index=dtime_range, columns=satnames) # empty DF
# fill df with 1 `satname` TS at a time
if not np.alltrue(np.isnan(ts)):
for sat in satnames:
ind, = np.where(sat == satname)
df[sat] = pn.Series(ts[ind], index=dtime[ind])
return df
'''
t = ap.year2date(f1.root.time[:])
lon = f1.root.lon[:]
lat = f1.root.lat[:]
h = f1.root.h[:]
df_gps = pd.DataFrame({'lon': lon, 'lat': lat, 'h': h}, index=t)
print df_gps.head()
del t, lon, lat, h
'''
# altimetry
h0 = f2.root.dh_mean_all[:]
#h1 = f2.root.dh_mean_corr_short_all[:]
#h2 = f2.root.h_firn[:]
#g = f2.root.dg_mean_all[:]
t0 = ap.num2date(f2.root.time_all[:])
#t2 = ap.num2date(f2.root.time_firn[:])
lon = f2.root.x_edges[:]
lat = f2.root.y_edges[:]
'''
# maps grid indexes to coordinates
ii, jj = xrange(len(lat)), xrange(len(lon))
ij2ll = dict([((i,j),(la,lo)) for i,j,la,lo in zip(ii,jj,lat,lon)])
df_h0 = pd.Panel(h0, items=t0, major_axis=ii, minor_axis=jj
).to_frame(filter_observations=False).T
del h0, t0, lon, lat
print df_h0
sys.exit()
'''
'''
t = ap.year2date(f1.root.time[:])
lon = f1.root.lon[:]
lat = f1.root.lat[:]
h = f1.root.h[:]
df_gps = pd.DataFrame({'lon': lon, 'lat': lat, 'h': h}, index=t)
print df_gps.head()
del t, lon, lat, h
'''
# altimetry
h0 = f2.root.dh_mean_all[:]
#h1 = f2.root.dh_mean_corr_short_all[:]
#h2 = f2.root.h_firn[:]
#g = f2.root.dg_mean_all[:]
t0 = ap.num2date(f2.root.time_all[:])
#t2 = ap.num2date(f2.root.time_firn[:])
lon = f2.root.x_edges[:]
lat = f2.root.y_edges[:]
'''
# maps grid indexes to coordinates
ii, jj = xrange(len(lat)), xrange(len(lon))
ij2ll = dict([((i,j),(la,lo)) for i,j,la,lo in zip(ii,jj,lat,lon)])
df_h0 = pd.Panel(h0, items=t0, major_axis=ii, minor_axis=jj
).to_frame(filter_observations=False).T
del h0, t0, lon, lat
print df_h0
sys.exit()
'''
def main():
fname_in = sys.argv[1]
din = GetData(fname_in, 'a')
time = ap.num2date(getattr(din, T_NAME)[:])
lon = din.lon[:]
lat = din.lat[:]
nrows = len(time)
nt, ny, nx = getattr(din, H_NAME).shape # i,j,k = t,y,x
RR = np.empty((nt,ny,nx), 'f8') * np.nan
SS = np.empty((nt,ny,nx), 'f8') * np.nan
if TINT:
intervals = [ap.year2date(tt) for tt in INTERVALS]
if TINT:
print 'using time-interval correlation'
elif TVAR:
print 'using time-variable correlation'
else:
print 'using constant correlation'
print 'processing time series:'
isfirst = True
# iterate over every grid cell (all times): i,j = y,x
#-----------------------------------------------------------------
for i in xrange(ny):
for j in xrange(nx):
print 'time series of grid-cell:', i, j
dh = getattr(din, H_NAME)[:nrows,i,j]
dg = getattr(din, G_NAME)[:nrows,i,j]
if np.alltrue(np.isnan(dh)): continue
#---------------------------------------------------------
if TINT:
# satellite-dependent R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr3(dh, dg, time, intervals,
diff=DIFF, robust=True)
elif TVAR:
# time-varying R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr2(dh, dg, diff=DIFF,
robust=True, npts=NPTS)
else:
# constant R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr(dh, dg, diff=DIFF, robust=True)
#---------------------------------------------------------
# plot figures
if PLOT_TS:
dh_cor = ap.referenced(dh_cor, to='first')
dh = ap.referenced(dh, to='first')
dg = ap.referenced(dg, to='first')
k, = np.where(~np.isnan(RR[:,i,j]))
r = np.mean(RR[k,i,j])
s = np.mean(SS[k,i,j])
fig = plot_rs(time, RR[:,i,j], SS[:,i,j])
fig = plot_ts(time, lon[j], lat[i], dh_cor, dh, dg,
r, s, diff=DIFF)
if fig is None: continue
plt.show()
# save one TS per grid cell at a time
#---------------------------------------------------------
if not SAVE_TO_FILE: continue
if isfirst:
# open or create output file
isfirst = False
atom = tb.Atom.from_type('float64', dflt=np.nan)
filters = tb.Filters(complib='zlib', complevel=9)
c1 = din.file.createCArray('/', SAVE_AS_NAME, atom,
(nt,ny,nx), '', filters)
c2 = din.file.createCArray('/', R_NAME, atom,
(nt,ny,nx), '', filters)
c3 = din.file.createCArray('/', S_NAME, atom,
(nt,ny,nx), '', filters)
c1[:,i,j] = dh_cor
if SAVE_TO_FILE:
c2[:] = RR
c3[:] = SS
if PLOT_MAP:
if TVAR:
# 3D -> 2D
RR = np.mean(RR[~np.isnan(RR)], axis=0)
SS = np.mean(SS[~np.isnan(SS)], axis=0)
plot_map(lon, lat, np.abs(RR), BBOX, MFILE, mres=1, vmin=0, vmax=1)
plt.title('Correlation Coefficient, R')
plt.savefig('map_r.png')
plot_map(lon, lat, SS, BBOX, MFILE, mres=1, vmin=-0.2, vmax=0.7)
plt.title('Correlation Gradient, S')
plt.savefig('map_s.png')
plt.show()
din.file.close()
if SAVE_TO_FILE:
print 'out file -->', fname_in
import seaborn as sns
import altimpy as ap
#fname = '/Volumes/RADARALT/fpaolo/data/shelves/all_19920716_20111015_shelf_tide_grids_mts.h5'
fname = '/Users/fpaolo/data/shelves/all_19920716_20111015_shelf_tide_grids_mts.h5.ice_oce'
def gradient(y, dt=.25):
return np.gradient(y.values, dt)
with tb.open_file(fname) as f:
alt0 = f.root.dh_mean_xcal[:]
#alt0 = f.root.dh_mean_xcal_short_const[:]
alt1 = f.root.dg_mean_xcal[:]
firn = f.root.h_firn[:]
t_alt = ap.num2date(f.root.time_xcal[:])
t_firn = ap.num2date(f.root.time_firn[:])
lon = f.root.lon[:]
lat = f.root.lat[:]
# map grid indexes to coordinates
ii, jj = xrange(len(lat)), xrange(len(lon))
ij2ll = dict([((i,j),(la,lo)) for i,j,la,lo in zip(ii,jj,lat,lon)])
df_alt0 = pd.Panel(alt0, items=t_alt, major_axis=ii, minor_axis=jj
).to_frame(filter_observations=False).T
df_alt1 = pd.Panel(alt1, items=t_alt, major_axis=ii, minor_axis=jj
).to_frame(filter_observations=False).T
df_firn = pd.Panel(firn, items=t_firn, major_axis=ii, minor_axis=jj
).to_frame().T
lats1 = d1["lat"][:] # 2d ism = d1["ism"][:] lsm = d1["lsm"][:] dt1 = ap.year2date(year1) # dt1 = year1 f2 = tb.openFile(FILE2) elev = f2.root.elev[:] time2 = f2.root.time[:] lon2 = f2.root.lon[:] # 1d lat2 = f2.root.lat[:] # 1d lon2 = ap.lon_180_360(lon2, inverse=True) lons2, lats2 = np.meshgrid(lon2, lat2) points2 = np.column_stack((lons2.ravel(), lats2.ravel())) # points2 = (lons2[6,4], lats2[6,4]) dt2 = ap.num2date(time2) # dt2 = ap.num2year(time2) """ f3 = tb.openFile(FILE3) d3 = f3.root.data[:] y3, x3 = d3[:,0], d3[:,1] x3 = ap.lon_180_360(x3, inverse=True) f4 = tb.openFile(FILE4) d4 = f4.root.data[:] y4, x4 = d4[:,0], d4[:,1] x4 = ap.lon_180_360(x4, inverse=True) """ # crop firn grid
#fname = '/Volumes/RADARALT/fpaolo/data/shelves/all_19920716_20111015_shelf_tide_grids_mts.h5'
fname = '/Users/fpaolo/data/shelves/all_19920716_20111015_shelf_tide_grids_mts.h5.ice_oce'
def gradient(y, dt=.25):
return np.gradient(y.values, dt)
with tb.open_file(fname) as f:
alt0 = f.root.dh_mean_xcal[:]
#alt0 = f.root.dh_mean_xcal_short_const[:]
alt1 = f.root.dg_mean_xcal[:]
firn = f.root.h_firn[:]
t_alt = ap.num2date(f.root.time_xcal[:])
t_firn = ap.num2date(f.root.time_firn[:])
lon = f.root.lon[:]
lat = f.root.lat[:]
# map grid indexes to coordinates
ii, jj = xrange(len(lat)), xrange(len(lon))
ij2ll = dict([((i, j), (la, lo))
for i, j, la, lo in zip(ii, jj, lat, lon)])
df_alt0 = pd.Panel(alt0, items=t_alt, major_axis=ii,
minor_axis=jj).to_frame(filter_observations=False).T
df_alt1 = pd.Panel(alt1, items=t_alt, major_axis=ii,
minor_axis=jj).to_frame(filter_observations=False).T
df_firn = pd.Panel(firn, items=t_firn, major_axis=ii,
minor_axis=jj).to_frame().T
def main():
fname_in = sys.argv[1]
din = GetData(fname_in, 'a')
time = ap.num2date(getattr(din, T_NAME)[:])
lon = din.lon[:]
lat = din.lat[:]
sat = din.satname[:]
nrows = len(time)
nt, ny, nx = getattr(din, H_NAME).shape # i,j,k = t,y,x
RR = np.empty((nt,ny,nx), 'f8') * np.nan
SS = np.empty((nt,ny,nx), 'f8') * np.nan
print 'processing time series:'
isfirst = True
# iterate over every grid cell (all times): i,j = y,x
#-----------------------------------------------------------------
for i in xrange(ny):
for j in xrange(nx):
print 'time series of grid-cell:', i, j
dh = getattr(din, H_NAME)[:nrows,i,j]
dg = getattr(din, G_NAME)[:nrows,i,j]
dh_cor = np.zeros_like(dh)
if np.alltrue(np.isnan(dh)): continue
#---------------------------------------------------------
# pull and correct a chunk of the array at a time
for s in np.unique(sat):
k = np.where(sat == s)
dh_cor[k], R, S = ap.backscatter_corr(dh[k], dg[k],
diff=DIFF, robust=True)
RR[k,i,j] = R
SS[k,i,j] = S
#---------------------------------------------------------
if PLOT_TS:
dh_cor = ap.referenced(dh_cor, to='first')
dh = ap.referenced(dh, to='first')
dg = ap.referenced(dg, to='first')
fig = plot_rs(time, RR[:,i,j], SS[:,i,j])
for s in np.unique(sat):
k = np.where(sat == s)
r, s = np.mean(RR[k,i,j]), np.mean(SS[k,i,j])
try:
fig = plot_ts(time[k], lon[j], lat[i], dh_cor[k],
dh[k], dg[k], r, s, diff=DIFF)
except:
print 'something wrong with ploting!'
print 'dh:', dh
print 'dg:', dg
if fig is None: continue
plt.show()
# save one TS per grid cell at a time
#---------------------------------------------------------
if not SAVE_TO_FILE: continue
if isfirst:
# open or create output file
isfirst = False
atom = tb.Atom.from_type('float64', dflt=np.nan)
filters = tb.Filters(complib='zlib', complevel=9)
try:
c1 = din.file.create_carray('/', SAVE_AS_NAME, atom,
(nt,ny,nx), '', filters)
except:
c1 = din.file.getNode('/', SAVE_AS_NAME)
c2 = din.file.create_carray('/', R_NAME, atom,
(nt,ny,nx), '', filters)
c3 = din.file.create_carray('/', S_NAME, atom,
(nt,ny,nx), '', filters)
c1[:,i,j] = dh_cor
if SAVE_TO_FILE:
c2[:] = RR
c3[:] = SS
if PLOT_MAP:
RR = RR[0] # change accordingly
SS = SS[0]
plot_map(lon, lat, np.abs(RR), BBOX, MASK_FILE, mres=1, vmin=0, vmax=1)
plt.title('Correlation Coefficient, R')
plt.savefig('map_r.png')
plot_map(lon, lat, SS, BBOX, MASK_FILE, mres=1, vmin=-0.2, vmax=0.7)
plt.title('Correlation Gradient, S')
plt.savefig('map_s.png')
plt.show()
din.file.close()
if SAVE_TO_FILE:
print 'out file -->', fname_in
def main():
fname_in = sys.argv[1]
din = GetData(fname_in, 'a')
time = ap.num2date(getattr(din, T_NAME)[:])
lon = din.lon[:]
lat = din.lat[:]
nrows = len(time)
nt, ny, nx = getattr(din, H_NAME).shape # i,j,k = t,y,x
RR = np.empty((nt, ny, nx), 'f8') * np.nan
SS = np.empty((nt, ny, nx), 'f8') * np.nan
if TINT:
intervals = [ap.year2date(tt) for tt in INTERVALS]
if TINT:
print 'using time-interval correlation'
elif TVAR:
print 'using time-variable correlation'
else:
print 'using constant correlation'
print 'processing time series:'
isfirst = True
# iterate over every grid cell (all times): i,j = y,x
#-----------------------------------------------------------------
for i in xrange(ny):
for j in xrange(nx):
print 'time series of grid-cell:', i, j
dh = getattr(din, H_NAME)[:nrows, i, j]
dg = getattr(din, G_NAME)[:nrows, i, j]
if np.alltrue(np.isnan(dh)): continue
#---------------------------------------------------------
if TINT:
# satellite-dependent R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr3(dh, dg, time, intervals,
diff=DIFF, robust=True)
elif TVAR:
# time-varying R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr2(dh, dg, diff=DIFF,
robust=True, npts=NPTS)
else:
# constant R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr(dh, dg, diff=DIFF, robust=True)
#---------------------------------------------------------
# plot figures
if PLOT_TS:
dh_cor = ap.referenced(dh_cor, to='first')
dh = ap.referenced(dh, to='first')
dg = ap.referenced(dg, to='first')
k, = np.where(~np.isnan(RR[:, i, j]))
r = np.mean(RR[k, i, j])
s = np.mean(SS[k, i, j])
fig = plot_rs(time, RR[:, i, j], SS[:, i, j])
fig = plot_ts(time,
lon[j],
lat[i],
dh_cor,
dh,
dg,
r,
s,
diff=DIFF)
if fig is None: continue
plt.show()
# save one TS per grid cell at a time
#---------------------------------------------------------
if not SAVE_TO_FILE: continue
if isfirst:
# open or create output file
isfirst = False
atom = tb.Atom.from_type('float64', dflt=np.nan)
filters = tb.Filters(complib='zlib', complevel=9)
c1 = din.file.createCArray('/', SAVE_AS_NAME, atom,
(nt, ny, nx), '', filters)
c2 = din.file.createCArray('/', R_NAME, atom, (nt, ny, nx), '',
filters)
c3 = din.file.createCArray('/', S_NAME, atom, (nt, ny, nx), '',
filters)
c1[:, i, j] = dh_cor
if SAVE_TO_FILE:
c2[:] = RR
c3[:] = SS
if PLOT_MAP:
if TVAR:
# 3D -> 2D
RR = np.mean(RR[~np.isnan(RR)], axis=0)
SS = np.mean(SS[~np.isnan(SS)], axis=0)
plot_map(lon, lat, np.abs(RR), BBOX, MFILE, mres=1, vmin=0, vmax=1)
plt.title('Correlation Coefficient, R')
plt.savefig('map_r.png')
plot_map(lon, lat, SS, BBOX, MFILE, mres=1, vmin=-0.2, vmax=0.7)
plt.title('Correlation Gradient, S')
plt.savefig('map_s.png')
plt.show()
din.file.close()
if SAVE_TO_FILE:
print 'out file -->', fname_in
