def plot_stress_strain_with_landsat(vx_stackfile,
vy_stackfile,
landsatfile,
idx=0,
fname='stress.svg'):
vx_stack = ice.Stack(vx_stackfile)
vy_stack = ice.Stack(vy_stackfile)
vx = np.array(vx_stack._datasets["data"][idx])
vy = np.array(vy_stack._datasets["data"][idx])
dx = vx_stack.hdr.dx
dy = vy_stack.hdr.dy
print(vx_stack.hdr.extent)
robust_opts = {'window_size': 250, 'order': 2}
# Compute stress strain
strain_dict, stress_dict = ice.compute_stress_strain(
vx,
vy,
dx=dx,
dy=dy,
grad_method='robust_l2',
inpaint=False,
**robust_opts)
fig, axs = plt.subplots(nrows=3, ncols=1, figsize=(6, 10))
e_xx = axs[0].imshow(strain_dict['e_xx'],
aspect='auto',
extent=vx_stack.hdr.extent,
cmap='coolwarm',
clim=[-2, 2])
axs[0].set_title('e_xx')
fig.colorbar(e_xx, ax=axs[0])
e_yy = axs[1].imshow(strain_dict['e_yy'],
aspect='auto',
extent=vx_stack.hdr.extent,
cmap='coolwarm',
clim=[-2, 2])
axs[1].set_title('e_yy')
fig.colorbar(e_yy, ax=axs[1])
e_xy = axs[2].imshow(strain_dict['e_xy'],
aspect='auto',
extent=vx_stack.hdr.extent,
cmap='viridis',
clim=[0, 4])
axs[2].set_title('e_xy')
fig.colorbar(e_xy, ax=axs[2])
plt.tight_layout()
plt.savefig('./figures/' + fname, format='svg')
plt.show()
vx_stack.fid.close()
vy_stack.fid.close()
def plot_mean_with_landsat(landsatfile,
stackfile,
title,
lb=float('-inf'),
clim=None,
fname=None):
stack = ice.Stack(stackfile, mode='r')
# Load Landsat 8 raster with same projection window
e = stack.hdr.extent
proj_win = [e[0], e[3], e[1], e[2]]
landsat = ice.Raster(rasterfile=landsatfile, projWin=proj_win)
fig, axs = plt.subplots(figsize=(9, 9))
data = np.array(stack._datasets["data"][0])
# data = np.nanmedian(data, axis=0)
data[data < lb] = np.nan
axs.imshow(landsat.data, extent=e, cmap='binary_r')
im = axs.imshow(data, extent=e, cmap='Spectral_r', alpha=0.6, clim=clim)
plt.title(title)
fig.colorbar(im)
if fname is not None:
plt.savefig('./figures/' + fname, dpi=300)
plt.show()
stack.fid.close()
def main(args):
# Load the stack
stack = ice.Stack(args.stackfile)
# Convert fps to interval (ms)
interval = 1000 / args.fps
cmap = ice.get_cmap(args.cmap)
fig, ax = plt.subplots(figsize=(7, 11))
ax.set_xlabel('X (m)')
ax.set_ylabel('Y (m)')
fig.set_tight_layout(True)
# Calculate frames
print('Creating frames for animation:')
ims = []
for i in tqdm(range(len(stack._datasets['data']))):
ims.append(frame(i, stack, ax, cmap, args.clim))
# Add colorbar
'''
cbar = plt.colorbar(ims[0][0], ax=ax)
cbar.set_label('Velocity (m/day')
'''
# Create animation
print('Saving animation to', args.save)
anim = animation.ArtistAnimation(fig, ims, interval=interval, repeat=True)
anim.save(args.save, dpi=args.dpi)
def create_stack(data_root, dtype, stackfile):
""" Create Stacks for the different data """
hdr = None
# Set a lower bound for valid data
lb = 0
if dtype in ['vx', 'vy']:
lb = -1e6 # m/yr
# Get dates and data from files
data_dict = {}
init_tdec, init_data = [], []
for root, _, files in os.walk(data_root):
if len(files) == 0:
continue
# Get filename (not very elegant, but it works)
rasterfile = ''
for f in files:
if dtype + '.tif' in f:
rasterfile = f
# Get date
date = root.split('/')[-1]
tdec = ice.datestr2tdec(yy=int(date[0:4]),
mm=int(date[4:6]),
dd=int(date[6:8]))
init_tdec.append(tdec)
# Get data
e = [-3130000, -3090000, 642500, 680000]
proj_win = [e[0], e[3], e[1], e[2]]
raster = ice.Raster(rasterfile=root + '/' + rasterfile,
projWin=proj_win)
# Apply data bounds
data = np.array(raster.data)
data[data < lb] = np.nan
# Store in dict so that data can be sorted by date before adding to Stack
data_dict[tdec] = data
# Store the first header
if hdr is None:
hdr = raster.hdr
# Sort data before adding to Stack
init_tdec.sort()
for t in init_tdec:
init_data.append(data_dict[t])
# Create Stack
stack = ice.Stack(stackfile,
mode='w',
init_tdec=np.array(init_tdec),
init_rasterinfo=hdr)
stack.fid.create_dataset("data", data=np.array(init_data))
stack.fid.close()
def create_stack(data_path,
data_table,
stack_path,
stack_fname,
proj_win=None):
""" Build a new Stack from a set of raster files.
Parameters
----------
data_path: str
path to directory storing all downloaded rasters to build Stack from
data_table: dict
table of information for each Raster
stack_path: str
path to directory that Stack will be saved to
stack_fname: str
Stackfile name
proj_win: list
Projection window to load each Raster in the Stack with. If None, the
optimal projection window will be calculated
Return
------
The projection window used for the Stack
"""
Path(stack_path).mkdir(parents=True, exist_ok=True)
# Calculate largest common extent between rasers
if proj_win is None:
proj_win = calc_optimal_proj_win(data_path, data_table['fname'])
print('Using projection window', proj_win)
# Convert date strings to tdec
dates = 'start_date' if 'start_date' in data_table.keys() else 'date'
init_tdec = dates_to_tdec(data_table[dates])
# Get stack data
print('Loading Stack data:')
init_data = get_stack_data(data_path, data_table['fname'], proj_win)
# Smooth data
print('Loaded data has shape,', init_data.shape)
# Get header to init stack with
init_rasterinfo = ice.Raster(data_path + data_table['fname'][0],
projWin=proj_win).hdr
# Initialize stack
print('Initializing Stack and saving to', stack_path + stack_fname)
stack = ice.Stack(stack_path + stack_fname,
mode='w',
init_tdec=init_tdec,
init_rasterinfo=init_rasterinfo)
stack.fid.create_dataset("data", data=init_data)
stack.fid.close()
return proj_win
def plot_stream(vx_stackfile, vy_stackfile, v_stackfile, landsatfile, idx=0):
vx_stack = ice.Stack(vx_stackfile)
vy_stack = ice.Stack(vy_stackfile)
v_stack = ice.Stack(v_stackfile)
e = vx_stack.hdr.extent
proj_win = [e[0], e[3], e[1], e[2]]
landsat = ice.Raster(rasterfile=landsatfile, projWin=proj_win)
v = np.array(v_stack._datasets['data'][idx])
vx = np.array(vx_stack._datasets['data'][idx])
vy = np.array(vy_stack._datasets['data'][idx])
dx = vx_stack.hdr.dx
dy = vy_stack.hdr.dy
x0, x1, y0, y1 = vx_stack.hdr.extent
x = np.arange(x0, x0 + len(vx[0]) * dx, dx)
y = np.arange(y1, y1 + len(vx) * dy, dy)
fig, axs = plt.subplots(figsize=(9, 9))
axs.imshow(landsat.data, extent=e, cmap='binary_r')
axs.set_ylim(bottom=y0, top=y1)
axs.set_xlim(left=x0, right=x1)
cmap = copy.copy(cm.get_cmap('plasma'))
cmap.set_under(alpha=0)
stream = axs.streamplot(x,
y,
vx,
vy,
color=v,
cmap=cmap,
norm=Normalize(vmin=150, vmax=5000),
density=5)
fig.colorbar(stream.lines, fraction=0.046, pad=0.04)
plt.tight_layout()
plt.savefig('./figures/streamline.jpg', dpi=300)
plt.show()
def main(args):
# Load the raster/stack and reference raster/stack
if args.raster.endswith('.h5'):
# Input stack
inobj = ice.Stack(args.raster)
# Reference stack
ref = ice.Stack(args.reference)
# Initialize output stack
outobj = ice.Stack(args.output, mode='w')
outobj.initialize(inobj.tdec, ref.hdr, data=False, weights=False)
# Loop over keys to resample
for key in args.keys:
print('Resampling', key)
inobj.resample(ref.hdr, outobj, key=key)
else:
# Input raster
inobj = ice.Raster(rasterfile=args.raster)
# Reference raster
ref = ice.Raster(rasterfile=args.reference)
# Resample
inobj.resample(ref.hdr)
# Write to disk
if args.epsg is not None:
out_epsg = args.epsg
else:
out_epsg = ref.hdr.epsg
inobj.write_gdal(args.output, epsg=out_epsg, driver=args.driver)
def main(args):
# Load stack
stack = ice.Stack(args.stackfile)
# Make sure output directory exists
if not os.path.isdir(args.outdir):
os.mkdir(args.outdir)
# Launch solver
ice.tseries.inversion(stack, args.user, args.outdir, nt_out=args.interp, dkey=args.dkey,
solver_type=args.solver, n_proc=args.n_proc, regParam=args.penalty,
rw_iter=args.rw_iter, n_min=args.n_min, no_weights=args.no_weights,
prior_cov=args.prior_cov, mask_raster=args.mask,
cleaned_stack=args.cleaned_stack, n_iter=args.n_iter)
def main(args):
# Load the stack
stack = ice.Stack(args.stackfile)
# Check if requested dataset is 2D. If so, view it directly
if stack[args.key].ndim == 2:
mean = stack[args.key][()]
# Otherwise, compute mean
else:
mean = stack.mean(key=args.key)
# Load reference SAR image
if args.ref is not None:
sar = ice.Raster(rasterfile=args.ref)
if sar.hdr != stack.hdr:
sar.resample(stack.hdr)
db = 10.0 * np.log10(sar.data)
low = np.percentile(db.ravel(), 5)
high = np.percentile(db.ravel(), 99.9)
else:
db = None
fig, ax = plt.subplots()
vmin, vmax = args.clim
cmap = ice.get_cmap(args.cmap)
if db is not None:
ref = ax.imshow(db,
aspect='auto',
cmap='gray',
vmin=low,
vmax=high,
extent=stack.hdr.extent)
im = ax.imshow(mean,
aspect='auto',
vmin=vmin,
vmax=vmax,
cmap=cmap,
extent=stack.hdr.extent,
alpha=args.alpha)
cbar = plt.colorbar(im, ax=ax, pad=0.02)
cbar.set_label(args.key)
plt.show()
if args.save is not None:
out = ice.Raster(data=mean, hdr=stack.hdr)
out.write_gdal(args.save, epsg=args.save_epsg)
def main():
enderlin_data_path = './data/enderlin2018/'
stack_path = './data/stacks/'
stack_fname = 'enderlin_velocity_stack.hdf5'
landsat8_raster_fname = './data/landsat8/LC08_L1TP_066017_20140224_20170306_01_T1/LC08_L1TP_066017_20140224_20170306_01_T1_B1.TIF'
# Get all raster files from manifest
rasterfiles = []
with open(enderlin_data_path + 'MANIFEST.TXT', 'r') as f:
for line in f:
rasterfiles.append(line.split()[0])
# plot_with_landsat(landsat8_raster_fname, enderlin_data_path + rasterfiles[0], 'Enderlin velocity on Landsat 8 Image')
# Get dates and data from files
init_tdec, init_data = [], []
for rasterfile in rasterfiles:
# Parse date from filename
date = rasterfile.split('_')[1]
tdec = ice.datestr2tdec(yy=int(date[0:4]),
mm=int(date[4:6]),
dd=int(date[6:8]))
init_tdec.append(tdec)
# Load file and get data
raster = ice.Raster(rasterfile=enderlin_data_path + rasterfile)
print(raster.hdr.extent)
# Data is flipped - not sure why
init_data.append(np.flip(raster.data, axis=0))
# Create Stack
hdr = ice.RasterInfo(rasterfile=enderlin_data_path + rasterfiles[0])
stack = ice.Stack(stack_path + stack_fname,
mode='w',
init_tdec=np.array(init_tdec),
init_rasterinfo=hdr)
stack.fid.create_dataset("data", data=np.array(init_data))
stack.fid.close()
plot_mean_with_landsat(landsat8_raster_fname, stack_path + stack_fname,
"Mean Velocity")
def get_stack_mean(stack_path, stack_fname, mean_fname):
""" Calculate the mean for a Stack and save to a file to save
future computational time.
Parameters
----------
stack_path: str
Path to directory containing stackfile
stack_fname:
Name of stackfile
mean_fname:
Name to save derived mean as
Returns
-------
mean: array_like
The loaded Stack mean
"""
if os.path.exists(stack_path + mean_fname):
# Load mean file if found
print('Loading mean from file')
raster = ice.Raster(rasterfile=stack_path + mean_fname)
return raster.data, raster.hdr
else:
# Calculate mean from Stack and save to file
print('Calculating mean from Stack')
stack = ice.Stack(stack_path + stack_fname)
data = np.array(stack._datasets['data'])
data[data < 0] = np.nan
mean = np.nanmean(data, axis=0)
# Save to file for next time
print('Saving mean to Raster for next time')
raster = ice.Raster(data=mean, hdr=stack.hdr)
raster.write_gdal(stack_path + mean_fname)
stack.fid.close()
return mean, raster.hdr
def main(args):
# Get rasterinfo
if args.rasterfile.endswith('.h5'):
stack = ice.Stack(args.rasterfile)
hdr = stack.hdr
tdec = stack.tdec
else:
hdr = ice.RasterInfo(args.rasterfile, match=args.match)
tdec = None
print('Image shape: (%d, %d)' % (hdr.ny, hdr.nx))
print('Geographic extent: %f %f %f %f' % tuple(hdr.extent))
print('Geographic spacing: (dy = %f, dx = %f)' % (hdr.dy, hdr.dx))
if tdec is not None:
print('Time span: %f -> %f' % (tdec[0], tdec[-1]))
print('Median time spacing: %f' % np.median(np.diff(tdec)))
print('EPSG:', hdr.epsg)
def main(args):
# Load the stack
stack = ice.Stack(args.stackfile)
# Get frame
if args.frame == 'initial':
mean = stack.slice(0, key=args.key)
elif args.frame == 'final':
mean = stack.slice(stack.Nt - 1, key=args.key)
elif args.frame == 'mean':
mean = stack.mean(key=args.key)
elif args.frame == 'std':
mean = stack.std(key=args.key)
else:
raise ValueError('Unsupported frame type.')
# If model directory is given, load model stack (full fit)
mstack = None
if args.mfile is not None:
mstack = ice.Stack(args.mfile)
# Load reference SAR image
if args.ref is not None:
sar = ice.Raster(rasterfile=args.ref)
if sar.hdr != stack.hdr:
sar.resample(stack.hdr)
db = 10.0 * np.log10(sar.data.astype(np.float32))
low = np.percentile(db.ravel(), 5)
high = np.percentile(db.ravel(), 99.9)
else:
db = None
# Initialize image plot
fig, ax = plt.subplots()
vmin, vmax = args.clim
cmap = ice.get_cmap(args.cmap)
if db is not None:
ref = ax.imshow(db,
aspect='auto',
cmap='gray',
vmin=low,
vmax=high,
extent=stack.hdr.extent)
im = ax.imshow(mean,
aspect='auto',
vmin=vmin,
vmax=vmax,
cmap=cmap,
extent=stack.hdr.extent,
alpha=args.alpha)
cbar = plt.colorbar(im, ax=ax, pad=0.02)
cbar.set_label(args.key)
# Initialize plot for time series for a given pixel
pts, axts = plt.subplots(figsize=(10, 6))
# Define action for clicking on deformation map
def printcoords(event):
if event.inaxes != ax:
return
# Get cursor coordinates
y, x = event.ydata, event.xdata
# Print out pixel locaton
i, j = stack.hdr.xy_to_imagecoord(x, y)
print('Row: %d Col: %d' % (i, j))
# Get time series for cursor location
d = stack.timeseries(xy=(x, y), key=args.key)
# Plot data and fit
axts.clear()
if args.sigma:
w = stack.timeseries(xy=(x, y), key='weights')
sigma = 1.0 / w
axts.errorbar(stack.tdec, d, yerr=sigma, fmt='o')
else:
axts.plot(stack.tdec, d, 'o')
if mstack is not None:
fit = mstack.timeseries(xy=(x, y), key=args.mkey)
axts.plot(mstack.tdec, fit, 'o')
axts.set_xlabel('Year')
axts.set_ylabel('Velocity')
pts.canvas.draw()
cid = fig.canvas.mpl_connect('button_press_event', printcoords)
plt.show()
fig.canvas.mpl_disconnect(cid)
def main(args):
# Check if nothing is passed
if args.projWin is None and args.srcWin is None and args.tWin is None:
print('No cropping parameters provided.')
return
# Load stack
stack = ice.Stack(args.stackfile)
# Construct spatial slices
if args.projWin is not None:
# Unpack projWin parameters
x0, y0, x1, y1 = args.projWin
# Convert geographic coordinates to image coordinates
i0, j0 = stack.hdr.xy_to_imagecoord(x0, y0)
i1, j1 = stack.hdr.xy_to_imagecoord(x1, y1)
islice = slice(i0, i1)
jslice = slice(j0, j1)
elif args.srcWin is not None:
# Unpack srcWin parameters
j0, i0, xsize, ysize = args.srcWin
j1 = j0 + xsize
i1 = i0 + ysize
islice = slice(i0, i1)
jslice = slice(j0, j1)
else:
islice = slice(0, stack.Ny)
jslice = slice(0, stack.Nx)
# Construct temporal subset if provided
tdec = stack.tdec
if args.tWin is not None:
t0, tf = args.tWin
k0 = np.argmin(np.abs(tdec - t0))
k1 = np.argmin(np.abs(tdec - tf))
tslice = slice(k0, k1)
tdec = tdec[tslice]
else:
tslice = slice(0, stack.Nt)
# Make meshgrid of coordinates
X, Y = stack.hdr.meshgrid()
# Apply slices
if islice is not None and jslice is not None:
X = X[islice, jslice]
Y = Y[islice, jslice]
# Create RasterInfo header
hdr = ice.RasterInfo(X=X, Y=Y)
# Create output stack
ostack = ice.Stack(args.output, mode='w')
ostack.initialize(tdec, hdr, data=True, weights=True,
chunks=(1, args.chunks[0], args.chunks[1]))
# Manually fill in the data
try:
ostack['data'][:, :, :] = stack['data'][tslice, islice, jslice]
except KeyError:
ostack['data'][:, :, :] = stack['igram'][tslice, islice, jslice]
ostack['weights'][:, :, :] = stack['weights'][tslice, islice, jslice]
def main(args):
# Load Stack
stack = ice.Stack(args.stackfile)
# Load reference SAR image
if args.ref is not None:
sar = ice.Raster(rasterfile=args.ref)
if sar.hdr != stack.hdr:
sar.resample(stack.hdr)
db = 10.0 * np.log10(sar.data)
low = np.percentile(db.ravel(), 5)
high = np.percentile(db.ravel(), 99.9)
else:
db = None
# Set up animation
fig, ax = plt.subplots(figsize=args.figsize)
data = stack._datasets[args.key]
cmap = ice.get_cmap(args.cmap)
# Add ref image if using
if db is not None:
ax.imshow(db,
aspect='auto',
cmap='gray',
vmin=low,
vmax=high,
extent=stack.hdr.extent)
# Extract reference frame if index provided
if args.rel_index is not None:
data_ref = data[args.rel_index]
else:
data_ref = 0.0
im = ax.imshow(data[0] - data_ref,
extent=stack.hdr.extent,
cmap=cmap,
clim=args.clim,
alpha=args.alpha)
# Create title
datestr = ice.tdec2datestr(stack.tdec[0])
tx = ax.set_title(args.title + ' ' + datestr, fontweight='bold')
ax.set_xlabel(args.xlabel)
ax.set_ylabel(args.ylabel)
# Add colorbar
div = make_axes_locatable(ax)
cax = div.append_axes('right', '5%', '5%')
cb = fig.colorbar(im, cax=cax)
cb.set_label(args.clabel)
# Update the frame
def animate(i):
im.set_data(data[i] - data_ref)
datestr = ice.tdec2datestr(stack.tdec[i])
tx.set_text(args.title + ' ' + datestr)
fig.set_tight_layout(True)
print('Generating animation and saving to', args.save)
interval = 1000 / args.fps # Convert fps to interval in milliseconds
anim = animation.FuncAnimation(fig,
animate,
interval=interval,
frames=len(data),
repeat=True)
anim.save(args.save, dpi=args.dpi)
if args.show:
plt.show()
def main():
stack_path = './data/stacks/'
stack_fname = 'joughin_v_stack.hdf5'
transect_fname = 'landsat_transect.npy'
print('Loading data and building model ..................................')
# Load transect coordinates and Stack
x, y = np.load(stack_path + transect_fname)
x, y = ice.transform_coordinates(x, y, 32606, 3413)
stack = ice.Stack(stack_path + stack_fname)
# Convert time vector to list of datetimes
t = ice.tdec2datestr(stack.tdec, returndate=True)
transect_velocities = np.array([stack.timeseries(xy=[x[i], y[i]], win_size=1) for i in range(len(x))])
# Create model
model = ice.tseries.build_temporal_model(t, poly=2, isplines=[])
# Create temporally coherent (smooth) G over entire timespan of data
stdec = ice.generateRegularTimeArray(min(stack.tdec), max(stack.tdec))
st = ice.tdec2datestr(stdec, returndate=True)
sG = ice.tseries.build_temporal_model(st, poly=2, isplines=[]).G
# Create image displaying found seasonal and secular variation along transect
print('Solving regression along transect ................................')
# Store the timeseries fit for each point along the transect
data = {
'total' : transect_velocities,
'seasonal' : [],
'secular' : []
}
fit = {
'seasonal' : [],
'secular' : []
}
transect_amp = []
transect_phase = []
mean_vel = []
transect_sec_phase = []
transect_sec_min = []
for tseries in transect_velocities:
tseries[tseries < 0] = np.nan
solver = ice.tseries.select_solver('ridge', reg_indices=model.itransient, penalty=0.25)
_, m, _ = solver.invert(model.G, tseries)
amp, phase = compute_seasonal_amp_phase(m, model)
transect_amp.append(amp)
transect_phase.append(phase)
a, b, c = m[model.isecular] # y = a + bx + cx**2
transect_sec_phase.append(-b/(2*c))
transect_sec_min.append(a - b**2/(4*c))
if m is None:
continue
seasonal_tseries = np.dot(sG[:,model.iseasonal], m[model.iseasonal])
secular_tseries = np.dot(sG[:,model.isecular], m[model.isecular])
mean_vel.append(np.nanmean(tseries))
# mean_vel.append(np.nanmedian(seasonal_tseries + secular_tseries))
fit['seasonal'].append(seasonal_tseries)
fit['secular'].append(secular_tseries)
seasonal_data = np.dot(model.G[:,model.iseasonal], m[model.iseasonal])
secular_data = np.dot(model.G[:,model.isecular], m[model.isecular])
data['seasonal'].append(tseries - secular_data)
data['secular'].append(tseries - seasonal_data)
# Calculate path length along the glacier in m
path_len = ice.compute_path_length(x, y)
# Get temporal extent of dataset
extent = [stack.tdec[0], stack.tdec[-1], 0, path_len[-1]]
# Generate plots
# plot_timeseries_fit(int(0.20*len(transect_velocities)), data, stack.tdec, fit, stdec)
# plot_transect_signal(fit, extent)
# Smooth amp/phase
smooth = True
if smooth:
win = 11
poly = 2
smooth_amp = savgol_filter(transect_amp, win, poly)
smooth_phase = savgol_filter(transect_phase, win, poly)
smooth_vel = savgol_filter(mean_vel, win, poly)
smooth_sec_phase = savgol_filter(transect_sec_phase, win, poly)
smooth_sec_min = savgol_filter(transect_sec_min, win, poly)
plot_transect_amp_phase(smooth_amp, smooth_phase, smooth_vel, path_len)
plot_transect_secular(smooth_sec_phase, smooth_phase, smooth_sec_min, smooth_vel, path_len)
else:
plot_transect_amp_phase(transect_amp, transect_phase, mean_vel, path_len)
# Close fd
stack.fid.close()