def main():
parser = getparser()
#Create dictionary of arguments
args = vars(parser.parse_args())
#Want to enable -full when -of is specified, probably a fancy way to do this with argparse
if args['of']:
args['full'] = True
#Note, imshow has many interpolation types:
#'none', 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming',
#'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos'
#{'interpolation':'bicubic', 'aspect':'auto'}
#args['imshow_kwargs']={'interpolation':'bicubic'}
args['imshow_kwargs']={'interpolation':'none'}
if args['clipped'] and args['overlay'] is None:
sys.exit("Must specify an overlay filename with option 'clipped'")
#Set this as the background numpy array
args['bg'] = None
if args['shp'] is not None:
print args['shp']
if args['link']:
fig = plt.figure(0)
n_ax = len(args['filelist'])
src_ds_list = [gdal.Open(fn) for fn in args['filelist']]
t_srs = geolib.get_ds_srs(src_ds_list[0])
res_stats = geolib.get_res_stats(src_ds_list, t_srs=t_srs)
#Use min res
res = res_stats[0]
extent = geolib.ds_geom_union_extent(src_ds_list, t_srs=t_srs)
#print res, extent
for n,fn in enumerate(args['filelist']):
if not iolib.fn_check(fn):
print 'Unable to open input file: %s' % fn
continue
#Note: this won't work if img1 has 1 band and img2 has 3 bands
#Hack for now
if not args['link']:
fig = plt.figure(n)
n_ax = 1
#fig.set_facecolor('black')
fig.set_facecolor('white')
fig.canvas.set_window_title(os.path.split(fn)[1])
#fig.suptitle(os.path.split(fn)[1], fontsize=10)
#Note: warplib SHOULD internally check to see if extent/resolution/projection are identical
#This eliminates the need for a clipped flag
#If user has already warped the background and source data
if args['overlay']:
if args['clipped']:
src_ds = gdal.Open(fn, gdal.GA_ReadOnly)
#Only load up the bg array once
if args['bg'] is None:
#Need to check that background fn exists
print "%s background" % args['overlay']
bg_ds = gdal.Open(args['overlay'], gdal.GA_ReadOnly)
#Check image dimensions
args['bg'] = get_bma(bg_ds, 1, args['full'])
else:
#Clip/warp background dataset to match overlay dataset
#src_ds, bg_ds = warplib.memwarp_multi_fn([fn, args['overlay']], extent='union')
src_ds, bg_ds = warplib.memwarp_multi_fn([fn, args['overlay']], extent='first')
#src_ds, bg_ds = warplib.memwarp_multi_fn([fn, args['overlay']], res='min', extent='first')
#Want to load up the unique bg array for each input
args['bg'] = get_bma(bg_ds, 1, args['full'])
else:
src_ds = gdal.Open(fn, gdal.GA_ReadOnly)
if args['link']:
#Not sure why, but this still warps all linked ds, even when identical res/extent/srs
#src_ds = warplib.warp(src_ds, res=res, extent=extent, t_srs=t_srs)
src_ds = warplib.memwarp_multi([src_ds,], res=res, extent=extent, t_srs=t_srs)[0]
cbar_kwargs={'extend':'both', 'orientation':'vertical', 'shrink':0.7, 'fraction':0.12, 'pad':0.02}
nbands = src_ds.RasterCount
b = src_ds.GetRasterBand(1)
dt = gdal.GetDataTypeName(b.DataType)
#Eventually, check dt of each band
print
print "%s (%i bands)" % (fn, nbands)
#Singleband raster
if (nbands == 1):
if args['cmap'] is None:
#Special case to handle ASP float32 grayscale data
if '-L_sub' in fn or '-R_sub' in fn:
args['cmap'] = 'gray'
else:
if (dt == 'Float64') or (dt == 'Float32') or (dt == 'Int32'):
args['cmap'] = 'cpt_rainbow'
#This is for WV images
elif (dt == 'UInt16'):
args['cmap'] = 'gray'
elif (dt == 'Byte'):
args['cmap'] = 'gray'
else:
args['cmap'] = 'cpt_rainbow'
"""
if 'count' in fn:
args['clim_perc'] = (0,100)
cbar_kwargs['extend'] = 'neither'
args['cmap'] = 'cpt_rainbow'
if 'mask' in fn:
args['clim'] = (0, 1)
#Could be (0, 255)
#args['clim_perc'] = (0,100)
#Want absolute clim of 0, then perc of 100
cbar_kwargs['extend'] = 'neither'
args['cmap'] = 'gray'
"""
args['cbar_kwargs'] = cbar_kwargs
bma = get_bma(src_ds, 1, args['full'])
#Note n+1 here ensures we're assigning subplot correctly here (n is 0-relative, subplot is 1)
bma_fig(fig, bma, n_subplt=n_ax, subplt=n+1, ds=src_ds, **args)
#3-band raster, likely disparity map
#This doesn't work when alpha band is present
elif (nbands == 3) and (dt == 'Byte'):
#For some reason, tifs are vertically flipped
if (os.path.splitext(fn)[1] == '.tif'):
args['imshow_kwargs']['origin'] = 'lower'
#Use gdal dataset here instead of imread(fn)?
imgplot = plt.imshow(plt.imread(fn), **args['imshow_kwargs'])
pltlib.hide_ticks(imgplot.axes)
#Handle the 3-band disparity map case here
#elif ((dt == 'Float32') or (dt == 'Int32')):
else:
if args['cmap'] is None:
args['cmap'] = 'cpt_rainbow'
bn = 1
while bn <= nbands:
bma = get_bma(src_ds, bn, args['full'])
bma_fig(fig, bma, n_subplt=nbands, subplt=bn, ds=src_ds, **args)
bn += 1
#Want to be better about this else case - lazy for now
#else:
# bma = get_bma(src_ds, 1, args['full'])
# bma_fig(fig, bma, **args)
ts = timelib.fn_getdatetime_list(fn)
if ts:
print "Timestamp list: ", ts
"""
if len(ts) == 1:
plt.title(ts[0].date())
elif len(ts) == 2:
plt.title("%s to %s" % (ts[0].date(), ts[1].date()))
"""
plt.tight_layout()
#Write out the file
#Note: make sure display is local for savefig
if args['of']:
outf = str(os.path.splitext(fn)[0])+'_fig.'+args['of']
#outf = str(os.path.splitext(fn)[0])+'_'+str(os.path.splitext(args['overlay'])[0])+'_fig.'+args['of']
#Note: need to account for colorbar (12%) and title - some percentage of axes beyond bma dimensions
#Should specify minimum text size for output
max_size = np.array((10.0,10.0))
max_dpi = 300.0
#If both outsize and dpi are specified, don't try to change, just make the figure
if (args['outsize'] is None) and (args['dpi'] is None):
args['dpi'] = 150.0
#Unspecified out figure size for a given dpi
if (args['outsize'] is None) and (args['dpi'] is not None):
args['outsize'] = np.array(bma.shape[::-1])/args['dpi']
if np.any(np.array(args['outsize']) > max_size):
args['outsize'] = max_size
#Specified output figure size, no specified dpi
elif (args['outsize'] is not None) and (args['dpi'] is None):
args['dpi'] = np.min([np.max(np.array(bma.shape[::-1])/np.array(args['outsize'])), max_dpi])
print
print "Saving output figure:"
print "Filename: ", outf
print "Size (in): ", args['outsize']
print "DPI (px/in): ", args['dpi']
print "Input dimensions (px): ", bma.shape[::-1]
print "Output dimensions (px): ", tuple(np.array(args['outsize'])*args['dpi'])
print
fig.set_size_inches(args['outsize'])
#fig.set_size_inches(54.427, 71.87)
#fig.set_size_inches(40, 87)
fig.savefig(outf, dpi=args['dpi'], bbox_inches='tight', pad_inches=0, facecolor=fig.get_facecolor(), edgecolor='none')
#Show the plot - want to show all at once
if not args['of']:
plt.show()
#z2_fn = '/nobackup/deshean/hma/hma1_2016dec22/hma_8m_tile/hma_8m.vrt'
#z2_fn = os.path.join(topdir,'hma/hma1_2016dec22/hma_8m_tile/hma_8m.vrt')
#z2_fn = os.path.join(topdir,'hma/hma1_2016dec22/hma_8m_tile_round2_20170220/hma_8m_round2.vrt')
#z2_fn = os.path.join(topdir,'hma/hma_8m_mos_20170410/hma_8m.vrt')
z2_fn = os.path.join(topdir,
'hma/mos/%s/mos_8m/%s_8m.vrt' % (mosdir, mosdir))
#z2_date = 2015.0
z2_date = datetime(2015, 1, 1)
elif site == 'other':
outdir = os.path.join(topdir, 'mb')
aea_srs = geolib.conus_aea_srs
#glac_shp_fn = '/Users/dshean/data/conus_glacierpoly_24k/rainier_24k_1970-2015_mb_aea.shp'
#glac_shp_fn = '/Users/dshean/data/conus_glacierpoly_24k/conus_glacierpoly_24k_aea.shp'
glac_shp_fn = '/Users/dshean/data/conus_glacierpoly_24k/conus_glacierpoly_24k_32610_scg_2008_aea.shp'
z1_fn = sys.argv[1]
z1_date = timelib.mean_date(timelib.fn_getdatetime_list(z1_fn))
z2_fn = sys.argv[2]
z2_date = timelib.mean_date(timelib.fn_getdatetime_list(z2_fn))
else:
sys.exit()
ts = datetime.now().strftime('%Y%m%d_%H%M')
out_fn = '%s_mb_%s.csv' % (site, ts)
out_fn = os.path.join(outdir, out_fn)
#Write out temporary file line by line, incase processes interrupted
import csv
f = open(os.path.splitext(out_fn)[0] + '_temp.csv', 'wb')
writer = csv.writer(f)
#List to hold output
import numpy as np
from pygeotools.lib import timelib
"""
Create clean filenames with center date for velocity maps generated from WV DEMs
"""
#Rename all
#mkdir vm
#for i in */2*vm.tif; do ln -s ../$i vm/$(~/src/vmap/vmap/wv_cdate.py $i)_vm.tif; done
#parallel "fn=$(~/src/vmap/vmap/wv_cdate.py {}); ln -s ../{} vm/${fn}_vm.tif" ::: */2*vm.tif
import os
fn = sys.argv[1]
dt_list = timelib.fn_getdatetime_list(fn)
dt_list = np.sort(dt_list)
dtmin = dt_list.min()
dtmax = dt_list.max()
c_date = timelib.mean_date(dt_list)
ndays = timelib.dt_ptp(dt_list)
ndays = timelib.dt_ptp((dtmin, dtmax))
nyears = ndays/365.25
#s = '%s_%04idays_%s-%s' % (c_date.strftime('%Y%m%d'), ndays, dtmin.strftime('%Y%m%d'), dtmax.strftime('%Y%m%d'))
#s = '%s_%s-%s_%0.2fyr' % (c_date.strftime('%Y%m%d'), dtmin.strftime('%Y%m%d'), dtmax.strftime('%Y%m%d'), nyears)
#s = '%s_%s-%s_%04iday' % (c_date.strftime('%Y%m%d'), dtmin.strftime('%Y%m%d'), dtmax.strftime('%Y%m%d'), ndays)
#Added %H%M here, as there were some inputs acquired on same days
s = '%s__%s-%s__%04iday' % (c_date.strftime('%Y%m%d_%H%M'), dtmin.strftime('%Y%m%d_%H%M'), dtmax.strftime('%Y%m%d_%H%M'), ndays)
print(s)
#outdir="vm"
def main():
if len(sys.argv) != 2:
sys.exit("Usage: %s dz.tif" % os.path.basename(sys.argv[0]))
#This is mean density for N Cascades snow
#rho = 0.5
#Density of pure ice
rho = 0.917
#Clip negative values to 0
filt = False
src_fn = sys.argv[1]
src_ds = iolib.fn_getds(src_fn)
res = geolib.get_res(src_ds, square=True)[0]
bma = iolib.ds_getma(src_ds)
#Attempt to extract t1 and t2 from input filename
ts = timelib.fn_getdatetime_list(src_fn)
#Hardcode timestamps
#ts = [datetime.datetime(2013,9,10), datetime.datetime(2014,5,14)]
dt_yr = None
if len(ts) == 2:
dt = ts[1] - ts[0]
year = datetime.timedelta(days=365.25)
dt_yr = dt.total_seconds() / year.total_seconds()
#Can add filter here to remove outliers, perc_fltr(0.01, 99.9)
if filt:
mask = np.ma.getmaskarray(bma)
bma[bma < 0] = 0
bma = np.ma.array(bma, mask=mask)
#Print out stats
print('\n')
stats = malib.print_stats(bma)
count = stats[0]
area = res**2 * count
mean = stats[3]
med = stats[5]
s_m3 = np.ma.sum(bma) * res**2
s_km3 = s_m3 / 1E9
s_mwe = mean * rho
s_gt = s_km3 * rho
s_mm = s_gt / 374
if dt_yr is not None:
print("%s to %s: %0.2f yr" % (ts[0], ts[1], dt_yr))
print("%0.0f m^3 (%0.0f m^3/yr)" % (s_m3, s_m3 / dt_yr))
print("%0.3f km^3 (%0.3f km^3/yr)" % (s_km3, s_km3 / dt_yr))
print("Density: %0.3f g/cc" % rho)
print("%0.3f GT (%0.3f GT/yr)" % (s_gt, s_gt / dt_yr))
print("%0.6f mm SLR (%0.6f mm/yr)" % (s_mm, s_mm / dt_yr))
print("%0.3f m.w.e. (%0.3f m.w.e./yr)" % (s_mwe, s_mwe / dt_yr))
else:
print("Area: %0.2f km2" % (area / 1E6))
print("%0.0f m^3" % s_m3)
print("%0.3f km^3" % s_km3)
print("Density: %0.3f g/cc" % rho)
print("%0.3f GT" % s_gt)
print("%0.6f mm SLR" % s_mm)
print("%0.3f m.w.e." % s_mwe)
#! /usr/bin/env python
"""
Create seasonal velocity composites for specified input relative date ranges
"""
import sys
import subprocess
from pygeotools.lib import timelib
from datetime import datetime, timedelta
fn_list = sys.argv[1:]
dt_list = [timelib.fn_getdatetime_list(fn) for fn in fn_list]
season = 'winter'
#season='summer'
#Define relative start/end dates for each season
if season == 'winter':
dt_min = (10, 1)
dt_max = (6, 30)
else:
dt_min = (6, 1)
dt_max = (10, 31)
#Dummy variable for year to compute day of year
yr = 2016
dt_min_doy = timelib.dt2doy(datetime(yr, *dt_min))
dt_max_doy = timelib.dt2doy(datetime(yr, *dt_max))
if dt_min_doy > dt_max_doy:
max_delta = datetime(yr + 1, *dt_max) - datetime(yr, *dt_min)
else:
def main():
parser = getparser()
#Create dictionary of arguments
args = vars(parser.parse_args())
#Want to enable -full when -of is specified, probably a fancy way to do this with argparse
if args['of']:
args['full'] = True
args['imshow_kwargs'] = pltlib.imshow_kwargs
#Need to implement better extent handling for link and overlay
#Can use warplib extent parsing
extent = 'first'
#extent = 'union'
#Should accept 'ts' or 'fn' or string here, default is 'ts'
#Can also accept list for subplots
title = args['title']
if args['link']:
fig = plt.figure(0)
n_ax = len(args['filelist'])
src_ds_list = [gdal.Open(fn) for fn in args['filelist']]
t_srs = geolib.get_ds_srs(src_ds_list[0])
res_stats = geolib.get_res_stats(src_ds_list, t_srs=t_srs)
#Use min res
res = res_stats[0]
extent = 'intersection'
extent = geolib.ds_geom_union_extent(src_ds_list, t_srs=t_srs)
#extent = geolib.ds_geom_intersection_extent(src_ds_list, t_srs=t_srs)
#print(res, extent)
for n, fn in enumerate(args['filelist']):
if not iolib.fn_check(fn):
print('Unable to open input file: %s' % fn)
continue
if title == 'ts':
ts = timelib.fn_getdatetime_list(fn)
if ts:
print("Timestamp list: ", ts)
if len(ts) == 1:
args['title'] = ts[0].date()
elif len(ts) > 1:
args['title'] = "%s to %s" % (ts[0].date(), ts[1].date())
else:
print("Unable to extract timestamp")
args['title'] = None
elif title == 'fn':
args['title'] = fn
#if title is not None:
# plt.title(title, fontdict={'fontsize':12})
#Note: this won't work if img1 has 1 band and img2 has 3 bands
#Hack for now
if not args['link']:
fig = plt.figure(n)
n_ax = 1
#fig.set_facecolor('black')
fig.set_facecolor('white')
fig.canvas.set_window_title(os.path.split(fn)[1])
#fig.suptitle(os.path.split(fn)[1], fontsize=10)
if args['overlay']:
#Should automatically search for shaded relief with same base fn
#bg_fn = os.path.splitext(fn)[0]+'_hs_az315.tif'
#Clip/warp background dataset to match overlay dataset
src_ds, bg_ds = warplib.memwarp_multi_fn([fn, args['overlay']],
extent=extent,
res='max')
#Want to load up the unique bg array for each input
args['bg'] = get_bma(bg_ds, 1, args['full'])
else:
src_ds = gdal.Open(fn, gdal.GA_ReadOnly)
if args['link']:
src_ds = warplib.memwarp_multi([
src_ds,
],
res=res,
extent=extent,
t_srs=t_srs)[0]
args['cbar_kwargs'] = pltlib.cbar_kwargs
if args['no_cbar']:
args['cbar_kwargs'] = None
nbands = src_ds.RasterCount
b = src_ds.GetRasterBand(1)
dt = gdal.GetDataTypeName(b.DataType)
#Eventually, check dt of each band
print("%s (%i bands)" % (fn, nbands))
#Singleband raster
if (nbands == 1):
if args['cmap'] is None:
#Special case to handle ASP float32 grayscale data
if '-L_sub' in fn or '-R_sub' in fn:
args['cmap'] = 'gray'
else:
if (dt == 'Float64') or (dt == 'Float32') or (dt
== 'Int32'):
args['cmap'] = 'cpt_rainbow'
#This is for WV images
elif (dt == 'UInt16'):
args['cmap'] = 'gray'
elif (dt == 'Byte'):
args['cmap'] = 'gray'
else:
args['cmap'] = 'cpt_rainbow'
"""
if 'count' in fn:
args['clim_perc'] = (0,100)
cbar_kwargs['extend'] = 'neither'
args['cmap'] = 'cpt_rainbow'
if 'mask' in fn:
args['clim'] = (0, 1)
#Could be (0, 255)
#args['clim_perc'] = (0,100)
#Want absolute clim of 0, then perc of 100
cbar_kwargs['extend'] = 'neither'
args['cmap'] = 'gray'
"""
bma = get_bma(src_ds, 1, args['full'])
if args['invert']:
bma *= -1
#Note n+1 here ensures we're assigning subplot correctly here (n is 0-relative, subplot is 1)
bma_fig(fig, bma, n_subplt=n_ax, subplt=n + 1, ds=src_ds, **args)
#3-band raster, likely disparity map
#This doesn't work when alpha band is present
elif (nbands == 3) and (dt == 'Byte'):
#For some reason, tifs are vertically flipped
if (os.path.splitext(fn)[1] == '.tif'):
args['imshow_kwargs']['origin'] = 'lower'
#Use gdal dataset here instead of imread(fn)?
imgplot = plt.imshow(plt.imread(fn), **args['imshow_kwargs'])
pltlib.hide_ticks(imgplot.axes)
#Handle the 3-band disparity map case here
#elif ((dt == 'Float32') or (dt == 'Int32')):
else:
if args['cmap'] is None:
args['cmap'] = 'cpt_rainbow'
bn = 1
while bn <= nbands:
bma = get_bma(src_ds, bn, args['full'])
bma_fig(fig,
bma,
n_subplt=nbands,
subplt=bn,
ds=src_ds,
**args)
bn += 1
#Want to be better about this else case - lazy for now
#else:
# bma = get_bma(src_ds, 1, args['full'])
# bma_fig(fig, bma, **args)
plt.tight_layout()
#Write out the file
#Note: make sure display is local for savefig
if args['of']:
outf = str(os.path.splitext(fn)[0]) + '_fig.' + args['of']
#outf = str(os.path.splitext(fn)[0])+'_'+str(os.path.splitext(args['overlay'])[0])+'_fig.'+args['of']
#Note: need to account for colorbar (12%) and title - some percentage of axes beyond bma dimensions
#Should specify minimum text size for output
max_size = np.array((10.0, 10.0))
max_dpi = 300.0
#If both outsize and dpi are specified, don't try to change, just make the figure
if (args['outsize'] is None) and (args['dpi'] is None):
args['dpi'] = 150.0
#Unspecified out figure size for a given dpi
if (args['outsize'] is None) and (args['dpi'] is not None):
args['outsize'] = np.array(bma.shape[::-1]) / args['dpi']
if np.any(np.array(args['outsize']) > max_size):
args['outsize'] = max_size
#Specified output figure size, no specified dpi
elif (args['outsize'] is not None) and (args['dpi'] is None):
args['dpi'] = np.min([
np.max(
np.array(bma.shape[::-1]) / np.array(args['outsize'])),
max_dpi
])
print()
print("Saving output figure:")
print("Filename: ", outf)
print("Size (in): ", args['outsize'])
print("DPI (px/in): ", args['dpi'])
print("Input dimensions (px): ", bma.shape[::-1])
print("Output dimensions (px): ",
tuple(np.array(args['outsize']) * args['dpi']))
print()
fig.set_size_inches(args['outsize'])
#fig.set_size_inches(54.427, 71.87)
#fig.set_size_inches(40, 87)
fig.savefig(outf,
dpi=args['dpi'],
bbox_inches='tight',
pad_inches=0,
facecolor=fig.get_facecolor(),
edgecolor='none')
#fig.savefig(outf, dpi=args['dpi'], facecolor=fig.get_facecolor(), edgecolor='none')
#Show the plot - want to show all at once
if not args['of']:
plt.show()
def main():
parser = getparser()
args = parser.parse_args()
fn = args.fn
#This is mean density for N Cascades snow
#rho = 0.5
#Density of pure ice
rho = args.rho
#If number is in kg/m^3 rather than g/cc
if rho > 10.:
rho /= 1000.
#Clip negative values to 0
filt = False
src_ds = iolib.fn_getds(fn)
res = geolib.get_res(src_ds, square=True)[0]
bma = iolib.ds_getma(src_ds)
#Attempt to extract t1 and t2 from input filename
ts = timelib.fn_getdatetime_list(fn)
#Hardcode timestamps
#ts = [datetime.datetime(2013,9,10), datetime.datetime(2014,5,14)]
dt_yr = None
if len(ts) == 2:
dt = ts[1] - ts[0]
year = datetime.timedelta(days=365.25)
dt_yr = dt.total_seconds() / year.total_seconds()
#Can add filter here to remove outliers, perc_fltr(0.01, 99.9)
if filt:
mask = np.ma.getmaskarray(bma)
bma[bma < 0] = 0
bma = np.ma.array(bma, mask=mask)
#Print out stats
print('\n')
stats = malib.print_stats(bma)
print('\n')
count = stats[0]
area = res**2 * count
mean = stats[3]
med = stats[5]
s_m3 = np.ma.sum(bma) * res**2
s_km3 = s_m3 / 1E9
s_mwe = mean * rho
s_gt = s_km3 * rho
#s_mm = s_gt/374
#https://climatesanity.wordpress.com/conversion-factors-for-ice-and-water-mass-and-volume/
s_mm = s_gt / 360
if dt_yr is not None:
print("%s to %s: %0.2f yr" % (ts[0], ts[1], dt_yr))
print("%0.0f m^3 (%0.0f m^3/yr)" % (s_m3, s_m3 / dt_yr))
print("%0.3f km^3 (%0.3f km^3/yr)" % (s_km3, s_km3 / dt_yr))
print("Density: %0.3f g/cc" % rho)
print("%0.3f GT (%0.3f GT/yr)" % (s_gt, s_gt / dt_yr))
print("%0.6f mm SLR (%0.6f mm/yr)" % (s_mm, s_mm / dt_yr))
print("%0.3f m.w.e. (%0.3f m.w.e./yr)" % (s_mwe, s_mwe / dt_yr))
else:
print("Area: %0.2f km2" % (area / 1E6))
print("%0.0f m^3" % s_m3)
print("%0.3f km^3" % s_km3)
print("Density: %0.3f g/cc" % rho)
print("%0.3f GT" % s_gt)
print("%0.6f mm SLR" % s_mm)
print("%0.3f m.w.e." % s_mwe)
print('\n')
#This will check input param for validity, could do beforehand
dem1_ds, dem2_ds = warplib.memwarp_multi_fn([dem1_fn, dem2_fn], extent='intersection', res=res, t_srs='first')
print("Loading input DEMs into masked arrays")
dem1 = iolib.ds_getma(dem1_ds, 1)
dem2 = iolib.ds_getma(dem2_ds, 1)
dem2_ts = timelib.fn_getdatetime(dem2_fn)
dz = dem2 - dem1
outprefix = os.path.splitext(os.path.split(dem1_fn)[1])[0]+'_'+os.path.splitext(os.path.split(dem2_fn)[1])[0]
elif args.dz_fn is not None:
dz_fn = args.dz_fn
dem1_ds, dz_ds = warplib.memwarp_multi_fn([dem1_fn, dz_fn], extent='intersection', res=res, t_srs='first')
print("Loading input DEM and Snow depth into masked arrays")
dem1 = iolib.ds_getma(dem1_ds, 1)
dz = iolib.ds_getma(dz_ds, 1)
#Try to pull out second timestamp from dz_fn
dem2_ts = timelib.fn_getdatetime_list(dz_fn)[-1]
outprefix = os.path.splitext(os.path.split(dz_fn)[1])[0]
outprefix = os.path.join(args.outdir, outprefix)
#Calculate water year
wy = dem1_ts.year + 1
if dem1_ts.month >= 10:
wy = dem1_ts.year
#These need to be updated in geolib to use gdaldem API
hs = geolib.gdaldem_mem_ds(dem1_ds, processing='hillshade', returnma=True)
hs_clim = (1,255)
dem_clim = malib.calcperc(dem1, (1,99))
res = geolib.get_res(dem1_ds)[0]
def main():
parser = getparser()
args = parser.parse_args()
t_unit = args.dt
plot = args.plot
remove_offsets = args.remove_offsets
mask_fn = args.mask_fn
if mask_fn is not None:
remove_offsets = True
#Input is 3-band disparity map, extract bands directly
src_fn = args.disp_fn
if not iolib.fn_check(src_fn):
sys.exit("Unable to locate input file: %s" % src_fn)
src_ds = iolib.fn_getds(src_fn)
if src_ds.RasterCount != 3:
sys.exit("Input file must be ASP disparity map (3 bands: x, y, mask)")
#Extract pixel resolution
h_res, v_res = geolib.get_res(src_ds)
#Horizontal scale factor
#If running on disparity_view output (gdal_translate -outsize 5% 5% F.tif F_5.tif)
#h_res /= 20
#v_res /= 20
#Load horizontal and vertical disparities
h = iolib.ds_getma(src_ds, bnum=1)
v = iolib.ds_getma(src_ds, bnum=2)
#ASP output has northward motion as negative values in band 2
v *= -1
t1, t2 = timelib.fn_getdatetime_list(src_fn)
dt = t2 - t1
#Default t_factor is in 1/years
t_factor = timelib.get_t_factor(t1, t2)
#Input timestamp arrays if inputs are mosaics
if False:
t1_fn = ''
t2_fn = ''
if os.path.exists(t1_fn) and os.path.exists(t2_fn):
t_factor = timelib.get_t_factor_fn(t1_fn, t2_fn)
if t_factor is None:
sys.exit("Unable to determine input timestamps")
if t_unit == 'day':
t_factor *= 365.25
print("Input dates:")
print(t1)
print(t2)
print(dt)
print(t_factor, t_unit)
#Scale values for polar stereographic distortion
srs = geolib.get_ds_srs(src_ds)
proj_scale_factor = 1.0
#Want to scale to get correct distances for polar sterographic
if srs.IsSame(geolib.nps_srs) or srs.IsSame(geolib.sps_srs):
proj_scale_factor = geolib.scale_ps_ds(src_ds)
#Convert disparity values in pixels to m/t_unit
h_myr = h * h_res * proj_scale_factor / t_factor
h = None
v_myr = v * v_res * proj_scale_factor / t_factor
v = None
#Velocity Magnitude
m = np.ma.sqrt(h_myr**2 + v_myr**2)
print("Velocity Magnitude stats")
malib.print_stats(m)
#Remove x and y offsets over control surfaces
offset_str = ''
if remove_offsets:
if mask_fn is None:
from demcoreg.dem_mask import get_mask
print(
"\nUsing demcoreg to prepare mask of stable control surfaces\n"
)
#TODO: Accept mask_list as in demcoreg
#mask_list = args.mask_list
# for now keep it simple, limit to non-glacier surfaces
mask_list = [
'glaciers',
]
mask = get_mask(src_ds, mask_list=mask_list, dem_fn=src_fn)
else:
print("\nWarping input raster mask")
#This can be from previous dem_mask.py run (e.g. *rockmask.tif)
mask_ds = warplib.memwarp_multi_fn([
mask_fn,
],
res=src_ds,
extent=src_ds,
t_srs=src_ds)[0]
mask = iolib.ds_getma(mask_ds)
#The default from ds_getma is a masked array, so need to isolate boolean mask
#Assume input is 0 for masked, 1 for unmasked (valid control surface)
mask = mask.filled().astype('bool')
#This should work, as the *rockmask.py is 1 for unmasked, 0 for masked, with ndv=0
#mask = np.ma.getmaskarray(mask)
#Vector mask - untested
if os.path.splitext(mask_fn)[1] == 'shp':
mask = geolib.shp2array(mask_fn, src_ds)
print("\nRemoving median x and y offset over static control surfaces")
h_myr_count = h_myr.count()
h_myr_static_count = np.ma.array(h_myr, mask=mask).count()
h_myr_mad, h_myr_med = malib.mad(np.ma.array(h_myr, mask=mask),
return_med=True)
v_myr_mad, v_myr_med = malib.mad(np.ma.array(v_myr, mask=mask),
return_med=True)
print("Static pixel count: %i (%0.1f%%)" %
(h_myr_static_count,
100 * float(h_myr_static_count) / h_myr_count))
print("median (+/-NMAD)")
print("x velocity offset: %0.2f (+/-%0.2f) m/%s" %
(h_myr_med, h_myr_mad, t_unit))
print("y velocity offset: %0.2f (+/-%0.2f) m/%s" %
(v_myr_med, v_myr_mad, t_unit))
h_myr -= h_myr_med
v_myr -= v_myr_med
offset_str = '_offsetcorr_h%0.2f_v%0.2f' % (h_myr_med, v_myr_med)
#Velocity Magnitude
m = np.ma.sqrt(h_myr**2 + v_myr**2)
print("Velocity Magnitude stats after correction")
malib.print_stats(m)
if plot:
fig_fn = os.path.splitext(src_fn)[0] + '.png'
label = 'Velocity (m/%s)' % t_unit
f, ax = make_plot(m, fig_fn, label)
plotvec(h_myr, v_myr)
plt.tight_layout()
plt.savefig(fig_fn,
dpi=300,
bbox_inches='tight',
pad_inches=0,
edgecolor='none')
print("Writing out files")
gt = src_ds.GetGeoTransform()
proj = src_ds.GetProjection()
dst_fn = os.path.splitext(src_fn)[0] + '_vm%s.tif' % offset_str
iolib.writeGTiff(m, dst_fn, create=True, gt=gt, proj=proj)
dst_fn = os.path.splitext(src_fn)[0] + '_vx%s.tif' % offset_str
iolib.writeGTiff(h_myr, dst_fn, create=True, gt=gt, proj=proj)
dst_fn = os.path.splitext(src_fn)[0] + '_vy%s.tif' % offset_str
iolib.writeGTiff(v_myr, dst_fn, create=True, gt=gt, proj=proj)
src_ds = None
#Ellispoidal heights at sea level should be above -100 m
zlim = (-200,8388)
zlim_pad = 150
#Window size for median and gauss filters (px)
size=3
#Input stack npz
stack_fn=sys.argv[1]
#User-provided timestamps with format YYYYMMDD
if len(sys.argv) > 2:
dt_list_str = sys.argv[2:]
if len(dt_list_str) == 1:
dt_list_str = dt_list_str[0].split(' ')
dt_list = [timelib.fn_getdatetime_list(dt_str)[0] for dt_str in dt_list_str]
else:
#SRTM, then systematic ASTER timestamps
dt_list = [datetime(2000,2,11), datetime(2000,5,31), datetime(2009,5,31), datetime(2018,5,31)]
#Use tif on disk if available
out_fn=os.path.splitext(stack_fn)[0]
#Otherwise load stack and compute trend/intercept if necessary
trend_fn=out_fn+'_trend.tif'
trend_ds = iolib.fn_getds(trend_fn)
trend = iolib.ds_getma(trend_ds)/365.25
intercept_fn=out_fn+'_intercept.tif'
#Hmmm, no 365.25 factor here. Clean up here and in stack generation
intercept = iolib.fn_getma(intercept_fn)