def rasterize_list_poly(list_poly, in_met, i):
print('Poly stack number ' + str(i + 1))
# create input image
gt, proj, npix_x, npix_y = in_met
drv = gdal.GetDriverByName('MEM')
dst = drv.Create('', npix_x, npix_y, 1, gdal.GDT_Float32)
sp = dst.SetProjection(proj)
sg = dst.SetGeoTransform(gt)
band = dst.GetRasterBand(1)
band.SetNoDataValue(0)
band.Fill(0, 0)
del sp, sg
img = GeoImg(dst)
out_density = np.zeros(np.shape(img.img))
srs = osr.SpatialReference()
srs.ImportFromWkt(proj)
for j, poly in enumerate(list_poly):
print('Poly ' + str(j + 1) + ' out of ' + str(len(list_poly)))
ds_shp = ot.create_mem_shp(poly, srs)
mask = ot.geoimg_mask_on_feat_shp_ds(ds_shp, img)
out_density[mask] += 1
return out_density
def worldwide_coverage_density(list_poly, fn_out, res=0.05, nproc=1):
#worldwide raster in lat/lon proj
xmin = -180
ymax = 90
gt = (xmin, res, 0, ymax, 0, -res)
npix_x = int(360 / res)
npix_y = int(180 / res)
proj = osr.SpatialReference()
proj.ImportFromEPSG(4326)
ds_out = gdal.GetDriverByName('GTiff').Create(fn_out, npix_x, npix_y, 1,
gdal.GDT_Int16)
ds_out.SetGeoTransform(gt)
ds_out.SetProjection(proj.ExportToWkt())
band = ds_out.GetRasterBand(1)
band.SetNoDataValue(0)
band.Fill(0, 0)
img = GeoImg(ds_out)
out_density = np.zeros(np.shape(img.img))
if nproc == 1:
for i, poly in enumerate(list_poly):
print('Rasterizing poly number ' + str(i + 1) + ' in ' +
str(len(list_poly)))
ds_shp = ot.create_mem_shp(poly, proj)
mask = ot.geoimg_mask_on_feat_shp_ds(ds_shp, img)
out_density[mask] += 1
else:
print('Using ' + str(nproc) + ' processors...')
# speed up things with multiprocessing
pool = mp.Pool(nproc, maxtasksperchild=1)
in_met = (img.gt, img.proj_wkt, img.npix_x, img.npix_y)
pack_size = int(np.ceil(len(list_poly) / nproc))
argsin_packs = [{
'list_poly':
list_poly[i:min(i + pack_size, len(list_poly))],
'in_met':
in_met,
'i':
k
} for k, i in enumerate(np.arange(0, len(list_poly), pack_size))]
outputs = pool.map(wrapper_rasterize, argsin_packs, chunksize=1)
pool.close()
pool.join()
for output in outputs:
out_density += output
ds_out.GetRasterBand(1).WriteArray(out_density)
ds_out = None
def out_of_poly_mask(geoimg, poly_coords):
poly = ot.poly_from_coords(inter_poly_coords(poly_coords))
srs = osr.SpatialReference()
srs.ImportFromEPSG(4326)
# put in a memory vector
ds_shp = ot.create_mem_shp(poly, srs)
return ot.geoimg_mask_on_feat_shp_ds(ds_shp, geoimg)
poly = feature.GetGeometryRef()
area = feature.GetField('Area')
break
layer.ResetReading()
list_inters = get_footprints_inters_ext(list_files,poly,epsg_base,use_l1a_met=False)
print('Found '+str(len(list_inters))+ ' DEMs out of '+str(len(list_files))+' intersecting glacier '+gla[1])
for fn_inters in list_inters:
print('Working on file:'+fn_inters)
tmp_img = GeoImg(fn_inters)
mask_feat = ot.geoimg_mask_on_feat_shp_ds(ds_shp, tmp_img, layer_name=layer_name, feat_id='RGIId',
feat_val=gla[0])
# nb_px_mask = np.count_nonzero(mask_feat)
nb_px_valid = np.count_nonzero(~np.isnan(tmp_img.img[mask_feat]))
area_valid = nb_px_valid*tmp_img.dx**2/1000000
cov = area_valid/area*100
print('DEM ' + fn_inters + ' has intersection of ' + str(cov))
if cov > 5.:
list_final.append(os.path.basename(fn_inters))
list_cov.append(cov)
list_date.append(tmp_img.datetime)
mt.create_zip_from_flist([fn_inters],os.path.join(gla_dir,os.path.splitext(os.path.basename(fn_inters))[0]+'.zip'))
df = pd.DataFrame()
df = df.assign(dem_name=list_final,perc_valid_coverage=list_cov,date=list_date)
df.to_csv(os.path.join(gla_dir,'setsm_dem_coverage.csv'))
os.path.basename(fn_dhdt))[0].split('_')
sens_early = split_fn[-2]
sens_late = split_fn[-4]
date_early = split_fn[-1]
date_late = split_fn[-3]
site = split_fn[1]
for rgiid_valid in list_rgiid_valid:
print('Working on ' + rgiid_valid)
dhdt.img[np.abs(dhdt.img) > 15] = np.nan
mask = ot.geoimg_mask_on_feat_shp_ds(ds_shp,
dhdt,
layer_name=layer_name,
feat_id='RGIId',
feat_val=rgiid_valid)
mean, err, perc, area = ddem_hypso(dhdt.img,
np.ones(np.shape(dhdt.img)),
mask, dhdt.dx)
df_tmp = pd.DataFrame()
df_tmp = df_tmp.assign(
rgiid=[rgiid_valid],
dhdt=[mean],
err_dhdt=[err],
area=[area],
perc_meas=[perc],
sensor_early=[sens_early],
