#with fiona.open(canal_path) as canal_src: # bounds = canal_src.bounds grid_extent = fiona.open(path + "/grid_extent.geojson") grid_feature = grid_extent[0] grid_shape = shape(grid_feature['geometry']) bounds = grid_shape.bounds rv_array, affine = rasterize(canal_path, pixel_size=pixel_size, bounds=bounds) # binary_raster_path = "/sciclone/aiddata10/REU/projects/afghanistan_gie/distance_to_canals/binary_canals.tif" # binary_raster_path = "/sciclone/aiddata10/REU/projects/afghanistan_gie/distance_to_canals/binary_starts.tif" binary_raster_path = path + "/binary_starts.tif" export_raster(rv_array, affine, binary_raster_path) # import tarfile # def make_tarfile(dst, src): # with tarfile.open(dst, "w:gz") as tar: # tar.add(src, arcname=os.path.basename(src)) # make_tarfile(dst=binary_raster_path + ".tar.gz" , src=binary_raster_path) # ----------------------------------------------------------------------------- # distance_raster_path = "/sciclone/aiddata10/REU/projects/afghanistan_gie/distance_to_canals/distance_canals.tif" # distance_raster_path = "/sciclone/aiddata10/REU/projects/afghanistan_gie/distance_to_canals/distance_starts.tif" distance_raster_path = path + "/distance_starts.tif"
affine = Affine(pixel_size, 0, xmin, 0, -pixel_size, ymax)
shape = (int((ymax - ymin) / pixel_size), int((xmax - xmin) / pixel_size))
shorelines, _ = rasterize(shorelines_path, affine=affine, shape=shape)
shorelines = np.logical_not(shorelines).astype(int)
lakes, _ = rasterize(lakes_path, affine=affine, shape=shape)
rivers, _ = rasterize(rivers_path, affine=affine, shape=shape)
water = shorelines + lakes + rivers
water_output_raster_path = "water_binary.tif"
export_raster(water, affine, water_output_raster_path)
# -----------------------------------------------------------------------------
# import rasterio
# water_src = rasterio.open(water_output_raster_path)
# water = water_src.read()[0]
# affine = water_src.affine
distance_output_raster_path = "water_distance.tif"
def raster_conditional(rarray):
return (rarray == 1)
xmin = -180
xmax = 180
ymin = -90
ymax = 90
affine = Affine(pixel_size, 0, xmin, 0, -pixel_size, ymax)
shape = (int((ymax - ymin) / pixel_size), int((xmax - xmin) / pixel_size))
print "Rasterizing"
shorelines, _ = rasterize(shorelines_path, affine=affine, shape=shape)
shorelines = np.logical_not(shorelines).astype(int)
water_output_raster_path = "/sciclone/aiddata10/REU/geo/data/rasters/distance_to/coast_236/binary/coast_binary.tif"
export_raster(shorelines, affine, water_output_raster_path)
# -----------------------------------------------------------------------------
# import rasterio
# water_src = rasterio.open(water_output_raster_path)
# water = water_src.read()[0]
# affine = water_src.affine
distance_output_raster_path = "/sciclone/aiddata10/REU/geo/data/rasters/distance_to/coast_236/coast_distance.tif"
print "Generating distance raster"
def raster_conditional(rarray):
return (rarray == 1)
for f in src_files:
i += 1
print "Processing: ", f
features = fiona.open(f)
rv_array, _ = rasterize(vectors=features, pixel_size=pixel_size,
affine=affine, shape=shape)
output += rv_array * i
# any cell with value > i must have multiple features
output = np.where(output > i, mixed_val, output)
export_raster(output, affine=affine, nodata=255, path=categorical_output_raster_path)
# ------------------------
# distance raster
print "Generating distance raster"
def raster_conditional(rarray):
return (rarray > 0)
dist = build_distance_array(output, affine=affine,
output=distance_output_raster_path,
conditional=raster_conditional)
ymin = -90
ymax = 90
affine = Affine(pixel_size, 0, xmin,
0, -pixel_size, ymax)
shape = (int((ymax-ymin)/pixel_size), int((xmax-xmin)/pixel_size))
borders, _ = rasterize(borders_path, affine=affine, shape=shape)
borders_output_raster_path = "/sciclone/aiddata10/REU/geo/data/rasters/distance_to/gadm28_borders/binary/gadm28_borders_binary.tif"
export_raster(borders, affine, borders_output_raster_path)
# -----------------------------------------------------------------------------
# import rasterio
# borders_src = rasterio.open(borders_output_raster_path)
# borders = borders_src.read()[0]
# affine = borders_src.affine
distance_output_raster_path = "/sciclone/aiddata10/REU/geo/data/rasters/distance_to/gadm28_borders/gadm28_borders_distance.tif"
def raster_conditional(rarray):
return (rarray == 1)
# if r == 200:
# print "Run time: {0} seconds for {1} rows ({2}s avg)".format(row_dur, row_count, row_dur/row_count)
# print "t1 total: {0}, count: {1}, avg: {2}".format(t1, t1c, t1/t1c)
# print "t11 total: {0}, count: {1}, avg: {2}".format(t11, t11c, t11/t11c)
# print "t111 total: {0}, count: {1}, avg: {2}".format(t111, t111c, t111/t111c)
# print "t2 total: {0}, count: {1}, avg: {2}".format(t2, t2c, t2/t2c)
# print "t22 total: {0}, count: {1}, avg: {2}".format(t22, t22c, t22/t22c)
# print "t3 total: {0}, count: {1}, avg: {2}".format(t3, t3c, t3/t3c)
# raise
# print rv_array
# print z
print rv_array.shape
print nrows * ncols
print "t1 total: {0}, count: {1}, avg: {2}".format(t1, t1c, t1 / t1c)
print "t111 total: {0}, count: {1}, avg: {2}".format(t111, t111c, t111 / t111c)
print "t2 total: {0}, count: {1}, avg: {2}".format(t2, t2c, t2 / t2c)
print "t22 total: {0}, count: {1}, avg: {2}".format(t22, t22c, t22 / t22c)
print "t3 total: {0}, count: {1}, avg: {2}".format(t3, t3c, t3 / t3c)
dur = time.time() - t_start
print "Run time: {0} seconds".format(round(dur, 2))
export_raster(z, affine, output_raster_path)
0, -pixel_size, ymax)
shape = (int((ymax-ymin)/pixel_size), int((xmax-xmin)/pixel_size))
roads = np.zeros(shape, dtype='byte')
for n in names:
path = "{0}/{1}".format("/sciclone/aiddata10/REU/geo/raw/groads",
"groads-v1-{0}-shp/gROADS-v1-{0}.shp".format(n))
rv_array, _ = rasterize(path, affine=affine, shape=shape)
roads = roads | rv_array
roads_output_raster_path = "/sciclone/aiddata10/REU/geo/data/rasters/distance_to/groads/binary/groads_binary.tif"
export_raster(roads, affine, roads_output_raster_path)
# -----------------------------------------------------------------------------
# import rasterio
# roads_src = rasterio.open(roads_output_raster_path)
# roads = roads_src.read()[0]
# affine = roads_src.affine
distance_output_raster_path = "/sciclone/aiddata10/REU/geo/data/rasters/distance_to/groads/groads_distance.tif"
def raster_conditional(rarray):
return (rarray == 1)
def mk_proj(inhdf, outdir):
# get data time stamp
datestring = inhdf.split(".")[1][1:]
newtime = get_time(datestring)
# get new data export name
dayout = os.path.join(outdir, "modis_lst_day_cmg_" + newtime + ".tif")
nightout = os.path.join(outdir, "modis_lst_night_cmg_" + newtime + ".tif")
hdf = SD(inhdf, SDC.READ)
# Read dataset.
data2D_day = hdf.select('LST_Day_CMG')
data_day = data2D_day[:, :].astype(np.float64)
data2D_night = hdf.select('LST_Night_CMG')
data_night = data2D_night[:, :].astype(np.float64)
# Read global attribute.
fattrs = hdf.attributes(full=1)
ga = fattrs["StructMetadata.0"]
gridmeta = ga[0]
# Construct the grid. The needed information is in a global attribute
# called 'StructMetadata.0'. Use regular expressions to tease out the
# extents of the grid.
ul_regex = re.compile(
r'''UpperLeftPointMtrs=\(
(?P<upper_left_x>[+-]?\d+\.\d+)
,
(?P<upper_left_y>[+-]?\d+\.\d+)
\)''', re.VERBOSE)
match = ul_regex.search(gridmeta)
x0 = np.float(match.group('upper_left_x'))
y0 = np.float(match.group('upper_left_y'))
lr_regex = re.compile(
r'''LowerRightMtrs=\(
(?P<lower_right_x>[+-]?\d+\.\d+)
,
(?P<lower_right_y>[+-]?\d+\.\d+)
\)''', re.VERBOSE)
match = lr_regex.search(gridmeta)
x1 = np.float(match.group('lower_right_x'))
y1 = np.float(match.group('lower_right_y'))
ny, nx = data_day.shape
xinc = (x1 - x0) / nx
yinc = (y1 - y0) / ny
x = np.linspace(x0, x0 + xinc * nx, nx)
y = np.linspace(y0, y0 + yinc * ny, ny)
xv, yv = np.meshgrid(x, y)
# In basemap, the sinusoidal projection is global, so we won't use it.
# Instead we'll convert the grid back to lat/lons.
#sinu = pyproj.Proj("+proj=sinu +R=6371007.181 +nadgrids=@null +wktext")
#+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs
#+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs
sinu = pyproj.Proj(
"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
)
wgs84 = pyproj.Proj("+proj=longlat +datum=WGS84 +no_defs")
lon, lat = pyproj.transform(sinu, wgs84, xv, yv)
xmin = lon[-1, 0]
ymax = lat[0, -1]
pixel_size = 0.05
affine = Affine(pixel_size, 0, xmin, 0, -pixel_size, ymax)
data_day = data_day * scale_factor
data_night = data_night * scale_factor
distancerasters.export_raster(data_day, affine, dayout, nodata=0)
distancerasters.export_raster(data_night, affine, nightout, nodata=0)
# build intermediary rasters for individual categories/years
print "selected {0} features for field: {1}, and year: {2}".format(
len(features_filtered), cat, year)
if len(features_filtered) == 0:
print "\tno feature selected for violence type {0} and year {1}".format(
cat, year)
pass
else:
cat_raster, _ = rasterize(features_filtered,
affine=affine,
shape=out_shape)
output_file = "ucdp_" + field_values[cat] + "_" + str(year) + ".tif"
output_path = os.path.join(final_output, output_file)
export_raster(cat_raster,
affine=affine,
path=output_path,
out_dtype='float64',
nodata=None)
t2 = time.time()
sum_time = t2 - t1
print sum_time