def extract_environment(layer_name, x, postproc=lambda x:x, id_=None, lock=None):
"Expects ALL locations to be in decimal degrees."
fname = hashlib.sha1(x.tostring()+layer_name+str(id_)).hexdigest()+'.npy'
path, name = os.path.split(layer_name)
name = os.path.splitext(name)[0]
if fname in os.listdir('anopheles-caches'):
return name, numpy.load(os.path.join('anopheles-caches',fname))
else:
# if lock is not None:
# lock.acquire()
grid_lon, grid_lat, grid_data, grid_type = map_utils.import_raster(name,path)
# Convert to centroids
grid_lon += (grid_lon[1]-grid_lon[0])/2.
grid_lat += (grid_lat[1]-grid_lat[0])/2.
# Interpolate
extracted = map_utils.interp_geodata(grid_lon, grid_lat, postproc(grid_data).data, x[:,0], x[:,1], grid_data.mask, chunk=None, view='y-x+', order=0)
del grid_data
# if lock is not None:
# lock.release()
numpy.save(os.path.join('anopheles-caches',fname), extracted)
return name, extracted
def trees_to_map(brt_evaluator, species_name, layer_names, glob_name, glob_channels, bbox, config_filename, memlim = 4e9):
"""
Makes maps and writes them out in flt format.
"""
all_names = get_names(layer_names, glob_name, glob_channels)
short_layer_names = all_names[:len(layer_names)]
short_glob_names = all_names[len(layer_names):]
result_dirname = get_result_dir(config_filename)
llclat,llclon,urclat,urclon = bbox
rasters = {}
n_rasters = len(layer_names)+len(glob_channels)
lon,lat,glob,t = map_utils.import_raster(*os.path.split(glob_name)[::-1])
orig_glob_shape = glob.shape
llclati = np.where(lat>=llclat)[0][0]
llcloni = np.where(lon>=llclon)[0][0]
urclati = np.where(lat<=urclat)[0][-1]
urcloni = np.where(lon<=urclon)[0][-1]
glob = subset_raster(glob, llclati, llcloni, urclati, urcloni)
where_notmask = np.where(True-glob.mask)
raster_size = np.prod(glob.shape)*4
if raster_size * n_rasters > memlim:
warnings.warn('Species %s: Generating this map would require too much memory. Make the bounding box smaller.'%species_name)
for n, ch in zip(short_glob_names, glob_channels):
rasters[n] = (glob==ch)[where_notmask]
for n, p in zip(short_layer_names, layer_names):
lon,lat,data,t = map_utils.import_raster(*os.path.split(p)[::-1])
if data.shape != orig_glob_shape:
raise ValueError, 'Shape of raster %s does not match shape of Glob raster for species %s. Check config file.'%(n,species_name)
rasters[n] = subset_raster(data, llclati, llcloni, urclati, urcloni)[where_notmask]
ravelledmap = brt_evaluator(rasters)
out_raster = glob.astype('float32')
out_raster[where_notmask] = pm.flib.invlogit(ravelledmap)
return lon[llcloni:urcloni],lat[llclati:urclati],out_raster
def get_pseudoabsences(eo, buffer_width, n_pseudoabsences, layer_names, glob_name, eo_expand=0):
if eo_expand:
eo = lomem_buffer(eo, eo_expand)
fname = hashlib.sha1(geojson.dumps(eo)+'_'+str(buffer_width)+'_'+str(n_pseudoabsences)).hexdigest()+'.npy'
if fname in os.listdir('anopheles-caches'):
pseudoabsences = np.load(os.path.join('anopheles-caches', fname))
else:
if buffer_width >= 0:
buff = lomem_buffer(eo, buffer_width)
diff_buffer = buff.difference(eo)
elif buffer_width == -1:
diff_buffer = eo
else:
raise ValueError, 'Buffer width is negative, but not -1.'
if len(layer_names)>0:
template = layer_names[0]
else:
template = glob_name
lon, lat, test_raster, rtype = map_utils.import_raster(*os.path.split(template)[::-1])
# Right the raster
test_raster = map_utils.grid_convert(test_raster,'y-x+','x+y+')
# Convert lower-left coords to centroids
lon += (lon[1]-lon[0])/2.
lat += (lat[1]-lat[0])/2.
def testfn(lon_test,lat_test,r=test_raster,lon=lon,lat=lat):
lon_ind = np.argmin(np.abs(lon-lon_test))
lat_ind = np.argmin(np.abs(lat-lat_test))
return True-test_raster.mask[lon_ind,lat_ind]
pseudoabsences = np.vstack(map_utils.shapefile_utils.multipoly_sample(n_pseudoabsences, diff_buffer, test=testfn)).T
if not np.all([testfn(l1,l2) for l1,l2 in pseudoabsences]):
raise ValueError, 'Test failed for some pseudoabsences.'
np.save(os.path.join('anopheles-caches',fname), pseudoabsences)
return pseudoabsences, eo
try:
os.mkdir('5k-covariates')
except OSError:
pass
# Parameters
modis_missing = 0
glob_missing = 210
modis_res = (21600, 43200)
lon = np.linspace(-180,180,modis_res[1])
lat = np.linspace(-90,90,modis_res[0])
af_lon, af_lat, af_data, af_type = map_utils.import_raster('africa','.')
lon_min, lon_max, lat_min, lat_max = af_lon.min(), af_lon.max(), af_lat.min(), af_lat.max()
pete_lon, pete_lat, pete_mask, junk = map_utils.import_raster('ls','.')
# Subset the rasters
lon_min_i, lon_max_i = ((np.array([lon_min, lon_max])+180.)/360.*modis_res[1]).astype('int')
lat_min_i, lat_max_i = ((np.array([lat_min, lat_max])+90.)/180.*modis_res[0]).astype('int')
pete_lat_min_i = np.argmin(np.abs(pete_lat - af_lat.min()))
pete_lat_max_i = np.argmin(np.abs(pete_lat - af_lat.max()))
clipped_pete_mask = grid_convert(grid_convert(pete_mask,'y-x+','x+y+')[lon_min_i/5.:lon_max_i/5.,pete_lat_min_i:pete_lat_max_i],'x+y+','y-x+')
af_lon = af_lon[:-1]
af_lat = af_lat[:-1]
af_data = af_data[:-1,:-1]
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