def __init__(self, shp, prefix='au', rebuild=False):
sf = shapefile.Reader(shp)
header = [field[0] for field in sf.fields][1:]
"""
Field name: the name describing the data at this column index.
Field type: the type of data at this column index. Types can be: Character, Numbers, Longs, Dates, or Memo.
Field length: the length of the data found at this column index.
Decimal length: the number of decimal places found in Number fields.
"""
# shapes = sf.shapes()
# print(len(shapes))
# records = sf.records()
# print(len(records))
gwc = RasterDatasetInterpolator(
'/geodata/weppcloud_runs/au/gwc_dnbr_barc4_utm.tif')
# gwc2 = RasterDatasetInterpolator('gwc_sbs2.tif')
# gwc6 = RasterDatasetInterpolator('gwc_sbs6.tif')
fp_hill = open('%s_hill_summary.csv' % prefix, 'w')
csv_wtr = csv.DictWriter(
fp_hill,
fieldnames=('huc', 'topaz_id', 'wepp_id', 'length', 'width',
'area', 'slope', 'centroid_lng', 'centroid_lat',
'landuse', 'soil_texture', 'sbs', 'ash_wind_transport',
'ash_water_transport', 'ash_transport'))
csv_wtr.writeheader()
fails = 0
for i, shape in enumerate(sf.iterShapes()):
record = {k: v for k, v in zip(header, sf.record(i))}
# print(record)
huc12 = str(record['ID'])
print(huc12)
if huc12 in blacklist:
print('in blacklist, skipping', huc12)
continue
bbox = shape.bbox
_y = haversine((bbox[0], bbox[1]), (bbox[0], bbox[3])) * 1000
_x = haversine((bbox[0], bbox[1]), (bbox[2], bbox[1])) * 1000
sqm2 = _y * _x
if sqm2 < 30 * 30 * 4:
print('too small, skipping', huc12)
continue
wd = _join('/geodata/weppcloud_runs/', prefix, huc12)
if _exists(wd):
if _exists(_join(wd, 'dem', 'topaz', 'SUBWTA.ARC')):
print('already delineated, skipping', huc12)
continue
shutil.rmtree(wd)
os.mkdir(wd)
print('initializing nodbs')
ron = Ron(wd, "au-fire.cfg")
#ron = Ron(wd, "au.cfg")
# ron = Ron(wd, "0.cfg")
ron.name = wd
print('setting map')
pad = max(abs(bbox[0] - bbox[2]), abs(bbox[1] - bbox[3])) * 0.4
map_center = (bbox[0] + bbox[2]) / 2.0, (bbox[1] + bbox[3]) / 2.0
l, b, r, t = bbox
bbox = [l - pad, b - pad, r + pad, t + pad]
print('bbox', bbox)
ron.set_map(bbox, map_center, zoom=13)
print('fetching dem')
ron.fetch_dem()
print('setting topaz parameters')
topaz = Topaz.getInstance(wd)
print('building channels')
topaz_pars = chn_routing_err_topaz_pars.get(
huc12, dict(csa=10, mcl=200))
topaz.build_channels(**topaz_pars)
map = ron.map
print('find raster indices')
utm2wgs_transformer = GeoTransformer(src_proj4=wgs84_proj4,
dst_proj4=map.srs_proj4)
points = [
utm2wgs_transformer.transform(lng, lat)
for lng, lat in shape.points
]
mask = build_mask(points, ron.dem_fn)
# plt.figure()
# plt.imshow(mask)
# plt.colorbar()
# plt.savefig(_join(topaz.topaz_wd, 'mask.png'))
if huc12 in outlet_locs:
out_lng, out_lat = outlet_locs[huc12]
rdi = RasterDatasetInterpolator(ron.dem_fn)
px, py = rdi.get_px_coord_from_lnglat(out_lng, out_lat)
print('px, py', px, py)
dem, transform, proj = read_raster(ron.dem_fn)
min_elev = dem[px, py]
else:
print('loading channel map')
channels, _, _ = read_raster(topaz.netful_arc)
mask[np.where(channels == 0)] = 1
plt.figure()
plt.imshow(mask)
plt.colorbar()
plt.savefig(_join(topaz.topaz_wd, 'mask.png'))
plt.close()
print('finding lowest point in HUC')
dem, transform, proj = read_raster(ron.dem_fn)
print(mask.shape, dem.shape)
print(np.sum(mask))
demma = ma.masked_array(dem, mask=mask)
plt.figure()
plt.imshow(demma)
plt.colorbar()
plt.savefig(_join(topaz.topaz_wd, 'demma.png'))
plt.close()
min_elev = np.min(demma)
px, py = np.unravel_index(np.argmin(demma), demma.shape)
px = int(px)
py = int(py)
print(min_elev, px, py, px / dem.shape[0], py / dem.shape[1])
print('building subcatchments')
topaz.set_outlet(px, py, pixelcoords=True)
try:
topaz.build_subcatchments()
except:
fails += 1
raise
print('abstracting watershed')
wat = Watershed.getInstance(wd)
wat.abstract_watershed()
translator = wat.translator_factory()
topaz_ids = [
top.split('_')[1] for top in translator.iter_sub_ids()
]
# is_gwc2 = is_gwc6 = False
for topaz_id, hill_summary in wat.sub_iter():
print(topaz_id)
_wat = hill_summary.as_dict()
# _landuse = landuse_summaries[str(topaz_id)]
# _soils = soils_summaries[str(topaz_id)]
_centroid_lng, _centroid_lat = _wat['centroid']
# try:
# _sbs2 = gwc2.get_location_info(_centroid_lng, _centroid_lat, method='near')
# if _sbs2 < 0:
# _sbs2 = None
# except RDIOutOfBoundsException:
# _sbs2 = None
#
# try:
# _sbs6 = gwc6.get_location_info(_centroid_lng, _centroid_lat, method='near')
# if _sbs6 < 0:
# _sbs6 = None
# except RDIOutOfBoundsException:
# _sbs6 = None
#
# if _sbs2 is None and _sbs6 is None:
# _sbs = 0
#
# elif _sbs2 is not None:
# _sbs = _sbs2
# is_gwc2 = True
#
# else:
# _sbs = _sbs6
# is_gwc6 = True
# _d = dict(huc=huc12, topaz_id=int(topaz_id), wepp_id=_wat['wepp_id'],
# length=_wat['length'], width=_wat['width'], area=_wat['area'],
# slope=_wat['slope_scalar'],
# centroid_lng=_centroid_lng,
# centroid_lat=_centroid_lat,
# landuse=_landuse['key'],
# soil_texture=_soils['simple_texture'],
# sbs=_sbs)
# csv_wtr.writerow(_d)
# if not is_gwc2 and not is_gwc6:
# continue
baer = Baer.getInstance(wd)
# if is_gwc2:
# shutil.copyfile('gwc_sbs2.tif', _join(baer.baer_dir, 'gwc_sbs2.tif'))
# baer.validate('gwc_sbs2.tif')
# if is_gwc6:
# shutil.copyfile('gwc_sbs6.tif', _join(baer.baer_dir, 'gwc_sbs6.tif'))
# baer.validate('gwc_sbs6.tif')
shutil.copyfile(
'/geodata/weppcloud_runs/au/gwc_dnbr_barc4_utm.tif',
_join(baer.baer_dir, 'gwc_dnbr_barc4_utm.tif'))
baer.validate('gwc_dnbr_barc4_utm.tif')
print('building landuse')
landuse = Landuse.getInstance(wd)
landuse.mode = LanduseMode.Gridded
landuse.build()
landuse = Landuse.getInstance(wd)
landuse_summaries = landuse.subs_summary
print('building soils')
soils = Soils.getInstance(wd)
soils.mode = SoilsMode.Gridded
soils.build()
soils_summaries = soils.subs_summary
print('building climate')
climate = Climate.getInstance(wd)
stations = climate.find_au_heuristic_stations()
climate.input_years = 100
climate.climatestation = stations[0]['id']
climate.climate_spatialmode = ClimateSpatialMode.Single
climate.build(verbose=True)
print('prepping wepp')
wepp = Wepp.getInstance(wd)
wepp.prep_hillslopes()
print('running hillslopes')
wepp.run_hillslopes()
print('prepping watershed')
wepp = Wepp.getInstance(wd)
wepp.prep_watershed()
print('running watershed')
wepp.run_watershed()
print('generating loss report')
loss_report = wepp.report_loss()
print('generating totalwatsed report')
fn = _join(ron.export_dir, 'totalwatsed.csv')
totwatsed = TotalWatSed(_join(ron.output_dir, 'totalwatsed.txt'),
wepp.baseflow_opts, wepp.phosphorus_opts)
totwatsed.export(fn)
assert _exists(fn)
ash = Ash.getInstance(wd)
ash.run_ash(fire_date='8/4',
ini_white_ash_depth_mm=16.5625,
ini_black_ash_depth_mm=17.166666666666668)
ashpost = AshPost.getInstance(wd)
ash_summary = ashpost.summary_stats
if ash_summary is not None:
_recurrence = ash_summary['recurrence']
_return_periods = ash_summary['return_periods']
_annuals = ash_summary['annuals']
_sev_annuals = ash_summary['sev_annuals']
ash_out = ashpost.ash_out
for topaz_id, hill_summary in wat.sub_iter():
print(topaz_id)
_wat = hill_summary.as_dict()
_landuse = landuse_summaries[str(topaz_id)]
_soils = soils_summaries[str(topaz_id)]
_centroid_lng, _centroid_lat = _wat['centroid']
_d = dict(huc=huc12,
topaz_id=int(topaz_id),
wepp_id=_wat['wepp_id'],
length=_wat['length'],
width=_wat['width'],
area=_wat['area'],
slope=_wat['slope_scalar'],
centroid_lng=_centroid_lng,
centroid_lat=_centroid_lat,
landuse=_landuse['key'],
soil_texture=_soils['simple_texture'],
ash_wind_transport=ash_out[str(
topaz_id)]['wind_transport (kg/ha)'],
ash_water_transport=ash_out[str(
topaz_id)]['water_transport (kg/ha)'],
ash_transport=ash_out[str(topaz_id)]
['ash_transport (kg/ha)'])
csv_wtr.writerow(_d)
print('exporting arcmap resources')
arc_export(wd)
print(fails, i + 1)
_topaz_ids = selector(landuse, None)
bare_tops = bare_or_sodgrass_or_bunchgrass_selector(
landuse, None)
_topaz_ids = [
top for top in _topaz_ids if top not in bare_tops
]
landuse.modify(_topaz_ids, dom)
tops.extend(_topaz_ids)
log_print('building soils')
if _exists(_join(wd, 'lt.nodb')):
lt = LakeTahoe.getInstance(wd)
lt.lc_lookup_fn = lc_lookup_fn
soils = Soils.getInstance(wd)
soils.mode = SoilsMode.Gridded
soils.build()
log_print('building climate')
if climate_mode == 'observed':
climate = Climate.getInstance(wd)
stations = climate.find_closest_stations()
climate.input_years = 30
climate.climatestation = stations[0]['id']
climate.climate_mode = ClimateMode.Observed
climate.climate_spatialmode = ClimateSpatialMode.Multiple
climate.set_observed_pars(start_year=1990, end_year=2019)
elif climate_mode == 'future':