def project_year(model, model_dir, what, scenario, year):
print("projecting %s for %d using %s" % (what, year, scenario))
models = select_models(model, model_dir)
# Read Sam's abundance model (forested and non-forested)
modf = modelr.load(models[0])
intercept_f = modf.intercept
predicts.predictify(modf)
modn = modelr.load(models[1])
intercept_n = modn.intercept
predicts.predictify(modn)
# Open forested/non-forested mask layer
fstnf = rasterio.open(utils.luh2_static('fstnf'))
# Import standard PREDICTS rasters
rastersf = predicts.rasterset('luh2', scenario, year, 'f')
rsf = RasterSet(rastersf, mask=fstnf, maskval=0.0)
rastersn = predicts.rasterset('luh2', scenario, year, 'n')
rsn = RasterSet(rastersn, mask=fstnf, maskval=1.0)
#rsn = RasterSet(rastersn)
if what == 'bii':
vname = 'bii'
rsf[vname] = SimpleExpr(
vname, 'exp(%s) / exp(%f)' % (modf.output, intercept_f))
rsn[vname] = SimpleExpr(
vname, 'exp(%s) / exp(%f)' % (modn.output, intercept_n))
rsf[modf.output] = modf
rsn[modn.output] = modn
else:
vname = modf.output
assert modf.output == modn.output
rsf[vname] = modf
rsn[vname] = modn
if what not in rsf:
print('%s not in rasterset' % what)
print(', '.join(sorted(rsf.keys())))
sys.exit(1)
stime = time.time()
datan, meta = rsn.eval(what, quiet=False)
dataf, _ = rsf.eval(what, quiet=True)
data_vals = dataf.filled(0) + datan.filled(0)
data = data_vals.view(ma.MaskedArray)
data.mask = np.logical_and(dataf.mask, datan.mask)
#data = datan
etime = time.time()
print("executed in %6.2fs" % (etime - stime))
oname = '%s/luh2/%s-%s-%d.tif' % (utils.outdir(), scenario, what, year)
with rasterio.open(oname, 'w', **meta) as dst:
dst.write(data.filled(meta['nodata']), indexes=1)
if None:
fig = plt.figure(figsize=(8, 6))
ax = plt.gca()
show(data, cmap='viridis', ax=ax)
plt.savefig('luh2-%s-%d.png' % (scenario, year))
return
def project(year):
print("projecting %d" % year)
# Open the mask shape file
shp_file = 'c:/data/from-adriana/tropicalforests.shp'
shapes = fiona.open(shp_file)
# Read Adriana's abundance model (mainland)
mod = modelr.load('../tropical-bii/simplifiedAbundanceModel.rds')
predicts.predictify(mod)
# Import standard PREDICTS rasters
rasters = predicts.rasterset('1km', '', year, 'medium')
rasters['primary_lightintense'] = SimpleExpr(
'primary_lightintense', 'primary_light + primary_intense')
rasters['cropland_lightintense'] = SimpleExpr(
'cropland_lightintense', 'cropland_light + cropland_intense')
rasters['pasture_lightintense'] = SimpleExpr(
'pasture_lightintense', 'pasture_light + pasture_intense')
rasters['rd_dist'] = Raster('rd_dist', '/out/1km/roads-adrid.tif')
rasters['hpd'] = Raster(
'hpd', '/vsizip//data/from-adriana/HPD01to12.zip/yr%d/hdr.adf' % year)
rs = RasterSet(rasters, shapes=shapes, crop=True, all_touched=True)
what = 'LogAbund'
rs[what] = mod
stime = time.time()
rs.write(what, utils.outfn('1km', 'adrid-%d.tif' % year))
etime = time.time()
print("executed in %6.2fs" % (etime - stime))
def project_year(model, model_dir, what, scenario, year):
print("projecting %s for %d using %s" % (what, year, scenario))
models = select_models(model, model_dir)
# Read Sam's abundance model (forested and non-forested)
mod = modelr.load(models[0])
predicts.predictify(mod)
# Import standard PREDICTS rasters
by_age = 'young_secondary' in mod.syms
print('by_age: %s' % str(by_age))
rasters = predicts.rasterset('luh5', scenario, year, by_age)
rs = RasterSet(rasters)
if what == 'bii':
vname = 'bii'
rs[vname] = SimpleExpr(vname, '%s / %f' % (mod.output, intercept))
else:
vname = mod.output
rs[vname] = mod
if what not in rs:
print('%s not in rasterset' % what)
print(', '.join(sorted(rs.keys())))
sys.exit(1)
stime = time.time()
data, meta = rs.eval(what, quiet=False)
etime = time.time()
print("executed in %6.2fs" % (etime - stime))
oname = os.path.join(os.environ['OUTDIR'],
'luh5/%s-%s-%d.tif' % (scenario, what, year))
#hpd, _ = rs.eval('hpd', quiet=False)
#hpd_max, meta2 = rs.eval('hpd_max', quiet=False)
with rasterio.open(oname, 'w', **meta) as dst:
#bb = dst.bounds
#ul = map(int, ~meta2['affine'] * (bb[0], bb[3]))
#lr = map(int, ~meta2['affine'] * (bb[2], bb[1]))
#cap = ma.where(hpd > hpd_max[ul[1]:lr[1], ul[0]:lr[0]], True, False)
#show(hpd, norm=colors.PowerNorm(gamma=0.2))
#show(cap * 1)
#data.mask = np.logical_or(data.mask, cap)
dst.write(data.filled(meta['nodata']), indexes=1)
if None:
fig = plt.figure(figsize=(8, 6))
ax = plt.gca()
show(data, cmap='viridis', ax=ax)
plt.savefig('luh2-%s-%d.png' % (scenario, year))
return
def project_year(model, model_dir, scenario, year):
"""Run a projection for a single year. Can be called in parallel when
projecting a range of years.
"""
print("projecting %s for %d using %s" % (model, year, scenario))
# Import standard PREDICTS rasters
rasters = predicts.rasterset('luh2', scenario, year)
rs = RasterSet(rasters)
what, model = select_model(model, model_dir)
# Read Sam's models
if model:
mod = modelr.load(model)
predicts.predictify(mod)
rs[mod.output] = mod
if what in ('CompSimAb', 'CompSimSR', 'Abundance', 'Richness'):
if what in ('CompSimAb', 'CompSimSR'):
expr = '(inv_logit(%s) - 0.01) / (inv_logit(%f) - 0.01)'
else:
expr = '(exp(%s) / exp(%f))'
rs[what] = SimpleExpr(what, expr % (mod.output, mod.intercept))
if what not in rs:
print('%s not in rasterset' % what)
print(', '.join(sorted(rs.keys())))
sys.exit(1)
stime = time.time()
data, meta = rs.eval(what, quiet=True)
etime = time.time()
print("executed in %6.2fs" % (etime - stime))
oname = '%s/luh2/%s-%s-%04d.tif' % (utils.outdir(), scenario, what, year)
with rasterio.open(oname, 'w', **meta) as dst:
dst.write(data.filled(meta['nodata']), indexes=1)
if None:
fig = plt.figure(figsize=(8, 6))
show(data, cmap='viridis', ax=plt.gca())
fig.savefig('luh2-%s-%d.png' % (scenario, year))
return
def plot(model, title, save, adjust):
"""Plot response curve of CS model versus HPD."""
mod = modelr.load(model)
df = pd.DataFrame(columns=tuple(luh2.LU.keys()))
for col in df.columns:
s = pd.Series(
mod.partial({
'LogHPD_s2': np.linspace(0, 11, 13),
'LogHPD_diff': np.linspace(0, -11, 13),
col: np.full((13), 1)
}))
df[col] = (inv_logit(s) - adjust) / (1 - 2 * adjust)
df.plot()
ax = plt.gca()
ax.set_title(title)
ax.set_xlabel('log(HPD + 1)')
ax.set_ylabel('CompSim')
if save:
plt.savefig(save, transparent=True, bbox_inches="tight", pad_inches=0)
plt.show()
import projections.predicts as predicts
import projections.r2py.modelr as modelr
import projections.utils as utils
scenarios = ['historical', 'ssp1_rcp2.6_image', 'ssp5_rcp8.5_remind-magpie']
models = ['ab_model.rds', 'sr_model.rds']
for model in models:
if model == 'ab_model.rds':
what = "abundance"
else:
what = "sprich"
# Read in the model
mod = modelr.load('D:/victoria_projections/' + model)
predicts.predictify(mod)
for scenario in scenarios:
if scenario == 'historical':
years = range(1970, 2015)
hpdtrend = 'wpp'
else:
years = range(2015, 2101)
hpdtrend = 'medium'
for year in years:
rasters = predicts.rasterset('luh2',
args = parser.parse_args()
if args.mainland:
ISLMAIN = 1
mask_file = os.path.join(utils.data_root(),
'1km/mainland-from-igor-edited-at.tif')
else:
ISLMAIN = 0
mask_file = os.path.join(utils.data_root(),
'1km/islands-from-igor-edited-at.tif')
# Open the mask raster file (Mainlands)
mask_ds = rasterio.open(mask_file)
# Read Katia's abundance model
mod = modelr.load('/home/vagrant/katia/models/best_model_abund.rds')
predicts.predictify(mod)
# Import standard PREDICTS rasters
rasters = predicts.rasterset('1km', 'medium', year=2005)
# create an ISL_MAINL raster
# set it to Mainlands this time round (set Mainlands to 1 and Islands to 0)
rasters['ISL_MAINLMAINLAND'] = SimpleExpr('ISL_MAINLMAINLAND', ISLMAIN)
# specify the plantation forest maps as 0
# not sure why it's plantations_pri rather than plantation, but hey ho
rasters['plantation_pri'] = SimpleExpr('plantation_pri', 0)
rasters['plantation_pri_minimal'] = SimpleExpr('plantation_pri_minimal', 0)
rasters['plantation_pri_light'] = SimpleExpr('plantation_pri_light', 0)
rasters['plantation_pri_intense'] = SimpleExpr('plantation_pri_intense', 0)
import rasterio
from rasterio.plot import show, show_hist
from projections.rasterset import RasterSet, Raster
from projections.simpleexpr import SimpleExpr
import projections.predicts as predicts
import projections.r2py.modelr as modelr
import projections.utils as utils
# Open the mask raster file
mask_file = os.path.join(utils.data_root(),
'1km/mainland-from-igor-edited.tif')
mask_ds = rasterio.open(mask_file)
# Richness compositional similarity model with updated PriMin-Urb coefficient
mod = modelr.load('/home/vagrant/katia/models/updated_compsim_rich.rds')
predicts.predictify(mod)
# Import standard PREDICTS rasters
rasters = predicts.rasterset('1km', 'medium', year=2005)
# create an ISL_MAINL raster
# set it to Mainlands this time round (set Mainlands to 1 and Islands to 0)
rasters['ISL_MAINMAINLAND'] = SimpleExpr('ISL_MAINMAINLAND', '2')
rasters['ISL_MAINISLAND'] = SimpleExpr('ISL_MAINISLAND', '0')
rasters['adjGeogDist'] = SimpleExpr('adjGeogDist', '0')
rasters['cubeRtEnvDist'] = SimpleExpr('cubeRtEnvDist', '0')
# set up the rasterset, cropping to mainlands
rs = RasterSet(rasters, mask=mask_ds, maskval=0, crop=True)
import time
from rasterio.plot import show
import matplotlib.pyplot as plt
from projections.rasterset import RasterSet, Raster
import projections.predicts as predicts
import projections.r2py.modelr as modelr
# Open the mask shape file
shp_file = os.path.join(os.environ['DATA_ROOT'],
'from-adriana/tropicalforests.shp')
shapes = fiona.open(shp_file)
# Read Adriana's abundance model (mainland)
mod = modelr.load(os.path.join(os.environ['MODEL_DIR'], 'ab-model.rds'))
predicts.predictify(mod)
# Import standard PREDICTS rasters
rasters = predicts.rasterset('luh5', 'historical', 1990, True)
rs = RasterSet(rasters, shapes=shapes, all_touched=True)
what = mod.output
rs[mod.output] = mod
stime = time.time()
data1, meta_data1 = rs.eval(what)
etime = time.time()
print("executed in %6.2fs" % (etime - stime))
show(data1)
##
import projections.utils as utils
RD_DIST_MIN = 0
RD_DIST_MAX = 195274.3
HPD_MIN = 0
HPD_MAX = 22490
CLIP = True # False
# Open the mask raster file (Mainlands)
mask_file = os.path.join(utils.data_root(),
'1km/mainland-from-igor-edited.tif')
mask_ds = rasterio.open(mask_file)
# Read Katia's richness model
mod = modelr.load('/home/vagrant/katia/models/best_model_rich.rds')
predicts.predictify(mod)
# Import standard PREDICTS rasters
rasters = predicts.rasterset('1km', 'medium', year=2005)
# create an ISL_MAINL raster
# set it to Mainlands this time round (set Mainlands to 1 and Islands to 0)
rasters['ISL_MAINLMAINLAND'] = SimpleExpr('ISL_MAINLMAINLAND', '2')
rasters['ISL_MAINLISLAND'] = SimpleExpr('ISL_MAINLISLAND', '0')
# specify the plantation forest maps as 0
# not sure why it's plantations_pri rather than plantation, but hey ho
rasters['plantation_pri'] = SimpleExpr('plantation_pri', '0')
rasters['plantation_pri_minimal'] = SimpleExpr('plantation_pri_minimal', '0')
rasters['plantation_pri_light'] = SimpleExpr('plantation_pri_light', '0')
# specify the model version
fldr = ['v1', 'v2', 'v3']
# Open the mask shape file
shp_file = 'c:/data/from-adriana/tropicalforests.shp'
shapes = fiona.open(shp_file)
# for each model version
for version in fldr:
# get the model name
mod_name = 'simplifiedAbundanceModel_' + version + '.rds'
# Read in the model
mod = modelr.load('C:/data/from-adriana/ModelsForProjections/' + mod_name)
predicts.predictify(mod)
# pull out the reference value for this model
df = pandas.read_csv(
'C:/data/from-adriana/ValuesForProjections/abData.csv')
value_name = 'reference_' + version
ref = float(df[df['Value'] == value_name]['zeroValue'])
# for each year
for year in range(2001, 2013):
# This line imports standard PREDICTS rasters
#rasters = predicts.rasterset('1km', 'version3.3', year, 'medium')
# we don't need this anymore as we're importing our own data
