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
years = range(1970, 2015)
hpdtrend = 'wpp'
else:
years = range(2015, 2101)
hpdtrend = 'medium'
for year in years:
rasters = predicts.rasterset('luh2',
scenario,
year,
hpd_trend=hpdtrend)
# set soil properties to 0
rasters['BD'] = SimpleExpr('BD', 0)
rasters['CLAY'] = SimpleExpr('CLAY', 0)
rasters['OC'] = SimpleExpr('OC', 0)
rasters['phkcl'] = SimpleExpr('phkcl', 0)
#note that rasters.keys() shows you all the names of the rasters in this object
rasters['secondary_vegetation_minimal'] = SimpleExpr(
'secondary_vegetation_minimal',
'young_secondary_minimal + intermediate_secondary_minimal + mature_secondary_minimal'
)
rasters['secondary_vegetation_light'] = SimpleExpr(
'secondary_vegetation_light',
'young_secondary_light + intermediate_secondary_light + mature_secondary_light'
)
rasters['secondary_vegetation_intense'] = SimpleExpr(
'secondary_vegetation_intense',
year) + '.tif'
ru.clip(inname, outname, 0, None)
# calculate bii (with only a lower bound)
# pull in all the rasters for computing bii
bii_rs = RasterSet({
'abundance':
Raster(
'abundance', 'C:/ds/temporal-bii/' + version +
'/bii-ab-low-bound-%d.tif' % year),
'comp_sim':
Raster(
'comp_sim', 'C:/ds/temporal-bii/' + version +
'/bii-cs-low-bound-%d.tif' % year),
'bii_ab':
SimpleExpr('bii_ab', 'abundance * comp_sim')
})
# write out bii raster
bii_rs.write(
'bii_ab',
utils.outfn('temporal-bii/' + version, 'bii-%d.tif' % year))
# calculate bounded bii
# clip the abundance rasters
inname = 'C:/ds/temporal-bii/' + version + '/bii-ab-' + str(
year) + '.tif'
outname = 'C:/ds/temporal-bii/' + version + '/bii-ab-bound-' + str(
year) + '.tif'
ru.clip(inname, outname, 0, abmax)
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)
## If CLIP is true, limit the predictor variable values to the max seen
## when fitting the model
if args.clip:
rasters['clip_hpd'] = SimpleExpr(
'clip_hpd', 'clip(hpd_ref, %f, %f)' % (HPD_MIN, HPD_MAX))
else:
rasters['clip_hpd'] = SimpleExpr('clip_hpd', 'hpd_ref')
# 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)
# if you're projecting the whole world, use this code instead
#rs = RasterSet(rasters)
# evaluate the model
# model is logit transformed with an adjustment, so back-transformation
rs[mod.output] = mod
rs['output'] = SimpleExpr(
'output', '(inv_logit(%s) - 0.01) / (inv_logit(%f) - 0.01)' %
def rset_add(rasters, name, expr): rasters[name] = SimpleExpr(name, expr)
import numpy as np
import time
import matplotlib.pyplot as plt
from rasterio.plot import show, show_hist
from projections.atlas import atlas
from projections.rasterset import RasterSet, Raster
from projections.simpleexpr import SimpleExpr
import projections.predicts as predicts
import projections.rds as rds
# Import standard PREDICTS rasters
rasters = predicts.rasterset('rcp', 'aim', 2020, 'medium')
rs = RasterSet(rasters)
rs['logHPD_rs2'] = SimpleExpr('logHPD_rs', 'scale(log(hpd + 1), 0.0, 1.0)')
data1 = rs.eval('logHPD_rs')
data2 = rs.eval('logHPD_rs2')
data3 = np.where(np.isclose(data1, data2, equal_nan=True), 1, 0)
diff = np.fabs(data1 - data2)
print("max diff %f" % diff.max())
print("max in hpd_ref: %f" % rs['hpd_ref'].data.values.max())
print("max in hpd: %f" % rs['hpd'].data.dropna().values.max())
fig, ((ax1, ax2, ax3), (hx1, hx2, hx3)) = plt.subplots(2, 3, figsize=(21, 7))
show(data1, ax=ax1, cmap='Greens', title='Global max/min')
show(data2, ax=ax2, cmap='Greens', title='Computed max/min')
show(diff, ax=ax3, cmap='viridis', title='Difference', vmin=0, vmax=1.0)
show_hist(data1, ax=hx1, histtype='stepfilled')
else:
suffix = 'islands'
ab_max = 1.443549
sr_max = 1.413479
folder = 'clip' if args.clip else 'no-clip'
# pull in all the rasters for computing bii
bii_rs = RasterSet({'abundance': Raster('abundance',
utils.outfn('katia',
folder,
'ab-%s.tif' % suffix)),
'comp_sim': Raster('comp_sim',
utils.outfn('katia',
'ab-cs-%s.tif' % suffix)),
'clip_ab': SimpleExpr('clip_ab',
'clip(abundance, 0, %f)' % ab_max),
'bii_ab': SimpleExpr('bii_ab', 'abundance * comp_sim'),
'bii_ab2': SimpleExpr('bii_ab2', 'clip_ab * comp_sim'),
})
# write out bii raster
bii_rs.write('bii_ab' if args.clip else 'bii_ab2',
utils.outfn('katia', folder,
'abundance-based-bii-%s.tif' % suffix))
# do the same for species richness
# pull in all the rasters for computing bii
bii_rs = RasterSet({'sp_rich': Raster('sp_rich',
utils.outfn('katia',
folder,
'sr-%s.tif' % suffix)),
# 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
# so set up an empty rasters object
rasters = dict()
# update the land-use classes that we need for the model
rasters['cropland_minimal'] = Raster(
'cropland_minimal', 'C:/ds/adrid/cropland_minimal-%d.tif' % year)
rasters['cropland_light'] = Raster(
'cropland_light', 'C:/ds/adrid/cropland_light-%d.tif' % year)
rasters['cropland_intense'] = Raster(
'cropland_intense', 'C:/ds/adrid/cropland_intense-%d.tif' % year)
rasters['cropland'] = SimpleExpr(
'cropland', 'cropland_minimal + cropland_light + cropland_intense')
rasters['pasture_minimal'] = Raster(
'pasture_minimal', 'C:/ds/adrid/pasture_minimal-%d.tif' % year)
rasters['pasture_light'] = Raster(
'pasture_light', 'C:/ds/adrid/pasture_light-%d.tif' % year)
rasters['pasture_intense'] = Raster(
'pasture_intense', 'C:/ds/adrid/pasture_intense-%d.tif' % year)
rasters['pasture'] = SimpleExpr(
'pasture', 'pasture_minimal + pasture_light + pasture_intense')
rasters['primary_minimal'] = Raster(
'primary_minimal', 'C:/ds/adrid/primary_minimal-%d.tif' % year)
rasters['primary_light'] = Raster(
'primary_light', 'C:/ds/adrid/primary_light-%d.tif' % year)
rasters['primary_intense'] = Raster(
from projections.simpleexpr import SimpleExpr
import projections.predicts as predicts
import projections.r2py.modelr as modelr
import projections.utils as utils
import projections.raster_utils as ru
CLIP = 'no-clip'
# pull in all the rasters for computing bii
bii_rs = RasterSet({
'abundance':
Raster('abundance', utils.outfn('katia', CLIP, 'bii-ab-mainlands.tif')),
'comp_sim':
Raster('comp_sim', utils.outfn('katia', 'bii-ab-cs-mainlands.tif')),
'clip_ab':
SimpleExpr('clip_ab', 'clip(abundance, 0, 1.655183)'),
'bii_ab':
SimpleExpr('bii_ab', 'abundance * comp_sim'),
'bii_ab2':
SimpleExpr('bii_ab2', 'clip_ab * comp_sim'),
})
# write out bii raster
bii_rs.write('bii_ab' if CLIP == 'clip' else 'bii_ab2',
utils.outfn('katia', CLIP, 'abundance-based-bii-mainlands.tif'))
# do the same for species richness
# pull in all the rasters for computing bii
bii_rs = RasterSet({
'sp_rich':
Raster('sp_rich', utils.outfn('katia', CLIP, 'bii-sr-mainlands.tif')),
# 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
# so set up an empty rasters object
rasters = dict()
# update the land-use classes that we need for the model
rasters['cropland_minimal'] = Raster(
'cropland_minimal', 'C:/ds/adrid/cropland_minimal-%d.tif' % year)
rasters['cropland_light'] = Raster(
'cropland_light', 'C:/ds/adrid/cropland_light-%d.tif' % year)
rasters['cropland_intense'] = Raster(
'cropland_intense', 'C:/ds/adrid/cropland_intense-%d.tif' % year)
rasters['cropland'] = SimpleExpr(
'cropland', 'cropland_minimal + cropland_light + cropland_intense')
rasters['pasture_minimal'] = Raster(
'pasture_minimal', 'C:/ds/adrid/pasture_minimal-%d.tif' % year)
rasters['pasture_light'] = Raster(
'pasture_light', 'C:/ds/adrid/pasture_light-%d.tif' % year)
rasters['pasture_intense'] = Raster(
'pasture_intense', 'C:/ds/adrid/pasture_intense-%d.tif' % year)
rasters['pasture'] = SimpleExpr(
'pasture', 'pasture_minimal + pasture_light + pasture_intense')
rasters['primary_minimal'] = Raster(
'primary_minimal', 'C:/ds/adrid/primary_minimal-%d.tif' % year)
rasters['primary_light'] = Raster(
'primary_light', 'C:/ds/adrid/primary_light-%d.tif' % year)
rasters['primary_intense'] = Raster(
