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))
)
rasters['secondary_vegetation_intense'] = SimpleExpr(
'secondary_vegetation_intense',
'young_secondary_intense + intermediate_secondary_intense + mature_secondary_intense'
)
rasters['primary_vegetation_intense'] = SimpleExpr(
'primary_vegetation_intense', 'primary_intense')
rasters['primary_vegetation_light'] = SimpleExpr(
'primary_vegetation_light', 'primary_light')
rasters['plantation_pri'] = SimpleExpr('plantation_pri',
'perennial + timber')
rasters['plantation_pri_intense'] = SimpleExpr(
'plantation_pri_intense', 'perennial_intense + timber_intense')
rasters['plantation_pri_light'] = SimpleExpr(
'plantation_pri_light', 'perennial_light + timber_light')
rasters['plantation_pri_minimal'] = SimpleExpr(
'plantation_pri_minimal', 'perennial_minimal + timber_minimal')
rs = RasterSet(rasters)
# back transform (they were transformed using log(x+1)
rs[mod.output] = mod
rs['output'] = SimpleExpr(
'output', 'clip((exp(%s) - 1) / (exp(%f) - 1), 0, 1e20)' %
(mod.output, mod.intercept))
rs.write(
'output', 'D:/victoria_projections/projections/' + what + '-' +
scenario + '-' + '%d.tif' % year)
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)
# clip the cs rasters
inname = 'C:/ds/temporal-bii/' + version + '/bii-cs-' + str(
year) + '.tif'
outname = 'C:/ds/temporal-bii/' + version + '/bii-cs-bound-' + str(
year) + '.tif'
#!/usr/bin/env python
import fiona
import time
from rasterio.plot import show, show_hist
from projections.rasterset import RasterSet, Raster
import projections.predicts as predicts
import projections.modelr as modelr
# Open the mask shape file
shp_file = '../../data/from-adriana/tropicalforests.shp'
shapes = fiona.open(shp_file)
# Read Adriana's abundance model (mainland)
mod = modelr.load('../models/ab-corrected.rds')
predicts.predictify(mod)
# Import standard PREDICTS rasters
rasters = predicts.rasterset('1km', 'foo', 2005, 'historical')
rs = RasterSet(rasters, shapes=shapes, all_touched=True)
rs[mod.output] = mod
what = mod.output
stime = time.time()
data = rs.write(what, 'hires.tif')
etime = time.time()
print("executed in %6.2fs" % (etime - stime))
###Added +100 to DistRd to deal with zero values
# 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 square root abundance so square it
# Note that the intercept value has been calculated for the baseline
# land use when all other variables are held at 0
# Therefore I calculate separatedly an intercept where DistRd is set to
# the max value, i.e. logDistRd_RS = 1.
intercept = mod.partial({'ISL_MAINLMAINLAND': ISLMAIN, 'logDistRd_rs': 1.0})
print("intercept: %.5f" % intercept)
if args.mainland:
assert math.isclose(intercept, 0.67184, rel_tol=0.001)
else:
## FIXME: Replace RHS with the R calculated value
assert math.isclose(intercept, 0.7270164, rel_tol=0.001)
rs[mod.output] = mod
rs['output'] = SimpleExpr(
'output', '(pow(%s, 2) / pow(%f, 2))' % (mod.output, intercept))
fname = 'ab-%s.tif' % ('mainland' if args.mainland else 'islands')
path = ('katia', 'clip' if args.clip else 'no-clip', fname)
rs.write('output', utils.outfn(*path))
'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)' %
(mod.output, mod.intercept))
rs.write('output', utils.outfn('katia', 'bii-sr-cs-mainlands.tif'))
assert np.allclose(data1, good, atol=1e-05, equal_nan=True)
del out
##
## Redo the projection using iterative API
##
mod = modelr.load('../models/ab-corrected.rds')
predicts.predictify(mod)
# Import standard PREDICTS rasters
rasters2 = predicts.rasterset('rcp', 'aim', 2020, 'medium')
rs2 = RasterSet(rasters2, shapes=shapes, all_touched=True)
rs2[mod.output] = mod
stime = time.time()
rs2.write(what, 'adrid.tif')
etime = time.time()
print("executed in %6.2fs" % (etime - stime))
out = rasterio.open('adrid.tif')
data2 = out.read(1, masked=True)
diff = np.fabs(data1 - data2)
print("max diff: %f" % diff.max())
plot = None
if plot:
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5))
show(data1, ax=ax1, cmap='Greens', title='Non-incremental')
show(data2, ax=ax2, cmap='Greens', title='Incremental')
show(diff, ax=ax3, cmap='viridis', title='Difference')
plt.show()
# 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', ISLMAIN)
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
intercept = mod.partial({'ISL_MAINMAINLAND': ISLMAIN})
print("intercept: %.5f" % intercept)
if args.mainland:
assert math.isclose(intercept, 1.939964, rel_tol=0.001)
else:
assert math.isclose(intercept, 1.439107176, rel_tol=0.001)
rs['output'] = SimpleExpr(
'output', '(inv_logit(%s) - 0.01) / (inv_logit(%f) - 0.01)' %
(mod.output, intercept))
fname = 'ab-cs-%s.tif' % ('mainland' if args.mainland else 'islands')
rs.write('output', utils.outfn('katia', fname))
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)),
'comp_sim': Raster('comp_sim',
utils.outfn('katia',
'sr-cs-%s.tif' % suffix)),
'clip_sr': SimpleExpr('clip_sr',
'clip(sp_rich, 0, %f)' % sr_max),
'bii_sr': SimpleExpr('bii_sr', 'sp_rich * comp_sim'),
'bii_sr2': SimpleExpr('bii_sr2', 'clip_sr * comp_sim')})
'CRootRdDens_50km_cs', '(clip_rd - %f) / %f' % (mean_rd, sd_rd))
# set up the rasterset, cropping by tropical forests
# set all_touched = False as there are some boundary issues with very small countries on the edge
# like Macao
rs = RasterSet(rasters, shapes=shapes, crop=True, all_touched=False)
# evaluate the model
# model is square root abundance so square it
# note that the intercept value has been calculated for the baseline land use when all other variables are held at 0
rs[mod.output] = mod
rs['output'] = SimpleExpr(
'output', '(pow(%s, 2) / pow(%f, 2))' % (mod.output, ref))
rs.write(
'output',
utils.outfn('temporal-bii/' + version, 'bii-ab-%d.tif' % year))
# if you're on the final version (i.e. the last time you're going round)
# write out the pressure data
if version == 'v3':
rs.write(
'primary',
utils.outfn('temporal-bii/primary', 'primary-%d.tif' % year))
rs.write(
'secondary',
utils.outfn('temporal-bii/secondary',
'secondary-%d.tif' % year))
rs.write(
'pasture',
utils.outfn('temporal-bii/pasture', 'pasture-%d.tif' % year))
# 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')),
'comp_sim':
Raster('comp_sim', utils.outfn('katia', 'bii-sr-cs-mainlands.tif')),
'clip_sr':
SimpleExpr('clip_sr', 'clip(sp_rich, 0, 1.636021)'),
'bii_sr':
SimpleExpr('bii_sr', 'sp_rich * comp_sim'),
'bii_sr2':
SimpleExpr('bii_sr2', 'clip_sr * comp_sim')
})
# get the differences
# diff = cuberoot(s1) - cuberoot(s2)
# we want cuberoot(s1+1) = 0
# so the difference is 0 - cuberoot(s2)
# then center and scale (subtract mean and divide by sd)
mean_rd50_diff = float(
df[df['Value'] == 'CRootDens_50km_diff']['mean'])
mean_rd1_diff = float(df[df['Value'] == 'CRootDens_1km_diff']['mean'])
sd_rd50_diff = float(df[df['Value'] == 'CRootDens_50km_diff']['sd'])
sd_rd1_diff = float(df[df['Value'] == 'CRootDens_1km_diff']['sd'])
rasters['CRootDens_50km_diff_cs'] = SimpleExpr(
'CRootDens_50km_diff_cs',
'((0 - clip_rd50) - %f) / %f' % (mean_rd50_diff, sd_rd50_diff))
rasters['CRootDens_1km_diff_cs'] = SimpleExpr(
'CRootDens_1km_diff_cs',
'((0 - clip_rd1) - %f) / %f' % (mean_rd1_diff, sd_rd1_diff))
# set up the rasterset, cropping by tropical forests
rs = RasterSet(rasters, shapes=shapes, crop=True, all_touched=False)
# 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)' %
(mod.output, ref))
rs.write(
'output',
utils.outfn('temporal-bii/' + version, 'bii-cs-%d.tif' % year))
import sys
import rasterio
from projections.rasterset import RasterSet
import projections.predicts as predicts
import projections.utils as utils
# Import standard PREDICTS rasters
rasters = predicts.rasterset('1km', 'medium', year = 2005)
for suffix in ('islands', 'mainland'):
# Open the BII raster file
mask_file = 'C:/Users/katis2/Desktop/Final_projections/Clip_variables/abundance-based-bii-%s.tif' % suffix
mask_ds = rasterio.open(mask_file)
# set up the rasterset, cropping to mainlands
rs = RasterSet(rasters, mask=mask_ds, maskval=-9999, crop=True)
# Run through each land-use
for lu in ('cropland', 'pasture', 'primary', 'secondary', 'urban'):
# And every use intensity
for ui in ('minimal', 'light', 'intense'):
name = '%s_%s' % (lu, ui)
print(name)
oname = utils.outfn('katia', '%s-%s.tif' % (name, suffix))
if os.path.isfile(oname) or name in ('secondary_intense',
'urnan_light'):
continue
rs.write(name, oname)
