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ipbes-summarize.py
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ipbes-summarize.py
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#!/usr/bin/env python
import click
import fiona
import numpy as np
import numpy.ma as ma
import pandas as pd
import rasterio
from rasterio.plot import show
import pdb
import projections.utils as utils
class YearRangeParamType(click.ParamType):
name = 'year range'
def convert(self, value, param, ctx):
try:
try:
return [int(value)]
except ValueError:
l, h = value.split(':')
return range(int(l), int(h))
except ValueError:
self.fail('%s is not a valid year range' % value, param, ctx)
YEAR_RANGE = YearRangeParamType()
def read_bounded(ds, bounds=None, height=None):
if bounds is None:
return ds.read(1, masked=True)
win = ds.window(*bounds)
if height and win[0][1] - win[0][0] > height:
win = ((win[0][0], win[0][0] + height), win[1])
return ds.read(1, masked=True, window=win)
def carea(bounds=None, height=None):
with rasterio.open(utils.luh2_static('carea')) as ds:
read_bounded(ds, bounds, height)
def tarea(bounds=None, height=None):
with rasterio.open(utils.luh2_static('icwtr')) as ds:
ice = read_bounded(ds, bounds, height)
return (1 - ice) * carea(bounds, height)
def weighted_mean_by(regions, data):
data.mask = np.logical_or(data.mask, regions.mask)
regions.mask = ma.getmask(data)
regions_idx = regions.compressed().astype(int)
sums = np.bincount(regions_idx, data.compressed())
ncells = np.bincount(regions_idx)
idx = np.where(ncells > 0)
carea = ncells[idx]
as_array = np.column_stack((idx[0].astype(int), carea, sums[idx] / carea))
return pd.DataFrame(as_array, columns=['ID', 'Cells', 'Mean'])
def find_bounds(sources):
bounds = [-180, -90, 180, 90]
width = 0
height = 0
for src in sources:
if rasterio.coords.disjoint_bounds(bounds, src.bounds):
raise ValueError("rasters do not intersect")
bounds[0] = max(bounds[0], src.bounds[0])
bounds[1] = max(bounds[1], src.bounds[1])
bounds[2] = min(bounds[2], src.bounds[2])
bounds[3] = min(bounds[3], src.bounds[3])
width = max(width, src.width)
height = max(height, src.height)
for src in sources:
win = src.window(*bounds)
width = min(width, win[1][1] - win[1][0])
height = min(height, win[0][1] - win[0][0])
return bounds, width, height
def get_ipbes_regions():
with fiona.open(utils.outfn('vector', 'ipbes_land_shape',
'ipbes_land.shp')) as shapes:
props = tuple(filter(lambda x: x.get('type') == 'Land',
(s['properties'] for s in shapes)))
return pd.DataFrame({'ID': tuple(int(s.get('OBJECTID')) for s in props),
'Name': tuple(s.get('IPBES_sub') for s in props)})
@click.group(invoke_without_command=True)
@click.pass_context
def cli(ctx):
if ctx.invoked_subcommand is None:
click.echo('I was invoked without subcommand')
summarize()
@cli.command()
@click.argument('what', type=click.Choice(['ab', 'sr',
'cs-ab', 'cs-sr',
'bii-ab', 'bii-sr']))
@click.argument('scenario', type=click.Choice(utils.luh2_scenarios()))
@click.argument('years', type=YEAR_RANGE)
@click.option('--npp', type=click.Path(dir_okay=False))
@click.option('--vsr', type=click.Path(dir_okay=False))
def summary(what, scenario, years, npp, vsr):
"""Generate a per-year and per-IPBES region summary of a diversity metric.
Diversity metric supported are
- ab: abundance (Abundance)
- sr: species richness (Richness)
- cs-ab: abundance-based compositional similarity (CompSimAb)
- cs-sr: species richness-based compositional similarity (CompSimSR)
- bii-ab: abundance-based BII (BIIAb)
- bii-sr: species richness-based BII (BIISR)
"""
if npp and vsr:
raise RuntimeError('Please specify --npp or --vsr, not both')
df = get_ipbes_regions()
df_global = pd.DataFrame({'ID': [-1], 'Name': ['Global']},
columns=('ID', 'Name'))
vname = 'Abundance' if what == 'ab' \
else 'Richness' if what =='sr' \
else 'CompSimAb' if what == 'cs-ab' \
else 'CompSimSR' if what == 'cs-sr' \
else 'BIIAb' if what == 'bii-ab' \
else 'BIISR'
template = '%s-%s-%%04d.tif' % (scenario, vname)
if scenario == 'historical':
# This is horrible kludge!
years = (900, 1000, 1100, 1200, 1300, 1400, 1500, 1600,
1700, 1710, 1720, 1730, 1740, 1750, 1760, 1770, 1780, 1790,
1800, 1810, 1820, 1830, 1840, 1850, 1860, 1870, 1880, 1890,
1900, 1910, 1920, 1930, 1940, 1950, 1960, 1970, 1980, 1990,
2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
2010, 2011, 2012, 2013, 2014)
for year in years:
fnames = [utils.outfn('luh2', template % year)]
fnames.append(utils.luh2_static('carea'))
if npp:
fnames.append(npp)
if vsr:
fnames.append(vsr)
fnames.append(utils.outfn('luh2', 'ipbes-subs.tif'))
sources = [rasterio.open(src) for src in fnames]
bounds, width, height = find_bounds(sources)
data = [read_bounded(src, bounds, height) for src in sources]
bii = ma.prod(data[0:-1], 0)
bii.mask = np.any(tuple(d.mask for d in data[0:-1]), 0)
intact = ma.prod([np.full(data[0].shape, 1), data[1]], 0)
if (npp or vsr):
intact *= data[-2]
intact.mask = bii.mask
by_subs = weighted_mean_by(data[-1], bii)
intact_by_subs = weighted_mean_by(data[-1], intact)
if 'Cells' not in df.columns:
df['Cells'] = by_subs.Cells
df[year] = by_subs.Mean / intact_by_subs.Mean
if 'Cells' not in df_global.columns:
df_global['Cells'] = (bii.shape[0] * bii.shape[1] -
ma.count_masked(bii))
df_global[year] = ma.average(bii) / ma.average(intact)
if len(years) < 10:
print(df)
weight = "-npp" if npp else "-vsr" if vsr else ""
df.to_csv('%s-%s%s-subreg-%04d-%04d.csv' % (scenario, vname, weight,
years[0], years[-1]))
df_global.to_csv('%s-%s%s-global-%04d-%04d.csv' % (scenario, vname, weight,
years[0], years[-1]))
@cli.command()
@click.argument('what', type=click.Choice(['ab', 'sr']))
@click.argument('scenario', type=click.Choice(utils.luh2_scenarios()))
@click.argument('years', type=YEAR_RANGE)
@click.option('--npp', type=click.Path(dir_okay=False))
def summarize(what, scenario, years, npp):
df = get_ipbes_regions()
for year in years:
template = '%s-%%s-%04d.tif' % (scenario, year)
vnames = ('Abundance', 'CompSimAb') if what == 'ab' else ('Richness',
'CompSimSR')
fnames = [utils.outfn('luh2', template % vname)
for vname in vnames]
fnames.append(utils.luh2_static('carea'))
if npp:
fnames.append(npp)
fnames.append(utils.outfn('luh2', 'ipbes-subs.tif'))
sources = [rasterio.open(src) for src in fnames]
bounds = find_bounds(sources)
data = [read_bounded(src, bounds) for src in sources]
bii = ma.prod(data[0:-1], 0)
bii.mask = np.any(tuple(d.mask for d in data[0:-1]), 0)
intact = ma.prod([np.full(data[0].shape, 1), data[2:-1]], 0)
intact.mask = bii.mask
by_subs = weighted_mean_by(data[-1], bii)
intact_by_subs = weighted_mean_by(data[-1], intact)
df[year] = by_subs.Mean / intact_by_subs.Mean
print(df)
if __name__ == '__main__':
#pylint: disable-msg=no-value-for-parameter
cli()
#pylint: enable-msg=no-value-for-parameter