def read_wid_csvs():
data = dict()
for fname in glob.glob(
os.path.join(utils.data_root(), 'wid', 'Data',
'WID_*_InequalityData.csv')):
bname = os.path.basename(fname)
_, iso2, _ = bname.split('_', 3)
iso2 = iso2.lower()
df = pd.read_csv(fname, sep=';', encoding='latin1', low_memory=False)
df.columns = df.iloc[6, :]
data[iso2] = df
return data
def cnames_df():
cnames = pd.read_csv(
os.path.join(utils.data_root(), 'ssp-data', 'country-names.csv'))
return cnames
default=False,
action='store_true',
help='Project using mainland coefficients '
'(default: islands)')
parser.add_argument('--clip',
'-c',
dest='clip',
default=False,
action='store_true',
help='Clip predictor variables to max value seen '
'during model fitting')
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)
win2[0][1])), (win1[1][0] + win2[1][0],
min(win1[1][1] + win2[1][0], win2[1][1])))
return win1
@click.command()
@click.option('--band', default=1, help='Band of raster to mask')
@click.argument('raster', type=click.Path())
@click.argument('mask', type=click.Path())
def maskit(raster, mask, band=1):
rds = rasterio.open(raster, 'r+')
ice = rasterio.open(mask)
minxs, minys, maxxs, maxys = zip(rds.bounds, ice.bounds)
bounds = (max(minxs), max(minys), min(maxxs), min(maxys))
ice_view = ice.window(*bounds)
nodata = rds.nodatavals[band - 1]
for ij, win in rds.block_windows():
if rds.height > 10000 and win[0][1] % 100 == 0:
print(win)
rr = rds.read(band, masked=True, window=win)
ii = ice.read(1, masked=True, window=window_inset(win, ice_view))
rr.mask = np.logical_or(rr.mask, np.where(ii == 1.0, True, False))
rds.write(rr.filled(nodata), window=win, indexes=band)
if __name__ == '__main__':
raster = 'ds/1km/roads.tif'
mask = 'zip:%s/1km/ICE.zip!ICE_1km_2005.bil' % utils.data_root()
maskit()
import click
#import matlibplot.pyplot as plt
import numpy as np
import numpy.ma as ma
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')
click.echo('processing %s' % region)
fips_names = map(wrap, regions[region])
features = filter(lambda s: s['properties']['FIPS'] in fips_names, src)
shapes = map(shapely.geometry.shape,
[f['geometry'] for f in features])
union = shapely.ops.unary_union(shapes)
props = OrderedDict({'name': region})
dst.write({'geometry': shapely.geometry.mapping(union),
'type': features[0]['type'],
'properties': props,
})
@click.command()
@click.argument('borders', type=click.Path())
@click.argument('regions', type=click.File('r'),
default=os.path.join(utils.data_root(), 'ssp-data',
'regions.json'))
@click.argument('country-names', type=click.Path(),
default=os.path.join(utils.data_root(), 'ssp-data',
'country-names.csv'))
@click.argument('output', type=click.Path(dir_okay=False, writable=True))
def main(borders, regions, country_names, output):
cnames = pd.DataFrame.from_csv(country_names)
region_data = json.load(regions)
with fiona.open(borders) as src:
meta = src.meta
meta['schema']['properties'] = OrderedDict({'name': 'str:8'})
with fiona.open(output, 'w', **meta) as dst:
merge(src, dst, region_data, cnames)
if __name__ == '__main__':
HPD_MAX = 22490
parser = argparse.ArgumentParser(description="sr.py -- richness projections")
parser.add_argument('--mainland', '-m', dest='mainland', default=False,
action='store_true',
help='Project using mainland coefficients '
'(default: islands)')
parser.add_argument('--clip', '-c', dest='clip', default=False,
action='store_true',
help='Clip predictor variables to max value seen '
'during model fitting')
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 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)