def process_map(lookupobj, csvfilename):
"""Produce a CSV file of areas per country from a dataset."""
shapefilename = 'data/ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp'
df = pd.DataFrame(columns=lookupobj.get_columns(), dtype=float)
df.index.name = 'Country'
shapefile = osgeo.ogr.Open(shapefilename)
assert shapefile.GetLayerCount() == 1
layer = shapefile.GetLayerByIndex(0)
for idx, feature in enumerate(layer):
admin = admin_names.lookup(feature.GetField("ADMIN"))
if admin is None:
continue
a3 = feature.GetField("SOV_A3")
if admin not in df.index:
df.loc[admin] = [0] * len(df.columns)
print(f"Processing {admin:<41} #{a3}_{idx}")
maskfilename = f"masks/{a3}_{idx}_{lookupobj.maskdim}_mask._tif"
maskimg = osgeo.gdal.Open(maskfilename, osgeo.gdal.GA_ReadOnly)
maskband = maskimg.GetRasterBand(1)
x_siz = maskband.XSize
y_siz = maskband.YSize
x_blksiz, y_blksiz = maskband.GetBlockSize()
for y in range(0, y_siz, y_blksiz):
nrows = geoutil.blklim(coord=y, blksiz=y_blksiz, totsiz=y_siz)
for x in range(0, x_siz, x_blksiz):
ncols = geoutil.blklim(coord=x, blksiz=x_blksiz, totsiz=x_siz)
if geoutil.is_sparse(band=maskband,
x=x,
y=y,
ncols=ncols,
nrows=nrows):
# sparse hole in image, no data to process
continue
maskblock = maskband.ReadAsArray(x, y, ncols, nrows)
km2block = geoutil.km2_block(nrows=nrows,
ncols=ncols,
y_off=y,
img=maskimg)
lookupobj.km2(x=x,
y=y,
ncols=ncols,
nrows=nrows,
maskblock=maskblock,
km2block=km2block,
df=df,
admin=admin)
outputfilename = os.path.join('results', csvfilename)
df.sort_index(axis='index').to_csv(outputfilename, float_format='%.2f')
return df
def map_ne_admin_to_drawdown_regions(row):
"""
Maps Natural Earth's ADMIN name to a list of Drawdown regions
"""
ne_name = row['ADMIN']
dd_name = admin_names.lookup(ne_name)
if dd_name in SPECIAL_COUNTRIES:
return [dd_name]
else:
dd_region = admin_names.region_mapping.get(dd_name)
if dd_region is None:
return None
elif isinstance(dd_region, list):
return dd_region
else:
raise ValueError(f"List expected, but got {dd_region}")
def produce_CSV():
"""Produce a CSV file of Thermal Moisture Regime + Agro-Ecological Zone per country."""
columns = []
for tmr in tmr_state.keys():
columns.extend([f"{tmr}|AEZ{x}" for x in range(1, 30)])
df = pd.DataFrame(columns=columns, dtype='float')
df.index.name = 'Country'
countrycsvfilename = 'results/AEZ-by-country.csv'
shapefilename = 'data/ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp'
kg_filename = 'data/Beck_KG_V1/Beck_KG_V1_present_0p0083.tif'
lc_filename = 'data/copernicus/C3S-LC-L4-LCCS-Map-300m-P1Y-2018-v2.1.1.tif'
sl_filename = 'data/ConsolidatedSlope.tif'
wk_filename = 'data/FAO/workability_FAO_sq7_1km.tif'
shapefile = osgeo.ogr.Open(shapefilename)
assert shapefile.GetLayerCount() == 1
features = shapefile.GetLayerByIndex(0)
kg_img = osgeo.gdal.Open(kg_filename, osgeo.gdal.GA_ReadOnly)
kg_band = kg_img.GetRasterBand(1)
lc_img = osgeo.gdal.Open(lc_filename, osgeo.gdal.GA_ReadOnly)
lc_band = lc_img.GetRasterBand(1)
sl_img = osgeo.gdal.Open(sl_filename, osgeo.gdal.GA_ReadOnly)
sl_band = {}
for idx in range(1, 9):
sl_band[idx] = sl_img.GetRasterBand(idx)
wk_img = osgeo.gdal.Open(wk_filename, osgeo.gdal.GA_ReadOnly)
wk_band = wk_img.GetRasterBand(1)
for idx, feature in enumerate(features):
admin = admin_names.lookup(feature.GetField("ADMIN"))
if admin is None:
continue
a3 = feature.GetField("SOV_A3")
if admin not in df.index:
df.loc[admin] = [0] * len(df.columns)
print(f"Processing {admin:<41} #{a3}_{idx}")
maskfilename = f"masks/{a3}_{idx}_1km_mask._tif"
maskimg = osgeo.gdal.Open(maskfilename, osgeo.gdal.GA_ReadOnly)
mask_band = maskimg.GetRasterBand(1)
x_siz = mask_band.XSize
y_siz = mask_band.YSize
x_blksiz, y_blksiz = mask_band.GetBlockSize()
for y in range(0, y_siz, y_blksiz):
nrows = geoutil.blklim(coord=y, blksiz=y_blksiz, totsiz=y_siz)
for x in range(0, x_siz, x_blksiz):
ncols = geoutil.blklim(coord=x, blksiz=x_blksiz, totsiz=x_siz)
if geoutil.is_sparse(band=mask_band,
x=x,
y=y,
ncols=ncols,
nrows=nrows):
# sparse hole in image, no data to process
continue
mask_blk = mask_band.ReadAsArray(x, y, ncols, nrows)
k = geoutil.km2_block(nrows=nrows,
ncols=ncols,
y_off=y,
img=maskimg)
k[np.logical_not(mask_blk)] = 0.0
km2_blk = (np.repeat(np.repeat(k, 3, axis=1), 3, axis=0)) / 9.0
k = kg_band.ReadAsArray(x, y, ncols, nrows)
kg_blk = np.repeat(np.repeat(k, 3, axis=1), 3, axis=0)
regime = populate_tmr(kg_blk)
sl_blk = {}
for idx in range(1, 9):
s = sl_band[idx].ReadAsArray(x, y, ncols, nrows)
sl_blk[idx] = np.repeat(np.repeat(s, 3, axis=1), 3, axis=0)
slope = populate_slope(sl_blk)
lc_blk = lc_band.ReadAsArray(3 * x, 3 * y, 3 * ncols,
3 * nrows)
land_use = populate_land_use(lc_blk)
w = wk_band.ReadAsArray(x, y, ncols, nrows)
wk_blk = np.repeat(np.repeat(w, 3, axis=1), 3, axis=0)
soil_health = populate_soil_health(wk_blk)
for tmr in tmr_state.keys():
n = 1
for aez in yield_AEZs(regime=regime,
tmr=tmr,
slope=slope,
land_use=land_use,
soil_health=soil_health):
df.loc[admin, f"{tmr}|AEZ{n}"] += (aez * km2_blk).sum()
n += 1
df.sort_index(axis='index').to_csv(countrycsvfilename, float_format='%.2f')
regions = [
'OECD90', 'Eastern Europe', 'Asia (Sans Japan)',
'Middle East and Africa', 'Latin America', 'China', 'India', 'EU',
'USA'
]
df_region = pd.DataFrame(0, index=regions, columns=df.columns.copy())
df_region.index.name = 'Region'
for country, row in df.iterrows():
region = admin_names.region_mapping[country]
if region is not None:
df_region.loc[region, :] += row
for tmr in [
'Tropical-Humid', 'Arid', 'Tropical-Semiarid', 'Temperate-Humid',
'Temperate-Semiarid', 'Boreal-Humid', 'Boreal-Semiarid', 'Arctic'
]:
tmrfilename = tmr.translate(str.maketrans('/', '-'))
filename = f"results/AEZ-{tmrfilename}-by-region.csv"
df_region.filter(regex=f'^{tmr.lower()}',
axis=1).to_csv(filename, float_format='%.2f')
def test_lookup():
assert admin_names.lookup('Cabo Verde') == 'Cape Verde'
assert admin_names.lookup('Scarborough Reef') is None
def produce_CSV():
"""Produce a CSV file of degraded land for {forest, cropland, grassland}."""
columns = [
'forest:good:degraded',
'forest:marginal:degraded',
'forest:poor:degraded',
'forest:verypoor:degraded',
'forest:good:nondegraded',
'forest:marginal:nondegraded',
'forest:poor:nondegraded',
'forest:verypoor:nondegraded',
'cropland:good:degraded',
'cropland:marginal:degraded',
'cropland:poor:degraded',
'cropland:verypoor:degraded',
'cropland:good:nondegraded',
'cropland:marginal:nondegraded',
'cropland:poor:nondegraded',
'cropland:verypoor:nondegraded',
'grassland:good:degraded',
'grassland:marginal:degraded',
'grassland:poor:degraded',
'grassland:verypoor:degraded',
'grassland:good:nondegraded',
'grassland:marginal:nondegraded',
'grassland:poor:nondegraded',
'grassland:verypoor:nondegraded',
'bare:good:degraded',
'bare:marginal:degraded',
'bare:poor:degraded',
'bare:verypoor:degraded',
'bare:good:nondegraded',
'bare:marginal:nondegraded',
'bare:poor:nondegraded',
'bare:verypoor:nondegraded',
'urban:good:degraded',
'urban:marginal:degraded',
'urban:poor:degraded',
'urban:verypoor:degraded',
'urban:good:nondegraded',
'urban:marginal:nondegraded',
'urban:poor:nondegraded',
'urban:verypoor:nondegraded',
'water:good:degraded',
'water:marginal:degraded',
'water:poor:degraded',
'water:verypoor:degraded',
'water:good:nondegraded',
'water:marginal:nondegraded',
'water:poor:nondegraded',
'water:verypoor:nondegraded',
'ice:good:degraded',
'ice:marginal:degraded',
'ice:poor:degraded',
'ice:verypoor:degraded',
'ice:good:nondegraded',
'ice:marginal:nondegraded',
'ice:poor:nondegraded',
'ice:verypoor:nondegraded',
]
df = pd.DataFrame(columns=columns, dtype='float')
df.index.name = 'Country'
shapefilename = 'data/ne_10m_admin_0_countries/ne_10m_admin_0_countries.shp'
shapefile = osgeo.ogr.Open(shapefilename)
assert shapefile.GetLayerCount() == 1
features = shapefile.GetLayerByIndex(0)
lc_filename = 'data/copernicus/ESACCI-LC-L4-LCCS-Map-300m-P1Y-2015-v2.0.7.tif'
lc_img = osgeo.gdal.Open(lc_filename, osgeo.gdal.GA_ReadOnly)
lc_band = lc_img.GetRasterBand(1)
lpd_filename = 'data/lpd_int2/lpd_int2.tif'
lpd_img = osgeo.gdal.Open(lpd_filename, osgeo.gdal.GA_ReadOnly)
lpd_band = lpd_img.GetRasterBand(1)
wk_filename = 'data/FAO/workability_FAO_sq7_1km.tif'
wk_img = osgeo.gdal.Open(wk_filename, osgeo.gdal.GA_ReadOnly)
wk_band = wk_img.GetRasterBand(1)
for idx, feature in enumerate(features):
admin = admin_names.lookup(feature.GetField("ADMIN"))
if admin is None:
continue
a3 = feature.GetField("SOV_A3")
if admin not in df.index:
df.loc[admin] = [0] * len(df.columns)
print(f"Processing {admin:<41} #{a3}_{idx}")
maskfilename = f"masks/{a3}_{idx}_1km_mask._tif"
maskimg = osgeo.gdal.Open(maskfilename, osgeo.gdal.GA_ReadOnly)
mask_band = maskimg.GetRasterBand(1)
x_siz = mask_band.XSize
y_siz = mask_band.YSize
x_blksiz, y_blksiz = mask_band.GetBlockSize()
for y in range(0, y_siz, y_blksiz):
nrows = geoutil.blklim(coord=y, blksiz=y_blksiz, totsiz=y_siz)
for x in range(0, x_siz, x_blksiz):
ncols = geoutil.blklim(coord=x, blksiz=x_blksiz, totsiz=x_siz)
if geoutil.is_sparse(band=mask_band,
x=x,
y=y,
ncols=ncols,
nrows=nrows):
# sparse hole in image, no data to process
continue
mask_blk = mask_band.ReadAsArray(x, y, ncols, nrows)
k = geoutil.km2_block(nrows=nrows,
ncols=ncols,
y_off=y,
img=maskimg)
k[np.logical_not(mask_blk)] = 0.0
km2_blk = (np.repeat(np.repeat(k, 3, axis=1), 3, axis=0)) / 9.0
lc_blk = lc_band.ReadAsArray(3 * x, 3 * y, 3 * ncols,
3 * nrows)
lc = {}
lc['forest'] = np.logical_or.reduce(
(lc_blk == 12, lc_blk == 50, lc_blk == 60, lc_blk == 61,
lc_blk == 62, lc_blk == 70, lc_blk == 71, lc_blk == 72,
lc_blk == 80, lc_blk == 81, lc_blk == 82, lc_blk == 90,
lc_blk == 160, lc_blk == 170))
lc['cropland'] = np.logical_or.reduce(
(lc_blk == 10, lc_blk == 30, lc_blk == 20))
lc['grassland'] = np.logical_or.reduce(
(lc_blk == 11, lc_blk == 40, lc_blk == 100, lc_blk == 110,
lc_blk == 120, lc_blk == 121, lc_blk == 122,
lc_blk == 130, lc_blk == 150, lc_blk == 151,
lc_blk == 152, lc_blk == 153, lc_blk == 180))
lc['bare'] = np.logical_or.reduce(
(lc_blk == 140, lc_blk == 200, lc_blk == 201,
lc_blk == 202))
lc['urban'] = (lc_blk == 190)
lc['water'] = (lc_blk == 210)
lc['ice'] = (lc_blk == 220)
k = lpd_band.ReadAsArray(x, y, ncols, nrows)
lpd = {}
lpd_blk = np.repeat(np.repeat(k, 3, axis=1), 3, axis=0)
lpd['degraded'] = (lpd_blk != 0.0)
lpd['nondegraded'] = (lpd_blk == 0.0)
k = wk_band.ReadAsArray(x, y, ncols, nrows)
wk_blk = np.repeat(np.repeat(k, 3, axis=1), 3, axis=0)
work = {}
work['good'] = (wk_blk == 1)
work['marginal'] = (wk_blk == 2)
work['poor'] = (wk_blk == 3)
work['verypoor'] = (wk_blk == 4)
for cover in lc.keys():
for degraded in lpd.keys():
for soil in work.keys():
key = f'{cover}:{soil}:{degraded}'
df.loc[admin, key] += (np.logical_and.reduce(
(lc[cover], lpd[degraded], work[soil])) *
km2_blk).sum()
csvfilename = 'results/degraded-cover-by-country.csv'
df.sort_index(axis='index').to_csv(csvfilename, float_format='%.2f')
regions = [
'OECD90', 'Eastern Europe', 'Asia (Sans Japan)',
'Middle East and Africa', 'Latin America', 'China', 'India', 'EU',
'USA'
]
df_region = pd.DataFrame(0, index=regions, columns=df.columns.copy())
df_region.index.name = 'Region'
for country, row in df.iterrows():
region = admin_names.region_mapping[country]
if region is not None:
df_region.loc[region, :] += row
csvfilename = f"results/degraded-cover-by-region.csv"
df_region.to_csv(csvfilename, float_format='%.2f')