def bf_reduction_with_gwsup(metadata, complete_data):
lu_tif = metadata['lu']
driver, NDV, xsize, ysize, GeoT, Projection = becgis.GetGeoInfo(lu_tif)
#new directories:
directory_bf = os.path.split(complete_data['bf'][0][0])[0] + '_corrbf'
if not os.path.exists(directory_bf):
os.makedirs(directory_bf)
directory_ro = os.path.split(complete_data['tr'][0][0])[0] + '_corrbf'
if not os.path.exists(directory_ro):
os.makedirs(directory_ro)
common_dates = becgis.CommonDates(
[complete_data['supply_gw'][1], complete_data['bf'][1]])
for date in common_dates:
gw_supply_tif = complete_data['supply_gw'][0][
complete_data['supply_gw'][1] == date][0]
SUP_GW = becgis.OpenAsArray(gw_supply_tif, nan_values=True)
bf_tif = complete_data['bf'][0][complete_data['bf'][1] == date][0]
BF = becgis.OpenAsArray(bf_tif, nan_values=True)
ro_tif = complete_data['tr'][0][complete_data['tr'][1] == date][0]
sro_tif = complete_data['sr'][0][complete_data['sr'][1] == date][0]
SRO = becgis.OpenAsArray(sro_tif, nan_values=True)
BF_new = BF - SUP_GW
BF_new[BF_new < 0] = 0.
RO_new = BF_new + SRO
outfile_bf = os.path.join(directory_bf, os.path.basename(bf_tif))
becgis.CreateGeoTiff(outfile_bf, BF_new, driver, NDV, xsize, ysize,
GeoT, Projection)
outfile_tr = os.path.join(directory_ro, os.path.basename(ro_tif))
becgis.CreateGeoTiff(outfile_tr, RO_new, driver, NDV, xsize, ysize,
GeoT, Projection)
complete_data['bf'] = becgis.SortFiles(directory_bf, [-10, -6],
month_position=[-6, -4])[0:2]
complete_data['tr'] = becgis.SortFiles(directory_ro, [-10, -6],
month_position=[-6, -4])[0:2]
return complete_data
def calc_mskd_mean(mask_map, pop_map, idc_name):
"""
Open a map and calculate the average of a masked area.
"""
target_maps = np.array([pop_map])
temp_dir = os.path.split(mask_map)[0]
pop_map = bg.MatchProjResNDV(mask_map, target_maps, temp_dir)[0]
lu = bg.OpenAsArray(mask_map, nan_values=True)
ppl_ha = bg.OpenAsArray(pop_map, nan_values=True)
ppl_ha[np.isnan(lu)] = np.nan
ppl = np.nanmean(ppl_ha)
os.remove(pop_map)
pop = {idc_name: ppl}
return pop
def calc_lu_areas(lu_map):
"""
Calculate the areas of the four different major WA+ landuse categories.
"""
lu = bg.OpenAsArray(lu_map, nan_values=True)
lu_types = gd.get_sheet1_classes()
area_km2 = bg.MapPixelAreakm(lu_map)
areas = dict()
for typ, classes in lu_types.items():
mask = np.logical_or.reduce([lu == value for value in classes])
area = np.nansum(area_km2[mask])
areas[typ] = area
return areas
def calc_mean(maps):
"""
Calculate the mean and the standard deviation per pixel for a
serie of maps.
"""
fh = os.path.join(tempfile.mkdtemp(), 'temp.tif')
geoinfo = bg.GetGeoInfo(maps[0])
data_sum = np.zeros((geoinfo[3], geoinfo[2]))
data_count = np.zeros((geoinfo[3], geoinfo[2]))
for filename in maps:
data = bg.OpenAsArray(filename, nan_values=True)
data_sum = np.nansum([data_sum, data], axis=0)
count = np.ones((geoinfo[3], geoinfo[2]))
count[np.isnan(data)] = 0
data_count += count
mean = data_sum / data_count
bg.CreateGeoTiff(fh, mean, *geoinfo)
return fh
def supply_return_natural_lu(metadata, complete_data):
lu_tif = metadata['lu']
LULC = becgis.OpenAsArray(lu_tif, nan_values=True)
lucs = gd.get_sheet4_6_classes()
#new directories:
directory_sup = os.path.split(
complete_data['supply_total'][0][0])[0] + '_corr'
directory_dro = os.path.split(complete_data['dro'][0][0])[0] + '_corr'
directory_dperc = os.path.split(complete_data['dperc'][0][0])[0] + '_corr'
directory_sro = os.path.split(complete_data['sr'][0][0])[0] + '_corr'
directory_tr = os.path.split(complete_data['tr'][0][0])[0] + '_corr'
if not os.path.exists(directory_sup):
os.makedirs(directory_sup)
if not os.path.exists(directory_dro):
os.makedirs(directory_dro)
if not os.path.exists(directory_dperc):
os.makedirs(directory_dperc)
if not os.path.exists(directory_sro):
os.makedirs(directory_sro)
if not os.path.exists(directory_tr):
os.makedirs(directory_tr)
#
driver, NDV, xsize, ysize, GeoT, Projection = becgis.GetGeoInfo(lu_tif)
common_dates = becgis.CommonDates([
complete_data['supply_total'][1], #complete_data['etb'][1],
complete_data['dro'][1],
complete_data['dperc'][1], #,
complete_data['tr'][1]
])
for date in common_dates:
total_supply_tif = complete_data['supply_total'][0][
complete_data['supply_total'][1] == date][0]
SUP = becgis.OpenAsArray(total_supply_tif, nan_values=True)
dperc_tif = complete_data['dperc'][0][complete_data['dperc'][1] ==
date][0]
DPERC = becgis.OpenAsArray(dperc_tif, nan_values=True)
DPERC[np.isnan(DPERC)] = 0
dro_tif = complete_data['dro'][0][complete_data['dro'][1] == date][0]
DRO = becgis.OpenAsArray(dro_tif, nan_values=True)
DRO[np.isnan(DRO)] = 0
sro_tif = complete_data['sr'][0][complete_data['sr'][1] == date][0]
SRO = becgis.OpenAsArray(sro_tif, nan_values=True)
SRO[np.isnan(SRO)] = 0
# et_blue_tif = complete_data['etb'][0][complete_data['etb'][1] == date][0]
# ETB = becgis.OpenAsArray(et_blue_tif, nan_values = True)
tr_tif = complete_data['tr'][0][complete_data['tr'][1] == date][0]
TR = becgis.OpenAsArray(tr_tif, nan_values=True)
# perc_tif = complete_data['perc'][0][complete_data['perc'][1] == date][0]
# PERC = becgis.OpenAsArray(perc_tif, nan_values = True)
natural_lus = [
'Forests', 'Shrubland', 'Rainfed Crops', 'Forest Plantations',
'Natural Water Bodies', 'Wetlands', 'Natural Grasslands',
'Other (Non-Manmade)'
]
natural_lu_codes = []
for lu_c in natural_lus:
natural_lu_codes.extend(lucs[lu_c])
for code in natural_lu_codes:
# PERC[LULC == code] = PERC[LULC == code] - DPERC[LULC == code]
TR[LULC == code] = TR[LULC == code] - DRO[LULC == code]
SRO[LULC == code] = SRO[LULC == code] - DRO[LULC == code]
SUP[LULC == code] = SUP[LULC == code] - DPERC[LULC == code] - DRO[
LULC == code]
DRO[LULC == code] = 0
DPERC[LULC == code] = 0
outfile_sup = os.path.join(directory_sup,
os.path.basename(total_supply_tif))
becgis.CreateGeoTiff(outfile_sup, SUP, driver, NDV, xsize, ysize, GeoT,
Projection)
outfile_dro = os.path.join(directory_dro, os.path.basename(dro_tif))
becgis.CreateGeoTiff(outfile_dro, DRO, driver, NDV, xsize, ysize, GeoT,
Projection)
outfile_sro = os.path.join(directory_sro, os.path.basename(sro_tif))
becgis.CreateGeoTiff(outfile_sro, SRO, driver, NDV, xsize, ysize, GeoT,
Projection)
outfile_dperc = os.path.join(directory_dperc,
os.path.basename(dperc_tif))
becgis.CreateGeoTiff(outfile_dperc, DPERC, driver, NDV, xsize, ysize,
GeoT, Projection)
# outfile_perc = os.path.join(directory_perc, os.path.basename(perc_tif))
# becgis.CreateGeoTiff(outfile_perc, PERC, driver, NDV, xsize, ysize, GeoT, Projection)
outfile_tr = os.path.join(directory_tr, os.path.basename(tr_tif))
becgis.CreateGeoTiff(outfile_tr, TR, driver, NDV, xsize, ysize, GeoT,
Projection)
complete_data['supply_total'] = becgis.SortFiles(directory_sup, [-10, -6],
month_position=[-6,
-4])[0:2]
complete_data['dro'] = becgis.SortFiles(directory_dro, [-10, -6],
month_position=[-6, -4])[0:2]
complete_data['sr'] = becgis.SortFiles(directory_sro, [-10, -6],
month_position=[-6, -4])[0:2]
complete_data['dperc'] = becgis.SortFiles(directory_dperc, [-10, -6],
month_position=[-6, -4])[0:2]
# complete_data['perc'] = becgis.SortFiles(directory_perc, [-10,-6], month_position = [-6,-4])[0:2]
complete_data['tr'] = becgis.SortFiles(directory_tr, [-10, -6],
month_position=[-6, -4])[0:2]
return complete_data