def main(arg):
all_filenames = []
for (dirpath, dirnames, filenames) in walk(TEST_DIRECTORY):
for fn in filenames:
all_filenames.append((dirpath, fn))
all_filenames = [(d, f) for (d, f) in all_filenames if f.endswith('.txt')]
for d, f in all_filenames:
if f.startswith('raw_'):
new_f = d + f[4:]
with open(new_f, 'w') as nf:
nf.write(parse_raw(d + f))
for d, f in all_filenames:
if not f.startswith(arg):
continue
test_map, num, ans = read_map(d + f)
find_solution(test_map, num, ans)
break
def game_loop():
field = read_map("map.txt")
speed = 100
initialize_render()
while True:
if blt.has_input():
key = blt.read()
if key == blt.TK_A:
speed = 1000
if key == blt.TK_S:
speed = 500
if key == blt.TK_D:
speed = 100
if key == blt.TK_F:
speed = 10
if key == blt.TK_CLOSE or key == blt.TK_Q:
break
update(field)
render()
blt.delay(speed)
terminate_render()
luc = len(luc_names) pas = 1 fea = 6 act = luc - (pas + fea) # Specify the maximum neighbourhood size distance considered max_distance = 5 # Set the minimum required percentage of conversions. min_convo_rate = 0.025 # Set the minimum required Enrichment Factor value at distance 1. min_EF_1 = 0.00 # Set the significance limit (recommended value is 1.96 z_limit = 1.96 # Read in the map for time slice 1. omap = read_map(omap_path) # Read in the map for time slice 2. amap = read_map(amap_path) # Read in the masking map. mask = read_map(mask_path) # Analyse the input maps for evaluation purposes map_dimensions = np.shape(omap) rows = map_dimensions[0] cols = map_dimensions[1] # Determine the distances that will be analysed using the module considered # distances. temp = considered_distances(max_distance) # Store the list of considered distances as a variable. cd = temp[0] # Store the total number of distances considered
def define_surface_co2_region(x, y, xedge, yedge, do_debug=False):
""" the state variables define following transcom definiation
see http://www.purdue.edu/transcom/images/smoothmap2_final_2.jpg
"""
import read_map
lon, lat, type_map=read_map.read_map(360, 180)
land_reg=['ice',\
'North American Boreal',\
'North American Temperate',\
'South American Tropical',\
'South American Temperate',\
'North Africa',\
'South Africa',\
'Eurasia Boreal', \
'Eurasia Temperate',\
'Tropical Asia', \
'Australia', \
'Europe']
ocean_reg=['North Pacific Temperate', \
'West Pacific Tropics', \
'East Pacific Tropics', \
'South Pacific Temperate',\
'Northern Ocean',
'Northen Atlantic Temperate',\
'Atlantic Tropics', \
'South Atlantic Temperate',\
'South Ocean', \
'Indian Tropical', \
'South Indian Temperate']
type_map=type_map.astype(int)
nz=max(type_map.flat)
type_map_sav=array(type_map)
JJPAR=gm.grid_mod.get_y_size()
JY=arange(1, JJPAR+1)
YY = gm.grid_mod.get_ymids( JY )
new_map=read_map.regrid_map(lon, lat, xedge, yedge,type_map_sav)
nx=size(xedge)-1
ny=size(yedge)-1
new_map=new_map[0:nx, 0:ny]
if (do_debug):
subplot(2,1,1)
nz=max(type_map.flat)
type_map=1.0*type_map
gpl.plot_map(type_map,lon, lat, use_pcolor=1)
subplot(2,1,2)
gpl.plot_map(new_map, x, y, use_pcolor=1)
show()
# the emission error are taken transcom prior flux file 3 for basis function error while the ice one is assigned to a small value (percentage) #
def_err=[0.15, \
0.726200, 1.500000, 1.412200, \
1.227300, 1.331700, 1.411400, \
1.514300, 1.728300, 0.865600, \
0.593600, 1.420900, 0.820000, \
0.500000, 0.560000, 1.220000, \
0.260000, 0.400000, 0.400000, \
0.480000, 1.500000, 0.740000, \
0.540000] # in Gt C /year
return new_map, land_reg, ocean_reg, def_err
def define_surface_co2_region_cell(x, y, xedge, yedge, \
sel_id, nlon_reg, nlat_reg, \
do_debug=False):
""" the state variables define following transcom definiation but increase some new regions
see http://www.purdue.edu/transcom/images/smoothmap2_final_2.jpg
"""
import read_map
lon, lat, type_map=read_map.read_map(360, 180)
land_reg=['ice',\
'North American Boreal',\
'North American Temperate',\
'South American Tropical',\
'South American Temperate',\
'North Africa',\
'South Africa',\
'Eurasia Boreal', \
'Eurasia Temperate',\
'Tropical Asia', \
'Australia', \
'Europe']
ocean_reg=['North Pacific Temperate', \
'West Pacific Tropics', \
'East Pacific Tropics', \
'South Pacific Temperate',\
'Northern Ocean',
'Northen Atlantic Temperate',\
'Atlantic Tropics', \
'South Atlantic Temperate',\
'South Ocean', \
'Indian Tropical', \
'South Indian Temperate']
type_map=type_map.astype(int)
nz=max(type_map.flat)
type_map_sav=array(type_map)
JJPAR=gm.grid_mod.get_y_size()
JY=arange(1, JJPAR+1)
YY = gm.grid_mod.get_ymids( JY )
new_map=read_map.regrid_map(lon, lat, xedge, yedge,type_map)
nx=size(xedge)-1
ny=size(yedge)-1
new_map=new_map[0:nx, 0:ny]
print(max(new_map.flat))
type_map=array(new_map)
lon=array(xedge[0:nx])
lat=array(xedge[0:ny])
new_map=zeros(shape(type_map))
idx=where(type_map==sel_id)
idx=squeeze(idx)
cnt_cell=0
idx_lon=idx[0]
idx_lat=idx[1]
npt=size(idx_lon)
for pt in range(npt):
new_map[idx_lon[pt], idx_lat[pt]]=cnt_cell
cnt_cell=cnt_cell+1
print('cnt_cell', cnt_cell)
type_map=where(type_map>sel_id, type_map+cnt_cell-1, type_map)
type_map=type_map+new_map
if (do_debug):
subplot(2,1,1)
nz=max(type_map.flat)
type_map=1.0*type_map
gpl.plot_map(type_map,lon, lat, use_pcolor=1)
subplot(2,1,2)
gpl.plot_map(new_map, x, y, use_pcolor=1)
show()
# the emission error are taken transcom prior flux file 3 for basis function error while the ice one is assigned to a small value (percentage) #
def_err_land=[0.15, \
0.726200, 1.500000, 1.412200, \
1.227300, 1.331700, 1.411400, \
1.514300, 1.728300, 0.865600, 0.593600, 1.420900]
# def_err_ocean=[0.820000, \
# 0.500000, 0.560000, 1.220000, \
# 0.260000, 0.400000, 0.400000, \
# 0.480000, 1.500000, 0.740000, \
# 0.540000] # in Gt C /year
def_err_ocean=[0.270000, \
0.390000, 0.370000, 0.630000, \
0.350000, 0.270000, 0.410000, \
0.550000, 0.720000, 0.480000, \
0.410000] # in Gt C /year
def_err_land[sel_id]=def_err_land[sel_id]/(sqrt(cnt_cell))
all_def_err_land=def_err_land[0:sel_id+1]+[def_err_land[sel_id]]*(cnt_cell-1)+def_err_land[sel_id+1:]
new_reg=list()
for ireg in range(1, cnt_cell):
regname='SUB_R%2.2dS%2.2d' % (sel_id, ireg)
new_reg.append(regname)
print(new_reg)
all_land_reg=land_reg[0:sel_id+1]+new_reg+land_reg[sel_id+1:]
print(len(all_land_reg))
def_err=all_def_err_land+def_err_ocean
# subplot(2,1,1)
# gpl.plot_map(type_map,x, y, use_pcolor=1,cmap=cm.jet)
# subplot(2,1,2)
# gpl.plot_map(new_map, x, y, use_pcolor=1,cmap=cm.jet)
# show()
new_map=array(type_map)
return new_map, all_land_reg, ocean_reg, def_err
def define_surface_co2_region_hr(x, y, xedge, yedge, \
sel_id, nlon_reg, nlat_reg, \
do_debug=False):
""" the state variables define following transcom definiation but increase some new regions
see http://www.purdue.edu/transcom/images/smoothmap2_final_2.jpg
"""
import read_map
lon, lat, type_map=read_map.read_map(360, 180)
land_reg=['ice',\
'North American Boreal',\
'North American Temperate',\
'South American Tropical',\
'South American Temperate',\
'North Africa',\
'South Africa',\
'Eurasia Boreal', \
'Eurasia Temperate',\
'Tropical Asia', \
'Australia', \
'Europe']
ocean_reg=['North Pacific Temperate', \
'West Pacific Tropics', \
'East Pacific Tropics', \
'South Pacific Temperate',\
'Northern Ocean',
'Northen Atlantic Temperate',\
'Atlantic Tropics', \
'South Atlantic Temperate',\
'South Ocean', \
'Indian Tropical', \
'South Indian Temperate']
type_map=type_map.astype(int)
nz=max(type_map.flat)
type_map_sav=array(type_map)
JJPAR=gm.grid_mod.get_y_size()
JY=arange(1, JJPAR+1)
YY = gm.grid_mod.get_ymids( JY )
idx=where(type_map==sel_id)
lon_reg_id=idx[0]
lat_reg_id=idx[1]
rlon_reg=lon[lon_reg_id]
rlat_reg=lat[lat_reg_id]
min_lon_reg, max_lon_reg=min(rlon_reg), max(rlon_reg)
min_lat_reg, max_lat_reg=min(rlat_reg), max(rlat_reg)
id_lon=where(logical_and(lon>=min_lon_reg, lon<=max_lon_reg))
id_lon=squeeze(id_lon)
id_lat=where(logical_and(lat>=min_lat_reg, lat<=max_lat_reg))
id_lat=squeeze(id_lat)
sel_id_list=list()
areas=list()
new_map=zeros(shape(type_map))
for ilat in id_lat:
sel_idx=where(lat_reg_id==ilat)
sel_idx=squeeze(sel_idx)
if (size(sel_idx)>0):
ix, iy=lon_reg_id[sel_idx], lat_reg_id[sel_idx]
area_y=abs(cos(pi*lat[ilat]/180.0))*size(sel_idx)
areas.append(area_y)
else:
areas.append(0)
sel_id_list.append(array(sel_idx))
areas=array(areas)
area_div=sum(areas)/nlat_reg
area_sum=0.0
nlat_div=size(id_lat)
area_lon=zeros(size(id_lon), float)
# print shape(area_lon)
# print id_lon
# print lon[id_lon]
# print id_lat
# print lat[id_lat]
sub_sel_idx=list()
region_count=0
print('nlat_div', nlat_div, area_div)
new_reg=list()
for ilat in range(nlat_div):
area_sum=area_sum+areas[ilat]
# print ilat, area_sum
# spread area into lon
sel_idx=sel_id_list[ilat]
if (size(sel_idx)>0):
tmp_lon_pos=lon_reg_id[sel_idx]
# print id_lon[40:50]
# print tmp_lon_pos
lon_pos=searchsorted(id_lon, tmp_lon_pos)
lon_pos=squeeze(lon_pos)
# print lon_pos
# print id_lon[lon_pos]
# print shape(area_lon)
# print max(lon_pos)
cell_area=abs(cos(pi*lat[lat_reg_id[sel_idx[0]]]/180.0))
for ii in range(size(sel_idx)):
area_lon[lon_pos[ii]]=area_lon[lon_pos[ii]]+cell_area
sub_sel_idx.append(sel_idx[ii])
if (area_sum>=area_div or ilat==nlat_div-1):
sub_sel_idx=array(sub_sel_idx)
# divide the along the longitude
area_lon_div=sum(area_lon)/nlon_reg
area_lon=add.accumulate(area_lon)
lon_div_ar=arange(0, nlon_reg,1.0)
lon_div_ar=area_lon_div*lon_div_ar
lon_pos_2=searchsorted(lon_div_ar, area_lon)
lon_pos_2=lon_pos_2-1
ix, iy=lon_reg_id[sub_sel_idx], lat_reg_id[sub_sel_idx]
print(shape(id_lon))
# print shape(ix), shape(iy)
tmp_ix_pos=searchsorted(id_lon, ix)
# print 'max lon_pos_2', max(lon_pos_2)
# print lon_div_ar
# print area_lon[tmp_ix_pos]
# print lon_pos_2[tmp_ix_pos]
# print lon_pos_2
print('nz', nz)
new_reg_id=lon_pos_2[tmp_ix_pos]+(region_count)*nlon_reg
print(min(new_reg_id), max(new_reg_id), region_count)
# new_reg_id=where(new_reg_id==0, 0, new_reg_id+nz)
new_map[ix, iy]=new_reg_id # -type_map[ix,iy]
region_count=region_count+1
area_sum=0.0
area_lon=zeros(size(id_lon), float)
sub_sel_idx=list()
type_map=where(type_map>sel_id, type_map+nlon_reg*nlat_reg-1, type_map)
type_map=type_map+new_map
# now we add some new regions into it
new_map=read_map.regrid_map(lon, lat, xedge, yedge,type_map)
nx=size(xedge)-1
ny=size(yedge)-1
new_map=new_map[0:nx, 0:ny]
print(max(new_map.flat))
# figure(2)
# gpl.plot_map(type_map,lon, lat, use_pcolor=1, cmap=cm.Paired) #Set1)
# savefig('test_div.png')
# savefig('test_div.ps')
# show()
if (do_debug):
subplot(2,1,1)
nz=max(type_map.flat)
type_map=1.0*type_map
gpl.plot_map(type_map,lon, lat, use_pcolor=1)
subplot(2,1,2)
gpl.plot_map(new_map, x, y, use_pcolor=1)
show()
# the emission error are taken transcom prior flux file 3 for basis function error while the ice one is assigned to a small value (percentage) #
def_err_land=[0.15, \
0.726200, 1.500000, 1.412200, \
1.227300, 1.331700, 1.411400, \
1.514300, 1.728300, 0.865600, 0.593600, 1.420900]
# def_err_ocean=[0.820000, \
# 0.500000, 0.560000, 1.220000, \
# 0.260000, 0.400000, 0.400000, \
# 0.480000, 1.500000, 0.740000, \
# 0.540000] # in Gt C /year
def_err_ocean=[0.270000, \
0.390000, 0.370000, 0.630000, \
0.350000, 0.270000, 0.410000, \
0.550000, 0.720000, 0.480000, \
0.410000] # in Gt C /year
def_err_land[sel_id]=def_err_land[sel_id]/(sqrt(nlon_reg)*sqrt(nlat_reg))
all_def_err_land=def_err_land[0:sel_id+1]+[def_err_land[sel_id]]*(nlon_reg*nlat_reg-1)+def_err_land[sel_id+1:]
for ireg in range(1, nlon_reg*nlat_reg):
regname='SUB_R%2.2dS%2.2d' % (sel_id, ireg)
new_reg.append(regname)
print(new_reg)
all_land_reg=land_reg[0:sel_id+1]+new_reg+land_reg[sel_id+1:]
def_err=all_def_err_land+def_err_ocean
return new_map, all_land_reg, ocean_reg, def_err
geo_cmd = "C:\\Program Files (x86)\\Geonamica\\Metronamica\\GeonamicaCmd.exe"
# Set the land-use class names.
luc_names = [
"Natural areas", "Arable land", "Permanent crops", "Pastures",
"Agricultural areas", "Residential", "Industry & commerce",
"Recreation areas", "Forest", "Road & rail", "Seaports", "Airports",
"Mine & dump sites", "Fresh water", "Marine water"
]
# Set the land-use class parameters: number of land-use classes, passive,
# feature, and active.
luc = len(luc_names)
pas = 1
fea = 6
act = luc - (pas + fea)
# Read in the map for the data at time slice 1.
omap = read_map(map1_path)
# Read in the map for the data at time slice 2.
amap = read_map(map2_path)
# Read in the masking map.
mask = read_map(mask_path)
# Analyse the input maps for evaluation purposes
map_dimensions = np.shape(omap)
rows = map_dimensions[0]
cols = map_dimensions[1]
# Specify the maximum neighbourhood size distance considered
max_distance = 5
# Determine the distances that will be analysed, use module: considered_distances.
temp = considered_distances(max_distance)
# Store the list of considered distances as a variable.
cd = temp[0]
# Store the total number of distances considered
geo_cmd = "C:\\Program Files (x86)\\Geonamica\\Metronamica\\GeonamicaCmd.exe"
# Set the land-use class names.
luc_names = [
"Natural areas", "Arable land", "Permanent crops", "Pastures",
"Agricultural areas", "Residential", "Industry & commerce",
"Recreation areas", "Forest", "Road & rail", "Seaports", "Airports",
"Mine & dump sites", "Fresh water", "Marine water"
]
# Set the land-use class parameters: number of land-use classes, passive,
# feature and active.
luc = len(luc_names)
pas = 1
fea = 6
act = luc - (pas + fea)
# Read in the map for the data at time slice 1.
map_1990 = read_map(map1_path)
# Read in the map for the data at time slice 2.
map_2000 = read_map(map2_path)
# Read in the map for the data at time slice 3.
map_2006 = read_map(map3_path)
# Read in the masking map
mask = read_map(mask_path)
# Analyse the input maps for evaluation purposes.
map_dimensions = np.shape(map_1990)
rows = map_dimensions[0]
cols = map_dimensions[1]
# Specify the maximum neighoburhood size distance considered.
max_distance = 5
# Determine the distances that will be analysed.
temp = considered_distances(max_distance)
# Store the list of considered distances as a variable.
#mask_path=str(sys.argv[3])
#map1_path = "/home/a1091793/Documents/Modelling/RunMetroCal/individual_2/replicate_0/Data2/lu1989.asc"
#map2_path = "/home/a1091793/Documents/Modelling/RunMetroCal/individual_2/replicate_0/Data2/lu2000.asc"
#mask_path = "/home/a1091793/Documents/Modelling/RunMetroCal/individual_2/replicate_0/Data2/region.asc"
map2_path = r"/home/a1091793/Documents/Modelling/Run/individual_0/replicate_0/Data2/lu2000.asc"
map1_path = r"/home/a1091793/Documents/Modelling/Run/individual_0/replicate_0/Log/Land_use/Land use map_2000-Jan-01 00_00_00.rst"
mask_path = r"/home/a1091793/Documents/Modelling/Run/individual_0/replicate_0/Data2/region.asc"
from read_map import read_map
import numpy as np
from clumpy_module import clumpiness_index
'''Maps----------------------------------------------------------------------'''
map1=read_map(map1_path)
map2=read_map(map2_path)
mask=read_map(mask_path)
'''Clumpiness_indices--------------------------------------------------------'''
CLUMPY1=clumpiness_index(map1,mask)
CLUMPY2=clumpiness_index(map2,mask)
'''Average_absolute_clumpiness_error-----------------------------------------'''
luc=np.amax(map1)+1
#pas=int(sys.argv[4])
#fea=int(sys.argv[5])
pas = 1
fea = 2
act=luc-(fea+pas)
ACE=[0]*act
for i in range(pas,luc-fea):
ACE[i-pas]=abs(CLUMPY1[i]-CLUMPY2[i])