def gen_normalize_dem_tif():
# for the purpose of examination
arr_dem = np.load(r'E:\MODIS\DEM\dem_normalize.npy')
plt.imshow(arr_dem)
plt.colorbar()
plt.show()
tif = r'E:\MODIS\DEM\china_1km_dem_wgs1984_resample.tif'
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(
tif)
to_raster.array2raster(
r'E:\MODIS\DEM\china_1km_dem_wgs1984_resample_normalize.tif', originX,
originY, pixelWidth, pixelHeight, arr_dem)
def nc_to_tif(nc,outdir):
# outdir = this_root+'SPEI\\tif\\'
import analysis
analysis.Tools().mk_dir(outdir)
ncin = Dataset(nc, 'r')
lat = ncin['lat'][::-1]
lon = ncin['lon']
pixelWidth = lon[1]-lon[0]
pixelHeight = lat[1]-lat[0]
longitude_start = lon[0]
latitude_start = lat[0]
time = ncin.variables['time']
# print(time)
# exit()
# time_bounds = ncin.variables['time_bounds']
# print(time_bounds)
start = datetime.datetime(1900, 01, 01)
# a = start + datetime.timedelta(days=5459)
# print(a)
# print(len(time_bounds))
# print(len(time))
# for i in time:
# print(i)
# exit()
# nc_dic = {}
flag = 0
valid_year = []
for i in range(1982, 2016):
valid_year.append(str(i))
for i in range(len(time)):
flag += 1
# print(time[i])
date = start + datetime.timedelta(days=int(time[i]))
year = str(date.year)
month = '%02d' % date.month
# day = '%02d'%date.day
date_str = year + month
if not date_str[:4] in valid_year:
continue
# print(date_str)
arr = ncin.variables['tmp'][i][::-1]
arr = np.array(arr)
grid = arr < 99999
arr[np.logical_not(grid)] = -999999
newRasterfn = outdir+date_str+'.tif'
to_raster.array2raster(newRasterfn,longitude_start,latitude_start,pixelWidth,pixelHeight,arr)
def read_hdf():
out_dir = this_root + 'NDVI\\tif\\'
fdir = this_root + 'NDVI\\GIMMS_NDVI\\'
for f in tqdm(os.listdir(fdir)):
# for f in os.listdir(fdir):
if not f.endswith('.hdf'):
continue
# if not f == 'ndvi3g_geo_v1_2015_0106.hdf':
# continue
# print(f)
year = f.split('.')[0].split('_')[-2]
# print(year)
f = h5py.File(fdir + f, 'r')
# print(len(f))
# continue
base_month = f['time'][0]
for i in range(len(f)):
arr = f['ndvi'][i]
lon = f['lon']
lat = f['lat']
time = f['time'][i]
month = base_month + (time - base_month) / 0.5
# print(time_)
# continue
month = int(month)
date = year + '%02d' % month
# print(date)
# continue
newRasterfn = out_dir + '{}.tif'.format(date)
longitude_start = lon[0]
latitude_start = lat[0]
pixelWidth = lon[1] - lon[0]
pixelHeight = lat[1] - lat[0]
arr = np.array(arr, dtype=float)
# print(arr.dtype)
grid = arr > -3000
arr[np.logical_not(grid)] = -999999
# plt.imshow(arr)
# plt.show()
to_raster.array2raster(newRasterfn, longitude_start,
latitude_start, pixelWidth, pixelHeight,
arr)
pass
def contour():
fdir = u'E:\\FVC内蒙古植被覆盖数据\\1km年值_1978_1985_1995_2005_2018\\'
outdir = u'E:\\FVC内蒙古植被覆盖数据\\tif\\'
flist = os.listdir(fdir)
arr_sum = 0
for f in flist:
if f.endswith('.tif'):
print(f)
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir + f)
array = np.array(array,dtype='float64')
arr_sum += array
arr_mean = arr_sum/5.
grid = arr_mean > -9999
arr_mean[np.logical_not(grid)] = 255
# plt.imshow(arr_mean)
# plt.colorbar()
# plt.show()
# exit()
to_raster.array2raster(outdir+'mean.tif',originX, originY, pixelWidth, pixelHeight,arr_mean)
def mean_1km():
fdir = r'E:\1km_fvc_monthly\glass\\'
out_dir = r'E:\1km_fvc_annual\glass\\'
flist = os.listdir(fdir)
one_year = {}
for y in range(2000, 2017):
one_year[str(y)] = []
for f in flist:
year = f.split('_')[1].split('-')[0]
for y in range(2000, 2017):
if year == str(y):
one_year[year].append(f)
for y in one_year:
print(y)
array_sum = 0.
flag = 0.
for mon in one_year[y]:
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(
fdir + mon)
# array = np.ma.masked_where(array == 255, array)
# plt.imshow(array, 'jet')
# plt.colorbar()
# plt.show()
array_sum += array
flag += 1.
array_mean = array_sum / flag
# array_mean = np.ma.masked_where(array_mean>254,array_mean)
newRasterfn = out_dir + y + '.tif'
longitude_start = originX
latitude_start = originY
pixelWidth = pixelWidth
pixelHeight = pixelHeight
array = array_mean
to_raster.array2raster(newRasterfn, longitude_start, latitude_start,
pixelWidth, pixelHeight, array)
def modify_2018_1km_tif():
fdir = u'E:\\FVC内蒙古植被覆盖数据\\1km月值_1978_1985_1995_2005_2018\\'
flist = os.listdir(fdir)
selected = []
for f in flist:
if f.endswith('.tif') and '_2018-' in f:
print(f)
selected.append(f)
selected = selected[::2]
print()
for f in selected:
# print(f)
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir+f)
break
# arrs.append(array)
maxarr = np.zeros_like(array)
for f in selected:
print(f)
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir+f)
maxarr = np.maximum(maxarr,array)
to_raster.array2raster(fdir+'2018_new.tif',originX, originY, pixelWidth, pixelHeight,maxarr)
temp = []
for j in i:
if j < -999:
val = 255
elif -999 < j < 0:
val = 0
elif 0 <= j <=1:
val = int(j*100)
elif j > 1:
val = 100
else:
val = 255
temp.append(val)
# print val
new_array.append(temp)
# new_array = np.array(new_array,dtype=int)
new_array = np.array(new_array,dtype=int)
# print 'ploting'
# plt.imshow(new_array)
# plt.colorbar()
# print 'done'
# plt.show()
# exit()
# print 'saving'
newRasterfn = (save_dir+f).decode('gbk').encode('utf-8')
to_raster.array2raster(newRasterfn,originX,originY,pixelWidth,pixelHeight,new_array)
time_end = time.time()
log_process.process_bar(flag,len(flist),time_init,time_start,time_end,f)
flag+=1
def nc_to_tif(nc,outdir):
# outdir = this_root+'SPEI\\tif\\'
import analysis
analysis.Tools().mk_dir(outdir,force=1)
ncin = Dataset(nc, 'r')
# print(ncin.variables)
# exit()
lat = ncin['lat']
lon = ncin['lon']
pixelWidth = lon[1]-lon[0]
pixelHeight = lat[1]-lat[0]
longitude_start = lon[0]
latitude_start = lat[0]
time = ncin.variables['time']
# print(time)
# exit()
# time_bounds = ncin.variables['time_bounds']
# print(time_bounds)
start = datetime.datetime(1900, 01, 01)
# a = start + datetime.timedelta(days=5459)
# print(a)
# print(len(time_bounds))
# print(len(time))
# for i in time:
# print(i)
# exit()
# nc_dic = {}
flag = 0
# valid_year = []
# for i in range(1982, 2016):
# valid_year.append(str(i))
# for i in time:
# print(i)
# exit()
for i in range(len(time)):
flag += 1
# print(time[i])
date = start + datetime.timedelta(days=int(time[i]))
year = str(date.year)
month = '%02d' % date.month
# day = '%02d'%date.day
date_str = year + month
# if not date_str[:4] in valid_year:
# continue
# print(date_str)
# exit()
ndv = np.nan
arr = ncin.variables['def'][i]
for name,variable in ncin.variables.items():
for var in variable.ncattrs():
if var == 'missing_value':
ndv = variable.getncattr(var)
if np.isnan(ndv):
raise IOError('no key missing_value')
arr = np.array(arr)
#### mask ####
grid = arr == 32768
# print ndv
arr[grid] = -999999
# arr[grid] = np.nan
# plt.imshow(arr)
# plt.colorbar()
# plt.show()
newRasterfn = outdir+date_str+'.tif'
to_raster.array2raster(newRasterfn,longitude_start,latitude_start,pixelWidth,pixelHeight,arr)