def plot_month_hist():
fdir = this_root + '1km月值_1978_1985_1995_2005_2018\\'
fdir = fdir.decode('gbk')
flist = os.listdir(fdir)
for year in ['1978','1985','1995','2005','2018']:
plt.figure(figsize=(3*4, 4*4))
flag = 0
for f in flist:
if year in f:
if f.endswith('.tif'):
print(f)
# flag += 1
# a = a.decode('gbk')
if not '2005' in f:
mon = f.split('_')[-3]
# print(mon)
else:
mon = f.split('_')[-2]
# print(mon)
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir + f)
hist = []
for i in range(len(array)):
for j in range(len(array[0])):
val = array[i][j]
if 0 <= val <= 100:
hist.append(val)
flag+=1
plt.subplot(4,3,flag)
plt.hist(hist,bins=40,normed=1)
plt.title(mon)
plt.savefig(this_root+year+'.png',dpi=300)
def plot_year_hist():
fdir = this_root + '1km月值_1978_1985_1995_2005_2018\\'
fdir = fdir.decode('gbk')
flist = os.listdir(fdir)
flag = 0
# plt.figure(figsize=(5,25))
flag = 0
for f in flist:
if f.endswith('.tif'):
# flag+=1
if not '-08-01_' in f:
continue
flag += 1
# a = a.decode('gbk')
print(f)
year = f.split('_')[1].split('.')[0]
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir + f)
hist = []
for i in range(len(array)):
for j in range(len(array[0])):
val = array[i][j]
if 0 <= val <= 100:
hist.append(val)
plt.subplot(1,5,flag)
plt.hist(hist,bins=40,normed=1)
plt.title(year)
if flag == 5:
break
# plt.xticks([])
# plt.yticks([]
plt.show()
def quanqu():
fdir = r'D:\FVC\FVC_1km_new\FVC-year_1km_clipped\\'
# fdir = fdir.decode('gbk')
flist = os.listdir(fdir)
fw = open(fdir+'quanqu.csv','w')
for f in flist:
if f.endswith('.tif'):
# print(f)
year = f.split('.')[0].split('fvc')[1]
# print(a.decode('gbk'))
# exit()
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir+f)
# plt.imshow(array)
# plt.colorbar()
# plt.show()
fenmu = 0.
gao = 0.
zhonggao = 0.
zhong = 0.
zhongdi = 0.
di = 0.
for i in range(len(array)):
for j in range(len(array[0])):
val = array[i][j]*100
if 0<=val<=100:
fenmu += 1.
if 75<val<=100:
gao+=1.
elif 60<val<=75:
zhonggao+=1.
elif 45<val<=60:
zhong+=1.
elif 30<val<=45:
zhongdi+=1.
elif 0<=val<=30:
di+=1.
else:
print(val)
gao_ratio = round(gao/fenmu,4)*100.
zhonggao_ratio = round(zhonggao/fenmu,4)*100.
zhong_ratio = round(zhong/fenmu,4)*100.
zhongdi_ratio = round(zhongdi/fenmu,4)*100.
di_ratio = round(di/fenmu,4)*100.
a = '{}年内蒙古植被高覆盖区面积为{}平方千米，占全区总面积比例为{}%，' \
'植被中高覆盖区面积为{}平方千米，占全区总面积比例为{}%，' \
'植被中覆盖区面积为{}平方千米，占全区总面积比例为{}%，' \
'植被中低覆盖区面积为{}平方千米，占全区总面积比例为{}%，' \
'植被低覆盖区面积为{}平方千米，占全区总面积比例为{}%'\
.format(year,gao,gao_ratio,
zhonggao,zhonggao_ratio,
zhong,zhong_ratio,
zhongdi,zhongdi_ratio,
di,di_ratio)
b='{},'*5
c=b.format(gao,zhonggao,zhong,zhongdi,di)
# print(c)
print(a.decode('gbk'))
fw.write(','.join(c.split(',')) + '\n')
def gen_dem_npy():
# for the purpose of generate non standardized dem dic
tif = r'E:\MODIS\DEM\china_1km_dem_wgs1984_resample.tif'
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(
tif)
np.save(r'E:\MODIS\DEM\china_1km_dem_wgs1984_resample_non_normalize',
array)
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 plot_fenqu():
fdir = 'E:\\FVC内蒙古植被覆盖数据\\30m年值_1978_1985_1995_2005_2018_clipped\\'.decode('gbk')
outdir = this_root+'png\\'
if not os.path.isdir(outdir):
os.makedirs(outdir)
shp_name = ['北方风沙区', '北方土石山区', '东北黑土区', '西北黄土高原区']
flist = os.listdir(fdir)
for y in ['1978','1985','1995','2005','2018']:
arrs = []
names = []
for z in shp_name:
for f in flist:
if f.endswith('.tif'):
zone = f.split('_')[-1].split('.')[0].encode('gbk')
year = f.split('_')[1].split('.')[0]
if zone == z and y == year:
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir+f)
arrs.append(array)
names.append(z.decode('gbk'))
fig, axs = plt.subplots(2, 2, figsize=(9,7))
images = []
flag = 0
for i in range(2):
for j in range(2):
arr = arrs[flag]
# classified = classify_arr(arr)
# exit()
arr = np.ma.masked_where(arr>120,arr)
# sns.
# plt.imshow()
cmap = ["#734e00", "#a87001", "#effa91", "#b1cc63", "#5d8c22"]
cmap_ = col.LinearSegmentedColormap.from_list('mycol',cmap)
cm.register_cmap(cmap=cmap_)
images.append(axs[i,j].imshow(arr, cmap='mycol',vmin=0,vmax=100))
# images.append(axs[i, j].imshow(arr,'jet'))
axs[i, j].set_title(names[flag])
axs[i,j].set_xticks([])
axs[i,j].set_yticks([])
# fig.yticks([])
# fig.yticks([])
flag += 1
fig.suptitle(y+'年内蒙古4大区域植被盖度(%)'.decode('gbk'))
fig.colorbar(images[0], ax=axs, orientation='horizontal', shrink=0.7, pad=0.05)
plt.show()
# plt.savefig(outdir+y+'.png',dpi=300)
pass
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)
def chutu():
### 内存不足 无法运行 ####
fdir = u'E:\\FVC内蒙古植被覆盖数据\\30m月值_1978_1985_1995_2005_2018\\'
flist = os.listdir(fdir)
for f in flist:
if f.endswith('.tif'):
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir+f)
array = np.array(array)
array = np.ma.masked_where(array<0,array)
array = np.ma.masked_where(array>100,array)
plt.imshow(array,'BrBG',vmin=0,vmax=100)
# plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.title(f)
plt.savefig(f+'.png',dpi=300)
exit()
def cal_trend_30m():
fdir = u'E:\\FVC内蒙古植被覆盖数据\\30m年值_1978_1985_1995_2005_2018\\'
# save_file = 'E:\\FVC内蒙古植被覆盖数据\\npy\\1km月值'
flist = os.listdir(fdir)
row = 66189
col = 80941
print('create void dic')
for r in tqdm(range(row)):
void_dic = {}
for c in range(col):
void_dic['%d.%d'%(r,c)] = []
for f in flist:
print(f)
if f.endswith('.tif'):
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir + f)
print(np.shape(array))
exit()
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 cal_trend():
fdir = u'E:\\FVC内蒙古植被覆盖数据\\1km年值_1978_1985_1995_2005_2018\\'
save_file = 'E:\\FVC内蒙古植被覆盖数据\\npy\\1km年值_new'
flist = os.listdir(fdir)
arrs = []
for y in [1978,1985,1995,2005,2018]:
for f in flist:
if f.endswith('.tif') and str(y) in f:
print(f)
# continue
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir+f)
arrs.append(array)
# exit()
_,row,col = np.shape(arrs)
trend = []
for r in tqdm(range(row)):
temp = []
for c in range(col):
vals = []
for arr in arrs:
val = arr[r][c]
vals.append(val)
if vals[0] < 0:
temp.append(-999999)
continue
else:
fit = stats.linregress(range(len(vals)),vals)
slope = float(fit.slope)
temp.append(slope)
# print stats.pearsonr(range(len(vals)),vals)
trend.append(temp)
trend = np.array(trend)
# plt.imshow(trend)
# plt.colorbar()
# plt.show()
# print('saving')
np.save(save_file,trend)
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 data_transform(self):
# 不可并行，内存不足
mode = 'SWE_max'
# mode = 'SWE_avg'
fdir = this_root+'GLOBSWE\\tif\\'+mode+'\\'
outdir = this_root+'GLOBSWE\\per_pix\\'+mode+'\\'
analysis.Tools().mk_dir(outdir,force=True)
# 将空间图转换为数组
# per_pix_data
flist = os.listdir(fdir)
date_list = []
for y in range(1982,2016):
for mon in range(1,13):
if mon in range(5,10):
continue
date_list.append('{}{}'.format(y,'%02d'%mon))
# for i in date_list:
# print i
# exit()
all_array = []
for d in tqdm(date_list, 'loading...'):
for f in flist:
# if not d in f:
# continue
if f.endswith('.tif'):
if f.split('.')[0] == d:
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir + f)
all_array.append(array)
# print len(all_array)
# exit()
row = len(all_array[0])
col = len(all_array[0][0])
void_dic = {}
void_dic_list = []
for r in range(row):
for c in range(col):
void_dic['%03d.%03d' % (r, c)] = []
void_dic_list.append('%03d.%03d' % (r, c))
# print(len(void_dic))
# exit()
for r in tqdm(range(row), 'transforming...'):
for c in range(col):
for arr in all_array:
val = arr[r][c]
void_dic['%03d.%03d' % (r, c)].append(val)
# for i in void_dic_list:
# print(i)
# exit()
flag = 0
temp_dic = {}
for key in tqdm(void_dic_list, 'saving...'):
flag += 1
# print('saving ',flag,'/',len(void_dic)/100000)
temp_dic[key] = void_dic[key]
if flag % 10000 == 0:
# print('\nsaving %02d' % (flag / 10000)+'\n')
np.save(outdir + 'per_pix_dic_%03d' % (flag / 10000), temp_dic)
temp_dic = {}
np.save(outdir + 'per_pix_dic_%03d' % 0, temp_dic)
fdir = r'F:\FVC内蒙古植被覆盖数据\1km月值_1978_1985_1995_2005_2018\\'
save_dir = r'F:\FVC内蒙古植被覆盖数据\1km_monthly\\'
flist = os.listdir(fdir)
time_init = time.time()
flag = 0
for f in flist:
time_start = time.time()
if not f.endswith('.tif'):
continue
# print(f)
# exit()
file_name = (fdir+f).decode('gbk').encode('utf-8')
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(file_name)
new_array = []
flag1 = 0
for i in array:
flag1 += 1
# print flag1,'/',len(array)
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:
def fenqu():
# fdir = r'D:\FVC\FVC_1km_new\4fenqu\\'.decode('gbk')
# fdir = r'D:\FVC\重点区域范围\1km_fenqu_clipped\\'.decode('gbk')
fdir = r'E:\FVC内蒙古植被覆盖数据\FVC_D\重点1\1km_clipped\\'.decode('gbk')
# shp_name = ['北方风沙区', '北方土石山区', '东北黑土区', '西北黄土高原区']
# shp_name = ['太行山山地丘陵区', '宁蒙覆沙黄土丘陵区', '晋陕蒙丘陵沟壑区', '燕山及辽西山地丘陵区']
# shp_name = ['内蒙古草地', '鄂尔多斯市', '内蒙古农牧交错带']
shp_name = ['赤峰市', '鄂尔多斯', '通辽市', '准格尔旗']
flist = os.listdir(fdir)
fw = open(fdir+'result_.csv','w')
for f in flist:
if f.endswith('.tif'):
# print(f.decode('gbk'))
zone = f.split('_')[-1].split('.')[0].encode('gbk')
# year = f.split('_')[0].split('fvc')[1]
year = f.split('_')[0]
# print(year)
# exit()
# print(zone)
array, originX, originY, pixelWidth, pixelHeight = to_raster.raster2array(fdir + f)
# plt.imshow(array)
# plt.colorbar()
# plt.show()
fenmu = 0.
gao = 0.
zhonggao = 0.
zhong = 0.
zhongdi = 0.
di = 0.
# plt.imshow(array)
# plt.show()
for i in range(len(array)):
for j in range(len(array[0])):
val = array[i][j]
if 0 <= val <= 100:
fenmu += 1.
if 75 < val <= 100:
gao += 1.
elif 60 < val <= 75:
zhonggao += 1.
elif 45 < val <= 60:
zhong += 1.
elif 30 < val <= 45:
zhongdi += 1.
elif 0 <= val <= 30:
di += 1.
else:
print(val)
gao_ratio = round(gao / fenmu, 4) * 100.
zhonggao_ratio = round(zhonggao / fenmu, 4) * 100.
zhong_ratio = round(zhong / fenmu, 4) * 100.
zhongdi_ratio = round(zhongdi / fenmu, 4) * 100.
di_ratio = round(di / fenmu, 4) * 100.
gao = int(gao)
zhonggao = int(zhonggao)
zhong = int(zhong)
zhongdi = int(zhongdi)
di = int(di)
a = '{}年内蒙古{}植被高覆盖区面积为{}平方千米，占全区总面积比例为{}%；' \
'植被中高覆盖区面积为{}平方千米，占全区总面积比例为{}%；' \
'植被中覆盖区面积为{}平方千米，占全区总面积比例为{}%；' \
'植被中低覆盖区面积为{}平方千米，占全区总面积比例为{}%；' \
'植被低覆盖区面积为{}平方千米，占全区总面积比例为{}%。' \
.format(year,zone,gao, gao_ratio,
zhonggao, zhonggao_ratio,
zhong, zhong_ratio,
zhongdi, zhongdi_ratio,
di, di_ratio)
b = '{},'*12
c = b.format(year,zone,gao, gao_ratio,
zhonggao, zhonggao_ratio,
zhong, zhong_ratio,
zhongdi, zhongdi_ratio,
di, di_ratio)
print(a.decode('gbk'))
fw.write(','.join(c.split(',')[2:])+'\n')