def draw_fine_global_map(title, ofile, lons, lats, value, vmin, vmax, fmt='%d', flag=0):
opath = os.path.dirname(ofile)
if not os.path.isdir(opath):
os.makedirs(opath)
fig = plt.figure(figsize=(6, 4)) # 图像大小
pos1 = [0.1, 0.3, 0.8, 0.6]
ax1 = add_ax(fig, *pos1)
p = dv_map(fig, ax=ax1)
p.colormap = 'jet'
p.delat = 30
p.delon = 30
# p.colorbar_fmt = "%0.2f"
p.show_countries = True
# p.show_china = True
# p.show_china_province = True
p.show_line_of_latlon = True
p.show_colorbar = False
p.easyplot(
lats, lons, value, vmin=vmin, vmax=vmax, markersize=1.5, marker='.')
p.draw()
# 直方图
pos2 = [0.1, 0.15, 0.8, 0.1]
ax2 = add_ax(fig, *pos2)
p2 = dv_hist(fig, ax=ax2)
p2.easyplot(
value.reshape((-1,)), bins=512, cmap="jet", range=(vmin, vmax))
p2.xlim_min = vmin
p2.xlim_max = vmax
p2.simple_axis(mode=0)
p2.draw()
# ColorBar
pos3 = [0.1, 0.115, 0.8, 0.03]
ax3 = add_ax(fig, *pos3)
add_colorbar_horizontal(ax3, vmin, vmax, cmap="jet", fmt=fmt)
# colorbar上打点
if flag == 1:
value = np.ma.masked_where(np.abs(value) > 3 * vmax, value)
mean = np.ma.mean(value)
print 'b', mean
else:
mean = np.ma.mean(value)
precent = (mean - vmin) / (vmax - vmin)
ax3.plot(precent, 0.5, 'ko', ms=5, zorder=5)
font_mono = get_DV_Font()
strlist = ["mean = %0.2f" % mean]
add_annotate(ax2, strlist, 'left_top', font_mono)
# 总标题
fig.suptitle(title, fontproperties=p.font, size=14)
p.savefig(ofile)
def plot_bias_map(lat=None,
lon=None,
data=None,
out_file=None,
title=None,
vmin=None,
vmax=None):
if title:
title = title
else:
title = "Map"
# 绘制偏差的全球分布图，保持0值永远在bar的中心
if vmin is not None and vmax is not None:
vmin = vmin
vmax = vmax
else:
datamax = np.max(data)
if datamax >= 0:
vmin = -1.0 * datamax
vmax = datamax
else:
vmin = datamax
vmax = -1.0 * datamax
p = dv_map.dv_map()
p.colorbar_fmt = '%0.3f'
color_list = [
'#000081', '#0000C8', '#1414FF', '#A3A3FF', '#FFA3A3', '#FF1414',
'#C70000', '#810000'
]
cmap = colors.ListedColormap(color_list, 'indexed')
p.easyplot(lat,
lon,
data,
vmin=vmin,
vmax=vmax,
ptype=None,
markersize=0.05,
marker='s',
colormap=cmap)
p.title = title
make_sure_path_exists(os.path.dirname(out_file))
p.savefig(out_file)
print '>>> {}'.format(out_file)
def plot_map_project(latitude,
longitude,
count,
out_file,
title=None,
ptype=None,
vmin=None,
vmax=None,
marker=None,
markersize=None):
if title is not None:
title = title
else:
title = "Map"
if vmin is not None:
vmin = vmin
if vmax is not None:
vmax = vmax
if marker is not None:
marker = marker
else:
marker = 's'
if markersize is not None:
markersize = markersize
else:
markersize = 1
p = dv_map.dv_map()
p.easyplot(latitude,
longitude,
count,
vmin=vmin,
vmax=vmax,
ptype=ptype,
markersize=markersize,
marker=marker)
p.title = title
p.add_landmark(lonlatlocation)
p.savefig(out_file)
print '>>> {}'.format(out_file)
def plot_map_project(
latitude,
longitude,
value,
out_file,
box=None,
title=None,
ptype=None,
vmin=None,
vmax=None,
marker=None,
markersize=None):
if box is not None:
box = box # nlat, slat, wlon, elon:北（小），南（大），东（大），西（小）
if title is not None:
title = title
else:
title = "Map"
if vmin is not None:
vmin = vmin
if vmax is not None:
vmax = vmax
if marker is not None:
marker = marker
else:
marker = 's'
if markersize is not None:
markersize = markersize
else:
markersize = 5
p = dv_map()
p.easyplot(latitude, longitude, value, vmin=vmin, vmax=vmax, box=box,
ptype=ptype, markersize=markersize, marker=marker)
p.title = title
p.savefig(out_file)
print('>>> {}'.format(out_file))
def plot_shanghai(latitude,
longitude,
value,
out_file,
box=None,
title=None,
ptype=None,
vmin=None,
vmax=None,
marker=None,
markersize=None,
AREA=None):
if box is not None:
box = box # nlat, slat, wlon, elon:北（小），南（大），东（大），西（小）
if title is not None:
title = title
else:
title = "Map"
if vmin is not None:
vmin = vmin
if vmax is not None:
vmax = vmax
if marker is not None:
marker = marker
else:
marker = 's'
if markersize is not None:
markersize = markersize
else:
markersize = 5
# 开始画图-----------------------
fig = plt.figure(figsize=(9., 8.)) # 图像大小
p = dv_map(fig=fig)
subplots_adjust(left=0.07, right=0.98, top=0.90, bottom=0.15)
p.show_colorbar = False
p.show_countries = False
p.show_coastlines = False
# p.show_line_of_latlon = False
# p.show_china = True
p.show_china_province = True
p.show_inside_china = True
p.show_inside_china_mini = False
p.delat = 2 # 纬度刻度线分辨率
p.delon = 2 # 经度刻度线分辨率
p.color_coast = "#3a3a3a" # 海岸线颜色
p.color_contry = "#3a3a3a" # 国家颜色
p.fontsize_tick = 15
# set color map
p.valmin = vmin
p.valmax = vmax
p.colormap = plt.get_cmap('jet') # mpl.cm.rainbow, summer
# p.colorbar_extend = "max"
# plot
p.easyplot(latitude, longitude, value, box=box, markersize=markersize, ptype="pcolormesh")
if AREA:
print('设置地区 ：{}'.format(AREA))
if AREA == 'YRD':
citys = ["江苏省", "安徽省", "浙江省", "上海市"]
elif AREA == 'BTH':
citys = ["北京市", "天津市", "河北省"]
elif AREA == 'FWP':
citys = ["陕西省", "山西省", "河南省"]
elif AREA == 'FWP':
citys = ["广东省"]
else:
citys = []
for city in citys:
p.city_boundary(city, linewidth=1.2, shape_name='中国省级行政区')
# 色标 ---------------------------
cb_loc = [0.12, 0.07, 0.76, 0.03]
# unit = r"$\mathregular{(10^{15}\/\/molec/cm^2)}$"
fontsize = 16
# p.add_custom_colorbar(cb_loc, p.valmin, p.valmax,
# fmt="%d",
# unit="(1E16 molec/cm^2)",
# fontsize=fontsize)
c_ax = fig.add_axes(cb_loc)
cbar = fig.colorbar(p.cs, cax=c_ax, orientation='horizontal')
for l in c_ax.xaxis.get_ticklabels():
l.set_fontproperties(p.font_mid)
l.set_fontsize(fontsize)
l.set_color(p.color_ticker)
# cbar写单位
# cbar.ax.set_title(unit, x=1.0382, y=0, color=p.color_ticker,
# ha='left', va='center',
# fontproperties=p.font_mid, fontsize=fontsize)
# 标题 ---------------------------
p.w_title = p.suptitle(title, fontsize=14, y=0.97)
# save
p.savefig(out_file, dpi=300)
print(">>> {}".format(out_file))
p.clean()
def proj_dcc(self):
# 初始化投影参数 rowMax=None, colMax=None
lookup_table = prj_gll(resLat=self.resLat, resLon=self.resLon)
newLats, newLons = lookup_table.generateLatsLons()
proj_data_num = np.full_like(newLats, 0)
row = proj_data_num.shape[0]
col = proj_data_num.shape[1]
if self.is_monthly: # 如果是月的，需要查找当前自然月所有数据
PERIOD = calendar.monthrange(int(self.ymd[:4]),
int(self.ymd[4:6]))[1] # 当前月份天数
_ymd = self.ymd[:6] + '%02d' % PERIOD # 当月最后一天
else:
PERIOD = 1
_ymd = self.ymd
file_list = []
for daydelta in xrange(PERIOD):
cur_ymd = pb_time.ymd_plus(_ymd, -daydelta)
# 判断文件是否存在
file_name = '%s_DCC_SLT_%s.H5' % (self.sat_sensor, cur_ymd)
ym = self.ymd[:6]
HDF_file = os.path.join(self.ifile, ym, file_name)
if not os.path.isfile(HDF_file):
print("file no exist : %s" % HDF_file)
else:
file_list.append(HDF_file)
if len(file_list) == 0:
return
# 开始进行投影
for HDF_file in file_list:
h5File = h5py.File(HDF_file, 'r')
lons = h5File.get('Longitude')[:]
lats = h5File.get('Latitude')[:]
h5File.close()
lons = lons / 100.
lats = lats / 100.
ii, jj = lookup_table.lonslats2ij(lons, lats)
for i in xrange(row):
for j in xrange(col):
condition = np.logical_and(ii[:] == i, jj[:] == j)
idx = np.where(condition)
proj_data_num[i][j] = proj_data_num[i][j] + len(idx[0])
proj_data_num = np.ma.masked_where(proj_data_num == 0, proj_data_num)
if self.is_monthly:
print("plot: %s" % self.ymd[0:6])
p = dv_map.dv_map()
p.easyplot(newLats,
newLons,
proj_data_num,
vmin=0,
vmax=10000,
ptype=None,
markersize=20,
marker='s')
title_name = '%s_dcc_projection_' % self.sat_sensor + str(
self.ymd[0:6])
p.title = u'dcc： ' + str(title_name) + u' (分辨率1度)'
opath_fig = os.path.join(self.ofile, 'Monthly',
'%s' % self.ymd[:6])
if not os.path.exists(opath_fig):
os.makedirs(opath_fig)
fig_name = os.path.join(
opath_fig,
'%s_%s_dcc_monthly.png' % (self.sat_sensor, self.ymd[:6]))
p.savefig(fig_name)
opath_hdf = os.path.join(self.ofile, 'Monthly',
'%s' % self.ymd[:6])
if not os.path.exists(opath_hdf):
os.mkdir(opath_hdf)
opath_hdf = os.path.join(
opath_hdf,
'%s_%s_dcc_monthly.hdf' % (self.sat_sensor, self.ymd[0:6]))
h5file_W = h5py.File(opath_hdf, 'w')
h5file_W.create_dataset('proj_data_nums',
dtype='i2',
data=proj_data_num,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.close()
###########################################
# read hdf file and cont values
print("write txt: %s" % self.ymd[0:6])
if not os.path.exists(self.ofile_txt):
os.mkdir(self.ofile_txt)
opath_txt = os.path.join(
self.ofile_txt, '%s_dcc_monthly_count.txt' % self.sat_sensor)
if self.ifile and len(self.ymd) == 8:
hdf = h5py.File(opath_hdf, 'r')
data_mat = hdf.get("proj_data_nums")[:]
hdf.close()
count_value = np.sum(data_mat)
self.data_count = '%8s\t%8s\n' % (self.ymd[0:6], count_value)
lock.acquire()
self.write_txt(opath_txt)
lock.release()
else:
print("plot: %s" % self.ymd)
p = dv_map.dv_map()
p.easyplot(newLats,
newLons,
proj_data_num,
vmin=0,
vmax=10000,
ptype=None,
markersize=20,
marker='s')
title_name = '%s_dcc_projection_' % self.sat_sensor + str(self.ymd)
p.title = u'dcc： ' + str(title_name) + u' (分辨率1度)'
opath_fig = os.path.join(self.ofile, 'Daily', '%s' % self.ymd)
if not os.path.exists(opath_fig):
os.makedirs(opath_fig)
fig_name = os.path.join(
opath_fig, '%s_%s_dcc_daily.png' % (self.sat_sensor, self.ymd))
p.savefig(fig_name)
opath_hdf = os.path.join(self.ofile, 'Daily', '%s' % self.ymd)
if not os.path.exists(opath_hdf):
os.mkdir(opath_hdf)
opath_hdf = os.path.join(
opath_hdf, '%s_%s_dcc_daily.hdf' % (self.sat_sensor, self.ymd))
h5file_W = h5py.File(opath_hdf, 'w')
h5file_W.create_dataset('proj_data_nums',
dtype='i2',
data=proj_data_num,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.close()
###########################################
# read hdf file and cont values
print("write txt: %s" % self.ymd)
if not os.path.exists(self.ofile_txt):
os.mkdir(self.ofile_txt)
opath_txt = os.path.join(
self.ofile_txt, '%s_dcc_daily_count.txt' % self.sat_sensor)
if self.ifile and len(self.ymd) == 8:
hdf = h5py.File(opath_hdf, 'r')
data_mat = hdf.get("proj_data_nums")[:]
count_value = np.sum(data_mat)
self.data_count = '%8s\t%8s\n' % (self.ymd, count_value)
lock.acquire()
self.write_txt(opath_txt)
lock.release()
fy4_lon_lat_lut = 'FY4X_LON_LAT_LUT.H5'
with h5py.File(fy4_lon_lat_lut, 'r') as hdf5_file:
lats = hdf5_file.get('/Latitude')[:]
lons = hdf5_file.get('/Longitude')[:]
lat_0 = 28.550000
lon_0 = 23.390000
lat_max = lat_0 + 3
lat_min = lat_0 - 3
lon_max = lon_0 + 3
lon_min = lon_0 - 3
box = [lat_max, lat_min, lon_min, lon_max]
# box = [60, 10, 70, 150]
p = dv_map()
p.easyplot(lats,
lons,
lats,
vmin=None,
vmax=None,
ptype=None,
markersize=0.1,
marker='o',
box=None)
lonlatlocation = [(123.0, -74.5, u"Dome_C"), (23.39, 28.55, u"Libya4")]
p.add_landmark(lonlatlocation)
out_picture = 'map_test.png'
p.savefig(out_picture)
print '>>> {}'.format(out_picture)
def main():
pickle_file = 'fy3d_mersi_global.pickle'
if not os.path.isfile(pickle_file):
dir_path = "/home/gsics/nas03/CMA_GSICS/SourceData/FENGYUN-3D/MERSI/L1/ORBIT/2018/20180630"
times = [
'0800',
'0805',
'0810',
'0815',
'0820',
'0825',
'0830',
'0835',
'0840',
'0845',
'0850',
'0855',
'0900',
'0905',
'0910',
'0915',
'0920',
'0925',
'0930',
'0935',
'0940',
'0945',
'0950',
'0955',
'1000',
'1005',
'1010',
'1015',
'1020',
'1025',
'1030',
'1035',
'1040',
'1045',
'1050',
'1055',
'1100',
'1110',
'1115',
'1120',
'1125',
'1130',
]
file_name = 'FY3D_MERSI_GBAL_L1_20180630_{}_1000M_MS.HDF'
lon_all = list()
lat_all = list()
tbb_all = list()
for i in times:
in_file = os.path.join(dir_path, file_name.format(i))
print in_file
read_mersi_l1 = ReadMersiL1(in_file)
lon = read_mersi_l1.get_longitude()
lat = read_mersi_l1.get_latitude()
tbb = read_mersi_l1.get_tbb()['CH_24']
for d in [lat, lon, tbb]:
index = np.isfinite(d)
lat = lat[index]
lon = lon[index]
tbb = tbb[index]
lon_all = np.append(lon_all, lon)
lat_all = np.append(lat_all, lat)
tbb_all = np.append(tbb_all, tbb)
print(lon_all.shape, lat_all.shape, tbb_all.shape)
data = {
'lon': lon_all,
'lat': lat_all,
'tbb': tbb_all,
}
with open(pickle_file, 'wb') as f:
pickle.dump(data, f)
else:
with open(pickle_file, 'rb') as f:
data = pickle.load(f)
lat = data['lat']
lon = data['lon']
tbb = data['tbb']
print(lat.shape, lon.shape, tbb.shape)
out_file = 'FY3D+MERSI_TBB_GLOBAL_DISTRIBUTION.png'
title = 'FY3D+MERSI TBB GLOBAL DISTRIBUTION CH_24 20180630'
vmin = int(np.nanmin(tbb))
vmax = int(np.nanmax(tbb))
p = dv_map.dv_map()
p.easyplot(lat, lon, tbb, markersize=1, marker='s', vmin=vmin, vmax=vmax)
p.title = title
p.savefig(out_file)
print '>>> {}'.format(out_file)
def run(satPair, ymd):
# 解析配置文件
shortsat1 = (satPair.split('_')[0]).split('+')[0]
sensor1 = (satPair.split('_')[0]).split('+')[1]
shortsat2 = (satPair.split('_')[1]).split('+')[0]
sensor2 = (satPair.split('_')[1]).split('+')[1]
#ipath = os.path.join(MATCH_DIR, satPair, ymd[:6])
ipath = os.path.join(MATCH_DIR, satPair, ymd[:4], ymd)
# 获取输入文件列表
pat = '[_,\-\+\w]+_%s\d{6}.hdf5' % ymd
FileLst = pb_io.FindFile(ipath, pat)
dictLon1 = {}
dictLat1 = {}
dictTbb1 = {}
dictLon2 = {}
dictLat2 = {}
dictTbb2 = {}
i = 0
if FileLst != []:
for iFile in FileLst:
try:
h5File_R = h5py.File(iFile, 'r')
obritType = h5File_R.attrs.get('obrit Direction1')[0]
Lon1 = h5File_R.get('S1_Lon')[:]
Lat1 = h5File_R.get('S1_Lat')[:]
Lon2 = h5File_R.get('S2_Lon')[:]
Lat2 = h5File_R.get('S2_Lat')[:]
if len(obritType) == 0:
print iFile
continue
if obritType not in dictLon1.keys():
dictLon1[obritType] = Lon1
dictLat1[obritType] = Lat1
dictLon2[obritType] = Lon2
dictLat2[obritType] = Lat2
dictTbb1[obritType] = {}
dictTbb2[obritType] = {}
for band in BandLst:
tbb1 = h5File_R.get('/%s/S1_FovTbbMean' % band)[:]
tbb2 = h5File_R.get('/%s/S2_FovTbbMean' % band)[:]
if band not in dictTbb1[obritType].keys():
dictTbb1[obritType][band] = tbb1
dictTbb2[obritType][band] = tbb2
else:
dictLon1[obritType] = np.concatenate(
(dictLon1[obritType], Lon1))
dictLat1[obritType] = np.concatenate(
(dictLat1[obritType], Lat1))
dictLon2[obritType] = np.concatenate(
(dictLon2[obritType], Lon2))
dictLat2[obritType] = np.concatenate(
(dictLat2[obritType], Lat2))
for band in BandLst:
tbb1 = h5File_R.get('/%s/S1_FovTbbMean' % band)[:]
tbb2 = h5File_R.get('/%s/S2_FovTbbMean' % band)[:]
dictTbb1[obritType][band] = np.concatenate(
(dictTbb1[obritType][band], tbb1))
dictTbb2[obritType][band] = np.concatenate(
(dictTbb2[obritType][band], tbb2))
h5File_R.close()
except Exception as e:
print str(e)
# 按照升降轨道 和 不同通道分别出图，暂时出tbb即可
for adType in dictLat1.keys():
for band in dictTbb1[adType].keys():
print adType, band
Tbb1 = dictTbb1[adType][band]
Tbb2 = dictTbb2[adType][band]
Tbb1 = np.ma.masked_where(Tbb1 <= 0, Tbb1)
Tbb2 = np.ma.masked_where(Tbb1 <= 0, Tbb2)
outName = '%s+%s_GBAL_MAP_%s_%s_%s.png' % (shortsat1, sensor1,
adType, ymd, band)
#opath = os.path.join(MATCH_DIR, satPair, outName)
opath = os.path.join(MATCH_DIR, satPair, ymd[:4], outName)
p = dv_map.dv_map(figsize=(6, 5))
p.title = '%s %s BT collocated with %s at %s %s (%s)' % (
shortsat1, sensor1, sensor2, band, ymd, adType)
# p.colorbar_fmt = '%0.2f'
vmin = 180
vmax = 300
p.delat = 30
p.delon = 30
p.easyplot(dictLat1[adType],
dictLon1[adType],
Tbb1,
vmin=vmin,
vmax=vmax,
markersize=1,
marker='.')
p.savefig(opath, dpi=300)
outName = '%s+%s_GBAL_MAP_%s_%s_%s.png' % (shortsat2, sensor2,
adType, ymd, band)
#opath = os.path.join(MATCH_DIR, satPair, outName)
opath = os.path.join(MATCH_DIR, satPair, ymd[:4], outName)
p = dv_map.dv_map(figsize=(6, 5))
p.title = '%s %s BT convoluted at %s at %s %s (%s)' % (
shortsat2, sensor2, sensor1, band, ymd, adType)
# p.colorbar_fmt = '%0.2f'
vmin = 180
vmax = 300
p.delat = 30
p.delon = 30
p.easyplot(dictLat2[adType],
dictLon2[adType],
Tbb2,
vmin=vmin,
vmax=vmax,
markersize=1,
marker='.')
p.savefig(opath, dpi=300)
outName = '%s+%s_%s+%s_GBAL_MAP_%s_%s_%s.png' % (
shortsat1, sensor1, shortsat2, sensor2, adType, ymd, band)
#opath = os.path.join(MATCH_DIR, satPair, outName)
opath = os.path.join(MATCH_DIR, satPair, ymd[:4], outName)
p = dv_map.dv_map(figsize=(6, 5))
p.title = '%s %s BT at %s %s (%s)' % (sensor1, sensor2, band,
ymd, adType)
# p.colorbar_fmt = '%0.2f'
vmin = -2
vmax = 2
p.delat = 30
p.delon = 30
diffTbb = Tbb1 - Tbb2
diffTbb = np.ma.masked_where(Tbb1 <= 0, diffTbb)
p.easyplot(dictLat1[adType],
dictLon1[adType],
diffTbb,
vmin=vmin,
vmax=vmax,
markersize=1,
marker='.')
p.savefig(opath, dpi=300)
def proj_fy2_dcc(self):
print self.ymd
# print 'start project %s %s' % (self.sat, self.sensor)
# 初始化投影参数 rowMax=None, colMax=None
lookup_table = prj_gll(nlat=self.nlat,
slat=self.slat,
wlon=self.wlon,
elon=self.elon,
resLat=self.resLat,
resLon=self.resLon)
newLats, newLons = lookup_table.generateLatsLons()
proj_data_num = np.full_like(newLats, 0)
row = proj_data_num.shape[0]
col = proj_data_num.shape[1]
for L1File in self.ifile:
# # print L1File
h5File = h5py.File(L1File, 'r')
lons = h5File.get('Longitude')[:]
lats = h5File.get('Latitude')[:]
lons = lons / 100.
lats = lats / 100.
h5File.close()
ii, jj = lookup_table.lonslats2ij(lons, lats)
for i in xrange(row):
for j in xrange(col):
condition = np.logical_and(ii[:, 0] == i, jj[:, 0] == j)
idx = np.where(condition)
# if len(idx[0]) > 0:
# print len(idx[0])
proj_data_num[i][j] = proj_data_num[i][j] + len(idx[0])
proj_data_num = np.ma.masked_where(proj_data_num == 0, proj_data_num)
p = dv_map.dv_map()
p.easyplot(newLats,
newLons,
proj_data_num,
vmin=0,
vmax=10000,
box=[30., -30., 60., 120.],
ptype=None,
markersize=20,
marker='s')
title_name = '%s_dcc_projection_' % self.sat + str(self.ymd)
p.title = u'dcc： ' + str(title_name) + u' (分辨率1度)'
fig_name = self.ofile + '/%s' % self.ymd[:6]
if not os.path.exists(fig_name):
os.mkdir(fig_name)
fig_name = fig_name + '/%s_%s_dcc.png' % (self.sat, self.ymd)
p.savefig(fig_name)
opath_hdf = self.ofile + '/%s' % self.ymd[:6]
if not os.path.exists(opath_hdf):
os.mkdir(opath_hdf)
opath_hdf = opath_hdf + '/%s_%s_dcc.hdf' % (self.sat, self.ymd)
h5file_W = h5py.File(opath_hdf, 'w')
h5file_W.create_dataset('proj_data_nums',
dtype='i2',
data=proj_data_num,
compression='gzip',
compression_opts=5,
shuffle=True)
h5file_W.close()
###########################################
# read hdf file and cont values
opath_txt = self.ofile_txt + '/%s_dcc_daily_count.txt' % self.sat
if len(self.ifile) == 1 and len(self.ymd) == 8:
daily_hdf = h5py.File(opath_hdf)
data_mat = daily_hdf.get("proj_data_nums", 'r')
count_value = np.sum(data_mat)
self.FileSave = '%8s\t%8s\n' % (self.ymd, count_value)
self.Write(opath_txt)
def draw(self, in_file, proj_file, dataset_name, vmin=None, vmax=None):
if self.error:
return
# 加载 Proj 数据
if os.path.isfile(proj_file):
try:
with h5py.File(proj_file, 'r') as h5:
lut_ii = h5.get("lut_ii")[:]
lut_jj = h5.get("lut_jj")[:]
data_ii = h5.get("data_ii")[:]
data_jj = h5.get("data_jj")[:]
except Exception as why:
print why
print "Can't open file: {}".format(proj_file)
return
else:
print "File does not exist: {}".format(proj_file)
return
with time_block("Draw load", switch=TIME_TEST):
# 加载产品数据
if os.path.isfile(in_file):
try:
with h5py.File(in_file, 'r') as h5:
proj_value = h5.get(dataset_name)[:][data_ii, data_jj]
except Exception as why:
print why
print "Can't open file: {}".format(in_file)
return
else:
print "File does not exist: {}".format(in_file)
return
if vmin is not None:
vmin = vmin
if vmax is not None:
vmax = vmax
p = dv_map.dv_map()
p.title = "{} {}".format(dataset_name, self.ymd)
# 增加省边界
# p1.show_china_province = True
p.delat = 30
p.delon = 30
p.show_line_of_latlon = False
# p.colormap = 'gist_rainbow'
# p.colormap = 'viridis'
# p.colormap = 'brg'
# 创建查找表
lookup_table = prj_core(
self.cmd, self.res, unit="deg", row=self.row, col=self.col)
lookup_table.grid_lonslats()
lons = lookup_table.lons
lats = lookup_table.lats
# 创建完整的数据投影
value = np.full((self.row, self.col), self.fill_value, dtype='f4')
value[lut_ii, lut_jj] = proj_value
value = np.ma.masked_less_equal(value, 0) # 掩掉 <=0 的数据
# 乘数据的系数，水色产品为 0.001
slope = 0.001
value = value * slope
p.easyplot(lats, lons, value, ptype=None, vmin=vmin,
vmax=vmax, markersize=0.1, marker='o')
out_png_path = os.path.dirname(in_file)
out_png = os.path.join(out_png_path, '{}.png'.format(dataset_name))
pb_io.make_sure_path_exists(os.path.dirname(out_png))
p.savefig(out_png, dpi=300)
def plot_map_picture(value,
longitude,
latitude,
title='',
vmin=-np.inf,
vmax=np.inf,
areas=None,
box=None,
ticks=None,
file_out=None,
ptype='pcolormesh',
mksize=5,
nanhai=False):
if file_out is None:
print('没有指定输出文件：file_out is None')
return
# 开始画图-----------------------
fig = plt.figure(figsize=(9, 8)) # 图像大小
p = dv_map(fig=fig)
subplots_adjust(left=0.07, right=0.98, top=0.90, bottom=0.15)
p.show_colorbar = False
p.show_countries = False
p.show_coastlines = False
# p.show_line_of_latlon = False
# p.show_china = True
if nanhai:
p.nanhai_loc = [0.83, 0.25, 0.15, 0.17]
p.nanhai_minimap()
p.show_china_province = True
p.show_inside_china = True
p.show_inside_china_mini = True
if box:
if abs(box[1] - box[0]) < 10:
p.delat = 2
else:
p.delat = 5
if abs(box[2] - box[3]) < 10:
p.delon = 2
elif abs(box[2] - box[3]) < 40:
p.delon = 5
else:
p.delon = 10
# else:
# p.delat = 2 # 纬度刻度线分辨率
# p.delon = 2 # 经度刻度线分辨率
p.color_coast = "#3a3a3a" # 海岸线颜色
p.color_contry = "#3a3a3a" # 国家颜色
p.fontsize_tick = 15
# set color map
p.valmin = vmin
p.valmax = vmax
p.colormap = plt.get_cmap('jet') # mpl.cm.rainbow, summer, jet, bwr
# p.colorbar_extend = "max"
# plot
p.easyplot(latitude,
longitude,
value,
vmin=vmin,
vmax=vmax,
box=box,
markersize=mksize,
ptype=ptype)
if areas is not None and len(areas) > 0:
print('设置地区 ：{}'.format(areas))
# aeres = ["江苏省", "安徽省", "浙江省", "上海市"]
for aere in areas:
p.city_boundary(aere, linewidth=1.2, shape_name='中国省级行政区')
# 色标 ---------------------------
cb_loc = [0.12, 0.07, 0.76, 0.03]
# unit = r"$\mathregular{(10^{15}\/\/molec/cm^2)}$"
fontsize = 16
# p.add_custom_colorbar(cb_loc, p.valmin, p.valmax,
# fmt="%d",
# unit="(1E16 molec/cm^2)",
# fontsize=fontsize)
c_ax = fig.add_axes(cb_loc)
# cbar = fig.colorbar(p.cs, cax=c_ax, ticks=np.arange(0, 1.6, 0.3), orientation='horizontal')
fig.colorbar(p.cs, cax=c_ax, ticks=ticks, orientation='horizontal')
for l in c_ax.xaxis.get_ticklabels():
l.set_fontproperties(p.font_mid)
l.set_fontsize(fontsize)
l.set_color(p.color_ticker)
# cbar写单位
# cbar.ax.set_title(unit, x=1.0382, y=0, color=p.color_ticker,
# ha='left', va='center',
# fontproperties=p.font_mid, fontsize=fontsize)
# 标题 ---------------------------
p.w_title = p.suptitle(title, fontsize=14, y=0.97)
# save
p.savefig(file_out, dpi=200)
print(">>> {}".format(file_out))
p.clean()
# wavenums, response = cris.get_spectral_response()
# # wavenums, response = cris.get_spectral_response_low()
# print_data_status(wavenums)
# print_data_status(response)
# print 'get_spectral_response1111:'
# wavenums1, response1 = cris.get_spectral_response_high()
# print_data_status(wavenums1)
# print_data_status(response1)
print 'longitude:'
t_data1 = cris.get_longitude()
print 'latitude:'
t_data2 = cris.get_latitude()
value = np.full_like(t_data2, 100.)
tbbs = tbb['CH_21']
p = dv_map.dv_map(figsize=(6, 5))
p.easyplot(t_data2,
t_data1,
tbbs,
vmin=180,
vmax=320,
markersize=1.5,
marker='.')
p.savefig('D:/data/CRIS/test1.png')
pass
# #
# print 'sensor_azimuth:'
# t_data = cris.get_sensor_azimuth()
# print_data_status(t_data)
# print 'sensor_zenith:'
# t_data = cris.get_sensor_zenith()
