def cumulated_precip_evo(initTime=None,
t_gap=6,
t_range=[6, 36],
day_back=0,
model='ECMWF',
data_source='MICAPS',
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
Global=False,
**kwargs):
fhours = np.arange(t_range[0], t_range[1] + 1, t_gap)
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % t_gap)
]
except KeyError:
raise ValueError('Can not find all directories needed')
if (initTime == None):
initTime = MICAPS_IO.get_latest_initTime(data_dir[0])
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
# retrieve data from micaps server
rain = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
rain2 = rain.copy(deep=True)
for itime in range(1, len(rain['forecast_period'].values)):
rain2['data'].values[itime, :, :] = np.sum(
rain['data'].values[0:itime + 1, :, :], axis=0)
if (data_source == 'CIMISS'):
if (initTime != None):
filename = utl.model_filename(initTime, 0, UTC=True)
else:
filename = utl.filename_day_back_model(day_back=0,
fhour=0,
UTC=True)
try:
TPE1 = CMISS_IO.cimiss_model_by_times(
'20' + filename[0:8],
valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TPE'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
except KeyError:
raise ValueError('Can not find all data needed')
rain = TPE1.copy(deep=True)
rain['data'].values = (TPE1['data'].values)
# set map extent
if (area != None):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
else:
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
mask1 = (rain['lon'] > map_extent[0] - delt_x) & (
rain['lon'] < map_extent[1] + delt_x) & (
rain['lat'] > map_extent[2] - delt_y) & (rain['lat'] <
map_extent[3] + delt_y)
rain2 = rain2.where(mask1, drop=True)
rain2.attrs['model'] = model
rain2.attrs['t_gap'] = t_gap
# draw
QPF_graphics.draw_cumulated_precip_evo(rain=rain2,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def T2m_mean24(initTime=None,
fhour=24,
day_back=0,
model='中央气象台中短期指导',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
data_source='MICAPS',
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
# prepare data
if (data_source == 'MICAPS'):
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='T2m')
]
fhours = np.arange(fhour - 21, fhour + 1, 3)
if (initTime is None):
initTime = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)[0:8]
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
T_2m = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
Tmean_2m = T_2m.isel(time=[-1]).copy()
Tmean_2m['data'].values[0, :, :] = np.mean(T_2m['data'].values, axis=0)
if (data_source == 'CIMISS'):
# get filename
fhours = np.arange(fhour - 18, fhour + 1, 3)
if (initTime is None):
initTime = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)[0:8]
T_2m = CMISS_IO.cimiss_model_by_times('20' + initTime,
valid_times=fhours,
data_code=utl.CMISS_data_code(
data_source=model,
var_name='TEF2'),
fcst_level=0,
fcst_ele='TEF2',
units='K',
allExists=False)
T_2m['data'].values = T_2m['data'].values - 273.15
Tmean_2m = T_2m.isel(time=[-1]).copy()
Tmean_2m['data'].values[0, :, :] = np.min(T_2m['data'].values, axis=0)
# set map extent
if (area != '全国'):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
mask1 = (Tmean_2m['lon'] > map_extent[0] - delt_x) & (
Tmean_2m['lon'] < map_extent[1] + delt_x) & (
Tmean_2m['lat'] > map_extent[2] - delt_y) & (
Tmean_2m['lat'] < map_extent[3] + delt_y)
Tmean_2m = Tmean_2m.where(mask1, drop=True)
#- to solve the problem of labels on all the contours
Tmean_2m.attrs['model'] = model
Tmean_2m.attrs['title'] = '2米平均温度'
elements_graphics.draw_T_2m(T_2m=Tmean_2m,
T_type='T_mean',
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def tmp_evo(initTime=None,
tmp_lev=850,
t_gap=6,
t_range=[6, 36],
model='ECMWF',
data_source='MICAPS',
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
**kwargs):
fhours = np.arange(t_range[0], t_range[1], t_gap)
#prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl=tmp_lev),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PSFC')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initTime == None):
initTime = MICAPS_IO.get_latest_initTime(data_dir[0])
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
# retrieve data from micaps server
tmp = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
psfc = MICAPS_IO.get_model_grids(data_dir[1], filenames=filenames)
if (data_source == 'CIMISS'):
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, 0, UTC=True)
else:
filename = utl.filename_day_back_model(day_back=0,
fhour=0,
UTC=True)
try:
tmp = CMISS_IO.cimiss_model_by_times(
'20' + filename[0:8],
valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TEM'),
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_level=tmp_lev,
fcst_ele="TEM",
units='K')
tmp['data'].values = tmp['data'].values - 273.15
psfc = CMISS_IO.cimiss_model_by_times(
'20' + filename[0:8],
valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,
var_name='PRS'),
fcst_level=0,
fcst_ele="PRS",
units='Pa')
psfc['data'] = psfc['data'] / 100.
except KeyError:
raise ValueError('Can not find all data needed')
# set map extent
if (area != None):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent, delt_x, delt_y = utl.get_map_extent(cntr_pnt, zoom_ratio,
map_ratio)
tmp = utl.cut_xrdata(map_extent, tmp, delt_x=delt_x, delt_y=delt_y)
tmp = tmp.rolling({'lon': 3, 'lat': 3}).mean()
psfc = utl.cut_xrdata(map_extent, psfc, delt_x=delt_x, delt_y=delt_y)
tmp = utl.mask_terrian(tmp_lev, psfc, tmp)
tmp.attrs['model'] = model
coldwave_graphics.draw_tmp_evo(tmp=tmp,
map_extent=map_extent,
south_China_sea=south_China_sea,
**kwargs)
def T2m_mx24(initTime=None,
fhour=24,
day_back=0,
model='中央气象台中短期指导',
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
data_source='MICAPS',
area=None,
**kargws):
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='Tmx_2m')
]
if (initTime != None):
filename = utl.model_filename(initTime, fhour)
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
Tmx_2m = MICAPS_IO.get_model_grid(data_dir[0], filename=filename)
except:
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='Tmx3_2m')
]
except:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='T2m')
]
fhours = np.arange(fhour - 21, fhour + 1, 3)
if (initTime is None):
initTime = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)[0:8]
filenames = [
initTime + '.' + str(fhour).zfill(3) for fhour in fhours
]
T_2m = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
Tmx_2m = T_2m.isel(time=[-1]).copy()
Tmx_2m['data'].values[0, :, :] = np.max(T_2m['data'].values,
axis=0)
if (data_source == 'CIMISS'):
# get filename
fhours = np.arange(fhour - 18, fhour + 1, 3)
if (initTime is None):
initTime = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)[0:8]
try:
# retrieve data from CMISS server
T_2m = CMISS_IO.cimiss_model_by_times(
'20' + initTime,
valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,
var_name='MX2T6'),
fcst_level=0,
fcst_ele='MX2T6',
units='K',
allExists=False)
except:
T_2m = CMISS_IO.cimiss_model_by_times(
'20' + initTime,
valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TEF2'),
fcst_level=0,
fcst_ele='TEF2',
units='K',
allExists=False)
if T_2m is None:
return
T_2m['data'].values = T_2m['data'].values - 273.15
Tmx_2m = T_2m.isel(time=[-1]).copy()
Tmx_2m['data'].values[0, :, :] = np.max(T_2m['data'].values, axis=0)
# set map extent
if (area != '全国'):
south_China_sea = False
if (area != None):
cntr_pnt, zoom_ratio = utl.get_map_area(area_name=area)
map_extent = [0, 0, 0, 0]
map_extent[0] = cntr_pnt[0] - zoom_ratio * 1 * map_ratio
map_extent[1] = cntr_pnt[0] + zoom_ratio * 1 * map_ratio
map_extent[2] = cntr_pnt[1] - zoom_ratio * 1
map_extent[3] = cntr_pnt[1] + zoom_ratio * 1
delt_x = (map_extent[1] - map_extent[0]) * 0.2
delt_y = (map_extent[3] - map_extent[2]) * 0.1
#+ to solve the problem of labels on all the contours
mask1 = (Tmx_2m['lon'] > map_extent[0] - delt_x) & (
Tmx_2m['lon'] < map_extent[1] + delt_x) & (
Tmx_2m['lat'] > map_extent[2] - delt_y) & (Tmx_2m['lat'] <
map_extent[3] + delt_y)
Tmx_2m = Tmx_2m.where(mask1, drop=True)
#- to solve the problem of labels on all the contours
Tmx2 = xr.DataArray(np.squeeze(Tmx_2m['data'].values, axis=0),
name='data',
coords={
'time': ('time', [Tmx_2m['time'].values[0]]),
'fhour': ('time', [fhour]),
'lat': ('lat', Tmx_2m['lat'].values),
'lon': ('lon', Tmx_2m['lon'].values)
},
dims=('time', 'lat', 'lon'),
attrs={
'model_name': '中央气象台中短期指导',
'var_name': '2米最高温度',
'vhours': 24
})
draw_SCMOC.draw_TMP2(TMP2=Tmx2, map_extent=map_extent, **kargws)
