def Time_Crossection_rh_uv_Temp(
initTime=None,
model='ECMWF',
points={
'lon': [116.3833],
'lat': [39.9]
},
levels=[1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 200],
t_gap=3,
t_range=[0, 48],
output_dir=None):
fhours = np.arange(t_range[0], t_range[1], t_gap)
# 读数据
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='TMP',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=''),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='RH',
lvl=''),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PSFC')
]
except KeyError:
raise ValueError('Can not find all directories needed')
if (initTime == None):
initTime = get_latest_initTime(data_dir[0][0:-1] + "850")
filenames = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours]
TMP_4D = get_model_3D_grids(directory=data_dir[0][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
TMP_2D = TMP_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
u_4D = get_model_3D_grids(directory=data_dir[1][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
u_2D = u_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
v_4D = get_model_3D_grids(directory=data_dir[2][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
v_2D = v_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
rh_4D = get_model_3D_grids(directory=data_dir[3][0:-1],
filenames=filenames,
levels=levels,
allExists=False)
rh_2D = rh_4D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
rh_2D.attrs['model'] = model
rh_2D.attrs['points'] = points
Psfc_3D = get_model_grids(directory=data_dir[4][0:-1],
filenames=filenames,
allExists=False)
Psfc_1D = Psfc_3D.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
v_2D2, pressure_2D = xr.broadcast(v_2D['data'], v_2D['level'])
v_2D2, Psfc_2D = xr.broadcast(v_2D['data'], Psfc_1D['data'])
terrain_2D = pressure_2D - Psfc_2D
crossection_graphics.draw_Time_Crossection_rh_uv_Temp(
rh_2D=rh_2D,
u_2D=u_2D,
v_2D=v_2D,
TMP_2D=TMP_2D,
terrain_2D=terrain_2D,
t_range=t_range,
model=model,
output_dir=output_dir)
def dT2m_mean24(initTime=None,
fhour=48,
day_back=0,
map_ratio=19 / 9,
zoom_ratio=20,
cntr_pnt=[102, 34],
south_China_sea=True,
area='全国',
city=False,
output_dir=None,
Global=False):
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source='ECMWF',
var_name='T2m')
]
fhours1 = 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]
filenames1 = [initTime + '.' + str(fhour).zfill(3) for fhour in fhours1]
if (fhour >= 48):
fhours2 = np.arange(fhour - 21 - 24, fhour + 1 - 24, 3)
filenames2 = [
initTime + '.' + str(fhour).zfill(3) for fhour in fhours2
]
if (fhour >= 36 and fhour < 48):
fhours2 = np.arange(fhour - 21 + 12 - 24, fhour + 1 + 12 - 24, 3)
initTime2 = (datetime.strptime('20' + initTime, '%Y%m%d%H') -
timedelta(hours=12)).strftime('%Y%m%d%H')[2:10]
filenames2 = [
initTime2 + '.' + str(fhour).zfill(3) for fhour in fhours2
]
if (fhour >= 24 and fhour < 36):
fhours2 = np.arange(fhour - 21 + 24 - 24, fhour + 1 + 24 - 24, 3)
initTime2 = (datetime.strptime('20' + initTime, '%Y%m%d%H') -
timedelta(hours=24)).strftime('%Y%m%d%H')[2:10]
filenames2 = [
initTime2 + '.' + str(fhour).zfill(3) for fhour in fhours2
]
if (fhour < 24):
print('fhour should > 24')
return
# prepare data
T_2m1 = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames1)
Tmn_2m1 = T_2m1.isel(time=[-1]).copy()
Tmn_2m1['data'].values[0, :, :] = np.mean(T_2m1['data'].values, axis=0)
T_2m2 = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames2)
Tmn_2m2 = T_2m2.isel(time=[-1]).copy()
Tmn_2m2['data'].values[0, :, :] = np.mean(T_2m2['data'].values, axis=0)
dTmn_2m = Tmn_2m1.copy()
dTmn_2m['data'].values = Tmn_2m1['data'].values - Tmn_2m2['data'].values
# 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 = (dTmn_2m['lon'] > map_extent[0] - delt_x) & (
dTmn_2m['lon'] < map_extent[1] + delt_x) & (
dTmn_2m['lat'] > map_extent[2] - delt_y) & (dTmn_2m['lat'] <
map_extent[3] + delt_y)
dTmn_2m = dTmn_2m.where(mask1, drop=True)
#- to solve the problem of labels on all the contours
dTmn_2m.attrs['model'] = 'ECMWF'
dTmn_2m.attrs['title'] = '2米最低温度24小时变温'
elements_graphics.draw_dT_2m(dT_2m=dTmn_2m,
T_type='dT2m_meann',
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def cumulated_precip(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.sum('time')
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_time('20' + filename[0:8],
valid_time=fhours[0],
data_code=utl.CMISS_data_code(
data_source=model,
var_name='TPE'),
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE1 is None:
return
TPE2 = CMISS_IO.cimiss_model_by_time('20' + filename[0:8],
valid_time=fhours[-1],
data_code=utl.CMISS_data_code(
data_source=model,
var_name='TPE'),
fcst_level=0,
fcst_ele="TPE",
units='kg*m^-2')
if TPE2 is None:
return
except KeyError:
raise ValueError('Can not find all data needed')
rain = TPE1.copy(deep=True)
rain['data'].values = (TPE2['data'].values - TPE1['data'].values)
rain2 = rain.sum('time')
# 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
rain = utl.cut_xrdata(map_extent=map_extent,
xr_input=rain,
delt_y=delt_y,
delt_x=delt_x)
rain2.attrs['model'] = model
rain2.attrs['t_gap'] = t_gap
rain2.attrs['initTime'] = datetime.strptime(initTime, '%y%m%d%H')
rain2.attrs['fhour1'] = fhours[0]
rain2.attrs['fhour2'] = fhours[-1]
# draw
QPF_graphics.draw_cumulated_precip(rain=rain2,
map_extent=map_extent,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def periodmean_gh_uv_pwat_ulj(initTimes=None,
fhours=[0],
day_back=0,
model='ECMWF',
gh_lev=500,
uv_lev=850,
ulj_lev=200,
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
data_source='MICAPS',
Global=False,
**kwargs):
if (area != None):
south_China_sea = False
# micaps data directory
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl=gh_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=uv_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=uv_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='UGRD',
lvl=ulj_lev),
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='VGRD',
lvl=ulj_lev),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='TCWV'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PSFC')
]
except KeyError:
raise ValueError('Can not find all directories needed')
filenames = []
# get filename
if (initTimes != None):
for initTime in initTimes:
for fhour in fhours:
filenames.append(utl.model_filename(initTime, fhour))
else:
filenames = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
# retrieve data from micaps server
gh = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
u = MICAPS_IO.get_model_grids(data_dir[1], filenames=filenames)
v = MICAPS_IO.get_model_grids(data_dir[2], filenames=filenames)
u2 = MICAPS_IO.get_model_grids(data_dir[3], filenames=filenames)
v2 = MICAPS_IO.get_model_grids(data_dir[4], filenames=filenames)
pwat = MICAPS_IO.get_model_grids(data_dir[5], filenames=filenames)
psfc = MICAPS_IO.get_model_grids(data_dir[6], filenames=filenames)
if (data_source == 'CIMISS'):
# get filename
filenames = []
if (initTimes != None):
for initTime in initTimes:
for fhour in fhours:
filenames.append(
'20' + utl.model_filename(initTime, fhour, UTC=True))
else:
filenames = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)
try:
# retrieve data from CIMISS server
gh = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='GPH'),
filenames=filenames,
fcst_level=gh_lev,
fcst_ele="GPH",
units='gpm')
gh['data'].values = gh['data'].values / 10.
u = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='WIU'),
filenames=filenames,
fcst_level=uv_lev,
fcst_ele="WIU",
units='m/s')
v = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='WIV'),
filenames=filenames,
fcst_level=uv_lev,
fcst_ele="WIV",
units='m/s')
u2 = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='WIU'),
filenames=filenames,
fcst_level=ulj_lev,
fcst_ele="WIU",
units='m/s')
v2 = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='WIV'),
filenames=filenames,
fcst_level=ulj_lev,
fcst_ele="WIV",
units='m/s')
if (model == 'ECMWF'):
pwat = utl.cimiss_model_ana_grids(
data_code=utl.CMISS_data_code(data_source=model,
var_name='TCWV'),
filenames=filenames,
fcst_level=0,
fcst_ele="TCWV",
units='kg m-2')
else:
pwat = utl.cimiss_model_ana_grids(
data_code=utl.CMISS_data_code(data_source=model,
var_name='TIWV'),
filenames=filenames,
fcst_level=0,
fcst_ele="TIWV",
units='kg m-2')
psfc = utl.cimiss_model_ana_grids(data_code=utl.CMISS_data_code(
data_source=model, var_name='PRS'),
filenames=filenames,
fcst_level=0,
fcst_ele="PRS",
units='Pa')
psfc['data'] = psfc['data'] / 100.
except KeyError:
raise ValueError('Can not find all data needed')
# prepare data
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=cntr_pnt,
zoom_ratio=zoom_ratio,
map_ratio=map_ratio)
gh = utl.cut_xrdata(map_extent, gh, delt_x=delt_x, delt_y=delt_y)
u = utl.cut_xrdata(map_extent, u, delt_x=delt_x, delt_y=delt_y)
v = utl.cut_xrdata(map_extent, v, delt_x=delt_x, delt_y=delt_y)
u2 = utl.cut_xrdata(map_extent, u2, delt_x=delt_x, delt_y=delt_y)
v2 = utl.cut_xrdata(map_extent, v2, delt_x=delt_x, delt_y=delt_y)
pwat = utl.cut_xrdata(map_extent, pwat, delt_x=delt_x, delt_y=delt_y)
gh = utl.mask_terrian(gh_lev, psfc, gh)
u = utl.mask_terrian(uv_lev, psfc, u)
v = utl.mask_terrian(uv_lev, psfc, v)
u2 = utl.mask_terrian(ulj_lev, psfc, u2)
v2 = utl.mask_terrian(ulj_lev, psfc, v2)
uv = xr.merge([u.rename({'data': 'u'}), v.rename({'data': 'v'})])
ulj = mpcalc.wind_speed(u2['data'].values * units('m/s'),
v2['data'].values * units('m/s'))
ulj_xr = u2.copy(deep=True)
ulj_xr['data'].values = ulj.magnitude
pwat_mean = pwat.mean('time')
gh_mean = gh.mean('time')
ulj_mean = ulj_xr.mean('time')
uv_mean = uv.mean('time')
gh_mean.attrs['model'] = model
gh_mean.attrs['st_time'] = gh['time'].values[0]
gh_mean.attrs['ed_time'] = gh['time'].values[-1]
synoptic_graphics.draw_gh_uv_pwat_ulj(pwat=pwat_mean,
gh=gh_mean,
uv=uv_mean,
ulj=ulj_mean,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir)
def get_model_points_gy(directory,
filenames,
points,
allExists=True,
fill_null=False,
Null_value=0):
"""
Retrieve point time series from MICAPS cassandra service.
Args:
directory (string): the data directory on the service.
filenames (list): the list of filenames.
points (dict): dictionary, {'lon':[...], 'lat':[...]}.
Examples:
>>> directory = "NWFD_SCMOC/TMP/2M_ABOVE_GROUND"
>>> fhours = np.arange(3, 75, 3)
>>> filenames = ["19083008."+str(fhour).zfill(3) for fhour in fhours]
>>> points = {'lon':[116.3833, 110.0], 'lat':[39.9, 32]}
>>> data = get_model_points(dataDir, filenames, points)
>>> allExists (boolean): all files should exist, or return None.
"""
data = get_model_grids(directory, filenames, allExists=allExists)
#if(fill_null is True):
# temp=np.array(data['data'].values)
# idx_null=np.where(temp == Null_value)
# #temp[idx_null]=np.nan
# temp2=gaussian_filter(temp,5)
# temp[idx_null]=temp2[idx_null]
# data['data'].values=temp
if (fill_null is True):
temp = np.array(data['data'].values)
dims = np.shape(temp)
grid_x = np.array(data['lon'].values)
grid_y = np.array(data['lat'].values)
x, y = np.meshgrid(data['lon'].values, data['lat'].values)
idx_x = np.squeeze(
np.where((grid_x > points['lon'][0] - 6)
& (grid_x < points['lon'][0] + 6)))
idx_y = np.squeeze(
np.where((grid_y > points['lat'][0] - 5)
& (grid_y < points['lat'][0] + 5)))
x2, y2 = np.meshgrid(grid_x[idx_x[0]:idx_x[-1]],
grid_y[idx_y[0]:idx_y[-1]])
nx2 = len(idx_x)
ny2 = len(idx_y)
x = x.reshape(dims[1] * dims[2])
y = y.reshape(dims[1] * dims[2])
nt = dims[0]
for it in range(0, nt):
temp2 = np.squeeze(temp[it, :, :])
temp2 = temp2.reshape(dims[1] * dims[2])
idx_ok = np.squeeze(
np.where((temp2 != Null_value) & (x < points['lon'][0] + 6)
& (x > points['lon'][0] - 6)
& (y < points['lat'][0] + 5)
& (y > points['lat'][0] - 5)))
n_ok = len(idx_ok)
data_new = griddata(
np.squeeze(np.dstack(([y[idx_ok], x[idx_ok]]))), temp2[idx_ok],
(y2, x2))
temp[it, idx_y[0]:idx_y[-1],
idx_x[0]:idx_x[-1]] = data_new.reshape(1, ny2 - 1, nx2 - 1)
data['data'].values = temp
if data:
return data.interp(lon=('points', points['lon']),
lat=('points', points['lat']))
else:
return None
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 cu_rain(initTime=None,
atime=6,
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,
**kwargs):
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source='CLDAS',
var_name='RAIN01')
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initTime == None):
initTime = (datetime.now() -
timedelta(hours=2)).strftime('%y%m%d%H')
filenames = []
for ihour in range(0, atime):
filenames.append((datetime.strptime(initTime, '%y%m%d%H') -
timedelta(hours=ihour)).strftime('%y%m%d%H') +
'.000')
# retrieve data from micaps server
rain = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames)
if rain is None:
return
if (data_source == 'CIMISS'):
# get filename
if (initTime == None):
initTime = (datetime.now() -
timedelta(hours=2 + 8)).strftime('%y%m%d%H')
filenames = []
for ihour in range(0, atime):
filenames.append((datetime.strptime(initTime, '%y%m%d%H') -
timedelta(hours=ihour)).strftime('%Y%m%d%H') +
'0000')
try:
# retrieve data from CIMISS server
rain = CIMISS_IO.cimiss_analysis_by_times(
times_str=filenames,
fcst_ele='PRE',
data_code=utl.CMISS_data_code(data_source='CLDAS',
var_name='PRE'),
)
if rain is None:
return
rain = rain.rename({'PRE': 'data'})
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=cntr_pnt,
zoom_ratio=zoom_ratio,
map_ratio=map_ratio)
rain = utl.cut_xrdata(map_extent, rain, delt_x=delt_x, delt_y=delt_y)
rain['data'].values[rain['data'].values == 9999.] = np.nan
cu_rain = rain.sum('time')
cu_rain.attrs['obs_time'] = datetime.strptime(initTime, '%y%m%d%H')
cu_rain.attrs['model'] = 'CLDAS'
cu_rain.attrs['atime'] = atime
cu_rain.attrs['var_name'] = '累积降水'
# draw
QPF_graphics.draw_obs_cu_rain(rain=cu_rain,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir)
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 cumulative_precip_and_rain_days(endtime=None,
cu_ndays=5,
rn_ndays=7,
map_ratio=19 / 11,
zoom_ratio=20,
cntr_pnt=[102, 34],
south_China_sea=True,
area=None,
city=False,
output_dir=None,
Global=False,
**kwargs):
# prepare data
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source='CLDAS',
var_name='RAIN24')
]
# get filename
cu_days = np.arange(0, cu_ndays)
filenames_cu = [
(datetime.strptime('20' + endtime, '%Y%m%d%H') -
timedelta(days=int(icu_days))).strftime('%Y%m%d%H')[2:] + '.000'
for icu_days in cu_days
]
rn_days = np.arange(0, rn_ndays)
filenames_days = [
(datetime.strptime('20' + endtime, '%Y%m%d%H') -
timedelta(days=int(irn_days))).strftime('%Y%m%d%H')[2:] + '.000'
for irn_days in rn_days
]
# retrieve data from micaps server
cu_rain_all = MICAPS_IO.get_model_grids(data_dir[0],
filenames=filenames_cu)
cu_days_rain = MICAPS_IO.get_model_grids(data_dir[0],
filenames=filenames_days)
coords = MICAPS_IO.get_model_grid(data_dir[0], filename=filenames_days[0])
# set map extent
if (area != None):
if (area != None):
south_China_sea = False
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
mask1 = (cu_rain_all['lon'] > map_extent[0] - delt_x) & (
cu_rain_all['lon'] < map_extent[1] + delt_x) & (
cu_rain_all['lat'] > map_extent[2] - delt_y) & (
cu_rain_all['lat'] < map_extent[3] + delt_y)
mask2 = (cu_days_rain['lon'] > map_extent[0] - delt_x) & (
cu_days_rain['lon'] < map_extent[1] + delt_x) & (
cu_days_rain['lat'] > map_extent[2] - delt_y) & (
cu_days_rain['lat'] < map_extent[3] + delt_y)
coords = coords.where(mask1, drop=True)
cu_rain_all = cu_rain_all.where(mask1, drop=True)
mask11 = (cu_rain_all['data'] == 9999.)
cu_rain_all['data'].values[mask11.values] = np.nan
cu_days_rain = cu_days_rain.where(mask2, drop=True)
mask22 = (cu_days_rain['data'] == 9999.)
cu_days_rain['data'].values[mask22.values] = 0
cu_rain = xr.DataArray([np.sum(cu_rain_all['data'].values, axis=0)],
coords=coords['data'].coords,
dims=coords['data'].dims)
mask3 = (cu_days_rain['data'] > 0)
cu_days_rain['data'].values[mask3.values] = 1
days_rain = xr.DataArray([np.sum(cu_days_rain['data'].values, axis=0)],
coords=coords['data'].coords,
dims=coords['data'].dims)
days_rain.attrs['rn_ndays'] = rn_ndays
cu_rain.attrs['cu_ndays'] = cu_ndays
# draw
observation_graphics.draw_cumulative_precip_and_rain_days(
cu_rain=cu_rain,
days_rain=days_rain,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def cu_Tmn2(endtime,
cu_ndays=1,
map_ratio=14 / 9,
zoom_ratio=20,
cntr_pnt=[104, 34],
area=None,
**kargws):
# prepare data
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source='CLDAS',
var_name='Tmn_2m')
]
# get filename
cu_days = np.arange(0, cu_ndays)
filenames_cu = [
(datetime.strptime('20' + endtime, '%Y%m%d%H') -
timedelta(days=int(icu_days))).strftime('%Y%m%d%H')[2:] + '.000'
for icu_days in cu_days
]
# retrieve data from micaps server
Tmn2m_all = MICAPS_IO.get_model_grids(data_dir[0], filenames=filenames_cu)
coords = MICAPS_IO.get_model_grid(data_dir[0], filename=filenames_cu[0])
# set map extent
if (area != None):
if (area != None):
south_China_sea = False
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
mask1 = ((Tmn2m_all['lon'] > map_extent[0] - delt_x) &
(Tmn2m_all['lon'] < map_extent[1] + delt_x) &
(Tmn2m_all['lat'] > map_extent[2] - delt_y) &
(Tmn2m_all['lat'] < map_extent[3] + delt_y))
coords = coords.where(mask1, drop=True)
Tmn2m_all = Tmn2m_all.where(mask1, drop=True)
mask11 = (Tmn2m_all['data'] == 9999.)
Tmn2m_all['data'].values[mask11.values] = np.nan
Tmn2 = xr.DataArray(np.max(Tmn2m_all['data'].values, axis=0),
name='data',
coords={
'time': ('time', [Tmn2m_all['time'].values[0]]),
'lat': ('lat', Tmn2m_all['lat'].values),
'lon': ('lon', Tmn2m_all['lon'].values)
},
dims=('time', 'lat', 'lon'),
attrs={
'model_name': 'CLDAS',
'var_name': '最低温度',
'vhours': cu_ndays * 24
})
# draw
draw_TMP2(TMP2=Tmn2, map_extent=map_extent, **kargws)
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 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)
def Time_Crossection_rh_uv_Temp(initTime=None,model='ECMWF',points={'lon':[116.3833], 'lat':[39.9]},
levels=[1000, 950, 925, 900, 850, 800, 700,600,500,400,300,200],data_source='MICAPS',
t_gap=3,t_range=[0,48],lw_ratio=[16,9],output_dir=None,**kwargs):
fhours = np.arange(t_range[0], t_range[1], t_gap)
# 读数据
if(data_source == 'MICAPS'):
try:
data_dir = [utl.Cassandra_dir(data_type='high',data_source=model,var_name='TMP',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='UGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='VGRD',lvl=''),
utl.Cassandra_dir(data_type='high',data_source=model,var_name='RH',lvl=''),
utl.Cassandra_dir(data_type='surface',data_source=model,var_name='PSFC')]
except KeyError:
raise ValueError('Can not find all directories needed')
if(initTime==None):
initTime = get_latest_initTime(data_dir[0][0:-1]+"850")
filenames = [initTime+'.'+str(fhour).zfill(3) for fhour in fhours]
TMP_4D=get_model_3D_grids(directory=data_dir[0][0:-1],filenames=filenames,levels=levels, allExists=False)
u_4D=get_model_3D_grids(directory=data_dir[1][0:-1],filenames=filenames,levels=levels, allExists=False)
v_4D=get_model_3D_grids(directory=data_dir[2][0:-1],filenames=filenames,levels=levels, allExists=False)
rh_4D=get_model_3D_grids(directory=data_dir[3][0:-1],filenames=filenames,levels=levels, allExists=False)
Psfc_3D=get_model_grids(directory=data_dir[4][0:-1],filenames=filenames,allExists=False)
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:
rh_4D=CMISS_IO.cimiss_model_3D_grids(init_time_str='20'+filename[0:8],valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='RHU'),
fcst_levels=levels, fcst_ele="RHU", units='%',pbar=True)
u_4D=CMISS_IO.cimiss_model_3D_grids(init_time_str='20'+filename[0:8],valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIU'),
fcst_levels=levels, fcst_ele="WIU", units='m/s',pbar=True)
v_4D=CMISS_IO.cimiss_model_3D_grids(init_time_str='20'+filename[0:8],valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='WIV'),
fcst_levels=levels, fcst_ele="WIV", units='m/s',pbar=True)
TMP_4D=CMISS_IO.cimiss_model_3D_grids(init_time_str='20'+filename[0:8],valid_times=fhours,
data_code=utl.CMISS_data_code(data_source=model,var_name='TEM'),
fcst_levels=levels, fcst_ele="TEM", units='K',pbar=True)
TMP_4D['data'].values=TMP_4D['data'].values-273.15
Psfc_3D=CMISS_IO.cimiss_model_grids(init_time_str='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',pbar=True)
except KeyError:
raise ValueError('Can not find all data needed')
TMP_2D=TMP_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
u_2D=u_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
v_2D=v_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
rh_2D=rh_4D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
rh_2D.attrs['model']=model
rh_2D.attrs['points']=points
Psfc_1D=Psfc_3D.interp(lon=('points', points['lon']), lat=('points', points['lat']))
v_2D2,pressure_2D = xr.broadcast(v_2D['data'],v_2D['level'])
v_2D2,Psfc_2D = xr.broadcast(v_2D['data'],Psfc_1D['data'])
terrain_2D=pressure_2D-Psfc_2D
crossection_graphics.draw_Time_Crossection_rh_uv_Temp(
rh_2D=rh_2D, u_2D=u_2D, v_2D=v_2D,TMP_2D=TMP_2D,terrain_2D=terrain_2D,
t_range=t_range,model=model,lw_ratio=lw_ratio,output_dir=output_dir)
