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 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 mslp_rain_snow(initTime=None,
fhour=24,
day_back=0,
model='ECMWF',
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,
Global=False,
**kwargs):
'''
issues:
1. CIMISS 上上没有上没有GRAPES-GFS的降雪，所以当data_source='CIMISS',model='GRAPES_GFS'无法出图
'''
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PRMSL'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % atime),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='SNOW' + '%02d' % atime),
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour)
if (atime > 3):
filename_mslp = utl.model_filename(initTime,
int(fhour - atime / 2))
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
if (atime > 3):
filename_mslp = utl.filename_day_back_model(
day_back=day_back, fhour=int(fhour - atime / 2))
# retrieve data from micaps server
mslp = get_model_grid(data_dir[0], filename=filename)
if mslp is None:
return
rain = get_model_grid(data_dir[1], filename=filename)
if rain is None:
return
snow = get_model_grid(data_dir[2], filename=filename)
if snow is None:
return
if (data_source == 'CIMISS'):
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour, UTC=True)
if (atime > 3):
filename_gh = utl.filename_day_back_model(initTime,
fhour=int(fhour -
atime / 2),
UTC=True)
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)
if (atime > 3):
filename_gh = utl.filename_day_back_model(day_back=day_back,
fhour=int(fhour -
atime / 2),
UTC=True)
try:
# retrieve data from CIMISS server
if (model == 'ECMWF'):
mslp = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='GSSP'),
levattrs={
'long_name': 'Mean_sea_level',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="GSSP",
units='Pa')
else:
mslp = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='SSP'),
levattrs={
'long_name': 'Mean_sea_level',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="SSP",
units='Pa')
if mslp is None:
return
mslp['data'] = mslp['data'] / 100.
TPE1 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
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')
if TPE1 is None:
return
TPE2 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour - atime,
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')
if TPE2 is None:
return
TTSP1 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TTSP'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TTSP",
units='kg*m^-2')
if TTSP1 is None:
return
TTSP2 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour - atime,
data_code=utl.CMISS_data_code(data_source=model,
var_name='TTSP'),
levattrs={
'long_name': 'Height above Ground',
'units': 'm',
'_CoordinateAxisType': '-'
},
fcst_level=0,
fcst_ele="TTSP",
units='kg*m^-2')
if TTSP2 is None:
return
except KeyError:
raise ValueError('Can not find all data needed')
rain = TPE1.copy(deep=True)
rain['data'].values = (TPE1['data'].values -
TPE2['data'].values) * 1000
snow = TTSP1.copy(deep=True)
snow['data'].values = (TTSP1['data'].values -
TTSP2['data'].values) * 1000
# 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 = [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 = (mslp['lon'] > map_extent[0] - delt_x) & (
mslp['lon'] < map_extent[1] + delt_x) & (
mslp['lat'] > map_extent[2] - delt_y) & (mslp['lat'] <
map_extent[3] + delt_y)
mask2 = (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)
mslp = mslp.where(mask1, drop=True)
mslp.attrs['model'] = model
rain = rain.where(mask2, drop=True)
snow = snow.where(mask2, drop=True)
snow.attrs['atime'] = atime
rain_snow = xr.merge(
[rain.rename({'data': 'rain'}),
snow.rename({'data': 'snow'})])
mask1 = ((rain_snow['rain'] - rain_snow['snow']) >
0.1) & (rain_snow['snow'] > 0.1)
sleet = rain_snow['rain'].where(mask1)
mask2 = ((rain_snow['rain'] - rain_snow['snow']) <
0.1) & (rain_snow['snow'] > 0.1)
snw = rain_snow['snow'].where(mask2)
mask3 = (rain_snow['rain'] > 0.1) & (rain_snow['snow'] < 0.1)
rn = rain_snow['rain'].where(mask3)
rn.attrs['atime'] = atime
# draw
QPF_graphics.draw_mslp_rain_snow(rain=rn,
snow=snw,
sleet=sleet,
mslp=mslp,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def gh_rain(initTime=None,
fhour=24,
day_back=0,
model='ECMWF',
gh_lev=500,
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,
Global=False,
**kwargs):
# prepare data
if (data_source == 'MICAPS'):
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl=str(gh_lev)),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % atime),
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):
filename = utl.model_filename(initTime, fhour)
if (atime > 3):
filename_gh = utl.model_filename(initTime,
int(fhour - atime / 2))
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour)
if (atime > 3):
filename_gh = utl.filename_day_back_model(day_back=day_back,
fhour=int(fhour -
atime / 2))
# retrieve data from micaps server
gh = MICAPS_IO.get_model_grid(data_dir[0], filename=filename_gh)
if gh is None:
return
rain = MICAPS_IO.get_model_grid(data_dir[1], filename=filename)
if rain is None:
return
psfc = MICAPS_IO.get_model_grid(data_dir[2], filename=filename)
if (data_source == 'CIMISS'):
# get filename
if (initTime != None):
filename = utl.model_filename(initTime, fhour, UTC=True)
if (atime > 3):
filename_gh = utl.model_filename(initTime,
fhour=int(fhour - atime / 2),
UTC=True)
else:
filename = utl.filename_day_back_model(day_back=day_back,
fhour=fhour,
UTC=True)
if (atime > 3):
filename_gh = utl.filename_day_back_model(day_back=day_back,
fhour=int(fhour -
atime / 2),
UTC=True)
try:
# retrieve data from CIMISS server
gh = CMISS_IO.cimiss_model_by_time(
'20' + filename_gh[0:8],
valid_time=fhour,
data_code=utl.CMISS_data_code(data_source=model,
var_name='GPH'),
levattrs={
'long_name': 'pressure_level',
'units': 'hPa',
'_CoordinateAxisType': '-'
},
fcst_level=gh_lev,
fcst_ele="GPH",
units='gpm')
if gh is None:
return
gh['data'].values = gh['data'].values / 10.
TPE1 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour,
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')
if TPE1 is None:
return
TPE2 = CMISS_IO.cimiss_model_by_time(
'20' + filename[0:8],
valid_time=fhour - atime,
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')
if TPE2 is None:
return
psfc = CMISS_IO.cimiss_model_by_time('20' + filename[0:8],
valid_time=fhour,
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')
rain = TPE1.copy(deep=True)
rain['data'].values = TPE1['data'].values - TPE2['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)
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
gh = utl.cut_xrdata(map_extent, gh, delt_x=delt_x, delt_y=delt_y)
rain = utl.cut_xrdata(map_extent, rain, delt_x=delt_x, delt_y=delt_y)
gh = utl.mask_terrian(gh_lev, psfc, gh)
gh.attrs['model'] = model
gh.attrs['lev'] = gh_lev
rain.attrs['atime'] = atime
# draw
QPF_graphics.draw_gh_rain(rain=rain,
gh=gh,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def gh_rain(initial_time=None,
fhour=24,
day_back=0,
model='ECMWF',
gh_lev='500',
atime=6,
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):
if (area != '全国'):
south_China_sea = False
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='high',
data_source=model,
var_name='HGT',
lvl=gh_lev),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % atime)
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
if (atime > 3):
filename_gh = utl.model_filename(initial_time, fhour / 2.)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
if (atime > 3):
filename_gh = utl.filename_day_back_model(day_back=day_back,
fhour=fhour / 2.)
# retrieve data from micaps server
gh = get_model_grid(data_dir[0], filename=filename_gh)
if gh is None:
return
rain = get_model_grid(data_dir[1], filename=filename)
init_time = gh.coords['forecast_reference_time'].values
# prepare data
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
idx_x1 = np.where((gh.coords['lon'].values > map_extent[0] - delt_x)
& (gh.coords['lon'].values < map_extent[1] + delt_x))
idx_y1 = np.where((gh.coords['lat'].values > map_extent[2] - delt_y)
& (gh.coords['lat'].values < map_extent[3] + delt_y))
idx_x2 = np.where((rain.coords['lon'].values > map_extent[0] - delt_x)
& (rain.coords['lon'].values < map_extent[1] + delt_x))
idx_y2 = np.where((rain.coords['lat'].values > map_extent[2] - delt_y)
& (rain.coords['lat'].values < map_extent[3] + delt_y))
#- to solve the problem of labels on all the contours
gh = {
'lon':
gh.coords['lon'].values[idx_x1],
'lat':
gh.coords['lat'].values[idx_y1],
'data':
gh['data'].values[0, 0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'lev':
gh_lev,
'model':
model,
'fhour':
fhour,
'init_time':
init_time
}
rain = {
'lon':
rain.coords['lon'].values[idx_x2],
'lat':
rain.coords['lat'].values[idx_y2],
'data':
copy.deepcopy(rain['data'].values[0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)])
}
QPF_graphics.draw_gh_rain(rain=rain,
gh=gh,
atime=atime,
map_extent=map_extent,
regrid_shape=20,
city=city,
south_China_sea=south_China_sea,
output_dir=output_dir,
Global=Global)
def mslp_rain_snow(initial_time=None,
fhour=24,
day_back=0,
model='ECMWF',
atime=6,
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):
if (area != '全国'):
south_China_sea = False
# micaps data directory
try:
data_dir = [
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='PRMSL'),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='RAIN' + '%02d' % atime),
utl.Cassandra_dir(data_type='surface',
data_source=model,
var_name='SNOW' + '%02d' % atime),
]
except KeyError:
raise ValueError('Can not find all directories needed')
# get filename
if (initial_time != None):
filename = utl.model_filename(initial_time, fhour)
if (atime > 3):
filename_mslp = utl.model_filename(initial_time, fhour / 2.)
else:
filename = utl.filename_day_back_model(day_back=day_back, fhour=fhour)
if (atime > 3):
filename_mslp = utl.filename_day_back_model(day_back=day_back,
fhour=fhour / 2.)
# retrieve data from micaps server
mslp = get_model_grid(data_dir[0], filename=filename)
if mslp is None:
return
rain = get_model_grid(data_dir[1], filename=filename)
snow = get_model_grid(data_dir[2], filename=filename)
init_time = mslp.coords['forecast_reference_time'].values
# prepare data
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
idx_x1 = np.where((mslp.coords['lon'].values > map_extent[0] - delt_x)
& (mslp.coords['lon'].values < map_extent[1] + delt_x))
idx_y1 = np.where((mslp.coords['lat'].values > map_extent[2] - delt_y)
& (mslp.coords['lat'].values < map_extent[3] + delt_y))
idx_x2 = np.where((rain.coords['lon'].values > map_extent[0] - delt_x)
& (rain.coords['lon'].values < map_extent[1] + delt_x))
idx_y2 = np.where((rain.coords['lat'].values > map_extent[2] - delt_y)
& (rain.coords['lat'].values < map_extent[3] + delt_y))
#- to solve the problem of labels on all the contours
rain_snow = xr.merge(
[rain.rename({'data': 'rain'}),
snow.rename({'data': 'snow'})])
mask1 = ((rain_snow['rain'] - rain_snow['snow']) >
0.1) & (rain_snow['snow'] > 0.1)
sleet = rain_snow['rain'].where(mask1)
mask2 = ((rain_snow['rain'] - rain_snow['snow']) <
0.1) & (rain_snow['snow'] > 0.1)
snw = rain_snow['snow'].where(mask2)
mask3 = (rain_snow['rain'] > 0.1) & (rain_snow['snow'] < 0.1)
rn = rain_snow['rain'].where(mask3)
mslp = {
'lon':
mslp.coords['lon'].values[idx_x1],
'lat':
mslp.coords['lat'].values[idx_y1],
'data':
mslp['data'].values[0, idx_y1[0][0]:(idx_y1[0][-1] + 1),
idx_x1[0][0]:(idx_x1[0][-1] + 1)],
'model':
model,
'fhour':
fhour,
'init_time':
init_time
}
rain = {
'lon':
rn.coords['lon'].values[idx_x2],
'lat':
rn.coords['lat'].values[idx_y2],
'data':
rn.values[0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)]
}
snow = {
'lon':
snw.coords['lon'].values[idx_x2],
'lat':
snw.coords['lat'].values[idx_y2],
'data':
snw.values[0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)]
}
sleet = {
'lon':
sleet.coords['lon'].values[idx_x2],
'lat':
sleet.coords['lat'].values[idx_y2],
'data':
sleet.values[0, idx_y2[0][0]:(idx_y2[0][-1] + 1),
idx_x2[0][0]:(idx_x2[0][-1] + 1)]
}
QPF_graphics.draw_mslp_rain_snow(rain=rain,
snow=snow,
sleet=sleet,
mslp=mslp,
atime=atime,
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)
