第六章
6.1 文本文件
6.1.2 CSV文件
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import pandas as pd
dat = pd.read_csv('/home/mw/input/pythonbook7300/sta.csv', parse_dates=['time'])
print(dat.dtypes)
print('--------')
print(dat)
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time datetime64[ns]
station int64
t float64
tmax float64
tmin float64
dtype: object
--------
time station t tmax tmin
0 2020-03-01 58238 8.7 12.5 1.3
1 2020-03-01 58235 7.5 11.3 0.8
2 2020-03-01 58237 8.5 11.8 1.2
3 2020-03-02 58238 9.5 13.3 4.4
4 2020-03-02 58235 8.3 12.8 4.0
5 2020-03-02 58237 8.8 12.9 4.2
6 2020-03-03 58238 13.1 14.5 10.1
7 2020-03-03 58235 12.4 13.7 9.6
8 2020-03-03 58237 13.0 14.1 10.0
9 2020-03-04 58238 10.3 13.8 5.2
10 2020-03-04 58235 9.8 12.9 4.5
11 2020-03-04 58237 10.1 13.1 4.8
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import numpy as np
import pandas as pd
dat = pd.read_csv('/home/mw/input/pythonbook7300/sta.csv',
parse_dates=['time'],
dtype={'station': np.unicode_})
print(dat.dtypes)
print('--------')
print(dat)
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time datetime64[ns]
station object
t float64
tmax float64
tmin float64
dtype: object
--------
time station t tmax tmin
0 2020-03-01 58238 8.7 12.5 1.3
1 2020-03-01 58235 7.5 11.3 0.8
2 2020-03-01 58237 8.5 11.8 1.2
3 2020-03-02 58238 9.5 13.3 4.4
4 2020-03-02 58235 8.3 12.8 4.0
5 2020-03-02 58237 8.8 12.9 4.2
6 2020-03-03 58238 13.1 14.5 10.1
7 2020-03-03 58235 12.4 13.7 9.6
8 2020-03-03 58237 13.0 14.1 10.0
9 2020-03-04 58238 10.3 13.8 5.2
10 2020-03-04 58235 9.8 12.9 4.5
11 2020-03-04 58237 10.1 13.1 4.8
6.1.3 空格（制表符）作为分隔符的文件
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import numpy as np
import pandas as pd
dat = pd.read_csv('/home/mw/input/pythonbook7300/sta.txt',
sep='\s',
parse_dates=['time'],
dtype={'station': np.unicode_})
print(dat.dtypes)
print('--------')
print(dat)
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time datetime64[ns]
station object
t float64
tmax float64
tmin float64
dtype: object
--------
time station t tmax tmin
2020-03-01 2023-03-02 58238 8.7 12.5 1.3
2020-03-01 2023-03-02 58235 7.5 11.3 0.8
2020-03-01 2023-03-02 58237 8.5 11.8 1.2
2020-03-02 2023-03-02 58238 9.5 13.3 4.4
2020-03-02 2023-03-02 58235 8.3 12.8 4.0
2020-03-02 2023-03-02 58237 8.8 12.9 4.2
2020-03-03 2023-03-02 58238 13.1 14.5 10.1
2020-03-03 2023-03-02 58235 12.4 13.7 9.6
2020-03-03 2023-03-02 58237 13.0 14.1 10.0
2020-03-04 2023-03-02 58238 10.3 13.8 5.2
2020-03-04 2023-03-02 58235 9.8 12.9 4.5
2020-03-04 2023-03-02 58237 10.1 13.1 4.8
/opt/conda/lib/python3.7/site-packages/pandas/util/_decorators.py:311: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.
return func(*args, **kwargs)
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import numpy as np
import xarray as xr
array_raw = np.loadtxt('/home/mw/input/pythonbook7300/grid_1.txt', dtype=np.float64)
print(array_raw)
lon = np.linspace(120, 122.5, 5)
lat = np.linspace(10, 13, 4)
print(lon)
print(lat)
t_grid = xr.DataArray(array_raw, coords=[lat, lon], dims=["lat", "lon"])
print(t_grid)
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Warning: ecCodes 2.21.0 or higher is recommended. You are running version 2.14.1
[[10.12 11.15 11.3 10.25 9.97]
[10.23 11.54 11.12 10.01 9.85]
[10.07 11.37 10.98 10.42 9.58]
[10.33 12.01 10.38 10.36 10.12]]
[120. 120.625 121.25 121.875 122.5 ]
[10. 11. 12. 13.]
<xarray.DataArray (lat: 4, lon: 5)>
array([[10.12, 11.15, 11.3 , 10.25, 9.97],
[10.23, 11.54, 11.12, 10.01, 9.85],
[10.07, 11.37, 10.98, 10.42, 9.58],
[10.33, 12.01, 10.38, 10.36, 10.12]])
Coordinates:
* lat (lat) float64 10.0 11.0 12.0 13.0
* lon (lon) float64 120.0 120.6 121.2 121.9 122.5
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import numpy as np
import xarray as xr
array_raw = np.loadtxt('/home/mw/input/pythonbook7300/grid_2.txt', dtype=np.float64)
print(array_raw)
array_raw = array_raw.reshape((4, 5))
print(array_raw)
lon = np.linspace(120, 122.5, 5)
lat = np.linspace(10, 13, 4)
print(lon)
print(lat)
t_grid = xr.DataArray(array_raw, coords=[lat, lon], dims=["lat", "lon"])
print(t_grid)
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[[10.12 11.15 11.3 10.25 9.97 10.23 11.54 11.12 10.01 9.85]
[10.07 11.37 10.98 10.42 9.58 10.33 12.01 10.38 10.36 10.12]]
[[10.12 11.15 11.3 10.25 9.97]
[10.23 11.54 11.12 10.01 9.85]
[10.07 11.37 10.98 10.42 9.58]
[10.33 12.01 10.38 10.36 10.12]]
[120. 120.625 121.25 121.875 122.5 ]
[10. 11. 12. 13.]
<xarray.DataArray (lat: 4, lon: 5)>
array([[10.12, 11.15, 11.3 , 10.25, 9.97],
[10.23, 11.54, 11.12, 10.01, 9.85],
[10.07, 11.37, 10.98, 10.42, 9.58],
[10.33, 12.01, 10.38, 10.36, 10.12]])
Coordinates:
* lat (lat) float64 10.0 11.0 12.0 13.0
* lon (lon) float64 120.0 120.6 121.2 121.9 122.5
6.2 Excel文件
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dat = pd.read_excel('/home/mw/input/pythonbook7300/read.xlsx', sheet_name='Sheet1')
print(dat)
print('-----')
print(dat.dtypes)
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时间 最高温度 降水
0 2020-01-01 10.3 0
1 2020-01-02 13.3 0
2 2020-01-03 12.1 0
3 2020-01-04 9.9 0
-----
时间 datetime64[ns]
最高温度 float64
降水 int64
dtype: object
6.3 NetCDF文件
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import xarray as xr
dataset = xr.open_dataset('/home/mw/input/pythonbook9857/1980060106.nc')
print(dataset)
t = dataset['t']
print(t)
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<xarray.Dataset>
Dimensions: (longitude: 721, latitude: 361, level: 13, time: 1)
Coordinates:
* longitude (longitude) float32 0.0 0.25 0.5 0.75 ... 179.2 179.5 179.8 180.0
* latitude (latitude) float32 90.0 89.75 89.5 89.25 ... 0.75 0.5 0.25 0.0
* level (level) int32 1000 975 925 850 700 600 ... 300 250 200 150 100
* time (time) datetime64[ns] 1980-06-01T06:00:00
Data variables:
pv (time, level, latitude, longitude) float32 ...
z (time, level, latitude, longitude) float32 ...
t (time, level, latitude, longitude) float32 ...
q (time, level, latitude, longitude) float32 ...
w (time, level, latitude, longitude) float32 ...
vo (time, level, latitude, longitude) float32 ...
d (time, level, latitude, longitude) float32 ...
u (time, level, latitude, longitude) float32 ...
v (time, level, latitude, longitude) float32 ...
r (time, level, latitude, longitude) float32 ...
clwc (time, level, latitude, longitude) float32 ...
Attributes:
Conventions: CF-1.6
history: 2017-03-27 13:09:45 GMT by grib_to_netcdf-2.1.0: grib_to_ne...
<xarray.DataArray 't' (time: 1, level: 13, latitude: 361, longitude: 721)>
[3383653 values with dtype=float32]
Coordinates:
* longitude (longitude) float32 0.0 0.25 0.5 0.75 ... 179.2 179.5 179.8 180.0
* latitude (latitude) float32 90.0 89.75 89.5 89.25 ... 0.75 0.5 0.25 0.0
* level (level) int32 1000 975 925 850 700 600 ... 300 250 200 150 100
* time (time) datetime64[ns] 1980-06-01T06:00:00
Attributes:
units: K
long_name: Temperature
standard_name: air_temperature
6.4 GRIB文件
6.4.1 使用PyNIO
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import xarray as xr
dataset = xr.open_dataset('/home/mw/input/pythonbook9857/fnl_201906011800.grib2', engine='pynio')
print(dataset)
data = dataset['TMP_P0_L1_GLL0']
print(data)
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<xarray.Dataset>
Dimensions: (lat_0: 181, lon_0: 360, lv_ISBL0: 31, lv_AMSL1: 3, lv_HTGL2: 3, lv_PVL3: 2, lv_HTGL4: 2, lv_SIGL5: 4, lv_ISBL6: 21, lv_HTGL7: 3, lv_ISBL8: 26, lv_SPDL9: 2, lv_ISBL10: 17, lv_DBLL11: 4)
Coordinates:
* lv_ISBL10 (lv_ISBL10) float32 100.0 200.0 300.0 ... 3.5e+04 4e+04
* lv_ISBL8 (lv_ISBL8) float32 1e+03 2e+03 3e+03 ... 9.75e+04 1e+05
* lv_HTGL7 (lv_HTGL7) float32 10.0 80.0 100.0
* lv_ISBL6 (lv_ISBL6) float32 1e+04 1.5e+04 ... 9.75e+04 1e+05
* lv_HTGL4 (lv_HTGL4) float32 2.0 80.0
* lv_PVL3 (lv_PVL3) float32 -2e-06 2e-06
* lv_HTGL2 (lv_HTGL2) float32 2.0 80.0 100.0
* lv_AMSL1 (lv_AMSL1) float32 1.829e+03 2.743e+03 3.658e+03
* lv_ISBL0 (lv_ISBL0) float32 100.0 200.0 300.0 ... 9.75e+04 1e+05
* lat_0 (lat_0) float32 90.0 89.0 88.0 ... -88.0 -89.0 -90.0
* lon_0 (lon_0) float32 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0
Dimensions without coordinates: lv_SIGL5, lv_SPDL9, lv_DBLL11
Data variables: (12/98)
TMP_P0_L1_GLL0 (lat_0, lon_0) float32 ...
TMP_P0_L6_GLL0 (lat_0, lon_0) float32 ...
TMP_P0_L7_GLL0 (lat_0, lon_0) float32 ...
TMP_P0_L100_GLL0 (lv_ISBL0, lat_0, lon_0) float32 ...
TMP_P0_L102_GLL0 (lv_AMSL1, lat_0, lon_0) float32 ...
TMP_P0_L103_GLL0 (lv_HTGL2, lat_0, lon_0) float32 ...
... ...
lv_DBLL11_l1 (lv_DBLL11) float32 ...
lv_DBLL11_l0 (lv_DBLL11) float32 ...
lv_SPDL9_l1 (lv_SPDL9) float32 ...
lv_SPDL9_l0 (lv_SPDL9) float32 ...
lv_SIGL5_l1 (lv_SIGL5) float32 ...
lv_SIGL5_l0 (lv_SIGL5) float32 ...
<xarray.DataArray 'TMP_P0_L1_GLL0' (lat_0: 181, lon_0: 360)>
array([[273.23535, 273.23535, 273.23535, ..., 273.23535, 273.23535, 273.23535],
[273.13535, 273.13535, 273.13535, ..., 273.13535, 273.13535, 273.13535],
[273.13535, 273.13535, 273.13535, ..., 273.03537, 273.03537, 273.13535],
...,
[219.43535, 219.43535, 219.53535, ..., 219.83536, 219.73535, 219.53535],
[218.63536, 218.63536, 218.63536, ..., 218.63536, 218.63536, 218.73535],
[219.93535, 219.93535, 219.93535, ..., 219.93535, 219.93535, 219.93535]],
dtype=float32)
Coordinates:
* lat_0 (lat_0) float32 90.0 89.0 88.0 87.0 ... -87.0 -88.0 -89.0 -90.0
* lon_0 (lon_0) float32 0.0 1.0 2.0 3.0 4.0 ... 356.0 357.0 358.0 359.0
Attributes:
center: US National Weather Servi...
production_status: Operational products
long_name: Temperature
units: K
grid_type: Latitude/longitude
parameter_discipline_and_category: Meteorological products, ...
parameter_template_discipline_category_number: [0 0 0 0]
level_type: Ground or water surface
level: [0.]
forecast_time: [0]
forecast_time_units: hours
initial_time: 06/01/2019 (18:00)
6.4.2 使用cfgrib
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import xarray as xr
dataset = xr.open_dataset('/home/mw/input/pythonbook9857/fnl_201906011800.grib2',
engine='cfgrib',
backend_kwargs={
'filter_by_keys': {
'typeOfLevel': 'isobaricInhPa'
}
}
)
print(dataset)
print(dataset['u'])
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skipping variable: paramId==260131 shortName='o3mr'
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 660, in build_dataset_components
dict_merge(variables, coord_vars)
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 593, in dict_merge
"key=%r value=%r new_value=%r" % (key, master[key], value)
cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,
650., 600., 550., 500., 450., 400., 350., 300., 250.,
200., 150., 100., 70., 50., 30., 20., 10., 7.,
5., 3., 2., 1.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([400., 350., 300., 250., 200., 150., 100., 70., 50., 30., 20.,
10., 7., 5., 3., 2., 1.]))
skipping variable: paramId==3041 shortName='absv'
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 660, in build_dataset_components
dict_merge(variables, coord_vars)
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 593, in dict_merge
"key=%r value=%r new_value=%r" % (key, master[key], value)
cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,
650., 600., 550., 500., 450., 400., 350., 300., 250.,
200., 150., 100., 70., 50., 30., 20., 10., 7.,
5., 3., 2., 1.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,
650., 600., 550., 500., 450., 400., 350., 300., 250.,
200., 150., 100., 70., 50., 30., 20., 10.]))
skipping variable: paramId==135 shortName='w'
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 660, in build_dataset_components
dict_merge(variables, coord_vars)
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 593, in dict_merge
"key=%r value=%r new_value=%r" % (key, master[key], value)
cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,
650., 600., 550., 500., 450., 400., 350., 300., 250.,
200., 150., 100., 70., 50., 30., 20., 10., 7.,
5., 3., 2., 1.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,
650., 600., 550., 500., 450., 400., 350., 300., 250.,
200., 150., 100.]))
skipping variable: paramId==260018 shortName='clwmr'
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 660, in build_dataset_components
dict_merge(variables, coord_vars)
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 593, in dict_merge
"key=%r value=%r new_value=%r" % (key, master[key], value)
cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,
650., 600., 550., 500., 450., 400., 350., 300., 250.,
200., 150., 100., 70., 50., 30., 20., 10., 7.,
5., 3., 2., 1.])) new_value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,
650., 600., 550., 500., 450., 400., 350., 300., 250.,
200., 150., 100.]))
skipping variable: paramId==260080 shortName='5wavh'
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 660, in build_dataset_components
dict_merge(variables, coord_vars)
File "/opt/conda/lib/python3.7/site-packages/cfgrib/dataset.py", line 593, in dict_merge
"key=%r value=%r new_value=%r" % (key, master[key], value)
cfgrib.dataset.DatasetBuildError: key present and new value is different: key='isobaricInhPa' value=Variable(dimensions=('isobaricInhPa',), data=array([1000., 975., 950., 925., 900., 850., 800., 750., 700.,
650., 600., 550., 500., 450., 400., 350., 300., 250.,
200., 150., 100., 70., 50., 30., 20., 10., 7.,
5., 3., 2., 1.])) new_value=Variable(dimensions=(), data=500.0)
<xarray.Dataset>
Dimensions: (isobaricInhPa: 31, latitude: 181, longitude: 360)
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
* isobaricInhPa (isobaricInhPa) float64 1e+03 975.0 950.0 ... 3.0 2.0 1.0
* latitude (latitude) float64 90.0 89.0 88.0 87.0 ... -88.0 -89.0 -90.0
* longitude (longitude) float64 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0
valid_time datetime64[ns] ...
Data variables:
gh (isobaricInhPa, latitude, longitude) float32 ...
t (isobaricInhPa, latitude, longitude) float32 ...
r (isobaricInhPa, latitude, longitude) float32 ...
u (isobaricInhPa, latitude, longitude) float32 ...
v (isobaricInhPa, latitude, longitude) float32 ...
Attributes:
GRIB_edition: 2
GRIB_centre: kwbc
GRIB_centreDescription: US National Weather Service - NCEP
GRIB_subCentre: 0
Conventions: CF-1.7
institution: US National Weather Service - NCEP
history: 2023-03-02T09:18 GRIB to CDM+CF via cfgrib-0.9.9...
<xarray.DataArray 'u' (isobaricInhPa: 31, latitude: 181, longitude: 360)>
[2019960 values with dtype=float32]
Coordinates:
time datetime64[ns] ...
step timedelta64[ns] ...
* isobaricInhPa (isobaricInhPa) float64 1e+03 975.0 950.0 ... 3.0 2.0 1.0
* latitude (latitude) float64 90.0 89.0 88.0 87.0 ... -88.0 -89.0 -90.0
* longitude (longitude) float64 0.0 1.0 2.0 3.0 ... 357.0 358.0 359.0
valid_time datetime64[ns] ...
Attributes: (12/29)
GRIB_paramId: 131
GRIB_dataType: fc
GRIB_numberOfPoints: 65160
GRIB_typeOfLevel: isobaricInhPa
GRIB_stepUnits: 1
GRIB_stepType: instant
... ...
GRIB_name: U component of wind
GRIB_shortName: u
GRIB_units: m s**-1
long_name: U component of wind
units: m s**-1
standard_name: eastward_wind
6.5 GrADS二进制文件
6.5.1 站点数据
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import numpy as np
import pandas as pd
def grads_sta(path_file, n_sta, n_time):
dt = np.dtype([('sta', 'S8'), ('lat', 'f4'), ('lon', 'f4'),
('time', 'f4'), ('nlev', 'i4'), ('nflag', 'i4'),
('var', 'f4')])
df_group = []
with open(path_file, 'rb') as fid:
for t in range(n_time):
data_raw = np.frombuffer(fid.read(n_sta * 32), dtype=dt)
fid.read(28)
df = pd.DataFrame(data_raw)
try:
df['sta'] = [x.decode() for x in df['sta']]
except:
pass
df_group.append(df)
return df_group
dat = grads_sta('/home/mw/input/pythonbook9857/r160.grd', 160, 1)
print(dat)
print(type(dat))
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[ sta lat lon time nlev nflag var
0 b'\x01 ' 51.720001 126.650002 0.0 1 1 0.098331
1 b'\x02 ' 48.770000 121.919998 0.0 1 1 -0.167619
2 b'\x03 ' 49.220001 119.750000 0.0 1 1 -0.064597
3 b'\x04 ' 50.450001 121.699997 0.0 1 1 -0.046360
4 b'\x05 ' 49.169998 125.230003 0.0 1 1 0.017577
.. ... ... ... ... ... ... ...
155 b'\x9c ' 47.730000 88.080002 0.0 1 1 -0.036620
156 b'\x9d ' 46.730000 83.000000 0.0 1 1 0.071207
157 b'\x9e ' 44.430000 84.660004 0.0 1 1 -0.205051
158 b'\x9f ' 43.950001 81.330002 0.0 1 1 0.170275
159 b'\xa0 ' 43.779999 87.620003 0.0 1 1 -0.103539
[160 rows x 7 columns]]
<class 'list'>
6.5.2 栅格数据
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import numpy as np
import xarray as xr
import pandas as pd
def grads_grid(path_file, nx, ny, nt,
lon_0, lon_step, lat_0, lat_step,
t_0, t_freq, var_list, z_list):
data_raw = np.fromfile(path_file, dtype=np.float32)
n_var = len(var_list)
nz = len(z_list)
data_raw = data_raw.reshape((nt, n_var, nz, ny, nx))
time_list = pd.date_range(t_0, periods=nt, freq=t_freq)
lon = np.arange(lon_0, lon_0 + lon_step * nx, lon_step)
lat = np.arange(lat_0, lat_0 + lat_step * ny, lat_step)
data = xr.DataArray(data_raw,
coords=[time_list, var_list, z_list, lat, lon],
dims=['time', 'var', 'level', 'lat', 'lon']
).to_dataset('var')
return data
dataset = grads_grid('/home/mw/input/pythonbook9857/hgt500.grd',
nx=144, ny=73, nt=732,
lon_0=0, lon_step=2.5, lat_0=-90, lat_step=2.5,
t_0='1948-01-01', t_freq='1M', var_list=['hgt'],
z_list=[500])
print(dataset)
|
<xarray.Dataset>
Dimensions: (time: 732, level: 1, lat: 73, lon: 144)
Coordinates:
* time (time) datetime64[ns] 1948-01-31 1948-02-29 ... 2008-12-31
* level (level) int64 500
* lat (lat) float64 -90.0 -87.5 -85.0 -82.5 -80.0 ... 82.5 85.0 87.5 90.0
* lon (lon) float64 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
Data variables:
hgt (time, level, lat, lon) float32 5.189e+03 5.189e+03 ... 5.075e+03
6.6 WRF-ARW输出文件
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import xarray as xr
import wrf
from netCDF4 import Dataset
ncfile = Dataset('/home/mw/input/pythonbook9857/wrfout_d01_2017-08-10_000000.nc')
p = wrf.getvar(ncfile, 'pressure', timeidx=wrf.ALL_TIMES)
rh = wrf.getvar(ncfile, 'rh', timeidx=wrf.ALL_TIMES)
print(rh)
rh_500 = wrf.interplevel(rh, p, 500)
rh_850_to_500 = wrf.interplevel(rh, p, [850, 700, 500])
print(rh_850_to_500)
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<xarray.DataArray 'rh' (Time: 5, bottom_top: 29, south_north: 49, west_east: 79)>
array([[[[87.765434 , 85.98064 , 84.91124 , ..., 81.3592 ,
79.81483 , 81.41704 ],
[87.995636 , 86.22767 , 85.25411 , ..., 83.49108 ,
81.253586 , 81.55239 ],
[88.70185 , 87.21322 , 86.30212 , ..., 84.36489 ,
83.808624 , 83.382866 ],
...,
[43.890274 , 45.305763 , 46.74152 , ..., 79.794525 ,
79.27631 , 78.8375 ],
[39.819405 , 41.26361 , 42.63604 , ..., 79.73092 ,
79.23843 , 78.76903 ],
[37.56467 , 38.025745 , 39.262104 , ..., 79.73279 ,
79.23924 , 78.71969 ]],
[[89.051445 , 86.849625 , 85.17071 , ..., 79.81156 ,
78.14068 , 79.16232 ],
[89.22471 , 87.07232 , 85.47679 , ..., 81.908 ,
80.53091 , 80.28641 ],
[89.795204 , 87.968445 , 86.57346 , ..., 83.796936 ,
82.867424 , 82.598114 ],
...
[ 4.049299 , 4.0986323, 4.12636 , ..., 6.4341807,
6.5087857, 6.608485 ],
[ 3.9992058, 4.052754 , 4.0801926, ..., 6.392681 ,
6.4727077, 6.563373 ],
[ 3.947622 , 3.9738657, 4.002276 , ..., 6.363432 ,
6.4367847, 6.513951 ]],
[[ 4.21838 , 4.2060227, 4.201893 , ..., 5.0465074,
5.0882134, 5.14203 ],
[ 4.2712355, 4.2646527, 4.2615657, ..., 5.06661 ,
5.1020627, 5.126723 ],
[ 4.3191833, 4.324505 , 4.3139987, ..., 5.089982 ,
5.105507 , 5.109809 ],
...,
[ 3.166993 , 3.1939094, 3.2173321, ..., 4.870332 ,
4.891847 , 4.9187264],
[ 3.1322336, 3.1641045, 3.1914012, ..., 4.8407125,
4.8697286, 4.8977094],
[ 3.100257 , 3.123251 , 3.1545112, ..., 4.8102155,
4.8391423, 4.8720045]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 113.2 113.3 113.4 ... 121.3 121.4
XLAT (south_north, west_east) float32 29.68 29.68 29.68 ... 33.99 33.99
XTIME (Time) float32 0.0 180.0 360.0 540.0 720.0
* Time (Time) datetime64[ns] 2017-08-10 ... 2017-08-10T12:00:00
Dimensions without coordinates: bottom_top, south_north, west_east
Attributes:
FieldType: 104
MemoryOrder: XYZ
description: relative humidity
units: %
stagger:
coordinates: XLONG XLAT XTIME
projection: LambertConformal(stand_lon=117.19999694824219, moad_cen_lat...
<xarray.DataArray 'rh_interp' (Time: 5, level: 3, south_north: 49, west_east: 79)>
array([[[[ 49.581455 , 49.90141 , 49.964336 , ..., 93.02744 ,
92.50517 , 91.436905 ],
[ 49.854908 , 50.216183 , 50.33729 , ..., 94.29466 ,
93.958336 , 92.881165 ],
[ 50.29943 , 50.652588 , 50.81059 , ..., 94.8869 ,
94.64059 , 94.331085 ],
...,
[ 43.85136 , 44.185024 , 44.534695 , ..., 65.72999 ,
66.07933 , 66.988106 ],
[ 43.501328 , 43.93895 , 44.335472 , ..., 66.38105 ,
66.68885 , 67.59361 ],
[ 43.071743 , 43.59577 , 44.11645 , ..., 67.04757 ,
67.32303 , 68.26489 ]],
[[ 59.510532 , 58.794968 , 58.20066 , ..., 88.38223 ,
89.8404 , 90.4845 ],
[ 60.2473 , 59.721775 , 59.27535 , ..., 88.13034 ,
89.4728 , 90.00002 ],
[ 60.890114 , 60.67282 , 60.48124 , ..., 87.15515 ,
88.844986 , 89.848656 ],
...
[ 32.703304 , 32.478714 , 32.44395 , ..., 50.050068 ,
50.41752 , 50.632175 ],
[ 33.32784 , 33.011208 , 32.852413 , ..., 48.91975 ,
49.255787 , 49.405983 ],
[ 34.13029 , 33.958523 , 33.643333 , ..., 47.629578 ,
47.947124 , 48.399437 ]],
[[ 24.025608 , 23.000294 , 21.980774 , ..., 16.391787 ,
17.90786 , 19.738607 ],
[ 20.825268 , 20.007761 , 19.12427 , ..., 12.906076 ,
14.448949 , 16.162752 ],
[ 18.05197 , 17.35045 , 16.605772 , ..., 12.18835 ,
12.27763 , 14.094156 ],
...,
[ 4.564085 , 4.439832 , 4.360229 , ..., 18.472599 ,
19.609535 , 19.641474 ],
[ 4.6399007, 4.5138626, 4.4195275, ..., 18.654158 ,
19.900602 , 20.3621 ],
[ 4.7261467, 4.597047 , 4.4673424, ..., 18.454733 ,
19.761063 , 20.774572 ]]]], dtype=float32)
Coordinates:
XLONG (south_north, west_east) float32 113.2 113.3 113.4 ... 121.3 121.4
XLAT (south_north, west_east) float32 29.68 29.68 29.68 ... 33.99 33.99
XTIME (Time) float32 0.0 180.0 360.0 540.0 720.0
* Time (Time) datetime64[ns] 2017-08-10 ... 2017-08-10T12:00:00
* level (level) int64 850 700 500
Dimensions without coordinates: south_north, west_east
Attributes:
FieldType: 104
units: %
stagger:
coordinates: XLONG XLAT XTIME
projection: LambertConformal(stand_lon=117.19999694824219, moad_cen_l...
missing_value: 9.969209968386869e+36
_FillValue: 9.969209968386869e+36
vert_units: hPa
6.7 雷达基数据文件
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| import cinrad
f = cinrad.io.CinradReader('/home/mw/input/pythonbook9857/RADA_CHN_DOR_L2_O-Z9551-SA-CAP-20200708032300.bin.bz2')
print(f.available_product(0))
tilt_number = 0
data_radius = 230
r = f.get_data(tilt_number, data_radius, 'REF')
print(r)
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['REF', 'VEL', 'SW', 'azimuth', 'RF']
<xarray.Dataset>
Dimensions: (azimuth: 366, distance: 230)
Coordinates:
* azimuth (azimuth) float64 1.38 1.397 1.415 1.432 ... 1.349 1.366 1.383
* distance (distance) float64 1.0 2.0 3.0 4.0 ... 227.0 228.0 229.0 230.0
Data variables:
REF (azimuth, distance) float64 nan -8.5 2.0 2.0 ... nan nan nan nan
longitude (azimuth, distance) float64 117.3 117.3 117.3 ... 119.7 119.7
latitude (azimuth, distance) float64 31.87 31.87 31.87 ... 32.25 32.25
height (azimuth, distance) float64 0.1764 0.1873 0.1983 ... 5.709 5.747
Attributes:
elevation: 0.615234375
range: 230
scan_time: 2020-07-08 03:23:11.811000
site_code: Z9551
site_name: 合肥
site_longitude: 117.25777777777778
site_latitude: 31.866944444444446
tangential_reso: 1.0
nyquist_vel: 8.52
task: VCP21
6.8 CIMISS的使用
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| import cimiss
import numpy as np
client = cimiss.Query(user_id='myuserid', password='mypasswd', host='myhost')
resp_array_2d = client.array_2d(
interface_id="getSurfEleByTime",
params={
"dataCode": "SURF_CHN_MUL_HOR",
"elements": "Station_ID_C,PRE_1h,PRS",
"times": "20181224000000",
"orderby": "Station_ID_C:ASC",
"limitCnt": "3",
},
dtypes={'PRE_1h': np.float, 'PRS': np.float}
)
print(resp_array_2d)
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