xarray基础之计算篇

import xarray as xr
# 数据
f = '/home/mw/input/cru3546/cru_ts4.07.2021.2022.pre.dat.nc'

# 打开数据集
ds = xr.open_dataset(f)
ds

Warning: ecCodes 2.21.0 or higher is recommended. You are running version 2.14.1

all_mean_pre = ds.pre.mean()

print('两年全球平均降水:', all_mean_pre)

两年全球平均降水: <xarray.DataArray 'pre' ()>
array(61.810722, dtype=float32)

first_mean_pre = ds.pre.sel(time=slice('2021-01-16', '2021-12-16')).mean()

print('2021年全球平均降水:', first_mean_pre)

2021年全球平均降水: <xarray.DataArray 'pre' ()>
array(61.74274, dtype=float32)

point_mean_pre = ds.pre.sel(time=slice('2021-01-16', '2021-12-16'),lat=32.75,lon=60.25).mean()

print('2021年某格点平均降水:', point_mean_pre)

2021年某格点平均降水: <xarray.DataArray 'pre' ()>
array(12.158334, dtype=float32)
Coordinates:
    lon      float32 60.25
    lat      float32 32.75

sum_pre = ds.pre.sum()
print('两年全球总降水:', sum_pre)

两年全球总降水: <xarray.DataArray 'pre' ()>
array(1.00014696e+08, dtype=float32)

std_pre = ds.pre.std()
print('pre std:', std_pre)

pre std: <xarray.DataArray 'pre' ()>
array(85.1408844)

max=ds.pre.max()
max

argmax=ds.pre.argmax(dim=['lon','lat'])
argmax

{'lon': <xarray.DataArray 'pre' (time: 24)>
 array([612, 641, 641, 256, 204, 548, 509, 545, 205, 576, 520, 612, 716,
        612, 612, 612, 556, 543, 506, 556, 641, 603, 205, 565])
 Coordinates:
   * time     (time) datetime64[ns] 2021-01-16 2021-02-15 ... 2022-12-16,
 'lat': <xarray.DataArray 'pre' (time: 24)>
 array([199, 170, 170, 189, 185, 216, 207, 220, 188, 212, 205, 199, 144,
        199, 199, 199, 204, 230, 218, 208, 168, 209, 188, 191])
 Coordinates:
   * time     (time) datetime64[ns] 2021-01-16 2021-02-15 ... 2022-12-16}

ds.pre[0].max()

ds.pre[0].argmax(dim=['lat','lon'])

{'lat': <xarray.DataArray 'pre' ()>
 array(199)
 Coordinates:
     time     datetime64[ns] 2021-01-16,
 'lon': <xarray.DataArray 'pre' ()>
 array(612)
 Coordinates:
     time     datetime64[ns] 2021-01-16}

ds.pre[0][199,612]

import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from matplotlib.colors import LinearSegmentedColormap
import cmaps
lons=ds.lon
lats=ds.lat
data=ds.pre[0]
fig = plt.figure(figsize=(20,18))

# 设置地图投影为PlateCarree
ax = plt.axes(projection=ccrs.PlateCarree())

# 绘制填充等值线图
cs = ax.contourf(lons, lats, data, transform=ccrs.PlateCarree(), cmap=cmaps.radar, levels=np.linspace(data.min(), data.max(), 20))
# 添加网格线
ax.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False)
# 添加海岸线
ax.coastlines()

/opt/conda/lib/python3.7/site-packages/cmaps/cmaps.py:3517: UserWarning: Trying to register the cmap 'radar' which already exists.
  matplotlib.cm.register_cmap(name=cname, cmap=cmap)

<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa2feef28d0>

import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from matplotlib.colors import LinearSegmentedColormap
import cmaps
lons=ds.lon
lats=ds.lat
data=ds.pre[0]
fig = plt.figure(figsize=(20,10))

# 设置地图投影为PlateCarree
ax = plt.axes(projection=ccrs.PlateCarree())

# 绘制填充等值线图
cs = ax.contourf(lons, lats, data, transform=ccrs.PlateCarree(), cmap=cmaps.radar_1, levels=np.linspace(data.min(), data.max(), 20))
# 添加网格线
ax.gridlines(draw_labels=True, dms=True, x_inline=False, y_inline=False)
# 设置经纬度范围
ax.set_extent([120, 130, 5, 15], crs=ccrs.PlateCarree())
# 最大值打点
ax.scatter([data.lon[612]],[data.lat[199]],color='b',s=10)
# 添加海岸线
ax.coastlines()
ax.scatter([data.lon[612]],[data.lat[199]],color='b',s=20)
cbar = plt.colorbar(cs, orientation='vertical')

/opt/conda/lib/python3.7/site-packages/cmaps/cmaps.py:3533: UserWarning: Trying to register the cmap 'radar_1' which already exists.
  matplotlib.cm.register_cmap(name=cname, cmap=cmap)

diff = ds.pre.diff(dim='time')
diff

diff[0].plot()

<matplotlib.collections.QuadMesh at 0x7fa2fe46a3d0>

df = ds.to_dataframe()
df

mean = df.mean()
mean

pre    61.771572
stn     4.029309
dtype: float64

max_value = df['pre'].max()
max_value

2311.800048828125

sum_by_row = df.sum(axis=0)
sum_by_row

pre    100014712.0
stn      6519744.0
dtype: float64

std_dev = df.std()
std_dev

pre    85.079056
stn     3.012599
dtype: float64

correlation = df.corr()
correlation

import pandas as pd
# 转格式
df = ds.to_dataframe()
print(df)
# 查找最大值
max_value = df["pre"].max()
print(max_value)
# 查找最大值所在的时间
idx = df["pre"].idxmax()
idx

                           pre  stn
lon     lat    time                
-179.75 -89.75 2021-01-16  NaN  NaN
               2021-02-15  NaN  NaN
               2021-03-16  NaN  NaN
               2021-04-16  NaN  NaN
               2021-05-16  NaN  NaN
...                        ...  ...
 179.75  89.75 2022-08-16  NaN  NaN
               2022-09-16  NaN  NaN
               2022-10-16  NaN  NaN
               2022-11-16  NaN  NaN
               2022-12-16  NaN  NaN

[6220800 rows x 2 columns]
2311.800048828125