xarray基础之计算篇
本帖最后由 气ython风雨 于 2024-3-27 11:31 编辑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)
dsWarning: ecCodes 2.21.0 or higher is recommended. You are running version 2.14.1
平均
In :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)
In :
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)
In :
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
总和
In :
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)
标准差
In :std_pre = ds.pre.std()
print('pre std:', std_pre)
pre std: <xarray.DataArray 'pre' ()>
array(85.1408844)
最大值与最大值坐标
In :
max=ds.pre.max()
maxOut:
In :
argmax=ds.pre.argmax(dim=['lon','lat'])
argmaxOut:
{'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 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 2021-01-16 2021-02-15 ... 2022-12-16}
输出了二十四时间点的最大值坐标，下面选一个时间点
In :
ds.pre.max()Out:
In :ds.pre.argmax(dim=['lat','lon'])Out:
{'lat': <xarray.DataArray 'pre' ()>
array(199)
Coordinates:
time datetime64 2021-01-16,
'lon': <xarray.DataArray 'pre' ()>
array(612)
Coordinates:
time datetime64 2021-01-16}
In :
ds.preOut
上面已找到最大值的经纬度，顺便画个图
In :
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
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)
Out:
<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa2feef28d0>
In :
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
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(, crs=ccrs.PlateCarree())
# 最大值打点
ax.scatter(],],color='b',s=10)
# 添加海岸线
ax.coastlines()
ax.scatter(],],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)
在东经126，北纬9的附近的小黑点就是最大值所在，格点数据放大看确实有点奇形怪状
还有对应的argmin，用法差不多就不多介绍了
获得下月减上月的数组
In :
diff = ds.pre.diff(dim='time')
diffOut:
diff.plot()
Out:
<matplotlib.collections.QuadMesh at 0x7fa2fe46a3d0>
转为pandas计算
In :
df = ds.to_dataframe()
dfOut:
6220800 rows × 2 columns
均值
In :
mean = df.mean()
meanOut:
pre 61.771572
stn 4.029309
dtype: float64
指定列最大值
In :max_value = df['pre'].max()
max_valueOut:
2311.800048828125
列总和
In :sum_by_row = df.sum(axis=0)
sum_by_rowOut:
pre 100014712.0
stn 6519744.0
dtype: float64
标准差与相关系数
In :
std_dev = df.std()
std_dev
Out:
pre 85.079056
stn 3.012599
dtype: float64
In :
correlation = df.corr()
correlationOut:
最大值坐标
In :
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
prestn
lon lat time
-179.75 -89.75 2021-01-16NaNNaN
2021-02-15NaNNaN
2021-03-16NaNNaN
2021-04-16NaNNaN
2021-05-16NaNNaN
... ......
179.7589.75 2022-08-16NaNNaN
2022-09-16NaNNaN
2022-10-16NaNNaN
2022-11-16NaNNaN
2022-12-16NaNNaN
2311.800048828125
Out:
(74.75, 13.75, Timestamp('2021-07-16 00:00:00'))
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