基于Python的Grib数据可视化

cosman 9年前

来自: http://www.cnblogs.com/kallan/p/5160017.html

利用Python语言实现Grib数据可视化主要依靠三个库——pygrib、numpy和matplotlib。pygrib是欧洲中期天气预报中心(ECMWF)的GRIG API C库的Python接口,通过这个库可以将Grib数据读取出来;numpy是Python的一种开源的数值计算扩展,这种工具可用来存储和处理大型矩阵;matplotlib是python著名的绘图库,它提供了一整套和matlab相似的命令API,十分适合交互式地进行制图;在数据可视化过程中,我们常需要将数据在地图上画出来,所以还需要matplotlib的一个子包basemap,负责地图绘制。

一、库的安装

(一) matplotlib 安装

  • matplotlib依赖

  1. nose

  2. numpy

  3. pyparsing

  4. python-dateutil

  5. cycler

  6. pkg-config

  7. freetype

  8. libpng

  • 安装过程

这里我都是通过源码包安装的,大家也可以再终端里通过pip install 命令来安装

1、安装nose

解压缩后,进入命令提示符 运行

1 python3 setup.py install

2、安装numpy

解压缩后,进入命令提示符 运行

1 python3 setup.py install

3、安装pyparsing

解压缩后,进入命令提示符 运行

1 python3 setup.py install 

4、安装python-dateutil

解压缩后,进入命令提示符 运行

1 python3 setup.py install

5、安装cycler

解压缩后,进入命令提示符 运行

1 python3 setup.py install

6、安装pkg-config

1 ./configure --with-intermal-glib  2 make && date  3 sudo make install && date

7、安装freetype

1 ./configure  2 make && date  3 sudo make install && date

8、安装libpng

1 ./configure  2 make && date  3 sudo make install && date

9、安装matplotlib-1.5.0

解压缩后,进入命令提示符 运行

1 python3 setup.py install

(二) basemap 安装

  • basemap依赖

  1. geos

  2. pyproj

  • 安装过程

1、安装GEOS

1 ./configure  2 make && date  3 sudo make install && date

2、安装pyproj

1 python3 setup.py install

3、安装basemap

1 python3 setup.py install

(三)pygrib安装

  • pygrib依赖

  1. Jasper

  2. GRIB API 

  3. numpy

  4. pyproj

  • 安装过程

由于之前已经安装了numpy和pyproj,这里只需安装Jasper和GRIB API即可安装pygrib

1、安装Jasper

1 ./configure  2 make && date  3 sudo make install && date

2、安装GRIB API

1 ./configure --with-jasper='/usr/local/'  2 make && date  3 sudo make install && date

3、安装pygrib

安装pygrib之前首先要根据自己的实际情况修改文件目录下的setup.cfg文件,最主要的就是修改grib_api_dir和jasper_dir,这两个是刚刚安装的Jasper和GRIB API的路径,如果这两个地址不正确安装会报错

  1 # Rename this file to setup.cfg to set pygrib's   2 # build options.   3 # Follow instructions below for editing.   4 [directories]   5 # uncomment and set to grib_api install location.   6 # Include files should be located in grib_api_dir/include and   7 # the library should be located in grib_api_dir/lib.   8 # If the libraries and include files are installed in separate locations,   9 # use grib_api_libdir and grib_api_incdir to specify the locations  10 # separately.    11 grib_api_dir = /usr/local  12 # if grib_api was built with jasper support for JPEG200,  13 # uncomment and set to jasper lib install location.  14 # If the libraries and include files are installed in separate locations,  15 # use jasper_libdir and jasper_incdir.  16 jasper_dir = /usr/local  17 # if grib_api was built with openjpeg support for JPEG200,  18 # uncomment and set to openjpeg lib install location.  19 # If the libraries and include files are installed in separate locations,  20 # use openjpeg_libdir and openjpeg_incdir.  21 #openjpeg_dir = /opt/local  22 # if grib_api was built with png support,  23 # uncomment and set to png lib install location.  24 # If the libraries and include files are installed in separate locations,  25 # use png_libdir and png_incdir.  26 png_dir = /usr  27 # if grib_api was built with png support,  28 # uncomment and set to zlib install location.  29 zlib_dir = /usr  30 # install man pages for command line utilities here  31 #man_dir = /usr/local/man    View Code

修改好就可以正常安装了

1 python3 setup.py install

二、grib数据读取

虽然我做的东西和气象沾边,但是我本身并不是气象专业出身,所有这些东西都是我慢慢研究琢磨出来的,所以有些方面可能讲的比较外行,有不对的地方欢迎大家留言指正。

(一)导入pygrib模块

1 >>> import pygrib

(二)打开Grib文件

1 >>> grbs = pygrib.open('/Users/Kallan/Documents/data/echhae50.082')

(三)提取文件信息

1 >>> grbs.seek(0)  2 >>> for grb in grbs:  3 grb  4 1:Geopotential Height:gpm (instant):regular_ll:isobaricInhPa:level 500:fcst time 24 :from 201507081200

信息解读

<span>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1 <span>:数据列表的行号,有的文件可能包括多个数据</span></span>

<span>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Geopotential<span> <span>Height<span>:数据的名称</span></span></span></span>

<span>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; gpm <span>(<span>instant<span>):数据的单位</span></span></span></span>

<span>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;regular_ll<span>:常规数据,其实这个字段我也不清楚</span></span>

<span>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;isobaricInhPa<span>:这个字段表示的是数据属性,此处表示是以hPa为单位的等压面</span></span>

<span>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;level <span>500<span>:这个字段表示的是高度层</span></span></span>

<span>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fcst time <span>24<span> <span>:预报时效</span></span></span></span>

<span>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;from<span> <span>201507081200 :起报时间</span></span></span>

综合上面的信息可以得出,这个文件是从2015年7月8日12时开始的24小时后500hPa等压面高度场数据

(四)导出文件数据

 1 >>> grb = grbs.select(name='Geopotential Height')[0]   2 >>> data = grb.values   3 >>> print(data.shape,data.min(),data.max())   4 (37, 37) 5368.6796875 5941.0390625   5 >>> lat,lon=grb.latlons()   6 >>> print(lat,'\n',lon)   7 [[  0.    0.    0.  ...,   0.    0.    0. ]   8 [  2.5   2.5   2.5 ...,   2.5   2.5   2.5]   9 [  5.    5.    5.  ...,   5.    5.    5. ]  10 ...,  11 [ 85.   85.   85.  ...,  85.   85.   85. ]  12 [ 87.5  87.5  87.5 ...,  87.5  87.5  87.5]  13 [ 90.   90.   90.  ...,  90.   90.   90. ]]  14 [[-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]  15 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]  16 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]  17 ...,  18 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]  19 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]  20 [-90.  -87.5 -85.  ...,  -5.   -2.5   0. ]]

三、grib数据可视化

(一)导入需要的模块

1 >>> import matplotlib.pyplot as plt  2 >>> from mpl_toolkits.basemap import Basemap  3 >>> import numpy as np

(二)创建一个figure

1 >>> plt.figure()  2 <matplotlib.figure.Figure object at 0x107e65198>

(三)创建一个basemap实例

 1 >>> m=Basemap(projection='mill',lat_ts=10,llcrnrlon=lon.min(), \   2  urcrnrlon=lon.max(),llcrnrlat=lat.min(),urcrnrlat=lat.max(), \   3  resolution='c')   4 >>> m.drawcoastlines(linewidth=0.25)   5 <matplotlib.collections.LineCollection object at 0x1091c1f28>   6 >>> m.drawcountries(linewidth=0.25)   7 <matplotlib.collections.LineCollection object at 0x10621d0f0>   8 >>> m.fillcontinents(color='coral',lake_color='aqua')   9 >>> m.drawmapboundary(fill_color='aqua')  10 <matplotlib.patches.Rectangle object at 0x10918b3c8>  11 >>> m.drawmeridians(np.arange(0,360,30))  12 >>> m.drawparallels(np.arange(-90,90,30))

(四)将lat,lon的数据格式转换成投影需要的格式存入x,y

1 >>> x, y = m(lon,lat)

(五)绘制等值线

1 >>> cs = m.contour(x,y,data,15,linewidths=1.5)

(六)命名并显示图像

1 >>> plt.title('Geopotential Height Contour from Grib')  2 <matplotlib.text.Text object at 0x10918bda0>  3 >>> plt.show()

(七)图像展示

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