选取
<p>[TOC]</p>
<pre><code class="language-python">%matplotlib inline
%load_ext autoreload
%autoreload 2
import meteva.base as meb
import pandas as pd
import numpy as np
import datetime
import copy</code></pre>
<p>随机生成包含多个层次，多个时刻，多个时效，多个成员的站点数据，用于测试示例</p>
<pre><code class="language-python">data = {&quot;id&quot;:[54511,54522],&quot;lon&quot;:[100,110],&quot;lat&quot;:[30,40],&quot;ob&quot;:[0,0],&quot;grapes&quot;:[0,0],&quot;ec&quot;:[0,0],}
df = pd.DataFrame(data)
sta = meb.sta_data(df)
meb.set_stadata_coords(sta,level = 1000,time = datetime.datetime(2019,12,31,8,0),dtime = 0)
sta1 = copy.deepcopy(sta)
meb.set_stadata_coords(sta1,level = 850)
sta = meb.combine_join(sta,sta1)
sta1 = copy.deepcopy(sta)
meb.set_stadata_coords(sta1,time = datetime.datetime(2020,1,1,20,0))
sta = meb.combine_join(sta,sta1)
sta1 = copy.deepcopy(sta)
meb.set_stadata_coords(sta1,dtime = 24)
sta_all = meb.combine_join(sta,sta1)
sta_all.iloc[:,-3:] = (np.random.rand(16,3)*100).astype(np.int16)
sta_all.iloc[10:13,-1] = meb.IV
print(sta_all)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<h1>通过综合字典选取数据</h1>
<p><strong>sele_by_dict(data,s):</strong><br />
通过包含多个维度选项的字典型参数，来确定所要选取的数据的范围，返回相应的数据样本。该函数和下文中sele_by_para函数是一一对应的，只是用于选择数据的参数形式以字典的形式统一成一个参数。</p>
<p><strong>参数说明：</strong><br />
<strong>data</strong>: <a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a><br />
<strong>s</strong>: 字典型变量,字典的关键次为字符串，可选项和下文中sele_by_para的选取参数一一对应。具体请参见下文<br />
<strong>return</strong>: <a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> </p>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">sta = meb.sele_by_dict(sta_all,s = {&quot;level&quot;:1000,&quot;dtime&quot;:24,&quot;member&quot;:[&quot;ob&quot;,&quot;ec&quot;]})
print(sta) </code></pre>
<pre><code> level time dtime id lon lat ob ec
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 17
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 59</code></pre>
<pre><code class="language-python">#上述示例等价于
sta = meb.sele_by_para(sta_all,level=1000,dtime=24,member =[&quot;ob&quot;,&quot;ec&quot;])
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob ec
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 17
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 59</code></pre>
<h1>通过参数提取部分数据</h1>
<p><strong>sele_by_para(data,member = None,level = None,time = None,time_range = None,year = None,month = None,day = None,dayofyear = None,hour = None,minute = None,ob_time=None, ob_time_range=None, ob_year=None, ob_month=None, ob_day=None, ob_dayofyear=None, ob_hour=None,ob_minute = None,dtime = None,dtime_range = None,dday = None, dhour = None,lon = None,lat = None,id = None,grid = None,gxy = None,gxyz = None,stadata = None,value = None,drop_IV = False,last = None,last_range = None,province_name = None,drop_last = True,ob_stadata = None,</strong>kwargs)** </p>
<p>从网格和站点数据中提取部分数据</p>
<table>
<thead>
<tr>
<th style="text-align: left;"><strong>参数</strong></th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"> <strong>data</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a></td>
</tr>
<tr>
<td style="text-align: left;"> <strong>member</strong></td>
<td style="text-align: left;">成员的名称，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>level</strong></td>
<td style="text-align: left;">层次的名称，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>time</strong></td>
<td style="text-align: left;">时间（以起报时刻为准），可以是datetime,datetime64或“2019010108”类似的字符串形式，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>time_range</strong></td>
<td style="text-align: left;">时间范围，列表形式，第一个元素为起始时间，第二个为结束时间，时间可以是datetime,datetime64或“2019010108”类似的字符串形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>year</strong></td>
<td style="text-align: left;">要提取的数据的年份（以起报时刻为准），同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>month</strong></td>
<td style="text-align: left;">要提取的数据的月份（以起报时刻为准），同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>day</strong></td>
<td style="text-align: left;">要提取的数据的日期（以起报时刻为准），可以是datetime,datetime64或“20190101”类似的字符串形式，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>dayofyear</strong></td>
<td style="text-align: left;">要提取的数据在一年中的排序（以起报时刻为准），整数形式，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>hour</strong></td>
<td style="text-align: left;">要提取的数据的小时数（以起报时刻为准），0-23的整数，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>minute</strong></td>
<td style="text-align: left;">要提取的数据的分钟数（以起报时刻为准），0-59的整数，同时提取多个采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>day_hour</strong></td>
<td style="text-align: left;">要提取的数据的日期-小时数，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>year_month</strong></td>
<td style="text-align: left;">要提取的数据的年份-月份，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_time</strong></td>
<td style="text-align: left;">时间（以观测时刻为准），可以是datetime,datetime64或“2019010108”类似的字符串形式，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_time_range</strong></td>
<td style="text-align: left;">观测时间范围，列表形式，第一个元素为起始时间，第二个为结束时间，时间可以是datetime,datetime64或 “2019010108”类似的字符串形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_year</strong></td>
<td style="text-align: left;">要提取的数据的年份（以观测时刻为准），同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_month</strong></td>
<td style="text-align: left;">要提取的数据的月份（以观测时刻为准），同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_day</strong></td>
<td style="text-align: left;">要提取的数据的日期（以观测时刻为准），可以是datetime,datetime64或“20190101”类似的字符串形式，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_dayofyear</strong></td>
<td style="text-align: left;">要提取的数据在一年中的排序（以观测时刻为准），整数形式，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_hour</strong></td>
<td style="text-align: left;">要提取的数据的小时数（以观测时刻为准），0-23的整数，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_minute</strong></td>
<td style="text-align: left;">要提取的数据的分钟数（以观测时刻为准），0-59的整数，同时提取多个采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_day_hour</strong></td>
<td style="text-align: left;">要提取的数据的日期-小时数，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_year_month</strong></td>
<td style="text-align: left;">要提取的数据的年份-月份，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>dtime</strong></td>
<td style="text-align: left;">要提取的数据的时效，整数形式，同时提取多个是采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>dtime_range</strong></td>
<td style="text-align: left;">时间范围，列表形式，第一个元素为起始时效，第二个为结束时效</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>dday</strong></td>
<td style="text-align: left;">要提取的数据的时效dtime整除以24的值，整数形式，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>dhour</strong></td>
<td style="text-align: left;">要提取的数据的时效dtime除24的余数，整数形式，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>lon</strong></td>
<td style="text-align: left;">要提取的数据的经度范围，列表形式，第一个元素为起始经度，第二个为结束经度</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>lat</strong></td>
<td style="text-align: left;">要提取的数据的纬度范围，列表形式，第一个元素为起始经度，第二个为结束经度</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>id</strong></td>
<td style="text-align: left;">要提取的数据的站号，同时提取多个是采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>grid</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/meteva?page_id=3975600815874861">网格信息类</a>变量，提取多维矩形网格范围内的数据，grid中size>1的维度的坐标范围会被用作选择的已经，size=1的维度会被忽略</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>gxy</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/meteva?page_id=3975600815874861">网格信息类</a>,采用其中经纬度范围提取水平矩形网格范围内的数据</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>gxyz</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/meteva?page_id=3975600815874861">网格信息类</a>,采用其中经纬度和层次范围提取三维矩形网格范围内的数据</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>stadata</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a>, 对于stadata中level，time，dtime，id四个坐标中非缺省的部分，从data中提取和stadata坐标一致的站点数据</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>value</strong></td>
<td style="text-align: left;">提取所有数据列都在给定取值范围的数据，列表形式第一个元素为数据最低值，第二个为数据最高值</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>drop_IV</strong></td>
<td style="text-align: left;">该参数为True时，删除包含缺省值的行</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>last</strong></td>
<td style="text-align: left;">取出最后一列包含last的行，如何选择多个类型，last采用列表形式，并删除最后一列的数据</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>last_range</strong></td>
<td style="text-align: left;">包含起始值和结束值的列表，取出最后一列取值在该取值范围的数据，并删除最后一列的数据</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>province_name</strong></td>
<td style="text-align: left;">按省份名称选取数据，例如参数值 ["北京","上海"]表示选取北京和上海两市的数据样本</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>ob_stadata</strong></td>
<td style="text-align: left;">将ob_stadata 和data用combine_on_obtime_id匹配，保留的样本再删除ob_stadata中的列</td>
</tr>
<tr>
<td style="text-align: left;"> **kwargs</td>
<td style="text-align: left;">通过该参数接受和数据列名称相关的参数，有两种具体情形，<br>情形1：如果参数名称正好等于数据列名称，参数的值为一个列表,当数据列的取值属于列表时，结果会保留；<br>情形2：如果参数名称=数据列名称+"_range时"，参数值必须设置为长度为2的列表，代表取值范围，当数据列的取值在参数设定的取值范围内会被保留</td>
</tr>
<tr>
<td style="text-align: left;"> <strong><font face="黑体" color=blue size=3>return</strong></font></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a></td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python"># 选取ob列取值 属于列表[5,15,25,55,60,75,85,90,95]的样本
# ob 是数据列名称，通过**kwargs 它构成一个参数。是文档中情形1
sta = meb.sele_by_para(sta_all,ob = [5,15,25,55,60,75,85,90,95])
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59</code></pre>
<pre><code class="language-python">#选取ec列取值为41 - 69 的样本
# ec_range 是数据列名称 和&quot;_range&quot; 构成，通过**kwargs 它构成一个参数。是文档中情形2
# 此时会返回ec列取值在41到69的数据行（两头都是闭区间）
sta = meb.sele_by_para(sta_all,ec_range = [41,69])
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,member = [&quot;grapes&quot;]) #提取单个成员的数据
print(sta) </code></pre>
<pre><code> level time dtime id lon lat grapes
0 1000 2019-12-31 08:00:00 0 54511 100 30 49
1 1000 2019-12-31 08:00:00 0 54522 110 40 51
2 850 2019-12-31 08:00:00 0 54511 100 30 95
3 850 2019-12-31 08:00:00 0 54522 110 40 37
4 1000 2020-01-01 20:00:00 0 54511 100 30 91
5 1000 2020-01-01 20:00:00 0 54522 110 40 8
6 850 2020-01-01 20:00:00 0 54511 100 30 40
7 850 2020-01-01 20:00:00 0 54522 110 40 83
8 1000 2019-12-31 08:00:00 24 54511 100 30 69
9 1000 2019-12-31 08:00:00 24 54522 110 40 72
10 850 2019-12-31 08:00:00 24 54511 100 30 4
11 850 2019-12-31 08:00:00 24 54522 110 40 38
12 1000 2020-01-01 20:00:00 24 54511 100 30 0
13 1000 2020-01-01 20:00:00 24 54522 110 40 27
14 850 2020-01-01 20:00:00 24 54511 100 30 87
15 850 2020-01-01 20:00:00 24 54522 110 40 28</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,member = [&quot;ob&quot;,&quot;ec&quot;]) #提取多个成员的数据
print(sta) </code></pre>
<pre><code> level time dtime id lon lat ob ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 60
2 850 2019-12-31 08:00:00 0 54511 100 30 41 81
3 850 2019-12-31 08:00:00 0 54522 110 40 92 80
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 38
6 850 2020-01-01 20:00:00 0 54511 100 30 78 90
7 850 2020-01-01 20:00:00 0 54522 110 40 57 60
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,level = 1000) #提取指定层数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,time = &quot;2019123108&quot;) #提取指定起报时刻数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,day = &quot;20191231&quot;) #提取指定起报日期
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,year = 2020) #提取指定起报年份数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,ob_year = 2020) #提取起报时刻+预报时效对应的观测时刻为指定年份的数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,dtime = 24) #提取指定时效数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,dtime_range = [0,24]) #提取指定时效范围的数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,dday = 1) #提取时效&gt;=1天 且 &lt; 2天的数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,dhour = 0) #时效 % 1天的余数为0 的数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,id = 54511) #提取指定站点数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,drop_IV=True) #删除包含缺省值的数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,value=[20,80]) #删除包含缺省值的数据
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">grid1 = meb.grid([100,105,1],[30,35,1],level_list=[925,700]) #grid1中有三个纬度size&gt;1
sta = meb.sele_by_para(sta_all,grid = grid1) #选择指定网格范围内的数据，
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,gxy = grid1) #仅采用grid1中水平方向的范围进行过滤
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,gxyz = grid1) #采用三维空间方向进行过滤
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38</code></pre>
<pre><code class="language-python">loc =pd.DataFrame({&quot;time&quot;:[datetime.datetime(2019,12,31,8),datetime.datetime(2020,1,1,20)],&quot;id&quot;:[54511,54522]})
print(loc) #试验不同日期选择不同站点的方式</code></pre>
<pre><code> time id
0 2019-12-31 08:00:00 54511
1 2020-01-01 20:00:00 54522</code></pre>
<pre><code class="language-python">sta_loc = meb.sta_data(loc)
print(sta_loc) #要选择的站点序列，它在19年12月31日08时提取54511站，在1月1日20时提取54522站，level和dtime维度不做区分</code></pre>
<pre><code> level time dtime id lon lat data0
0 NaN 2019-12-31 08:00:00 NaN 54511 NaN NaN 0
1 NaN 2020-01-01 20:00:00 NaN 54522 NaN NaN 0</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,stadata = sta_loc) # 19年12月31日08时和54511和1月1日20时54522站的所有层次和时效数据被提取
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81
2 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
3 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60
4 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
5 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
6 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
7 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<p>如果数据选择的依据完全不基于level，time，dtime，id，lon，lat等时空坐标，比如以站点高度，下垫面类型作为选择依据时上述方法都无法涵盖，更有甚者如果我们选择涡度散度水汽条件这些条件来作为选择的依据，则这个判据是随所有的时空坐标的是变化的，此时就要用到lastR或lastL参数，方法如下。</p>
<pre><code class="language-python">sta = meb.drop_by_para(sta_all,stadata = sta_loc) # 19年12月31日08时和54511和1月1日20时54522站的所有层次和时效数据被提取
print(sta)</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46</code></pre>
<pre><code class="language-python">sta_all[&quot;div&quot;] = (np.random.randn(16) * 100).astype(np.int16)
sta_all.iloc[0,-1] = 0
sta_all.iloc[1,-1] = 100
print(sta_all) # 首先我们需要在数据的最后一列增加一列，这一列是你选择的依据，它可以是散点，涡度，下垫面类型等等</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec div
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57 0
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60 100
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81 -166
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80 12
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21 74
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38 54
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90 177
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60 39
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 69 32 -101
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 72 17 -75
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999 -124
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999 125
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 0 999999 -26
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 27 59 117
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38 -107
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46 -100</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,last=[0,100])
print(sta) #选择最后一列取值在为0或100的数据，并删除最后一列，如果div代表下垫面类型，采用这种方式可以选择指定下垫面类型的数据</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,last_range=[0,100])
print(sta) #选择最后一列取值在0到100范围的数据，并删除最后一列，如果div代表散度，则该方法相当于选择了所有散度在该范围的数据</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60</code></pre>
<pre><code class="language-python">sta = meb.sele_by_para(sta_all,level = 1000,dtime=24,member = [&quot;ob&quot;,&quot;ec&quot;])
print(sta) #以上所有参数也可组合使用</code></pre>
<pre><code> level time dtime id lon lat ob ec
8 1000 2019-12-31 08:00:00 24 54511 100 30 29 32
9 1000 2019-12-31 08:00:00 24 54522 110 40 68 17
12 1000 2020-01-01 20:00:00 24 54511 100 30 60 999999
13 1000 2020-01-01 20:00:00 24 54522 110 40 15 59</code></pre>
<h1>通过参数剔除部分数据</h1>
<p><strong>drop_by_para(data,level = None,time = None,time_range = None,year = None,month = None,day = None,dayofyear = None,hour = None,minute = None,ob_time=None, ob_time_range=None, ob_year=None, ob_month=None, ob_day=None, ob_dayofyear=None, ob_hour=None,ob_minute = None,dtime = None,dtime_range = None,dday = None, dhour = None,lon = None,lat = None,id = None,grid = None,gxy = None,gxyz = None,stadata = None,value = None,drop_IV = False,last = None,last_range = None,province_name = None,drop_last = True,ob_stadata = None,</strong>kwargs)** </p>
<p>从网格和站点数据中提取部分数据，除了没有member参数之外，其参数和sele_by_para用法完全一样，但使用效果是返回剔除所选部分的数据后的结果。<br />
<strong>调用示例</strong></p>
<pre><code class="language-python">sta = meb.drop_by_para(sta_all,level = 1000,dtime = 24)
print(sta) #剔除的部分是了level =1000 且 dtime =24的部分。</code></pre>
<pre><code> level time dtime id lon lat ob grapes ec div
0 1000 2019-12-31 08:00:00 0 54511 100 30 60 49 57 0
1 1000 2019-12-31 08:00:00 0 54522 110 40 39 51 60 100
2 850 2019-12-31 08:00:00 0 54511 100 30 41 95 81 -166
3 850 2019-12-31 08:00:00 0 54522 110 40 92 37 80 12
4 1000 2020-01-01 20:00:00 0 54511 100 30 13 91 21 74
5 1000 2020-01-01 20:00:00 0 54522 110 40 36 8 38 54
6 850 2020-01-01 20:00:00 0 54511 100 30 78 40 90 177
7 850 2020-01-01 20:00:00 0 54522 110 40 57 83 60 39
10 850 2019-12-31 08:00:00 24 54511 100 30 71 4 999999 -124
11 850 2019-12-31 08:00:00 24 54522 110 40 8 38 999999 125
14 850 2020-01-01 20:00:00 24 54511 100 30 64 87 38 -107
15 850 2020-01-01 20:00:00 24 54522 110 40 52 28 46 -100</code></pre>
<h1>从数据集中提取观测数据</h1>
<p><strong>get_ob_from_combined_data(sta_all,ob_column=1):</strong><br />
从已经匹配好的观测预报数据集中提取观测数据，并将时效重置为0</p>
<table>
<thead>
<tr>
<th style="text-align: left;"><strong>参数</strong></th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"> <strong>sta_all</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a></td>
</tr>
<tr>
<td style="text-align: left;"> ob_column</td>
<td style="text-align: left;">观测数据占据的列数，对于一般的物理量ob_column = 1,对于风、台风路径等矢量数据ob_column =2</td>
</tr>
<tr>
<td style="text-align: left;"> <strong><font face="黑体" color=blue size=3>return</strong></font></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a></td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">sta_all = pd.read_hdf(r&quot;H:\test_data\input\mpd\Example_data\sta_all.h5&quot;)
sta_all = meb.sele_by_para(sta_all,id = 54398,time_range = [&quot;2019070108&quot;,&quot;2019070208&quot;],dtime_range = [0,24],
member = [&quot;OBS&quot;,&quot;ECMWF&quot;])
print(sta_all)</code></pre>
<pre><code> level time dtime id lon lat OBS ECMWF
0 0 2019-07-01 08:00:00 0 54398 116.6 40.1 25.8 24.724
744 0 2019-07-01 08:00:00 6 54398 116.6 40.1 32.6 32.672
1470 0 2019-07-01 08:00:00 12 54398 116.6 40.1 31.4 27.944
2214 0 2019-07-01 08:00:00 18 54398 116.6 40.1 26.8 23.420
2577 0 2019-07-01 08:00:00 24 54398 116.6 40.1 24.4 23.008
6 0 2019-07-01 20:00:00 0 54398 116.6 40.1 31.4 27.820
750 0 2019-07-01 20:00:00 6 54398 116.6 40.1 26.8 22.852
1476 0 2019-07-01 20:00:00 12 54398 116.6 40.1 24.4 24.448
2220 0 2019-07-01 20:00:00 18 54398 116.6 40.1 32.1 32.208
2583 0 2019-07-01 20:00:00 24 54398 116.6 40.1 25.8 27.976
12 0 2019-07-02 08:00:00 0 54398 116.6 40.1 24.4 25.132
756 0 2019-07-02 08:00:00 6 54398 116.6 40.1 32.1 33.268
1482 0 2019-07-02 08:00:00 12 54398 116.6 40.1 25.8 27.868
2226 0 2019-07-02 08:00:00 18 54398 116.6 40.1 21.2 22.272
2589 0 2019-07-02 08:00:00 24 54398 116.6 40.1 24.0 26.572</code></pre>
<pre><code class="language-python">sta_ob = meb.get_ob_from_combined_data(sta_all)
print(sta_ob)</code></pre>
<pre><code> level time dtime id lon lat OBS
0 0 2019-07-01 08:00:00 0 54398 116.6 40.1 25.8
744 0 2019-07-01 14:00:00 0 54398 116.6 40.1 32.6
1470 0 2019-07-01 20:00:00 0 54398 116.6 40.1 31.4
2214 0 2019-07-02 02:00:00 0 54398 116.6 40.1 26.8
2577 0 2019-07-02 08:00:00 0 54398 116.6 40.1 24.4
2220 0 2019-07-02 14:00:00 0 54398 116.6 40.1 32.1
2583 0 2019-07-02 20:00:00 0 54398 116.6 40.1 25.8
2226 0 2019-07-03 02:00:00 0 54398 116.6 40.1 21.2
2589 0 2019-07-03 08:00:00 0 54398 116.6 40.1 24.0</code></pre>
<h1>选取部分成员</h1>
<p><strong>in_member_list(data,member_list,name_or_index = "name"):</strong><br />
从站点数据或网格数据中选取部分成员的数据</p>
<table>
<thead>
<tr>
<th style="text-align: left;"><strong>参数</strong></th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"> <strong>data</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
<tr>
<td style="text-align: left;"> <strong>member_list</strong></td>
<td style="text-align: left;">成员的名称或索引，同时提取多个时采用列表形式</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>name_or_index</strong></td>
<td style="text-align: left;">该参数是"name"时 member_list 中元素是成员的名称，如果该参数是"index"则，member_list中元素是索引号</td>
</tr>
<tr>
<td style="text-align: left;"> <strong><font face="黑体" color=blue size=3>return</strong></font></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">grd = meb.read_griddata_from_nc(r&quot;H:\test_data\input\meb\grd_sele_test.nc&quot;)
print(grd)</code></pre>
<pre><code>&lt;xarray.DataArray 'data0' (member: 3, level: 1, time: 10, dtime: 4, lat: 81, lon: 101)&gt;
array([[[[[[ 2.59400e+00, 2.88400e+00, 1.60200e+00, ...,
7.60000e-02, 3.21000e-01, 4.58000e-01],
[ 4.63900e+00, 5.85900e+00, 9.62000e-01, ...,
1.60000e-02, 2.59000e-01, 8.24000e-01],
[ 6.62200e+00, 3.31100e+00, 1.81500e+00, ...,
0.00000e+00, 4.50000e-02, 4.12000e-01],
...,
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
9.10900e+00, 1.01930e+01, 1.11390e+01],
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
1.12450e+01, 1.05900e+01, 8.63600e+00],
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
7.20200e+00, 1.03300e+01, 9.93300e+00]],
[[ 2.36050e+01, 2.25220e+01, 1.80360e+01, ...,
6.10000e-02, 3.00000e-02, 1.50000e-02],
[ 2.42310e+01, 2.88090e+01, 2.12860e+01, ...,
4.60000e-02, 7.60000e-02, 1.99000e-01],
[ 1.75020e+01, 2.56500e+01, 2.76190e+01, ...,
7.60000e-02, 6.10000e-02, 1.38000e-01],
...
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00],
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00],
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00]],
[[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00],
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00],
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00],
...,
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00],
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00],
[ 0.00000e+00, 0.00000e+00, 0.00000e+00, ...,
0.00000e+00, 0.00000e+00, 0.00000e+00]]]]]])
Coordinates:
* member (member) object 'ECMWF' '网格预报指导报' '国省融合预报'
* level (level) float64 0.0
* time (time) datetime64[ns] 2021-07-15T20:00:00 ... 2021-07-20T08:00:00
* dtime (dtime) int32 24 48 72 96
* lat (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 37.6 37.7 37.8 37.9 38.0
* lon (lon) float64 108.0 108.1 108.2 108.3 ... 117.7 117.8 117.9 118.0</code></pre>
<pre><code class="language-python">grd1 = meb.in_member_list(grd,member_list=[&quot;ECMWF&quot;,&quot;国省融合预报&quot;])
print(grd1)</code></pre>
<pre><code>&lt;xarray.DataArray 'data0' (member: 2, level: 1, time: 10, dtime: 4, lat: 81, lon: 101)&gt;
array([[[[[[ 2.5940e+00, 2.8840e+00, 1.6020e+00, ..., 7.6000e-02,
3.2100e-01, 4.5800e-01],
[ 4.6390e+00, 5.8590e+00, 9.6200e-01, ..., 1.6000e-02,
2.5900e-01, 8.2400e-01],
[ 6.6220e+00, 3.3110e+00, 1.8150e+00, ..., 0.0000e+00,
4.5000e-02, 4.1200e-01],
...,
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 9.1090e+00,
1.0193e+01, 1.1139e+01],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.1245e+01,
1.0590e+01, 8.6360e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 7.2020e+00,
1.0330e+01, 9.9330e+00]],
[[ 2.3605e+01, 2.2522e+01, 1.8036e+01, ..., 6.1000e-02,
3.0000e-02, 1.5000e-02],
[ 2.4231e+01, 2.8809e+01, 2.1286e+01, ..., 4.6000e-02,
7.6000e-02, 1.9900e-01],
[ 1.7502e+01, 2.5650e+01, 2.7619e+01, ..., 7.6000e-02,
6.1000e-02, 1.3800e-01],
...
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00]],
[[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00]]]]]])
Coordinates:
* member (member) &lt;U6 'ECMWF' '国省融合预报'
* level (level) float64 0.0
* time (time) datetime64[ns] 2021-07-15T20:00:00 ... 2021-07-20T08:00:00
* dtime (dtime) int32 24 48 72 96
* lat (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 37.6 37.7 37.8 37.9 38.0
* lon (lon) float64 108.0 108.1 108.2 108.3 ... 117.7 117.8 117.9 118.0</code></pre>
<pre><code class="language-python"># 按序号选取成员
grd2 = meb.in_member_list(grd,member_list=[0,2],name_or_index = &quot;index&quot;)
print(grd2)</code></pre>
<pre><code>&lt;xarray.DataArray 'data0' (member: 2, level: 1, time: 10, dtime: 4, lat: 81, lon: 101)&gt;
array([[[[[[ 2.5940e+00, 2.8840e+00, 1.6020e+00, ..., 7.6000e-02,
3.2100e-01, 4.5800e-01],
[ 4.6390e+00, 5.8590e+00, 9.6200e-01, ..., 1.6000e-02,
2.5900e-01, 8.2400e-01],
[ 6.6220e+00, 3.3110e+00, 1.8150e+00, ..., 0.0000e+00,
4.5000e-02, 4.1200e-01],
...,
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 9.1090e+00,
1.0193e+01, 1.1139e+01],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.1245e+01,
1.0590e+01, 8.6360e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 7.2020e+00,
1.0330e+01, 9.9330e+00]],
[[ 2.3605e+01, 2.2522e+01, 1.8036e+01, ..., 6.1000e-02,
3.0000e-02, 1.5000e-02],
[ 2.4231e+01, 2.8809e+01, 2.1286e+01, ..., 4.6000e-02,
7.6000e-02, 1.9900e-01],
[ 1.7502e+01, 2.5650e+01, 2.7619e+01, ..., 7.6000e-02,
6.1000e-02, 1.3800e-01],
...
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00]],
[[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00]]]]]])
Coordinates:
* member (member) &lt;U6 'ECMWF' '国省融合预报'
* level (level) float64 0.0
* time (time) datetime64[ns] 2021-07-15T20:00:00 ... 2021-07-20T08:00:00
* dtime (dtime) int32 24 48 72 96
* lat (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 37.6 37.7 37.8 37.9 38.0
* lon (lon) float64 108.0 108.1 108.2 108.3 ... 117.7 117.8 117.9 118.0</code></pre>
<h1>选取部分时间数据</h1>
<p><strong>in_time_list(data,time_list):</strong><br />
从站点数据或网格数据中选取部分起报时间的数据</p>
<table>
<thead>
<tr>
<th style="text-align: left;"><strong>参数</strong></th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"> <strong>data</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
<tr>
<td style="text-align: left;"> <strong>time_list</strong></td>
<td style="text-align: left;">需要提取的数据的时间列表</td>
</tr>
<tr>
<td style="text-align: left;"> <strong><font face="黑体" color=blue size=3>return</strong></font></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">grd3 = meb.in_time_list(grd,time_list=[&quot;2021071520&quot;,&quot;2021071608&quot;])
print(grd3)</code></pre>
<pre><code>&lt;xarray.DataArray 'data0' (member: 3, level: 1, time: 2, dtime: 4, lat: 81, lon: 101)&gt;
array([[[[[[2.59400e+00, 2.88400e+00, 1.60200e+00, ..., 7.60000e-02,
3.21000e-01, 4.58000e-01],
[4.63900e+00, 5.85900e+00, 9.62000e-01, ..., 1.60000e-02,
2.59000e-01, 8.24000e-01],
[6.62200e+00, 3.31100e+00, 1.81500e+00, ..., 0.00000e+00,
4.50000e-02, 4.12000e-01],
...,
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 9.10900e+00,
1.01930e+01, 1.11390e+01],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.12450e+01,
1.05900e+01, 8.63600e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 7.20200e+00,
1.03300e+01, 9.93300e+00]],
[[2.36050e+01, 2.25220e+01, 1.80360e+01, ..., 6.10000e-02,
3.00000e-02, 1.50000e-02],
[2.42310e+01, 2.88090e+01, 2.12860e+01, ..., 4.60000e-02,
7.60000e-02, 1.99000e-01],
[1.75020e+01, 2.56500e+01, 2.76190e+01, ..., 7.60000e-02,
6.10000e-02, 1.38000e-01],
...
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00]],
[[0.00000e+00, 2.00000e-01, 2.80000e-01, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
...,
[2.22000e+00, 2.32000e+00, 2.32000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[2.11000e+00, 2.30000e+00, 2.40000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[1.81000e+00, 2.23000e+00, 2.44000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00]]]]]])
Coordinates:
* member (member) object 'ECMWF' '网格预报指导报' '国省融合预报'
* level (level) float64 0.0
* time (time) datetime64[ns] 2021-07-15T20:00:00 2021-07-16T08:00:00
* dtime (dtime) int32 24 48 72 96
* lat (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 37.6 37.7 37.8 37.9 38.0
* lon (lon) float64 108.0 108.1 108.2 108.3 ... 117.7 117.8 117.9 118.0</code></pre>
<h1>按时间范围选取数据</h1>
<p><strong>between_time_range(data,start_time,end_time):</strong><br />
从站点数据或网格数据中选取部分时间的数据</p>
<table>
<thead>
<tr>
<th style="text-align: left;"><strong>参数</strong></th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"> <strong>data</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
<tr>
<td style="text-align: left;"> <strong>start_time</strong></td>
<td style="text-align: left;">选取数据的起始时间</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>end_time</strong></td>
<td style="text-align: left;">选取数据的结束时间</td>
</tr>
<tr>
<td style="text-align: left;"> <strong><font face="黑体" color=blue size=3>return</strong></font></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">grd4 = meb.between_time_range(grd,start_time=&quot;2021071620&quot;,end_time=&quot;2021071720&quot;)
print(grd4)</code></pre>
<pre><code>&lt;xarray.DataArray 'data0' (member: 3, level: 1, time: 3, dtime: 4, lat: 81, lon: 101)&gt;
array([[[[[[7.7700e+00, 9.6020e+00, 6.9960e+00, ..., 2.6240e+00,
1.7390e+00, 1.6170e+00],
[6.6830e+00, 1.0422e+01, 8.7320e+00, ..., 1.9650e+00,
2.0300e+00, 1.9680e+00],
[6.7940e+00, 1.1661e+01, 1.2085e+01, ..., 2.1290e+00,
3.3990e+00, 7.0600e-01],
...,
[5.9500e-01, 8.3900e-01, 1.2660e+00, ..., 1.3690e+00,
1.0560e+00, 7.9400e-01],
[6.4100e-01, 4.8800e-01, 6.5600e-01, ..., 1.7510e+00,
9.2700e-01, 7.1700e-01],
[5.9500e-01, 7.3200e-01, 7.7800e-01, ..., 1.1980e+00,
1.0410e+00, 1.6250e+00]],
[[1.2177e+01, 1.5121e+01, 1.4999e+01, ..., 6.6680e+00,
1.3733e+01, 1.3992e+01],
[1.4023e+01, 1.5717e+01, 1.5701e+01, ..., 5.8900e+00,
1.0620e+01, 1.1246e+01],
[8.2700e+00, 6.6380e+00, 1.1505e+01, ..., 5.9200e+00,
9.8420e+00, 6.6840e+00],
...
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00]],
[[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00]]]]]])
Coordinates:
* member (member) object 'ECMWF' '网格预报指导报' '国省融合预报'
* level (level) float64 0.0
* time (time) datetime64[ns] 2021-07-16T20:00:00 ... 2021-07-17T20:00:00
* dtime (dtime) int32 24 48 72 96
* lat (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 37.6 37.7 37.8 37.9 38.0
* lon (lon) float64 108.0 108.1 108.2 108.3 ... 117.7 117.8 117.9 118.0</code></pre>
<h1>选取部分时效数据</h1>
<p><strong>in_dtime_list(data,dtime_list):</strong><br />
从站点数据或网格数据中选取部分时效的数据</p>
<table>
<thead>
<tr>
<th style="text-align: left;"><strong>参数</strong></th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"> <strong>data</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
<tr>
<td style="text-align: left;"> <strong>dtime_list</strong></td>
<td style="text-align: left;">需要提取的数据的时效列表</td>
</tr>
<tr>
<td style="text-align: left;"> <strong><font face="黑体" color=blue size=3>return</strong></font></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">grd5 = meb.in_dtime_list(grd,dtime_list=[24,96])
print(grd5)</code></pre>
<pre><code>&lt;xarray.DataArray 'data0' (member: 3, level: 1, time: 10, dtime: 2, lat: 81, lon: 101)&gt;
array([[[[[[2.59400e+00, 2.88400e+00, 1.60200e+00, ..., 7.60000e-02,
3.21000e-01, 4.58000e-01],
[4.63900e+00, 5.85900e+00, 9.62000e-01, ..., 1.60000e-02,
2.59000e-01, 8.24000e-01],
[6.62200e+00, 3.31100e+00, 1.81500e+00, ..., 0.00000e+00,
4.50000e-02, 4.12000e-01],
...,
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 9.10900e+00,
1.01930e+01, 1.11390e+01],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 1.12450e+01,
1.05900e+01, 8.63600e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 7.20200e+00,
1.03300e+01, 9.93300e+00]],
[[8.05600e+00, 6.24100e+00, 6.82000e+00, ..., 5.80000e-01,
2.44000e-01, 3.97000e-01],
[7.47700e+00, 8.25500e+00, 1.06960e+01, ..., 2.30400e+00,
2.05900e+00, 3.47900e+00],
[2.93000e+00, 1.39770e+01, 7.38500e+00, ..., 4.33400e+00,
6.05800e+00, 4.70000e+00],
...
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00]],
[[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
...,
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00],
[0.00000e+00, 0.00000e+00, 0.00000e+00, ..., 0.00000e+00,
0.00000e+00, 0.00000e+00]]]]]])
Coordinates:
* member (member) object 'ECMWF' '网格预报指导报' '国省融合预报'
* level (level) float64 0.0
* time (time) datetime64[ns] 2021-07-15T20:00:00 ... 2021-07-20T08:00:00
* dtime (dtime) int32 24 96
* lat (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 37.6 37.7 37.8 37.9 38.0
* lon (lon) float64 108.0 108.1 108.2 108.3 ... 117.7 117.8 117.9 118.0</code></pre>
<h1>按时效范围选取数据</h1>
<p><strong>between_dtime_range(data,start_dtime,end_dtime):</strong><br />
从站点数据或网格数据中选取部分时效的数据</p>
<table>
<thead>
<tr>
<th style="text-align: left;"><strong>参数</strong></th>
<th style="text-align: left;">说明</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"> <strong>data</strong></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
<tr>
<td style="text-align: left;"> <strong>start_dtime</strong></td>
<td style="text-align: left;">选取数据的起始时效</td>
</tr>
<tr>
<td style="text-align: left;"> <strong>end_dtime</strong></td>
<td style="text-align: left;">选取数据的结束时效</td>
</tr>
<tr>
<td style="text-align: left;"> <strong><font face="黑体" color=blue size=3>return</strong></font></td>
<td style="text-align: left;"><a href="https://www.showdoc.cc/nmc?page_id=3744334022014027">站点数据</a> 或<a href="https://www.showdoc.com.cn/meteva/3975600815874861">网格数据</a></td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">grd6 = meb.between_dtime_range(grd,start_dtime=24,end_dtime=72)
print(grd6)</code></pre>
<pre><code>&lt;xarray.DataArray 'data0' (member: 3, level: 1, time: 10, dtime: 3, lat: 81, lon: 101)&gt;
array([[[[[[ 2.5940e+00, 2.8840e+00, 1.6020e+00, ..., 7.6000e-02,
3.2100e-01, 4.5800e-01],
[ 4.6390e+00, 5.8590e+00, 9.6200e-01, ..., 1.6000e-02,
2.5900e-01, 8.2400e-01],
[ 6.6220e+00, 3.3110e+00, 1.8150e+00, ..., 0.0000e+00,
4.5000e-02, 4.1200e-01],
...,
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 9.1090e+00,
1.0193e+01, 1.1139e+01],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.1245e+01,
1.0590e+01, 8.6360e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 7.2020e+00,
1.0330e+01, 9.9330e+00]],
[[ 2.3605e+01, 2.2522e+01, 1.8036e+01, ..., 6.1000e-02,
3.0000e-02, 1.5000e-02],
[ 2.4231e+01, 2.8809e+01, 2.1286e+01, ..., 4.6000e-02,
7.6000e-02, 1.9900e-01],
[ 1.7502e+01, 2.5650e+01, 2.7619e+01, ..., 7.6000e-02,
6.1000e-02, 1.3800e-01],
...
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00]],
[[ 0.0000e+00, 2.0000e-01, 1.1800e+00, ..., 1.7000e+00,
2.1000e+00, 2.1000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.3000e+00,
2.1000e+00, 2.9000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.5000e+00,
2.1000e+00, 3.1000e+00],
...,
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[ 0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00]]]]]])
Coordinates:
* member (member) object 'ECMWF' '网格预报指导报' '国省融合预报'
* level (level) float64 0.0
* time (time) datetime64[ns] 2021-07-15T20:00:00 ... 2021-07-20T08:00:00
* dtime (dtime) int32 24 48 72
* lat (lat) float64 30.0 30.1 30.2 30.3 30.4 ... 37.6 37.7 37.8 37.9 38.0
* lon (lon) float64 108.0 108.1 108.2 108.3 ... 117.7 117.8 117.9 118.0</code></pre>
<pre><code class="language-python"></code></pre>