功率谱
<p>[TOC]</p>
<pre><code class="language-python">%matplotlib inline
%load_ext autoreload
%autoreload 2
import pandas as pd
import meteva.method as mem
import meteva.base as meb
import meteva.product as mpd
import meteva.perspact as mps # 透视分析模块
import datetime
import meteva
import numpy as np
import os
import copy</code></pre>
<h2>功率谱检验方法概述</h2>
<p>根据二维傅里叶变换方法获取实况和预报场（矩形网格场）的功率谱，绘制在图像中进行对比</p>
<p>在二维网格场中计算能谱通常可以通过傅里叶变换的方法来实现。以下是一般的步骤：</p>
<ol>
<li>离散傅里叶变换（Discrete Fourier Transform，DFT）：<br />
假设你的二维网格场为f(x,y)，其中x和y分别表示网格的两个维度。
对这个二维场进行离散傅里叶变换，得到F(u,v)，其中u和v是傅里叶空间的频率变量。
可以使用快速傅里叶变换（FFT）算法来高效地计算傅里叶变换。</li>
<li>
<p>计算能量谱：<br />
能量谱通常定义为傅里叶变换后的幅值的平方。即E(u,v) = |F(u,v)|^2。
这表示在频率(u,v)处的能量强度。</p>
</li>
<li>平均得到一维能谱曲线：<br />
首先计算二维能谱中每个点到中心的距离，<br />
然后对距离进行离散化，<br />
最后通过对不同距离区间内的能谱值进行加权平均得到一维能谱曲线。</li>
</ol>
<h1>单时刻功率谱计算</h1>
<p>spectrum_fft2_one_field(grd)
根据单个网格场计算功率谱数据</p>
<table>
<thead>
<tr>
<th style="text-align: left;">参数</th>
<th style="text-align: center;">说明</th>
<th style="text-align: left;">备注</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"><strong><font face="黑体" color=Blue size=5>grd</font></strong></td>
<td style="text-align: center;">网格数据</td>
<td style="text-align: left;"><a href="https://www.showdoc.com.cn/meteva?page_id=3975600815874861">网格数据格式</a></td>
</tr>
<tr>
<td style="text-align: left;"><font face="黑体" color=blue size=5>return</font></td>
<td style="text-align: center;">n一维umpy数组</td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">path =r&quot;\\10.40.23.69\u02\data\model\ecmwf\2024100108\gh\500\2024100108.000&quot;
grd = meb.read_griddata_from_micaps4(path)
sp = mem.spectrum_fft2_one_field(grd)
print(sp)</code></pre>
<pre><code>[1.53036919e+17 9.49452974e+12 9.06320650e+11 1.82987521e+11
1.43288589e+11 4.99675146e+10 3.58800451e+10 2.13815317e+10
1.52172652e+10 1.04741717e+10 7.52550938e+09 6.85066736e+09
4.28379176e+09 3.29577688e+09 3.04947706e+09 2.29628912e+09
2.09519762e+09 1.56164772e+09 1.30934585e+09 1.20547097e+09
1.04020638e+09 8.20827829e+08 7.82316579e+08 7.08476790e+08
5.93157807e+08 5.07387525e+08 4.40695976e+08 4.39271802e+08
4.00464136e+08 3.27656365e+08 3.11877214e+08 2.70843888e+08
2.61605387e+08 2.35190326e+08 2.05435308e+08 2.12538214e+08
1.81080009e+08 1.76025483e+08 1.48016142e+08 1.45381735e+08
1.46868211e+08 1.20413241e+08 1.19839390e+08 1.05885578e+08
1.04920854e+08 1.02832883e+08 9.08689942e+07 8.90953628e+07
7.97303430e+07 7.93205417e+07 6.93603815e+07 6.92128697e+07
6.96179458e+07 6.10487927e+07 6.29405736e+07 5.28303532e+07
5.54872768e+07 5.17396940e+07 5.00669732e+07 4.78015779e+07
4.38824344e+07 4.30031495e+07 4.14055936e+07 3.96868078e+07
4.00474920e+07 3.50543342e+07 3.69400082e+07 3.41757189e+07
3.13491710e+07 3.33801909e+07 3.03279840e+07 3.11625847e+07
2.71013031e+07 2.76182266e+07 2.71167170e+07 2.59457593e+07
2.53045279e+07 2.40389677e+07 2.46584904e+07 2.32724926e+07
2.18540528e+07 2.23787720e+07 2.05123697e+07 2.15447920e+07
2.00138870e+07 1.92680799e+07 1.91891993e+07 1.87104188e+07
1.93631422e+07 1.67536043e+07 1.80051484e+07 1.70631040e+07
1.67434358e+07 1.64584008e+07 1.56566461e+07 1.62432659e+07
1.55737197e+07 1.46535435e+07 1.51102064e+07 1.41289805e+07
1.48530955e+07 1.35301701e+07 1.38488248e+07 1.41811091e+07
1.30899188e+07 1.35819689e+07 1.24756366e+07 1.28814378e+07
1.29594547e+07 1.25216425e+07 1.23946330e+07 1.17970390e+07
1.20819403e+07 1.18506250e+07 1.13078604e+07 1.16538000e+07
1.16852713e+07 1.13510328e+07 1.10947618e+07 1.10160993e+07
1.10752604e+07 1.34233106e+06 1.31450233e+06 1.25019594e+06
1.41142543e+06 1.32263130e+06 1.45038836e+06 1.34331973e+06
1.35458527e+06 1.46940231e+06 1.37407416e+06 1.43857033e+06
1.36378636e+06 1.41373323e+06 1.52217082e+06 1.39078999e+06
1.46546199e+06 1.41594817e+06 1.45917223e+06 1.51919599e+06
1.40313917e+06 2.74126829e+04 2.07472131e+04 1.76896991e+04
1.58340550e+04 1.47585660e+04 1.42861474e+04 1.31137320e+04
1.31264348e+04 1.19277333e+04 1.22784000e+04 1.22486316e+04
1.17596190e+04 1.13847778e+04 1.07697067e+04 1.07060299e+04
1.04886250e+04 1.02056119e+04 1.04485714e+04 9.73122581e+03
1.00340741e+04 9.79362963e+03 9.08592000e+03 9.28018182e+03
9.29420408e+03 9.63726829e+03 9.02709091e+03 9.16205714e+03
8.90836364e+03 9.00294737e+03 9.52110345e+03 9.44640000e+03
8.79753846e+03 8.58260870e+03 8.61184000e+03 9.15368421e+03
8.97431579e+03 8.18000000e+03 8.33846154e+03 9.37733333e+03
8.56177778e+03 9.73142857e+03 7.99733333e+03 8.02933333e+03
9.71200000e+03]</code></pre>
<h1>任意时段功率谱计算</h1>
<p>spectrum_fft2(para,plot = None,save_path = None,title = None)
批量读取一段时间的时刻和预报数据，绘制成功率谱对比图</p>
<table>
<thead>
<tr>
<th style="text-align: left;">参数</th>
<th style="text-align: center;">说明</th>
<th style="text-align: left;">备注</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;"><strong><font face="黑体" color=Blue size=5>para</font></strong></td>
<td style="text-align: center;">待统计的观测和预报数据和检验参数描述</td>
<td style="text-align: left;">字典形式，内容见下面的示例</td>
</tr>
<tr>
<td style="text-align: left;"><font face="黑体" color=blue size=5>return</font></td>
<td style="text-align: center;">包含实况和各类预报的功率谱数据的字典</td>
<td style="text-align: left;">字典，关键词时obs和预报数据名称</td>
</tr>
</tbody>
</table>
<p><strong>调用示例</strong></p>
<pre><code class="language-python">para = {
&quot;grid&quot;: meteva.base.grid([70,140,0.25],[10,60,0.25]), # 检验区域
&quot;begin_time&quot;: datetime.datetime(2024, 9, 1, 8), # 时段开始时刻(基于起报时间)
&quot;end_time&quot;: datetime.datetime(2024, 9, 3, 20), # 时段结束时刻（基于起报时间）
&quot;time_step&quot;: 12, # 起报时间间隔
&quot;dtime&quot;: 120, # 预报时效
&quot;time_type&quot;: &quot;BT&quot;, # 最终检验结果呈现时，采用北京时还是世界时，UT代表世界时，BT代表北京时
&quot;ob_data&quot;: {
&quot;dir_ob&quot;: r&quot;\\10.40.23.69\u02\data\model\ecmwf\YYYYMMDDHH\gh\500\YYYYMMDDHH.TTT&quot;, # 实况场数据路径
&quot;hour&quot;: None,
&quot;read_method&quot;: meteva.base.io.read_griddata_from_micaps4, # 读取数据的函数
&quot;operation&quot;: None, # 预报数据读取后处理函数
&quot;operation_para&quot;: {}, # 预报数据读取后处理参数，用于对单位进行变换的操作
&quot;read_para&quot;: {}, # 读取数据的函数参数
&quot;time_type&quot;: &quot;BT&quot;, # 数据文件中的时间类型，UT代表世界时
},
&quot;fo_data&quot;: {
&quot;ECMWF&quot;: {
&quot;dir_fo&quot;: r&quot;\\10.40.23.69\u02\data\model\ecmwf\YYYYMMDDHH\gh\500\YYYYMMDDHH.TTT&quot;, # 数据路径
&quot;read_method&quot;: meteva.base.io.read_griddata_from_micaps4, # 读取数据的函数
&quot;read_para&quot;: {}, # 读取数据的函数参数
&quot;reasonable_value&quot;: [0, 1000], # 合理的预报值的取值范围，超出范围观测将被过滤掉
&quot;operation&quot;: None, # 预报数据读取后处理函数
&quot;operation_para&quot;: {}, # 预报数据读取后处理参数，用于对单位进行变换的操作
&quot;time_type&quot;: &quot;BT&quot;, # 预报数据时间类型是北京时，即08时起报
&quot;move_fo_time&quot;: 0 # 是否对预报的时效进行平移，12 表示将1月1日08时的36小时预报转换成1月1日20时的24小时预报后参与对比
},
&quot;CMA_GFS&quot;: {
&quot;dir_fo&quot;: r&quot;\\10.40.23.69\u02\data\model\cma_gfs\YYYYMMDDHH\gh\500\YYYYMMDDHH.TTT&quot;, # 数据路径
&quot;read_method&quot;: meteva.base.io.read_griddata_from_micaps4, # 读取数据的函数
&quot;read_para&quot;: {}, # 读取数据的函数参数
&quot;reasonable_value&quot;: [0, 1000], # 合理的预报值的取值范围，超出范围观测将被过滤掉
&quot;operation&quot;: None, # 预报数据读取后处理函数，用于对单位进行变换的操作
&quot;operation_para&quot;: {}, # #预报数据读取后处理参数
&quot;time_type&quot;: &quot;BT&quot;, # 预报数据时间类型是北京时，即08时起报
&quot;move_fo_time&quot;: 0 # 是否对预报的时效进行平移，12 表示将1月1日08时的36小时预报转换成1月1日20时的24小时预报后参与对比
},
},
&quot;output_dir&quot;: None # 观测站点合并数据的输出路径，设置为None时不输出收集数据的中间结果
}
</code></pre>
<pre><code class="language-python">sp_dict = mem.spectrum_fft2(para,save_path=r&quot;H:\test_data\output\method\space\spectrum\test.png&quot;,show = True)</code></pre>
<pre><code>success read from \\10.40.23.69\u02\data\model\ecmwf\2024090608\gh\500\2024090608.000
success read from \\10.40.23.69\u02\data\model\ecmwf\2024090108\gh\500\2024090108.120
success read from \\10.40.23.69\u02\data\model\cma_gfs\2024090108\gh\500\2024090108.120
success read from \\10.40.23.69\u02\data\model\ecmwf\2024091108\gh\500\2024091108.000
success read from \\10.40.23.69\u02\data\model\ecmwf\2024090608\gh\500\2024090608.120
success read from \\10.40.23.69\u02\data\model\cma_gfs\2024090608\gh\500\2024090608.120
png result has been output to H:\test_data\output\method\space\spectrum\test.png</code></pre>
<p><img src="https://www.showdoc.com.cn/server/api/attachment/visitFile?sign=ded09908afa73376dbb9cfcc0cfb9ea3&amp;file=file.png" alt="" /></p>
<pre><code class="language-python">print(sp_dict)</code></pre>
<pre><code>{'obs': array([1.06683716e+17, 5.30016323e+12, 7.50091708e+11, 2.24815803e+11,
1.01641588e+11, 3.65625835e+10, 2.47595635e+10, 1.35127059e+10,
8.78412763e+09, 7.47247868e+09, 4.88370282e+09, 4.14043724e+09,
2.81491555e+09, 2.09667797e+09, 1.91648108e+09, 1.53160846e+09,
1.32586176e+09, 9.58828882e+08, 8.26627644e+08, 7.61999520e+08,
6.65528439e+08, 5.16332016e+08, 4.98161673e+08, 4.45934248e+08,
3.81155440e+08, 3.21156890e+08, 2.85403466e+08, 2.82950386e+08,
2.54463118e+08, 2.10872659e+08, 2.01887500e+08, 1.71941511e+08,
1.67968889e+08, 1.51499039e+08, 1.30509034e+08, 1.36866820e+08,
1.16437933e+08, 1.13264897e+08, 9.59324431e+07, 9.42507954e+07,
9.54766699e+07, 7.95364472e+07, 7.86683154e+07, 6.86847501e+07,
6.92275776e+07, 6.72987728e+07, 5.95649981e+07, 5.84974947e+07,
5.26928421e+07, 5.23327378e+07, 4.59305322e+07, 4.61525828e+07,
4.65063256e+07, 4.09811697e+07, 4.22654614e+07, 3.54335775e+07,
3.77097573e+07, 3.50420302e+07, 3.37774106e+07, 3.25072853e+07,
2.98648889e+07, 2.94304839e+07, 2.85148358e+07, 2.73754701e+07,
2.76754447e+07, 2.43752974e+07, 2.57501457e+07, 2.38630346e+07,
2.20399346e+07, 2.34722780e+07, 2.14062875e+07, 2.19962529e+07,
1.93685275e+07, 1.96890534e+07, 1.93961198e+07, 1.86437192e+07,
1.82352575e+07, 1.74355890e+07, 1.79246635e+07, 1.70151415e+07,
1.60742372e+07, 1.65037794e+07, 1.52239376e+07, 1.60428823e+07,
1.49634976e+07, 1.45151601e+07, 1.44977669e+07, 1.41750964e+07,
1.48147487e+07, 1.28645714e+07, 1.39060039e+07, 1.32434456e+07,
1.30807958e+07, 1.29317857e+07, 1.23697691e+07, 1.29218994e+07,
1.24389998e+07, 1.17802490e+07, 1.22199949e+07, 1.15175705e+07,
1.21895838e+07, 9.50491036e+05, 1.00090272e+06, 1.05267559e+06,
9.77739240e+05, 1.06219373e+06, 9.68922946e+05, 1.02977329e+06,
1.04465947e+06, 1.02594087e+06, 1.02790916e+06, 9.96848591e+05,
1.03398247e+06, 1.01109635e+06, 9.94178295e+05, 1.02309484e+06,
1.03128964e+06, 1.02074510e+06, 9.92371984e+05, 1.00929951e+06,
1.01291170e+06, 1.04035860e+06, 1.01985827e+06, 9.35588964e+05,
1.05285546e+06, 9.74419983e+05, 1.05428583e+06, 9.96916910e+05,
9.70660351e+05, 1.04521633e+06, 9.74208034e+05, 1.02827587e+06,
9.75301413e+05, 9.89583134e+05, 1.07886631e+06, 9.55599264e+05,
1.03049604e+06, 9.82945212e+05, 1.02680011e+06, 1.04572428e+06,
9.71464481e+05, 2.55096774e+04, 1.89510000e+04, 1.61649756e+04,
1.45498701e+04, 1.38985067e+04, 1.29589851e+04, 1.26040645e+04,
1.20654769e+04, 1.17078621e+04, 1.11774545e+04, 1.10763137e+04,
1.09335510e+04, 1.05648511e+04, 1.02555238e+04, 1.05176000e+04,
1.04847368e+04, 1.00737778e+04, 9.69825000e+03, 9.81020690e+03,
9.49793103e+03, 9.48492308e+03, 9.89690909e+03, 9.41371429e+03,
9.69294118e+03, 9.19458824e+03, 8.97028571e+03, 8.45920000e+03,
9.09480000e+03, 9.52800000e+03, 8.97680000e+03, 8.58400000e+03,
8.80400000e+03]), 'ECMWF': array([1.06827978e+17, 4.88175750e+12, 6.57153783e+11, 2.05865476e+11,
9.78045265e+10, 3.33746378e+10, 2.37441067e+10, 1.21012661e+10,
7.99944957e+09, 6.54362014e+09, 4.34061600e+09, 3.83835511e+09,
2.65080104e+09, 1.93541888e+09, 1.75821974e+09, 1.41231901e+09,
1.23168226e+09, 8.92472662e+08, 7.58097862e+08, 6.89630162e+08,
6.14566688e+08, 4.84872065e+08, 4.62692072e+08, 4.10549776e+08,
3.50351924e+08, 2.96204942e+08, 2.62653292e+08, 2.59677410e+08,
2.36663275e+08, 1.93682739e+08, 1.86197831e+08, 1.60033794e+08,
1.56314385e+08, 1.39831002e+08, 1.20502777e+08, 1.25510727e+08,
1.07088215e+08, 1.04893063e+08, 8.87916457e+07, 8.65776574e+07,
8.77379728e+07, 7.29525196e+07, 7.24012386e+07, 6.37410093e+07,
6.39060310e+07, 6.20823507e+07, 5.51333011e+07, 5.39173118e+07,
4.88733044e+07, 4.82364319e+07, 4.21614007e+07, 4.24313227e+07,
4.28116932e+07, 3.76871247e+07, 3.89750142e+07, 3.26296046e+07,
3.46377560e+07, 3.23229770e+07, 3.11590403e+07, 3.00952141e+07,
2.75843670e+07, 2.71111155e+07, 2.62639693e+07, 2.52064538e+07,
2.54022485e+07, 2.24699891e+07, 2.36983789e+07, 2.19747473e+07,
2.02376572e+07, 2.16044218e+07, 1.97277393e+07, 2.02695204e+07,
1.78301245e+07, 1.81186141e+07, 1.78739582e+07, 1.71941812e+07,
1.68401504e+07, 1.60438404e+07, 1.65321956e+07, 1.56689013e+07,
1.47936472e+07, 1.52187958e+07, 1.40410030e+07, 1.47913867e+07,
1.37660087e+07, 1.33634005e+07, 1.33435024e+07, 1.30603420e+07,
1.36441016e+07, 1.18464797e+07, 1.27939797e+07, 1.21913119e+07,
1.20344826e+07, 1.19097472e+07, 1.13810265e+07, 1.18947600e+07,
1.14579185e+07, 1.08478590e+07, 1.12455692e+07, 1.06002015e+07,
1.12111854e+07, 8.84869465e+05, 9.28211644e+05, 9.74694875e+05,
9.11529147e+05, 9.86204894e+05, 8.99464635e+05, 9.56651798e+05,
9.68995378e+05, 9.52423874e+05, 9.51430502e+05, 9.23799409e+05,
9.58284865e+05, 9.37583968e+05, 9.26905753e+05, 9.54300747e+05,
9.55796285e+05, 9.47303431e+05, 9.20670073e+05, 9.38593105e+05,
9.38347105e+05, 9.66349310e+05, 9.49203124e+05, 8.67650087e+05,
9.79839928e+05, 9.03438373e+05, 9.77688615e+05, 9.27322232e+05,
9.00467916e+05, 9.71989394e+05, 9.02659139e+05, 9.54278960e+05,
9.05393328e+05, 9.20110234e+05, 1.00411363e+06, 8.86743130e+05,
9.55185412e+05, 9.13943481e+05, 9.53065955e+05, 9.70897143e+05,
9.04158833e+05, 2.59584086e+04, 1.94643636e+04, 1.64173171e+04,
1.51811429e+04, 1.38193600e+04, 1.30475821e+04, 1.28930323e+04,
1.23027692e+04, 1.18326207e+04, 1.15731636e+04, 1.10969412e+04,
1.08187755e+04, 1.09340426e+04, 1.04895238e+04, 1.04528000e+04,
9.94442105e+03, 9.94344444e+03, 9.90625000e+03, 9.94524138e+03,
9.70124138e+03, 9.70369231e+03, 9.62581818e+03, 9.54590476e+03,
9.68305882e+03, 9.14023529e+03, 9.03828571e+03, 9.16120000e+03,
9.22880000e+03, 8.88514286e+03, 8.30240000e+03, 9.93200000e+03,
9.09200000e+03]), 'CMA_GFS': array([1.06606377e+17, 5.17975346e+12, 7.19653300e+11, 1.83128429e+11,
7.96606491e+10, 3.03657537e+10, 2.38327294e+10, 1.24416959e+10,
8.48480138e+09, 7.07922425e+09, 4.41681234e+09, 3.85975270e+09,
2.69566354e+09, 1.96747751e+09, 1.74586209e+09, 1.38690524e+09,
1.24099026e+09, 9.04147785e+08, 7.79809916e+08, 7.00925958e+08,
6.12910769e+08, 4.78607838e+08, 4.62834011e+08, 4.17048792e+08,
3.52786945e+08, 2.92779683e+08, 2.59487755e+08, 2.60757132e+08,
2.36921298e+08, 1.92255937e+08, 1.85939175e+08, 1.57594519e+08,
1.54093902e+08, 1.40302684e+08, 1.21355615e+08, 1.25781237e+08,
1.07428602e+08, 1.04464466e+08, 8.92898029e+07, 8.76995433e+07,
8.81555435e+07, 7.31710752e+07, 7.21742085e+07, 6.34602262e+07,
6.38230376e+07, 6.22184462e+07, 5.52084115e+07, 5.38638377e+07,
4.87222184e+07, 4.82940593e+07, 4.23976089e+07, 4.25589060e+07,
4.30877812e+07, 3.78352947e+07, 3.90788988e+07, 3.27478023e+07,
3.46605223e+07, 3.23778631e+07, 3.12495454e+07, 3.01950031e+07,
2.76567578e+07, 2.73144672e+07, 2.63224733e+07, 2.52833158e+07,
2.56000217e+07, 2.24422055e+07, 2.38118459e+07, 2.20112148e+07,
2.02681102e+07, 2.16286097e+07, 1.97725525e+07, 2.03400174e+07,
1.79057058e+07, 1.82140247e+07, 1.79150609e+07, 1.72372924e+07,
1.68561655e+07, 1.61015269e+07, 1.65693333e+07, 1.57142939e+07,
1.48246158e+07, 1.52264658e+07, 1.40677796e+07, 1.48301109e+07,
1.38056405e+07, 1.33835288e+07, 1.33862565e+07, 1.30910126e+07,
1.36665263e+07, 1.18848406e+07, 1.28368915e+07, 1.22366154e+07,
1.20823899e+07, 1.19326450e+07, 1.14182374e+07, 1.19323679e+07,
1.14828048e+07, 1.08768196e+07, 1.12794287e+07, 1.06414596e+07,
1.12405573e+07, 8.95852752e+05, 9.52122961e+05, 1.00461314e+06,
9.37318222e+05, 1.01101424e+06, 9.20824693e+05, 9.91345214e+05,
1.00217661e+06, 9.87662672e+05, 9.85824542e+05, 9.52882957e+05,
9.93490776e+05, 9.69412659e+05, 9.59170422e+05, 9.87914523e+05,
9.90353908e+05, 9.85633339e+05, 9.52921861e+05, 9.73560234e+05,
9.78127629e+05, 1.00557135e+06, 9.81766112e+05, 8.99185992e+05,
1.01555953e+06, 9.35206739e+05, 1.01709528e+06, 9.63957803e+05,
9.37907481e+05, 1.01024116e+06, 9.36890093e+05, 9.95246457e+05,
9.42143455e+05, 9.53390580e+05, 1.04167795e+06, 9.18340318e+05,
9.93168109e+05, 9.50666632e+05, 9.89313231e+05, 1.00901910e+06,
9.38003262e+05, 1.13110538e+04, 8.94236364e+03, 7.85170732e+03,
7.20041558e+03, 6.57829333e+03, 6.54913433e+03, 5.95780645e+03,
5.83876923e+03, 5.32220690e+03, 5.26523636e+03, 5.46015686e+03,
5.44097959e+03, 4.93727660e+03, 4.83171429e+03, 5.13950000e+03,
4.56978947e+03, 4.59277778e+03, 4.50875000e+03, 4.36441379e+03,
4.74041379e+03, 4.62292308e+03, 4.34127273e+03, 4.54019048e+03,
4.03129412e+03, 4.34305882e+03, 4.83485714e+03, 4.58160000e+03,
4.47600000e+03, 4.04285714e+03, 4.44640000e+03, 4.54800000e+03,
6.12400000e+03])}</code></pre>
<pre><code class="language-python"></code></pre>
<pre><code class="language-python"></code></pre>
<pre><code class="language-python"></code></pre>
<pre><code class="language-python"></code></pre>
<pre><code class="language-python"></code></pre>
<pre><code class="language-python"></code></pre>
<pre><code class="language-python"></code></pre>