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<time datetime="2016-02-22T20:44:52+08:00" itemprop="datePublished">2016/2/22</time>
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<div class="tags">posted in
<a class='category' href='%E7%90%83%E5%87%BD%E6%95%B0.html'>球函数</a>
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<h1 class="title" itemprop="name"><a href="14561450925713.html" itemprop="url">
球谐系数可视化</a></h1>
<div class="entry-content" itemprop="articleBody">
<p>可视化的三种方式</p>
<h2 id="toc_0">1. 单个文件，所有阶次的二维图</h2>
<ul>
<li><code>plot_SH(SH1)</code>
<img src="media/14561450925713/14561454957218.jpg" alt=""/>
<strong>图</strong>：2003年1月的结果（上图是取对数结果，下图是正负号）
<img src="media/14561450925713/14561455499740.jpg" alt=""/>
<strong>图</strong>：2003年6月结果</li>
</ul>
<p>结论：<br/>
1. 1月结果明显振幅大于6月（其实这是噪声）<br/>
2. 固定一个m看，明显有正负和振幅的震荡（即去相关滤波的出发点）</p>
<h2 id="toc_1">2. 单个文件，按阶或者次的序列</h2>
<ul>
<li><p><code>plot_SH_by_nm(SH1,n,m)</code></p></li>
</ul>
<ol>
<li>当m省略（不指定范围，默认从0到n），或者m为一列向量时（指定范围），以m为变量按照固定的n画图</li>
<li>当n为NaN（不指定范围，默认从m到Nmax），或者m为一个数值n为一列向量时（指定范围），以n为变量，按照固定的m画图</li>
</ol>
<p><img src="media/14561450925713/14561494999702.jpg" alt=""/><br/>
<strong>图</strong>：<code>plot_SH_by_nm(SH1,60,0:40)</code>：指定m范围</p>
<p><img src="media/14561450925713/14561495435469.jpg" alt=""/><br/>
<strong>图</strong>：<code>plot_SH_by_nm(SH1,60)</code>：省略m</p>
<p><img src="media/14561450925713/14561498507695.jpg" alt=""/><br/>
<strong>图</strong>：<code>plot_SH_by_nm(SH(1),nan,21)</code>：按照固定的m画图，自动分别连接奇偶项</p>
<h2 id="toc_2">3. 多个文件，某一阶次的时间序列</h2>
<ul>
<li><code>plot_SH_by_tt(SH1,n,m)</code>：n与m必须是scaler数值
<img src="media/14561450925713/14561514305613.jpg" alt=""/>
<strong>图</strong>：(10,10)的时间序列
<img src="media/14561450925713/14561515481615.jpg" alt=""/>
<strong>图</strong>：(30,30)的时间序列</li>
</ul>
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<div class="date">
<time datetime="2016-01-22T13:26:26+08:00" itemprop="datePublished">2016/1/22</time>
</div>
<div class="tags">posted in
<a class='category' href='%E7%90%83%E5%87%BD%E6%95%B0.html'>球函数</a>
</div>
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<h1 class="title" itemprop="name"><a href="14534403861434.html" itemprop="url">
质量反演方法</a></h1>
<div class="entry-content" itemprop="articleBody">
<h1 id="toc_0">通用公式</h1>
<h2 id="toc_1">一般球谐展开式</h2>
<pre><code>\\[f\left( {\theta ,\varphi } \right) = \sum\limits_{l = 0}^\infty {\sum\limits_{m = 0}^l {{{\tilde P}_{lm}}\left( {\cos \theta } \right)\left\{ {{C_{lm}}\cos m\varphi + {S_{lm}}\sin m\varphi } \right\}} } \\]
\\[\left\{ {\begin{array}{*{20}{c}}
{{C_{lm}}}\\
{{S_{lm}}}
\end{array}} \right\}\\]
</code></pre>
<h1 id="toc_2">SADI</h1>
<h2 id="toc_3">Matlab代码</h2>
<ul>
<li><code>[mass, LLZ_mass_trend, LLZ_d_F] =
ctl_SADI( SH_G, SH_d, LLZ_ind_obs, LLZ_ind_mass, m2matrix, N_lonlat, smooth, alpha )</code><br/>
空间域反演控制文件。其中：
<ul>
<li>SH_G:</li>
<li>SH_d:</li>
<li>LLZ_ind_obs</li>
<li>LLZ_ind_mass</li>
<li>m2matrix</li>
<li>N_lonlat, smooth</li>
<li>当alpha为矩阵的时候（如：<code>alpha=10^(-10:1:0)</code>），会调用这个函数内部的<code>[std_m,std_fit] = plot_l_curve(G,d,alpha)</code>画L曲线</li>
<li>当alpha为一个确定的值时，会反演并返回结果<br/></li>
</ul></li>
</ul>
<h3 id="toc_4">辅助代码</h3>
<ul>
<li><code>plot_inversion_map(LLZ_mass_trend, LLZ_d_F, cran, N_lonlat, N_lonlat_G, smooth)</code><br/>
画出反演结果的平面图。</li>
</ul>
<h1 id="toc_5">ITER</h1>
<h2 id="toc_6">Matlab代码</h2>
<ul>
<li><code>[rg_mass, SH_residue, totalmass]= ctrl_ITER_batch(SHs, LLZ_mask, smooth, Nstep, icheck, imethod)</code><br/>
调用<code>ctl_ITER_yi</code>或者<code>ctl_ITER_chen</code>，imethod设定</li>
<li><code>[rg_mass, s_list, varargout]= ctl_ITER_chen(SH_model0, LLZ_mask, smooth, Nstep)</code><br/>
chen的反演方法，海洋覆盖一层负质量</li>
<li><p><code>[rg_mass, s_list, sh_residue]= ctl_ITER_yi(SH_model0, LLZ_mask, smooth, Nstep)</code><br/>
我的反演方法，只有陆地有质量。rg_mass是格网值，只有mask为1的地方才有值；LLZ_mask只包含0和1；sh_residue为拟合残余系数，无量纲化。</p>
<p>s_list, 包含Nstep每一步的结果: <br/>
rg_F（rg_mass的展开结果）, <br/>
rg_rsd（原始观测值减去rg_F）, <br/>
rg_mass（反演出来的质量，第Nstep步就是最终返回值）, <br/>
totalmass（rg_mass的总质量), <br/>
ratio（在mask为1的位置拟合残差比例）</p></li>
</ul>
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<article class="post" itemprop="blogPost" itemscope itemtype="http://schema.org/BlogPosting">
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<time datetime="2016-01-22T10:47:15+08:00" itemprop="datePublished">2016/1/22</time>
</div>
<div class="tags">posted in
<a class='category' href='%E5%85%B6%E4%BB%96.html'>其他</a>
</div>
</div>
<h1 class="title" itemprop="name"><a href="14534308351747.html" itemprop="url">
Matlab与Google earth和GMT格式的交互</a></h1>
<div class="entry-content" itemprop="articleBody">
<h1 id="toc_0">Google earth</h1>
<h2 id="toc_1">使用google-earth获得范围内的点</h2>
<p>一个常见的需求是：怎么样划定一个范围，然后得到范围内的点？这里讲述怎么结合google earth和matlab实现这个功能。<br/>
google earth操作步骤：<br/>
* 1. 打开软件，缩放到合适的视图，然后点击添加路径：<br/>
<img src="media/14534308351747/14534308904208.jpg" alt=""/><br/>
* 2. 如图，我们添加了5个点，大概组成了一个矩形区域<br/>
<img src="media/14534308351747/14534309232558.jpg" alt=""/>
<br/>
可以给这个路径命名为myPath，并改变线条的颜色（样式/颜色），点击确定后会保存这个路径。<br/>
<strong>注意</strong>：已经放置的路径点可以用鼠标拖动修改；滚轮可以缩放调节视图，有利于更细节的修改；键盘的上下左右键可以移动视图；右键删除上一个放置的点（如果要删除中间的某个点，先左键单击它，再右键）；如果想插入一个点，先在要插入位置的上一个点左键选中，再在需要插入位置左键点击。<br/>
* 3. 上一步中我们保存了名为myPath的路径，在左侧位置可以找到它，右击，选择将位置另存为<br/>
<img src="media/14534308351747/14534310265726.jpg" alt=""/><br/>
文件类型选择kml<br/>
<img src="media/14534308351747/14534310365955.jpg" alt=""/><br/>
这样我们得到了<code>myPath.kml</code>，用matlab代码可以读取<br/>
* 4. 用<code>read_kml</code>程序获得点的边界txt文件，保存在paths/路径下<br/>
* 5. 用<code>grid_in_bound</code>获得边界内的点，我们寻找所有在边界内的1°×1°的点。<br/>
* 6. 用<code>xyz2kml_points</code>把找到的点转为<code>points_myPath.kml</code>文件，导入到google earth中<br/>
<img src="media/14534308351747/14534310630825.jpg" alt=""/><br/>
可以看到，一共有87个点，每个点的编号标注在旁边。
这3个命令在<code>test_kml_data.m</code>中有具体代码。</p>
<h2 id="toc_2">matlab代码</h2>
<ul>
<li><code>read_kml(ip_kml,op_dir)</code><br/>
ip_kml为kml文件名称，op_dir为输出路径</li>
<li><code>[LLZ_ind,xyz]=grid_in_bound( BD, LLZ )</code><br/>
获得边界内的点</li>
<li><code>read_kml_to_a_single(ip_kml,op_file)</code><br/>
读取kml文件，写入到一个文件中，每个记录用 <code>> 记录名</code>隔开。这种文件适合GMT作图。</li>
<li><code>xyz2kml(op_kml,xy)</code><br/>
将多个线段写成kml文件，输入的xy可以通过命令<code>read_BD_and_name</code>生成。xy包含三个域：<code>xy(ii).x, xy(ii).y, xy(ii).name</code>，当name缺省时，自动转为1：N的字符串</li>
<li><code>xyz2kml_points(op_kml,xy,o_namelist)</code><br/>
将多个点写成kml文件。xy形状为(N,2)，<code>o_namelist{ii}</code>为结构体</li>
<li><code>xy=read_BD_and_name(ipdir,namepattern)</code><br/>
例如：<code>read_BD_and_name(‘a/','*txt')</code>，专门供<code>xyz2kml</code>使用，生成其需要的xy</li>
</ul>
<h3 id="toc_3">批量处理模板: 从kml到mask</h3>
<pre><code>clear;
ip_kml = 'wang-Remote-sensing.kml';
bd_dir = 'BD';
op_xy = 'xy_with_nu.mat';
op_mask = 'mask_wang.mat';
iact = 4;
if iact == 1 % 1: kml2xyz
read_kml(ip_kml,bd_dir)
elseif iact == 2 % plot all
xy=read_BD_and_name(bd_dir,'*txt');
% xy = xy([6,7,4,8,2,3,5,1]); % re-order
Nxy = numel(xy);
ik = 0;
for ii = 1:Nxy
xy(ii).group = ii;
end
save( op_xy, 'xy')
elseif iact == 3 % generate group number
load(op_xy);%'xy');
Nxy = numel(xy);
% plot boundary and number
for ii = 1:Nxy
plot(xy(ii).x,xy(ii).y)
h = text(mean(xy(ii).x),mean(xy(ii).y),num2str(xy(ii).group));
set( h,'color','r','fontsize',14)
hold on;
end
all_plot('coast');
hold off
elseif iact == 4 % generate mask
load(op_xy);% 'xy');
OBS = mlonmlat(8);
Nfile = numel(xy);
LLZ_mask = OBS;
LLZ_mask.rg = zeros(size(LLZ_mask.lon));
for ii = 1:Nfile
info(ii).name = xy(ii).name;
info(ii).nu = xy(ii).group;
BD = [xy(ii).x,xy(ii).y];
[LLZ_ind0,vLLZ]=grid_in_bound( BD, OBS );
LLZ_mask.rg(LLZ_ind0.rg==1) = xy(ii).group;
end
LLZ_mask2 = LLZ_mask;
LLZ_mask2.rg(LLZ_mask2.rg==0) = NaN;
rg_plot(LLZ_mask2);
myaxis('tibet-L');
% save(op_mask,'LLZ_mask','info')
end
</code></pre>
<h3 id="toc_4">判断在边界内部：inpolygon</h3>
<p>一个附带的常见需求是求出边界内部的点。matlab自带的函数<code>inpolygon</code>可以解决这个问题<br/>
一个自带的例子：</p>
<pre><code>xv = rand(6,1); yv = rand(6,1);
xv = [xv ; xv(1)]; yv = [yv ; yv(1)];
x = rand(1000,1); y = rand(1000,1);
in = inpolygon(x,y,xv,yv);
plot(xv,yv,x(in),y(in),’.r’,x(~in),y(~in),'.b')
</code></pre>
<p><img src="media/14534308351747/14534323980173.jpg" alt=""/></p>
<h1 id="toc_5">GMT</h1>
<h2 id="toc_6">matlab代码</h2>
<ul>
<li><code>xys=read_segments(ip_xy)</code><br/>
从片段文件中（<code>></code>隔开）读取各个片段，组成<code>xys(ik)</code>，包含域<code>.x, .y, .name</code></li>
</ul>
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