Log Scale Matplotlib PatchCollection颜色

时间:2015-10-04 06:58:13

标签: python matplotlib patch colorbar

我有一个生成异构网格的函数,然后绘制补丁。它为每个bin指定了较低和较高的xy边缘。例如,单个bin由向量[x0, x1, y0, y1]定义。这些坐标转换为bin:

    y1|---------|   
      |         |  
      |   bin   | 
      |         |
    y0|---------|
     x0         x1   

我有一个(Nx4) mesh,其中包含N[x0, x1, y0, y1]个列。要绘制数据,我会执行以下操作:

z_plot  = z_stat / (dx * dy)     # ``z_stat`` is a calculated z-value 
z_plot  = z_plot / z_plot.max()  # for any given bin.

colors = mpl.cm.jet(z_plot)                   # Let fill data be white.
colors[z_stat == fill] = (1.0, 1.0, 1.0, 1.0) # fill=-9999.0, typically.

dx = mesh[:, 1] - mesh[:, 0]  # x1-x0
dy = mesh[:, 3] - mesh[:, 2]  # y1-y0.

xy = zip(mesh[:, 0], mesh[:, 2])  # (x,y) coordinates of each
                                  # bin's lower left corner.

patches = [mpl.patches.Rectangle(xy[i], dx[i], dy[i],         # I dont want
                                 ec=None, lw=0, fc=colors[i]) # visible edges.
            for i in range(mesh.shape[0])
          ]

patches = mpl.collections.PatchCollection(patches, match_original=True)
ax.add_collection(patches)

if z_stat is not None:

    kwargs = {'orientation': 'vertical'}
    cax, kw = _mpl.colorbar.make_axes_gridspec(plot_ax, **kwargs)

    cbar = mpl.colorbar.ColorbarBase(cax, cmap=_mpl.cm.jet)

结果如下:

The <code>x</code> and <code>y</code> data is converted to a standard 2D histogram in <code>y vs. x</code>. The spiral mesh is generated. <code>Binned Data</code> is the number of data points in each bin. <code>Bin Mean</code> is the mean value in the bin. It would be very useful to log scale this.

This question does something similar, but without the logscale colors。我不知道如何让颜色记录比例。简单地将mpl.colors.LogNorm()之类的内容传递给mpl.colorbar.ColorbarBase()对我来说无效。

编辑1 :生成网格。

我有一个生成异构网格的函数,然后绘制补丁。它以2D数组开始:

mesh = [[x00, x10, y00, y01], 
        [x10, x11, y10, y11], 
        ..., 
        [xN0, xN1, yN0, yN1]] 

我通过网格读取并将每个bin分成四个:

#    y1|----|----|          x0, x1, y0, y1 = mesh[i, :]
#      | p4 | p3 |          xh = [x0 + .5*(x1-x0)]
#      |----|----| <- yh    yh = [y0 + .5 *(y1-y0)]
#      | p1 | p2 |
#    y0|----|----|
#     x0    ^-xh x1       

如果[p1, p2, p3, p4]中的每一个都有超过最小数据点数(例如50),我将行[x0, x1, y0, y1]替换为此数组:

        new_mesh = _np.array([[x0, xh, xh, x0],  # Define the 16 edges of  
                              [xh, x1, x1, xh],  # the 4 new bins that are  
                              [y0, y0, yh, yh],  # going to replace the bin 
                              [yh, yh, y1, y1]]  # originally defined by 
                            ).T                  # [x0, x1, y0, y1].

        if i == 0:  # 0th edge is a special case for indexing.

            mesh_h = _np.concatenate([new_mesh, mesh[1:]])

        else:

            mesh_h = _np.concatenate([mesh[:i], new_mesh, mesh[i+1:]])         


        mesh = mesh_h  # Set the new edges.

1 个答案:

答案 0 :(得分:2)

虽然我无法测试您的确切情况,因为您没有提供可独立运行的示例,但您应该(如果我对您理想的行为的理解是正确的)能够按照以下方式完成您想要的操作。

首先编辑此行以删除颜色和边缘信息的手动设置:

    function isSame(arr1,arr2) {
        var same=true;
        for(var i=0;i < arr1.length;i++) {
            if(!~jQuery.inArray(arr1[i],arr2) || arr1.length!=arr2.length){
                same=false;
                }
            }
        return same;
        }

看起来应该是这样的:

patches = [mpl.patches.Rectangle(xy[i], dx[i], dy[i],         # I dont want
                                 ec=None, lw=0, fc=colors[i]) # visible edges.
            for i in range(mesh.shape[0])
          ]

然后将patches = [mpl.patches.Rectangle(xy[i], dx[i], dy[i]) for i in range(mesh.shape[0])] LogNorm和您的边缘参数传递给jet。这是因为我们希望matplotlib尽可能多地处理它,以便它可以为你挑选颜色。

PatchCollection

然后使用patch_collection = mpl.collections.PatchCollection(patches,cmap=matplotlib.cm.jet, norm=matplotlib.colors.LogNorm(), lw=0) 为PatchCollection提供z信息:

set_array

最后将集合添加到绘图中,创建颜色栏并显示图:

patch_collection.set_array(z_plot)

这个答案很大程度上基于here给出的可能有用的示例。