離散的な観測点データから2次元grid作成　綺麗に内挿したい

離散的な観測点データから2次元gridデータへ変換をしたのですが、
もっと見た目を綺麗にしたいです。

試してみたこと
griddata.py で定義された方法で、x,y,z をgrid化しました。
grid の値は、griddata.pyのsizeで定義された範囲で取得される点の中央値（median）を返しています。

python
1#!/usr/bin/env python
2
3
4import pandas as pd
5import griddata
6import numpy as np
7import sys
8import matplotlib.pyplot as plt
9from mpl_toolkits.basemap import Basemap, maskoceans
10df = pd.read_csv(sys.argv[1])
11
12#filter
13pm10 = df[(df.pm10 != "-")]
14# データの準備
15X = pm10.lon.values.astype(np.float32)
16Y = pm10.lat.values.astype(np.float32)
17Z = pm10.pm10.values.astype(np.float32)
18
19print(X.min(),X.max())
20print(Y.min(),Y.max())
21
22
23binsize = 0.01
24extent = (124.50,129.50,33.10,39.10)
25grid, bins, binloc,xi,yi = griddata.griddata(X, Y, Z, extent,binsize=binsize)  # see this routine's docstring
26xx,yy = np.meshgrid(xi,yi)
27print(grid.shape)
28
29# minimum values for colorbar. filter our nans which are in the grid
30zmin    = grid[np.where(np.isnan(grid) == False)].min()
31zmax    = grid[np.where(np.isnan(grid) == False)].max()
32grid[np.isnan(grid)] = zmin
33grid = maskoceans(xx,yy,grid)
34bmap = Basemap(projection='cyl',llcrnrlon=124.50,urcrnrlon=129.50,llcrnrlat=33.10,urcrnrlat=39.10,resolution='i')
35
36bmap.imshow(grid, extent=extent,cmap=plt.cm.rainbow, origin='lower', vmin=0, vmax=zmax, aspect='auto')
37bmap.drawcoastlines(linewidth=0.3, linestyle='solid', color='black')
38bmap.drawcountries(linewidth=0.3, linestyle='solid', color='black')
39bmap.drawparallels(np.arange(-100, 90.0, 1.0), linewidth=0.1, color='black')
40bmap.drawmeridians(np.arange(0.0, 360.0, 1.0), linewidth=0.1, color='black')
41plt.colorbar() # draw colorbar
42plt.scatter(X,Y,marker='o',c='b',s=5)
43plt.title('Obs.Analysis TEST grid=0.01')
44#plt.show()
45plt.savefig("obs_grid_0p01.png",bbox_inches='tight')

python
1# griddata.py - 2010-07-11 ccampo
2import numpy as np
3
4def griddata(x, y, z, extent,binsize=0.01, retbin=True, retloc=True):
5    """
6    Place unevenly spaced 2D data on a grid by 2D binning (nearest
7    neighbor interpolation).
8    
9    Parameters
10    ----------
11    x : ndarray (1D)
12        The idependent data x-axis of the grid.
13    y : ndarray (1D)
14        The idependent data y-axis of the grid.
15    z : ndarray (1D)
16        The dependent data in the form z = f(x,y).
17    binsize : scalar, optional
18        The full width and height of each bin on the grid.  If each
19        bin is a cube, then this is the x and y dimension.  This is
20        the step in both directions, x and y. Defaults to 0.01.
21    retbin : boolean, optional
22        Function returns `bins` variable (see below for description)
23        if set to True.  Defaults to True.
24    retloc : boolean, optional
25        Function returns `wherebins` variable (see below for description)
26        if set to True.  Defaults to True.
27   
28    Returns
29    -------
30    grid : ndarray (2D)
31        The evenly gridded data.  The value of each cell is the median
32        value of the contents of the bin.
33    bins : ndarray (2D)
34        A grid the same shape as `grid`, except the value of each cell
35        is the number of points in that bin.  Returns only if
36        `retbin` is set to True.
37    wherebin : list (2D)
38        A 2D list the same shape as `grid` and `bins` where each cell
39        contains the indicies of `z` which contain the values stored
40        in the particular bin.
41
42    Revisions
43    ---------
44    2010-07-11  ccampo  Initial version
45    """
46    # get extrema values.
47    #xmin, xmax = x.min(), x.max()
48    #ymin, ymax = y.min(), y.max()
49    xmin,xmax = extent[0],extent[1]
50    ymin,ymax = extent[2],extent[3]
51    # make coordinate arrays.
52    xo      = np.arange(xmin, xmax+binsize, binsize)
53    yo      = np.arange(ymin, ymax+binsize, binsize)
54    xi, yi  = np.meshgrid(xo,yo)
55
56    # make the grid.
57    grid       = np.zeros(xi.shape, dtype=x.dtype)
58    nrow, ncol = grid.shape
59    if retbin: bins = np.copy(grid)
60
61    # create list in same shape as grid to store indices
62    if retloc:
63        wherebin = np.copy(grid)
64        wherebin = wherebin.tolist()
65
66    # fill in the grid.
67    for row in range(nrow):
68        for col in range(ncol):
69            xc = xi[row, col]    # x coordinate.
70            yc = yi[row, col]    # y coordinate.
71            
72            # find the position that xc and yc correspond to.
73            posx = np.abs(x - xc)
74            posy = np.abs(y - yc)
75            size = 0.5
76            #ibin = np.logical_and(posx < binsize/2., posy < binsize/2.)
77            ibin = np.logical_and(posx < size/2., posy < size/2.)
78            ind  = np.where(ibin == True)[0]
79            
80            # fill the bin.
81            bin = z[ibin]
82            if retloc: wherebin[row][col] = ind
83            if retbin: bins[row, col] = bin.size
84            if bin.size != 0:
85                binval         = np.median(bin)
86                grid[row, col] = binval
87            else:
88                grid[row, col] = np.nan   # fill empty bins with nans.
89
90    # return the grid
91    if retbin:
92        if retloc:
93#            return grid, bins, wherebin
94            return grid, bins, wherebin,xo,yo
95        else:
96            return grid, bins
97    else:
98        if retloc:
99            return grid, wherebin
100        else:
101            return grid