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POHow to concatenate data from multiple netCDF files with Python
 

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  1. POHow to concatenate data from multiple netCDF files with Python
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    <p>I have some netCDF files, 24 for each of the directions (<code>x</code>, <code>y</code>, <code>z</code>) and 24 with values for different times. At the final point I have to plot the data for all time steps.</p> <p>For the plotting I need to interpolate at specific point so I have to knew the nearest neighbor. My plan is to divide the data in to 3D cells so I don't have to search the nearest neighbor in the whole dataset. </p> <p>So in my first step I read in my data files and create an array witch contains <code>[x,y,z,v[:]]</code> the coordinates of each point and the value for each time.</p> <p>After that I calculate for each point the cell it belongs to and append it to a Array of 4 dimensions: <code>x</code>, <code>y</code>, <code>z</code> and <code>v</code>:</p> <pre><code>for vec in vecs: x_ind = int((vec[0]-xmin) / stepWidthX) y_ind = int((vec[1]-ymin) / stepWidthY) z_ind = int((vec[2]-zmin) / stepWidthZ) if x_ind==gridPointsInXdirection: x_ind = x_ind-1 if y_ind==gridPointsInYdirection: y_ind = y_ind-1 if z_ind==gridPointsInZdirection: z_ind = z_ind-1 #print z_ind, y_ind,x_ind XGridPoints[z_ind, y_ind, x_ind] = np.append(XGridPoints[z_ind, y_ind, x_ind], vec[0]) YGridPoints[z_ind, y_ind, x_ind] = np.append(YGridPoints[z_ind, y_ind, x_ind], vec[1]) ZGridPoints[z_ind, y_ind, x_ind] = np.append(ZGridPoints[z_ind, y_ind, x_ind], vec[2]) VGridPoints[z_ind, y_ind, x_ind] = np.append(VGridPoints[z_ind, y_ind, x_ind], vec[3]) </code></pre> <p>Where <code>vecs</code> is the array with all data points. So far it's working but my problem now is in <code>VGridPoints</code>: I have a long list of values and not a list of arrays. Is there a solution to append an array to an array element so that I can access it later something like:</p> <pre><code>x = XGridPoints[2,3,4][2] y = YGridPoints[2,3,4][2] z = ZGridPoints[2,3,4][2] v[:] = VGridPoints[2,3,4][2] </code></pre> <p>When I take only one time step it's working but I have a large overdrive if I recalculate the cells and the nearest neighbour for each time step and they do not change the location over time.</p>
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    1. COIts not your question, but why don't you use scipy.spatial.cKDTree, you seem to reinvent the wheel? Also scipy.ndimage has some interpolations that might already do what you want. Simplest solution to your problem though, create one big array that is already large enough to hold _everything_.
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