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1. INTERPOLATE NUMPY RASTER X Y Z HOW TO
2. INTERPOLATE NUMPY RASTER X Y Z CODE
3. INTERPOLATE NUMPY RASTER X Y Z SERIES

There are some useful things that work very well, other things that were hastily written for a one-off task several years ago, and some confusing things that were never finished.Ĭontributions, bug reports, and general feedback are all welcome. This originated as a personal repo that I am slowly cleaning up and distributing. # Optional requirements (needed for some functionality)

INTERPOLATE NUMPY RASTER X Y Z CODE

The -e flag (“editable mode”, setuptools “develop mode”) will allow you to modify source code and immediately see changes. Note: by default, this will deploy executable scripts in /usr/local/bin Make_stack.py -tr ‘min’ -te ‘union’ 20*.tif ` from pygeotools.lib import malib fn_list = s = malib.DEMStack(fn_list, res='min', extent='union') #Stack standard deviation s.stack_std #Stack linear trend s.stack_trend ` Reproject and clip to user-defined extent, preserving original resolution of each input raster: Warptool.py -tr raster2.tif -te raster2.tif -t_srs raster2.tif raster1.tif Warptool.py -tr max -te intersection -t_srs first raster1.tif raster2.tif raster3.tifĬreate version of raster1.tif that matches resolution, extent, and projection of raster2.tif: Warp all to match raster1.tif projection with common intersection and largest pixel size: ` from pygeotools.lib import iolib, warplib, malib fn1 = 'raster1.tif' fn2 = 'raster2.tif' ds_list = mwarp_multi_fn(, res='max', extent='intersection', t_srs='first', r='cubic') r1 = iolib.ds_getma(ds_list) r2 = iolib.ds_getma(ds_list) rdiff = r1 - r2 malib.print_stats(rdiff) out_fn = 'raster_diff.tif' iolib.writeGTiff(rdiff, out_fn, ds_list) ` # Warping multiple datasets to common grid, computing difference, writing out raster2shp.py - Create polygon shapefile of input raster footprints proj_select.py - Automatically determine projection for input lat/lon or raster trim_ndv.py - Remove rows/cols containing only NoData from raster margins filter.py - Apply various filters available in filtlib apply_mask.py - Apply mask from one raster to another clip_raster_by_shp.py - Clip and mask an input raster using a polygon shapefile

INTERPOLATE NUMPY RASTER X Y Z SERIES

make_stack.py - Create a “stack” of input rasters (a raster time series object) and compute stats warptool.py - Warp arbitrary rasters to common res/extent/proj

# Command-line utilities (run with no arguments for usage)

filtlib - Collection of filters for 2D masked arrays (Gauss, rolling median, high pass, etc.) timelib - Time conversions, extract timestamps from filenames, useful for raster time series analysis iolib - File input/output, wrappers for GDAL I/O, masked array write to disk warplib - On-the-fly GDAL warp operations for abitrary number of input datasets malib - NumPy Masked Array operations, DEMStack class geolib - Coordinate transformations, raster to vector, vector to raster Point data coordinate transformations, sampling, and interpolation routines (e.g., arrays of xyz points) Many functions to handle rasters with NoData gaps using ( ) Resample/warp rasters to common resolution/extent/projection At the left is your original dataset and the interpolated values are on the right.Libraries and command line tools for geospatial data processing and analysis One caveat is that you are going to obtain the interpolation on the convex hull of your data (unless you use the Nearest neighbor method).įollowing is an example. One option available in scipy.interpolate is griddata, where you pass your points and values and interpolate in another set of points that you pass. The data comes to me as a table: x y z f(x,y,z)Īnd I have to organize the f(x,y,z) function in an array of fxyz = ,]].ĭo you guys recommend another method to create this interpolation function assuming that the data comes in this form?Īs I mentioned in my comment, your data is not defined over a regular grid and that's why you should not be using that function. The problem I am having is related to the format the data is given to me and how the function receives it. It gets three one dimensional arrays (x,y,z) and the function answer (f(x,y,z)) to return the interpolating function. Interp_func = RegularGridInterpolator((x,y,z), fxyz) Using the RegularGridInterpolator, I created this function: def interp_3d(x,y,z,fxyz,x_desired,y_desired,z_desired):

INTERPOLATE NUMPY RASTER X Y Z HOW TO

The problem is more related with organizing the data than how to do the interpolations. I am trying to finish a series of interpolation functions.