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When running paver setup attempting to configure Geonode (Windows 10-64bit, Python 39), I get a raise of django being improperly configured. The computer suggests that if I have installed GDAL successfully then I should try setting GDAL_LIBRARY_PATH in my settings. I have successfully installed gdal via pip install GDAL-3.3.1-cp39-cp39-win_amd64.whl. The settings for the geonode project are as follows (C:\Users\amell\Documents\geonode\geonode): if os.name == 'nt': import platform OSGEO4W = r"C:\OSGeo4W" if '64' in platform.architecture()[0]: OSGEO4W += "64" assert os.path.isdir(OSGEO4W), "Directory does not exist: " + OSGEO4W os.environ['OSGEO4W_ROOT'] = OSGEO4W os.environ['GDAL_DATA'] = OSGEO4W + r"\share\gdal" os.environ['PROJ_LIB'] = OSGEO4W + r"\share\proj" os.environ['PATH'] = OSGEO4W + r"\bin;" + os.environ['PATH'] I then added the below code to the above and still get the same issue: GDAL_LIBRARY_PATH = r'C:\OSGeo4W64\bin\gdal301.dll' I have in addition checked the lib names to see if the GDAL version I installed (331) is listed (C:\Users\amell\Documents\geonode_env\Lib\site-packages\django\contrib\gis\gdal\libgdal). Strangely, the .dll in C:\OSGeo4W64\bin is 301 and not 331 (others suggested renaming this which failed), though I installed 331 via pip using the .whl file. I am currently not sure what the problem is, I tried possible solutions suggested by those with the same issue and they do not seem to work for my case. Perhaps I am missing something.

I'm new to R and using terra and cannot figure out this permission error. This exact code works on another researcher's computer, so I'm not sure why it's not working on my Rstudios. The folder and directory is correct, but I keep getting the same error. The wc2.1_30s folder contains some .tif files. I want to name a folder with my variables baseline using this code: baseline

Sequential, diverging and bivariate color palettes with perceptually even lightness. Export to CSS, Python, R, QGIS, ArcGIS Pro, GDAL, RustGIS or GMT.

https://postgr.es/p/9zE

Sequential, diverging and bivariate color palettes with perceptually even lightness. Export to CSS, Python, R, QGIS, ArcGIS Pro, GDAL, RustGIS or GMT.

I am testing Savitzky-Golay filter with my MODIS dataset. Currently I am testing for 10 rasters (but later on will use my whole dataset) only just to test how should I create my script. So my initial workflow is to: * Access my raster files * each file I need to convert to 2D array using GDAL or GDAL Numeric * Append all those created 2D arrays to a list * Implement numpy.dstack() to stack my 2D arrays and hopefully I can begin some analysis. However, I getting error in the 4th step (it says memory error) -- converting my list of 2D arrays to a stack. Could you guide me some advice how to proceed. I am just building my script so for now its not so much. import os, sys import gdal, numpy as np import gdalnumeric as gd from scipy.signal import savgol_filter rasters = list() ws = 'G:/Test/Raster' for folder, subs, files in os.walk(ws): for filename in files: aSrc = gd.LoadFile(os.path.join(folder,filename)) rasters.append(aSrc) stackRast = np.dstack(rasters)

Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) &lt;<a href="https://doi.org/10.3390%2Fdata4030092" target="_top">doi:10.3390/data4030092</a>&gt; for further details.

Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let 'gdalcubes' automatically apply cropping, reprojection, and resampling using the 'Geospatial Data Abstraction Library' ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as 'netCDF' or 'GeoTIFF' files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the 'GDAL', 'netCDF', 'CURL', and 'SQLite' libraries. See Appel and Pebesma (2019) &lt;<a href="https://doi.org/10.3390%2Fdata4030092" target="_top">doi:10.3390/data4030092</a>&gt; for further details.

A while ago, I asked for a method to assign new QgsRasterBlock objects to a QgsRasterDataProvider objects at Assign new QgsRasterBlock object to a QgsRasterDataProvider object. The reason was for using only PyQGIS methods to manipulate raster layers avoiding GDAL/OGR python module. Recently, I found out a potential solution, posted as an answer; however original provider is not preserved in original raster. I found out that QgsRasterDataProvider missing methods in my first post were 'setEditable' and 'writeBlock'. I also found out, for avoiding QGIS crashing during each test, it is necessary to add as last line provider = None. Following simplified version of original code looks as follow: layer = iface.activeLayer() provider = layer.dataProvider() extent = provider.extent() rows = layer.height() cols = layer.width() xsize = layer.rasterUnitsPerPixelX() ysize = layer.rasterUnitsPerPixelY() block = provider.block(1, extent, cols, rows) provider.setEditable(True) for i in range(rows): for j in range(cols): if block.value(i,j) == 4: block.setValue(i, j, 25) provider.writeBlock(block, 1) pipe = QgsRasterPipe() pipe.set(provider) rasterWriter = QgsRasterFileWriter("/home/zeito/pyqgis_data/test_write.tif") xSize = provider.xSize() ySize = provider.ySize() CRS = layer.crs() error = rasterWriter.writeRaster(pipe, xSize, ySize, provider.extent(), CRS) if error == QgsRasterFileWriter.NoError: print ("Raster was saved successfully!") else: print ("Raster was not saved!") provider = None After running above code in Python Console of QGIS, with a copy of test_sample, this one looks identical to original; as it can be observed in following image: However, Value Tool Plugin corroborates that it effectively changed: When I load test_write resulting layer and test_sample_copy (from hard disk), both layers look as expected (corroborated again by Value Tool Plugin). However, after erasing test_write and test_sample_copy and reloading both layers again, it was corroborated that test_sample_copy effectively and unexpectedly changed. So, there is something wrong in the way I created the provider for resulting layer because modify unexpectedly the provider of original raster layer. How can the provider of original raster layer be preserved when it is modified for another raster layer?