Hello Rasterio community, while I have been using GDAL-python for a few years, I am delighted to learn about Rasterio. Both in GDAL and Rasterio (matter of fact any programming) require detail and to

Hi Sean, Thank you for you help and excuse the late reply! I will find another way of accomplishing this. Best regards, Niklas Am Mi., 9. Jan. 2019 um 22:12 Uhr schrieb Sean Gillies via Groups.Io

In https://github.com/OSGeo/gdal/issues/1189#issuecomment-452716305, Even reminds us that GDAL does not support writing TIFFs to S3 and I'm pretty sure that this applies to Azure as well. See the note

Hi Guy, Let's try to answer your questions on the main discussion group. The dev group is for discussing development of the library itself: new API methods, infrastructure, governance, &c. I've

Hi Niklas, First off, in my original reply I missed the chance to suggest that we move this discussion to the main (not dev) discussion group, which has more users. The dev group is for discussing and

The `r+` trick is very neat for changing the metadata in-place. But for actually changing the data values to match the new metadata, both your suggestions are fine - either used masked arrays or do

Hi, I'm sorry this isn't more obviously documented. You can change the nodata value of an existing dataset by opening it in r+ mode and setting the attribute. with rasterio.open("example.tif", "r+")

Hi Everyone, Using RasterIO, what is the correct way to change (or assign if it isn't already set) the no data value for a single or multi band TIF file? For example using the test rgb.byte.tif file,

Hi, The thing that is tripping Guy up is that reproject has an optional init_dest_nodata keyword argument that defaults to True: the output array is filled with the nodata value (0) before warped

Hi Guy, The dest array will be overwritten by the reprojected data based on your source array. So, to get ones in the destination, you would need to modify the source array to have ones in it. Hope

Hi guys I am trying to use a method `rasterio.warp.reproject` I create a source numpy array full of zeros and dest numpy array full of ones, I reprojected using the nearset resmapling and

Some tests results. I tried these methods on a 2.2GB image using sleep(0.05) as the compute function. Results: TimePeak memorySingle thread window iteration15:31.97454668simple

Hi Sean, I have tried your suggestion and it indeed works like a charm now. I totally forgot about the header file that was needed to open the binary data file. Thanks for your help and your awesome

Hi Nick, I'm not an expert in the ENVI format or HPC, but I have a suggestion for you. As I understand from reading https://www.gdal.org/frmt_various.html, your ENVI dataset is represented by multiple

Not at all! This is great stuff. I'm a little short of time right now, but as soon as I can I'll test all of this. I don't know how much of an overhead opening a dataset per window is, but a dataset

Hello, In my project I’m dealing with 217 hyperspectral raster files in ENVI format. These rasters contain 420 bands, this means that I have to deal with files that are 30GB+ in size. To keep

Hi Pablo, I don't want to preempt you, but here's what I've come up with: https://gist.github.com/sgillies/b90a79917d7ec5ca0c074b5f6f4857e3#file-cfrw-py. If the window reads are done by the worker

Hi guys Wanted to share my 10 cent about this subject. I am trying to do the concurrency support for reading by creating a vrt on the windows, VRTs are just XML so they can be created eagerly in the

Thanks for the clarification, Even!-- Sean Gillies

I didn't build gdal but I am using the base gdal image from quay.io/mojodna/gdal:v2.3.2. s3 access is working perfectly fine. If GDAL was not built SSL, AWS S3 wouldn't have worked correct? Also is