[Edited Message Follows]

Thanks for your answer Even, and be assured that I always read the manual before asking question :-) 


```

$ more /proc/version

Linux version 4.9.184-linuxkit (root@a8c33e955a82) (gcc version 8.3.0 (Alpine 8.3.0) ) #1 SMP Tue Jul 2 22:58:16 UTC 2019
...
# From gdal /configure

  NetCDF support:            yes

  NetCDF has netcdf_mem.h:   yes
...
# NetCDF install

ENV NETCDF_VERSION=4.6.3

# NetCDF

RUN mkdir /tmp/netcdf \

&& curl -sfL https://github.com/Unidata/netcdf-c/archive/v$NETCDF_VERSION.tar.gz | tar zxf - -C /tmp/netcdf --strip-components=1

RUN cd /tmp/netcdf \

&& CPPFLAGS="-I${PREFIX}/include" LDFLAGS="-L${PREFIX}/lib" \

./configure \

--with-default-chunk-size=67108864 \

--with-chunk-cache-size=67108864 \

--prefix=$PREFIX \

--disable-static \

--enable-netcdf4 \

--enable-dap \

--with-pic \

&& make -j $(nproc) --silent && make install && make clean \

&& rm -rf /tmp/netcdf

```

My configuration meets the current spec from the docs, and still fallback to HDF5

Hi all,

I'm using a VRT file to represent a bunch of individual map tiles as one
big image (following the help of Dion Haefner via this list - thanks
again!). I noticed that some of the data is invalid but not marked as
such (via the invalid data value). I'd like to define these regions as
invalid using a file (e.g. a shapefile) in a way that would allow me to
call `dataset.read(..., masked=True)` and get a masked array that masks
out both data that has the dataset's invalid data value, and data that I
manually define to be invalid in my file.

I found the examples on the site using Fiona to load a shapefile
(https://rasterio.readthedocs.io/en/stable/topics/masking-by-shapefile.html)
and use it to mask some data. I figured that would be useful, so I built
a shapefile with my invalid regions as polygons. I'm now having trouble
reading from my VRT file with these shapes masked out. I've got the
following code:

import rasterio
import fiona

with rasterio.open(VRT_FILE) as dataset, fiona.open(SHAPE_FILE, "r") as
shapefile:
     shapes = [feature["geometry"] for feature in shapefile]
     masked_data = rasterio.mask.mask(dataset, shapes, invert=True)
     ...

but this throws the error `MemoryError: Unable to allocate array with
shape (216640, 282250) and data type bool`.

It seems the `rasterio.mask.mask` method tries to read the WHOLE
dataset, and apparently has no way to define a window. Until now (before
playing with shapefiles) I've been reading the dataset with
`dataset.read(... window=Window(...))`, which lets me define a window to
avoid reading the whole (huge) dataset at once. I'd like to do this for
my shape-masked data too.

I have two questions:

  - Is my approach to defining these invalid regions - i.e. with a
shapefile - reasonable? Or is there a better way, ideally one that
doesn't involve loading, manually masking and then writing a huge file
back to disk?
  - If my approach is indeed reasonable, then is there a way to create a
`DatasetReader`-like object that supports `.read(...)` with a window
parameter, using data masked from my shapefile?

Best wishes,


Sean

Hello,

I could not figure out how to export a single band with a colormap as an RGB.tif

A snippet of my code:

with rasterio.open(new_f_cmp,'w',**raster_cmp_p) as dst:
    dst.write(file_np_man_cs_int8.data,1)
    dst.write_colormap(1, f_dic)
    cmap=dst.colormap(1)

thanks

--

Eyal Saiet

The mind is not a vessel to be filled, but a fire to be kindled. Plutarch

I found my problem. I had extra double quote from a copy paste around for this env var CPL_VSIL_CURL_ALLOWED_EXTENSIONS=".TIF,.ovr,.jp2,.tif" which I was setting via .env file and referencing it in the docker-compose.yml file. Once I removed the quotes for the curl allowed extensions, it started working again.