Installation of lasR R package

[samuelgrubinger]

This package, https://github.com/r-lidar/lasR, is a new open-source development from the author of lidR that is programmed for efficient pipeline processing of point clouds. It will likely be essential for GEO-TREES data processing. Currently, it is not on conda-forge and cannot be installed in R on MAAP. Any solutions?

[wildintellect]

The easiest path might be to build a conda-forge feedstock, at a minimum we can learn a lot from the lidR feedstock https://github.com/conda-forge/r-lidr-feedstock/blob/main/recipe/meta.yaml

[wildintellect]

I just tested on https://hub.openveda.cloud which uses Rocker images (includes GDAL), and this build fine with

install.packages('lasR', repos = 'https://r-lidar.r-universe.dev')

Which is not surprising since the core is Ubuntu, build tools are included, and conda is note involved. Just noting here as a temporary option, if it's needed soon.

Also noted on Slack that PDAL pipelines might be capable of similar workflows. One concern with lidR and lasR is the custom read/write modules and the lack of full COPC support. Not blockers just things to be aware of.

[samuelgrubinger]

Thanks Alex! I'll give it a shot. *Samuel Grubinger* Postdoctoral researcher University of Maryland He/him/his ***@***.***

…

On Wed, Nov 6, 2024 at 2:46 PM Alex I. Mandel ***@***.***> wrote: I just tested on https://hub.openveda.cloud which uses Rocker images <https://github.com/rocker-org/rocker-versioned2/blob/master/dockerfiles/binder_4.3.3.Dockerfile> (includes GDAL), and this build fine with install.packages('lasR', repos = 'https://r-lidar.r-universe.dev') Which is not surprising since the core is Ubuntu, build tools are included, and conda is note involved. Just noting here as a temporary option, if it's needed soon. Also noted on Slack that PDAL pipelines might be capable of similar workflows. One concern with lidR and lasR is the custom read/write modules and the lack of full COPC support. Not blockers just things to be aware of. — Reply to this email directly, view it on GitHub <#1136 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AKGGXTNYZ6EGQ6SHWD6ZYKLZ7KL4DAVCNFSM6AAAAABRHONT3OVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDINRQHE2DKNRRGA> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[wildintellect]

Following next line of thought:
I hacked grayskull and generated a meta.yaml for building a conda package (still testing if it works)
r-lasr/meta.yaml

{% set name = "lasr" %}
{% set version = "0.10.3" %}
{% set posix = "m2-" if win else "" %}
{% set native = "m2w64-" if win else "" %}

package:
  name: r-{{ name }}
  version: '{{ version }}'

source:
  url: https://r-lidar.r-universe.dev/src/contrib/lasR_{{ version }}.tar.gz
  sha256: 147f2c50e30c2d5d9f7386be4fb12c12c9cf401bcb288f1e541faf9701db4499

build:
  number: 0
  merge_build_host: true
  script: R CMD INSTALL --build .
  rpaths:
    - lib/R/lib/
    - lib/

requirements:
  build:
    - cross-r-base {{ r_base }}  # [build_platform != target_platform]
    - autoconf  # [unix]
    - "{{ compiler('c') }}"  # [unix]
    - "{{ compiler('m2w64_c') }}"  # [win]
    - "{{ compiler('cxx') }}"  # [unix]
    - "{{ compiler('m2w64_cxx') }}"  # [win]
    - posix  # [win]
  host:
    - r-base
    # - r-methods
    # - r-stats
    - r-r.utils
  run:
    - r-base
    # - r-methods
    # - r-stats
    - r-r.utils

test:
  commands:
    - $R -e "library('lasR')"  # [not win]
    - "\"%R%\" -e \"library('lasR')\""  # [win]

about:
  home: https://github.com/r-lidar/lasR
  summary: Fast and pipeable airborne lidar processing tools. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, normalization, individual tree segmentation and other manipulations in a powerful and versatile processing chain.
  license: NGPL

extra:
  recipe-maintainers:
    - AddYourGitHubIdHere

conda build needed version, hard coded for now

conda install -c conda-forge conda-libmamba-solver
conda build -c conda-forge r-lasr

Had to comment out r-methods and r-stats, core packages? then rename r-r.utils
stuck now on

ValueError: Incompatible component merge:
  - 'h193b22a_2'
  - 'h4bf12b8_2'

[samuelgrubinger]

Hi Alex. Any update on this as a solution?

[wildintellect]

I got the build to work with this meta.yaml https://gist.github.com/wildintellect/874cd8e8b4054430948e01d938e22819
But a little stumped on the install conda install -c local r-lasr fails to find the build files in /opt/conda/envs/r/conda-bld/

[wildintellect]

Resource usage statistics from building r-lasr:
   Process count: 10
   CPU time: Sys=0:00:08.5, User=0:01:37.6
   Memory: 630.2M
   Disk usage: 84.4K
   Time elapsed: 0:03:09.0


STOPPING BUILD BEFORE POST: r-lasr-0.10.3-r42_0
Renaming work directory '/opt/conda/envs/r/conda-bld/r-lasr_1731101455092/work' to '/opt/conda/envs/r/conda-bld/r-lasr_1731101455092/work_moved_r-lasr-0.10.3-r42h3fd9d12_0_linux-64_main_build_loop'
shutil.move(work)=/opt/conda/envs/r/conda-bld/r-lasr_1731101455092/work, dest=/opt/conda/envs/r/conda-bld/r-lasr_1731101455092/work_moved_r-lasr-0.10.3-r42h3fd9d12_0_linux-64_main_build_loop)

INFO :: The inputs making up the hashes for the built packages are as follows:
{}


####################################################################################
Resource usage summary:

Total time: 0:14:02.4
CPU usage: sys=0:00:08.5, user=0:01:37.6
Maximum memory usage observed: 630.2M
Total disk usage observed (not including envs): 84.4K

I'm just puzzled as conda install --use-local r-lasr can't seem to find the locally built package.