Quickstart

Introduction

It’s a giant pain to build everything yourself. The quickest way to start using PDAL is to leverage builds that were constructed by the PDAL development team using Conda.

Directly from the Conda front page,

Conda is an open source package management system and environment management system that runs on Windows, macOS and Linux. Conda quickly installs, runs and updates packages and their dependencies. Conda easily creates, saves, loads and switches between environments on your local computer.

This exercise will print the first point of an ASPRS LAS file. It will utilize the PDAL command line application to inspect the file.

Note

If you need to compile your own copy of PDAL, see Compilation for more details.

Install Conda

Conda installation instructions can be found at the following links. Read through them a bit for your platform so you have an idea what to expect.

Note

We will assume you are running on Windows, but the same commands should work in macOS or Linux too – though definition of file paths might provide a significant difference.

Run Conda

On macOS and Linux, all Conda commands are typed into a terminal window. On Windows, commands are typed into the Anaconda Prompt window. Instructions can be found in the Conda Getting Started guide.

Test Installation

To test your installation, simply run the command conda list from your terminal window or the Anaconda Prompt. A list of installed packages should appear.

Install the PDAL Package

A PDAL package based on the latest release, including all recent patches, is pushed to the conda-forge channel on anaconda.org with every code change on the PDAL maintenance branch.

Warning

It is actually a very good idea to install PDAL in it’s own environment (or add it to an existing one). You will NOT want to add it to your default environment named base. Managing environments is beyond the scope of the quickstart, but you should read more about it over at https://conda.io/docs/user-guide/getting-started.html#managing-envs.

To install the PDAL package so that we can use it to run PDAL commands, we run the following command to create an environment named myenv, installing PDAL from the conda-forge channel.

conda create --yes --name myenv --channel conda-forge pdal

Depending on what packages you may or may not have already installed, the output should look something like:

Solving environment: done

## Package Plan ##

  environment location: C:\Miniconda3\envs\myenv

  added / updated specs:
    - pdal


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    pdal-1.7.2                 |   py35h33f895e_1         8.6 MB  conda-forge
    setuptools-39.2.0          |           py35_0         591 KB  conda-forge
    numpy-1.14.3               |   py35h9fa60d3_2          42 KB
    ------------------------------------------------------------
                                           Total:         9.2 MB

The following NEW packages will be INSTALLED:

    boost:           1.66.0-py35_vc14_1    conda-forge [vc14]
    boost-cpp:       1.66.0-vc14_1         conda-forge [vc14]
    ca-certificates: 2018.4.16-0           conda-forge
    cairo:           1.14.10-vc14_0        conda-forge [vc14]
    certifi:         2018.4.16-py35_0      conda-forge
    curl:            7.60.0-vc14_0         conda-forge [vc14]
    expat:           2.2.5-vc14_0          conda-forge [vc14]
    flann:           1.9.1-h0953f56_2      conda-forge
    freexl:          1.0.5-vc14_0          conda-forge [vc14]
    geos:            3.6.2-vc14_1          conda-forge [vc14]
    geotiff:         1.4.2-vc14_1          conda-forge [vc14]
    hdf4:            4.2.13-vc14_0         conda-forge [vc14]
    hdf5:            1.10.1-vc14_2         conda-forge [vc14]
    hexer:           1.4.0-vc14_1          conda-forge [vc14]
    icc_rt:          2017.0.4-h97af966_0
    icu:             58.2-vc14_0           conda-forge [vc14]
    intel-openmp:    2018.0.3-0
    jpeg:            9b-vc14_2             conda-forge [vc14]
    jsoncpp:         1.8.1-vc14_0          conda-forge [vc14]
    kealib:          1.4.7-vc14_4          conda-forge [vc14]
    krb5:            1.14.6-vc14_0         conda-forge [vc14]
    laszip:          3.2.2-vc14_0          conda-forge [vc14]
    laz-perf:        1.2.0-vc14_1          conda-forge [vc14]
    libgdal:         2.2.4-vc14_4          conda-forge [vc14]
    libiconv:        1.15-vc14_0           conda-forge [vc14]
    libnetcdf:       4.6.1-vc14_2          conda-forge [vc14]
    libpng:          1.6.34-vc14_0         conda-forge [vc14]
    libpq:           9.6.3-vc14_0          conda-forge [vc14]
    libspatialite:   4.3.0a-vc14_19        conda-forge [vc14]
    libssh2:         1.8.0-vc14_2          conda-forge [vc14]
    libtiff:         4.0.9-vc14_0          conda-forge [vc14]
    libxml2:         2.9.8-vc14_0          conda-forge [vc14]
    libxslt:         1.1.32-vc14_0         conda-forge [vc14]
    mkl:             2018.0.3-1
    mkl_fft:         1.0.2-py35_0          conda-forge
    mkl_random:      1.0.1-py35_0          conda-forge
    nitro:           2.7.dev2-vc14_0       conda-forge [vc14]
    numpy:           1.14.3-py35h9fa60d3_2
    numpy-base:      1.14.3-py35h5c71026_0
    openjpeg:        2.3.0-vc14_2          conda-forge [vc14]
    openssl:         1.0.2o-vc14_0         conda-forge [vc14]
    pcl:             1.8.1-hd76163c_1      conda-forge
    pdal:            1.7.2-py35h33f895e_1  conda-forge
    pip:             9.0.3-py35_0          conda-forge
    pixman:          0.34.0-vc14_2         conda-forge [vc14]
    postgresql:      10.3-py35_vc14_0      conda-forge [vc14]
    proj4:           4.9.3-vc14_5          conda-forge [vc14]
    python:          3.5.5-1               conda-forge
    setuptools:      39.2.0-py35_0         conda-forge
    sqlite:          3.20.1-vc14_2         conda-forge [vc14]
    vc:              14-0                  conda-forge
    vs2015_runtime:  14.0.25420-0          conda-forge
    wheel:           0.31.0-py35_0         conda-forge
    wincertstore:    0.2-py35_0            conda-forge
    xerces-c:        3.2.0-vc14_0          conda-forge [vc14]
    xz:              5.2.3-0               conda-forge
    zlib:            1.2.11-vc14_0         conda-forge [vc14]

Downloading and Extracting Packages
pdal-1.7.2           |  8.6 MB | ###################################### | 100%
setuptools-39.2.0    |  591 KB | ###################################### | 100%
numpy-1.14.3         |   42 KB | ###################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate myenv
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Note

PDAL’s Python extension is managed separately from the PDAL package. To install it, replace pdal with python-pdal in any of the commands in this section. Seeing as how PDAL is a dependency of the Python extension, you will actually get two for the price of one!

To install PDAL to an existing environment names myenv, we would run the following command.

conda install --name myenv --channel conda-forge pdal

Finally, to update PDAL to the latest version, run the following.

conda update pdal

Fetch Sample Data

We need some sample data to play with, so we’re going to download the autzen.laz file. Inside your terminal (assuming Windows), issue the following command:

explorer.exe https://github.com/PDAL/data/raw/master/autzen/autzen.laz

In the download dialog, save the file to your Downloads folder, e.g., C:\Users\hobu\Downloads.

What’s next?

  • Visit Applications to find out how to utilize PDAL applications to process data on the command line yourself.
  • Visit Development to learn how to embed and use PDAL in your own applications.
  • Readers lists the formats that PDAL can read, Filters lists the kinds of operations you can do with PDAL, and Writers lists the formats PDAL can write.
  • Tutorials contains a number of walk-through tutorials for achieving many tasks with PDAL.
  • The PDAL workshop contains numerous hands-on examples with screenshots and example data of how to use PDAL Applications to tackle point cloud data processing tasks.
  • Python describes how PDAL embeds and extends Python and how you can leverage these capabilities in your own programs.

See also

Community is a good source to reach out to when you’re stuck.