Installation

One can either install pre-built binaries using conda or build from source.

Warning

Before building from source make sure that you have the dependencies installed. If installing using conda, conda will manage the dependencies. Avoid also installing packages with pip.

Install binaries using conda

Conda is not only useful to manage python environments but can also be used as a user space package manager. Dates in the tag (for eg. 2020.07.23.dev0) are from the developer branch. Please use released tags for stability.

We have four different packages available:

Package

Description

slsdetlib

shared libraries and command line utilities

slsdetgui

GUI

slsdet

Python bindings

moenchzmq

moench

#Add channels for dependencies and our library
conda config --add channels conda-forge
conda config --add channels slsdetectorgroup
conda config --set channel_priority strict

#create and activate an environment with our library
#replace 6.1.1 with the required tag
conda create -n myenv slsdetlib=6.1.1
conda activate myenv

#ready to use
sls_detector_get exptime
...
#List available versions
# lib and binaries
conda search slsdetlib
# python
conda search slsdet
# gui
conda search slsdetgui
# moench
conda search moenchzmq

Build from source

1. Download Source Code from github

git clone https://github.com/slsdetectorgroup/slsDetectorPackage.git --branch 6.1.1

Note

For v6.x.x of slsDetectorPackage and older, refer pybind11 notes on cloning.

2. Build from Source

One can either build using cmake or use the in-built cmk.sh script.

Build using CMake

# outside slsDetecorPackage folder
mkdir build && cd build

# configure & generate Makefiles using cmake
# by listing all your options (alternately use ccmake described below)
# cmake3 for some systems
cmake ../slsDetectorPackage -DCMAKE_INSTALL_PREFIX=/your/install/path

# compiled to the build/bin directory
make -j12 #or whatever number of cores you are using to build

# install headers and libs in /your/install/path directory
make install

Instead of the cmake command, one can use ccmake to get a list of options to configure and generate Makefiles at ease.

# ccmake3 for some systems
ccmake ..

# choose the options
# first press [c] - configure (until you see [g])
# then press [g] - generate

Example cmake options

Comment

-DSLS_USE_PYTHON=ON

Python

-DPython_FIND_VIRTUALENV=ONLY

Python from the conda env

-DSLS_USE_GUI=ON

GUI

-DSLS_USE_HDF5=ON

HDF5

-DSLS_USE_SIMULATOR=ON

Simulator

Note

For v7.x.x of slsDetectorPackage and older, refer zeromq notes for cmake option to hint library location.

Build using in-built cmk.sh script

The binaries are generated in slsDetectorPackage/build/bin directory.

Usage: $0 [-b] [-c] [-d <HDF5 directory>] [-e] [-g] [-h] [-i]
[-j <Number of threads>] [-k <CMake command>] [-l <Install directory>]
[-m] [-n] [-p] [-r] [-s] [-t] [-u] [-z]
-[no option]: only make
-b: Builds/Rebuilds CMake files normal mode
-c: Clean
-d: HDF5 Custom Directory
-e: Debug mode
-g: Build/Rebuilds gui
-h: Builds/Rebuilds Cmake files with HDF5 package
-i: Builds tests
-j: Number of threads to compile through
-k: CMake command
-l: Install directory
-m: Manuals
-n: Manuals without compiling doxygen (only rst)
-p: Builds/Rebuilds Python API
-r: Build/Rebuilds only receiver
-s: Simulator
-t: Build/Rebuilds only text client
-u: Chip Test Gui
-z: Moench zmq processor


# display all options
./cmk.sh -?

# new build and compile in parallel (recommended basic option):
./cmk.sh -cbj5

# new build, python and compile in parallel:
./cmk.sh -cbpj5

#For rebuilding only certain sections
./cmk.sh -tg #only text client and gui
./cmk.sh -r #only receiver

Note

For v7.x.x of slsDetectorPackage and older, refer zeromq notes for cmk script option to hint library location.

Build on old distributions

If your linux distribution doesn’t come with a C++11 compiler (gcc>4.8) then it’s possible to install a newer gcc using conda and build the slsDetectorPackage using this compiler

#Create an environment with the dependencies
conda create -n myenv gxx_linux-64 cmake
conda activate myenv

# outside slsDetecorPackage folder
mkdir build && cd build
cmake ../slsDetectorPackage -DCMAKE_PREFIX_PATH=$CONDA_PREFIX
make -j12

Note

For v7.x.x of slsDetectorPackage and older, refer zeromq notes for dependencies for conda.

Build slsDetectorGui (Qt5)

  1. Using pre-built binary on conda

    conda create -n myenv slsdetgui=7.0.0
    conda activate myenv
    
  2. Using system installation on RHEL7

    yum install qt5-qtbase-devel.x86_64
    yum install qt5-qtsvg-devel.x86_64
    
  3. Using system installation on RHEL8

    yum install qt5-qtbase-devel.x86_64
    yum install qt5-qtsvg-devel.x86_64
    yum install expat-devel.x86_64
    
  4. Using conda

    #Add channels for dependencies and our library
    conda config --add channels conda-forge
    conda config --add channels slsdetectorgroup
    conda config --set channel_priority strict
    
    # create environment to compile
    # on rhel7
    conda create -n slsgui gxx_linux-64 gxx_linux-64 mesa-libgl-devel-cos6-x86_64 qt
    # on fedora or newer systems
    conda create -n slsgui qt
    
    # when using conda compilers, would also need libgl, but no need for it on fedora unless maybe using it with ROOT
    
    # activate environment
    conda activate slsgui
    
    # compile with cmake outside slsDetecorPackage folder
    mkdir build && cd build
    cmake ../slsDetectorPackage -DSLS_USE_GUI=ON
    make -j12
    
    # or compile with cmk.sh
    cd slsDetectorPackage
    ./cmk.sh -cbgj9
    

Note

For v7.x.x of slsDetectorPackage and older, refer zeromq notes for dependencies for conda.

Build this documentation

The documentation for the slsDetectorPackage is build using a combination of Doxygen, Sphinx and Breathe. The easiest way to install the dependencies is to use conda

conda create -n myenv python=3.12 sphinx sphinx_rtd_theme breathe doxygen numpy
# using cmake or ccmake to enable DSLS_BUILD_DOCS
# outside slsDetecorPackage folder
mkdir build && cd build
cmake ../slsDetectorPackage -DSLS_BUILD_DOCS=ON

make docs # generate API docs and build Sphinx RST
make rst # rst only, saves time in case the API did not change

Pybind and Zeromq

Pybind11 for Python
v8.0.0+:
pybind11 is built
* by default from tar file in repo (libs/pybind/v2.1x.0.tar.gz)
* or use advanced option SLS_FETCH_PYBIND11_FROM_GITHUB [link].
* v9.0.0+: pybind11 (v2.13.6)
* v8.x.x : pybind11 (v2.11.0)

v7.x.x:
pybind11 packaged into ‘libs/pybind’. No longer a submodule. No need for “recursive” or “submodule update”.

Older versions:
pybind11 is a submodule. Must be cloned using “recursive” and updated when switching between versions using the following commands.
# Note: Only for v6.x.x versions and older

# clone using recursive to get pybind11 submodule
git clone --recursive https://github.com/slsdetectorgroup/slsDetectorPackage.git

# update submodule when switching between releases
cd slsDetectorPackage
git submodule update --init
Zeromq
v8.0.0+:
zeromq (v4.3.4) is built
* by default from tar file in repo (libs/libzmq/libzmq-4.3.4.tar.gz)
* or use advanced option SLS_FETCH_ZMQ_FROM_GITHUB [link].

v7.x.x and older:
zeromq-devel must be installed and one can hint its location using
* cmake option:’-DZeroMQ_HINT=/usr/lib64’ or
* option ‘-q’ in cmk.sh script: : ./cmk.sh -cbj5 -q /usr/lib64
* ‘zeromq’ dependency added when installing using conda