Dependencies

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

Documentaion

Detailed documentation can be found in the software wiki and on the official site.

Installation

1. 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 three different packages available:

• slsdetlib shared libraries and command line utilities
• slsdetgui GUI
• slsdet Python bindings

#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
etc ...

# List available versions
# lib and binaries
conda search slsdetlib
# python
conda search slsdet
# gui
conda search slsdetgui

2. Build from source

2.1 Download Source Code from github

git clone https://github.com/slsdetectorgroup/slsDetectorPackage.git --branch 7.0.0

Pybind for Python

• v7.0.0+: 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.

# 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

2.2 Build from source

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
# then press [g] - generate

Example cmake options	Comment
-DSLS_USE_PYTHON=ON	Python
-DPython_FIND_VIRTUALENV=ONLY	Python from only the conda environment
-DZeroMQ_HINT=/usr/lib64	Use system zmq instead
-DSLS_USE_GUI=ON	GUI

Build using in-built cmk.sh script

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

Usage: ./cmk.sh [-b] [-c] [-d <HDF5 directory>] [e] [g] [-h] [i] [-j <Number of threads>] 
[-k <CMake command>] [-l <Install directory>] [m] [n] [-p] [-q <Zmq hint directory>] 
[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
-q: Zmq hint directory
-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

#To use the system zmq (/usr/lib64) instead
./cmk.sh -cbj5 -q /usr/lib64

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 zmq
conda activate myenv

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

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 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 zeromq gxx_linux-64 gxx_linux-64 mesa-libgl-devel-cos6-x86_64 qt
# on fedora or newer systems
conda create -n slsgui zeromq 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

Build documentation from package

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 sphinx_rtd_theme breathe

# 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

Support

dhanya.thattil@psi.ch
erik.frojdh@psi.ch