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.
Installation
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
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
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 (maybe multiple times till 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)
Using pre-built binary on conda
conda create -n myenv slsdetgui=7.0.0 conda activate myenv
Using system installation on RHEL7
yum install qt5-qtbase-devel.x86_64 yum install qt5-qtsvg-devel.x86_64
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
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
# 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