Update README file

.github/workflows/conda-build.yml

@@ -0,0 +1,43 @@
+name: Conda Build
+
+on:
+  push:
+    branches:
+      - development  # You can change this to the branch you want to trigger on
+
+jobs:
+  build-linux:
+    name: CondaBuild (${{ matrix.python-version }}, ${{ matrix.os }})
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: ["ubuntu-latest", "macos-latest", "windows-latest"]
+        python-version: ["3.8", "3.11"]
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v2
+
+      - name: Set up Conda
+        uses: conda-incubator/setup-miniconda@v3
+        with:
+          auto-update-conda: true
+          python-version: ${{ matrix.python-version }}
+          mamba-version: "*"
+          channels: conda-forge,defaults
+          channel-priority: true
+
+      - name: Display Conda Settings
+        shell: bash -el {0}
+        run: |
+          conda info
+          conda list
+          conda config --show-sources
+          conda config --show
+          printenv | sort
+
+      - name: Build Conda Package
+        shell: bash -el {0}
+        run: |
+          mamba install conda-build boa
+          conda mambabuild conda-recipe

.gitignore

@@ -4,6 +4,7 @@ dist/
 doc/auto_examples
 tmp/
 .vscode
+.cache
 .vs
 .ipynb_checkpoints
 cmake-build-*/

.gitlab-ci.yml

@@ -2,23 +2,10 @@ variables:
   GIT_SSL_NO_VERIFY: "1"
 
 stages:
-#  - sync
   - build
   - test
   - deploy
 
-# github:push:
-#   stage: sync
-#   image: condaforge/mambaforge
-#   tags:
-#     - linux
-#   before_script:
-#     - git submodule update --init --recursive --remote
-#   script:
-#     - git switch -c ${CI_COMMIT_REF_NAME}
-#     - git push --force https://${GITHUB_TOKEN}@github.com/fluorescence-tools/imp.bff
-
-
 # general definitions
 .build: &build
   stage: build
@@ -47,21 +34,21 @@ conda:build:lnx:x86:
     - linux
     - x86_64
 conda:build:lnx:ppc64le:
-  <<: *conda_build_lnx
-  image:
-    entrypoint: [ "/bin/bash", "-i", "-c" ]
-    name: condaforge/linux-anvil-ppc64le
-  tags:
-    - linux
-    - ppc64le
+ <<: *conda_build_lnx
+ image:
+   entrypoint: [ "/bin/bash", "-i", "-c" ]
+   name: condaforge/linux-anvil-ppc64le
+ tags:
+   - linux
+   - ppc64le
 conda:build:lnx:aarch64:
-  <<: *conda_build_lnx
-  image:
-    entrypoint: [ "/bin/bash", "-i", "-c" ]
-    name: condaforge/linux-anvil-aarch64
-  tags:
-    - linux
-    - aarch64
+ <<: *conda_build_lnx
+ image:
+   entrypoint: [ "/bin/bash", "-i", "-c" ]
+   name: condaforge/linux-anvil-aarch64
+ tags:
+   - linux
+   - aarch64
 
 conda:build:osx:
   <<: *conda_build_posix

README.md

@@ -1,11 +1,12 @@
 # tttrlib
 [![Anaconda-Server Badge](https://anaconda.org/tpeulen/tttrlib/badges/version.svg)](https://anaconda.org/tpeulen/tttrlib)
 [![PyPI version](https://badge.fury.io/py/tttrlib.svg)](https://pypi.org/project/tttrlib/)
-[![pipeline status](https://gitlab.peulen.xyz/tpeulen/tttrlib/badges/master/pipeline.svg)](https://gitlab.peulen.xyz/tpeulen/tttrlib/badges/master/pipeline.svg)
+![conda build](https://github.com/fluorescence-tools/tttrlib/actions/workflows/conda-build.yml/badge.svg)
+
 
 ## General description
 tttrlib is a file format agnostic high performance library to
-read and process time-tagged-time resolved (TTTR) data acquired by
+read, process, and write time-tagged-time resolved (TTTR) data acquired by
 PicoQuant (PQ) and Becker & Hickl measurement devices/cards or TTTR
 files in the open Photon-HDF format.
 
@@ -25,10 +26,90 @@ Scilab and R. Currently, tttrlib is wrapped for the use in Python.
 
 ![tttrlib FLIM][3]
 
-tttrlib is a library that facilitates the interaction with TTTR data that can be
-used to develop data analysis pipelines e.g. for single-molecule and image
-spectroscopy. tttrlib is not intended as end-user software for specific application
-purposes.  
+`tttrlib` is programmed in C++ and wrapped for python. Thus, it can be used to integrate time-resolved data into 
+advanced data analysis pipelines.
+
+### Capabilities
+
+* Fast reading TTTR files (IO limited)
+* Generation / analysis of fluorescence decays
+* Time window analysis
+* Correlation of time event traces
+* Filtering of time event traces to generate instrument response functions for fluorescence decays analysis without the need of independent measurements..
+* Fast photon distribution analysis
+* Fast selection of photons from a photon stream
+
+Generation of fluorescence decay histograms tttrlib outperforms pure numpy and Python based
+libraries by a factor of ~40.
+
+## Documentation
+
+### Installation
+In an [anaconda](https://www.anaconda.com/) environment the library can
+be installed by the following command:
+
+```console
+conda install -c tpeulen tttrlib
+```
+
+Alternatively, you can use pip to install `tttrlib`
+
+```console
+pip install tttrlib
+```
+
+### Usage
+The API of tttrlib as well as some use cases are documented on its [web page](https://docs.peulen.xyz/tttrlib).
+Below you find a small selection of code snippets.
+
+Access photon data as follows:
+```python
+import tttrlib
+fn = 'photon_stream.ptu'
+data = tttrlib.TTTR(fn)
+
+macro_times = data.macro_times
+micro_times = data.micro_times
+routing_channels = data.routing_channels
+```
+
+Print header-information:
+```python
+import tttrlib
+fn = 'photon_stream.ptu'
+data = tttrlib.TTTR(fn)
+print(data.json)
+```
+
+Correlate photon streams:
+```python
+import tttrlib
+fn = 'photon_stream.ptu'
+data = tttrlib.TTTR(fn)
+correlator = tttrlib.Correlator(
+    channels=([1], [2]),
+    tttr=data
+)
+taus = correlator.x_axis,
+correlation_amplitude = correlator.correlation
+```
+
+Create intensity images from CLSM data:
+```python
+import tttrlib
+fn = 'image.ptu'
+data = tttrlib.TTTR(fn)
+clsm = tttrlib.CLSM(data)
+
+channels = [0, 1]
+prompt_range = [0, 16000]
+clsm.fill(channels=channels, micro_time_ranges=[prompt_range])
+
+intensity_image = clsm.intensity
+```
+
+tttrlib is in active development. In case you notice unusual behaviour do not
+hesitate to contact the authors.
 
 ## Supported file formats
 
@@ -44,27 +125,9 @@ purposes.
 ### Photon HDF5
 
 ## Design goals
-* Low memory footprint (keep objective large datasets, e.g.  FLIM in memory).
+* Low memory footprint (keep objective large datasets, e.g., FLIM in memory).
 * Platform independent C/C++ library with interfaces for scripting libraries
 
-## Capabilities
-
-* Fast (IO limited) Reading TTTR files
-* Generation / analysis of fluorescence decays
-* Time window analysis
-* Correlation of time event traces
-* Filtering of time event traces to generate instrument response functions for fluorescence decays analysis without the need of independent measurements..
-* Fast photon distribution analysis
-* Fast selection of photons from a photon stream
-
-Generation of fluorescence decay histograms tttrlib outperforms pure numpy and Python based
-libraries by a factor of ~40.
-
-## Implementation
-
-Pure pure C/C++ high performance algorithms for real-time and interactive
-analysis of TTTR data.
-
 ## Building and Installation
 
 ### C++ shared library
@@ -78,7 +141,7 @@ cmake ..
 sudo make install
 ```
 
-On Linux you can build and install a package instead (prefered):
+On Linux you can build and install a package instead:
 
 ### Python bindings
 
@@ -100,40 +163,18 @@ be installed by the following command:
 conda install -c tpeulen tttrlib
 ```
 
-For most users the later approach is recommended. Currently, pre-compiled
+For most users, the latter approach is recommended. Currently, pre-compiled
 packages for the anaconda distribution system are available for Windows (x86),
 Linux (x86, ARM64, PPCle), and macOS (x86). Precompiled libary are linked against 
 conda-forge HDF5 & Boost. Thus, the use of [miniforge](https://github.com/conda-forge/miniforge) 
-is highly recommended.
+is recommended.
 
 Legacy 32-bit platforms and versions of programming languages, e.g., Python 2.7
 are not supported.
 
-### Pip install
-
-Ubuntu: 
-
-Self compiled
-
-```console
-sudo apt-get install libhdf5-dev boost-dev swig
-```
-
-```console
-pip install https://github.com/fluorescence-tools/tttrlib
-```
-
-## Documentation
-
-The API of tttrlib as well as some use cases are documented
-on its [web page](https://docs.peulen.xyz/tttrlib)
-
-Note, tttrlib is highly experimental library in current development. In
-case you notice unusual behaviour do not hesitate to contact the authors.
-
 ## License
 
-Copyright 2007-2023 tttrlib developers.
+Copyright 2007-2024 tttrlib developers.
 Licensed under the BSD-3-Clause
 
-[3]: https://docs.peulen.xyz/tttrlib/_images/sphx_glr_plot_read_clsm_data_002.png "tttrlib FLIM"
+[3]: https://github.com/fluorescence-tools/tttrlib/doc/logos/mashup.png "tttrlib FLIM"

conda-recipe/bld.bat

@@ -1,5 +1,4 @@
 cd %SRC_DIR%
-git submodule update --recursive --init --remote
 
 echo "Build app wrapper"
 :: build app wrapper
@@ -10,20 +9,25 @@ if errorlevel 1 exit 1
 rmdir b2 /s /q
 mkdir b2
 cd b2
-for /f %%A in ('python -c "import platform; print(platform.python_version())"') do set python_version=%%A
-echo Python version: %python_version%
+
+REM Call Python with the --version flag to get the version information
+for /f "tokens=2 delims= " %%v in ('%PYTHON% --version 2^>^&1') do set PYTHON_VERSION=%%v
+
+REM Extract only the numeric part of the version
+for /f "tokens=1-3 delims=." %%a in ("%PYTHON_VERSION%") do set PYTHON_VERSION_NUMERIC=%%a.%%b.%%c
+
+echo Python version: %PYTHON_VERSION_NUMERIC%
 cmake .. -G "NMake Makefiles" ^
  -DCMAKE_INSTALL_PREFIX="%LIBRARY_PREFIX%" ^
  -DCMAKE_PREFIX_PATH="%PREFIX%" ^
  -DBUILD_PYTHON_INTERFACE=ON ^
  -DCMAKE_BUILD_TYPE=Release ^
  -DCMAKE_LIBRARY_OUTPUT_DIRECTORY="%SP_DIR%" ^
  -DCMAKE_SWIG_OUTDIR="%SP_DIR%" ^
- -DPYTHON_VERSION="%python_version%" ^
+ -DPYTHON_VERSION="%PYTHON_VERSION_NUMERIC%" ^
  -DBUILD_LIBRARY=ON
 nmake install
 
-
 :: Add wrappers to path for each Python command line tool
 :: (all files without an extension)
 cd %SRC_DIR%\bin

conda-recipe/build.sh

@@ -1,4 +1,3 @@
-git submodule update --recursive --init --remote
 mkdir b2 && cd b2
 
 if [[ "${target_platform}" == osx-* ]]; then