Merge pull request #72 from amepproject/website-readme-optimization

Website readme optimization
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diff --git a/README.md b/README.md
@@ -22,68 +22,38 @@ format. To be fast and usable on modern HPC (**H**igh **P**erformance
 **C**omputing) hardware, the methods are optimized to run also in parallel.
 
 
-# Description
+# How to cite AMEP
 
-The **AMEP** Python library provides a unified framework for handling 
-both particle-based and continuum simulation data. It is made for the analysis 
-of molecular-dynamics (MD), Brownian-dynamics (BD), and continuum simulation 
-data of condensed matter systems and active matter systems in particular. 
-**AMEP** provides a huge variety of analysis methods for both data types that 
-allow to evaluate various dynamic and static observables based on the 
-trajectories of the particles or the time evolution of continuum fields. For 
-fast and efficient data handling, **AMEP** provides a unified framework for 
-loading and storing simulation data and analysis results in a compressed, 
-HDF5-based data format. **AMEP** is written purely in Python and uses powerful 
-libraries such as NumPy, SciPy, Matplotlib, and scikit-image commonly used in 
-computational physics. Therefore, understanding, modifying, and building up on 
-the provided framework is comparatively easy. All evaluation functions are 
-optimized to run efficiently on HPC hardware to provide fast computations. To 
-plot and visualize simulation data and analysis results, **AMEP** provides an 
-optimized plotting framework based on the Matplotlib Python library, which 
-allows to easily plot and animate particles, fields, and lines. Compared to 
-other analysis libraries, the huge variety of analysis methods combined with 
-the possibility to handle both most common data types used in soft-matter 
-physics and in the active matter community in particular, enables the analysis 
-of a much broader class of simulation data including not only classical 
-molecular-dynamics or Brownian-dynamics simulations but also any kind of 
-numerical solutions of partial differential equations. The following table 
-gives an overview on the observables provided by **AMEP** and on their 
-capability of running in parallel and processing particle-based and continuum 
-simulation data.
+If you use **AMEP** for a project that leads to a scientific publication, please acknowledge 
+the use of **AMEP** within the body of your publication for example by copying or adapting 
+the following formulation:
+
+*Data analysis for this publication utilized the AMEP library [1].*
 
+> [1] L. Hecht, K.-R. Dormann, K. L. Spanheimer, M. Ebrahimi, M. Cordts, S. Mandal, 
+>     A. K. Mukhopadhyay, and B. Liebchen, AMEP: The Active Matter Evaluation Package for Python, 
+>     *arXiv [Cond-Mat.Soft]* (2024). Available at: http://arxiv.org/abs/2404.16533.
 
-| Observable | Parallel | Particles | Fields |
-|:-----------|:--------:|:---------:|:------:|
-| **Spatial Correlation Functions:** ||||
-| RDF (radial pair distribution function) | ✔ | ✔ | ❌ |
-| PCF2d (2d pair correlation function) | ✔ | ✔ | ❌ |
-| PCFangle (angular pair correlation function) | ✔ | ✔ | ❌ |
-| SFiso (isotropic static structure factor) | ✔ | ✔ | ✔ |
-| SF2d (2d static structure factor) | ✔ | ✔ | ✔ |
-| SpatialVelCor (spatial velocity correlation function) | ✔ | ✔ | ❌ |
-| PosOrderCor (positional order correlation function) | ✔ | ✔ | ❌ |
-| HexOrderCor (hexagonal order correlation function) | ✔ | ✔ | ❌ |
-| **Local Order:** ||||
-| Voronoi tesselation | ❌ | ✔ | ❌ |
-| Local density | ❌ | ✔ | ✔ |
-| Local packing fraction | ❌ | ✔ | ❌ |
-| k-atic bond order parameter | ❌ | ✔ | ❌ |
-| Next/nearest neighbor search | ❌ | ✔ | ❌ |
-| **Time Correlation Functions:** ||||
-| MSD (mean square displacement) | ❌ | ✔ | ❌ |
-| VACF (velocity autocorrelation function) | ❌ | ✔ | ❌ |
-| OACF (orientation autocorrelation function) | ❌ | ✔ | ❌ |
-| **Cluster Analysis:** ||||
-| Clustersize distribution | ❌ | ✔ | ✔ |
-| Cluster growth | ❌ | ✔ | ✔ |
-| Radius of gyration | ❌ | ✔ | ✔ |
-| Linear extension | ❌ | ✔ | ✔ |
-| Center of mass | ❌ | ✔ | ✔ |
-| Gyration tensor | ❌ | ✔ | ✔ |
-| Inertia tensor | ❌ | ✔ | ✔ |
-| **Miscellaneous:** ||||
-| Translational/rotational kinetic energy | ❌ | ✔ | ❌ |
-| Kinetic temperature | ❌ | ✔ | ❌ |
+The pre-print is freely available on [arXiv](https://arxiv.org/abs/2404.16533). To cite this reference, 
+you can use the following BibTeX entry:
+
+```bibtex
+@misc{hecht2024amep,
+    title = {AMEP: The Active Matter Evaluation Package for Python}, 
+    author = {Lukas Hecht and 
+              Kay-Robert Dormann and 
+              Kai Luca Spanheimer and 
+              Mahdieh Ebrahimi and 
+              Malte Cordts and 
+              Suvendu Mandal and 
+              Aritra K. Mukhopadhyay and 
+              Benno Liebchen},
+    year = {2024},
+    eprint = {2404.16533},
+    archivePrefix = {arXiv},
+    primaryClass = {cond-mat.soft}
+}
+```
 
 
 # Installation
@@ -146,40 +116,6 @@ Anaconda installation path.
 to download FFmpeg and to get further information on how to install FFmpeg on your machine.
 
 
-# Citation
-
-If you use **AMEP** for a project that leads to a scientific publication, please acknowledge 
-the use of **AMEP** within the body of your publication for example by copying or adapting 
-the following formulation:
-
-*Data analysis for this publication utilized the AMEP library [1].*
-
-> [1] L. Hecht, K.-R. Dormann, K. L. Spanheimer, M. Ebrahimi, M. Cordts, S. Mandal, 
->     A. K. Mukhopadhyay, and B. Liebchen, AMEP: The Active Matter Evaluation Package for Python, 
->     *arXiv [Cond-Mat.Soft]* (2024). Available at: http://arxiv.org/abs/2404.16533.
-
-The pre-print is freely available on [arXiv](https://arxiv.org/abs/2404.16533). To cite this reference, 
-you can use the following BibTeX entry:
-
-```bibtex
-@misc{hecht2024amep,
-    title = {AMEP: The Active Matter Evaluation Package for Python}, 
-    author = {Lukas Hecht and 
-              Kay-Robert Dormann and 
-              Kai Luca Spanheimer and 
-              Mahdieh Ebrahimi and 
-              Malte Cordts and 
-              Suvendu Mandal and 
-              Aritra K. Mukhopadhyay and 
-              Benno Liebchen},
-    year = {2024},
-    eprint = {2404.16533},
-    archivePrefix = {arXiv},
-    primaryClass = {cond-mat.soft}
-}
-```
-
-
 # Getting started
 
 The following example briefly demonstrates the **AMEP** workflow. A typical 
@@ -218,6 +154,70 @@ fig.savefig(rdf.name + '.pdf')
 For more detailed examples, check the [examples](https://github.com/amepproject/amep/tree/main/examples) directory.
 
 
+# Project description
+
+The **AMEP** Python library provides a unified framework for handling 
+both particle-based and continuum simulation data. It is made for the analysis 
+of molecular-dynamics (MD), Brownian-dynamics (BD), and continuum simulation 
+data of condensed matter systems and active matter systems in particular. 
+**AMEP** provides a huge variety of analysis methods for both data types that 
+allow to evaluate various dynamic and static observables based on the 
+trajectories of the particles or the time evolution of continuum fields. For 
+fast and efficient data handling, **AMEP** provides a unified framework for 
+loading and storing simulation data and analysis results in a compressed, 
+HDF5-based data format. **AMEP** is written purely in Python and uses powerful 
+libraries such as NumPy, SciPy, Matplotlib, and scikit-image commonly used in 
+computational physics. Therefore, understanding, modifying, and building up on 
+the provided framework is comparatively easy. All evaluation functions are 
+optimized to run efficiently on HPC hardware to provide fast computations. To 
+plot and visualize simulation data and analysis results, **AMEP** provides an 
+optimized plotting framework based on the Matplotlib Python library, which 
+allows to easily plot and animate particles, fields, and lines. Compared to 
+other analysis libraries, the huge variety of analysis methods combined with 
+the possibility to handle both most common data types used in soft-matter 
+physics and in the active matter community in particular, enables the analysis 
+of a much broader class of simulation data including not only classical 
+molecular-dynamics or Brownian-dynamics simulations but also any kind of 
+numerical solutions of partial differential equations. The following table 
+gives an overview on the observables provided by **AMEP** and on their 
+capability of processing particle-based and continuum 
+simulation data.
+
+
+| Observable | Particles | Fields |
+|:-----------|:---------:|:------:|
+| **Spatial Correlation Functions:** |||
+| RDF (radial pair distribution function) | ✔ | ➖ |
+| PCF2d (2d pair correlation function) | ✔ | ➖ |
+| PCFangle (angular pair correlation function) | ✔ | ➖ |
+| SFiso (isotropic static structure factor) | ✔ | ✔ |
+| SF2d (2d static structure factor) | ✔ | ✔ |
+| SpatialVelCor (spatial velocity correlation function) | ✔ | ➖ |
+| PosOrderCor (positional order correlation function) | ✔ | ➖ |
+| HexOrderCor (hexagonal order correlation function) | ✔ | ➖ |
+| **Local Order:** |||
+| Voronoi tesselation | ✔ | ➖ |
+| Local density | ✔ | ✔ |
+| Local packing fraction | ✔ | ➖ |
+| k-atic bond order parameter | ✔ | ➖ |
+| Next/nearest neighbor search | ✔ | ➖ |
+| **Time Correlation Functions:** |||
+| MSD (mean square displacement) | ✔ | ➖ |
+| VACF (velocity autocorrelation function) | ✔ | ➖ |
+| OACF (orientation autocorrelation function) | ✔ | ➖ |
+| **Cluster Analysis:** |||
+| Clustersize distribution | ✔ | ✔ |
+| Cluster growth | ✔ | ✔ |
+| Radius of gyration | ✔ | ✔ |
+| Linear extension | ✔ | ✔ |
+| Center of mass | ✔ | ✔ |
+| Gyration tensor | ✔ | ✔ |
+| Inertia tensor | ✔ | ✔ |
+| **Miscellaneous:** |||
+| Translational/rotational kinetic energy | ✔ | ➖ |
+| Kinetic temperature | ✔ | ➖ |
+
+
 # Module descriptions
 
 In the following, we provide a list of all **AMEP** modules together with a 

diff --git a/amep/load.py b/amep/load.py
@@ -33,7 +33,7 @@
 import os
 import h5py
 
-from .reader import LammpsReader, H5amepReader, ContinuumReader, GSDReader
+from .reader import LammpsReader, H5amepReader, ContinuumReader
 from .trajectory import ParticleTrajectory,FieldTrajectory
 from .base import TRAJFILENAME, BaseEvalData, BaseDatabase, LOADMODES
 from .base import check_path, get_module_logger
@@ -226,16 +226,7 @@ def traj(
             **kwargs
         )
         return FieldTrajectory(reader)
-    elif mode == 'gsd':
-        reader = GSDReader(
-            directory,
-            savedir,
-            trajfile = trajfile,
-            deleteold = deleteold,
-            verbose = verbose,
-            **kwargs
-        )
-        return ParticleTrajectory(reader)
+
     # here one has to check both the amep version with which the file has been
     # created (reader.version) and the data type (particles or fields) -
     # the latter is needed to decide whether a ParticleTrajectory or a

diff --git a/amep/plot.py b/amep/plot.py
@@ -909,7 +909,7 @@ def box(axis: mpl.axes.Axes, box_boundary: np.ndarray, **kwargs) -> None:
 
 
 def particles(
-        ax: mpl.axes, coords: np.ndarray, box_boundary: np.ndarray,
+        ax: mpl.axes.Axes, coords: np.ndarray, box_boundary: np.ndarray,
         radius: np.ndarray | float, scalefactor: float = 1.0,
         values: np.ndarray | None = None,
         cmap: list | str = 'viridis', set_ax_limits: bool = True,
@@ -1081,7 +1081,7 @@ def particles(
 
 
 def field(
-        ax: mpl.axes, density: np.ndarray, X: np.ndarray, Y: np.ndarray,
+        ax: mpl.axes.Axes, density: np.ndarray, X: np.ndarray, Y: np.ndarray,
         cmap: list | str = 'plasma', box_boundary: np.ndarray | None = None,
         vmin: float | None = None, vmax: float | None = None,
         cscale: str = 'lin', verbose: bool = False, **kwargs