Merge branch 'python' into cph-tutorial-cleanup

.codecov.yml

@@ -47,6 +47,7 @@ ignore:
   - "libs"
   - "doc/tutorials"
   - "samples"
+  - "maintainer/benchmarks"
 fixes:
   - "testsuite/python/save_checkpoint*::testsuite/python/save_checkpoint.py"
   - "testsuite/python/test_checkpoint*::testsuite/python/test_checkpoint.py"

.gitlab-ci.yml

@@ -290,7 +290,7 @@ tutorials-samples-maxset:
      make_check_python: 'false'
      make_check_tutorials: 'true'
      make_check_samples: 'true'
-     make_check_benchmarks: 'false'
+     make_check_benchmarks: 'true'
      test_timeout: '1200'
   script:
     - bash maintainer/CI/build_cmake.sh
@@ -313,7 +313,7 @@ tutorials-samples-default:
      make_check_python: 'false'
      make_check_tutorials: 'true'
      make_check_samples: 'true'
-     make_check_benchmarks: 'false'
+     make_check_benchmarks: 'true'
      test_timeout: '1200'
   script:
     - bash maintainer/CI/build_cmake.sh
@@ -338,7 +338,7 @@ tutorials-samples-empty:
      make_check_python: 'false'
      make_check_tutorials: 'true'
      make_check_samples: 'true'
-     make_check_benchmarks: 'false'
+     make_check_benchmarks: 'true'
      test_timeout: '1200'
      with_scafacos: 'false'
   script:
@@ -364,7 +364,7 @@ tutorials-samples-no-gpu:
      make_check_python: 'false'
      make_check_tutorials: 'true'
      make_check_samples: 'true'
-     make_check_benchmarks: 'false'
+     make_check_benchmarks: 'true'
      test_timeout: '1200'
      hide_gpu: 'true'
   script:

AUTHORS

@@ -75,6 +75,7 @@ Jonas Landsgesell
 Jon Halverson
 Joost de Graaf
 Josh Berryman
+Julian Hoßbach
 Julius Herb
 Kai Grass
 Kai Kratzer
@@ -102,6 +103,7 @@ Nils Binz
 Nishchay Suri
 Oleg V. Rud
 Owen A. Hickey-Moriarty
+Pablo Miguel Blanco Andrés
 Pascal Hebbeker
 Patrick Diggins
 Patrick Kreissl

doc/doxygen/bibliography.bib

@@ -589,17 +589,6 @@ @Article{tironi95a
   doi       = {10.1063/1.469273},
 }
 
-@Article{troester05a,
-  title = {{Wang-Landau sampling with self-adaptive range}},
-  author = {Tr\"{o}ster, Andreas and Dellago, Christoph},
-  journal = {Physical Review E},
-  volume = {71},
-  number = {6},
-  pages = {066705},
-  year = {2005},
-  doi = {10.1103/PhysRevE.71.066705},
-}
-
 @Article{tyagi10a,
   author    = {Tyagi, Sandeep and S\"{u}zen, Mehmet and Sega, Marcello and Barbosa, Marcia C. and Kantorovich, Sofia S. and Holm, Christian},
   title     = {{An iterative, fast, linear-scaling method for computing induced charges on arbitrary dielectric boundaries}},
@@ -630,17 +619,6 @@ @Article{wang01a
   doi       = {10.1063/1.1398588},
 }
 
-@Article{yan02b,
-author = {Yan, Qiliang and Faller, Roland and {de Pablo}, Juan J.},
-title = {{Density-of-states Monte Carlo method for simulation of fluids}},
-journal = {The Journal of Chemical Physics},
-volume = {116},
-number = {20},
-pages = {8745-8749},
-year = {2002},
-doi = {10.1063/1.1463055},
-}
-
 @Article{yeh99a,
   title                    = {{Ewald summation for systems with slab geometry}},
   author                   = {Yeh, In-Chul and Berkowitz, Max L.},

doc/sphinx/advanced_methods.rst

@@ -26,10 +26,10 @@ Several modes are available for different types of binding.
 * ``"bind_centers"``: adds a pair-bond between two particles at their first collision. By making the bonded interaction *stiff* enough, the particles can be held together after the collision. Note that the particles can still slide on each others' surface, as the pair bond is not directional. This mode is set up as follows::
 
     import espressomd
-    from espressomd.interactions import HarmonicBond
+    import espressomd.interactions
 
     system = espressomd.System(box_l=[1, 1, 1])
-    bond_centers = HarmonicBond(k=1000, r_0=<CUTOFF>)
+    bond_centers = espressomd.interactions.HarmonicBond(k=1000, r_0=<CUTOFF>)
     system.bonded_inter.add(bond_centers)
     system.collision_detection.set_params(mode="bind_centers", distance=<CUTOFF>,
                                           bond_centers=bond_centers)
@@ -100,7 +100,7 @@ Several modes are available for different types of binding.
 
         n_angle_bonds = 181  # 0 to 180 degrees in one degree steps
         for i in range(0, res, 1):
-            self.system.bonded_inter[i] = Angle_Harmonic(
+            self.system.bonded_inter[i] = espressomd.interactions.Angle_Harmonic(
                 bend=1, phi0=float(i) / (res - 1) * np.pi)
 
         # Create the bond passed to bond_centers here and add it to the system
@@ -454,9 +454,8 @@ end:
 
     for i in range(1, 101):
         system.integrator.run(steps=500)
-        cell.output_vtk_pos_folded(filename="output/sim1/cell_"
-                                   + str(i) + ".vtk")
-        print("time: ", str(i * time_step))
+        cell.output_vtk_pos_folded(filename=f"output/sim1/cell_{i}.vtk")
+        print(f"time: {i * time_step}")
     print("Simulation completed.")
 
 This simulation runs for 100 cycles. In each cycle, 500 integration
@@ -510,7 +509,7 @@ In the script, we have used the commands such as
 
 ::
 
-    cell.output_vtk_pos_folded(filename="output/sim1/cell_" + str(i) + ".vtk")
+    cell.output_vtk_pos_folded(filename=f"output/sim1/cell_{i}.vtk")
 
 to output the information about cell in every pass of the simulation
 loop. These files can then be used for inspection in ParaView and
@@ -1531,8 +1530,8 @@ and can be used to conveniently add a Drude particle to a given core particle.
 As it also adds the first two bonds between Drude and core, these bonds have to
 be created beforehand::
 
-    from drude_functions import *
-    add_drude_particle_to_core(<system>, <harmonic_bond>, <thermalized_bond>,
+    import espressomd.drude_helpers
+    espressomd.drude_helpers(<system>, <harmonic_bond>, <thermalized_bond>,
         <core particle>, <id drude>, <type drude>, <alpha>, <mass drude>,
         <coulomb_prefactor>, <thole damping>, <verbose>)
 
@@ -1562,7 +1561,7 @@ One bond type of this kind is needed per Drude type. The above helper function a
 tracks particle types, ids and charges of Drude and core particles, so a simple call of
 another helper function::
 
-    drude_helpers.setup_and_add_drude_exclusion_bonds(S)
+    espressomd.drude_helpers.setup_and_add_drude_exclusion_bonds(S)
 
 will use this data to create a :ref:`Subtract P3M short-range bond` per Drude type
 and set it up it between all Drude and core particles collected in calls of :meth:`~espressomd.drude_helpers.add_drude_particle_to_core`.
@@ -1588,7 +1587,7 @@ long-range part of the electrostatic interaction. Two helper methods assist
 with setting up this exclusion. If used, they have to be called
 after all Drude particles are added to the system::
 
-    setup_intramol_exclusion_bonds(<system>, <molecule drude types>,
+    espressomd.drude_helpers.setup_intramol_exclusion_bonds(<system>, <molecule drude types>,
         <molecule core types>, <molecule core partial charges>, <verbose>)
 
 This function creates the requires number of bonds which are later added to the
@@ -1605,7 +1604,7 @@ Parameters are:
 After setting up the bonds, one has to add them to each molecule with the
 following method::
 
-    add_intramol_exclusion_bonds(<system>, <drude ids>, <core ids>, <verbose>)
+    espressomd.drude_helpers.add_intramol_exclusion_bonds(<system>, <drude ids>, <core ids>, <verbose>)
 
 This method has to be called for all molecules and needs the following parameters:
 
@@ -1770,27 +1769,6 @@ be converted to :math:`K_c` as
 where :math:`p^{\ominus}=1\,\mathrm{atm}` is the standard pressure.
 Consider using the python module pint for unit conversion.
 
-.. _Wang-Landau Reaction Ensemble:
-
-Wang-Landau Reaction Ensemble
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Combination of the Reaction Ensemble with the Wang-Landau algorithm
-:cite:`wang01a`. Allows for enhanced sampling of the reacting system
-and for the determination of the density of states with respect
-to the reaction coordinate or with respect to some other collective
-variable :cite:`landsgesell17a`. Here the 1/t Wang-Landau
-algorithm :cite:`belardinelli07a` is implemented since it
-does not suffer from systematic errors.
-
-Multiple reactions and multiple collective variables can be set.
-
-An example script can be found here:
-
-* `Wang-Landau reaction ensemble <https://github.com/espressomd/espresso/blob/python/samples/wang_landau_reaction_ensemble.py>`__
-
-For a description of the available methods, see :class:`espressomd.reaction_ensemble.ReactionEnsemble`.
-
 .. _Grand canonical ensemble simulation using the Reaction Ensemble:
 
 Grand canonical ensemble simulation

doc/sphinx/analysis.rst

@@ -366,8 +366,8 @@ follows
 
 ::
 
-    from espressomd.observables import ParticlePositions
-    part_pos = ParticlePositions(ids=(1, 2, 3, 4, 5))
+    import espressomd.observables
+    part_pos = espressomd.observables.ParticlePositions(ids=(1, 2, 3, 4, 5))
 
 Here, the keyword argument ``ids`` specifies the ids of the particles,
 which the observable should take into account.
@@ -768,14 +768,14 @@ The cluster analysis is available in parallel simulations, but the analysis is c
 Whether or not two particles are neighbors is defined by a pair criterion. The available criteria can be found in :mod:`espressomd.pair_criteria`.
 For example, a distance criterion which will consider particles as neighbors if they are closer than 0.11 is created as follows::
 
-    from espressomd.pair_criteria import DistanceCriterion
-    dc = DistanceCriterion(cut_off=0.11)
+    import espressomd.pair_criteria
+    dc = espressomd.pair_criteria.DistanceCriterion(cut_off=0.11)
 
 To obtain the cluster structure of a system, an instance of :class:`espressomd.cluster_analysis.ClusterStructure` has to be created.
 To to create a cluster structure with above criterion::
 
-    from espressomd.cluster_analysis import ClusterStructure
-    cs = ClusterStructure(distance_criterion=dc)
+    import espressomd.cluster_analysis
+    cs = espressomd.cluster_analysis.ClusterStructure(distance_criterion=dc)
 
 In most cases, the cluster analysis is carried out by calling the :any:`espressomd.cluster_analysis.ClusterStructure.run_for_all_pairs` method. When the pair criterion is purely based on bonds,  :any:`espressomd.cluster_analysis.ClusterStructure.run_for_bonded_particles` can be used.
 

doc/sphinx/constraints.rst

@@ -173,8 +173,8 @@ Available shapes
 
 Python syntax::
 
-    import espressomd from espressomd.shapes import <SHAPE>
-    system = espressomd.System(box_l=[1, 1, 1])
+    import espressomd.shapes
+    shape = espressomd.shapes.<SHAPE>
 
 ``<SHAPE>`` can be any of the available shapes.
 

doc/sphinx/electrostatics.rst

@@ -189,8 +189,8 @@ surface. ICC relies on a Coulomb solver that is already initialized. So far, it
 is implemented and well tested with the Coulomb solver P3M. ICC is an |es|
 actor and can be activated via::
 
-    from espressomd.electrostatic_extensions import ICC
-    icc = ICC(...)
+    import espressomd.electrostatic_extensions
+    icc = espressomd.electrostatic_extensions.ICC(...)
     system.actors.add(icc)
 
 The ICC particles are setup as normal |es| particles. Note that they should
@@ -336,10 +336,10 @@ the details.
 
 MMM1D is used with::
 
-    from espressomd.electrostatics import MMM1D
-    mmm1d = MMM1D(prefactor=C, far_switch_radius=fr, maxPWerror=err, tune=False,
-                  bessel_cutoff=bc)
-    mmm1d = MMM1D(prefactor=C, maxPWerror=err)
+    import espressomd.electrostatics
+    mmm1d = espressomd.electrostatics.MMM1D(prefactor=C, far_switch_radius=fr,
+                                            maxPWerror=err, tune=False, bessel_cutoff=bc)
+    mmm1d = espressomd.electrostatics.MMM1D(prefactor=C, maxPWerror=err)
 
 where the prefactor :math:`C` is defined in Eqn. :eq:`coulomb_prefactor`.
 MMM1D Coulomb method for systems with periodicity (0 0 1). Needs the
@@ -350,7 +350,8 @@ does not need to be specified as it is automatically determined from the
 particle distances and maximal pairwise error. The second tuning form
 just takes the maximal pairwise error and tries out a lot of switching
 radii to find out the fastest one. If this takes too long, you can
-change the value of the property :attr:`espressomd.system.System.timings`,
+change the value of the ``timings`` argument of the
+:class:`~espressomd.electrostatics.MMM1D` class,
 which controls the number of test force calculations.
 
 .. _MMM1D on GPU:
@@ -365,10 +366,10 @@ counterpart, it does not need the N-squared cell system.
 
 ::
 
-    from espressomd.electrostatics import MMM1DGPU
-    mmm1d_gpu = MMM1DGPU(prefactor=C, far_switch_radius=fr, maxPWerror=err,
-                         tune=False, bessel_cutoff=bc)
-    mmm1d_gpu = MMM1DGPU(prefactor=C, maxPWerror=err)
+    import espressomd.electrostatics
+    mmm1d = espressomd.electrostatics.MMM1DGPU(prefactor=C, far_switch_radius=fr,
+                                               maxPWerror=err, tune=False, bessel_cutoff=bc)
+    mmm1d = espressomd.electrostatics.MMM1DGPU(prefactor=C, maxPWerror=err)
 
 The first form sets parameters manually. The switch radius determines at which
 xy-distance the force calculation switches from the near to the far
@@ -404,9 +405,10 @@ To use a specific electrostatics solver from ScaFaCoS for your system,
 e.g. ``ewald``, set its cutoff to :math:`1.5` and tune the other parameters
 for an accuracy of :math:`10^{-3}`::
 
-   from espressomd.electrostatics import Scafacos
-   scafacos = Scafacos(prefactor=1, method_name="ewald",
-                       method_params={"ewald_r_cut": 1.5, "tolerance_field": 1e-3})
+   import espressomd.electrostatics
+   scafacos = espressomd.electrostatics.Scafacos(
+      prefactor=1, method_name="ewald",
+      method_params={"ewald_r_cut": 1.5, "tolerance_field": 1e-3})
    system.actors.add(scafacos)
 
 For details of the various methods and their parameters please refer to