Merge branch 'develop' into Relax_similarity_condition

.github/workflows/pythonpublish.yml

@@ -20,8 +20,8 @@ jobs:
         pip install setuptools wheel twine
     - name: Build and publish
       env:
-        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
+        TWINE_USERNAME: __token__
+        TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
       run: |
         python setup.py sdist
         twine upload dist/*

CHANGELOG.md

@@ -2,6 +2,13 @@
 
 ## latest
 
+- Pass an ID to the micro simulation object so that it is aware of its own uniqueness https://github.com/precice/micro-manager/pull/66
+- Resolve bug which led to an error when global adaptivity was used with unequal number of simulations on each rank https://github.com/precice/micro-manager/pull/78
+- Make the `initialize()` method of the MicroManager class private https://github.com/precice/micro-manager/pull/77
+- Add reference paper via a CITATION.cff file https://github.com/precice/micro-manager/commit/6c08889c658c889d6ab5d0867802522585abcee5
+- Add JOSS DOI badge https://github.com/precice/micro-manager/commit/2e3c2a4c77732f56a957abbad9e4d0cb64029725
+- Update pyprecice API calls to their newer variants https://github.com/precice/micro-manager/pull/51
+
 ## v0.3.0
 
 - Add global variant to adaptivity (still experimental) https://github.com/precice/micro-manager/pull/42

docs/micro-simulation-convert-to-library.md

@@ -14,9 +14,14 @@ Restructure your micro simulation code into a Python class with the structure gi
 
 ```python
 class MicroSimulation: # Name is fixed
-    def __init__(self):
+    def __init__(self, sim_id):
         """
         Constructor of class MicroSimulation.
+
+        Parameters
+        ----------
+        sim_id : int
+            ID of the simulation instance, that the Micro Manager has set for it.
         """
 
     def solve(self, macro_data: dict, dt: float) -> dict:

docs/running.md

@@ -18,8 +18,6 @@ from micro_manager import MicroManager
 
 manager = MicroManager("micro-manager-config.json")
 
-manager.initialize()
-
 manager.solve()
 ```
 

examples/cpp-dummy/micro_cpp_dummy.cpp

@@ -13,7 +13,7 @@
 #include "micro_cpp_dummy.hpp"
 
 // Constructor
-MicroSimulation::MicroSimulation() : _micro_scalar_data(0), _state(0) {}
+MicroSimulation::MicroSimulation(int sim_id) : _sim_id(sim_id), _micro_scalar_data(0), _state(0) {}
 
 // Solve
 py::dict MicroSimulation::solve(py::dict macro_data, double dt)
@@ -64,7 +64,7 @@ PYBIND11_MODULE(micro_dummy, m) {
     m.doc() = "pybind11 micro dummy plugin";
 
     py::class_<MicroSimulation>(m, "MicroSimulation")
-        .def(py::init())
+        .def(py::init<int>())
         .def("solve", &MicroSimulation::solve)
         .def("get_state", &MicroSimulation::get_state)
         .def("set_state", &MicroSimulation::set_state)
@@ -77,7 +77,7 @@ PYBIND11_MODULE(micro_dummy, m) {
                     throw std::runtime_error("Invalid state!");
 
                 /* Create a new C++ instance */
-                MicroSimulation ms;
+                MicroSimulation ms(0);
 
                 ms.set_state(t);
 

examples/cpp-dummy/micro_cpp_dummy.hpp

@@ -14,14 +14,15 @@ namespace py = pybind11;
 class MicroSimulation
 {
 public:
-    MicroSimulation();
+    MicroSimulation(int sim_id);
     // solve takes a python dict data, and the timestep dt as inputs, and returns a python dict
     py::dict solve(py::dict macro_write_data, double dt);
 
     void set_state(py::list state);
     py::list get_state() const;
 
 private:
+    int _sim_id;
     double _micro_scalar_data;
     std::vector<double> _micro_vector_data;
     double _state;

examples/cpp-dummy/run_micro_manager.py

@@ -11,6 +11,4 @@
 
 manager = MicroManager(args.config)
 
-manager.initialize()
-
 manager.solve()

examples/python-dummy/micro_dummy.py

@@ -6,10 +6,11 @@
 
 class MicroSimulation:
 
-    def __init__(self):
+    def __init__(self, sim_id):
         """
         Constructor of MicroSimulation class.
         """
+        self._sim_id = sim_id
         self._dims = 3
         self._micro_scalar_data = None
         self._micro_vector_data = None

examples/python-dummy/run_micro_manager.py

@@ -11,6 +11,4 @@
 
 manager = MicroManager(args.config)
 
-manager.initialize()
-
 manager.solve()

micro_manager/adaptivity/adaptivity.py

@@ -8,6 +8,7 @@
 from typing import Callable
 import re
 import xml.etree.ElementTree as ET
+from warnings import warn
 
 
 class AdaptivityCalculator:
@@ -172,9 +173,15 @@ def _update_active_sims(
             self._coarse_const = self._get_adaptive_similarity_const(self._coarse_const_input)
         if self._adaptive_refine_const:
             self._refine_const = self._get_adaptive_similarity_const(self._refine_const_input)
-        self._coarse_tol = self._coarse_const * self._refine_const * \
-            np.amax(similarity_dists)
 
+        max_similarity_dist = np.amax(similarity_dists)
+
+        if max_similarity_dist == 0.0:
+            warn("All similarity distances are zero, probably because all the data for adaptivity is the same. Coarsening tolerance will be manually set to minimum float number.")
+            self._coarse_tol = sys.float_info.min
+        else:
+            self._coarse_tol = self._coarse_const * self._refine_const * max_similarity_dist
+
         _is_sim_active = np.copy(is_sim_active)  # Input is_sim_active is not longer used after this point
 
         # Update the set of active micro sims

micro_manager/adaptivity/global_adaptivity.py

@@ -18,8 +18,7 @@ def __init__(
             self,
             configurator,
             logger,
-            is_sim_on_this_rank: list,
-            rank_of_sim: np.ndarray,
+            global_number_of_sims: float,
             global_ids: list,
             rank: int,
             comm) -> None:
@@ -32,10 +31,8 @@ def __init__(
             Object which has getter functions to get parameters defined in the configuration file.
         logger : object of logging
             Logger defined from the standard package logging
-        is_sim_on_this_rank : list
+        global_number_of_sims : float
             List of booleans. True if simulation is on this rank, False otherwise.
-        rank_of_sim : numpy array
-            1D array consisting of rank on which the simulation lives.
         global_ids : list
             List of global IDs of simulations living on this rank.
         rank : int
@@ -44,12 +41,26 @@ def __init__(
             Global communicator of MPI.
         """
         super().__init__(configurator, logger)
-        self._is_sim_on_this_rank = is_sim_on_this_rank
-        self._rank_of_sim = rank_of_sim
         self._global_ids = global_ids
         self._comm = comm
         self._rank = rank
 
+        local_number_of_sims = len(global_ids)
+
+        # Create a map of micro simulation global IDs and the ranks on which they are
+        micro_sims_on_this_rank = np.zeros(local_number_of_sims, dtype=np.intc)
+        for i in range(local_number_of_sims):
+            micro_sims_on_this_rank[i] = self._rank
+
+        self._rank_of_sim = np.zeros(global_number_of_sims, dtype=np.intc)  # DECLARATION
+
+        self._comm.Allgatherv(micro_sims_on_this_rank, self._rank_of_sim)
+
+        self._is_sim_on_this_rank = [False] * global_number_of_sims  # DECLARATION
+        for i in range(global_number_of_sims):
+            if self._rank_of_sim[i] == self._rank:
+                self._is_sim_on_this_rank[i] = True
+
     def compute_adaptivity(
             self,
             dt: float,
@@ -94,11 +105,15 @@ def compute_adaptivity(
         is_sim_active = self._update_active_sims(similarity_dists, is_sim_active_nm1)
 
         is_sim_active, sim_is_associated_to = self._update_inactive_sims(
-            similarity_dists, is_sim_active_nm1, sim_is_associated_to_nm1, micro_sims)
+            similarity_dists, is_sim_active, sim_is_associated_to_nm1, micro_sims)
+
+        print("sim_is_associated_to: {}".format(sim_is_associated_to))
 
         sim_is_associated_to = self._associate_inactive_to_active(
             similarity_dists, is_sim_active, sim_is_associated_to)
 
+        print("sim_is_associated_to: {}".format(sim_is_associated_to))
+
         self._logger.info(
             "{} active simulations, {} inactive simulations".format(
                 np.count_nonzero(

micro_manager/adaptivity/local_adaptivity.py

@@ -61,7 +61,7 @@ def compute_adaptivity(
         is_sim_active = self._update_active_sims(similarity_dists, is_sim_active_nm1)
 
         is_sim_active, sim_is_associated_to = self._update_inactive_sims(
-            similarity_dists, is_sim_active_nm1, sim_is_associated_to_nm1, micro_sims)
+            similarity_dists, is_sim_active, sim_is_associated_to_nm1, micro_sims)
 
         sim_is_associated_to = self._associate_inactive_to_active(
             similarity_dists, is_sim_active, sim_is_associated_to)

micro_manager/config.py

@@ -4,6 +4,7 @@
 
 import json
 import os
+from warnings import warn
 
 
 class Config:
@@ -125,6 +126,12 @@ def read_json(self, config_filename):
             for dname in data["simulation_params"]["adaptivity"]["data"]:
                 self._data_for_adaptivity[dname] = exchange_data[dname]
 
+            if self._data_for_adaptivity.keys() == self._write_data_names.keys():
+                warn(
+                    "Only micro simulation data is used for similarity computation in adaptivity. This would lead to the"
+                    " same set of active and inactive simulations for the entire simulation time. If this is not intended,"
+                    " please include macro simulation data as well.")
+
             self._adaptivity_history_param = data["simulation_params"]["adaptivity"]["history_param"]
             self._adaptivity_coarsening_constant = data["simulation_params"]["adaptivity"]["coarsening_constant"]
             self._adaptivity_refining_constant = data["simulation_params"]["adaptivity"]["refining_constant"]