Merge branch 'waning-immunity' of github.com:SABS-R3-Epidemiology/epi…

…abm into waning-immunity

.gitignore

@@ -53,6 +53,7 @@ share/python-wheels/
 .installed.cfg
 *.egg
 build/
+_build/
 build_dir/
 MANIFEST
 

README.md

@@ -131,6 +131,8 @@ Note that cmake must be installed on your system. The following command can be u
 sudo apt-get install cmake cmake-data
 ```
 
+We recommend the use of the G++ compiler (version 9 or higher) with cEpiabm. If other compilers are used, users should ensure they include the filesystem library, or include this explicitly by inserting `link_libraries(stdc++fs)` in [CMakeLists.txt](cEpiabm/CMakeLists.txt).
+
 ### Compiling cEpiabm Python Bindings
 
 Pybind11 is required for compiling cEpiabm with Python bindings. Pybind11 is added as a git submodule, make sure this submodule is cloned along with the main epiabm repository.

cEpiabm/README.rst

@@ -0,0 +1,58 @@
+cEpiabm
+=======
+
+We provide an efficient and scalable backend in C++, which can run
+simulations for populations comparable to the UK in a reasonable
+timeframe. This code may be harder than pyEpiabm for new users to
+understand, but the parallels with the python code should be
+sufficiently informative for those who wish to look deeper into the
+code.
+
+Set up
+------
+
+Installation of cEpiabm
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Cmake is used for installation of the cEpiabm software. The following
+procedure will compile the code and run all unit tests:
+
+.. code:: console
+
+   mkdir build_dir
+   cd build_dir
+   cmake ../cEpiabm/. -DCMAKE_BUILD_TYPE=Debug
+   cmake --build . --parallel 2 --target unit_tests
+   ctest -j2 --output-on-failure
+
+Note that cmake must be installed on your system. The following command
+can be used on ubuntu systems:
+
+.. code:: console
+
+   sudo apt-get install cmake cmake-data
+
+We recommend the use of the G++ compiler (version 9 or higher) with
+cEpiabm. If other compilers are used, users should ensure they include 
+the filesystem library, or include this explicitly by inserting 
+``link_libraries(stdc++fs)`` in the ``cEpiabm/CMakeLists.txt`` file.
+
+Compiling cEpiabm Python Bindings
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Pybind11 is required for compiling cEpiabm with Python bindings.
+Pybind11 is added as a git submodule, make sure this submodule is cloned
+along with the main epiabm repository.
+
+Compiling Python bindings follows similar procedure to compiling the
+cEpiabm tests:
+
+.. code:: console
+
+   mkdir build_dir
+   cd build_dir
+   cmake ../cEpiabm/. -DCMAKE_BUILD_TYPE=Release -DENABLE_COVERAGE=OFF
+   cmake --build . --parallel 6 --target epiabm
+
+cEpiabm’s python bindings will be compiled to a python module named
+epiabm, located in ``build_dir/src/epiabm.cpython-(version info).so``.

cEpiabm/docs/.readthedocs.yaml

@@ -0,0 +1,20 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the required OS and Python version
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: cEpiabm/docs/conf.py
+
+python:
+  install:
+    - requirements: cEpiabm/docs/requirements.txt

cEpiabm/docs/conf.py

@@ -80,7 +80,7 @@ def configure_doxyfile(_input_dir, _output_dir):
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'sphinx_rtd_theme'
+html_theme = 'alabaster'
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,

cEpiabm/docs/index.rst

@@ -6,8 +6,7 @@
 Welcome to C++ Epiabm's documentation!
 ======================================
 
-This is the C++ Backend for the EpiAbm Package.
-
+.. include:: ../README.rst
 
 Contents
 ========

pyEpiabm/README.rst

@@ -0,0 +1,134 @@
+pyEpiabm
+========
+
+The pyEpiabm backend is written in python, chosen for its readability
+and user-friendliness. It is not able to cope with large population
+sizes, but can configure a toy population to explore the functionality
+of the model and better understand the underlying code. Additionally,
+toy models may be quicker for model comparison and parameter inference,
+though care should obviously be taken when scaling up to a full
+population. We provide a variety of workflows to show the utility of
+code.
+
+Installation of pyEpiabm
+------------------------
+
+pyEpiabm is not yet available on `PyPI <https://pypi.org/>`__, but the
+module can be pip installed locally. The directory should first be
+downloaded to your local machine, and can then be installed using the
+command:
+
+.. code:: console
+
+   pip install -e .
+
+from the ``pyEpiabm`` directory. If you also wish to build the docs
+locally, this requires additional dependencies, which must be specified:
+
+.. code:: console
+
+   pip install -e .[docs]
+
+Running a simulation
+--------------------
+
+A number of example simulations are included the in the
+``python_examples`` directory. The simplest complete workflow for
+running a simulation is provided in
+``python_examples/basic_example/simulation_flow.py``, but all others
+follow a similar format. Other example simulations include an
+``age_stratified_example``, and ``spatial_example`` which demonstrate
+these aspects of the module. ``gilbraltar_example`` combines age and
+spatial stratification using census data from Gibraltar, and can be used
+to benchmark against CovidSIM.
+
+There are a number of steps to any simulation:
+
+Set Random Seed *(Optional)*
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This allows the random seed to be set for all random modules used in the
+simulation, to enable reproducible simulations. The recommended approach
+here is to set one seed at the start of the script (before configuring
+the population or the simulation objects), so that both are generated
+according to this seed. It is also possible to set a separate seed for
+one or other object, by passing ``population_seed`` or
+``simulation_seed`` into their respective parameter dictionaries,
+however care should be exercised to ensure the two objects are
+configured sequentially. For example, generating a second population
+after setting the simulation seed would be done according to
+``simulation_seed`` not ``population_seed``. Setting the seed is not
+currently compatible with multi-threaded execution.
+
+Configure Population
+~~~~~~~~~~~~~~~~~~~~
+
+Create a population based on the parameters given, from the following
+list:
+
+-  ``population_size``: Number of people in population
+-  ``cell_number``: Number of cells in population
+-  ``microcell_number``: Number of microcells in each cell
+-  ``household_number``: Number of households in each microcell
+   *(Optional)*
+-  ``place_number``: Number of places in each microcell *(Optional)*
+-  ``population_seed``: Random seed for reproducible populations - see
+   above *(Optional)*
+
+Import Population
+~~~~~~~~~~~~~~~~~
+
+Alternatively, it is possible to import a population from a ``.csv``
+file, with the following headings:
+
+-  ``cell``: ID code for cell
+-  ``microcell``: ID code for microcell
+-  ``location_x``: The x coordinate of the parent cell location
+-  ``location_y``: The y coordinate of the parent cell location
+-  ``household_number``: Number of households in that microcell
+-  Any number of columns with titles from the ``InfectionStatus`` enum
+   (such as ``InfectionStatus.Susceptible``), giving the number of
+   people with that status in that cell
+
+File of this format can also be exported using the
+``pyEpiabm.routine.FilePopulationConfig.print_population()`` method,
+i.e. for reproducibility or use in further simulations.
+
+Configure Simulation
+~~~~~~~~~~~~~~~~~~~~
+
+Configure a simulation with a number of parameters. These are split into
+three categories:
+
+sim_params
+""""""""""
+
+- ``simulation_start_time``: The initial time at the start of the simulation
+- ``simulation_end_time``: The final time at which to stop the simulation
+- ``initial_infected_number``: The initial number of infected individuals in the population
+- ``initial_infect_cell``: Whether to choose initial infected individuals from a single cell
+- ``simulation_seed``: Random seed for reproducible simulations - see above *(Optional)*
+
+file_params
+"""""""""""
+*(For controlling output location)*
+
+- ``output_file``: String for the name of the output .csv file
+- ``output_dir``: String for the location of the output file, as a relative path
+- ``spatial_output``: Boolean to determine whether a spatial output should be used *(Default false)*
+- ``age_stratified``: Boolean to determine whether the output will be age stratified *(Default false)*
+
+inf_history_params
+""""""""""""""""""
+*(For controlling the infection history output - Default None)*
+
+- ``output_dir``: String for the location for the output files, as a relative path
+- ``status_output``: Boolean to determine whether we need a csv file containing infection status values (Default false)
+- ``infectiousness_output``: Boolean to determine whether we need a csv file containing infectiousness (viral load) values (Default false)
+- ``compress``: Boolean to determine whether we compress a csv file containing infection status values and/or a csv file containing infectiousness (viral load) values if they are written (Default false)
+
+Two lists of sweeps must also be passed to this function - the first
+will be executed once at the start of the simulation (i.e. to determine
+the initial infections in the population), while the second list will be
+ran at every timestep (i.e. to propagate the infection through the
+population).

pyEpiabm/docs/source/index.rst

@@ -1,21 +1,19 @@
 .. pyEpiabm documentation master file, created by
-   sphinx-quickstart on Wed Oct 28 05:13:22 2020.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
+    sphinx-quickstart on Wed Oct 28 05:13:22 2020.
+    You can adapt this file completely to your liking, but it should at least
+    contain the root `toctree` directive.
 
 Welcome to pyEpiabm's documentation!
 ====================================
 
-This is a python backend for the EpiAbm package.
-
+.. include:: ../../README.rst
 
 Contents
 ========
 
 .. module:: pyEpiabm
 
 .. toctree::
-
     core
     interventions
     outputs

pyEpiabm/pyEpiabm/tests/test_unit/test_sweep/test_host_progression_sweep.py

@@ -286,7 +286,7 @@ def test__get_waning_weights_no_age(self):
     def test__get_waning_weights(self):
         """Tests the _get_waning_weights() function with ages
         """
-        with (mock.patch('pyEpiabm.Parameters.instance') as mock_param):
+        with mock.patch('pyEpiabm.Parameters.instance') as mock_param:
             mock_param.return_value.use_ages = 1.0
             mock_param.return_value.use_waning_immunity = 1.0
             mock_param.return_value.asympt_infect_period = 14

pyEpiabm/pyEpiabm/utility/rate_multiplier.py

@@ -4,6 +4,7 @@
 #
 
 import math
+import typing
 
 
 class RateMultiplier:
@@ -47,7 +48,7 @@ def __init__(self, p_1: float, p_2: float,
         self.t_2 = t_2
         self.a, self.b = self._calculate_parameters()
 
-    def _calculate_parameters(self) -> tuple[float, float]:
+    def _calculate_parameters(self) -> typing.Tuple:
         """Given the initial data points p(t_1) = p_1 and p(t_2) = p_2, this
         method determines the parameters a and b of the function.
         Note that p(t) = 1 - be^(-at).

pyEpiabm/pyEpiabm/version_info.py

@@ -6,7 +6,7 @@
 #  - Changes to minor indicate new features, possible slight backwards
 #    incompatibility
 #  - Changes to revision indicate bugfixes, tiny new features
-VERSION_INT = 0, 0, 3
+VERSION_INT = 1, 1, 0
 
 # String version of the version number
 VERSION = '.'.join([str(x) for x in VERSION_INT])

pyEpiabm/setup.py

@@ -25,9 +25,9 @@ def get_version():
 
 def get_readme():
     """
-    Load README.md text for use as description.
+    Load README.rst text for use as description.
     """
-    with open('README.md') as f:
+    with open('README.rst') as f:
         return f.read()
 
 

python_examples/NZ_example/NZ_flow.py

@@ -94,6 +94,7 @@
 plt.savefig(os.path.join(os.path.dirname(__file__),
             "simulation_outputs/SIR.png"))
 plt.close()
+# Default file format is .png, but can be changed to .pdf, .svg, etc.
 
 # Create plot to show new cases and new deaths against time
 newdf = pd.DataFrame()

python_examples/age_stratified_example/age_hist/age_hist.py

@@ -30,6 +30,7 @@
 plt.xlabel('Age')
 plt.title('Histogram of toy population')
 plt.savefig('images/Histograms/Withoutsim.png')
+# Default file format is .png, but can be changed to .pdf, .svg, etc.
 
 # Set simulation parameters
 sim_params = {"simulation_start_time": 0, "simulation_end_time": 90,

python_examples/age_stratified_example/age_stratified_plot.py

@@ -163,11 +163,11 @@ def barchart(self, outfile: str,
                  param_file=None):
         """Function which creates a bar chart from csv data, with
         capability to stratify by age if required. Plot is automatically
-        saved to a png file.
+        saved to a file of the specified format.
         Parameters
         ----------
         outfile : str
-            Path to the .png file where the bar chart will be saved
+            Path to the file where the bar chart will be saved
         infection_category : str
             Category to be plotted, defaults to Total Infectious
         write_Df_toFile : str
@@ -242,9 +242,13 @@ def barchart(self, outfile: str,
 
 if __name__ == '__main__':
     dirname = os.path.dirname(os.path.abspath(__file__))
+    savename = os.path.join("simulation_outputs", "age_stratify.png")
+    # Default file format is .png, but can be changed to .pdf, .svg, etc.
+
     p = Plotter(os.path.join(os.path.dirname(__file__),
                 "simulation_outputs/output_with_age.csv"),
                 start_date='01-01-2020', sum_weekly=False)
+
     p.barchart(os.path.join(os.path.dirname(__file__),
                "simulation_outputs/age_stratify.png"),
                write_Df_toFile=os.path.join(os.path.dirname(__file__),

python_examples/age_stratified_example/simulation_flow_with_age.py

@@ -88,6 +88,7 @@
                    "InfectionStatus.Recovered"])
 plt.savefig(os.path.join(os.path.dirname(__file__),
             "simulation_outputs/simulation_flow_SIR_plot.png"))
+# Default file format is .png, but can be changed to .pdf, .svg, etc.
 
 # Creation of a plot of results with age stratification
 p = Plotter(os.path.join(os.path.dirname(__file__),

python_examples/basic_simulation/simulation_flow.py

@@ -72,3 +72,4 @@
                          "simulation_outputs",
                          "simulation_flow_SIR_plot.png")
 )
+# Default file format is .png, but can be changed to .pdf, .svg, etc.

python_examples/gibraltar_example/covidsim_results/plot_covidsim.py

@@ -74,3 +74,4 @@
            "covidsim_daily_cases.csv"),
            param_file=os.path.join(os.path.dirname(__file__),
            os.path.pardir, "./gibraltar_parameters.json"))
+# Default file format is .png, but can be changed to .pdf, .svg, etc.

python_examples/gibraltar_example/gibraltar_flow.py

@@ -106,6 +106,7 @@
 SIRdf.plot(y=["Susceptible", "Infected", "Recovered"])
 plt.savefig(os.path.join(os.path.dirname(__file__),
             "simulation_outputs/simulation_flow_SIR_plot.png"))
+# Default file format is .png, but can be changed to .pdf, .svg, etc.
 
 # Creation of a plot of results with age stratification
 # if file_params["age_stratified"]: