Source code for pycoalescence.simulation

Run spatially explicit neutral simulations on provided landscapes with support for a wide range of scenarios and
parameters. Detailed :ref:`here <performing_simulations>`.

The main class is :class:`.Simulation`, which contains routines for setting up and running simulations, plus basic tree
generation after simulations have been completed.

    - Simulation parameters (such as dispersal kernel, speciation rate)
    - Map files representing density over space
    - [optional] map files representing relative reproductive ability
    - [optional] map files representing dispersal potential
    - [optional] historical density map files

    - Database containing generated coalescence tree, simulation parameters and basic biodiversity metrics.
    - If the simulation does not complete, will instead dump data to a Dump_main_*_*.csv file for resuming simulations.
from __future__ import absolute_import
from __future__ import print_function

import copy
import logging
import os
import types

import numpy as np

global sqlite_import

# noinspection PyCompatibility
from configparser import ConfigParser

    from io import StringIO
# Python 2.x support
except ImportError as ie:  # pragma: no cover
    from cStringIO import StringIO

    NumberTypes = (types.IntType, types.LongType, types.FloatType, types.ComplexType)
except AttributeError:
    # No support for complex numbers compile
    NumberTypes = (int, float)

    from osgeo import gdal
except ImportError as ie:  # pragma: no cover
        import gdal
    except ImportError:
        gdal = None
        logging.warning("Problem importing gdal modules: {}".format(ie))

    import sqlite3

    from .coalescence_tree import CoalescenceTree
except ImportError as ie:  # pragma: no cover
    CoalescenceTree = None
    logging.error("Problem importing sqlite or coalescence_tree modules: {}".format(ie))

from pycoalescence.necsim import libnecsim
from pycoalescence.future_except import FileNotFoundError, FileExistsError
from pycoalescence.landscape import Landscape
from import Map
from pycoalescence.system_operations import write_to_log, check_parent

[docs]class Simulation(Landscape): """ A class containing routines to set up and run simulations, including detecting map dimensions from tif files. """ def __init__(self, logging_level=logging.WARNING, log_output=None, **kwargs): """ Sets up the simulation object, setting the logging object and assigning default variables. :param int logging_level: the minimal level of logging to display :param string log_output: a file to output log data to. Use None for writing to console. :param kwargs: additional keyword arguments to supply to logging.basicConfig() """ # All the parameters used for the simulation Landscape.__init__(self) # Contains the C object for performing simulations # This will be set depending on the simulation type self.c_simulation = None self._fine_map_sum_res = None self.count_total = None # Create the logging object and pass on the required arguments for its creation passedKwards = {k: v for k, v in kwargs.items() if "stream" not in k} self.logger = logging.Logger("pycoalescence.simulation", **passedKwards) self._create_logger(file=log_output, logging_level=logging_level, **kwargs) self.output_directory = "" self.pause_directory = "" self.config = ConfigParser() self.config_string = StringIO() self.full_config_file = None self.is_setup = False self.is_setup_param = False self.is_setup_complete = False self.seed = 0 self.min_speciation_rate = 0 self.sigma = 2 self.deme = 1 self.sample_size = 1 self.max_time = 100 self.dispersal_relative_cost = ( 1 ) # the relative cost of moving through non-matrix self.job_type = 0 self.min_num_species = 1 self.tau = 0 self.times = None self.speciation_rates = [] # parameters for the application of speciation rates self.is_setup_speciation = False self.speciation_file = "" self.record_spatial = False self.record_fragments = False # The output database location self.output_database = None self.dispersal_method = None self.m_prob = 0 self.cutoff = 0 self.restrict_self = False # The protracted variables self.protracted = False self.min_speciation_gen = 0.0 self.max_speciation_gen = 0.0 # This variable stores whether a new config file has been opened or not self.config_open = False # Grid dimensions sizing self.grid = Map() # Dispersal map data self.dispersal_map = Map() self.death_map = Map() self.reproduction_map = Map() self.fine_map_array = None self.sample_map_array = None self.fine_map_average = None self.is_spatial = True self.uses_spatial_sampling = False self.uses_gillespie = False self.gillespie_threshold = None def __del__(self): """Safely destroys the logger and the C++ objects.""" handlers = copy.copy(self.logger.handlers) for handler in handlers: handler.close() self.logger.removeHandler(handler) self.logger = None self.c_simulation = None
[docs] def add_sample_time(self, time): """ Adds an extra sample time to the list of times. This allows for multiple temporal sample points from within the same simulation. :param time: the sample time to add """ if isinstance(time, list): [self.add_sample_time(x) for x in time] else: if not self.times or len(self.times) == 0: self.times = [0.0] self.times.append(time)
[docs] def set_config_file(self, output_file=None): """ Sets the config file to the output, over-writing any existing config file that has been stored. :param output_file: path to config file to output to """ if ( self.full_config_file and output_file != self.full_config_file and output_file ): self.logger.warning( "Config file has already been set to {}, changing to {}.".format( self.full_config_file, output_file ) ) if output_file is not None: self.full_config_file = output_file
[docs] def load_config(self, config_file): """ Loads the config file by reading the lines in order. :param str config_file: the config file to read in. """ # New method using ConfigParser self.full_config_file = config_file self.seed = self.config.getint("main", "seed") self.job_type = self.config.getint("main", "job_type") self.output_directory = self.config.get("main", "output_directory") self.min_speciation_rate = self.config.getfloat("main", "min_spec_rate") self.sigma = self.config.getfloat("main", "sigma", fallback=1.0) self.tau = self.config.getfloat("main", "tau", fallback=1.0) self.dispersal_relative_cost = self.config.getint( "main", "dispersal_relative_cost", fallback=1.0 ) self.deme = self.config.getint("main", "deme", fallback=1.0) self.sample_size = self.config.getfloat("main", "sample_size", fallback=1.0) self.max_time = self.config.getint("main", "max_time", fallback=1.0) self.min_num_species = self.config.getint("main", "min_species") if self.config.has_section("protracted"): self.min_speciation_gen = self.config.getfloat( "protracted", "min_speciation_gen" ) self.max_speciation_gen = self.config.getfloat( "protracted", "max_speciation_gen" ) self.protracted = True if self.config.has_section("spec_rates"): opts = self.config.options("spec_rates") for each in opts: self.speciation_rates.append(self.config.getfloat("spec_rates", each)) if self.config.has_section("times"): opts = self.config.options("times") self.times = [] for each in opts: self.times.append(self.config.getfloat("times", each)) if self.config.has_section("sample_grid"): self.sample_map.set_dimensions( self.config.get("sample_grid", "path"), self.config.getint("sample_grid", "x", fallback=0), self.config.getint("sample_grid", "y", fallback=0), 0, 0, ) self.uses_spatial_sampling = self.config.getboolean( "sample_grid", "uses_spatial_sampling", fallback=False ) self.fine_map.set_dimensions( self.config.get("fine_map", "path"), self.config.getint("fine_map", "x", fallback=0), self.config.getint("fine_map", "y", fallback=0), self.config.getint("fine_map", "x_off", fallback=0), self.config.getint("fine_map", "y_off", fallback=0), ) self.coarse_map.set_dimensions( self.config.get("coarse_map", "path"), self.config.getint("coarse_map", "x", fallback=0), self.config.getint("coarse_map", "y", fallback=0), self.config.getint("coarse_map", "x_off", fallback=0), self.config.getint("coarse_map", "y_off", fallback=0), ) self.coarse_scale = self.config.getfloat( "coarse_map", "scale", fallback=1.0 ) i = 0 while True: tmp_fine = "historical_fine{}".format(i) tmp_coarse = "historical_coarse{}".format(i) if not self.config.has_section(tmp_fine): break self.historical_fine_list.append(self.config.get(tmp_fine, "path")) self.historical_coarse_list.append(self.config.get(tmp_coarse, "path")) self.rates_list.append(self.config.getfloat(tmp_fine, "rate")) self.times_list.append(self.config.getfloat(tmp_fine, "time")) i += 1 if self.config.has_section("grid_map"): self.grid.file_name = "set" self.grid.x_size = self.config.getint("grid_map", "x") self.grid.y_size = self.config.getint("grid_map", "y") self.sample_map.x_offset = self.config.getint("sample_grid", "x_off") self.sample_map.y_offset = self.config.getint("sample_grid", "y_off") if self.config.has_section("death"): self.death_map.file_name = self.config.get("death", "map") if self.config.has_section("reproduction"): self.reproduction_map.file_name = self.config.get("reproduction", "map") if self.config.has_section("dispersal"): self.dispersal_method = self.config.get("dispersal", "method") self.m_prob = self.config.getfloat( "dispersal", "m_probability", fallback=0.0 ) self.cutoff = self.config.getfloat("dispersal", "cutoff", fallback=0.0) self.restrict_self = self.config.getboolean( "dispersal", "restrict_self", fallback=False ) self.landscape_type = self.config.get( "dispersal", "landscape_type", fallback="closed" ) self.dispersal_map = self.config.get( "dispersal", "dispersal_file", fallback=None )
[docs] def write_config(self, config_file): """ Writes the config to the config file provided, overwriting any existing config files. :param config_file: the config file to write out to :rtype: None """ if not self.config.has_section("main"): self.create_config() self.create_map_config() if config_file is not None: check_parent(config_file) if os.path.exists(config_file) and not self.config_open: os.remove(config_file) with open(config_file, "w") as f: self.config.write(f) self.config_open = True self.full_config_file = config_file
[docs] def create_temporal_sampling_config(self): """ Creates the time-sampling config file. Function is called automatically when creating a config file, and should not be manually called. """ if self.times and len(self.times) != 0: self.config.add_section("times") for i, j in enumerate(sorted(set(self.times))): self.config.set("times", "time{}".format(i), str(j)) if self.full_config_file is not None: self.write_config(self.full_config_file)
[docs] def create_map_config(self, output_file=None): """ Generates the map config file from reading the spatial structure of each of the provided files. :param str output_file: the file to output configuration data to (the map config file) """ self.set_config_file(output_file) if self.is_setup_map: if self.is_spatial: self.config.add_section("sample_grid") if self.sample_map.file_name is None: self.config.set("sample_grid", "path", "null") else: self.config.set("sample_grid", "path", self.sample_map.file_name) x, y = map(str, self.sample_map.get_x_y()) self.config.set("sample_grid", "x", str(x)) self.config.set("sample_grid", "y", str(y)) self.config.set( "sample_grid", "uses_spatial_sampling", str(int(self.uses_spatial_sampling)), ) self.config.add_section("fine_map") self.config.set("fine_map", "path", self.fine_map.file_name) x, y = map(str, self.fine_map.get_x_y()) self.config.set("fine_map", "x", str(x)) self.config.set("fine_map", "y", str(y)) self.config.set("fine_map", "x_off", str(self.fine_map.x_offset)) self.config.set("fine_map", "y_off", str(self.fine_map.y_offset)) self.config.add_section("coarse_map") if self.coarse_map.file_name is not None: self.config.set("coarse_map", "path", self.coarse_map.file_name) x, y = map(str, self.coarse_map.get_x_y()) self.config.set("coarse_map", "x", str(x)) self.config.set("coarse_map", "y", str(y)) self.config.set( "coarse_map", "x_off", str(self.coarse_map.x_offset) ) self.config.set( "coarse_map", "y_off", str(self.coarse_map.y_offset) ) self.config.set("coarse_map", "scale", str(self.coarse_scale)) else: self.config.set("coarse_map", "path", "none") self.config.set("coarse_map", "x", "0") self.config.set("coarse_map", "y", "0") self.config.set("coarse_map", "x_off", "0") self.config.set("coarse_map", "y_off", "0") self.config.set("coarse_map", "scale", "1") self.sort_historical_maps() if ( len(self.historical_coarse_list) != 0 and len(self.historical_fine_list) != 0 ): for i, t in enumerate(self.times_list): tmp_fine = "historical_fine{}".format(i) self.config.add_section(tmp_fine) self.config.set(tmp_fine, "path", self.historical_fine_list[i]) self.config.set(tmp_fine, "number", str(i)) self.config.set(tmp_fine, "time", str(t)) self.config.set(tmp_fine, "rate", str(self.rates_list[i])) tmp_coarse = "historical_coarse{}".format(i) self.config.add_section(tmp_coarse) self.config.set( tmp_coarse, "path", self.historical_coarse_list[i] ) self.config.set(tmp_coarse, "number", str(i)) self.config.set(tmp_coarse, "time", str(t)) self.config.set(tmp_coarse, "rate", str(self.rates_list[i])) if self.grid.file_name == "set": self.config.add_section("grid_map") self.config.set("grid_map", "x", str(self.grid.x_size)) self.config.set("grid_map", "y", str(self.grid.y_size)) self.config.set( "sample_grid", "x_off", str(self.sample_map.x_offset) ) self.config.set( "sample_grid", "y_off", str(self.sample_map.y_offset) ) if self.death_map.file_name not in [None, "none", "null"]: self.config.add_section("death") self.config.set("death", "map", self.death_map.file_name) if self.reproduction_map.file_name not in [None, "none", "null"]: self.config.add_section("reproduction") self.config.set( "reproduction", "map", self.reproduction_map.file_name ) if self.dispersal_method is not None: self.config.add_section("dispersal") self.config.set("dispersal", "method", self.dispersal_method) self.config.set("dispersal", "m_probability", str(self.m_prob)) self.config.set("dispersal", "cutoff", str(self.cutoff)) if self.restrict_self: self.config.set( "dispersal", "restrict_self", str(int(self.restrict_self)) ) if self.landscape_type != "closed": if not self.config.has_section("dispersal"): self.config.add_section("dispersal") self.config.set("dispersal", "landscape_type", self.landscape_type) if self.dispersal_map.file_name not in ["none", None]: if not self.config.has_section("dispersal"): self.config.add_section("dispersal") self.config.set( "dispersal", "dispersal_file", self.dispersal_map.file_name ) if self.full_config_file is not None: self.write_config(self.full_config_file) else: raise RuntimeError( "Cannot generate map config file without setting up map variables" )
[docs] def create_config(self, output_file=None): """ Generates the configuration. This will be written out either by providing an output file here, or by calling write_config(); :param str output_file: the file to generate the config option. Must be a path to a .txt file. """ self.set_config_file(output_file) if self.is_setup_map and self.is_setup_param: # New method using ConfigParser self.config.add_section("main") self.config.set("main", "seed", str(self.seed)) self.config.set("main", "job_type", str(self.job_type)) self.config.set("main", "output_directory", self.output_directory) self.config.set("main", "min_spec_rate", str(self.min_speciation_rate)) if self.is_spatial: self.config.set("main", "sigma", str(self.sigma)) self.config.set("main", "tau", str(self.tau)) self.config.set( "main", "dispersal_relative_cost", str(self.dispersal_relative_cost) ) self.config.set("main", "deme", str(self.deme)) self.config.set("main", "sample_size", str(self.sample_size)) self.config.set("main", "max_time", str(self.max_time)) self.config.set("main", "min_species", str(self.min_num_species)) if self.protracted: self.config.add_section("protracted") self.config.set( "protracted", "has_protracted", str(int(self.protracted)) ) self.config.set( "protracted", "min_speciation_gen", str(float(self.min_speciation_gen)), ) self.config.set( "protracted", "max_speciation_gen", str(float(self.max_speciation_gen)), ) if len(self.speciation_rates) != 0: self.config.add_section("spec_rates") for i, j in enumerate(set([x for x in self.speciation_rates])): spec_rate = "spec_{}".format(i) self.config.set("spec_rates", spec_rate, str(j)) if self.full_config_file is not None: self.write_config(self.full_config_file) else: raise RuntimeError( "Setup has not been completed, cannot create config file" ) self.create_temporal_sampling_config()
[docs] def run_simple(self, seed, task, output, speciation_rate, sigma, size): """ Runs a simple coalescence simulation on a square infinite landscape with the provided parameters. This requires a separate compilation of the inf_land version of the coalescence simulator. Note that this function returns richness=0 for failure to read from the file. It is assumed that there will be at least one species in the simulation. Note that the maximum time for this function is set as 10 hours (36000 seconds) and will raise an exception if the simulation does not complete in this time). :raises RuntimeError: if the simulation didn't complete in time. :param seed: the simulation seed :param task: the task (for file naming) :param output: the output directory :param speciation_rate: the probability of speciation :param sigma: the normal distribution sigma value for dispersal :param size: the size of the world (so there will be size^2 individuals simulated) :return: the species richness in the simulation """ self.set_simulation_parameters( seed=seed, task=task, output_directory=output, min_speciation_rate=speciation_rate, sigma=sigma, tau=1, deme=1.0, sample_size=1.0, max_time=36000, dispersal_relative_cost=1, min_num_species=1, ) self.set_map("null", size, size) self.set_speciation_rates([speciation_rate]) # Now read the species richness from the database try: richness = self.get_species_richness() if richness == 0: raise AssertionError( "Richness is 0, error in program. This should never be the case for a complete " "sim." ) return richness except IOError: raise RuntimeError( "Simulation didn't complete in 10 hours (maximum time for run_simple). Try running a " "custom simulation instead." )
[docs] def get_species_richness(self, reference=1): """ Calls coal_analyse.get_species_richness() with the supplied variables. Requires successful import of coal_analyse and sqlite3. :param int reference: the community reference to obtain the metrics for. :return: the species richness. """ self.calculate_sql_database() db = self.output_database t = CoalescenceTree() t.set_database(db) return t.get_species_richness(reference)
[docs] def get_protracted(self): """ Gets whether the simulation pointed to by this object is a protracted simulation or not. """ if self.output_database is None or not os.path.exists(self.output_database): return self.protracted else: t = CoalescenceTree() try: t.set_database(self.output_database) except IOError: # Catches errors thrown if the simulation is paused pass return t.is_protracted()
[docs] def set_map_files( self, sample_file, fine_file=None, coarse_file=None, historical_fine_file=None, historical_coarse_file=None, dispersal_map=None, death_map=None, reproduction_map=None, ): """ Sets the map files (or to null, if none specified). It then calls detect_map_dimensions() to correctly read in the specified dimensions. If sample_file is "null", dimension values will remain at 0. If coarse_file is "null", it will default to the size of fine_file with zero offset. If the coarse file is "none", it will not be used. If the historical fine or coarse files are "none", they will not be used. .. note:: the dispersal map should be of dimensions xy by xy where x, y are the fine map dimensions. Dispersal rates from each row/column index represents dispersal from the row index to the column index according to index = x+(y*xdim), where x,y are the coordinates of the cell and xdim is the x dimension of the fine map. See the :class:`PatchedLandscape class <pycoalescence.patched_landscape.PatchedLandscape>` for routines for generating these landscapes. :param str sample_file: the sample map file. Provide "null" if on samplemask is required :param str fine_file: the fine map file. Defaults to "null" if none provided :param str coarse_file: the coarse map file. Defaults to "none" if none provided :param str historical_fine_file: the historical fine map file. Defaults to "none" if none provided :param str historical_coarse_file: the historical coarse map file. Defaults to "none" if none provided :param str dispersal_map: the dispersal map for reading dispersal values. Default to "none" if none provided :param str death_map: a map of relative death probabilities, at the scale of the fine map :param str reproduction_map: a map of relative reproduction probabilities, at the scale of the fine map :rtype: None :return: None """ if dispersal_map is None: self.dispersal_map.file_name = "none" else: self.dispersal_map.file_name = dispersal_map if death_map is None: self.death_map.file_name = "none" else: self.death_map.file_name = death_map if reproduction_map is None: self.reproduction_map.file_name = "none" else: self.reproduction_map.file_name = reproduction_map Landscape.set_map_files( self, sample_file, fine_file, coarse_file, historical_fine_file, historical_coarse_file, )
[docs] def detect_map_dimensions(self): """ Detects all the map dimensions for the provided files (where possible) and sets the respective values. This is intended to be run after set_map_files() :return: None """ Landscape.detect_map_dimensions(self) # Now do detection for dispersal map self.check_dispersal_map() self.check_reproduction_map() self.check_death_map() self.check_maps()
[docs] def check_dispersal_map(self): """Checks that the dispersal map dimensions match the fine map dimensions.""" if self.dispersal_map.file_name not in {"none", "null", None}: if self.coarse_map.file_name not in {None, "none"}: raise ValueError( "Cannot use a coarse map if using a dispersal map. " "This feature is currently unsupported." ) self.dispersal_map.set_dimensions() if ( self.dispersal_map.x_size != self.fine_map.x_size * self.fine_map.y_size or self.dispersal_map.y_size != self.fine_map.x_size * self.fine_map.y_size ): raise ValueError( "Dimensions of dispersal map do not match dimensions of fine map. This is currently" " unsupported." )
[docs] def check_reproduction_map(self): """Checks that the reproduction map dimensions match the fine map dimensions.""" if self.reproduction_map.file_name not in {None, "none", "null"}: if self.coarse_map.file_name not in {None, "none", "null"}: raise ValueError( "Cannot use a coarse map if using a reproduction map. " "This feature is currently unsupported." ) self.check_dimensions_match_fine(self.reproduction_map, "reproduction")
[docs] def check_death_map(self): """Checks that the death map dimensions match the fine map dimensions.""" if self.death_map.file_name not in {None, "none", "null"}: if self.coarse_map.file_name not in {None, "none", "null"}: raise ValueError( "Cannot use a coarse map if using a death map. " "This feature is currently unsupported." ) self.check_dimensions_match_fine(self.death_map, "death")
[docs] def add_dispersal_map(self, dispersal_map): """ Adds a dispersal map to the simulation. :param str/pycoalescence.Map dispersal_map: the dispersal map to import :rtype: None """ if isinstance(dispersal_map, Map): self.dispersal_map = dispersal_map else: self.dispersal_map.file_name = dispersal_map self.check_dispersal_map()
[docs] def add_death_map(self, death_map): """ Adds a death map to the simulation. :param str/pycoalescence.Map death_map: the death map to import :rtype: None """ if isinstance(death_map, Map): self.death_map = death_map else: self.death_map.file_name = death_map self.check_death_map()
[docs] def add_reproduction_map(self, reproduction_map): """ Adds a death map to the simulation. :param str/pycoalescence.Map reproduction_map: the death map to import :rtype: None """ if isinstance(reproduction_map, Map): self.reproduction_map = reproduction_map else: self.reproduction_map.file_name = reproduction_map self.check_reproduction_map()
[docs] def add_gillespie(self, generations=0.0): """ Uses the Gillespie algorithm from the given number of generations into the simulation. :param generations: the number of generations at which to use gillespie. :rtype: None :return: None """ FutureWarning( "The gillespie algorithm is not implemented beyond the base functionality and is awaiting a future update." ) if generations < 0: raise ValueError("Number of generations cannot be < 0.") if not self.check_can_use_gillespie(): self.uses_gillespie = False raise ValueError( "Cannot use Gillespie with this simulation. Gillespie algorithm can only be used for spatial " "simulations involving a dispersal map and point-speciation. Ensure your simulation is correctly set up." ) self.uses_gillespie = True self.gillespie_threshold = float(generations)
[docs] def check_can_use_gillespie(self): """ Checks if the simulation can use the Gillespie algorithm. :return: true if the simulation can use Gillespie :rtype: bool """ if not (self.is_setup_map and self.is_setup_param): raise RuntimeError( "Cannot check if Gillespie is possible before simulation is setup." ) if ( self.dispersal_map.file_name in ["none", None] or not self.is_spatial or self.coarse_map.file_name not in ["none", None] or self.protracted ): return False return True
[docs] def check_dimensions_match_fine(self, map_to_check, name=""): """ Checks that the dimensions of the provided map matches the fine map. :param Map map_to_check: map to check the dimensions of against the fine map :param str name: name to write out in error message :return: true if the dimensions match """ if map_to_check.file_name not in {"none", "null", None}: map_to_check.set_dimensions() if not map_to_check.has_equal_dimensions(self.fine_map): # if the sizes match, then proceed with a warning if ( map_to_check.x_size == self.fine_map.x_size and map_to_check.y_size == self.fine_map.y_size ): self.logger.warning( "Coordinates of {} map did not match fine map.".format(name) ) map_to_check.x_offset = self.fine_map.x_offset map_to_check.y_offset = self.fine_map.y_offset map_to_check.x_res = self.fine_map.x_res map_to_check.y_res = self.fine_map.y_res else: raise ValueError( "Dimensions of the {} map do not match the fine map. This is currently " "unsupported.".format(name) )
[docs] def set_simulation_parameters( self, seed, task, output_directory, min_speciation_rate, sigma=1.0, tau=1.0, deme=1.0, sample_size=1.0, max_time=3600, dispersal_method=None, m_prob=0.0, cutoff=0, dispersal_relative_cost=1, min_num_species=1, restrict_self=False, landscape_type=False, protracted=False, min_speciation_gen=None, max_speciation_gen=None, spatial=True, uses_spatial_sampling=False, times=None, ): """ Set all the simulation parameters apart from the map objects. :param int seed: the unique job number for this simulation set :param int task: the task reference number (used for easy file identification after simulations are complete) :param str output_directory: the output directory to store the SQL database :param float min_speciation_rate: the minimum speciation rate to simulate :param float sigma: the dispersal sigma value :param float tau: the fat-tailed dispersal tau value :param float deme: the deme size (in individuals per cell) :param float sample_size: the sample size of the deme (decimal 0-1) :param float max_time: the maximum allowed simulation time (in seconds) :param str dispersal_method: the dispersal kernel method. Should be one of [normal, fat-tail, norm-uniform] :param float m_prob: the probability of drawing from the uniform dispersal. Only relevant for uniform dispersals :param float cutoff: the maximum value for the uniform dispersal. Only relevant for uniform dispersals. :param float dispersal_relative_cost: the relative cost of travelling through non-habitat (defaults to 1) :param int min_num_species: the minimum number of species known to exist (defaults to 1 :param bool restrict_self: if true, restricts dispersal from own cell :param bool/str landscape_type: if false or "closed", restricts dispersal to the provided maps, otherwise can be "infinite", or a tiled landscape using "tiled_coarse" or "tiled_fine", or a clamped landscape using "clamped_coarse" or "clamped_fine". :param bool protracted: if true, uses protracted speciation application :param float min_speciation_gen: the minimum amount of time a lineage must exist before speciation occurs. :param float max_speciation_gen: the maximum amount of time a lineage can exist before speciating. :param bool spatial: if true, means that the simulation is spatial :param bool uses_spatial_sampling: if true, the sample mask is interpreted as a proportional sampling mask, where the number of individuals sampled in the cell is equal to the density * deme_sample * cell sampling proportion :param list times: list of temporal sampling points to apply (in generations) """ if not self.is_setup_param: self.set_seed(seed) self.output_directory = output_directory self.job_type = task self.min_speciation_rate = min_speciation_rate self.sigma = sigma self.tau = tau self.deme = deme self.sample_size = sample_size self.max_time = max_time self.dispersal_relative_cost = dispersal_relative_cost self.min_num_species = min_num_species self.dispersal_method = dispersal_method self.m_prob = m_prob self.cutoff = cutoff self.calculate_sql_database() self.is_setup_param = True self.restrict_self = restrict_self self.is_spatial = spatial if uses_spatial_sampling and not spatial: raise ValueError( "Must use a spatial simulation to define spatial sampling regime." ) self.uses_spatial_sampling = uses_spatial_sampling if not spatial: self.is_setup_map = True if landscape_type in {False, "closed"}: self.landscape_type = "closed" elif landscape_type in {True, "infinite"}: self.landscape_type = "infinite" elif landscape_type in { "tiled_coarse", "tiled_fine", "clamped_coarse", "clamped_fine", }: self.landscape_type = landscape_type else: raise ValueError( "Supplied landscape type is not recognised: {}".format( landscape_type ) ) if protracted: self.protracted = True if max_speciation_gen is None: self.logger.warning( "Using protracted speciation, but no maximum speciation generation supplied." " Default to 10^100" ) self.max_speciation_gen = 10 ** 100 else: self.max_speciation_gen = max_speciation_gen if min_speciation_gen is None: self.logger.warning( "Using protracted speciation, but no minimum speciation generation supplied." " Default to 0.0" ) self.min_speciation_gen = 0.0 else: self.min_speciation_gen = min_speciation_gen if times: self.add_sample_time(times) else: self.logger.warning("Parameters already set up.")
[docs] def set_seed(self, seed): """ Sets the seed for the simulation. A seed < 1 should not be set for the necsim, as equivalent behaviour is produce for seed and abs(seed), plus for seed = 1 and seed = 0. Consequently, for any values less than 1, we take a very large number plus the seed, instead. Therefore a error is raised if the seed exceeds this very large number (this is an acceptable decrease in userability as a seed that large is unlikely to ever be used). :param int seed: the random number seed """ limit_val = int(2147483647 / 2) if seed > limit_val: raise ValueError("Seed cannot be larger than 2,147,483,647") if seed < 1: new_seed = abs(seed) + limit_val self.logger.critical( "Seed of {} is < 1, so will be changed to {}\n".format(seed, new_seed) ) self.seed = new_seed else: self.seed = seed
[docs] def check_simulation_parameters(self, expected=False, ignore_errors=False): """ Checks that simulation parameters have been correctly set and the program is ready for running. :param ignore_errors: if true, any FileNotFoundError and FileExistsError raised by checking the output database are ignored :param expected: set to true if we expect the output file to exist """ if not self.is_setup_param or (self.seed == 0 and self.job_type == 0): raise RuntimeError("Simulation parameters have not been set.") if self.output_directory in {"", None, "null"}: raise RuntimeError("Output directory not set.") if self.full_config_file is not None: check_parent(self.full_config_file) if self.uses_gillespie and not self.check_can_use_gillespie(): raise RuntimeError("Cannot use Gillespie algorithm with current map setup.") try: if self.output_database is None: self.calculate_sql_database() self.check_file_parameters(expected=expected) except (FileExistsError, FileNotFoundError) as err: # pragma: no cover if not ignore_errors: raise err else:
[docs] def resume_coalescence( self, pause_directory, seed, job_type, max_time, out_directory=None, protracted=None, spatial=None, ): """ Resumes the simulation from the specified directory, looking for the simulation with the specified seed and task referencing. :param pause_directory: the directory to search for the paused simulation :param seed: the seed of the paused simulation :param job_type: the task of the paused simulation :param max_time: the maximum time to run simulations for :param out_directory: optionally provide an alternative output location. Defaults to same location as pause_directory :param bool protracted: protractedness of the simulation :param bool spatial: if the simulation is to be run with spatial complexity .. note: A max time of 0 uses the previous simulation maximum time. :return: None """ if protracted is not None: self.protracted = protracted if spatial is not None: self.is_spatial = spatial if out_directory is None: out_directory = pause_directory self.output_directory = out_directory self.pause_directory = pause_directory self.seed = seed self.job_type = job_type self.max_time = max_time self.calculate_sql_database() file_path = os.path.join( pause_directory, "Pause", "Dump_main_{}_{}.csv".format(job_type, seed) ) if not os.path.exists(file_path): raise IOError( "Paused file " + file_path + " not found. Ensure pause directory is correct and is accessible." ) self.setup_necsim() self.c_simulation.setup_resume( pause_directory, out_directory, int(seed), int(job_type), int(max_time) ) self.is_setup_complete = True self._run_and_output()
[docs] def persistent_ram_usage(self): """ This is the persistent RAM usage which cannot be optimised by the program for a particular set of maps :return: the total persistent RAM usage in bytes """ total_ram = 0 total_ram += self.fine_map.x_size * self.fine_map.y_size * 4 # First add the maps if self.coarse_map.file_name not in [None, "none"]: total_ram += self.coarse_map.x_size * self.coarse_map.y_size * 4 if len(self.historical_fine_list) != 0: total_ram += self.fine_map.x_size * self.fine_map.y_size * 4 if len(self.historical_coarse_list) != 0: total_ram += self.coarse_map.x_size * self.coarse_map.y_size * 4 if self.death_map.file_name not in [None, "none"]: total_ram += self.death_map.x_size * self.death_map.y_size * 8 if self.sample_map.file_name not in [None, "none", "null"]: total_ram += self.sample_map.x_size * self.sample_map.y_size * 1 if self.dispersal_map.file_name not in [None, "none"]: total_ram += self.fine_map.x_size * self.fine_map.y_size * 8 # Now add the other large memory objects # The data object total_ram += 2 * self.count_individuals() * 91 * min(len(self.times_list), 1) # The active object total_ram += 72 * self.count_individuals() * min(len(self.times_list), 1) return total_ram
[docs] def count_individuals(self): """ Estimates the number of individuals to be simulated. This may be inaccurate if using multiple time points and historical maps. :return: a count of the number of individuals to be simulated :rtype: float """ if self.count_total is None: if self.sample_map.file_name in [None, "none", "null"]: ds_array = np.ones(shape=(self.fine_map.x_size, self.fine_map.y_size)) else: ds = gdal.Open(self.sample_map.file_name) ds_array = ds.GetRasterBand(1).ReadAsArray() self.count_total = ( np.sum(ds_array) * self.sample_size * self.get_average_density() ) if len(self.times_list) > 0: self.count_total *= len(self.times_list) return self.count_total else: return self.count_total
[docs] def import_fine_map_array(self): """ Imports the fine map array to the in-memory object, subsetted to the same size as the sample grid. :rtype: None """ if self.fine_map_array is None: if self.fine_map.file_name == "null": self.fine_map_array = np.ones( (self.fine_map.x_size, self.fine_map.y_size) ) else: self.fine_map_array = self.fine_map.get_subset( self.fine_map.x_offset, self.fine_map.y_offset, self.sample_map.x_size, self.sample_map.y_size, )
[docs] def import_sample_map_array(self): """ Imports the sample map array to the in-memory object. :rtype: None """ if self.sample_map_array is None: if self.sample_map.file_name == "null": self.sample_map_array = np.ones( (self.sample_map.x_size, self.sample_map.y_size) ) else: if self.uses_spatial_sampling: self.sample_map_array = else: self.sample_map_array = >= 0.5, ).mask = None
[docs] def grid_density_estimate(self, x_off, y_off, x_dim, y_dim): """ Counts the density total for a subset of the grid by sampling from the fine map :note: This is an approximation (based on the average density of the fine map) and does not produce a perfect value. This is done for performance reasons. The actual value can be obtained with grid_density_actual(). :param x_off: the x offset of the grid map subset :param y_off: the y offset of the grid map subset :param x_dim: the x dimension of the grid map subset :param y_dim: the y dimension of the grid map subset :return: an estimate of the total individuals that exist in the subset. :rtype: int """ self.import_fine_map_array() if ( self.sample_map.file_name not in ["none", "null", None] and self.sample_map.file_name != self.fine_map.file_name ): self.import_sample_map_array() # Less accurate, but faster way. arr_subset = self.sample_map_array[ y_off : y_off + y_dim, x_off : x_off + x_dim ] return int( np.sum( np.floor(arr_subset * self.get_average_density() * self.sample_size) ) ) return int(np.sum(np.floor(self.fine_map_array * self.deme * self.sample_size)))
[docs] def grid_density_actual(self, x_off, y_off, x_dim, y_dim): """ Counts the density total for a subset of the grid by sampling from the fine map. Note that for large maps this can take a very long time. :param x_off: the x offset of the grid map subset :param y_off: the y offset of the grid map subset :param x_dim: the x dimension of the grid map subset :param y_dim: the y dimension of the grid map subset :return: the total individuals that exist in the subset. :rtype: int """ self.import_fine_map_array() if ( self.sample_map.file_name not in ["none", "null", None] and self.sample_map.file_name != self.fine_map.file_name ): self.import_sample_map_array() # Less accurate, but faster way. return int( np.sum( np.floor( np.multiply( self.fine_map_array[ y_off : y_off + y_dim, x_off : x_off + x_dim ], self.sample_map_array[ y_off : y_off + y_dim, x_off : x_off + x_dim ], ) * self.deme * self.sample_size ) ) ) else: return int( np.sum( np.floor( self.fine_map_array[ y_off : y_off + y_dim, x_off : x_off + x_dim ] * self.deme * self.sample_size ) ) )
[docs] def get_average_density(self): """ Gets the average density across the fine map, subsetted for the sample grid. """ if self.fine_map_average is None: self.import_fine_map_array() self.fine_map_average = self.deme * np.mean(self.fine_map_array) return self.fine_map_average
[docs] def check_sample_map_equals_sample_grid(self): """ Checks if the grid and sample map are the same size and offset (in which case, future operations can be simplified). :return: true if the grid and sample map dimensions and offsets are equal :rtype: bool """ return ( self.grid.x_size == self.sample_map.x_size and self.grid.y_size == self.sample_map.x_size and self.grid.x_offset == 0 and self.grid.y_offset == 0 )
[docs] def optimise_ram(self, ram_limit): """ Optimises the maps for a specific RAM usage. If ram_limit is None, this function does nothing. :note: Assumes that the C++ compiler has sizeof(long) = 8 bytes for calculating space usage. :note: Only optimises RAM for a square area of the map. For rectangular shapes, will use the shortest length as a maximum size. :param ram_limit: the desired amount of RAM to limit to, in GB :raises MemoryError: if the desired simulation cannot be compressed into available RAM """ if self.sample_size <= 0 or self.deme <= 0: raise ValueError( "Sample size is 0, or deme is 0. set_simulation_parameters() before optimising RAM." ) self.grid = copy.deepcopy(self.sample_map) # Over-estimate static usage slightly static_usage = 1.1 * self.persistent_ram_usage() / 1024 ** 3 if static_usage > ram_limit: raise MemoryError( "Cannot achieve RAM limit: minimum requirements are {}GB.".format( round(static_usage, 2) ) ) remaining_space = (ram_limit - static_usage) * 1024 ** 3 "Remaining space is {}GB.\n".format(round(remaining_space / 1024 ** 3, 2)) ) # Rough calculation of number of cells we have remaining (aim for 75% to be sure) average_density = self.get_average_density() # This is the size of the grid object in memory tmp_space = 0.95 * (remaining_space / ((16 * average_density) + 32)) size = int(np.rint(tmp_space ** 0.5)) if size < self.sample_map.x_size or size < self.sample_map.y_size: if ( size > self.sample_map.x_size or size > self.sample_map.y_size ): # pragma: no cover size = min(self.sample_map.x_size, self.sample_map.y_size) if size < 2: raise MemoryError( "Could not find square grid to achieve RAM limit. Please set this manually." ) max_attained = 0 max_x = 0 max_y = 0 # Now loop over every square of the required size on the grid, until we find one with the maximum density if ( self.sample_map.x_size * self.sample_map.y_size ) > 1000000: # pragma: no cover printing = True else: printing = False end_x = self.sample_map.x_size - size - 1 if size < 10: # pragma: no cover self.logger.warning( "Extremely small grid size: {}. Disabling maximum density checking.".format( size ) ) max_x = int(self.sample_map.x_size / 2) max_y = int(self.sample_map.y_size / 2) max_attained = 1 else: for x in range( 0, max(self.sample_map.x_size - size - 1, 1), int(round(max(10.0, size / 10))), ): if printing: # pragma: no cover "\rChecking {} / {} : {}%".format( x, end_x, round(100 * x / end_x) ) ) for y in range( 0, max(self.sample_map.y_size - size - 1, 1), int(round(max(10.0, size / 10))), ): # print("x, y: {}, {}".format(x, y)) tmp_total = self.grid_density_actual(x, y, size, size) if tmp_total > max_attained: max_x = x max_y = y max_attained = tmp_total if printing: # pragma: no cover"\rChecking complete \n") if max_attained > 0: # Now do one last check for consistency while self._fine_map_check(max_x, max_y, size): if printing: # pragma: no cover printing = False "Shrinking due to high densities in sample zone.\n" ) # Brute-force dividing by 2 each time size = max(1, int(size / 2)) if size == 1: break if ( max_x + size > self.sample_map.x_size or max_y + size > self.sample_map.y_size ): # pragma: no cover self.logger.debug("Max x,y: {}, {}".format(max_x, max_y)) self.logger.debug("Size: {}".format(size)) self.logger.debug( "Fine map size: {}, {}".format( self.fine_map.x_size, self.fine_map.y_size ) ) self.logger.debug( "Sample map size: {}, {}".format( self.sample_map.x_size, self.sample_map.y_size ) ) raise RuntimeError( "Incorrect dimension setting - please report this bug" ) self.grid.x_size = size self.grid.y_size = size self.sample_map.x_offset = max_x self.sample_map.y_offset = max_y self.grid.file_name = "set" if self.check_sample_map_equals_sample_grid(): # pragma: no cover self.grid.file_name = "none" else: # pragma: no cover raise MemoryError( "Could not optimise maps for desired memory usage. " "Attempted grid size of {} and achived max of {}".format( size, max_attained ) ) self._wipe_objects()
[docs] def get_optimised_solution(self): """ Gets the optimised solution as a dictionary containing the important optimised variables. This can be read back in with set_optimised_solution :return: dict containing the important optimised variables :rtype: dict """ return { "grid_x_size": self.grid.x_size, "grid_y_size": self.grid.y_size, "sample_x_offset": self.sample_map.x_offset, "sample_y_offset": self.sample_map.y_offset, "grid_file_name": self.grid.file_name, }.copy()
[docs] def set_optimised_solution(self, dict_in): """ Sets the optimised RAM solution from the variables in the provided dictionary. This should contain the grid_x_size, grid_y_size, grid_file_name, sample_x_offset and sample_y_offset. :param dict dict_in: the dictionary containing the optimised RAM solution variables :rtype: None """ self.grid.x_size = dict_in["grid_x_size"] self.grid.y_size = dict_in["grid_y_size"] self.sample_map.x_offset = dict_in["sample_x_offset"] self.sample_map.y_offset = dict_in["sample_y_offset"] self.grid.file_name = dict_in["grid_file_name"]
[docs] def finalise_setup(self, expected=False, ignore_errors=False): """ Runs all setup routines to provide a complete simulation. Should be called immediately before run_coalescence() to ensure the simulation setup is complete. :param ignore_errors: if true, any FileNotFoundError and FileExistsError raised by checking the output database are ignored :param expected: set to true if we expect the output file to exist """ self.check_simulation_parameters(expected=expected, ignore_errors=ignore_errors) self.create_config() self.create_map_config() self.calculate_sql_database() try: from pycoalescence.__init__ import __version__ "Simulation set-up under pycoalescence version {}".format(__version__) ) except ImportError: # pragma: no cover"pycoalescence version not found.") self.setup_necsim() if self.full_config_file is not None: # pragma: no cover self.c_simulation.import_from_config(self.full_config_file) else: self.config.write(self.config_string) self.c_simulation.import_from_config_string(self.config_string.getvalue()) self.c_simulation.setup() if self.uses_gillespie and self.check_can_use_gillespie(): self.c_simulation.add_gillespie(self.gillespie_threshold) self.is_setup_complete = True
[docs] def setup_necsim(self): """ Calculates the type of the simulation (spatial/non-spatial, protracted/non-protracted) and sets the c object appropriately. :rtype: None """ if self.protracted: if self.is_spatial: self.c_simulation = libnecsim.CPSpatialSimulation( self.logger, write_to_log ) else: self.c_simulation = libnecsim.CPNSESimulation(self.logger, write_to_log) elif self.is_spatial: self.c_simulation = libnecsim.CSpatialSimulation(self.logger, write_to_log) else: self.c_simulation = libnecsim.CNSESimulation(self.logger, write_to_log)
[docs] def set_speciation_rates(self, speciation_rates): """Add speciation rates for analysis at the end of the simulation. This is optional :param list speciation_rates: a list of speciation rates to apply at the end of the simulation """ self.speciation_rates.extend(speciation_rates)
[docs] def calculate_sql_database(self): """ Saves the output database location to self.output_database. """ self.output_database = os.path.join( self.output_directory, "data_{}_{}.db".format(str(self.job_type), str(self.seed)), )
[docs] def check_sql_database(self, expected=False): """ Checks whether the output database exists. If the existance does not match the expected variable, raises an error. :raises FileExistsError: if the file already exists when it's not expected to :raises FileNotExistsError: if the file does not exist when we expect it to :param expected: boolean for expected existance of the output file :rtype: None """ check_parent(self.output_database) if not expected: if os.path.exists(self.output_database): try: t = CoalescenceTree(self.output_database) except IOError: pass else: raise FileExistsError("Output sql database already exists.") else: if not os.path.exists(self.output_database): raise FileNotFoundError( "Output sql database {} not found.".format(self.output_database) )
[docs] def check_maps(self): """ Checks that the maps all exist and that the file structure makes sense. :raises ValueError: if the grid is not within the sample map. :return: None """ if self.is_spatial: Landscape.check_maps(self) if ( self.grid.file_name == "set" and self.sample_map.x_offset + self.grid.x_size > self.sample_map.x_size or self.sample_map.y_offset + self.grid.y_size > self.sample_map.y_size ): raise ValueError( "Grid is not within the sample map - please check offsets of sample map." ) self.check_dispersal_map() self.check_reproduction_map() self.check_death_map()
[docs] def check_file_parameters(self, expected=False): """ Check that all the required files exist for the simulation and the output doesn't already exist. :param expected: set to true if we expect the output file to already exist :raises RuntimeError: if previous set-up routines are not complete """ if self.is_setup_map and self.is_setup_param: self.check_maps() else: raise RuntimeError("Set up of maps or parameters is incomplete.") self.check_sql_database(expected=expected)
[docs] def run(self): """ Convenience function which completes setup, runs the simulation and calculates the coalescence tree for the set speciation rates in one step. :rtype: None """ self.finalise_setup() self._run_and_output()
def _run_and_output(self): """ Runs the simulation and outputs to the database. Should only be called internally. :return: """ if self.run_coalescence(): self.apply_speciation_rates() else: self.logger.warning("Simulation did not complete in time specified.\n") self.logger.warning("Resume with extra time to continue.\n")
[docs] def run_coalescence(self): """ Attempt to run the simulation with the given simulation set-up. This is the main routine performing the actual simulation which will take a considerable amount of time. :return: True if the simulation completes successfully, False if the simulation pauses. :rtype: bool """ if self.is_setup_complete: # Make the output directory if it doesn't yet exist if not os.path.exists(self.output_directory): os.makedirs(self.output_directory) # Call the C++ object and run the simulation return else: raise RuntimeError("Set up is incomplete.")
[docs] def apply_speciation_rates(self, speciation_rates=None): """ Applies the speciation rates to the coalescence tree and outputs to the database. :param speciation_rates: a list of speciation rates to apply :rtype: None """ if speciation_rates: for each in speciation_rates: self.speciation_rates.append(each) self.speciation_rates = [x for x in set(self.speciation_rates)] self.c_simulation.apply_speciation_rates(self.speciation_rates)
def _wipe_objects(self): """ Clears the arrays from memory :return: """ self.fine_map_array = None self.sample_map_array = None def _fine_map_check(self, max_x, max_y, size): """ Checks the total number of individuals in a subset from the fine map is less than the average density multiplied by the size of the square. :param max_x: the x value to start subsetting from :param max_y: the y value to start subsetting from :param size: the size of the square :return: bool true if it is smaller :rtype: bool """ self._fine_map_sum_res = self.grid_density_actual(max_x, max_y, size, size) return self._fine_map_sum_res > size * size * self.get_average_density()