CSS_Project/docs/search.js at main · codegithubka/CSS_Project

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window.pdocSearch = (function(){

/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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this.docInfo[e],this.length--)},t.DocumentStore.prototype.addFieldLength=function(e,t,n){null!==e&&void 0!==e&&0!=this.hasDoc(e)&&(this.docInfo[e]||(this.docInfo[e]={}),this.docInfo[e][t]=n)},t.DocumentStore.prototype.updateFieldLength=function(e,t,n){null!==e&&void 0!==e&&0!=this.hasDoc(e)&&this.addFieldLength(e,t,n)},t.DocumentStore.prototype.getFieldLength=function(e,t){return null===e||void 0===e?0:e in this.docs&&t in this.docInfo[e]?this.docInfo[e][t]:0},t.DocumentStore.prototype.toJSON=function(){return{docs:this.docs,docInfo:this.docInfo,length:this.length,save:this._save}},t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},t={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",o=n+"[^aeiouy]*",r=i+"[aeiou]*",s="^("+o+")?"+r+o,u="^("+o+")?"+r+o+"("+r+")?$",a="^("+o+")?"+r+o+r+o,l="^("+o+")?"+i,c=new RegExp(s),d=new RegExp(a),f=new RegExp(u),h=new RegExp(l),p=/^(.+?)(ss|i)es$/,v=/^(.+?)([^s])s$/,g=/^(.+?)eed$/,m=/^(.+?)(ed|ing)$/,y=/.$/,S=/(at|bl|iz)$/,x=new RegExp("([^aeiouylsz])\\1$"),w=new 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0===e)throw new Error("token should not be undefined");return e.replace(/^\W+/,"").replace(/\W+$/,"")},t.Pipeline.registerFunction(t.trimmer,"trimmer"),t.InvertedIndex=function(){this.root={docs:{},df:0}},t.InvertedIndex.load=function(e){var t=new this;return t.root=e.root,t},t.InvertedIndex.prototype.addToken=function(e,t,n){for(var n=n||this.root,i=0;i<=e.length-1;){var o=e[i];o in n||(n[o]={docs:{},df:0}),i+=1,n=n[o]}var r=t.ref;n.docs[r]?n.docs[r]={tf:t.tf}:(n.docs[r]={tf:t.tf},n.df+=1)},t.InvertedIndex.prototype.hasToken=function(e){if(!e)return!1;for(var t=this.root,n=0;n<e.length;n++){if(!t[e[n]])return!1;t=t[e[n]]}return!0},t.InvertedIndex.prototype.getNode=function(e){if(!e)return null;for(var t=this.root,n=0;n<e.length;n++){if(!t[e[n]])return null;t=t[e[n]]}return t},t.InvertedIndex.prototype.getDocs=function(e){var t=this.getNode(e);return null==t?{}:t.docs},t.InvertedIndex.prototype.getTermFrequency=function(e,t){var n=this.getNode(e);return null==n?0:t in n.docs?n.docs[t].tf:0},t.InvertedIndex.prototype.getDocFreq=function(e){var t=this.getNode(e);return null==t?0:t.df},t.InvertedIndex.prototype.removeToken=function(e,t){if(e){var n=this.getNode(e);null!=n&&t in n.docs&&(delete n.docs[t],n.df-=1)}},t.InvertedIndex.prototype.expandToken=function(e,t,n){if(null==e||""==e)return[];var t=t||[];if(void 0==n&&(n=this.getNode(e),null==n))return t;n.df>0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default 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/** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"models.CA": {"fullname": "models.CA", "modulename": "models.CA", "kind": "module", "doc": "<h1 id=\"cellular-automaton-framework\">Cellular Automaton Framework</h1>\n\nThis module provides the base cellular automaton class and the\nPredator-Prey (PP) implementation with Numba-accelerated kernels.\n\n<h6 id=\"classes\">Classes</h6>\n\nCA: Abstract base class for spatial cellular automata.\n\nPP: Predator-Prey model with configurable hunting behavior.\n\n<h6 id=\"example\">Example</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>from models.CA import PP\n\n# Basic usage\nmodel = PP(rows=100, cols=100, densities=(0.3, 0.15), seed=42)\nmodel.run(steps=1000)\n\n# With evolution enabled\nmodel = PP(rows=100, cols=100, seed=42)\nmodel.evolve("prey_death", sd=0.05, min_val=0.01, max_val=0.15)\nmodel.run(steps=500)\n\n# With directed hunting\nmodel = PP(rows=100, cols=100, directed_hunting=True, seed=42)\n</code></pre>\n</div>\n"}, "models.CA.CA": {"fullname": "models.CA.CA", "modulename": "models.CA", "qualname": "CA", "kind": "class", "doc": "Base cellular automaton class for spatial simulations.\n\nThis class provides a framework for multi-species cellular automata with\nsupport for global parameters, per-cell evolving parameters, and\ngrid initialization based on density.\n\n<h6 id=\"attributes\">Attributes</h6>\n\n<ul>\n<li>grid (np.ndarray):\n2D numpy array containing integers in range [0, n_species].</li>\n<li>params (Dict[str, Any]):\nGlobal parameters shared by all cells.</li>\n<li>cell_params (Dict[str, Any]):\nLocal per-cell parameters, typically stored as numpy arrays matching the grid shape.</li>\n<li>neighborhood (str):\nThe adjacency rule used ('neumann' or 'moore').</li>\n<li>generator (np.random.Generator):\nThe random number generator instance for reproducibility.</li>\n<li>species_names (Tuple[str, ...]):\nHuman-readable names for each species state.</li>\n</ul>\n"}, "models.CA.CA.__init__": {"fullname": "models.CA.CA.__init__", "modulename": "models.CA", "qualname": "CA.__init__", "kind": "function", "doc": "Initialize the cellular automaton grid and configurations.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>rows (int):\nNumber of rows in the grid (must be > 0).</li>\n<li>cols (int):\nNumber of columns in the grid (must be > 0).</li>\n<li>densities (Tuple[float, ...]):\nInitial density for each species. Length defines <code>n_species</code>.\nValues must sum to <= 1.0.</li>\n<li>neighborhood ({'neumann', 'moore'}):\nType of neighborhood connectivity.</li>\n<li>params (Dict[str, Any]):\nInitial global parameter values.</li>\n<li>cell_params (Dict[str, Any]):\nInitial local per-cell parameters.</li>\n<li>seed (int, optional):\nSeed for the random number generator.</li>\n</ul>\n", "signature": "(\trows: int,\tcols: int,\tdensities: Tuple[float, ...],\tneighborhood: str,\tparams: Dict[str, object],\tcell_params: Dict[str, object],\tseed: Optional[int] = None)"}, "models.CA.CA.rows": {"fullname": "models.CA.CA.rows", "modulename": "models.CA", "qualname": "CA.rows", "kind": "variable", "doc": "int: Number of rows in the grid.\n", "annotation": ": int"}, "models.CA.CA.cols": {"fullname": "models.CA.CA.cols", "modulename": "models.CA", "qualname": "CA.cols", "kind": "variable", "doc": "int: Number of columns in the grid.\n", "annotation": ": int"}, "models.CA.CA.densities": {"fullname": "models.CA.CA.densities", "modulename": "models.CA", "qualname": "CA.densities", "kind": "variable", "doc": "Tuple[float, ...]: Initial density fraction for each species.\n", "annotation": ": Tuple[float, ...]"}, "models.CA.CA.n_species": {"fullname": "models.CA.CA.n_species", "modulename": "models.CA", "qualname": "CA.n_species", "kind": "variable", "doc": "int: Number of distinct species states (excluding empty state 0).\n", "annotation": ": int"}, "models.CA.CA.validate": {"fullname": "models.CA.CA.validate", "modulename": "models.CA", "qualname": "CA.validate", "kind": "function", "doc": "Validate core CA invariants and grid dimensions.\n\nChecks that the neighborhood is valid, the grid matches initialized dimensions,\nand that local parameter arrays match the grid shape.\n\n<h6 id=\"raises\">Raises</h6>\n\n<ul>\n<li>ValueError: If any structural invariant is violated.</li>\n</ul>\n", "signature": "(self) -> None:", "funcdef": "def"}, "models.CA.CA.evolve": {"fullname": "models.CA.CA.evolve", "modulename": "models.CA", "qualname": "CA.evolve", "kind": "function", "doc": "Enable per-cell evolution for a specific parameter on a given species.\n\nThis method initializes a spatial parameter array (local parameter map)\nfor a global parameter. It allows individual cells to carry their own\nvalues for that parameter, which can then mutate and evolve during\nthe simulation.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>param (str):\nThe name of the global parameter to enable for evolution.\nMust exist in <code>self.params</code>.</li>\n<li>species (int, optional):\nThe 1-based index of the species to which this parameter applies.\nIf None, the method attempts to infer the species from the\nparameter name prefix.</li>\n<li>sd (float, default 0.05):\nThe standard deviation of the Gaussian mutation applied during\ninheritance/reproduction.</li>\n<li>min_val (float, optional):\nThe minimum allowable value for the parameter (clamping).\nDefaults to 0.01 if not provided.</li>\n<li>max_val (float, optional):\nThe maximum allowable value for the parameter (clamping).\nDefaults to 0.99 if not provided.</li>\n</ul>\n\n<h6 id=\"raises\">Raises</h6>\n\n<ul>\n<li>ValueError: If the parameter is not in <code>self.params</code>, the species cannot be\ninferred, or the species index is out of bounds.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe local parameter is stored in <code>self.cell_params</code> as a 2D numpy\narray initialized with the current global value for all cells of\nthe target species, and <code>NaN</code> elsewhere.\n", "signature": "(\tself,\tparam: str,\tspecies: Optional[int] = None,\tsd: float = 0.05,\tmin_val: Optional[float] = None,\tmax_val: Optional[float] = None) -> None:", "funcdef": "def"}, "models.CA.CA.update": {"fullname": "models.CA.CA.update", "modulename": "models.CA", "qualname": "CA.update", "kind": "function", "doc": "Perform one update step of the cellular automaton.\n\nThis is an abstract method that defines the transition rules of the\nsystem. It must be implemented by concrete subclasses to specify\nhow cell states and parameters change over time based on their\ncurrent state and neighborhood.\n\n<h6 id=\"raises\">Raises</h6>\n\n<ul>\n<li>NotImplementedError: If called directly on the base class instead of an implementation.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>None</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nIn a typical implementation, this method handles the logic for\nstochastic transitions, movement, or predator-prey interactions.\n", "signature": "(self) -> None:", "funcdef": "def"}, "models.CA.CA.run": {"fullname": "models.CA.CA.run", "modulename": "models.CA", "qualname": "CA.run", "kind": "function", "doc": "Execute the cellular automaton simulation for a specified number of steps.\n\nThis method drives the simulation loop, calling <code>update()</code> at each\niteration. It manages visualization updates, directory creation for\ndata persistence, and handles the freezing of evolving parameters\nat a specific time step.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>steps (int):\nThe total number of iterations to run (must be non-negative).</li>\n<li>stop_evolution_at (int, optional):\nThe 1-based iteration index after which parameter mutation is\ndisabled. Useful for observing system stability after a period\nof adaptation.</li>\n<li>snapshot_iters (List[int], optional):\nA list of specific 1-based iteration indices at which to save\nthe current grid state to the results directory.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>None</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nIf snapshots are requested, a results directory is automatically created\nusing a timestamped subfolder (e.g., 'results/run-1700000000/').\nVisualization errors are logged but do not terminate the simulation.\n", "signature": "(\tself,\tsteps: int,\tstop_evolution_at: Optional[int] = None,\tsnapshot_iters: Optional[list] = None) -> None:", "funcdef": "def"}, "models.CA.PP": {"fullname": "models.CA.PP", "modulename": "models.CA", "qualname": "PP", "kind": "class", "doc": "Predator-Prey Cellular Automaton model with Numba-accelerated kernels.\n\nThis model simulates a stochastic predator-prey system where species\ninteract on a 2D grid. It supports evolving per-cell death rates,\nperiodic boundary conditions, and both random and directed hunting\nbehaviors.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>rows (int, default 10):\nNumber of rows in the simulation grid.</li>\n<li>cols (int, default 10):\nNumber of columns in the simulation grid.</li>\n<li>densities (Tuple[float, ...], default (0.2, 0.1)):\nInitial population densities for (prey, predator).</li>\n<li>neighborhood ({'moore', 'neumann'}, default 'moore'):\nThe neighborhood type for cell interactions.</li>\n<li>params (Dict[str, object], optional):\nGlobal parameters: \"prey_death\", \"predator_death\", \"prey_birth\",\n\"predator_birth\".</li>\n<li>cell_params (Dict[str, object], optional):\nInitial local parameter maps (2D arrays).</li>\n<li>seed (int, optional):\nRandom seed for reproducibility.</li>\n<li>synchronous (bool, default True):\nIf True, updates the entire grid at once. If False, updates\ncells asynchronously.</li>\n<li>directed_hunting (bool, default False):\nIf True, predators selectively hunt prey rather than choosing\nneighbors at random.</li>\n</ul>\n\n<h6 id=\"attributes\">Attributes</h6>\n\n<ul>\n<li>species_names (Tuple[str, ...]):\nLabels for the species ('prey', 'predator').</li>\n<li>synchronous (bool):\nCurrent update mode.</li>\n<li>directed_hunting (bool):\nCurrent hunting strategy logic.</li>\n</ul>\n", "bases": "CA"}, "models.CA.PP.__init__": {"fullname": "models.CA.PP.__init__", "modulename": "models.CA", "qualname": "PP.__init__", "kind": "function", "doc": "Initialize the Predator-Prey CA with validated parameters and kernels.\n", "signature": "(\trows: int = 10,\tcols: int = 10,\tdensities: Tuple[float, ...] = (0.2, 0.1),\tneighborhood: str = 'moore',\tparams: Dict[str, object] = None,\tcell_params: Dict[str, object] = None,\tseed: Optional[int] = None,\tsynchronous: bool = True,\tdirected_hunting: bool = False)"}, "models.CA.PP.validate": {"fullname": "models.CA.PP.validate", "modulename": "models.CA", "qualname": "PP.validate", "kind": "function", "doc": "Validate Predator-Prey specific invariants and spatial parameter arrays.\n\nExtends the base CA validation to ensure that numerical parameters are\nwithin the [0, 1] probability range and that evolved parameter maps\n(e.g., prey_death) correctly align with the species locations.\n\n<h6 id=\"raises\">Raises</h6>\n\n<ul>\n<li>ValueError: If grid shapes, parameter ranges, or species masks are inconsistent.</li>\n<li>TypeError: If parameters are non-numeric.</li>\n</ul>\n", "signature": "(self) -> None:", "funcdef": "def"}, "models.CA.PP.update_async": {"fullname": "models.CA.PP.update_async", "modulename": "models.CA", "qualname": "PP.update_async", "kind": "function", "doc": "Execute an asynchronous update using the optimized Numba kernel.\n\nThis method retrieves the evolved parameter maps and delegates the\nstochastic transitions to the <code>PPKernel</code>. Asynchronous updates\ntypically handle cell-by-cell logic where changes can be\nimmediately visible to neighbors.\n", "signature": "(self) -> None:", "funcdef": "def"}, "models.CA.PP.update": {"fullname": "models.CA.PP.update", "modulename": "models.CA", "qualname": "PP.update", "kind": "function", "doc": "Dispatch the simulation step based on the configured update mode.\n", "signature": "(self) -> None:", "funcdef": "def"}, "models.config": {"fullname": "models.config", "modulename": "models.config", "kind": "module", "doc": "<h1 id=\"experiment-configuration\">Experiment Configuration</h1>\n\nThis module provides the configuration dataclass and pre-defined phase\nconfigurations for Predator-Prey Hydra Effect experiments.\n\n<h6 id=\"classes\">Classes</h6>\n\nConfig\n Central configuration dataclass with all experiment parameters.\n\n<h6 id=\"functions\">Functions</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>get_phase_config: Retrieve configuration for a specific experimental phase.\n</code></pre>\n</div>\n\n<h6 id=\"phase-configurations\">Phase Configurations</h6>\n\n<ul>\n<li><code>PHASE1_CONFIG</code>: Parameter sweep to find critical point</li>\n<li><code>PHASE2_CONFIG</code>: Self-organization (evolution toward criticality)</li>\n<li><code>PHASE3_CONFIG</code>: Finite-size scaling at critical point</li>\n<li><code>PHASE4_CONFIG</code>: Sensitivity analysis (4D parameter sweep)</li>\n<li><code>PHASE5_CONFIG</code>: Directed hunting comparison</li>\n</ul>\n\n<h6 id=\"example\">Example</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>from models.config import Config, get_phase_config\n\n# Use predefined phase config\ncfg = get_phase_config(1)\n\n# Create custom config\ncfg = Config(grid_size=200, n_replicates=10)\n\n# Generate parameter sweep values\nprey_deaths = cfg.get_prey_deaths()\n</code></pre>\n</div>\n"}, "models.config.Config": {"fullname": "models.config.Config", "modulename": "models.config", "qualname": "Config", "kind": "class", "doc": "Central configuration for Predator-Prey Hydra Effect experiments.\n\n<h6 id=\"attributes\">Attributes</h6>\n\n<ul>\n<li>grid_size (int):\nSide length of the square simulation grid.</li>\n<li>densities (Tuple[float, float]):\nInitial population fractions for (prey, predator).</li>\n<li>grid_sizes (Tuple[int, ...]):\nGrid dimensions for Finite-Size Scaling (FSS) analysis (Phase 3).</li>\n<li>prey_birth (float):\nGlobal birth rate for prey species.</li>\n<li>prey_death (float):\nGlobal death rate for prey species.</li>\n<li>predator_birth (float):\nGlobal birth rate for predator species.</li>\n<li>predator_death (float):\nGlobal death rate for predator species.</li>\n<li>critical_prey_birth (float):\nCritical birth rate identified from Phase 1.</li>\n<li>critical_prey_death (float):\nCritical death rate identified from Phase 1.</li>\n<li>prey_death_range (Tuple[float, float]):\nBounds for prey death rate sweep.</li>\n<li>n_prey_death (int):\nNumber of points in prey death rate sweep.</li>\n<li>n_replicates (int):\nIndependent stochastic runs per parameter set.</li>\n<li>warmup_steps (int):\nIterations before data collection begins.</li>\n<li>measurement_steps (int):\nIterations for collecting statistics.</li>\n<li>evolve_sd (float):\nStandard deviation for parameter mutation.</li>\n<li>evolve_min (float):\nLower bound for evolving parameters.</li>\n<li>evolve_max (float):\nUpper bound for evolving parameters.</li>\n<li>directed_hunting (bool):\nToggle for targeted predator movement.</li>\n<li>save_timeseries (bool):\nToggle for recording population time series.</li>\n<li>timeseries_subsample (int):\nSubsample rate for time series data.</li>\n<li>collect_pcf (bool):\nToggle for Pair Correlation Function analysis.</li>\n<li>pcf_sample_rate (float):\nFraction of runs that compute PCFs.</li>\n<li>pcf_max_distance (float):\nMaximum radial distance for PCF.</li>\n<li>pcf_n_bins (int):\nNumber of bins in PCF histogram.</li>\n<li>min_density_for_analysis (float):\nPopulation threshold for spatial analysis.</li>\n<li>n_jobs (int):\nCPU cores for parallelization (-1 = all).</li>\n</ul>\n"}, "models.config.Config.get_prey_deaths": {"fullname": "models.config.Config.get_prey_deaths", "modulename": "models.config", "qualname": "Config.get_prey_deaths", "kind": "function", "doc": "Generate array of prey death rates for parameter sweep.\n", "signature": "(self) -> numpy.ndarray:", "funcdef": "def"}, "models.config.Config.get_warmup_steps": {"fullname": "models.config.Config.get_warmup_steps", "modulename": "models.config", "qualname": "Config.get_warmup_steps", "kind": "function", "doc": "Get warmup steps (can be extended for size-dependent scaling).\n", "signature": "(self, L: int) -> int:", "funcdef": "def"}, "models.config.Config.get_measurement_steps": {"fullname": "models.config.Config.get_measurement_steps", "modulename": "models.config", "qualname": "Config.get_measurement_steps", "kind": "function", "doc": "Get measurement steps (can be extended for size-dependent scaling).\n", "signature": "(self, L: int) -> int:", "funcdef": "def"}, "models.config.Config.estimate_runtime": {"fullname": "models.config.Config.estimate_runtime", "modulename": "models.config", "qualname": "Config.estimate_runtime", "kind": "function", "doc": "Estimate wall-clock time for the experiment.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>n_cores (int):\nNumber of available CPU cores.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>str: Human-readable runtime estimate.</li>\n</ul>\n", "signature": "(self, n_cores: int = 32) -> str:", "funcdef": "def"}, "models.config.get_phase_config": {"fullname": "models.config.get_phase_config", "modulename": "models.config", "qualname": "get_phase_config", "kind": "function", "doc": "Retrieve configuration for a specific experimental phase.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>phase (int):\nPhase number (1-5).</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>Config: Configuration instance for the requested phase.</li>\n</ul>\n\n<h6 id=\"raises\">Raises</h6>\n\n<ul>\n<li>ValueError: If phase number is invalid.</li>\n</ul>\n", "signature": "(phase: int) -> models.config.Config:", "funcdef": "def"}, "models.numba_optimized": {"fullname": "models.numba_optimized", "modulename": "models.numba_optimized", "kind": "module", "doc": "<h1 id=\"numba-optimized-kernels\">Numba-Optimized Kernels</h1>\n\nThis module provides Numba-accelerated kernels for the predator-prey\ncellular automaton, including update kernels and spatial analysis functions.\n\n<h6 id=\"classes\">Classes</h6>\n\nPPKernel\n Wrapper for predator-prey update kernels with pre-allocated buffers.\n\n<h6 id=\"cluster-analysis\">Cluster Analysis</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>measure_cluster_sizes_fast # Fast cluster size measurement (sizes only).\ndetect_clusters_fast # Full cluster detection with labels.\nget_cluster_stats_fast # Comprehensive cluster statistics.\n</code></pre>\n</div>\n\n<h6 id=\"pair-correlation-functions\">Pair Correlation Functions</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>compute_pcf_periodic_fast # PCF for two position sets with periodic boundaries.\ncompute_all_pcfs_fast #Compute prey-prey, pred-pred, and prey-pred PCFs.\n</code></pre>\n</div>\n\n<h6 id=\"utilities\">Utilities</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>set_numba_seed # Seed Numba's internal RNG.\nwarmup_numba_kernels # Pre-compile kernels to avoid first-run latency.\n</code></pre>\n</div>\n\n<h6 id=\"example\">Example</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>from models.numba_optimized import (\n PPKernel,\n get_cluster_stats_fast,\n compute_all_pcfs_fast,\n)\n\n# Cluster analysis\nstats = get_cluster_stats_fast(grid, species=1)\nprint(f"Largest cluster: {stats['largest']}")\n\n# PCF computation\npcfs = compute_all_pcfs_fast(grid, max_distance=20.0)\nprey_prey_dist, prey_prey_gr, _ = pcfs['prey_prey']\n</code></pre>\n</div>\n"}, "models.numba_optimized.set_numba_seed": {"fullname": "models.numba_optimized.set_numba_seed", "modulename": "models.numba_optimized", "qualname": "set_numba_seed", "kind": "function", "doc": "Seed Numba's internal random number generator from within a JIT context.\n\nThis function ensures that Numba's independent random number generator\nis synchronized with the provided seed, enabling reproducibility for\njit-compiled functions that use NumPy's random operations.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>seed (int):\nThe integer value used to initialize the random number generator.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>None</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nBecause Numba maintains its own internal state for random number\ngeneration, calling <code>np.random.seed()</code> in standard Python code will not\naffect jit-compiled functions. This helper must be called to bridge\nthat gap.\n", "signature": "(seed: int) -> None:", "funcdef": "def"}, "models.numba_optimized.PPKernel": {"fullname": "models.numba_optimized.PPKernel", "modulename": "models.numba_optimized", "qualname": "PPKernel", "kind": "class", "doc": "Wrapper for predator-prey kernel with pre-allocated buffers.\n\nThis class manages the spatial configuration and memory buffers required\nfor the Numba-accelerated update kernels. By pre-allocating the\n<code>occupied_buffer</code>, it avoids expensive memory allocations during the\nsimulation loop.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>rows (int):\nNumber of rows in the simulation grid.</li>\n<li>cols (int):\nNumber of columns in the simulation grid.</li>\n<li>neighborhood ({'moore', 'von_neumann'}, optional):\nThe neighborhood type determining adjacent cells. 'moore' includes\ndiagonals (8 neighbors), 'von_neumann' does not (4 neighbors).\nDefault is 'moore'.</li>\n<li>directed_hunting (bool, optional):\nIf True, uses the directed behavior kernel where species search for\ntargets. If False, uses random neighbor selection. Default is False.</li>\n</ul>\n\n<h6 id=\"attributes\">Attributes</h6>\n\n<ul>\n<li>rows (int):\nGrid row count.</li>\n<li>cols (int):\nGrid column count.</li>\n<li>directed_hunting (bool):\nToggle for intelligent behavior logic.</li>\n</ul>\n"}, "models.numba_optimized.PPKernel.update": {"fullname": "models.numba_optimized.PPKernel.update", "modulename": "models.numba_optimized", "qualname": "PPKernel.update", "kind": "function", "doc": "Execute a single asynchronous update step using the configured kernel.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid (np.ndarray):\nThe current 2D simulation grid.</li>\n<li>prey_death_arr (np.ndarray):\n2D array of individual prey mortality rates.</li>\n<li>prey_birth (float):\nPrey reproduction probability.</li>\n<li>prey_death (float):\nBase prey mortality probability.</li>\n<li>pred_birth (float):\nPredator reproduction (hunting success) probability.</li>\n<li>pred_death (float):\nPredator mortality probability.</li>\n<li>evolve_sd (float, optional):\nMutation standard deviation (default 0.1).</li>\n<li>evolve_min (float, optional):\nMinimum evolved death rate (default 0.001).</li>\n<li>evolve_max (float, optional):\nMaximum evolved death rate (default 0.1).</li>\n<li>evolution_stopped (bool, optional):\nWhether to disable mutation during this step (default True).</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>np.ndarray: The updated grid after one full asynchronous pass.</li>\n</ul>\n", "signature": "(\tself,\tgrid: numpy.ndarray,\tprey_death_arr: numpy.ndarray,\tprey_birth: float,\tprey_death: float,\tpred_birth: float,\tpred_death: float,\tevolve_sd: float = 0.1,\tevolve_min: float = 0.001,\tevolve_max: float = 0.1,\tevolution_stopped: bool = True) -> numpy.ndarray:", "funcdef": "def"}, "models.numba_optimized.measure_cluster_sizes_fast": {"fullname": "models.numba_optimized.measure_cluster_sizes_fast", "modulename": "models.numba_optimized", "qualname": "measure_cluster_sizes_fast", "kind": "function", "doc": "Measure cluster sizes for a specific species using Numba-accelerated flood fill.\n\nThis function provides a high-performance interface for calculating cluster\nsize statistics without the overhead of generating a full label map. It is\noptimized for large-scale simulation analysis where only distribution\nmetrics (e.g., mean size, max size) are required.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid (np.ndarray):\nA 2D array representing the simulation environment.</li>\n<li>species (int):\nThe target species identifier (e.g., 1 for Prey, 2 for Predator).</li>\n<li>neighborhood ({'moore', 'neumann'}, optional):\nThe connectivity rule. 'moore' uses 8-way connectivity (including diagonals);\n'neumann' uses 4-way connectivity. Default is 'moore'.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>cluster_sizes (np.ndarray):\nA 1D array of integers, where each element is the cell count of an\nidentified cluster.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe input grid is cast to <code>int32</code> to ensure compatibility with the\nunderlying JIT-compiled <code>_measure_clusters</code> kernel.\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> sizes = measure_cluster_sizes_fast(grid, species=1, neighborhood='moore')\n>>> if sizes.size > 0:\n... print(f"Largest cluster: {sizes.max()}")\n</code></pre>\n</div>\n", "signature": "(\tgrid: numpy.ndarray,\tspecies: int,\tneighborhood: str = 'moore') -> numpy.ndarray:", "funcdef": "def"}, "models.numba_optimized.detect_clusters_fast": {"fullname": "models.numba_optimized.detect_clusters_fast", "modulename": "models.numba_optimized", "qualname": "detect_clusters_fast", "kind": "function", "doc": "Perform full cluster detection with labels using Numba acceleration.\n\nThis function returns a label array for spatial analysis and a dictionary\nof cluster sizes. It is significantly faster than standard Python or\nSciPy equivalents for large simulation grids.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid (np.ndarray):\nA 2D array representing the simulation environment.</li>\n<li>species (int):\nThe target species identifier (e.g., 1 for Prey, 2 for Predator).</li>\n<li>neighborhood ({'moore', 'neumann'}, optional):\nThe connectivity rule. 'moore' uses 8-way connectivity; 'neumann'\nuses 4-way connectivity. Default is 'moore'.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>labels (np.ndarray):\nA 2D int32 array where each cell contains its unique cluster ID.\nCells not belonging to the target species are 0.</li>\n<li>sizes (dict):\nA dictionary mapping cluster IDs to their respective cell counts.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe underlying Numba kernel uses a stack-based flood fill to avoid\nrecursion limits and handles periodic boundary conditions.\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> labels, sizes = detect_clusters_fast(grid, species=1)\n>>> if sizes:\n... largest_id = max(sizes, key=sizes.get)\n... print(f"Cluster {largest_id} size: {sizes[largest_id]}")\n</code></pre>\n</div>\n", "signature": "(\tgrid: numpy.ndarray,\tspecies: int,\tneighborhood: str = 'moore') -> Tuple[numpy.ndarray, Dict[int, int]]:", "funcdef": "def"}, "models.numba_optimized.get_cluster_stats_fast": {"fullname": "models.numba_optimized.get_cluster_stats_fast", "modulename": "models.numba_optimized", "qualname": "get_cluster_stats_fast", "kind": "function", "doc": "Compute comprehensive cluster statistics for a species using Numba acceleration.\n\nThis function integrates cluster detection and labeling to provide a\nfull suite of spatial metrics. It calculates the cluster size distribution\nand the largest cluster fraction, which often serves as an order\nparameter in percolation theory and Phase 1-3 analyses.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid (np.ndarray):\nA 2D array representing the simulation environment.</li>\n<li>species (int):\nThe target species identifier (e.g., 1 for Prey, 2 for Predator).</li>\n<li>neighborhood ({'moore', 'neumann'}, optional):\nThe connectivity rule. 'moore' uses 8-way connectivity; 'neumann'\nuses 4-way connectivity. Default is 'moore'.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>stats (dict):\nA dictionary containing:\n<ul>\n<li>'n_clusters': Total count of isolated clusters.</li>\n<li>'sizes': Sorted array (descending) of all cluster sizes.</li>\n<li>'largest': Size of the single largest cluster.</li>\n<li>'largest_fraction': Size of the largest cluster divided by\nthe total population of the species.</li>\n<li>'mean_size': Average size of all clusters.</li>\n<li>'size_distribution': Frequency mapping of {size: count}.</li>\n<li>'labels': 2D array of unique cluster IDs.</li>\n<li>'size_dict': Mapping of {label_id: size}.</li>\n</ul></li>\n</ul>\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> stats = get_cluster_stats_fast(grid, species=1)\n>>> print(f"Found {stats['n_clusters']} prey clusters.")\n>>> print(f"Order parameter: {stats['largest_fraction']:.3f}")\n</code></pre>\n</div>\n", "signature": "(grid: numpy.ndarray, species: int, neighborhood: str = 'moore') -> Dict:", "funcdef": "def"}, "models.numba_optimized.compute_pcf_periodic_fast": {"fullname": "models.numba_optimized.compute_pcf_periodic_fast", "modulename": "models.numba_optimized", "qualname": "compute_pcf_periodic_fast", "kind": "function", "doc": "Compute the Pair Correlation Function (PCF) using cell-list acceleration.\n\nThis high-level function coordinates the spatial hashing and histogram\ncalculation to determine the $g(r)$ function. It normalizes the resulting\nhistogram by the expected number of pairs in an ideal gas of the same\ndensity, accounting for the toroidal area of each radial bin.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>positions_i (np.ndarray):\n(N, 2) array of coordinates for species I.</li>\n<li>positions_j (np.ndarray):\n(M, 2) array of coordinates for species J.</li>\n<li>grid_shape (tuple of int):\nThe (rows, cols) dimensions of the simulation grid.</li>\n<li>max_distance (float):\nThe maximum radius to calculate correlations for.</li>\n<li>n_bins (int, optional):\nNumber of bins for the radial distribution (default 50).</li>\n<li>self_correlation (bool, optional):\nSet to True if computing the correlation of a species with itself\nto avoid self-counting (default False).</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>bin_centers (np.ndarray):\nThe central radial distance for each histogram bin.</li>\n<li>pcf (np.ndarray):\nThe normalized $g(r)$ values. A value of 1.0 indicates no spatial\ncorrelation; > 1.0 indicates clustering; < 1.0 indicates repulsion.</li>\n<li>total_pairs (int):\nThe total count of pairs found within the <code>max_distance</code>.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe function dynamically determines the optimal number of cells for the\nspatial hash based on the <code>max_distance</code> and grid dimensions to maintain\nlinear time complexity.\n", "signature": "(\tpositions_i: numpy.ndarray,\tpositions_j: numpy.ndarray,\tgrid_shape: Tuple[int, int],\tmax_distance: float,\tn_bins: int = 50,\tself_correlation: bool = False) -> Tuple[numpy.ndarray, numpy.ndarray, int]:", "funcdef": "def"}, "models.numba_optimized.compute_all_pcfs_fast": {"fullname": "models.numba_optimized.compute_all_pcfs_fast", "modulename": "models.numba_optimized", "qualname": "compute_all_pcfs_fast", "kind": "function", "doc": "Compute all three species Pair Correlation Functions (PCFs) using cell-list acceleration.\n\nThis function calculates the spatial auto-correlations (Prey-Prey,\nPredator-Predator) and the cross-correlation (Prey-Predator) for a given\nsimulation grid. It identifies particle positions and leverages\nNumba-accelerated cell lists to handle the computations efficiently.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid (np.ndarray):\n2D integer array where 1 represents prey and 2 represents predators.</li>\n<li>max_distance (float, optional):\nThe maximum radial distance for the correlation. Defaults to 1/4\nof the minimum grid dimension if not provided.</li>\n<li>n_bins (int, optional):\nNumber of distance bins for the histogram. Default is 50.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>results (dict):\nA dictionary with keys 'prey_prey', 'pred_pred', and 'prey_pred'.\nEach value is a tuple containing:\n<ul>\n<li>bin_centers (np.ndarray): Radial distances.</li>\n<li>pcf_values (np.ndarray): Normalized g(r) values.</li>\n<li>pair_count (int): Total number of pairs found.</li>\n</ul></li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe PCF provides insight into the spatial organization of the system.\ng(r) > 1 at short distances indicates aggregation (clustering),\nwhile g(r) < 1 indicates exclusion or repulsion.\n", "signature": "(\tgrid: numpy.ndarray,\tmax_distance: Optional[float] = None,\tn_bins: int = 50) -> Dict[str, Tuple[numpy.ndarray, numpy.ndarray, int]]:", "funcdef": "def"}, "models.numba_optimized.warmup_numba_kernels": {"fullname": "models.numba_optimized.warmup_numba_kernels", "modulename": "models.numba_optimized", "qualname": "warmup_numba_kernels", "kind": "function", "doc": "Pre-compile all Numba-accelerated kernels to avoid first-run latency.\n\nThis function executes a single step of the simulation and each analysis\nroutine on a dummy grid. Because Numba uses Just-In-Time (JIT) compilation,\nthe first call to a decorated function incurs a compilation overhead.\nRunning this warmup ensures that subsequent experimental runs are timed\naccurately and perform at full speed.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid_size (int, optional):\nThe side length of the dummy grid used for warmup (default 100).</li>\n<li>directed_hunting (bool, optional):\nIf True, also warms up the directed behavior update kernel (default False).</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>None</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThis function checks for <code>NUMBA_AVAILABLE</code> before execution. It warms up\nthe <code>PPKernel</code> (random and optionally directed), as well as the\nspatial analysis functions (<code>compute_all_pcfs_fast</code>, <code>detect_clusters_fast</code>, etc.).\n", "signature": "(grid_size: int = 100, directed_hunting: bool = False):", "funcdef": "def"}, "models.numba_optimized.benchmark_kernels": {"fullname": "models.numba_optimized.benchmark_kernels", "modulename": "models.numba_optimized", "qualname": "benchmark_kernels", "kind": "function", "doc": "Benchmark the execution performance of random vs. directed update kernels.\n\nThis utility measures the average time per simulation step for both the\nstochastic (random neighbor) and heuristic (directed hunting/reproduction)\nupdate strategies. It accounts for the computational overhead introduced\nby the \"intelligent\" search logic used in directed mode.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid_size (int, optional):\nThe side length of the square simulation grid (default 100).</li>\n<li>n_runs (int, optional):\nThe number of iterations to perform for averaging performance (default 20).</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>t_random (float):\nAverage time per step for the random kernel in milliseconds.</li>\n<li>t_directed (float):\nAverage time per step for the directed kernel in milliseconds.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe function ensures a fair comparison by:\n\n<ol>\n<li>Using a fixed seed for reproducible initial grid states.</li>\n<li>Warming up Numba kernels before timing to exclude JIT compilation latency.</li>\n<li>Copying the grid and death arrays for each iteration to maintain\nconsistent population densities throughout the benchmark.</li>\n</ol>\n", "signature": "(grid_size: int = 100, n_runs: int = 20):", "funcdef": "def"}, "models.numba_optimized.benchmark_cluster_detection": {"fullname": "models.numba_optimized.benchmark_cluster_detection", "modulename": "models.numba_optimized", "qualname": "benchmark_cluster_detection", "kind": "function", "doc": "Benchmark the performance of different cluster detection and analysis routines.\n\nThis function evaluates three levels of spatial analysis:\n\n<ol>\n<li>Size measurement only (fastest, no label map).</li>\n<li>Full detection (returns label map and size dictionary).</li>\n<li>Comprehensive statistics (calculates distributions, means, and order parameters).</li>\n</ol>\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid_size (int, optional):\nSide length of the square grid for benchmarking (default 100).</li>\n<li>n_runs (int, optional):\nNumber of iterations to average for performance results (default 20).</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>stats (dict):\nThe result dictionary from the final comprehensive statistics run.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe benchmark uses a fixed prey density of 30% to ensure a representative\ndistribution of clusters. It pre-warms the Numba kernels to ensure that\nthe measurements reflect execution speed rather than compilation time.\n", "signature": "(grid_size: int = 100, n_runs: int = 20):", "funcdef": "def"}, "experiments": {"fullname": "experiments", "modulename": "experiments", "kind": "module", "doc": "<h1 id=\"predator-prey-hydra-effect-experiments\">Predator-Prey Hydra Effect Experiments</h1>\n\nHPC-ready experiment runner for investigating the Hydra effect in\npredator-prey cellular automata.\n\n<h6 id=\"experimental-phases\">Experimental Phases</h6>\n\n<ul>\n<li>Phase 1: Parameter sweep to find critical point (bifurcation + cluster analysis)</li>\n<li>Phase 2: Self-organization analysis (evolution toward criticality)</li>\n<li>Phase 3: Finite-size scaling at critical point</li>\n<li>Phase 4: Sensitivity analysis across parameter regimes</li>\n<li>Phase 5: Model extensions (directed hunting comparison)</li>\n</ul>\n\n<h6 id=\"functions\">Functions</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>run_single_simulation # Execute one simulation run and collect metrics.\nrun_phase1, run_phase2, run_phase3, run_phase4, run_phase5 # Phase-specific experiment runners.\n</code></pre>\n</div>\n\n<h6 id=\"utilities\">Utilities</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>generate_unique_seed # Deterministic seed generation from parameters.\ncount_populations # Count species populations on grid.\nget_evolved_stats # Statistics for evolved parameters.\naverage_pcfs # Average multiple PCF measurements.\nsave_results_jsonl, load_results_jsonl, save_results_npz # I/O functions for experiment results.\n</code></pre>\n</div>\n\n<h6 id=\"command-line-usage\">Command Line Usage</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>python experiments.py --phase 1 # Run phase 1\npython experiments.py --phase 1 --dry-run # Estimate runtime\npython experiments.py --phase all # Run all phases\npython experiments.py --phase 1 --output results/ # Custom output\n</code></pre>\n</div>\n\n<h6 id=\"programmatic-usage\">Programmatic Usage</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>from experiments import run_single_simulation, run_phase1\nfrom models.config import PHASE1_CONFIG\n\n# Single simulation\nresult = run_single_simulation(\n prey_birth=0.2,\n prey_death=0.05,\n predator_birth=0.8,\n predator_death=0.1,\n grid_size=100,\n seed=42,\n cfg=PHASE1_CONFIG,\n)\n\n# Full phase (writes to output directory)\nimport logging\nresults = run_phase1(PHASE1_CONFIG, Path("results/"), logging.getLogger())\n</code></pre>\n</div>\n"}, "experiments.generate_unique_seed": {"fullname": "experiments.generate_unique_seed", "modulename": "experiments", "qualname": "generate_unique_seed", "kind": "function", "doc": "Create a deterministic seed from a dictionary of parameters and a repetition index.\n\nThis function serializes the input dictionary into a sorted JSON string,\nappends the repetition count, and hashes the resulting string using SHA-256.\nThe first 8 characters of the hex digest are then converted to an integer\nto provide a stable, unique seed for random number generators.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>params (dict):\nA dictionary of configuration parameters. Keys are sorted to ensure\ndeterminism regardless of insertion order.</li>\n<li>rep (int):\nThe repetition or iteration index, used to ensure different seeds\nare generated for the same parameter set across multiple runs.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>int: A unique integer seed derived from the input parameters.</li>\n</ul>\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> params = {'learning_rate': 0.01, 'batch_size': 32}\n>>> generate_unique_seed(params, 1)\n3432571217\n>>> generate_unique_seed(params, 2)\n3960013583\n</code></pre>\n</div>\n", "signature": "(params: dict, rep: int) -> int:", "funcdef": "def"}, "experiments.count_populations": {"fullname": "experiments.count_populations", "modulename": "experiments", "qualname": "count_populations", "kind": "function", "doc": "Count the number of empty, prey, and predator cells in the simulation grid.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>grid (np.ndarray):\nA 2D NumPy array representing the simulation environment, where:\n<ul>\n<li>0: Empty cell</li>\n<li>1: Prey</li>\n<li>2: Predator</li>\n</ul></li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>empty_count (int):\nTotal number of cells with a value of 0.</li>\n<li>prey_count (int):\nTotal number of cells with a value of 1.</li>\n<li>predator_count (int):\nTotal number of cells with a value of 2.</li>\n</ul>\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> grid = np.array([[0, 1], [2, 1]])\n>>> count_populations(grid)\n(1, 2, 1)\n</code></pre>\n</div>\n", "signature": "(grid: numpy.ndarray) -> Tuple[int, int, int]:", "funcdef": "def"}, "experiments.get_evolved_stats": {"fullname": "experiments.get_evolved_stats", "modulename": "experiments", "qualname": "get_evolved_stats", "kind": "function", "doc": "Get statistics of an evolved parameter from the model.\n\nThis function retrieves parameter values from the model's internal storage,\nfilters out NaN values, and calculates basic descriptive statistics.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>model (object):\nThe simulation model instance containing a <code>cell_params</code> attribute\nwith a <code>.get()</code> method.</li>\n<li>param (str):\nThe name of the parameter to calculate statistics for.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>stats (dict):\nA dictionary containing the following keys:\n<ul>\n<li>'mean': Arithmetic mean of valid values.</li>\n<li>'std': Standard deviation of valid values.</li>\n<li>'min': Minimum valid value.</li>\n<li>'max': Maximum valid value.</li>\n<li>'n': Count of non-NaN values.\nIf no valid data is found, all stats return NaN and n returns 0.</li>\n</ul></li>\n</ul>\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> stats = get_evolved_stats(my_model, "speed")\n>>> print(stats['mean'])\n1.25\n</code></pre>\n</div>\n", "signature": "(model, param: str) -> Dict:", "funcdef": "def"}, "experiments.average_pcfs": {"fullname": "experiments.average_pcfs", "modulename": "experiments", "qualname": "average_pcfs", "kind": "function", "doc": "Average multiple Pair Correlation Function (PCF) measurements and calculate standard error.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>pcf_list (list of tuple):\nA list where each element is a tuple containing:\n<ul>\n<li>distances (np.ndarray): The radial distances (r).</li>\n<li>pcf_values (np.ndarray): The correlation values g(r).</li>\n<li>count (int): Metadata or weight (not used in current calculation).</li>\n</ul></li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>distances (np.ndarray):\nThe radial distances from the first entry in the list.</li>\n<li>pcf_mean (np.ndarray):\nThe element-wise mean of the PCF values across all measurements.</li>\n<li>pcf_se (np.ndarray):\nThe standard error of the mean for the PCF values.</li>\n</ul>\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> data = [(np.array([0, 1]), np.array([1.0, 2.0]), 10),\n... (np.array([0, 1]), np.array([1.2, 1.8]), 12)]\n>>> dist, mean, se = average_pcfs(data)\n>>> mean\narray([1.1, 1.9])\n</code></pre>\n</div>\n", "signature": "(\tpcf_list: List[Tuple[numpy.ndarray, numpy.ndarray, int]]) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]:", "funcdef": "def"}, "experiments.save_results_jsonl": {"fullname": "experiments.save_results_jsonl", "modulename": "experiments", "qualname": "save_results_jsonl", "kind": "function", "doc": "Save a list of dictionaries to a file in JSON Lines (JSONL) format.\n\nEach dictionary in the list is serialized into a single JSON string and\nwritten as a new line. Non-serializable objects are converted to strings\nusing the default string representation.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>results (list of dict):\nThe collection of result dictionaries to be saved.</li>\n<li>output_path (Path):\nThe file system path (pathlib.Path) where the JSONL file will be created.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>None</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe file is opened in 'w' (write) mode, which will overwrite any existing\ncontent at the specified path.\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> data = [{"id": 1, "score": 0.95}, {"id": 2, "score": 0.88}]\n>>> save_results_jsonl(data, Path("results.jsonl"))\n</code></pre>\n</div>\n", "signature": "(results: List[Dict], output_path: pathlib.Path):", "funcdef": "def"}, "experiments.save_results_npz": {"fullname": "experiments.save_results_npz", "modulename": "experiments", "qualname": "save_results_npz", "kind": "function", "doc": "Save simulation results to a compressed NumPy (.npz) binary file.\n\nThis function flattens a list of result dictionaries into a single\ndictionary of NumPy arrays, prefixing keys with the run index to\nmaintain data separation. The resulting file is compressed to\nreduce storage space.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>results (list of dict):\nA list where each dictionary contains key-value pairs of\nsimulation data (e.g., arrays, lists, or scalars).</li>\n<li>output_path (Path):\nThe file system path (pathlib.Path) where the compressed\nNPZ file will be saved.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>None</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe keys in the saved file follow the format 'run_{index}_{original_key}'.\nValues are automatically converted to NumPy arrays if they are not\nalready.\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> results = [{"energy": [1, 2]}, {"energy": [3, 4]}]\n>>> save_results_npz(results, Path("output.npz"))\n</code></pre>\n</div>\n", "signature": "(results: List[Dict], output_path: pathlib.Path):", "funcdef": "def"}, "experiments.load_results_jsonl": {"fullname": "experiments.load_results_jsonl", "modulename": "experiments", "qualname": "load_results_jsonl", "kind": "function", "doc": "Load simulation results from a JSON Lines (JSONL) formatted file.\n\nThis function reads a file line-by-line, parsing each line as an\nindependent JSON object and aggregating them into a list of dictionaries.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>input_path (Path):\nThe file system path (pathlib.Path) to the JSONL file.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>results (list of dict):\nA list of dictionaries reconstructed from the file content.</li>\n</ul>\n\n<h6 id=\"raises\">Raises</h6>\n\n<ul>\n<li>FileNotFoundError: If the specified input path does not exist.</li>\n<li>json.JSONDecodeError: If a line in the file is not valid JSON.</li>\n</ul>\n\n<h6 id=\"examples\">Examples</h6>\n\n<div class=\"pdoc-code codehilite\">\n<pre><code>>>> data = load_results_jsonl(Path("results.jsonl"))\n>>> len(data)\n2\n</code></pre>\n</div>\n", "signature": "(input_path: pathlib.Path) -> List[Dict]:", "funcdef": "def"}, "experiments.run_single_simulation": {"fullname": "experiments.run_single_simulation", "modulename": "experiments", "qualname": "run_single_simulation", "kind": "function", "doc": "Run a single Predator-Prey (PP) simulation and collect comprehensive metrics.\n\nThis function initializes a Cellular Automata model, executes a warmup phase\nto reach steady state, and then performs a measurement phase to track\npopulation dynamics, spatial clustering, and evolutionary changes.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>prey_birth (float):\nThe probability or rate of prey reproduction.</li>\n<li>prey_death (float):\nThe base probability or rate of prey mortality.</li>\n<li>predator_birth (float):\nThe probability or rate of predator reproduction upon consuming prey.</li>\n<li>predator_death (float):\nThe probability or rate of predator mortality.</li>\n<li>grid_size (int):\nThe side length of the square simulation grid.</li>\n<li>seed (int):\nRandom seed for ensuring reproducibility of the simulation run.</li>\n<li>cfg (Config):\nA configuration object containing simulation hyperparameters (densities,\nsampling rates, timing, etc.).</li>\n<li>with_evolution (bool, optional):\nIf True, enables the evolution of the 'prey_death' parameter within\nthe model (default is False).</li>\n<li>compute_pcf (bool, optional):\nExplicit toggle for Pair Correlation Function calculation. If None,\nit is determined by <code>cfg.pcf_sample_rate</code> (default is None).</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>result (dict):\nA dictionary containing simulation results including:\n<ul>\n<li>Input parameters and survival flags.</li>\n<li>Population mean and standard deviation for both species.</li>\n<li>Cluster statistics (number of clusters, sizes, largest fractions).</li>\n<li>Evolutionary statistics (mean, std, min, max, and final values).</li>\n<li>PCF data and spatial indices (segregation and clustering).</li>\n<li>Optional time series for populations and evolved parameters.</li>\n</ul></li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe function relies on several external utilities: <code>count_populations</code>,\n<code>get_evolved_stats</code>, <code>get_cluster_stats_fast</code>, <code>compute_all_pcfs_fast</code>,\nand <code>average_pcfs</code>.\n", "signature": "(\tprey_birth: float,\tprey_death: float,\tpredator_birth: float,\tpredator_death: float,\tgrid_size: int,\tseed: int,\tcfg: models.config.Config,\twith_evolution: bool = False,\tcompute_pcf: Optional[bool] = None) -> Dict:", "funcdef": "def"}, "experiments.run_phase1": {"fullname": "experiments.run_phase1", "modulename": "experiments", "qualname": "run_phase1", "kind": "function", "doc": "Execute Phase 1 of the simulation: a parameter sweep to identify critical points.\n\nThis function performs a 1D sweep across varying prey mortality rates while\nkeeping other parameters fixed. It utilizes parallel execution via joblib\nand saves results incrementally to a JSONL file to ensure data integrity\nduring long-running batches.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>cfg (Config):\nConfiguration object containing simulation hyperparameters, sweep\nranges, and execution settings (n_jobs, grid_size, etc.).</li>\n<li>output_dir (Path):\nDirectory where result files (JSONL) and metadata (JSON) will be stored.</li>\n<li>logger (logging.Logger):\nLogger instance for tracking simulation progress and recording\noperational metadata.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>all_results (list of dict):\nA list of dictionaries containing the metrics collected from every\nindividual simulation run in the sweep.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe function performs the following steps:\n\n<ol>\n<li>Pre-warms Numba kernels for performance.</li>\n<li>Generates a deterministic set of simulation jobs using unique seeds.</li>\n<li>Executes simulations in parallel using a generator for memory efficiency.</li>\n<li>Records metadata including a timestamp and a serialized snapshot of\nthe configuration.</li>\n</ol>\n", "signature": "(\tcfg: models.config.Config,\toutput_dir: pathlib.Path,\tlogger: logging.Logger) -> List[Dict]:", "funcdef": "def"}, "experiments.run_phase2": {"fullname": "experiments.run_phase2", "modulename": "experiments", "qualname": "run_phase2", "kind": "function", "doc": "Execute Phase 2 of the simulation: self-organization and criticality analysis.\n\nThis phase tests the Self-Organized Criticality (SOC) hypothesis by\ninitializing simulations at different points in the parameter space and\nobserving whether evolutionary pressure drives the system toward a\ncommon critical point, regardless of initial prey mortality rates.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>cfg (Config):\nConfiguration object containing simulation hyperparameters, evolution\nsettings, and execution constraints.</li>\n<li>output_dir (Path):\nDirectory where result files (JSONL) and metadata (JSON) will be stored.</li>\n<li>logger (logging.Logger):\nLogger instance for tracking progress and evolutionary convergence.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>all_results (list of dict):\nA list of dictionaries containing metrics from the evolutionary\nsimulation runs.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe function captures:\n\n<ol>\n<li>Convergence of 'prey_death' across multiple replicates.</li>\n<li>Final steady-state population distributions.</li>\n<li>Incremental saving of results to prevent data loss.</li>\n</ol>\n", "signature": "(\tcfg: models.config.Config,\toutput_dir: pathlib.Path,\tlogger: logging.Logger) -> List[Dict]:", "funcdef": "def"}, "experiments.run_phase3": {"fullname": "experiments.run_phase3", "modulename": "experiments", "qualname": "run_phase3", "kind": "function", "doc": "Phase 3: Finite-size scaling at critical point.\n\n<ul>\n<li>Multiple grid sizes at (critical_prey_birth, critical_prey_death)</li>\n<li>Analyze cluster size cutoffs vs L</li>\n</ul>\n", "signature": "(\tcfg: models.config.Config,\toutput_dir: pathlib.Path,\tlogger: logging.Logger) -> List[Dict]:", "funcdef": "def"}, "experiments.run_phase4": {"fullname": "experiments.run_phase4", "modulename": "experiments", "qualname": "run_phase4", "kind": "function", "doc": "Execute Phase 3 of the simulation: Finite-Size Scaling (FSS) analysis.\n\nThis phase investigates how spatial structures, specifically cluster size\ncutoffs, scale with the system size (L) at the critical point identified\nin Phase 1. This is essential for determining the universality class of\nthe phase transition.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>cfg (Config):\nConfiguration object containing critical point parameters, the list of\ngrid sizes to test, and execution settings.</li>\n<li>output_dir (Path):\nDirectory where result files (JSONL) and FSS metadata (JSON) will be\nstored.</li>\n<li>logger (logging.Logger):\nLogger instance for tracking progress across different grid sizes.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>all_results (list of dict):\nA list of dictionaries containing metrics and cluster statistics for\neach grid size and replicate.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe function performs the following:\n\n<ol>\n<li>Iterates through multiple grid sizes defined in <code>cfg.grid_sizes</code>.</li>\n<li>Generates parallel jobs for each size using critical birth/death rates.</li>\n<li>Saves results incrementally to allow for post-simulation analysis of\npower-law exponents.</li>\n</ol>\n", "signature": "(\tcfg: models.config.Config,\toutput_dir: pathlib.Path,\tlogger: logging.Logger) -> List[Dict]:", "funcdef": "def"}, "experiments.run_phase5": {"fullname": "experiments.run_phase5", "modulename": "experiments", "qualname": "run_phase5", "kind": "function", "doc": "Execute Phase 5 of the simulation: Global 4D parameter sweep with directed hunting.\n\nThis phase performs a comprehensive sensitivity analysis by varying four key\nparameters (prey birth/death and predator birth/death) while directed\nhunting is enabled. The results allow for a direct comparison with Phase 4\nto determine how predator search behavior shifts the system's critical\nthresholds and stability.\n\n<h6 id=\"parameters\">Parameters</h6>\n\n<ul>\n<li>cfg (Config):\nConfiguration object containing simulation hyperparameters, parallel\nexecution settings, and the fixed grid size for this phase.</li>\n<li>output_dir (Path):\nDirectory where the result JSONL file and execution metadata will\nbe stored.</li>\n<li>logger (logging.Logger):\nLogger instance for tracking the progress of the high-volume\nsimulation batch.</li>\n</ul>\n\n<h6 id=\"returns\">Returns</h6>\n\n<ul>\n<li>all_results (list of dict):\nA list of dictionaries containing metrics for every simulation in\nthe 4D parameter grid.</li>\n</ul>\n\n<h6 id=\"notes\">Notes</h6>\n\nThe function utilizes a Cartesian product of parameter ranges to build a\njob list of over 13,000 unique parameter sets (multiplied by replicates).\nSeeds are uniquely generated to distinguish these runs from other phases\neven if parameter values overlap.\n", "signature": "(\tcfg: models.config.Config,\toutput_dir: pathlib.Path,\tlogger: logging.Logger) -> List[Dict]:", "funcdef": "def"}, "experiments.main": {"fullname": "experiments.main", "modulename": "experiments", "qualname": "main", "kind": "function", "doc": "Organize the predator-prey experimental suite across multiple phases.\n\nThis entry point handles command-line arguments, sets up logging and output\ndirectories, and executes the requested simulation phases (1-5). It\nsupports parallel execution, dry runs for runtime estimation, and\nautomated configuration persistence.\n\n<h6 id=\"notes\">Notes</h6>\n\nThe script dynamically retrieves phase-specific configurations using\n<code>get_phase_config</code> and dispatches execution to the corresponding runner\nin the <code>PHASE_RUNNERS</code> mapping.\n", "signature": "():", "funcdef": "def"}}, "docInfo": {"models.CA": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 369}, "models.CA.CA": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 151}, "models.CA.CA.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 132, "bases": 0, "doc": 138}, "models.CA.CA.rows": {"qualname": 2, "fullname": 4, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 10}, "models.CA.CA.cols": {"qualname": 2, "fullname": 4, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 10}, "models.CA.CA.densities": {"qualname": 2, "fullname": 4, "annotation": 3, "default_value": 0, "signature": 0, "bases": 0, "doc": 11}, "models.CA.CA.n_species": {"qualname": 3, "fullname": 5, "annotation": 2, "default_value": 0, "signature": 0, "bases": 0, "doc": 13}, "models.CA.CA.validate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 52}, "models.CA.CA.evolve": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 119, "bases": 0, "doc": 267}, "models.CA.CA.update": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 110}, "models.CA.CA.run": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 77, "bases": 0, "doc": 186}, "models.CA.PP": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 1, "doc": 270}, "models.CA.PP.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 229, "bases": 0, "doc": 13}, "models.CA.PP.validate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 81}, "models.CA.PP.update_async": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 48}, "models.CA.PP.update": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 14, "bases": 0, "doc": 13}, "models.config": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 284}, "models.config.Config": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 419}, "models.config.Config.get_prey_deaths": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 19, "bases": 0, "doc": 12}, "models.config.Config.get_warmup_steps": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 24, "bases": 0, "doc": 13}, "models.config.Config.get_measurement_steps": {"qualname": 4, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 24, "bases": 0, "doc": 13}, "models.config.Config.estimate_runtime": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 48}, "models.config.get_phase_config": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 66}, "models.numba_optimized": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 412}, "models.numba_optimized.set_numba_seed": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 19, "bases": 0, "doc": 126}, "models.numba_optimized.PPKernel": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 192}, "models.numba_optimized.PPKernel.update": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 181, "bases": 0, "doc": 194}, "models.numba_optimized.measure_cluster_sizes_fast": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 63, "bases": 0, "doc": 327}, "models.numba_optimized.detect_clusters_fast": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 85, "bases": 0, "doc": 353}, "models.numba_optimized.get_cluster_stats_fast": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 55, "bases": 0, "doc": 400}, "models.numba_optimized.compute_pcf_periodic_fast": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 145, "bases": 0, "doc": 301}, "models.numba_optimized.compute_all_pcfs_fast": {"qualname": 4, "fullname": 7, "annotation": 0, "default_value": 0, "signature": 107, "bases": 0, "doc": 235}, "models.numba_optimized.warmup_numba_kernels": {"qualname": 3, "fullname": 6, 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{"experiments.save_results_jsonl": {"tf": 1}, "experiments.load_results_jsonl": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"models.numba_optimized.measure_cluster_sizes_fast": {"tf": 1}, "models.numba_optimized.detect_clusters_fast": {"tf": 1}, "models.numba_optimized.get_cluster_stats_fast": {"tf": 1}, "models.numba_optimized.benchmark_cluster_detection": {"tf": 1.4142135623730951}}, "df": 4, "s": {"docs": {"models.CA.PP": {"tf": 1}, "models.numba_optimized": {"tf": 1}, "models.numba_optimized.detect_clusters_fast": {"tf": 1.7320508075688772}, "models.numba_optimized.get_cluster_stats_fast": {"tf": 1}}, "df": 4}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"models.numba_optimized.get_cluster_stats_fast": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "y": {"docs": {"models.numba_optimized": {"tf": 1}, "models.numba_optimized.warmup_numba_kernels": {"tf": 1}, "models.numba_optimized.benchmark_kernels": {"tf": 1}}, "df": 3}}}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"models.numba_optimized.measure_cluster_sizes_fast": {"tf": 1}, "models.numba_optimized.detect_clusters_fast": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "t": {"docs": {"models.numba_optimized": {"tf": 1.4142135623730951}, "models.numba_optimized.measure_cluster_sizes_fast": {"tf": 1}, "models.numba_optimized.detect_clusters_fast": {"tf": 1.7320508075688772}, "models.numba_optimized.get_cluster_stats_fast": {"tf": 2.449489742783178}, "experiments.run_single_simulation": {"tf": 1}}, "df": 5}}}}}, "w": {"docs": {"experiments.run_phase4": {"tf": 1}}, "df": 1}}}, "j": {"docs": {"models.numba_optimized.compute_pcf_periodic_fast": {"tf": 1.4142135623730951}}, "df": 1, "o": {"docs": {}, "df": 0, "b": {"docs": {"experiments.run_phase5": {"tf": 1}}, "df": 1, "s": {"docs": {"models.config.Config": {"tf": 1}, "experiments.run_phase1": {"tf": 1.4142135623730951}, "experiments.run_phase4": {"tf": 1}}, "df": 3}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {"experiments.run_phase1": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"models.numba_optimized.set_numba_seed": {"tf": 1.7320508075688772}, "models.numba_optimized.measure_cluster_sizes_fast": {"tf": 1}, "models.numba_optimized.warmup_numba_kernels": {"tf": 1}, "models.numba_optimized.benchmark_kernels": {"tf": 1}}, "df": 4}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"models.numba_optimized.warmup_numba_kernels": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"experiments.generate_unique_seed": {"tf": 1}, "experiments.save_results_jsonl": {"tf": 1.4142135623730951}, "experiments.load_results_jsonl": {"tf": 2}, "experiments.run_phase1": {"tf": 1}, "experiments.run_phase2": {"tf": 1}, "experiments.run_phase4": {"tf": 1}}, "df": 6, "l": {"docs": {"experiments": {"tf": 1.4142135623730951}, "experiments.save_results_jsonl": {"tf": 2}, "experiments.load_results_jsonl": {"tf": 2}, "experiments.run_phase1": {"tf": 1.4142135623730951}, "experiments.run_phase2": {"tf": 1}, "experiments.run_phase4": {"tf": 1}, "experiments.run_phase5": {"tf": 1}}, "df": 7}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"experiments.load_results_jsonl": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};

// mirrored in build-search-index.js (part 1)

// Also split on html tags. this is a cheap heuristic, but good enough.

elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/);

let searchIndex;

if (docs._isPrebuiltIndex) {

console.info("using precompiled search index");

searchIndex = elasticlunr.Index.load(docs);

} else {

console.time("building search index");

// mirrored in build-search-index.js (part 2)

searchIndex = elasticlunr(function () {

this.pipeline.remove(elasticlunr.stemmer);

this.pipeline.remove(elasticlunr.stopWordFilter);

this.addField("qualname");

this.addField("fullname");

this.addField("annotation");

this.addField("default_value");

this.addField("signature");

this.addField("bases");

this.addField("doc");

this.setRef("fullname");

});

for (let doc of docs) {

searchIndex.addDoc(doc);

}

console.timeEnd("building search index");

}

return (term) => searchIndex.search(term, {

fields: {

qualname: {boost: 4},

fullname: {boost: 2},

annotation: {boost: 2},

default_value: {boost: 2},

signature: {boost: 2},

bases: {boost: 2},

doc: {boost: 1},

expand: true

});

})();

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