Merge pull request #104 from ucl-cssb/CRM

This extends CRM modeling

Author: Fontanapink

examples/CRM/examples-bayes-CRM.ipynb

@@ -10,6 +10,7 @@
 import os
 from typing import Optional, Union, List, Dict, Any
 
+
 from mimic.utilities import *
 from mimic.model_simulate.sim_gLV import *
 from mimic.model_infer.base_infer import BaseInfer
@@ -43,12 +44,44 @@ class infergLVbayes(BaseInfer):
     Args:
         X (np.ndarray): The design matrix
         F (np.ndarray): The observed values
-        mu (np.ndarray): The growth rates matrix
-        M (np.ndarray): The interaction matrix
+        mu (int, float, List): The growth rates
+        M (int, float, List): The interaction matrix
+        epsilon ((int, float, List)): The perturbation matrix
+
+        prior_mu_mean (int, float, List): The prior mean for mu
+        prior_mu_sigma (int, float, List): The prior sigma for mu
+        prior_Mii_mean (int, float, List): The prior mean for Mii
+        prior_Mii_sigma (int, float, List): The prior sigma for Mii
+        prior_Mij_sigma (int, float, List): The prior sigma for Mij
+        prior_eps_mean (int, float, List): The prior mean for epsilon
+        prior_eps_sigma (int, float, List): The prior sigma for epsilon
+
+        draws (int): The number of draws for the MCMC sampler
+        tune (int): The number of tuning steps for the MCMC sampler
+        chains (int): The number of chains for the MCMC sampler
+        cores (int): The number of cores to use for the MCMC sampler
+        DA (int): The number of off-diagonal elements to be non-zero
+        DA0 (int, float): The number of off-diagonal elements to be non-zero
+        N (int): The number of samples
+        noise_stddev (int, float): The standard deviation of the noise
+
 
 
     Methods:
 
+        set_parameters: Set or update simulation parameters.
+        import_data: Import data from a .csv file.
+        calculate_DA0: Calculate the number of off-diagonal elements to be non-zero.
+
+        run_inference: Run Bayesian inference with uniform priors and specified bounds.
+        run_inference_shrinkage: Run Bayesian inference with horseshoe priors for shrinkage.
+        run_inference_shrinkage_pert: Run Bayesian inference with horseshoe priors for shrinkage and perturbation.
+
+        plot_posterior: Plot the posterior distribution of the parameters.
+        plot_posterior_pert: Plot the posterior distribution of the parameters with perturbation.
+        plot_interaction_matrix: Plot the interaction matrix.
+
+
 
     Returns:
         None

examples/CRM/examples-sim-CRM.ipynb

@@ -2,6 +2,7 @@
 from typing import List, Optional, Union
 
 import numpy  # do not change this to np, it will break the code
+from numpy.typing import NDArray
 from scipy import stats
 from scipy.integrate import odeint
 
@@ -27,7 +28,13 @@ class sim_CRM(BaseModel):
     def __init__(
             self,
             num_species=2,
-            num_resources=0):
+            num_resources=2,
+            tau=None,
+            w=None,
+            c=None,
+            m=None,
+            r=None,
+            K=None):
         """
         Initializes the CRM simulation with given parameters or defaults.
 
@@ -126,7 +133,7 @@ def simulate(self,
 
         Returns:
             tuple: Tuple containing the simulation results for species (yobs), metabolites (sobs),
-            initial conditions (sy0), growth rates (mu), interaction matrix (M), and metabolite production rates (beta).
+            initial conditions (sy0)
         """
         # self.check_params(self.parameters, 'CRM')
         syobs = odeint(
@@ -144,7 +151,7 @@ def simulate(self,
                 self.K))
         yobs = syobs[:, 0:self.nsp]  # species
         sobs = syobs[:, self.nsp:]  # resources
-        self.data = syobs  # QUESTION: should this be yobs or sobs?
+        self.data = syobs
         return yobs, sobs
 
 

examples/gLV/examples-bayes-gLV.ipynb

@@ -27,10 +27,10 @@ def __init__(
         If no parameters are given for mu and M, a model is randomly generated
 
         :param num_species: number of species
-        :param mu: specific growth rates vector
+        :param mu: specific growth rates vectors
         :param M: interaction matrix
         """
-        # QUESTION: is this not supposed to have num_metabolites?
+
         super().__init__()
         self.model = "gLV"
         self.nsp = num_species