43 moisture decay uptake rate modification in script

grassland/parameters.csv

@@ -53,4 +53,6 @@ Vmax_Km_int,0
 Uptake_Vmax_Km,0.2
 Uptake_Vmax_Km_int,0
 Km_error,0
-Specif_factor,1
+Specif_factor,1
+R_moist_decay,0.05
+R_moist_uptake,0.1

grassland/scrubland/parameters.csv

@@ -53,4 +53,6 @@ Vmax_Km_int,0
 Uptake_Vmax_Km,0.2
 Uptake_Vmax_Km_int,0
 Km_error,0
-Specif_factor,1
+Specif_factor,1
+R_moist_decay,0.05
+R_moist_uptake,0.1

input/parameters.csv

@@ -53,4 +53,6 @@ Vmax_Km_int,0
 Uptake_Vmax_Km,0.2
 Uptake_Vmax_Km_int,0
 Km_error,0
-Specif_factor,1
+Specif_factor,1
+R_moist_decay,0.05
+R_moist_uptake,0.1

input/runtime.txt

@@ -24,4 +24,5 @@ Input_NH4	0
 Input_PO4	0
 direct	0.95
 dist	1
-interval	1
+interval	1
+output_CSV	1

pyproject.toml

@@ -7,6 +7,7 @@ requires-python = ">=3.12"
 dependencies = [
     "pandas>=2.2.3",
     "scipy>=1.15.2",
+    "xarray>=2025.12.0"
 ]
 
 [dependency-groups]

src/dementpy.py

@@ -45,6 +45,7 @@ def main():
     pulse      = int(runtime.loc['pulse',1])         # number of pulses
     cycle      = int(runtime.loc['end_time',1])      # number of time steps in each pulse
     interval   = int(runtime.loc['interval',1])      # interval of time step to record outputs
+    Export_format = int(runtime.loc['output_CSV',1])      # interval of time step to record outputs
     mode       = int(runtime.loc['dispersal',1])     # 0:'default' or 1:'dispersal'
 
     #...Initialize data by calling the Function: Initialize_Data()
@@ -101,7 +102,10 @@ def main():
 
     #...export the Output_init object to the output_folder using the export() funtion in the utility module 
     os.chdir('../'+output_folder)
-    export(Output_init, site, outname)
+    if Export_format == 1:
+        Output_init.export_to_csv(outname)
+    else:
+        Output_init.export_to_netcdf(outname)
 
 if __name__ == '__main__':
     main()

src/grid.py

@@ -59,6 +59,7 @@ def __init__(self,runtime,data_init):
         self.Km0             = data_init['Km0']                           # Half-saturation constant
         self.SubstrateRatios = np.float32('nan')                          # Substrate stoichiometry
         self.DecayRates      = np.float32('nan')                          # Substrate decay rate
+        self.R_moist_decay   = data_init['R_moist_decay']                 # Rate for change of moisture reaction in decay module
 
         #Uptake
         #self.Microbes_init   = data_init['Microbes_pp']                   # microbial community before placement
@@ -75,7 +76,8 @@ def __init__(self,runtime,data_init):
         self.Taxon_Uptake_C  = np.float32('nan')                          # taxon uptake of C 
         self.Taxon_Uptake_N  = np.float32('nan')                          # taxon uptake of N 
         self.Taxon_Uptake_P  = np.float32('nan')                          # taxon uptake of P
-
+        self.R_moist_uptake  = data_init['R_moist_uptake']                # Rate for change of moisture reaction in uptake module
+
         #Metabolism
         self.Consti_Enzyme_C   = data_init["EnzProdConstit"]    # C cost of encoding constitutive enzyme
         self.Induci_Enzyme_C   = data_init["EnzProdInduce"]     # C Cost of encoding inducible enzyme 
@@ -154,7 +156,7 @@ def degradation(self,day):
         SubstrateRatios = SubstrateRatios.fillna(0) # NOTE:ensure NA(b/c of 0/0 in df) = 0
 
         # Arrhenius equation for Vmax and Km multiplied by exponential decay for Psi sensitivity
-        Vmax = Arrhenius(self.Vmax0, self.Ea,    self.temp[day]) * Allison(0.05, self.wp_fc, self.psi[day])  # Vmax: (enz*gridsize) * sub
+        Vmax = Arrhenius(self.Vmax0, self.Ea,    self.temp[day]) * Allison(self.R_moist_decay, self.wp_fc, self.psi[day])  # Vmax: (enz*gridsize) * sub
         Km   = Arrhenius(self.Km0,   self.Km_Ea, self.temp[day])                                             # Km:   (sub*gridsize) * enz
 
         # Multiply Vmax by enzyme concentration
@@ -231,7 +233,7 @@ def uptake(self,day):
 
         # Start calculating monomer uptake
         # Caculate uptake enzyme kinetic parameters, multiplied by moisture multiplier accounting for the diffusivity implications
-        Uptake_Vmax = Arrhenius(self.Uptake_Vmax0, self.Uptake_Ea, self.temp[day]) * Allison(0.1, self.wp_fc, self.psi[day])
+        Uptake_Vmax = Arrhenius(self.Uptake_Vmax0, self.Uptake_Ea, self.temp[day]) * Allison(self.R_moist_uptake, self.wp_fc, self.psi[day])
         Uptake_Km   = Arrhenius(self.Uptake_Km0,   self.Km_Ea,     self.temp[day])
 
         # Equation for hypothetical potential uptake (per unit of compatible uptake protein)
@@ -715,6 +717,7 @@ def reinitialization(self,initialization,microbes_pp,output,mode,pulse,*args):
         # last: assign microbes to each grid box randomly based on prior densities
         choose_taxa = np.zeros((self.n_taxa,self.gridsize), dtype='int8')
         for i in range(self.n_taxa):
+            choose_taxa[i,:] = np.random.binomial(1, frequencies[i], self.gridsize)
             freq=np.float64(frequencies.iloc[1]) #working independent of indexing (based on location not index labels)
             p_vec = np.array([freq, 1.0 - freq], dtype=np.float64) #solve numoy issues
             p_vec = p_vec / p_vec.sum()

src/initialization.py

@@ -5,6 +5,12 @@
 """
 import pandas as pd
 import numpy as np
+import xarray as xr
+
+import warnings
+import numbers
+from pathlib import Path
+from typing import Union
 
 from substrate import Substrate
 from monomer   import Monomer
@@ -159,8 +165,137 @@ def initialize_data(runtime_parameters, site):
         'wp_fc':             parameters.loc['wp_fc',1],             # threshold below which microbes start to respond to drought
         'wp_th':             parameters.loc['wp_th',1],             # threshold below which microbes in full swing to respond to drought
         'alpha':             parameters.loc['alpha',1],             # factor delineating curve concavity of microbial response to drought
+        'R_moist_decay':     parameters.loc['R_moist_decay',1],         # factor in the Allison equation for moisture (Decay part)
+        'R_moist_uptake':    parameters.loc['R_moist_uptake',1],        # factor in the Allison equation for moisture (Uptake part)
         'Temp': daily_temp,                                         # temperature
         'Psi':  daily_psi                                           # water potential
     }
 
-    return Data_Dictionary
+    return Data_Dictionary
+
+
+def export_initialization_dict_to_csv(base_path: Path | str, d: dict) -> None:
+    """Export contents of the initialisation directory to a folder.
+
+    Writes each of the items of a type below to a separate CSV file
+      - pandas.DataFrame
+      - pandas.Series
+      - numpy.ndarray of rank below 2
+    All scalar numbers are grouped in a single CSV 'scalars.csv' file.
+
+    Note:
+        All other items are ignored following a warning!
+        If you need them written you need to add extra entry.
+    """
+
+    # Create space for output
+    base_path = Path(base_path)
+    base_path.mkdir(parents=True, exist_ok=True)
+
+    # Collect all scalar numbers
+    scalar_numbers = dict()
+
+    for name, member in d.items():
+        if isinstance(member, (pd.DataFrame, pd.Series)):
+            fname = name + ".csv"
+            member.to_csv(base_path / fname)
+        elif isinstance(member, np.ndarray):
+            if len(member.shape) <= 2:
+                fname = name + ".csv"
+                np.savetxt(fname, member, delimiter=",")
+            else:
+                warnings.warn(
+                    f"Member '{name}' of initialisation dictionary could not be saved since "
+                    f"it is an array of rank higher than 2 (rank: {len(member.shape)})."
+                )
+        elif isinstance(member, numbers.Number):
+            scalar_numbers[name] = member
+        else:
+            warnings.warn(
+                f"Initialisation member '{name}' has unsupported type '{type(member)}'. "
+                f"It has not been exported to the output directory '{base_path}'."
+            )
+
+    # Print numbers
+    pd.Series(scalar_numbers).to_csv(base_path / "scalars.csv")
+
+    def export_to_netcdf(self, base_path: Path | str) -> None:
+        """Export contents of the output file to a directory in NetCDF format.
+        - Each pandas.DataFrame member is saved to a separate .nc file.
+        - All pandas.Series members are combined and saved to a single 'series.nc' file.
+        - All scalar numerical members are grouped and saved to 'scalars.nc'.
+
+        Parameters:
+          base_path : Path
+            A path that names the root directory where contents will be exported.
+            If the directory does not exist it will be created.
+        """
+        # Create space for output
+        base_path = Path(base_path)
+        base_path.mkdir(parents=True, exist_ok=True)
+
+        # Collect all series and scalar data
+        series_data = dict()
+        scalar_numbers = dict()
+
+        for name, member in vars(self).items():
+            # convert each DataFrame to an xarray Dataset and save to .nc
+            if isinstance(member, pd.DataFrame):
+                # Ensure column names are strings
+                member.columns = member.columns.astype(str)
+                if member.index.name is not None:
+                    member.index.name = str(member.index.name)
+                fname = name + ".nc" # use the .nc extension
+                try:
+                    xarray_member = xr.Dataset.from_dataframe(member)
+                    xarray_member.to_netcdf(base_path / fname)
+                except Exception as e:
+                    warnings.warn(
+                        f"Could not export DataFrame '{name}' to NetCDF. Error: {e}"
+                    )
+
+            elif isinstance(member, pd.Series):
+                series_data[name] = member
+
+            elif isinstance(member, numbers.Number):
+                scalar_numbers[name] = member
+
+            elif name == "Initialization":
+                # Special case - Initialization dictionary
+                # Serialise it to a subfolder
+                path = base_path / name
+                export_initialization_dict_to_netcdf(path, member)
+            elif isinstance(member, pd.Series):
+                xrmember = xr.DataArray(member)
+                fname = name + ".nc"
+                xrmember.to_netcdf(base_path / fname)
+
+            else:
+                warnings.warn(
+                    f"Output member '{name}' has unsupported type '{type(member)}'. "
+                    f"It has not been exported to the output directory '{base_path}'."
+                )
+
+        # process and save Series
+        if series_data:
+            try:
+                # Combine all Series into a single DataFrame.
+                combined_series_df = pd.concat(series_data, axis=1)
+                # Convert the combined DataFrame to an xarray Dataset.
+                series_dataset = xr.Dataset.from_dataframe(combined_series_df)
+                # Save the Series Dataset to a single NetCDF file.
+                series_dataset.to_netcdf(base_path / "series.nc")
+            except ValueError as e:
+                # This handles the "duplicate labels" error if it occurs.
+                warnings.warn(
+                    f"Could not export combined series due to an error: {e}. "
+                    "Consider cleaning the index of your Series data first."
+                )
+
+        if scalar_numbers:
+            # Create an xarray Dataset directly from the dictionary of scalars.
+            # Each key will become a variable in the NetCDF file.
+            scalars_dataset = xr.Dataset(scalar_numbers)
+            # Save the scalars Dataset to a NetCDF file.
+            scalars_dataset.to_netcdf(base_path / "scalars.nc")
+

src/microbe.py

@@ -162,7 +162,7 @@ def microbial_community_initialization(self,microbes_pp,fb_grid):
         pb = self.taxa_per_box
         choose_taxa = np.random.choice([1,0], self.n_taxa*self.gridsize,replace=True, p=[pb,(1-pb)])
         pf = pb * self.max_size_b/self.max_size_f
-        choose_taxa[fb_grid==1] = np.random.choice([1,0], sum(fb_grid),replace=True, p=[pf,(1-pf)])
+        choose_taxa[fb_grid==1] = np.random.choice([1,0], np.sum(fb_grid),replace=True, p=[pf,(1-pf)])
         microbes_df.loc[choose_taxa==0] = np.float32(0)
 
 
@@ -534,4 +534,4 @@ def microbe_osmo_psi(alfa,Psi_fc,Psi):
     else:
         f_osmo = 1 - alfa * Psi
 
-    return np.float32(f_osmo)
+    return np.float32(f_osmo)