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Update grid.py #54

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67 changes: 44 additions & 23 deletions src/grid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,13 +67,16 @@ def __init__(self, runtime, data_init):
] # Rate for change of moisture reaction in decay module

# Uptake
# self.Microbes_init = data_init['Microbes_pp'] # microbial community before placement
self.Microbes_init = data_init['Microbes_pp'] # microbial community before placement
self.Microbes = data_init["Microbes"].copy(
deep=True
) # microbial community after placement
# self.Monomers_init = data_init['Monomers'] # Monomers initialized
self.Monomers_init = data_init['Monomers'] # Monomers initialized
self.Monomers = data_init["Monomers"].copy(deep=True) # Monomers
self.MonInput = data_init["MonInput"] # Inputs of monomers
self.C_frac_org = np.tile(data_init['elem_fracs']['C'].values, self.gridsize)
self.N_frac_org = np.tile(data_init['elem_fracs']['N'].values, self.gridsize)
self.P_frac_org = np.tile(data_init['elem_fracs']['P'].values, self.gridsize)
self.Uptake_Ea = data_init["Uptake_Ea"] # transporter enzyme Ea
self.Uptake_Vmax0 = data_init["Uptake_Vmax0"] # transporter Vmax
self.Uptake_Km0 = data_init["Uptake_Km0"] # transporter Km
Expand Down Expand Up @@ -235,7 +238,7 @@ def degradation(self, day):
)

# Update Substrates Pool by removing decayed C, N, & P. Depending on specific needs, adding inputs of substrates can be done here
self.Substrates -= SubstrateRatios.mul(DecayRates, axis=0) # + self.SubInput
self.Substrates -= SubstrateRatios.mul(DecayRates, axis=0) + self.SubInput

# Pass these two back to the global variables to be used in the next method
self.SubstrateRatios = SubstrateRatios
Expand Down Expand Up @@ -264,9 +267,11 @@ def uptake(self, day):
self.Monomers.index != "PO4"
) # organic monomers
# is_mineral = (Monomers.index == "NH4") | (Monomers.index == "PO4")

is_NH4 = self.Monomers.index == "NH4"
is_PO4 = self.Monomers.index == "PO4"

# Update monomer ratios in each time step with organic monomers following the substrates
self.Monomer_ratios[is_org] = self.SubstrateRatios.values
self.Monomer_ratios[is_org] = self.SubstrateRatios.values

# Determine monomer pool from decay and input
# Organic monomers derived from substrate-decomposition
Expand All @@ -276,6 +281,25 @@ def uptake(self, day):
# Input_Mineral = MR_transition[is_mineral].mul((self.MonInput[is_mineral]).tolist(),axis=0)
# Monomer pool determined
self.Monomers.loc[is_org] += Decay_Org # + Input_Org

# --- Route monomer inputs by pool ---
input_vals = self.MonInput.values # positionally aligned with self.Monomers

# Organic monomers: input is carbon, goes to the C column
org_input = input_vals[is_org]
self.Monomers.loc[is_org, 'C'] += org_input * self.C_frac_org
self.Monomers.loc[is_org, 'N'] += org_input * self.N_frac_org
self.Monomers.loc[is_org, 'P'] += org_input * self.P_frac_org

# Mineral N: NH4 input goes to the N column
self.Monomers.loc[is_NH4, 'N'] += input_vals[is_NH4]

# Mineral P: PO4 input goes to the P column
self.Monomers.loc[is_PO4, 'P'] += input_vals[is_PO4]

self.Monomers = self.Monomers.fillna(0.0)
self.Monomers[self.Monomers < 0] = np.float32(0)

# self.Monomers.loc[is_mineral] += Input_Mineral

# Get the total mass of each monomer: C+N+P
Expand Down Expand Up @@ -330,6 +354,7 @@ def uptake(self, day):
# Update Monomers
# By monomer: total uptake (monomer*gridsize) * 3(C-N-P)
self.Monomers -= self.Monomer_ratios.mul(Uptake.sum(axis=1), axis=0)
self.Monomers[self.Monomers < 0] = np.float32(0)

# Derive Taxon-specific total uptake of C, N, & P
# By taxon: total uptake; (monomer*gridsize) * taxon
Expand Down Expand Up @@ -744,6 +769,7 @@ def mortality(self, day):
# Update monomer pools
self.Monomers.loc[is_NH4, "N"] += sum(MicLoss["N"]) / self.gridsize
self.Monomers.loc[is_PO4, "P"] += sum(MicLoss["P"]) / self.gridsize
self.Monomers[self.Monomers < 0] = np.float32(0)

# Update Substrates pool by adding dead microbial biomass
self.Substrates.loc[is_DeadMic] += Death_gridcell.values
Expand Down Expand Up @@ -911,6 +937,15 @@ def reinitialization(self, initialization, microbes_pp, output, mode, pulse, *ar
self.Substrates = initialization["Substrates"].copy(deep=True)
self.Monomers = initialization["Monomers"].copy(deep=True)
self.Enzymes = initialization["Enzymes"].copy(deep=True)
self.SubstrateRatios = pd.DataFrame(
np.zeros_like(initialization['Substrates']),
index=initialization['Substrates'].index,
columns=initialization['Substrates'].columns
)
self.DecayRates = pd.Series(
np.zeros(len(initialization['Substrates'])),
index=initialization['Substrates'].index
)

# reinitialize microbial community in a new pulse as per the mode in three steps
# first: retrieve the microbial pool; NOTE: copy()
Expand Down Expand Up @@ -940,24 +975,10 @@ def reinitialization(self, initialization, microbes_pp, output, mode, pulse, *ar
# calculate frequency of every taxon
frequencies = cum_abundance / cum_abundance.sum()
frequencies = frequencies.fillna(0)
frequencies = frequencies.clip(
lower=0, upper=1
) # guard against any stray negative/over-1 values (sum of 1)
frequencies = (
frequencies / frequencies.sum() if frequencies.sum() > 0 else frequencies
) # renormalize to exactly 1

# last: assign microbes to each grid box randomly based on prior densities
choose_taxa = np.zeros((self.n_taxa, self.gridsize), dtype="int8")
choose_taxa = np.zeros((self.n_taxa,self.gridsize), dtype='int8')
for i in range(self.n_taxa):
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()
choose_taxa[i, :] = np.random.choice(
[1, 0], self.gridsize, replace=True, p=p_vec
)
self.Microbes.loc[np.ravel(choose_taxa, order="F") == 0] = np.float32(
0
) # NOTE order='F'
choose_taxa[i,:] = np.random.binomial(1, frequencies.iloc[i], self.gridsize)
self.Microbes.loc[np.ravel(choose_taxa,order='F')==0] = np.float32(0) # NOTE order='F'

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