Nutrient concentrations

pysimdeum/core/end_use.py

@@ -103,6 +103,7 @@ def __post_init__(self):
             **kwargs: keyword arguments for super classes.
         """
         self.name = "Bathtub"
+        self.wastewater_type = "greywater"
 
     def fct_frequency(self, age=None):
         """Random function computing the frequency of use for the Bathtub end-use class.
@@ -214,6 +215,7 @@ class BathroomTap(EndUse):
 
     def __post_init__(self):
         self.name = "BathroomTap"
+        self.wastewater_type = "greywater"
 
     def fct_frequency(self):
 
@@ -302,6 +304,7 @@ class Dishwasher(EndUse):
 
     def __post_init__(self):
         self.name = "Dishwasher"
+        self.wastewater_type = "blackwater"
 
     def fct_frequency(self, numusers=None):
 
@@ -326,7 +329,7 @@ def calculate_discharge(self, discharge, start, j, ind_enduse, pattern_num, day_
             elif ((day_num + 1) == total_days) and (discharge_time > end_of_day):
                 pass
             else:
-                discharge[discharge_time, j, ind_enduse, pattern_num, 0] = discharge_pattern[time]
+                discharge[discharge_time, j, ind_enduse, pattern_num, 1] = discharge_pattern[time]
 
         return discharge
 
@@ -381,6 +384,7 @@ class KitchenTap(EndUse):
 
     def __post_init__(self):
         self.name = "KitchenTap"
+        self.wastewater_type = "blackwater"
 
     def fct_frequency(self, numusers=None):
 
@@ -451,10 +455,9 @@ def calculate_discharge(self, discharge, start, duration, intensity, temperature
             end = int(start + discharge_duration)
             # check if subtype = consumption (drinking), if so the discharge flow rate is set to 0
             if self.subtype == 'consumption':
-                discharge[start:end, j, ind_enduse, pattern_num, 0] = 0
+                discharge[start:end, j, ind_enduse, pattern_num, 1] = 0
             else:
-                discharge[start:end, j, ind_enduse, pattern_num, 0] = discharge_flow_rate         
-            #discharge[start:end, j, ind_enduse, pattern_num, 0] = discharge_flow_rate
+                discharge[start:end, j, ind_enduse, pattern_num, 1] = discharge_flow_rate         
             remaining_water -= discharge_flow_rate * discharge_duration
             start = end
 
@@ -570,6 +573,7 @@ class Shower(EndUse):
 
     def __post_init__(self):
         self.name = "Shower"
+        self.wastewater_type = "greywater"
 
     def fct_frequency(self, age=None):
 
@@ -655,17 +659,20 @@ class NormalShower(Shower):
 
     def __post_init__(self):
         self.name = "NormalShower"
+        self.wastewater_type = "greywater"
 
 class FancyShower(Shower):
 
     def __post_init__(self):
         self.name = "FancyShower"
+        self.wastewater_type = "greywater"
 
 
 class WashingMachine(EndUse):
 
     def __post_init__(self):
         self.name = "WashingMachine"
+        self.wastewater_type = "blackwater"
 
     def fct_frequency(self, numusers=None):
 
@@ -691,7 +698,7 @@ def calculate_discharge(self, discharge, start, j, ind_enduse, pattern_num, day_
             elif ((day_num + 1) == total_days) and (discharge_time > end_of_day):
                 pass
             else:
-                discharge[discharge_time, j, ind_enduse, pattern_num, 0] = discharge_pattern[time]
+                discharge[discharge_time, j, ind_enduse, pattern_num, 1] = discharge_pattern[time]
 
         return discharge
 
@@ -748,6 +755,7 @@ class Wc(EndUse):
 
     def __post_init__(self):
         self.name = "Wc"
+        self.wastewater_type = "blackwater"
 
 
     def fct_frequency(self, age=None, gender=None):
@@ -790,7 +798,7 @@ def calculate_discharge(self, discharge, start, duration, intensity, temperature
         while incoming_water > 0:
             discharge_duration = incoming_water / discharge_flow_rate
             start = int(end - discharge_duration)
-            discharge[start:end, j, ind_enduse, pattern_num, 0] = discharge_flow_rate
+            discharge[start:end, j, ind_enduse, pattern_num, 1] = discharge_flow_rate
             incoming_water -= discharge_flow_rate * discharge_duration
             end = start
 
@@ -831,21 +839,25 @@ class WcNormal(Wc):
 
     def __post_init__(self):
         self.name = 'WcNormal'
+        self.wastewater_type = "blackwater"
 
 @dataclass
 class WcNormalSave(Wc):
 
     def __post_init__(self):
         self.name = "WcNormalSave"
+        self.wastewater_type = "blackwater"
 
 @dataclass
 class WcNew(Wc):
 
     def __post_init__(self):
         self.name = "WcNew"
+        self.wastewater_type = "blackwater"
 
 @dataclass
 class WcNewSave(Wc):
 
     def __post_init__(self):
         self.name = "WcNewSave"
+        self.wastewater_type = "blackwater"

pysimdeum/core/house.py

@@ -287,7 +287,7 @@ def simulate(self, date=None, duration='1 day', num_patterns=1, simulate_dischar
         number_of_days = int(timedelta/pd.to_timedelta('1 day'))
 
         if simulate_discharge:
-            dischargetype = ['discharge']
+            dischargetype = ['greywater', 'blackwater']
             discharge = np.zeros((len(time), len(users), len(enduse), num_patterns, len(dischargetype)))
         else:
             discharge = None

pysimdeum/core/utils.py

@@ -3,6 +3,10 @@
 import numpy as np
 from dataclasses import dataclass
 from typing import Union
+import toml
+import xarray as xr
+import os
+from pysimdeum.data import DATA_DIR
 
 
 def chooser(data: Union[pd.Series, pd.DataFrame], myproperty: str=''):
@@ -195,12 +199,10 @@ def handle_discharge_spillover(discharge, discharge_pattern, time, discharge_tim
     """
     if discharge_time >= (total_days * 24 * 60 * 60):
         spillover_time = discharge_time - end_of_day
-        discharge[spillover_time, j, ind_enduse, pattern_num, 0] = discharge_pattern[time]
+        discharge[spillover_time, j, ind_enduse, pattern_num, 1] = discharge_pattern[time]
     else:
         # Continue to the next day
-        discharge[discharge_time, j, ind_enduse, pattern_num, 0] = discharge_pattern[time]
-
-    #discharge[spillover_time, j, ind_enduse, pattern_num, 0] = discharge_pattern[time]
+        discharge[discharge_time, j, ind_enduse, pattern_num, 1] = discharge_pattern[time]
 
     return discharge
 
@@ -354,6 +356,67 @@ def complex_discharge_pattern(config, enduse_pattern, resolution='1s'):
     return discharge_pattern
 
 
+def process_discharge_nutrients(discharge):
+    """Process discharge data and add nutrient concentrations based on values from a .toml file.
+
+    This function reads nutrient multipliers from a TOML file, converts the discharge data from an
+    xarray.DataArray to a pandas DataFrame, calculates the nutrient concentrations based on the
+    multipliers, and adds the nutrient data back to an xarray.Dataset.
+
+    Args:
+        discharge (xr.DataArray): The discharge data.
+
+    Returns:
+        xr.Dataset: The updated xarray.Dataset containing the discharge data and the nutrient concentrations.
+    """
+
+    toml_file_path = os.path.join(DATA_DIR, 'NL', 'ww_nutrients.toml')
+
+    # Read the .toml file
+    nutrient_data = toml.load(toml_file_path)
+
+    # Convert a xarray.DataArray to a pd.DataFrame
+    df = discharge.to_dataframe(name='flow').reset_index()
+
+    # list of nutrient types
+    nutrients = ['n', 'p', 'cod', 'bod5', 'ss', 'amm']
+
+    # Add new columns for each nutrient initialised to zero
+    for nutrient in nutrients:
+        df[nutrient] = 0.0
+
+    # Set the values for each nutrient based on the multipliers from the TOML file
+    for nutrient in nutrients:
+        for enduse in nutrient_data.keys():
+            low = nutrient_data[enduse][nutrient]['low']
+            high = nutrient_data[enduse][nutrient]['high']
+            multiplier = np.random.uniform(low, high) # sample from uniform distribution
+            df.loc[df['enduse'] == enduse, nutrient] = df['flow'] * multiplier
+
+    # Create an xarray.Dataset and add the discharge DataArray to it
+    ds = xr.Dataset({'discharge': discharge})
+
+    # Add the pandas DataFrame to the xarray.Dataset as a new variable
+    ds['df'] = (('index', 'columns'), df.values)
+    ds['df_index'] = ('index', df.index)
+    ds['df_columns'] = ('columns', df.columns)
+
+    return ds
+
+
+def dataset_to_df(ds):
+    """Convert an xarray.Dataset to a pd.DataFrame.
+
+    Args:
+        ds (xr.Dataset): The input xarray.Dataset containing the 'df', 'df_index', and 'df_columns' variables.
+
+    Returns:
+        pd.DataFrame: The converted pandas DataFrame.
+    """
+    df_from_ds = pd.DataFrame(data=ds['df'].values, index=ds['df_index'].values, columns=ds['df_columns'].values)
+
+    return df_from_ds
+
 @dataclass
 class Base:
     """Base class of pysimdeum for generating objects. 

pysimdeum/data/NL/ww_nutrients.toml

@@ -0,0 +1,141 @@
+# Nutrient statistics per enduse
+
+[Shower]
+    [Shower.n]
+        low = 0.01
+        high = 0.017
+    [Shower.p]
+        low = 0.002
+        high = 0.002
+    [Shower.cod]
+        low = 0.35
+        high = 0.424
+    [Shower.bod5]
+        low = 0.17
+        high = 0.25
+    [Shower.ss]
+        low = 0.076
+        high = 0.12
+    [Shower.amm]
+        low = 0.0015
+        high = 0.002
+
+[Bathtub]
+    [Bathtub.n]
+        low = 0.006
+        high = 0.017
+    [Bathtub.p]
+        low = 0.002
+        high = 0.002
+    [Bathtub.cod]
+        low = 0.282
+        high = 0.424
+    [Bathtub.bod5]
+        low = 0.17
+        high = 0.25
+    [Bathtub.ss]
+        low = 0.076
+        high = 0.12
+    [Bathtub.amm]
+        low = 0.0013
+        high = 0.002
+
+[Wc]
+    [Wc.n]
+        low = 0.3
+        high = 0.3
+    [Wc.p]
+        low = 0.0361
+        high = 0.0361
+    [Wc.cod]
+        low = 0.751
+        high = 0.751
+    [Wc.bod5]
+        low = 0.4
+        high = 0.4
+    [Wc.ss]
+        low = 0.391
+        high = 0.391
+    [Wc.amm]
+        low = 0.04
+        high = 0.04
+
+[KitchenTap]
+    [KitchenTap.n]
+        low = 0.04
+        high = 0.068
+    [KitchenTap.p]
+        low = 0.013
+        high = 0.074
+    [KitchenTap.cod]
+        low = 0.936
+        high = 1.38
+    [KitchenTap.bod5]
+        low = 0.536
+        high = 0.8
+    [KitchenTap.ss]
+        low = 0.72
+        high = 0.72
+    [KitchenTap.amm]
+        low = 0.0042
+        high = 0.006
+
+[BathroomTap]
+    [BathroomTap.n]
+        low = 0.012
+        high = 0.012
+    [BathroomTap.p]
+        low = 0.028
+        high = 0.049
+    [BathroomTap.cod]
+        low = 0.383
+        high = 0.433
+    [BathroomTap.bod5]
+        low = 0.148
+        high = 0.252
+    [BathroomTap.ss]
+        low = 0.04
+        high = 0.15
+    [BathroomTap.amm]
+        low = 0.00016
+        high = 0.00115
+
+[WashingMachine]
+    [WashingMachine.n]
+        low = 0.013
+        high = 0.02
+    [WashingMachine.p]
+        low = 0.032
+        high = 0.171
+    [WashingMachine.cod]
+        low = 0.725
+        high = 0.725
+    [WashingMachine.bod5]
+        low = 0.11
+        high = 0.662
+    [WashingMachine.ss]
+        low = 0.09
+        high = 0.19
+    [WashingMachine.amm]
+        low = 0.013
+        high = 0.02
+
+[Dishwasher]
+    [Dishwasher.n]
+        low = 0.04
+        high = 0.05
+    [Dishwasher.p]
+        low = 0.068
+        high = 0.068
+    [Dishwasher.cod]
+        low = 1
+        high = 1
+    [Dishwasher.bod5]
+        low = 0.11
+        high = 1.04
+    [Dishwasher.ss]
+        low = 0.09
+        high = 0.44
+    [Dishwasher.amm]
+        low = 0.0045
+        high = 0.05