integrated code with ecologits

src/calculator.py

@@ -45,28 +45,28 @@ def calculator_mode():
         if df_filtered['warning_arch'].values[0] and df_filtered['warning_multi_modal'].values[0]:
             st.warning(WARNING_BOTH)
             
-    try:
+    #try:
 
-        impacts = llm_impacts(
-                        provider=provider_raw,
-                        model_name=model_raw,
-                        output_token_count=[x[1] for x in PROMPTS if x[0] == output_tokens][0],
-                        request_latency=100000
-                    )
+    impacts = llm_impacts(
+                    provider=provider_raw,
+                    model_name=model_raw,
+                    output_token_count=[x[1] for x in PROMPTS if x[0] == output_tokens][0],
+                    request_latency=100000
+                )
 
-        impacts, _, _ = format_impacts(impacts)
-        
-        with st.container(border=True):
+    impacts, _, _ = format_impacts(impacts)
+    
+    with st.container(border=True):
 
-            st.markdown('<h3 align = "center">Environmental impacts</h3>', unsafe_allow_html=True)
-            st.markdown('<p align = "center">To understand how the environmental impacts are computed go to the 📖 Methodology tab.</p>', unsafe_allow_html=True)
-            display_impacts(impacts, provider, location="🌎 World")
+        st.markdown('<h3 align = "center">Environmental impacts</h3>', unsafe_allow_html=True)
+        st.markdown('<p align = "center">To understand how the environmental impacts are computed go to the 📖 Methodology tab.</p>', unsafe_allow_html=True)
+        display_impacts(impacts, provider, location="🌎 World")
+    
+    with st.container(border=True):
         
-        with st.container(border=True):
-            
-            st.markdown('<h3 align = "center">That\'s equivalent to ...</h3>', unsafe_allow_html=True)
-            st.markdown('<p align = "center">Making this request to the LLM is equivalent to the following actions :</p>', unsafe_allow_html=True)
-            display_equivalent(impacts, provider, location="🌎 World")
+        st.markdown('<h3 align = "center">That\'s equivalent to ...</h3>', unsafe_allow_html=True)
+        st.markdown('<p align = "center">Making this request to the LLM is equivalent to the following actions :</p>', unsafe_allow_html=True)
+        display_equivalent(impacts, provider, location="🌎 World")
             
-    except Exception as e:
-        st.error('Could not find the model in the repository. Please try another model.')
+    # except Exception as e:
+    #     st.error('Could not find the model in the repository. Please try another model.')

src/content.py

@@ -171,27 +171,15 @@ Where
 
 Additionally, the environmental impacts are expressed in **four dimensions 
 (multi-criteria impacts)**:
-* 🌍 **Global Warming Potential** (GWP): Potential impact on global warming in kilograms of CO2 equivalent, 
+* 🌍 **Global Warming Potential** (GWP): Potential impact on Climate Change in kilograms of CO2 equivalent, 
 or kgCO2eq, also commonly known as green house gases (GHG) or carbon emissions.
 * 🪨 **Abiotic Depletion Potential for Elements** (ADPe): Mesures the impact on the depletion of non-organic resources such as 
 minerals or metals in kilograms of antimony equivalent. This is to say the impact equating to that amount of antimony extracted.
 * ⛽️ **Primary Energy** (PE): Total energy consumed from primary sources in megajoules.
-* 💧 **Water Use** : Water consumption from this request. The formula for quantifying this is:
-$$
-WCF_{request} = E_{\text{server}} \times \left( \text{WUE}_{\text{on-site}} + \text{PUE} \times \text{WUE}_{\text{off-site}} \right) + \color{orange}{\frac{\Delta T \times {WCF}_{embodied}}{\Delta L \times N_{requests}} }
-$$
-Where
-* $WCF_{request}$ : Water consumption footprint for the request
-* $E_{\text{server}}$ : Energy cost at the server for the request 
-* $WUE_{on-site}$ : Water usage efficiency at the data center 
-* $PUE$: Power usage efficiency at the data center 
-* $WUE_{off-site}$ : Water usage efficiency of the local electricity mix 
-* $\color{orange}{\Delta T}$ : Generation latency, or the time it takes for the server to process the request, in seconds
-* $\color{orange}{\Delta L}$ : Server lifespan in seconds
-* $\color{orange}{N_{requests}}$ : Number of simultanous reqeusts handled by the server
-* $\color{orange}{WCF_{embodied}}$ : Embodied water consumption footprint for manufacturing the server
+* 💧 **Water Use** : Water consumption from this request. 
+
+Again, if you are interested in seeing the detailed calculations we employ, please consider checking out the methodology page on our website for [ecologits]https://ecologits.ai/latest/methodology/llm_inference/)!
 
-The variables in $\color{orange}{orange}$ represent the embodied water consumption and are not presently integrated into the calculations due to the lack of data. According to many sources, once integrated, they could double or more our water consumption estimation.
 ### Principles, Data and Hypotheses
 We use a **bottom-up methodology** to model impacts, meaning that we estimate the impacts of low-level physical 
 components to then estimate the impacts at software level (in our case an LLM inference). We also rely on **Life 
@@ -277,7 +265,7 @@ CITATION_TEXT = r"""@misc{ecologits-calculator,
   howpublished= {\url{https://huggingface.co/spaces/genai-impact/ecologits-calculator}},
 }
 @software{ecologits,
-  author = {Samuel Rincé, Adrien Banse, Vinh Nguyen and Luc Berton},
+  author = {Samuel Rincé, Adrien Banse, Vinh Nguyen, Luc Berton, and Chieh Hsu},
   publisher = {GenAI Impact},
   title = {EcoLogits: track the energy consumption and environmental footprint of using generative AI models through APIs.},
 }"""

src/expert.py

@@ -32,6 +32,8 @@ def expert_mode():
                 key = 1
             )
 
+        provider_selected = st.session_state[1].lower()
+
         with col2:
             model_exp = st.selectbox(
                 label = 'Model',
@@ -79,7 +81,7 @@ def expert_mode():
 
         location = st.selectbox('Location', [x[0] for x in COUNTRY_CODES])
 
-        col4, col5, col6 = st.columns(3)
+        col4, col5 = st.columns(2)
 
         with col4:
             try:
@@ -97,6 +99,8 @@ def expert_mode():
                 mix_adpe = st.number_input('Electricity mix - Abiotic resources [kgSbeq / kWh]', float(find_electricity_mix(["WOR"][0])[0]), format="%0.13f")
                 st.warning(f"Lacking data on {location}, showing global average data.")
 
+        col6, col7 = st.columns(2)
+
         with col6:
             try: 
                 mix_pe = st.number_input('Electricity mix - Primary energy [MJ / kWh]', float(find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[1]), format="%0.3f")
@@ -104,6 +108,13 @@ def expert_mode():
                 mix_pe = st.number_input('Electricity mix - Primary energy [MJ / kWh]', float(find_electricity_mix(["WOR"][0])[1]), format="%0.3f")
                 st.warning(f"Lacking data on {location}, showing global average data.")
 
+        with col7:
+            try: 
+                mix_water = st.number_input('Electricity mix - Water consumption factor [L / kWh]', float(find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[4]), format="%0.3f")
+            except: 
+                mix_water = st.number_input('Electricity mix - Water consumption factor [L / kWh]', float(find_electricity_mix(["WOR"][0])[4]), format="%0.3f")
+                st.warning(f"Lacking data on {location}, showing global average data.")
+
 
     impacts = compute_llm_impacts(model_active_parameter_count=active_params,
                 model_total_parameter_count=total_params,
@@ -111,7 +122,9 @@ def expert_mode():
                 request_latency=100000,
                 if_electricity_mix_gwp=mix_gwp,
                 if_electricity_mix_adpe=mix_adpe,
-                if_electricity_mix_pe=mix_pe
+                if_electricity_mix_pe=mix_pe,
+                if_electricity_mix_wcf=mix_water,
+                provider = provider_selected
             )
     
     impacts, usage, embodied = format_impacts(impacts)

src/impacts.py

@@ -34,58 +34,72 @@ def display_impacts(impacts, provider, location):
     col_energy, col_ghg, col_adpe, col_pe, col_water = st.columns(5)
 
     with col_energy:
-        st.markdown('<h4 align="center">⚡️ Energy</h4>', unsafe_allow_html = True)
+        st.markdown("""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">⚡️</div>
+        <div style="font-size: 25px;">Energy</div>
+        </div>
+        """, unsafe_allow_html = True)
         st.latex(f'\Large {impacts.energy.magnitude:.3g} \ \large {impacts.energy.units}')
-        st.markdown(f'<p align="center"><i>Evaluates the electricity consumption<i></p>', unsafe_allow_html = True)
+        st.markdown("""
+        <div style="height: 10px;"></div>            
+        <div style="text-align: center;"><i>Evaluates the electricity consumption<i>
+        </div>
+        """, unsafe_allow_html = True)
 
     with col_ghg:
-        st.markdown('<h4 align="center">🌍️ GHG Emissions</h4>', unsafe_allow_html = True)
+        st.markdown("""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">🌍️</div>
+        <div style="font-size: 18px;">GHG Emissions</div>
+        </div>
+        """, unsafe_allow_html = True)
         st.latex(f'\Large {impacts.gwp.magnitude:.3g} \ \large {impacts.gwp.units}')
-        st.markdown(f'<p align="center"><i>Evaluates the effect on global warming<i></p>', unsafe_allow_html = True)
+        st.markdown("""
+        <div style="text-align: center;"><i>Evaluates the effect on climate change<i>
+        </div>
+        """, unsafe_allow_html = True)
 
     with col_adpe:
-        st.markdown('<h4 align="center">🪨 Abiotic Resources</h4>', unsafe_allow_html = True)
+        st.markdown("""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">🪨</div>
+        <div style="font-size: 16px;">Abiotic Resources</div>
+        </div>
+        """, unsafe_allow_html = True)
         st.latex(f'\Large {impacts.adpe.magnitude:.3g} \ \large {impacts.adpe.units}')
-        st.markdown(f'<p align="center"><i>Evaluates the use of metals and minerals<i></p>', unsafe_allow_html = True)
+        st.markdown("""
+        <div style="text-align: center;"><i>Evaluates the use of metals and minerals<i>
+        </div>
+        """, unsafe_allow_html = True)
 
     with col_pe:
-        st.markdown('<h4 align="center">⛽️ Primary Energy</h4>', unsafe_allow_html = True)
+        st.markdown("""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">⛽️</div>
+        <div style="font-size: 18px;">Primary Energy</div>
+        </div>
+        """, unsafe_allow_html = True)
         st.latex(f'\Large {impacts.pe.magnitude:.3g} \ \large {impacts.pe.units}')
-        st.markdown(f'<p align="center"><i>Evaluates the use of energy resources<i></p>', unsafe_allow_html = True)
-
-
-    with col_water: #je sais pas où se trouve magnitude ou impact, donc j'ai commencé par une approche locale
-        st.markdown('<h4 align="center">🚰 Water</h4>', unsafe_allow_html = True)
-        water = water_impact(impacts, provider, location)
-        if water >= 1 : 
-            st.latex(f'\Large {water:.3g} \ \large {"Liters"}')
-        else :
-            st.latex(f'\Large {water * 1000 :.3g} \ \large {"mLiters"}')
-        st.markdown(f'<p align="center"><i>Evaluates the use of water<i></p>', unsafe_allow_html = True)
-
-
-# WCF = E_server * [WUE_on-site + PUE * WUE_off-site] + embodied (embodied not yet implemented, embodied = T * WCF_embodied / lifetime)
-# WCF : Water Consumption Footprint for the request
-# E_server : energy cost at the server for the request 
-# WUE_on-site : Water usage efficiency at the data center 
-# PUE: Power usage efficiency at the data center 
-# WUE_off-site: Water usage efficiency of the local electricity mix 
-def water_impact(impacts, provider, location):
-    energy = impacts.energy.magnitude 
-    PUE = PROVIDER_PUE[AI_COMPANY_TO_DATA_CENTER_PROVIDER[provider.lower()]]
-    WUE_onsite = PROVIDER_WUE_ONSITE[AI_COMPANY_TO_DATA_CENTER_PROVIDER[provider.lower()]]
-    #WUE_on-site = 
-    #pas de variation régionale pour le simulateur simple mais oui pour le simulateur expert mode
-    try:
-        WUE_offsite = float(find_electricity_mix([x[1] for x in COUNTRY_CODES if x[0] ==location][0])[4])
-    except :    
-        WUE_offsite = float(find_electricity_mix(["WOR"][0])[4])
-        st.warning(f"Lacking data on {location}, showing global average data.")
-
-    water_consumption = energy * (WUE_onsite + PUE * WUE_offsite) /1000 #5.04 est la valeur WUF moyenne du globe
-
-    #/1000 parce que les WUE et PUE sont en kWh
-    return water_consumption
+        st.markdown("""
+        <div style="height: 10px;"></div>
+        <div style="text-align: center;"><i>Evaluates the use of energy resources<i>
+        </div>
+        """, unsafe_allow_html = True)
+
+    with col_water: 
+        st.markdown("""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">🚰</div>
+        <div style="font-size: 25px;">Water</div>
+        </div>
+        """, unsafe_allow_html = True)
+        st.latex(f'\Large {impacts.water.magnitude:.3g} \ \large {impacts.water.units}')
+        st.markdown("""
+        <div style="text-align: center;"><i>Evaluates the use of water<i>
+        </div>
+        """, unsafe_allow_html = True)
+
 
 ############################################################################################################
 
@@ -102,31 +116,53 @@ def display_equivalent(impacts, provider, location):
     with col1:
         physical_activity, distance = format_energy_eq_physical_activity(impacts.energy)
         if physical_activity == PhysicalActivity.WALKING:
-            physical_activity = "🚶 " + physical_activity.capitalize()
+            physical_activity_emoji = "🚶 " 
+            physical_activity = physical_activity.capitalize()
         if physical_activity == PhysicalActivity.RUNNING:
-            physical_activity = "🏃 " + physical_activity.capitalize()
-
-        st.markdown(f'<h4 align="center">{physical_activity}</h4>', unsafe_allow_html = True)
+            physical_activity_emoji = "🏃 " 
+            physical_activity = physical_activity.capitalize()
+
+        st.markdown(f"""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">{physical_activity_emoji}</div>
+        <div style="font-size: 25px;">{physical_activity}</div>
+        </div>
+        """, unsafe_allow_html = True)
         st.latex(f'\Large {distance.magnitude:.3g} \ \large {distance.units}')
         st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
 
     with col2:
         ev_eq = format_energy_eq_electric_vehicle(impacts.energy)
-        st.markdown(f'<h4 align="center">🔋 Electric Vehicle</h4>', unsafe_allow_html = True)
+        st.markdown(f"""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">🔋</div>
+        <div style="font-size: 22px;">Electric Vehicle</div>
+        </div>
+        """, unsafe_allow_html = True)
         st.latex(f'\Large {ev_eq.magnitude:.3g} \ \large {ev_eq.units}')
         st.markdown(f'<p align="center"><i>Based on energy consumption<i></p>', unsafe_allow_html = True)
 
     with col3:
         streaming_eq = format_gwp_eq_streaming(impacts.gwp)
-        st.markdown(f'<h4 align="center">⏯️ Streaming</h4>', unsafe_allow_html = True)
+        st.markdown(f"""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">⏯️</div>
+        <div style="font-size: 25px;">Streaming</div>
+        </div>
+        """, unsafe_allow_html = True)
         st.latex(f'\Large {streaming_eq.magnitude:.3g} \ \large {streaming_eq.units}')
         st.markdown(f'<p align="center"><i>Based on GHG emissions<i></p>', unsafe_allow_html = True)
     
     with col4:
-        water = water_impact(impacts, provider, location)
-        water_eq = format_water_eq_bottled_waters(water)
-        st.markdown(f'<h4 align="center">🚰 Bottled waters</h4>', unsafe_allow_html = True)
-        st.latex(f'\Large {water_eq:.3g} \ \large {"bottles"}')
+        #water = water_impact(impacts, provider, location)
+        water_eq = format_water_eq_bottled_waters(impacts.water)
+        st.markdown(f"""
+        <div style="text-align: center;">
+        <div style="font-size: 30px;">🚰</div>
+        <div style="font-size: 25px;">Bottled Waters</div>
+        </div>
+        """, unsafe_allow_html = True)
+        st.latex(f'\Large {water_eq.magnitude:.3g} \ \large {"bottles"}')
         st.markdown(f'<p align="center"><i>Based on water consumption, measured in 0.75 L bottles.<i></p>', unsafe_allow_html = True)
     
 
@@ -160,8 +196,7 @@ def display_equivalent(impacts, provider, location):
         st.markdown(f'<p align="center"><i>Based on GHG emissions<i></p>', unsafe_allow_html = True)
 
     with col8:
-        water = water_impact(impacts, provider, location)
-        water_eq = format_water_eq_olympic_sized_swimming_pool(water)
-        st.markdown(f'<h4 align="center">🏊🏼 {round(water_eq):,} Olympic-sized swimming pools</h4>', unsafe_allow_html = True)
+        olympic_swimming_pool = format_water_eq_olympic_sized_swimming_pool(impacts.water)
+        st.markdown(f'<div style="text-align: center; font-size: 20px; font-weight: bold;">🏊🏼 {round(olympic_swimming_pool.magnitude):,} Olympic-sized swimming pools</h4>', unsafe_allow_html = True)
         st.markdown(f'<p align="center"><i>Based on water consumption<i></p>', unsafe_allow_html = True)
 

src/models.py

@@ -15,9 +15,13 @@ def clean_models_data(df, with_filter = True):
         'cohere': 'Cohere',
         'microsoft': 'Microsoft',
         'mistral-community': 'Mistral Community',
-        'databricks': 'Databricks'
+        'databricks': 'Databricks',
+        "azureopenai" : "Microsoft", #présent dans ecologits mais pas ici. traité comme Openai
+        "huggingfacehub" : "AWS" #présent dans ecologits mais pas ici. traité comme Openai
     }
 
+
+
     models_to_keep = MAIN_MODELS
     
     df.drop('type', axis=1, inplace=True)

src/utils.py

@@ -2,7 +2,7 @@ from dataclasses import dataclass
 from enum import Enum
 
 from ecologits.model_repository import models
-from ecologits.impacts.modeling import Impacts, Energy, GWP, ADPe, PE
+from ecologits.impacts.modeling import Impacts, Energy, GWP, ADPe, PE, Water
 #from ecologits.tracers.utils import llm_impacts
 from pint import UnitRegistry, Quantity
 
@@ -27,9 +27,9 @@ u.define('km = kilometer')
 u.define('s = second')
 u.define('min = minute')
 u.define('h = hour')
-# u.define('L = liters')
-# u.define('mL = milliliters')
-# u.define('bottled waters')
+u.define('L = liter')
+u.define('mL = milliliter')
+#u.define('bottled waters = bottled waters')
 q = u.Quantity
 
 @dataclass
@@ -38,7 +38,7 @@ class QImpacts:
     gwp: Quantity
     adpe: Quantity
     pe: Quantity
-#    water: Quantity
+    water: Quantity
 
 
 class PhysicalActivity(str, Enum):
@@ -73,7 +73,7 @@ EV_ENERGY_EQ = q("0.17 kWh / km")
 # From https://impactco2.fr/outils/comparateur?value=1&comparisons=streamingvideo
 STREAMING_GWP_EQ = q("15.6 h / kgCO2eq")
 
-BOTTLED_WATERS_EQ = 0.75
+BOTTLED_WATERS_EQ = q("0.75 L")
 
 # From https://ourworldindata.org/population-growth
 ONE_PERCENT_WORLD_POPULATION = 80_000_000
@@ -97,7 +97,7 @@ IRELAND_POPULATION_MILLION = 5
 AIRPLANE_PARIS_NYC_GWP_EQ = q("177000 kgCO2eq")
 
 # From https://www.patagoniaalliance.org/wp-content/uploads/2014/08/How-much-water-does-an-Olympic-sized-swimming-pool-hold.pdf
-OLYMPIC_SWIMMING_POOL = 2500000 #2.5 million 
+OLYMPIC_SWIMMING_POOL = q("2500000 L") #2.5 million 
 
 # From https://docs.google.com/spreadsheets/d/1uj8yA601uBtJ7GSf7k96Lv1NoQBfsCnVmTCII2HgZvo/edit?gid=0#gid=0
 # Google : https://www.gstatic.com/gumdrop/sustainability/google-2025-environmental-report.pdf
@@ -172,9 +172,11 @@ def format_pe(pe: PE) -> Quantity:
         val = val.to("kJ")
     return val
 
-# def format_water(water: PE) -> Quantity:
-#     val = q(water.value, water.unit)
-#     return val
+def format_water(water: Water) -> Quantity:
+    val = q(water.value, water.unit)
+    if val < q("1 L"):
+        val = val.to("mL")
+    return val
 
 def format_impacts(impacts: Impacts) -> QImpacts:
 
@@ -183,18 +185,21 @@ def format_impacts(impacts: Impacts) -> QImpacts:
         impacts.gwp.value = (impacts.gwp.value.max + impacts.gwp.value.min)/2
         impacts.adpe.value = (impacts.adpe.value.max + impacts.adpe.value.min)/2
         impacts.pe.value = (impacts.pe.value.max + impacts.pe.value.min)/2
+        impacts.water.value = (impacts.water.value.max + impacts.water.value.min)/2
         return QImpacts(
             energy=format_energy(impacts.energy),
             gwp=format_gwp(impacts.gwp),
             adpe=format_adpe(impacts.adpe),
             pe=format_pe(impacts.pe),
+            water=format_water(impacts.water)
         ), impacts.usage, impacts.embodied
     except: #when no range
         return QImpacts(
             energy=format_energy(impacts.energy),
             gwp=format_gwp(impacts.gwp),
             adpe=format_adpe(impacts.adpe),
-            pe=format_pe(impacts.pe)
+            pe=format_pe(impacts.pe),
+            water=format_water(impacts.water)
         ), impacts.usage, impacts.embodied
 
 def split_impacts_u_e(impacts: Impacts) -> QImpacts:
@@ -214,7 +219,8 @@ def format_impacts_expert(impacts: Impacts, display_range: bool) -> QImpacts:
             energy=format_energy(impacts.energy),
             gwp=format_gwp(impacts.gwp),
             adpe=format_adpe(impacts.adpe),
-            pe=format_pe(impacts.pe)
+            pe=format_pe(impacts.pe),
+            water=format_water(impacts.water)
         ), impacts.usage, impacts.embodied
     
     else:
@@ -226,7 +232,8 @@ def format_impacts_expert(impacts: Impacts, display_range: bool) -> QImpacts:
             energy=format_energy(energy),
             gwp=format_gwp(gwp),
             adpe=format_adpe(adpe),
-            pe=format_pe(pe)
+            pe=format_pe(pe),
+            water=format_water(impacts.water)
         ), impacts.usage, impacts.embodied
 
 #####################################################################################
@@ -260,8 +267,8 @@ def format_gwp_eq_streaming(gwp: Quantity) -> Quantity:
         streaming_eq = streaming_eq.to("s")
     return streaming_eq
 
-def format_water_eq_bottled_waters(water):
-    #water = water.to("bottled waters")
+def format_water_eq_bottled_waters(water: Quantity) -> Quantity:
+    water = water.to("L")
     bottled_water_eq = water / BOTTLED_WATERS_EQ
     return bottled_water_eq
 
@@ -284,8 +291,13 @@ def format_gwp_eq_airplane_paris_nyc(gwp: Quantity) -> Quantity:
     gwp_eq = gwp_eq.to("kgCO2eq")
     return gwp_eq / AIRPLANE_PARIS_NYC_GWP_EQ
 
-def format_water_eq_olympic_sized_swimming_pool(water):
+def format_water_eq_olympic_sized_swimming_pool(water: Quantity) -> Quantity:
     water_eq = water * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+    water_eq = water_eq.to("L")
     return water_eq / OLYMPIC_SWIMMING_POOL
 
+# def format_water_eq_olympic_sized_swimming_pool(water):
+#     water_eq = water * ONE_PERCENT_WORLD_POPULATION * DAYS_IN_YEAR
+#     return water_eq / OLYMPIC_SWIMMING_POOL
+
 ####################################################################################### MODELS PARAMETER####################################################################################