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Split emissions layer categories #3917

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f5692d5
Add logging for missing file requirement parameters
reyery Sep 24, 2025
ce3e934
Split emissions map layer into embodied and operational
reyery Sep 25, 2025
4e42ee2
Add schema for get_total_yearly_operational_building
reyery Sep 25, 2025
83933bd
Remove redundant slicing by building
reyery Sep 25, 2025
77a0903
Refactor comfort chart to pass building_name explicitly
reyery Sep 25, 2025
e8852de
Fix building HVAC data access in comfort_chart.py
reyery Sep 25, 2025
f69569d
Update emissions keys in plot metrics and analytics
reyery Sep 26, 2025
4b00f27
Add error handling for invalid plot_cea_feature values
reyery Sep 26, 2025
3dccbd9
Add embodied emissions support to bar_plot
reyery Sep 26, 2025
25260e0
Add support for operational emissions plots
reyery Sep 26, 2025
84e9577
Refactor LCA emissions calculation logic
reyery Sep 26, 2025
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29 changes: 8 additions & 21 deletions cea/analysis/lca/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,10 +3,7 @@
This script is used to calculate the LCA
"""





import datetime
import os

from cea.analysis.lca.embodied import lca_embodied
Expand All @@ -28,28 +25,18 @@


def emissions_simplified(locator, config):

# Force the simplified embodied and operation to be true
# embodied = config.emissions.embodied
# operation = config.emissions.operational
embodied = True
operation = True

# embodied emissions
if embodied:
year_to_calculate = config.emissions.year_to_calculate
if year_to_calculate is None:
year_to_calculate = 2025
lca_embodied(year_to_calculate, locator)
# get current year
year_to_calculate = datetime.datetime.now().year
print(f'Running embodied emissions for year {year_to_calculate}')
lca_embodied(year_to_calculate, locator)

# operation emissions
if operation:
lca_operation(locator)
lca_operation(locator)

def emissions_detailed(config):
if config.emissions.include_emission_timeline:
operational_hourly(config)
total_yearly(config)
operational_hourly(config)
total_yearly(config)
Comment on lines +38 to +39
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🛠️ Refactor suggestion | 🟠 Major

🧩 Analysis chain

Hourly + timeline runs: confirm default building selection and add logging

Running both unconditionally can be heavy; ensure this matches CLI expectations and that config.emissions.buildings has sensible defaults when unset.

Apply this diff:

-    operational_hourly(config)
-    total_yearly(config)
+    logger.info("Running hourly operational emissions (per-building and district)")
+    operational_hourly(config)
+    logger.info("Running total emissions timeline")
+    total_yearly(config)

To verify buildings default semantics:

🏁 Script executed:

#!/bin/bash
# Find where emissions.buildings is defined/used to confirm defaults
rg -n -C3 -P '\bemissions\.buildings\b' --type=py

Length of output: 1230

🏁 Script executed:

#!/bin/bash
# Display imports and logger setup in main.py
sed -n '1,100p' cea/analysis/lca/main.py

Length of output: 1502

🏁 Script executed:

#!/bin/bash
# Show Configuration class and emissions section in cea/config.py
awk '/class Configuration/,/^$/ {print}' cea/config.py
grep -R -n "emissions" -n cea/config.py

Length of output: 110

🏁 Script executed:

#!/bin/bash
# Search cea/config.py for configparser usage and dynamic attribute access
grep -R -n -E "ConfigParser|parser|__getattr__|__getattribute__" cea/config.py

Length of output: 3362

Add logger setup, logging calls and verify default buildings

  • In cea/analysis/lca/main.py, import logging and add at top: 
    logger = logging.getLogger(__name__)
  • Around lines 38–39, wrap calls with logging: 
    -    operational_hourly(config)
-    total_yearly(config)
+    logger.info("Running hourly operational emissions (per-building and district)")
+    operational_hourly(config)
+    logger.info("Running total emissions timeline")
+    total_yearly(config)
  • Manually confirm that config.emissions.buildings has a sensible default in your config file or handle the unset case in code.
🤖 Prompt for AI Agents 
In cea/analysis/lca/main.py around lines 38–39, add logging setup at the top by
importing logging and creating logger = logging.getLogger(__name__); before
calling operational_hourly(config) and total_yearly(config) insert
logger.debug/info calls indicating each step start and logger.info on successful
completion (and logger.exception on errors if you wrap calls in try/except);
finally, ensure config.emissions.buildings is validated before use—either check
for presence and set a sensible default or emit a clear logger.warning/error and
return/raise so the code does not proceed with an unset value.



def main(config):
Expand Down
13 changes: 0 additions & 13 deletions cea/default.config
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Original file line number Diff line number Diff line change
Expand Up @@ -364,19 +364,6 @@ operational.type = BooleanParameter
operational.help = Estimate Operational costs (variable and fixed) due to supply systems operation (allocated according to the supply-systems input database)

[emissions]
year-to-calculate =
year-to-calculate.type = IntegerParameter
year-to-calculate.nullable = True
year-to-calculate.help = Defines the year to calculate the aggregated emissions for each building. Leave blank to use the current year. Different from the emission timeline calculation, for simplified calculations, if a building is greater than 60 years old, its embodied emissions are not considered.
year-to-calculate.category = CEA-LCA Simplified Settings

include-emission-timeline = false
include-emission-timeline.type = BooleanParameter
include-emission-timeline.help = True to record (8760) hourly operational emissions for heating, cooling, hot water and electricity use, and embodied emissions for each selected building,
and all activities every year until year-end, assuming all components are renovated or replaced until the building is demolished.
"Year-start" is the oldest of the (construction_)year of all selected buildings.
"Year-end" is customisable below.

year-end =
year-end.type = IntegerParameter
year-end.nullable = True
Expand Down
1 change: 1 addition & 0 deletions cea/demand/building_properties/building_hvac.py
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Original file line number Diff line number Diff line change
Expand Up @@ -191,6 +191,7 @@ def verify_overlap_season(building_name, has_heating_season, has_cooling_season,
return False


# TODO: Remove building_name from function signature, not useful in function
def verify_has_season(building_name, start, end):
def invalid_date(date):
if len(date) != 5 or "|" not in date:
Expand Down
5 changes: 5 additions & 0 deletions cea/interfaces/dashboard/map_layers/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,10 @@
from cea import MissingInputDataException
from cea.config import Configuration, DEFAULT_CONFIG
from cea.inputlocator import InputLocator
from cea.interfaces.dashboard.lib.logs import getCEAServerLogger


logger = getCEAServerLogger("cea-server-map-layers")


# locator_func = Callable[..., str]
Expand Down Expand Up @@ -44,6 +48,7 @@ def get_required_files(self, layer: "MapLayer", current_params: Dict[str, Any] =
if self.depends_on is not None:
# Check if the current parameters meet the requirements
if not all(value in current_params for value in self.depends_on):
logger.error(f"Missing required parameters for file requirement: {self.depends_on}")
raise ValueError("Missing required parameters")

Comment on lines 48 to 53
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⚠️ Potential issue | 🟡 Minor

Guard against None parameters and log more context

Membership test on current_params can raise if None. Also, include locator name and provided params for easier debugging.

Apply:

-        if self.depends_on is not None:
-            # Check if the current parameters meet the requirements
-            if not all(value in current_params for value in self.depends_on):
-                logger.error(f"Missing required parameters for file requirement: {self.depends_on}")
-                raise ValueError("Missing required parameters")
+        if self.depends_on is not None:
+            # Check if the current parameters meet the requirements
+            missing = [k for k in self.depends_on if not current_params or k not in current_params]
+            if missing:
+                logger.error(
+                    f"Missing required parameters for file requirement {self.file_locator}: "
+                    f"missing={missing}, provided_keys={list((current_params or {}).keys())}"
+                )
+                raise ValueError(f"Missing required parameters: {missing}")
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
if self.depends_on is not None:
# Check if the current parameters meet the requirements
if not all(value in current_params for value in self.depends_on):
logger.error(f"Missing required parameters for file requirement: {self.depends_on}")
raise ValueError("Missing required parameters")
if self.depends_on is not None:
# Check if the current parameters meet the requirements
missing = [k for k in self.depends_on if not current_params or k not in current_params]
if missing:
logger.error(
f"Missing required parameters for file requirement {self.file_locator}: "
f"missing={missing}, provided_keys={list((current_params or {}).keys())}"
)
raise ValueError(f"Missing required parameters: {missing}")
🤖 Prompt for AI Agents 
In cea/interfaces/dashboard/map_layers/base.py around lines 48 to 53, guard
against current_params being None before performing membership tests and improve
the error log: first check that current_params is not None (or convert it to an
empty container) before using all(value in current_params for value in
self.depends_on); if the check fails, include the locator name (self.locator or
appropriate attribute) and the actual provided current_params in the
logger.error message and raise a ValueError with a more descriptive message
mentioning the missing parameters and locator context.

# Parse string locator
Expand Down
148 changes: 112 additions & 36 deletions cea/interfaces/dashboard/map_layers/life_cycle_analysis/layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,17 +9,14 @@
from cea.plots.colors import color_to_hex


class EmissionsMapLayer(MapLayer):
class EmbodiedEmissionsMapLayer(MapLayer):
category = LifeCycleAnalysisCategory
name = "life-cycle-analysis"
label = "GHG Emissions"
description = "GHG Emissions"
name = "embodied-emissions"
label = "Embodied Emissions"
description = ""
Comment on lines +12 to +16
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🛠️ Refactor suggestion | 🟠 Major

❓ Verification inconclusive

Public rename may break imports—add a back‑compat alias

Renaming EmissionsMapLayer to EmbodiedEmissionsMapLayer is a breaking change. Provide a temporary alias to avoid downstream breakage.

Run this to find existing references:

Add after class definitions:

# Backward compatibility alias (remove in a future major release)
EmissionsMapLayer = EmbodiedEmissionsMapLayer

🏁 Script executed:

#!/bin/bash
set -euo pipefail
rg -nP -C2 '\bEmissionsMapLayer\b'

Length of output: 0

Add backward-compat alias for EmissionsMapLayer
No internal references to EmissionsMapLayer were found, but the public rename is breaking for downstream users. After the EmbodiedEmissionsMapLayer class (e.g. after line 16), add:

# Backward compatibility alias (remove in a future major release)
EmissionsMapLayer = EmbodiedEmissionsMapLayer
🤖 Prompt for AI Agents 
In cea/interfaces/dashboard/map_layers/life_cycle_analysis/layers.py around
lines 12 to 16, create a backward-compatibility alias for the renamed class by
adding an alias assignment after the EmbodiedEmissionsMapLayer class definition
that binds the old public name EmissionsMapLayer to the new
EmbodiedEmissionsMapLayer, and include a short comment indicating it is for
backward compatibility and slated for removal in a future major release.


def _get_emissions_types(self):
return ["embodied", "operational"]

def _get_data_columns(self, parameters: dict) -> Optional[list]:
results_path = self._get_results_files(parameters)
def _get_data_columns(self) -> Optional[list]:
results_path = self.locator.get_lca_embodied()

try:
emissions_df = pd.read_csv(results_path)
Expand All @@ -29,34 +26,121 @@ def _get_data_columns(self, parameters: dict) -> Optional[list]:

return sorted(list(columns - {"name", "GFA_m2"}))

def _get_results_files(self, parameters: dict) -> str:
emissions_type = parameters['emissions-type']
if emissions_type == "embodied":
return self.locator.get_lca_embodied()
elif emissions_type == "operational":
return self.locator.get_lca_operation()
else:
raise ValueError(f"Invalid emissions type: {emissions_type}")

@classmethod
def expected_parameters(cls):
return {
'emissions-type':
'data-column':
ParameterDefinition(
"Emissions Type",
"Data Column",
"string",
description="Type of emissions",
options_generator="_get_emissions_types",
description="Data column to use",
options_generator="_get_data_columns",
selector="choice",
),
'radius':
ParameterDefinition(
"Radius",
"number",
default=5,
description="Radius of hexagon bin in meters",
selector="input",
range=[0, 100],
filter="radius",
),
'scale':
ParameterDefinition(
"Scale",
"number",
default=1,
description="Scale of hexagon bin height",
selector="input",
range=[0.1, 10],
filter="scale",
),
}

@classmethod
def file_requirements(cls):
return [
FileRequirement(
"Zone Buildings Geometry",
file_locator="locator:get_zone_geometry",
),
FileRequirement(
"Embodied Emissions Results",
file_locator="locator:get_lca_embodied",
),
]

@cache_output
def generate_data(self, parameters):
"""Generates the output for this layer"""

# FIXME: Hardcoded to zone buildings for now
buildings = self.locator.get_zone_building_names()

data_column = parameters['data-column']

output = {
"data": [],
"properties": {
"name": self.name,
"label": "Embodied Emissions",
"description": self.description,
"colours": {
"colour_array": [color_to_hex("grey_lighter"), color_to_hex("black")],
"points": 12
}
Comment on lines +90 to +93
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⚠️ Potential issue | 🔴 Critical

🧩 Analysis chain

Verify colours payload shape: 'colour_array' vs 'color_array'

ColourGradient.to_dict() uses 'color_array' (US). Here it's 'colour_array' (UK). Confirm which the frontend expects to avoid broken colour gradients.

🏁 Script executed:

#!/bin/bash
set -euo pipefail
echo "Occurrences of colour/color array keys:"
rg -nP -C1 '\b(colo[u]?r_array)\b'
echo "Occurrences of the 'colours' property structure:"
rg -nP -C2 '\bcolou?rs\b'

Length of output: 28889

🏁 Script executed:

#!/bin/bash
set -euo pipefail
echo "---- ColourGradient definition ----"
rg -nP 'class ColourGradient' -C10 cea/interfaces/dashboard/map_layers/base.py
echo "---- ColourGradient.to_dict ----"
rg -nP 'def to_dict' -C5 cea/interfaces/dashboard/map_layers/base.py
echo "---- MapLayer.colours usage in to_dict ----"
rg -nP 'colours' -C5 cea/interfaces/dashboard/map_layers/base.py

Length of output: 1541

Replace ‘colour_array’ with ‘color_array’ in cea/interfaces/dashboard/map_layers/life_cycle_analysis/layers.py:90-93; ColourGradient.to_dict() emits “color_array”, so this must match to avoid broken gradients.

🤖 Prompt for AI Agents 
In cea/interfaces/dashboard/map_layers/life_cycle_analysis/layers.py around
lines 90 to 93, the dictionary key "colour_array" must be renamed to
"color_array" to match ColourGradient.to_dict() output; update the key name in
that dict (and any nearby occurrences) so it uses "color_array":
[color_to_hex("grey_lighter"), color_to_hex("black")], leaving values and
structure unchanged.

}
}

df = gpd.read_file(self.locator.get_zone_geometry()).set_index("name").loc[buildings]
building_centroids = df.geometry.centroid.to_crs(CRS.from_epsg(4326))

results_path = self.locator.get_lca_embodied()
emissions_df = pd.read_csv(results_path, usecols=["name", data_column], index_col="name")[data_column].loc[
buildings]

output['data'] = [{"position": [centroid.x, centroid.y], "value": emissions} for centroid, emissions in
zip(building_centroids, emissions_df)]
output['properties']['range'] = {
'total': {
'label': 'Total Range',
'min': float(min(emissions_df)),
'max': float(max(emissions_df))
},
}

return output


class OperationalEmissionsMapLayer(MapLayer):
category = LifeCycleAnalysisCategory
name = "operational-emissions"
label = "Operational Emissions"
description = ""

def _get_data_columns(self) -> Optional[list]:
results_path = self.locator.get_lca_operation()

try:
emissions_df = pd.read_csv(results_path)
columns = set(emissions_df.columns)
except (pd.errors.EmptyDataError, FileNotFoundError):
return

return sorted(list(columns - {"name", "GFA_m2"}))

@classmethod
def expected_parameters(cls):
return {
'data-column':
ParameterDefinition(
"Data Column",
"string",
description="Data column to use",
options_generator="_get_data_columns",
selector="choice",
depends_on=["emissions-type"],
),
'radius':
ParameterDefinition(
Expand Down Expand Up @@ -88,9 +172,8 @@ def file_requirements(cls):
file_locator="locator:get_zone_geometry",
),
FileRequirement(
"Demand Results",
file_locator="layer:_get_results_files",
depends_on=["emissions-type"],
"Operational Emissions Results",
file_locator="locator:get_lca_operation",
),
]

Expand All @@ -101,14 +184,13 @@ def generate_data(self, parameters):
# FIXME: Hardcoded to zone buildings for now
buildings = self.locator.get_zone_building_names()

emissions_type = parameters['emissions-type']
data_column = parameters['data-column']

output = {
"data": [],
"properties": {
"name": self.name,
"label": f"GHG Emissions: {emissions_type}",
"label": "Operational Emissions",
"description": self.description,
"colours": {
"colour_array": [color_to_hex("grey_lighter"), color_to_hex("black")],
Expand All @@ -119,14 +201,8 @@ def generate_data(self, parameters):

df = gpd.read_file(self.locator.get_zone_geometry()).set_index("name").loc[buildings]
building_centroids = df.geometry.centroid.to_crs(CRS.from_epsg(4326))

if emissions_type == "embodied":
results_path = self.locator.get_lca_embodied()
elif emissions_type == "operational":
results_path = self.locator.get_lca_operation()
else:
raise ValueError(f"Invalid emissions type: {emissions_type}")


results_path = self.locator.get_lca_operation()
emissions_df = pd.read_csv(results_path, usecols=["name", data_column], index_col="name")[data_column].loc[
buildings]

Expand Down
51 changes: 51 additions & 0 deletions cea/schemas.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -19658,6 +19658,57 @@ get_thermal_network_velocity_edges_file:
values: '{0.0...n}'
min: 0.0
used_by: []
get_total_yearly_operational_building:
created_by:
- emission_time_dependent
file_path: outputs/data/emissions/timeline/Total_yearly_operational_building.csv
file_type: csv
schema:
columns:
hour:
description: Hour of the year.
type: int
unit: '[h]'
values: '{0...8759}'
min: 0
max: 8759
heating_tonCO2:
description: Total operational emissions from heating systems.
type: float
unit: '[ton CO2-eq]'
values: '{0.0...n}'
min: 0.0
hot_water_tonCO2:
description: Total operational emissions from hot water systems.
type: float
unit: '[ton CO2-eq]'
values: '{0.0...n}'
min: 0.0
cooling_tonCO2:
description: Total operational emissions from cooling systems.
type: float
unit: '[ton CO2-eq]'
values: '{0.0...n}'
min: 0.0
electricity_tonCO2:
description: Total operational emissions from electricity consumption.
type: float
unit: '[ton CO2-eq]'
values: '{0.0...n}'
min: 0.0
Qcs_sys_NONE_tonCO2:
description: Total operational emissions from cooling systems (NONE category).
type: float
unit: '[ton CO2-eq]'
values: '{0.0...n}'
min: 0.0
E_sys_GRID_tonCO2:
description: Total operational emissions from grid electricity systems.
type: float
unit: '[ton CO2-eq]'
values: '{0.0...n}'
min: 0.0
used_by: []
get_total_demand:
created_by:
- demand
Expand Down
8 changes: 4 additions & 4 deletions cea/visualisation/a_data_loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,17 +35,17 @@
'pv': solar_metrics,
'pvt': solar_metrics,
'sc': solar_metrics,
'emissions-embodied': embodied_emission_metrics,
'emissions-operational': operational_emission_metrics
'embodied-emissions': embodied_emission_metrics,
'operational-emissions': operational_emission_metrics
}

dict_plot_analytics_cea_feature = {
'demand': demand_analytics,
'pv': solar_analytics,
'pvt': [],
'sc': [],
'emissions-embodied': [],
'emissions-operational': []
'embodied-emissions': [],
'operational-emissions': []
}

# Trigger the summary feature and point to the csv results file
Expand Down
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