Collect all weather data from the Gateway#676
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WalkthroughThe pull request introduces a comprehensive update to the Plugwise integration, focusing on enhancing weather data representation across multiple JSON fixtures and test files. The primary change involves replacing the Changes
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Actionable comments posted: 4
🧹 Nitpick comments (14)
plugwise/helper.py (1)
569-569: Refactor note: Consolidate or rename
Calling _get_gateway_measurements() here is intuitive, but consider naming it more specifically (e.g., _get_gateway_weather_measurements()) if the function is intended solely for capturing weather info. This can assist maintainability and clarity of usage.tests/data/anna/anna_elga_2_cooling_UPDATED_DATA.json (1)
88-93: Consider varying test data valuesSeveral weather attributes have identical values across different test files:
- humidity: 42
- wind_bearing: 120.0
- wind_speed: 3.6
Consider varying these values to ensure the system handles different scenarios correctly.
tests/data/anna/anna_elga_2_cooling.json (1)
86-94: LGTM! Consider adding documentation for weather data unitsThe weather data structure is well-organized with appropriate data types and reasonable value ranges. However, it would be helpful to document the units of measurement (e.g., temperature in °C, wind speed in m/s, wind bearing in degrees, solar irradiance in W/m²) to ensure consistent interpretation.
tests/data/anna/anna_elga_2.json (1)
86-94: Consider varying weather test dataWhile the weather data structure is consistent, several values (humidity, wind_bearing, wind_speed, weather_description, solar_irradiance) are identical to anna_elga_2_cooling.json. Consider varying these values to test different weather scenarios and ensure the system handles a broader range of conditions.
fixtures/anna_v4/all_data.json (1)
48-55: Consider extracting common weather data to a shared fixtureThe weather data structure and values are identical between
anna_v4/all_data.jsonandanna_v4_dhw/all_data.json. Consider extracting this into a shared fixture to maintain consistency and reduce duplication.#!/bin/bash # Find similar weather data patterns across fixtures rg --json -l '"weather":\s*{.*"humidity"' fixtures/tests/data/anna/anna_loria_heating_idle.json (1)
50-57: Consider diversifying weather test dataWhile the weather data structure is consistent, consider adding test cases with different weather conditions to verify system behavior across various scenarios (e.g., extreme temperatures, different wind conditions).
Example variations to consider:
- High temperature + high humidity for cooling tests
- Low temperature + high wind for heating tests
- Various weather descriptions affecting solar gain
Would you like me to help create additional test fixtures with diverse weather scenarios?
fixtures/anna_elga_no_cooling/all_data.json (1)
16-23: Consider implementing a Weather Data InterfaceThe restructuring of weather data from 'sensors' to a dedicated 'weather' object is a good architectural decision. To further improve this:
- Create a formal interface or schema for the weather data structure
- Implement data validation for all weather attributes
- Document the expected ranges and units for each attribute
- Define an enum for possible weather descriptions
This will ensure consistency and reliability across the entire integration.
Would you like help creating a schema definition for the weather data structure?
fixtures/anna_elga_2/all_data.json (1)
87-94: LGTM! Consistent weather data implementation across fixturesThe weather data structure is consistently implemented across all test fixtures with:
- Same attribute names and data types
- Reasonable value ranges
- Correct item count adjustments
Consider adding schema validation to ensure this structure remains consistent as more weather attributes are added in the future.
Also applies to: 101-101
fixtures/m_adam_cooling/all_data.json (1)
93-100: LGTM! Consider documenting units in comments.The weather data structure is comprehensive and well-organized. However, it would be helpful to document the units for each measurement.
Add comments to clarify units:
"weather": { - "humidity": 90, - "outdoor_temperature": 29.65, - "solar_irradiance": 3.0, - "weather_description": "rain", - "wind_bearing": 291.0, - "wind_speed": 6.26 + "humidity": 90, # Percentage (%) + "outdoor_temperature": 29.65, # Celsius (°C) + "solar_irradiance": 3.0, # Watts per square meter (W/m²) + "weather_description": "rain", + "wind_bearing": 291.0, # Degrees (°) + "wind_speed": 6.26 # Meters per second (m/s) }plugwise/util.py (1)
206-213: LGTM! Consider using dict.get() for more concise code.The changes correctly handle the new weather data structure and include proper existence checks. However, the code could be more concise using Python's dict.get() method.
Consider this more concise implementation:
- if "binary_sensors" in data and not data["binary_sensors"]: - data.pop("binary_sensors") - if "sensors" in data and not data["sensors"]: - data.pop("sensors") - if "switches" in data and not data["switches"]: - data.pop("switches") - if "weather" in data and not data["weather"]: - data.pop("weather") + for key in ("binary_sensors", "sensors", "switches", "weather"): + if not data.get(key): + data.pop(key, None)tests/data/adam/adam_plus_anna_new_regulation_off.json (1)
176-183: LGTM! Consider adding documentation for the weather attributes.The new weather object structure is well-organized and includes comprehensive weather data. The data types and values are appropriate for each attribute.
Consider adding documentation (in a README or schema file) to describe:
- Units for each measurement (e.g., wind speed in m/s or km/h)
- Expected value ranges
- Update frequency
fixtures/adam_plus_anna_new_regulation_off/all_data.json (1)
176-183: Consider adding JSON schema validation.To ensure data consistency and provide better developer experience, consider adding a JSON schema that defines:
- Required weather attributes
- Data types and constraints
- Units and value ranges
This would help catch potential issues early in the development process.fixtures/m_adam_jip/all_data.json (1)
Line range hint
176-183: Well-structured approach to weather data representation.The replacement of the
sensorsfield with a comprehensiveweatherobject across all fixtures demonstrates good architectural decisions:
- Consistent schema across all gateway devices
- Logical grouping of weather-related attributes
- Appropriate data types for each measurement
- Clear separation of concerns between weather data and other sensor data
Consider documenting the weather data schema and its attributes in the project's documentation to help maintainers and contributors understand the expected format and units of measurement.
Also applies to: 229-236, 232-239
plugwise/constants.py (1)
146-152: Consider adding proper units of measurement for weather-related measurements.Some weather measurements are using
NONEas their unit when they should have specific units:
solar_irradiancetypically uses W/m² (watts per square meter)wind_vectorshould be split intowind_bearing(degrees) andwind_speed(m/s)- "solar_irradiance": UOM(NONE), - "weather_description": UOM(NONE), - "wind_vector": UOM(NONE), + "solar_irradiance": UOM("W/m²"), + "weather_description": UOM(NONE), + "wind_bearing": UOM(DEGREE), + "wind_speed": UOM("m/s"),
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📒 Files selected for processing (59)
fixtures/adam_heatpump_cooling/all_data.json(2 hunks)fixtures/adam_jip/all_data.json(2 hunks)fixtures/adam_multiple_devices_per_zone/all_data.json(1 hunks)fixtures/adam_onoff_cooling_fake_firmware/all_data.json(2 hunks)fixtures/adam_plus_anna/all_data.json(2 hunks)fixtures/adam_plus_anna_new/all_data.json(2 hunks)fixtures/adam_plus_anna_new_regulation_off/all_data.json(2 hunks)fixtures/adam_zone_per_device/all_data.json(1 hunks)fixtures/anna_elga_2/all_data.json(1 hunks)fixtures/anna_elga_2_cooling/all_data.json(1 hunks)fixtures/anna_elga_2_schedule_off/all_data.json(1 hunks)fixtures/anna_elga_no_cooling/all_data.json(2 hunks)fixtures/anna_heatpump_cooling/all_data.json(2 hunks)fixtures/anna_heatpump_cooling_fake_firmware/all_data.json(2 hunks)fixtures/anna_heatpump_heating/all_data.json(2 hunks)fixtures/anna_loria_cooling_active/all_data.json(2 hunks)fixtures/anna_loria_driessens/all_data.json(2 hunks)fixtures/anna_loria_heating_idle/all_data.json(2 hunks)fixtures/anna_v4/all_data.json(2 hunks)fixtures/anna_v4_dhw/all_data.json(2 hunks)fixtures/anna_v4_no_tag/all_data.json(2 hunks)fixtures/anna_without_boiler_fw441/all_data.json(2 hunks)fixtures/m_adam_cooling/all_data.json(1 hunks)fixtures/m_adam_heating/all_data.json(1 hunks)fixtures/m_adam_jip/all_data.json(2 hunks)fixtures/m_adam_multiple_devices_per_zone/all_data.json(1 hunks)fixtures/m_anna_heatpump_cooling/all_data.json(2 hunks)fixtures/m_anna_heatpump_idle/all_data.json(2 hunks)plugwise/common.py(1 hunks)plugwise/constants.py(4 hunks)plugwise/helper.py(5 hunks)plugwise/util.py(1 hunks)scripts/manual_fixtures.py(3 hunks)tests/data/adam/adam_heatpump_cooling.json(1 hunks)tests/data/adam/adam_jip.json(1 hunks)tests/data/adam/adam_multiple_devices_per_zone.json(1 hunks)tests/data/adam/adam_onoff_cooling_fake_firmware.json(1 hunks)tests/data/adam/adam_plus_anna.json(1 hunks)tests/data/adam/adam_plus_anna_new.json(1 hunks)tests/data/adam/adam_plus_anna_new_regulation_off.json(1 hunks)tests/data/adam/adam_zone_per_device.json(1 hunks)tests/data/anna/anna_elga_2.json(1 hunks)tests/data/anna/anna_elga_2_cooling.json(1 hunks)tests/data/anna/anna_elga_2_cooling_UPDATED_DATA.json(1 hunks)tests/data/anna/anna_elga_2_schedule_off.json(1 hunks)tests/data/anna/anna_elga_no_cooling.json(1 hunks)tests/data/anna/anna_heatpump_cooling.json(1 hunks)tests/data/anna/anna_heatpump_cooling_fake_firmware.json(1 hunks)tests/data/anna/anna_heatpump_heating.json(1 hunks)tests/data/anna/anna_loria_cooling_active.json(1 hunks)tests/data/anna/anna_loria_driessens.json(1 hunks)tests/data/anna/anna_loria_heating_idle.json(1 hunks)tests/data/anna/anna_v4.json(1 hunks)tests/data/anna/anna_v4_UPDATED_DATA.json(1 hunks)tests/data/anna/anna_v4_dhw.json(1 hunks)tests/data/anna/anna_v4_no_tag.json(1 hunks)tests/data/anna/anna_without_boiler_fw441.json(1 hunks)tests/test_adam.py(7 hunks)tests/test_anna.py(15 hunks)
🔇 Additional comments (68)
plugwise/helper.py (5)
19-19: Import reference recognized
The addition of ATTR_UNIT_OF_MEASUREMENT helps tie measurement values to specific units, improving clarity and reducing hard-coded references.
47-47: New WeatherType import
Adding WeatherType is a logical step for storing multiple weather-related values in a typed, robust manner.
531-536: Initialization of the 'weather' dictionary
The introduction of the new "weather" field in GwEntityData is a clear and maintainable enhancement for storing exterior conditions separately from other sensor data.
773-799: New weather data gathering logic
The function clearly populates weather data for the gateway. Ensure that any gateway not providing valid or complete weather data still gracefully handles partial or missing attributes to avoid unexpected key errors.
800-821: Enhanced flexibility for measurement retrieval
The updated _object_value method now handles multiple measurement types (humidity, solar, etc.). Great approach for centralizing logic. Verify that potentially missing or null values (e.g., partial logs) are handled gracefully.
tests/data/anna/anna_v4_UPDATED_DATA.json (1)
50-56: Weather field introduction
Replacing the previous sensors block with weather ensures a richer set of weather attributes (humidity, solar, wind). Confirm that consumers of the old sensors structure have been updated to handle the new field.
tests/data/anna/anna_without_boiler_fw441.json (1)
47-55: Comprehensive weather data
Adding a dedicated weather block for humidity, temperature, solar, and wind attributes provides clarity and future extensibility. Double-check that references to the old sensors block are removed or redirected.
fixtures/anna_without_boiler_fw441/all_data.json (2)
47-55: Introduction of 'weather' field
Good move to store environmental data in one location. Ensure all references to the removed sensors field are updated accordingly.
71-71: Item count updated from 39 to 44
The new item_count matches the expanded set of weather attributes. Confirm consistency by verifying counts in upstream or downstream references.
tests/data/anna/anna_v4_dhw.json (1)
48-55: LGTM! Weather data structure looks good.
The new weather object contains all essential weather attributes with appropriate data types and reasonable values.
Please verify that the units for these weather measurements are documented in the integration's documentation:
- wind_speed (m/s?)
- solar_irradiance (W/m²?)
- wind_bearing (degrees clockwise from north?)
tests/data/anna/anna_v4.json (1)
48-55: Weather data structure is consistent with other test fixtures.
tests/data/anna/anna_v4_no_tag.json (2)
48-55: Weather data structure is consistent with other test fixtures.
48-55: Verify test coverage for new weather attributes.
The weather data structure is consistently implemented across test fixtures. Please ensure there are corresponding test cases that validate the parsing and handling of these new weather attributes.
tests/data/anna/anna_elga_no_cooling.json (2)
16-23: LGTM! Weather data structure looks good.
The weather object contains all necessary attributes with reasonable values.
18-18: Verify outdoor temperature consistency
The outdoor temperature is reported in two places:
- Gateway weather: 20.2°C
- Heater sensors: 3.0°C
This significant difference might indicate:
- Different data sources
- Different update frequencies
- Test data inconsistency
Also applies to: 45-45
✅ Verification successful
Let's check the actual temperature values in these files to understand if this is a test data inconsistency or an expected variation.
Let's search for any heater sensor temperature values in the file to verify the claim about the 3.0°C reading mentioned in the review comment.
Temperature difference is expected - different measurement points
The temperature difference in the file is not an inconsistency but rather represents two different measurements:
outdoor_temperature(20.2°C): Current gateway weather readingoutdoor_air_temperature(3.0°C): Heater sensor reading
This pattern is consistent with test data representing different temperature sensors in the system. The variation could represent:
- Historical vs current readings
- Different sensor locations
- Different measurement timestamps
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for other outdoor temperature occurrences in test files
rg -l "outdoor.*temperature" tests/data/
Length of output: 1165
Script:
#!/bin/bash
# Search for outdoor temperature values in test files
rg -A 1 '"outdoor_temperature"' tests/data/anna/anna_elga_no_cooling.json
# Check a few other files to compare temperature values
echo "=== Other test files ==="
rg -A 1 '"outdoor_temperature"' tests/data/anna/anna_v4.json tests/data/anna/anna_loria_heating_idle.json tests/data/anna/anna_heatpump_heating.json
Length of output: 812
Script:
#!/bin/bash
# Search for temperature-related values in the specific file
rg "temperature|temp" tests/data/anna/anna_elga_no_cooling.json
Length of output: 473
tests/data/anna/anna_elga_2_schedule_off.json (1)
87-94: LGTM! Consistent weather data.
The weather object structure matches the expected format and shows good consistency with sensor values.
tests/data/anna/anna_elga_2_cooling_UPDATED_DATA.json (1)
87-94: LGTM! Weather data structure is consistent.
The weather object follows the established pattern and shows good consistency with sensor values.
tests/data/anna/anna_heatpump_cooling.json (1)
15-23: LGTM! Good variety in test data
This file demonstrates good test coverage with varied weather conditions:
- Non-zero solar irradiance (207.2)
- Different weather description ("overcast")
- Varied wind conditions (0.0° bearing, 2.1 speed)
- Different humidity (35%) and temperature (22.0°C)
tests/data/anna/anna_heatpump_cooling_fake_firmware.json (1)
16-23: Verify weather data units and ranges
The weather data structure looks good, but please ensure these units are correct:
- Temperature in °C
- Solar irradiance in W/m²
- Wind speed in m/s
- Wind bearing in degrees (0-360°)
- Humidity in percentage (0-100%)
✅ Verification successful
The previous search was too narrow. Let's broaden our search to:
- Find weather-related data structures and their context
- Look for schema/validation files that might define units
- Search source code for weather-related constants or configurations
Based on the search results from examining the test data files, I can verify the weather data structure and its units:
Weather data structure and units are consistent across all test files
The search results show consistent patterns across multiple test files that confirm:
- Temperature values are in °C (range observed: -1.25 to 31.0)
- Solar irradiance values are in W/m² (range observed: 0.0 to 653.6)
- Wind speed values are in m/s (range observed: 1.79 to 8.2)
- Wind bearing values are in degrees (range observed: 0.0 to 291.0)
- Humidity values are in percentage (range observed: 35 to 91)
All values across test files are within reasonable ranges and use consistent units.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for weather data validation or unit tests
rg -l "weather.*test|test.*weather" tests/
Length of output: 44
Script:
#!/bin/bash
# Search for weather-related files and patterns more broadly
rg -i "weather" --type json -A 5 .
Length of output: 37725
Script:
#!/bin/bash
# Look for any schema or validation files
fd -e json -e schema.json -e yaml -e yml . | grep -i "schema\|valid\|weather"
Length of output: 79
Script:
#!/bin/bash
# Search for weather-related constants or configurations
rg -i "weather" --type python --type typescript --type javascript .
Length of output: 98
fixtures/anna_v4_dhw/all_data.json (1)
48-55: Verify item_count increment
The item_count increase from 58 to 63 (+5) aligns with the addition of 5 new weather fields (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed). The outdoor_temperature was moved from sensors to weather, so it doesn't affect the count.
Also applies to: 98-98
fixtures/anna_v4_no_tag/all_data.json (2)
48-55: LGTM: Weather data structure is well-defined and complete
The new weather object includes all essential weather metrics with appropriate data types:
- Temperature in Celsius
- Humidity as percentage
- Solar irradiance in W/m²
- Wind speed in m/s
- Wind bearing in degrees
- Weather description as string
98-98: Verify item_count increase
The item_count increase from 58 to 63 (+5) aligns with the addition of new weather attributes (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed).
tests/data/anna/anna_loria_cooling_active.json (1)
50-57: LGTM: Weather data structure is consistent
The weather object maintains consistency with other fixtures and includes appropriate test values for a cooling scenario (e.g., outdoor_temperature: 15.5°C with overcast conditions).
tests/data/anna/anna_heatpump_heating.json (1)
16-23: Consider adding data validation constraints for weather attributes
The weather data structure needs validation constraints to ensure data integrity:
humidity: Should be 0-100%wind_bearing: Should be 0-360 degreesweather_description: Should be a defined enum of possible values
Consider defining a schema or data model that enforces these constraints. This will help prevent invalid data from being processed.
Let's check if there are any validation constraints defined elsewhere:
fixtures/anna_elga_no_cooling/all_data.json (1)
103-103: Verify item count increase matches new weather attributes
The item count increase from 63 to 68 (+5) appears to correspond with the newly added weather attributes: humidity, solar_irradiance, weather_description, wind_bearing, and wind_speed.
Let's verify this change is consistent across other files:
✅ Verification successful
Let me verify the item count changes by checking the actual weather attributes in the relevant files.
Item count increase correctly reflects added weather attributes
The verification confirms that the item count increase from 63 to 68 (+5) in anna_elga_no_cooling exactly matches the five newly added weather attributes that are present in the file:
- humidity
- solar_irradiance
- weather_description
- wind_bearing
- wind_speed
The outdoor_temperature attribute was already present in both files, so it doesn't contribute to the count increase.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for item_count changes in other files
rg -B 1 -A 1 '"item_count":\s*\d+'
Length of output: 8602
Script:
#!/bin/bash
# Search for weather-related attributes in anna_v4 and anna_elga_no_cooling files
rg -A 5 '"outdoor_temperature"|"humidity"|"solar_irradiance"|"weather_description"|"wind_bearing"|"wind_speed"' fixtures/anna_v4/all_data.json fixtures/anna_elga_no_cooling/all_data.json
Length of output: 1617
fixtures/anna_elga_2_schedule_off/all_data.json (2)
87-94: LGTM! Weather data structure is well-designed and consistent
The new weather object provides a comprehensive set of weather attributes with appropriate data types:
- Temperature as float (13.0°)
- Humidity as integer percentage (42%)
- Wind measurements as floats (bearing: 120.0°, speed: 3.6)
- Text description ("cloudy")
- Solar irradiance as float (0.0)
The outdoor temperature (13.0°) correctly matches the OpenTherm sensor reading.
101-101: LGTM! Item count increase is accurate
The increase from 63 to 68 items correctly reflects the addition of 5 new weather attributes (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed).
fixtures/anna_elga_2_cooling/all_data.json (1)
89-89: Verify temperature discrepancy between sensors
The outdoor temperature reported by the Gateway (31.0°) differs from the OpenTherm sensor reading (30.0°). While this small difference might be expected due to different measurement times or sources, it would be good to confirm this is intended behavior.
Consider documenting which temperature source should be considered authoritative for automation purposes.
fixtures/anna_heatpump_cooling/all_data.json (2)
16-23: LGTM! Weather data structure and values look valid.
The weather object contains all expected fields with appropriate data types and reasonable value ranges:
- Humidity: 35% (0-100 range)
- Temperature: 22.0°C (typical outdoor range)
- Solar irradiance: 207.2 W/m² (typical daylight range)
- Wind speed: 2.1 m/s (light breeze)
- Wind bearing: 0.0° (valid 0-360 range)
102-102: Verify item count increase matches new weather attributes
The gateway's item_count increase from 64 to 69 (+5) correctly reflects the addition of new weather attributes (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed).
✅ Verification successful
I'm requesting these scripts to:
- Verify the previous item_count value to confirm the increase from 64 to 69
- Confirm the presence of the mentioned weather attributes in the file
This will help us validate if the review comment's assertion about the +5 increase matching the new weather attributes is correct.
Weather attributes match item count increase
The verification confirms that all 5 new weather attributes (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed) are present in the file, which matches the reported item_count increase from 64 to 69 (+5). The attributes are properly integrated into the weather object structure.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Verify consistent item_count changes across all gateway fixtures
rg -l '"item_count"' | while read -r file; do
echo "=== $file ==="
rg '"item_count".*(\d+)' "$file"
done
Length of output: 8636
Script:
#!/bin/bash
# Let's check the git history for the specific file to see the previous item_count value
git log -p fixtures/anna_heatpump_cooling/all_data.json | grep -A 1 -B 1 '"item_count":'
# Also let's check for weather-related attributes in this file
rg -A 1 -B 1 '"(humidity|solar_irradiance|weather_description|wind_bearing|wind_speed)"' fixtures/anna_heatpump_cooling/all_data.json
Length of output: 356
fixtures/anna_heatpump_cooling_fake_firmware/all_data.json (1)
16-23: LGTM! Weather data structure matches the non-fake firmware fixture.
The weather object maintains consistency with the previous fixture, which is appropriate for testing purposes.
tests/data/adam/adam_onoff_cooling_fake_firmware.json (1)
63-70: LGTM! Weather data structure and value correlations are valid.
The weather object:
- Maintains consistent structure across different gateway models
- Shows proper correlation between values:
- High humidity (88%) matches clouded weather description
- Moderate solar irradiance (208.2 W/m²) appropriate for cloudy conditions
- Light wind speed (1.79 m/s) at 228.0° bearing
- Preserves the outdoor temperature value (13.4°C) from the previous sensor implementation
fixtures/anna_loria_cooling_active/all_data.json (2)
50-57: LGTM! Weather data structure looks good.
The new weather object provides a clear and comprehensive representation of weather conditions with appropriate units and value ranges.
106-106: Verify item_count increase.
The increase in item_count from 66 to 71 correctly reflects the addition of 5 new weather attributes.
fixtures/anna_loria_heating_idle/all_data.json (1)
50-57: LGTM! Consistent weather data implementation.
The weather data structure and item_count changes are consistent with the cooling_active fixture, which is correct as these are structural changes independent of the heating/cooling state.
Also applies to: 106-106
fixtures/m_anna_heatpump_idle/all_data.json (2)
16-23: LGTM! Weather data values are internally consistent.
The weather attributes show good internal consistency - the "clear" weather description aligns well with the high solar irradiance (653.6 W/m²) and temperature (28.2°C).
107-107: LGTM! Correct item_count adjustment.
The item_count increase from 67 to 72 correctly reflects the addition of 5 new weather attributes.
fixtures/m_anna_heatpump_cooling/all_data.json (2)
107-107: Verify gateway item count increment
The item_count increase from 67 to 72 (+5) matches the number of new weather attributes added (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed).
16-23: Verify weather data structure and ranges
The weather data structure looks good with all required attributes. Values are within expected ranges:
- Temperature (28.2°C) aligns with cooling scenario
- Humidity (42%) is within 0-100% range
- Wind bearing (50.0°) is within 0-360° range
- Solar irradiance (653.6) is consistent with "clear" weather
✅ Verification successful
Weather data structure and values are consistent with test fixtures
The weather data structure in fixtures/m_anna_heatpump_cooling/all_data.json follows the same schema found across all fixture files that contain weather data. The values are within reasonable ranges and consistent with other cooling scenario fixtures:
- All required attributes (humidity, outdoor_temperature, solar_irradiance, weather_description, wind_bearing, wind_speed) are present
- Values align with other cooling scenarios in the test fixtures (e.g., anna_heatpump_cooling_fake_firmware: 22°C, m_adam_cooling: 29.65°C)
- The structure matches the standardized format used across the test suite
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Verify consistent weather data structure across all fixture files
fd -e json -x jq -r '.devices[] | select(.weather != null) | .weather | keys' {} \; | sort | uniq -c
Length of output: 3677
Script:
#!/bin/bash
# Check the weather data structure in the specific file
jq -r '.devices[] | select(.weather != null) | .weather' fixtures/m_anna_heatpump_cooling/all_data.json
# Find all files with weather data to compare structure
fd -e json -x sh -c 'echo "=== {} ==="; jq -r ".devices[] | select(.weather != null) | .weather" {}' \;
Length of output: 12812
fixtures/anna_heatpump_heating/all_data.json (1)
18-18: Verify temperature consistency between gateway and OpenTherm
There's a significant discrepancy between temperatures reported by:
- Gateway weather: 20.2°C
- OpenTherm sensor: 3.0°C (outdoor_air_temperature)
Please verify which temperature reading should be considered authoritative.
Also applies to: 47-47
fixtures/anna_loria_driessens/all_data.json (1)
16-23: Weather data values are internally consistent
The weather data shows good internal consistency:
- Lower solar irradiance (97.6) aligns with "clouded" weather
- Higher humidity (88%) is typical for cloudy conditions
- Gateway temperature (6.81°C) closely matches OpenTherm sensor (7.5°C)
fixtures/adam_onoff_cooling_fake_firmware/all_data.json (2)
63-70: LGTM: Weather data structure is well-organized
The new weather object provides comprehensive environmental data with appropriate data types:
- Temperature in Celsius
- Humidity as percentage
- Solar irradiance in W/m²
- Wind speed in m/s
- Wind bearing in degrees
- Weather description as string
122-122: Verify item count increase
The item count increase from 64 to 69 (+5) correctly reflects the addition of new weather attributes (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed).
tests/data/adam/adam_plus_anna.json (1)
90-97: LGTM: Test data matches fixture format
The weather object in the test data correctly mirrors the structure from the fixtures, maintaining consistency across the codebase.
fixtures/adam_plus_anna/all_data.json (2)
90-97: LGTM: Weather data structure maintains consistency
The weather object implementation maintains consistency with other files in the codebase, using the same structure and data types.
136-136: Verify item count increase
The item count increase from 80 to 85 (+5) correctly reflects the addition of new weather attributes.
fixtures/m_adam_heating/all_data.json (1)
92-99: LGTM! Weather data structure is consistent.
The weather data structure maintains consistency with other fixtures and correctly handles negative temperatures.
tests/data/adam/adam_plus_anna_new.json (1)
176-183: LGTM! Weather data structure is consistent across test fixtures.
The weather object maintains the same structure and data types across different test scenarios, which is good for testing reliability.
fixtures/adam_plus_anna_new_regulation_off/all_data.json (2)
176-183: LGTM! Weather data structure is consistent with other fixtures.
The weather object implementation maintains consistency across all test fixtures, which is crucial for reliable testing.
296-296: Verify the updated item count.
The increase in item_count from 177 to 182 appears to correspond with the addition of new weather attributes.
fixtures/adam_plus_anna_new/all_data.json (1)
176-183: LGTM! Weather data structure is well-organized.
The new weather object provides a comprehensive and logically structured representation of weather data, with appropriate data types for each attribute.
tests/data/adam/adam_jip.json (1)
229-236: LGTM! Consistent weather data implementation.
The weather data structure maintains consistency with other fixtures while containing appropriate test values.
fixtures/m_adam_jip/all_data.json (2)
380-380: LGTM! Item count update is consistent.
The item count increase to 249 aligns with the addition of weather attributes, maintaining consistency with the schema changes.
232-239: Verify the duplicated weather data values.
The weather data values are identical to those in adam_jip.json. While this might be intentional for testing purposes, it's worth verifying.
✅ Verification successful
The script results show that the weather data in m_adam_jip/all_data.json is indeed identical to adam_jip.json, but this appears to be intentional. Looking at the broader context, we can see that files prefixed with m_ appear to be variants of their corresponding non-prefixed fixtures, likely for testing different scenarios. For example:
m_adam_coolingvsadam_heatpump_coolingm_adam_multiple_devices_per_zonevsadam_multiple_devices_per_zone
Weather data duplication is expected in test fixtures
The identical weather data between m_adam_jip/all_data.json and adam_jip.json is intentional, as these are related test fixtures. Files prefixed with m_ appear to be variants of their base fixtures, maintaining the same weather data where appropriate for testing consistency.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Compare weather data across test fixtures
# Extract and compare weather objects
echo "Comparing weather data in test fixtures:"
for file in fixtures/*adam*/all_data.json; do
echo "=== $file ==="
jq -c '.. | select(.weather?) | .weather' "$file"
done
Length of output: 4657
fixtures/adam_jip/all_data.json (1)
233-240: LGTM! Weather data structure is well-organized.
The new weather object contains all essential weather metrics with appropriate value ranges. The item_count increase (from 244 to 249) correctly reflects the addition of these weather attributes.
scripts/manual_fixtures.py (1)
142-142: LGTM! Temperature values are appropriate for each mode.
The outdoor temperature values are correctly set for different scenarios:
- Cooling mode: 29.65°C (appropriately warm)
- Heating mode: -1.25°C (appropriately cold)
- Anna cooling mode: 28.2°C (appropriately warm)
Also applies to: 223-223, 298-298
plugwise/common.py (1)
150-151: LGTM! Weather data counting implementation is consistent.
The implementation correctly follows the existing pattern for counting dictionary items, properly accounting for the weather dictionary itself by subtracting 1 from the total count.
tests/data/adam/adam_zone_per_device.json (1)
581-588: LGTM! Consider adding data validation for weather attributes.
The weather data structure looks good with comprehensive attributes. However, it would be beneficial to ensure the values are within expected ranges:
- humidity: 0-100%
- wind_bearing: 0-360 degrees
- wind_speed: non-negative
- solar_irradiance: non-negative
✅ Verification successful
All weather data values are within valid ranges
After analyzing the weather data across all test fixtures, I can confirm that:
- All humidity values are between 0-100% (ranging from 35% to 91%)
- All wind bearing values are between 0-360 degrees (ranging from 0.0° to 291.0°)
- All wind speed values are non-negative (ranging from 1.79 to 8.2)
- All solar irradiance values are non-negative (ranging from 0.0 to 653.6)
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify weather data ranges across all test fixtures
# Search for weather data in JSON files and validate ranges
rg -U --multiline '(?s)"weather":\s*{\s*"humidity":\s*(\d+).*?"wind_bearing":\s*(\d+\.?\d*).*?"wind_speed":\s*(\d+\.?\d*).*?"solar_irradiance":\s*(\d+\.?\d*)' -g '*.json' | \
while IFS= read -r line; do
echo "Validating weather data ranges in: $line"
# Extract and validate values using awk
echo "$line" | gawk '
match($0, /"humidity":\s*(\d+)/, h) &&
match($0, /"wind_bearing":\s*(\d+\.?\d*)/, wb) &&
match($0, /"wind_speed":\s*(\d+\.?\d*)/, ws) &&
match($0, /"solar_irradiance":\s*(\d+\.?\d*)/, si) {
humidity = h[1]
wind_bearing = wb[1]
wind_speed = ws[1]
solar_irradiance = si[1]
if (humidity < 0 || humidity > 100)
print "Invalid humidity:", humidity
if (wind_bearing < 0 || wind_bearing > 360)
print "Invalid wind_bearing:", wind_bearing
if (wind_speed < 0)
print "Invalid wind_speed:", wind_speed
if (solar_irradiance < 0)
print "Invalid solar_irradiance:", solar_irradiance
}'
done
Length of output: 433
Script:
#!/bin/bash
# Description: Verify weather data ranges across all test files using a simpler pattern
rg -U --multiline '"weather":\s*{[^}]+}' -g '*.json' | while IFS= read -r line; do
echo "$line" | gawk '
match($0, /"humidity":\s*([0-9.]+)/, h) { humidity = h[1] }
match($0, /"wind_bearing":\s*([0-9.]+)/, wb) { wind_bearing = wb[1] }
match($0, /"wind_speed":\s*([0-9.]+)/, ws) { wind_speed = ws[1] }
match($0, /"solar_irradiance":\s*([0-9.]+)/, si) { solar_irradiance = si[1] }
END {
if (humidity != "" && (humidity < 0 || humidity > 100))
print "Invalid humidity:", humidity
if (wind_bearing != "" && (wind_bearing < 0 || wind_bearing > 360))
print "Invalid wind_bearing:", wind_bearing
if (wind_speed != "" && wind_speed < 0)
print "Invalid wind_speed:", wind_speed
if (solar_irradiance != "" && solar_irradiance < 0)
print "Invalid solar_irradiance:", solar_irradiance
if (humidity == "" && wind_bearing == "" && wind_speed == "" && solar_irradiance == "")
print "No weather data found"
else
print "Found values - humidity:", humidity, "wind_bearing:", wind_bearing, "wind_speed:", wind_speed, "solar_irradiance:", solar_irradiance
}
'
done
Length of output: 193
Script:
#!/bin/bash
# Description: Search for weather data in JSON files using simple grep
rg '"weather":' -g '*.json' -A 10 | while IFS= read -r line; do
echo "$line" | grep -E '"(humidity|wind_bearing|wind_speed|solar_irradiance)":'
done
Length of output: 80636
tests/data/adam/adam_multiple_devices_per_zone.json (1)
584-591: LGTM! Weather data structure is consistent.
The weather data structure matches the format in other test fixtures, maintaining consistency across the test suite.
tests/test_adam.py (1)
39-39: LGTM! Entity count updates reflect new weather attributes.
The consistent increase in entity_items count (by 5) across all test methods correctly reflects the addition of new weather attributes (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed).
Also applies to: 210-210, 290-290, 328-328, 351-351, 376-376, 422-422
✅ Verification successful
Let me analyze the test files further to verify the entity count changes.
Entity count increases are consistent with the addition of 5 weather attributes
The git diff confirms that all entity_items counts in test_adam.py have been increased by exactly 5 across all test cases:
- 177 → 182 (+5)
- 369 → 374 (+5)
- 497 → 502 (+5)
- 64 → 69 (+5)
- 80 → 85 (+5)
- 244 → 249 (+5)
This consistent increment of 5 entities aligns perfectly with the addition of the five new weather attributes (humidity, solar_irradiance, weather_description, wind_bearing, wind_speed).
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify consistent entity count increase across test files
# Search for entity_items assertions and their values
rg 'assert self\.entity_items == \d+' tests/ | \
while IFS= read -r line; do
echo "Found entity count in: $line"
done
Length of output: 6165
Script:
#!/bin/bash
# Get the git diff for test_adam.py to see the actual changes in entity counts
git diff origin/main -- tests/test_adam.py | grep -A 1 -B 1 "entity_items =="
Length of output: 1450
fixtures/m_adam_multiple_devices_per_zone/all_data.json (2)
575-582: LGTM! Weather data structure looks well-designed.
The new weather object provides a comprehensive set of environmental data with appropriate data types and reasonable value ranges.
590-590: LGTM! Item count update is consistent.
The increase from 369 to 374 correctly reflects the addition of 5 new weather-related fields.
fixtures/adam_zone_per_device/all_data.json (1)
581-588: Changes match the pattern from other files.
The weather object structure and item count update follow the same well-designed pattern as in fixtures/m_adam_multiple_devices_per_zone/all_data.json.
Also applies to: 596-596
fixtures/adam_multiple_devices_per_zone/all_data.json (1)
584-591: Changes match the pattern from other files.
The weather object structure and item count update follow the same well-designed pattern as in the previous files.
Also applies to: 599-599
plugwise/constants.py (1)
524-533: LGTM! Well-structured weather data class.
The WeatherData class is well-defined with appropriate types for all weather-related fields.
tests/data/adam/adam_heatpump_cooling.json (1)
309-316: LGTM! Well-structured weather data.
The weather data structure in the test fixture is consistent with the WeatherData TypedDict and contains realistic values for all fields.
fixtures/adam_heatpump_cooling/all_data.json (2)
817-817: LGTM! Item count correctly updated.
The item count increase from 497 to 502 accurately reflects the addition of the new weather attributes.
309-316: LGTM! Consider adding value range validation.
The weather data structure is well-organized and comprehensive, including all essential weather metrics. The values appear reasonable:
- Temperature: 13.4°C (typical outdoor temperature)
- Humidity: 88% (valid percentage)
- Wind speed: 1.79 m/s (reasonable wind speed)
- Wind bearing: 228.0° (valid degree range)
- Solar irradiance: 208.2 W/m² (typical daylight value)
✅ Verification successful
Weather data structure is consistent across test fixtures
The weather data structure is consistently implemented across all test fixtures that include weather information, with values within reasonable ranges:
- Humidity: 35-91%
- Temperature: -1.25°C to 31°C
- Solar irradiance: 0-653.6 W/m²
- Wind bearing: 0-291°
- Wind speed: 1.79-8.2 m/s
- Weather descriptions: clear, cloudy, clouded, overcast, rain
The data in the reviewed file aligns with these ranges and follows the same structure used throughout the test fixtures.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Verify the weather data structure is consistent across all test fixtures
fd -e json -x jq -r '.devices | .[] | select(.model == "Gateway") | .weather // empty' {}
Length of output: 8552
| "weather": { | ||
| "humidity": 88, | ||
| "outdoor_temperature": 6.81, | ||
| "solar_irradiance": 97.6, | ||
| "weather_description": "clouded", | ||
| "wind_bearing": 230.0, | ||
| "wind_speed": 4.12 | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Standardize decimal precision across weather measurements
There are inconsistencies in decimal precision between different test fixtures:
outdoor_temperature: 6.81 vs 20.2wind_speed: 4.12 vs 4.1
Standardize the decimal precision across all weather measurements to ensure consistent data representation. Consider using a fixed precision of 1 decimal place for all float values.
"weather": {
"humidity": 88,
- "outdoor_temperature": 6.81,
+ "outdoor_temperature": 6.8,
"solar_irradiance": 97.6,
"weather_description": "clouded",
"wind_bearing": 230.0,
- "wind_speed": 4.12
+ "wind_speed": 4.1
}📝 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.
| "weather": { | |
| "humidity": 88, | |
| "outdoor_temperature": 6.81, | |
| "solar_irradiance": 97.6, | |
| "weather_description": "clouded", | |
| "wind_bearing": 230.0, | |
| "wind_speed": 4.12 | |
| } | |
| "weather": { | |
| "humidity": 88, | |
| "outdoor_temperature": 6.8, | |
| "solar_irradiance": 97.6, | |
| "weather_description": "clouded", | |
| "wind_bearing": 230.0, | |
| "wind_speed": 4.1 | |
| } |
| "gateway_id": "da224107914542988a88561b4452b0f6", | ||
| "heater_id": "056ee145a816487eaa69243c3280f8bf", | ||
| "item_count": 177, | ||
| "item_count": 182, |
There was a problem hiding this comment.
💡 Codebase verification
Item count in JSON file needs correction
The declared item_count of 182 in the JSON file appears to be incorrect. The actual count of scalar properties in the JSON structure is 208, and this count remains unchanged from the previous version. This suggests that either:
- The
item_countfield is not meant to represent the total number of scalar properties - Or there's an error in the count that needs to be corrected
🔗 Analysis chain
Verify the updated item count.
The item count has been increased from 177 to 182, which aligns with the addition of new weather attributes.
Let me try a different approach to count the items correctly.
Let's try one more approach to understand the structure and count the items correctly.
Let's verify the exact item count by checking all the properties in the JSON structure.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Verify that the item count matches the actual number of items
# Count all unique keys in the JSON structure
jq '[.. | objects | keys[]] | unique | length' fixtures/adam_plus_anna_new/all_data.json
Length of output: 93
Script:
#!/bin/bash
# Let's check the actual structure of the JSON file first
jq 'keys' fixtures/adam_plus_anna_new/all_data.json
# Then count the total number of items in the main data array
jq '.data | length' fixtures/adam_plus_anna_new/all_data.json
Length of output: 146
Script:
#!/bin/bash
# Let's examine the actual content structure
jq '.devices | length' fixtures/adam_plus_anna_new/all_data.json
# Also check the gateway structure
jq '.gateway | keys' fixtures/adam_plus_anna_new/all_data.json
# Count all non-null values in the entire JSON structure
jq '[.. | select(. != null)] | length' fixtures/adam_plus_anna_new/all_data.json
Length of output: 339
Script:
#!/bin/bash
# Count all properties in the JSON structure
jq '[paths(scalars)] | length' fixtures/adam_plus_anna_new/all_data.json
# Also let's see the previous version's count for comparison
git show HEAD^:fixtures/adam_plus_anna_new/all_data.json | jq '[paths(scalars)] | length'
Length of output: 176
| WeatherType = Literal[ | ||
| "humidity", | ||
| "outdoor_temperature", | ||
| "solar_irradiance", | ||
| "weather_description", | ||
| "wind_bearing", | ||
| "wind_speed", | ||
| ] | ||
| WEATHER: Final[tuple[str, ...]] = get_args(WeatherType) |
There was a problem hiding this comment.
Align weather types with device measurements.
There's an inconsistency between WeatherType and DEVICE_MEASUREMENTS:
WeatherTypehas separatewind_bearingandwind_speedDEVICE_MEASUREMENTShas combinedwind_vector
Please align these definitions to use consistent field names.
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That might make sense, unless one is from the Smile and the other the HA standardised form?
There was a problem hiding this comment.
@CoMPaTech, indeed, if “wind_vector” is coming directly from the Smile in a combined format while “wind_bearing” and “wind_speed” follow HA’s standard naming, keeping them separate could be fine. Otherwise, if both are describing the same data from the same source, consolidating them into a consistent naming (either “wind_vector” or separate “wind_bearing”/“wind_speed”) would avoid confusion. Let me know if you prefer aligning these fields for a more uniform format, or if they should remain distinct.
|
I think we came across this earlier, but decided (back then) not to incorporate it as it was also just generic data from the internet (or maybe from a Core member, I don't really remember) - then again, it is provided data and as such could at least mimic what, for instance Anna, displays. |
CoMPaTech
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CoMPaTech
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While reviewing, what do we do with outdoor_temperature if one has both? I remember we decided to opt for the physical sensor instead of the perceived one, just to be clear that should be (also) obvious to end users.
We solved that a long time ago by renaming it to |
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #676 +/- ##
=========================================
Coverage 100.00% 100.00%
=========================================
Files 20 20
Lines 3469 3503 +34
=========================================
+ Hits 3469 3503 +34 ☔ View full report in Codecov by Sentry. |
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Actionable comments posted: 1
🧹 Nitpick comments (1)
plugwise/helper.py (1)
801-819: LGTM with minor type hint suggestionThe implementation is clean and handles different measurement types appropriately. Consider adding a more specific return type hint:
- ) -> float | int | str | None: + ) -> float | int | str | None: # float for temp/irradiance, int for humidity, str for others
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
plugwise/helper.py(5 hunks)
🔇 Additional comments (2)
plugwise/helper.py (2)
531-536: LGTM: Clean data structure initialization
The addition of the "weather" dictionary follows the existing pattern and maintains consistency with the codebase structure.
773-819: Verify weather data integration with thermostat devices
The implementation aligns with the PR objectives. Let's verify the integration:
| def _get_gateway_measurements(self, entity_id: str, data: GwEntityData) -> None: | ||
| """Adam & Anna: the Gateway weather-data is present under the Home location. | ||
|
|
||
| Available under the Home location. | ||
| """ | ||
| if self._is_thermostat and entity_id == self.gateway_id: | ||
| outdoor_temperature = self._object_value( | ||
| self._home_location, "outdoor_temperature" | ||
| ) | ||
| if outdoor_temperature is not None: | ||
| data.update({"sensors": {"outdoor_temperature": outdoor_temperature}}) | ||
| self._count += 1 | ||
| if not (self._is_thermostat and entity_id == self.gateway_id): | ||
| return | ||
|
|
||
| def _object_value(self, obj_id: str, measurement: str) -> float | int | None: | ||
| measurements = DEVICE_MEASUREMENTS | ||
| for measurement, attrs in measurements.items(): | ||
| value = self._location_value(self._home_location, measurement, attrs) | ||
| if value is not None: | ||
| if measurement == "wind_vector": | ||
| value_list: list[str] = str(value).split(",") | ||
| data["weather"]["wind_speed"] = format_measure( | ||
| value_list[0].strip("("), | ||
| getattr(attrs, ATTR_UNIT_OF_MEASUREMENT), | ||
| ) | ||
| data["weather"]["wind_bearing"] = format_measure( | ||
| value_list[1].strip(")"), | ||
| getattr(attrs, ATTR_UNIT_OF_MEASUREMENT), | ||
| ) | ||
| self._count += 2 | ||
| else: | ||
| key = cast(WeatherType, measurement) | ||
| data["weather"][key] = value | ||
| self._count += 1 | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider enhancing wind vector parsing robustness
While the implementation is functional, the wind vector parsing could be more robust:
- No validation of the value_list length before accessing indices
- No error handling for malformed vector strings
Consider this safer implementation:
- value_list: list[str] = str(value).split(",")
- data["weather"]["wind_speed"] = format_measure(
- value_list[0].strip("("),
- getattr(attrs, ATTR_UNIT_OF_MEASUREMENT),
- )
- data["weather"]["wind_bearing"] = format_measure(
- value_list[1].strip(")"),
- getattr(attrs, ATTR_UNIT_OF_MEASUREMENT),
- )
+ try:
+ value_list: list[str] = str(value).split(",")
+ if len(value_list) >= 2:
+ data["weather"]["wind_speed"] = format_measure(
+ value_list[0].strip("("),
+ getattr(attrs, ATTR_UNIT_OF_MEASUREMENT),
+ )
+ data["weather"]["wind_bearing"] = format_measure(
+ value_list[1].strip(")"),
+ getattr(attrs, ATTR_UNIT_OF_MEASUREMENT),
+ )
+ self._count += 2
+ except (ValueError, IndexError) as err:
+ LOGGER.warning("Failed to parse wind vector: %s", err)📝 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.
| def _get_gateway_measurements(self, entity_id: str, data: GwEntityData) -> None: | |
| """Adam & Anna: the Gateway weather-data is present under the Home location. | |
| Available under the Home location. | |
| """ | |
| if self._is_thermostat and entity_id == self.gateway_id: | |
| outdoor_temperature = self._object_value( | |
| self._home_location, "outdoor_temperature" | |
| ) | |
| if outdoor_temperature is not None: | |
| data.update({"sensors": {"outdoor_temperature": outdoor_temperature}}) | |
| self._count += 1 | |
| if not (self._is_thermostat and entity_id == self.gateway_id): | |
| return | |
| def _object_value(self, obj_id: str, measurement: str) -> float | int | None: | |
| measurements = DEVICE_MEASUREMENTS | |
| for measurement, attrs in measurements.items(): | |
| value = self._location_value(self._home_location, measurement, attrs) | |
| if value is not None: | |
| if measurement == "wind_vector": | |
| value_list: list[str] = str(value).split(",") | |
| data["weather"]["wind_speed"] = format_measure( | |
| value_list[0].strip("("), | |
| getattr(attrs, ATTR_UNIT_OF_MEASUREMENT), | |
| ) | |
| data["weather"]["wind_bearing"] = format_measure( | |
| value_list[1].strip(")"), | |
| getattr(attrs, ATTR_UNIT_OF_MEASUREMENT), | |
| ) | |
| self._count += 2 | |
| else: | |
| key = cast(WeatherType, measurement) | |
| data["weather"][key] = value | |
| self._count += 1 | |
| def _get_gateway_measurements(self, entity_id: str, data: GwEntityData) -> None: | |
| """Adam & Anna: the Gateway weather-data is present under the Home location. | |
| Available under the Home location. | |
| """ | |
| if not (self._is_thermostat and entity_id == self.gateway_id): | |
| return | |
| measurements = DEVICE_MEASUREMENTS | |
| for measurement, attrs in measurements.items(): | |
| value = self._location_value(self._home_location, measurement, attrs) | |
| if value is not None: | |
| if measurement == "wind_vector": | |
| try: | |
| value_list: list[str] = str(value).split(",") | |
| if len(value_list) >= 2: | |
| data["weather"]["wind_speed"] = format_measure( | |
| value_list[0].strip("("), | |
| getattr(attrs, ATTR_UNIT_OF_MEASUREMENT), | |
| ) | |
| data["weather"]["wind_bearing"] = format_measure( | |
| value_list[1].strip(")"), | |
| getattr(attrs, ATTR_UNIT_OF_MEASUREMENT), | |
| ) | |
| self._count += 2 | |
| except (ValueError, IndexError) as err: | |
| LOGGER.warning("Failed to parse wind vector: %s", err) | |
| else: | |
| key = cast(WeatherType, measurement) | |
| data["weather"][key] = value | |
| self._count += 1 |
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🧹 Nitpick comments (1)
plugwise/helper.py (1)
804-822: Consider adding error handling for value conversionsWhile the method is well-structured, it could benefit from error handling around the value conversions, particularly for the humidity and measurement formatting operations.
Consider this safer implementation:
def _loc_value( self, loc_id: str, measurement: str, attrs: DATA | UOM ) -> float | int | str | None: locator = f"./location[@id='{loc_id}']/logs/point_log[type='{measurement}']/period/measurement" if (found := self._domain_objects.find(locator)) is None: return None value: str = found.text try: match measurement: case "humidity": return int(float(value)) case "outdoor_temperature" | "solar_irradiance": return format_measure(value, getattr(attrs, ATTR_UNIT_OF_MEASUREMENT)) case _: return value + except (ValueError, TypeError) as err: + LOGGER.warning("Failed to convert measurement value: %s", err) + return None
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🔇 Additional comments (2)
plugwise/helper.py (2)
531-536: LGTM: Clean initialization of weather data structure
The addition of the "weather" dictionary to GwEntityData follows the established pattern for data organization.
773-803: LGTM: Well-structured gateway measurements collection
The method effectively collects weather data from the gateway, with clear separation of concerns and proper entity validation.
The wind vector parsing could be more robust, as noted in a previous review comment.
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@CoMPaTech I was preparing a little bit for the Anna P1 device and I realized there is also interesting data available at the Gateway / Home location for thermostat devices.
Also, this will allow us to add the weather-platform to the Integrations.
Summary by CodeRabbit
New Features
weatherdata structure for devices, replacing the previoussensorsstructure, which includes attributes like humidity, outdoor temperature, solar irradiance, weather description, wind bearing, and wind speed.Bug Fixes