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This commit is contained in:
J. Nick Koston
2025-10-15 19:28:51 -10:00
parent 589c25e65a
commit 92d54ffb09
1 changed files with 29 additions and 31 deletions
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58
esphome/components/sensor/__init__.py
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@@ -453,30 +453,19 @@ async def skip_initial_filter_to_code(config, filter_id):
return cg.new_Pvariable(filter_id, config) return cg.new_Pvariable(filter_id, config)
def _create_sliding_window_filter(config, filter_id, sliding_class, streaming_class): @FILTER_REGISTRY.register("min", MinFilter, MIN_SCHEMA)
"""Helper to create sliding window or streaming filter based on config. async def min_filter_to_code(config, filter_id):
window_size: int = config[CONF_WINDOW_SIZE]
When window_size == send_every, use streaming filter (O(1) memory). send_every: int = config[CONF_SEND_EVERY]
Otherwise, use sliding window filter (O(n) memory). send_first_at: int = config[CONF_SEND_FIRST_AT]
"""
window_size = config[CONF_WINDOW_SIZE]
send_every = config[CONF_SEND_EVERY]
send_first_at = config[CONF_SEND_FIRST_AT]
# Optimization: Use streaming filter for batch windows (window_size == send_every) # Optimization: Use streaming filter for batch windows (window_size == send_every)
# Saves 99.98% memory for large windows (e.g., 20KB → 4 bytes for window_size=5000) # Saves 99.98% memory for large windows (e.g., 20KB → 4 bytes for window_size=5000)
if window_size == send_every: if window_size == send_every:
return cg.new_Pvariable(filter_id, streaming_class, window_size, send_first_at) # Use streaming filter - O(1) memory instead of O(n)
return cg.new_Pvariable( return cg.Pvariable(filter_id, StreamingMinFilter, window_size, send_first_at)
filter_id, sliding_class, window_size, send_every, send_first_at # Use sliding window filter - maintains ring buffer
) return cg.new_Pvariable(filter_id, window_size, send_every, send_first_at)
@FILTER_REGISTRY.register("min", Filter, MIN_SCHEMA)
async def min_filter_to_code(config, filter_id):
return _create_sliding_window_filter(
config, filter_id, MinFilter, StreamingMinFilter
)
MAX_SCHEMA = cv.All( MAX_SCHEMA = cv.All(
@@ -491,11 +480,16 @@ MAX_SCHEMA = cv.All(
) )
@FILTER_REGISTRY.register("max", Filter, MAX_SCHEMA) @FILTER_REGISTRY.register("max", MaxFilter, MAX_SCHEMA)
async def max_filter_to_code(config, filter_id): async def max_filter_to_code(config, filter_id):
return _create_sliding_window_filter( window_size: int = config[CONF_WINDOW_SIZE]
config, filter_id, MaxFilter, StreamingMaxFilter send_every: int = config[CONF_SEND_EVERY]
) send_first_at: int = config[CONF_SEND_FIRST_AT]
# Optimization: Use streaming filter for batch windows (window_size == send_every)
if window_size == send_every:
return cg.Pvariable(filter_id, StreamingMaxFilter, window_size, send_first_at)
return cg.new_Pvariable(filter_id, window_size, send_every, send_first_at)
SLIDING_AVERAGE_SCHEMA = cv.All( SLIDING_AVERAGE_SCHEMA = cv.All(
@@ -512,16 +506,20 @@ SLIDING_AVERAGE_SCHEMA = cv.All(
@FILTER_REGISTRY.register( @FILTER_REGISTRY.register(
"sliding_window_moving_average", "sliding_window_moving_average",
Filter, SlidingWindowMovingAverageFilter,
SLIDING_AVERAGE_SCHEMA, SLIDING_AVERAGE_SCHEMA,
) )
async def sliding_window_moving_average_filter_to_code(config, filter_id): async def sliding_window_moving_average_filter_to_code(config, filter_id):
return _create_sliding_window_filter( window_size: int = config[CONF_WINDOW_SIZE]
config, send_every: int = config[CONF_SEND_EVERY]
filter_id, send_first_at: int = config[CONF_SEND_FIRST_AT]
SlidingWindowMovingAverageFilter,
StreamingMovingAverageFilter, # Optimization: Use streaming filter for batch windows (window_size == send_every)
if window_size == send_every:
return cg.Pvariable(
filter_id, StreamingMovingAverageFilter, window_size, send_first_at
) )
return cg.new_Pvariable(filter_id, window_size, send_every, send_first_at)
EXPONENTIAL_AVERAGE_SCHEMA = cv.All( EXPONENTIAL_AVERAGE_SCHEMA = cv.All(