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tweak
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@@ -453,8 +453,12 @@ async def skip_initial_filter_to_code(config, filter_id):
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return cg.new_Pvariable(filter_id, config)
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return cg.new_Pvariable(filter_id, config)
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@FILTER_REGISTRY.register("min", MinFilter, MIN_SCHEMA)
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def _create_sliding_window_filter(config, filter_id, sliding_class, streaming_class):
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async def min_filter_to_code(config, filter_id):
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"""Helper to create sliding window or streaming filter based on config.
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When window_size == send_every, use streaming filter (O(1) memory).
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Otherwise, use sliding window filter (O(n) memory).
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"""
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window_size = config[CONF_WINDOW_SIZE]
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window_size = config[CONF_WINDOW_SIZE]
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send_every = config[CONF_SEND_EVERY]
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send_every = config[CONF_SEND_EVERY]
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send_first_at = config[CONF_SEND_FIRST_AT]
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send_first_at = config[CONF_SEND_FIRST_AT]
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@@ -462,17 +466,14 @@ async def min_filter_to_code(config, filter_id):
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# Optimization: Use streaming filter for batch windows (window_size == send_every)
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# Optimization: Use streaming filter for batch windows (window_size == send_every)
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# Saves 99.98% memory for large windows (e.g., 20KB → 4 bytes for window_size=5000)
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# Saves 99.98% memory for large windows (e.g., 20KB → 4 bytes for window_size=5000)
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if window_size == send_every:
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if window_size == send_every:
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return cg.new_Pvariable(
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return cg.new_Pvariable(filter_id, streaming_class, window_size, send_first_at)
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filter_id,
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return cg.new_Pvariable(filter_id, window_size, send_every, send_first_at)
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StreamingMinFilter,
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window_size,
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send_first_at,
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@FILTER_REGISTRY.register("min", MinFilter, MIN_SCHEMA)
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)
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async def min_filter_to_code(config, filter_id):
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return cg.new_Pvariable(
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return _create_sliding_window_filter(
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filter_id,
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config, filter_id, MinFilter, StreamingMinFilter
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config[CONF_WINDOW_SIZE],
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config[CONF_SEND_EVERY],
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config[CONF_SEND_FIRST_AT],
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)
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)
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@@ -490,24 +491,8 @@ MAX_SCHEMA = cv.All(
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@FILTER_REGISTRY.register("max", MaxFilter, MAX_SCHEMA)
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@FILTER_REGISTRY.register("max", MaxFilter, MAX_SCHEMA)
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async def max_filter_to_code(config, filter_id):
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async def max_filter_to_code(config, filter_id):
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window_size = config[CONF_WINDOW_SIZE]
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return _create_sliding_window_filter(
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send_every = config[CONF_SEND_EVERY]
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config, filter_id, MaxFilter, StreamingMaxFilter
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send_first_at = config[CONF_SEND_FIRST_AT]
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# Optimization: Use streaming filter for batch windows (window_size == send_every)
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# Saves 99.98% memory for large windows (e.g., 20KB → 4 bytes for window_size=5000)
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if window_size == send_every:
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return cg.new_Pvariable(
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filter_id,
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StreamingMaxFilter,
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window_size,
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send_first_at,
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)
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return cg.new_Pvariable(
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filter_id,
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config[CONF_WINDOW_SIZE],
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config[CONF_SEND_EVERY],
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config[CONF_SEND_FIRST_AT],
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)
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)
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@@ -529,24 +514,11 @@ SLIDING_AVERAGE_SCHEMA = cv.All(
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SLIDING_AVERAGE_SCHEMA,
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SLIDING_AVERAGE_SCHEMA,
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)
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)
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async def sliding_window_moving_average_filter_to_code(config, filter_id):
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async def sliding_window_moving_average_filter_to_code(config, filter_id):
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window_size = config[CONF_WINDOW_SIZE]
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return _create_sliding_window_filter(
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send_every = config[CONF_SEND_EVERY]
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config,
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send_first_at = config[CONF_SEND_FIRST_AT]
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# Optimization: Use streaming filter for batch windows (window_size == send_every)
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# Saves 99.94% memory for large windows (e.g., 20KB → 12 bytes for window_size=5000)
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if window_size == send_every:
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return cg.new_Pvariable(
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filter_id,
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StreamingMovingAverageFilter,
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window_size,
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send_first_at,
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)
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return cg.new_Pvariable(
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filter_id,
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filter_id,
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config[CONF_WINDOW_SIZE],
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SlidingWindowMovingAverageFilter,
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config[CONF_SEND_EVERY],
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StreamingMovingAverageFilter,
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config[CONF_SEND_FIRST_AT],
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)
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)
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