Migrate light to new file structure
JulioV
parent
eda58d9d2c
commit
e643881058
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@ -83,10 +83,12 @@ if config["SCREEN"]["COMPUTE"]:
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files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"]))
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if config["LIGHT"]["COMPUTE"]:
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for provider in config["LIGHT"]["PROVIDERS"].keys():
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if config["LIGHT"]["PROVIDERS"][provider]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"]))
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files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"]))
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files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower()))
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if config["ACCELEROMETER"]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"]))
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@ -136,10 +136,14 @@ SCREEN:
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EPISODE_TYPES: ["unlock"]
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LIGHT:
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COMPUTE: False
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DB_TABLE: light
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DAY_SEGMENTS: *day_segments
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PROVIDERS:
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RAPIDS:
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COMPUTE: TRUE
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FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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SRC_FOLDER: "rapids" # inside src/features/light
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SRC_LANGUAGE: "python"
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ACCELEROMETER:
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COMPUTE: False
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@ -173,16 +173,29 @@ rule screen_features:
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script:
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"../src/features/screen_features.py"
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rule light_features:
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rule light_r_features:
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input:
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expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]),
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sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]),
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day_segments_labels = "data/interim/day_segments_labels.csv"
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params:
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day_segment = "{day_segment}",
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features = config["LIGHT"]["FEATURES"],
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provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key],
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provider_key = "{provider_key}"
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output:
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"data/processed/{pid}/light_{day_segment}.csv"
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"data/interim/{pid}/light_features/light_r_{provider_key}.csv"
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script:
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"../src/features/light_features.py"
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"../src/features/light/light_entry.R"
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rule light_python_features:
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input:
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sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]),
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day_segments_labels = "data/interim/day_segments_labels.csv"
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params:
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provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key],
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provider_key = "{provider_key}"
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output:
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"data/interim/{pid}/light_features/light_python_{provider_key}.csv"
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script:
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"../src/features/light/light_entry.py"
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rule conversation_features:
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input:
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@ -1,34 +0,0 @@
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import pandas as pd
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import numpy as np
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def base_light_features(light_data, day_segment, requested_features):
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# name of the features this function can compute
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base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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light_features = pd.DataFrame(columns=["local_date"] + ["light_" + day_segment + "_" + x for x in features_to_compute])
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if not light_data.empty:
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if day_segment != "daily":
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light_data =light_data[light_data["local_day_segment"] == day_segment]
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if not light_data.empty:
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light_features = pd.DataFrame()
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if "count" in features_to_compute:
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light_features["light_" + day_segment + "_count"] = light_data.groupby(["local_date"]).count()["timestamp"]
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# get light ambient luminance related features
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if "maxlux" in features_to_compute:
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light_features["light_" + day_segment + "_maxlux"] = light_data.groupby(["local_date"])["double_light_lux"].max()
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if "minlux" in features_to_compute:
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light_features["light_" + day_segment + "_minlux"] = light_data.groupby(["local_date"])["double_light_lux"].min()
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if "avglux" in features_to_compute:
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light_features["light_" + day_segment + "_avglux"] = light_data.groupby(["local_date"])["double_light_lux"].mean()
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if "medianlux" in features_to_compute:
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light_features["light_" + day_segment + "_medianlux"] = light_data.groupby(["local_date"])["double_light_lux"].median()
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if "stdlux" in features_to_compute:
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light_features["light_" + day_segment + "_stdlux"] = light_data.groupby(["local_date"])["double_light_lux"].std().fillna('NA')
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light_features = light_features.reset_index()
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return light_features
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@ -0,0 +1,13 @@
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source("renv/activate.R")
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source("src/features/utils/utils.R")
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library("dplyr")
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library("tidyr")
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sensor_data_file <- snakemake@input[["sensor_data"]]
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day_segments_file <- snakemake@input[["day_segments_labels"]]
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provider <- snakemake@params["provider"][["provider"]]
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provider_key <- snakemake@params["provider_key"]
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sensor_features <- fetch_provider_features(provider, provider_key, "light", sensor_data_file, day_segments_file)
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write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)
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@ -0,0 +1,18 @@
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import pandas as pd
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from importlib import import_module, util
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from pathlib import Path
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# import fetch_provider_features from src/features/utils/utils.py
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spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
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mod = util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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fetch_provider_features = getattr(mod, "fetch_provider_features")
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sensor_data_file = snakemake.input["sensor_data"][0]
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day_segments_file = snakemake.input["day_segments_labels"]
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provider = snakemake.params["provider"]
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provider_key = snakemake.params["provider_key"]
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sensor_features = fetch_provider_features(provider, provider_key, "light", sensor_data_file, day_segments_file)
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sensor_features.to_csv(snakemake.output[0], index=False)
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@ -0,0 +1,34 @@
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import pandas as pd
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import numpy as np
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def rapids_features(light_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
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requested_features = provider["FEATURES"]
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# name of the features this function can compute
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base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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light_features = pd.DataFrame(columns=["local_segment"] + ["light_rapids_" + "_" + x for x in features_to_compute])
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if not light_data.empty:
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light_data = filter_data_by_segment(light_data, day_segment)
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if not light_data.empty:
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light_features = pd.DataFrame()
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if "count" in features_to_compute:
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light_features["light_rapids_" + "_count"] = light_data.groupby(["local_segment"]).count()["timestamp"]
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# get light ambient luminance related features
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if "maxlux" in features_to_compute:
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light_features["light_rapids_" + "_maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max()
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if "minlux" in features_to_compute:
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light_features["light_rapids_" + "_minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min()
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if "avglux" in features_to_compute:
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light_features["light_rapids_" + "_avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean()
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if "medianlux" in features_to_compute:
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light_features["light_rapids_" + "_medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median()
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if "stdlux" in features_to_compute:
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light_features["light_rapids_" + "_stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std().fillna('NA')
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light_features = light_features.reset_index()
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return light_features
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