Revert "Start support for phone_keyboard"

This reverts commit dd95b4f941.

Snakefile

@@ -100,15 +100,6 @@ for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
 
-for provider in config["PHONE_KEYBOARD"]["PROVIDERS"].keys():
-    if config["PHONE_KEYBOARD"]["PROVIDERS"][provider]["COMPUTE"]:
-        files_to_compute.extend(expand("data/raw/{pid}/phone_keyboard_raw.csv", pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/raw/{pid}/phone_keyboard_with_datetime.csv", pid=config["PIDS"]))
-        files_to_compute.extend(expand("data/interim/{pid}/phone_keyboard_features/phone_keyboard_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_KEYBOARD"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
-        files_to_compute.extend(expand("data/processed/features/{pid}/phone_keyboard.csv", pid=config["PIDS"],))
-        files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
-        files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-
 for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys():
     if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]:
         files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"]))

config.yaml

@@ -189,15 +189,6 @@ PHONE_DATA_YIELD:
       SRC_LANGUAGE: "r"
       SRC_FOLDER: "rapids" # inside src/features/phone_data_yield
 
-PHONE_KEYBOARD:
-  TABLE: keyboard
-  PROVIDERS:
-    RAPIDS:
-      COMPUTE: False
-      FEATURES: []
-      SRC_FOLDER: "rapids" # inside src/features/phone_keyboard
-      SRC_LANGUAGE: "python"
-
 # See https://www.rapids.science/latest/features/phone-light/
 PHONE_LIGHT:
   TABLE: light

rules/features.smk

@@ -262,32 +262,6 @@ rule phone_light_r_features:
     script:
         "../src/features/entry.R"
 
-rule phone_keyboard_python_features:
-    input:
-        sensor_data = "data/raw/{pid}/phone_keyboard_with_datetime.csv",
-        time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
-    params:
-        provider = lambda wildcards: config["PHONE_KEYBOARD"]["PROVIDERS"][wildcards.provider_key.upper()],
-        provider_key = "{provider_key}",
-        sensor_key = "phone_keyboard"
-    output:
-        "data/interim/{pid}/phone_keyboard_features/phone_keyboard_python_{provider_key}.csv"
-    script:
-        "../src/features/entry.py"
-
-rule phone_keyboard_r_features:
-    input:
-        sensor_data = "data/raw/{pid}/phone_keyboard_with_datetime.csv",
-        time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
-    params:
-        provider = lambda wildcards: config["PHONE_KEYBOARD"]["PROVIDERS"][wildcards.provider_key.upper()],
-        provider_key = "{provider_key}",
-        sensor_key = "phone_keyboard"
-    output:
-        "data/interim/{pid}/phone_keyboard_features/phone_keyboard_r_{provider_key}.csv"
-    script:
-        "../src/features/entry.R"
-
 rule phone_locations_python_features:
     input:
         sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv",

src/features/phone_keyboard/rapids/main.py

@@ -1,40 +0,0 @@
-import pandas as pd
-import numpy as np
-
-def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-
-    # I copied this from light, modify it to make it work for keyboard
-
-    light_data = pd.read_csv(sensor_data_files["sensor_data"])
-    print(light_data)
-    raise ValueError("Test")
-    requested_features = provider["FEATURES"]
-    # name of the features this function can compute
-    base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
-    # the subset of requested features this function can compute
-    features_to_compute = list(set(requested_features) & set(base_features_names))
-
-    light_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
-    if not light_data.empty:
-        light_data = filter_data_by_segment(light_data, time_segment)
-        
-        if not light_data.empty:
-            light_features = pd.DataFrame()
-            if "count" in features_to_compute:
-                light_features["count"] = light_data.groupby(["local_segment"]).count()["timestamp"]
-            
-            # get light ambient luminance related features
-            if "maxlux" in features_to_compute:
-                light_features["maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max()
-            if "minlux" in features_to_compute:
-                light_features["minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min()
-            if "avglux" in features_to_compute:
-                light_features["avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean()
-            if "medianlux" in features_to_compute:
-                light_features["medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median()
-            if "stdlux" in features_to_compute:
-                light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std()
-            
-            light_features = light_features.reset_index()
-
-    return light_features