Merge pull request #160 from BIDMCDigitalPsychiatry/add-text-features

Added messages_usage text features

Author: carlan1

cortex/secondary/call_number.py

@@ -56,8 +56,7 @@ def call_number(call_direction="all", **kwargs):
 
     else:
         number = None
-        log.info(""" %s was passed but is not an acceptable argument.
-        Acceptable arguments include 'all','incoming', or 'outgoing'" """,
-                 call_direction)
+        raise Exception(f"{call_direction} is not a proper argument. "
+                        + "Must be incoming, outgoing, or all")
 
     return {'timestamp': kwargs['start'], 'value': number}

cortex/secondary/text_degree.py

@@ -0,0 +1,41 @@
+""" Module text_degree from raw feature messages_usage """
+import numpy as np
+
+from ..feature_types import secondary_feature, log
+from ..raw.messages_usage import messages_usage
+
+MS_IN_A_DAY = 86400000
+
+
+@secondary_feature(
+    name='cortex.feature.text_degree',
+    dependencies=[messages_usage]
+)
+def text_degree(**kwargs):
+    """The number of unique phone numbers a participant texted.
+
+    Args:
+        **kwargs:
+            id (string): The participant's LAMP id. Required.
+            start (int): The initial UNIX timestamp (in ms) of the window for
+                which the feature is being generated. Required.
+            end (int): The last UNIX timestamp (in ms) of the window for
+                which the feature is being generated. Required.
+
+    Returns:
+        A dict consisting:
+            timestamp (int): The beginning of the window (same as
+            kwargs['start']).
+            value (float): Number of unique phone numbers texted.
+    """
+    _texts = messages_usage(**kwargs)['data']
+    
+    if len(_texts) == 0:
+        unique_contacts =  None
+        return {'timestamp': kwargs['start'], 'value': unique_contacts} 
+    else:
+        unique_contacts = np.sum(text['totalUniqueContacts'] for text in _texts)
+    
+    return {'timestamp': kwargs['start'], 'value': unique_contacts}
+
+

cortex/secondary/text_number.py

@@ -0,0 +1,50 @@
+""" Module for text_number from raw feature messages_usage """
+import numpy as np
+from ..feature_types import secondary_feature, log
+from ..raw.messages_usage import messages_usage
+
+MS_IN_A_DAY = 86400000
+@secondary_feature(
+    name='cortex.feature.text_number',
+    dependencies=[messages_usage]
+)
+def text_number(text_direction="all", **kwargs):
+    """Returns the number of texts made.
+
+    Args:
+        text_direction (string): Returns all texts if "all",
+            returns received texts if "incoming", returns
+            sent texts if "outgoing".
+        **kwargs:
+            id (string): The participant's LAMP id. Required.
+            start (int): The initial UNIX timestamp (in ms) of the window
+                for which the feature is being generated. Required.
+            end (int): The last UNIX timestamp (in ms) of the window
+                for which the feature is being generated. Required.
+
+    Returns:
+        A dict consisting:
+            timestamp (int): The beginning of the window
+            (same as kwargs['start']).
+            value (float): The number of texts.
+    """
+    _texts = messages_usage(**kwargs)['data']
+
+    incoming_text_count = np.sum(text['totalIncomingMessages'] for text in _texts)
+    outgoing_text_count = np.sum(text['totalOutgoingMessages'] for text in _texts)
+    
+    if len(_texts) == 0:
+        number = None
+        return {'timestamp': kwargs['start'], 'value': number}
+    elif text_direction == "all":
+        number = incoming_text_count + outgoing_text_count
+    elif text_direction == "incoming":
+        number = incoming_text_count
+    elif text_direction == "outgoing":
+        number = outgoing_text_count
+    else:
+        number = None
+        raise Exception(f"{text_direction} is not a proper argument. "
+                        + "Must be incoming, outgoing, or all")
+
+    return {'timestamp': kwargs['start'], 'value': number}