From f95e7875e8f30f539b2853739c16a82f2700e8e3 Mon Sep 17 00:00:00 2001 From: Zachary Clement Date: Fri, 15 Sep 2023 11:21:54 -0400 Subject: [PATCH] add indegree and outdegree for calls OR texts --- forest/willow/log_stats.py | 69 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) diff --git a/forest/willow/log_stats.py b/forest/willow/log_stats.py index 1418871b..2ec4a502 100644 --- a/forest/willow/log_stats.py +++ b/forest/willow/log_stats.py @@ -140,6 +140,67 @@ def text_analysis( ) +def text_and_call_analysis(df_call: pd.DataFrame, df_text: pd.DataFrame, stamp: int, step_size: int) -> tuple: + """Calculate the summary statistics for the call data + in the given time interval. + + Args: + df_call: pd.DataFrame + dataframe of the call data + df_text: pd.DataFrame + dataframe of the text data + stamp: int + starting timestamp of the study + step_size: int + ending timestamp of the study + + Returns: + tuple of summary statistics containing: + num_uniq_in_call_or_text: int + number of people making incoming calls or texts to the Beiwe + user + num_uniq_out_call_or_text: int + number of people receiving outgoing calls or texts from the + Beiwe user + + """ + # filter the data based on the timestamp + if df_call.shape > 0 + temp_call = df_call[ + (df_call["timestamp"] / 1000 >= stamp) + & (df_call["timestamp"] / 1000 < stamp + step_size) + ] + index_in_call = np.array(temp_call["call type"]) == "Incoming Call" + index_out_call = np.array(temp_call["call type"]) == "Outgoing Call" + calls_in = np.array(temp_call["hashed phone number"])[index_in_call] + calls_out = np.array(temp_call["hashed phone number"])[index_out_call] + else: ## no calls were received, so no unique numbers will be used + calls_in = np.array([]) + calls_out = np.array([]) + + if df_text.shape > 0: + temp_text = df_text[ + (df_text["timestamp"] / 1000 >= stamp) + & (df_text["timestamp"] / 1000 < stamp + step_size) + ] + + index_s = np.array(temp_text["sent vs received"]) == "sent SMS" + index_r = np.array(temp_text["sent vs received"]) == "received SMS" + texts_in = np.array(temp_text["hashed phone number"])[index_r] + texts_out = np.array(temp_text["hashed phone number"])[index_s] + else: ## no texts were received, so no unique numbers will be used + texts_in = np.array([]) + texts_out = np.array([]) + + num_uniq_in_call_or_text = len(np.unique(np.hstack(calls_in, texts_in))) + num_uniq_out_call_or_text = len(np.unique(np.hstack(texts_out, calls_out))) + + return ( + num_uniq_in_call_or_text, + num_uniq_out_call_or_text + ) + + def call_analysis(df_call: pd.DataFrame, stamp: int, step_size: int) -> tuple: """Calculate the summary statistics for the call data in the given time interval. @@ -288,6 +349,11 @@ def comm_logs_summaries( newline += list(call_stats) else: newline += [pd.NA] * 8 + if df_text.shape[0] > 0 or df_call.shape[0] > 0: + text_and_call_stats = text_and_call_analysis(df_text, df_call, stamp, step_size, frequency) + newline += list(text_and_call_stats) + else: + newline += [pd.NA] * 2 if df_text.shape[0] > 0: text_stats = text_analysis(df_text, stamp, step_size, frequency) @@ -295,6 +361,7 @@ def comm_logs_summaries( else: newline += [pd.NA] * 10 + if frequency == Frequency.DAILY: newline = [year, month, day] + newline else: @@ -311,6 +378,8 @@ def comm_logs_summaries( "num_mis_caller", "total_mins_in_call", "total_mins_out_call", + "num_uniq_in_call_or_text", + "num_uniq_out_call_or_text", "num_s", "num_r", "num_mms_s",