def query_count(QY_dataset, start_time, end_time, params={}):
"""
Count the number of queries which started in the given time window.
Args:
QY_dataset: (Dataset) the query dataset
start_time: (pd.Timestamp) the start time of the window
end_time: (pd.Timestamp) the ends time of the window
params: (dict) optional parameters
Returns:
a Dataset of counts
"""
if QY_dataset.raw:
logging.error("Data must be preprocessed before counts")
raise ValueError
time_range = Time_Range(start_time, end_time)
if time_range.start_column != "start_date":
logging.error("Query data can only be binned by date")
raise ValueError
in_range = QY_dataset.get_data_from_time_range(time_range)
if "query_type" in params:
query_type = params["query_type"]
if query_type == "m":
in_type_bool = in_range.QueryRecipient != "System to Site"
in_type = in_range[in_type_bool]
query_type_name_prefix = "manual_"
elif query_type == "a":
in_type_bool = in_range.QueryRecipient == "System to Site"
in_type = in_range[in_type_bool]
query_type_name_prefix = "automatic_"
else:
in_type = in_range
else:
query_type_name_prefix = ""
in_type = in_range
site_counts = in_type.groupby("Site")["STUDYID"].count()
if "site_list" in params:
site_counts = site_counts.reindex(params["site_list"], fill_value=0)
site_count_frame = series_to_frame(site_counts, time_range,
query_type_name_prefix + "query_count")
out = Dataset(
dataset=site_count_frame,
params={
"count": True,
"raw": False,
"time_format": time_range.type
},
)
return out
def lab_count(LB_dataset, start_time, end_time, params={}):
"""
Count the number of labs which started in the given time window.
Args:
LB_dataset: (Dataset) the labs dataset
start_time: (int, pd.Timestamp) the start time of the window
end_time: (int, pd.Timestamp) the ends time of the window
params: (dict) optional parameters
Returns:
a Dataset of counts
"""
if LB_dataset.raw:
logging.error("Data must be preprocessed before counts")
raise ValueError
time_range = Time_Range(start_time, end_time)
in_range = LB_dataset.get_data_from_time_range(time_range)
if "lab_type" in params:
lab_type = params["lab_type"]
if lab_type == "u":
unsched_bool = in_range.VISITNUM.apply(lambda x: x % 1) != 0
in_type = in_range[unsched_bool]
lab_type_name_prefix = "unscheduled_"
elif lab_type == "m":
miss_bool = in_range.LBSTAT == "NOT DONE"
in_type = in_range[miss_bool]
lab_type_name_prefix = "missed_"
else:
in_type = in_range
else:
lab_type_name_prefix = ""
in_type = in_range
site_counts = in_type.groupby("Site")["STUDYID"].count()
if "site_list" in params:
site_counts = site_counts.reindex(params["site_list"], fill_value=0)
site_count_frame = series_to_frame(site_counts, time_range,
lab_type_name_prefix + "lab_count")
out = Dataset(
dataset=site_count_frame,
params={
"count": True,
"raw": False,
"time_format": time_range.type
},
)
return out
def hanging_query_count(QY_dataset, start_time, end_time, params={}):
"""
Count the number of hanging queries which started in the given time window.
Args:
QY_dataset: (Dataset) the query dataset
start_time: (pd.Timestamp) the start time of the window
end_time: (pd.Timestamp) the ends time of the window
params: (dict) optional parameters
Returns:
a Dataset of counts
"""
if QY_dataset.raw:
logging.error("Data must be preprocessed before counts")
raise ValueError
time_range = Time_Range(start_time, end_time)
if time_range.start_column != "start_date":
logging.error("Query data can only be binned by date")
raise ValueError
left_bool = QY_dataset.dataset["OpenDate"] <= time_range.start_time
right_bool = QY_dataset.dataset["CloseDate"] > time_range.end_time
in_range = QY_dataset.dataset[left_bool & right_bool]
site_counts = in_range.groupby("Site")["STUDYID"].count()
if "site_list" in params:
site_counts = site_counts.reindex(params["site_list"], fill_value=0)
site_count_frame = series_to_frame(site_counts, time_range,
"hanging_query_count")
out = Dataset(
dataset=site_count_frame,
params={
"count": True,
"raw": False,
"time_format": time_range.type
},
)
return out
def missed_dose_count(EX_dataset, start_time, end_time, params={}):
"""
Count the number of missed doses which started in the given time window.
Args:
EX_dataset: (Dataset) the exposure dataset
start_time: (int, pd.Timestamp) the start time of the window
end_time: (int, pd.Timestamp) the ends time of the window
params: (dict) optional parameters
Returns:
a Dataset of counts
"""
if EX_dataset.raw:
logging.error("Data must be preprocessed before counts")
raise ValueError
time_range = Time_Range(start_time, end_time)
in_range = EX_dataset.get_data_from_time_range(time_range)
in_type_bool = in_range["EXDOSE"] == 0
in_type = in_range[in_type_bool]
site_counts = in_type.groupby("Site")["STUDYID"].count()
site_counts = site_counts.reindex(EX_dataset.get_site_array(),
fill_value=0)
site_count_frame = series_to_frame(site_counts, time_range,
"missed_dose_count")
out = Dataset(
dataset=site_count_frame,
params={
"count": True,
"raw": False,
"time_format": time_range.type
},
)
return out
def dosage_variance(EX_dataset, start_time, end_time, params={}):
"""
Compute the number of dosage variance per site in the given time window.
Args:
EX_dataset: (Dataset) the exposure dataset for deriving subject counts
start_time: (int, pd.Timestamp) the start time of the window
end_time: (int, pd.Timestamp) the ends time of the window
params: (dict) optional parameters
Returns:
a Dataset of dosage variances
"""
if EX_dataset.raw:
logging.error("Data must be preprocessed before counts")
raise ValueError
time_range = Time_Range(start_time, end_time)
in_range = EX_dataset.get_data_from_time_range(time_range)
patient_var_group = in_range.groupby(["Site", "USUBJID"])
patient_vars = patient_var_group.EXDOSE.agg("std").fillna(0)
site_vars = patient_vars.groupby("Site").agg("mean")
site_vars = site_vars.reindex(EX_dataset.get_site_array(), fill_value=0)
site_var_frame = series_to_frame(site_vars, time_range, "dosage_variance")
out = Dataset(
dataset=site_var_frame,
params={
"count": True,
"raw": False,
"time_format": time_range.type
},
)
return out
def query_response_time(QY_dataset, start_time, end_time, params={}):
"""
Compute the query response time in the given time window.
Args:
QY_dataset: (Dataset) the query dataset
start_time: (pd.Timestamp) the start time of the window
end_time: (pd.Timestamp) the ends time of the window
params: (dict) optional parameters
Returns:
a list of Datasets of response times
"""
if QY_dataset.raw:
logging.error("Data must be preprocessed before counts")
raise ValueError
time_range = Time_Range(start_time, end_time)
if time_range.start_column != "start_date":
logging.error("Query data can only be binned by date")
raise ValueError
in_range = QY_dataset.get_data_from_time_range(time_range)
if "query_type" in params:
query_type = params["query_type"]
if query_type == "m":
in_type_bool = in_range.QueryRecipient != "System to Site"
in_type = in_range[in_type_bool]
query_type_name_prefix = "manual_"
elif query_type == "a":
in_type_bool = in_range.QueryRecipient == "System to Site"
in_type = in_range[in_type_bool]
query_type_name_prefix = "automatic_"
else:
in_type = in_range
else:
query_type_name_prefix = ""
in_type = in_range
closed_queries_bool = in_type.CloseDate.notna()
in_type = in_type[closed_queries_bool]
in_type["Response_Time"] = in_type["CloseDate"] - in_type["OpenDate"]
in_type["Response_Time_Hours"] = in_type["Response_Time"].apply(
lambda x: float(x.seconds / 3600))
if not in_type.empty:
site_means = in_type.groupby("Site")["Response_Time_Hours"].mean()
if "site_list" in params:
site_means = site_means.reindex(params["site_list"],
fill_value=np.nan)
site_mean_frame = series_to_frame(
site_means, time_range,
query_type_name_prefix + "query_response_time")
out = Dataset(
dataset=site_mean_frame.dropna(),
params={
"count": True,
"raw": False,
"time_format": time_range.type
},
)
return out
def adverse_event_count(AE_dataset, start_time, end_time, params={}):
"""
Count the number of adverse events which started in the given time window.
Args:
AE_dataset: (Dataset) the adverse events dataset
start_time: (int, pd.Timestamp) the start time of the window
end_time: (int, pd.Timestamp) the ends time of the window
params: (dict) optional parameters
Returns:
a Dataset of counts
"""
if AE_dataset.raw:
logging.error("Data must be preprocessed before counts")
raise ValueError
time_range = Time_Range(start_time, end_time)
in_range = AE_dataset.get_data_from_time_range(time_range)
if "adverse_event_type" in params:
adverse_event_type = params["adverse_event_type"]
if adverse_event_type == "s":
ser_bool = in_range.AESER == "Y"
in_type = in_range[ser_bool]
adverse_event_name_prefix = "serious_"
elif adverse_event_type == "r":
rel_bool = in_range.AEREL == "RELATED"
in_type = in_range[rel_bool]
adverse_event_name_prefix = "related_"
elif adverse_event_type == "sr":
ser_bool = in_range.AESER == "Y"
rel_bool = in_range.AEREL == "RELATED"
both_bool = ser_bool & rel_bool
in_type = in_range[both_bool]
adverse_event_name_prefix = "serious_related_"
else:
in_type = in_range
else:
adverse_event_name_prefix = ""
in_type = in_range
site_counts = in_type.groupby("Site")["STUDYID"].count()
if "site_list" in params:
site_counts = site_counts.reindex(params["site_list"], fill_value=0)
site_count_frame = series_to_frame(
site_counts, time_range,
adverse_event_name_prefix + "adverse_event_count")
out = Dataset(
dataset=site_count_frame,
params={
"count": True,
"raw": False,
"time_format": time_range.type
},
)
return out