def test_get_visits():
udb.set_host("staging")
# get N visits
visit_df = udb.get_visits(n=13)
assert len(visit_df) == 13
# get specific visits:
test_visit_ids = [
'1b3cf7c8-e45c-41f3-87c3-c416c78f6305',
'be6b4716-db1b-42bc-b614-7982f4614deb',
'90a88ed5-0bf5-4ffd-b46c-91720d868cef',
'98315020-68a4-46e6-b07f-599acf0c507d',
'8e0444d4-9b86-47b4-a02c-d0df06005441',
'837edd81-4b82-4111-920b-46e2e2726aeb',
'fa65a8a0-425f-4cc4-9c75-a873627248c1',
'bded41a8-d478-4f96-9706-30a833e431b1',
'52929e2f-fa57-4e5a-a8aa-c2b890f4c200',
'971a1e47-dc29-48dd-b6fd-6ee3172fcb5c',
'20f61c59-0312-4cd5-b4c3-df34b5cfe204',
'cfd1f60b-5b3d-442b-a115-33b5023389c8',
'417a7d6e-75c7-4211-be0c-4d07a0f30258'
]
test_subject_ids = [
'66999214-048e-4a41-a51b-4e8197c2e668',
'66999214-048e-4a41-a51b-4e8197c2e668',
'66999214-048e-4a41-a51b-4e8197c2e668',
'66999214-048e-4a41-a51b-4e8197c2e668',
'66999214-048e-4a41-a51b-4e8197c2e668',
'66999214-048e-4a41-a51b-4e8197c2e668',
'66999214-048e-4a41-a51b-4e8197c2e668',
'66999214-048e-4a41-a51b-4e8197c2e668',
'b1d62c65-c6bb-4839-ac57-59ab4ea73fc7',
'66999214-048e-4a41-a51b-4e8197c2e668',
'66999214-048e-4a41-a51b-4e8197c2e668',
'675bcea5-2bbd-4cdd-b2b3-abae53b33a80',
'66999214-048e-4a41-a51b-4e8197c2e668'
]
visit_df = udb.get_visits(visit_id=test_visit_ids)
assert list(visit_df.visit_id.values) == test_visit_ids
assert list(visit_df.subject_id.values) == test_subject_ids
def test_get_visit_exams():
# test the various elements needed for app.py
print("test loading visits")
visit_id = "4347d97f-cd56-4865-a505-3506246e9ed7"
expected_exams = [
'TrailMaking2', 'CategoryFluency', 'LetterFluency', 'TrailMaking',
'Prosaccade'
]
visit_exams_df = udb.get_visit_exams(udb.get_visits(visit_id=visit_id))
# check that all 7 visit_exams get returned
assert len(visit_exams_df) == 5
# check that they are the correct ones
assert all([w in expected_exams for w in visit_exams_df['exam']])
def concatenate_history(subj_series):
"""
Get all the visit_exams for a specific user & exam combination
:param subj_series:
:return:
"""
# todo: check get_visits is working with user_id here
visits = udb.get_visits(subject_id=subj_series['subject_id'])
visit_exams_df = udb.get_visit_exams(visits)
visit_exams_df.dropna(subset=['exam'], inplace=True)
filter_exam = visit_exams_df.exam == subj_series['exam']
visit_exams_df = visit_exams_df[filter_exam]
return visit_exams_df
def get_visit_exams_per_user(one_per_user=True):
"""
Get the most recent exam of each type per user
:return:
"""
visit_exams_df = utils_db.get_visit_exams(utils_db.get_visits(n=np.inf))
# remove anyone who is a test subject
test_subjects = utils_db.get_test_subjects()
visit_exams_df = visit_exams_df[visit_exams_df['subject_id'].apply(
lambda w: w not in test_subjects)]
# pick just the most recent exam for each visit exam type for each subject
# sort the exams by date
visit_exams_df = visit_exams_df.sort_values('created_date_visit', axis=0)
# jut pick the last exam that each user took
if one_per_user:
visit_exams_df = visit_exams_df.groupby(['subject_id',
'exam']).last().reset_index()
return visit_exams_df
def concatenate_control(subj_series,
n=CONTROL_SUBJECT_QTY,
year_range=20,
max_controls=False):
"""
:param subj_series: the series for the visit_exam we are looking to get controls for
:param n: maximum number of controls
:param year_range: +- year range around which control subjects will be taken
:param max_controls: True if there are no restrictions on who is a control - except that it not be the subject
:return:
"""
visits = udb.get_visits(n=np.inf)
# todo: test getting a single exam in the query
visit_exams_df = udb.get_visit_exams(visits)
visit_exams_df.dropna(subset=['exam'], inplace=True)
visit_exams_df.dropna(subset=['exam_version'], inplace=True)
# just the visit_exams with our exam
filter_exam = visit_exams_df.exam == subj_series.exam
# just the visit_exams *not including any from our subject
filter_username = np.invert(
visit_exams_df.subject_id == subj_series.subject_id)
# do the filter step
visit_exams_df = visit_exams_df[filter_exam & filter_username]
if max_controls:
control_df = visit_exams_df
else:
if (subj_series['date_of_birth'] is None) or (subj_series['gender'] is
None):
# do not return any controls until we know the person's date of birth
control_df = pd.DataFrame()
logger.warning("missing DOB or Gender for: " +
str(subj_series['subject_id']))
else:
visit_exams_df.dropna(subset=['date_of_birth'], inplace=True)
visit_exams_df.dropna(subset=['gender'], inplace=True)
# filter
filter_gender = visit_exams_df.gender == subj_series.gender
# calculate age of subj_series and filter based on year_range
subj_series_age = subj_series['created_date_visit'] - subj_series[
'date_of_birth']
visit_exams_df['age'] = visit_exams_df[
'created_date_visit'] - visit_exams_df['date_of_birth']
filter_age = (visit_exams_df.age > subj_series_age - datetime.timedelta(days=year_range * 365.25)) & \
(visit_exams_df.age < subj_series_age + datetime.timedelta(days=year_range * 365.25))
visit_exams_df = visit_exams_df[filter_gender & filter_age]
# drop the age column once we're done using so nothing farther downstream breaks
visit_exams_df.drop(['age'], axis=1, inplace=True)
control_df = visit_exams_df.iloc[:n]
control_and_subject_df = control_df.append(subj_series)
return control_and_subject_df
def get_processed_df_for_visit(visit_id, params={}):
visit_exams_df = udb.get_visit_exams(udb.get_visits(visit_id=visit_id))
processed_df = udb.load_visit_exams_processed(visit_exams_df, params)
return processed_df
def test_load_processed():
print("testing load processed")
test_begin_time = datetime.datetime.now(datetime.timezone.utc)
visit_id = "6feeef57-4047-4c2d-b5f1-7e02f60a0188"
# todo: add each exam as it is finished
visit_exams_df = udb.get_visit_exams(udb.get_visits(visit_id=visit_id))
# filter out any we know haven't passed the test yet
# todo: remove this filter because everything should pass the test
exams_to_test = [
'TrailMaking', 'TrailMaking2', 'CategoryFluency', 'LetterFluency'
] #, 'Prosaccade']
visit_exams_df = visit_exams_df[visit_exams_df['exam'].apply(
lambda w: w in exams_to_test)]
# note: host is AWS so that this doesn't leave a mess
params = {
'videos': False,
'host': 'aws',
'control_subj_quantity': 2,
'exams': exams_to_test,
'overwrite': True
}
print("Test recomputing results")
processed_df = udb.load_visit_exams_processed(visit_exams_df, params)
# Check that all the status passed
for idx, subj_series in processed_df.iterrows():
# check that each exam
assert 'has_error' in subj_series, "missing error status field"
assert subj_series[
'has_error'] == False, "error occured in: " + subj_series['exam']
assert 'has_error' in subj_series[
'processed'], "missing status element in exam: " + subj_series[
'exam']
# check that the status is true
assert subj_series['processed']['has_error'] == False, "processing has error in exam: " + subj_series['exam'] +\
", has_error: " + subj_series['processed']['has_error']
# test that the processed files have all been modified by that process
for idx, subj_series in processed_df.iterrows():
response = udb.load_s3_object(udb.get_processed_path(subj_series))
assert response[
'LastModified'] > test_begin_time, "overwrite: True, processed file was not modified when it should have been for exam: " + subj_series[
'exam']
# tests below measure a few cases of the overwrite_now function
# check that the files don't get reprocessed again if param is set to false
test_begin_time = datetime.datetime.now(datetime.timezone.utc)
params['overwrite'] = False
processed_df2 = udb.load_visit_exams_processed(visit_exams_df, params)
for idx, subj_series in processed_df2.iterrows():
response = udb.load_s3_object(udb.get_processed_path(subj_series))
assert response[
'LastModified'] < test_begin_time, "overwrite:'False' processed file was modified when it shouldn't have been for exam: " + subj_series[
'exam']
# test batch overwrite: the files don't get reprocessed again for a long-running batch
params['overwrite'] = 'batch'
# pretend the batch started 2 hours ago
test_begin_time = datetime.datetime.now(datetime.timezone.utc)
params['batch_begin_time'] = test_begin_time - datetime.timedelta(hours=2)
processed_df2 = udb.load_visit_exams_processed(visit_exams_df, params)
for idx, subj_series in processed_df2.iterrows():
response = udb.load_s3_object(udb.get_processed_path(subj_series))
assert response[
'LastModified'] < test_begin_time, "overwrite:'batch' processed file was modified when it shouldn't have been for exam: " + subj_series[
'exam']
# Test batch overwrite: the files *do* get reprocessed if older than when the batch started
params['batch_begin_time'] = datetime.datetime.now(datetime.timezone.utc)
processed_df2 = udb.load_visit_exams_processed(visit_exams_df, params)
for idx, subj_series in processed_df2.iterrows():
response = udb.load_s3_object(udb.get_processed_path(subj_series))
assert response['LastModified'] > params[
'batch_begin_time'], "overwrite:'batch' processed file was not modified when it should have been for exam: " + subj_series[
'exam']
def test_processing_functions():
"""
test results of the processing functions
:return:
"""
visit_id = "6feeef57-4047-4c2d-b5f1-7e02f60a0188"
# todo: add each exam as it is finished
exams_to_test = [
'TrailMaking2', 'CategoryFluency', 'LetterFluency', 'TrailMaking'
] #, 'Prosaccade']
# note: assumption is that the processing was already re-done in the earlier test function.
params = {
'videos': False,
'host': 'aws',
'control_subj_quantity': 2,
'exams': exams_to_test,
'overwrite': False
}
visit_exams_df = udb.get_visit_exams(udb.get_visits(visit_id=visit_id))
processed_df = udb.load_visit_exams_processed(visit_exams_df, params)
# filter out any we know haven't passed the test yet
# todo: remove this filter because everything should pass the test
processed_df = processed_df[processed_df['exam'].apply(
lambda w: w in exams_to_test)]
for idx, subj_series in processed_df.iterrows():
if subj_series['exam'] == 'TrailMaking':
assert subj_series['processed']['metrics'][
'error_count'] == 13, "trailmaking produced wrong error count"
assert subj_series['processed']['metrics'][
'repeat_count'] == 14, "trailmaking produced wrong repeat count"
assert subj_series['processed'][
'active time'] == 22.0552, "trailmaking produced wrong active time"
elif subj_series['exam'] == 'TrailMaking2':
assert subj_series['processed']['metrics'][
'error_count'] == 16, "trailmaking2 produced wrong error count"
assert subj_series['processed']['metrics'][
'repeat_count'] == 11, "trailmaking2 produced wrong repeat count"
assert subj_series['processed'][
'active time'] == 19.5775, "trailmaking2 produced wrong active time"
elif subj_series['exam'] == 'CategoryFluency':
assert subj_series['processed']['data']['responses'].iloc[0][
'transcript'] == 'cucumbers', "transcript first word incorrect"
assert subj_series['processed']['data']['responses'].iloc[1][
'transcript'] == 'carrots', "transcript second word incorrect"
assert subj_series['processed']['data']['responses'].iloc[2][
'transcript'] == 'celery', "transcript second word incorrect"
assert subj_series['processed']['metrics'][
'num_correct'] == 6, "number correct wrong"
elif subj_series['exam'] == 'LetterFluency':
assert subj_series['processed']['data']['responses'].iloc[0][
'transcript'] == 'apples'
assert subj_series['processed']['data']['responses'].iloc[1][
'transcript'] == 'asparagus'
assert subj_series['processed']['data']['responses'].iloc[2][
'transcript'] == 'australia'
assert subj_series['processed']['metrics']['num_correct'] == 4