def set_experiment_complete(experiment_id):
"""
"""
sql = f'''
UPDATE experiment
SET "status"='COMPLETED'
WHERE id='{experiment_id}'
'''
query(sql)
def set_experiment_failed(experiment_id):
"""
"""
sql = f'''
UPDATE experiment
SET "status"='STOPPED'
WHERE id='{experiment_id}'
'''
query(sql)
def remove_orphan_evals():
"""
removes evaluations that don't have an output
"""
sql = '''
DELETE FROM study_evaluation
WHERE "modelOutput" is NULL
'''
query(sql)
print('cleaned orphan evals')
def remove_orphan_studies():
"""
removes studies that don't have a type
"""
sql = '''
DELETE FROM study
WHERE type is NULL
'''
query(sql)
print('cleaned orphan studies')
def remove_study_by_id(orthanc_id: str):
"""
Removes a study from the db by its orthanc ID
Args:
orthanc_id (str): the study id of the orthanc study
"""
sql = f'''
DELETE FROM study
WHERE "orthancStudyId"='{orthanc_id}'
'''
query(sql)
def stop_all_models():
"""
sets a model as quickstarted
Args:
model_ids (List[int]): the models to mark as quick started
"""
sql = f'''
UPDATE model
SET "running"=false
'''
query(sql)
def fail_classifer(study_id: int):
"""
Updates study evalutation status to failed
Args:
eval_id: the db id of the failed evaluation
"""
sql = f'''
UPDATE study
SET failed=true
WHERE "orthancStudyId"='{study_id}'
'''
query(sql)
def mark_model_as_stopped(model_id):
"""
sets a model as quickstarted
Args:
model_ids (List[int]): the models to mark as quick started
"""
sql = f'''
UPDATE model
SET "running"=false
WHERE id = {model_id}
'''
query(sql)
def mark_models_as_quickstarted(model_ids):
"""
sets a model as quickstarted
Args:
model_ids (List[int]): the models to mark as quick started
"""
sql = f'''
UPDATE model
SET "running"=true
WHERE "id" in ({join_for_in_clause(model_ids)})
'''
query(sql)
def fail_eval(eval_id: int):
"""
Updates study evalutation status to failed
Args:
eval_id (int): the db id of the study evaluation
"""
sql = f'''
UPDATE study_evaluation
SET status='FAILED'
WHERE id={eval_id}
'''
query(sql)
def add_stdout_to_eval(eval_ids: List[int], lines: List[str]):
studies = get_study_evals(eval_ids)
stdout = []
if studies[0]['stdout'] is not None:
stdout = studies[0]['stdout']
stdout = stdout + lines
sql = f'''
UPDATE study_evaluation
SET stdout=(%s)
WHERE id in ({join_for_in_clause(eval_ids)})
'''
query(sql, json.dumps(stdout))
def test_query(self):
print("Running query test")
self.assertIsNotNone(self.table)
self.assertEqual(self.table.table_status, "ACTIVE")
response = db.query("email", "*****@*****.**", self.table)
self.assertEqual(response.get("Count"), 1)
self.assertEqual(
response.get("Items")[0].get("email"), "*****@*****.**")
def save_patient_id(patient_id: str, orthanc_id: str, modality: str,
study_uid: str, series_uid: str):
"""
Saves a patient id to the database for a study
Args:
patient_id (str): the patient id from orthanc
orthanc_id (str): the study id from orthanc
modality (str): the study id from orthanc
study_uid (str): the study uid from the dicom
"""
sql = f'''
UPDATE study
SET "patientId"='{patient_id}', modality='{modality}', "studyUid"='{study_uid}', "seriesUid"='{series_uid}'
WHERE "orthancStudyId"='{orthanc_id}'
'''
query(sql)
def save_study_type(orthanc_id: str, study_type: str) -> Dict:
"""
Saves a study to the database with it accompanying type
Args:
orthanc_id (str): the study ID from orthanc
study_type (str): the type of the study (e.g. Frontal_CXR)
Returns:
Dict: the inserted study
"""
sql = f'''
UPDATE study
SET type='{study_type}'
WHERE "orthancStudyId"='{orthanc_id}'
'''
query(sql)
def restart_failed_evals(eval_ids: List[int]):
"""
sets a failed evaluation to status 'RUNNING' to restart it
Args:
eval_ids (List[int]): a list of the ids of evals to be restarted
"""
if len(eval_ids) == 0:
return
# join ids by , so that it can be used in WHERE ... IN clause
ids = ','.join([str(eval_id) for eval_id in eval_ids])
sql = f'''
UPDATE study_evaluation
SET status='RUNNING'
WHERE id in ({ids})
'''
query(sql)
def update_eval_status_and_save(output: ModelOutput, eval_id: int):
"""
Updates study evalutation status to completed and saves the model output
Args:
output (ModelOutput): the output of the model
eval_id (int): the id of the eval to be update
"""
# checks output to see if it output an image and adds imgOutputPath to SQL string
update_sql_string = ''
if output['image']:
img_path = output['image']
update_sql_string = f', "imgOutputPath"=\'{img_path}\''
### set eval as completed and save model output as json
sql = f'''
UPDATE study_evaluation
SET status='COMPLETED', "modelOutput"=('{json.dumps(output)}') {update_sql_string}
WHERE id={eval_id}
'''
query(sql)
axes.set_axis_bgcolor('white')
#
axes.yaxis.label.set_size(22)
axes.xaxis.label.set_size(22)
city = "pisa"
dow = "wd"
hours = 5
# method = "krandomtraj"
# method = "fullrandtraj"
method = "distrandtraj"
query_tsd = queries.create_tsd_from_table("(select * from " + method + "." + city + "_" + str(hours) + "h_" + dow + " ) a")
query_tsd_original = queries.create_tsd(city, dow, hours)
dist_tsd = dist.compute_probability_distribution(db_utils.query(query_tsd), is_cum_sum=False)
dist_tsd_original = dist.compute_probability_distribution(db_utils.query(query_tsd_original), is_cum_sum=False)
print "TSD ",dist_tsd[0], dist_tsd[1]
print "TSD ORIGINAL", dist_tsd_original[0], dist_tsd_original[1]
plt.plot(dist_tsd[0], dist_tsd[1], label="Random", marker='s', markersize=5, alpha=alpha)
plt.plot(dist_tsd_original[0], dist_tsd_original[1], marker='3', markersize=5, label="Original", alpha=alpha)
plt.tick_params(axis='both', which='major', labelsize=24, colors="#000000")
plt.xlabel('x')
plt.ylabel('P')
leg = plt.legend(loc=1, prop={'size': 18})
leg.get_frame().set_facecolor("white")
#
axes.yaxis.label.set_size(22)
axes.xaxis.label.set_size(22)
city = "pisa"
dow = "wd"
hours = 5
# method = "krandomtraj"
# method = "fullrandtraj"
method = "distrandtraj"
query_tsd = queries.create_tsd_from_table("(select * from " + method + "." +
city + "_" + str(hours) + "h_" +
dow + " ) a")
query_tsd_original = queries.create_tsd(city, dow, hours)
dist_tsd = dist.compute_probability_distribution(db_utils.query(query_tsd),
is_cum_sum=False)
dist_tsd_original = dist.compute_probability_distribution(
db_utils.query(query_tsd_original), is_cum_sum=False)
print "TSD ", dist_tsd[0], dist_tsd[1]
print "TSD ORIGINAL", dist_tsd_original[0], dist_tsd_original[1]
plt.plot(dist_tsd[0],
dist_tsd[1],
label="Random",
marker='s',
markersize=5,
alpha=alpha)
plt.plot(dist_tsd_original[0],
dist_tsd_original[1],
def generate_random_trajectories(city, dow, hours):
print city, dow, hours
trajectory_result = dict()
place_list = db.query_places(city, dow, hours)
################################## QUERIES ##################################
tsd = queries.create_tsd(city, dow, hours)
tpu = queries.create_tpu(city, dow, hours)
################################## END QUERIES ##################################
# LOAD DATA
# Compute the distributions
n_users = db.get_number_of_users(city, dow, hours)
n_trajectories = db.get_number_of_trajectories(city, dow, hours)
max_trajectories_per_user = np.max(db.query(tpu)['key'])
max_trajectory_size = np.max(db.query(tsd)['key'])
tsd = queries.create_tsd(city, dow, hours)
tpu = queries.create_tpu(city, dow, hours)
trajectories_per_user_distribution = dist.compute_probability_distribution(
db.query(tpu))
trajectory_size_distribution = dist.compute_probability_distribution(
db.query(tsd))
print n_users
n_trajs_total = 0
u = 0
while u < n_users and n_trajs_total < n_trajectories:
# number of trajectories
print "%s/%s and %s/%s" % (u, n_users, n_trajs_total, n_trajectories)
user_trajectories = dist.random_from_probability(
trajectories_per_user_distribution)
# print 'User '+str(u) + ' with ' + str(n_trajectories)
trajectories_count = 0
today = datetime.datetime.fromtimestamp(time.time())
trajectory_result[u] = []
# create n trajectories
while trajectories_count < user_trajectories and n_trajs_total < n_trajectories:
# number of points
n_points = dist.random_from_probability(
trajectory_size_distribution)
print "Traj Size ", n_points
places_count = 0
traj = []
# pick n_points places
while places_count < n_points:
tomorrow = today + TIME_DELTA
place = get_next_place(place_list)
places = get_close_places(place, place_list)
traj.append((places, today, tomorrow))
places_count += 1
# go forward in time
today = tomorrow + TIME_DELTA
trajectories_count += 1
n_trajs_total += 1
trajectory_result[u].append(traj)
# user increment
u += 1
print "Total trajectories: %s(%s) " % (str(n_trajs_total), n_trajectories)
table = TABLE_SCHEMA + city + "_" + str(hours) + "h_" + dow
db.store_trajectories(trajectory_result, table)
def generate_random_trajectories(city, dow, hours):
print city, dow, hours
trajectory_result = dict()
place_list = db.query_places(city, dow, hours)
################################## QUERIES ##################################
tsd = queries.create_tsd(city, dow, hours)
tpu = queries.create_tpu(city, dow, hours)
# ted = queries.create_ted(city, dow, hours)
################################## END QUERIES ##################################
# LOAD DATA
# Compute the distributions
n_users = db.get_number_of_users(city, dow, hours)
trajectories_per_user_distribution = dist.compute_probability_distribution(db.query(tpu))
trajectory_size_distribution = dist.compute_probability_distribution(db.query(tsd))
print "Trajectory Size Distri ", trajectory_size_distribution
trajectory_extent_distribution = dist.compute_probability_density_function(db.query_trajectory_extent( city, dow, hours ))
print "Trajectory Extent Dist ", trajectory_extent_distribution
print "N of users:", n_users
n_trajs_total = 0
for u in range(0, n_users):
# number of trajectories
n_trajectories = dist.random_from_probability(trajectories_per_user_distribution)
print 'User ' + str(u) + ' with ' + str(n_trajectories)
trajectories_count = 0
trajectory_result[u] = []
# create n trajectories
# TODO melhorar a criacao das trajetorias para satisfazer a distribuicao de extent
while trajectories_count < n_trajectories:
today = datetime.datetime.fromtimestamp(time.time())
aux = dist.random_from_probability_2(trajectory_size_distribution, trajectory_extent_distribution)
n_points = aux[0]
extent = aux[1]
# # number of points
# n_points = dist.random_from_probability(trajectory_size_distribution)
#
# # extent of the trajectory
# extent = dist.random_from_probability(trajectory_extent_distribution)
# print "Generating Traj ", trajectories_count+1, extent
traj = generate_trajectory(n_points, extent, place_list, today)
trajectories_count += 1
n_trajs_total += 1
# print "Generated Traj ", utils.compute_trajectory_extent(traj)
trajectory_result[u].append(traj)
max_extent = np.max(trajectory_extent_distribution[0])
max_points = np.max(trajectory_size_distribution[0])
print max_extent
today = datetime.datetime.fromtimestamp(time.time())
traj = generate_trajectory(max_points, max_extent, place_list, today)
print "Generated Traj ", utils.compute_trajectory_extent(traj)
trajectory_result[n_users] = [traj]
n_trajs_total += 1
print "Total trajectories: " + str(n_trajs_total)
table = TABLE_SCHEMA + city + "_" + str(hours) +"h_" + dow
db.store_trajectories(trajectory_result, table)
def generate_random_trajectories(city, dow, hours):
print city, dow, hours
trajectory_result = dict()
place_list = db.query_places(city, dow, hours)
################################## QUERIES ##################################
tsd = queries.create_tsd(city, dow, hours)
tpu = queries.create_tpu(city, dow, hours)
################################## END QUERIES ##################################
# LOAD DATA
# Compute the distributions
n_users = db.get_number_of_users(city, dow, hours)
n_trajectories = db.get_number_of_trajectories(city, dow, hours)
max_trajectories_per_user = np.max(db.query(tpu)['key'])
max_trajectory_size = np.max(db.query(tsd)['key'])
tsd = queries.create_tsd(city, dow, hours)
tpu = queries.create_tpu(city, dow, hours)
trajectories_per_user_distribution = dist.compute_probability_distribution(db.query(tpu))
trajectory_size_distribution = dist.compute_probability_distribution(db.query(tsd))
print n_users
n_trajs_total = 0
u = 0
while u < n_users and n_trajs_total < n_trajectories:
# number of trajectories
print "%s/%s and %s/%s" % (u, n_users, n_trajs_total, n_trajectories)
user_trajectories = dist.random_from_probability(trajectories_per_user_distribution)
# print 'User '+str(u) + ' with ' + str(n_trajectories)
trajectories_count = 0
today = datetime.datetime.fromtimestamp(time.time())
trajectory_result[u] = []
# create n trajectories
while trajectories_count < user_trajectories and n_trajs_total < n_trajectories:
# number of points
n_points = dist.random_from_probability(trajectory_size_distribution)
print "Traj Size ", n_points
places_count = 0
traj = []
# pick n_points places
while places_count < n_points:
tomorrow = today + TIME_DELTA
place = get_next_place(place_list)
places = get_close_places(place, place_list)
traj.append((places, today, tomorrow))
places_count += 1
# go forward in time
today = tomorrow + TIME_DELTA
trajectories_count += 1
n_trajs_total += 1
trajectory_result[u].append(traj)
# user increment
u += 1
print "Total trajectories: %s(%s) " % (str(n_trajs_total), n_trajectories)
table = TABLE_SCHEMA+city+"_"+str(hours)+"h_"+dow
db.store_trajectories(trajectory_result, table)
