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lasair_zooniverse.py
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lasair_zooniverse.py
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import os
import time
import json
import wget
import lasair_consumer
import logging
import matplotlib
from pathlib import Path
from lasair_consumer import msgConsumer
from PIL import Image, ImageDraw
from lasair_zooniverse_base import lasair_zooniverse_base_class
# 3rd party imports
import matplotlib.pyplot as plt
import numpy as np
from panoptes_client import Panoptes, Project, SubjectSet, Subject, Workflow
from panstamps import __version__
from panstamps import cl_utils
from panstamps import utKit
from panstamps.downloader import downloader
class lasair_object:
def __init__(self):
'default constructor'
def __init__(self, objectId, ra, dec, stamp, lightcurve_plot):
self.objectId = objectId
self.ramean = ra
self.decmean = dec
self.stamp = stamp
self.lightcurve_plot = lightcurve_plot
self.Detections = []
def __str__(self):
print (self.objectId, self.ramean, self.decmean)
def get_objectId(msg):
msgString = msg.decode("utf-8")
msgDict = json.loads(msgString)
objectname = msgDict['objectId']
return objectname
def handle_object(objectId, L):
# from the objectId, we can get all the info that Lasair has
objectInfo = L.objects([objectId])[0]
if not objectInfo:
return None
#print(json.dumps(objectInfo['image_urls'], indent=2))
return objectInfo
class lasair_zooniverse_class(lasair_zooniverse_base_class):
def __init__(self, kafka_server, ENDPOINT, PANOPTES_APP_ID):
self.kafka_server = kafka_server
self.ENDPOINT = ENDPOINT
self.PANOPTES_APP_ID = PANOPTES_APP_ID,
self.log = logging.getLogger("lasair-zooniverse-logger")
def query_lasair_topic(self, group_id, topic):
c = msgConsumer(self.kafka_server, group_id)
c.subscribe(topic)
start = time.time()
objectIds = []
while 1:
msg = c.poll()
print(msg)
if msg == None:
break
else:
objectname = get_objectId(msg)
objectIds.append(objectname)
print('======= %.1f seconds =========' % ((time.time()-start)))
print('poll done')
#remove duplicates from the list
objectIds = list(set(objectIds))
return objectIds
def wget_objectdata(self, objectId, url, data_dir):
try:
url = url % (objectId)
dirpath = os.path.join(data_dir, time.strftime("%m-%d-%Y", time.gmtime()))
if not os.path.exists(dirpath):
os.makedirs(dirpath)
print(dirpath)
wget.download(url, os.path.join(dirpath, objectId + '.json'))
except Exception:
self.log.exception("Error in wget for object: " + objectId)
# 2022-11-29 KWS Added API equivalent of wget_objectdata above
def get_objectdata_via_api(self, objectId, data_dir, L):
# Initially let's do this as before and write the JSON to disk.
try:
dirpath = os.path.join(data_dir, 'pending')
if not os.path.exists(dirpath):
os.makedirs(dirpath)
print(dirpath)
obj = handle_object(objectId, L)
with open(dirpath + '/' + objectId + '.json', 'w') as fp:
json.dump(obj, fp)
except Exception as e:
self.log.exception("Error in API get for object: " + objectId)
def get_proto_subjects(self, data_dir):
proto_subjects = []
try:
dirpath = os.path.join(data_dir, 'pending')
if not os.path.exists(dirpath):
return proto_subjects
files = Path(dirpath).glob('*.json')
for file in files:
print(file.name)
f=open(file, "r")
data = json.load(f)
f.close()
lasair_zobject = self.create_lasair_object(data)
proto_subject = self.build_proto_subject(lasair_zobject, data_dir)
if (proto_subject != None):
proto_subjects.append(proto_subject)
return proto_subjects
except Exception as e:
self.log.exception("Error reading JSON object files from " + data_dir)
def produce_proto_subject(self, unique_id, data_dir):
# produce plots and gather metadata for each subject to be created
lasair_zobject = self.parse_object_data(unique_id, data_dir)
if(lasair_zobject != None):
return self.build_proto_subject(lasair_zobject, data_dir)
return None
def build_proto_subject(self, lasair_zobject, data_dir):
try:
light_curve, panstamps = self.build_plots(lasair_zobject, data_dir)
metadata = {
'objectId': lasair_zobject.objectId,
'ramean': lasair_zobject.ramean,
'decmean': lasair_zobject.decmean,
'ZTF_URL': 'https://lasair-ztf.lsst.ac.uk/objects/' + lasair_zobject.objectId
}
proto_subject = {}
proto_subject['location_lc'] = light_curve
proto_subject['location_ps'] = panstamps
proto_subject['metadata'] = metadata
return (proto_subject)
except Exception:
self.log.exception("Error in build_proto_subject for object: " + lasair_zobject.objectId)
return None
def create_subjects_and_link_to_project(self, proto_subjects, project_id, workflow_id, subject_set_id):
panoptes_client = Panoptes(
endpoint = self.ENDPOINT,
client_id = self.PANOPTES_APP_ID,
client_secret = os.getenv('PANOPTES_CLIENT_SECRET')
)
try:
with panoptes_client:
project = Project.find(project_id)
workflow = Workflow().find(workflow_id)
if subject_set_id == None:
subject_set = SubjectSet()
ts = time.gmtime()
subject_set.display_name = time.strftime("%Y-%m-%d %H:%M:%S", ts)
subject_set.links.project = project
subject_set.save()
else:
subject_set = SubjectSet().find(subject_set_id)
subjects = []
for proto_subject in proto_subjects:
subject = Subject()
subject.links.project = project
subject.add_location(proto_subject['location_lc'])
subject.add_location(proto_subject['location_ps'])
subject.metadata.update(proto_subject['metadata'])
subject.save()
subjects.append(subject)
subject_set.add(subjects)
workflow.add_subject_sets(subject_set)
except Exception:
self.log.exception("Error in create_subjects_and_link_to_project ")
def parse_object_data(self, objectId, data_dir):
try:
dirpath = os.path.join(data_dir, 'pending')
f=open(os.path.join(dirpath, objectId + '.json'), "r")
data = json.load(f)
f.close()
lo = self.create_lasair_object(data)
return lo
except Exception as e:
print(repr(e))
return None
def create_lasair_object(self, data):
lo = lasair_object(data['objectId'], 0,0,0,0)
for key, value in data.items():
if key == 'objectData':
objectData = value
for objData in objectData:
for dkey, dvalue in objectData.items():
if dkey == 'ramean':
lo.ramean = dvalue
elif dkey == 'decmean':
lo.decmean = dvalue
elif key == 'candidates':
candidates = value
print(len(candidates))
for candidate in candidates:
mjd = candidate['mjd']
fid = candidate['fid']
mag = candidate['magpsf']
try:
error = candidate['sigmapsf']
flag = True
except KeyError:
mag = candidate['diffmaglim']
flag = False
lo.Detections.append({'mjd':mjd, 'mag':mag, 'fid':fid, 'error':error, 'detect_flag': flag})
return lo
def gather_metadata(self, ramean, decmean, dirpath):
#logger = logging.getLogger("Panstamps")
fitsPaths, jpegPaths, colorPath = downloader(
log=logging.getLogger(__name__),
settings=False,
fits=False,
jpeg=True,
arcsecSize=75,
filterSet='gri',
color=True,
singleFilters=False,
ra=ramean,
dec=decmean,
imageType="stack",
downloadDirectory=dirpath,
mjdStart=False,
mjdEnd=False,
window=False
).get()
return colorPath
def build_plots(self, lasair_object, data_dir):
mjd_red = []
mag_red = []
yerr_red = []
mjd_red_limit = []
mag_red_limit = []
mjd_blue = []
mag_blue = []
yerr_blue = []
mjd_blue_limit = []
mag_blue_limit = []
mjd_first = np.inf
print(len(lasair_object.Detections))
for detection in lasair_object.Detections:
print(detection)
if detection['detect_flag'] == True:
if detection['mjd'] < mjd_first:
mjd_first = detection['mjd']
if detection['fid'] == 2:
mjd_red.append(detection['mjd'])
mag_red.append(detection['mag'])
yerr_red.append(detection['error'])
elif detection['fid'] == 1:
mjd_blue.append(detection['mjd'])
mag_blue.append(detection['mag'])
yerr_blue.append(detection['error'])
elif detection['detect_flag'] == False:
if detection['fid'] == 2:
mjd_red_limit.append(detection['mjd'])
mag_red_limit.append(detection['mag'])
elif detection['fid'] == 1:
mjd_blue_limit.append(detection['mjd'])
mag_blue_limit.append(detection['mag'])
dirpath = os.path.join(data_dir, time.strftime("%m-%d-%Y", time.gmtime()))
if not os.path.exists(dirpath):
os.makedirs(dirpath)
font = {'family' : 'sans-serif',
'size' : 22}
matplotlib.rc('font', **font)
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
ax.errorbar(np.array(mjd_red) - mjd_first,
mag_red,
yerr=yerr_red,
marker='o',
markersize=10,
color='#D1495B',
ls='none')
ax.errorbar(np.array(mjd_blue) - mjd_first,
mag_blue,
yerr=yerr_blue,
marker='D',
markersize=10,
color='#26547C',
ls='none')
ax.scatter(np.array(mjd_red_limit) - mjd_first,
mag_red_limit,
marker='v',
s=100,
color='#DE7C89')
ax.scatter(np.array(mjd_blue_limit) - mjd_first,
mag_blue_limit,
marker='v',
s=100,
color='#4489C5')
ax.set_xlabel('Days since First Detection')
ax.set_ylabel('Brightness')
plt.grid()
plt.gca().invert_yaxis()
plt.savefig(os.path.join(dirpath, "%s_light_curve.jpeg"%(lasair_object.objectId)))
#now get the panstamps image
colorPath = self.gather_metadata(lasair_object.ramean,lasair_object.decmean, dirpath)
#put crosshairs on the panstamps image
self.draw_crosshairs(lasair_object.ramean,lasair_object.decmean, colorPath)
light_curve = os.path.join(dirpath, "%s_light_curve.jpeg"%(lasair_object.objectId))
plots = dict({ 'light_curve': light_curve, 'panstamps': colorPath })
return light_curve, colorPath[0]
def draw_crosshairs(self, ramean, decmean, colorPath):
im = Image.open(colorPath[0], mode='r')
# DETERMINE THE SIZE OF THE IMAGE
imWidth, imHeight = im.size
# THE CROSS HAIRS SHOULD BE 1/6 THE LENGTH OF THE SMALLEST DIMENSON
chLen = int(min(imWidth, imHeight) / 6)
# THE GAP IN THE CENTRE SHOULD BE 1/60 OF THE LENGTH OF THE SMALLEST DIMENSON
gapLen = int(min(imWidth, imHeight) / 60)
# LINE WIDTH SHOULD BE EASILY VIEWABLE AT ALL SIZES - 0.2% OF THE WIDTH SEEMS GOOD
# SEEMS FINE
lineWidth = int(max(imWidth, imHeight) / 300)
lines = []
l = (imWidth / 2 - gapLen - chLen, imHeight /
2, imWidth / 2 - gapLen, imHeight / 2)
lines.append(l)
l = (imWidth / 2 + gapLen, imHeight /
2, imWidth / 2 + gapLen + chLen, imHeight / 2)
lines.append(l)
l = (imWidth / 2, imHeight /
2 - gapLen - chLen, imWidth / 2, imHeight / 2 - gapLen)
lines.append(l)
l = (imWidth / 2, imHeight /
2 + gapLen, imWidth / 2, imHeight / 2 + gapLen + chLen)
lines.append(l)
# GENERATE THE DRAW OBJECT AND DRAW THE CROSSHAIRS
draw = ImageDraw.Draw(im)
draw.line(l, fill='#00ff00', width=lineWidth)
for l in lines:
draw.line(l, fill='#00ff00', width=lineWidth)
del draw
im.thumbnail((300, 300), Image.LANCZOS)
im.save(colorPath[0], "JPEG")