from planobs.plan import PlanObservation

name = "testalert" # Name of the alert object
date = "2020-05-05" #This is optional, defaults to today
ra = 133.7
dec = 13.37

plan = PlanObservation(name=name, date=date, ra=ra, dec=dec)
plan.plot_target() # Plots the observing conditions
plan.request_ztf_fields() # Checks in which ZTF fields this 
# object is observable and generates plots for them.

from planobs.plan import PlanObservation

name = "IC201007A" # Name of the alert object
date = "2020-10-08" #This is optional, defaults to today

# No RA and Dec values are given, because we set alertsource to icecube, which leads to automatic GCN parsing.

plan = PlanObservation(name=name, date=date, alertsource="icecube")
plan.plot_target() # Plots the observing conditions.
plan.request_ztf_fields() # Checks which ZTF fields cover the target (and have references).
print(plan.recommended_field) # This give you the field with the most overlap.

from planobs.api import Queue

q = Queue(user="yourname")

existing_too_requests = get_too_queues(names_only=True)
print(existing_too_requests)

from planobs.api import Queue, get_too_queues
from planobs.models import TooTarget

trigger_name = "ToO_IC220513A_test"

# Instantiate the API connection
q = Queue(user="yourname")

# Add a trigger to the internal submission queue. Filter ID is 1 for r-, 2 for g- and 3 for i-band. Exposure time is given in seconds.
q.add_trigger_to_queue(
    trigger_name=trigger_name,
    validity_window_start_mjd=59719.309333333334,
    targets=[
        TooTarget(
            field_id=427,
            filter_id=1,
            exposure_time=300,
        )
    ],
)

q.submit_queue()

# Now we verify that our trigger has been successfully submitted
existing_too_requests = get_too_queues(names_only=True)
print(existing_too_requests)
assert trigger_name in existing_too_requests

from planobs.api import Queue

q = Queue(user="yourname")

trigger_name = "ToO_IC220513A_test"

res = q.delete_trigger(trigger_name=trigger_name)