import numpy as np
import lsst.sims.featureScheduler as fs
from lsst.sims.speedObservatory import Speed_observatory
import matplotlib.pylab as plt
import healpy as hp
import time
import matplotlib.pylab as plt
from drive_cadence import Cadence_enhance_basis_function


survey_length = 365.25*10  # days
nside = fs.set_default_nside(nside=32)
years = np.round(survey_length/365.25)
t0 = time.time()

target_map = fs.standard_goals(nside=nside)
norm_factor = fs.calc_norm_factor(target_map)

# Set up a map of where to drive the cadence
cadence_area = target_map['r'] * 0
cadence_area[np.where(target_map['r'] == 1)] = 1.
# hp.mollview(cadence_area, title='Where to drive cadence')

# set up a cloud map
cloud_map = target_map['r']*0 + 0.7


# Set up observations to be taken in blocks
surveys = []
filter1s = ['u', 'g', 'r', 'i', 'z', 'y']
filter2s = [None, 'r', 'i', 'g', None, None]
pair_surveys = []
for filtername, filtername2 in zip(filter1s, filter2s):
    bfs = []
    bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))
    bfs.append(fs.Target_map_basis_function(filtername=filtername,
                                            target_map=target_map[filtername],
                                            out_of_bounds_val=hp.UNSEEN, nside=nside,
                                            norm_factor=norm_factor))
    if filtername2 is not None:
        bfs.append(fs.Target_map_basis_function(filtername=filtername2,
                                                target_map=target_map[filtername2],
                                                out_of_bounds_val=hp.UNSEEN, nside=nside,
                                                norm_factor=norm_factor))
    bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))
    bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
    bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=60., max_alt=76.))
    bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., zenith_pad=20.,
                                                   nside=nside))
    bfs.append(fs.Quadrant_basis_function(quadrants=['N', 'E', 'S'], azWidth=90.))
    bfs.append(fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
    bfs.append(fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))
    weights = np.array([0., 0.3, 0.3, 3., 1., 0., 0., 0., 0., 0.])
    if filtername2 is None:
        # Need to scale weights up so filter balancing still works properly.
        weights = np.array([0., 0.6, 3., 1., 0., 0., 0., 0., 0.])

    if filtername2 is None:
        survey_name = 'blob, %s' % filtername
    else:
        survey_name = 'blob, %s%s' % (filtername, filtername2)
    surveys.append(fs.Blob_survey(bfs, weights, filtername=filtername,
                                  filter2=filtername2,
                                  survey_note=survey_name, az_range=180.,
                                  search_radius=90, ignore_obs='DD'))
    pair_surveys.append(surveys[-1])


#filters = ['u', 'g', 'r', 'i', 'z', 'y']
filters = ['y']
greedy_surveys = []
for filtername in filters:
    bfs = []
    bfs.append(fs.M5_diff_basis_function(filtername=filtername, nside=nside))
    bfs.append(fs.Target_map_basis_function(filtername=filtername,
                                            target_map=target_map[filtername],
                                            out_of_bounds_val=hp.UNSEEN, nside=nside,
                                            norm_factor=norm_factor))

    bfs.append(fs.North_south_patch_basis_function(zenith_min_alt=50., nside=nside))
    bfs.append(fs.Slewtime_basis_function(filtername=filtername, nside=nside))
    bfs.append(fs.Strict_filter_basis_function(filtername=filtername))
    bfs.append(fs.Zenith_shadow_mask_basis_function(nside=nside, shadow_minutes=0., max_alt=76.))
    bfs.append(fs.Moon_avoidance_basis_function(nside=nside, moon_distance=40.))
    bfs.append(fs.Bulk_cloud_basis_function(max_cloud_map=cloud_map, nside=nside))
    weights = np.array([3.0, 0.3, 1., 3., 3., 0, 0., 0.])
    # Might want to try ignoring DD observations here, so the DD area gets covered normally--DONE
    surveys.append(fs.Greedy_survey_fields(bfs, weights, block_size=1, filtername=filtername,
                                           dither=True, nside=nside, ignore_obs='DD'))
    greedy_surveys.append(surveys[-1])


# Set up the DD surveys
dd_surveys = fs.generate_dd_surveys()
surveys.extend(dd_surveys)

survey_list_o_lists = [dd_surveys, pair_surveys, greedy_surveys]

scheduler = fs.Core_scheduler(survey_list_o_lists, nside=nside)
n_visit_limit = None
observatory = Speed_observatory(nside=nside, quickTest=True)
observatory, scheduler, observations = fs.sim_runner(observatory, scheduler,
                                                     survey_length=survey_length,
                                                     filename='tight_mask_simple_%iyrs.db' % years,
                                                     delete_past=True, n_visit_limit=n_visit_limit)
t1 = time.time()
delta_t = t1-t0
print('ran in %.1f min = %.1f hours' % (delta_t/60., delta_t/3600.))