# Apache OCW lib immports
from ocw.dataset import Dataset, Bounds
import ocw.data_source.local as local
import ocw.data_source.rcmed as rcmed
import ocw.dataset_processor as dsp
import ocw.evaluation as evaluation
import ocw.metrics as metrics
import ocw.plotter as plotter
import ocw.utils as utils

import datetime
import numpy as np
import numpy.ma as ma

from os import path
import urllib
import ssl
if hasattr(ssl, '_create_unverified_context'):
    ssl._create_default_https_context = ssl._create_unverified_context

# File URL leader
FILE_LEADER = "http://zipper.jpl.nasa.gov/dist/"
# Three Local Model Files
FILE_1 = "AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc"
FILE_2 = "AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc"
FILE_3 = "AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc"
# Filename for the output image/plot (without file extension)
OUTPUT_PLOT = "portrait_diagram"

# Spatial and temporal configurations
LAT_MIN = -45.0
LAT_MAX = 42.24
LON_MIN = -24.0
LON_MAX = 60.0
START = datetime.datetime(2000, 01, 1)
END = datetime.datetime(2007, 12, 31)
EVAL_BOUNDS = Bounds(LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)

# variable that we are analyzing
varName = 'pr'

# regridding parameters
gridLonStep = 0.5
gridLatStep = 0.5

# some vars for this evaluation
target_datasets_ensemble = []
target_datasets = []
allNames = []

# Download necessary NetCDF file if not present
if not path.exists(FILE_1):
    urllib.urlretrieve(FILE_LEADER + FILE_1, FILE_1)

if not path.exists(FILE_2):
    urllib.urlretrieve(FILE_LEADER + FILE_2, FILE_2)

if not path.exists(FILE_3):
    urllib.urlretrieve(FILE_LEADER + FILE_3, FILE_3)

""" Step 1: Load Local NetCDF File into OCW Dataset Objects and store in list"""
target_datasets.append(local.load_file(FILE_1, varName, name="KNMI"))
target_datasets.append(local.load_file(FILE_2, varName, name="REGCM"))
target_datasets.append(local.load_file(FILE_3, varName, name="UCT"))

""" Step 2: Fetch an OCW Dataset Object from the data_source.rcmed module """
print("Working with the rcmed interface to get CRU3.1 Monthly Mean Precipitation")
# the dataset_id and the parameter id were determined from
# https://rcmes.jpl.nasa.gov/content/data-rcmes-database
CRU31 = rcmed.parameter_dataset(
    10, 37, LAT_MIN, LAT_MAX, LON_MIN, LON_MAX, START, END)

""" Step 3: Processing Datasets so they are the same shape """
print("Processing datasets ...")
CRU31 = dsp.normalize_dataset_datetimes(CRU31, 'monthly')
print("... on units")
CRU31 = dsp.water_flux_unit_conversion(CRU31)

for member, each_target_dataset in enumerate(target_datasets):
    target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)
    target_datasets[member] = dsp.water_flux_unit_conversion(target_datasets[
                                                             member])
    target_datasets[member] = dsp.normalize_dataset_datetimes(
        target_datasets[member], 'monthly')

print("... spatial regridding")
new_lats = np.arange(LAT_MIN, LAT_MAX, gridLatStep)
new_lons = np.arange(LON_MIN, LON_MAX, gridLonStep)
CRU31 = dsp.spatial_regrid(CRU31, new_lats, new_lons)

for member, each_target_dataset in enumerate(target_datasets):
    target_datasets[member] = dsp.spatial_regrid(
        target_datasets[member], new_lats, new_lons)

# find the total annual mean. Note the function exists in util.py as def
# calc_climatology_year(dataset):
_, CRU31.values = utils.calc_climatology_year(CRU31)

for member, each_target_dataset in enumerate(target_datasets):
    _, target_datasets[member].values = utils.calc_climatology_year(target_datasets[
                                                                    member])

# make the model ensemble
target_datasets_ensemble = dsp.ensemble(target_datasets)
target_datasets_ensemble.name = "ENS"

# append to the target_datasets for final analysis
target_datasets.append(target_datasets_ensemble)

for target in target_datasets:
    allNames.append(target.name)

list_of_regions = [
    Bounds(-10.0, 0.0, 29.0, 36.5),
    Bounds(0.0, 10.0,  29.0, 37.5),
    Bounds(10.0, 20.0, 25.0, 32.5),
    Bounds(20.0, 33.0, 25.0, 32.5),
    Bounds(-19.3, -10.2, 12.0, 20.0),
    Bounds(15.0, 30.0, 15.0, 25.0),
    Bounds(-10.0, 10.0, 7.3, 15.0),
    Bounds(-10.9, 10.0, 5.0, 7.3),
    Bounds(33.9, 40.0,  6.9, 15.0),
    Bounds(10.0, 25.0,  0.0, 10.0),
    Bounds(10.0, 25.0, -10.0,  0.0),
    Bounds(30.0, 40.0, -15.0,  0.0),
    Bounds(33.0, 40.0, 25.0, 35.00)]

region_list = ["R" + str(i + 1) for i in xrange(13)]

# metrics
pattern_correlation = metrics.PatternCorrelation()

# create the Evaluation object
RCMs_to_CRU_evaluation = evaluation.Evaluation(CRU31,  # Reference dataset for the evaluation
                                               # 1 or more target datasets for
                                               # the evaluation
                                               target_datasets,
                                               # 1 or more metrics to use in
                                               # the evaluation
                                               [pattern_correlation],
                                               # list of subregion Bounds
                                               # Objects
                                               list_of_regions)
RCMs_to_CRU_evaluation.run()

new_patcor = np.squeeze(np.array(RCMs_to_CRU_evaluation.results), axis=1)

plotter.draw_portrait_diagram(np.transpose(
    new_patcor), allNames, region_list, fname=OUTPUT_PLOT, fmt='png', cmap='coolwarm_r')