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"""
    multi_model_taylor_diagram.py

    Use OCW to download, normalize and evaluate three datasets
    against a reference dataset and OCW standard metrics
    drawing a Taylor diagram of the results of the evaluation.

    In this example:

    1. Download three netCDF files from a local site.
        AFRICA_KNMI-RACMO2.2b_CTL_ERAINT_MM_50km_1989-2008_pr.nc
        AFRICA_ICTP-REGCM3_CTL_ERAINT_MM_50km-rg_1989-2008_pr.nc
        AFRICA_UCT-PRECIS_CTL_ERAINT_MM_50km_1989-2008_pr.nc
    2. Load the local files into OCW dataset objects.
    3. Process each dataset to the same same shape.
        a.) Restrict the datasets re: geographic and time boundaries.
        b.) Temporally rebin the data (monthly).
        c.) Spatially regrid each dataset.
    4.  Extract the metrics used for the evaluation and evaluate
        against a reference dataset and standard OCW metrics.
    5.  Draw evaluation results Taylor diagram.

    OCW modules demonstrated:

    1. datasource/local
    2. dataset
    3. dataset_processor
    4. evaluation
    5. metrics
    6. plotter
    7. utils

"""
from __future__ import print_function

import datetime
import ssl
import sys
from os import path

import numpy as np

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
from ocw.dataset import Bounds

if sys.version_info[0] >= 3:
    from urllib.request import urlretrieve
else:
    # Not Python 3 - today, it is most likely to be Python 2
    # But note that this might need an update when Python 4
    # might be around one day
    from urllib import urlretrieve

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 = "pr_africa_taylor"

# Spatial and temporal configurations
LAT_MIN = -45.0
LAT_MAX = 42.24
LON_MIN = -24.0
LON_MAX = 60.0
START = datetime.datetime(2000, 1, 1)
END = datetime.datetime(2007, 12, 31)
EVAL_BOUNDS = Bounds(lat_min=LAT_MIN, lat_max=LAT_MAX,
                     lon_min=LON_MIN, lon_max=LON_MAX, start=START, end=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 = []
ref_datasets = []

# Download necessary NetCDF file if not present
if not path.exists(FILE_1):
    urlretrieve(FILE_LEADER + FILE_1, FILE_1)

if not path.exists(FILE_2):
    urlretrieve(FILE_LEADER + FILE_2, FILE_2)

if not path.exists(FILE_3):
    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="REGM3"))
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: Resample Datasets so they are the same shape.
print("Resampling datasets ...")
print("... on units")
CRU31 = dsp.water_flux_unit_conversion(CRU31)
print("... temporal")
CRU31 = dsp.temporal_rebin(CRU31, temporal_resolution='monthly')

for member, each_target_dataset in enumerate(target_datasets):
    target_datasets[member] =\
        dsp.water_flux_unit_conversion(target_datasets[member])
    target_datasets[member] =\
        dsp.temporal_rebin(target_datasets[member], temporal_resolution='monthly')
    target_datasets[member] = dsp.subset(target_datasets[member], EVAL_BOUNDS)

# Regrid
print("... regrid")
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 mean values
# way to get the mean. Note the function exists in util.py as def
# calc_climatology_year(dataset):
CRU31.values = utils.calc_temporal_mean(CRU31)

# 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 member, each_target_dataset in enumerate(target_datasets):
    target_datasets[member].values =\
        utils.calc_temporal_mean(target_datasets[member])

allNames = []

for target in target_datasets:
    allNames.append(target.name)

# calculate the metrics
taylor_diagram = metrics.SpatialPatternTaylorDiagram()


# 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
                                               [taylor_diagram])
RCMs_to_CRU_evaluation.run()

taylor_data = RCMs_to_CRU_evaluation.results[0]

plotter.draw_taylor_diagram(taylor_data,
                            allNames,
                            "CRU31",
                            fname=OUTPUT_PLOT,
                            fmt='png',
                            frameon=False)