#
#  Licensed to the Apache Software Foundation (ASF) under one or more
#  contributor license agreements.  See the NOTICE file distributed with
#  this work for additional information regarding copyright ownership.
#  The ASF licenses this file to You under the Apache License, Version 2.0 
#  (the "License"); you may not use this file except in compliance with
#  the License.  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#

import unittest
import datetime
import os

from ocw import dataset_processor as dp
from ocw import dataset as ds
from ocw.data_source import local
import numpy as np
import numpy.ma as ma

import logging
logging.basicConfig(level=logging.CRITICAL)

class TestEnsemble(unittest.TestCase): 
    def test_unequal_dataset_shapes(self):
        self.ten_year_dataset = ten_year_monthly_dataset()
        self.two_year_dataset = two_year_daily_dataset()
        with self.assertRaises(ValueError):
            self.ensemble_dataset = dp.ensemble([self.ten_year_dataset, self.two_year_dataset])
    
    def test_ensemble_logic(self):
        self.datasets = []
        self.datasets.append(build_ten_cube_dataset(1))
        self.datasets.append(build_ten_cube_dataset(2))
        self.three = build_ten_cube_dataset(3)
        self.datasets.append(self.three)
        self.datasets.append(build_ten_cube_dataset(4))
        self.datasets.append(build_ten_cube_dataset(5))
        self.ensemble = dp.ensemble(self.datasets)
        self.ensemble_flat = self.ensemble.values.flatten()
        self.three_flat = self.three.values.flatten()
        np.testing.assert_array_equal(self.ensemble_flat, self.three_flat)
    
    def test_ensemble_name(self):
        self.ensemble_dataset_name = "Dataset Ensemble"
        self.datasets = []
        self.datasets.append(build_ten_cube_dataset(1))
        self.datasets.append(build_ten_cube_dataset(2))
        self.ensemble = dp.ensemble(self.datasets)
        self.assertEquals(self.ensemble.name, self.ensemble_dataset_name)
        

class TestTemporalRebin(unittest.TestCase):
    
    def setUp(self):
        self.ten_year_monthly_dataset = ten_year_monthly_dataset()
        self.ten_year_annual_times = np.array([datetime.datetime(year, 1, 1) for year in range(2000, 2010)])
        self.two_years_daily_dataset = two_year_daily_dataset()
    
    def test_monthly_to_annual_rebin(self):
        annual_dataset = dp.temporal_rebin(self.ten_year_monthly_dataset, datetime.timedelta(days=365))
        np.testing.assert_array_equal(annual_dataset.times, self.ten_year_annual_times)
    
    def test_monthly_to_full_rebin(self):
        full_dataset = dp.temporal_rebin(self.ten_year_monthly_dataset, datetime.timedelta(days=3650))
        full_times = [datetime.datetime(2004, 12, 16)]
        self.assertEqual(full_dataset.times, full_times)
    
    def test_daily_to_monthly_rebin(self):
        """This test takes a really long time to run.  TODO: Figure out where the performance drag is"""
        monthly_dataset = dp.temporal_rebin(self.two_years_daily_dataset, datetime.timedelta(days=31))
        bins = list(set([datetime.datetime(time_reading.year, time_reading.month, 1) for time_reading in self.two_years_daily_dataset.times]))
        bins = np.array(bins)
        bins.sort()
        np.testing.assert_array_equal(monthly_dataset.times, bins)
    
    def test_daily_to_annual_rebin(self):
        annual_dataset = dp.temporal_rebin(self.two_years_daily_dataset, datetime.timedelta(days=366))
        bins = list(set([datetime.datetime(time_reading.year, 1, 1) for time_reading in self.two_years_daily_dataset.times]))
        bins = np.array(bins)
        bins.sort()
        np.testing.assert_array_equal(annual_dataset.times, bins)
    
    def test_non_rebin(self):
        """This will take a monthly dataset and ask for a monthly rebin of 28 days.  The resulting
        dataset should have the same time values"""
        monthly_dataset = dp.temporal_rebin(self.ten_year_monthly_dataset, datetime.timedelta(days=28))
        good_times = self.ten_year_monthly_dataset.times
        np.testing.assert_array_equal(monthly_dataset.times, good_times)

    def test_variable_propagation(self):
        annual_dataset = dp.temporal_rebin(self.ten_year_monthly_dataset,
                                           datetime.timedelta(days=365))
        self.assertEquals(annual_dataset.name,
                          self.ten_year_monthly_dataset.name)
        self.assertEquals(annual_dataset.variable,
                          self.ten_year_monthly_dataset.variable)


class TestRcmesSpatialRegrid(unittest.TestCase):

    def test_return_array_shape(self):
        spatial_values = np.ones([90,180])
        spatial_values = ma.array(spatial_values)
        
        lat_range = ma.array(range(-89, 90, 2))
        lon_range = ma.array(range(-179, 180, 2))
        
        lons, lats = np.meshgrid(lon_range, lat_range)
        # Convert these to masked arrays
        lats = ma.array(lats)
        lons = ma.array(lons)
        
        lat2_range = np.array(range(-89, 90, 4))
        lon2_range = np.array(range(-179, 180, 4))
        
        lons2, lats2 = np.meshgrid(lon2_range, lat2_range)
        # Convert to masked arrays
        lats2 = ma.array(lats2)
        lons2 = ma.array(lons2)

        regridded_values = dp._rcmes_spatial_regrid(spatial_values, lats, lons, lats2, lons2)
        self.assertEqual(regridded_values.shape, lats2.shape)
        self.assertEqual(regridded_values.shape, lons2.shape)

class TestSpatialRegrid(unittest.TestCase):
    
    def setUp(self):
        self.input_dataset = ten_year_monthly_dataset()
        self.new_lats = np.array(range(-89, 90, 4))
        self.new_lons = np.array(range(-179, 180, 4))
        self.regridded_dataset = dp.spatial_regrid(self.input_dataset, self.new_lats, self.new_lons)


    def test_returned_lats(self):
        np.testing.assert_array_equal(self.regridded_dataset.lats, self.new_lats)

    def test_returned_lons(self):
        np.testing.assert_array_equal(self.regridded_dataset.lons, self.new_lons)

    def test_shape_of_values(self):
        regridded_data_shape = self.regridded_dataset.values.shape
        expected_data_shape = (len(self.input_dataset.times), len(self.new_lats), len(self.new_lons))
        self.assertSequenceEqual(regridded_data_shape, expected_data_shape)

    def test_variable_propagation(self):
        self.assertEquals(self.input_dataset.name, self.regridded_dataset.name)
        self.assertEquals(self.input_dataset.variable, self.regridded_dataset.variable)

class TestNormalizeDatasetDatetimes(unittest.TestCase):
    def setUp(self):
        self.monthly_dataset = ten_year_monthly_15th_dataset()
        self.daily_dataset = two_year_daily_2hr_dataset()

    def test_daily(self):
        new_ds = dp.normalize_dataset_datetimes(self.monthly_dataset, 'daily')

        # Check that all the days have been shifted to the first of the month
        self.assertTrue(all(x.hour == 0 for x in new_ds.times))

    def test_montly(self):
        new_ds = dp.normalize_dataset_datetimes(self.monthly_dataset, 'monthly')

        # Check that all the days have been shifted to the first of the month
        self.assertTrue(all(x.day == 1 for x in new_ds.times))

class TestSubset(unittest.TestCase):
    def setUp(self):
        self.target_dataset = ten_year_monthly_dataset()

        self.subregion = ds.Bounds(
            -81, 81, 
            -161, 161, 
            datetime.datetime(2001, 1, 1), 
            datetime.datetime(2004, 1, 1)
        )
        self.non_exact_spatial_subregion = ds.Bounds(
            -80.25, 80.5, 
            -160.25, 160.5, 
            datetime.datetime(2001, 1, 1), 
            datetime.datetime(2004, 1, 1)
        )
        self.non_exact_temporal_subregion = ds.Bounds(
            -80.25, 80.5, 
            -160.25, 160.5,
            datetime.datetime(2001, 1, 15), 
            datetime.datetime(2004, 2, 15)
        )

    def test_subset(self):
        subset = dp.subset(self.subregion, self.target_dataset)
        self.assertEqual(subset.lats.shape[0], 82)
        self.assertSequenceEqual(list(np.array(range(-81, 82, 2))), 
                list(subset.lats))
        self.assertEqual(subset.lons.shape[0], 162)
        self.assertEqual(subset.times.shape[0], 37)
        self.assertEqual(subset.values.shape, (37, 82, 162))
    
    def test_subset_using_non_exact_spatial_bounds(self):
        index_slices = dp._get_subregion_slice_indices(self.non_exact_spatial_subregion,  self.target_dataset)
        control_index_slices = {"lat_start"  : 5,
                                "lat_end"    : 84,
                                "lon_start"  : 10,
                                "lon_end"    : 169,
                                "time_start" : 12, 
                                "time_end"   : 48}
        self.assertDictEqual(index_slices, control_index_slices)

    def test_subset_using_non_exact_temporal_bounds(self):
        index_slices = dp._get_subregion_slice_indices(self.non_exact_temporal_subregion,  self.target_dataset)
        control_index_slices = {"lat_start"  : 5,
                                "lat_end"    : 84,
                                "lon_start"  : 10,
                                "lon_end"    : 169,
                                "time_start" : 13, 
                                "time_end"   : 49}
        self.assertDictEqual(index_slices, control_index_slices)

class TestSafeSubset(unittest.TestCase):
    def setUp(self):
        lats = np.array(range(-60, 61, 1))
        lons = np.array(range(-170, 171, 1))
        times = np.array([datetime.datetime(year, month, 1)
                          for year in range(2000, 2010)
                          for month in range(1, 13)])
        values = np.ones([len(times), len(lats), len(lons)])
        self.target_dataset = ds.Dataset(lats,
                                         lons,
                                         times,
                                         values,
                                         variable="test variable name",
                                         name='foo')

        self.spatial_out_of_bounds = ds.Bounds(
            -165, 165,
            -180, 180,
            datetime.datetime(2001, 1, 1),
            datetime.datetime(2004, 1, 1)
        )

        self.temporal_out_of_bounds = ds.Bounds(
            -40, 40,
            -160.25, 160.5,
            datetime.datetime(1999, 1, 15),
            datetime.datetime(2222, 2, 15)
        )

        self.everything_out_of_bounds = ds.Bounds(
            -165, 165,
            -180, 180,
            datetime.datetime(1999, 1, 15),
            datetime.datetime(2222, 2, 15)
        )

    def test_partial_spatial_overlap(self):
        '''Ensure that safe_subset can handle out of bounds spatial values'''
        ds = dp.safe_subset(self.spatial_out_of_bounds, self.target_dataset)
        spatial_bounds = ds.spatial_boundaries()
        self.assertEquals(spatial_bounds[0], -60)
        self.assertEquals(spatial_bounds[1], 60)
        self.assertEquals(spatial_bounds[2], -170)
        self.assertEquals(spatial_bounds[3], 170)

    def test_partial_temporal_overlap(self):
        '''Ensure that safe_subset can handle out of bounds temporal values'''
        ds = dp.safe_subset(self.temporal_out_of_bounds, self.target_dataset)
        temporal_bounds = ds.time_range()
        start = datetime.datetime(2000, 1, 1)
        end = datetime.datetime(2009, 12, 1)

        self.assertEquals(temporal_bounds[0], start)
        self.assertEquals(temporal_bounds[1], end)

    def test_entire_bounds_overlap(self):
        ds = dp.safe_subset(self.everything_out_of_bounds, self.target_dataset)
        spatial_bounds = ds.spatial_boundaries()
        temporal_bounds = ds.time_range()
        start = datetime.datetime(2000, 1, 1)
        end = datetime.datetime(2009, 12, 1)

        self.assertEquals(spatial_bounds[0], -60)
        self.assertEquals(spatial_bounds[1], 60)
        self.assertEquals(spatial_bounds[2], -170)
        self.assertEquals(spatial_bounds[3], 170)
        self.assertEquals(temporal_bounds[0], start)
        self.assertEquals(temporal_bounds[1], end)

class TestFailingSubset(unittest.TestCase):
    def setUp(self):
        self.target_dataset = ten_year_monthly_dataset()
        self.target_dataset.lats = np.array(range(-89, 88, 2))
        self.target_dataset.lons = np.array(range(-179, 178, 2))

        self.subregion = ds.Bounds(
            -81, 81, 
            -161, 161, 
            datetime.datetime(2001, 1, 1), 
            datetime.datetime(2004, 1, 1)
        )

    def test_out_of_dataset_bounds_lat_min(self):
        self.subregion.lat_min = -90
        with self.assertRaises(ValueError):
            dp.subset(self.subregion, self.target_dataset)

    def test_out_of_dataset_bounds_lat_max(self):
        self.subregion.lat_max = 90
        with self.assertRaises(ValueError):
            dp.subset(self.subregion, self.target_dataset)

    def test_out_of_dataset_bounds_lon_min(self):
        self.subregion.lon_min = -180
        with self.assertRaises(ValueError):
            dp.subset(self.subregion, self.target_dataset)

    def test_out_of_dataset_bounds_lon_max(self):
        self.subregion.lon_max = 180
        with self.assertRaises(ValueError):
            dp.subset(self.subregion, self.target_dataset)

    def test_out_of_dataset_bounds_start(self):
        self.subregion.start = datetime.datetime(1999, 1, 1)
        with self.assertRaises(ValueError):
            dp.subset(self.subregion, self.target_dataset)

    def test_out_of_dataset_bounds_end(self):
        self.subregion.end = datetime.datetime(2011, 1, 1)
        with self.assertRaises(ValueError):
            dp.subset(self.subregion, self.target_dataset)

class TestNetCDFWrite(unittest.TestCase):
    def setUp(self):
        self.ds = ten_year_monthly_dataset()
        self.file_name = 'test.nc'

    def tearDown(self):
        if os.path.isfile(self.file_name):
            os.remove(self.file_name)

    def test_file_write(self):
        dp.write_netcdf(self.ds, self.file_name)
        self.assertTrue(os.path.isfile(self.file_name))

    def test_that_file_contents_are_valid(self):
        dp.write_netcdf(self.ds, self.file_name)
        new_ds = local.load_file(self.file_name, self.ds.variable)

        self.assertEqual(self.ds.variable, new_ds.variable)
        np.testing.assert_array_equal(self.ds.lats, new_ds.lats)
        np.testing.assert_array_equal(self.ds.lons, new_ds.lons)
        np.testing.assert_array_equal(self.ds.times, new_ds.times)
        np.testing.assert_array_equal(self.ds.values, new_ds.values)

def ten_year_monthly_dataset():
    lats = np.array(range(-89, 90, 2))
    lons = np.array(range(-179, 180, 2))
    # Ten Years of monthly data
    times = np.array([datetime.datetime(year, month, 1) for year in range(2000, 2010) for month in range(1, 13)])
    values = np.ones([len(times), len(lats), len(lons)])
    input_dataset = ds.Dataset(lats, lons, times, values, variable="test variable name", name='foo')
    return input_dataset

def ten_year_monthly_15th_dataset():
    lats = np.array(range(-89, 90, 2))
    lons = np.array(range(-179, 180, 2))
    # Ten Years of monthly data
    times = np.array([datetime.datetime(year, month, 15) for year in range(2000, 2010) for month in range(1, 13)])
    values = np.ones([len(times), len(lats), len(lons)])
    input_dataset = ds.Dataset(lats, lons, times, values, variable="test variable name")
    return input_dataset

def two_year_daily_dataset():
    lats = np.array(range(-89, 90, 2))
    lons = np.array(range(-179, 180, 2))
    times = np.array([datetime.datetime(2001, 1, 1) + datetime.timedelta(days=d) for d in range(730)])
    values = np.ones([len(times), len(lats), len(lons)])
    dataset = ds.Dataset(lats, lons, times, values, variable='random data')
    return dataset    

def two_year_daily_2hr_dataset():
    lats = np.array(range(-89, 90, 2))
    lons = np.array(range(-179, 180, 2))
    times = np.array([datetime.datetime(2001, 1, 1) + datetime.timedelta(days=d, hours=2) for d in range(730)])
    values = np.ones([len(times), len(lats), len(lons)])
    dataset = ds.Dataset(lats, lons, times, values, variable='random data')
    return dataset    

def build_ten_cube_dataset(value):
    lats = np.array(range(-89, 90, 18))
    lons = np.array(range(-179, 180, 36))
    times = np.array([datetime.datetime(year, 1, 1) for year in range(2000, 2010)])
    values = np.ones([len(times), len(lats), len(lons)])
    values = values * value
    dataset = ds.Dataset(lats, lons, times, values)
    return dataset

if __name__ == '__main__':
    unittest.main()