tidal_range.py

from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import click
import functools
import sys
import os
import csv 
import numpy as np
import rasterio
import datetime as DT
import xarray as xr
from datetime import datetime
from itertools import product
import logging
import logging.handlers as lh
import dask.array as da
import datacube.api
import fnmatch
import copy
from collections import defaultdict
from dateutil.relativedelta import relativedelta
from datacube.api.geo_xarray import append_solar_day
from datacube.storage.masking import make_mask
from dateutil.rrule import rrule, YEARLY
from datacube.ui import click as ui
# from datacube.ui.expression import parse_expressions
from enum import Enum
from datacube.api import GridWorkflow
from pathlib import Path
from datacube.storage.storage import write_dataset_to_netcdf
from rasterio.enums import ColorInterp
from gqa_filter import list_gqa_filtered_cells, get_gqa
from dateutil.rrule import rrule, YEARLY
from otps import TimePoint
from otps.predict_wrapper import predict_tide
from datacube.api.grid_workflow import Tile
from datacube_stats import statistics
from math import cos, asin, sqrt

logging.basicConfig()
_log = logging.getLogger('agdc-temporal-range-test')
_log.setLevel(logging.INFO)
#: pylint: disable=invalid-name
required_option = functools.partial(click.option, required=True)
MY_DATA = {}
COUNT = 0
DEFAULT_PROFILE = {
    'blockxsize': 256,
    'blockysize': 256,
    'compress': 'lzw',
    'driver': 'GTiff',
    'interleave': 'band',
    'nodata': -999,
    'tiled': True}

#: pylint: disable=too-many-arguments
@ui.cli.command(name='tidal-temporal-range')
@ui.global_cli_options
# @ui.executor_cli_options
# @click.command()
@required_option('--epoch', 'epoch', default=2, type=int, help='epoch like 2 5 10')
@required_option('--lon_range', 'lon_range', type=str, required=True, help='like (130.01, 130.052) under quote')
@required_option('--lat_range', 'lat_range', type=str, required=True, help='like (-13.023,-13.052) under quote')
@required_option('--year_range', 'year_range', type=str, required=True, help='2010-2017 i.e 2010-01-01 to 2017-01-01')
@click.option('--tide_post', 'tide_post', type=str, default='',
              help='pick up tide post from epoch_tide_post_model.csv in current directory using Haversin algorithm for a closest cluster or provide from google map like (130.0123, -11.01)')
@click.option('--per', 'per', default=10, type=int, help='10 25 50 for low tide/high tide 10/10 25/25 50/50' )
@click.option('--season', 'season', default='dummy', type=str, help='summer winter autumn spring')
@click.option('--stats', 'stats', default='MEDOID', type=click.Choice(['NDWI', 'MNDWI', 'NDBI', 'NDVI', 'MEDOID']), help='ndwi mndwi ndbi ndvi medoid')
@click.option('--ls7fl', default=True, is_flag=True, help='To include all LS7 data set it to False')
@click.option('--debug', default=False, is_flag=True, help='Build in debug mode to get details of tide height within time range')
# @ui.parsed_search_expressions
# @ui.pass_index(app_name='agdc-tidal-analysis-app')

def main(epoch, lon_range, lat_range, year_range, tide_post, per, season, stats, ls7fl, debug):
    # dc = datacube.Datacube(app="tidal-range")
    products = ['ls5_nbar_albers', 'ls7_nbar_albers', 'ls8_nbar_albers']
    dc=datacube.Datacube(app='tidal_temporal_test')
    td_info = MyTide(dc, lon_range, lat_range, products, epoch, year_range, tide_post, per, season, stats, ls7fl, debug)
    print ("Input date range " + year_range )
    for (acq_min, acq_max) in td_info.get_epochs():
        if season == "dummy":
            print ("running task for epoch " + str(acq_min) + " TO " + str(acq_max) + " on percentile " + str(per
               ) + " tide post " + tide_post + " for lon/lat range " + lon_range + lat_range + " epoch " + str(epoch))
        else:
            print ("running task for epoch " + str(acq_min) + " TO " + str(acq_max) + " on percentile " + str(per
                   ) + " tide post " + tide_post + " for lon/lat range " + lon_range + lat_range + " epoch " + str(epoch
                   ) + " for season " + season)
        
        td_info.tidal_task(acq_min, acq_max)


def pq_fuser(dest, src):
    valid_bit = 8
    valid_val = (1 << valid_bit)
    no_data_dest_mask = ~(dest & valid_val).astype(bool)
    np.copyto(dest, src, where=no_data_dest_mask)
    both_data_mask = (valid_val & dest & src).astype(bool)
    np.copyto(dest, src & dest, where=both_data_mask)


class MyTide():
    def __init__(self, dc, lon_range, lat_range, products, epoch, year_range, tide_post, per, season, stats, ls7fl, debug):
        self.dc = dc
        self.lon = eval(lon_range)
        self.lat = eval(lat_range)
        self.products = products
        self.epoch = epoch
        self.start_epoch = datetime.strptime(year_range.split('-')[0] +"-01-01", "%Y-%m-%d").date()
        self.end_epoch = datetime.strptime(year_range.split('-')[1]+"-01-01", "%Y-%m-%d").date()
        self.tide_post = tide_post
        self.per = per
        self.season = season
        self.stats = stats
        self.ls7fl = ls7fl
        self.debug = debug

    def get_epochs(self):
        for dt in rrule(YEARLY, interval=self.epoch, dtstart=self.start_epoch, until=self.end_epoch):

            if dt.date() >= self.end_epoch:
               print ("medoid calculation finished and data is available in MY_DATA dictionary ")
               return 
            acq_min = dt.date()
            acq_max = acq_min + relativedelta(years=self.epoch, days=-1)
            acq_min = max(self.start_epoch, acq_min)
            acq_max = min(self.end_epoch, acq_max)
            yield acq_min, acq_max

    def distance(self, lat1, lon1, lat2, lon2):
        p = 0.017453292519943295
        a = 0.5 - cos((lat2 - lat1) * p)/2 + cos(lat1 * p) * cos(lat2 * p) * (1 - cos((lon2 - lon1) * p)) / 2
        return 12742 * asin(sqrt(a))

    def extract_otps_range(self, date_list):
        # open the otps lon/lat file
        tp = list()
        ln = 0
        la = 0
        if self.tide_post:
            ln = eval(self.tide_post)[0]
            la = eval(self.tide_post)[1]
        else:
            print ("reading from tidal model file and using Haversine algorithm to extract shortest distance")
            from operator import itemgetter 
            # first find centroid of lat lon range
            la = (self.lat[0] + self.lat[1])/2
            ln = (self.lon[0] + self.lon[1])/2
            rdlist = list()
            fname = "./epoch_tide_post_model.csv"
            try:
                with open (fname, 'rt') as f:
                    reader = csv.reader(f, delimiter=',')
                    for rd in reader:
                        rdlist.append((rd[0], rd[1], rd[2],
                                      self.distance(la, ln, float(rd[1]), float(rd[0]))))
                rdlist = sorted(rdlist, key=itemgetter(3))
                print ( "Found tide post coordinates,depth and shortest distance", rdlist[0] )
                la = float(rdlist[0][1])
                ln = float(rdlist[0][0])
            except IOError as e:
                print ("Unable to open file: " +str(fname))
                sys.exit() 
        for dt in date_list:
            tp.append(TimePoint(ln, la, dt)) 
        tides = predict_tide(tp)
        if len(tides) == 0:
           print ("No tide height observed from OTPS model within lat/lon range")
           sys.exit()
        print ("received from predict tides ", str(datetime.now()))
        date_low = list()
        date_high = list()
        tide_dict = dict()
        for tt in tides:
            tide_dict[datetime.strptime(tt.timepoint.timestamp.isoformat()[0:19], "%Y-%m-%dT%H:%M:%S")] = tt.tide_m
        tide_dict = sorted(tide_dict.items(), key=lambda x: x[1])
        # lowest = round(float(self.per)*len(tide_dict)/100)
        dr = float(tide_dict[len(tide_dict)-1][1]) - float(tide_dict[0][1])
        lmr = float(tide_dict[0][1]) + dr*float(self.per)*0.01   # low tide max range
        hlr = float(tide_dict[len(tide_dict)-1][1]) - dr*float(self.per)*0.01   # low tide max range
        date_low = [x for x  in tide_dict if x[1] <= lmr]
        date_high = [x for x  in tide_dict if x[1] > hlr] 
        # date_high = tide_dict[-int(lowest):]
        print ("lowest tides range and number " +  str(date_low[0][1]) + "," + str(date_low[len(date_low)-1][1])
               + " " + str(len(date_low)))
        print ("highest tides range and number " +  str(date_high[0][1]) + "," + str(date_high[len(date_high)-1][1])
               + " " + str(len(date_high)))
        if self.debug:
            print ("lowest tides list ", [[datetime.strftime(date[0], "%Y-%m-%d"), date[1]]  for date in date_low])
            print ("")
            print ("highest tides list", [[datetime.strftime(date[0], "%Y-%m-%d"), date[1]] for date in date_high])
            print ("")
            print ("ALL TIDES LIST", [[datetime.strftime(date[0], "%Y-%m-%d"), date[1]] for date in tide_dict])
        date_low = [dd[0] for dd in date_low]
        date_high = [dd[0] for dd in date_high]
        return date_low, date_high 

    def build_my_dataset(self, acq_min, acq_max):
        
        nbar_data = None 
        dt5 = "2011-12-01"
        dtt7 = "1999-07-01"
        dt7 = "2003-03-01"
        dt8 = "2013-04-01"
        for i, st in enumerate(self.products):
            prod = None        
            print (" doing for sensor",  st )
            if st == 'ls5_nbar_albers' and acq_max > datetime.strptime(dt5, "%Y-%m-%d").date() and  \
                  acq_min > datetime.strptime(dt5, "%Y-%m-%d").date():
                print ("LS5 post 2011 Dec data is not exist")
                continue
            elif st == 'ls5_nbar_albers' and acq_max > datetime.strptime(dt5, "%Y-%m-%d").date() and \
                  acq_min < datetime.strptime(dt5, "%Y-%m-%d").date():
                acq_max = datetime.strptime(dt5, "%Y-%m-%d").date()
                print (" epoch end date is reset for LS5 2011/12/01")
            if st == 'ls7_nbar_albers' and self.ls7fl and acq_max > datetime.strptime(dt7, "%Y-%m-%d").date() and  \
                  acq_min > datetime.strptime(dt7, "%Y-%m-%d").date():
                print ("LS7 post 2003 March data is not included")
                continue
            elif st == 'ls7_nbar_albers' and self.ls7fl and acq_max > datetime.strptime(dt7, "%Y-%m-%d").date() and \
                  acq_min < datetime.strptime(dt7, "%Y-%m-%d").date():
                acq_max = datetime.strptime(dt7, "%Y-%m-%d").date()
                print (" epoch end date is reset for LS7 2003/03/01")
            if st == 'ls7_nbar_albers' and acq_max < datetime.strptime(dtt7, "%Y-%m-%d").date() and \
                  acq_min < datetime.strptime(dtt7, "%Y-%m-%d").date():
                continue
            if st == 'ls8_nbar_albers' and acq_max < datetime.strptime(dt8, "%Y-%m-%d").date() and \
                  acq_min < datetime.strptime(dt8, "%Y-%m-%d").date():
                continue
            elif st == 'ls8_nbar_albers' and acq_max > datetime.strptime(dt8, "%Y-%m-%d").date() and \
                acq_min < datetime.strptime(dt8, "%Y-%m-%d").date():
                acq_min = datetime.strptime(dt8, "%Y-%m-%d").date()
            if st == 'ls5_nbar_albers':
                prod = 'ls5_pq_albers'
            elif st == 'ls7_nbar_albers':
                prod = 'ls7_pq_albers'
            else:
                prod = 'ls8_pq_albers'
            # add extra day to the maximum range to include the last day in the search
            # end_ep = acq_max + relativedelta(days=1)
            indexers = {'time':(acq_min, acq_max), 'x':(str(self.lon[0]), str(self.lon[1])), 'y':(str(self.lat[0]), str(self.lat[1]))}
            pq = self.dc.load(product=prod, fuse_func=pq_fuser, **indexers)   
            if st == 'ls5_nbar_albers' and len(pq) == 0:
                print ("No LS5 data found")
                continue
            if nbar_data is not None and st == 'ls7_nbar_albers' and len(pq) == 0:
                print ("No LS7 data found")
                continue
            indexers = {'time':(acq_min, acq_max), 'x':(str(self.lon[0]), str(self.lon[1])), 'y':(str(self.lat[0]), str(self.lat[1])),
                        'measurements':['blue', 'green', 'red', 'nir', 'swir1', 'swir2']}
            mask_clear = pq['pixelquality'] & 15871 == 15871
            if nbar_data is not None:
                new_data = self.dc.load(product=st, **indexers)
                new_data = new_data.where(mask_clear)
                nbar_data = xr.concat([nbar_data, new_data], dim='time')
            else:
                nbar_data = self.dc.load(product=st, **indexers)
                nbar_data = nbar_data.where(mask_clear)
        # if season then filtered only season data
        if self.season.upper() == 'WINTER':
            nbar_data = nbar_data.isel(time=nbar_data.groupby('time.season').groups['JJA'])
        elif self.season.upper() == 'SUMMER':
            nbar_data = nbar_data.isel(time=nbar_data.groupby('time.season').groups['DJF'])
        elif self.season.upper() == 'SPRING':
            nbar_data = nbar_data.isel(time=nbar_data.groupby('time.season').groups['SON'])
        elif self.season.upper() == 'AUTUMN':
            nbar_data = nbar_data.isel(time=nbar_data.groupby('time.season').groups['MAM'])
        # filtered out lowest and highest date range
        date_list = nbar_data.time.values.astype('M8[s]').astype('O').tolist() 
        date_low, date_high = self.extract_otps_range(date_list) 
        date_low = [s.strftime("%Y-%m-%d %H:%M:%S") for s in date_low]
        date_high = [s.strftime("%Y-%m-%d %H:%M:%S") for s in date_high]
        date_all = [s.strftime("%Y-%m-%d %H:%M:%S") for s in date_list]
        low_match = [i for i, item in enumerate(date_all) if item in date_low] 
        high_match = [i for i, item in enumerate(date_all) if item in date_high]
        nbar_low = nbar_data.isel(time=low_match)
        nbar_high = nbar_data.isel(time=high_match)
        print (" loaded nbar data with low " +  str(datetime.now().time())) 
        return nbar_low, nbar_high 

    def compute_stats(self, data):
        ndata = None
        if self.stats.upper() == 'NDWI':
            # green = data.green.where(data.green != data.green.attrs['nodata'])
            # nir = data.nir.where(data.nir != data.nir.attrs['nodata'])
            green = data.green
            nir = data.nir 
            ndata = ((green - nir) / (green + nir ))
        if self.stats.upper() == 'MNDWI':
            green = data.green
            swir1 = data.swir1
            ndata = ((green - swir1) / (green + swir1 ))
        if self.stats.upper() == 'NDBI':
            nir = data.nir
            swir1 = data.swir1
            ndata = ((nir - swir1) / (nir + swir1 ))
        if self.stats.upper() == 'NDVI':
            nir = data.nir
            red = data.red
            ndata = ((nir - red) / (nir + red ))
        if ndata is not None:
            return ndata.median(dim='time').data
        else:
            return ndata
      

    def tidal_task(self, acq_min, acq_max):
        #  gather latest datasets as per product names 
           
        ds_low, ds_high = self.build_my_dataset(acq_min, acq_max)
        # calculate medoid
        # For a slice of 1000:1000 for entire time seried do like  
        # smallds = ds_high.isel(x=slice(None, None, 4), y=slice(None, None, 4)) 
        if self.stats.upper() != "MEDOID":
            print ("creating median image for " + self.stats + " for lower range " + str(datetime.now().time()))      
            med_low = self.compute_stats(ds_low)
            print ("creating median image for " + self.stats + " for higher range " + str(datetime.now().time()))      
            med_high = self.compute_stats(ds_high)
        else:    
            print ("creating  " + self.stats + " for lower range " + str(datetime.now().time()))      
            med_low = statistics.combined_var_reduction(ds_low, statistics.nanmedoid)         
            print ("creating " + self.stats + " for higher range " + str(datetime.now().time()))      
            med_high = statistics.combined_var_reduction(ds_high, statistics.nanmedoid)         
        print ("calculation for " + self.stats + " finished for this epoch " + str(datetime.now().time()))      
        if (acq_max == self.end_epoch):
            print (self.stats.upper(), " calculation finished and data is available in MY_DATA dictionary ", 
                   str(datetime.now().time()))      
        key = ''
        if self.season.upper() != 'DUMMY':
            key = self.stats + "_" + str(acq_min) + "_" + str(acq_max) + "_" + self.season.upper() + "_LOW"
        else:
            key = self.stats + "_" + str(acq_min) + "_" + str(acq_max) + "_LOW"
        MY_DATA[key] = copy.deepcopy(med_low)
        key = ''
        if self.season.upper() != 'DUMMY':
            key = self.stats + "_" + str(acq_min) + "_" + str(acq_max) + "_" + self.season.upper() + "_HIGH"
        else:
            key = self.stats + "_" + str(acq_min) + "_" + str(acq_max) + "_HIGH" 
        MY_DATA[key] = copy.deepcopy(med_high)
        return


if __name__ == '__main__':
    '''
    The program gets all LANDSAT datasets excluding post March 2003 LS7 datasets and applied cloud free pq data.
    It accepts lon and lat ranges and optional tide_post. If it is not available, it pulls tidal post model data and 
    extracts epoch/seasonal data and calculates medoid for six bands and output to a dictionary MY_DATA.
    The dictionary has LOW and HIGH xarray medoid datasets. It needs small spatial range to cover wide time range.
    '''
    main()