forked from kilianv/CoastSat_WRL
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137 lines
4.2 KiB
Python
137 lines
4.2 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 20 16:15:51 2018
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@author: z5030440
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"""
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import scipy.io as sio
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import os
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import ee
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta
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import pickle
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import pdb
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import pytz
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# image processing modules
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import skimage.filters as filters
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import skimage.exposure as exposure
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import skimage.transform as transform
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import skimage.morphology as morphology
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import skimage.measure as measure
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import sklearn.decomposition as decomposition
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from scipy import spatial
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# my functions
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import functions.utils as utils
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import functions.sds as sds
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#plt.rcParams['axes.grid'] = True
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au_tz = pytz.timezone('Australia/Sydney')
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# load quadbike dates and convert from datenum to datetime
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suffix = '.mat'
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dir_name = os.getcwd()
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file_name = 'data\quadbike_dates'
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file_path = os.path.join(dir_name, file_name + suffix)
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quad_dates = sio.loadmat(file_path)['dates']
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dt_quad = []
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for i in range(quad_dates.shape[0]):
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dt_quad.append(datetime(quad_dates[i,0], quad_dates[i,1], quad_dates[i,2], tzinfo=au_tz))
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# load satellite datetimes (in UTC) and convert to AEST time
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
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# location (Narrabeen-Collaroy beach)
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rect_narra = [[[151.3473129272461,-33.69035274454718],
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[151.2820816040039,-33.68206818063878],
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[151.27281188964844,-33.74775138989556],
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[151.3425064086914,-33.75231878701767],
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[151.3473129272461,-33.69035274454718]]];
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))
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n_img = flt_col.size().getInfo()
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print('Number of images covering Narrabeen:', n_img)
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im_all = flt_col.getInfo().get('features')
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# extract datetimes from image metadata
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dt_sat = [_['properties']['system:time_start'] for _ in im_all]
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dt_sat = [datetime.fromtimestamp(_/1000, tz=pytz.utc) for _ in dt_sat]
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dt_sat = [_.astimezone(au_tz) for _ in dt_sat]
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# calculate days difference
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diff_days = [ [(x - _).days for _ in dt_quad] for x in dt_sat]
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day_thresh = 15
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idx = [utils.find_indices(_, lambda e: abs(e) < day_thresh) for _ in diff_days]
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dt_diff = []
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idx_nogt = []
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for i in range(n_img):
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if not idx[i]:
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idx_nogt.append(i)
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continue
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dt_diff.append({"sat dt": dt_sat[i],
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"quad dt": [dt_quad[_] for _ in idx[i]],
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"days diff": [diff_days[i][_] for _ in idx[i]] })
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with open('idx_nogt.pkl', 'wb') as f:
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pickle.dump(idx_nogt, f)
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#%%
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dates_sat = mdates.date2num(dt_sat)
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dates_quad = mdates.date2num(dt_quad)
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plt.figure()
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plt.plot_date(dates_sat, np.zeros((n_img,1)))
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plt.plot_date(dates_quad, np.ones((len(dates_quad),1)))
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plt.show()
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data = pd.read_pickle('data_2016.pkl')
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dt_sat = [_.astimezone(au_tz) for _ in data['dt']]
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[ (_ - dt_sat[0]).days for _ in dt_quad]
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dn_sat = []
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for i in range(len(dt_sat)): dn_sat.append(dt_sat[i].toordinal())
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dn_sat = np.array(dn_sat)
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dn_sur = []
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for i in range(len(dt_survey)): dn_sur.append(dt_survey[i].toordinal())
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dn_sur = np.array(dn_sur)
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distances = np.zeros((len(dn_sat),4)).astype('int32')
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indexes = np.zeros((len(dn_sat),2)).astype('int32')
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for i in range(len(dn_sat)):
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distances[i,0] = np.sort(abs(dn_sat[i] - dn_sur))[0]
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distances[i,1] = np.sort(abs(dn_sat[i] - dn_sur))[1]
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distances[i,2] = dt_sat[i].year
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distances[i,3] = dt_sat[i].month
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indexes[i,0] = np.where(abs(dn_sat[i] - dn_sur) == np.sort(abs(dn_sat[i] - dn_sur))[0])[0][0]
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indexes[i,1] = np.where(abs(dn_sat[i] - dn_sur) == np.sort(abs(dn_sat[i] - dn_sur))[1])[0][0]
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years = [2013, 2014, 2015, 2016]
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months = mdates.MonthLocator()
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days = mdates.DayLocator()
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month_fmt = mdates.DateFormatter('%b')
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f, ax = plt.subplots(4, 1)
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for i, ca in enumerate(ax):
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ca.xaxis.set_major_locator(months)
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ca.xaxis.set_major_formatter(month_fmt)
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ca.xaxis.set_minor_locator(days)
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ca.set_ylabel(str(years[i]))
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for j in range(len(dt_sat)):
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if dt_sat[j].year == years[i]:
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ca.plot(dt_sat[j],0, 'bo', markerfacecolor='b')
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#f.subplots_adjust(hspace=0)
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#plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False)
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plt.plot(dt_survey, np.zeros([len(dt_survey),1]), 'bo')
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plt.plot(dt_sat, np.ones([len(dt_sat),1]), 'ro')
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plt.yticks([])
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plt.show()
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