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383 lines
14 KiB
Python
383 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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#==========================================================#
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# Compare Narrabeen SDS with 3D quadbike surveys
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#==========================================================#
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# Initial settings
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import pdb
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import ee
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import matplotlib.dates as mdates
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import matplotlib.cm as cm
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from datetime import datetime, timedelta
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import pickle
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import pytz
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import scipy.io as sio
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import scipy.interpolate as interpolate
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import statsmodels.api as sm
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import skimage.measure as measure
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# my functions
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import functions.utils as utils
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# some settings
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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plt.rcParams['axes.grid'] = True
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plt.rcParams['figure.max_open_warning'] = 100
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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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filename = 'data\quadbike\survey_dates.mat'
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filepath = os.path.join(os.getcwd(), filename)
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dates_quad = sio.loadmat(filepath)['dates'] # matrix containing year, month, day
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dates_quad = [datetime(dates_quad[i,0], dates_quad[i,1], dates_quad[i,2],
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tzinfo=au_tz) for i in range(dates_quad.shape[0])]
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# load timestamps from satellite images
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satname = 'L8'
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sitename = 'NARRA'
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filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
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with open(os.path.join(filepath, sitename + '_output_new' + '.pkl'), 'rb') as f:
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output = pickle.load(f)
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dates_l8 = output['t']
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# convert to AEST
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dates_l8 = [_.astimezone(au_tz) for _ in dates_l8]
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# remove duplicates
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dates_l8_str = [_.strftime('%Y%m%d') for _ in dates_l8]
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dupl = utils.duplicates_dict(dates_l8_str)
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idx_remove = []
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for k,v in dupl.items():
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idx1 = v[0]
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idx2 = v[1]
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c1 = output['cloud_cover'][idx1]
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c2 = output['cloud_cover'][idx2]
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g1 = output['acc_georef'][idx1]
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g2 = output['acc_georef'][idx2]
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if c1 < c2 - 0.01:
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idx_remove.append(idx2)
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elif g1 < g2 - 0.1:
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idx_remove.append(idx2)
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else:
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idx_remove.append(idx1)
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idx_remove = sorted(idx_remove)
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idx_all = np.linspace(0,70,71)
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idx_keep = list(np.where(~np.isin(idx_all,idx_remove))[0])
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output['t'] = [output['t'][k] for k in idx_keep]
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output['shorelines'] = [output['shorelines'][k] for k in idx_keep]
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output['cloud_cover'] = [output['cloud_cover'][k] for k in idx_keep]
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output['acc_georef'] = [output['acc_georef'][k] for k in idx_keep]
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# convert to AEST
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dates_l8 = output['t']
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dates_l8 = [_.astimezone(au_tz) for _ in dates_l8]
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# load wave data (already AEST)
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filename = 'data\wave\SydneyProcessed.mat'
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filepath = os.path.join(os.getcwd(), filename)
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wave_data = sio.loadmat(filepath)
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idx = utils.find_indices(wave_data['dates'][:,0], lambda e: e >= dates_l8[0].year and e <= dates_l8[-1].year)
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hsig = np.array([wave_data['Hsig'][i][0] for i in idx])
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wdir = np.array([wave_data['Wdir'][i][0] for i in idx])
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dates_wave = [datetime(wave_data['dates'][i,0], wave_data['dates'][i,1],
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wave_data['dates'][i,2], wave_data['dates'][i,3],
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wave_data['dates'][i,4], wave_data['dates'][i,5],
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tzinfo=au_tz) for i in idx]
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# load tide data (already AEST)
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filename = 'SydTideData.mat'
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filepath = os.path.join(os.getcwd(), 'data', 'tide', filename)
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tide_data = sio.loadmat(filepath)
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idx = utils.find_indices(tide_data['dates'][:,0], lambda e: e >= dates_l8[0].year and e <= dates_l8[-1].year)
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tide = np.array([tide_data['tide'][i][0] for i in idx])
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dates_tide = [datetime(tide_data['dates'][i,0], tide_data['dates'][i,1],
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tide_data['dates'][i,2], tide_data['dates'][i,3],
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tide_data['dates'][i,4], tide_data['dates'][i,5],
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tzinfo=au_tz) for i in idx]
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#%% make a plot of all the dates with wave data
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orange = [255/255,140/255,0]
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blue = [0,191/255,255/255]
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f = plt.figure()
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months = mdates.MonthLocator()
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month_fmt = mdates.DateFormatter('%b %Y')
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days = mdates.DayLocator()
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years = [2013,2014,2015,2016]
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for k in range(len(years)):
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sel_year = years[k]
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ax = plt.subplot(4,1,k+1)
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idx_year = utils.find_indices(dates_wave, lambda e : e.year >= sel_year and e.year <= sel_year)
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plt.plot([dates_wave[i] for i in idx_year], [hsig[i] for i in idx_year], 'k-', linewidth=0.5)
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hsigmax = np.nanmax([hsig[i] for i in idx_year])
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cbool = True
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for j in range(len(dates_quad)):
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if dates_quad[j].year == sel_year:
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if cbool:
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plt.plot([dates_quad[j], dates_quad[j]], [0, hsigmax], color=orange, label='survey')
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cbool = False
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else:
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plt.plot([dates_quad[j], dates_quad[j]], [0, hsigmax], color=orange)
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cbool = True
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for j in range(len(dates_l8)):
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if dates_l8[j].year == sel_year:
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if cbool:
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plt.plot([dates_l8[j], dates_l8[j]], [0, hsigmax], color=blue, label='landsat8')
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cbool = False
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else:
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plt.plot([dates_l8[j], dates_l8[j]], [0, hsigmax], color=blue)
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if k == 3:
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plt.legend()
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plt.xlim((datetime(sel_year,1,1), datetime(sel_year,12,31, tzinfo=au_tz)))
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plt.ylim((0, hsigmax))
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plt.ylabel('Hs [m]')
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ax.xaxis.set_major_locator = months
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ax.xaxis.set_major_formatter(month_fmt)
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f.subplots_adjust(hspace=0.2)
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plt.draw()
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#%% calculate difference between dates (quad and sat)
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diff_days = [ [(x - _).days for _ in dates_quad] for x in dates_l8]
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max_diff = 14
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idx_closest = [utils.find_indices(_, lambda e: abs(e) <= max_diff) for _ in diff_days]
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# store in dates_diff dictionnary
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dates_diff = []
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cloud_cover = []
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for i in range(len(idx_closest)):
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if not idx_closest[i]:
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continue
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elif len(idx_closest[i]) > 1:
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idx_best = np.argmin(np.abs([diff_days[i][_] for _ in idx_closest[i]]))
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dates_temp = [dates_quad[_] for _ in idx_closest[i]]
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days_temp = [diff_days[i][_] for _ in idx_closest[i]]
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dates_diff.append({"date sat": dates_l8[i],
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"date quad": dates_temp[idx_best],
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"days diff": days_temp[idx_best]})
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else:
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dates_diff.append({"date sat": dates_l8[i],
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"date quad": dates_quad[idx_closest[i][0]],
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"days diff": diff_days[i][idx_closest[i][0]]
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})
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# store cloud data
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cloud_cover.append(output['cloud_cover'][i])
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# store wave data
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wave_hsig = []
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for i in range(len(dates_diff)):
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wave_hsig.append(hsig[np.argmin(np.abs(np.array([(dates_diff[i]['date sat'] - _).total_seconds() for _ in dates_wave])))])
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# make a plot
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plt.figure()
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counter = 0
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for i in range(len(dates_diff)):
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counter = counter + 1
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if dates_diff[i]['date quad'] > dates_diff[i]['date sat']:
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date_min = dates_diff[i]['date sat']
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date_max = dates_diff[i]['date quad']
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color1 = orange
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color2 = blue
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else:
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date_min = dates_diff[i]['date quad']
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date_max = dates_diff[i]['date sat']
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color1 = blue
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color2 = orange
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idx_t = utils.find_indices(dates_wave, lambda e : e >= date_min and e <= date_max)
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hsigmax = np.nanmax([hsig[i] for i in idx_t])
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hsigmin = np.nanmin([hsig[i] for i in idx_t])
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if counter > 9:
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counter = 1
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plt.figure()
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ax = plt.subplot(3,3,counter)
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plt.plot([dates_wave[i] for i in idx_t], [hsig[i] for i in idx_t], 'k-', linewidth=1.5)
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plt.plot([date_min, date_min], [0, 4.5], color=color2, label='survey')
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plt.plot([date_max, date_max], [0, 4.5], color=color1, label='landsat8')
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plt.ylabel('Hs [m]')
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ax.xaxis.set_major_locator(mdates.DayLocator(tz=au_tz))
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ax.xaxis.set_minor_locator(mdates.HourLocator(tz=au_tz))
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%d'))
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ax.xaxis.set_minor_locator(months)
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plt.title(dates_diff[i]['date sat'].strftime('%b %Y') + ' (' + str(abs(dates_diff[i]['days diff'])) + ' days)')
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plt.draw()
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plt.gcf().subplots_adjust(hspace=0.5)
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# mean day difference
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np.mean([ np.abs(_['days diff']) for _ in dates_diff])
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#%% Compare shorelines in elevation
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dist_buffer = 50 # buffer of points selected for interpolation
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# load quadbike .mat files
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foldername = 'data\quadbike\surveys3D'
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folderpath = os.path.join(os.getcwd(), foldername)
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filenames = os.listdir(folderpath)
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# get the satellite shorelines
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sl = output['shorelines']
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# get dates from filenames
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dates_quad = [datetime(int(_[6:10]), int(_[11:13]), int(_[14:16]), tzinfo= au_tz) for _ in filenames]
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zav = []
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ztide = []
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sl_gt = []
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for i in range(len(dates_diff)):
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sl_smooth = sl[i]
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# select closest 3D survey and load .mat file
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idx_closest = np.argmin(np.abs(np.array([(dates_diff[i]['date sat'] - _).days for _ in dates_quad])))
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survey3d = sio.loadmat(os.path.join(folderpath, filenames[idx_closest]))
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# reshape to a vector
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xs = survey3d['x'].reshape(survey3d['x'].shape[0] * survey3d['x'].shape[1])
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ys = survey3d['y'].reshape(survey3d['y'].shape[0] * survey3d['y'].shape[1])
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zs = survey3d['z'].reshape(survey3d['z'].shape[0] * survey3d['z'].shape[1])
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# remove nan values
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idx_nan = np.isnan(zs)
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xs = xs[~idx_nan]
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ys = ys[~idx_nan]
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zs = zs[~idx_nan]
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# find water level at the time the image was acquired
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idx_closest = np.argmin(np.abs(np.array([(dates_diff[i]['date sat'] - _).total_seconds() for _ in dates_tide])))
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tide_level = tide[idx_closest]
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ztide.append(tide_level)
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# find contour corresponding to the water level on 3D surface (if below minimum, add 0.05m increments)
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if tide_level < np.nanmin(survey3d['z']):
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tide_level = np.nanmin(survey3d['z'])
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sl_tide = measure.find_contours(survey3d['z'], tide_level)
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sl_tide = sl_tide[np.argmax(np.array([len(_) for _ in sl_tide]))]
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count = 0
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while len(sl_tide) < 900:
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count = count + 1
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tide_level = tide_level + 0.05*count
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sl_tide = measure.find_contours(survey3d['z'], tide_level)
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sl_tide = sl_tide[np.argmax(np.array([len(_) for _ in sl_tide]))]
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print('added ' + str(0.05*count) + ' cm - contour with ' + str(len(sl_tide)) + ' points')
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else:
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sl_tide = measure.find_contours(survey3d['z'], tide_level)
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sl_tide = sl_tide[np.argmax(np.array([len(_) for _ in sl_tide]))]
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# remove nans
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if np.any(np.isnan(sl_tide)):
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index_nan = np.where(np.isnan(sl_tide))[0]
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sl_tide = np.delete(sl_tide, index_nan, axis=0)
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# get x,y coordinates
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xtide = [survey3d['x'][int(np.round(sl_tide[m,0])), int(np.round(sl_tide[m,1]))] for m in range(sl_tide.shape[0])]
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ytide = [survey3d['y'][int(np.round(sl_tide[m,0])), int(np.round(sl_tide[m,1]))] for m in range(sl_tide.shape[0])]
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sl_gt.append(np.transpose(np.array([np.array(xtide), np.array(ytide)])))
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# interpolate SDS on 3D surface to get elevation (point by point)
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zq = []
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for j in range(sl_smooth.shape[0]):
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xq = sl_smooth[j,0]
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yq = sl_smooth[j,1]
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dist_q = np.linalg.norm(np.transpose(np.array([[xq - _ for _ in xs],[yq - _ for _ in ys]])), axis=1)
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idx_buffer = dist_q <= dist_buffer
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if sum(idx_buffer) > 0:
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tck = interpolate.bisplrep(xs[idx_buffer], ys[idx_buffer], zs[idx_buffer])
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zq.append(interpolate.bisplev(xq, yq, tck))
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zq = np.array(zq)
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plt.figure()
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plt.hist(zq, bins=100)
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plt.draw()
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# plt.figure()
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# plt.axis('equal')
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# plt.scatter(xs, ys, s=10, c=zs, marker='o', cmap=cm.get_cmap('jet'),
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# label='quad data')
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# plt.plot(xs[idx_buffer], ys[idx_buffer], 'ko')
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# plt.plot(xq,yq,'ro')
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# plt.draw()
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# store the alongshore median elevation
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zav.append(np.median(utils.reject_outliers(zq, m=2)))
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# make plot
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red = [255/255, 0, 0]
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gray = [0.75, 0.75, 0.75]
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plt.figure()
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plt.subplot(121)
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plt.axis('equal')
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plt.scatter(xs, ys, s=10, c=zs, marker='o', cmap=cm.get_cmap('jet'),
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label='3D survey')
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plt.plot(xtide, ytide, '--', color=gray, linewidth=2.5, label='tide level contour')
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plt.plot(sl_smooth[:,0], sl_smooth[:,1], '-', color=red, linewidth=2.5, label='SDS')
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# plt.plot(sl[i][idx_beach,0], sl[i][idx_beach,1], 'w-', linewidth=2)
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plt.xlabel('Eastings [m]')
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plt.ylabel('Northings [m]')
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plt.title('Shoreline comparison')
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plt.colorbar(label='mAHD')
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plt.legend()
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plt.ylim((6266100, 6267000))
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plt.subplot(122)
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plt.plot(np.linspace(0,1,len(zq)), zq, 'ko-', markersize=5)
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plt.plot([0, 1], [zav[i], zav[i]], 'r-', label='median')
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plt.plot([0, 1], [ztide[i], ztide[i]], 'g--', label = 'measured tide')
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plt.xlabel('Northings [m]')
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plt.ylabel('Elevation [mAHD]')
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plt.title('Alongshore SDS elevation')
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plt.legend()
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mng = plt.get_current_fig_manager()
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mng.window.showMaximized()
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plt.tight_layout()
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plt.draw()
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print(i)
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#%% Calculate some error statistics
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zav = np.array(zav)
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ztide = np.array(ztide)
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f = plt.figure()
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plt.subplot(3,1,1)
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plt.bar(np.linspace(1,len(zav),len(zav)), zav-ztide)
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plt.ylabel('Error in z [m]')
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plt.title('Elevation error')
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plt.xticks([])
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plt.draw()
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plt.subplot(3,1,2)
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plt.bar(np.linspace(1,len(zav),len(zav)), wave_hsig, color=orange)
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plt.ylabel('Hsig [m]')
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plt.xticks([])
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plt.draw()
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plt.subplot(3,1,3)
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plt.bar(np.linspace(1,len(zav),len(zav)), np.array(cloud_cover)*100, color='g')
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plt.ylabel('Cloud cover %')
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plt.xlabel('comparison #')
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plt.grid(False)
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plt.grid(axis='y')
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f.subplots_adjust(hspace=0)
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plt.draw()
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np.sqrt(np.mean((zav - ztide)**2))
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#%% plot to show LOWESS smoothing
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#i = 0
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#idx_beach = [np.min(np.linalg.norm(sl[i][k,:] - narrabeach, axis=1)) < dist_thresh for k in range(sl[i].shape[0])]
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#x = sl[i][idx_beach,0]
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#y = sl[i][idx_beach,1]
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#sl_smooth = lowess(x,y, frac=1./10, it = 10)
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#
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#plt.figure()
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#plt.axis('equal')
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#plt.scatter
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#plt.plot(x,y,'bo', linewidth=2, label='original SDS')
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#plt.plot(sl_smooth[:,1], sl_smooth[:,0], 'ro', linewidth=2, label='smoothed SDS')
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#plt.legend()
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#plt.xlabel('Eastings [m]')
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#plt.ylabel('Northings [m]')
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#plt.title('Local weighted scatterplot smoothing (LOWESS)')
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#plt.draw()
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