From 1093ecfeba0f712cbe3c719fbf7e90cea777e080 Mon Sep 17 00:00:00 2001 From: kvos Date: Tue, 3 Apr 2018 20:55:19 +1000 Subject: [PATCH] reorganisation --- .gitignore | 3 +- data/L7/NARRA/NARRA_epsgcode.pkl | 1 + data/L7/NARRA/NARRA_timestamps.pkl | Bin 0 -> 10770 bytes data/L8/NARRA/NARRA_epsgcode.pkl | 1 + data/L8/NARRA/NARRA_output.pkl | Bin 0 -> 1287432 bytes data/L8/NARRA/NARRA_timestamps.pkl | Bin 0 -> 5688 bytes data/quadbike_dates.mat | Bin 425 -> 0 bytes download_images.py | 16 +- download_images_L7.py | 118 +++++++++ functions/sds.py | 21 +- box_head.py => oldcodes/box_head.py | 0 .../compare_shorelines.py | 0 .../compare_shorelines2.py | 0 geocheck.py => oldcodes/geocheck.py | 0 .../get_coordinates.py | 0 .../plot_cloud_cover.py | 0 sds_extract.py => oldcodes/sds_extract.py | 0 .../sds_extract_allcode.py | 0 .../sds_extract_test.py | 0 oldcodes/time_coverage.py | 136 ++++++++++ {data => olddata}/data_2016.pkl | Bin {data => olddata}/data_gt_l8.pkl | Bin {data => olddata}/idx_nogt.pkl | Bin {data => olddata}/narra_beach.pkl | Bin {data => olddata}/wl_final.pkl | Bin {data => olddata}/wl_l8_comparison.pkl | Bin read_images.py | 37 ++- test_L7image.py | 75 ++++++ time_coverage.py | 242 ++++++++++-------- 29 files changed, 529 insertions(+), 121 deletions(-) create mode 100644 data/L7/NARRA/NARRA_epsgcode.pkl create mode 100644 data/L7/NARRA/NARRA_timestamps.pkl create mode 100644 data/L8/NARRA/NARRA_epsgcode.pkl create mode 100644 data/L8/NARRA/NARRA_output.pkl create mode 100644 data/L8/NARRA/NARRA_timestamps.pkl delete mode 100644 data/quadbike_dates.mat create mode 100644 download_images_L7.py rename box_head.py => oldcodes/box_head.py (100%) rename compare_shorelines.py => oldcodes/compare_shorelines.py (100%) rename compare_shorelines2.py => oldcodes/compare_shorelines2.py (100%) rename geocheck.py => oldcodes/geocheck.py (100%) rename get_coordinates.py => oldcodes/get_coordinates.py (100%) rename plot_cloud_cover.py => oldcodes/plot_cloud_cover.py (100%) rename sds_extract.py => oldcodes/sds_extract.py (100%) rename sds_extract_allcode.py => oldcodes/sds_extract_allcode.py (100%) rename sds_extract_test.py => oldcodes/sds_extract_test.py (100%) create mode 100644 oldcodes/time_coverage.py rename {data => olddata}/data_2016.pkl (100%) rename {data => olddata}/data_gt_l8.pkl (100%) rename {data => olddata}/idx_nogt.pkl (100%) rename {data => olddata}/narra_beach.pkl (100%) rename {data => olddata}/wl_final.pkl (100%) rename {data => olddata}/wl_l8_comparison.pkl (100%) create mode 100644 test_L7image.py diff --git a/.gitignore b/.gitignore index 3fd6e7e..c633bc7 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ *.pyc -*.mat \ No newline at end of file +*.mat +*.tif \ No newline at end of file diff --git a/data/L7/NARRA/NARRA_epsgcode.pkl b/data/L7/NARRA/NARRA_epsgcode.pkl new file mode 100644 index 0000000..a8a7a3d --- /dev/null +++ b/data/L7/NARRA/NARRA_epsgcode.pkl @@ -0,0 +1 @@ +M. \ No 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zcmeZu4DoSvQZUssQ1EpO(M`+DN!3vZ$Vn_o%P-2cQV4Jk_w+L}(NSB|pfD>5=LRLprhIXkb}fv1I6 zXTk*$p#_|cExSXr{4e}@{k5uf-+cF%?U7TI6xdfhN)#`%k&D(A zPg_pgz0>Z_y^m&V*gKAY=vdBa&{dF;`TQk}VE(A)6*U>0M-bQCksT)OoTp8ay$c%a-M>n<=pO za9)G9cdWIQN#DeC(~6=trtetxVd=RDBmXnkePV6*8vB=W);x~CSlYX8L-F%9QP1yg mKE7xBm;0CW9VQ=4OK8?RnCYLo^#1|Nb06 0: + morphology.remove_small_objects(cloud_mask, min_size=5, connectivity=8, in_place=True) + + if plot_bool: + plt.figure() + plt.imshow(cloud_mask, cmap='gray') + plt.draw() #cloud_shadow_values = [2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020] #cloud_shadow_mask = np.isin(im_qa, cloud_shadow_values) diff --git a/box_head.py b/oldcodes/box_head.py similarity index 100% rename from box_head.py rename to oldcodes/box_head.py diff --git a/compare_shorelines.py b/oldcodes/compare_shorelines.py similarity index 100% rename from compare_shorelines.py rename to oldcodes/compare_shorelines.py diff --git a/compare_shorelines2.py b/oldcodes/compare_shorelines2.py similarity index 100% rename from compare_shorelines2.py rename to oldcodes/compare_shorelines2.py diff --git a/geocheck.py b/oldcodes/geocheck.py similarity index 100% rename from geocheck.py rename to oldcodes/geocheck.py diff --git a/get_coordinates.py b/oldcodes/get_coordinates.py similarity index 100% rename from get_coordinates.py rename to oldcodes/get_coordinates.py diff --git a/plot_cloud_cover.py b/oldcodes/plot_cloud_cover.py similarity index 100% rename from plot_cloud_cover.py rename to oldcodes/plot_cloud_cover.py diff --git a/sds_extract.py b/oldcodes/sds_extract.py similarity index 100% rename from sds_extract.py rename to oldcodes/sds_extract.py diff --git a/sds_extract_allcode.py b/oldcodes/sds_extract_allcode.py similarity index 100% rename from sds_extract_allcode.py rename to oldcodes/sds_extract_allcode.py diff --git a/sds_extract_test.py b/oldcodes/sds_extract_test.py similarity index 100% rename from sds_extract_test.py rename to oldcodes/sds_extract_test.py diff --git a/oldcodes/time_coverage.py b/oldcodes/time_coverage.py new file mode 100644 index 0000000..90c7971 --- /dev/null +++ b/oldcodes/time_coverage.py @@ -0,0 +1,136 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Mar 20 16:15:51 2018 + +@author: z5030440 +""" + +import scipy.io as sio +import os +import ee +import matplotlib.pyplot as plt +import matplotlib.dates as mdates +import numpy as np +import pandas as pd +from datetime import datetime, timedelta +import pickle +import pdb +import pytz + + +# image processing modules +import skimage.filters as filters +import skimage.exposure as exposure +import skimage.transform as transform +import skimage.morphology as morphology +import skimage.measure as measure +import sklearn.decomposition as decomposition +from scipy import spatial +# my functions +import functions.utils as utils +import functions.sds as sds +#plt.rcParams['axes.grid'] = True +au_tz = pytz.timezone('Australia/Sydney') + +# load quadbike dates and convert from datenum to datetime +suffix = '.mat' +dir_name = os.getcwd() +file_name = 'data\quadbike_dates' +file_path = os.path.join(dir_name, file_name + suffix) +quad_dates = sio.loadmat(file_path)['dates'] +dt_quad = [] +for i in range(quad_dates.shape[0]): + dt_quad.append(datetime(quad_dates[i,0], quad_dates[i,1], quad_dates[i,2], tzinfo=au_tz)) + +# load satellite datetimes (in UTC) and convert to AEST time +input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') +# location (Narrabeen-Collaroy beach) +rect_narra = [[[151.3473129272461,-33.69035274454718], + [151.2820816040039,-33.68206818063878], + [151.27281188964844,-33.74775138989556], + [151.3425064086914,-33.75231878701767], + [151.3473129272461,-33.69035274454718]]]; +flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra)) +n_img = flt_col.size().getInfo() +print('Number of images covering Narrabeen:', n_img) +im_all = flt_col.getInfo().get('features') + +# extract datetimes from image metadata +dt_sat = [_['properties']['system:time_start'] for _ in im_all] +dt_sat = [datetime.fromtimestamp(_/1000, tz=pytz.utc) for _ in dt_sat] +dt_sat = [_.astimezone(au_tz) for _ in dt_sat] +# calculate days difference +diff_days = [ [(x - _).days for _ in dt_quad] for x in dt_sat] +day_thresh = 15 +idx = [utils.find_indices(_, lambda e: abs(e) < day_thresh) for _ in diff_days] + +dt_diff = [] +idx_nogt = [] +for i in range(n_img): + if not idx[i]: + idx_nogt.append(i) + continue + dt_diff.append({"sat dt": dt_sat[i], + "quad dt": [dt_quad[_] for _ in idx[i]], + "days diff": [diff_days[i][_] for _ in idx[i]] }) + +with open('idx_nogt.pkl', 'wb') as f: + pickle.dump(idx_nogt, f) + + +#%% +dates_sat = mdates.date2num(dt_sat) +dates_quad = mdates.date2num(dt_quad) +plt.figure() +plt.plot_date(dates_sat, np.zeros((n_img,1))) +plt.plot_date(dates_quad, np.ones((len(dates_quad),1))) +plt.show() + +data = pd.read_pickle('data_2016.pkl') + +dt_sat = [_.astimezone(au_tz) for _ in data['dt']] + +[ (_ - dt_sat[0]).days for _ in dt_quad] + + + +dn_sat = [] +for i in range(len(dt_sat)): dn_sat.append(dt_sat[i].toordinal()) +dn_sat = np.array(dn_sat) +dn_sur = [] +for i in range(len(dt_survey)): dn_sur.append(dt_survey[i].toordinal()) +dn_sur = np.array(dn_sur) + +distances = np.zeros((len(dn_sat),4)).astype('int32') +indexes = np.zeros((len(dn_sat),2)).astype('int32') +for i in range(len(dn_sat)): + distances[i,0] = np.sort(abs(dn_sat[i] - dn_sur))[0] + distances[i,1] = np.sort(abs(dn_sat[i] - dn_sur))[1] + distances[i,2] = dt_sat[i].year + distances[i,3] = dt_sat[i].month + indexes[i,0] = np.where(abs(dn_sat[i] - dn_sur) == np.sort(abs(dn_sat[i] - dn_sur))[0])[0][0] + indexes[i,1] = np.where(abs(dn_sat[i] - dn_sur) == np.sort(abs(dn_sat[i] - dn_sur))[1])[0][0] + + +years = [2013, 2014, 2015, 2016] +months = mdates.MonthLocator() +days = mdates.DayLocator() +month_fmt = mdates.DateFormatter('%b') +f, ax = plt.subplots(4, 1) +for i, ca in enumerate(ax): + ca.xaxis.set_major_locator(months) + ca.xaxis.set_major_formatter(month_fmt) + ca.xaxis.set_minor_locator(days) + ca.set_ylabel(str(years[i])) + for j in range(len(dt_sat)): + if dt_sat[j].year == years[i]: + ca.plot(dt_sat[j],0, 'bo', markerfacecolor='b') +#f.subplots_adjust(hspace=0) +#plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False) + + +plt.plot(dt_survey, np.zeros([len(dt_survey),1]), 'bo') +plt.plot(dt_sat, np.ones([len(dt_sat),1]), 'ro') +plt.yticks([]) +plt.show() + diff --git a/data/data_2016.pkl b/olddata/data_2016.pkl similarity index 100% rename from data/data_2016.pkl rename to olddata/data_2016.pkl diff --git a/data/data_gt_l8.pkl b/olddata/data_gt_l8.pkl similarity index 100% rename from data/data_gt_l8.pkl rename to olddata/data_gt_l8.pkl diff --git a/data/idx_nogt.pkl b/olddata/idx_nogt.pkl similarity index 100% rename from data/idx_nogt.pkl rename to olddata/idx_nogt.pkl diff --git a/data/narra_beach.pkl b/olddata/narra_beach.pkl similarity index 100% rename from data/narra_beach.pkl rename to olddata/narra_beach.pkl diff --git a/data/wl_final.pkl b/olddata/wl_final.pkl similarity index 100% rename from data/wl_final.pkl rename to olddata/wl_final.pkl diff --git a/data/wl_l8_comparison.pkl b/olddata/wl_l8_comparison.pkl similarity index 100% rename from data/wl_l8_comparison.pkl rename to olddata/wl_l8_comparison.pkl diff --git a/read_images.py b/read_images.py index 4b08ff9..d99cefe 100644 --- a/read_images.py +++ b/read_images.py @@ -31,6 +31,8 @@ import functions.utils as utils import functions.sds as sds np.seterr(all='ignore') # raise/ignore divisions by 0 and nans +plt.rcParams['axes.grid'] = True +plt.rcParams['figure.max_open_warning'] = 100 ee.Initialize() # initial settings @@ -73,7 +75,7 @@ for i in range(N): im_ms = np.stack(bands, 2) # cloud mask im_qa = im_ms[:,:,5] - cloud_mask = sds.create_cloud_mask(im_qa) + cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool) cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]), order=0, preserve_range=True, mode='constant').astype('bool_') @@ -105,16 +107,19 @@ for i in range(N): # convert to output epsg spatial reference wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg) -# plt.figure() -# plt.imshow(im_ms_ps[:,:,[2,1,0]]) -# for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2) -# plt.axis('image') -# plt.title(file_names_pan[i]) -# plt.show() - plt.figure() - centroids = [] cmap = cm.get_cmap('jet') + + plt.subplot(121) + plt.imshow(im_ms_ps[:,:,[2,1,0]]) + for j,contour in enumerate(wl_pix): + colours = cmap(np.linspace(0, 1, num=len(wl))) + plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color=colours[j,:]) + plt.axis('image') + plt.title(file_names_pan[i]) + + plt.subplot(122) + centroids = [] for j,contour in enumerate(wl): colours = cmap(np.linspace(0, 1, num=len(wl))) centroids.append([np.mean(contour[:, 0]),np.mean(contour[:, 1])]) @@ -122,8 +127,18 @@ for i in range(N): plt.plot(np.mean(contour[:, 0]), np.mean(contour[:, 1]), 'o', color=colours[j,:]) plt.axis('equal') plt.title(file_names_pan[i]) + mng = plt.get_current_fig_manager() + mng.window.showMaximized() + plt.tight_layout() plt.draw() + pt_in = np.array(ginput(1)) + + if pt_in[0][0] < 10000: + print('skipped m ' + str(i)) + idx_high_cloud.append(i) + continue + dist_centroid = [np.linalg.norm(_ - pt_in) for _ in centroids] shorelines.append(wl[np.argmin(dist_centroid)]) t.append(timestamps_sorted[i]) @@ -137,4 +152,6 @@ for i in range(N): output = {'t':t, 'shorelines':shorelines} with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'wb') as f: - pickle.dump(output, f) \ No newline at end of file + pickle.dump(output, f) + + diff --git a/test_L7image.py b/test_L7image.py new file mode 100644 index 0000000..945c588 --- /dev/null +++ b/test_L7image.py @@ -0,0 +1,75 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Mar 27 17:12:35 2018 + +@author: Kilian +""" + +# Initial settings +import os +import numpy as np +import matplotlib.pyplot as plt +import ee +import pdb + +# other modules +from osgeo import gdal, ogr, osr +import pickle +import matplotlib.cm as cm +from pylab import ginput + +# image processing modules +import skimage.filters as filters +import skimage.exposure as exposure +import skimage.transform as transform +import sklearn.decomposition as decomposition +import skimage.measure as measure + + +# import own modules +import functions.utils as utils +import functions.sds as sds + +np.seterr(all='ignore') # raise/ignore divisions by 0 and nans +plt.rcParams['axes.grid'] = True +plt.rcParams['figure.max_open_warning'] = 100 +ee.Initialize() + +# initial settings +cloud_thresh = 0.5 # threshold for cloud cover +plot_bool = False # if you want the plots +prob_high = 99.9 # upper probability to clip and rescale pixel intensity +min_contour_points = 100# minimum number of points contained in each water line +output_epsg = 28356 # GDA94 / MGA Zone 56 + +satname = 'L7' +sitename = 'NARRA' +filepath = os.path.join(os.getcwd(), 'data', satname, sitename) +with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f: + timestamps = pickle.load(f) +timestamps_sorted = sorted(timestamps) +with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f: + input_epsg = pickle.load(f) + +file_path_pan = os.path.join(filepath, 'pan') +file_path_ms = os.path.join(filepath, 'ms') +file_names_pan = os.listdir(file_path_pan) +file_names_ms = os.listdir(file_path_ms) +N = len(file_names_pan) +idx_high_cloud = [] +t = [] +shorelines = [] + +for i in range(N): + # read pan image + fn_pan = os.path.join(file_path_pan, file_names_pan[i]) + data = gdal.Open(fn_pan, gdal.GA_ReadOnly) + georef = np.array(data.GetGeoTransform()) + bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)] + im_pan = np.stack(bands, 2)[:,:,0] + # read ms image + fn_ms = os.path.join(file_path_ms, file_names_ms[i]) + data = gdal.Open(fn_ms, gdal.GA_ReadOnly) + bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)] + im_ms = np.stack(bands, 2) + diff --git a/time_coverage.py b/time_coverage.py index 90c7971..e84ff07 100644 --- a/time_coverage.py +++ b/time_coverage.py @@ -5,18 +5,18 @@ Created on Tue Mar 20 16:15:51 2018 @author: z5030440 """ -import scipy.io as sio import os -import ee +import numpy as np import matplotlib.pyplot as plt +import pdb +import ee + import matplotlib.dates as mdates -import numpy as np -import pandas as pd +import matplotlib.cm as cm from datetime import datetime, timedelta import pickle -import pdb import pytz - +import scipy.io as sio # image processing modules import skimage.filters as filters @@ -29,108 +29,150 @@ from scipy import spatial # my functions import functions.utils as utils import functions.sds as sds -#plt.rcParams['axes.grid'] = True + +np.seterr(all='ignore') # raise/ignore divisions by 0 and nans +plt.rcParams['axes.grid'] = True +plt.rcParams['figure.max_open_warning'] = 100 + au_tz = pytz.timezone('Australia/Sydney') # load quadbike dates and convert from datenum to datetime -suffix = '.mat' -dir_name = os.getcwd() -file_name = 'data\quadbike_dates' -file_path = os.path.join(dir_name, file_name + suffix) -quad_dates = sio.loadmat(file_path)['dates'] -dt_quad = [] -for i in range(quad_dates.shape[0]): - dt_quad.append(datetime(quad_dates[i,0], quad_dates[i,1], quad_dates[i,2], tzinfo=au_tz)) - -# load satellite datetimes (in UTC) and convert to AEST time -input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') -# location (Narrabeen-Collaroy beach) -rect_narra = [[[151.3473129272461,-33.69035274454718], - [151.2820816040039,-33.68206818063878], - [151.27281188964844,-33.74775138989556], - [151.3425064086914,-33.75231878701767], - [151.3473129272461,-33.69035274454718]]]; -flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra)) -n_img = flt_col.size().getInfo() -print('Number of images covering Narrabeen:', n_img) -im_all = flt_col.getInfo().get('features') - -# extract datetimes from image metadata -dt_sat = [_['properties']['system:time_start'] for _ in im_all] -dt_sat = [datetime.fromtimestamp(_/1000, tz=pytz.utc) for _ in dt_sat] -dt_sat = [_.astimezone(au_tz) for _ in dt_sat] +filename = 'data\quadbike\survey_dates.mat' +filepath = os.path.join(os.getcwd(), filename) +dates_quad = sio.loadmat(filepath)['dates'] # matrix containing year, month, day +dates_quad = [datetime(dates_quad[i,0], dates_quad[i,1], dates_quad[i,2], + tzinfo=au_tz) for i in range(dates_quad.shape[0])] + +# load timestamps from satellite images +satname = 'L8' +sitename = 'NARRA' +filepath = os.path.join(os.getcwd(), 'data', satname, sitename) +with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f: + output = pickle.load(f) +dates_l8 = output['t'] +# convert to AEST +dates_l8 = [_.astimezone(au_tz) for _ in dates_l8] + +# load wave data +filename = 'data\wave\SydneyProcessed.mat' +filepath = os.path.join(os.getcwd(), filename) +wave_data = sio.loadmat(filepath) +idx = utils.find_indices(wave_data['dates'][:,0], lambda e: e >= dates_l8[0].year and e <= dates_l8[-1].year) +hsig = np.array([wave_data['Hsig'][i][0] for i in idx]) +wdir = np.array([wave_data['Wdir'][i][0] for i in idx]) +dates_wave = [datetime(wave_data['dates'][i,0], wave_data['dates'][i,1], + wave_data['dates'][i,2], wave_data['dates'][i,3], + wave_data['dates'][i,4], wave_data['dates'][i,5], + tzinfo=au_tz) for i in idx] +#%% make a plot of all the dates +f = plt.figure() +months = mdates.MonthLocator() +month_fmt = mdates.DateFormatter('%b %Y') +days = mdates.DayLocator() +years = [2013,2014,2015,2016] +for k in range(len(years)): + sel_year = years[k] + ax = plt.subplot(4,1,k+1) + idx_year = utils.find_indices(dates_wave, lambda e : e.year >= sel_year and e.year <= sel_year) + plt.plot([dates_wave[i] for i in idx_year], [hsig[i] for i in idx_year], 'k-', linewidth=0.5) + hsigmax = np.nanmax([hsig[i] for i in idx_year]) + cbool = True + for j in range(len(dates_quad)): + if dates_quad[j].year == sel_year: + if cbool: + plt.plot([dates_quad[j], dates_quad[j]], [0, hsigmax], color=[255/255,140/255,0], label='survey') + cbool = False + else: + plt.plot([dates_quad[j], dates_quad[j]], [0, hsigmax], color=[255/255,140/255,0]) + cbool = True + for j in range(len(dates_l8)): + if dates_l8[j].year == sel_year: + if cbool: + plt.plot([dates_l8[j], dates_l8[j]], [0, hsigmax], color=[0,191/255,255/255], label='landsat8') + cbool = False + else: + plt.plot([dates_l8[j], dates_l8[j]], [0, hsigmax], color=[0,191/255,255/255]) + if k == 3: + plt.legend() + plt.xlim((datetime(sel_year,1,1), datetime(sel_year,12,31, tzinfo=au_tz))) + plt.ylim((0, hsigmax)) + plt.ylabel('Hs [m]') + ax.xaxis.set_major_locator = months + ax.xaxis.set_major_formatter(month_fmt) +f.subplots_adjust(hspace=0.2) +plt.draw() +#%% + # calculate days difference -diff_days = [ [(x - _).days for _ in dt_quad] for x in dt_sat] -day_thresh = 15 -idx = [utils.find_indices(_, lambda e: abs(e) < day_thresh) for _ in diff_days] - -dt_diff = [] -idx_nogt = [] -for i in range(n_img): - if not idx[i]: - idx_nogt.append(i) +diff_days = [ [(x - _).days for _ in dates_quad] for x in dates_l8] +max_diff = 5 +idx_closest = [utils.find_indices(_, lambda e: abs(e) <= max_diff) for _ in diff_days] +dates_diff = [] +for i in range(len(idx_closest)): + if not idx_closest[i]: continue - dt_diff.append({"sat dt": dt_sat[i], - "quad dt": [dt_quad[_] for _ in idx[i]], - "days diff": [diff_days[i][_] for _ in idx[i]] }) - -with open('idx_nogt.pkl', 'wb') as f: - pickle.dump(idx_nogt, f) - + elif len(idx_closest[i]) > 1: + idx_best = np.argmin(np.abs([diff_days[i][_] for _ in idx_closest[i]])) + dates_temp = [dates_quad[_] for _ in idx_closest[i]] + days_temp = [diff_days[i][_] for _ in idx_closest[i]] + dates_diff.append({"date sat": dates_l8[i], + "date quad": dates_temp[idx_best], + "days diff": days_temp[idx_best]}) + else: + dates_diff.append({"date sat": dates_l8[i], + "date quad": dates_quad[idx_closest[i][0]], + "days diff": diff_days[i][idx_closest[i][0]] + }) + +np.mean([ np.abs(_['days diff']) for _ in dates_diff]) + +#%% + +# load quadbike .mat files +foldername = 'data\quadbike\surveys3D' +folderpath = os.path.join(os.getcwd(), foldername) +filenames = os.listdir(folderpath) + +# load the satellite shorelines +sl = output['shorelines'] + +dates_quad = [datetime(int(_[6:10]), int(_[11:13]), int(_[14:16]), tzinfo= au_tz) for _ in filenames] +# for each satellite shoreline, load the corresponding 3D survey +for i in range(len(dates_diff)): + idx_closest = np.argmin(np.abs(np.array([(dates_diff[i]['date sat'] - _).days for _ in dates_quad]))) + survey3d = sio.loadmat(os.path.join(folderpath, filenames[idx_closest])) -#%% -dates_sat = mdates.date2num(dt_sat) -dates_quad = mdates.date2num(dt_quad) -plt.figure() -plt.plot_date(dates_sat, np.zeros((n_img,1))) -plt.plot_date(dates_quad, np.ones((len(dates_quad),1))) -plt.show() - -data = pd.read_pickle('data_2016.pkl') - -dt_sat = [_.astimezone(au_tz) for _ in data['dt']] - -[ (_ - dt_sat[0]).days for _ in dt_quad] - - - -dn_sat = [] -for i in range(len(dt_sat)): dn_sat.append(dt_sat[i].toordinal()) -dn_sat = np.array(dn_sat) -dn_sur = [] -for i in range(len(dt_survey)): dn_sur.append(dt_survey[i].toordinal()) -dn_sur = np.array(dn_sur) + plt.figure() + plt.axis('equal') + plt.scatter(survey3d['x'], survey3d['y'], s=10, c=survey3d['z'], marker='o', cmap=cm.get_cmap('jet'), + label='quad data') + plt.plot(sl[i][:,0], sl[i][:,1], 'ko') + plt.draw() + +import statsmodels.api as sm +lowess = sm.nonparametric.lowess -distances = np.zeros((len(dn_sat),4)).astype('int32') -indexes = np.zeros((len(dn_sat),2)).astype('int32') -for i in range(len(dn_sat)): - distances[i,0] = np.sort(abs(dn_sat[i] - dn_sur))[0] - distances[i,1] = np.sort(abs(dn_sat[i] - dn_sur))[1] - distances[i,2] = dt_sat[i].year - distances[i,3] = dt_sat[i].month - indexes[i,0] = np.where(abs(dn_sat[i] - dn_sur) == np.sort(abs(dn_sat[i] - dn_sur))[0])[0][0] - indexes[i,1] = np.where(abs(dn_sat[i] - dn_sur) == np.sort(abs(dn_sat[i] - dn_sur))[1])[0][0] -years = [2013, 2014, 2015, 2016] -months = mdates.MonthLocator() -days = mdates.DayLocator() -month_fmt = mdates.DateFormatter('%b') -f, ax = plt.subplots(4, 1) -for i, ca in enumerate(ax): - ca.xaxis.set_major_locator(months) - ca.xaxis.set_major_formatter(month_fmt) - ca.xaxis.set_minor_locator(days) - ca.set_ylabel(str(years[i])) - for j in range(len(dt_sat)): - if dt_sat[j].year == years[i]: - ca.plot(dt_sat[j],0, 'bo', markerfacecolor='b') -#f.subplots_adjust(hspace=0) -#plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False) - - -plt.plot(dt_survey, np.zeros([len(dt_survey),1]), 'bo') -plt.plot(dt_sat, np.ones([len(dt_sat),1]), 'ro') -plt.yticks([]) +# For the 1D case: +x = sl[i][:,0] +y = sl[i][:,1] +x0 = x +f_hat = lo.lowess(x, y, x) +fig,ax = plt.subplots(1) +ax.scatter(x,y) +ax.plot(x0,f_hat,'ro') plt.show() +# 2D case (and more...) +x = np.random.randn(2, 100) +f = -1 * np.sin(x[0]) + 0.5 * np.cos(x[1]) + 0.2*np.random.randn(100) +x0 = np.mgrid[-1:1:.1, -1:1:.1] +x0 = np.vstack([x0[0].ravel(), x0[1].ravel()]) +f_hat = lo.lowess(x, f, x0, kernel=lo.tri_cube) +from mpl_toolkits.mplot3d import Axes3D +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x[0], x[1], f) +ax.scatter(x0[0], x0[1], f_hat, color='r') +