From ea32adf79bfe62997d0c306f0e30ec64375bb595 Mon Sep 17 00:00:00 2001 From: Kilian Vos Date: Thu, 29 Mar 2018 14:56:57 +1100 Subject: [PATCH] clean master most things transferred to development branch --- compare_shorelines.py | 222 -------------------------------- compare_shorelines2.py | 47 ------- data/data_2016.pkl | Bin 506207 -> 0 bytes data/data_gt_l8.pkl | Bin 616802 -> 0 bytes data/idx_nogt.pkl | Bin 210 -> 0 bytes data/narra_beach.pkl | Bin 520 -> 0 bytes data/quadbike_dates.mat | Bin 425 -> 0 bytes data/wl_final.pkl | Bin 58880652 -> 0 bytes data/wl_l8_comparison.pkl | Bin 365411 -> 0 bytes geocheck.py | 221 -------------------------------- get_coordinates.py | 96 -------------- plot_cloud_cover.py | 119 ----------------- sds_extract_test.py | 260 -------------------------------------- time_coverage.py | 136 -------------------- 14 files changed, 1101 deletions(-) delete mode 100644 compare_shorelines.py delete mode 100644 compare_shorelines2.py delete mode 100644 data/data_2016.pkl delete mode 100644 data/data_gt_l8.pkl delete mode 100644 data/idx_nogt.pkl delete mode 100644 data/narra_beach.pkl delete mode 100644 data/quadbike_dates.mat delete mode 100644 data/wl_final.pkl delete mode 100644 data/wl_l8_comparison.pkl delete mode 100644 geocheck.py delete mode 100644 get_coordinates.py delete mode 100644 plot_cloud_cover.py delete mode 100644 sds_extract_test.py delete mode 100644 time_coverage.py diff --git a/compare_shorelines.py b/compare_shorelines.py deleted file mode 100644 index 629a702..0000000 --- a/compare_shorelines.py +++ /dev/null @@ -1,222 +0,0 @@ -# -*- coding: utf-8 -*- - -# Preamble -import ee -import matplotlib.pyplot as plt -import matplotlib.cm as cm -import numpy as np -import pandas as pd -from datetime import datetime -import pickle -import pdb -import pytz -from pylab import ginput -import scipy.io as sio -import scipy.interpolate -import os - -# 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.morphology as morphology -import skimage.measure as measure - -# my functions -import functions.utils as utils -import functions.sds as sds - -np.seterr(all='ignore') # raise/ignore divisions by 0 and nans -ee.Initialize() - -au_tz = pytz.timezone('Australia/Sydney') - -#%% -# load SDS shorelines -with open('data\data_gt_l8.pkl', 'rb') as f: - data = pickle.load(f) - -# 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)) - -# remove overlapping images, keep the one with lowest cloud_cover -n = len(data['cloud_cover']) -idx_worst = [] -for i in range(n): - date_im = data['date_acquired'][i] - idx_double = np.isin(data['date_acquired'], date_im) - if sum(idx_double.astype(int)) > 1: - idx_worst.append(np.where(idx_double)[0][np.argmax(np.array(data['cloud_cover'])[idx_double])]) -dt_sat = [] -new_meta = {'contours':[], - 'cloud_cover':[], - 'geom_rmse_model':[], - 'gcp_model':[], - 'quality':[], - 'sun_azimuth':[], - 'sun_elevation':[]} -for i in range(n): - if not np.isin(i,idx_worst): - dt_sat.append(data['dt'][i].astimezone(au_tz)) - new_meta['contours'].append(data['contours'][i]) - new_meta['cloud_cover'].append(data['cloud_cover'][i]) - new_meta['geom_rmse_model'].append(data['geom_rmse_model'][i]) - new_meta['gcp_model'].append(data['gcp_model'][i]) - new_meta['quality'].append(data['quality'][i]) - new_meta['sun_azimuth'].append(data['sun_azimuth'][i]) - new_meta['sun_elevation'].append(data['sun_elevation'][i]) - -# calculate difference between days -diff_days = [ [(x - _).days for _ in dt_quad] for x in dt_sat] -day_thresh = 15 -idx_close = [utils.find_indices(_, lambda e: abs(e) < day_thresh) for _ in diff_days] - -# put everything in a dictionnary and save it -wl_comp = [] -for i in range(len(dt_sat)): - wl_comp.append({'sat dt': dt_sat[i], - 'quad dt': [dt_quad[_] for _ in idx_close[i]], - 'days diff': [diff_days[i][_] for _ in idx_close[i]], - 'contours': new_meta['contours'][i], - 'cloud_cover': new_meta['cloud_cover'][i], - 'geom_rmse_model': new_meta['geom_rmse_model'][i], - 'gcp_model': new_meta['gcp_model'][i], - 'quality': new_meta['quality'][i], - 'sun_azimuth': new_meta['sun_azimuth'][i], - 'sun_elevation': new_meta['sun_elevation'][i]}) - -with open('wl_l8_comparison.pkl', 'wb') as f: - pickle.dump(wl_comp, f) - -#%% -with open('data\wl_l8_comparison.pkl', 'rb') as f: - wl = pickle.load(f) -# load quadbike dates and convert from datenum to datetime -suffix = '.mat' -dir_name = os.getcwd() -subfolder_name = 'data\quadbike_surveys' -file_path = os.path.join(dir_name, subfolder_name) -file_names = os.listdir(file_path) - -for i in range(len(file_names)): - fn_mat = os.path.join(file_path, file_names[i]) - years = int(file_names[i][6:10]) - months = int(file_names[i][11:13]) - days = int(file_names[i][14:16]) - for j in range(len(wl)): - if wl[j]['quad dt'][0] == datetime(years, months, days, tzinfo=au_tz): - quad_mat = sio.loadmat(fn_mat) - wl[j].update({'quad_data':{'x':quad_mat['x'], - 'y':quad_mat['y'], - 'z':quad_mat['z'], - 'dt': datetime(years, months, days, tzinfo=au_tz)}}) -with open('data\wl_final.pkl', 'wb') as f: - pickle.dump(wl, f) -#%% -with open('data\wl_final.pkl', 'rb') as f: - wl = pickle.load(f) - -i = 0 -x = wl[i]['quad_data']['x'] -y = wl[i]['quad_data']['y'] -z = wl[i]['quad_data']['z'] -x = x.reshape(x.shape[0] * x.shape[1]) -y = y.reshape(y.shape[0] * y.shape[1]) -z = z.reshape(z.shape[0] * z.shape[1]) -idx_nan = np.isnan(z) - -x_nan = x[idx_nan] -y_nan = y[idx_nan] -z_nan = z[idx_nan] - -x_nonan = x[~idx_nan] -y_nonan = y[~idx_nan] -z_nonan = z[~idx_nan] - -xs = x_nonan[::10] -ys = y_nonan[::10] -zs = z_nonan[::10] - -xq = wl[i]['contours'][:,0] -yq = wl[i]['contours'][:,1] - -# cut xq around xs -np.min(xs) -np.max(xs) -np.min(ys) -np.max(ys) - -idx_x = np.logical_and(xq < np.max(xs), xq > np.min(xs)) -idx_y = np.logical_and(yq < np.max(ys), yq > np.min(ys)) -idx_in = np.logical_and(idx_x, idx_y) - -xq = xq[idx_in] -yq = yq[idx_in] - -for i in range(len(xq)): - idx_x = np.logical_and(xs < xq[i] + 10, xs > xq[i] - 10) - idx_y = np.logical_and(ys < yq[i] + 10, ys > yq[i] - 10) - xint = xs[idx_x] - yint = ys[idx_y] - -f = interpolate.interp2d(xs, ys, zs, kind='linear') -zq = f(xq,yq) - -plt.figure() -plt.grid() -plt.scatter(xs, ys, s=10, c=zs, marker='o', cmap=cm.get_cmap('jet'), - label='quad data') -plt.plot(xq,yq,'r-o', markersize=5, label='SDS') -plt.axis('equal') -plt.legend() -plt.colorbar(label='mAHD') -plt.xlabel('Eastings [m]') -plt.ylabel('Northings [m]') -plt.show() - -plt.figure() -plt.plot(zq[:,0]) -plt.show() - - -plt.figure() -plt.grid() -plt.scatter(x_nonan, y_nonan, s=10, c=z_nonan, marker='o', cmap=cm.get_cmap('jet'), - label='quad data') -#plt.plot(x_nan, y_nan, 'k.', label='nans') -plt.plot(xq,yq,'r-o', markersize=5, label='SDS') -plt.axis('equal') -plt.legend() -plt.colorbar(label='mAHD') -plt.xlabel('Eastings [m]') -plt.ylabel('Northings [m]') -plt.show() - - -z2 = scipy.interpolate.griddata([x, y], z, [xq, yq], method='linear') - -f_interp = scipy.interpolate.interp2d(x1,y1,z1, kind='linear') - - -sio.savemat('shoreline1.mat', {'x':xq, 'y':yq}) - -from scipy import interpolate -x = np.arange(-5.01, 5.01, 0.01) -y = np.arange(-5.01, 5.01, 0.01) -xx, yy = np.meshgrid(x, y) -z = np.sin(xx**2+yy**2) -f = interpolate.interp2d(x, y, z, kind='cubic') - -xnew = np.arange(-5.01, 5.01, 1e-2) -ynew = np.arange(-5.01, 5.01, 1e-2) -znew = f(xnew, ynew) -plt.plot(x, z[:, 0], 'ro-', xnew, znew[:, 0], 'b-') -plt.show() \ No newline at end of file diff --git a/compare_shorelines2.py b/compare_shorelines2.py deleted file mode 100644 index b38ba1a..0000000 --- a/compare_shorelines2.py +++ /dev/null @@ -1,47 +0,0 @@ -# -*- coding: utf-8 -*- - -# Preamble -import ee -import matplotlib.pyplot as plt -import matplotlib.cm as cm -import numpy as np -import pandas as pd -from datetime import datetime -import pickle -import pdb -import pytz -from pylab import ginput -import scipy.io as sio -import scipy.interpolate -import os - -# 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.morphology as morphology -import skimage.measure as measure - -# my functions -import functions.utils as utils -import functions.sds as sds - -np.seterr(all='ignore') # raise/ignore divisions by 0 and nans -ee.Initialize() - -with open('data\wl_final.pkl', 'rb') as f: - wl = pickle.load(f) - -i = 0 -x = wl[i]['quad_data']['x'] 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