diff --git a/Narra1.csv b/Narra1.csv
new file mode 100644
index 0000000..e50615e
--- /dev/null
+++ b/Narra1.csv
@@ -0,0 +1,28385 @@
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+29777,0.14731335772914395,0.12418860226915607,0.11078543766556499,0.19401404550932078,0.20050530772186065,0.11813389509916306,0.2194370277169255,0.2730602000266564,0.14152689092452767,0.23504199833873088,3.0
+29778,0.1255248614384584,0.09244819173311622,0.07513320719075675,0.18006731092351105,0.19335853662884353,0.0809386745095253,0.32151976066035387,0.4111829572609619,0.25113196091216033,0.3530719709576937,3.0
+29779,0.1428394890725579,0.11059947973545065,0.10220278978882937,0.20873829259455698,0.19748691158387252,0.10615827143192291,0.307319776621002,0.3426227952547316,0.165835461017384,0.2820229464740149,3.0
+29780,0.1472677286434667,0.11168756775041636,0.10634836508140401,0.23336709486049365,0.2012673415315959,0.11022879928350449,0.3526384086201593,0.373897407556353,0.1613437182202368,0.28623859579865785,3.0
+29781,0.14160295707965412,0.11423876997342976,0.1074561154997382,0.2191909748373682,0.20170749940918467,0.11028779298067093,0.31476557354981255,0.34206598694118895,0.1371033836522095,0.2768468499617867,3.0
diff --git a/functions/sds.py b/functions/sds.py
index 8bb8fa6..b028c91 100644
--- a/functions/sds.py
+++ b/functions/sds.py
@@ -397,10 +397,6 @@ def pansharpen(im_ms, im_pan, cloud_mask, plot_bool):
     vec_pcs[:,0] = hist_match(vec_pan, vec_pcs[:,0])
     vec_ms_ps = pca.inverse_transform(vec_pcs)
     
-    # normalise between 0 and 1
-    for i in range(vec_pcs.shape[1]):
-        vec_ms_ps[:,i] = np.divide(vec_ms_ps[:,i] - np.min(vec_ms_ps[:,i]),
-                         np.max(vec_ms_ps[:,i]) - np.min(vec_ms_ps[:,i]))
     # reshape vector into image
     vec_ms_ps_full = np.ones((len(vec_mask), im_ms.shape[2])) * np.nan
     vec_ms_ps_full[~vec_mask,:] = vec_ms_ps
@@ -409,11 +405,11 @@ def pansharpen(im_ms, im_pan, cloud_mask, plot_bool):
     if plot_bool:
         plt.figure()
         ax1 = plt.subplot(121)
-        plt.imshow(im_ms[:,:,[2,1,0]])
+        plt.imshow(rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 100, False))
         plt.axis('off')
         plt.title('Original')
         ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
-        plt.imshow(im_ms_ps[:,:,[2,1,0]])
+        plt.imshow(rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False))
         plt.axis('off')
         plt.title('Pansharpened')
         plt.show()
@@ -669,15 +665,16 @@ def classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size
     # find the class with maximum reflection in the B,G,R,Pan
     im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
     im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+    im_sand = morphology.binary_erosion(im_sand, morphology.disk(1))
 #    im_sand = morphology.binary_dilation(im_sand, morphology.disk(1))
     
     if plot_bool:
-        im = np.copy(im_ms_ps)
+        im = np.copy(rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False))
         im[im_sand,0] = 0
         im[im_sand,1] = 0
         im[im_sand,2] = 1
         plt.figure()
-        plt.imshow(im[:,:,[2,1,0]])
+        plt.imshow(im)
         plt.axis('image')
         plt.title('Sand classification')
         plt.show()
diff --git a/functions/sds_old1.py b/functions/sds_old1.py
new file mode 100644
index 0000000..8bb8fa6
--- /dev/null
+++ b/functions/sds_old1.py
@@ -0,0 +1,685 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  1 11:20:35 2018
+
+@author: z5030440
+"""
+
+"""This script contains the functions needed for satellite derived shoreline (SDS) extraction"""
+
+# Initial settings
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import ee
+
+# other modules
+from osgeo import gdal, ogr, osr
+import tempfile
+from urllib.request import urlretrieve
+import zipfile
+
+# 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 skimage.morphology as morphology
+from sklearn.cluster import KMeans
+
+
+# import own modules
+from functions.utils import *
+
+
+# Download from ee server function
+
+def download_tif(image, polygon, bandsId):
+    """downloads tif image (region and bands) from the ee server and stores it in a temp file"""
+    url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
+        'image': image.serialize(),
+        'region': polygon,
+        'bands': bandsId,
+        'filePerBand': 'false',
+        'name': 'data',
+        }))
+    local_zip, headers = urlretrieve(url)
+    with zipfile.ZipFile(local_zip) as local_zipfile:
+        return local_zipfile.extract('data.tif', tempfile.mkdtemp())
+
+def load_image(image, polygon, bandsId): 
+    """
+    Loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database.
+    The geographic area and bands to select can be specified
+    
+    KV WRL 2018
+    
+    Arguments:
+    -----------
+        image: ee.Image()
+            image objec from the EE database
+        polygon: list
+            coordinates of the points creating a polygon. Each point is a list with 2 values
+        bandsId: list
+            bands to select, each band is a dictionnary in the list containing the following keys:
+            crs, crs_transform, data_type and id. NOTE: you have to remove the key dimensions, otherwise
+            the entire image is retrieved.
+            
+    Returns:
+    -----------
+        image_array : np.ndarray
+            An array containing the image (2D if one band, otherwise 3D)
+        georef : np.ndarray
+            6 element vector containing the crs_parameters 
+            [X_ul_corner Xscale Xshear Y_ul_corner Yshear Yscale]
+    """
+    
+    local_tif_filename = download_tif(image, polygon, bandsId)
+    dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
+    georef = np.array(dataset.GetGeoTransform())
+    bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
+    return np.stack(bands, 2), georef
+
+def create_cloud_mask(im_qa, satname, plot_bool):
+    """
+    Creates a cloud mask from the image containing the QA band information
+    
+    KV WRL 2018
+    
+    Arguments:
+    -----------
+        im_qa: np.ndarray
+            Image containing the QA band
+        satname: string
+            short name for the satellite (L8, L7, S2)
+        plot_bool: boolean
+            True if plot is wanted
+            
+    Returns:
+    -----------
+        cloud_mask : np.ndarray of booleans
+            A boolean array with True where the cloud are present
+    """
+    
+    # convert QA bits
+    if satname == 'L8':
+        cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
+    elif satname == 'L7':
+        cloud_values = [752, 756, 760, 764]
+        
+    cloud_mask = np.isin(im_qa, cloud_values)
+    # remove isolated cloud pixels (there are some in the swash and they cause problems)
+    if sum(sum(cloud_mask)) > 0:
+        morphology.remove_small_objects(cloud_mask, min_size=10, connectivity=1, 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)
+    
+    return cloud_mask
+
+def read_eeimage(im, polygon, sat_name, plot_bool):
+    """
+    Read an ee.Image() object and returns the panchromatic band, multispectral bands (B, G, R, NIR, SWIR)
+    and a cloud mask. All outputs are at 15m resolution (bilinear interpolation for the multispectral bands)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im: ee.Image()
+            Image to read from the Google Earth Engine database
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_pan: np.ndarray (2D)
+            The panchromatic band (15m)
+        im_ms: np.ndarray (3D)
+            The multispectral bands interpolated at 15m
+        im_cloud: np.ndarray (2D)
+            The cloud mask at 15m
+        crs_params: list
+            EPSG code and affine transformation parameters
+    """   
+
+    im_dic = im.getInfo()
+    # save metadata
+    im_meta = im_dic.get('properties')
+    meta = {'timestamp':im_meta['system:time_start'],
+            'date_acquired':im_meta['DATE_ACQUIRED'],
+            'geom_rmse_model':im_meta['GEOMETRIC_RMSE_MODEL'],
+            'gcp_model':im_meta['GROUND_CONTROL_POINTS_MODEL'],
+            'quality':im_meta['IMAGE_QUALITY_OLI'],
+            'sun_azimuth':im_meta['SUN_AZIMUTH'],
+            'sun_elevation':im_meta['SUN_ELEVATION']}
+    
+    im_bands = im_dic.get('bands')
+    
+    # delete dimensions key from dictionnary, otherwise the entire image is extracted
+    for i in range(len(im_bands)): del im_bands[i]['dimensions']
+    
+    # load panchromatic band
+    pan_band = [im_bands[7]]
+    im_pan, crs_pan = load_image(im, polygon, pan_band)
+    im_pan = im_pan[:,:,0]
+    
+    # load the multispectral bands (B2,B3,B4,B5,B6) = (blue,green,red,nir,swir1)
+    ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5]]
+    im_ms_30m, crs_ms = load_image(im, polygon, ms_bands)
+        
+    # create cloud mask
+    qa_band = [im_bands[11]]
+    im_qa, crs_qa = load_image(im, polygon, qa_band)
+    im_qa = im_qa[:,:,0]
+    im_cloud = create_cloud_mask(im_qa, sat_name, plot_bool)
+    im_cloud = transform.resize(im_cloud, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, mode='constant').astype('bool_')
+    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms_30m,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    
+    # check if -inf values (means out of image) and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    im_cloud = np.logical_or(np.logical_or(im_cloud, im_inf), im_nan)
+
+    # get the crs parameters for the image at 15m and 30m resolution
+    crs = {'crs_15m':crs_pan, 'crs_30m':crs_ms, 'epsg_code':int(pan_band[0]['crs'][5:])}
+
+    if plot_bool:
+        
+        # if there are -inf in the image, set them to 0 before plotting
+        if sum(sum(np.isin(im_ms_30m[:,:,0], -np.inf).astype(int))) > 0:
+            idx = np.isin(im_ms_30m[:,:,0], -np.inf)
+            im_ms_30m[idx,0] = 0; im_ms_30m[idx,1] = 0; im_ms_30m[idx,2] = 0; 
+            im_ms_30m[idx,3] = 0; im_ms_30m[idx,4] = 0
+
+        plt.figure()
+        
+        plt.subplot(221)
+        plt.imshow(im_pan, cmap='gray')
+        plt.title('PANCHROMATIC')
+        
+        plt.subplot(222)
+        plt.imshow(im_ms_30m[:,:,[2,1,0]])
+        plt.title('RGB')
+
+
+        plt.subplot(223)
+        plt.imshow(im_ms_30m[:,:,3], cmap='gray')
+        plt.title('NIR')
+        
+        plt.subplot(224)
+        plt.imshow(im_ms_30m[:,:,4], cmap='gray')
+        plt.title('SWIR')
+        
+        plt.show()
+    
+    return im_pan, im_ms, im_cloud, crs, meta
+
+
+def rescale_image_intensity(im, cloud_mask, prob_high, plot_bool):
+    """
+    Rescales the intensity of an image (multispectral or single band) by applying
+    a cloud mask and clipping the prob_high upper percentile. This functions allows
+    to stretch the contrast of an image.
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im: np.ndarray
+            Image to rescale, can be 3D (multispectral) or 2D (single band)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        prob_high: float
+            probability of exceedence used to calculate the upper percentile
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_adj: np.ndarray
+            The rescaled image
+    """
+    prc_low = 0 # lower percentile
+    vec_mask = cloud_mask.reshape(im.shape[0] * im.shape[1])
+    
+    if plot_bool:
+        plt.figure()
+        
+    if len(im.shape) > 2:
+        vec =  im.reshape(im.shape[0] * im.shape[1], im.shape[2])    
+        vec_adj = np.ones((len(vec_mask), im.shape[2])) * np.nan
+
+        for i in range(im.shape[2]):
+            prc_high = np.percentile(vec[~vec_mask, i], prob_high)
+            vec_rescaled = exposure.rescale_intensity(vec[~vec_mask, i], in_range=(prc_low, prc_high))
+            vec_adj[~vec_mask,i] = vec_rescaled
+            
+            if plot_bool:
+                plt.subplot(np.floor(im.shape[2]/2) + 1, np.floor(im.shape[2]/2), i+1)
+                plt.hist(vec[~vec_mask, i], bins=200, label='original')
+                plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
+                plt.legend()
+                plt.title('Band' + str(i+1))
+                plt.show()
+        
+        im_adj = vec_adj.reshape(im.shape[0], im.shape[1], im.shape[2])
+        
+        if plot_bool:
+            plt.figure()
+            ax1 = plt.subplot(121)
+            plt.imshow(im[:,:,[2,1,0]])
+            plt.axis('off')
+            plt.title('Original')
+            ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+            plt.imshow(im_adj[:,:,[2,1,0]])
+            plt.axis('off')
+            plt.title('Rescaled')
+            plt.show()
+            
+    else:
+        vec =  im.reshape(im.shape[0] * im.shape[1])
+        vec_adj = np.ones(len(vec_mask)) * np.nan
+        prc_high = np.percentile(vec[~vec_mask], prob_high)
+        vec_rescaled = exposure.rescale_intensity(vec[~vec_mask], in_range=(prc_low, prc_high))
+        vec_adj[~vec_mask] = vec_rescaled
+        
+        if plot_bool:
+            plt.hist(vec[~vec_mask], bins=200, label='original')
+            plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
+            plt.legend()
+            plt.title('Single band')
+            plt.show()
+        
+        im_adj = vec_adj.reshape(im.shape[0], im.shape[1])
+        
+        if plot_bool:
+            plt.figure()
+            ax1 = plt.subplot(121)
+            plt.imshow(im, cmap='gray')
+            plt.axis('off')
+            plt.title('Original')
+            ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+            plt.imshow(im_adj, cmap='gray')
+            plt.axis('off')
+            plt.title('Rescaled')
+            plt.show()
+
+    return im_adj
+    
+    
+def hist_match(source, template):
+    """
+    Adjust the pixel values of a grayscale image such that its histogram
+    matches that of a target image
+
+    Arguments:
+    -----------
+        source: np.ndarray
+            Image to transform; the histogram is computed over the flattened
+            array
+        template: np.ndarray
+            Template image; can have different dimensions to source
+    Returns:
+    -----------
+        matched: np.ndarray
+            The transformed output image
+    """
+
+    oldshape = source.shape
+    source = source.ravel()
+    template = template.ravel()
+
+    # get the set of unique pixel values and their corresponding indices and
+    # counts
+    s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
+                                            return_counts=True)
+    t_values, t_counts = np.unique(template, return_counts=True)
+
+    # take the cumsum of the counts and normalize by the number of pixels to
+    # get the empirical cumulative distribution functions for the source and
+    # template images (maps pixel value --> quantile)
+    s_quantiles = np.cumsum(s_counts).astype(np.float64)
+    s_quantiles /= s_quantiles[-1]
+    t_quantiles = np.cumsum(t_counts).astype(np.float64)
+    t_quantiles /= t_quantiles[-1]
+
+    # interpolate linearly to find the pixel values in the template image
+    # that correspond most closely to the quantiles in the source image
+    interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
+
+    return interp_t_values[bin_idx].reshape(oldshape)
+
+def pansharpen(im_ms, im_pan, cloud_mask, plot_bool):
+    """
+    Pansharpens a multispectral image (3D), using the panchromatic band (2D)
+    and a cloud mask
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms: np.ndarray
+            Multispectral image to pansharpen (3D)
+        im_pan: np.ndarray
+            Panchromatic band (2D)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened multisoectral image (3D)
+    """
+    
+    # reshape image into vector and apply cloud mask
+    vec = im_ms.reshape(im_ms.shape[0] * im_ms.shape[1], im_ms.shape[2])
+    vec_mask = cloud_mask.reshape(im_ms.shape[0] * im_ms.shape[1])
+    vec = vec[~vec_mask, :]
+    # apply PCA to RGB bands
+    pca = decomposition.PCA()
+    vec_pcs = pca.fit_transform(vec)
+    # replace 1st PC with pan band (after matching histograms)
+    vec_pan = im_pan.reshape(im_pan.shape[0] * im_pan.shape[1])
+    vec_pan = vec_pan[~vec_mask]
+    vec_pcs[:,0] = hist_match(vec_pan, vec_pcs[:,0])
+    vec_ms_ps = pca.inverse_transform(vec_pcs)
+    
+    # normalise between 0 and 1
+    for i in range(vec_pcs.shape[1]):
+        vec_ms_ps[:,i] = np.divide(vec_ms_ps[:,i] - np.min(vec_ms_ps[:,i]),
+                         np.max(vec_ms_ps[:,i]) - np.min(vec_ms_ps[:,i]))
+    # reshape vector into image
+    vec_ms_ps_full = np.ones((len(vec_mask), im_ms.shape[2])) * np.nan
+    vec_ms_ps_full[~vec_mask,:] = vec_ms_ps
+    im_ms_ps = vec_ms_ps_full.reshape(im_ms.shape[0], im_ms.shape[1], im_ms.shape[2])
+    
+    if plot_bool:
+        plt.figure()
+        ax1 = plt.subplot(121)
+        plt.imshow(im_ms[:,:,[2,1,0]])
+        plt.axis('off')
+        plt.title('Original')
+        ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+        plt.imshow(im_ms_ps[:,:,[2,1,0]])
+        plt.axis('off')
+        plt.title('Pansharpened')
+        plt.show()
+
+    return im_ms_ps
+    
+def nd_index(im1, im2, cloud_mask, plot_bool):
+    """
+    Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im1, im2: np.ndarray
+            Images (2D) with which to calculate the ND index
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:    -----------
+        im_nd: np.ndarray
+
+            Image (2D) containing the ND index
+    """
+    vec_mask = cloud_mask.reshape(im1.shape[0] * im1.shape[1])
+    vec_nd = np.ones(len(vec_mask)) * np.nan
+    vec1 = im1.reshape(im1.shape[0] * im1.shape[1])
+    vec2 = im2.reshape(im2.shape[0] * im2.shape[1])
+    temp = np.divide(vec1[~vec_mask] - vec2[~vec_mask],
+                     vec1[~vec_mask] + vec2[~vec_mask])
+    vec_nd[~vec_mask] = temp
+    im_nd = vec_nd.reshape(im1.shape[0], im1.shape[1])
+    
+    if plot_bool:
+        plt.figure()
+        plt.imshow(im_nd, cmap='seismic')
+        plt.colorbar()
+        plt.title('Normalised index')
+        plt.show()
+    
+    return im_nd
+
+def find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool):
+    """
+    Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ndwi: np.ndarray
+            Image (2D) with the NDWI (water index)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        min_contour_points: int
+            minimum number of points in each contour line
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        contours_wl: list of np.arrays 
+            contains the (row,column) coordinates of the contour lines
+
+    """  
+    
+    # reshape image to vector
+    vec_ndwi = im_ndwi.reshape(im_ndwi.shape[0] * im_ndwi.shape[1])
+    vec_mask = cloud_mask.reshape(cloud_mask.shape[0] * cloud_mask.shape[1])
+    vec = vec_ndwi[~vec_mask]
+    # apply otsu's threshold
+    t_otsu = filters.threshold_otsu(vec)
+    # use Marching Squares algorithm to detect contours on ndwi image
+    contours = measure.find_contours(im_ndwi, t_otsu)
+    # filter water lines
+    contours_wl = []
+    for i, contour in enumerate(contours):
+        # remove contour points that are around clouds (nan values)
+        if np.any(np.isnan(contour)):
+            index_nan = np.where(np.isnan(contour))[0]
+            contour = np.delete(contour, index_nan, axis=0) 
+        # remove contours that have only few points (less than min_contour_points)
+        if contour.shape[0] > min_contour_points:
+            contours_wl.append(contour)
+        
+    if plot_bool:
+        # plot otsu's histogram segmentation
+        plt.figure()
+        vals = plt.hist(vec, bins=200)
+        plt.plot([t_otsu, t_otsu],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
+        plt.legend()
+        plt.show()
+        
+        # plot the water line contours on top of water index
+        plt.figure()
+        plt.imshow(im_ndwi, cmap='seismic')
+        plt.colorbar()
+        for i,contour in enumerate(contours_wl): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+        plt.axis('image')
+        plt.title('Detected water lines')
+        plt.show()
+    
+    return contours_wl
+
+def convert_pix2world(points, crs_vec):
+    """
+    Converts pixel coordinates (row,columns) to world projected coordinates
+    performing an affine transformation
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        points: np.ndarray or list of np.ndarray
+            array with 2 columns (rows first and columns second)
+        crs_vec: np.ndarray
+            vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
+                
+    Returns:    -----------
+        points_converted: np.ndarray or list of np.ndarray 
+            converted coordinates, first columns with X and second column with Y
+        
+    """
+    
+    # make affine transformation matrix
+    aff_mat = np.array([[crs_vec[1], crs_vec[2], crs_vec[0]],
+                       [crs_vec[4], crs_vec[5], crs_vec[3]],
+                       [0, 0, 1]])
+    # create affine transformation
+    tform = transform.AffineTransform(aff_mat)
+
+    if type(points) is list:
+        points_converted = []
+        # iterate over the list
+        for i, arr in enumerate(points): 
+            tmp = arr[:,[1,0]]
+            points_converted.append(tform(tmp))
+            
+    elif type(points) is np.ndarray:
+        tmp = points[:,[1,0]]
+        points_converted = tform(tmp)
+        
+    else:
+        print('invalid input type')
+        raise
+        
+    return points_converted
+
+def convert_epsg(points, epsg_in, epsg_out):
+    """
+    Converts from one spatial reference to another using the epsg codes
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        points: np.ndarray or list of np.ndarray
+            array with 2 columns (rows first and columns second)
+        epsg_in: int
+            epsg code of the spatial reference in which the input is
+        epsg_out: int
+            epsg code of the spatial reference in which the output will be            
+                
+    Returns:    -----------
+        points_converted: np.ndarray or list of np.ndarray 
+            converted coordinates
+        
+    """
+    
+    # define input and output spatial references
+    inSpatialRef = osr.SpatialReference()
+    inSpatialRef.ImportFromEPSG(epsg_in)
+    outSpatialRef = osr.SpatialReference()
+    outSpatialRef.ImportFromEPSG(epsg_out)
+    # create a coordinates transform
+    coordTransform = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)
+    # transform points
+    if type(points) is list:
+        points_converted = []
+        # iterate over the list
+        for i, arr in enumerate(points): 
+            points_converted.append(np.array(coordTransform.TransformPoints(arr)))
+            
+    elif type(points) is np.ndarray:
+        points_converted = np.array(coordTransform.TransformPoints(points))
+        
+    else:
+        print('invalid input type')
+        raise
+        
+    return points_converted
+
+def classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool):
+    """
+    Classifies sand pixels using an unsupervised algorithm (Kmeans)
+    Set buffer size to False if you 
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_pan:
+            Panchromatic band
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        wl_pix: list of np.ndarray
+            list of arrays containig the pixel coordinates of the water line
+        buffer_size: int or False
+            radius of the disk used to create a buffer around the water line
+            when False, the entire image is considered for kmeans
+        min_beach_size: int
+            minimum number of connected pixels belonging to a single beach
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        im_sand: np.ndarray
+            2D binary image containing True where sand pixels are located
+
+    """     
+    # reshape the 2D images into vectors
+    vec_ms_ps = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2])
+    vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1])
+    vec_mask = cloud_mask.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    # add B,G,R,NIR and pan bands to the vector of features
+    vec_features = np.zeros((vec_ms_ps.shape[0], 5))
+    vec_features[:,[0,1,2,3]] = vec_ms_ps[:,[0,1,2,3]]
+    vec_features[:,4] = vec_pan
+    
+    if buffer_size:
+        # create binary image with ones where the detected water lines is
+        im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1]))
+        for i, contour in enumerate(wl_pix):
+                indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))]
+                for j, idx in enumerate(indices):
+                    im_buffer[idx] = 1
+        # perform a dilation on the binary image
+        se = morphology.disk(buffer_size)
+        im_buffer = morphology.binary_dilation(im_buffer, se)
+        vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    else:
+        vec_buffer = np.ones((vec_pan.shape[0]))
+    # add cloud mask to buffer
+    vec_buffer= np.logical_and(vec_buffer, ~vec_mask)
+    # perform kmeans (6 clusters)
+    kmeans = KMeans(n_clusters=6, random_state=0).fit(vec_features[vec_buffer,:])
+    labels = np.ones((len(vec_mask))) * np.nan
+    labels[vec_buffer] = kmeans.labels_
+    im_labels = labels.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1])
+    # find the class with maximum reflection in the B,G,R,Pan
+    im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
+    im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+#    im_sand = morphology.binary_dilation(im_sand, morphology.disk(1))
+    
+    if plot_bool:
+        im = np.copy(im_ms_ps)
+        im[im_sand,0] = 0
+        im[im_sand,1] = 0
+        im[im_sand,2] = 1
+        plt.figure()
+        plt.imshow(im[:,:,[2,1,0]])
+        plt.axis('image')
+        plt.title('Sand classification')
+        plt.show()
+    
+    return im_sand
diff --git a/sand_classification/NARRA_other.pkl b/sand_classification/NARRA_other.pkl
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diff --git a/sand_classification/NARRA_swash.pkl b/sand_classification/NARRA_swash.pkl
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diff --git a/sand_classification/NARRA_water.pkl b/sand_classification/NARRA_water.pkl
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diff --git a/sand_classification/NN1.pkl b/sand_classification/NN1.pkl
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diff --git a/sand_classification/Narra1.csv b/sand_classification/Narra1.csv
new file mode 100644
index 0000000..e50615e
--- /dev/null
+++ b/sand_classification/Narra1.csv
@@ -0,0 +1,28385 @@
+,B,G,R,NIR,SWIR,Pan,WI,VI,BR,mWI,class
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diff --git a/sand_createNN.py b/sand_createNN.py
new file mode 100644
index 0000000..55299ed
--- /dev/null
+++ b/sand_createNN.py
@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Train Neural Network
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import ee
+import pdb
+import time
+import pandas as pd
+# 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 skimage.morphology as morphology
+from scipy import ndimage
+import random
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+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()
+#%%
+
+# load training data
+sitename = 'NARRA'
+with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_sand.pkl'), 'rb') as f:    
+    train_sand = pickle.load(f)
+with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_swash.pkl'), 'rb') as f:    
+    train_swash = pickle.load(f)   
+with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_water.pkl'), 'rb') as f:    
+    train_water = pickle.load(f) 
+with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_other.pkl'), 'rb') as f:    
+    train_other = pickle.load(f) 
+
+
+train_water = train_water[np.random.choice(train_water.shape[0], 1500),:]
+train_other = train_other[np.random.choice(train_other.shape[0], 1500),:]
+
+n_features = train_sand.shape[1]
+n_sand = len(train_sand)
+n_swash = len(train_swash)
+n_water = len(train_water)
+n_other = len(train_other)
+
+training_data = np.zeros((n_sand+n_swash+n_water+n_other, n_features+1))
+training_data[:n_sand,:n_features] = train_sand
+training_data[n_sand:n_sand+n_swash,:n_features] = train_swash
+training_data[n_sand+n_swash:n_sand+n_swash+n_water,:n_features] = train_water
+training_data[n_sand+n_swash+n_water:n_sand+n_swash+n_water+n_other,:n_features] = train_other
+training_data[:n_sand,n_features] = 1*np.ones((n_sand))
+training_data[n_sand:n_sand+n_swash,n_features] = 2*np.ones((n_swash))
+training_data[n_sand+n_swash:n_sand+n_swash+n_water,n_features] = 3*np.ones((n_water))
+    
+X = training_data[:,0:n_features]
+y = training_data[:,n_features]
+
+# divide in train and test
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
+
+# standardize data
+#scaler = StandardScaler()
+#scaler.fit(X_train)
+#X_train = scaler.transform(X_train)
+#X_test = scaler.transform(X_test)
+
+# run NN on train dat and evaluate on test data
+clf = MLPClassifier()
+clf.fit(X,y)
+clf.score(X_test,y_test)
+
+# save NN model
+joblib.dump(clf, os.path.join(os.getcwd(), 'sand_classification', 'NN1.pkl'))
\ No newline at end of file
diff --git a/sand_create_train.py b/sand_pixtrain_beach.py
similarity index 62%
rename from sand_create_train.py
rename to sand_pixtrain_beach.py
index 827ddc7..0a4803e 100644
--- a/sand_create_train.py
+++ b/sand_pixtrain_beach.py
@@ -26,6 +26,7 @@ import sklearn.decomposition as decomposition
 import skimage.measure as measure
 import skimage.morphology as morphology
 from scipy import ndimage
+import random
 
 # machine learning modules
 from sklearn.model_selection import train_test_split
@@ -44,9 +45,9 @@ plt.rcParams['figure.max_open_warning'] = 100
 ee.Initialize()
 
 # parameters
-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
+cloud_thresh = 0.3      # threshold for cloud cover
+plot_bool = False       # if you want the plots
+prob_high = 100         # 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
 buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
@@ -54,8 +55,8 @@ min_beach_size = 50     # number of pixels in a beach (pixel classification)
 
 # load metadata (timestamps and epsg code) for the collection
 satname = 'L8'
-sitename = 'NARRA_all'
-#sitename = 'NARRA'
+#sitename = 'NARRA_all'
+sitename = 'NARRA'
 #sitename = 'OLDBAR'
 
 # Load metadata
@@ -74,16 +75,23 @@ idx_nocloud = []
 n_features = 10
 train_pos = np.nan*np.ones((1,n_features))
 train_neg = np.nan*np.ones((1,n_features))
-columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'SAND')
+train_other = np.nan*np.ones((1,n_features))
+train_water = np.nan*np.ones((1,n_features))
+columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
 
 #%%
+date_acquired_ts = []
+fig = plt.figure()
 for i in range(N):
+    plt.close(fig)
     # 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]
+    nrow = im_pan.shape[0]
+    ncol = im_pan.shape[1]
     # read ms image
     fn_ms = os.path.join(file_path_ms, file_names_ms[i])
     data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
@@ -109,47 +117,81 @@ for i in range(N):
         idx_skipped.append(i)
         continue
     idx_nocloud.append(i)
-            
-    # rescale intensities
-    im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool)
-    im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool)
+    if file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] in date_acquired_ts:
+        idx_skipped.append(i)
+        continue
     # pansharpen rgb image
     im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
-    nrow = im_ms_ps.shape[0]
-    ncol = im_ms_ps.shape[1]
     # add down-sized bands for NIR and SWIR (since pansharpening is not possible)
     im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2) 
     # calculate NDWI
     im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
     # detect edges
     wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
+    if not wl_pix:
+        idx_skipped.append(i)
+        continue
+    
     # classify sand pixels with Kmeans
-    im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool)
+    im_sandKmeans = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool)
     
     # plot a figure to manually select which images to keep
-    im = np.copy(im_ms_ps)
-    im[im_sand,0] = 0
-    im[im_sand,1] = 0
-    im[im_sand,2] = 1
-    plt.figure()
-    plt.imshow(im[:,:,[2,1,0]])
+    im = np.copy(sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False))
+    im[im_sandKmeans,0] = 0
+    im[im_sandKmeans,1] = 0
+    im[im_sandKmeans,2] = 1
+    
+    # select water pixels on image
+    pt_water = np.array([140, 70])
+    lenrow = 20
+    lencol = 20
+    idx_row = np.linspace(0,lenrow-1,lenrow).astype(int) + int(pt_water[0])
+    idx_col = np.linspace(0,lencol-1,lencol).astype(int) + int(pt_water[1])
+    xx, yy = np.meshgrid(idx_row,idx_col, indexing='ij')
+    row_water = xx.reshape(lenrow*lencol)
+    col_water = yy.reshape(lenrow*lencol)
+    im_water = np.zeros((nrow,ncol)).astype(bool)
+    for k in range(len(row_water)): 
+        im_water[row_water[k],col_water[k]] = True
+        im[row_water[k],col_water[k],0] = 0
+        im[row_water[k],col_water[k],1] = 1
+        im[row_water[k],col_water[k],2] = 1
+    
+    # select other pixels on image (vegetation + buildings)
+    pt_other = np.array([random.randint(150,235), 7])
+    lenrow = 40
+    lencol = 15
+    idx_row = np.linspace(0,lenrow-1,lenrow).astype(int) + int(pt_other[0])
+    idx_col = np.linspace(0,lencol-1,lencol).astype(int) + int(pt_other[1])
+    xx, yy = np.meshgrid(idx_row,idx_col, indexing='ij')
+    row_other = xx.reshape(lenrow*lencol)
+    col_other = yy.reshape(lenrow*lencol)
+    im_other = np.zeros((nrow,ncol)).astype(bool)
+    for k in range(len(row_other)): 
+        im_other[row_other[k],col_other[k]] = True
+        im[row_other[k],col_other[k],0] = 1
+        im[row_other[k],col_other[k],1] = 1
+        im[row_other[k],col_other[k],2] = 0
+        
+    # plot image
+    fig = plt.figure()
+    plt.imshow(im)
     plt.axis('image')
     plt.title('Sand classification')
-    plt.show()
+    plt.draw()
     mng = plt.get_current_fig_manager()                                         
     mng.window.showMaximized()
     plt.tight_layout()   
     plt.draw()
     
-    
-    
     # click a point
-    # top-left quadrant:   keep classif as pos and click somewhere for neg
-    # bottom-left:         keep classif as neg
+    # top-left quadrant: sand
+    # bottom-left quadrant: swash       
     # any right quadrant:  discard image
     pt_in = np.array(ginput(n=1, timeout=1000))
     if pt_in[0][0] < im_ms_ps.shape[1]/2:
         
+        # calculate features
         im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features))
         im_features[:,:,[0,1,2,3,4]] = im_ms_ps
         im_features[:,:,5] = im_pan
@@ -160,86 +202,47 @@ for i in range(N):
         # win = np.ones((3,3))
         # im_features[:,:,9] = ndimage.generic_filter(im_features[:,:,5], np.std, footprint=win)
         # im_features[:,:,10] = ndimage.generic_filter(im_features[:,:,5], np.max, footprint=win) - ndimage.generic_filter(im_features[:,:,5], np.min, footprint=win)
-        
-        if pt_in[0][1] < im_ms_ps.shape[0]/2:
             # positive examples
-            vec_pos = im_features[im_sand,:]
-            train_pos = np.append(train_pos, vec_pos, axis=0)   
-            
-            # click where negative examples are
-            pt_neg = np.round(np.array(ginput(n=1, timeout=1000))[0])
-            radius = int(round(np.sqrt(sum(sum(im_sand)))))
-            idx_rows = np.linspace(0,radius-1,radius).astype(int) + int(pt_neg[1])
-            idx_cols = np.linspace(0,radius-1,radius).astype(int) + int(pt_neg[0])
-            xx, yy = np.meshgrid(idx_rows,idx_cols, indexing='ij')
-            row_neg = xx.reshape(radius*radius)
-            col_neg = yy.reshape(radius*radius)
-            im_nosand = np.zeros((nrow,ncol)).astype(bool)
-            for i in range(len(row_neg)): 
-                im_nosand[row_neg[i],col_neg[i]] = True
-                im_ms_ps[row_neg[i],col_neg[i],0] = 1
-                im_ms_ps[row_neg[i],col_neg[i],1] = 1
-                im_ms_ps[row_neg[i],col_neg[i],2] = 0
-            plt.imshow(im_ms_ps[:,:,[2,1,0]])
-            plt.draw()
         
-            # negative examples
-            vec_neg = im_features[im_nosand,:]
-            train_neg = np.append(train_neg, vec_neg, axis=0)
-            
+        # fill training data 
+        vec_other = im_features[im_other,:]
+        train_other = np.append(train_other, vec_other, axis=0) 
+        vec_water = im_features[im_water,:]
+        train_water = np.append(train_water, vec_water, axis=0)
+        
+        if pt_in[0][1] < im_ms_ps.shape[0]/2:
+            # sand examples
+            vec_pos = im_features[im_sandKmeans,:]
+            train_pos = np.append(train_pos, vec_pos, axis=0)             
         else:
-            # negative examples
-            vec_neg = im_features[im_sand,:]
-            train_neg = np.append(train_neg, vec_neg, axis=0)  
+            # swash examples
+            vec_neg = im_features[im_sandKmeans,:]
+            train_neg = np.append(train_neg, vec_neg, axis=0) 
 
     else:
         print('skip ' + str(i))
         idx_skipped.append(i)
+        continue
+    
+    date_acquired_ts.append(file_names_pan[i][9:19])
         
 # format data 
 train_pos = train_pos[1:,:]
 train_neg = train_neg[1:,:]
-n_pos = len(train_pos)
-n_neg = len(train_neg)
-training_data = np.zeros((n_pos+n_neg, n_features+1))
-training_data[:n_pos,:n_features] = train_pos
-training_data[n_pos:n_pos+n_neg,:n_features] = train_neg
-training_data[:n_pos,n_features] = np.ones((n_pos))
-df_train = pd.DataFrame(training_data, columns=columns)
-df_train.dropna(axis=0, how='any', inplace=True)
-sand_train = np.array(df_train)
+train_water = train_water[1:,:]
+train_other = train_other[1:,:]
 
 # save data
-#with open(os.path.join(filepath, sitename + '_sand_idxskip' + '.pkl'), 'wb') as f:
-#    pickle.dump(idx_skipped, f)
-#with open(os.path.join(filepath, sitename + '_sand_train' + '.pkl'), 'wb') as f:
-#    pickle.dump(sand_train, f)
-#df_train.to_csv('training_data.csv')  
-
-#%% Train neural network on data
-
-# load training data
-with open(os.path.join(filepath, sitename + '_sand_train' + '.pkl'), 'rb') as f:
-    sand_train = pickle.load(f)
-    
-n_features = sand_train.shape[1] - 1
-X = sand_train[:,0:n_features]
-y = sand_train[:,n_features]
-
-# divide in train and test
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
+#with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_sand.pkl'), 'wb') as f:
+#    pickle.dump(train_pos, f)
+#with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_swash.pkl'), 'wb') as f:
+#    pickle.dump(train_neg, f)
+#with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_water.pkl'), 'wb') as f:
+#    pickle.dump(train_water, f)
+#with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_other.pkl'), 'wb') as f:
+#    pickle.dump(train_other, f) 
 
-#scaler = StandardScaler()
-#scaler.fit(X_train)
-#X_train = scaler.transform(X_train)
-#X_test = scaler.transform(X_test)
 
-# run NN on train dat and evaluate on test data
-clf = MLPClassifier()
-clf.fit(X_train,y_train)
-clf.score(X_test,y_test)
 
-# save NN model
-joblib.dump(clf, os.path.join(os.getcwd(), 'sand_classification', 'NN_small.pkl'))
 
 
diff --git a/sand_runNN.py b/sand_runNN.py
index 55deaac..3b122f4 100644
--- a/sand_runNN.py
+++ b/sand_runNN.py
@@ -44,7 +44,7 @@ plt.rcParams['figure.max_open_warning'] = 100
 ee.Initialize()
 
 # parameters
-cloud_thresh = 0.5      # threshold for cloud cover
+cloud_thresh = 0.3      # threshold for cloud cover
 plot_bool = False      # if you want the plots
 prob_high = 100        # upper probability to clip and rescale pixel intensity
 min_contour_points = 100# minimum number of points contained in each water line
@@ -54,9 +54,11 @@ min_beach_size = 50     # number of pixels in a beach (pixel classification)
 
 # load metadata (timestamps and epsg code) for the collection
 satname = 'L8'
-sitename = 'NARRA_all'
+#sitename = 'NARRA_all'
 #sitename = 'NARRA'
 #sitename = 'OLDBAR'
+sitename = 'OLDBAR_inlet'
+
 
 # Load metadata
 filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
@@ -74,9 +76,9 @@ idx_nocloud = []
 n_features = 10
 train_pos = np.nan*np.ones((1,n_features))
 train_neg = np.nan*np.ones((1,n_features))
-columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'SAND')
+columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
 
-clf = joblib.load(os.path.join(os.getcwd(), 'sand_classification', 'NN_new.pkl'))
+clf = joblib.load(os.path.join(os.getcwd(), 'sand_classification', 'NN1.pkl'))
 #%%
 for i in range(N):
     # read pan image
@@ -85,6 +87,8 @@ for i in range(N):
     georef = np.array(data.GetGeoTransform())
     bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
     im_pan = np.stack(bands, 2)[:,:,0]
+    nrow = im_pan.shape[0]
+    ncol = im_pan.shape[1]
     # read ms image
     fn_ms = os.path.join(file_path_ms, file_names_ms[i])
     data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
@@ -111,30 +115,25 @@ for i in range(N):
         continue
     idx_nocloud.append(i)
             
-    # rescale intensities
-    im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool)
-    im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool)
     # pansharpen rgb image
     im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
-    nrow = im_ms_ps.shape[0]
-    ncol = im_ms_ps.shape[1]
     # add down-sized bands for NIR and SWIR (since pansharpening is not possible)
     im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2) 
-    # calculate NDWI
-    im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
-    # detect edges
-    wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
-    # classify sand pixels with Kmeans
-    im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool)
+#    # calculate NDWI
+#    im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
+#    # detect edges
+#    wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
+#    # classify sand pixels with Kmeans
+#    im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool)
         
     # calculate features
     im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features))
     im_features[:,:,[0,1,2,3,4]] = im_ms_ps
     im_features[:,:,5] = im_pan
-    im_features[:,:,6] = im_ndwi
+    im_features[:,:,6] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, False) # (NIR-G)
     im_features[:,:,7] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask, False) # ND(NIR-R)
     im_features[:,:,8] = sds.nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask, False) # ND(B-R)
-    im_features[:,:,9] = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # (SWIR-G)
+    im_features[:,:,9] = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # ND(SWIR-G)
     # remove NaNs and clouds
     vec = im_features.reshape((im_ms_ps.shape[0] * im_ms_ps.shape[1], n_features))
     vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1])
@@ -147,30 +146,28 @@ for i in range(N):
     vec_new = np.zeros((cloud_mask.shape[0]*cloud_mask.shape[1])) 
     vec_new[~vec_mask] = y
     im_classif = vec_new.reshape((im_ms_ps.shape[0], im_ms_ps.shape[1]))
-    im_classif = im_classif.astype(bool)
-    im_classif = morphology.remove_small_objects(im_classif, min_size=min_beach_size, connectivity=2)
-    
-    # make comparison plot between NN and Kmeans
-    im1 = np.copy(im_ms_ps)
-    im1[im_classif,0] = 0
-    im1[im_classif,1] = 0
-    im1[im_classif,2] = 1
+#    im_classif = morphology.remove_small_objects(im_classif, min_size=min_beach_size, connectivity=2)
     
-    im2 = np.copy(im_ms_ps)
-    im2[im_sand,0] = 0
-    im2[im_sand,1] = 0
-    im2[im_sand,2] = 1
+    # plot NN labels
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+    im = np.copy(im_display)
+    colours = np.array([[1,0,0],[1,1,0],[0,1,1],[0,0,1]])
+    for k in range(4):
+        im[im_classif == k,0] = colours[k,0]
+        im[im_classif == k,1] = colours[k,1]
+        im[im_classif == k,2] = colours[k,2]
     
     plt.figure()
     ax1 = plt.subplot(121)
-    plt.imshow(im1[:,:,[2,1,0]])
+    plt.imshow(im_display)
     plt.axis('off')
-    plt.title('NN')
+    plt.title('Image')
     ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
-    plt.imshow(im2[:,:,[2,1,0]])
+    plt.imshow(im)
     plt.axis('off')
-    plt.title('Kmeans')
+    plt.title('NN')
     mng = plt.get_current_fig_manager()                                         
     mng.window.showMaximized()
     plt.tight_layout()   
-    plt.draw()
\ No newline at end of file
+    plt.draw()
+