diff --git a/read_images.py b/read_images.py
deleted file mode 100644
index 261b452..0000000
--- a/read_images.py
+++ /dev/null
@@ -1,199 +0,0 @@
-#==========================================================#
-#==========================================================#
-# Extract shorelines from Landsat images
-#==========================================================#
-#==========================================================#
-
-
-#==========================================================#
-# 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
-from shapely.geometry import LineString
-
-# 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
-
-# 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 other 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()
-
-#==========================================================#
-# Parameters
-#==========================================================#
-
-sitename = 'NARRA'
-
-cloud_thresh = 0.7      # threshold for cloud cover
-plot_bool = False       # if you want the plots
-output_epsg = 28356     # GDA94 / MGA Zone 56
-buffer_size = 7         # radius (in pixels) of disk for buffer (pixel classification)
-min_beach_size = 20     # number of pixels in a beach (pixel classification)
-dist_ref = 100          # maximum distance from reference point
-min_length_wl = 200     # minimum length of shoreline LineString to be kept 
-
-output = dict([])
-
-#==========================================================#
-# Metadata
-#==========================================================#
-
-filepath = os.path.join(os.getcwd(), 'data', sitename)
-with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
-    metadata = pickle.load(f)
-    
-#%%
-#==========================================================#
-# Read S2 images
-#==========================================================#
-
-satname = 'S2'
-dates = metadata[satname]['dates']
-input_epsg = metadata[satname]['epsg']
-
-# path to images
-filepath10 = os.path.join(os.getcwd(), 'data', sitename, satname, '10m')
-filenames10 = os.listdir(filepath10)
-filepath20 = os.path.join(os.getcwd(), 'data', sitename, satname, '20m')
-filenames20 = os.listdir(filepath20)
-filepath60 = os.path.join(os.getcwd(), 'data', sitename, satname, '60m')
-filenames60 = os.listdir(filepath60)
-if (not len(filenames10) == len(filenames20)) or (not len(filenames20) == len(filenames60)):
-    raise 'error: not the same amount of files for 10, 20 and 60 m'
-N = len(filenames10)
-
-# initialise variables
-cloud_cover_ts = []
-acc_georef_ts = []
-date_acquired_ts = []
-filename_ts = []
-satname_ts = []
-timestamp = []
-shorelines = []
-idx_skipped = []
-
-spacing = '=========================================================='
-msg = ' %s\n %s\n %s' % (spacing, satname, spacing)
-print(msg)
-
-for i in range(N):
-    
-    # read 10m bands
-    fn = os.path.join(filepath10, filenames10[i])
-    data = gdal.Open(fn, gdal.GA_ReadOnly)
-    georef = np.array(data.GetGeoTransform())
-    bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
-    im10 = np.stack(bands, 2)
-    im10 = im10/10000 # TOA scaled to 10000
-    
-    # if image is only zeros, skip it
-    if sum(sum(sum(im10))) < 1:
-        print('skip ' + str(i) + ' - no data')
-        idx_skipped.append(i)
-        continue        
-    
-    nrows = im10.shape[0]
-    ncols = im10.shape[1]
-    
-    # read 20m band (SWIR1)
-    fn = os.path.join(filepath20, filenames20[i])
-    data = gdal.Open(fn, gdal.GA_ReadOnly)
-    bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
-    im20 = np.stack(bands, 2)
-    im20 = im20[:,:,0]
-    im20 = im20/10000 # TOA scaled to 10000
-    im_swir = transform.resize(im20, (nrows, ncols), order=1, preserve_range=True, mode='constant')
-    im_swir = np.expand_dims(im_swir, axis=2)
-    
-    # append down-sampled swir band to the 10m bands
-    im_ms = np.append(im10, im_swir, axis=2)
-    
-    # read 60m band (QA)
-    fn = os.path.join(filepath60, filenames60[i])
-    data = gdal.Open(fn, gdal.GA_ReadOnly)
-    bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
-    im60 = np.stack(bands, 2)
-    im_qa = im60[:,:,0]
-    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
-    cloud_mask = transform.resize(cloud_mask,(nrows, ncols), order=0, preserve_range=True, mode='constant')
-    # check if -inf or nan values on any band and add to cloud mask
-    for k in range(im_ms.shape[2]):   
-            im_inf = np.isin(im_ms[:,:,k], -np.inf)
-            im_nan = np.isnan(im_ms[:,:,k])
-            cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
-            
-    # calculate cloud cover and if above threshold, skip it
-    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
-    if cloud_cover > cloud_thresh:
-        print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
-        idx_skipped.append(i)
-        continue
-    
-    # rescale image intensity for display purposes
-    im_display = sds.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False)
-    
-    # plot rgb image
-    plt.figure()
-    plt.subplot(121)
-    plt.axis('off')
-    plt.imshow(im_display)
-    
-    # classify image in 4 classes (sand, whitewater, water, other) with NN classifier
-    im_classif, im_labels = sds.classify_image_NN_nopan(im_ms, cloud_mask, min_beach_size, plot_bool)
-    
-    plt.subplot(122)
-    plt.axis('off')
-    im = np.copy(im_display)
-    colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
-    for k in range(0,im_labels.shape[2]):
-        im[im_labels[:,:,k],0] = colours[k,0]
-        im[im_labels[:,:,k],1] = colours[k,1]
-        im[im_labels[:,:,k],2] = colours[k,2]
-    plt.imshow(im)
-    # store the data
-    cloud_cover_ts.append(cloud_cover)
-    acc_georef_ts.append(metadata[satname]['acc_georef'][i])
-    filename_ts.append(filenames10[i])
-    satname_ts.append(satname)
-    date_acquired_ts.append(filenames10[i][:10])
-    timestamp.append(metadata[satname]['dates'][i])
-
-# store in output structure
-output[satname] = {'dates':timestamp, 'idx_skipped':idx_skipped,
-      'metadata':{'filenames':filename_ts, 'satname':satname_ts, 'cloud_cover':cloud_cover_ts,
-                  'acc_georef':acc_georef_ts}}
-
-# save output
-#with open(os.path.join(filepath, sitename + '_output' + satname + '.pkl'), 'wb') as f:
-#    pickle.dump(output, f)
-
-
-    
\ No newline at end of file