xpixel = 0.00027
ypixel = 0.00027
x_extent = lon_limits[1] - lon_limits[0]
y_extent = lat_limits[1] - lat_limits[0]
xsize = np.ceil(x_extent / xpixel)
ysize = np.ceil(y_extent / ypixel)
print('x extent {} : y extent {}'.format(str(x_extent), str(y_extent)))
print('Num of 30m pixels in x {} | Num of 30m pixels in y {}'.format(
str(xsize), str(ysize)))
wkt_list = list(
Vector.wkt_from_coords(coords, geom_type='point')
for coords in pos_arr.tolist())
attrib = {'cover': 'float', 'cover_error': 'float'}
attr_list = list({
'cover': cover[0],
'cover_error': cover[1]
} for cover in zip(cover_arr, cover_err_arr))
vector = Vector.vector_from_string(wkt_list,
geom_string_type='wkt',
out_epsg=4326,
vector_type='point',
attributes=attr_list,
attribute_types=attrib,
def read_gee_extract_data(filename):
"""
Method to read sample data in the form of a site dictionary with samples dicts by year
:param filename: Input data file name
:return: dict of list of dicts by year
"""
lines = Handler(filename).read_from_csv(return_dicts=True)
site_dict = dict()
line_counter = 0
for j, line in enumerate(lines):
include = True
for key, val in line.items():
if type(val).__name__ == 'str':
if val == 'None':
include = False
if saturated_bands(line['radsat_qa']) \
or line['GEOMETRIC_RMSE_MODEL'] > 15.0 \
or unclear_value(line['pixel_qa']):
include = False
if include:
line_counter += 1
site_year = str(line['site']) + '_' + str(line['year'])
if site_year not in site_dict:
geom_wkt = Vector.wkt_from_coords((line['longitude'], line['latitude']))
site_dict[site_year] = {'geom': geom_wkt,
'decid_frac': line['decid_frac'],
'data': dict(),
'site_year': line['year'],
'site': line['site']}
temp_dict = dict()
sensor_dict = extract_date(line['LANDSAT_ID'])
temp_dict['img_jday'] = sensor_dict['date'].timetuple().tm_yday
temp_dict['img_year'] = sensor_dict['date'].timetuple().tm_year
temp_dict['sensor'] = sensor_dict['sensor']
bands = list('B' + str(ii + 1) for ii in range(7)) + ['slope', 'elevation', 'aspect']
band_dict = dict()
for band in bands:
if band in line:
band_dict[band] = line[band]
temp_dict['bands'] = correct_landsat_sr(band_dict,
sensor_dict['sensor'],
scale=0.0001)
site_dict[site_year]['data'].update({'{}_{}'.format(str(temp_dict['img_jday']),
str(temp_dict['img_year'])): temp_dict})
# print(line_counter)
return site_dict
[-95.185546875, 67.57571741708057],
[-113.466796875, 68.13885164925574],
[-125.947265625, 70.4073476760681],
[-140.09765625, 70.31873847853124],
[-156.708984375, 71.63599288330609],
[-167.431640625, 69.3493386397765],
[-165.146484375, 66.72254132270653],
[-168.662109375, 65.47650756256367],
[-165.673828125, 63.31268278043484],
[-168.837890625, 60.326947742998414],
[-166.552734375, 56.218923189166624]]
# boundary = ee.Geometry.Polygon(bound_coords)
# boundary = ee.FeatureCollection('users/masseyr44/shapefiles/NAboreal_10kmbuffer').first().geometry()
boundary_geom = Vector.get_osgeo_geom(Vector.wkt_from_coords(
bound_coords, 'polygon'),
geom_type='wkt')
# values to copy from fusion table/feature collection
feat_properties = ['site', 'year', 'decid_frac']
KEYS = ee.List(feat_properties)
# properties to retrieve from scene
scene_properties = [
'CLOUD_COVER', 'GEOMETRIC_RMSE_MODEL', 'LANDSAT_ID',
'SOLAR_ZENITH_ANGLE'
]
PROPERTY_LIST = ee.List(scene_properties)
# Bands to retrieve
bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'pixel_qa', 'radsat_qa']
wkt_list = list()
attr_list = list()
spref_str = '+proj=longlat +datum=WGS84'
latlon = list()
count = 0
for row in samp_data:
print('Reading elem: {}'.format(str(count + 1)))
elem = dict()
for header in list(attr):
elem[header] = row[header]
samp_geom = Vector.get_osgeo_geom(
Vector.wkt_from_coords([row['Lon'], row['Lat']]))
latlon.append([row['Lon'], row['Lat']])
if elem['Lat'] < 52.0 and samp_geom.Intersects(bounds_geom):
wkt_list.append(Vector.wkt_from_coords([row['Lon'], row['Lat']]))
attr_list.append(elem)
count += 1
uniq, indices, inverse, count = np.unique(ar=latlon,
axis=0,
return_index=True,
return_counts=True,
return_inverse=True)
# take mean of all samples of the same site that fall in the same year bin
for i, samp_list in enumerate(year_samp):
print('year: {}'.format(str(year_bins[i])))
samp_count = 0
site_ids = list(set(list(attr_dict['site']
for attr_dict in samp_list)))
for site_id in site_ids[0:100]:
same_site_samp_list = list(samp for samp in samp_list
if samp['site'] == site_id)
lat = same_site_samp_list[0]['Latitude']
lon = same_site_samp_list[0]['Longitude']
samp_wkt = Vector.wkt_from_coords([lon, lat])
samp_geom = Vector.get_osgeo_geom(samp_wkt)
if boreal_geom.Intersects(samp_geom):
decid_frac = np.mean(
list(site_samp['decid_frac']
for site_samp in same_site_samp_list))
year = int(
np.mean(
list(site_samp['year']
for site_samp in same_site_samp_list)))
# remove spaces in site names,
# and add year to site name eg: 'site1' + '2007' = 'site1_2007'
year_samp_reduced[i].append({