def read_timeseries(self, input_path) -> Timeseries:
timeseries = Timeseries()
with open(input_path, 'r') as file:
reader = csv.reader(file, delimiter=',')
line_count = 0
for row in reader:
if line_count > 0:
timeseries.push(*row)
line_count += 1
return timeseries
def write_timeseries(self, timeseries: Timeseries, output_path: str):
data = list(timeseries.get_zip())
field_names = timeseries.get_attributes()
with open(output_path, 'wt') as file:
writer = csv.writer(file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerow(field_names)
for ts in data:
writer.writerow(ts)
def average(self, raster_paths: List[str]) -> Timeseries:
timeseries = Timeseries()
raster_paths.sort()
for raster_path in raster_paths:
data = self.tif_reader.read_tif(raster_path)
avg = np.nanmean(data)
date_str = get_date_from_filename(
get_filename_from_path(raster_path))
timeseries.push(date_str, avg)
return timeseries
def get_all_reference_timeseries(self,
build: bool = False,
plot: bool = False) -> List[Timeseries]:
return [
Timeseries(['2019-01', '2019-02', '2019-03'],
[254. / 255, 253. / 255, 255. / 255], 'broadleaf',
'VH'),
Timeseries(['2019-01', '2019-02', '2019-03'],
[252. / 255, 251. / 255, 253. / 255], 'broadleaf',
'VV'),
Timeseries(['2019-01', '2019-02', '2019-03'],
[0. / 255, 1. / 255, 5. / 255], 'coniferous', 'VH'),
Timeseries(['2019-01', '2019-02', '2019-03'],
[2. / 255, 3. / 255, 4. / 255], 'coniferous', 'VV'),
]
def plot_multiple_timeseries(self,
timeseries: List[Timeseries],
figsize: Tuple[int, int] = None,
save_path: str = None):
figsize = figsize if figsize else (10, 10)
num_ts = len(timeseries)
x: np.array = None
ts: Timeseries = Timeseries()
plt.figure(figsize=figsize)
for idx, ts in enumerate(timeseries):
ts_size = ts.get_size()
x = np.arange(ts_size)
# TODO ylim should be same for all plots
plt.subplot(num_ts, 1, idx + 1)
plt.ylabel('Mean sig0 [dB]')
plt.title(ts.get_description())
plt.xticks([], [])
plt.plot(x, ts.sig0s)
plt.xticks(x[::3], ts.dates[::3], rotation=20)
plt.xlabel('Date')
if save_path:
plt.savefig(save_path)
else:
plt.show()
def get_rmsd(self, reference_timeseries: Timeseries,
actual_paths: List[str]) -> np.array:
rmsd_cubes = []
raster_segmenter = RasterSegmenter()
ts_size = reference_timeseries.get_size()
print(f'Timeseries: {reference_timeseries.get_description()}')
counter = 1
cube = raster_segmenter.get_next_cube(actual_paths)
timeseries_array = np.array(reference_timeseries.sig0s)
while cube:
print(f'RMSD Segment Counter: {counter}')
cube.data = np.square(cube.data -
timeseries_array[None, None, :]) # per pixel
cube.data = np.nansum(cube.data,
axis=2) # sums all nan values to 0
cube.data[cube.data == 0] = np.nan # set nan sums to nan again
cube.data = np.sqrt(cube.data / ts_size) # combining pixel
rmsd_cubes.append(cube)
counter += 1
cube = raster_segmenter.get_next_cube(actual_paths)
raster = raster_segmenter.get_rmsd_from_cubes(rmsd_cubes)
del rmsd_cubes
return raster
def test_csv_read_write():
# Paths
output_path = get_filepath(test_folder, 'csv_read_write.csv')
# timeseries
dates = ['201901', '201902', '201801', '201807']
sig0 = [12345, 3452, 12345, 9875]
input_timeseries = Timeseries(dates, sig0)
csv_read_writer = CsvReaderWriter()
csv_read_writer.write_timeseries(input_timeseries, output_path)
output_timeseries = csv_read_writer.read_timeseries(output_path)
assert input_timeseries == output_timeseries