def beach_slope(filepath_data, sitename, slope=None):
with open(
os.path.join(filepath_data, sitename,
sitename + "_output" + ".pkl"), "rb") as f:
output = pickle.load(f)
sf = shapefile.Reader(os.path.join(filepath_data, sitename,
"Perfiles.shp"))
transects = dict([])
i = 0
records = sf.records()
for shape in sf.shapes():
transects[str(int(records[i]["ID_perfil"]))] = np.array(shape.points)
i = i + 1
del records, sf
# remove S2 shorelines (the slope estimation algorithm needs only Landsat)
if "S2" in output["satname"]:
idx_S2 = np.array([_ == "S2" for _ in output["satname"]])
for key in output.keys():
output[key] = [output[key][_] for _ in np.where(~idx_S2)[0]]
# remove duplicates
output = SDS_slope.remove_duplicates(output)
# remove shorelines from images with poor georeferencing (RMSE > 10 m)
output = SDS_slope.remove_inaccurate_georef(output, 10)
# plot shorelines and transects
plots.plot_shorelines_transects(filepath_data, sitename, output, transects)
# a more robust method to compute intersection is needed here to avoid outliers
# as these can affect the slope detection algorithm
settings_transects = { # parameters for shoreline intersections
"along_dist": 25, # along-shore distance to use for intersection
"max_std": 15, # max std for points around transect
"max_range": 30, # max range for points around transect
"min_val":
-100, # largest negative value along transect (landwards of transect origin)
# parameters for outlier removal
"nan/max": "auto", # mode for removing outliers ('auto', 'nan', 'max')
"prc_std":
0.1, # percentage to use in 'auto' mode to switch from 'nan' to 'max'
"max_cross_change":
40, # two values of max_cross_change distance to use
}
# compute intersections [advanced version]
cross_distance = SDS_slope.compute_intersection(output, transects,
settings_transects)
# remove outliers [advanced version]
cross_distance = SDS_slope.reject_outliers(cross_distance, output,
settings_transects)
# plot time-series
SDS_slope.plot_cross_distance(filepath_data, sitename, output["dates"],
cross_distance)
# slope estimation settings
days_in_year = 365.2425
seconds_in_day = 24 * 3600
settings_slope = {
"slope_min": 0.005,
"slope_max": 0.6,
"delta_slope": 0.005,
"date_range":
[1950, 2050], # range of dates over which to perform the analysis
"n_days": 7, # sampling period [days]
"n0": 50, # for Nyquist criterium
"freqs_cutoff": 1.0 / (seconds_in_day * 30), # 1 month frequency
"delta_f": 100 *
1e-10, # deltaf for buffer around max peak # True to save some plots of the spectrums
}
settings_slope["date_range"] = [
pytz.utc.localize(datetime(settings_slope["date_range"][0], 5, 1)),
pytz.utc.localize(datetime(settings_slope["date_range"][1], 1, 1)),
]
beach_slopes = SDS_slope.range_slopes(
settings_slope["slope_min"],
settings_slope["slope_max"],
settings_slope["delta_slope"],
)
idx_dates = [
np.logical_and(_ > settings_slope["date_range"][0],
_ < settings_slope["date_range"][1])
for _ in output["dates"]
]
dates_sat = [output["dates"][_] for _ in np.where(idx_dates)[0]]
tides = []
slope_est = dict([])
_, _, filenames = next(
walk(os.path.join(filepath_data, "Marea_Astronomica")))
for key in cross_distance.keys():
cross_distance[key] = cross_distance[key][idx_dates]
transect = transects[key]
startPoint = transects[key][0]
endPoint = transects[key][1]
centroidX = startPoint[0]
centroidY = startPoint[1]
inProj = Proj(init='epsg:32628')
outProj = Proj(init='epsg:4326')
centroidXWgs84, centroidYWgs84 = transform(inProj, outProj, centroidX,
centroidY)
minDist = 1000.0
tideFile = None
for filename in filenames:
y = float(filename[7:14])
x = float(filename[16:24])
dist = distance.cdist([(x, y)], [(centroidXWgs84, centroidYWgs84)])
if dist < minDist:
minDist = dist
tideFile = filename
del dist
filepath = os.path.join(filepath_data, "Marea_Astronomica", tideFile)
mat = sio.loadmat(filepath)
dates_raw = mat['time'].flatten()
tides_ts = mat["tide"].flatten()
time_py = matDatenum2PYDatetime(dates_raw, unitTime='D')[0]
dates_ts = []
for time in time_py:
dates_ts.append(
datetime(time.year,
time.month,
time.day,
time.hour,
time.minute,
tzinfo=pytz.utc))
dates_sat = output["dates"]
# get tide levels corresponding to the time of image acquisition
tides_sat = SDS_tools.get_closest_datapoint(dates_sat, dates_ts,
tides_ts)
#plots.plot_water_levels(filepath_data, sitename, tide_data, dates_sat, tides_sat)
plots.plot_tide_time_series(filepath_data, sitename, dates_sat,
tides_sat)
t = np.array([_.timestamp() for _ in dates_sat]).astype("float64")
delta_t = np.diff(t)
plots.plot_time_step_distribution(filepath_data, sitename, delta_t,
seconds_in_day, settings_slope)
# find tidal peak frequency
settings_slope["freqs_max"] = SDS_slope.find_tide_peak(
filepath_data, sitename, dates_sat, tides_sat, settings_slope)
# remove NaNs
idx_nan = np.isnan(cross_distance[key])
dates = [dates_sat[_] for _ in np.where(~idx_nan)[0]]
tide = tides_sat[~idx_nan]
composite = cross_distance[key][~idx_nan]
# apply tidal correction
tsall = SDS_slope.tide_correct(composite, tide, beach_slopes)
try:
SDS_slope.plot_spectrum_all(filepath_data, sitename, key, dates,
composite, tsall, settings_slope)
slope_est[key] = SDS_slope.integrate_power_spectrum(
filepath_data, sitename, key, dates, tsall, settings_slope)
print("Beach slope at transect %s: %.3f" % (key, slope_est[key]))
except:
pass
del mat, dates_raw, tides_ts, dates_ts, tides_sat, composite, tsall, idx_nan
with open(os.path.join(filepath_data, sitename, "transects_slope.csv"),
mode="w") as csv_file:
writer = csv.writer(csv_file,
delimiter=",",
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
writer.writerow(["transect", "slope"])
for key in cross_distance.keys():
try:
writer.writerow(["transect" + str(key), slope_est[key]])
except:
pass
return slope_est
dates_ts = []
for time in time_py:
dates_ts.append(
datetime(time.year,
time.month,
time.day,
time.hour,
time.minute,
tzinfo=pytz.utc))
dates_sat = output["dates"]
# get tide levels corresponding to the time of image acquisition
tides_sat = SDS_tools.get_closest_datapoint(
dates_sat, dates_ts, tides_ts)
correction = (tides_sat - reference_elevation) / slope_est[key]
cross_distance_tidally_corrected[
key] = cross_distance_f[key] + correction
del mat, dates_raw, tides_ts, dates_ts, tides_sat
# store the tidally-corrected time-series in a .csv file
out_dict = dict([])
out_dict["dates"] = dates_sat
out_dict["geoaccuracy"] = output["geoaccuracy"]
out_dict["satname"] = output["satname"]
for key in cross_distance_tidally_corrected.keys():
try:
out_dict["Transect " +
str(key)] = cross_distance_tidally_corrected[key]
