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"""
Coastal analyses on Digital Earth Africa data.
"""
# Import required packages
# Force GeoPandas to use Shapely instead of PyGEOS
# In a future release, GeoPandas will switch to using Shapely by default.
import os
os.environ['USE_PYGEOS'] = '0'
import requests
import numpy as np
import xarray as xr
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
from scipy import stats
from otps import TimePoint
from otps import predict_tide
from shapely.geometry import box
from datacube.utils.geometry import CRS
from owslib.wfs import WebFeatureService
from deafrica_tools.datahandling import parallel_apply
# Fix converters for tidal plot
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
# URL for the DE Africa Coastlines data on Geoserver.
WFS_ADDRESS = "https://geoserver.digitalearth.africa/geoserver/wfs"
def model_tides(
x,
y,
time,
model="FES2014",
directory="/var/share/tide_models",
epsg=4326,
method="bilinear",
extrapolate=True,
cutoff=10.0,
):
"""
Compute tides at points and times using tidal harmonics.
If multiple x, y points are provided, tides will be
computed for all timesteps at each point.
This function supports any tidal model supported by
`pyTMD`, including the FES2014 Finite Element Solution
tide model, and the TPXO8-atlas and TPXO9-atlas-v5
TOPEX/POSEIDON global tide models.
This function requires access to tide model data files
to work. These should be placed in a folder with
subfolders matching the formats specified by `pyTMD`:
https://pytmd.readthedocs.io/en/latest/getting_started/Getting-Started.html#directories
For FES2014 (https://www.aviso.altimetry.fr/es/data/products/auxiliary-products/global-tide-fes/description-fes2014.html):
- {directory}/fes2014/ocean_tide/
{directory}/fes2014/load_tide/
For TPXO8-atlas (https://www.tpxo.net/tpxo-products-and-registration):
- {directory}/tpxo8_atlas/
For TPXO9-atlas-v5 (https://www.tpxo.net/tpxo-products-and-registration):
- {directory}/TPXO9_atlas_v5/
This function is a minor modification of the `pyTMD`
package's `compute_tide_corrections` function, adapted
to process multiple timesteps for multiple input point
locations. For more info:
https://pytmd.readthedocs.io/en/stable/user_guide/compute_tide_corrections.html
Parameters:
-----------
x, y : float or list of floats
One or more x and y coordinates used to define
the location at which to model tides. By default these
coordinates should be lat/lon; use `epsg` if they
are in a custom coordinate reference system.
time : A datetime array or pandas.DatetimeIndex
An array containing 'datetime64[ns]' values or a
'pandas.DatetimeIndex' providing the times at which to
model tides in UTC time.
model : string
The tide model used to model tides. Options include:
- "FES2014" (only pre-configured option on DEA Sandbox)
- "TPXO8-atlas"
- "TPXO9-atlas-v5"
directory : string
The directory containing tide model data files. These
data files should be stored in sub-folders for each
model that match the structure provided by `pyTMD`:
https://pytmd.readthedocs.io/en/latest/getting_started/Getting-Started.html#directories
For example:
- {directory}/fes2014/ocean_tide/
{directory}/fes2014/load_tide/
- {directory}/tpxo8_atlas/
- {directory}/TPXO9_atlas_v5/
epsg : int
Input coordinate system for 'x' and 'y' coordinates.
Defaults to 4326 (WGS84).
method : string
Method used to interpolate tidal contsituents
from model files. Options include:
- bilinear: quick bilinear interpolation
- spline: scipy bivariate spline interpolation
- linear, nearest: scipy regular grid interpolations
extrapolate : bool
Whether to extrapolate tides for locations outside of
the tide modelling domain using nearest-neighbor
cutoff : int or float
Extrapolation cutoff in kilometers. Set to `np.inf`
to extrapolate for all points.
Returns
-------
A pandas.DataFrame containing tide heights for every
combination of time and point coordinates.
"""
import os
import pyproj
import numpy as np
import pyTMD.time
import pyTMD.model
import pyTMD.utilities
from pyTMD.calc_delta_time import calc_delta_time
from pyTMD.infer_minor_corrections import infer_minor_corrections
from pyTMD.predict_tide_drift import predict_tide_drift
from pyTMD.read_tide_model import extract_tidal_constants
from pyTMD.read_netcdf_model import extract_netcdf_constants
from pyTMD.read_GOT_model import extract_GOT_constants
from pyTMD.read_FES_model import extract_FES_constants
# Check that tide directory is accessible
try:
os.access(directory, os.F_OK)
except:
raise FileNotFoundError("Invalid tide directory")
# Get parameters for tide model
model = pyTMD.model(directory, format="netcdf", compressed=False).elevation(model)
# If time passed as a single Timestamp, convert to datetime64
if isinstance(time, pd.Timestamp):
time = time.to_datetime64()
# Handle numeric or array inputs
x = np.atleast_1d(x)
y = np.atleast_1d(y)
time = np.atleast_1d(time)
# Determine point and time counts
assert len(x) == len(y), "x and y must be the same length"
n_points = len(x)
n_times = len(time)
# Converting x,y from EPSG to latitude/longitude
try:
# EPSG projection code string or int
crs1 = pyproj.CRS.from_string("epsg:{0:d}".format(int(epsg)))
except (ValueError, pyproj.exceptions.CRSError):
# Projection SRS string
crs1 = pyproj.CRS.from_string(epsg)
crs2 = pyproj.CRS.from_string("epsg:{0:d}".format(4326))
transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)
lon, lat = transformer.transform(x.flatten(), y.flatten())
# Assert delta time is an array and convert datetime
time = np.atleast_1d(time)
t = pyTMD.time.convert_datetime(time, epoch=(1992, 1, 1, 0, 0, 0)) / 86400.0
# Delta time (TT - UT1) file
delta_file = pyTMD.utilities.get_data_path(["data", "merged_deltat.data"])
# Read tidal constants and interpolate to grid points
if model.format in ("OTIS", "ATLAS"):
amp, ph, D, c = extract_tidal_constants(
lon,
lat,
model.grid_file,
model.model_file,
model.projection,
TYPE=model.type,
METHOD=method,
EXTRAPOLATE=extrapolate,
CUTOFF=cutoff,
GRID=model.format,
)
deltat = np.zeros_like(t)
elif model.format == "netcdf":
amp, ph, D, c = extract_netcdf_constants(
lon,
lat,
model.grid_file,
model.model_file,
TYPE=model.type,
METHOD=method,
EXTRAPOLATE=extrapolate,
CUTOFF=cutoff,
SCALE=model.scale,
GZIP=model.compressed,
)
deltat = np.zeros_like(t)
elif model.format == "GOT":
amp, ph, c = extract_GOT_constants(
lon,
lat,
model.model_file,
METHOD=method,
EXTRAPOLATE=extrapolate,
CUTOFF=cutoff,
SCALE=model.scale,
GZIP=model.compressed,
)
# Interpolate delta times from calendar dates to tide time
deltat = calc_delta_time(delta_file, t)
elif model.format == "FES":
amp, ph = extract_FES_constants(
lon,
lat,
model.model_file,
TYPE=model.type,
VERSION=model.version,
METHOD=method,
EXTRAPOLATE=extrapolate,
CUTOFF=cutoff,
SCALE=model.scale,
GZIP=model.compressed,
)
# Available model constituents
c = model.constituents
# Interpolate delta times from calendar dates to tide time
deltat = calc_delta_time(delta_file, t)
# Calculate complex phase in radians for Euler's
cph = -1j * ph * np.pi / 180.0
# Calculate constituent oscillation
hc = amp * np.exp(cph)
# Repeat constituents to length of time and number of input
# coords before passing to `predict_tide_drift`
t, hc, deltat = (
np.tile(t, n_points),
hc.repeat(n_times, axis=0),
np.tile(deltat, n_points),
)
# Predict tidal elevations at time and infer minor corrections
npts = len(t)
tide = np.ma.zeros((npts), fill_value=np.nan)
tide.mask = np.any(hc.mask, axis=1)
# Depending on pyTMD version (<=1.06 vs > 1.06), use different params
# TODO: Remove once Sandbox is updated to use pyTMD version 1.0.9
try:
tide.data[:] = predict_tide_drift(
t, hc, c, deltat=deltat, corrections=model.format
)
minor = infer_minor_corrections(
t, hc, c, deltat=deltat, corrections=model.format
)
except:
tide.data[:] = predict_tide_drift(
t, hc, c, DELTAT=deltat, CORRECTIONS=model.format
)
minor = infer_minor_corrections(
t, hc, c, DELTAT=deltat, CORRECTIONS=model.format
)
tide.data[:] += minor.data[:]
# Replace invalid values with fill value
tide.data[tide.mask] = tide.fill_value
# Export data as a dataframe
return pd.DataFrame(
{
"time": np.tile(time, n_points),
"x": np.repeat(x, n_times),
"y": np.repeat(y, n_times),
"tide_m": tide,
}
).set_index("time")
def pixel_tides(
ds,
times=None,
resample=True,
calculate_quantiles=None,
resolution=None,
buffer=None,
resample_method="bilinear",
**model_tides_kwargs,
):
"""
Obtain tide heights for each pixel in a dataset by modelling
tides into a low-resolution grid surrounding the dataset,
then (optionally) spatially resample this low-res data back
into the original higher resolution dataset extent and resolution.
Parameters:
-----------
ds : xarray.Dataset
A dataset whose geobox (`ds.odc.geobox`) will be used to define
the spatial extent of the low resolution tide modelling grid.
times : pandas.DatetimeIndex or list of pandas.Timestamps, optional
By default, the function will model tides using the times
contained in the `time` dimension of `ds`. Alternatively, this
param can be used to model tides for a custom set of times
instead. For example:
`times=pd.date_range(start="2000", end="2001", freq="5h")`
resample : bool, optional
Whether to resample low resolution tides back into `ds`'s original
higher resolution grid. Set this to `False` if you do not want
low resolution tides to be re-projected back to higher resolution.
calculate_quantiles : list or np.array, optional
Rather than returning all individual tides, low-resolution tides
can be first aggregated using a quantile calculation by passing in
a list or array of quantiles to compute. For example, this could
be used to calculate the min/max tide across all times:
`calculate_quantiles=[0.0, 1.0]`.
resolution: int, optional
The desired resolution of the low-resolution grid used for tide
modelling. The default None will create a 5000 m resolution grid
if `ds` has a projected CRS (i.e. metre units), or a 0.05 degree
resolution grid if `ds` has a geographic CRS (e.g. degree units).
Note: higher resolutions do not necessarily provide better
tide modelling performance, as results will be limited by the
resolution of the underlying global tide model (e.g. 1/16th
degree / ~5 km resolution grid for FES2014).
buffer : int, optional
The amount by which to buffer the higher resolution grid extent
when creating the new low resolution grid. This buffering is
important as it ensures that ensure pixel-based tides are seamless
across dataset boundaries. This buffer will eventually be clipped
away when the low-resolution data is re-projected back to the
resolution and extent of the higher resolution dataset. To
ensure that at least two pixels occur outside of the dataset
bounds, the default None applies a 12000 m buffer if `ds` has a
projected CRS (i.e. metre units), or a 0.12 degree buffer if
`ds` has a geographic CRS (e.g. degree units).
resample_method : string, optional
If resampling is requested (see `resample` above), use this
resampling method when converting from low resolution to high
resolution pixels. Defaults to "bilinear"; valid options include
"nearest", "cubic", "min", "max", "average" etc.
**model_tides_kwargs :
Optional parameters passed to the `dea_tools.coastal.model_tides`
function. Important parameters include "model" and "directory",
used to specify the tide model to use and the location of its files.
Returns:
--------
If `resample` is True:
tides_lowres : xr.DataArray
A low resolution data array giving either tide heights every
timestep in `ds` (if `times` is None), tide heights at every
time in `times` (if `times` is not None), or tide height quantiles
for every quantile provided by `calculate_quantiles`.
If `resample` is False:
tides_highres, tides_lowres : tuple of xr.DataArrays
In addition to `tides_lowres` (see above), a high resolution
array of tide heights will be generated that matches the
exact spatial resolution and extent of `ds`. This will contain
either tide heights every timestep in `ds` (if `times` is None),
tide heights at every time in `times` (if `times` is not None),
or tide height quantiles for every quantile provided by
`calculate_quantiles`.
"""
import odc.geo.xr
from odc.geo.geobox import GeoBox
# First test if no time dimension and nothing passed to `times`
if ('time' not in ds.dims) & (times is None):
raise ValueError(
"`ds` does not contain a 'time' dimension. Times are required "
"for modelling tides: please pass in a set of custom tides "
"using the `times` parameter. For example: "
"`times=pd.date_range(start='2000', end='2001', freq='5h')`"
)
# If custom times are provided, convert them to a consistent
# pandas.DatatimeIndex format
if times is not None:
if isinstance(times, list):
time_coords = pd.DatetimeIndex(times)
elif isinstance(times, pd.Timestamp):
time_coords = pd.DatetimeIndex([times])
else:
time_coords = times
# Otherwise, use times from `ds` directly
else:
time_coords = ds.coords["time"]
# Determine spatial dimensions
y_dim, x_dim = ds.odc.spatial_dims
# Determine resolution and buffer, using different defaults for
# geographic (i.e. degrees) and projected (i.e. metres) CRSs:
crs_units = ds.odc.geobox.crs.units[0][0:6]
if ds.odc.geobox.crs.geographic:
if resolution is None:
resolution = 0.05
elif resolution > 360:
raise ValueError(f"A resolution of greater than 360 was "
f"provided, but `ds` has a geographic CRS "
f"in {crs_units} units. Did you accidently "
f"provide a resolution in projected "
f"(i.e. metre) units?")
if buffer is None:
buffer = 0.12
else:
if resolution is None:
resolution = 5000
elif resolution < 1:
raise ValueError(f"A resolution of less than 1 was provided, "
f"but `ds` has a projected CRS in "
f"{crs_units} units. Did you accidently "
f"provide a resolution in geographic "
f"(degree) units?")
if buffer is None:
buffer = 12000
# Raise error if resolution is less than dataset resolution
dataset_res = ds.odc.geobox.resolution.x
if resolution < dataset_res:
raise ValueError(f"The resolution of the low-resolution tide "
f"modelling grid ({resolution:.2f}) is less "
f"than `ds`'s pixel resolution ({dataset_res:.2f}). "
f"This can cause extremely slow tide modelling "
f"performance. Please select provide a resolution "
f"greater than {dataset_res:.2f} using "
f"`pixel_tides`'s 'resolution' parameter.")
# Create a new reduced resolution tide modelling grid after
# first buffering the grid
print(f"Creating reduced resolution {resolution} x {resolution} "
f"{crs_units} tide modelling array")
buffered_geobox = ds.odc.geobox.buffered(buffer)
rescaled_geobox = GeoBox.from_bbox(
bbox=buffered_geobox.boundingbox, resolution=resolution
)
rescaled_ds = odc.geo.xr.xr_zeros(rescaled_geobox)
# Flatten grid to 1D, then add time dimension
flattened_ds = rescaled_ds.stack(z=(x_dim, y_dim))
flattened_ds = flattened_ds.expand_dims(dim={"time": time_coords.values})
# Model tides for each timestep
model = (
"FES2014" if "model" not in model_tides_kwargs else model_tides_kwargs["model"]
)
print(f"Modelling tides using {model} tide model")
tide_df = model_tides(
x=flattened_ds[x_dim],
y=flattened_ds[y_dim],
time=flattened_ds.time,
epsg=ds.odc.geobox.crs.epsg,
**model_tides_kwargs,
)
# Rename x and y coordinates to match satellite array
tide_df = tide_df.rename({"x": x_dim, "y": y_dim}, axis=1)
# Insert modelled tide values back into flattened array, then unstack
# back to 3D (y, x, time)
tides_lowres = (
# Convert dataframe to xarray format
tide_df.set_index([x_dim, y_dim], append=True)
.to_xarray()
# Re-index and transpose back into 3D
.tide_m.reindex_like(rescaled_ds)
.transpose("time", y_dim, x_dim)
.astype(np.float32)
)
# Optionally calculate and return quantiles rather than raw data
if calculate_quantiles is not None:
print("Computing tide quantiles")
tides_lowres = tides_lowres.quantile(q=calculate_quantiles, dim="time")
reproject_dim = "quantile"
else:
reproject_dim = "time"
# Ensure CRS is present
tides_lowres = tides_lowres.odc.assign_crs(ds.odc.geobox.crs)
# Reproject each timestep into original high resolution grid
if resample:
print("Reprojecting tides into original array")
tides_highres = parallel_apply(
tides_lowres,
reproject_dim,
odc.algo.xr_reproject,
ds.odc.geobox.compat,
resample_method,
)
return tides_highres, tides_lowres
else:
print("Returning low resolution tide array")
return tides_lowres
def tidal_tag(
ds,
ebb_flow=False,
swap_dims=False,
tidepost_lat=None,
tidepost_lon=None,
return_tideposts=False,
**model_tides_kwargs,
):
"""
Takes an xarray.Dataset and returns the same dataset with a new
`tide_m` variable giving the height of the tide at the exact
moment of each satellite acquisition.
The function models tides at the centroid of the dataset by default,
but a custom tidal modelling location can be specified using
`tidepost_lat` and `tidepost_lon`.
The default settings use the FES2014 global tidal model, implemented
using the pyTMD Python package. FES2014 was produced by NOVELTIS,
LEGOS, CLS Space Oceanography Division and CNES. It is distributed
by AVISO, with support from CNES (http://www.aviso.altimetry.fr/).
Parameters
----------
ds : xarray.Dataset
An xarray.Dataset object with x, y and time dimensions
ebb_flow : bool, optional
An optional boolean indicating whether to compute if the
tide phase was ebbing (falling) or flowing (rising) for each
observation. The default is False; if set to True, a new
`ebb_flow` variable will be added to the dataset with each
observation labelled with 'Ebb' or 'Flow'.
swap_dims : bool, optional
An optional boolean indicating whether to swap the `time`
dimension in the original xarray.Dataset to the new
`tide_m` variable. Defaults to False.
tidepost_lat, tidepost_lon : float or int, optional
Optional coordinates used to model tides. The default is None,
which uses the centroid of the dataset as the tide modelling
location.
return_tideposts : bool, optional
An optional boolean indicating whether to return the `tidepost_lat`
and `tidepost_lon` location used to model tides in addition to the
xarray.Dataset. Defaults to False.
**model_tides_kwargs :
Optional parameters passed to the `dea_tools.coastal.model_tides`
function. Important parameters include "model" and "directory",
used to specify the tide model to use and the location of its files.
Returns
-------
The original xarray.Dataset with a new `tide_m` variable giving
the height of the tide (and optionally, its ebb-flow phase) at the
exact moment of each satellite acquisition (if `return_tideposts=True`,
the function will also return the `tidepost_lon` and `tidepost_lat`
location used in the analysis).
"""
import odc.geo.xr
# If custom tide modelling locations are not provided, use the
# dataset centroid
if not tidepost_lat or not tidepost_lon:
tidepost_lon, tidepost_lat = ds.odc.geobox.geographic_extent.centroid.coords[0]
print(
f"Setting tide modelling location from dataset centroid: "
f"{tidepost_lon:.2f}, {tidepost_lat:.2f}"
)
else:
print(
f"Using user-supplied tide modelling location: "
f"{tidepost_lon:.2f}, {tidepost_lat:.2f}"
)
# Use tidal model to compute tide heights for each observation:
model = (
"FES2014" if "model" not in model_tides_kwargs else model_tides_kwargs["model"]
)
print(f"Modelling tides using {model} tidal model")
tide_df = model_tides(
x=tidepost_lon,
y=tidepost_lat,
time=ds.time,
epsg="EPSG:4326",
**model_tides_kwargs,
)
# If tides cannot be successfully modeled (e.g. if the centre of the
# xarray dataset is located is over land), raise an exception
if tide_df.tide_m.isnull().all():
raise ValueError(
f"Tides could not be modelled for dataset centroid located "
f"at {tidepost_lon:.2f}, {tidepost_lat:.2f}. This can occur if "
f"this coordinate occurs over land. Please manually specify "
f"a tide modelling location located over water using the "
f"`tidepost_lat` and `tidepost_lon` parameters."
)
# Assign tide heights to the dataset as a new variable
ds["tide_m"] = xr.DataArray(tide_df.tide_m, coords=[ds.time])
# Optionally calculate the tide phase for each observation
if ebb_flow:
# Model tides for a time 15 minutes prior to each previously
# modelled satellite acquisition time. This allows us to compare
# tide heights to see if they are rising or falling.
print("Modelling tidal phase (e.g. ebb or flow)")
tide_pre_df = model_tides(
x=tidepost_lon,
y=tidepost_lat,
time=(ds.time - pd.Timedelta("15 min")),
epsg="EPSG:4326",
**model_tides_kwargs,
)
# Compare tides computed for each timestep. If the previous tide
# was higher than the current tide, the tide is 'ebbing'. If the
# previous tide was lower, the tide is 'flowing'
tidal_phase = [
"Ebb" if i else "Flow"
for i in tide_pre_df.tide_m.values > tide_df.tide_m.values
]
# Assign tide phase to the dataset as a new variable
ds["ebb_flow"] = xr.DataArray(tidal_phase, coords=[ds.time])
# If swap_dims = True, make tide height the primary dimension
# instead of time
if swap_dims:
# Swap dimensions and sort by tide height
ds = ds.swap_dims({"time": "tide_m"})
ds = ds.sortby("tide_m")
ds = ds.drop_vars("time")
if return_tideposts:
return ds, tidepost_lon, tidepost_lat
else:
return ds
def tidal_stats(
ds,
tidepost_lat=None,
tidepost_lon=None,
plain_english=True,
plot=True,
modelled_freq="2h",
linear_reg=False,
round_stats=3,
**model_tides_kwargs,
):
"""
Takes an xarray.Dataset and statistically compares the tides
modelled for each satellite observation against the full modelled
tidal range. This comparison can be used to evaluate whether the
tides observed by satellites (e.g. Landsat) are biased compared to
the natural tidal range (e.g. fail to observe either the highest or
lowest tides etc).
For more information about the tidal statistics computed by this
function, refer to Figure 8 in Bishop-Taylor et al. 2018:
https://www.sciencedirect.com/science/article/pii/S0272771418308783#fig8
The function models tides at the centroid of the dataset by default,
but a custom tidal modelling location can be specified using
`tidepost_lat` and `tidepost_lon`.
The default settings use the FES2014 global tidal model, implemented
using the pyTMD Python package. FES2014 was produced by NOVELTIS,
LEGOS, CLS Space Oceanography Division and CNES. It is distributed
by AVISO, with support from CNES (http://www.aviso.altimetry.fr/).
Parameters
----------
ds : xarray.Dataset
An xarray.Dataset object with x, y and time dimensions
tidepost_lat, tidepost_lon : float or int, optional
Optional coordinates used to model tides. The default is None,
which uses the centroid of the dataset as the tide modelling
location.
plain_english : bool, optional
An optional boolean indicating whether to print a plain english
version of the tidal statistics to the screen. Defaults to True.
plot : bool, optional
An optional boolean indicating whether to plot how satellite-
observed tide heights compare against the full tidal range.
Defaults to True.
modelled_freq : str, optional
An optional string giving the frequency at which to model tides
when computing the full modelled tidal range. Defaults to '2h',
which computes a tide height for every two hours across the
temporal extent of `ds`.
linear_reg: bool, optional
Experimental: whether to return linear regression stats that
assess whether dstellite-observed and all available tides show
any decreasing or increasing trends over time. Not currently
recommended as all observed regressions always return as
significant due to far larger sample size.
round_stats : int, optional
The number of decimal places used to round the output statistics.
Defaults to 3.
**model_tides_kwargs :
Optional parameters passed to the `dea_tools.coastal.model_tides`
function. Important parameters include "model" and "directory",
used to specify the tide model to use and the location of its files.
Returns
-------
A pandas.Series object containing the following statistics:
tidepost_lat: latitude used for modelling tide heights
tidepost_lon: longitude used for modelling tide heights
observed_min_m: minimum tide height observed by the satellite
all_min_m: minimum tide height from all available tides
observed_max_m: maximum tide height observed by the satellite
all_max_m: maximum tide height from all available tides
observed_range_m: tidal range observed by the satellite
all_range_m: full astronomical tidal range based on all
available tides
spread_m: proportion of the full astronomical tidal range observed
by the satellite (see Bishop-Taylor et al. 2018)
low_tide_offset: proportion of the lowest tides never observed
by the satellite (see Bishop-Taylor et al. 2018)
high_tide_offset: proportion of the highest tides never observed
by the satellite (see Bishop-Taylor et al. 2018)
If `linear_reg = True`, the output will also contain:
observed_slope: slope of any relationship between observed tide
heights and time
all_slope: slope of any relationship between all available tide
heights and time
observed_pval: significance/p-value of any relationship between
observed tide heights and time
all_pval: significance/p-value of any relationship between
all available tide heights and time
"""
# Model tides for each observation in the supplied xarray object
ds_tides, tidepost_lon, tidepost_lat = tidal_tag(
ds,
tidepost_lat=tidepost_lat,
tidepost_lon=tidepost_lon,
return_tideposts=True,
**model_tides_kwargs,
)
# Drop spatial ref for nicer plotting
if "spatial_ref" in ds_tides:
ds_tides = ds_tides.drop_vars("spatial_ref")
# Generate range of times covering entire period of satellite record
all_timerange = pd.date_range(
start=ds_tides.time.min().item(),
end=ds_tides.time.max().item(),
freq=modelled_freq,
)
# Model tides for each timestep
all_tides_df = model_tides(
x=tidepost_lon,
y=tidepost_lat,
time=all_timerange,
epsg="EPSG:4326",
**model_tides_kwargs,
)
# Get coarse statistics on all and observed tidal ranges
obs_mean = ds_tides.tide_m.mean().item()
all_mean = all_tides_df.tide_m.mean()
obs_min, obs_max = ds_tides.tide_m.quantile([0.0, 1.0]).values
all_min, all_max = all_tides_df.tide_m.quantile([0.0, 1.0]).values
# Calculate tidal range
obs_range = obs_max - obs_min
all_range = all_max - all_min
# Calculate Bishop-Taylor et al. 2018 tidal metrics
spread = obs_range / all_range
low_tide_offset = abs(all_min - obs_min) / all_range
high_tide_offset = abs(all_max - obs_max) / all_range
# Extract x (time in decimal years) and y (distance) values
all_x = (
all_tides_df.index.year
+ ((all_tides_df.index.dayofyear - 1) / 365)
+ ((all_tides_df.index.hour - 1) / 24)
)
all_y = all_tides_df.tide_m.values.astype(np.float32)
time_period = all_x.max() - all_x.min()
# Extract x (time in decimal years) and y (distance) values
obs_x = (
ds_tides.time.dt.year
+ ((ds_tides.time.dt.dayofyear - 1) / 365)
+ ((ds_tides.time.dt.hour - 1) / 24)
)
obs_y = ds_tides.tide_m.values.astype(np.float32)
# Compute linear regression
obs_linreg = stats.linregress(x=obs_x, y=obs_y)
all_linreg = stats.linregress(x=all_x, y=all_y)
if plain_english:
print(
f"\n{spread:.0%} of the {all_range:.2f} m modelled astronomical "
f"tidal range is observed at this location.\nThe lowest "
f"{low_tide_offset:.0%} and highest {high_tide_offset:.0%} "
f"of astronomical tides are never observed.\n"
)
if linear_reg:
if obs_linreg.pvalue > 0.05:
print(
f"Observed tides show no significant trends "
f"over the ~{time_period:.0f} year period."
)
else:
obs_slope_desc = "decrease" if obs_linreg.slope < 0 else "increase"
print(
f"Observed tides {obs_slope_desc} significantly "
f"(p={obs_linreg.pvalue:.3f}) over time by "
f"{obs_linreg.slope:.03f} m per year (i.e. a "
f"~{time_period * obs_linreg.slope:.2f} m "
f"{obs_slope_desc} over the ~{time_period:.0f} year period)."
)
if all_linreg.pvalue > 0.05:
print(
f"All tides show no significant trends "
f"over the ~{time_period:.0f} year period."
)
else:
all_slope_desc = "decrease" if all_linreg.slope < 0 else "increase"
print(
f"All tides {all_slope_desc} significantly "
f"(p={all_linreg.pvalue:.3f}) over time by "
f"{all_linreg.slope:.03f} m per year (i.e. a "
f"~{time_period * all_linreg.slope:.2f} m "
f"{all_slope_desc} over the ~{time_period:.0f} year period)."
)
if plot:
# Create plot and add all time and observed tide data
fig, ax = plt.subplots(figsize=(10, 5))
all_tides_df.tide_m.plot(ax=ax, alpha=0.4)
ds_tides.tide_m.plot.line(
ax=ax, marker="o", linewidth=0.0, color="black", markersize=2
)
# Add horizontal lines for spread/offsets
ax.axhline(obs_min, color="black", linestyle=":", linewidth=1)
ax.axhline(obs_max, color="black", linestyle=":", linewidth=1)
ax.axhline(all_min, color="black", linestyle=":", linewidth=1)
ax.axhline(all_max, color="black", linestyle=":", linewidth=1)
# Add text annotations for spread/offsets
ax.annotate(
f" High tide\n offset ({high_tide_offset:.0%})",
xy=(all_timerange.max(), np.mean([all_max, obs_max])),
va="center",
)
ax.annotate(
f" Spread\n ({spread:.0%})",
xy=(all_timerange.max(), np.mean([obs_min, obs_max])),
va="center",
)
ax.annotate(
f" Low tide\n offset ({low_tide_offset:.0%})",
xy=(all_timerange.max(), np.mean([all_min, obs_min])),
)
# Remove top right axes and add labels
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
ax.set_ylabel("Tide height (m)")
ax.set_xlabel("")
ax.margins(x=0.015)
# Export pandas.Series containing tidal stats
output_stats = {
"tidepost_lat": tidepost_lat,
"tidepost_lon": tidepost_lon,
"observed_mean_m": obs_mean,
"all_mean_m": all_mean,
"observed_min_m": obs_min,
"all_min_m": all_min,
"observed_max_m": obs_max,
"all_max_m": all_max,
"observed_range_m": obs_range,
"all_range_m": all_range,
"spread": spread,
"low_tide_offset": low_tide_offset,
"high_tide_offset": high_tide_offset,
}
if linear_reg:
output_stats.update(
{
"observed_slope": obs_linreg.slope,
"all_slope": all_linreg.slope,
"observed_pval": obs_linreg.pvalue,
"all_pval": all_linreg.pvalue,
}
)
return pd.Series(output_stats).round(round_stats)
def transect_distances(transects_gdf, lines_gdf, mode='distance'):
"""
Take a set of transects (e.g. shore-normal beach survey lines), and
determine the distance along the transect to each object in a set of
lines (e.g. shorelines). Distances are measured in the CRS of the
input datasets.
For coastal applications, transects should be drawn from land to
water (with the first point being on land so that it can be used
as a consistent location from which to measure distances.
The distance calculation can be performed using two modes:
- 'distance': Distances are measured from the start of the
transect to where it intersects with each line. Any transect
that intersects a line more than once is ignored. This mode is
useful for measuring e.g. the distance to the shoreline over
time from a consistent starting location.
- 'width' Distances are measured between the first and last
intersection between a transect and each line. Any transect
that intersects a line only once is ignored. This is useful
for e.g. measuring the width of a narrow area of coastline over
time, e.g. the neck of a spit or tombolo.
Parameters
----------
transects_gdf : geopandas.GeoDataFrame
A GeoDataFrame containing one or multiple vector profile lines.
The GeoDataFrame's index column will be used to name the rows in
the output distance table.
lines_gdf : geopandas.GeoDataFrame
A GeoDataFrame containing one or multiple vector line features
that intersect the profile lines supplied to `transects_gdf`.
The GeoDataFrame's index column will be used to name the columns
in the output distance table.
mode : string, optional
Whether to use 'distance' (for measuring distances from the
start of a profile) or 'width' mode (for measuring the width
between two profile intersections). See docstring above for more
info; defaults to 'distance'.
Returns
-------
distance_df : pandas.DataFrame
A DataFrame containing distance measurements for each profile
line (rows) and line feature (columns).
"""
import warnings
from shapely.errors import ShapelyDeprecationWarning
from shapely.geometry import Point
def _intersect_dist(transect_gdf, lines_gdf, mode=mode):
"""
Take an individual transect, and determine the distance along
the transect to each object in a set of lines (e.g. shorelines).
"""
# Identify intersections between transects and lines
intersect_points = lines_gdf.apply(
lambda x: x.geometry.intersection(transect_gdf.geometry), axis=1)
# In distance mode, identify transects with one intersection only,
# and use this as the end point and the start of the transect as the
# start point when measuring distances
if mode == 'distance':
start_point = Point(transect_gdf.geometry.coords[0])
point_df = intersect_points.apply(
lambda x: pd.Series({'start': start_point, 'end': x})
if x.type == 'Point'
else pd.Series({'start': None, 'end': None}))
# In width mode, identify transects with multiple intersections, and
# use the first intersection as the start point and the second
# intersection for the end point when measuring distances
if mode == 'width':
point_df = intersect_points.apply(
lambda x: pd.Series({'start': x.geoms[0], 'end': x.geoms[-1]})
if x.type == 'MultiPoint'
else pd.Series({'start': None, 'end': None}))
# Calculate distances between valid start and end points
distance_df = point_df.apply(
lambda x: x.start.distance(x.end) if x.start else None, axis=1)
return distance_df
# Run code after ignoring Shapely pre-v2.0 warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
# Assert that both datasets use the same CRS
assert transects_gdf.crs == lines_gdf.crs, ('Please ensure both '
'input datasets use the same CRS.')
# Run distance calculations
distance_df = transects_gdf.apply(
lambda x: _intersect_dist(x, lines_gdf), axis=1)
return pd.DataFrame(distance_df)
def get_coastlines(bbox: tuple,
crs="EPSG:4326",
layer="shorelines",
drop_wms=True) -> gpd.GeoDataFrame:
"""
Get DE Africa Coastlines data for a provided bounding box using WFS.
For a full description of the DE Africa Coastlines dataset, refer to the
official Digital Earth Africa product description:
Parameters
----------
bbox : (xmin, ymin, xmax, ymax), or geopandas object
Bounding box expressed as a tuple. Alternatively, a bounding
box can be automatically extracted by suppling a
geopandas.GeoDataFrame or geopandas.GeoSeries.
crs : str, optional
Optional CRS for the bounding box. This is ignored if `bbox`
is provided as a geopandas object.
layer : str, optional
Which DE Africa Coastlines layer to load. Options include the annual
shoreline vectors ("shorelines") and the rates of change
statistics points ("statistics"). Defaults to "shorelines".
drop_wms : bool, optional
Whether to drop WMS-specific attribute columns from the data.
These columns are used for visualising the dataset on DE Africa Maps,
and are unlikely to be useful for scientific analysis. Defaults
to True.
Returns
-------
gpd.GeoDataFrame
A GeoDataFrame containing shoreline or point features and
associated metadata.
"""
# If bbox is a geopandas object, convert to bbox.
try:
crs = str(bbox.crs)
bbox = bbox.total_bounds
except:
pass
# Get the available layers in the coastlines:DEAfrica_Coastlines group.
describe_layer_url = "https://geoserver.digitalearth.africa/geoserver/wms?service=WMS&version=1.1.1&request=DescribeLayer&layers=coastlines:DEAfrica_Coastlines&outputFormat=application/json"
describe_layer_response = requests.get(describe_layer_url).json()
available_layers = [layer["layerName"] for layer in describe_layer_response['layerDescriptions']]
# Get the layer name.
if layer == "shorelines":
layer_name = [i for i in available_layers if "shorelines" in i]
else:
layer_name = [i for i in available_layers if "rates_of_change" in i]
# Query WFS.
wfs = WebFeatureService(url=WFS_ADDRESS, version="1.1.0")
response = wfs.getfeature(typename=layer_name,
bbox=tuple(bbox) + (crs,),
outputFormat="json")
# Load data as a geopandas.GeoDataFrame.
coastlines_gdf = gpd.read_file(response)
# Clip to extent of bounding box.
extent = gpd.GeoSeries(box(*bbox), crs=crs).to_crs(coastlines_gdf.crs)
coastlines_gdf = coastlines_gdf.clip(extent)
# Optionally drop WMS-specific columns.
if drop_wms:
coastlines_gdf = coastlines_gdf.loc[:, ~coastlines_gdf.columns.str.contains("wms_")]
return coastlines_gdf
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