From 772a95cae3707f26cc0f599e731806be1a1b3830 Mon Sep 17 00:00:00 2001 From: ClaireP Date: Tue, 24 Sep 2024 15:43:36 +1000 Subject: [PATCH] Extents updates (#91) * Integrate new tide modelling, connectivity * Update all, bugfinding * Update all, bugfinding * Add latest versions * Fix import * Update packaging * Update packaging * Commit to repo to enable PR * manually creating new requirements.txt file * Update nodata value * Fix tests * Automatically update integration test validation results * Update notebooks and docstrings * Automatically update integration test validation results --------- Co-authored-by: Robbi Bishop-Taylor Co-authored-by: robbibt --- intertidal/elevation.py | 114 +-- intertidal/exposure.py | 10 +- intertidal/extents.py | 134 ++-- intertidal/io.py | 57 -- intertidal/tide_modelling.py | 154 ---- ...a_s2ls_intertidal_cyear_3.odc-product.yaml | 5 + notebooks/Intertidal_CLI.ipynb | 128 ++-- notebooks/Intertidal_elevation.ipynb | 642 +++++++++------- notebooks/Intertidal_workflow.ipynb | 724 +++++++++--------- requirements.in | 31 +- requirements.txt | 41 +- setup.py | 3 +- tests/README.md | 4 +- tests/test_intertidal.py | 8 +- tests/validation.csv | 2 + tests/validation.jpg | Bin 73687 -> 75031 bytes 16 files changed, 922 insertions(+), 1135 deletions(-) delete mode 100644 intertidal/tide_modelling.py diff --git a/intertidal/elevation.py b/intertidal/elevation.py index 20c454d2..b22de71d 100644 --- a/intertidal/elevation.py +++ b/intertidal/elevation.py @@ -22,7 +22,6 @@ load_data, load_topobathy_mask, load_aclum_mask, - load_ocean_mask, prepare_for_export, tidal_metadata, export_dataset_metadata, @@ -31,46 +30,10 @@ configure_logging, round_date_strings, ) -from intertidal.tide_modelling import pixel_tides_ensemble -from intertidal.extents import extents#, ocean_connection +from intertidal.extents import extents, load_connectivity_mask from intertidal.exposure import exposure from intertidal.tidal_bias_offset import bias_offset -def ocean_connection(water, ocean_da, connectivity=2): - """ - Identifies areas of water pixels that are adjacent to or directly - connected to intertidal pixels. - - Parameters: - ----------- - water : xarray.DataArray - An array containing True for water pixels. - ocean_da : xarray.DataArray - An array containing True for ocean pixels. - connectivity : integer, optional - An integer passed to the 'connectivity' parameter of the - `skimage.measure.label` function. - - Returns: - -------- - ocean_connection : xarray.DataArray - An array containing the a mask consisting of identified - ocean-connected pixels as True. - """ - - # First, break `water` array into unique, discrete - # regions/blobs. - blobs = xr.apply_ufunc(label, water, 0, False, connectivity) - - # For each unique region/blob, use region properties to determine - # whether it overlaps with a feature from `intertidal`. If - # it does, then it is considered to be adjacent or directly connected - # to intertidal pixels - ocean_connection = blobs.isin( - [i.label for i in regionprops(blobs.values, ocean_da.values) if i.max_intensity] - ) - - return ocean_connection def ds_to_flat( satellite_ds, @@ -138,27 +101,31 @@ def ds_to_flat( corr : xr.DataArray Correlation of NDWI pixel wetness with tide height. """ - - # If an overall valid data mask is provided, apply to the data first - if valid_mask is not None: - satellite_ds = satellite_ds.where(valid_mask) - - # Flatten satellite dataset by stacking "y" and "x" dimensions, then - # drop any pixels that are empty across all-of-time - flat_ds = satellite_ds.stack(z=("y", "x")).dropna(dim="time", how="all") - # Calculate frequency of wet per pixel, then threshold # to exclude always wet and always dry freq = ( - (flat_ds[index] > ndwi_thresh) - .where(~flat_ds[index].isnull()) + (satellite_ds[index] > ndwi_thresh) + .where(~satellite_ds[index].isnull()) .mean(dim="time") .rename("qa_ndwi_freq") ) + + # Mask out pixels outside of frequency bounds freq_mask = (freq >= min_freq) & (freq <= max_freq) + satellite_ds = satellite_ds.where(freq_mask) - # Flatten to 1D, dropping any pixels that are not in frequency mask - flat_ds = flat_ds.where(freq_mask, drop=True) + # If an overall valid data mask is provided, apply to the data + if valid_mask is not None: + satellite_ds = satellite_ds.where(valid_mask) + + # Flatten satellite and freq data by stacking "y" and "x" dims. + # Drop any pixels that are always empty, or empty timesteps + flat_ds = ( + satellite_ds.stack(z=("y", "x")) + .dropna(dim="time", how="all") + .dropna(dim="z", how="all") + ) + freq = freq.stack(z=("y", "x")) # Calculate correlations between NDWI water observations and tide # height. Because we are only interested in pixels with inundation @@ -176,6 +143,7 @@ def ds_to_flat( else: tide_array = flat_ds.tide_m + # Calculate correlation if corr_method == "pearson": corr = xr.corr(wet_dry, tide_array, dim="time").rename("qa_ndwi_corr") elif corr_method == "spearman": @@ -805,7 +773,6 @@ def clean_edge_pixels(ds): def elevation( satellite_ds, valid_mask=None, - ocean_mask=None, ndwi_thresh=0.1, min_freq=0.01, max_freq=0.99, @@ -834,12 +801,6 @@ def elevation( this could be a mask generated from a topo-bathy DEM, used to limit the analysis to likely intertidal pixels. Default is None, which will not apply a mask. - ocean_mask : xr.DataArray, optional - An optional mask identifying ocean pixels within the analysis - area, with the same spatial dimensions as `satellite_ds`. - If provided, this will be used to restrict the analysis to pixels - that are directly connected to ocean waters. Defaults is None, - which will not apply a mask. ndwi_thresh : float, optional A threshold value for the normalized difference water index (NDWI) above which pixels are considered water, by default 0.1. @@ -916,13 +877,14 @@ def elevation( # dataset (x by y by time). If `model` is "ensemble" this will model # tides by combining the best local tide models. log.info(f"{run_id}: Modelling tide heights for each pixel") - tide_m, _ = pixel_tides_ensemble( + tide_m, _ = pixel_tides( ds=satellite_ds, - ancillary_points="data/raw/tide_correlations_2017-2019.geojson", model=tide_model, directory=tide_model_dir, + ranking_points="data/raw/tide_correlations_2017-2019.geojson", + ensemble_top_n=3, ) - + # Set tide array pixels to nodata if the satellite data array pixels # contain nodata. This ensures that we ignore any tide observations # where we don't have matching satellite imagery @@ -999,16 +961,6 @@ def elevation( elevation_bands = [d for d in ds.data_vars if "elevation" in d] ds[elevation_bands] = clean_edge_pixels(ds[elevation_bands]) - # Mask out any non-ocean connected elevation pixels. - # `~(ds.qa_ndwi_freq < min_freq)` ensures that nodata pixels are - # treated as wet - if ocean_mask is not None: - log.info(f"{run_id}: Restricting outputs to ocean-connected waters") - ocean_connected_mask = ocean_connection( - ~(ds.qa_ndwi_freq < min_freq), ocean_mask - ) - ds[elevation_bands] = ds[elevation_bands].where(ocean_connected_mask) - # Return output data and tide height array log.info(f"{run_id}: Successfully completed intertidal elevation modelling") return ds, tide_m @@ -1252,11 +1204,11 @@ def intertidal_cli( satellite_ds.load() # Load topobathy mask from GA's AusBathyTopo 250m 2023 Grid, - # urban land use class mask from ABARES CLUM, and ocean mask - # from geodata_coast_100k - topobathy_mask = load_topobathy_mask(dc, satellite_ds.odc.geobox.compat) - reclassified_aclum = load_aclum_mask(dc, satellite_ds.odc.geobox.compat) - ocean_mask = load_ocean_mask(dc, satellite_ds.odc.geobox.compat) + # urban land use class mask from ABARES CLUM, and coastal mask + # from least-cost connectivity analysis + topobathy_mask = load_topobathy_mask(dc, satellite_ds.odc.geobox) + urban_mask = load_aclum_mask(dc, satellite_ds.odc.geobox) + coastal_mask, _ = load_connectivity_mask(dc, satellite_ds.odc.geobox) # Also load ancillary dataset IDs to use in metadata # (both layers are continental continental products with only @@ -1269,8 +1221,7 @@ def intertidal_cli( log.info(f"{run_id}: Calculating Intertidal Elevation") ds, tide_m = elevation( satellite_ds, - valid_mask=topobathy_mask, - # ocean_mask=ocean_mask, + valid_mask=topobathy_mask & coastal_mask, ndwi_thresh=ndwi_thresh, min_freq=min_freq, max_freq=max_freq, @@ -1287,8 +1238,11 @@ def intertidal_cli( # Calculate extents (to be included in next version) log.info(f"{run_id}: Calculating Intertidal Extents") ds["extents"] = extents( - dc=dc, - ds=ds + dem=ds.elevation, + freq=ds.qa_ndwi_freq, + corr=ds.qa_ndwi_corr, + coastal_mask=coastal_mask, + urban_mask=urban_mask, ) if exposure_offsets: diff --git a/intertidal/exposure.py b/intertidal/exposure.py index 1708f015..8d5cfac5 100644 --- a/intertidal/exposure.py +++ b/intertidal/exposure.py @@ -9,8 +9,7 @@ import pandas as pd from math import ceil -from dea_tools.coastal import _pixel_tides_resample -from intertidal.tide_modelling import pixel_tides_ensemble +from dea_tools.coastal import _pixel_tides_resample, pixel_tides from intertidal.utils import configure_logging, round_date_strings @@ -441,12 +440,13 @@ def exposure( ), f'Nominated filter "{x}" is not in {all_filters}. Check spelling and retry' # Run tide model at low resolution - modelledtides_lowres = pixel_tides_ensemble( - dem, + modelledtides_lowres = pixel_tides( + ds=dem, model=tide_model, times=time_range, directory=tide_model_dir, - ancillary_points="data/raw/tide_correlations_2017-2019.geojson", + ranking_points="data/raw/tide_correlations_2017-2019.geojson", + ensemble_top_n=3, resample=False, ) diff --git a/intertidal/extents.py b/intertidal/extents.py index 6cb60596..60045265 100644 --- a/intertidal/extents.py +++ b/intertidal/extents.py @@ -15,9 +15,7 @@ from dea_tools.spatial import xr_rasterize from rasterio.features import sieve from intertidal.io import ( - load_ocean_mask, load_aclum_mask, - load_topobathy_mask, extract_geobox, ) @@ -251,95 +249,75 @@ def load_gmw_mask(ds, gmw_path="/gdata1/data/mangroves/gmw_v3_2007_vec_aus.geojs return gmw_da -def extents(dc, ds, buffer=20000, min_correlation=0.15, sieve_size=5): +def extents( + dem, freq, corr, coastal_mask, urban_mask, min_correlation=0.15, sieve_size=5 +): """ - Classify coastal ecosystems into broad classes based - on their respective patterns of wetting frequency, - proximity to the ocean, and relationships to tidal - inundation, elevation and urban land use (to mask misclassifications). + Classify coastal ecosystems into broad classes based on + wetting frequency, proximity to ocean, and relationships + to tidal inundation, elevation, and urban land use. - Parameters: - ----------- - dc : Datacube - A Datacube instance for loading data. - ds : xarray.Dataset - An xarray.Dataset that must include xarray.DataArrays for - at least 'elevation', 'qa_ndwi_freq' and 'qa_ndwi_corr'. - These arrays are generated as an output from the - intertidal.elevation algorithm. - buffer : int, optional - The distance by which to buffer the ds GeoBox to reduce edge - effects. This buffer will eventually be removed and clipped back - to the original GeoBox extent. Defaults to 20,000 metres. - min_correlation : int, optional + Parameters + ---------- + dem : xarray.DataArray + An intertidal Digital Elevation Model, as produced by + the `intertidal.elevation` algorithm. + freq : xarray.DataArray + Frequency of wetness for each pixel, generated by the + `intertidal.elevation` algorithm. + corr : xarray.DataArray + Correlation data between water index and tide height, + as generated by the `intertidal.elevation` algorithm. + coastal_mask : xarray.DataArray + A boolean mask identifying likely coastal pixels. This + is used to separate inland vs. ocean and coastal waters. + For Australia, this is obtained using the + `intertidal.extents.load_connectivity_mask` function, + which uses cost-distance modelling to identify likely + coastal pixels. + urban_mask : xarray.DataArray + A boolean mask identifying urban pixels that are applied + to remove false positive water observations over urban + areas. For Australia, this is obtained using the + `intertidal.io.load_aclum_mask` function. + min_correlation : float, optional Minimum correlation between water index and tide height - required for a pixel to be included in the intertidal pixel - analysis, 0.15 by default, aligning with the default value - used in the intertidal.elevation algorithm. - sieve_size : int, optional - Maximum number of grouped pixels belonging to any single - class that are sieved out to remove small noisy dataset - features. Class values are replaced with the dominant - neighbouring class. - - Returns: - -------- - extents: xarray.DataArray - A categorical xarray.DataArray depicting the following pixel classes: - - Nodata (0), - - Ocean and coastal water (1), - - Exposed intertidal (low confidence) (2), - - Exposed intertidal (high confidence) (3), - - Inland waters (4), - - Land (5) - - Notes: - ------ - Classes are defined as follows: - - 0 : Nodata - 1 : Ocean and coastal water - Pixels that are wet in 50 % or more observations - 2 : Exposed intertidal (low confidence) - Pixels with water index and tidal correlations higher than 0.15. - Pixels must be located within the costdist_mask connectivity mask - and not be included in the intertidal elevation (high confidence) - dataset - 3 : Exposed intertidal (high confidence) - Pixels that are included in the intertidal elevation dataset - 4 : Inland waters - Pixels that are wet in more than 50 % of observations and fall - outside of the costdist_mask connectivity mask. - 5 : Land - Pixels that are wet in less than 50 % of observations + for identifying low confidence intertidal pixels. Default + is 0.15. + sieve_size : int, optional + Maximum number of grouped pixels to be sieved out to + remove small noisy features. Default is 5. + + Returns + ------- + xarray.DataArray + A categorical DataArray with the following classes: + - 255: Nodata + - 1: Ocean and coastal waters (wet ≥50% of observations, within coastal mask) + - 2: Exposed intertidal (low confidence) (correlation > 0.15, within coastal mask) + - 3: Exposed intertidal (high confidence) (included in intertidal elevation dataset) + - 4: Inland waters (wet >50% of observations, outside coastal mask) + - 5: Land (wet <50% of observations) """ # Identify dataset geobox - geobox = ds.odc.geobox - - # Load other inputs - ocean_mask = load_ocean_mask(dc, geobox) - urban_mask = load_aclum_mask(dc, geobox) - bathy_mask = load_topobathy_mask(dc, geobox) - costdist_mask, _ = load_connectivity_mask(dc, geobox, buffer=buffer) + geobox = dem.odc.geobox - # Identify any pixels that are nodata in both frequency and bathy mask - # (this is a temporary hack due to us not having any other way of telling - # ocean nodata pixels apart from inland nodata pixels - is_nan = (ds.qa_ndwi_freq.isnull()) & bathy_mask + # Identify any pixels that are nodata in frequency + is_nan = freq.isnull() # Spilt pixels into those that were mostly wet vs mostly dry. # Identify subset of mostly wet pixels that were inland - mostly_dry = (ds.qa_ndwi_freq < 50) & ~is_nan - mostly_wet = (ds.qa_ndwi_freq >= 50) & ~is_nan - mostly_wet_inland = mostly_wet & ~costdist_mask + mostly_dry = (freq < 0.50) & ~is_nan + mostly_wet = (freq >= 0.50) & ~is_nan + mostly_wet_inland = mostly_wet & ~coastal_mask # Identify low-confidence pixels as those with greater than 0.15 # correlation. Use connectivity mask to mask out any that are "inland" - intertidal_lc = (ds.qa_ndwi_corr >= min_correlation) & costdist_mask + intertidal_lc = (corr >= min_correlation) & coastal_mask # Identify high confidence intertidal as those in our elevation data - intertidal_hc = ds.elevation.notnull() + intertidal_hc = dem.notnull() # Identify likely misclassified urban pixels as those that overlap with # the urban mask @@ -347,7 +325,7 @@ class that are sieved out to remove small noisy dataset # Combine all classifications - this is done one-by-one, pasting each # new layer over the top of the existing data - extents = xr_zeros(geobox=geobox, dtype="int16") # start with 0 + extents = xr_zeros(geobox=geobox, dtype="int16") + 255 # start with 255 extents.values[mostly_wet] = 1 # Add in mostly wet pixels extents.values[mostly_wet_inland] = 4 # Add in mostly wet inland pixels on top extents.values[urban_misclass] = ( @@ -364,6 +342,6 @@ class that are sieved out to remove small noisy dataset extents.values[intertidal_hc] = 3 # Export to file - extents.attrs["nodata"] = 0 + extents.attrs["nodata"] = 255 return extents diff --git a/intertidal/io.py b/intertidal/io.py index 2df8f470..f8e911a7 100644 --- a/intertidal/io.py +++ b/intertidal/io.py @@ -622,63 +622,6 @@ def load_aclum_mask( return odc.geo.xr.xr_zeros(geobox).astype(bool) -def load_ocean_mask( - dc, - geobox, - product="geodata_coast_100k", - band="land", - resampling="nearest", - mask_invalid=False, -): - """ - Loads an ocean mask for the extents of the loaded satellite data. - This is used to determine connectivity to the ocean for each wet or - intertidal pixel. - - Parameters - ---------- - dc : Datacube - A Datacube instance for loading data. - geobox : ndarray - The GeoBox of the loaded satellite data, used to ensure the data - is loaded into the same pixel grid (e.g. resolution, extents, CRS). - product : str, optional - The name of the ocean mask dataset to load from the datacube. - Defaults to "geodata_coast_100k". - band : str, optional - The name of the band containing the ocean classification. - Defaults to "land". - resampling : str, optional - The resampling method to use, by default "nearest". - mask_invalid : bool, optional - Whether to mask invalid/nodata values in the array by setting - them to NaN, by default True. - - Returns - ------- - ocean_mask : xarray.DataArray - An output boolean mask, where True represent pixels to use in the - following analysis. - """ - try: - # Load from datacube, reprojecting to GeoBox of input satellite data - ocean_ds = dc.load( - product="geodata_coast_100k", like=geobox, resampling=resampling - ).squeeze("time") - - # Mask invalid data - if mask_invalid: - ocean_ds = mask_invalid_data(ocean_ds) - - # Return ocean pixels as True - ocean_mask = ocean_ds[band] == 0 - return ocean_mask - - # Return an array of all True (i.e. ocean) if no data is returned - except AttributeError: - return odc.geo.xr.xr_zeros(geobox) == 0 - - def _is_s3(path): """ Determine whether output location is on S3. diff --git a/intertidal/tide_modelling.py b/intertidal/tide_modelling.py deleted file mode 100644 index 0470e8c1..00000000 --- a/intertidal/tide_modelling.py +++ /dev/null @@ -1,154 +0,0 @@ -import xarray as xr -import geopandas as gpd - -from dea_tools.coastal import pixel_tides, _pixel_tides_resample -from dea_tools.spatial import xr_interpolate - - -def pixel_tides_ensemble( - ds, - ancillary_points, - model="ensemble", - top_n=3, - interp_method="idw", - reduce_method="mean", - ancillary_valid_perc=0.02, - **pixel_tides_kwargs, -): - """ - Generate an ensemble tide model, combining the top local tide models - for any coastal location using ancillary point data (e.g. altimetry - observations or NDWI correlations along the coastline). - - This function generates an ensemble of tidal height predictions for - each pixel in a satellite dataset. Firstly, tides from multiple tide - models are modelled into a low resolution grid using `pixel_tides`. - Ancillary point data is then loaded and interpolated to the same - grid to serve as weightings. These weightings are used to retain - only the top N tidal models, and remaining top models are reduced/ - combined into a single ensemble output for each time/x/y. - The resulting ensemble tides are then resampled and reprojected to - match the high-resolution satellite data. - - 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. - ancillary_points : str - Path to a file containing point correlations for different tidal - models. - model : list or None, optional - The default of "ensemble" will combine results from "FES2014", - "FES2012", "EOT20", "TPXO8-atlas-v1", "TPXO9-atlas-v5", "GOT4.10" - and "HAMTIDE11" into a single locally optimised ensemble model. - All other options will skip the ensemble model step and - run `pixel_tides` directly instead. - top_n : integer, optional - The number of top models to use in the ensemble calculation. - Default is 3, which will reduce values from the top 3 models. - interp_method : str, optional - Interpolation method used to interpolate correlations onto the - low-resolution tide grid. Default is "nearest". - reduce_method : str, optional - Method used to reduce values from the `top_n` tide models into - a single enemble output. Defaults to "mean", supports "median". - ancillary_valid_perc : float, optional - The minimum valid percent used to filter input point correlations. - Defaults to 0.02. - **pixel_tides_kwargs - All other optional keyword arguments to provide to the underlying - `pixel_tides` function. - - Returns: - -------- - tides_highres_ensemble : xarray.Dataset - High-resolution ensemble tidal heights dataset. - tides_lowres_ensemble : xarray.Dataset - Low-resolution ensemble tidal heights dataset. - weights_ds : xarray.Dataset - Dataset containing weights for each tidal model used in the ensemble. - """ - - # Run pixel tides directly if "ensemble" is not specified - if (model != "ensemble") & ("ensemble" not in model): - return pixel_tides( - ds, - model=model, - **pixel_tides_kwargs, - ) - - # Otherwise, run pixel tides on all models in preperation for - # ensemble tide modelling - else: - print("Running ensemble tide modelling") - # Extract the `resample` param if it exists so we can run - # `pixel_tides` with `resample=False`, and then resample later - resample_param = pixel_tides_kwargs.pop("resample", True) - - # Run `pixel_tides` on all tide models and return low-res output - ensemble_models = [ - "FES2014", - "FES2012", - "TPXO8-atlas-v1", - "TPXO9-atlas-v5", - "EOT20", - "HAMTIDE11", - "GOT4.10", - ] - tides_lowres = pixel_tides( - ds, - resample=False, - model=ensemble_models, - **pixel_tides_kwargs, - ) - - # Load ancillary points from file, filter by minimum valid data perc - # and drop any empty points/unnecessary columns - print("Generating ensemble tide model from point inputs") - corr_gdf = ( - gpd.read_file(ancillary_points) - .query(f"valid_perc > {ancillary_valid_perc}") - .dropna()[ensemble_models + ["geometry"]] - ) - - # Spatially interpolate each tide model - print(f"Interpolating model weights using '{interp_method}' interpolation") - weights_ds = xr_interpolate( - tides_lowres, gdf=corr_gdf, columns=ensemble_models, method=interp_method - ).to_array(dim="tide_model") - - # Print models in order of correlation - print( - weights_ds.drop("spatial_ref") - .mean(dim=["x", "y"]) - .to_dataframe("weights") - .sort_values("weights", ascending=False) - ) - - # Mask out all but the top N models - tides_top_n = tides_lowres.where( - (weights_ds.rank(dim="tide_model") > (len(ensemble_models) - top_n)) - ) - - # Reduce remaining models to produce a single ensemble output - # for each time/x/y - if reduce_method == "median": - print("Reducing multiple models into single ensemble model using 'median'") - tides_lowres_ensemble = tides_top_n.median("tide_model") - elif reduce_method == "mean": - print("Reducing multiple models into single ensemble model using 'mean'") - tides_lowres_ensemble = tides_top_n.mean("tide_model") - - # Optionally resample/reproject ensemble tides to match high-res - # satellite data - if resample_param: - print("Reprojecting ensemble tides into original array") - tides_highres_ensemble, _ = _pixel_tides_resample( - tides_lowres=tides_lowres_ensemble, ds=ds - ) - return tides_highres_ensemble, tides_lowres_ensemble - - else: - print("Returning low resolution ensemble tide array") - return tides_lowres_ensemble diff --git a/metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml b/metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml index 5c2b3381..09d8bb42 100644 --- a/metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml +++ b/metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml @@ -31,6 +31,11 @@ measurements: units: "percent" nodata: 255 + - name: extents + dtype: uint8 + units: "class" + nodata: 255 + - name: ta_hat dtype: float32 units: "metres above MSL" diff --git a/notebooks/Intertidal_CLI.ipynb b/notebooks/Intertidal_CLI.ipynb index e168f441..12d4a929 100644 --- a/notebooks/Intertidal_CLI.ipynb +++ b/notebooks/Intertidal_CLI.ipynb @@ -28,7 +28,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/jovyan/dea_intertidal/dea-intertidal\n" + "/home/jovyan/Robbi/dea-intertidal\n" ] } ], @@ -46,22 +46,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "8f7bb11f", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ - "pip install -r requirements.in --quiet" + "pip install -r requirements.in --quiet" ] }, { @@ -87,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "0e979f2b", "metadata": { "tags": [] @@ -96,11 +88,12 @@ "source": [ "# General params\n", "study_area = \"testing\" # \"x094y145\" # To match the default 32 km tile GridSpec\n", - "start_date = \"2023\" # Start date for analysis\n", - "label_date = \"2023\" # Date used to label arrays\n", - "end_date = \"2023\" # End date for analysis\n", + "start_date = \"2019\" # Start date for analysis\n", + "label_date = \"2020\" # Date used to label arrays\n", + "end_date = \"2021\" # End date for analysis\n", "tide_model = \"ensemble\" # Tide model to use in analysis \n", "tide_model_dir = \"/gdata1/data/tide_models_clipped\" # Directory containing tide model files\n", + "# tide_model_dir = \"/home/jovyan/tide_models_clipped\" # Directory containing tide model files\n", "output_version = \"0.0.1\" # Version number to label output files and metadata\n", "output_dir = \"data/processed/\" # Path for output files, can also be e.g. \"s3://dea-public-data-dev/derivative/\"\n", "\n", @@ -134,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "415108cd", "metadata": { "tags": [] @@ -258,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "3f36e01e-a0d9-4525-a88a-8d89b669d527", "metadata": { "tags": [] @@ -268,71 +261,50 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-08-21 07:27:55 INFO [0.0.1] [2023] [testing]: Using parameters {'study_area': 'testing', 'start_date': '2023', 'end_date': '2023', 'label_date': '2023', 'output_version': '0.0.1', 'output_dir': 'data/processed/', 'product_maturity': 'provisional', 'dataset_maturity': 'final', 'resolution': 10, 'ndwi_thresh': 0.1, 'min_freq': 0.01, 'max_freq': 0.99, 'min_correlation': 0.15, 'windows_n': 100, 'window_prop_tide': 0.15, 'correct_seasonality': False, 'tide_model': ('ensemble',), 'tide_model_dir': '/gdata1/data/tide_models_clipped', 'modelled_freq': '3h', 'exposure_offsets': True, 'aws_unsigned': True}\n", - "2024-08-21 07:27:55 INFO [0.0.1] [2023] [testing]: Loading satellite data\n", - "\n", - "2024-08-21 07:27:59 INFO [0.0.1] [2023] [testing]: Running in testing mode using custom study area\n", - "2024-08-21 07:28:17 INFO [0.0.1] [2023] [testing]: Calculating Intertidal Elevation\n", - "2024-08-21 07:28:17 INFO [0.0.1] [2023] [testing]: Modelling tide heights for each pixel\n", - "Running ensemble tide modelling\n", + "2024-09-24 04:47:05 INFO [0.0.1] [2020] [testing]: Using parameters {'study_area': 'testing', 'start_date': '2019', 'end_date': '2021', 'label_date': '2020', 'output_version': '0.0.1', 'output_dir': 'data/processed/', 'product_maturity': 'provisional', 'dataset_maturity': 'final', 'resolution': 10, 'ndwi_thresh': 0.1, 'min_freq': 0.01, 'max_freq': 0.99, 'min_correlation': 0.15, 'windows_n': 100, 'window_prop_tide': 0.15, 'correct_seasonality': False, 'tide_model': ('ensemble',), 'tide_model_dir': '/gdata1/data/tide_models_clipped', 'modelled_freq': '3h', 'exposure_offsets': True, 'aws_unsigned': True}\n", + "2024-09-24 04:47:05 INFO [0.0.1] [2020] [testing]: Loading satellite data\n", + "\n", + "2024-09-24 04:47:09 INFO [0.0.1] [2020] [testing]: Running in testing mode using custom study area\n", + "2024-09-24 04:48:07 INFO [0.0.1] [2020] [testing]: Calculating Intertidal Elevation\n", + "2024-09-24 04:48:07 INFO [0.0.1] [2020] [testing]: Modelling tide heights for each pixel\n", "Creating reduced resolution 5000 x 5000 metre tide modelling array\n", - "Modelling tides using FES2014, FES2012, TPXO8-atlas-v1, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10 in parallel\n", - "100%|███████████████████████████████████████████| 35/35 [00:51<00:00, 1.48s/it]\n", - "Returning low resolution tide array\n", - "Generating ensemble tide model from point inputs\n", - "Interpolating model weights using 'idw' interpolation\n", - " weights\n", - "tide_model \n", - "TPXO9-atlas-v5 0.453527\n", - "GOT4.10 0.452426\n", - "EOT20 0.451006\n", - "FES2014 0.450247\n", - "FES2012 0.446300\n", - "HAMTIDE11 0.437319\n", - "TPXO8-atlas-v1 0.433992\n", - "Reducing multiple models into single ensemble model using 'mean'\n", - "Reprojecting ensemble tides into original array\n", - "2024-08-21 07:29:12 INFO [0.0.1] [2023] [testing]: Masking nodata and adding tide heights to satellite data array\n", - "2024-08-21 07:29:12 INFO [0.0.1] [2023] [testing]: Flattening satellite data array and filtering to intertidal candidate pixels\n", - "2024-08-21 07:29:12 INFO [0.0.1] [2023] [testing]: Applying valid data mask to constrain study area\n", - "Reducing analysed pixels from 7125 to 5678 (79.69%)\n", - "2024-08-21 07:29:12 INFO [0.0.1] [2023] [testing]: Running per-pixel rolling median\n", - "100%|█████████████████████████████████████████| 105/105 [00:01<00:00, 64.18it/s]\n", - "2024-08-21 07:29:15 INFO [0.0.1] [2023] [testing]: Modelling intertidal elevation\n", + "Running ensemble tide modelling\n", + "Modelling tides using FES2014, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10, FES2012, TPXO8-atlas-v1 in parallel\n", + "100%|███████████████████████████████████████████| 35/35 [00:22<00:00, 1.58it/s]\n", + "Interpolating model rankings using IDW interpolation \n", + "Combining models into single ensemble model\n", + "Reprojecting tides into original array\n", + "2024-09-24 04:48:33 INFO [0.0.1] [2020] [testing]: Masking nodata and adding tide heights to satellite data array\n", + "2024-09-24 04:48:33 INFO [0.0.1] [2020] [testing]: Flattening satellite data array and filtering to intertidal candidate pixels\n", + "2024-09-24 04:48:33 INFO [0.0.1] [2020] [testing]: Applying valid data mask to constrain study area\n", + "Reducing analysed pixels from 7125 to 5248 (73.66%)\n", + "2024-09-24 04:48:33 INFO [0.0.1] [2020] [testing]: Running per-pixel rolling median\n", + "100%|█████████████████████████████████████████| 105/105 [00:04<00:00, 23.61it/s]\n", + "2024-09-24 04:48:39 INFO [0.0.1] [2020] [testing]: Modelling intertidal elevation\n", "Applying tidal interval interpolation to 200 intervals\n", "Applying rolling mean smoothing with radius 20\n", - "2024-08-21 07:29:15 INFO [0.0.1] [2023] [testing]: Modelling intertidal uncertainty\n", - "2024-08-21 07:29:16 INFO [0.0.1] [2023] [testing]: Unflattening data back to its original spatial dimensions\n", - "2024-08-21 07:29:16 INFO [0.0.1] [2023] [testing]: Cleaning inaccurate upper intertidal pixels\n", - "2024-08-21 07:29:16 INFO [0.0.1] [2023] [testing]: Successfully completed intertidal elevation modelling\n", - "2024-08-21 07:29:16 INFO [0.0.1] [2023] [testing]: Calculating Intertidal Extents\n", - "2024-08-21 07:29:24 INFO [0.0.1] [2023] [testing]: Calculating Intertidal Exposure\n", - "Running ensemble tide modelling\n", + "2024-09-24 04:48:39 INFO [0.0.1] [2020] [testing]: Modelling intertidal uncertainty\n", + "2024-09-24 04:48:39 INFO [0.0.1] [2020] [testing]: Unflattening data back to its original spatial dimensions\n", + "2024-09-24 04:48:39 INFO [0.0.1] [2020] [testing]: Cleaning inaccurate upper intertidal pixels\n", + "2024-09-24 04:48:39 INFO [0.0.1] [2020] [testing]: Successfully completed intertidal elevation modelling\n", + "2024-09-24 04:48:39 INFO [0.0.1] [2020] [testing]: Calculating Intertidal Extents\n", + "2024-09-24 04:48:39 INFO [0.0.1] [2020] [testing]: Calculating Intertidal Exposure\n", "Creating reduced resolution 5000 x 5000 metre tide modelling array\n", - "Modelling tides using FES2014, FES2012, TPXO8-atlas-v1, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10 in parallel\n", - "100%|███████████████████████████████████████████| 35/35 [01:03<00:00, 1.81s/it]\n", + "Running ensemble tide modelling\n", + "Modelling tides using FES2014, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10, FES2012, TPXO8-atlas-v1 in parallel\n", + "100%|███████████████████████████████████████████| 35/35 [00:25<00:00, 1.35it/s]\n", + "Interpolating model rankings using IDW interpolation \n", + "Combining models into single ensemble model\n", "Returning low resolution tide array\n", - "Generating ensemble tide model from point inputs\n", - "Interpolating model weights using 'idw' interpolation\n", - " weights\n", - "tide_model \n", - "TPXO9-atlas-v5 0.453527\n", - "GOT4.10 0.452426\n", - "EOT20 0.451006\n", - "FES2014 0.450247\n", - "FES2012 0.446300\n", - "HAMTIDE11 0.437319\n", - "TPXO8-atlas-v1 0.433992\n", - "Reducing multiple models into single ensemble model using 'mean'\n", - "Returning low resolution ensemble tide array\n", "Calculating unfiltered exposure\n", - "2024-08-21 07:30:31 INFO [0.0.1] [2023] [testing]: Calculating spread, offset and HAT/LAT/LOT/HOT layers\n", - "2024-08-21 07:30:31 INFO [0.0.1] [2023] [testing]: Assembling dataset\n", - "2024-08-21 07:30:31 INFO [0.0.1] [2023] [testing]: Writing output arrays\n", - "2024-08-21 07:30:31 INFO [0.0.1] [2023] [testing]: Assembled dataset: /tmp/tmpi_bwussj/ga_s2ls_intertidal_cyear_3/0-0-1/tes/ting/2023--P1Y/ga_s2ls_intertidal_cyear_3_testing_2023--P1Y_final.odc-metadata.yaml\n", - "2024-08-21 07:30:31 INFO [0.0.1] [2023] [testing]: Writing data locally: data/processed/ga_s2ls_intertidal_cyear_3/0-0-1/tes/ting/2023--P1Y/\n", - "\u001b[0m2024-08-21 07:30:32 INFO [0.0.1] [2023] [testing]: Completed DEA Intertidal workflow\n", - "\u001b[0m" + "2024-09-24 04:49:12 INFO [0.0.1] [2020] [testing]: Calculating spread, offset and HAT/LAT/LOT/HOT layers\n", + "2024-09-24 04:49:12 INFO [0.0.1] [2020] [testing]: Assembling dataset\n", + "2024-09-24 04:49:12 INFO [0.0.1] [2020] [testing]: Writing output arrays\n", + "2024-09-24 04:49:13 INFO [0.0.1] [2020] [testing]: Assembled dataset: /tmp/tmpy3ce3ev7/ga_s2ls_intertidal_cyear_3/0-0-1/tes/ting/2020--P1Y/ga_s2ls_intertidal_cyear_3_testing_2020--P1Y_final.odc-metadata.yaml\n", + "2024-09-24 04:49:13 INFO [0.0.1] [2020] [testing]: Writing data locally: data/processed/ga_s2ls_intertidal_cyear_3/0-0-1/tes/ting/2020--P1Y/\n", + "\u001b[0m2024-09-24 04:49:14 INFO [0.0.1] [2020] [testing]: Completed DEA Intertidal workflow\n", + "\u001b[0mCPU times: user 1.63 s, sys: 337 ms, total: 1.97 s\n", + "Wall time: 2min 13s\n" ] } ], @@ -478,7 +450,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/Intertidal_elevation.ipynb b/notebooks/Intertidal_elevation.ipynb index ef94c3e0..c7944949 100644 --- a/notebooks/Intertidal_elevation.ipynb +++ b/notebooks/Intertidal_elevation.ipynb @@ -41,10 +41,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "d67433e6-0408-40d1-be61-db49c52f88dc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "pip install -r requirements.in --quiet" ] @@ -59,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "2520e8de-ac2c-4571-99a0-f7fb7b932f56", "metadata": {}, "outputs": [], @@ -81,12 +89,11 @@ "from odc.geo.geobox import GeoBox\n", "from odc.ui import select_on_a_map\n", "from dea_tools.dask import create_local_dask_cluster\n", + "from dea_tools.coastal import pixel_tides\n", "\n", - "from intertidal.tide_modelling import pixel_tides_ensemble\n", "from intertidal.io import (\n", " load_data,\n", " load_topobathy_mask,\n", - " load_ocean_mask,\n", " prepare_for_export,\n", ")\n", "from intertidal.elevation import (\n", @@ -99,7 +106,7 @@ " clean_edge_pixels,\n", " elevation,\n", ")\n", - "from intertidal.extents import ocean_connection" + "from intertidal.extents import load_connectivity_mask" ] }, { @@ -122,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "96122c69-8f4d-439a-a945-8f19294d2e07", "metadata": { "tags": [] @@ -130,8 +137,8 @@ "outputs": [], "source": [ "# Intertidal Elevation variables\n", - "start_date = \"2020\" # Start date for analysis\n", - "end_date = \"2022\" # End date for analysis\n", + "start_date = \"2019\" # Start date for analysis\n", + "end_date = \"2021\" # End date for analysis\n", "resolution = 10 # Spatial resolution used for output files\n", "crs = \"EPSG:3577\" # Coordinate Reference System (CRS) to use for output files\n", "min_freq = 0.01 # Minimum wetness freq required for pixel to be included in analysis\n", @@ -142,8 +149,6 @@ "include_ls = True # Include Landsat data in the analysis?\n", "filter_gqa = True # Filter to remove poorly georeferenced scenes?\n", "tide_model = \"ensemble\" # Tide model to use in analysis \n", - "# tide_model_dir = \"/var/share/tide_models\" # Directory containing tide model files\n", - "# tide_model = [\"FES2014\", \"FES2012\", \"TPXO9-atlas-v5\"]\n", "tide_model_dir = \"/gdata1/data/tide_models_clipped\"" ] }, @@ -159,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "8c1dfca3-543d-4e07-9a0f-2eeddf582835", "metadata": {}, "outputs": [], @@ -181,22 +186,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "e67929eb-8a55-4a15-be7a-fcda29ec1f66", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "" + ], + "text/plain": [ + "Geometry(MULTIPOLYGON (((131.87329168896437 -12.220584319739181, 131.87329168896437 -12.200502049369382, 131.90178150419615 -12.200502049369382, 131.90178150419615 -12.220584319739181, 131.87329168896437 -12.220584319739181))), EPSG:4326)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# # Set study area to load from file\n", - "# study_area = \"pointstuart\"\n", + "# Set study area to load from file\n", + "study_area = \"pointstuart\"\n", "\n", - "# # Read file, select study area and convert to geom\n", - "# studyarea_gdf = gpd.read_file(\n", - "# \"data/raw/intertidal_development_polygons.geojson\"\n", - "# ).set_index(\"id\")\n", - "# geom = Geometry(studyarea_gdf.loc[study_area].geometry, crs=studyarea_gdf.crs)\n", - "# geom" + "# Read file, select study area and convert to geom\n", + "studyarea_gdf = gpd.read_file(\n", + " \"data/raw/intertidal_development_polygons.geojson\"\n", + ").set_index(\"id\")\n", + "geom = Geometry(studyarea_gdf.loc[study_area].geometry, crs=studyarea_gdf.crs)\n", + "geom" ] }, { @@ -209,46 +228,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "bdcf1c79-ae5a-4453-a7e8-d3f021b0b65a", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0df7b8dd2f624378b5517dae2b60c01c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Map(center=[-26, 135], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out_t…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "Geometry(POLYGON ((117.922611 -20.490494, 117.922611 -20.458813, 117.964926 -20.458813, 117.964926 -20.490494, 117.922611 -20.490494)), EPSG:4326)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# Set study area name for outputs\n", - "study_area = \"testing\"\n", + "# # Set study area name for outputs\n", + "# study_area = \"testing\"\n", "\n", - "# Plot interactive map to select area\n", - "basemap = basemap_to_tiles(basemaps.Esri.WorldImagery)\n", - "geom = select_on_a_map(height=\"600px\", layers=(basemap,), center=(-26, 135), zoom=4)\n", - "geom" + "# # Plot interactive map to select area\n", + "# basemap = basemap_to_tiles(basemaps.Esri.WorldImagery)\n", + "# geom = select_on_a_map(height=\"600px\", layers=(basemap,), center=(-26, 135), zoom=4)\n", + "# geom" ] }, { @@ -261,12 +252,22 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "98e930ff-c5a4-45fa-a043-b8902c606d63", "metadata": { "tags": [] }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/env/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 36173 instead\n", + " warnings.warn(\n" + ] + }, { "data": { "text/html": [ @@ -274,7 +275,7 @@ "
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\n", - " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status\n", + " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/36173/status\n", "
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LocalCluster

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  • " ], "text/plain": [ - "\n", - "Dimensions: (time: 341, y: 400, x: 476)\n", + " Size: 84MB\n", + "Dimensions: (time: 303, y: 219, x: 317)\n", "Coordinates:\n", - " * time (time) datetime64[ns] 2020-01-02T02:08:40.010559 ... 2022-12...\n", - " * y (y) float64 -2.275e+06 -2.275e+06 ... -2.279e+06 -2.279e+06\n", - " * x (x) float64 -1.458e+06 -1.458e+06 ... -1.453e+06 -1.453e+06\n", - " spatial_ref int32 3577\n", + " * time (time) datetime64[ns] 2kB 2019-01-07T01:16:28.775870 ... 202...\n", + " * y (y) float64 2kB -1.286e+06 -1.286e+06 ... -1.288e+06 -1.288e+06\n", + " * x (x) float64 3kB -1.404e+04 -1.404e+04 ... -1.09e+04 -1.088e+04\n", + " spatial_ref int32 4B 3577\n", "Data variables:\n", - " ndwi (time, y, x) float32 nan nan nan ... -0.3259 -0.3428 -0.3729\n", + " ndwi (time, y, x) float32 84MB 0.3482 0.3449 ... -0.08957 -0.092\n", "Attributes:\n", " crs: EPSG:3577\n", " grid_mapping: spatial_ref" ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -980,24 +976,11 @@ ] }, { - "cell_type": "code", - "execution_count": 6, - "id": "92d23ccd-f088-4815-8a56-08bc0f69ccbe", + "cell_type": "markdown", + "id": "93f45ad8-b23d-425a-91c1-a227b67d1372", "metadata": { "tags": [] }, - "outputs": [], - "source": [ - "# # Experiment of removing mostly empty scenes to reduce memory/speed up\n", - "# satellite_ds = satellite_ds.sel(\n", - "# time=satellite_ds.ndwi.notnull().mean(dim=[\"y\", \"x\"]) > 0.9\n", - "# )" - ] - }, - { - "cell_type": "markdown", - "id": "93f45ad8-b23d-425a-91c1-a227b67d1372", - "metadata": {}, "source": [ "### Load optional masks\n", "Loads a topo-bathymetric DEM for the extents of the loaded satellite data.\n", @@ -1006,7 +989,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "b3348b1b-ea4c-4637-958e-c0d89212e25a", "metadata": { "tags": [] @@ -1016,7 +999,7 @@ "# Load data from GA's AusBathyTopo 250m 2023 Grid\n", "topobathy_mask = load_topobathy_mask(\n", " dc=dc,\n", - " geobox=satellite_ds.odc.geobox.compat,\n", + " geobox=satellite_ds.odc.geobox,\n", " product=\"ga_ausbathytopo250m_2023\",\n", " resampling=\"bilinear\",\n", " min_threshold=-15,\n", @@ -1029,23 +1012,29 @@ "id": "6042da34-dd8a-4b67-b039-ca6e5946d112", "metadata": {}, "source": [ - "Load a mask identifying ocean pixels. This can be used to limit the output elevation outputs to unambiguously tidal waters." + "Generate a coastal mask, based on the connectivity of each pixel to the ocean.\n", + "Coastal pixels are identified by calculating \"least-cost\" distances from ocean weighted by elevation, where low, flat areas of terrain are assigned lower weights than high, steep pixels. \n", + "This produces a coastal mask that extends further inland in low-lying coastal areas." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "2a8449dd-967f-4f85-a52d-6ded977e3b89", + "execution_count": 10, + "id": "82a0802f-c1a8-47fc-bb0d-798229255e50", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# Load mask identifying ocean pixels\n", - "ocean_mask = load_ocean_mask(\n", + "# Generate coastal connectivity mask to exclude non-tidal areas\n", + "coastal_mask, _ = load_connectivity_mask(\n", " dc=dc,\n", - " geobox=satellite_ds.odc.geobox.compat,\n", - " product=\"geodata_coast_100k\",\n", + " geobox=satellite_ds.odc.geobox,\n", + " product='ga_srtm_dem1sv1_0',\n", + " elevation_band='dem_h',\n", + " resampling='bilinear',\n", + " buffer=20000,\n", + " max_threshold=100,\n", ")" ] }, @@ -1059,56 +1048,44 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "1cc505df-daf3-4592-858e-6695aa1f78b4", - "metadata": {}, + "execution_count": 11, + "id": "861a8d3c-d98c-477c-9491-b0de24131a02", + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Running ensemble tide modelling\n", "Creating reduced resolution 5000 x 5000 metre tide modelling array\n", - "Modelling tides using FES2014, FES2012, TPXO8-atlas-v1, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10 in parallel\n" + "Running ensemble tide modelling\n", + "Modelling tides using FES2014, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10, FES2012, TPXO8-atlas-v1 in parallel\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 35/35 [00:19<00:00, 1.76it/s]\n" + "100%|██████████| 35/35 [00:22<00:00, 1.53it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Returning low resolution tide array\n", - "Generating ensemble tide model from point inputs\n", - "Interpolating model weights using 'idw' interpolation\n", - " weights\n", - "tide_model \n", - "EOT20 0.562335\n", - "FES2014 0.561498\n", - "HAMTIDE11 0.561064\n", - "GOT4.10 0.561034\n", - "TPXO9-atlas-v5 0.560845\n", - "FES2012 0.558731\n", - "TPXO8-atlas-v1 0.510388\n", - "Reducing multiple models into single ensemble model using 'mean'\n", - "Reprojecting ensemble tides into original array\n" + "Interpolating model rankings using IDW interpolation \n", + "Combining models into single ensemble model\n", + "Reprojecting tides into original array\n" ] } ], "source": [ - "# Model tides into every pixel in the three-dimensional satellite\n", - "# dataset (x by y by time). If `model` is \"ensemble\" this will model\n", - "# tides by combining the best local tide models.\n", - "tide_m, _ = pixel_tides_ensemble(\n", + "# Model tides into every pixel in the three-dimensional (x, y, time)\n", + "# satellite dataset. If `model` is \"ensemble\" this generate optimised\n", + "# tide modelling by combining the best local tide models.\n", + "tide_m, _ = pixel_tides(\n", " ds=satellite_ds,\n", - " ancillary_points=\"data/raw/tide_correlations_2017-2019.geojson\",\n", - " top_n=3,\n", - " reduce_method='mean',\n", " model=tide_model,\n", " directory=tide_model_dir,\n", " )" @@ -1116,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "0dcac52f-78d5-41f3-81a4-199509949a96", "metadata": {}, "outputs": [], @@ -1148,22 +1125,31 @@ "Flatten array to only pixels with positive correlations between water observations and tide height. This greatly improves processing time by ensuring only a narrow strip of pixels along the coastline are analysed, rather than the entire x * y array:\n", "\n", "\n", - "![](../data/figures/tide_array_flattening.JPG)" + "![](../data/figures/tide_array_flattening.JPG)\n", + "\n", + "
    \n", + "\n", + "**Note:** For Australia, we pass in a custom `valid_mask` to constrain the analysis to probable coastal pixels. \n", + "This is optional, and can be skipped (at the expense of slightly less clean outputs and longer runtimes) by commenting out `valid_mask=...`.\n", + "\n", + "
    " ] }, { "cell_type": "code", - "execution_count": 11, - "id": "457ee569-b862-4a4c-b04e-66b39c9fa931", - "metadata": {}, + "execution_count": 13, + "id": "3cd6667b-38a2-497b-a994-2fae1254537a", + "metadata": { + "tags": [] + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Reducing analysed pixels from 190400 to 108689 (57.08%)\n", - "CPU times: user 2.39 s, sys: 1.57 s, total: 3.96 s\n", - "Wall time: 3.78 s\n" + "Reducing analysed pixels from 69423 to 43276 (62.34%)\n", + "CPU times: user 885 ms, sys: 515 ms, total: 1.4 s\n", + "Wall time: 1.36 s\n" ] } ], @@ -1174,7 +1160,7 @@ " min_freq=min_freq,\n", " max_freq=max_freq,\n", " min_correlation=min_correlation,\n", - " valid_mask=topobathy_mask,\n", + " valid_mask=topobathy_mask & coastal_mask,\n", ")" ] }, @@ -1188,12 +1174,15 @@ "### Pixel-wise rolling median\n", "This function performs a rolling median calculation along the tide heights of our satellite images. \n", "It breaks our tide range into `windows_n` individual rolling windows, each of which covers `windows_prop_tide` of the full tidal range. \n", - "For each window, the function returns the median of all tide heights and NDWI index values within the window, and returns an array with a new \"interval\" dimension that summarises these values from low to high tide." + "For each window, the function returns the median of all tide heights and NDWI index values within the window, and returns an array with a new \"interval\" dimension that summarises these values from low to high tide.\n", + "\n", + "More windows (e.g. `windows_n=100`) produces detailed elevation maps that can capture small differences in intertidal morphology - at the expense of slower run times.\n", + "Fewer windows (e.g. `windows_n=50`) will run faster, but produce less smooth, less detailed elevation maps." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "230cb5d2-2d11-4d9a-a29d-d24d2fa44c18", "metadata": { "tags": [] @@ -1202,12 +1191,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d63a67678d7e458db37d483057ed1b2f", + "model_id": "69cdfffe42644e4fbfebf14f7b79b54d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/105 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "x, y = -1455867.2,-2277114.2\n", - "interval_pixel, interval_smoothed_pixel = pixel_dem_debug(\n", - " x,\n", - " y,\n", - " flat_ds,\n", - " interval_ds, \n", - " interp_intervals=200,\n", - " smooth_radius=20,\n", - " min_periods=5,\n", - " # plot_style=\"season\"\n", - ")" + "# x, y = -1455867.2,-2277114.2\n", + "# interval_pixel, interval_smoothed_pixel = pixel_dem_debug(\n", + "# x,\n", + "# y,\n", + "# flat_ds,\n", + "# interval_ds, \n", + "# interp_intervals=200,\n", + "# smooth_radius=20,\n", + "# min_periods=5,\n", + "# # plot_style=\"season\"\n", + "# )" ] }, { @@ -1300,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "0a8e0556-c698-4628-b0f3-cf35e722a293", "metadata": {}, "outputs": [ @@ -1326,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "cec14155-eeb5-4a78-a30f-54b329e6e7e2", "metadata": {}, "outputs": [], @@ -1354,7 +1324,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "1f1d3490-59aa-4001-8ea9-ea46f809c944", "metadata": { "tags": [] @@ -1376,16 +1346,12 @@ "# Clean upper edge of intertidal zone in elevation layers \n", "# (likely to be inaccurate edge pixels)\n", "elevation_bands = [d for d in ds.data_vars if \"elevation\" in d]\n", - "ds[elevation_bands] = clean_edge_pixels(ds[elevation_bands])\n", - "\n", - "# Mask out any non-ocean connected elevation pixels\n", - "ocean_connected_mask = ocean_connection(~(ds.qa_ndwi_freq < min_freq), ocean_mask)\n", - "ds[elevation_bands] = ds[elevation_bands].where(ocean_connected_mask)" + "ds[elevation_bands] = clean_edge_pixels(ds[elevation_bands])\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "75b41fb9-6271-4c8a-a1d5-f4800d03f789", "metadata": { "tags": [] @@ -1394,16 +1360,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
    " ] @@ -1422,12 +1388,142 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "87202b19-b854-4140-9bb7-ca05d324901c", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
    Make this Notebook Trusted to load map: File -> Trust Notebook
    " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds.elevation.odc.explore()" ] @@ -1442,7 +1538,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "id": "ae5db6ce-c55c-4d3c-8a90-e7db3f792039", "metadata": { "tags": [] @@ -1473,7 +1569,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "dbad6b6a-081b-4360-9ffe-120c76b43bd2", "metadata": {}, "outputs": [], @@ -1498,7 +1594,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/notebooks/Intertidal_workflow.ipynb b/notebooks/Intertidal_workflow.ipynb index d0574be5..a36c2871 100644 --- a/notebooks/Intertidal_workflow.ipynb +++ b/notebooks/Intertidal_workflow.ipynb @@ -25,7 +25,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/jovyan/dea_intertidal/dea-intertidal\n" + "/home/jovyan/Robbi/dea-intertidal\n" ] } ], @@ -43,34 +43,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "55beb087", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "pip install -r requirements.in --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "efb4577c-72a6-434b-a976-bfa859c2fd90", - "metadata": { - "tags": [] - }, "outputs": [], "source": [ - "%reload_ext autoreload" + "pip install -r requirements.in --quiet" ] }, { @@ -83,21 +63,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "e1804f40", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -116,23 +87,24 @@ "from odc.ui import select_on_a_map\n", "import cmocean\n", "\n", + "from dea_tools.dask import create_local_dask_cluster\n", + "from dea_tools.coastal import pixel_tides\n", + "\n", "from intertidal.utils import (\n", " round_date_strings,\n", " intertidal_hillshade,\n", ")\n", - "from intertidal.tide_modelling import pixel_tides_ensemble\n", "from intertidal.io import (\n", " load_data,\n", " load_topobathy_mask,\n", " load_aclum_mask,\n", " prepare_for_export,\n", ")\n", - "from intertidal.elevation import elevation#, ocean_connection\n", - "from intertidal.extents import extents\n", + "from intertidal.elevation import elevation\n", + "from intertidal.extents import extents, load_connectivity_mask\n", "from intertidal.exposure import exposure\n", "from intertidal.tidal_bias_offset import bias_offset\n", - "\n", - "from dea_tools.dask import create_local_dask_cluster" + "\n" ] }, { @@ -157,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "523efad1", "metadata": { "tags": [] @@ -165,8 +137,8 @@ "outputs": [], "source": [ "# Intertidal Elevation variables\n", - "start_date = \"2020\" # Start date for analysis\n", - "end_date = \"2022\" # End date for analysis\n", + "start_date = \"2019\" # Start date for analysis\n", + "end_date = \"2021\" # End date for analysis\n", "resolution = 10 # Spatial resolution used for output files\n", "crs = \"EPSG:3577\" # Coordinate Reference System (CRS) to use for output files\n", "ndwi_thresh = 0.1 # Threshold used to identify dry/wet transition\n", @@ -192,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "79f0fb35", "metadata": { "tags": [] @@ -216,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "da9b6a07", "metadata": { "tags": [] @@ -231,7 +203,7 @@ "Geometry(MULTIPOLYGON (((131.87329168896437 -12.220584319739181, 131.87329168896437 -12.200502049369382, 131.90178150419615 -12.200502049369382, 131.90178150419615 -12.220584319739181, 131.87329168896437 -12.220584319739181))), EPSG:4326)" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -258,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "d0938611", "metadata": { "tags": [] @@ -286,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "a13da6ce", "metadata": { "tags": [] @@ -299,7 +271,7 @@ "
    \n", "
    \n", "

    Client

    \n", - "

    Client-65f5cf49-5f8c-11ef-8e10-96d779865243

    \n", + "

    Client-b9dfb6ce-7a32-11ef-b7c1-563a0aa6a178

    \n", "
    \n", - " Comm: tcp://127.0.0.1:44895\n", + " Comm: tcp://127.0.0.1:35389\n", " \n", " Total threads: 62\n", @@ -400,7 +401,7 @@ "
    \n", - " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/45793/status\n", + " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/37593/status\n", " \n", " Memory: 477.21 GiB\n", @@ -408,13 +409,13 @@ "
    \n", - " Nanny: tcp://127.0.0.1:35591\n", + " Nanny: tcp://127.0.0.1:43701\n", "
    \n", - " Local directory: /tmp/dask-worker-space/worker-i9j5am58\n", + " Local directory: /tmp/dask-scratch-space/worker-jfw082c2\n", "
    \n", "\n", " \n", @@ -312,7 +284,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -321,7 +293,7 @@ "
    \n", - " Dashboard: /user/claire.phillips@ga.gov.au/proxy/8787/status\n", + " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status\n", "
    \n", "\n", " \n", - " \n", " \n", @@ -334,11 +306,11 @@ " \n", "
    \n", "

    LocalCluster

    \n", - "

    a1280977

    \n", + "

    4ca2a37f

    \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -371,11 +343,11 @@ "
    \n", "
    \n", "

    Scheduler

    \n", - "

    Scheduler-995cb46b-8e8e-4a06-99f4-770dee573d90

    \n", + "

    Scheduler-09e6c777-ad3e-407d-98c4-faad8910d552

    \n", "
    \n", - " Dashboard: /user/claire.phillips@ga.gov.au/proxy/8787/status\n", + " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status\n", " \n", " Workers: 1\n", @@ -346,10 +318,10 @@ "
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    \n", - " Comm: tcp://127.0.0.1:39501\n", + " Comm: tcp://127.0.0.1:44619\n", " \n", " Workers: 1\n", @@ -383,10 +355,10 @@ "
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  • " ], "text/plain": [ - " Size: 76MB\n", - "Dimensions: (time: 272, y: 219, x: 317)\n", + " Size: 84MB\n", + "Dimensions: (time: 303, y: 219, x: 317)\n", "Coordinates:\n", - " * time (time) datetime64[ns] 2kB 2020-01-02T00:57:27.736492 ... 202...\n", + " * time (time) datetime64[ns] 2kB 2019-01-07T01:16:28.775870 ... 202...\n", " * y (y) float64 2kB -1.286e+06 -1.286e+06 ... -1.288e+06 -1.288e+06\n", " * x (x) float64 3kB -1.404e+04 -1.404e+04 ... -1.09e+04 -1.088e+04\n", " spatial_ref int32 4B 3577\n", "Data variables:\n", - " ndwi (time, y, x) float32 76MB nan nan nan nan ... nan nan nan nan\n", + " ndwi (time, y, x) float32 84MB 0.3482 0.3449 ... -0.08957 -0.092\n", "Attributes:\n", " crs: EPSG:3577\n", " grid_mapping: spatial_ref" ] }, - "execution_count": 9, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -975,12 +948,12 @@ "%%time\n", "\n", "# Connect to datacube\n", - "dc = datacube.Datacube(app=\"Intertidal_elevation\")\n", + "dc = datacube.Datacube(app=\"Intertidal_workflow\")\n", "\n", "# Create local dask cluster to improve data load time\n", "client = create_local_dask_cluster(return_client=True)\n", "\n", - "satellite_ds,_,_ = load_data(\n", + "satellite_ds, _, _ = load_data(\n", " dc=dc,\n", " study_area=study_area,\n", " geom=geom,\n", @@ -997,30 +970,70 @@ "print(satellite_ds)\n", "\n", "# Load data\n", - "satellite_ds.load()" + "satellite_ds.load()\n" ] }, { "cell_type": "markdown", - "id": "a5bf5430", - "metadata": {}, + "id": "155e10ba-4d92-4925-a13e-a8d49c2b7a0e", + "metadata": { + "tags": [] + }, "source": [ - "### Load optional topobathy mask\n", + "### Load optional masks\n", "Loads a topo-bathymetric DEM for the extents of the loaded satellite data.\n", "This is used as a coarse mask to constrain the analysis to the coastal zone, improving run time and reducing clear false positives over deep water or elevated land." ] }, { "cell_type": "code", - "execution_count": 10, - "id": "188c4fd2", + "execution_count": 6, + "id": "b3348b1b-ea4c-4637-958e-c0d89212e25a", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Load data from GA's AusBathyTopo 250m 2023 Grid\n", - "topobathy_mask = load_topobathy_mask(dc, geobox=satellite_ds.odc.geobox.compat)" + "topobathy_mask = load_topobathy_mask(\n", + " dc=dc,\n", + " geobox=satellite_ds.odc.geobox,\n", + " product=\"ga_ausbathytopo250m_2023\",\n", + " resampling=\"bilinear\",\n", + " min_threshold=-15,\n", + " mask_filters=[(\"dilation\", 25)],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bb100b29-f558-4cdc-bcaa-a01a01599bf9", + "metadata": {}, + "source": [ + "Generate a coastal mask, based on the connectivity of each pixel to the ocean. \n", + "Coastal pixels are identified by calculating \"least-cost\" distances from ocean weighted by elevation, where low, flat areas of terrain are assigned lower weights than high, steep pixels. \n", + "This produces a coastal mask that extends further inland in low-lying coastal areas." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "82a0802f-c1a8-47fc-bb0d-798229255e50", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Generate coastal connectivity mask to exclude non-tidal areas\n", + "coastal_mask, _ = load_connectivity_mask(\n", + " dc=dc,\n", + " geobox=satellite_ds.odc.geobox,\n", + " product='ga_srtm_dem1sv1_0',\n", + " elevation_band='dem_h',\n", + " resampling='bilinear',\n", + " buffer=20000,\n", + " max_threshold=100,\n", + ")" ] }, { @@ -1029,12 +1042,18 @@ "metadata": {}, "source": [ "### Intertidal elevation\n", - "To run without the topobathy DEM mask, comment out `valid_mask=...`." + "\n", + "
    \n", + "\n", + "**Note:** For Australia, we pass in a custom `valid_mask` to constrain the analysis to probable coastal pixels. \n", + "This is optional, and can be skipped (at the expense of slightly less clean outputs and longer runtimes) by commenting out `valid_mask=...`.\n", + "\n", + "
    " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "86591395", "metadata": { "tags": [] @@ -1044,66 +1063,55 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-08-21 07:11:13 INFO Processing: Modelling tide heights for each pixel\n" + "2024-09-24 05:08:29 INFO Processing: Modelling tide heights for each pixel\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Running ensemble tide modelling\n", "Creating reduced resolution 5000 x 5000 metre tide modelling array\n", - "Modelling tides using FES2014, FES2012, TPXO8-atlas-v1, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10 in parallel\n" + "Running ensemble tide modelling\n", + "Modelling tides using FES2014, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10, FES2012, TPXO8-atlas-v1 in parallel\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 35/35 [00:57<00:00, 1.65s/it]\n" + "100%|██████████| 35/35 [00:22<00:00, 1.52it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Returning low resolution tide array\n", - "Generating ensemble tide model from point inputs\n", - "Interpolating model weights using 'idw' interpolation\n", - " weights\n", - "tide_model \n", - "HAMTIDE11 0.343471\n", - "EOT20 0.341870\n", - "FES2014 0.338001\n", - "TPXO9-atlas-v5 0.332146\n", - "GOT4.10 0.326536\n", - "TPXO8-atlas-v1 0.316927\n", - "FES2012 0.308876\n", - "Reducing multiple models into single ensemble model using 'mean'\n", - "Reprojecting ensemble tides into original array\n" + "Interpolating model rankings using IDW interpolation \n", + "Combining models into single ensemble model\n", + "Reprojecting tides into original array\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2024-08-21 07:12:16 INFO Processing: Masking nodata and adding tide heights to satellite data array\n", - "2024-08-21 07:12:16 INFO Processing: Flattening satellite data array and filtering to intertidal candidate pixels\n", - "2024-08-21 07:12:16 INFO Processing: Applying valid data mask to constrain study area\n", - "2024-08-21 07:12:17 INFO Processing: Running per-pixel rolling median\n" + "2024-09-24 05:08:58 INFO Processing: Masking nodata and adding tide heights to satellite data array\n", + "2024-09-24 05:08:58 INFO Processing: Flattening satellite data array and filtering to intertidal candidate pixels\n", + "2024-09-24 05:08:58 INFO Processing: Applying valid data mask to constrain study area\n", + "2024-09-24 05:08:59 INFO Processing: Running per-pixel rolling median\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Reducing analysed pixels from 69423 to 43493 (62.65%)\n" + "Reducing analysed pixels from 69423 to 43264 (62.32%)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d85a25f972874733bbee3ac710fb2bf1", + "model_id": "5c72cdcfe7e941c0ab89853311673427", "version_major": 2, "version_minor": 0 }, @@ -1118,7 +1126,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-08-21 07:12:46 INFO Processing: Modelling intertidal elevation\n" + "2024-09-24 05:09:45 INFO Processing: Modelling intertidal elevation\n" ] }, { @@ -1133,10 +1141,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-08-21 07:12:47 INFO Processing: Modelling intertidal uncertainty\n", - "2024-08-21 07:12:47 INFO Processing: Unflattening data back to its original spatial dimensions\n", - "2024-08-21 07:12:47 INFO Processing: Cleaning inaccurate upper intertidal pixels\n", - "2024-08-21 07:12:47 INFO Processing: Successfully completed intertidal elevation modelling\n" + "2024-09-24 05:09:46 INFO Processing: Modelling intertidal uncertainty\n", + "2024-09-24 05:09:46 INFO Processing: Unflattening data back to its original spatial dimensions\n", + "2024-09-24 05:09:46 INFO Processing: Cleaning inaccurate upper intertidal pixels\n", + "2024-09-24 05:09:46 INFO Processing: Successfully completed intertidal elevation modelling\n" ] } ], @@ -1144,7 +1152,7 @@ "# Model elevation for each pixel\n", "ds, tide_m = elevation(\n", " satellite_ds,\n", - " valid_mask=topobathy_mask,\n", + " valid_mask=topobathy_mask & coastal_mask,\n", " tide_model=tide_model,\n", " tide_model_dir=tide_model_dir,\n", ")" @@ -1161,15 +1169,27 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "id": "ebd67431-d173-4179-b75d-3cff5913a1a6", "metadata": { "tags": [] }, "outputs": [], "source": [ + "# Load urban mask used to remove inland false positives\n", + "urban_mask = load_aclum_mask(\n", + " dc=dc,\n", + " geobox=satellite_ds.odc.geobox,\n", + ")\n", + "\n", "# Calculate extents\n", - "ds[\"extents\"] = extents(dc=dc, ds=ds)" + "ds[\"extents\"] = extents(\n", + " dem=ds.elevation,\n", + " freq=ds.qa_ndwi_freq,\n", + " corr=ds.qa_ndwi_corr,\n", + " coastal_mask=coastal_mask,\n", + " urban_mask=urban_mask,\n", + ")" ] }, { @@ -1180,15 +1200,15 @@ }, "source": [ "### Intertidal exposure\n", - "Calculate exposure using the script function.\n", - "To calculate exposure for the full time period, leave `filters` commented out or set as ['unfiltered'].\n", - "See the function documentation for the full range of available filters and filter_combinations.\n", - "The code accepts lists of multiple filters and filter_combination tuples." + "Calculate intertidal exposure for each pixel with an intertidal elevation.\n", + "To calculate exposure for the full time period, leave `filters` commented out or set as `[\"unfiltered\"]`.\n", + "See the function documentation for the full range of available `filters` and `filters_combined`.\n", + "The code accepts lists of multiple `filters` and `filter_combined` tuples." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "159f4544", "metadata": { "tags": [] @@ -1198,36 +1218,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running ensemble tide modelling\n", "Creating reduced resolution 5000 x 5000 metre tide modelling array\n", - "Modelling tides using FES2014, FES2012, TPXO8-atlas-v1, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10 in parallel\n" + "Running ensemble tide modelling\n", + "Modelling tides using FES2014, TPXO9-atlas-v5, EOT20, HAMTIDE11, GOT4.10, FES2012, TPXO8-atlas-v1 in parallel\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 35/35 [01:13<00:00, 2.10s/it]\n" + "100%|██████████| 35/35 [00:25<00:00, 1.36it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Interpolating model rankings using IDW interpolation \n", + "Combining models into single ensemble model\n", "Returning low resolution tide array\n", - "Generating ensemble tide model from point inputs\n", - "Interpolating model weights using 'idw' interpolation\n", - " weights\n", - "tide_model \n", - "HAMTIDE11 0.343471\n", - "EOT20 0.341870\n", - "FES2014 0.338001\n", - "TPXO9-atlas-v5 0.332146\n", - "GOT4.10 0.326536\n", - "TPXO8-atlas-v1 0.316927\n", - "FES2012 0.308876\n", - "Reducing multiple models into single ensemble model using 'mean'\n", - "Returning low resolution ensemble tide array\n", "Calculating unfiltered exposure\n" ] } @@ -1267,7 +1276,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "20801964", "metadata": { "tags": [] @@ -1283,7 +1292,7 @@ " ds[\"ta_offset_low\"],\n", " ds[\"ta_offset_high\"],\n", ") = bias_offset(\n", - " tide_m=tide_m, tide_cq=modelledtides_ds['unfiltered'], lot_hot=True, lat_hat=True\n", + " tide_m=tide_m, tide_cq=modelledtides_ds[\"unfiltered\"], lot_hot=True, lat_hat=True\n", ")" ] }, @@ -1297,7 +1306,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "b362949e", "metadata": { "tags": [] @@ -1337,6 +1346,7 @@ "}\n", "\n", "html[theme=dark],\n", + "html[data-theme=dark],\n", "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", @@ -1669,205 +1679,207 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    <xarray.Dataset> Size: 5MB\n",
    +       "
    <xarray.Dataset> Size: 7MB\n",
            "Dimensions:                (y: 219, x: 317)\n",
            "Coordinates:\n",
            "  * y                      (y) float64 2kB -1.286e+06 -1.286e+06 ... -1.288e+06\n",
            "  * x                      (x) float64 3kB -1.404e+04 -1.404e+04 ... -1.088e+04\n",
            "    spatial_ref            int32 4B 3577\n",
    +       "    tide_model             <U8 32B 'ensemble'\n",
            "Data variables: (12/13)\n",
    -       "    elevation              (y, x) float64 555kB nan nan nan ... -1.013 -1.026\n",
    -       "    elevation_uncertainty  (y, x) float64 555kB nan nan nan ... 0.1344 0.1211\n",
    -       "    qa_ndwi_corr           (y, x) float64 555kB nan nan nan ... 0.4645 0.4465\n",
    -       "    qa_ndwi_freq           (y, x) float64 555kB 0.9948 0.9948 ... 0.9344 0.9399\n",
    -       "    extents                (y, x) int16 139kB 5 5 5 5 5 5 5 5 ... 3 3 3 3 3 3 3\n",
    -       "    exposure_unfiltered    (y, x) float64 555kB nan nan nan ... 17.0 17.0 16.0\n",
    +       "    elevation              (y, x) float64 555kB nan nan nan ... -1.192 -1.193\n",
    +       "    elevation_uncertainty  (y, x) float64 555kB nan nan nan ... 0.2468 0.2466\n",
    +       "    qa_ndwi_corr           (y, x) float64 555kB nan nan nan ... 0.5159 0.5036\n",
    +       "    qa_ndwi_freq           (y, x) float64 555kB 0.9959 0.9959 ... 0.9083 0.9125\n",
    +       "    extents                (y, x) int16 139kB 1 1 1 1 1 1 1 1 ... 3 3 3 3 3 3 3\n",
    +       "    exposure_unfiltered    (y, x) float64 555kB nan nan nan ... 18.0 18.0 18.0\n",
            "    ...                     ...\n",
    -       "    ta_hat                 (y, x) float32 278kB 1.973 1.973 ... 1.998 1.999\n",
    -       "    ta_lot                 (y, x) float32 278kB -1.7 -1.7 -1.7 ... -1.679 -1.679\n",
    -       "    ta_hot                 (y, x) float32 278kB 1.809 1.809 ... 1.854 1.854\n",
    -       "    ta_spread              (y, x) float32 278kB 79.58 79.58 ... 79.04 79.04\n",
    -       "    ta_offset_low          (y, x) float32 278kB 16.69 16.69 ... 17.73 17.73\n",
    -       "    ta_offset_high         (y, x) float32 278kB 3.733 3.732 ... 3.228 3.226
  • " ], "text/plain": [ - " Size: 5MB\n", + " Size: 7MB\n", "Dimensions: (y: 219, x: 317)\n", "Coordinates:\n", " * y (y) float64 2kB -1.286e+06 -1.286e+06 ... -1.288e+06\n", " * x (x) float64 3kB -1.404e+04 -1.404e+04 ... -1.088e+04\n", " spatial_ref int32 4B 3577\n", + " tide_model " ] @@ -2018,36 +2030,6 @@ ")\n", "ax_dict[\"K\"].set_title(\"Highest Observed Tide\")\n", "\n", - "# # Plot the high and low tidelines with respective offset\n", - "# ax_dict[\"L\"].set_title(\"Lowtide line and lowtide offset\")\n", - "# # lowtideline.plot(\n", - "# # column=\"offset_lowtide\",\n", - "# # legend=True,\n", - "# # vmin=0,\n", - "# # vmax=40,\n", - "# # cmap=\"magma\",\n", - "# # ax=ax_dict[\"L\"],\n", - "# # zorder=2,\n", - "# # )\n", - "# # tidelines_gdf.loc[[0], \"geometry\"].plot(ax=ax_dict[\"L\"], zorder=1)\n", - "# ax_dict[\"L\"].set_xlim(left=ds.elevation.x.min(), right=ds.elevation.x.max())\n", - "# ax_dict[\"L\"].set_ylim(bottom=ds.elevation.y.min(), top=ds.elevation.y.max())\n", - "\n", - "# ax_dict[\"M\"].set_title(\"Hightide line and hightide offset\")\n", - "# # hightideline.plot(\n", - "# # column=\"offset_hightide\",\n", - "# # legend=True,\n", - "# # vmin=0,\n", - "# # vmax=40,\n", - "# # cmap=\"magma\",\n", - "# # ax=ax_dict[\"M\"],\n", - "# # zorder=2,\n", - "# # )\n", - "# # tidelines_gdf.loc[[1], \"geometry\"].plot(ax=ax_dict[\"M\"], zorder=1)\n", - "# ax_dict[\"M\"].set_yticks([])\n", - "# ax_dict[\"M\"].set_xlim(left=ds.elevation.x.min(), right=ds.elevation.x.max())\n", - "# ax_dict[\"M\"].set_ylim(bottom=ds.elevation.y.min(), top=ds.elevation.y.max())\n", - "\n", "# Remove axis labels\n", "for label, ax in ax_dict.items():\n", " ax.set_yticks([])\n", @@ -2068,7 +2050,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "709274f2", "metadata": {}, "outputs": [], @@ -2087,7 +2069,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "42d6814d", "metadata": {}, "outputs": [], @@ -2108,7 +2090,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "id": "b1f2c3f2", "metadata": {}, "outputs": [], @@ -2133,7 +2115,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/requirements.in b/requirements.in index 3aa53bca..310aef85 100644 --- a/requirements.in +++ b/requirements.in @@ -2,32 +2,31 @@ aiohttp botocore click==8.1.7 -datacube[s3,performance]==1.8.18 -dea-tools==0.3.2 -eodatasets3==0.30.5 -geopandas==0.14.3 -matplotlib==3.8.4 +datacube[s3,performance]==1.8.19 +dea-tools==0.3.4 +eodatasets3==0.30.6 +geopandas==0.14.4 +matplotlib==3.9.1 mdutils numpy==1.26.4 odc-algo==0.2.3 -odc-geo==0.4.3 +odc-geo==0.4.8 odc-ui pandas==2.2.2 -pygeos==0.14 pyproj==3.6.1 pytest pytest-dependency pytest-cov -pyTMD==2.1.0 +pyTMD==2.1.4 pytz==2024.1 -rioxarray==0.15.5 -rasterio==1.3.8 +rasterio==1.3.10 +rioxarray==0.17.0 seaborn==0.13.2 -scikit-image==0.22.0 -scikit-learn==1.4.2 -scipy==1.13.0 +scikit-image==0.24.0 +scikit-learn==1.5.1 +scipy==1.14.0 sunriset==1.0 -shapely==2.0.1 -tqdm==4.66.2 -xarray==2024.3.0 +shapely==2.0.5 +tqdm==4.66.5 +xarray==2024.7.0 xskillscore==0.0.24 diff --git a/requirements.txt b/requirements.txt index 697d3cba..0174fb93 100644 --- a/requirements.txt +++ b/requirements.txt @@ -152,7 +152,7 @@ dask-image==2023.3.0 # via odc-algo dask-ml==1.0.0 # via dea-tools -datacube[performance,s3]==1.8.18 +datacube[performance,s3]==1.8.19 # via # -r requirements.in # datacube-ows @@ -162,7 +162,7 @@ datacube[performance,s3]==1.8.18 # odc-ui datacube-ows==1.8.39 # via dea-tools -dea-tools==0.3.2 +dea-tools==0.3.4 # via -r requirements.in decorator==5.1.1 # via ipython @@ -178,7 +178,7 @@ distributed==2024.3.1 # dask-ml # datacube # odc-algo -eodatasets3==0.30.5 +eodatasets3==0.30.6 # via -r requirements.in exceptiongroup==1.2.0 # via @@ -218,7 +218,7 @@ geoalchemy2==0.14.6 # datacube-ows geographiclib==2.0 # via geopy -geopandas==0.14.3 +geopandas==0.14.4 # via # -r requirements.in # dea-tools @@ -308,11 +308,12 @@ lxml==5.1.0 # owslib # pyows # pytmd + # timescale markupsafe==2.1.5 # via # jinja2 # werkzeug -matplotlib==3.8.4 +matplotlib==3.9.1 # via # -r requirements.in # datacube-ows @@ -360,6 +361,7 @@ numpy==1.26.4 # dea-tools # eodatasets3 # folium + # geopandas # h5py # hdstats # imageio @@ -374,7 +376,6 @@ numpy==1.26.4 # pandas # pims # properscoring - # pygeos # pytmd # rasterio # rasterstats @@ -387,6 +388,7 @@ numpy==1.26.4 # snuggs # sparse # tifffile + # timescale # timezonefinder # xarray # xhistogram @@ -395,7 +397,7 @@ odc-algo==0.2.3 # via # -r requirements.in # odc-ui -odc-geo==0.4.3 +odc-geo==0.4.8 # via # -r requirements.in # dea-tools @@ -481,8 +483,6 @@ pydantic==2.6.4 # via planetary-computer pydantic-core==2.16.3 # via pydantic -pygeos==0.14 - # via -r requirements.in pygments==2.17.2 # via ipython pyows==0.2.7 @@ -533,13 +533,14 @@ python-dateutil==2.9.0.post0 # pystac # pystac-client # pytmd + # timescale python-dotenv==1.0.1 # via planetary-computer python-rapidjson==1.16 # via eodatasets3 python-slugify==8.0.4 # via datacube-ows -pytmd==2.1.0 +pytmd==2.1.4 # via # -r requirements.in # dea-tools @@ -559,7 +560,7 @@ pyyaml==6.0.1 # datacube # distributed # owslib -rasterio==1.3.8 +rasterio==1.3.10 # via # -r requirements.in # datacube @@ -587,7 +588,7 @@ requests==2.31.0 # owslib # planetary-computer # pystac-client -rioxarray==0.15.5 +rioxarray==0.17.0 # via # -r requirements.in # dea-tools @@ -603,19 +604,19 @@ ruamel-yaml-clib==0.2.8 # via ruamel-yaml s3transfer==0.10.1 # via boto3 -scikit-image==0.22.0 +scikit-image==0.24.0 # via # -r requirements.in # dea-tools # odc-algo -scikit-learn==1.4.2 +scikit-learn==1.5.1 # via # -r requirements.in # dask-glm # dask-ml # dea-tools # xskillscore -scipy==1.13.0 +scipy==1.14.0 # via # -r requirements.in # dask-glm @@ -630,6 +631,7 @@ scipy==1.13.0 # scikit-image # scikit-learn # sparse + # timescale # xskillscore seaborn==0.13.2 # via -r requirements.in @@ -637,7 +639,8 @@ setuptools-scm==8.0.4 # via # datacube-ows # pytmd -shapely==2.0.1 + # timescale +shapely==2.0.5 # via # -r requirements.in # datacube @@ -681,6 +684,8 @@ tifffile==2024.2.12 # via # dask-image # scikit-image +timescale==0.0.5 + # via pytmd timezonefinder==6.5.0 # via datacube-ows tomli==2.0.1 @@ -699,7 +704,7 @@ toolz==0.12.1 # xskillscore tornado==6.4 # via distributed -tqdm==4.66.2 +tqdm==4.66.5 # via # -r requirements.in # dea-tools @@ -733,7 +738,7 @@ widgetsnbextension==4.0.10 # via ipywidgets wrapt==1.16.0 # via deprecat -xarray==2024.3.0 +xarray==2024.7.0 # via # -r requirements.in # datacube diff --git a/setup.py b/setup.py index 336b4aa9..41a2f175 100644 --- a/setup.py +++ b/setup.py @@ -24,9 +24,8 @@ "odc-ui", "odc-algo", "pandas", - "pygeos", "pyproj", - "pyTMD>=2.0.0", + "pyTMD>=2.0.0,<2.1.5", "pytest", "pytest-dependency", "pytest-cov", diff --git a/tests/README.md b/tests/README.md index 56d01435..92926d92 100644 --- a/tests/README.md +++ b/tests/README.md @@ -10,8 +10,8 @@ Integration tests This directory contains tests that are run to verify that DEA Intertidal code runs correctly. The ``test_intertidal.py`` file runs a small-scale full workflow analysis over an intertidal flat in the Gulf of Carpentaria using the DEA Intertidal [Command Line Interface (CLI) tools](../notebooks/Intertidal_CLI.ipynb), and compares these results against a LiDAR validation DEM to produce some simple accuracy metrics. -The latest integration test completed at **2024-08-23 09:54**. Compared to the previous run, it had an: -- RMSE accuracy of **0.14 m ( :heavy_minus_sign: no change)** +The latest integration test completed at **2024-09-24 15:42**. Compared to the previous run, it had an: +- RMSE accuracy of **0.15 m ( :heavy_minus_sign: no change)** - MAE accuracy of **0.12 m ( :heavy_minus_sign: no change)** - Bias of **0.12 m ( :heavy_minus_sign: no change)** - Pearson correlation of **0.975 ( :heavy_minus_sign: no change)** diff --git a/tests/test_intertidal.py b/tests/test_intertidal.py index 88137aa8..1d69fe4c 100644 --- a/tests/test_intertidal.py +++ b/tests/test_intertidal.py @@ -26,7 +26,13 @@ def satellite_ds(): """ Loads a pre-generated timeseries of satellite data from NetCDF. """ - return xr.open_dataset("tests/data/satellite_ds.nc") + satellite_ds = xr.open_dataset("tests/data/satellite_ds.nc") + + # Hack to fix malformed CRS + del satellite_ds["spatial_ref"] + satellite_ds = satellite_ds.odc.assign_crs("EPSG:3577") + + return satellite_ds @pytest.mark.dependency() diff --git a/tests/validation.csv b/tests/validation.csv index 2681fe70..9f8fc126 100644 --- a/tests/validation.csv +++ b/tests/validation.csv @@ -65,3 +65,5 @@ time,Correlation,RMSE,MAE,R-squared,Bias,Regression slope 2024-08-22 02:43:49.292628+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-08-22 05:41:31.102000+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-08-22 23:54:49.456210+00:00,0.975,0.141,0.121,0.95,0.116,1.11 +2024-09-24 00:07:47.380872+00:00,0.975,0.146,0.125,0.95,0.119,1.112 +2024-09-24 05:42:35.688710+00:00,0.975,0.146,0.125,0.95,0.119,1.112 diff --git a/tests/validation.jpg b/tests/validation.jpg index 91ed615c2c5b7c358bd24582da7ef64e76eedb30..b339ef82583be4179bee6e6be272dccec3f89dad 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    \n", - " Comm: tcp://127.0.0.1:40365\n", + " Comm: tcp://127.0.0.1:33487\n", " \n", - " Total threads: 7\n", + " Total threads: 62\n", "
    \n", - " Dashboard: /user/claire.phillips@ga.gov.au/proxy/40679/status\n", + " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/46813/status\n", " \n", - " Memory: 59.21 GiB\n", + " Memory: 477.21 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:39863\n", + " Nanny: tcp://127.0.0.1:41429\n", "
    \n", - " Local directory: /tmp/dask-scratch-space/worker-h2ecmu6p\n", + " Local directory: /tmp/dask-scratch-space/worker-ibmg97mu\n", "