From a894c6077f10fb53d5c9c099a3469d06bad81aee Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Sun, 24 Mar 2024 23:57:46 +0000 Subject: [PATCH 01/12] Pre-publication fixes --- intertidal/elevation.py | 42 +++++-- intertidal/extents.py | 99 ++++++++++++++++ intertidal/io.py | 164 ++++++++++++++++++-------- metadata/eo3_intertidal.odc-type.yaml | 4 +- 4 files changed, 244 insertions(+), 65 deletions(-) diff --git a/intertidal/elevation.py b/intertidal/elevation.py index 645dc31..11777da 100644 --- a/intertidal/elevation.py +++ b/intertidal/elevation.py @@ -82,7 +82,7 @@ def ds_to_flat( If True, remove any seasonal signal from the tide height data by subtracting monthly mean tide height from each value. This can reduce false tide correlations in regions where tide heights - correlate with seasonal changes in surface water. Note that + correlate with seasonal changes in surface water. Note that seasonally corrected tides are only used to identify potentially tide influenced pixels - not for elevation modelling itself. valid_mask : xr.DataArray, optional @@ -131,15 +131,15 @@ def ds_to_flat( # correlation. This prevents small changes in NDWI beneath the water # surface from producing correlations with tide height. wet_dry = flat_ds[index] > ndwi_thresh - + # Use either tides directly or correct to remove seasonal signal if correct_seasonality: print("Removing seasonal signal before calculating tide correlations") - gb = flat_ds.tide_m.groupby('time.month') - tide_array = (gb - gb.mean()) + gb = flat_ds.tide_m.groupby("time.month") + tide_array = gb - gb.mean() else: - tide_array = flat_ds.tide_m - + tide_array = flat_ds.tide_m + if corr_method == "pearson": corr = xr.corr(wet_dry, tide_array, dim="time").rename("qa_ndwi_corr") elif corr_method == "spearman": @@ -558,10 +558,11 @@ def pixel_uncertainty( max_q=0.75, ): """ - Calculate uncertainty bounds around a modelled elevation based on - observations that were misclassified by a given NDWI threshold. + Calculate one-sided uncertainty bounds around a modelled elevation + based on observations that were misclassified by a given NDWI + threshold. - The function identifies observations that were misclassified by the + Uncertainty is based observations that were misclassified by the modelled elevation, i.e., wet observations (NDWI > threshold) at lower tide heights than the modelled elevation, or dry observations (NDWI < threshold) at higher tide heights than the modelled @@ -603,7 +604,8 @@ def pixel_uncertainty( ------- dem_flat_low, dem_flat_high, dem_flat_uncertainty : xarray.DataArray The lower and upper uncertainty bounds around the modelled - elevation, and the summary uncertainty range between them. + elevation, and the summary uncertainty range between them + (expressed as one-sided uncertainty). misclassified_sum : xarray.DataArray The sum of individual satellite observations misclassified by the modelled elevation and NDWI threshold. @@ -666,8 +668,9 @@ def pixel_uncertainty( dem_flat_low = np.minimum(uncertainty_low, flat_dem.elevation) dem_flat_high = np.maximum(uncertainty_high, flat_dem.elevation) - # Subtract low from high DEM to summarise uncertainy range - dem_flat_uncertainty = dem_flat_high - dem_flat_low + # Subtract low from high DEM to summarise uncertainty range + # (and divide by two to give one-sided uncertainty) + dem_flat_uncertainty = (dem_flat_high - dem_flat_low) / 2.0 return ( dem_flat_low, @@ -1067,6 +1070,18 @@ def elevation( help="Proportion of the tide range to use for each window radius " "in the per-pixel rolling median calculation, by default 0.15.", ) +@click.option( + "--correct_seasonality/--no-correct_seasonality", + is_flag=True, + default=False, + help="If True, remove any seasonal signal from the tide height data " + "by subtracting monthly mean tide height from each value prior to " + "correlation calculations. This can reduce false tide correlations " + "in regions where tide heights correlate with seasonal changes in " + "surface water. Note that seasonally corrected tides are only used " + "to identify potentially tide influenced pixels - not for elevation " + "modelling itself.", +) @click.option( "--tide_model", type=str, @@ -1126,6 +1141,7 @@ def intertidal_cli( min_correlation, windows_n, window_prop_tide, + correct_seasonality, tide_model, tide_model_dir, modelled_freq, @@ -1199,7 +1215,7 @@ def intertidal_cli( min_correlation=min_correlation, windows_n=windows_n, window_prop_tide=window_prop_tide, - correct_seasonality=True, + correct_seasonality=correct_seasonality, tide_model=tide_model, tide_model_dir=tide_model_dir, run_id=run_id, diff --git a/intertidal/extents.py b/intertidal/extents.py index 7bceb47..e1d0e50 100644 --- a/intertidal/extents.py +++ b/intertidal/extents.py @@ -246,3 +246,102 @@ def extents( extents = extents.combine_first(0) return extents + + +# from rasterio.features import sieve + + +# def ocean_connection(water, ocean_da, connectivity=1): +# """ +# 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 extents_ocean_masking( +# dem, +# freq, +# corr, +# ocean_mask, +# urban_mask, +# min_freq=0.01, +# max_freq=0.99, +# mostly_dry_freq=0.5, +# min_correlation=0.15, +# ): +# """ +# Experimental ocean masking extents code +# """ +# # Set NaN values (i.e. pixels masked out over deep water) in frequency to 1 +# freq = freq.fillna(1) + +# # Identify broad classes based on wetness frequency +# intermittent = (freq >= min_freq) & (freq <= max_freq) # wet and dynamic +# wet_all = freq >= min_freq # all occasionally wet pixels incl. intertidal +# mostly_dry = freq < mostly_dry_freq # dry for majority of the timeseries + +# # Classify 'wet_all' pixels into 'wet_ocean' and 'wet_inland' based +# # on connectivity to ocean pixels, and mask out `wet_inland` pixels +# # identified as intensive urban use +# wet_ocean = ocean_connection(wet_all, (ocean_mask | (corr >= 0.5))) +# wet_inland = wet_all & ~wet_ocean & ~urban_mask + +# # Distinguish mostly dry intermittent inland from other wet inland +# wet_inland_intermittent = wet_inland & mostly_dry + +# # Separate all intertidal from high confidence intertidal pixels +# intertidal = intermittent & (corr >= min_correlation) +# intertidal_hc = dem.notnull() & wet_ocean + +# # Identify intertidal fringe pixels (e.g. non-tidally correlated +# # ocean pixels that appear in close proximity to the intertidal zone +# # that are dry for at least half the timeseries. +# intertidal_dilated = mask_cleanup(mask=intertidal, mask_filters=[("dilation", 3)]) +# intertidal_fringe = intertidal_dilated & wet_ocean & mostly_dry + +# # Combine all layers +# extents = odc.geo.xr.xr_zeros(dem.odc.geobox).astype(np.uint8) +# extents.values[wet_ocean.values] = 3 +# extents.values[wet_inland.values] = 2 +# extents.values[wet_inland_intermittent.values] = 1 +# extents.values[intertidal_fringe.values] = 0 +# extents.values[intertidal.values] = 4 + +# # Reduce noise by sieving all classes except high confidence intertidal. +# # This merges small areas of isolated pixels with their most common neighbour +# extents.values[:] = sieve(extents, 3, connectivity=4) + +# # Finally add intertidal high confidence extents over the top +# extents.values[intertidal_hc.values] = 5 + +# return extents diff --git a/intertidal/io.py b/intertidal/io.py index de4907d..1ad32e3 100644 --- a/intertidal/io.py +++ b/intertidal/io.py @@ -533,7 +533,7 @@ def load_aclum_mask( product="abares_clum_2020", class_band="alum_class", resampling="nearest", - mask_invalid=True, + mask_invalid=False, ): """ Loads an ABARES derived land use classification of Australia @@ -566,55 +566,117 @@ def load_aclum_mask( An output boolean mask, where True equals intensive urban and False equals all other classes. """ - # Load from datacube, reprojecting to GeoBox of input satellite data - aclum_ds = dc.load(product=product, like=geobox, resampling=resampling).squeeze( - "time" - ) + try: + # Load from datacube, reprojecting to GeoBox of input satellite data + aclum_ds = dc.load(product=product, like=geobox, resampling=resampling).squeeze( + "time" + ) - # Mask invalid data - if mask_invalid: - aclum_ds = mask_invalid_data(aclum_ds) - - # Manually isolate the 'intensive urban' land use summary class, set - # all other pixels to False. For class definitions, refer to - # gdata1/data/land_use/ABARES_CLUM/geotiff_clum_50m1220m/Land use, 18-class summary.qml) - reclassified_aclum = aclum_ds[class_band].isin( - [ - 500, - 530, - 531, - 532, - 533, - 534, - 535, - 536, - 537, - 538, - 540, - 541, - 550, - 551, - 552, - 553, - 554, - 555, - 560, - 561, - 562, - 563, - 564, - 565, - 566, - 567, - 570, - 571, - 572, - 573, - 574, - 575, - ] - ) - return reclassified_aclum + # Mask invalid data + if mask_invalid: + aclum_ds = mask_invalid_data(aclum_ds) + + # Manually isolate the 'intensive urban' land use summary class, set + # all other pixels to False. For class definitions, refer to + # gdata1/data/land_use/ABARES_CLUM/geotiff_clum_50m1220m/Land use, 18-class summary.qml) + reclassified_aclum = aclum_ds[class_band].isin( + [ + 500, + 530, + 531, + 532, + 533, + 534, + 535, + 536, + 537, + 538, + 540, + 541, + 550, + 551, + 552, + 553, + 554, + 555, + 560, + 561, + 562, + 563, + 564, + 565, + 566, + 567, + 570, + 571, + 572, + 573, + 574, + 575, + ] + ) + return reclassified_aclum + + # Return an array of all False (i.e. no urban) if no data is returned + except AttributeError: + 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): @@ -782,12 +844,14 @@ def tidal_metadata(ds): ) # Calculate category - metadata_dict["intertidal:category"] = ( + metadata_dict["intertidal:tr_class"] = ( "microtidal" if metadata_dict["intertidal:tr"] < 2 else "mesotidal" if 2 <= metadata_dict["intertidal:tr"] <= 4 else "macrotidal" + if metadata_dict["intertidal:tr"] > 4 + else np.nan ) return metadata_dict diff --git a/metadata/eo3_intertidal.odc-type.yaml b/metadata/eo3_intertidal.odc-type.yaml index a2c15a6..8197f4d 100644 --- a/metadata/eo3_intertidal.odc-type.yaml +++ b/metadata/eo3_intertidal.odc-type.yaml @@ -71,13 +71,13 @@ dataset: type: double # Intertidal-specific metadata below - intertidal_category: + intertidal_tr_class: description: | Tide range classification - one of microtidal|mesotidal|macrotidal indexed: false offset: - properties - - intertidal:category + - intertidal:tr_class intertidal_hat: description: | From 2b1e06ffc77c861114604bc0e4bac91d697f579a Mon Sep 17 00:00:00 2001 From: robbibt Date: Mon, 25 Mar 2024 00:32:23 +0000 Subject: [PATCH 02/12] Automatically update integration test validation results --- tests/README.md | 2 +- tests/validation.csv | 1 + tests/validation.jpg | Bin 72218 -> 72109 bytes 3 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/README.md b/tests/README.md index 10f0074..37b360c 100644 --- a/tests/README.md +++ b/tests/README.md @@ -10,7 +10,7 @@ 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 **2024-03-22 14:25**. Compared to the previous run, it had an: +The latest integration test completed at **2024-03-25 11:32**. Compared to the previous run, it had an: - RMSE accuracy of **0.14 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)** diff --git a/tests/validation.csv b/tests/validation.csv index e8f3be4..2b46be3 100644 --- a/tests/validation.csv +++ b/tests/validation.csv @@ -48,3 +48,4 @@ time,Correlation,RMSE,MAE,R-squared,Bias,Regression slope 2024-03-13 23:17:46.582372+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-14 01:50:20.512235+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-22 03:25:50.523558+00:00,0.975,0.141,0.121,0.95,0.116,1.11 +2024-03-25 00:32:00.748385+00:00,0.975,0.141,0.121,0.95,0.116,1.11 diff --git a/tests/validation.jpg b/tests/validation.jpg index 46d3fbf9a357229dc9e6c3d7ff1292436171ec63..be3103f1c7a3587444fa142991a3ed2437bbb6de 100644 GIT binary patch delta 42180 zcmcG#bx>SS*EKo>4Ix2-22Frq!QCanLvVKqF2OBukl@Y)2u^S($Y6uJ1RX56yXz2O 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zsw2a`GdwH)dUeK8JmKNvvdeXzxL3W3(o)&9QC6T^8C@X8Mw;d}->yf*^2I*+&7jb4 zG8sL%?83x|4w;4gyS|x<+zgAnI~_uZ(r8X!UqPwi1uh9VZ=GNWcwY$#9H9I^7G~q$ zD)v9Wi>nH|u^>$cLf)7r4O`sbRL?07KG?vsFYHFE3U!(?0T!c>>f{9kb{=u;+~%#BBV4~3#5QURFDfA*@nn}oVc@D(>f^^$&pm@#Ha}vSe-Q*!roB%{vd2u%CNo@jWOwV-U(f bJ*}XXRwz!2{y$jYKhePdHhJ2Mf6V+3HRn}U From b5d4c5d166eca0472bcf20cb5b19f763c8b3e035 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 25 Mar 2024 11:49:20 +1100 Subject: [PATCH 03/12] Use precompiled pip --- .gitignore | 1 + Dockerfile | 5 +- requirements.txt | 760 +++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 764 insertions(+), 2 deletions(-) create mode 100644 requirements.txt diff --git a/.gitignore b/.gitignore index 9bce7cc..6018e4d 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ !*.yaml !*.yml !*.in +!*.txt !**.github/workflows !*.gitignore !*.dockerignore diff --git a/Dockerfile b/Dockerfile index 390f3bf..4f5192d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -29,8 +29,9 @@ RUN pip install pip-tools # Pip installation RUN mkdir -p /conf -COPY requirements.in /conf/ -RUN pip-compile --extra-index-url=https://packages.dea.ga.gov.au/ --output-file=/conf/requirements.txt /conf/requirements.in +# COPY requirements.in /conf/ +# RUN pip-compile --extra-index-url=https://packages.dea.ga.gov.au/ --output-file=/conf/requirements.txt /conf/requirements.in +COPY requirements.txt /conf/ RUN pip install -r /conf/requirements.txt \ && pip install --no-cache-dir awscli diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..0dbb5e4 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,760 @@ +# +# This file is autogenerated by pip-compile with Python 3.10 +# by the following command: +# +# pip-compile --extra-index-url=https://packages.dea.ga.gov.au/ --output-file=requirements.txt requirements.in +# +--extra-index-url https://packages.dea.ga.gov.au/ + +affine==2.4.0 + # via + # datacube + # datacube-ows + # eodatasets3 + # odc-algo + # odc-geo + # odc-stac + # rasterio + # rasterstats +aiohttp==3.9.3 + # via + # -r requirements.in + # dea-tools +aiosignal==1.3.1 + # via aiohttp +annotated-types==0.6.0 + # via pydantic +asttokens==2.4.1 + # via stack-data +async-timeout==4.0.3 + # via aiohttp +attrs==23.2.0 + # via + # aiohttp + # cattrs + # datacube + # eodatasets3 + # fiona + # jsonschema + # rasterio + # referencing +babel==2.14.0 + # via + # datacube-ows + # flask-babel +blinker==1.7.0 + # via flask +boltons==23.1.1 + # via eodatasets3 +boto3==1.34.69 + # via + # datacube + # dea-tools + # eodatasets3 +botocore==1.34.69 + # via + # -r requirements.in + # boto3 + # datacube + # dea-tools + # eodatasets3 + # s3transfer +bottleneck==1.3.8 + # via + # datacube + # xskillscore +branca==0.7.1 + # via + # dea-tools + # folium + # ipyleaflet +cachetools==5.3.3 + # via + # datacube + # odc-geo +cattrs==23.2.3 + # via eodatasets3 +certifi==2024.2.2 + # via + # fiona + # netcdf4 + # pyproj + # rasterio + # requests +cffi==1.16.0 + # via timezonefinder +cftime==1.6.3 + # via + # netcdf4 + # xskillscore +charset-normalizer==3.3.2 + # via requests +ciso8601==2.3.1 + # via + # datacube + # dea-tools + # eodatasets3 +click==8.1.3 + # via + # -r requirements.in + # click-plugins + # cligj + # dask + # datacube + # datacube-ows + # distributed + # eodatasets3 + # fiona + # flask + # planetary-computer + # rasterio + # rasterstats +click-plugins==1.1.1 + # via + # fiona + # rasterio +cligj==0.7.2 + # via + # fiona + # rasterio + # rasterstats +cloudpickle==3.0.0 + # via + # dask + # dask-glm + # datacube + # distributed +colour==0.1.5 + # via datacube-ows +comm==0.2.2 + # via ipywidgets +contourpy==1.2.0 + # via matplotlib +coverage[toml]==7.4.4 + # via pytest-cov +cycler==0.12.1 + # via matplotlib +dask[array]==2024.3.1 + # via + # dask-glm + # dask-image + # dask-ml + # datacube + # dea-tools + # distributed + # odc-algo + # odc-stac + # xhistogram + # xskillscore +dask-glm==0.3.2 + # via dask-ml +dask-image==2023.3.0 + # via odc-algo +dask-ml==1.0.0 + # via dea-tools +datacube[performance,s3]==1.8.13 + # via + # -r requirements.in + # datacube-ows + # dea-tools + # eodatasets3 + # odc-algo + # odc-ui +datacube-ows==1.8.39 + # via dea-tools +dea-tools==0.3.2.dev54 + # via -r requirements.in +decorator==5.1.1 + # via ipython +deepdiff==6.7.1 + # via datacube-ows +defusedxml==0.7.1 + # via eodatasets3 +distributed==2024.3.1 + # via + # dask-glm + # dask-ml + # datacube + # odc-algo +eodatasets3==0.30.2 + # via -r requirements.in +exceptiongroup==1.2.0 + # via + # cattrs + # ipython + # pytest +executing==2.0.1 + # via stack-data +fiona==1.9.6 + # via + # dea-tools + # eodatasets3 + # geopandas + # rasterstats +flask==3.0.2 + # via + # datacube-ows + # flask-babel + # prometheus-flask-exporter +flask-babel==4.0.0 + # via datacube-ows +folium==0.16.0 + # via dea-tools +fonttools==4.50.0 + # via matplotlib +frozenlist==1.4.1 + # via + # aiohttp + # aiosignal +fsspec==2024.3.1 + # via + # dask + # datacube-ows +geoalchemy2==0.14.6 + # via + # datacube + # datacube-ows +geographiclib==2.0 + # via geopy +geopandas==0.13.2 + # via + # -r requirements.in + # dea-tools +geopy==2.4.1 + # via dea-tools +greenlet==3.0.3 + # via sqlalchemy +h3==3.7.7 + # via timezonefinder +h5py==3.10.0 + # via eodatasets3 +hdstats==0.2.1 + # via dea-tools +idna==3.6 + # via + # requests + # yarl +imageio==2.34.0 + # via + # pims + # scikit-image +importlib-metadata==7.1.0 + # via dask +iniconfig==2.0.0 + # via pytest +ipyleaflet==0.18.2 + # via odc-ui +ipython==8.22.2 + # via + # ipywidgets + # jupyter-ui-poll + # odc-ui +ipywidgets==8.1.2 + # via + # ipyleaflet + # odc-ui +iso8601==2.1.0 + # via pyows +itsdangerous==2.1.2 + # via flask +jedi==0.19.1 + # via ipython +jinja2==3.1.3 + # via + # branca + # distributed + # flask + # flask-babel + # folium +jmespath==1.0.1 + # via + # boto3 + # botocore +joblib==1.3.2 + # via + # dea-tools + # scikit-learn +jsonschema==4.21.1 + # via + # datacube + # eodatasets3 + # pystac +jsonschema-specifications==2023.12.1 + # via jsonschema +jupyter-ui-poll==0.2.2 + # via odc-ui +jupyterlab-widgets==3.0.10 + # via ipywidgets +kiwisolver==1.4.5 + # via matplotlib +lark==1.1.9 + # via + # datacube + # datacube-ows +llvmlite==0.42.0 + # via numba +locket==1.0.0 + # via + # distributed + # partd +lxml==5.1.0 + # via + # datacube-ows + # dea-tools + # owslib + # pyows + # pytmd +markupsafe==2.1.5 + # via + # jinja2 + # werkzeug +matplotlib==3.7.1 + # via + # -r requirements.in + # datacube-ows + # dea-tools + # odc-ui + # seaborn +matplotlib-inline==0.1.6 + # via ipython +mdutils==1.6.0 + # via -r requirements.in +msgpack==1.0.8 + # via distributed +multidict==6.0.5 + # via + # aiohttp + # yarl +multipledispatch==1.0.0 + # via + # dask-glm + # dask-ml +netcdf4==1.6.5 + # via + # datacube + # pytmd +networkx==3.2.1 + # via scikit-image +numba==0.59.1 + # via + # dask-ml + # sparse + # xskillscore +numexpr==2.9.0 + # via odc-algo +numpy==1.24.3 + # via + # -r requirements.in + # bottleneck + # cftime + # contourpy + # dask + # dask-image + # dask-ml + # datacube + # datacube-ows + # dea-tools + # eodatasets3 + # folium + # h5py + # hdstats + # imageio + # matplotlib + # netcdf4 + # numba + # numexpr + # odc-algo + # odc-geo + # odc-stac + # odc-ui + # pandas + # pims + # properscoring + # pygeos + # pytmd + # pywavelets + # rasterio + # rasterstats + # rioxarray + # scikit-image + # scikit-learn + # scipy + # seaborn + # shapely + # snuggs + # sparse + # tifffile + # timezonefinder + # xarray + # xhistogram + # xskillscore +odc-algo==0.2.3 + # via + # -r requirements.in + # odc-ui +odc-geo==0.4.3 + # via + # -r requirements.in + # dea-tools + # odc-stac +odc-stac==0.3.9 + # via dea-tools +odc-ui==0.2.1 + # via + # -r requirements.in + # dea-tools +ordered-set==4.1.0 + # via deepdiff +owslib==0.30.0 + # via dea-tools +packaging==24.0 + # via + # dask + # dask-ml + # datacube + # dea-tools + # distributed + # geoalchemy2 + # geopandas + # matplotlib + # planetary-computer + # pytest + # rioxarray + # scikit-image + # setuptools-scm + # xarray +pandas==1.5.3 + # via + # -r requirements.in + # dask-ml + # datacube + # dea-tools + # geopandas + # odc-stac + # odc-ui + # seaborn + # sunriset + # xarray +parso==0.8.3 + # via jedi +partd==1.4.1 + # via dask +pexpect==4.9.0 + # via ipython +pillow==10.2.0 + # via + # datacube-ows + # imageio + # matplotlib + # scikit-image +pims==0.6.1 + # via dask-image +planetary-computer==1.0.0 + # via dea-tools +pluggy==1.4.0 + # via pytest +prometheus-client==0.20.0 + # via prometheus-flask-exporter +prometheus-flask-exporter==0.23.0 + # via datacube-ows +prompt-toolkit==3.0.43 + # via ipython +properscoring==0.1 + # via xskillscore +psutil==5.9.8 + # via distributed +psycopg2==2.9.9 + # via + # datacube + # datacube-ows +ptyprocess==0.7.0 + # via pexpect +pure-eval==0.2.2 + # via stack-data +pycparser==2.21 + # via cffi +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 + # via datacube-ows +pyparsing==3.1.2 + # via + # datacube-ows + # matplotlib + # snuggs +pyproj==3.4.1 + # via + # -r requirements.in + # datacube + # dea-tools + # eodatasets3 + # geopandas + # odc-geo + # pytmd + # rioxarray +pystac[validation]==1.9.0 + # via + # eodatasets3 + # odc-stac + # planetary-computer + # pystac-client +pystac-client==0.7.6 + # via + # dea-tools + # planetary-computer +pytest==8.1.1 + # via + # -r requirements.in + # pytest-cov + # pytest-dependency +pytest-cov==5.0.0 + # via -r requirements.in +pytest-dependency==0.6.0 + # via -r requirements.in +python-dateutil==2.9.0.post0 + # via + # botocore + # datacube + # datacube-ows + # dea-tools + # matplotlib + # owslib + # pandas + # pystac + # pystac-client + # pytmd +python-dotenv==1.0.1 + # via planetary-computer +python-rapidjson==1.16 + # via eodatasets3 +python-slugify==8.0.4 + # via datacube-ows +pytmd==2.0.5 + # via + # -r requirements.in + # dea-tools +pytz==2023.3 + # via + # -r requirements.in + # datacube-ows + # dea-tools + # flask-babel + # owslib + # pandas + # planetary-computer + # sunriset +pywavelets==1.5.0 + # via scikit-image +pyyaml==6.0.1 + # via + # dask + # datacube + # distributed + # owslib + # pytmd +rasterio==1.3.4 + # via + # -r requirements.in + # datacube + # datacube-ows + # dea-tools + # eodatasets3 + # odc-algo + # odc-stac + # odc-ui + # rasterstats + # rioxarray +rasterstats==0.19.0 + # via dea-tools +referencing==0.34.0 + # via + # jsonschema + # jsonschema-specifications +regex==2023.12.25 + # via datacube-ows +requests==2.31.0 + # via + # datacube-ows + # dea-tools + # folium + # owslib + # planetary-computer + # pystac-client +rioxarray==0.15.1 + # via + # -r requirements.in + # dea-tools +rpds-py==0.18.0 + # via + # jsonschema + # referencing +ruamel-yaml==0.18.6 + # via + # datacube + # eodatasets3 +ruamel-yaml-clib==0.2.8 + # via ruamel-yaml +s3transfer==0.10.1 + # via boto3 +scikit-image==0.19.3 + # via + # -r requirements.in + # dea-tools + # odc-algo +scikit-learn==1.2.2 + # via + # -r requirements.in + # dask-glm + # dask-ml + # dea-tools + # xskillscore +scipy==1.10.1 + # via + # -r requirements.in + # dask-glm + # dask-image + # dask-ml + # datacube-ows + # dea-tools + # eodatasets3 + # hdstats + # properscoring + # pytmd + # scikit-image + # scikit-learn + # sparse + # xskillscore +seaborn==0.13.0 + # via -r requirements.in +setuptools-scm==8.0.4 + # via + # datacube-ows + # pytmd +shapely==2.0.1 + # via + # -r requirements.in + # datacube + # dea-tools + # eodatasets3 + # geopandas + # odc-geo + # rasterstats +simplejson==3.19.2 + # via rasterstats +six==1.16.0 + # via + # asttokens + # fiona + # python-dateutil +slicerator==1.1.0 + # via pims +snuggs==1.4.7 + # via rasterio +sortedcontainers==2.4.0 + # via distributed +sparse==0.15.1 + # via dask-glm +sqlalchemy==1.4.52 + # via + # datacube + # geoalchemy2 +stack-data==0.6.3 + # via ipython +structlog==24.1.0 + # via eodatasets3 +sunriset==1.0 + # via -r requirements.in +tblib==3.0.0 + # via distributed +text-unidecode==1.3 + # via python-slugify +threadpoolctl==3.4.0 + # via scikit-learn +tifffile==2024.2.12 + # via + # dask-image + # scikit-image +timezonefinder==6.5.0 + # via datacube-ows +tomli==2.0.1 + # via + # coverage + # pytest + # setuptools-scm +toolz==0.12.1 + # via + # dask + # datacube + # distributed + # odc-algo + # odc-stac + # partd + # xskillscore +tornado==6.4 + # via distributed +tqdm==4.65.0 + # via + # -r requirements.in + # dea-tools +traitlets==5.14.2 + # via + # comm + # ipython + # ipywidgets + # matplotlib-inline + # traittypes +traittypes==0.2.1 + # via ipyleaflet +typing-extensions==4.10.0 + # via + # cattrs + # pydantic + # pydantic-core + # setuptools-scm +urllib3==2.2.1 + # via + # botocore + # distributed + # requests +wcwidth==0.2.13 + # via prompt-toolkit +werkzeug==3.0.1 + # via flask +widgetsnbextension==4.0.10 + # via ipywidgets +xarray==2023.1.0 + # via + # -r requirements.in + # datacube + # datacube-ows + # dea-tools + # eodatasets3 + # odc-algo + # odc-stac + # odc-ui + # rioxarray + # xhistogram + # xskillscore +xhistogram==0.3.2 + # via xskillscore +xskillscore==0.0.24 + # via -r requirements.in +xyzservices==2023.10.1 + # via + # folium + # ipyleaflet +yarl==1.9.4 + # via aiohttp +zict==3.0.0 + # via distributed +zipp==3.18.1 + # via importlib-metadata + +# The following packages are considered to be unsafe in a requirements file: +# setuptools From 8109edf92702e19a143681281e588fc4b9b2e5ce Mon Sep 17 00:00:00 2001 From: robbibt Date: Mon, 25 Mar 2024 01:04:58 +0000 Subject: [PATCH 04/12] Automatically update integration test validation results --- tests/README.md | 2 +- tests/validation.csv | 1 + tests/validation.jpg | Bin 72109 -> 72097 bytes 3 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/README.md b/tests/README.md index 37b360c..4671a48 100644 --- a/tests/README.md +++ b/tests/README.md @@ -10,7 +10,7 @@ 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 **2024-03-25 11:32**. Compared to the previous run, it had an: +The latest integration test completed at **2024-03-25 12:04**. Compared to the previous run, it had an: - RMSE accuracy of **0.14 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)** diff --git a/tests/validation.csv b/tests/validation.csv index 2b46be3..a79fcd9 100644 --- a/tests/validation.csv +++ b/tests/validation.csv @@ -49,3 +49,4 @@ time,Correlation,RMSE,MAE,R-squared,Bias,Regression slope 2024-03-14 01:50:20.512235+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-22 03:25:50.523558+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-25 00:32:00.748385+00:00,0.975,0.141,0.121,0.95,0.116,1.11 +2024-03-25 01:04:32.512436+00:00,0.975,0.141,0.121,0.95,0.116,1.11 diff --git a/tests/validation.jpg b/tests/validation.jpg index be3103f1c7a3587444fa142991a3ed2437bbb6de..af2df53d9c0e41e36c315e8fe9a2fc64b2bf9805 100644 GIT binary patch delta 53360 zcmc$_bx>T<7be(9@E}2gy9IaG5G*(ZcS&$}eMoQ%5Fo(`?m-%NcbCvO1h;O2G`{?1 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z_OT^ja*9jgkIM+nttT^gX(PK6WCN}HOcW-(y0a4eo1nu#`!hHZFUK?DUC}VOamRjt zh1QP3b^vb;35(xR6R@Bz%FGyA&Zt+N;3UxA9icCeQjd5Can6!JC&ru}FxgJfZCZgn zMqA-2cs6GdCFVJ$ANeTSkgC84I7o^1dEi5GqD}f}l#>ZZ4E7l6QkP7aL|GRlx5pv2 zPj+t0GrO03iZsAnZ&m1;-WFT+RNAJvAk-@NN7<7jvC`mxRU);cic|iYqWl|_;|sk2 z+R+|Rj!BT-Y@4?{19`^y{ zFGs`(+CWZ*KB9Uiq2AWH0m2w8p1BgaE|-2Tx~=9R??kA!?)T{YP!FZjh^^;$AJJX$ zC|_2_t1j{R$ag~ZSbM$*Pwm)4_GKaa~rqdR#6)Qek zRPl4&V^0}&6aSg9Eei&)7LB?8S#74i?Q@?IH)C*Y+*{p`W`vpLPE39PSje!zm zeP6X*g3gHopKowE%>>w9i4uh^W#9L)6CvI^tQ@wfykpU=N|wXYa<22=ARyH#W6Nu> zs=y|qF9;y%1{nb?mJ4Azhl`(dZ=khf%Q*M77cz!~RB}YIkr5k5IHdXe}3&kQlh&{NbANdg3fpMIuFRoT(ta%*{AT z+JLRbR4%By7QF_=TbtaBN07lmp&T((5;V2PjZee+fwS)*awu28+Xu@MvT@+~5-8kG z@J@Ftn48Xkl9?sXjG^uKtEFJyt>4cJkG=0ZuC8KbgPT;qHT+V;Ks@wWlx?a$MzR7f z(=*`Dc?@LJ#1%?kDHOVpX=qC57X`dyinAn9w5ard7%(5i`?I-rvWK|r!QzjEKIQK` z&k7a#nxm~LX?)y671e5D+y50e(jMOyQf(v@lQ zb*D#E{5t0feqr>&eC<=T*IX$5UrwVhN{{Wn&n z8~u9UgzZa_P`C_E*k`=t*(B_M*gDT-rH%X~%54WI>NsAdN{~|`)$6+xZ+WQEZ z7UnW5oB8t?He-YuR53uaqD?}ISo89CWBOsBsX9c+7s%7@KS4o#5IG_siazH?q5xQg8;?^RAZ eEm|s8Aq Date: Mon, 25 Mar 2024 05:37:20 +0000 Subject: [PATCH 05/12] Temporarily remove extents, add ocean masking --- intertidal/elevation.py | 53 +- intertidal/extents.py | 67 +- intertidal/io.py | 110 +-- intertidal/tidal_bias_offset.py | 22 +- metadata/eo3_intertidal.odc-type.yaml | 26 +- ...a_s2ls_intertidal_cyear_3.odc-product.yaml | 16 - notebooks/Intertidal_CLI.ipynb | 130 +-- notebooks/Intertidal_elevation.ipynb | 908 ++++++++---------- 8 files changed, 617 insertions(+), 715 deletions(-) diff --git a/intertidal/elevation.py b/intertidal/elevation.py index 11777da..a483dbe 100644 --- a/intertidal/elevation.py +++ b/intertidal/elevation.py @@ -22,6 +22,7 @@ load_data, load_topobathy_mask, load_aclum_mask, + load_ocean_mask, prepare_for_export, tidal_metadata, export_dataset_metadata, @@ -31,7 +32,7 @@ round_date_strings, ) from intertidal.tide_modelling import pixel_tides_ensemble -from intertidal.extents import extents +from intertidal.extents import extents, ocean_connection from intertidal.exposure import exposure from intertidal.tidal_bias_offset import bias_offset @@ -766,6 +767,7 @@ 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, @@ -794,6 +796,12 @@ 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. @@ -953,6 +961,12 @@ 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 + 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 @@ -1191,17 +1205,18 @@ def intertidal_cli( ) satellite_ds.load() - # Load topobathy mask from GA's AusBathyTopo 250m 2023 Grid - topobathy_mask = load_topobathy_mask(dc, satellite_ds.odc.geobox.compat) - - # Load urban land use class mask from ABARES CLUM + # 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) + # Also load ancillary dataset IDs to use in metadata # (both layers are continental continental products with only # a single dataset, so no need for a spatial/temporal query) dss_ancillary = dc.find_datasets( - product=["ga_ausbathytopo250m_2023", "abares_clum_2020"] + product=["ga_ausbathytopo250m_2023", "abares_clum_2020",] ) # Calculate elevation @@ -1209,6 +1224,7 @@ def intertidal_cli( ds, tide_m = elevation( satellite_ds, valid_mask=topobathy_mask, + ocean_mask=ocean_mask, ndwi_thresh=ndwi_thresh, min_freq=min_freq, max_freq=max_freq, @@ -1222,17 +1238,17 @@ def intertidal_cli( log=log, ) - # Calculate extents - log.info(f"{run_id}: Calculating Intertidal Extents") - ds["extents"] = extents( - dem=ds.elevation, - freq=ds.qa_ndwi_freq, - corr=ds.qa_ndwi_corr, - reclassified_aclum=reclassified_aclum, - min_freq=min_freq, - max_freq=max_freq, - min_correlation=min_correlation, - ) + # # Calculate extents (to be included in next version) + # log.info(f"{run_id}: Calculating Intertidal Extents") + # ds["extents"] = extents( + # dem=ds.elevation, + # freq=ds.qa_ndwi_freq, + # corr=ds.qa_ndwi_corr, + # reclassified_aclum=reclassified_aclum, + # min_freq=min_freq, + # max_freq=max_freq, + # min_correlation=min_correlation, + # ) if exposure_offsets: log.info(f"{run_id}: Calculating Intertidal Exposure") @@ -1267,7 +1283,6 @@ def intertidal_cli( ) = bias_offset( tide_m=tide_m, tide_cq=tide_cq, - extents=ds.extents, lot_hot=True, lat_hat=True, ) diff --git a/intertidal/extents.py b/intertidal/extents.py index e1d0e50..6a8f1bc 100644 --- a/intertidal/extents.py +++ b/intertidal/extents.py @@ -248,44 +248,45 @@ def extents( return extents -# from rasterio.features import sieve +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. -# def ocean_connection(water, ocean_da, connectivity=1): -# """ -# 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. -# """ + 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 -# # 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 +# from rasterio.features import sieve # def extents_ocean_masking( diff --git a/intertidal/io.py b/intertidal/io.py index 1ad32e3..9590626 100644 --- a/intertidal/io.py +++ b/intertidal/io.py @@ -622,61 +622,61 @@ 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 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): diff --git a/intertidal/tidal_bias_offset.py b/intertidal/tidal_bias_offset.py index 3d60859..f06a87c 100644 --- a/intertidal/tidal_bias_offset.py +++ b/intertidal/tidal_bias_offset.py @@ -4,7 +4,7 @@ from dea_tools.spatial import subpixel_contours, points_on_line -def bias_offset(tide_m, tide_cq, extents, lat_hat=True, lot_hot=None): +def bias_offset(tide_m, tide_cq, lat_hat=True, lot_hot=None): """ Calculate the pixel-based sensor-observed spread and high/low offsets in tide heights compared to the full modelled tide range. @@ -21,10 +21,6 @@ def bias_offset(tide_m, tide_cq, extents, lat_hat=True, lot_hot=None): An xarray.DataArray representing modelled tidal heights for each pixel. Should have 'quantile', 'x' and 'y' in its dimensions. - extents : xr.DataArray - An xarray.DataArray representing 5 ecosystem class extents - of the intertidal zone. Should have the same - dimensions as `tide_m` and `tide_cq`. lat_hat : bool, optional Lowest/highest astronomical tides. This work considers the modelled tides to be equivalent to the astronomical tides. @@ -67,22 +63,16 @@ def bias_offset(tide_m, tide_cq, extents, lat_hat=True, lot_hot=None): # heights as a percentage of the modelled highest and lowest tides. offset_hightide = (abs(max_mod - max_obs)) / mod_range * 100 offset_lowtide = (abs(min_mod - min_obs)) / mod_range * 100 - + # Add the lowest and highest astronomical tides - valid_mask = extents.isin([5, 4, 3]) if lat_hat: - lat = min_mod.where(valid_mask) - hat = max_mod.where(valid_mask) + lat = min_mod + hat = max_mod # Add the lowest and highest sensor-observed tides if lot_hot: - lot = min_obs.where(valid_mask) - hot = max_obs.where(valid_mask) - - # Mask out non-intertidal pixels using ds extents - spread = spread.where(valid_mask) - offset_hightide = offset_hightide.where(valid_mask) - offset_lowtide = offset_lowtide.where(valid_mask) + lot = min_obs + hot = max_obs if lat_hat: if lot_hot: diff --git a/metadata/eo3_intertidal.odc-type.yaml b/metadata/eo3_intertidal.odc-type.yaml index 8197f4d..a79ac55 100644 --- a/metadata/eo3_intertidal.odc-type.yaml +++ b/metadata/eo3_intertidal.odc-type.yaml @@ -70,15 +70,7 @@ dataset: - eo:gsd type: double - # Intertidal-specific metadata below - intertidal_tr_class: - description: | - Tide range classification - one of microtidal|mesotidal|macrotidal - indexed: false - offset: - - properties - - intertidal:tr_class - + # Intertidal-specific metadata below intertidal_hat: description: | Highest astronomical tide height (metres above Mean Sea Level) @@ -144,6 +136,15 @@ dataset: - properties - intertidal:spread + intertidal_otr: + description: | + Observed tide range (difference between highest and lowest observed tides) + indexed: false + type: double + offset: + - properties + - intertidal:otr + intertidal_tr: description: | Tide range (difference between highest and lowest astronomical tides) @@ -153,11 +154,10 @@ dataset: - properties - intertidal:tr - intertidal_otr: + intertidal_tr_class: description: | - Observed tide range (difference between highest and lowest observed tides) + Tide range classification - one of microtidal|mesotidal|macrotidal indexed: false - type: double offset: - properties - - intertidal:otr \ No newline at end of file + - intertidal:tr_class \ No newline at end of file diff --git a/metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml b/metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml index 5978349..5c2b338 100644 --- a/metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml +++ b/metadata/ga_s2ls_intertidal_cyear_3.odc-product.yaml @@ -31,22 +31,6 @@ measurements: units: "percent" nodata: 255 - - name: extents - dtype: uint8 - units: "class" - nodata: 255 - flags_definition: - extents: - description: Intertidal extents class - bits: [0, 1, 2, 3, 4, 5, 6, 7] - values: - 0: Dry - 1: Inland intermittent wet - 2: Inland persistent wet - 3: Tidal influenced persistent wet - 4: Intertidal low confidence - 5: Intertidal high confidence - - name: ta_hat dtype: float32 units: "metres above MSL" diff --git a/notebooks/Intertidal_CLI.ipynb b/notebooks/Intertidal_CLI.ipynb index ed940a1..12676c4 100644 --- a/notebooks/Intertidal_CLI.ipynb +++ b/notebooks/Intertidal_CLI.ipynb @@ -188,6 +188,17 @@ " --window_prop_tide FLOAT Proportion of the tide range to use for each\n", " window radius in the per-pixel rolling\n", " median calculation, by default 0.15.\n", + " --correct_seasonality / --no-correct_seasonality\n", + " If True, remove any seasonal signal from the\n", + " tide height data by subtracting monthly mean\n", + " tide height from each value prior to\n", + " correlation calculations. This can reduce\n", + " false tide correlations in regions where\n", + " tide heights correlate with seasonal changes\n", + " in surface water. Note that seasonally\n", + " corrected tides are only used to identify\n", + " potentially tide influenced pixels - not for\n", + " elevation modelling itself.\n", " --tide_model TEXT The model used for tide modelling, as\n", " supported by the `pyTMD` Python package.\n", " Options include 'FES2014' (default),\n", @@ -235,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "1da1e270-43bd-4a11-9d5b-6be26cb1a74f", "metadata": { "tags": [] @@ -245,82 +256,81 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-03-08 03:57:00 INFO [0.0.1] [2023] [testing]: Loading satellite data\n", - "\n", - "2024-03-08 03:57:04 INFO [0.0.1] [2023] [testing]: Running in testing mode using custom study area\n", - "2024-03-08 03:57:26 INFO [0.0.1] [2023] [testing]: Calculating Intertidal Elevation\n", - "2024-03-08 03:57:26 INFO [0.0.1] [2023] [testing]: Modelling tide heights for each pixel\n", + "2024-03-25 04:57:07 INFO [0.0.1] [2023] [testing]: Loading satellite data\n", + "/env/lib/python3.10/site-packages/distributed/node.py:183: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 37479 instead\n", + " warnings.warn(\n", + "\n", + "2024-03-25 04:57:11 INFO [0.0.1] [2023] [testing]: Running in testing mode using custom study area\n", + "2024-03-25 04:57:46 INFO [0.0.1] [2023] [testing]: Calculating Intertidal Elevation\n", + "2024-03-25 04:57:46 INFO [0.0.1] [2023] [testing]: Modelling tide heights for each pixel\n", "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", - "100%|███████████████████████████████████████████| 35/35 [00:19<00:00, 1.83it/s]\n", + "100%|███████████████████████████████████████████| 35/35 [00:17<00:00, 1.97it/s]\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", - "GOT4.10 0.472108\n", - "EOT20 0.472078\n", - "TPXO9-atlas-v5 0.471788\n", - "FES2014 0.470052\n", - "FES2012 0.464148\n", - "HAMTIDE11 0.455962\n", - "TPXO8-atlas-v1 0.448392\n", - "Reducing multiple model outputs using 'mean'\n", - "Reprojecting tides into original array\n", - "2024-03-08 03:57:47 INFO [0.0.1] [2023] [testing]: Masking nodata and adding tide heights to satellite data array\n", - "2024-03-08 03:57:47 INFO [0.0.1] [2023] [testing]: Flattening satellite data array and filtering to intertidal candidate pixels\n", - "2024-03-08 03:57:47 INFO [0.0.1] [2023] [testing]: Applying valid data mask to constrain study area\n", - "Reducing analysed pixels from 7125 to 5719 (80.27%)\n", - "2024-03-08 03:57:47 INFO [0.0.1] [2023] [testing]: Running per-pixel rolling median\n", - "100%|█████████████████████████████████████████| 105/105 [00:01<00:00, 73.75it/s]\n", - "2024-03-08 03:57:49 INFO [0.0.1] [2023] [testing]: Modelling intertidal elevation\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", + "2024-03-25 04:58:06 INFO [0.0.1] [2023] [testing]: Masking nodata and adding tide heights to satellite data array\n", + "2024-03-25 04:58:06 INFO [0.0.1] [2023] [testing]: Flattening satellite data array and filtering to intertidal candidate pixels\n", + "2024-03-25 04:58:06 INFO [0.0.1] [2023] [testing]: Applying valid data mask to constrain study area\n", + "Reducing analysed pixels from 7125 to 5673 (79.62%)\n", + "2024-03-25 04:58:06 INFO [0.0.1] [2023] [testing]: Running per-pixel rolling median\n", + "100%|█████████████████████████████████████████| 105/105 [00:01<00:00, 66.46it/s]\n", + "2024-03-25 04:58:08 INFO [0.0.1] [2023] [testing]: Modelling intertidal elevation\n", "Applying tidal interval interpolation to 200 intervals\n", "Applying rolling mean smoothing with radius 20\n", - "2024-03-08 03:57:49 INFO [0.0.1] [2023] [testing]: Modelling intertidal uncertainty\n", - "2024-03-08 03:57:49 INFO [0.0.1] [2023] [testing]: Unflattening data back to its original spatial dimensions\n", - "2024-03-08 03:57:49 INFO [0.0.1] [2023] [testing]: Cleaning inaccurate upper intertidal pixels\n", - "2024-03-08 03:57:49 INFO [0.0.1] [2023] [testing]: Successfully completed intertidal elevation modelling\n", - "2024-03-08 03:57:49 INFO [0.0.1] [2023] [testing]: Calculating Intertidal Extents\n", - "2024-03-08 03:57:49 INFO [0.0.1] [2023] [testing]: Calculating Intertidal Exposure\n", - "2024-03-08 03:57:49 INFO [0.0.1] [2023] [testing]: Modelling tide heights for each pixel\n", + "2024-03-25 04:58:08 INFO [0.0.1] [2023] [testing]: Modelling intertidal uncertainty\n", + "2024-03-25 04:58:08 INFO [0.0.1] [2023] [testing]: Unflattening data back to its original spatial dimensions\n", + "2024-03-25 04:58:08 INFO [0.0.1] [2023] [testing]: Cleaning inaccurate upper intertidal pixels\n", + "2024-03-25 04:58:08 INFO [0.0.1] [2023] [testing]: Restricting outputs to ocean-connected waters\n", + "2024-03-25 04:58:08 INFO [0.0.1] [2023] [testing]: Successfully completed intertidal elevation modelling\n", + "2024-03-25 04:58:09 INFO [0.0.1] [2023] [testing]: Calculating Intertidal Exposure\n", + "2024-03-25 04:58:09 INFO [0.0.1] [2023] [testing]: Modelling tide heights for each pixel\n", "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", - "100%|███████████████████████████████████████████| 35/35 [00:19<00:00, 1.81it/s]\n", + "100%|███████████████████████████████████████████| 35/35 [00:18<00:00, 1.94it/s]\n", "Computing tide quantiles\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", - "GOT4.10 0.472108\n", - "EOT20 0.472078\n", - "TPXO9-atlas-v5 0.471788\n", - "FES2014 0.470052\n", - "FES2012 0.464148\n", - "HAMTIDE11 0.455962\n", - "TPXO8-atlas-v1 0.448392\n", - "Reducing multiple model outputs using 'mean'\n", - "Reprojecting tides into original array\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Calculating spread, offset and HAT/LAT/LOT/HOT layers\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Assembling dataset\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Writing array elevation\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Writing array elevation_uncertainty\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Writing array qa_ndwi_corr\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Writing array qa_ndwi_freq\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Writing array extents\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Writing array exposure\n", - "2024-03-08 03:58:11 INFO [0.0.1] [2023] [testing]: Writing array ta_lat\n", - "2024-03-08 03:58:12 INFO [0.0.1] [2023] [testing]: Writing array ta_hat\n", - "2024-03-08 03:58:12 INFO [0.0.1] [2023] [testing]: Writing array ta_lot\n", - "2024-03-08 03:58:12 INFO [0.0.1] [2023] [testing]: Writing array ta_hot\n", - "2024-03-08 03:58:12 INFO [0.0.1] [2023] [testing]: Writing array ta_spread\n", - "2024-03-08 03:58:12 INFO [0.0.1] [2023] [testing]: Writing array ta_offset_low\n", - "2024-03-08 03:58:12 INFO [0.0.1] [2023] [testing]: Writing array ta_offset_high\n", - "2024-03-08 03:58:12 INFO [0.0.1] [2023] [testing]: Assembled dataset: /tmp/tmp9l1pja4f/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", - "2024-03-08 03:58:12 INFO [0.0.1] [2023] [testing]: Writing data locally: data/processed/ga_s2ls_intertidal_cyear_3/0-0-1/tes/ting/2023--P1Y/\n", - "2024-03-08 03:58:13 INFO [0.0.1] [2023] [testing]: Completed DEA Intertidal workflow\n", - "CPU times: user 867 ms, sys: 226 ms, total: 1.09 s\n", - "Wall time: 1min 16s\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", + "2024-03-25 04:58:29 INFO [0.0.1] [2023] [testing]: Calculating spread, offset and HAT/LAT/LOT/HOT layers\n", + "2024-03-25 04:58:29 INFO [0.0.1] [2023] [testing]: Assembling dataset\n", + "/env/lib/python3.10/site-packages/eodatasets3/properties.py:435: UserWarning: Unknown Stac property 'intertidal:tr_class'. If this is valid property, please tell us on Github here so we can add it: \n", + "\thttps://github.com/GeoscienceAustralia/eo-datasets/issues/new?title=Include+property+%27intertidal%3Atr_class%27&labels=known-properties&body=Hello%21+The+property+%27intertidal%3Atr_class%27+does+not+appear+to+be+in+the+KNOWN_PROPERTIES+list%2C%0Abut+I+believe+it+to+be+valid.%0A%0AAn+example+value+of+this+property+is%3A+%27mesotidal%27%0A%0AThank+you%21%0A\n", + " warnings.warn(\n", + "2024-03-25 04:58:29 INFO [0.0.1] [2023] [testing]: Writing output arrays\n", + "/env/lib/python3.10/site-packages/eodatasets3/assemble.py:937: IncompleteDatasetWarning: unknown_property: Unknown stac property 'intertidal:tr_class'\n", + " warnings.warn(IncompleteDatasetWarning(m))\n", + "2024-03-25 04:58:30 INFO [0.0.1] [2023] [testing]: Assembled dataset: /tmp/tmpounco2k1/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", + "2024-03-25 04:58:30 INFO [0.0.1] [2023] [testing]: Writing data locally: data/processed/ga_s2ls_intertidal_cyear_3/0-0-1/tes/ting/2023--P1Y/\n", + "2024-03-25 04:58:31 INFO [0.0.1] [2023] [testing]: Completed DEA Intertidal workflow\n", + "CPU times: user 814 ms, sys: 200 ms, total: 1.01 s\n", + "Wall time: 1min 27s\n" ] } ], diff --git a/notebooks/Intertidal_elevation.ipynb b/notebooks/Intertidal_elevation.ipynb index fcfe26a..5891dbc 100644 --- a/notebooks/Intertidal_elevation.ipynb +++ b/notebooks/Intertidal_elevation.ipynb @@ -83,7 +83,12 @@ "from dea_tools.dask import create_local_dask_cluster\n", "\n", "from intertidal.tide_modelling import pixel_tides_ensemble\n", - "from intertidal.io import load_data, load_topobathy_mask, prepare_for_export\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", " ds_to_flat,\n", " pixel_rolling_median,\n", @@ -93,7 +98,8 @@ " flat_to_ds,\n", " clean_edge_pixels,\n", " elevation,\n", - ")" + ")\n", + "from intertidal.extents import ocean_connection" ] }, { @@ -128,6 +134,9 @@ "end_date = \"2022\" # 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", + "max_freq = 0.99 # Maximum wetness freq required for pixel to be included in analysis\n", + "min_correlation = 0.15 # Minimum correlation between water index and tide height \n", "ndwi_thresh = 0.1 # Threshold used to identify dry/wet transition\n", "include_s2 = True # Include Sentinel-2 data in the analysis?\n", "include_ls = True # Include Landsat data in the analysis?\n", @@ -150,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "8c1dfca3-543d-4e07-9a0f-2eeddf582835", "metadata": {}, "outputs": [], @@ -172,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "e67929eb-8a55-4a15-be7a-fcda29ec1f66", "metadata": { "tags": [] @@ -200,14 +209,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "bdcf1c79-ae5a-4453-a7e8-d3f021b0b65a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e27e28f5a5db4ec296e30fdeb00e96e4", + "model_id": "0df7b8dd2f624378b5517dae2b60c01c", "version_major": 2, "version_minor": 0 }, @@ -221,13 +230,13 @@ { "data": { "image/svg+xml": [ - "" + "" ], "text/plain": [ - "Geometry(POLYGON ((131.86409 -12.2291, 131.86409 -12.184471, 131.912498 -12.184471, 131.912498 -12.2291, 131.86409 -12.2291)), EPSG:4326)" + "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": 6, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -252,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "98e930ff-c5a4-45fa-a043-b8902c606d63", "metadata": { "tags": [] @@ -265,7 +274,7 @@ "
\n", "
\n", "

Client

\n", - "

Client-300c2b16-dcfe-11ee-85c3-a647befe7275

\n", + "

Client-85e8529b-ea5e-11ee-8cdb-e23cd9911b2b

\n", " \n", "\n", " \n", @@ -300,7 +309,7 @@ " \n", "
\n", "

LocalCluster

\n", - "

e50b41f5

\n", + "

8e72b4bb

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -337,11 +346,11 @@ "
\n", "
\n", "

Scheduler

\n", - "

Scheduler-de259d81-50fb-48c7-832a-844dfbdf5a04

\n", + "

Scheduler-c7e0e95f-4053-454c-8edb-4138cf4cab1f

\n", "
\n", @@ -312,10 +321,10 @@ "
\n", - " Total threads: 31\n", + " Total threads: 62\n", " \n", - " Total memory: 237.21 GiB\n", + " Total memory: 477.21 GiB\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -360,7 +369,7 @@ " Started: Just now\n", " \n", " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:41033\n", + " Comm: tcp://127.0.0.1:44359\n", " \n", " Workers: 1\n", @@ -352,7 +361,7 @@ " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/8787/status\n", " \n", - " Total threads: 31\n", + " Total threads: 62\n", "
\n", - " Total memory: 237.21 GiB\n", + " Total memory: 477.21 GiB\n", "
\n", @@ -383,29 +392,29 @@ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -432,7 +441,7 @@ "" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -443,14 +452,14 @@ "output_type": "stream", "text": [ "\n", - "Dimensions: (time: 306, y: 486, x: 538)\n", + "Dimensions: (time: 341, y: 400, x: 476)\n", "Coordinates:\n", - " * time (time) datetime64[ns] 2020-01-02T00:57:27.736492 ... 2022-12...\n", - " * y (y) float64 -1.284e+06 -1.284e+06 ... -1.289e+06 -1.289e+06\n", - " * x (x) float64 -1.508e+04 -1.506e+04 ... -9.715e+03 -9.705e+03\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", "Data variables:\n", - " ndwi (time, y, x) float32 dask.array\n", + " ndwi (time, y, x) float32 dask.array\n", "Attributes:\n", " crs: EPSG:3577\n", " grid_mapping: spatial_ref\n" @@ -465,6 +474,8 @@ "/env/lib/python3.10/site-packages/rasterio/warp.py:344: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n", " _reproject(\n", "/env/lib/python3.10/site-packages/rasterio/warp.py:344: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n", + " _reproject(\n", + "/env/lib/python3.10/site-packages/rasterio/warp.py:344: NotGeoreferencedWarning: Dataset has no geotransform, gcps, or rpcs. The identity matrix will be returned.\n", " _reproject(\n" ] }, @@ -472,8 +483,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 7.43 s, sys: 761 ms, total: 8.19 s\n", - "Wall time: 54.7 s\n" + "CPU times: user 7.34 s, sys: 785 ms, total: 8.13 s\n", + "Wall time: 1min 3s\n" ] }, { @@ -843,20 +854,20 @@ " fill: currentColor;\n", "}\n", "
<xarray.Dataset>\n",
-       "Dimensions:      (time: 306, y: 486, x: 538)\n",
+       "Dimensions:      (time: 341, y: 400, x: 476)\n",
        "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 2020-01-02T00:57:27.736492 ... 2022-12...\n",
-       "  * y            (y) float64 -1.284e+06 -1.284e+06 ... -1.289e+06 -1.289e+06\n",
-       "  * x            (x) float64 -1.508e+04 -1.506e+04 ... -9.715e+03 -9.705e+03\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",
        "Data variables:\n",
-       "    ndwi         (time, y, x) float32 nan nan nan nan nan ... nan nan nan nan\n",
+       "    ndwi         (time, y, x) float32 nan nan nan ... -0.3259 -0.3428 -0.3729\n",
        "Attributes:\n",
        "    crs:           EPSG:3577\n",
-       "    grid_mapping:  spatial_ref
  • crs :
    EPSG:3577
    grid_mapping :
    spatial_ref
  • " ], "text/plain": [ "\n", - "Dimensions: (time: 306, y: 486, x: 538)\n", + "Dimensions: (time: 341, y: 400, x: 476)\n", "Coordinates:\n", - " * time (time) datetime64[ns] 2020-01-02T00:57:27.736492 ... 2022-12...\n", - " * y (y) float64 -1.284e+06 -1.284e+06 ... -1.289e+06 -1.289e+06\n", - " * x (x) float64 -1.508e+04 -1.506e+04 ... -9.715e+03 -9.705e+03\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", "Data variables:\n", - " ndwi (time, y, x) float32 nan nan nan nan nan ... nan nan nan nan\n", + " ndwi (time, y, x) float32 nan nan nan ... -0.3259 -0.3428 -0.3729\n", "Attributes:\n", " crs: EPSG:3577\n", " grid_mapping: spatial_ref" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -970,7 +981,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "92d23ccd-f088-4815-8a56-08bc0f69ccbe", "metadata": { "tags": [] @@ -988,14 +999,14 @@ "id": "93f45ad8-b23d-425a-91c1-a227b67d1372", "metadata": {}, "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": 8, + "execution_count": 7, "id": "b3348b1b-ea4c-4637-958e-c0d89212e25a", "metadata": { "tags": [] @@ -1013,6 +1024,31 @@ ")" ] }, + { + "cell_type": "markdown", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2a8449dd-967f-4f85-a52d-6ded977e3b89", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load mask identifying ocean pixels\n", + "ocean_mask = load_ocean_mask(\n", + " dc=dc,\n", + " geobox=satellite_ds.odc.geobox.compat,\n", + " product=\"geodata_coast_100k\",\n", + ")" + ] + }, { "cell_type": "markdown", "id": "c34949eb-96f1-4844-ad6e-aeb71399e9f5", @@ -1040,7 +1076,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 35/35 [00:19<00:00, 1.79it/s]\n" + "100%|██████████| 35/35 [00:19<00:00, 1.76it/s]\n" ] }, { @@ -1049,17 +1085,18 @@ "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 0.360616\n", - "EOT20 0.344405\n", - "FES2014 0.341572\n", - "TPXO9-atlas-v5 0.334541\n", - "GOT4.10 0.329943\n", - "TPXO8-atlas-v1 0.320947\n", - "FES2012 0.313632\n", - "Reducing multiple model outputs using 'mean'\n", - "Reprojecting tides into original array\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" ] } ], @@ -1080,17 +1117,6 @@ { "cell_type": "code", "execution_count": 10, - "id": "32fcc597-9145-4f0e-bd4e-cef454d4a919", - "metadata": {}, - "outputs": [], - "source": [ - "## Experimental: testing ebb flow filtering\n", - "# ebb_flow_da, tide_m_offset = pixel_ebb_flow(tide_m, offset_min=15)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, "id": "0dcac52f-78d5-41f3-81a4-199509949a96", "metadata": {}, "outputs": [], @@ -1127,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "457ee569-b862-4a4c-b04e-66b39c9fa931", "metadata": {}, "outputs": [ @@ -1135,9 +1161,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reducing analysed pixels from 261468 to 74374 (28.44%)\n", - "CPU times: user 1.95 s, sys: 1.21 s, total: 3.16 s\n", - "Wall time: 3.03 s\n" + "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" ] } ], @@ -1145,10 +1171,9 @@ "%%time\n", "flat_ds, freq, corr = ds_to_flat(\n", " satellite_ds,\n", - " ndwi_thresh=0.0,\n", - " min_freq=0.01,\n", - " max_freq=0.99,\n", - " min_correlation=0.15,\n", + " min_freq=min_freq,\n", + " max_freq=max_freq,\n", + " min_correlation=min_correlation,\n", " valid_mask=topobathy_mask,\n", ")" ] @@ -1168,7 +1193,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "230cb5d2-2d11-4d9a-a29d-d24d2fa44c18", "metadata": { "tags": [] @@ -1177,7 +1202,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5fdb78a7313b4c96b69a7fe81f26c1f2", + "model_id": "d63a67678d7e458db37d483057ed1b2f", "version_major": 2, "version_minor": 0 }, @@ -1192,8 +1217,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 13.3 s, sys: 22.5 s, total: 35.8 s\n", - "Wall time: 51.3 s\n" + "CPU times: user 22 s, sys: 38 s, total: 1min\n", + "Wall time: 1min 26s\n" ] } ], @@ -1226,7 +1251,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "c079977e-cf56-499e-b016-819d3cd5110c", "metadata": { "tags": [] @@ -1242,7 +1267,7 @@ }, { "data": { - "image/png": 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", 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", 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    " ] @@ -1252,7 +1277,7 @@ } ], "source": [ - "x, y = -12845.7,-1287223.4\n", + "x, y = -1455867.2,-2277114.2\n", "interval_pixel, interval_smoothed_pixel = pixel_dem_debug(\n", " x,\n", " y,\n", @@ -1275,7 +1300,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "0a8e0556-c698-4628-b0f3-cf35e722a293", "metadata": {}, "outputs": [ @@ -1301,7 +1326,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "cec14155-eeb5-4a78-a30f-54b329e6e7e2", "metadata": {}, "outputs": [], @@ -1329,7 +1354,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "1f1d3490-59aa-4001-8ea9-ea46f809c944", "metadata": { "tags": [] @@ -1351,12 +1376,16 @@ "# 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])" + "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)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "id": "75b41fb9-6271-4c8a-a1d5-f4800d03f789", "metadata": { "tags": [] @@ -1365,16 +1394,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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    " ] @@ -1386,147 +1415,19 @@ "source": [ "fix, axes = plt.subplots(1, 4, figsize=(12, 3))\n", "ds.elevation.plot.imshow(cmap=\"viridis\", ax=axes[0])\n", - "ds.elevation_uncertainty.plot.imshow(cmap=\"inferno\", ax=axes[1])\n", + "ds.elevation_uncertainty.plot.imshow(cmap=\"inferno\", vmin=0, vmax=0.5, ax=axes[1])\n", "ds.qa_ndwi_corr.plot.imshow(cmap=\"RdBu\", vmin=-0.7, vmax=0.7, ax=axes[2])\n", "ds.qa_ndwi_freq.plot.imshow(cmap=\"Blues\", vmin=0, vmax=1, ax=axes[3])" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "87202b19-b854-4140-9bb7-ca05d324901c", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
    Make this Notebook Trusted to load map: File -> Trust Notebook
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} } }, "version_major": 2, From d1287677dfa0327745057e4eb56251b553d4cf12 Mon Sep 17 00:00:00 2001 From: robbibt Date: Mon, 25 Mar 2024 05:51:05 +0000 Subject: [PATCH 06/12] Automatically update integration test validation results --- tests/README.md | 2 +- tests/validation.csv | 1 + tests/validation.jpg | Bin 72097 -> 72262 bytes 3 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/README.md b/tests/README.md index 4671a48..65e7e6d 100644 --- a/tests/README.md +++ b/tests/README.md @@ -10,7 +10,7 @@ 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 **2024-03-25 12:04**. Compared to the previous run, it had an: +The latest integration test completed at **2024-03-25 16:50**. Compared to the previous run, it had an: - RMSE accuracy of **0.14 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)** diff --git a/tests/validation.csv b/tests/validation.csv index a79fcd9..c8d3667 100644 --- a/tests/validation.csv +++ b/tests/validation.csv @@ -50,3 +50,4 @@ time,Correlation,RMSE,MAE,R-squared,Bias,Regression slope 2024-03-22 03:25:50.523558+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-25 00:32:00.748385+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-25 01:04:32.512436+00:00,0.975,0.141,0.121,0.95,0.116,1.11 +2024-03-25 05:50:44.245009+00:00,0.975,0.141,0.121,0.95,0.116,1.11 diff --git a/tests/validation.jpg b/tests/validation.jpg index af2df53d9c0e41e36c315e8fe9a2fc64b2bf9805..710de76ebf12d25cb02563378497f2b6071a54aa 100644 GIT binary patch delta 50060 zcmeFYWl&sU&@MPQfdm4)u=r+s1kb53#Cn7 z_?XPlQNv+`ZS+Lvy2os!sPLI7?e~S{g0K!j9#&hHzi_wxi9iv1mOfFo&2nmJ_!CGC 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+++++++++++++-------------- 2 files changed, 260 insertions(+), 256 deletions(-) diff --git a/intertidal/elevation.py b/intertidal/elevation.py index a483dbe..2f6a77f 100644 --- a/intertidal/elevation.py +++ b/intertidal/elevation.py @@ -961,10 +961,14 @@ 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 + # 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) + 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 @@ -1205,18 +1209,18 @@ def intertidal_cli( ) satellite_ds.load() - # Load topobathy mask from GA's AusBathyTopo 250m 2023 Grid, + # 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) + 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) - + # Also load ancillary dataset IDs to use in metadata # (both layers are continental continental products with only # a single dataset, so no need for a spatial/temporal query) dss_ancillary = dc.find_datasets( - product=["ga_ausbathytopo250m_2023", "abares_clum_2020",] + product=["ga_ausbathytopo250m_2023", "abares_clum_2020"] ) # Calculate elevation diff --git a/notebooks/Intertidal_elevation.ipynb b/notebooks/Intertidal_elevation.ipynb index 5891dbc..ef94c3e 100644 --- a/notebooks/Intertidal_elevation.ipynb +++ b/notebooks/Intertidal_elevation.ipynb @@ -1379,7 +1379,7 @@ "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", + "ocean_connected_mask = ocean_connection(~(ds.qa_ndwi_freq < min_freq), ocean_mask)\n", "ds[elevation_bands] = ds[elevation_bands].where(ocean_connected_mask)" ] }, @@ -1503,85 +1503,41 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0df7b8dd2f624378b5517dae2b60c01c": { + "052e66fc68db4577bab0e0eab5a08fa2": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "width": "5em" + } + }, + "0c6168b3e44a4826a20dda25eb41abd4": { "model_module": "jupyter-leaflet", "model_module_version": "^0.17", - "model_name": "LeafletMapModel", + "model_name": 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This step # also ensures all previous data was written out correctly. - if "dea-public-data-dev" in output_location: - explorer_url = "https://explorer.dev.dea.ga.gov.au" - else: - explorer_url = "https://explorer.dea.ga.gov.au" _write_stac( dataset_assembler, destination_path=destination_path, - explorer_base_url=explorer_url, + explorer_base_url="https://explorer.dea.ga.gov.au", ) # Either sync to S3 or copy files to local destination From 49cbbefe292aca493c2ebb4c1ff440dcf4f6f347 Mon Sep 17 00:00:00 2001 From: robbibt Date: Mon, 25 Mar 2024 06:09:17 +0000 Subject: [PATCH 09/12] Automatically update integration test validation results --- tests/README.md | 2 +- tests/validation.csv | 1 + tests/validation.jpg | Bin 72262 -> 72238 bytes 3 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/README.md b/tests/README.md index 65e7e6d..db21c96 100644 --- a/tests/README.md +++ b/tests/README.md @@ -10,7 +10,7 @@ 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 **2024-03-25 16:50**. Compared to the previous run, it had an: +The latest integration test completed at **2024-03-25 17:08**. Compared to the previous run, it had an: - RMSE accuracy of **0.14 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)** diff --git a/tests/validation.csv b/tests/validation.csv index c8d3667..d280b7c 100644 --- a/tests/validation.csv +++ b/tests/validation.csv @@ -51,3 +51,4 @@ time,Correlation,RMSE,MAE,R-squared,Bias,Regression slope 2024-03-25 00:32:00.748385+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-25 01:04:32.512436+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-25 05:50:44.245009+00:00,0.975,0.141,0.121,0.95,0.116,1.11 +2024-03-25 06:08:57.564906+00:00,0.975,0.141,0.121,0.95,0.116,1.11 diff --git a/tests/validation.jpg b/tests/validation.jpg index 710de76ebf12d25cb02563378497f2b6071a54aa..63f1d07bf070dd7349945aa044fe18a3843c7435 100644 GIT binary patch delta 50145 zcmc$F1yI~Um*)^PxVw9T26qX;gS!(P1_{9i{Ubp_aKey4kl;2D+}$C#ySv*2=lJ&O 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zOVRXd6a20a=vEOk)XUNZI~?~BSY(%ieH;~1&xP>e&hA`a(;jg3*>s}y=UV5_J0#a4 zj5=7BjX}8?FFqwnHZ?Xk*3EA(==R0T4kPAwWQL_M`2dwifBZwgsX_g25m3CYq}a{y z#kllOhBl^Q1E?uBS})fdx5)6FR?J!zn2U<_i6EK{+h1%i1^_?W+AgwT-eM)hu#TeX ztTB+X3So%ne^e6O{~{%X@j%d(UTkKkL47)vyws~u9_q+}l8_(4*tWuA9|0t>!4{IH zGSDBWr2Hi+E!|A(Q&R_Hh8(%%=pw{Wf<@ZwUO(YStb+NLs{IkU-z^7zU+hStzkd8T DlgN>_ From 674a984213ac45ae7024c3b215ae95d3c86fa0fc Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 25 Mar 2024 17:25:19 +1100 Subject: [PATCH 10/12] Allow commit action to be skipped --- .github/workflows/dea-intertidal-image.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/dea-intertidal-image.yml b/.github/workflows/dea-intertidal-image.yml index c702572..b13525a 100644 --- a/.github/workflows/dea-intertidal-image.yml +++ b/.github/workflows/dea-intertidal-image.yml @@ -111,6 +111,7 @@ jobs: - name: Commit validation results into repository uses: stefanzweifel/git-auto-commit-action@v4 if: github.event_name == 'pull_request' + continue-on-error: true with: commit_message: Automatically update integration test validation results file_pattern: 'tests/validation.jpg tests/validation.csv tests/README.md' From 8085950199629c0597e1844460a3f22ec83e4488 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 25 Mar 2024 17:26:17 +1100 Subject: [PATCH 11/12] Update to latest eodatasets --- requirements.in | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.in b/requirements.in index 7178328..ce2cac2 100644 --- a/requirements.in +++ b/requirements.in @@ -4,7 +4,7 @@ botocore click==8.1.3 datacube[s3,performance]==1.8.13 dea-tools==0.3.2.dev54 -eodatasets3==0.30.2 +eodatasets3==0.30.4 geopandas==0.13.2 matplotlib==3.7.1 mdutils diff --git a/requirements.txt b/requirements.txt index 0dbb5e4..de858c1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -176,7 +176,7 @@ distributed==2024.3.1 # dask-ml # datacube # odc-algo -eodatasets3==0.30.2 +eodatasets3==0.30.4 # via -r requirements.in exceptiongroup==1.2.0 # via From bb7bef88f5200dcd2f4397c80e3f36d391325b58 Mon Sep 17 00:00:00 2001 From: robbibt Date: Mon, 25 Mar 2024 06:39:20 +0000 Subject: [PATCH 12/12] Automatically update integration test validation results --- tests/README.md | 2 +- tests/validation.csv | 1 + tests/validation.jpg | Bin 72238 -> 72233 bytes 3 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/README.md b/tests/README.md index db21c96..2e8efe7 100644 --- a/tests/README.md +++ b/tests/README.md @@ -10,7 +10,7 @@ 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 **2024-03-25 17:08**. Compared to the previous run, it had an: +The latest integration test completed at **2024-03-25 17:38**. Compared to the previous run, it had an: - RMSE accuracy of **0.14 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)** diff --git a/tests/validation.csv b/tests/validation.csv index d280b7c..bcd5258 100644 --- a/tests/validation.csv +++ b/tests/validation.csv @@ -52,3 +52,4 @@ time,Correlation,RMSE,MAE,R-squared,Bias,Regression slope 2024-03-25 01:04:32.512436+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-25 05:50:44.245009+00:00,0.975,0.141,0.121,0.95,0.116,1.11 2024-03-25 06:08:57.564906+00:00,0.975,0.141,0.121,0.95,0.116,1.11 +2024-03-25 06:38:58.505117+00:00,0.975,0.141,0.121,0.95,0.116,1.11 diff --git a/tests/validation.jpg b/tests/validation.jpg index 63f1d07bf070dd7349945aa044fe18a3843c7435..ba337a033dfe747f1477999d7998a2c80dd87db2 100644 GIT binary patch delta 50954 zcmd?QWl&sE7cJNXhoHeBKxhaF?yezNfIx6)EWxF5w~HhY+yVr5cWD|YxCM824-SpP 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    \n", - " Comm: tcp://127.0.0.1:40093\n", + " Comm: tcp://127.0.0.1:44895\n", " \n", - " Total threads: 31\n", + " Total threads: 62\n", "
    \n", - " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/40695/status\n", + " Dashboard: /user/robbi.bishoptaylor@ga.gov.au/proxy/45793/status\n", " \n", - " Memory: 237.21 GiB\n", + " Memory: 477.21 GiB\n", "
    \n", - " Nanny: tcp://127.0.0.1:37443\n", + " Nanny: tcp://127.0.0.1:35591\n", "
    \n", - " Local directory: /tmp/dask-worker-space/worker-exlgetrp\n", + " Local directory: /tmp/dask-worker-space/worker-i9j5am58\n", "