From afb94dd552d01f39c5b30d3dd35260318680ac57 Mon Sep 17 00:00:00 2001 From: Robbi Bishop-Taylor Date: Mon, 4 Mar 2024 15:43:27 +1100 Subject: [PATCH] Fix missing uncertainty bug (#67) * Fix missing uncertainty bug * Automatically update integration test validation results --------- Co-authored-by: robbibt --- intertidal/elevation.py | 39 ++++++++++++++++++++++++++++----------- tests/README.md | 2 +- tests/validation.csv | 1 + tests/validation.jpg | Bin 70269 -> 70423 bytes 4 files changed, 30 insertions(+), 12 deletions(-) diff --git a/intertidal/elevation.py b/intertidal/elevation.py index dc6c1e7..7d63f96 100644 --- a/intertidal/elevation.py +++ b/intertidal/elevation.py @@ -493,7 +493,13 @@ def pixel_dem_debug( def pixel_uncertainty( - flat_ds, flat_dem, ndwi_thresh=0.1, method="mad", min_q=0.25, max_q=0.75 + flat_ds, + flat_dem, + ndwi_thresh=0.1, + method="mad", + min_misclassified=3, + min_q=0.25, + max_q=0.75, ): """ Calculate uncertainty bounds around a modelled elevation based on @@ -524,8 +530,14 @@ def pixel_uncertainty( taking upper/lower tide height quantiles of miscalssified points. Defaults to "mad" for Median Absolute Deviation; use "quantile" to use quantile calculation instead. + min_misclassified : int, optional + If `method == "mad"`: This sets the minimum number of misclassified + observations required to calculate a valid MAD uncertainty. Pixels + with fewer misclassified observations will be assigned an output + uncertainty of 0 metres (reflecting how sucessfully the provided + elevation and NDWI threshold divide observations into dry and wet). min_q, max_q : float, optional - If `method == "quantile": the minimum and maximum quantiles used + If `method == "quantile"`: the minimum and maximum quantiles used to estimate uncertainty bounds based on misclassified points. Defaults to interquartile range, or 0.25, 0.75. This provides a balance between capturing the range of uncertainty at each @@ -554,14 +566,26 @@ def pixel_uncertainty( misclassified_all = misclassified_wet | misclassified_dry misclassified_ds = flat_ds.where(misclassified_all) + # Calculate sum of misclassified points + misclassified_sum = ( + misclassified_all.sum(dim="time") + .rename("misclassified_px_count") + .where(~flat_dem.elevation.isnull()) + ) + # Calculate uncertainty by taking the Median Absolute Deviation of # all misclassified points. if method == "mad": # Calculate median of absolute deviations - # TODO: Account for large MAD on pixels with very few - # misclassified points. Set < n misclassified points to 0 MAD? mad = abs(misclassified_ds.tide_m - flat_dem.elevation).median(dim="time") + # Set any pixels with < n misclassified points to 0 MAD. This + # avoids extreme MAD values being calculated when we have only + # a small set of misclassified observations, as well as missing + # data caused by being unable to calculate MAD on zero + # misclassified observations. + mad = mad.where(misclassified_sum >= min_misclassified, 0) + # Calculate low and high bounds uncertainty_low = flat_dem.elevation - mad uncertainty_high = flat_dem.elevation + mad @@ -589,13 +613,6 @@ def pixel_uncertainty( # Subtract low from high DEM to summarise uncertainy range dem_flat_uncertainty = dem_flat_high - dem_flat_low - # Calculate sum of misclassified points - misclassified_sum = ( - misclassified_all.sum(dim="time") - .rename("misclassified_px_count") - .where(~flat_dem.elevation.isnull()) - ) - return ( dem_flat_low, dem_flat_high, diff --git a/tests/README.md b/tests/README.md index ed91018..6a95f22 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 **2024-03-04 13:57**. Compared to the previous run, it had an: +The latest integration test completed at **2024-03-04 15:35**. 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 9ea6bbe..e10fa05 100644 --- a/tests/validation.csv +++ b/tests/validation.csv @@ -37,3 +37,4 @@ time,Correlation,RMSE,MAE,R-squared,Bias,Regression slope 2024-03-04 00:51:34.386727+00:00,0.976,0.141,0.121,0.952,0.117,1.109 2024-03-04 02:24:13.813543+00:00,0.976,0.141,0.121,0.952,0.117,1.109 2024-03-04 02:57:33.436682+00:00,0.976,0.141,0.121,0.952,0.117,1.109 +2024-03-04 04:35:38.183061+00:00,0.976,0.141,0.121,0.952,0.117,1.109 diff --git a/tests/validation.jpg b/tests/validation.jpg index 3ee564aafb1ee8d2f3611283532a28d6571a4856..67701cfac63c924711035b0321c3ce0aecd4bc8b 100644 GIT binary patch delta 51481 zcmeFZWl&sEw;ZtD8iE9U z+&6FPeKYTynp=0?y|-#=e(XP|&RKh{y_d_m%STHoMEiM!1DyH`uFuAvm!-jKBwp0x 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