Increase sample size of empirical variograms in advanced examples for…

… clearer plots (GlacioHack#492)
uw-cryo · Mar 18, 2024 · 1ad4ad1 · 1ad4ad1
1 parent 981d7db
commit 1ad4ad1
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Showing 2 changed files with 2 additions and 4 deletions.
diff --git a/examples/advanced/plot_standardization.py b/examples/advanced/plot_standardization.py
@@ -128,7 +128,7 @@
 df_vgm = xdem.spatialstats.sample_empirical_variogram(
     values=z_dh.data.squeeze(),
     gsd=dh.res[0],
-    subsample=300,
+    subsample=1000,
     n_variograms=10,
     estimator="dowd",
     random_state=42,

diff --git a/examples/advanced/plot_variogram_estimation_modelling.py b/examples/advanced/plot_variogram_estimation_modelling.py
@@ -78,9 +78,7 @@
 # conveniently by :func:`xdem.spatialstats.sample_empirical_variogram`:
 # Dowd's variogram is used for robustness in conjunction with the NMAD (see :ref:`robuststats-corr`).
 
-df = xdem.spatialstats.sample_empirical_variogram(
-    values=dh.data, gsd=dh.res[0], subsample=100, n_variograms=10, estimator="dowd", random_state=42
-)
+df = xdem.spatialstats.sample_empirical_variogram(values=dh, subsample=1000, n_variograms=10, estimator="dowd", random_state=42)
 
 # %%
 # *Note: in this example, we add a* ``random_state`` *argument to yield a reproducible random sampling of pixels within