Merge pull request #38 from chenyangkang/chenyangkang-JOSS-review

Fix bugs
chenyangkang · Jan 25, 2024 · 7111a7d · 7111a7d
2 parents f7b89fe + 0e49335
commit 7111a7d
Show file tree

Hide file tree

Showing 9 changed files with 21 additions and 8 deletions.
diff --git a/docs/.DS_Store b/docs/.DS_Store
diff --git a/docs/A_brief_introduction/A_brief_introduction.md b/docs/A_brief_introduction/A_brief_introduction.md
@@ -34,7 +34,7 @@ In the [demo](https://chenyangkang.github.io/stemflow/Examples/01.AdaSTEM_demo.h
 In the first case, the classifier and regressor "talk" to each other in each separate stixel (hereafter, "hurdle in Ada"); In the second case, the classifiers and regressors form two "unions" separately, and these two unions only "talk" to each other at the final combination, instead of in each stixel (hereafter, "Ada in hurdle"). In [Johnston (2015)](https://esajournals.onlinelibrary.wiley.com/doi/full/10.1890/14-1826.1) the first method was used. See section "[Hurdle in AdaSTEM or AdaSTEM in hurdle?](https://chenyangkang.github.io/stemflow/Examples/05.Hurdle_in_ada_or_ada_in_hurdle.html)" for further comparisons.
 
 ## Choose the gird size
-User can define the size of the stixels (spatial temporal grids) in terms of space and time. Larger stixel promotes generalizability but loses precision in fine resolution; Smaller stixel may have better predictability in the exact area but reduced ability of extrapolation for points outside the stixel. See section [Optimizing stixel size](https://chenyangkang.github.io/stemflow/Examples/07.Optimizing_stixel_size.html) for discussion about selecting gridding parameters.
+User can define the size of the stixels (spatial temporal grids) in terms of space and time. Larger stixel promotes generalizability but loses precision in fine resolution; Smaller stixel may have better predictability in the exact area but reduced ability of extrapolation for points outside the stixel. See section [Optimizing stixel size](https://chenyangkang.github.io/stemflow/Examples/07.Optimizing_stixel_size.html) for discussion about selecting gridding parameters and [Tips for spatiotemporal indexing](https://chenyangkang.github.io/stemflow/Tips/Tips_for_spatiotemporal_indexing.html).
 
 ## A simple demo
 In the demo, we first split the training data using temporal sliding windows with a size of 50 day of year (DOY) and step of 20 DOY (`temporal_start = 1`, `temporal_end=366`, `temporal_step=20`, `temporal_bin_interval=50`). For each temporal slice, a spatial gridding is applied, where we force the stixel to be split into smaller 1/4 pieces if the edge is larger than 25 units (measured in longitude and latitude, `grid_len_upper_threshold=25`), and stop splitting to prevent the edge length being chunked below 5 units (`grid_len_lower_threshold=5`) or containing less than 50 checklists (`points_lower_threshold=50`).  Model fitting is run using 1 core (`njobs=1`).

diff --git a/docs/Examples/04.SphereAdaSTEM_demo.ipynb b/docs/Examples/04.SphereAdaSTEM_demo.ipynb
@@ -46406,6 +46406,20 @@
     "model.gridding_plot"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Sphere Gridding](https://chenyangkang.github.io/stemflow/assets/Sphere_gridding.png)\n",
+    "\n",
+    "Here for an interactive plot [Interactive spherical gridding plot](https://chenyangkang.github.io/stemflow/assets/Sphere_gridding.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 12,

diff --git a/docs/Tips/Tips_for_spatiotemporal_indexing.md b/docs/Tips/Tips_for_spatiotemporal_indexing.md
@@ -207,7 +207,7 @@ model = SphereAdaSTEMRegressor(
 
 See [SphereAdaSTEM demo](https://chenyangkang.github.io/stemflow/Examples/04.SphereAdaSTEM_demo.html) and [Interactive spherical gridding plot](https://chenyangkang.github.io/stemflow/assets/Sphere_gridding.html).
 
-![Sphere Gridding](https://chenyangkang.github.io/stemflow/assets/Sphere_gridding.png)
+![Sphere Gridding](https://chenyangkang.github.io/stemflow/assets/Sphere_gridding.png){: style="display: block; margin: auto; width: 50%;"}
 
 -----
 ## References:

diff --git a/docs/assets/Untitled-1.psd b/docs/assets/Untitled-1.psd
diff --git a/docs/assets/logo_with_words.png b/docs/assets/logo_with_words.png
diff --git a/docs/index.md b/docs/index.md
@@ -186,7 +186,7 @@ Here, each color shows an ensemble generated during model fitting. In each of th
 
 If you use `SphereAdaSTEM` module, the gridding plot is a `plotly` generated interactive object by default:
 
-![Sphere Gridding](https://chenyangkang.github.io/stemflow/assets/Sphere_gridding.png)
+![Sphere Gridding](https://chenyangkang.github.io/stemflow/assets/Sphere_gridding.png){: style="display: block; margin: auto; width: 50%;"}
 
 See [SphereAdaSTEM demo](https://chenyangkang.github.io/stemflow/Examples/04.SphereAdaSTEM_demo.html) and [Interactive spherical gridding plot](https://chenyangkang.github.io/stemflow/assets/Sphere_gridding.html).
 

diff --git a/stemflow/model/SphereAdaSTEM.py b/stemflow/model/SphereAdaSTEM.py
@@ -520,8 +520,7 @@ def __init__(
             plot_empty,
         )
 
-    def predict(self, *args, **kwargs):
-        return AdaSTEMClassifier().predict(*args, **kwargs)
+        self.predict = MethodType(AdaSTEMClassifier.predict, self)
 
 
 class SphereAdaSTEMRegressor(SphereAdaSTEM):

diff --git a/stemflow/utils/sphere_quadtree.py b/stemflow/utils/sphere_quadtree.py
@@ -148,9 +148,9 @@ def get_ensemble_sphere_quadtree(
                 x, y, z = lonlat_cartesian_3D_transformer.transform(
                     sub_data["longitude"], sub_data["latitude"], radius=radius
                 )
-                sub_data["x_3D"] = x
-                sub_data["y_3D"] = y
-                sub_data["z_3D"] = z
+                sub_data.loc[:, "x_3D"] = x
+                sub_data.loc[:, "y_3D"] = y
+                sub_data.loc[:, "z_3D"] = z
 
                 QT_obj = Sphere_QTree(
                     grid_len_upper_threshold=grid_len_upper_threshold,