-m

joshdorrington · Jun 22, 2023 · a059009 · a059009
1 parent 4fcdab8
commit a059009
Show file tree

Hide file tree

Showing 15 changed files with 1,852 additions and 191 deletions.
diff --git a/.ipynb_checkpoints/readme-checkpoint.md b/.ipynb_checkpoints/readme-checkpoint.md
@@ -3,21 +3,24 @@
 
 ## Documentation
 
-See https://github.com/joshdorrington/domino/blob/master/docbuild/domino-composite.pdf
+See our API reference here: https://github.com/joshdorrington/domino/blob/master/docbuild/domino-composite.pdf
 
 ## Examples
 
-A basic introduction to using the package out of the box, with three example applications:
+See our Jupyter notebook examples for more detailed discussion of how to apply Domino to different use cases.
 
-https://github.com/joshdorrington/domino/blob/master/examples/basic_usage.ipynb
+Our [basic](https://github.com/joshdorrington/domino/blob/master/examples/basic_compositing.ipynb) and [advanced](https://github.com/joshdorrington/domino/blob/master/examples/advanced_compositing.ipynb) compositing guides cover the use of Domino's flexible LaggedAnalyser class to easily compute time-lagged composites and apply bootstrap significance tests to them.
 
-A more in-depth discussion of how to customise the behaviour of the LaggedAnalyser class:
+Producing filtered precursor patterns from composites, and computing precursor activity indices from those is covered in our [IndexGenerator](https://github.com/joshdorrington/domino/blob/master/examples/precursor_index_computation.ipynb) guide.
 
-https://github.com/joshdorrington/domino/blob/master/examples/advanced_usage.ipynb
 
 ## Install
 
 domino can be installed using pip:
 ```
-python -m pip install "domino-composite==0.14"
+python -m pip install "domino-composite==0.272"
 ```
+If you want to run the worked examples in the Jupyter notebooks you will need:
+```
+TO BE DECIDED
+```
diff --git a/.ipynb_checkpoints/setup-checkpoint.py b/.ipynb_checkpoints/setup-checkpoint.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name='domino-composite',
-    version='0.272',
+    version='0.273',
     author='Josh Dorrington',
     author_email='joshua.dorrington@kit.edu',
     description='A package for compositing atmospheric datasets',

diff --git a/dist/domino-composite-0.273.tar.gz b/dist/domino-composite-0.273.tar.gz
diff --git a/dist/domino_composite-0.273-py3-none-any.whl b/dist/domino_composite-0.273-py3-none-any.whl
diff --git a/domino/.ipynb_checkpoints/filtering-checkpoint.py b/domino/.ipynb_checkpoints/filtering-checkpoint.py
@@ -73,7 +73,7 @@ def get_area_regions(grid,area_thresh,area):
     connected_da=xr.DataArray(connected,coords=area.coords)
     ix_areas=np.array([area.where(connected_da==i).sum().values\
               for i in ix])
-
+    print(ix_areas)
     for m in ix[ix_areas<area_thresh]:
         connected[connected==m]=0
     return connected>0

diff --git a/domino/filtering.py b/domino/filtering.py
@@ -73,7 +73,7 @@ def get_area_regions(grid,area_thresh,area):
     connected_da=xr.DataArray(connected,coords=area.coords)
     ix_areas=np.array([area.where(connected_da==i).sum().values\
               for i in ix])
-
+    print(ix_areas)
     for m in ix[ix_areas<area_thresh]:
         connected[connected==m]=0
     return connected>0

diff --git a/domino_composite.egg-info/PKG-INFO b/domino_composite.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: domino-composite
-Version: 0.272
+Version: 0.273
 Summary: A package for compositing atmospheric datasets
 Home-page: https://github.com/joshdorrington/domino
 Author: Josh Dorrington
@@ -13,21 +13,24 @@ Description-Content-Type: text/markdown
 
 ## Documentation
 
-See https://github.com/joshdorrington/domino/blob/master/docbuild/domino-composite.pdf
+See our API reference here: https://github.com/joshdorrington/domino/blob/master/docbuild/domino-composite.pdf
 
 ## Examples
 
-A basic introduction to using the package out of the box, with three example applications:
+See our Jupyter notebook examples for more detailed discussion of how to apply Domino to different use cases.
 
-https://github.com/joshdorrington/domino/blob/master/examples/basic_usage.ipynb
+Our [basic](https://github.com/joshdorrington/domino/blob/master/examples/basic_compositing.ipynb) and [advanced](https://github.com/joshdorrington/domino/blob/master/examples/advanced_compositing.ipynb) compositing guides cover the use of Domino's flexible LaggedAnalyser class to easily compute time-lagged composites and apply bootstrap significance tests to them.
 
-A more in-depth discussion of how to customise the behaviour of the LaggedAnalyser class:
+Producing filtered precursor patterns from composites, and computing precursor activity indices from those is covered in our [IndexGenerator](https://github.com/joshdorrington/domino/blob/master/examples/precursor_index_computation.ipynb) guide.
 
-https://github.com/joshdorrington/domino/blob/master/examples/advanced_usage.ipynb
 
 ## Install
 
 domino can be installed using pip:
 ```
-python -m pip install "domino-composite==0.14"
+python -m pip install "domino-composite==0.272"
+```
+If you want to run the worked examples in the Jupyter notebooks you will need:
+```
+TO BE DECIDED
 ```
diff --git a/examples/.ipynb_checkpoints/precursor_index_computation-checkpoint.ipynb b/examples/.ipynb_checkpoints/precursor_index_computation-checkpoint.ipynb
diff --git a/...dask-worker-space/worker-8p129i7_.dirlock → ...dask-worker-space/worker-1kl9fbp2.dirlock b/...dask-worker-space/worker-8p129i7_.dirlock → ...dask-worker-space/worker-1kl9fbp2.dirlock
diff --git a/...dask-worker-space/worker-dtexfm8u.dirlock → ...dask-worker-space/worker-3o61nr85.dirlock b/...dask-worker-space/worker-dtexfm8u.dirlock → ...dask-worker-space/worker-3o61nr85.dirlock
diff --git a/...dask-worker-space/worker-h7021oyq.dirlock → ...dask-worker-space/worker-uavvjpfn.dirlock b/...dask-worker-space/worker-h7021oyq.dirlock → ...dask-worker-space/worker-uavvjpfn.dirlock
diff --git a/...dask-worker-space/worker-kbr57x_n.dirlock → ...dask-worker-space/worker-w9o0i7dt.dirlock b/...dask-worker-space/worker-kbr57x_n.dirlock → ...dask-worker-space/worker-w9o0i7dt.dirlock
diff --git a/examples/precursor_index_computation.ipynb b/examples/precursor_index_computation.ipynb
diff --git a/readme.md b/readme.md
@@ -3,21 +3,24 @@
 
 ## Documentation
 
-See https://github.com/joshdorrington/domino/blob/master/docbuild/domino-composite.pdf
+See our API reference here: https://github.com/joshdorrington/domino/blob/master/docbuild/domino-composite.pdf
 
 ## Examples
 
-A basic introduction to using the package out of the box, with three example applications:
+See our Jupyter notebook examples for more detailed discussion of how to apply Domino to different use cases.
 
-https://github.com/joshdorrington/domino/blob/master/examples/basic_usage.ipynb
+Our [basic](https://github.com/joshdorrington/domino/blob/master/examples/basic_compositing.ipynb) and [advanced](https://github.com/joshdorrington/domino/blob/master/examples/advanced_compositing.ipynb) compositing guides cover the use of Domino's flexible LaggedAnalyser class to easily compute time-lagged composites and apply bootstrap significance tests to them.
 
-A more in-depth discussion of how to customise the behaviour of the LaggedAnalyser class:
+Producing filtered precursor patterns from composites, and computing precursor activity indices from those is covered in our [IndexGenerator](https://github.com/joshdorrington/domino/blob/master/examples/precursor_index_computation.ipynb) guide.
 
-https://github.com/joshdorrington/domino/blob/master/examples/advanced_usage.ipynb
 
 ## Install
 
 domino can be installed using pip:
 ```
-python -m pip install "domino-composite==0.14"
+python -m pip install "domino-composite==0.272"
 ```
+If you want to run the worked examples in the Jupyter notebooks you will need:
+```
+TO BE DECIDED
+```
diff --git a/setup.py b/setup.py
@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name='domino-composite',
-    version='0.272',
+    version='0.273',
     author='Josh Dorrington',
     author_email='joshua.dorrington@kit.edu',
     description='A package for compositing atmospheric datasets',