Merge pull request #22 from TerrainBento/barnhark/update_to_ll2

Update to work with Landlab 2b

.travis.yml

@@ -4,8 +4,9 @@ os:
 - osx
 env:
   matrix:
-  - CONDA_ENV=3.7
   - CONDA_ENV=3.6
+  - CONDA_ENV=3.7
+  - CONDA_ENV=3.8
   global:
   - MPLBACKEND=Agg
   - secure: 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
@@ -24,11 +25,11 @@ jobs:
     - conda activate umami_docs
     - pip install -e .
     script:
-    - make -C docs clean html
+    - make -C docs clean html linkcheck
   - stage: deploy
     if: tag =~ v.*$
     os: osx
-    env: CONDA_ENV=3.7
+    env: CONDA_ENV=3.8
     script:
     - pip install twine wheel
     - python setup.py bdist_wheel
@@ -61,6 +62,7 @@ install:
 - pip install -e .
 script:
 - pip install pytest pytest-cov coveralls
-- pip install jupyter pandas plotnine holoviews terrainbento tqdm rasterio
+- pip install jupyter pandas plotnine holoviews tqdm rasterio
+- pip install terrainbento --pre
 - pytest umami tests/ --doctest-modules --cov=umami --cov-report=xml:$(pwd)/coverage.xml -vvv
 after_success: coveralls

Makefile

@@ -51,7 +51,7 @@ clean-test: ## remove test and coverage artifacts
 	rm -fr .pytest_cache
 
 lint: ## check style with flake8
-	flake8 umami --exclude=examples
+	flake8 umami 
 	flake8 tests
 
 pretty: ## reformat files to make them look pretty

README.md

@@ -3,7 +3,7 @@
 [![Build status](https://ci.appveyor.com/api/projects/status/0ehba569dttgsuyv?svg=true)](https://ci.appveyor.com/project/kbarnhart/umami)
 [![Coverage Status](https://coveralls.io/repos/github/TerrainBento/umami/badge.svg?branch=master)](https://coveralls.io/github/TerrainBento/umami?branch=master)
 [![Anaconda-Server Badge](https://anaconda.org/conda-forge/umami/badges/installer/conda.svg)](https://conda.anaconda.org/conda-forge)
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TerrainBento/umami/master?filepath=notebooks%2FWelcome.ipynb)
+[![Binder](https://static.mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TerrainBento/umami/master?filepath=notebooks%2FWelcome.ipynb)
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.01776/status.svg)](https://doi.org/10.21105/joss.01776)
 
 
@@ -15,7 +15,7 @@ with
 [terrainbento](https://github.com/TerrainBento/terrainbento) and other models built with the
 [Landlab Toolkit](https://github.com/landlab/landlab). Examples can be
 found in the `notebooks` directory (or on Binder
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TerrainBento/umami/master?filepath=notebooks%2FWelcome.ipynb)
+[![Binder](https://static.mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/TerrainBento/umami/master?filepath=notebooks%2FWelcome.ipynb)
 ).
 
 Umami offers two primary classes:

appveyor.yml

@@ -6,12 +6,17 @@ environment:
     - TARGET_ARCH: x64
       CONDA_NPY: 111
       CONDA_INSTALL_LOCN: C:\\Miniconda37-x64
-      CONDA_PY: 3.7
+      CONDA_PY: 3.6
 
     - TARGET_ARCH: x64
       CONDA_NPY: 111
       CONDA_INSTALL_LOCN: C:\\Miniconda36-x64
-      CONDA_PY: 3.6
+      CONDA_PY: 3.7
+
+    - TARGET_ARCH: x64
+      CONDA_NPY: 111
+      CONDA_INSTALL_LOCN: C:\\Miniconda37-x64
+      CONDA_PY: 3.8
 
 platform:
   - x64
@@ -28,7 +33,8 @@ install:
   - cmd: set PYTHONUNBUFFERED=1
   - cmd: conda config --set always_yes yes
   - cmd: pip install pytest
-  - cmd: pip install jupyter pandas plotnine holoviews terrainbento tqdm
+  - cmd: pip install jupyter pandas plotnine holoviews tqdm
+  - cmd: pip install terrainbento --pre
   - cmd: conda install rasterio -c conda-forge
   - cmd: conda install landlab -c conda-forge
   - cmd: conda info

binder/postBuild

@@ -7,7 +7,7 @@ conda install jupyter
 conda install holoviews
 conda install pandas
 conda install plotnine>=0.6.0
-conda install terrainbento>=1.10.1
+conda install terrainbento>=2b
 conda install tqdm
 conda install rasterio
 

docs/Makefile

@@ -19,4 +19,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -W

docs/source/index.rst

@@ -52,9 +52,9 @@ To get help or ask questions, please make an `issue on GitHub`_.
 Installation Instructions
 -------------------------
 
-Installation is described on `this`_ section of the README.
+Installation is described in `the README`_ .
 
-.. _this: https://github.com/TerrainBento/umami#where-to-get-it
+.. _the README: https://github.com/TerrainBento/umami
 
 Contributing
 ------------

environment-dev.yml

@@ -12,13 +12,13 @@ dependencies:
    # for the package
    - scipy
    - numpy
-   - landlab>=1.10.1, <2.0
+   - landlab>=2.0.0b4
    # for the notebooks
    - jupyter
    - holoviews
    - pandas
    - tqdm
    - plotnine>=0.6.0
-   - terrainbento>=1.1
+   - terrainbento>=2.0.0b1
    - gdal
    - rasterio

notebooks/OtherIO_options.ipynb

@@ -177,9 +177,8 @@
    "outputs": [],
    "source": [
     "rmg = RasterModelGrid((nrows, ncols),\n",
-    "                       dx=dx,\n",
-    "                       dy=dy,\n",
-    "                       xy_lower_left=xy_lower_left)\n",
+    "                       xy_spacing=(dx, dy),\n",
+    "                       xy_of_lower_left=xy_lower_left)\n",
     "\n",
     "z = rmg.add_field(\"topographic__elevation\", elevations.astype(float))"
    ]
@@ -268,17 +267,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "factor = 10\n",
+    "factor = 11\n",
     "nnodes = int(rmg.x_of_node.size / factor)\n",
-    "np.random.seed(42)\n",
+    "np.random.seed(27)\n",
     "\n",
-    "random_x = np.random.uniform(low=rmg.x_of_node.min(),\n",
-    "                             high=rmg.x_of_node.max(),\n",
-    "                             size=nnodes)\n",
-    "\n",
-    "random_y = np.random.uniform(low=rmg.y_of_node.min(),\n",
-    "                             high=rmg.y_of_node.max(),\n",
-    "                             size=nnodes)"
+    "# select a random subset of x_of_node and y_of_node and permute by a small quantity.\n",
+    "# permute only in x, which allows the ordering of nodes to be maintained \n",
+    "index = np.linspace(0, rmg.x_of_node.size-1, nnodes, dtype=int)\n",
+    "random_x = rmg.x_of_node[index] + 0.4 * rmg.spacing[0] * np.random.randn(index.size)\n",
+    "random_y = rmg.y_of_node[index] "
    ]
   },
   {
@@ -296,8 +293,8 @@
    "source": [
     "plt.plot(rmg.x_of_node, rmg.y_of_node, 'k.',  markersize=2, label=\"Raster Points\")\n",
     "plt.plot(random_x, random_y, 'm.', label=\"Irregular Points\")\n",
-    "plt.ylim(0, 0.025)\n",
-    "plt.xlim(0, 0.025)"
+    "plt.xlim(-105.40, -105.375)\n",
+    "plt.ylim(40.00, 40.025)"
    ]
   },
   {
@@ -313,35 +310,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "interp_obj = RegularGridInterpolator((rmg.y_of_node.reshape(\n",
-    "    rmg.shape)[:, 0], rmg.x_of_node.reshape(rmg.shape)[0, :]),\n",
-    "                                     z.reshape(rmg.shape))\n",
+    "interp_obj = RegularGridInterpolator((rmg.y_of_node.reshape(rmg.shape)[:, 0], \n",
+    "                                      rmg.x_of_node.reshape(rmg.shape)[0, :]),\n",
+    "                                     z.reshape(rmg.shape), bounds_error=False, fill_value=None)\n",
     "\n",
     "interp_z = interp_obj((random_y, random_x))"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before we create the `VoronoiDelaunayGrid` we have one more step to do. The Landlab model grid uses a standard method of ordering model grid nodes. Node order starts in the lower left corner and ends in the upper right. If we want to be able to add our elevation data to the grid, we need to sort the grid nodes into the Landlab grid order, so the order will not change when the grid is made. \n",
-    "\n",
-    "We do this by creating a numpy array that combines the random x and y locations, and the interpolated z values. We then sort first by x and then by y. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vals = np.vstack((random_x, random_y, interp_z))\n",
-    "\n",
-    "# sort by x then by y\n",
-    "sort_by_x = vals[:, vals[0, :].argsort()]\n",
-    "sort_by_xy = sort_by_x[:, sort_by_x[1, :].argsort()]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -357,8 +332,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vdg = VoronoiDelaunayGrid(sort_by_xy[0], sort_by_xy[1])\n",
-    "z = vdg.add_field(\"node\", \"topographic__elevation\", sort_by_xy[2])\n",
+    "vdg = VoronoiDelaunayGrid(random_x, random_y)\n",
+    "z = vdg.add_field(\"topographic__elevation\", interp_z, at=\"node\")\n",
     "\n",
     "imshow_grid(vdg, z, cmap=\"terrain\")"
    ]
@@ -418,7 +393,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Comparing the metric values for the two grids, we can see that they are slightly different in absolute value but very close based on percent change. "
+    "Comparing the metric values for the two grids, we can see that the mean is slightly different in absolute value but very close based on percent change and the 10th percentile is identical. "
    ]
   },
   {
@@ -459,7 +434,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.1"
   }
  },
  "nbformat": 4,

setup.cfg

@@ -16,6 +16,7 @@ filterwarnings =
 testpaths = umami tests
 norecursedirs = .* *.egg* build dist examples
 addopts =
+  --disable-pytest-warnings
   --ignore setup.py
   --ignore versioneer.py
   --ignore umami/_version.py

setup.py

@@ -28,5 +28,5 @@
     long_description_content_type="text/markdown",
     zip_safe=False,
     packages=find_packages(),
-    install_requires=["scipy", "numpy", "landlab>=1.10.1, <2.0"],
+    install_requires=["scipy", "numpy", "landlab>=2.0.0b4"],
 )

umami/calculations/metric/chi_intercept_gradient.py

@@ -15,14 +15,14 @@ def chi_intercept(chi_finder):
     This is a loose wrapper around the Landlab function
     `ChiFinder.best_fit_chi_elevation_gradient_and_intercept`_.
 
-    .. _ChiFinder.best_fit_chi_elevation_gradient_and_intercept: https://landlab.readthedocs.io/en/release/landlab.components.chi_index.html#landlab.components.chi_index.ChiFinder.best_fit_chi_elevation_gradient_and_intercept
+    .. _ChiFinder.best_fit_chi_elevation_gradient_and_intercept: https://landlab.readthedocs.io/en/master/reference/components/index.html#landlab.components.ChiFinder.best_fit_chi_elevation_gradient_and_intercept
 
     Parameters
     ----------
     chi_finder : an instance of a `ChiFinder`_
 
 
-    .. _ChiFinder: https://landlab.readthedocs.io/en/release/landlab.components.chi_index.html
+    .. _ChiFinder: https://landlab.readthedocs.io/en/master/reference/components/chi_index.html
 
 
     Returns
@@ -78,14 +78,14 @@ def chi_gradient(chi_finder):
     This is a loose wrapper around the Landlab function
     `ChiFinder.best_fit_chi_elevation_gradient_and_intercept`_.
 
-    .. _ChiFinder.best_fit_chi_elevation_gradient_and_intercept: https://landlab.readthedocs.io/en/release/landlab.components.chi_index.html#landlab.components.chi_index.ChiFinder.best_fit_chi_elevation_gradient_and_intercept
+    .. _ChiFinder.best_fit_chi_elevation_gradient_and_intercept: https://landlab.readthedocs.io/en/master/reference/components/index.html#landlab.components.ChiFinder.best_fit_chi_elevation_gradient_and_intercept
 
     Parameters
     ----------
     chi_finder : an instance of a `ChiFinder`_
 
 
-    .. _ChiFinder: https://landlab.readthedocs.io/en/release/landlab.components.chi_index.html
+    .. _ChiFinder: https://landlab.readthedocs.io/en/master/reference/components/chi_index.html
 
 
     Returns

umami/calculations/residual/discretized_misfit.py

@@ -18,7 +18,7 @@ def discretized_misfit(
     The following Binder notebook shows example usage of this umami
     calculation.
 
-    .. image:: https://mybinder.org/badge_logo.svg
+    .. image:: https://static.mybinder.org/badge_logo.svg
         :target: https://mybinder.org/v2/gh/TerrainBento/umami/master?filepath=notebooks%2FDiscretizedMisfit.ipynb
 
     The ``discretized_misfit`` calculation first classifies each grid cell in

umami/utils/create_landlab_components.py

@@ -12,10 +12,10 @@ def _create_landlab_components(
 
     # Run ChiFinder
     kwds = chi_finder_kwds or {}
-    cf = ChiFinder(grid, noclobber=False, **kwds)
+    cf = ChiFinder(grid, clobber=True, **kwds)
     cf.calculate_chi()
 
     # run distance upstream.
-    _ = calculate_flow__distance(grid, add_to_grid=True, noclobber=False)
+    _ = calculate_flow__distance(grid, add_to_grid=True, clobber=True)
 
     return fa, cf