Fix warnings and clean notebooks (#116)

* Fix warnings and clean notebooks * Add fixed vmin and vmax for plots of data-arrays
gwmod · Oct 31, 2022 · afd1dad · afd1dad
1 parent e454fbf
commit afd1dad
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Showing 8 changed files with 78 additions and 79 deletions.
diff --git a/docs/examples/02_surface_water.ipynb b/docs/examples/02_surface_water.ipynb
@@ -47,7 +47,9 @@
     "import geopandas as gpd\n",
     "import matplotlib.pyplot as plt\n",
     "import nlmod\n",
-    "import numpy as np\n"
+    "import numpy as np\n",
+    "import warnings\n",
+    "from shapely.errors import ShapelyDeprecationWarning"
    ]
   },
   {
@@ -57,10 +59,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "warnings.filterwarnings(\"ignore\", category=ShapelyDeprecationWarning)\n",
+    "\n",
     "print(f\"nlmod version: {nlmod.__version__}\")\n",
     "\n",
     "# toon informatie bij het aanroepen van functies\n",
-    "logging.basicConfig(level=logging.INFO)\n"
+    "logging.basicConfig(level=logging.INFO)"
    ]
   },
   {
@@ -106,7 +110,7 @@
     "# set the stage of the Lek to 0.0 m NAP and the botm to -3 m NAP\n",
     "mask = sfw[\"bronhouder\"] == \"L0002\"\n",
     "sfw.loc[mask, \"stage\"] = 0.0\n",
-    "sfw.loc[mask, \"botm\"] = -3.0\n"
+    "sfw.loc[mask, \"botm\"] = -3.0"
    ]
   },
   {
@@ -147,7 +151,7 @@
     "ax.set_xlabel(\"X (m RD)\")\n",
     "ax.set_ylabel(\"Y (m RD)\")\n",
     "plt.yticks(rotation=90, va=\"center\")\n",
-    "fig.tight_layout()\n"
+    "fig.tight_layout()"
    ]
   },
   {
@@ -172,7 +176,7 @@
     "ax.set_xlabel(\"X (m RD)\")\n",
     "ax.set_ylabel(\"Y (m RD)\")\n",
     "plt.yticks(rotation=90, va=\"center\")\n",
-    "fig.tight_layout()\n"
+    "fig.tight_layout()"
    ]
   },
   {
@@ -206,7 +210,7 @@
     "figdir, cachedir = nlmod.util.get_model_dirs(model_ws)\n",
     "\n",
     "delr = delc = 50.0\n",
-    "start_time = \"2021-01-01\"\n"
+    "start_time = \"2021-01-01\""
    ]
   },
   {
@@ -224,7 +228,7 @@
     "    cachedir=cachedir,\n",
     "    cachename=\"combined_layer_ds.nc\",\n",
     ")\n",
-    "layer_model\n"
+    "layer_model"
    ]
   },
   {
@@ -242,7 +246,7 @@
     "# create model time dataset\n",
     "ds = nlmod.mdims.set_ds_time(ds, start_time=start_time, steady_state=True)\n",
     "\n",
-    "ds\n"
+    "ds"
    ]
   },
   {
@@ -274,7 +278,7 @@
     "ic = nlmod.gwf.ic(ds, gwf, starting_head=1.0)\n",
     "\n",
     "# Create the output control package\n",
-    "oc = nlmod.gwf.oc(ds, gwf)\n"
+    "oc = nlmod.gwf.oc(ds, gwf)"
    ]
   },
   {
@@ -311,7 +315,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sfw_grid = nlmod.mdims.gdf_to_grid(sfw, gwf)\n"
+    "sfw_grid = nlmod.mdims.gdf_to_grid(sfw, gwf)"
    ]
   },
   {
@@ -359,7 +363,7 @@
     "cid = (107, 6)  # for 50 x 50 m grid\n",
     "# cid = (5, 45)  # for 100 x 100 m grid\n",
     "mask = sfw_grid.cellid == cid\n",
-    "sfw_grid.loc[mask]\n"
+    "sfw_grid.loc[mask]"
    ]
   },
   {
@@ -421,7 +425,7 @@
     "try:\n",
     "    nlmod.gwf.surface_water.aggregate(sfw_grid, \"area_weighted\")\n",
     "except ValueError as e:\n",
-    "    print(e)\n"
+    "    print(e)"
    ]
   },
   {
@@ -439,7 +443,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sfw_grid[\"c0\"] = 1.0  # days\n"
+    "sfw_grid[\"c0\"] = 1.0  # days"
    ]
   },
   {
@@ -459,7 +463,7 @@
    "source": [
     "celldata = nlmod.gwf.surface_water.aggregate(\n",
     "    sfw_grid, \"area_weighted\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -512,7 +516,7 @@
    "outputs": [],
    "source": [
     "new_celldata = celldata.loc[~celldata.rbot.isna()]\n",
-    "print(f\"removed {len(celldata)-len(new_celldata)} reaches because rbot is nan\")\n"
+    "print(f\"removed {len(celldata)-len(new_celldata)} reaches because rbot is nan\")"
    ]
   },
   {
@@ -522,7 +526,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "riv_spd = nlmod.gwf.surface_water.build_spd(new_celldata, \"RIV\", ds)\n"
+    "riv_spd = nlmod.gwf.surface_water.build_spd(new_celldata, \"RIV\", ds)"
    ]
   },
   {
@@ -560,7 +564,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "riv = flopy.mf6.ModflowGwfriv(gwf, stress_period_data=riv_spd)\n"
+    "riv = flopy.mf6.ModflowGwfriv(gwf, stress_period_data=riv_spd)"
    ]
   },
   {
@@ -580,7 +584,7 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=(10, 8), constrained_layout=True)\n",
     "mv = flopy.plot.PlotMapView(model=gwf, ax=ax, layer=0)\n",
-    "mv.plot_bc(\"RIV\")\n"
+    "mv.plot_bc(\"RIV\")"
    ]
   },
   {
@@ -602,7 +606,7 @@
    "source": [
     "nlmod.sim.write_and_run(\n",
     "    sim, ds, write_ds=True, nb_path=\"02_surface_water.ipynb\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -694,14 +698,6 @@
     "col = gwf.modelgrid.ncol // 2\n",
     "ax.set_title(f\"Cross-section along column {col}\");\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4478d66",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -720,7 +716,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.9.7"
   },
   "vscode": {
    "interpreter": {

diff --git a/docs/examples/03_local_grid_refinement.ipynb b/docs/examples/03_local_grid_refinement.ipynb
@@ -37,7 +37,9 @@
     "import flopy\n",
     "import geopandas as gpd\n",
     "import matplotlib.pyplot as plt\n",
-    "import nlmod\n"
+    "import nlmod\n",
+    "import warnings\n",
+    "from shapely.errors import ShapelyDeprecationWarning"
    ]
   },
   {
@@ -46,6 +48,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "warnings.filterwarnings(\"ignore\", category=ShapelyDeprecationWarning)\n",
+    "\n",
     "print(f'nlmod version: {nlmod.__version__}')\n",
     "\n",
     "# toon informatie bij het aanroepen van functies\n",
@@ -85,7 +89,7 @@
     "regis_botm_layer = \"MSz1\"\n",
     "use_geotop = True\n",
     "add_northsea = True\n",
-    "starting_head = 1.0\n"
+    "starting_head = 1.0"
    ]
   },
   {
@@ -116,7 +120,7 @@
     "    steady_start=steady_start,\n",
     "    transient_timesteps=transient_timesteps,\n",
     "    perlen=perlen,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -140,7 +144,7 @@
     ")\n",
     "\n",
     "if add_northsea:\n",
-    "    ds = nlmod.read.rws.add_northsea(ds, cachedir=cachedir)\n"
+    "    ds = nlmod.read.rws.add_northsea(ds, cachedir=cachedir)"
    ]
   },
   {
@@ -171,7 +175,7 @@
     "ic = nlmod.gwf.ic(ds, gwf, starting_head=starting_head)\n",
     "\n",
     "# Create the output control package\n",
-    "oc = nlmod.gwf.oc(ds, gwf)\n"
+    "oc = nlmod.gwf.oc(ds, gwf)"
    ]
   },
   {
@@ -197,7 +201,7 @@
     "\n",
     "# add constant head cells at model boundaries\n",
     "ds.update(nlmod.mgrid.mask_model_edge(ds, ds[\"idomain\"]))\n",
-    "chd = nlmod.gwf.chd(ds, gwf, chd=\"edge_mask\", head=\"starting_head\")\n"
+    "chd = nlmod.gwf.chd(ds, gwf, chd=\"edge_mask\", head=\"starting_head\")"
    ]
   },
   {
@@ -213,7 +217,7 @@
     "ds.update(knmi_ds)\n",
     "\n",
     "# create recharge package\n",
-    "rch = nlmod.gwf.rch(ds, gwf)\n"
+    "rch = nlmod.gwf.rch(ds, gwf)"
    ]
   },
   {
@@ -240,7 +244,7 @@
    "source": [
     "nlmod.sim.write_and_run(\n",
     "    sim, ds, write_ds=True, nb_path=\"03_local_grid_refinement.ipynb\"\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -318,7 +322,7 @@
     "nlmod.plot.data_array(ds[\"bathymetry\"], ds, ax=axes[0][0])\n",
     "nlmod.plot.data_array(ds[\"northsea\"], ds, ax=axes[0][1])\n",
     "nlmod.plot.data_array(ds[\"kh\"][1], ds, ax=axes[1][0])\n",
-    "nlmod.plot.data_array(ds[\"recharge\"][:, 0], ds, ax=axes[1][1])\n"
+    "nlmod.plot.data_array(ds[\"recharge\"][:, 0], ds, ax=axes[1][1])"
    ]
   },
   {

diff --git a/docs/examples/07_resampling.ipynb b/docs/examples/07_resampling.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "# Resampling \n",
     "\n",
-    "Resampling data is a very common operation when building a Modflow model. Usually it is used to project data from one grid onto the other. There are many different ways to do this. This notebook shows some examples of resampling methods that are incorporated in the `nlmod` package. These methods rely heavily on resampling methods in other packages such as `scipy.interpolate` and `xarray`."
+    "Resampling data is a very common operation when building a Modflow model. Usually it is used to project data from one grid onto the other. There are many different ways to do this. This notebook shows some examples of resampling methods that are incorporated in the `nlmod` package. These methods rely heavily on resampling methods in packages such as `rioxarray` and `scipy.interpolate`."
    ]
   },
   {
@@ -198,7 +198,7 @@
     "# create vertextured dataarray\n",
     "coords = dict(x=xr.DataArray(xvc, dims=['icell2d',]), y=xr.DataArray(yvc, dims=['icell2d',]))\n",
     "vertex1 = xr.DataArray(values, dims=('icell2d'), coords=coords)\n",
-    "nlmod.visualise.plots.plot_vertex_array(vertex1, vertices, gridkwargs={'edgecolor': 'k'});"
+    "nlmod.plot.plot_vertex_array(vertex1, vertices, gridkwargs={'edgecolor': 'k'});"
    ]
   },
   {
@@ -217,7 +217,7 @@
     "vertex1_nan = vertex1.copy().astype(float)\n",
     "vertex1_nan.values[7] = np.nan\n",
     "\n",
-    "nlmod.visualise.plots.plot_vertex_array(vertex1_nan, vertices, gridkwargs={'edgecolor': 'k'});"
+    "nlmod.plot.plot_vertex_array(vertex1_nan, vertices, gridkwargs={'edgecolor': 'k'});"
    ]
   },
   {
@@ -247,10 +247,10 @@
    "source": [
     "def compare_structured_data_arrays(da1, da2, method, edgecolor='k'):\n",
     "    fig, axes = plt.subplots(ncols=2, figsize=(12,6))\n",
-    "    da1.plot(ax=axes[0], edgecolor=edgecolor)\n",
+    "    da1.plot(ax=axes[0], edgecolor=edgecolor, vmin=0, vmax=9)\n",
     "    axes[0].set_aspect('equal')\n",
     "    axes[0].set_title('original grid')\n",
-    "    da2.plot(ax=axes[1], edgecolor=edgecolor)\n",
+    "    da2.plot(ax=axes[1], edgecolor=edgecolor, vmin=0, vmax=9)\n",
     "    axes[1].set_aspect('equal')\n",
     "    axes[1].set_title(f'resampled grid, method {method}')"
    ]
@@ -367,10 +367,10 @@
    "source": [
     "def compare_struct_to_vertex(struc2d, res_vertex2d_n, vertices, method):\n",
     "    fig, axes = plt.subplots(ncols=2, figsize=(12,6))\n",
-    "    struc2d.plot(ax=axes[0], edgecolor='k')\n",
+    "    struc2d.plot(ax=axes[0], edgecolor='k', vmin=0, vmax=9)\n",
     "    axes[0].set_aspect('equal')\n",
     "    axes[0].set_title('structured grid')\n",
-    "    nlmod.visualise.plots.plot_vertex_array(res_vertex2d_n, vertices, ax=axes[1], gridkwargs={'edgecolor': 'k'})\n",
+    "    nlmod.plot.plot_vertex_array(res_vertex2d_n, vertices, ax=axes[1], gridkwargs={'edgecolor': 'k'}, vmin=0, vmax=9)\n",
     "    axes[1].set_title(f'locally refined grid, method {method}')"
    ]
   },
@@ -419,9 +419,9 @@
    "source": [
     "def compare_vertex_to_struct(vertex1, struc_out_n, method):\n",
     "    fig, axes = plt.subplots(ncols=2, figsize=(12,6))\n",
-    "    nlmod.visualise.plots.plot_vertex_array(vertex1, vertices, ax=axes[0], gridkwargs={'edgecolor': 'k'})\n",
+    "    nlmod.plot.plot_vertex_array(vertex1, vertices, ax=axes[0], gridkwargs={'edgecolor': 'k'}, vmin=0, vmax=9)\n",
     "    axes[0].set_title('original')\n",
-    "    struc_out_n.plot(ax=axes[1], edgecolor='k')\n",
+    "    struc_out_n.plot(ax=axes[1], edgecolor='k', vmin=0, vmax=9)\n",
     "    axes[1].set_title(f'resampled, method {method}')\n",
     "    axes[1].set_aspect('equal')"
    ]
@@ -502,9 +502,9 @@
    "source": [
     "def compare_vertex_arrays(vertex1, vertex2, method):\n",
     "    fig, axes = plt.subplots(ncols=2, figsize=(12,6))\n",
-    "    nlmod.visualise.plots.plot_vertex_array(vertex1, vertices, ax=axes[0], gridkwargs={'edgecolor': 'k'})\n",
+    "    nlmod.plot.plot_vertex_array(vertex1, vertices, ax=axes[0], gridkwargs={'edgecolor': 'k'}, vmin=0, vmax=9)\n",
     "    axes[0].set_title('original')\n",
-    "    nlmod.visualise.plots.plot_vertex_array(vertex2, vertices, ax=axes[1], gridkwargs={'edgecolor': 'k'})\n",
+    "    nlmod.plot.plot_vertex_array(vertex2, vertices, ax=axes[1], gridkwargs={'edgecolor': 'k'}, vmin=0, vmax=9)\n",
     "    axes[1].set_title(f'resampled, method {method}')\n",
     "    axes[1].set_aspect('equal')"
    ]
@@ -549,7 +549,9 @@
    "source": [
     "point_geom = [Point(x,y) for x, y in zip([1000, 1200, 1225, 1300],[20200, 20175, 20175, 20425])]\n",
     "point_gdf = gpd.GeoDataFrame({'values':[1,52,66,24]}, geometry=point_geom)\n",
-    "line_geom = [LineString([point_geom[0], point_geom[1]]), LineString([point_geom[2], point_geom[3]]), LineString([point_geom[0], point_geom[3]])]\n",
+    "line_geom = [LineString([point_geom[0], point_geom[1]]),\n",
+    "             LineString([point_geom[2], point_geom[3]]),\n",
+    "             LineString([point_geom[0], point_geom[3]])]\n",
     "line_gdf = gpd.GeoDataFrame({'values':[1,52,66]}, geometry=line_geom)\n",
     "pol_geom = [shp_polygon([point_geom[0], point_geom[1], point_geom[2], point_geom[3], point_geom[0]]),\n",
     "            shp_polygon([point_geom[0], point_geom[1], point_geom[2], Point(1200,20300), point_geom[0]])]\n",
@@ -1100,13 +1102,6 @@
     "    axes[1].set_aspect('equal')\n",
     "    axes[1].set_title(f'resampled grid, method {method}')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {