{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1a496643",
   "metadata": {},
   "source": [
    "# 06: FloPy class project: Quadtree grid version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc15635f-e887-417c-a9b6-583f1d0c758e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "\n",
    "import flopy\n",
    "from flopy.utils.gridgen import Gridgen\n",
    "from flopy.discretization import VertexGrid\n",
    "from flopy.utils.triangle import Triangle as Triangle\n",
    "from flopy.utils.gridintersect import GridIntersect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f55b0c73-ec82-4654-ac95-d840845c6a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ws = \"../temp/quadtree/\"\n",
    "grid_ws = f\"{model_ws}grid/\"\n",
    "Path(grid_ws).mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "output_folder = Path('../figures')\n",
    "output_folder.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff71738e-4c13-40f1-88e2-ac42cf408c5a",
   "metadata": {},
   "source": [
    "Load a few raster files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c1f45d54-e585-4b6f-aa44-8db54d57b68c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bottom = flopy.utils.Raster.load(\"../data_project/aquifer_bottom.asc\")\n",
    "top = flopy.utils.Raster.load(\"../data_project/aquifer_top.asc\")\n",
    "kaq = flopy.utils.Raster.load(\"../data_project/aquifer_k.asc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27b2059-7d15-48be-89e4-5ea3a0a4f246",
   "metadata": {},
   "source": [
    "Load a few shapefiles with geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a056df43-3d04-41c4-9e6d-641fd118f331",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "river = gpd.read_file(\"../data_project/Flowline_river.shp\")\n",
    "inactive = gpd.read_file(\"../data_project/inactive_area.shp\")\n",
    "active = gpd.read_file(\"../data_project/active_area.shp\")\n",
    "wells = gpd.read_file(\"../data_project/pumping_well_locations.shp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec28213-31e6-45f4-9899-56772618afb5",
   "metadata": {},
   "source": [
    "Plot the shapefiles with geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "802e1ec8-04c4-42c6-905d-e28ec06d3515",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  warnings.warn(\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  warnings.warn(\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  warnings.warn(\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  warnings.warn(\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = river.plot(color=\"cyan\")\n",
    "active.plot(ax=ax, color=\"blue\")\n",
    "inactive.plot(ax=ax, color=\"white\")\n",
    "wells.plot(ax=ax, color=\"red\", markersize=8);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f70862-cc56-45bd-b208-512aa9186d9f",
   "metadata": {},
   "source": [
    "#### Make a quadtree grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6ad60d3-234a-49dd-a01b-c63f256bc26f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/flopy/utils/gridgen.py:232: UserWarning: Supplying a dis object is deprecated, and support will be removed in version 3.3.7. Please supply StructuredGrid.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# quadtree grid\n",
    "sim = flopy.mf6.MFSimulation()\n",
    "gwf = flopy.mf6.ModflowGwf(sim)\n",
    "dx = dy = 250.0\n",
    "nrow = 40\n",
    "ncol = 20\n",
    "dis6 = flopy.mf6.ModflowGwfdis(\n",
    "    gwf,\n",
    "    nrow=nrow,\n",
    "    ncol=ncol,\n",
    "    delr=dy,\n",
    "    delc=dx,\n",
    ")\n",
    "g = Gridgen(dis6, model_ws=grid_ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76073787-d788-4319-a142-6dd54e044bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PosixPath('/Users/aleaf/Documents/Python_course/python-for-hydrology/notebooks/part1_flopy/data_project/Flowline_river'),\n",
       " PosixPath('/Users/aleaf/Documents/Python_course/python-for-hydrology/notebooks/part1_flopy/data_project/pumping_well_locations'))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "river_pth = (Path(\"../data_project\") / \"Flowline_river\").resolve()\n",
    "well_pth = (Path(\"../data_project\") / \"pumping_well_locations\").resolve()\n",
    "river_pth, well_pth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "54d5790e-5e06-42b3-ba68-5392d6a51bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "g.add_refinement_features(str(river_pth), \"line\", 4, range(1))\n",
    "g.add_refinement_features(str(well_pth), \"point\", 4, range(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a435862-467f-41e4-9e21-8e582c47a896",
   "metadata": {},
   "outputs": [],
   "source": [
    "g.build(verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a533621d-cf14-47fe-b2d9-e0e2fb5c04c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "gridprops = g.get_gridprops_vertexgrid()\n",
    "base_grid = VertexGrid(**gridprops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ae8e837a-26fd-44d9-a712-0a16135b0d07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['nlay', 'ncpl', 'top', 'botm', 'vertices', 'cell2d'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridprops.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd6d13ba-7e29-48a0-af5a-c85bce0df6c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  warnings.warn(\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  warnings.warn(\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_grid.plot()\n",
    "river.plot(color=\"cyan\", ax=plt.gca(), linewidth=0.5)\n",
    "wells.plot(ax=plt.gca(), color=\"red\", markersize=8, zorder=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649393e3-e455-41ee-968d-8131c0057a79",
   "metadata": {},
   "source": [
    "#### Define the number of layers and some simple shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "99fee6a1-dba6-4c47-9239-f0b2c0e81e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlay = 3\n",
    "shape2d, shape3d = (base_grid.ncpl), (nlay, base_grid.ncpl)\n",
    "xlen, ylen = 5000.0, 10000.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e67c65-3a48-4489-b284-8a535c195c8a",
   "metadata": {},
   "source": [
    "### Intersect the modelgrid with the shapefiles\n",
    "\n",
    "#### Create an intersection object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9eb9a7bd-02f1-4966-b522-4bdbabeb0d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "ix = GridIntersect(base_grid, rtree=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed099929-5257-4c83-93da-337aecfab0a1",
   "metadata": {},
   "source": [
    "#### Intersect inactive and active shapefiles with the modelgrid\n",
    "\n",
    "After all of the intersection operations, take a look at the data contained in the returned objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8e5ef38-e57c-41f7-8c45-a953ae1a5568",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "bedrock = ix.intersect(inactive.geometry[0])\n",
    "active_cells = ix.intersect(active.geometry[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d5ee1df-925f-4d93-8d06-7adaff89522f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(rec.array([(0, <POLYGON ((250 10000, 250 9750, 0 9750, 0 10000, 250 10000))>, 62500.),\n",
       "            (1, <POLYGON ((500 10000, 500 9750, 250 9750, 250 10000, 500 10000))>, 62500.),\n",
       "            (2, <POLYGON ((750 10000, 750 9750, 500 9750, 500 10000, 750 10000))>, 62500.),\n",
       "            (3, <POLYGON ((1000 10000, 1000 9750, 750 9750, 750 10000, 1000 10000))>, 62500.)],\n",
       "           dtype=[('cellids', 'O'), ('ixshapes', 'O'), ('areas', '<f8')]),\n",
       " dtype((numpy.record, [('cellids', 'O'), ('ixshapes', 'O'), ('areas', '<f8')])))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_cells[:4], active_cells.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01ea9e5f-0aa0-4219-9bdf-cc8c639188e4",
   "metadata": {},
   "source": [
    "#### Intersect well shapefile with the modelgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07742516-d0b2-4bb3-9afe-e25afcd55bcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "well_cells = []\n",
    "for g in wells.geometry:\n",
    "    v = ix.intersect(g)\n",
    "    well_cells += v[\"cellids\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71700525-1e82-4334-868a-33c84af18c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1003, 1220, 2562, 3221, 3604, 4385]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_cells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04ecb26d-183a-4629-8adb-6dd22a22d787",
   "metadata": {},
   "source": [
    "#### Intersect river shapefile with the modelgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "26e2f376-7ba5-42ae-8b8f-822f244cc888",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "river_cells = ix.intersect(river.geometry[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0fa25077-4dc4-4b64-9629-42733843cf9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(rec.array([(18, <LINESTRING (3347.204 10000, 3348.246 9984.375)>, 15.65968373),\n",
       "            (20, <LINESTRING (3348.246 9984.375, 3349.287 9968.75)>, 15.65968373),\n",
       "            (23, <LINESTRING (3349.287 9968.75, 3350.329 9953.125)>, 15.65968373),\n",
       "            (25, <LINESTRING (3350.329 9953.125, 3351.371 9937.5)>, 15.65968373)],\n",
       "           dtype=[('cellids', 'O'), ('ixshapes', 'O'), ('lengths', '<f8')]),\n",
       " dtype((numpy.record, [('cellids', 'O'), ('ixshapes', 'O'), ('lengths', '<f8')])))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "river_cells[:4], river_cells.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8d7508-eaa8-4a66-a0a9-6a9a3302a957",
   "metadata": {},
   "source": [
    "### Intersect constant head line with the modelgrid\n",
    "\n",
    "Use a line with two points to defined the location of the constant head cells. The line verticase are `[(1250, 0.1), (4250, 0.1)]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d1ad7c18-b221-4272-bd4c-aa8b4145373b",
   "metadata": {},
   "outputs": [],
   "source": [
    "constant_cells = ix.intersect(\n",
    "    [(1250, 0.1), (4250, 0.1)], shapetype=\"linestring\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b452111-1be5-40a9-afeb-878fb48875eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(rec.array([(5018, <LINESTRING (1250 0.1, 1500 0.1)>, 250.),\n",
       "            (5019, <LINESTRING (1500 0.1, 1750 0.1)>, 250.),\n",
       "            (5020, <LINESTRING (1750 0.1, 2000 0.1)>, 250.),\n",
       "            (5021, <LINESTRING (2000 0.1, 2250 0.1)>, 250.)],\n",
       "           dtype=[('cellids', 'O'), ('ixshapes', 'O'), ('lengths', '<f8')]),\n",
       " dtype((numpy.record, [('cellids', 'O'), ('ixshapes', 'O'), ('lengths', '<f8')])))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constant_cells[:4], constant_cells.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b16d9318-24e8-417e-8944-343da5262632",
   "metadata": {},
   "source": [
    "### Resample the raster data to the modelgrid\n",
    "\n",
    "Use the `resample_to_grid()` method on each raster object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00e8eed1-2735-48c8-8bc7-cb2ea88a78a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rtop = top.resample_to_grid(\n",
    "    base_grid,\n",
    "    band=top.bands[0],\n",
    "    method=\"linear\",\n",
    "    extrapolate_edges=True,\n",
    ")\n",
    "rbot = bottom.resample_to_grid(\n",
    "    base_grid,\n",
    "    band=bottom.bands[0],\n",
    "    method=\"linear\",\n",
    "    extrapolate_edges=True,\n",
    ")\n",
    "rkaq = (\n",
    "    kaq.resample_to_grid(\n",
    "        base_grid, band=kaq.bands[0], method=\"linear\", extrapolate_edges=True\n",
    "    )\n",
    "    * 86400.0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d38c553-7319-445a-bda1-b9f1b324ba87",
   "metadata": {},
   "source": [
    "### Plot the resampled data \n",
    "\n",
    "Plot the aquifer top, bottom, and hydraulic conductivity. Also plot the aquifer thickness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d3e680a2-7112-4c79-81b8-5c9dc15c0f89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = flopy.plot.PlotMapView(modelgrid=base_grid)\n",
    "cb = mm.plot_array(rtop)\n",
    "mm.plot_grid(lw=0.5, color=\"0.5\")\n",
    "plt.colorbar(cb, ax=plt.gca());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e344430-3dd6-40f3-99dc-eb3ff4525fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = flopy.plot.PlotMapView(modelgrid=base_grid)\n",
    "cb = mm.plot_array(rbot)\n",
    "mm.plot_grid(lw=0.5, color=\"0.5\")\n",
    "plt.colorbar(cb, ax=plt.gca());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6025c075-3b2f-4bc4-929c-6ded94d7fb49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = flopy.plot.PlotMapView(modelgrid=base_grid)\n",
    "cb = mm.plot_array(rtop - rbot)\n",
    "mm.plot_grid(lw=0.5, color=\"0.5\")\n",
    "plt.colorbar(cb);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fc131289-28c2-4c27-9c28-e2393760ee7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = flopy.plot.PlotMapView(modelgrid=base_grid)\n",
    "cb = mm.plot_array(rkaq)\n",
    "mm.plot_grid(lw=0.5, color=\"0.5\")\n",
    "plt.colorbar(cb);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f459b8-7ba3-4778-a790-25ea654f5b49",
   "metadata": {},
   "source": [
    "#### Build the model data\n",
    "\n",
    "_Create the bottom of each model layer_\n",
    "\n",
    "Assume that the thickness of each layer at a row, column location is equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fa75d47d-2a36-4d14-9e3a-8e690933315a",
   "metadata": {},
   "outputs": [],
   "source": [
    "botm = np.zeros(shape3d, dtype=float)\n",
    "botm[-1, :] = rbot[:]\n",
    "layer_thickness = (rtop - rbot) / nlay\n",
    "for k in reversed(range(nlay - 1)):\n",
    "    botm[k] = botm[k + 1] + layer_thickness"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78271f86-4d9b-4e95-8f6d-159340ba0030",
   "metadata": {},
   "source": [
    "_Create the idomain array_\n",
    "\n",
    "Use the intersection data from the active and inactive shapefiles to create the idomain array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6bb26307-5b6f-46e1-b633-45b6a851c2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "idomain = np.zeros(shape3d, dtype=float)\n",
    "for node in active_cells[\"cellids\"]:\n",
    "    idomain[:, node] = 1\n",
    "for node in bedrock[\"cellids\"]:\n",
    "    idomain[:, node] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410fd6be-b9b2-44d8-8e45-cf022470d4a5",
   "metadata": {},
   "source": [
    "_Build the well package stress period data_\n",
    "\n",
    "* The pumping rates are in the `wells` geopandas dataframe\n",
    "* Pumping rates are in m/sec\n",
    "* The wells are located in model layer 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8abad8ba-f38b-4c85-bafe-040b8bd5de26",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FID</th>\n",
       "      <th>Q</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.00820</td>\n",
       "      <td>POINT (3875.000 7875.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.00410</td>\n",
       "      <td>POINT (3125.000 7375.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.00390</td>\n",
       "      <td>POINT (3375.000 5125.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.00083</td>\n",
       "      <td>POINT (2375.000 3625.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.00072</td>\n",
       "      <td>POINT (1375.000 2875.000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.00430</td>\n",
       "      <td>POINT (2875.000 1625.000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FID        Q                   geometry\n",
       "0    0 -0.00820  POINT (3875.000 7875.000)\n",
       "1    1 -0.00410  POINT (3125.000 7375.000)\n",
       "2    2 -0.00390  POINT (3375.000 5125.000)\n",
       "3    3 -0.00083  POINT (2375.000 3625.000)\n",
       "4    4 -0.00072  POINT (1375.000 2875.000)\n",
       "5    5 -0.00430  POINT (2875.000 1625.000)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "92672b02-13cb-4253-b009-cfa23ff572be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[2, 1003, -708.48],\n",
       " [2, 1220, -354.24],\n",
       " [2, 2562, -336.96],\n",
       " [2, 3221, -71.712],\n",
       " [2, 3604, -62.208000000000006],\n",
       " [2, 4385, -371.52]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_spd = []\n",
    "for (cellid, q) in zip(well_cells, wells[\"Q\"]):\n",
    "    well_spd.append([2, cellid, q * 86400.0])\n",
    "well_spd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9acc234-e7b7-434a-8a1d-8533c7225003",
   "metadata": {},
   "source": [
    "_Build the river package stress period data_\n",
    "\n",
    "* Calculate the length of the river using the `\"lengths\"` key. \n",
    "* The vertical hydraulic conductivity of the river bed sediments is 3.5 m/d.\n",
    "* The thickness of river bottom sediments at the upstream (North) and downstream (South) end of the river is 0.5 and 1.5 meters, respectively. \n",
    "* The river bottom at the upstream and downstream end of the river is 16.5 and 14.5 meters, respectively. The river width at the upstream and downstream end of the river is 5.0 and 10.0 meters, respectively. \n",
    "* The river stage at the upstream and downstream end of the river is 16.6 and 15.5 meters, respectively.\n",
    "* Use the boundname `upstream` for river cells where the upstream end of the river cell is less than 5000 m from the North end of the model. Use the boundname `downstream` for all other river cells.\n",
    "\n",
    "Use the upstream and downstream values to interpolate the river sediment thickness, bottom, width, and stage for each river cell.\n",
    "\n",
    "The river cells will be connected to model layer 1. The river bottom, width, and stage values should be calculated at the center of the river reach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1f0b8a27-6dba-4f62-b11c-f92bdada50a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "river_kv = 3.5\n",
    "river_thickness_up, river_thickness_down = 0.5, 1.5\n",
    "river_bot_up, river_bot_down = 16.5, 14.5\n",
    "river_width_up, river_width_down = 5.0, 10.0\n",
    "stage_up, stage_down = 16.6, 15.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d0013ce6-3c75-4f25-b7a4-17cf7ce7e456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12419.290359618695"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "river_length = river_cells[\"lengths\"].sum()\n",
    "river_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a565b394-c57c-45bd-a947-8565c86ed87b",
   "metadata": {},
   "outputs": [],
   "source": [
    "river_thickness_slope = (\n",
    "    river_thickness_down - river_thickness_up\n",
    ") / river_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "488d4e1b-6ee5-474e-91a0-6fe1dc764dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "river_bot_slope = (river_bot_down - river_bot_up) / river_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9f7fca4f-b31a-4679-a06c-9fe79abd4318",
   "metadata": {},
   "outputs": [],
   "source": [
    "river_width_slope = (river_width_down - river_width_up) / river_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9d5cafa8-be40-4055-a56b-b67878c78e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "stage_slope = (stage_down - stage_up) / river_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d693f849-55a8-450c-934b-2cb7a2ddcc80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[0,\n",
       "   18,\n",
       "   16.599306496120093,\n",
       "   547.7438185279854,\n",
       "   16.498739083854712,\n",
       "   'upstream'],\n",
       "  [0,\n",
       "   20,\n",
       "   16.597919488360276,\n",
       "   547.056195764861,\n",
       "   16.496217251564133,\n",
       "   'upstream']],\n",
       " [[0,\n",
       "   5093,\n",
       "   15.502077782281075,\n",
       "   365.0282982693656,\n",
       "   14.50377778596559,\n",
       "   'downstream'],\n",
       "  [0,\n",
       "   5095,\n",
       "   15.500692594093692,\n",
       "   364.9515826007674,\n",
       "   14.50125926198853,\n",
       "   'downstream']])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boundname = \"upstream\"\n",
    "total_length = 0.0\n",
    "river_spd = []\n",
    "river_top_delta = []\n",
    "for idx, (cellid, length) in enumerate(\n",
    "    zip(river_cells[\"cellids\"], river_cells[\"lengths\"])\n",
    "):\n",
    "    if total_length >= 5000.0 and boundname == \"upstream\":\n",
    "        boundname = \"downstream\"\n",
    "    dx = 0.5 * length\n",
    "    total_length += dx\n",
    "\n",
    "    river_thickness = river_thickness_up + river_thickness_slope * total_length\n",
    "    river_bot = river_bot_up + river_bot_slope * total_length\n",
    "    river_width = river_width_up + river_width_slope * total_length\n",
    "    river_stage = stage_up + stage_slope * total_length\n",
    "    conductance = river_kv * length * river_width / river_thickness\n",
    "    river_spd.append(\n",
    "        [0, cellid, river_stage, conductance, river_bot, boundname]\n",
    "    )\n",
    "    river_top_delta.append(river_bot - rtop[cellid])\n",
    "\n",
    "    total_length += dx\n",
    "river_top_delta = np.array(river_top_delta)\n",
    "river_spd[:2], river_spd[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "713c3406-48ca-4e60-80f5-bd9a9d78aa9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.2690004091658569,\n",
       " -0.09015671387478008,\n",
       " -0.6221983009423475,\n",
       " array([-1.00821085, -1.01072938]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "river_top_delta.min(), river_top_delta.max(), river_top_delta.mean(), river_top_delta[\n",
    "    -2:\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "837ae702-a313-4e4e-90f0-6bc6c6b5ef61",
   "metadata": {},
   "source": [
    "_Define river observations_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "452fa4f3-aac7-422b-b38b-1045e8b4cd1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "riv_obs = {\n",
    "    \"riv_obs.csv\": [\n",
    "        (\"UPSTREAM\", \"RIV\", \"UPSTREAM\"),\n",
    "        (\"DOWNSTREAM\", \"RIV\", \"DOWNSTREAM\"),\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6972f4c-a24c-4622-a665-e1b5fbe06530",
   "metadata": {},
   "source": [
    "_Build SFR datasets_\n",
    "\n",
    "`<rno> <cellid(ncelldim)> <rlen> <rwid> <rgrd> <rtp> <rbth> <rhk> <man> <ncon> <ustrf> <ndv> [<aux(naux)>] [<boundname>]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b4a497cd-22af-468a-adb7-5b640c2f9c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nreaches = river_cells.shape[0]\n",
    "nreaches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "07b90a0d-a474-4f8a-90f7-9d62d2268e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "boundname = \"upstream\"\n",
    "gage1_loc = None\n",
    "total_length = 0.0\n",
    "sfr_packagedata = []\n",
    "sfr_connectivity = []\n",
    "for idx, (cellid, length) in enumerate(\n",
    "    zip(river_cells[\"cellids\"], river_cells[\"lengths\"])\n",
    "):\n",
    "    if total_length >= 5000.0 and boundname == \"upstream\":\n",
    "        boundname = \"downstream\"\n",
    "        gage1_loc = idx - 1\n",
    "    dx = 0.5 * length\n",
    "    total_length += dx\n",
    "\n",
    "    river_thickness = river_thickness_up + river_thickness_slope * total_length\n",
    "    river_bot = river_bot_up + river_bot_slope * total_length\n",
    "    river_width = river_width_up + river_width_slope * total_length\n",
    "\n",
    "    if idx == 0:\n",
    "        nconn = 1\n",
    "        sfr_connectivity.append((idx, -(idx + 1)))\n",
    "    elif idx == nreaches - 1:\n",
    "        nconn = 1\n",
    "        sfr_connectivity.append((idx, (idx - 1)))\n",
    "    else:\n",
    "        nconn = 2\n",
    "        sfr_connectivity.append((idx, (idx - 1), -(idx + 1)))\n",
    "\n",
    "    sfr_layer = None\n",
    "    for k in range(nlay):\n",
    "        if river_bot - river_thickness > botm[k, cellid]:\n",
    "            sfr_layer = k\n",
    "\n",
    "    if sfr_layer is None:\n",
    "        sfr_cellid = \"none\"\n",
    "    else:\n",
    "        sfr_cellid = (sfr_layer, cellid)\n",
    "\n",
    "    leakance = river_kv * river_thickness\n",
    "    sfr_packagedata.append(\n",
    "        (\n",
    "            idx,\n",
    "            sfr_cellid,\n",
    "            length,\n",
    "            river_width,\n",
    "            -river_bot_slope,\n",
    "            river_bot,\n",
    "            river_thickness,\n",
    "            river_kv,\n",
    "            0.035,\n",
    "            nconn,\n",
    "            1.0,\n",
    "            0,\n",
    "            boundname,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    total_length += dx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "34430a13-a043-4ab8-9f87-6aebb407f17b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: [(0, 'inflow', 864000.0)]}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfr_spd = {0: [(0, \"inflow\", 864000.0)]}\n",
    "sfr_spd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a655722c-29ac-460c-919d-c599d4f1dfc3",
   "metadata": {},
   "source": [
    "`<rno> <cellid(ncelldim)> <rlen> <rwid> <rgrd> <rtp> <rbth> <rhk> <man> <ncon> <ustrf> <ndv> [<aux(naux)>] [<boundname>]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c43db498-27a6-425c-8cae-f9622d700c03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(0,\n",
       "   (2, 18),\n",
       "   15.6596837274718,\n",
       "   5.0031522903632215,\n",
       "   0.00016103979712906924,\n",
       "   16.498739083854712,\n",
       "   0.5006304580726444,\n",
       "   3.5,\n",
       "   0.035,\n",
       "   1,\n",
       "   1.0,\n",
       "   0,\n",
       "   'upstream'),\n",
       "  (1,\n",
       "   (2, 20),\n",
       "   15.65968372747177,\n",
       "   5.009456871089665,\n",
       "   0.00016103979712906924,\n",
       "   16.496217251564133,\n",
       "   0.5018913742179331,\n",
       "   3.5,\n",
       "   0.035,\n",
       "   2,\n",
       "   1.0,\n",
       "   0,\n",
       "   'upstream')],\n",
       " [(998,\n",
       "   (2, 5093),\n",
       "   15.63914027438707,\n",
       "   9.990555535086022,\n",
       "   0.00016103979712906924,\n",
       "   14.50377778596559,\n",
       "   1.4981111070172046,\n",
       "   3.5,\n",
       "   0.035,\n",
       "   2,\n",
       "   1.0,\n",
       "   0,\n",
       "   'downstream'),\n",
       "  (999,\n",
       "   (2, 5095),\n",
       "   15.639140274387051,\n",
       "   9.996851845028672,\n",
       "   0.00016103979712906924,\n",
       "   14.50125926198853,\n",
       "   1.4993703690057347,\n",
       "   3.5,\n",
       "   0.035,\n",
       "   1,\n",
       "   1.0,\n",
       "   0,\n",
       "   'downstream')])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfr_packagedata[:2], sfr_packagedata[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4f0f0568-b1ef-45a1-8ca1-4e2c0c549363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(0, -1), (1, 0, -2)], [(998, 997, -999), (999, 998)])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfr_connectivity[:2], sfr_connectivity[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a2bebce1-274b-47a6-b078-4d532e14f4d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "sfr_obs = {\n",
    "    \"sfr_obs.csv\": [\n",
    "        (\"UPSTREAM\", \"SFR\", \"UPSTREAM\"),\n",
    "        (\"DOWNSTREAM\", \"SFR\", \"DOWNSTREAM\"),\n",
    "        (\"GAGE1\", \"downstream-flow\", (gage1_loc,)),\n",
    "        (\"GAGE2\", \"ext-outflow\", (nreaches - 1,)),\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "831eea1f-8e29-4e76-b668-9758abcb2122",
   "metadata": {},
   "source": [
    "_Define the constant head cells_\n",
    "\n",
    "Assume the constant head cells are located in all three layers and have values equal to the downstream river stage (`stage_down`). Make sure the constant head stage is greater that the bottom of the layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bcb8ad80-b1ba-4510-bb62-d658976418c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([(1, 5018, 15.5), (2, 5018, 15.5)], [(1, 5104, 15.5), (2, 5104, 15.5)])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chd_spd = []\n",
    "for node in constant_cells[\"cellids\"]:\n",
    "    for k in range(nlay):\n",
    "        if stage_down > botm[k, node]:\n",
    "            chd_spd.append((k, node, stage_down))\n",
    "chd_spd[:2], chd_spd[-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f85bfe-d6df-42ff-b475-3d63773a3c2f",
   "metadata": {},
   "source": [
    "_Define recharge rates_\n",
    "\n",
    "* The recharge rate is 0.16000000E-08 m/sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "08edb467-a90f-47c9-8465-0b619ea02e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00013824"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recharge_rate = 0.16000000e-08 * 86400.0\n",
    "recharge_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6353df62-e5b5-4bab-9e32-0f4cf3e679df",
   "metadata": {},
   "source": [
    "#### Build the model\n",
    "\n",
    "Build a steady-state model using the data that you have created. Packages to create:\n",
    "\n",
    "* Simulation\n",
    "* TDIS (1 stress period, `TIME_UNITS='days'`)\n",
    "* IMS (default parameters)\n",
    "\n",
    "* GWF model (`SAVE_FLOWS=True`)\n",
    "* DISV (`LENGTH_UNITS='meters'`)\n",
    "* IC (`STRT=40.`)\n",
    "* NPF (Unconfined, same K for all layers, save `SAVE_SPECIFIC_DISCHARGE=True`)\n",
    "* RCH (array based)\n",
    "* SFR (`BOUNDNAMES=True`,add `SFR` observations for defined boundnames)\n",
    "* CHD\n",
    "* WEL\n",
    "* OC (Save `HEAD ALL` and `BUDGET ALL`)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b98023a9-21da-4916-a419-4047a423b5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mf6'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = \"project\"\n",
    "# exe_name = Path(\"/Users/jdhughes/Documents/Training/python-for-hydrology/notebooks/part1_flopy/day0\").resolve() / \"mf6\"  #\"/Users/jdhughes/Documents/Development/modflow6/modflow6/bin/mf6\"\n",
    "exe_name = \"mf6\"\n",
    "exe_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ceeb9cf4-c2a5-4eed-bb6e-058b141739d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = flopy.mf6.MFSimulation(\n",
    "    sim_name=name, sim_ws=model_ws, exe_name=str(exe_name)\n",
    ")\n",
    "tdis = flopy.mf6.ModflowTdis(sim, time_units=\"days\")\n",
    "ims = flopy.mf6.ModflowIms(\n",
    "    sim,\n",
    "    linear_acceleration=\"bicgstab\",\n",
    "    outer_maximum=200,\n",
    "    inner_maximum=100,\n",
    "    print_option=\"all\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9c85c033-c73a-43ec-8682-e866c094fcb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "gwf = flopy.mf6.ModflowGwf(\n",
    "    sim,\n",
    "    modelname=name,\n",
    "    save_flows=True,\n",
    "    newtonoptions=\"NEWTON UNDER_RELAXATION\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0ce841a8-14ce-408a-844d-49251d91b091",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Unable to resolve dimension of ('gwf6', 'disv', 'cell2d', 'cell2d', 'icvert') based on shape \"ncvert\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n"
     ]
    }
   ],
   "source": [
    "dis = flopy.mf6.ModflowGwfdisv(\n",
    "    gwf,\n",
    "    length_units=\"meters\",\n",
    "    nlay=nlay,\n",
    "    ncpl=base_grid.ncpl,\n",
    "    nvert=base_grid.nvert,\n",
    "    vertices=gridprops[\"vertices\"],\n",
    "    cell2d=gridprops[\"cell2d\"],\n",
    "    top=rtop,\n",
    "    botm=botm,\n",
    "    idomain=idomain,\n",
    ")\n",
    "ic = flopy.mf6.ModflowGwfic(gwf, strt=[rtop for k in range(nlay)])\n",
    "npf = flopy.mf6.ModflowGwfnpf(\n",
    "    gwf, save_specific_discharge=True, k=[rkaq, rkaq, rkaq], icelltype=1\n",
    ")\n",
    "rch = flopy.mf6.ModflowGwfrcha(gwf, recharge=recharge_rate)\n",
    "# riv = flopy.mf6.ModflowGwfriv(gwf, boundnames=True, stress_period_data=river_spd)\n",
    "# riv.obs.initialize(\n",
    "#     filename=f\"{name}.riv.obs\",\n",
    "#     print_input=True,\n",
    "#     continuous=riv_obs,\n",
    "# )\n",
    "sfr = flopy.mf6.ModflowGwfsfr(\n",
    "    gwf,\n",
    "    unit_conversion=86400.0,\n",
    "    boundnames=True,\n",
    "    print_flows=True,\n",
    "    print_stage=True,\n",
    "    nreaches=nreaches,\n",
    "    packagedata=sfr_packagedata,\n",
    "    connectiondata=sfr_connectivity,\n",
    "    perioddata=sfr_spd,\n",
    ")\n",
    "sfr.obs.initialize(\n",
    "    filename=f\"{name}.sfr.obs\",\n",
    "    print_input=True,\n",
    "    continuous=sfr_obs,\n",
    ")\n",
    "wel = flopy.mf6.ModflowGwfwel(gwf, stress_period_data=well_spd)\n",
    "chd = flopy.mf6.ModflowGwfchd(gwf, stress_period_data=chd_spd)\n",
    "oc = flopy.mf6.ModflowGwfoc(\n",
    "    gwf,\n",
    "    head_filerecord=f\"{name}.hds\",\n",
    "    budget_filerecord=f\"{name}.cbc\",\n",
    "    saverecord=[(\"HEAD\", \"ALL\"), (\"BUDGET\", \"ALL\")],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a6d3a06-05b3-4f68-aa34-00dec952ef7d",
   "metadata": {},
   "source": [
    "#### Write the model files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6a671453-1197-41db-9905-cc6549f705e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "writing simulation...\n",
      "  writing simulation name file...\n",
      "  writing simulation tdis package...\n",
      "  writing solution package ims_-1...\n",
      "  writing model project...\n",
      "    writing model name file...\n",
      "    writing package disv...\n",
      "    writing package ic...\n",
      "    writing package npf...\n",
      "    writing package rcha_0...\n",
      "    writing package sfr_0...\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "WARNING: Unable to resolve dimension of ('gwf6', 'sfr', 'connectiondata', 'connectiondata', 'ic') based on shape \"nconifno\".\n",
      "    writing package obs_0...\n",
      "    writing package wel_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'wel', 'dimensions') changed to 6 based on size of stress_period_data\n",
      "    writing package chd_0...\n",
      "INFORMATION: maxbound in ('gwf6', 'chd', 'dimensions') changed to 56 based on size of stress_period_data\n",
      "    writing package oc...\n"
     ]
    }
   ],
   "source": [
    "sim.write_simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b53e1ada-e0a7-43b0-a72a-cc5ed8a4bc31",
   "metadata": {},
   "source": [
    "#### Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8f6a82ba-1005-484e-8a5a-3f59e732eb58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FloPy is using the following executable to run the model: ../../../../../../software/modflow_exes/mf6\n",
      "                                   MODFLOW 6\n",
      "                U.S. GEOLOGICAL SURVEY MODULAR HYDROLOGIC MODEL\n",
      "                            VERSION 6.4.2 06/28/2023\n",
      "\n",
      "   MODFLOW 6 compiled Jun 28 2023 18:34:54 with Intel(R) Fortran Intel(R) 64\n",
      "   Compiler Classic for applications running on Intel(R) 64, Version 2021.7.0\n",
      "                             Build 20220726_000000\n",
      "\n",
      "This software has been approved for release by the U.S. Geological \n",
      "Survey (USGS). Although the software has been subjected to rigorous \n",
      "review, the USGS reserves the right to update the software as needed \n",
      "pursuant to further analysis and review. No warranty, expressed or \n",
      "implied, is made by the USGS or the U.S. Government as to the \n",
      "functionality of the software and related material nor shall the \n",
      "fact of release constitute any such warranty. Furthermore, the \n",
      "software is released on condition that neither the USGS nor the U.S. \n",
      "Government shall be held liable for any damages resulting from its \n",
      "authorized or unauthorized use. Also refer to the USGS Water \n",
      "Resources Software User Rights Notice for complete use, copyright, \n",
      "and distribution information.\n",
      "\n",
      " \n",
      " Run start date and time (yyyy/mm/dd hh:mm:ss): 2024/02/03 10:41:28\n",
      " \n",
      " Writing simulation list file: mfsim.lst\n",
      " Using Simulation name file: mfsim.nam\n",
      " \n",
      "    Solving:  Stress period:     1    Time step:     1\n",
      " \n",
      " Run end date and time (yyyy/mm/dd hh:mm:ss): 2024/02/03 10:41:29\n",
      " Elapsed run time:  0.777 Seconds\n",
      " \n",
      "\n",
      "WARNING REPORT:\n",
      "\n",
      "  1. OPTIONS BLOCK VARIABLE 'UNIT_CONVERSION' IN FILE 'project.sfr' WAS\n",
      "     DEPRECATED IN VERSION 6.4.2. SETTING UNIT_CONVERSION DIRECTLY.\n",
      " Normal termination of simulation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.run_simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab370b7-c023-402a-bf3d-792cc82ad4de",
   "metadata": {},
   "source": [
    "#### Post-process the results\n",
    "\n",
    "Use `gwf.output.` method to get the observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6b1b0a5d-b1e6-4824-94c3-c849e176146b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('totim', '<f8'), ('UPSTREAM', '<f8'), ('DOWNSTREAM', '<f8'), ('GAGE1', '<f8'), ('GAGE2', '<f8')])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myobs = gwf.sfr.output.obs().get_data()\n",
    "myobs.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5f66f01c-9f11-4d83-be31-874c4df3717a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1139.41061029]), array([-1905.93621052]))"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myobs[\"UPSTREAM\"], myobs[\"DOWNSTREAM\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b64389-c690-4c21-bf74-2d4a5479de23",
   "metadata": {},
   "source": [
    "Use `gwf.output.` method to get the heads and specific discharge. Make a map and cross-sections of the data using `flopy.plot` methods. Plot specific discharge vectors on the map and cross-sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5d872228-ac57-4cba-8873-fb6430dac670",
   "metadata": {},
   "outputs": [],
   "source": [
    "head = gwf.output.head().get_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4d75de40-e830-4c20-9b16-c5edfbdbb628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'    FLOW-JA-FACE',\n",
       " b'      DATA-SPDIS',\n",
       " b'             WEL',\n",
       " b'            RCHA',\n",
       " b'             CHD',\n",
       " b'             SFR']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gwf.output.budget().get_unique_record_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cc988112-38dd-43dc-b4f0-77ec2f41b627",
   "metadata": {},
   "outputs": [],
   "source": [
    "spdis = gwf.output.budget().get_data(text=\"DATA-SPDIS\")[0]\n",
    "qx, qy, qz = flopy.utils.postprocessing.get_specific_discharge(\n",
    "    spdis, gwf, head=head\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "263dd83d-0579-4340-9107-fdb9c7bc600a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  warnings.warn(\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    ncols=1,\n",
    "    nrows=1,\n",
    "    figsize=(5, 8),\n",
    "    constrained_layout=True,\n",
    "    subplot_kw=dict(aspect=\"equal\"),\n",
    ")\n",
    "mm = flopy.plot.PlotMapView(model=gwf, ax=ax, layer=0)\n",
    "cb = mm.plot_array(head, masked_values=[1e30], vmin=10, vmax=30)\n",
    "river.plot(color=\"cyan\", ax=mm.ax)\n",
    "mm.plot_grid(lw=0.5, color=\"0.5\")\n",
    "mm.plot_vector(qx, qy, qz, normalize=True)\n",
    "cs = mm.contour_array(\n",
    "    head,\n",
    "    colors=\"red\",\n",
    "    levels=np.arange(10, 28, 1),\n",
    "    linestyles=\":\",\n",
    "    linewidths=1.0,\n",
    ")\n",
    "ax.clabel(\n",
    "    cs,\n",
    "    inline=True,\n",
    "    fmt=\"%1.0f\",\n",
    "    fontsize=10,\n",
    "    inline_spacing=0.5,\n",
    ")\n",
    "plt.colorbar(cb, ax=mm.ax, shrink=0.5)\n",
    "\n",
    "fig.savefig(output_folder / \"freyberg-quadtree.png\", dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cef15171-ee4d-4085-8a5d-b7de5e1e304d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to GeoDataFrame is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  warnings.warn(\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geodataframe.py:1645: DeprecationWarning: Passing a SingleBlockManager to Series is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  srs = pd.Series(*args, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n",
      "/Users/aleaf/mambaforge/envs/pyclass/lib/python3.11/site-packages/geopandas/geoseries.py:221: DeprecationWarning: Passing a SingleBlockManager to GeoSeries is deprecated and will raise in a future version. Use public APIs instead.\n",
      "  super().__init__(data, index=index, name=name, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    ncols=1,\n",
    "    nrows=1,\n",
    "    figsize=(5, 8),\n",
    "    constrained_layout=True,\n",
    "    subplot_kw=dict(aspect=\"equal\"),\n",
    ")\n",
    "\n",
    "mm = flopy.plot.PlotMapView(model=gwf, ax=ax, layer=0)\n",
    "cb = mm.plot_array(gwf.dis.top.array, masked_values=[1e30], vmin=15, vmax=30)\n",
    "river.plot(color=\"cyan\", ax=mm.ax)\n",
    "mm.plot_grid(lw=0.5, color=\"0.5\", zorder=11)\n",
    "mm.plot_inactive(zorder=10)\n",
    "cs = mm.contour_array(\n",
    "    gwf.dis.top.array,\n",
    "    colors=\"red\",\n",
    "    levels=np.arange(15, 31, 1),\n",
    "    linestyles=\":\",\n",
    "    linewidths=1.0,\n",
    ")\n",
    "ax.clabel(\n",
    "    cs,\n",
    "    inline=True,\n",
    "    fmt=\"%1.0f\",\n",
    "    fontsize=10,\n",
    "    inline_spacing=0.5,\n",
    ")\n",
    "plt.colorbar(cb, ax=mm.ax, shrink=0.5)\n",
    "\n",
    "fig.savefig(output_folder / \"freyberg-quadtree-grid.png\", dpi=300);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59af47b-83cb-448b-8a0b-a1ef0039229d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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