diff --git a/examples/alternative_networkx_2d.ipynb b/examples/alternative_networkx_2d.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..892d86c39150831eb7cda162c6716e9f740b3a15
--- /dev/null
+++ b/examples/alternative_networkx_2d.ipynb
@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "attachments": {
+    "3f058920-7fc9-4aed-8362-427d19722cc2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "65e78811-2a04-463e-b94a-3e72bf4eee4c",
+   "metadata": {},
+   "source": [
+    "# Alternative implementation using graph construction and breadth-first search implemented by networkx python library\n",
+    "\n",
+    "https://networkx.org/\n",
+    "![grafik.png](attachment:3f058920-7fc9-4aed-8362-427d19722cc2.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e0dcf723-d338-4b62-b5bc-d2b665527fab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "\n",
+    "import tricco\n",
+    "from tricco.alternatives import nwx "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b89f508-be18-470c-a595-6b3c00ffe1bf",
+   "metadata": {},
+   "source": [
+    "## 1. Load grid and construct full graph of the grid that includes all connection edges between all cells\n",
+    "\n",
+    "Do this separately for vertex and edge connectivity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "46fa620f-0e90-492a-8ea4-7b0218bf5c72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gridfile = \"./data/icon-grid_nawdex_78w40e23n80n_R80000m.nc\"\n",
+    "grid = nwx.prepare_grid(model=\"ICON\", file=gridfile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0b3926a9-ecbe-4619-8ee2-98b6d07f84eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fgraph_vert = nwx.compute_fullgraph(grid, connectivity=\"vertex\")\n",
+    "fgraph_edge = nwx.compute_fullgraph(grid, connectivity=\"edge\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc23ddda-7010-47ff-9bc7-8360b243e057",
+   "metadata": {},
+   "source": [
+    "## 2. Load cloud data and identify connected components.  Result is a list of connected components ordered by size, where each connected component is a list of cell indices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5e57f10e-a411-4021-af4a-3d6adaa28842",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datafile = \"./data/nawdexnwp-80km-mis-0001_2016092200_2d_30min_DOM01_ML_0060.nc\"\n",
+    "data = xr.open_dataset(datafile)[\"clct\"].squeeze()\n",
+    "\n",
+    "components_vert = nwx.compute_connected_components_2d(data, fgraph_vert, 85.0)\n",
+    "components_edge = nwx.compute_connected_components_2d(data, fgraph_edge, 85.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aff336f5-6d16-48f5-8fc8-191a9de67759",
+   "metadata": {},
+   "source": [
+    "Map connected components on the structure of the field input data, with the value indicating the connected component. Needed for plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "51a1bff0-3fab-46f8-a822-4ce37bdb5fc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ncells = grid.cell.size\n",
+    "\n",
+    "field_cc_vert = np.zeros(ncells)\n",
+    "for icomp in range(len(components_vert)):\n",
+    "    field_cc_vert[list(components_vert[icomp])]=icomp+1\n",
+    "\n",
+    "field_cc_edge = np.zeros(ncells)\n",
+    "for icomp in range(len(components_edge)):\n",
+    "    field_cc_edge[list(components_edge[icomp])]=icomp+1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5c472e6-d101-42eb-9ad6-876cc1f4821c",
+   "metadata": {},
+   "source": [
+    "## 3. Plotting. Same as in example-2d.ipynb for the tricco cubulation implementation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2592faa4-0263-4bc7-9ca0-bb53bc9faaed",
+   "metadata": {},
+   "source": [
+    "Define a qualitative colormap that cycles through the colors of the matplotlib Set1 colormap, and that always plots 0 as white."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "250ef78f-b9b7-4cff-990b-3bd2c7b416ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_colormap(ncolors):\n",
+    "    import matplotlib as mpl    \n",
+    "    cmap_base = plt.cm.Set1 # note: number of colors of base color map is given by cmap_base.N\n",
+    "    cmaplist_base = [cmap_base(i) for i in range(cmap_base.N)]\n",
+    "    cmaplist = [(1, 1, 1, 1.0)] # need to have white as first color for the triangles with no connected component\n",
+    "    for i in range(ncolors-1):\n",
+    "        icolor = np.mod(i,cmap_base.N)\n",
+    "        cmaplist.append(cmaplist_base[icolor])\n",
+    "    return mpl.colors.LinearSegmentedColormap.from_list('Custom cmap', cmaplist, ncolors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27e8f072-364c-4947-b1ab-e35d5a6cb814",
+   "metadata": {},
+   "source": [
+    "Create 2x2 panel plot with original cloud field, thresholded cloud field and connected components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b4a01f77-110b-4237-b62c-b87e82d6569e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x504 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.tri as tri\n",
+    "\n",
+    "rad2deg=180.0/np.pi\n",
+    "\n",
+    "import xarray as xr\n",
+    "grid_plot  = xr.load_dataset(\"./data/icon-grid_nawdex_78w40e23n80n_R80000m.nc\")\n",
+    "vlat = rad2deg*grid_plot[\"vlat\"].values\n",
+    "vlon = rad2deg*grid_plot[\"vlon\"].values\n",
+    "# we need to subtract -1 from vertex_of_cell as python starts counting at 0, but fortran starts at 1\n",
+    "vertex_of_cell= grid_plot[\"vertex_of_cell\"].values-1\n",
+    "del grid_plot\n",
+    "\n",
+    "datafile = \"./data/nawdexnwp-80km-mis-0001_2016092200_2d_30min_DOM01_ML_0060.nc\"\n",
+    "field    = xr.open_dataset(datafile)[\"clct\"].squeeze()\n",
+    "field_thresholded = np.where(field<85.0, 0, 1)\n",
+    "\n",
+    "def make_niceplot(ax):\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['left'].set_bounds(20,80)\n",
+    "    ax.spines['bottom'].set_bounds(-80,40)\n",
+    "    ax.yaxis.set_ticks([20,40,60,80])\n",
+    "    ax.yaxis.set_ticklabels(['20N','40N','60N','80N'], fontsize=6)\n",
+    "    ax.xaxis.set_ticks([-80,-60,-40,-20,0,20,40])\n",
+    "    ax.xaxis.set_ticklabels(['80W','60W','40W','20W','0','20E','40E'], fontsize=6)\n",
+    "    plt.xlim(-85,45); plt.ylim(20,81);\n",
+    "    plt.ylabel('latitude', fontsize=8)\n",
+    "    \n",
+    "# plotting\n",
+    "plt.figure(figsize=(12,7))\n",
+    "\n",
+    "ax=plt.subplot(2,2,1); make_niceplot(ax)\n",
+    "plt.title('Total cloud cover (%)', fontsize=10)\n",
+    "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field,\n",
+    "                vmin=0, vmax=100, edgecolors='none', cmap=plt.get_cmap('Blues_r'))\n",
+    "plt.colorbar(c, ticks=[0,100]);\n",
+    "plt.text(-90,88, 'a)', color='k', size=14, ha='left', va='top')\n",
+    "\n",
+    "ax=plt.subplot(2,2,3); make_niceplot(ax)\n",
+    "plt.title('Total cloud cover (thresholded at 85%)', fontsize=10)\n",
+    "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_thresholded, \n",
+    "                vmin=0, vmax=1, edgecolors='none', cmap=plt.get_cmap('Blues_r'))\n",
+    "plt.colorbar(c, ticks=[0,1]);\n",
+    "plt.text(-90,88, 'b)', color='k', size=14, ha='left', va='top')\n",
+    "\n",
+    "ax=plt.subplot(2,2,2); make_niceplot(ax)\n",
+    "plt.title('Vertex connectivity', fontsize=10); make_niceplot(ax)\n",
+    "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_cc_vert, \n",
+    "                vmin=-0.5, vmax=len(components_vert)+0.5, edgecolors='none', cmap=make_colormap(ncolors=len(components_vert)+1))\n",
+    "plt.colorbar(c, ticks=[i for i in range(0,len(components_vert)+1,10)]);\n",
+    "plt.text(-90,88, 'c)', color='k', size=14, ha='left', va='top')\n",
+    "\n",
+    "ax=plt.subplot(2,2,4); make_niceplot(ax)\n",
+    "plt.title('Edge connectivity', fontsize=10); make_niceplot(ax)\n",
+    "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_cc_edge, \n",
+    "                vmin=-0.5, vmax=len(components_edge)+0.5, edgecolors='none', cmap=make_colormap(ncolors=len(components_edge)+1))\n",
+    "plt.colorbar(c, ticks=[i for i in range(0,len(components_edge)+1,10)]);\n",
+    "plt.xlabel('longitude', fontsize=8);\n",
+    "plt.text(-90,88, 'd)', color='k', size=14, ha='left', va='top');\n",
+    "\n",
+    "plt.savefig(\"./alternative_networkx_2d.pdf\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (based on the module python3/2022.01)",
+   "language": "python",
+   "name": "python3_2022_01"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/alternative_networkx_2d.pdf b/examples/alternative_networkx_2d.pdf
new file mode 100644
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diff --git a/examples/alternative_own-bfs-2d.ipynb b/examples/alternative_own-bfs-2d.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "65e78811-2a04-463e-b94a-3e72bf4eee4c",
+   "metadata": {},
+   "source": [
+    "# Alternative Implementation using a self-programmed breadth-first search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e0dcf723-d338-4b62-b5bc-d2b665527fab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "\n",
+    "import tricco\n",
+    "from tricco.alternatives import ownbfs "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8817b29a-6fd0-473c-8c94-39bd7cc1a06a",
+   "metadata": {},
+   "source": [
+    "## 1. Load grid and data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5a0ef8ea-60f5-4c2d-a7a9-07406af011a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gridfile = \"./data/icon-grid_nawdex_78w40e23n80n_R80000m.nc\"\n",
+    "grid = ownbfs.prepare_grid(model=\"ICON\", file=gridfile)\n",
+    "\n",
+    "datafile = \"./data/nawdexnwp-80km-mis-0001_2016092200_2d_30min_DOM01_ML_0060.nc\"\n",
+    "data = xr.open_dataset(datafile)[\"clct\"].squeeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2231bcaa-3a0f-480c-ab12-b30e3601e447",
+   "metadata": {},
+   "source": [
+    "## 2. Compute connected components for vertex and edge connectivity. Result is a list of connected components ordered by size, where each connected component is a list of cell indices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0601f8e1-8c27-48d7-b512-857cf0c4c7b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "components_vert=ownbfs.compute_components_2d(grid, data, 85.0, connectivity=\"vertex\")\n",
+    "components_edge=ownbfs.compute_components_2d(grid, data, 85.0, connectivity=\"edge\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bc1c488-c480-4278-bd2f-ff974d0b46dd",
+   "metadata": {},
+   "source": [
+    "Map connected components on the structure of the field input data, with the value indicating the connected component. Needed for plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "825fa2f1-7b41-4908-bedf-9a044ac9786c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ncells = grid.cell.size\n",
+    "\n",
+    "field_cc_vert = np.zeros(ncells)\n",
+    "for icomp in range(len(components_vert)):\n",
+    "    field_cc_vert[list(components_vert[icomp])]=icomp+1\n",
+    "\n",
+    "field_cc_edge = np.zeros(ncells)\n",
+    "for icomp in range(len(components_edge)):\n",
+    "    field_cc_edge[list(components_edge[icomp])]=icomp+1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4f95b7d-1f5b-45d7-b0d7-28d3f91b597f",
+   "metadata": {},
+   "source": [
+    "## 3. Plotting. Same as in example-2d.ipynb for the tricco cubulation implementation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97b56353-d6c3-453a-b707-235ee38d6659",
+   "metadata": {},
+   "source": [
+    "Define a qualitative colormap that cycles through the colors of the matplotlib Set1 colormap, and that always plots 0 as white."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e5ce15f7-2587-4646-af67-ee624aaaf31f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_colormap(ncolors):\n",
+    "    import matplotlib as mpl    \n",
+    "    cmap_base = plt.cm.Set1 # note: number of colors of base color map is given by cmap_base.N\n",
+    "    cmaplist_base = [cmap_base(i) for i in range(cmap_base.N)]\n",
+    "    cmaplist = [(1, 1, 1, 1.0)] # need to have white as first color for the triangles with no connected component\n",
+    "    for i in range(ncolors-1):\n",
+    "        icolor = np.mod(i,cmap_base.N)\n",
+    "        cmaplist.append(cmaplist_base[icolor])\n",
+    "    return mpl.colors.LinearSegmentedColormap.from_list('Custom cmap', cmaplist, ncolors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3822109d-f15f-48c9-9d58-8cbb1f134ec0",
+   "metadata": {},
+   "source": [
+    "Create 2x2 panel plot with original cloud field, thresholded cloud field and connected components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2650edab-808d-4e6b-b528-84d45e7f0391",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x504 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.tri as tri\n",
+    "\n",
+    "rad2deg=180.0/np.pi\n",
+    "\n",
+    "import xarray as xr\n",
+    "grid_plot  = xr.load_dataset(\"./data/icon-grid_nawdex_78w40e23n80n_R80000m.nc\")\n",
+    "vlat = rad2deg*grid_plot[\"vlat\"].values\n",
+    "vlon = rad2deg*grid_plot[\"vlon\"].values\n",
+    "# we need to subtract -1 from vertex_of_cell as python starts counting at 0, but fortran starts at 1\n",
+    "vertex_of_cell= grid_plot[\"vertex_of_cell\"].values-1\n",
+    "del grid_plot\n",
+    "\n",
+    "datafile = \"./data/nawdexnwp-80km-mis-0001_2016092200_2d_30min_DOM01_ML_0060.nc\"\n",
+    "field    = xr.open_dataset(datafile)[\"clct\"].squeeze()\n",
+    "field_thresholded = np.where(field<85.0, 0, 1)\n",
+    "\n",
+    "def make_niceplot(ax):\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['left'].set_bounds(20,80)\n",
+    "    ax.spines['bottom'].set_bounds(-80,40)\n",
+    "    ax.yaxis.set_ticks([20,40,60,80])\n",
+    "    ax.yaxis.set_ticklabels(['20N','40N','60N','80N'], fontsize=6)\n",
+    "    ax.xaxis.set_ticks([-80,-60,-40,-20,0,20,40])\n",
+    "    ax.xaxis.set_ticklabels(['80W','60W','40W','20W','0','20E','40E'], fontsize=6)\n",
+    "    plt.xlim(-85,45); plt.ylim(20,81);\n",
+    "    plt.ylabel('latitude', fontsize=8)\n",
+    "    \n",
+    "# plotting\n",
+    "plt.figure(figsize=(12,7))\n",
+    "\n",
+    "ax=plt.subplot(2,2,1); make_niceplot(ax)\n",
+    "plt.title('Total cloud cover (%)', fontsize=10)\n",
+    "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field,\n",
+    "                vmin=0, vmax=100, edgecolors='none', cmap=plt.get_cmap('Blues_r'))\n",
+    "plt.colorbar(c, ticks=[0,100]);\n",
+    "plt.text(-90,88, 'a)', color='k', size=14, ha='left', va='top')\n",
+    "\n",
+    "ax=plt.subplot(2,2,3); make_niceplot(ax)\n",
+    "plt.title('Total cloud cover (thresholded at 85%)', fontsize=10)\n",
+    "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_thresholded, \n",
+    "                vmin=0, vmax=1, edgecolors='none', cmap=plt.get_cmap('Blues_r'))\n",
+    "plt.colorbar(c, ticks=[0,1]);\n",
+    "plt.text(-90,88, 'b)', color='k', size=14, ha='left', va='top')\n",
+    "\n",
+    "ax=plt.subplot(2,2,2); make_niceplot(ax)\n",
+    "plt.title('Vertex connectivity', fontsize=10); make_niceplot(ax)\n",
+    "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_cc_vert, \n",
+    "                vmin=-0.5, vmax=len(components_vert)+0.5, edgecolors='none', cmap=make_colormap(ncolors=len(components_vert)+1))\n",
+    "plt.colorbar(c, ticks=[i for i in range(0,len(components_vert)+1,10)]);\n",
+    "plt.text(-90,88, 'c)', color='k', size=14, ha='left', va='top')\n",
+    "\n",
+    "ax=plt.subplot(2,2,4); make_niceplot(ax)\n",
+    "plt.title('Edge connectivity', fontsize=10); make_niceplot(ax)\n",
+    "c=plt.tripcolor(vlon, vlat, vertex_of_cell.transpose(), facecolors=field_cc_edge, \n",
+    "                vmin=-0.5, vmax=len(components_edge)+0.5, edgecolors='none', cmap=make_colormap(ncolors=len(components_edge)+1))\n",
+    "plt.colorbar(c, ticks=[i for i in range(0,len(components_edge)+1,10)]);\n",
+    "plt.xlabel('longitude', fontsize=8);\n",
+    "plt.text(-90,88, 'd)', color='k', size=14, ha='left', va='top');\n",
+    "\n",
+    "plt.savefig(\"./alternative_own-bfs_2d.pdf\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (based on the module python3/2022.01)",
+   "language": "python",
+   "name": "python3_2022_01"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/alternative_own-bfs_2d.pdf b/examples/alternative_own-bfs_2d.pdf
new file mode 100644
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diff --git a/tricco/alternatives/__init__.py b/tricco/alternatives/__init__.py
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index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
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+++ b/tricco/alternatives/__init__.py
@@ -0,0 +1 @@
+
diff --git a/tricco/alternatives/nwx.py b/tricco/alternatives/nwx.py
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index 0000000000000000000000000000000000000000..2a2daba449d35bf898d80a32310d2d3afc51523c
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+++ b/tricco/alternatives/nwx.py
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+# Implementation by means of graphs and networkx python library
+
+import numpy as np
+import xarray as xr
+import networkx as nx
+
+
+def prepare_grid(model, file):
+    """
+    Return required grid information as a xarray dataset.
+    
+       Input:
+           model   string  model name
+           file    string  name of the file containing the grid (including full path)
+           
+       Note that we only need the following information about the horizontal triangular grid:
+           - the edge neighbors of a given triangle (for edge connectivity)
+           - the triangles that share a given vertex (for vertex connectivity)
+    """
+    if model == "ICON":
+        _grid = _grid_icon(file)
+    return _grid
+
+
+# implementation for ICON model
+def _grid_icon(_gridfile):
+    _grid = xr.open_dataset(_gridfile)
+    _grid = _grid[["neighbor_cell_index", "cells_of_vertex"]]
+    # in the ICON model grid, the indexing of the triangle cells and vertices 
+    # starts with 1 and not with 0 as assumed by tricco --> we need to subtract 1 here
+    _grid["neighbor_cell_index"] = _grid["neighbor_cell_index"] - 1
+    _grid["cells_of_vertex"]     = _grid["cells_of_vertex"] - 1
+    # after the substraction of 1, "missing" triangles at the border of the grid domain are indexed as -1
+    # --> we set these values to -9999 to clearly flag these "missing" triangles
+    _grid["neighbor_cell_index"] = xr.where(_grid["neighbor_cell_index"]!=-1, _grid["neighbor_cell_index"], -9999)
+    _grid["cells_of_vertex"]     = xr.where(_grid["cells_of_vertex"]!=-1, _grid["cells_of_vertex"], -9999)
+    return _grid
+
+
+def compute_fullgraph(grid, connectivity="vertex"):
+    """
+    Returns full graph of the grid that contains all cells as nodes and with edges between all neighboring cells,
+    depending on the type of connectivity
+    
+       Input:
+          grid            xarray dataset    grid information
+          connectivity    string            type of connectivity, either vertex or edge
+    """
+    ncells = grid.cell.size
+    fgraph = nx.Graph()
+    fgraph.add_nodes_from(range(0, ncells))
+    # full graph for vertex connectivity
+    if connectivity == "vertex":
+        nverts = grid.vertex.size
+        for vertex in range(0, nverts):
+            cells_of_vertex = grid.cells_of_vertex[:, vertex].values
+            for cell1 in cells_of_vertex:
+                for cell2 in cells_of_vertex:
+                    if cell1 >=0 and cell2>=0:
+                        fgraph.add_edge(cell1, cell2)
+    # full graph for edge connectivity
+    elif connectivity == "edge":
+        for cell in range(0, ncells):
+            for neighbor in grid.neighbor_cell_index[:, cell].values:
+                if neighbor >=0:
+                    fgraph.add_edge(neighbor, cell)
+    # unknown type of connectivity
+    else:
+        print("compute fullgraph: unknown type of connectivity:", connectivity, "--> full graph of grid not defined!")
+        fgraph = None
+    return fgraph
+
+
+def compute_connected_components_2d(field, fgraph, threshold):
+    """
+    Returns connected components for 2-d data based on a thresholded field and the full graph of the grid.
+        
+        Input: 
+            field : 1d-data for which connected components will be calculated, defined on each grid cell
+            fgraph: full graph of the grid with all grid cells being nodes and edges defined between all neighboring
+                    cells, hence the graph encodes whether edge of vertex connectivity will be considered
+            threshold: set field to False if smaller than threshold, and to True otherwise
+        Output: 
+            list of connected components sorted by size of connected components (largest component first), 
+            each connected component is a list of grid cells
+    """
+    # copy full graph to local graph
+    _graph = fgraph.copy(as_view=False)
+    # threshold field
+    field = np.where(field<threshold, False, True)
+    # remove grid cells/nodes from graph for which field < threshold, i.e., False
+    for i in range(0, field.size):
+        if not field[i]: _graph.remove_node(i)
+    # compute connected components and sort by size
+    components = []
+    [components.append(list(cc)) for cc in nx.connected_components(_graph)];
+    components = sorted(components, key=len, reverse=True)
+    return components
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diff --git a/tricco/alternatives/ownbfs.py b/tricco/alternatives/ownbfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bceb042875d294e4cca5e4d687429d68f85553f
--- /dev/null
+++ b/tricco/alternatives/ownbfs.py
@@ -0,0 +1,118 @@
+# Implementation by means of a self-programmed breadth-first search
+
+import numpy as np
+import xarray as xr
+from collections import deque  # for using lists as queues
+
+
+def prepare_grid(model, file):
+    """
+    Return required grid information as a xarray dataset.
+    
+       Input:
+           model   string  model name
+           file    string  name of the file containing the grid (including full path)
+           
+       Note that we only need the following information about the horizontal triangular grid:
+           - the edge neighbors of a given triangle (for edge connectivity)
+           - the vertices of each triangle and the triangles that share a given vertex (for vertex connectivity)
+    """
+    if model == "ICON":
+        _grid = _grid_icon(file)
+    return _grid
+
+
+# implementation for ICON model
+def _grid_icon(_gridfile):
+    _grid = xr.open_dataset(_gridfile)
+    _grid = _grid[["neighbor_cell_index", "vertex_of_cell", "cells_of_vertex"]]
+    # in the ICON model grid, the indexing of the triangle cells and vertices 
+    # starts with 1 and not with 0 as assumed by tricco --> we need to subtract 1 here
+    _grid["neighbor_cell_index"] = _grid["neighbor_cell_index"] - 1
+    _grid["vertex_of_cell"] = _grid["vertex_of_cell"] - 1
+    _grid["cells_of_vertex"] = _grid["cells_of_vertex"] - 1
+    # after the substraction of 1, "missing" triangles at the border of the grid domain are indexed as -1
+    # --> we set these values to -9999 to clearly flag these "missing" triangles
+    _grid["neighbor_cell_index"] = xr.where(_grid["neighbor_cell_index"]!=-1, _grid["neighbor_cell_index"], -9999)
+    _grid["vertex_of_cell"] = xr.where(_grid["vertex_of_cell"]!=-1, _grid["vertex_of_cell"], -9999)
+    _grid["cells_of_vertex"] = xr.where(_grid["cells_of_vertex"]!=-1, _grid["cells_of_vertex"], -9999)
+    return _grid
+
+
+def _add_neighbors_to_queue(cell, field, grid, connectivity, explored, queue):
+    """
+    Add neighbors of a given cell to the queue if they are in the same connected component and not yet explored.
+    
+       Input: 
+           cell            int               the index of the cell currently considered
+           field           numpy array       thresholded 1-d array with values of True or False,
+                                             defined on each grid cell
+           grid            xarray dataset    grid information
+           connectivity    string            type of connectivity, either vertex or edge
+           explored        1-darray          information on whether cells have been explored yet
+       Input/Output:
+           queue           list-queue        cell indices for breadth-first search of current connected component
+    """
+    ncells=field.size
+    # vertex connectivity
+    if connectivity == "vertex":
+        vertex_of_cell = grid.vertex_of_cell[:, cell].values
+        for vertex in vertex_of_cell:
+            for neigh in grid.cells_of_vertex[:,vertex].values:
+                if neigh!=cell and neigh>=0 and field[neigh] and not explored[neigh] and neigh not in queue:
+                    queue.append(neigh)
+    # edge connectivity
+    elif connectivity == "edge":
+        for neigh in grid.neighbor_cell_index[:, cell].values:
+            if neigh>=0 and field[neigh] and not explored[neigh] and neigh not in queue:
+                queue.append(neigh)
+    # unknown type of connectivity
+    else:
+        print("_add_neighbors_to_queue: unknown type of connectivity:", connectivity, "--> ownbfs cannot work!")
+    
+
+def compute_components_2d(grid, field, threshold, connectivity="vertex"):
+    """
+    Returns connected components for given grid, field and connectivity type.
+    
+        The implementation runs through all cells. If a cell is not yet explored and has a value>=threshold, then
+        its connected component is explored via breadth-first-search marking all considered cells as explored. 
+    
+        Input: 
+        
+            grid            xarray dataset    grid information
+            field           numpy array       1-d array of field for which connected components will be calculated,
+                                              defined on each grid cell
+            threshold       float             set field to False if smaller than threshold, and to True otherwise
+            connectivity    string            type of connectivity, either vertex or edge
+            
+        Output: 
+            list of connected components sorted by size of connected components (largest component first), 
+            each connected component is a list of grid cells
+    """
+    ncells = field.size
+    # threshold field
+    field = np.where(field<threshold, False, True)
+    # init lists for connected components and explored cells
+    components = []
+    explored = [False] * ncells
+    # loop over all cells
+    for cell in range(ncells):
+        if not explored[cell]:  # cell must not be explored yet
+            explored[cell] = True
+            if field[cell]:  # if cell is covered, it belongs to a new connected component
+                # init component list and queue for BFS of this connected component
+                comp = [cell]
+                queue = deque([])
+                _add_neighbors_to_queue(cell, field, grid, connectivity, explored, queue)
+                # perform BFS
+                while queue:
+                    cell = queue.pop()
+                    explored[cell] = True
+                    comp.append(cell)
+                    _add_neighbors_to_queue(cell, field, grid, connectivity, explored, queue)
+                # append found component to list of components
+                components.append(comp)
+    # sort connected components by size, starting with the largest component
+    components = sorted(components, key=len, reverse=True)
+    return components
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