diff --git a/jupyter/network_main.ipynb b/jupyter/network_main.ipynb index 159827a..96904b7 100644 --- a/jupyter/network_main.ipynb +++ b/jupyter/network_main.ipynb @@ -7,23 +7,24 @@ "metadata": {}, "outputs": [], "source": [ - "# import sys\n", - "# sys.path.append('..')\n", + "import sys\n", + "sys.path.append('..')\n", "\n", - "# %reload_ext autoreload\n", - "# %autoreload 2\n", - "# # %aimport sponge_networks\n", + "%reload_ext autoreload\n", + "%autoreload 2\n", + "%aimport sponge_networks\n", "# # %aimport sponge_networks.utils" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "d2e6c76f-f590-4fee-897e-c0ede2a2367b", "metadata": {}, "outputs": [], "source": [ "import sponge_networks as sn\n", + "# from sponge_networks import sponge_networks \n", "\n", "import matplotlib.pyplot as plt\n", "plt.rcParams[\"figure.figsize\"] = (10, 7)\n", @@ -46,13 +47,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "d8d29daf-9181-420f-a3c6-01fec02b531c", "metadata": {}, "outputs": [], "source": [ "nw = sn.build_sponge_network(\n", - " grid_type=\"triangular\", n_cols=4, n_rows=2,\n", + " grid_type=\"hexagonal\", n_cols=1, n_rows=1,\n", " layout={\n", " \"weights_sink_edge\": 1,\n", " \"weights_loop\": 1,\n", @@ -61,25 +62,58 @@ " \"weights_down_up\": 1,\n", " },\n", " generate_sinks=True,\n", - " visual_sink_edge_length=0.6\n", + " visual_sink_edge_length=1.\n", ")" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "id": "d53a35c9-77e4-48d5-a8e4-13f7b3072e1c", + "metadata": {}, + "outputs": [], + "source": [ + "# nw.resource_network.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "14e9d69d-7cb0-4ab8-9e08-98c8a7625c77", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'sim' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43msim\u001b[49m\u001b[38;5;241m.\u001b[39msimple_protocol()\n\u001b[1;32m 2\u001b[0m df\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;241m1\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'sim' is not defined" + ] + } + ], + "source": [ + "df = sim.simple_protocol()\n", + "df.sum(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "id": "8b91dcb0-fb07-4922-999a-6589be0ace63", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6f3604e0323444249eb7619618f2004a", + "model_id": "de8f94305f3d414e8e0667b91c83f47f", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(IntSlider(value=0, description='№ of iteration', max=49), Output()), _dom_classes=('widg…" + "interactive(children=(IntSlider(value=0, description='№ of iteration', max=99), Output()), _dom_classes=('widg…" ] }, "metadata": {}, @@ -87,34 +121,33 @@ } ], "source": [ - "sim = nw.run_sponge_simulation([50, 20, 20], n_iters=50)\n", - "nw.plot_simulation(sim, scale=1.4)" + "sim = nw.run_sponge_simulation([50, 0], n_iters=100)\n", + "nw.plot_simulation(sim, scale=1.2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2785446-8bca-445d-bf45-0c18e03c3fc9", + "metadata": {}, + "outputs": [], + "source": [ + "4 * 5/6" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "e681f0f5-579a-4e67-9a1f-36cd39ad336d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "NodeView(((0, 0), (1, 0), (2, 0), (0, 1), (1, 1), (2, 1), (0, 2), (1, 2), (2, 2), (0, -1), (1, -1), (2, -1)))" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nw.resource_network.G.nodes()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "2e608e24-410f-4ee6-bfe0-4b6586d97940", "metadata": {}, "outputs": [], @@ -129,69 +162,30 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "6c162550-6e3c-49d4-86f5-151fbaba098a", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0, 1, 2],\n", - " [1, 0, 0],\n", - " [1, 3, 0]])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "R" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "696aa541-e692-4e73-bc41-6d6c20397449", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0. , 0.33333333, 0.66666667],\n", - " [1. , 0. , 0. ],\n", - " [0.25 , 0.75 , 0. ]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "rn.stochastic_matrix" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "67202550-b3ac-4aca-a647-c0416320e15a", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[1, 1, 1],\n", - " [1, 1, 1],\n", - " [2, 2, 2]]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "v = np.array([[1, 1, 2]])\n", "np.tensordot(v, np.array([1, 1, 1]), 0)" @@ -199,21 +193,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "dc586d88-457b-454a-9431-bee826c2886b", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([2, 4, 2])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "(np.array([1, 1, 1])@R)" ] diff --git a/pyproject.toml b/pyproject.toml index bd115ef..244b597 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "sponge-networks" -version = "0.2.2" +version = "0.2.3" description = "a generalization of the resource network model with greedy vertices" authors = ["heinwol "] license = "MIT"