{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "orig_nbformat": 2, "kernelspec": { "name": "python3810jvsc74a57bd01b27a185e5e38addd349bee67c436665dc7832e161e2a923b2540665280bf8fe", "display_name": "Python 3.8.10 64-bit ('base': conda)" } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "# Spatial skill\n", "Demonstrate nice plotting functionality etc by the SpatialSkill class" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from matplotlib_inline.backend_inline import set_matplotlib_formats\n", "set_matplotlib_formats('png')\n", "%matplotlib inline\n", "\n", "from fmskill import ModelResult, Connector\n", "from fmskill import PointObservation, TrackObservation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "c:\\users\\jem\\source\\fmskill\\fmskill\\utils.py:38: UserWarning: Time axis has duplicate entries. Now adding milliseconds to non-unique entries to make index unique.\n warnings.warn(\n" ] } ], "source": [ "fn = '../tests/testdata/NorthSeaHD_and_windspeed.dfsu'\n", "mr = ModelResult(fn, name='HD', item=0)\n", "fn = '../tests/testdata/altimetry_NorthSea_20171027.csv'\n", "df = pd.read_csv(fn, index_col=0, parse_dates=True)\n", "o1 = TrackObservation(df, item=2, name='alti')\n", "con = Connector(o1, mr)\n", "cc = con.extract()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ss = cc.spatial_skill()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\n", "Dimensions: (x: 5, y: 5)\n", "Coordinates:\n", " * y (y) float64 50.6 51.66 52.7 53.75 54.8\n", " * x (x) float64 -0.436 1.543 3.517 5.492 7.466\n", " observation ", "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n 2021-05-28T00:25:08.036903\r\n image/svg+xml\r\n \r\n \r\n Matplotlib v3.3.4, 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}, "metadata": { "needs_background": "light" } } ], "source": [ "ss.plot('rmse');" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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}, "metadata": { "needs_background": "light" } } ], "source": [ "ss.plot('n', figsize=(3,3));" ] }, { "source": [ "## Multi models" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "fn = \"../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu\"\n", "mr1 = ModelResult(fn, name=\"SW_1\", item=0)\n", "fn = \"../tests/testdata/SW/HKZN_local_2017_DutchCoast_v2.dfsu\"\n", "mr2 = ModelResult(fn, name=\"SW_2\", item=0)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "o1 = PointObservation(\"../tests/testdata/SW/HKNA_Hm0.dfs0\", item=0, x=4.2420, y=52.6887, name=\"HKNA\")\n", "o2 = PointObservation(\"../tests/testdata/SW/eur_Hm0.dfs0\", item=0, x=3.2760, y=51.9990, name=\"EPL\")\n", "o3 = TrackObservation(\"../tests/testdata/SW/Alti_c2_Dutch.dfs0\", item=3, name=\"c2\")\n", "con = Connector([o1, o2, o3], [mr1, mr2])\n", "cc = con.extract()" ] }, { "source": [ "## Spatial_skill\n", "Group by model" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "ss = cc.spatial_skill(by='model', bins=6)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['SW_1', 'SW_2']" ] }, "metadata": {}, "execution_count": 11 } ], "source": [ "ss.mod_names" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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HvVNJ242w+mngEduLK4cYERFRibqxB62fSLoPeAH4UPmq74tVDqzas9aNwHfK6UbgB8CvJe3RXLwRERH1jadoWtKeku6X9KCkY0fYLklfK7ffUeeBc2zx2sdS9EI52/Yi4Dlg3yrHVnn2nwscZvtuAElbUpR7fx74EXBFEzFHRESMbBw9a5UdUH0T2J2irvYmSRfbvqdmt72AzctpB+Db5ed4bQTsLmmlmnXfH+2gKhnxa4YyYQDb90ja1vZDaUgdERETb1wdemwPPGj7IQBJP6B4Mq3NiPcFvm/bwI2S1pS0ge3Hmo5YOgHYBdgSuJQis7+OCcqI75f0bYriaIADKIqlVwQWNRNwRDudd+u8TocQ0VL3/K4HX2RpXEe8rqSba5ZPs31aOb8R8JuabfNY/ml3pH02AprOiCl6ndwauNX2ByStD5xe5cAqGfGhwIeAj1L0rHUdcAxFJrxrE8GOmQZg6srta0E3eNi72eHKq9p2PYCPXXBbW6835MgdZ3XkurGsVaaIrVdbafQdJ9BVW2zPkU+1t5+eI8+/ra3XG3LwdjM7ct1ojhGDNCxxXWB7dp1tIx3oJvYZqxfKTrAWS1odeBx4VZUDq7y+9IKkbwGX2L5/2OZnxx5rREREI2aJm84X5wEb1yzPBOY3sc9Y3SxpTYpGzXMo8sf/qXLgqK2mJb0TuA24rFzeRtLFzUYaERHRiIFB159GcROwuaRNyyF7DwSG51kXA+8vW0/vCDw9nvphANsfsv2U7VMoGoodYvsDVY6tUt57AkXl99XlxW6TNKvJWCMiIhoysKRCjjvisfZiSUcBlwNTgDNs3y3piHL7KRSNqfYGHgSeByplmCOR9Brb9430CpSk7WzfMto5qmTEi20/nRbSERHRFoYl46ixtX0pRWZbu+6UmnkDH27+Csv4GHA48GWWrWdWubzbaCeo0qHHXZLeC0yRtLmkrwM3NBFsRETEqAwssetOk4ntw8vZvYGfUvQ8+RRF8ffeVc5RJSP+CPA64CXgfOAZihbUo5I0V9Kdkm4bamou6fNlTya3SbpC0oZVzhUREf3Drj9NUmcBrwW+Bny9nB/1HWKo1mr6eeD4cmrGrrYX1CyfbPuzAJL+AfgccEST546IiB5jN19H3EFb2N66ZvkqSbdXObBuRizpJzR4r8r2O6vHt8xxtW+er9roGhER0Z/GU0fcIbdK2tH2jQCSdgCur3Jgoyfify0/3w28AjinXH4PRf/TVRi4QpKBU4d6PpH0z8D7KcrSR+wURNLhFBXgvGKlFSteLqJ71KbxtdV1Q75FtEzx+lJ35MSS7qQIeRrFK1GPlsuvZNluNeuq+7/f9jXlRT5v+y01m34i6dqKMe5se76k9YArJd1n+1rbxwPHSzoOOIriFanh1z8NOA3gtatP745/kYgxqE3jr5y6UtJ4RI0ueiLeZ7wnqPIzfIakV9V0oL0pMKPKyW3PLz8fl3QRxfvItZn4eRStzJbLiCMioj95fD1rtZXtR8Z7jioZ8f8Frpb0ULk8i7I4rRFJqwIDtheW83sAJ0ra3PYD5W7vBNrb2W1ERExuhiWDnQ6ifaq0mr5M0ubAa8pV99l+qcK51wcuKjsCmQqcV57rQklbAIPAI6TFdERE1DCT+jWlCdeo1fSfuuYqM97lmmE36r6rLMreeoT1+zUfbkRE9INuKZqeCI2eiL8naRdGHi5qyHeBbScyoIiI6G82LOqi1lrj1SgjXoNiKKdGGfETExtORET0u256fWkiNHp9aVYb44iIiACKVtOLBvuntVZ6EYiIiEklRdMREREdZGBR9/U13bRkxBERMbl056APTRt1GEQVDpL0uXJ5E0nbtz60iIjoR0Udcf2p11QZj/hbwE4Ugz0ALAS+2bKIIiKirxV1xIN1p/GQtLakKyU9UH6uNcI+G0u6StK9ku6WdPS4LjqKKkXTO9jeTtKtALaflLRCK4OK3vXwgoWdDiGi5Z5/4cVOh9DVWvz60rHAz22fJOnYcvlTw/ZZDHzc9i2SpgNzJF1pu9JoSmNVJSNeJGkK5bjBkmZQdE/ZNqu+egt2uPKqdl6y7X4396lOh9Czdtp4DTZdd3rbr/u+Mew7Y5utOPLmm1sWy2Qx74E/dDqEnjRrjRVYZeWVOh3GhGlxY619gV3K+bOAqxmWEdt+DHisnF8o6V5gIyoOazhWVTLirwEXAeuV4wjvD3ymFcFERETYHu31pXUl1f5yPW1ovPsK1i8zWmw/Vg7TW5ekWRQ9SP6q4vnHrMqgD+dKmgO8jaKXrXfZvrdVAUVERH8rnogbFrwusD273kZJPwNeMcKm48cSh6TVgAuBj9p+ZizHjkWjQR/Wrll8HDi/dpvtP7YqqIiI6GOGwXEUTdt+e71tkn4vaYPyaXgDivxtpP2mUWTC59r+UdPBVNDoiXgOxQ8TAZsAT5bzawKPApu2MrCIiOhPLa4jvhg4BDip/PzP4TuoGL/3u8C9tr/SqkCG1H19yfamtl8FXA78pe11ba8D7AO09NdBRET0L9u8vGSw7jROJwG7S3oA2L1cRtKGki4t99kZOBjYTdJt5bT3eC9cT5XGWm+0fcTQgu3/kvT5VgUUERH9zbSuZy3bf6Bo8zR8/Xxg73L+OhqPPDihqmTECyR9BjiH4u9zEJB3ECIioiVseHlxRl+q9R7gBIpXmACuZWkvWxERERPKdjLiWmXr6JZ27xURETFkME/Ey5J0FWWvWrVs79aSiCIiou/10+hLVYqmj6mZXwnYj6IfzoiIiAk3aPNSnoiXsj1n2KrrJV3TongiIqLPpbHWMMN62BoA3sDIXYdFRERMiCWtG31p0qlSNF3bw9Zi4GHgsFYGFRER/WswraaX81rbywyuKWnFFsUTERF9zsDLi5d0Ooy2qdvFZY0bRlj3y4kOJCIiApa+R1xv6jWNRl96BcVAyCtL2pal3X2tDqzShtgiIqIP2bA4ry8B8BfAocBMoHb0iYXAp1sYU0RE9LG0mi7ZPgs4S9J+ti9s5uSS5lJk3EuAxbZnSzoZ+EvgZeB/gQ/YfqqZ80dERO8xaawFgKSDbJ8DzJL0seHbxzBG4662F9QsXwkcZ3uxpC8CxwGfGkvQERHRu+z0rDVk1fJztRG2Nf0Xsn1FzeKNwP7NnisiInqPDUvyRAy2Ty1nf2b7+tptknaueH4DV0gycKrt04Zt/1vggpEOlHQ4cDjAJptsUvFyEd0jaTyiHjO4pH8y4iqvL3294rqR7Gx7O2Av4MOS3jK0QdLxFB2EnDvSgbZPsz3b9uwZM2ZUvFxE90gajxhZ8UTsulOvaVRHvBPwJmDGsDri1YEpVU5ue375+biki4DtgWslHQLsA7zNnnz9mL1wyTc7cNUNO3DN6GfPX/ivHbjqZh24ZnSjVmUNZbfNFwCzgLnA39h+ss6+U4Cbgd/a3qclAdG4jngFivrhqcD0mvXPUKFeV9KqwIDtheX8HsCJkvakaJz1VtvPNx15jzlgn9d0OoS22HTd6aPvFD1r/7236HQIbbHKyit1OoTuZreyjvhY4Oe2T5J0bLlcr8Hw0cC9FA+gLdOojvga4BpJZ9p+pIlzrw9cJGnoOufZvkzSg8CKwJXlthttH9HE+SMiogfZsKR1dcT7AruU82cBVzNCRixpJvAO4J+B5d4cmkhV+pp+vnz393UU4xEDYHu3RgfZfgjYeoT1fzbWICMior+4da8vrW/7MQDbj0lar85+XwU+ybIlwi1RJSM+l6I8fR/gCOAQ4IlWBhUREf3LoxdNryvp5prl02rfypH0M0Yervf4KteXtA/wuO05knapcsx4VMmI17H9XUlH1xRXX9PqwCIion8NLmn4RLzA9ux6G22/vd42Sb+XtEH5NLwB8PgIu+0MvFPS3hQlwatLOsf2QRXDH5Mqry8tKj8fk/SOcgCIma0IJiIiYqhDj3rTOF1MUbJL+fmfy1/fx9meaXsWcCDwi1ZlwlDtifgLktYAPk7x/vDqwEdbFVBERPQ5t7SO+CTgh5IOAx4F/hpA0obA6bb3btWF6xk1I7Z9STn7NLArgKSPtjCmiIjoY8YtazVt+w/A20ZYPx9YLhO2fTVFy+qWqVI0PZKWNuWOiIg+1tqi6UmnStH0SDShUURERNSYhJ0utkyzGXH//IUiIqKtKry+1FMa9TW9kJEzXAErtyyiiIjobzaDi1/udBRt06iLy3QKHBERbWebwUXJiCMiIjrEeHBJp4Nom2TEERExuaRoOiIionOcjDgiIqKTzGCKpiMiIjojT8QRERGdlFbTERERHeTBPBFHRER0igEPpmetiIiIzkgdcUREROfYZknqiCMiIjrE6VkrIiKig/qraFrdMOajpCeAR+psXhdY0MZwWqmX7gV6636auZdX2p5RZcek8a7VS/fT0jQ+FpIuK+OpZ4HtPSf6up3SFRlxI5Jutj2703FMhF66F+it++nkveTvOHn10v300r10m4FOBxAREdHPkhFHRER0UC9kxKd1OoAJ1Ev3Ar11P528l/wdJ69eup9eupeu0vV1xBEREd2sF56IIyIiulYy4oiIiA7q2oxY0hmSHpd0V6djGS9JG0u6StK9ku6WdHSnY2qWpJUk/Y+k28t7+adOxzRekqZIulXSJW2+btL4JNSLaRw6l86jizNi4EygV17oXgx83PZrgR2BD0vassMxNeslYDfbWwPbAHtK2rGzIY3b0cC9HbjumSSNT0a9mMahc+m873VtRmz7WuCPnY5jIth+zPYt5fxCiv8MG3U2qua48Gy5OK2curZFoKSZwDuA09t97aTxyanX0jh0Np1HF2fEvUrSLGBb4FcdDqVpZRHXbcDjwJW2u/ZegK8CnwT6Z3DUFksan5S+StJ5xyQjnkQkrQZcCHzU9jOdjqdZtpfY3gaYCWwvaasOh9QUSfsAj9ue0+lYekXS+OSTdN55yYgnCUnTKL6gzrX9o07HMxFsPwVcTffWc+4MvFPSXOAHwG6SzulsSN0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}, "metadata": { "needs_background": "light" } } ], "source": [ "ss.plot(\"bias\");" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "ss.plot(\"rmse\", model='SW_1', cmap='YlOrRd');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }