{ "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": "3.8.5" }, "orig_nbformat": 2, "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" }, "interpreter": { "hash": "fa576ebcd40e010bdc0ae86b06ce09151f3424f9e9aed6893ff04f39a9299d89" } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "# Combine comparers\n", "FMskill comparers can be combined by using the \"+\" operator. You may want to add a new ModelResult to your existing comparison or a new observation or a new time period:\n", "\n", " cc = cc1 + cc2" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from fmskill import ModelResult, PointObservation, TrackObservation, Connector\n", "%matplotlib inline" ] }, { "source": [ "## Observations" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 2, "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\")" ] }, { "source": [ "## Model Results" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " 'CMEMS'\n", "- Item: VHM0" ] }, "metadata": {}, "execution_count": 3 } ], "source": [ "fn = \"../tests/testdata/SW/CMEMS_DutchCoast_*.nc\"\n", "mr1 = ModelResult(fn, name='CMEMS', item='VHM0')\n", "mr1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "mr2 = ModelResult('../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu', name='MIKE21SW', item=0)" ] }, { "source": [ "## Connect and extract\n", "Notice that the two ModelResults doesn't cover the exact same period." ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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}, "metadata": { "needs_background": "light" } } ], "source": [ "con1 = Connector([o1,o2,o3],mr1)\n", "con2 = Connector([o1,o2,o3],mr2)\n", "con1.plot_temporal_coverage(limit_to_model_period=False)\n", "con2.plot_temporal_coverage(limit_to_model_period=False);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "cc1 = con1.extract()\n", "cc2 = con2.extract()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " n bias rmse urmse mae cc si \\\n", "observation \n", "EPL 43 -0.440930 0.518713 0.273210 0.443256 0.920165 0.087342 \n", "HKNA 242 -0.741920 0.881698 0.476388 0.741920 0.902987 0.123507 \n", "c2 123 -0.349376 0.448139 0.280651 0.383551 0.912560 0.062328 \n", "\n", " r2 \n", "observation \n", "EPL 0.445177 \n", "HKNA 0.221544 \n", "c2 0.529142 " ], "text/html": "
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nbiasrmseurmsemaeccsir2
observation
EPL43-0.4409300.5187130.2732100.4432560.9201650.0873420.445177
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c2123-0.3493760.4481390.2806510.3835510.9125600.0623280.529142
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" }, "metadata": {}, "execution_count": 7 } ], "source": [ "cc1.skill()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " n bias rmse urmse mae cc si \\\n", "observation \n", "EPL 67 -0.066597 0.223597 0.213449 0.188513 0.969846 0.078296 \n", "HKNA 386 -0.194260 0.351964 0.293499 0.251839 0.971194 0.088669 \n", "c2 113 -0.001210 0.351796 0.351794 0.294585 0.974335 0.118511 \n", "\n", " r2 \n", "observation \n", "EPL 0.932596 \n", "HKNA 0.905300 \n", "c2 0.899507 " ], "text/html": "
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nbiasrmseurmsemaeccsir2
observation
EPL67-0.0665970.2235970.2134490.1885130.9698460.0782960.932596
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c2113-0.0012100.3517960.3517940.2945850.9743350.1185110.899507
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" }, "metadata": {}, "execution_count": 8 } ], "source": [ "cc2.skill()" ] }, { "source": [ "## Add the two Comparers" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "cc = cc1 + cc2" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " n bias rmse urmse mae cc \\\n", "model observation \n", "CMEMS EPL 43 -0.440930 0.518713 0.273210 0.443256 0.920165 \n", " HKNA 242 -0.741920 0.881698 0.476388 0.741920 0.902987 \n", " c2 41 -0.311355 0.474520 0.358089 0.402269 0.937073 \n", "MIKE21SW EPL 43 -0.078281 0.204842 0.189294 0.173804 0.973262 \n", " HKNA 242 -0.229809 0.411363 0.341185 0.295643 0.948802 \n", " c2 41 0.327152 0.410106 0.247303 0.357670 0.964173 \n", "\n", " si r2 \n", "model observation \n", "CMEMS EPL 0.087342 0.445177 \n", " HKNA 0.123507 0.221544 \n", " c2 0.087109 0.740606 \n", "MIKE21SW EPL 0.060515 0.913476 \n", " HKNA 0.088455 0.830548 \n", " c2 0.060159 0.806250 " ], "text/html": "
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nbiasrmseurmsemaeccsir2
modelobservation
CMEMSEPL43-0.4409300.5187130.2732100.4432560.9201650.0873420.445177
HKNA242-0.7419200.8816980.4763880.7419200.9029870.1235070.221544
c241-0.3113550.4745200.3580890.4022690.9370730.0871090.740606
MIKE21SWEPL43-0.0782810.2048420.1892940.1738040.9732620.0605150.913476
HKNA242-0.2298090.4113630.3411850.2956430.9488020.0884550.830548
c2410.3271520.4101060.2473030.3576700.9641730.0601590.806250
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" }, "metadata": {}, "execution_count": 10 } ], "source": [ "cc.skill()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }