{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Instrumentación computacional inteligente\n", "## Estudio de datos de estaciones meteorológicas\n", "\n", "### Fernando Ayats Llamas\n", "### 2023-04-11" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "hide-cell" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m\u001b[1m Activating\u001b[22m\u001b[39m project at `c:\\Users\\ayats\\Documents\\ici-parsing`\n" ] }, { "data": { "application/vnd.webio.node+json": { "children": [], "instanceArgs": { "namespace": "html", "tag": "div" }, "nodeType": "DOM", "props": {}, "type": "node" }, "text/html": [ "
\n", "

The WebIO Jupyter extension was not detected. See the\n", "\n", " WebIO Jupyter integration documentation\n", "\n", "for more information.\n", "

\n" ], "text/plain": [ "WebIO._IJuliaInit()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using Pkg\n", "Pkg.activate(@__DIR__);\n", "Pkg.instantiate()\n", "\n", "using StatsPlots\n", "using CSV, DataFrames\n", "\n", "import Dates: DateTime" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
120×2 DataFrame
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53.82021-01-01T05:00:00
63.32021-01-01T06:00:00
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" ], "text/latex": [ "\\begin{tabular}{r|cc}\n", "\t& value & ts\\\\\n", "\t\\hline\n", "\t& Float64 & DateTime\\\\\n", "\t\\hline\n", "\t1 & 5.4 & 2021-01-01T01:00:00 \\\\\n", "\t2 & 4.2 & 2021-01-01T02:00:00 \\\\\n", "\t3 & 4.1 & 2021-01-01T03:00:00 \\\\\n", "\t4 & 4.1 & 2021-01-01T04:00:00 \\\\\n", "\t5 & 3.8 & 2021-01-01T05:00:00 \\\\\n", "\t6 & 3.3 & 2021-01-01T06:00:00 \\\\\n", "\t7 & 2.7 & 2021-01-01T07:00:00 \\\\\n", "\t8 & 2.9 & 2021-01-01T08:00:00 \\\\\n", "\t9 & 3.6 & 2021-01-01T09:00:00 \\\\\n", "\t10 & 4.2 & 2021-01-01T10:00:00 \\\\\n", "\t11 & 4.8 & 2021-01-01T11:00:00 \\\\\n", "\t12 & 5.5 & 2021-01-01T12:00:00 \\\\\n", "\t13 & 6.5 & 2021-01-01T13:00:00 \\\\\n", "\t14 & 6.4 & 2021-01-01T14:00:00 \\\\\n", "\t15 & 4.3 & 2021-01-01T15:00:00 \\\\\n", "\t16 & 3.4 & 2021-01-01T16:00:00 \\\\\n", "\t17 & 3.5 & 2021-01-01T17:00:00 \\\\\n", "\t18 & 3.6 & 2021-01-01T18:00:00 \\\\\n", "\t19 & 3.6 & 2021-01-01T19:00:00 \\\\\n", "\t20 & 3.7 & 2021-01-01T20:00:00 \\\\\n", "\t21 & 3.7 & 2021-01-01T21:00:00 \\\\\n", "\t22 & 3.6 & 2021-01-01T22:00:00 \\\\\n", "\t23 & 3.5 & 2021-01-01T23:00:00 \\\\\n", "\t24 & 3.0 & 2021-01-02T00:00:00 \\\\\n", "\t25 & 2.4 & 2021-01-02T01:00:00 \\\\\n", "\t26 & 2.0 & 2021-01-02T02:00:00 \\\\\n", "\t27 & 1.6 & 2021-01-02T03:00:00 \\\\\n", "\t28 & 1.0 & 2021-01-02T04:00:00 \\\\\n", "\t29 & 0.4 & 2021-01-02T05:00:00 \\\\\n", "\t30 & 1.0 & 2021-01-02T06:00:00 \\\\\n", "\t$\\dots$ & $\\dots$ & $\\dots$ \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\u001b[1m120×2 DataFrame\u001b[0m\n", "\u001b[1m Row \u001b[0m│\u001b[1m value \u001b[0m\u001b[1m ts \u001b[0m\n", " │\u001b[90m Float64 \u001b[0m\u001b[90m DateTime \u001b[0m\n", "─────┼──────────────────────────────\n", " 1 │ 5.4 2021-01-01T01:00:00\n", " 2 │ 4.2 2021-01-01T02:00:00\n", " 3 │ 4.1 2021-01-01T03:00:00\n", " 4 │ 4.1 2021-01-01T04:00:00\n", " 5 │ 3.8 2021-01-01T05:00:00\n", " 6 │ 3.3 2021-01-01T06:00:00\n", " 7 │ 2.7 2021-01-01T07:00:00\n", " 8 │ 2.9 2021-01-01T08:00:00\n", " 9 │ 3.6 2021-01-01T09:00:00\n", " 10 │ 4.2 2021-01-01T10:00:00\n", " 11 │ 4.8 2021-01-01T11:00:00\n", " ⋮ │ ⋮ ⋮\n", " 111 │ 6.9 2021-01-05T15:00:00\n", " 112 │ 6.1 2021-01-05T16:00:00\n", " 113 │ 5.6 2021-01-05T17:00:00\n", " 114 │ 5.1 2021-01-05T18:00:00\n", " 115 │ 4.7 2021-01-05T19:00:00\n", " 116 │ 3.9 2021-01-05T20:00:00\n", " 117 │ 2.9 2021-01-05T21:00:00\n", " 118 │ 1.5 2021-01-05T22:00:00\n", " 119 │ 2.3 2021-01-05T23:00:00\n", " 120 │ 2.3 2021-01-06T00:00:00\n", "\u001b[36m 99 rows omitted\u001b[0m" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = CSV.File(\n", " open(\"data/Grazalema_AEMET_2021-2022.csv\");\n", " normalizenames=true,\n", " select=[:ts, :value],\n", " types=Dict(:ts => DateTime),\n", " dateformat=\"yyyy-mm-dd HH:MM:SS.s\"\n", ") |> DataFrame\n", "\n", "data = data[1:24*5, :]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { 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\n", " \n" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "@df data plot(:ts, :value, \n", " label=\"Measured\", \n", " marker=:circle,\n", " markersize=2,\n", ")\n", "\n", "macro plot_common()\n", " return quote\n", " (-5, 15) |> ylims!\n", " \"Date\" |> xlabel!\n", " \"Temperature\" |> ylabel!\n", " end\n", "end\n", "\n", "@plot_common()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# import SingularSpectrumAnalysis as SSA\n", "\n", "# data_trend, data_season = SSA.analyze(\n", "# data[:, :value],\n", "# 20,\n", "# ) \n", "\n", "\n", "# plot(data[:, :ts], data_trend, lab=\"Trend\")\n", "# plot!(data[:, :ts], data_season, lab=\"Seasonality\")\n", "# @lims()" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.8.5", "language": "julia", "name": "julia-1.8" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }