clearsky_phoenix_today.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "clearsky_phoenix_today.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGWADDlfJuUH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "4582cae5-a115-45f1-cbf9-90e3852ad9a1"
      },
      "source": [
        "!pip install pvlib"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pvlib in /usr/local/lib/python3.6/dist-packages (0.7.2)\n",
            "Requirement already satisfied: pandas>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from pvlib) (1.0.4)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from pvlib) (1.18.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from pvlib) (2.23.0)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.6/dist-packages (from pvlib) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.18.1->pvlib) (2.8.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->pvlib) (2020.4.5.2)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->pvlib) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->pvlib) (2.9)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->pvlib) (3.0.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas>=0.18.1->pvlib) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ri2K8FTKE4q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import datetime\n",
        "from pytz import timezone\n",
        "\n",
        "#import pvlib\n",
        "from pvlib import clearsky, atmosphere\n",
        "from pvlib.location import Location"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-Qa6X-pRPIU",
        "colab_type": "text"
      },
      "source": [
        "## Define the current location and time zone"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IJfo2QQKZpH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "outputId": "5780bfdd-1e9a-4aff-d733-2379ba952e79"
      },
      "source": [
        "name = 'Phoenix'\n",
        "latitude = 33.4484 \n",
        "longitude = -112.0740\n",
        "altitude = 331 # (meters)\n",
        "time_zone = 'US/Arizona'\n",
        "phx = Location(latitude, longitude, time_zone, altitude, name)\n",
        "print(phx)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Location: \n",
            "  name: Phoenix\n",
            "  latitude: 33.4484\n",
            "  longitude: -112.074\n",
            "  altitude: 331\n",
            "  tz: US/Arizona\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLmA1X0PRQYH",
        "colab_type": "text"
      },
      "source": [
        "## List the insolation for 10 minutes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZI1j_80LfjI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "243416f8-be3a-4bc3-d9bd-95533f008928"
      },
      "source": [
        "dt = '2020-04-20 13:14:00'\n",
        "#uncomment the following line to get the next 10 minutes\n",
        "#dt = datetime.datetime.now(tz=timezone('US/Arizona'))\n",
        "times = pd.date_range(start=dt, periods=10, freq='1min', tz=phx.tz)\n",
        "cs = phx.get_clearsky(times)  # ineichen with climatology table by default\n",
        "print('global horizontal (W/m2)')\n",
        "print(cs['ghi'])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "global horizontal (W/m2)\n",
            "2020-04-20 13:14:00-07:00    947.947653\n",
            "2020-04-20 13:15:00-07:00    947.081845\n",
            "2020-04-20 13:16:00-07:00    946.198035\n",
            "2020-04-20 13:17:00-07:00    945.296238\n",
            "2020-04-20 13:18:00-07:00    944.376475\n",
            "2020-04-20 13:19:00-07:00    943.438764\n",
            "2020-04-20 13:20:00-07:00    942.483123\n",
            "2020-04-20 13:21:00-07:00    941.509570\n",
            "2020-04-20 13:22:00-07:00    940.518128\n",
            "2020-04-20 13:23:00-07:00    939.508814\n",
            "Freq: T, Name: ghi, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wwUtmgwARu6j",
        "colab_type": "text"
      },
      "source": [
        "## Plot the insolation for one day"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjZRL2kZKN-a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "bcc8f03f-5dcf-41ed-d899-5f5cf51f43bd"
      },
      "source": [
        "times = pd.date_range(start='2020-04-20', periods=24*6, freq='10min', tz=phx.tz)\n",
        "cs = phx.get_clearsky(times)  # ineichen with climatology table by default\n",
        "cs.plot()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6085c3c588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}