Practical2_plus_answers.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.0"
    },
    "colab": {
      "name": "Practical2_plus_answers.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fclubb/SciRes-Earthquakes/blob/main/Practical2_plus_answers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18elycEG8Eot"
      },
      "source": [
        "# Scientific Research Project 7 - Practical 2 Plus Answers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzmzd0iD8Eot"
      },
      "source": [
        "## Spatial and temporal analysis of the earthquake dataset\n",
        "In the first week, we downloaded a CSV file of earthquake information from the USGS database and used Python to make some map plots of the dataset. In this notebook, we're going to use some more Python to perform some analysis on the spatial and temporal distribution of earthquakes in the San Francisco region.\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8ehFr-K8Eot"
      },
      "source": [
        "**Firstly, copy the notebook to your Google Drive using the COPY TO DRIVE button.**\r\n",
        "\r\n",
        "Now we'll import the packages that we need again. This should be familiar from last time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99OwXatz8Eou"
      },
      "source": [
        "# import several helpful packages that we'll use\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing. We can read in csv files using pd.read_csv('path/to/csv-file')\n",
        "import matplotlib.pyplot as plt # package for making figures\n",
        "import matplotlib.cm as cm # for choosing colourmaps"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ognwypYUnccz"
      },
      "source": [
        "\n",
        "Now we have to load our earthquake data into Google Colab. This is the exact same as we did last week using the USGS CSV file that you downloaded. To do this, click the small folder icon on the left hand bar and then click `Upload to Session Storage`. You can then navigate to the CSV file we downloaded and add it to Google Colab.\n",
        "\n",
        "**NOTE** - you will have to re-upload the data to Google Colab whenever you want to run this notebook. \n",
        "\n",
        "![practical1_fig4.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBtiRUgtor4E"
      },
      "source": [
        "Once you have uploaded the file and you can see it in the left hand bar, we can then load it into Python again using the code in the cell below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5cjQtFWWmkW4"
      },
      "source": [
        "df = pd.read_csv('San_Francisco_earthquakes_1900-2020.csv') # This needs to correspond to the name of the file you uploaded. You can change it to represent your own file "
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hT1uNKvX8nZd"
      },
      "source": [
        "## Spatial analysis\n",
        "\n",
        "Last week, we made some plots of how earthquakes were distributed, but we didn't think about how to **quantitatively measure** that distribution. One way of doing this is to do a _cluster analysis_: we will try to identify groups of earthquakes which have a similar spatial location.  One of the most common types of cluster analysis is called `K-means` clustering: this analysis will split the dataset into a user-defined number of clusters, or $K$ clusters (that's why it's called K-means). \n",
        "\n",
        "K-means clustering is a type of unsupervised machine learning algorithm. You can read this tutorial for details about how K-means clustering works: https://stanford.edu/~cpiech/cs221/handouts/kmeans.html\n",
        "You can also look at the Python user guide for K-means which provides a lot of information: https://scikit-learn.org/stable/modules/clustering.html#k-means\n",
        "\n",
        "The good thing about this algorithm is that it's very simple and easy to understand. However, one of the downsides is that we have to decide the number of clusters, or groups, that we want to end up with in advance. The result will be dependent on the number of clusters, or value of $K$, that you choose. Here we start with clustering into 5 groups ($K = 5$) but you should experiment and find a value of $K$ that looks appropriate for your dataset. We will start by importing the K-means package:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuJqBReZ8Eou"
      },
      "source": [
        "# import K means clustering package from scikit-learn: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\n",
        "from sklearn.cluster import KMeans"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rO3yXjy2_zF6"
      },
      "source": [
        "Now that we've got our dataframe, let's run the K-means clustering algorithm. We'll define a variable $K=5$ which sets the number of clusters that we want."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjPj8rAw_6q_"
      },
      "source": [
        "# set K = number of clusters you want\n",
        "K = 5"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TWwxK69AWDa"
      },
      "source": [
        "The next step is to get the X and Y values for clustering from our data. In our case, the X value is the longitude and the Y value is the latitude. We then run the clustering on the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLMaoKiS_7RY"
      },
      "source": [
        "# get the longitude and latitude\n",
        "data = df[['longitude', 'latitude']]\n",
        "# run the K-means clustering on the data with K number of clusters\n",
        "kmeans = KMeans(n_clusters=K)\n",
        "clusters = kmeans.fit_predict(data)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "da1RQyp4Dzx1"
      },
      "source": [
        "The `clusters` variable is now a list of the cluster number that was assigned to each earthquake. Python always starts counting at 0, so we have 5 clusters: cluster 0, cluster 1, cluster 2, cluster 3, and cluster 4:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2YxmmNrBDgh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2935e7b-6f20-479c-f7fa-bc47bbf0e705"
      },
      "source": [
        "clusters"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 3, 1, 3, 0, 2, 1, 0, 3, 1, 1, 1, 0, 2, 0, 1, 3, 2, 0, 0, 0, 0,\n",
              "       0, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2,\n",
              "       3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 2, 0, 0, 4, 4, 4, 4, 4, 0, 2, 3,\n",
              "       0, 3, 2, 0, 3, 3, 3, 2, 2, 2, 4, 0, 1, 1, 1, 2, 2, 4, 2, 0, 2, 3,\n",
              "       3, 0, 0, 0, 0, 1, 0, 3, 1, 3, 4, 2, 4, 0, 0, 3, 3, 3, 2, 0, 0, 0,\n",
              "       0, 2, 0, 4, 2, 0, 3, 2, 2, 0, 0, 0, 2, 2, 0, 4, 4, 0, 3, 3, 2, 4,\n",
              "       2, 2, 2, 3, 0, 0, 0, 2, 0, 1, 2, 2, 2, 2, 3, 2, 0, 0, 2, 1, 2, 2,\n",
              "       3, 4, 2, 2, 2, 2, 0, 0, 0, 0, 4, 0, 1, 4, 2, 2, 4, 0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vOGbW-6E_Zi"
      },
      "source": [
        "We can now make a simple scatter plot of the earthquakes coloured by the cluster number."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23EBDPFUEJuV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "b14b7186-c485-45a7-df0f-bd16c11d03a4"
      },
      "source": [
        "# make a simple X and Y plot with the points coloured by the cluster number\n",
        "cbar = plt.scatter(data['longitude'], data['latitude'], c=clusters)\n",
        "plt.xlabel('Longitude ($^\\circ$)')\n",
        "plt.ylabel('Latitude ($^\\circ$)')\n",
        "# add a colour bar\n",
        "plt.colorbar(cbar, label='Cluster number')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f7bb12af8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrdcnfCWSRr4"
      },
      "source": [
        "We can add the cluster number back in to our original dataframe, and then use pandas to save it as a new CSV file:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjFfDFp7SYVt"
      },
      "source": [
        "df['cluster_number'] = clusters\n",
        "df.to_csv('San_Francisco_earthquakes_plus_clustering.csv', index=False)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46N8ftqpGlB3"
      },
      "source": [
        "### Testing if points are randomly distributed\n",
        "Another test, which might be more useful, would be to check whether the earthquakes are randomly distributed, often called _Complete Spatial Randomness (CSR)_, or whether they are clustered into groups. To do that, we will install and import some libraries for analysing point patterns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKWn5sjPHPE5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f37b43b-24cf-47ca-8d89-32df62e71f53"
      },
      "source": [
        "# download and install the point pattern library\n",
        "!pip install libpysal pointpats\n",
        "import libpysal as ps\n",
        "from pointpats import PointPattern\n",
        "import pointpats.quadrat_statistics as qs"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: libpysal in /usr/local/lib/python3.6/dist-packages (4.3.5)\n",
            "Requirement already satisfied: pointpats in /usr/local/lib/python3.6/dist-packages (2.2.0)\n",
            "Requirement already satisfied: scipy>=0.11 in /usr/local/lib/python3.6/dist-packages (from libpysal) (1.4.1)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.6/dist-packages (from libpysal) (4.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from libpysal) (2.23.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from libpysal) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.3 in /usr/local/lib/python3.6/dist-packages (from libpysal) (1.19.5)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.6/dist-packages (from libpysal) (2.11.2)\n",
            "Requirement already satisfied: opencv-contrib-python>=4.2.0 in /usr/local/lib/python3.6/dist-packages (from pointpats) (4.5.1.48)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from pointpats) (3.2.2)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->libpysal) (2.10)\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->libpysal) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->libpysal) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->libpysal) (2020.12.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->libpysal) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->libpysal) (2.8.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->libpysal) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->pointpats) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->pointpats) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->pointpats) (2.4.7)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->libpysal) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J8FdCXuTHsYk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "762a738d-4f51-470a-9165-76828920ad6a"
      },
      "source": [
        "# make a point pattern from the latitude and longitude and print the summary\n",
        "pp = PointPattern(data)\n",
        "pp.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Point Pattern\n",
            "172 points\n",
            "Bounding rectangle [(-122.92,36.5), (-120.1,38.8)]\n",
            "Area of window: 6.486000000000047\n",
            "Intensity estimate for window: 26.518655565833914\n",
            "            x          y\n",
            "0 -121.274000  36.645500\n",
            "1 -121.244333  36.630500\n",
            "2 -122.312333  38.215167\n",
            "3 -121.180833  36.584333\n",
            "4 -121.496333  36.771167\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ALmp-D_MJqw"
      },
      "source": [
        "To test whether points in the pattern are randomly distributed, we will use what's called a _Quadrat Statistic_. This comes from ecology, where a common way of counting the distribution of, say, beetles or plants, is by dividing an area up in to rectangles and counting the number of organisms in each rectangle. If the organisms are randomly distributed, then there should be an even number of them in each rectangle. You can see more explanation in this document: https://www.seas.upenn.edu/~ese502/NOTEBOOK/Part_I/3_Testing_Spatial_Randomness.pdf\n",
        "\n",
        "Here we will set up our quadrats to be rectangle shapes with 3 rows (`ny`)and 3 columns (`nx`). One disadvantage of this method is that it can be sensitive to the number of rectangles that you choose. You can test the sensitivity by changing `nx` and `ny` in the function below. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1DLmYdNIKF4"
      },
      "source": [
        "q_r = qs.QStatistic(pp,shape= \"rectangle\",nx = 3, ny = 3)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1T8vyWYYN9Hq"
      },
      "source": [
        "We can plot the quadrats, which will show you how many earthquakes are located in each one. Just from looking at the numbers, we can see that there is a very uneven distribution of earthquakes across the region."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtAj21AlIKtg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "53f6e530-c6c1-4a21-a736-1b7a1222f918"
      },
      "source": [
        "q_r.plot()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yS5kZyNyOJWX"
      },
      "source": [
        "But how can we say that statistically? The easiest way to test the CSR hypothesis is to use the Pearson $\\chi^2$ goodness-of-fit test. This works by comparing the _expected_ number of points in each rectangle if the dataset was random, to the _actual_ number of points in each rectangle that we observe.\n",
        "You can read this notebook for a very nice explanation of how this works (focus on Section 3.1): https://joparga3.github.io/spatial_point_pattern/\n",
        "\n",
        "Our null hypothesis, that the points are randomly distributed, would occur if we have a $\\chi^2$ statistic with a $p$-value greater than 0.05. If $p > 0.05$ it means we can't reject the null hypothesis at a 95% confidence level.\n",
        "\n",
        "Our alternative hypothesis, that the points are clustered, would occur if $p < 0.05$. So let's look at our results:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wn1pCIayJhIA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30cd8a49-258f-422d-ddc7-38d7d5582dae"
      },
      "source": [
        "q_r.chi2 "
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "407.9767441860466"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIF8MHyUNVLj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b26930be-076f-40ad-9df7-ee0ec658121c"
      },
      "source": [
        "q_r.chi2_pvalue"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.681655621499739e-83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwnfsIsGQhdR"
      },
      "source": [
        "We can see that we have a $p$ value _much_ smaller than 0.05! This means we can reject the null hypothesis and state that the earthquakes are not randomly located."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51fEogjpQ-Ph"
      },
      "source": [
        "### Exercise 1: Spatial analysis\n",
        "In this section, we've explored a couple of statistical techniques that you could use to analyse the spatial distribution of earthquakes. Try the following:\n",
        "\n",
        "1. Think about what the K-means clusters tell you. Does the machine learning algorithm relate to the tectonic setting around San Francisco?\n",
        "\n",
        "_The machine learning algorithm hasn’t worked very well – the faults in the region tend to trend NW-SE, whereas the clusters appear to be banded mainly by latitude! This is a common issue with using unsupervised classification algorithms – the computer doesn’t know anything about the physical processes going on._\n",
        "\n",
        "2. Re-run the K-means clustering with different numbers of clusters (change the value of $K$). How does this affect the results?\n",
        "\n",
        "_To do this you need to change the value of K in the second code block. The default value is 5 – you could try creating more or fewer clusters to see if this affects the clustering. Here I have changed to 10 clusters – this seems to break down the earthquakes in a slightly more sensible way (the San Andreas Fault is now separated from the Calaveras Fault)_\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mm1BNLs8RAOu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "0f278f35-8ad8-4472-c116-c53dfe9f8b1f"
      },
      "source": [
        "K = 10\r\n",
        "\r\n",
        "# get the longitude and latitude\r\n",
        "data = df[['longitude', 'latitude']]\r\n",
        "\r\n",
        "# run the K-means clustering on the data with K number of clusters\r\n",
        "kmeans = KMeans(n_clusters=K)\r\n",
        "clusters = kmeans.fit_predict(data)\r\n",
        "\r\n",
        "# make a simple X and Y plot with the points coloured by the cluster number\r\n",
        "cbar = plt.scatter(data['longitude'], data['latitude'], c=clusters)\r\n",
        "plt.xlabel('Longitude ($^\\circ$)')\r\n",
        "plt.ylabel('Latitude ($^\\circ$)')\r\n",
        "# add a colour bar\r\n",
        "plt.colorbar(cbar, label='Cluster number')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f7bb0353358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C33rHNj83d_b"
      },
      "source": [
        "3. Practice your Python by modifying the scatter plot of earthquakes coloured by cluster number. You can try changing the colourscale, the size of the points, the marker style, etc. Can you work out how to save this plot as an image? (HINT: see notebook from last week for example of how to do this).\r\n",
        "\r\n",
        "_It’s up to you how to modify it – you could try changing to a discrete colourscale which might make the clusters easier to see._\r\n",
        "_To save it as an image you need to add the line:\r\n",
        "\r\n",
        "```\r\n",
        "plt.savefig('name of fig.png')\r\n",
        "```\r\n",
        "\r\n",
        "_at the end of the code block._\r\n",
        "\r\n",
        "4. The quadrat method for testing whether points are randomly located can be dependent on the number and size of rectangles that are used. Try varying the number of rectangles, and look at the difference in the $\\chi^2$ statistic and $p$ value. Does it have a significant impact on our hypothesis testing? If it does, this would be quite worrying, as it means our statistic wouldn't be very reliable.\r\n",
        "\r\n",
        "_On testing I found that the p value was still less than 0.05 when you vary nx/ny between 2 and 7 (making the number of rectangles higher than that becomes a bit pointless). This means we can be very confident that the earthquakes are not randomly distributed. \r\n",
        "Worth pointing out that, while this might sound obvious (we know earthquakes are occurring along faults), probabilistic seismic hazard assumes that earthquakes occur randomly within a source region. However, from our plots it’s clear that the southern faults have had many more earthquakes than the northern ones._\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "zEVVAUkM4TJV",
        "outputId": "1d728b7a-c580-4167-80e2-229d01362195"
      },
      "source": [
        "q_r = qs.QStatistic(pp,shape= \"rectangle\",nx = 7, ny = 7)\r\n",
        "q_r.plot()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHRCFmfd4ezu",
        "outputId": "06b9a0fb-e1ed-48e6-f425-3129c522946c"
      },
      "source": [
        "q_r.chi2 "
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "664.4186046511627"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-hP-gldX4ezy",
        "outputId": "d5b4f1c1-e8d0-4ebb-f704-2c242ccad8d6"
      },
      "source": [
        "q_r.chi2_pvalue"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.1595605233150263e-109"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8PIctyZ4Q6P"
      },
      "source": [
        "**BONUS QUESTION** for those who want to mess around with Python more:\r\n",
        "\r\n",
        "5. At the end of the K-means clustering, we saved the output to a new CSV file. In a new cell, try to read this to a dataframe using pandas. Then use the code from the first notebook to make a `cartopy` map plot of the earthquakes coloured by cluster number. (HINT - modify the last code block in Practical 1).\r\n",
        "\r\n",
        "_We'll do this in this notebook by importing `cartopy` and then copying the code from Practical 1. You could also load the new CSV file into last week's notebook._"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-UDQ56R34kwO",
        "outputId": "a215a0fc-0998-4407-af50-c2b5aae475e9"
      },
      "source": [
        "!pip install cartopy\r\n",
        "import cartopy"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: cartopy in /usr/local/lib/python3.6/dist-packages (0.17.0)\n",
            "Requirement already satisfied: shapely>=1.5.6 in /usr/lib/python3/dist-packages (from cartopy) (1.6.4)\n",
            "Requirement already satisfied: six>=1.3.0 in /usr/local/lib/python3.6/dist-packages (from cartopy) (1.15.0)\n",
            "Requirement already satisfied: setuptools>=0.7.2 in /usr/local/lib/python3.6/dist-packages (from cartopy) (51.3.3)\n",
            "Requirement already satisfied: numpy>=1.10 in /usr/local/lib/python3.6/dist-packages (from cartopy) (1.19.5)\n",
            "Requirement already satisfied: pyshp>=1.1.4 in /usr/lib/python3/dist-packages (from cartopy) (1.2.12)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "HcAHk_sc4pe0",
        "outputId": "574b5519-8ee9-47f3-cda0-aac95618d619"
      },
      "source": [
        "# set up a figure and choose the size by defining the width,height in inches.\r\n",
        "plt.figure(figsize=(10,10))\r\n",
        "# define an axis for the figure. We need to define the map projection for the figure - we will use Plate Carree.\r\n",
        "ax = plt.axes(projection=cartopy.crs.PlateCarree())\r\n",
        "# add gridlines to show the latitude and longitude\r\n",
        "gl = ax.gridlines(crs=cartopy.crs.PlateCarree(), draw_labels=True,\r\n",
        "                  linewidth=2, color='gray', alpha=0.5, linestyle='--', zorder=1)\r\n",
        "\r\n",
        "# The title of the plot\r\n",
        "ax.set_title('San Francisco Earthquakes, 1900-2020', fontsize=14, pad=25)\r\n",
        "\r\n",
        "# We will use the natural earth dataset, which provides free mapping data, to add some background to the figure.\r\n",
        "# display coastlines\r\n",
        "ax.coastlines(resolution='50m')\r\n",
        "# add the land\r\n",
        "land = cartopy.feature.NaturalEarthFeature(category='physical', scale='50m', name='land', facecolor='wheat', zorder=0)\r\n",
        "_ = ax.add_feature(land)\r\n",
        "# add the ocean\r\n",
        "ocean = cartopy.feature.NaturalEarthFeature(category='physical', scale='50m', name='ocean', facecolor='skyblue', zorder=0)\r\n",
        "_ = ax.add_feature(ocean)\r\n",
        "\r\n",
        "# make a scatter plot of the earthquakes. To colour by the cluster number, we set the variable c equal to the column in the dataframe (df['cluster_number'])\r\n",
        "cmap = cm.rainbow # this is the colour scale for the magnitudes.\r\n",
        "cbar = plt.scatter(x=df['longitude'], y=df['latitude'], zorder=2,    # X values = longitudes, Y values = latitudes. zorder controls the order of the layers to make the points plot above the land\r\n",
        "                   c=df['cluster_number'], cmap=cmap, edgecolors='black', s=60) # c = colour of points, edgecolors = colour of point outlines, s = size of points.\r\n",
        "\r\n",
        "# add a colourbar. You can change the label, orientation, etc.\r\n",
        "plt.colorbar(cbar, cmap=cmap, label='Cluster number', shrink=0.85, orientation='horizontal')\r\n",
        "\r\n",
        "# save the figure. You can change the name to whatever you want the image to be called.\r\n",
        "plt.savefig('San_Francisco_Earthquakes_Cluster_1900-2020.png')\r\n",
        "# show the figure in the notebook\r\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXD0AgecTwDK"
      },
      "source": [
        "## Temporal analysis\n",
        "Now we're going to move on and think about analysing the distribution of earthquakes through _time_. Time series analysis is essential for many, many things in geographical research: just a few examples of time series data could be precipitation or temperature records, sea level, ice sheet volume, river discharge, carbon emissions, air pollution levels, etc., etc. \n",
        "\n",
        "Alongside geographical research, time series are widely used in finance, economics, business, or pretty much any area where a variable changes through time.\n",
        "I would argue that dealing with time series is a fundamental and employable skill that you can learn from your degree!\n",
        "\n",
        "We're going to learn how to make some simple plots and analyses of the earthquake time series from our pandas dataframe. One of the first things we could do is to simply plot the earthquakes through time according to their magnitude. \n",
        "\n",
        "`pandas` has a very nice function called `datetime` that is specially designed for dealing with timeseries. Our first step is to convert the index (the row numbers) of the dataframe to a datetime:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ujee2PRTzph"
      },
      "source": [
        "# Convert the dataframe index to a pandas datetime\n",
        "df.index = pd.to_datetime(df['time'])"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wC4puY6zWeY7"
      },
      "source": [
        "If you print out the dataframe, you can see that the row numbers in the very left hand column have been replaced with the date. This means that we can use the date to refer to each row."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55wYsJieWcWV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "outputId": "666ae9a0-271e-4ed3-9caf-bf146926a22c"
      },
      "source": [
        "df"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>depth</th>\n",
              "      <th>mag</th>\n",
              "      <th>magType</th>\n",
              "      <th>nst</th>\n",
              "      <th>gap</th>\n",
              "      <th>dmin</th>\n",
              "      <th>rms</th>\n",
              "      <th>net</th>\n",
              "      <th>id</th>\n",
              "      <th>updated</th>\n",
              "      <th>place</th>\n",
              "      <th>type</th>\n",
              "      <th>horizontalError</th>\n",
              "      <th>depthError</th>\n",
              "      <th>magError</th>\n",
              "      <th>magNst</th>\n",
              "      <th>status</th>\n",
              "      <th>locationSource</th>\n",
              "      <th>magSource</th>\n",
              "      <th>cluster_number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-10-15 19:42:30.540000+00:00</th>\n",
              "      <td>2019-10-15T19:42:30.540Z</td>\n",
              "      <td>36.645500</td>\n",
              "      <td>-121.274000</td>\n",
              "      <td>10.100</td>\n",
              "      <td>4.71</td>\n",
              "      <td>mw</td>\n",
              "      <td>89.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.04759</td>\n",
              "      <td>0.14</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc73292360</td>\n",
              "      <td>2020-09-29T22:06:48.876Z</td>\n",
              "      <td>17km SSE of Tres Pinos, CA</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-13 19:31:29.050000+00:00</th>\n",
              "      <td>2017-11-13T19:31:29.050Z</td>\n",
              "      <td>36.630500</td>\n",
              "      <td>-121.244333</td>\n",
              "      <td>6.310</td>\n",
              "      <td>4.58</td>\n",
              "      <td>mw</td>\n",
              "      <td>82.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0.05988</td>\n",
              "      <td>0.11</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc72923380</td>\n",
              "      <td>2018-10-05T14:00:40.985Z</td>\n",
              "      <td>22km NE of Gonzales, California</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-08-24 10:20:44.070000+00:00</th>\n",
              "      <td>2014-08-24T10:20:44.070Z</td>\n",
              "      <td>38.215167</td>\n",
              "      <td>-122.312333</td>\n",
              "      <td>11.120</td>\n",
              "      <td>6.02</td>\n",
              "      <td>mw</td>\n",
              "      <td>369.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.03604</td>\n",
              "      <td>0.18</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc72282711</td>\n",
              "      <td>2020-08-14T23:04:26.676Z</td>\n",
              "      <td>South Napa</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-08-27 07:18:21.150000+00:00</th>\n",
              "      <td>2011-08-27T07:18:21.150Z</td>\n",
              "      <td>36.584333</td>\n",
              "      <td>-121.180833</td>\n",
              "      <td>7.500</td>\n",
              "      <td>4.64</td>\n",
              "      <td>mw</td>\n",
              "      <td>97.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.08108</td>\n",
              "      <td>0.08</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc71627835</td>\n",
              "      <td>2020-05-02T02:50:33.967Z</td>\n",
              "      <td>Central California</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-01-12 08:51:03.680000+00:00</th>\n",
              "      <td>2011-01-12T08:51:03.680Z</td>\n",
              "      <td>36.771167</td>\n",
              "      <td>-121.496333</td>\n",
              "      <td>7.661</td>\n",
              "      <td>4.50</td>\n",
              "      <td>mw</td>\n",
              "      <td>134.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.01802</td>\n",
              "      <td>0.19</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc71508850</td>\n",
              "      <td>2020-05-02T02:42:59.011Z</td>\n",
              "      <td>Central California</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>nc</td>\n",
              "      <td>nc</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1904-07-30 10:26:00+00:00</th>\n",
              "      <td>1904-07-30T10:26:00.000Z</td>\n",
              "      <td>38.500000</td>\n",
              "      <td>-122.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.50</td>\n",
              "      <td>ml</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ushis</td>\n",
              "      <td>ushis430</td>\n",
              "      <td>2018-06-04T20:43:44.000Z</td>\n",
              "      <td>Northern California</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>ushis</td>\n",
              "      <td>cdmg</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1903-08-03 06:49:00+00:00</th>\n",
              "      <td>1903-08-03T06:49:00.000Z</td>\n",
              "      <td>37.300000</td>\n",
              "      <td>-121.800000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.80</td>\n",
              "      <td>ml</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ushis</td>\n",
              "      <td>ushis421</td>\n",
              "      <td>2018-06-04T20:43:44.000Z</td>\n",
              "      <td>San Francisco Bay area, California</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>ushis</td>\n",
              "      <td>cdmg</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1903-06-11 13:12:00+00:00</th>\n",
              "      <td>1903-06-11T13:12:00.000Z</td>\n",
              "      <td>37.400000</td>\n",
              "      <td>-121.900000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.80</td>\n",
              "      <td>ml</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ushis</td>\n",
              "      <td>ushis418</td>\n",
              "      <td>2018-06-04T20:43:44.000Z</td>\n",
              "      <td>San Francisco Bay area, California</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>ushis</td>\n",
              "      <td>cdmg</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1902-05-19 18:31:00+00:00</th>\n",
              "      <td>1902-05-19T18:31:00.000Z</td>\n",
              "      <td>38.300000</td>\n",
              "      <td>-121.900000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.40</td>\n",
              "      <td>ml</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ushis</td>\n",
              "      <td>ushis402</td>\n",
              "      <td>2018-06-04T20:43:44.000Z</td>\n",
              "      <td>Northern California</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>ushis</td>\n",
              "      <td>cdmg</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1900-04-30 22:41:14+00:00</th>\n",
              "      <td>1900-04-30T22:41:14.000Z</td>\n",
              "      <td>36.900000</td>\n",
              "      <td>-121.600000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.50</td>\n",
              "      <td>ml</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ushis</td>\n",
              "      <td>ushis389</td>\n",
              "      <td>2018-06-04T20:43:44.000Z</td>\n",
              "      <td>Central California</td>\n",
              "      <td>earthquake</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>reviewed</td>\n",
              "      <td>ushis</td>\n",
              "      <td>cdmg</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>172 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                      time  ...  cluster_number\n",
              "time                                                        ...                \n",
              "2019-10-15 19:42:30.540000+00:00  2019-10-15T19:42:30.540Z  ...               3\n",
              "2017-11-13 19:31:29.050000+00:00  2017-11-13T19:31:29.050Z  ...               3\n",
              "2014-08-24 10:20:44.070000+00:00  2014-08-24T10:20:44.070Z  ...               1\n",
              "2011-08-27 07:18:21.150000+00:00  2011-08-27T07:18:21.150Z  ...               3\n",
              "2011-01-12 08:51:03.680000+00:00  2011-01-12T08:51:03.680Z  ...               0\n",
              "...                                                    ...  ...             ...\n",
              "1904-07-30 10:26:00+00:00         1904-07-30T10:26:00.000Z  ...               4\n",
              "1903-08-03 06:49:00+00:00         1903-08-03T06:49:00.000Z  ...               2\n",
              "1903-06-11 13:12:00+00:00         1903-06-11T13:12:00.000Z  ...               2\n",
              "1902-05-19 18:31:00+00:00         1902-05-19T18:31:00.000Z  ...               4\n",
              "1900-04-30 22:41:14+00:00         1900-04-30T22:41:14.000Z  ...               0\n",
              "\n",
              "[172 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdX1L6-EXQ5B"
      },
      "source": [
        "We'll now make what's called a \"stem plot\" to show the earthquakes through time. This is a plot where there is a point for each earthquake, with a vertical line connecting it to the X axis. It makes it easier to see what year each earthquake occurred in."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9HQs5rKWcyy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "7bad76d5-ef02-467a-94f2-c75c0ea36935"
      },
      "source": [
        "# set up a figure\n",
        "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10,4))\n",
        "# add the grid lines\n",
        "ax.grid(color='0.8', linestyle='--', which='both')\n",
        "\n",
        "# make a stem plot\n",
        "markerline, stemlines, baseline = ax.stem(df.index, df.mag, linefmt='black', use_line_collection=True)\n",
        "\n",
        "# adjust the markers and lines\n",
        "markerline.set_markeredgecolor('k')\n",
        "markerline.set_markerfacecolor('red')\n",
        "stemlines.set_linewidth(0.5)\n",
        "baseline.set_color('white')\n",
        "markerline.set_markersize(10)\n",
        "\n",
        "# add x and y labels\n",
        "plt.xlabel('Year', fontsize=14)\n",
        "plt.ylabel('Magnitude ($M$)', fontsize=14, labelpad=20)\n",
        "# set the Y axis to start at magnitude 3\n",
        "plt.ylim(3,)\n",
        "\n",
        "# save as a figure\n",
        "plt.savefig('San_Francisco_earthquakes_stem_plot.png')"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apuILFbBX90A"
      },
      "source": [
        "### Number of earthquakes through time\n",
        "\n",
        "Another analysis that we might want to do is to analyse how the number of earthquakes has changed through time. To do that, we can count the number of earthquakes in a given year. Our `pandas datetime` object can help us with this: we can simply group times by year:   "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "019e4_jxYLju",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "497b450c-7784-4966-b2c7-2a7c02eaabcd"
      },
      "source": [
        "# get the years from the dataframe\n",
        "y = df.index.year\n",
        "# group the dataframe by the year, and then get the count\n",
        "freq_df = df.groupby(y)['id'].count().reset_index()\n",
        "idx = freq_df['id'].argmax()\n",
        "freq_df['time'][idx]\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1989"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwTtfVYiuLF2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "234f20cd-9180-490c-8049-78a554c108f3"
      },
      "source": [
        "plt.plot(freq_df['time'], freq_df['id'])\n",
        "plt.ylabel('Number of earthquakes')\n",
        "plt.xlabel('Time')\n",
        "plt.savefig('n_quakes.png')"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO29eXxkZZX//z5VlVT29JKk6aS76WZpoLtZumkYxI1NQUBQRFFx+Y58xZlx1Jmv+zKKP2dznVGZcWQcXGYUWQZQXFBEARWBXtiSXuiV7k66O+mFVNZKLef3x723UpVUkltJKlWpnPfrlVeqnrrLc+ve+txzzznPeURVMQzDMOYOgUJ3wDAMw5hZTPgNwzDmGCb8hmEYcwwTfsMwjDmGCb9hGMYcI1ToDvihoaFBly9fXuhuGIZhzCo2bdp0RFUbR7bPCuFfvnw5GzduLHQ3DMMwZhUi8mK2dnP1GIZhzDFM+A3DMOYYJvyGYRhzDBN+wzCMOYYJv2EYxhwjb8IvIreLSKeItI5o/4CIbBORNhH5Ur72bxiGYWQnnxb/94Ar0htE5GLgWuBsVV0NfCWP+zcMwzCykDfhV9XHgGMjmv8S+GdVjbrLdOZr/4ZhFBe/297JgeP9he6Gwcz7+FcCrxSRJ0XkURE5b6wFReRmEdkoIhu7urpmsIuGYeSDD/7oaX7wp6zjiYwZZqaFPwQsAC4APgrcJSKSbUFVvU1V16vq+sbGUSOODcOYZQzGEwzFk4XuhsHMC/8B4F51eApIAg0z3AfDMGYYVSWWUOJJE/5iYKaF/37gYgARWQmUA0dmuA+GYcwwiaRm/DcKS96KtInIHcBFQIOIHAA+B9wO3O6meA4B71ab9NcwSp64K/jxhP3ci4G8Cb+qvm2Mj96Rr30ahlGcxBKOi8cs/uLARu4ahpF3PEs/ZsJfFJjwG4aRd2JJz+K34G4xYMJvGEbe8Sx+8/EXByb8hmHkHU/wzcdfHJjwG4aRdzxXT9yEvygw4TcMI++YxV9cmPAbhpF3vHROG7lbHJjwG4aRd2wAV3Fhwm8YRt6JJ8zHX0yY8BuGkXdi5uMvKkz4DcPIO3HL6ikqTPgNw8g7w1k9FtwtBkz4DcPIOzHz8RcVJvyGYeSduNXjLypM+A3DyDspi9/SOYsCE37DMPJOqkib+fiLAhN+wzDyTjxpE7EUE3kTfhG5XUQ63WkWR372YRFREbGJ1g1jDhBLWfwm/MVAPi3+7wFXjGwUkaXAa4F9edy3YRhFRGqydfPxFwV5E35VfQw4luWjfwE+BtgVYBhzBEvnLC5m1McvItcC7ar67Ezu1zCMwmLpnMVFaKZ2JCJVwKdw3Dx+lr8ZuBlg2bJleeyZYRj5xivSFrOsnqJgJi3+k4EVwLMishdYAmwWkROyLayqt6nqelVd39jYOIPdNAxjuvGCu6qQNKu/4MyYxa+qzwNN3ntX/Ner6pGZ6oNhGIUhPX8/nlTKA1LA3hj5TOe8A/gTcJqIHBCRm/K1L8Mwipv0Ebvm5y88ebP4VfVtE3y+PF/7NgyjuIilCb9j/QcL1xnDRu4ahpF/0l09ZvEXHhN+wzDyTqbFb8JfaEz4DcPIO146p/PahL/QmPAbhpF30q18q9BZeEz4DcPIO7GE+fiLCRN+wzDyTtx8/EWFCb9hGHnHsnqKCxN+wzDyTkZWjwV3C44Jv2EYeccs/uLChN8wjLyTbvFbhc7CM6Hwi8jLRaTaff0OEfmaiJyY/64ZhlEqxC2rp6jwY/F/C+gXkbOBDwO7gB/ktVeGYZQU8aQSDjlyYz7+wuNH+OOqqsC1wK2q+m9AbX67ZRhGKRFLKBVlTmE2s/gLjx/h7xGRTwLvBH4uIgGgLL/dMgyjlIgnklSUuRa/+fgLjh/hvwGIAu9R1UM4M2d9Oa+9MgyjpIgnlUqz+IuGCYXfFfv/BcJu0xHgvnx2yjCM0iKWSKZcPTZyt/D4yep5L3AP8G23qQW4P5+dMgyjtIgnlLAn/BbcLTh+XD3vB14ORABUdQdpc+cahmFMRDyZpCJkPv5iwY/wR1V1yHsjIiFgwlu2iNwuIp0i0prW9mUR2SYiz4nIfSIyb3LdNgxjNmFZPcWFH+F/VEQ+BVSKyGuAu4EHfKz3PeCKEW0PAWtU9SzgBeCTOfTVMIxZSmZWjwl/ofEj/J8AuoDngfcBv1DVT0+0kqo+Bhwb0fZrVY27b5/AyRAyDKPEiSXN4i8mQj6WuUVVPwv8J4CIBEXkh6p64xT3/R7gzrE+FJGbgZsBli1bNsVdGYZRSBJJpSJkWT3Fgh+Lf6k7gAsRKcdJ7dwxlZ2KyKeBOPDDsZZR1dtUdb2qrm9sbJzK7gzDKCCqSiKpVJa7Fn/CgruFxo/wvwc40xX/nwGPquotk92hiPwf4GrgRrcUhGEYJYxXmTNsPv6iYUxXj4isS3v7dZw8/j/iBHvXqermXHcmIlcAHwNerar9ua5vGMbsw0vfNFdP8TCej/+rI94fB1a57QpcMt6GReQO4CKgQUQOAJ/DyeIJAw+JCMATqvoXk+q5YRizAs/it+Bu8TCm8KvqxVPZsKq+LUvzf01lm4ZhzD68WvypdE4buVtw/GT1ICJXAauBCq9NVf+/fHXKMIzSwXPtlIcCiEDCRu4WHD+1ev4Dp0LnBwAB3gzYDFyGYfgi5lr8ZYEAoYCYj78I8JPVc6Gqvgs4rqqfB14GrMxvtwzDKBU8104oKAQDYj7+IsCP8A+4//tFpBmIAYvz1yXDMEoJL6snFAxQFghkTLxuFAY/Pv6fucXUvgxsxsno+U5ee2UYRsngCX1ZQAgGxXz8RcCEwq+qX3Bf/q+I/AyoUNXu/HbLMIxSYdjVYz7+YmFC4ReRd2VpQ1V/kJ8uGYZRSsRSrh7z8RcLflw956W9rgAuxXH5mPAbhjEh8ZSrJ0AoEDCLvwjw4+r5QPp719//47z1yDCMksIbwGUWf/HgJ6tnJH3AiunuiGEYpUnMFfqyoJiPv0jw4+N/gOGpFgM49XruymenDMMoHVIWfyBAKCip90bh8OPj/0ra6zjwoqoeyFN/DMMoMWIZA7jMx18M+PHxPzoTHTEMozTxBnCVuemc5uMvPH5cPT0Mu3oyPgJUVeumvVeGYZQMqTz+gBPcNYu/8Phx9fwrcBD4bxyxvxFY7M7DaxiGMS6pIm0pi998/IXGT1bPNar676rao6oRVf0WcG2+O2YYRmngWfheOqfV4y88foS/T0RuFJGgiARE5EaclE7DMIwJGZnVYz7+wuNH+N8OvAU47P692W0bFxG5XUQ6RaQ1rW2BiDwkIjvc//Mn23HDMGYHqSJtblZPzIS/4Ewo/Kq6V1WvVdUGVW1U1Teo6l4f2/4ecMWItk8AD6vqqcDD7nvDMEqYzLLM5uMvBvxk9TQC7wWWpy+vqu8Zbz1VfUxElo9ovhZnAnaA7wOPAB/32VfDMGYhsZFZPebjLzh+snp+Avwe+A2QmOL+FqnqQff1IWDRWAuKyM3AzQDLli2b4m4NwygUqSJtQfPxFwt+hL9KVafdKldVFZExrwBVvQ24DWD9+vV2pRjGLCWeTCICwYDj4zfhLzx+grs/E5Erp2l/h0VkMYD7v3OatmsYRpESSyhlAUdqrEhbcTCm8ItIj4hEgA/hiP+AiETS2ifDT4F3u6/fjeNGMgyjhEkkk4SCAmBlmYuEMV09qlo7lQ2LyB04gdwGETkAfA74Z+AuEbkJeBEnTdQwjBImllCCAUf4QwFJjeQ1CoefrJ6HVfXSidpGoqpvG+OjcdczDKO0iCeTlAVdV48Fd4uCMYVfRCqAahyLfT5OnR6AOqBlBvpmGEYJEE8ooZTFb2WZi4HxLP73AX8DNAObGBb+CHBrnvtlGEaJEEtoyuI3H39xMJ6P/+sicivwKVX9wgz2yTCMEiKeFtx1snrMx19oxk3nVNUEcN0M9cUwjBIk3dVjFn9x4CeP/2EReZOIyMSLGoZhZBJLpAV3LY+/KPAj/O8D7gai05DHbxjGHCOe1LQ8/gCqmNVfYPzMuTulfH7DMOY2sUSSUGA4nRMcv38wECxkt+Y0fmr14KZzngpUeG2q+li+OmUYRukQTyhlacFdMIu/0PgZwPV/cco2LAGeAS4A/gRckt+uGYZRCsSTwxa/N4LX/PyFxY+P/0PAecCLqnoxsBZ4Ka+9MgyjZIglNCOdEyBhNfkLih/hH1TVQQARCavqNuC0/HbLMIxSIb1kQ9D9bxZ/YfHj4z8gIvOA+4GHROQ4ToE1wzCMCcks2WA+/mLAT1bPG92Xt4jI74B64MG89sowjJIhPY/f8/Fbhc7C4jer5xXAqar6XXcO3hZgT157ZhhGSZCex+9l95jFX1gm9PGLyOdwJkT/pNtUBvxPPjtlGEbp4Lh6PIvffPzFgJ/g7huBa4A+AFXtAGxQl2EYvnBcPebjLyb8CP+QqiqgACJSnd8uGYZRSmSWbBgeuWsUDj/Cf5eIfBuYJyLvBX4D/Gd+u2UYRqmQUbLBLP6iwE9Wz1dE5DU4E7CcBnxWVR+ayk5F5G+B/4vzFPE88OfeWAHDMEqL9JINNnK3OPCV1eMK/ZTE3kNEWoAPAqtUdUBE7gLeCnxvOrZvGEZx4UzE4ln8zn+z+AuLH1dPPggBlSISAqqAjgL1wzCMPKKqztSLgUyL3/L4C8uMC7+qtgNfAfYBB4FuVf31yOVE5GYR2SgiG7u6uma6m4ZhTAOeZe9Z/JbHXxyMKfwi8rD7/4vTuUO3xPO1wAqcidyrReQdI5dT1dtUdb2qrm9sbJzOLhiGMUPEU8JvPv5iYjwf/2IRuRC4RkR+DGRMvaiqmye5z8uAParaBSAi9wIXYoPCDKPk8Fw6ZYERPn6rzllQxhP+zwJ/h1OH/2sjPlMmX49/H3CBiFQBA8ClwMZJbsswjCImnjCLvxgZU/hV9R7gHhH5O1X9wnTtUFWfFJF7gM1AHHgauG26tm8YRvEQcwdqpbJ6zMdfFPjJ4/+CiFwDvMptekRVfzaVnarq54DPTWUbhmEUP57Aj8zqsZG7hcVPkbZ/wpmFa4v79yER+cd8d8wwjNnPsKsnc+Ru3Hz8BcXPAK6rgHNUNQkgIt/Hcc98Kp8dMwxj9pMK7npF2oI2gKsY8JvHPy/tdX0+OmIYRumRSuccUavHgruFxY/F/0/A0+7sW4Lj6/9EXntlGEZJ4Fn8I7N6EubjLyh+grt3iMgjwHlu08dV9VBee2UYRkng+fJH1uM3i7+w+C3SdhD4aZ77YhhGieFl7wzPwGXpnMVAoYq0GYYxB4iNGMAVsqkXiwITfsMw8sawqyfT4o9bdc6CMq7wi0hQRLbNVGcMwygtUiN3A+bjLybGFX5VTQDbRWTZDPXHMIwSYqTFHwgIATEff6HxE9ydD7SJyFNAn9eoqtfkrVeGYZQE8RHpnOD4+c3iLyx+hP/v8t4LwzBKktiIAVzg+PnN4i8sfvL4HxWRE4FTVfU3bjnlYP67ZhjGbCdl8QfSLX6xWj0Fxk+RtvcC9wDfdptagPvz2SnDMEqDkfX4AYJBsZG7BcZPOuf7gZcDEQBV3QE05bNThmGUBl5WjxfcBcfij5mrp6D4Ef6oqg55b0QkhDMDl2EYxrikLP5AZnDXpl4sLH6E/1ER+RRQKSKvAe4GHshvtwzDKAWGi7RlBnctq6ew+BH+TwBdwPPA+4BfAJ+Zyk5FZJ6I3CMi20Rkq4i8bCrbMwyjOPEEviw9ndN8/AXHT1ZP0p185UkcF892VZ3q7frrwIOqer2IlANVU9yeYRhFyHBWj1n8xYSfrJ6rgF3AN4BbgZ0i8rrJ7lBE6nFq+v8XgKoOqepLk92eYRjTx9d/s4PN+45P2/ZiiSwWv+XxFxw/A7i+ClysqjsBRORk4OfALye5zxU4rqPvisjZwCbgQ6ral76QiNwM3AywbJlVjDCMfKOq/OvDLxAZjLFu2fxp2ebB7gEWVpcjkpbOaSN3C44fH3+PJ/ouu4GeKewzBKwDvqWqa3HKQIya0UtVb1PV9aq6vrGxcQq7MwzDDwOxBKrQF41P2zZb2yOsbsmcrdUZwGU+/kIypsUvIte5LzeKyC+Au3B8/G8GNkxhnweAA6r6pPv+HmwqR8MoOH3RhPN/KDEt24vGE7xwuIdXn5ZpuJmPv/CM5+p5fdrrw8Cr3dddQOVkd6iqh0Rkv4icpqrbgUuBLZPdnmEY00P/kGPp90+Txb/jcC/xpLKmOdPiLwuaj7/QjCn8qvrnedzvB4Afuhk9u4F87sswDB/0uoLfO03C39reDcCalrqMdrP4C8+EwV0RWYEj1MvTl59KWWZVfQZYP9n1DcOYfvpdF0//NLl6Wju6qQ2HWDo/M1s7FAgwEJuefRiTw09Wz/04qZcPABaRMYwSxbP0pyu429YRYVVzHYG0cg1gFn8x4Ef4B1X1G3nviWEYBaU/FdyduvDHE0m2Hoxw45+dOOozJ4/fbMhC4kf4vy4inwN+DUS9RlXdnLdeGYYx4/SlLP6pu2F2H+ljMJZkdXPdqM+CVo+/4PgR/jOBdwKXMOzqUfe9YRglgmfp9w3FUdWMQVe5MhzYrR/1WShorp5C40f43wyclF6a2TCM0sML6qrCYCxJZfnkJ9pr64hQURbgpIbqUZ+FAgFL5ywwfkbutgLz8t0RwzAKS3oa51RTOlvbuzn9hLqMcsweoYAQNx9/QfFj8c8DtonIBjJ9/JNO5zRKnwdbD3L6CXUsz2LxGcVJ+sAtZzBXeFLbSSaVLR0Rrl3bnPXzYEBsIpYC40f4P5f3Xhglx9/e+Sw3nLeUW65ZXeiuGD7pTQvqTsXi33+8n55ofNSIXQ/z8RceP/X4H52JjhilQzyRZCCWoHsgVuiuGDnQP5Ru8U8+s6e1PQLA6jGEP2hlmQuOn5G7PQzPsVsOlAF9qjo6T8swGC7yZcI/u+iNxgkIJHVqFn9rRzehgLDyhJqsn4cCgdSUjEZh8GPx13qvxcnvuha4IJ+dMmY3nuUYMeGfVfQPJVhYE6arJ5oazDUZWtu7WbmolnAoe1aQWfyFx09WTwp1uB+4PE/9MUoAbyCQWfyzi75onKZaJ6A72dG7qk5gN9vALQ/z8RceP66e69LeBnCKqw3mrUfGrMcb+RkZNOGfTfQNxTmlsYY2Jl+v51BkkKN9Q1kHbnnY1IuFx09WT3pd/jiwF8fdYxhZ8axFs/hnF/3RBI2uxT/Z4K4X2B1Zijkdb+rFqY4ONiaPHx+/1co3csKz+AdjSaLxxJi+XqO46I3GmV9VTiggkw7utnV0IwKnnzCOq8et1plUCJruF4Txpl787Djrqap+IQ/9MUqA9LTAyECcxloT/mInnkgSjSepDoeoDocmPQtXa3uEkxqqqQ6PbVMGXeGPJ5MEA3ZtFILxgrt9Wf4AbgI+nud+GbOYdGvR/PyzAy8Ft6o8SHV5MGMwVy60dXSP69+HYYvfKnQWjvGmXvyq91pEaoEP4UyR+GPgq2Ot5xcRCQIbgXZVvXqq2zOKh/RUQPPzzw68p7Qaz+KfRFbP0d4oB7sHx83ogXSL34S/UIybzikiC0Tk74HncG4S61T146raOQ37/hCwdRq2YxQZ6Ra/Cf/swIvLVIVDVIVDqSeAXGjrcAO7Y4zY9ShzC7dZZk/hGFP4ReTLwAagBzhTVW9R1ePTsVMRWQJcBXxnOrZnTMymF48zOMl5Tvcf62ff0X7fy2f6+GdW+JNJ5fGdR1AtfVEZjCXYvG9afpKp9M2acJCacHBS6ZytHU4N/rFKNXik+/iNwjCexf9hoBn4DNAhIhH3r0dEIlPc778CH2OcOXxF5GYR2SgiG7u6uqa4u7lNx0sDXP8fj3P3pgOTWv/T97fykXue9b1831ACb5rVmRb+J/cc4+3feZIHnjs4o/stBHdu2M/133qc7v6pf8deCm5VeYiq8tCkhH9nZy8n1FVQX1U27nLlrsU/FDfhLxRjCr+qBlS1UlVrVbUu7a92KnV6RORqoFNVN423nKrepqrrVXV9Y2PjZHdnAM8d6EYV2o8PTGr9rp5oTuv2ReMsqqsAIDI4PRN3+6X9Jaefd27YN6P7LQQHjveTVDjWP/U5kjxXT3V5iOry4KRG7nb3x1hQXT7hcrUVTmgxMjCz14YxTE4lG6aJlwPXiMhenEDxJSLyPwXox5yhzX0E7+qJTrBkdiIDMbp6or7dJ33RBPOqyqkoC8y4j7+zxxlU/sedR9l/zL97ajbinc/p+I4991x1OOimc+buFuweiFFfOb61D6SWsYyvwjHjwq+qn1TVJaq6HHgr8FtVfcdM92Mu4c1/6olirkQGYgwlkr4Fpi8apyYcpK6ibMZdPZ2RKOXBACJw18b9M7rvmabTFf7p+I69gLyXxz+ZAVyRwRh1lRMXA6hzhd8C/4WjEBa/McN42RaTsfgTSaXHFYFOn+v3D8WpKg9RX1k24z/urp4oS+ZX8qpTG7ln04GSzhzpnE6L33P1hENUl4eIxpPEcyydnLPFb8JfMAoq/Kr6iOXw55fOyCCdPVHKguJbuNPpSXsc74z4W783GqcmXBjh7+wZpKE2zFvPW8rB7kEe21G6iQGdEecJbjpcJp5Pv7IsSHXYGU3bn2MWWGQgTl3FxMLvLWMWf+Ewi7/E8az985Yv4FjfUM6ZFOk/Tr+uov6hBFXlQeoqy2bcj9vZE6WpNsylZyxiYXU5dz5Vmu6ewVgiFTifDgHti8apLAsSDAhV5aFUm1+G4s6sa34s/tqKECJm8RcSE/4SxwvsXnSakxl1pDc3qz9dVPy6inqjcaoLZPF39URpqq2gPBTgjWtb+M3Wwzkf82wg/VxMR3ZM31AiVV/Hs/j7cgjwejf4iVI5AQIBoTYcmvGML2MYE/4Sp7U9wvKFVZzU4EyDl6ufP11U/LiKVJX+oQTV4SB1FaEZTdnrjcbpH0rQVOeUFr7hvKXEk8p9m9tnrA8zRfq5mC6L3xP86klY/F4f/Lh6wAnwmquncJjwlzitHd2sbq5PiWGufn7vxynib91oPEkiqSmLPzIYIzlDAVbP5+3NInXqolrWLZvHjzfsK7mRvF2u201kmnz80URK8D3LP5dcfu868ePq8ZYzV0/hMOEvYbr7Yxw4PsDqljqaap0BVbmmdHqi0jKvMiWs4+FZidXlIeoqy1CF3klO45cr3o3JO1ZwrP5dXX3TVtqgWPCOtWVe5bQIaIbFPxlXj2fx+0jnBOfJwCz+wmHCX8J4/v01zfU01JQ7VrvPzBwP78d5alONLzdRf1p531S+9jSUFPBDSvjdpxuAq89qpro8yI9LLMjbGYkSEFjRUD1tA7iGffyhVJtfJmXx2wCugmHCX8IMF82qIxQMsLC6PGdXT2QgRiggnLiw2pfw90aHy/tO5wjNeCLJ8b7xSxN4/WusGRb+6nCIq89q5ufPH/Q1KKm7P0Y0PrGlOxhLTHqWqskQSyQzbqCdPYM01ISZX1U+PRb/UJqrJ+Xjd76HnsHYhAX+vECtfx9/yCz+AmLCX8K0dURYXF/BQlcIG2rCKd+wX7oHYtRVltFUF6YnGmdggnK9npVYFQ5Na7729x7fy6u//LtxBaizZ5DyYIB5IzJLbjh/Kf1DCe54cvz6ParK1bf+ns8/sGXC/nzqvud5z/c2+Ov8NHDrb3dy2b88moqXdPZEaaoLT5uA9kXjVJU7Lp6qlKvHOZc3fPsJPv9A27jrD7t6cvHxW1ZPoTDhL2Fa27szSuQ21VVMKrhbX1nmO0bgzdxUEw5O6wjNp/YcIzIYZ/uhnjGX6YpEaawNj5rAe+3Sebx6ZSPfeHjHuE8thyNR9h8b4L7N7RkD17Lx5O5j7Orsze0gpsBTe47R1RNl71FnIjwvbdVxmcSnHLzui6a5esqHg7uRwRhbDkZ4as+xcdePDMQoDwWoKPM3lWJdRRkDsYRV6CwQJvwlSl80zu4jfaxpGS6k2lQbzj2dczBOXWUZjbX+soK8uVqrykOpQN90WHbeQDTPfZWNzp5oqp/piAh/d/UqBmIJvvKr7WOu79U0GogleODZscs6H+8bov2lAY72DRHLsazBZFDV1HG3ut9DZ0+UxpowdRVlJJI6qYlT0rff56bgglMvv6IsQF80zhZ3f7uP9I2b3um3XIOHl+9v7p7CYMJfomw7FEE1c1IMT/hzSa/sHohRVxFKpUhOFBz2BCjdxz/VH7cntOCMSxiLzp7BVD9HckpTDf/nwuXctWk/zx/IfvNo7ehG3IDpneMUePNuQpD7gLjJsP/YAD2uD72tvZtEUjna67h6puOpKj0F16PGnYXLuxmqwtaDY3/3kcEchd8qdBYUE/4SxRPIkRZ/PKkcz6F+e0/K1eMI6kQxgr6UxR+kujxEYBryzD2hrSoPpjKVstHl+r3H4oOXncrC6nJueaAtq2uktT3CSQ3VvOOCE3l2/0tsO5Rd6NKfOnLNkpoM3jFXlQdp7ejmaG+UpDrnczpurukpuB7eZCxbOiIp3793E8iGZyD4xer1FBYT/hKlraObhdXlnFA3nNPeVOf56f2LlRfcnV9VTigwcaG3vqHh8r6BgEzLCE1PaK8+azHbDvVkda8MxZMc749l5PCPpK6ijI9efhqbXjzOT57pGPX5lo5u1rTU88a1LZQFhTs3ZLf60y3+yc5xkAutHd0EA8IVa06grSOSOgeNtRWpYOpULH4vBTfd4q8Oh+iLJmjt6OaCkxbSUFOecdwjydXVMx39NiaPCX+J0toeYVVzXUag06+f3kNVU4/wgYDQWBueWPijcYIBIRxyLq3pqMnf2t5Ny7xKXn5KA0PxJDuzBFW7ej0xHNviB3jzuUs5s6Wef/rl1gyf9dHeKB3dg6xprmdBdTmvXX0C9z3dnjW1s629m7OXOC60yVQ8zZXW9ginNtWwbtl8XuqP8bQ7GC3d1TMli9+7WZcPB2ary4Mc7Yuys7OXNc11rG6uT8UXshEZiPvO6AGod+M/ZvEXBhP+EiQaT/DC4R7WtGROej3sp/eX0jkQSxBLaOqx3J/wO5U5vRvOdBRq29IRYU1LXSpekc3lMLJcw1gEAsIt16zicCTKvz+yM9XuWbOrmx3X2A3rl/JSf4yHtsRf+VMAAB3XSURBVBzOWL/XDZq/emWjW8ZicpPb+EVVaXOfRLy+/W67U2raC+7C1Ka4TLnn0iz+qnCItvYISYVVzfWsaaljx+GeMcc4TNrit0JtBcGEvwTZcbiXeFJZ0zxS+B03SJfPgOTI0ZhNteEJbxp9bi1+Dy/dcLL0DMbYfaSP1c31rGiodv38oy3PbOUaxuLcExfwhnOa+c/f72HfUWd6xuHBbs539opTGmiZVznK3eMFOM9ZNo8FVbkPiMuVzp4oR3qHWNNcxxmL6wgGhMd3HQGcG/H0+PiHU3A9asJBhlyXmnfTjSeVFw6NftpKJpWewZjvwVsw7OM3V09hMOEvQTyLOD2wC1BZHqQ2HPIdkPTSMD1xaaytmNCn7dXi95jqAKOtB528/TUtjuitWlyXNcDblaVcw3h84nVnEAoIf/9zZ7BWW0eEpQsqU2mGgYDw5vVL+MPOIxlz96a+2+Z65wkoz8Fdb3+rW+qpKAtySmMNg7Ek9ZVlVJQFqamYusukLy0F18N7Pa+qjJZ5lSkjIls6be9QnKT6L9cAUFEWJBwKmPAXCBP+EqS1o5vacIil86tGfdZY5z+XP1Vq1/XHNtWGOdY/fu5639Boi38qopRebwgcV8yWjsiolNTOnigisLC63Nd2T6iv4P0Xn8KvtxzmDzuO0NbezerFmU9Ib16/FIC7Nx1ItbW2R2ioCdNUV0Fjbe4joXOlrSOCCJyx2LmJe+4ez6UVDAi1FaEpCWh6Cq6H93q1GydauqCS2opQVjdbZMSToV8KMV+D4TDjwi8iS0XkdyKyRUTaRORDM92HUqetwwnsBgIy6rOm2rBvv/TIH3RTXRhVONo7djqoM/R/WECmGtxtbY/QWBtOZSStbqmnbyiRGsHq0dUzyMLqMKGg/0v6plesYNmCKv7uJ63sPdo/6gmpZV4lrzy1kXs27k/N3ev42z3xnfgJaKq0tnezoqF6WIjduE36k81Uv+P0FFwP77V3wxURVjfXZQ3wjjQQ/FKIGdoMh0JY/HHgw6q6CrgAeL+IrCpAP6ZMIqmj/gpd9z2eSLL1YGRUYNejsdYp25Ct796fx8jJNfyUbeiLJjLSAusqy4jGk/QPxSc18XlbR3fKygXSXA6ZAtQZyT5qdzwqyoJ8+qoz2HPEuYmszvKd3bB+KR3u3L39Q3F2dPam+tBUF6arN5pxzqf7mmjriGTEatY0D990PKZa6TI9BdejesSNxtl3PdsORkZNwu65BHPJ6oGxLX7Vsa/NkddosVHo379fcrtFTwOqehA46L7uEZGtQAswcWWsaeatt/2JS05v4uZXnZzzut/5/W7+/udbR7WfUFfBIx+9KGvNkr/+0WbmV5XzhTesSbV95VfbeeFwD7e9a/2o5aPxBFd/4w+895Un8Zbzlvrq1xO7jzEYS2aIZWb/wjxwtJ+TP/WLMbfx/otP5qOXn541uAuwq6uXs5bMy7pu39BwXXeA+VWO62XVZ38FwPtedRKfvPIMX8cyGEuwo7OXy85YlGo7dVEN5cEAre3dXHN2c6r98DijdsfjtasW8YpTGvjDziNZv7PLVjWxoLqcP//ucEG2YYs/TCyhHO+PsaC6fFLXhMfOzh7eetuT/PdN56fcOt6I5Xe97MTUck6K7giLPy2O8tyBl7jp+xu5+30vY3lDta/v4FD3IKG0FFwgNRhrTfpNt6WeaDzJrq4+TjuhNtWe6+xb6fvIlmjw0Xue454099pIRODL15/N9ecuGXf77//RZpbMq/R9vU2FZFL5f3c9w9G+Ib7/5+dnfdouJmZc+NMRkeXAWuDJLJ/dDNwMsGzZsmnf9/G+IZ7YfYykMinhf2R7Fy3zKrkhTZD3Hu3j3s3tbD0YYe2y+RnLv3i0j589d5DF9RUZwv/QlsPs6uolGk8QDmUKw6/bDrOjs5ffbuv0JfydPYP8v7ueYfnCKl6zalHWZd594XLqK8sYy2h64NkOfruti49efnrKiqx1RWBVcx0nN1bz9d/s4MozF4/qL3jpnMOX1ZVnnkD3QIxYIsnPnzvI77Z3+v4hbj/UQyKpGS6YsmCA006ozQjwxhNJdhzu5YILFvrabjoiwtfecjZP7T2WNSMoHApy69vXsnGvkztfWRbk4tObgMwnoAXV5TlfE+n8959e5EhvlD/uPJISfi97Kf3prbaijO+8a31GW31lGXuPOAHox17ooqsnyh1P7fP1Pe8/1s+dG/Zz5ZmLM8Z8XHNOCwuqw5zUWJNq826Mre3dGcI/FR//rq5Ml11nzyD3Pd3Oq1Y2sv7E7N/XvZsPcPsf9owr/PFEkt9sOUzLDAn/tx7dxf3uoMB7Nh3wbagVioIJv4jUAP8L/I2qjnIcquptwG0A69evn/bnJ+9HtdUNFOZyh/Zyqy9ffQIfvPTUVPuB4/3cu7mdto7RP/K7NzoWzMHuQY72RllYE2YwlmBnVy+JpLLjcO8o98xdbr2YtoNjD5X3iCWS/PWPniYyGOMHN51P7RjW15L5Vfz1Jadm/QwcK/u2x3YTjSfoHohREw6l/OZlwQCfff1q3n37U9z+h7385UWjb5hOOufwDWFeVXlquXhSufW3OxgYSlBZPnEVx5Eplh5rWur4ZeshVBURYVdXH9F4ckz31kQ01VVw9VnNY35+4ckNXHhyQ5b1husXnbYo92vCYzCWSIlGeqpq+nwK6Vx6RuZNPd1l4q3/v5sP8JHLT6NsgpjHP/5iKwERPnnl6aO2edVZizPaTmqsoaIsQFtHhDedO9zuGQi5unqyjeq+d3M7iaTy2atXcUpTTdb15lWV8dmftNHa3j3mOfeuiT1H++gdkWI83fxhxxG++uvtvP7sZjpeGuBLv9rGFWeekPMT0ExSkKweESnDEf0fquq9heiD96PqicbZl5au54eO7kGO98dG/SBb5lVSX1k2Kt0wnkhyz6YDNLh18b0f5zbXooXRg5L2H+vnDzuP0FATZv+xgQlnsfryr7bz1J5j/NN1Z3L6CdndPH5Iz9eODMRHWXGvXtnIZWc0cetvd4zK6U8klYFYpsWfue06ksqYNXBG0tYRob6yjCXzK0f18aX+WKpwW9sYAplvmtJGQud6TaTzq7ZDdA/EaKgJZ1wHbR0RWuZVMq9q/EyluophH39rRzcNNWGO9A7x222d4673+K4j/LL1EH910cksrq8cd1kglU47MqWzeyCGCNTmKK71lWX0pM3JrKrctWE/60+cP6boA1x7dgvlocCYJTUA38XlpkrHSwN88MdPc3JjDf983Zl87vWrONo3xDcf3pG3fU4HhcjqEeC/gK2q+rWZ3r9Ha3s3IdfKH6/U71jrwuhgoIiwpqVuVAXJx3Z0cSgyyMcuPy1jf952QgEZ1QcvhfAjr10JMK5w/PL5g9z22G7eecGJvHHt+H7PifDcKm0d3XQPxFJunnQ+c9UqYgnlnx/cltHuTcIylnXlWWfjDf1Pp629O5VOmM6wyyGS+l9RFshwS8wEwyUwBnO+JtK5c8N+lsyv5G3nL2VXV29qspu29u5RmUbZqK8so38owZFeZz6Bd7/sRJpqw+MKYzyR5PM/3cKS+ZW891UnTbgPj9XN9aPSaSMDMWrd2ky5UFfhuBy9OZk37D3O7iN9E7pJ6qvKeN2aE7j/mfYxJ+Zp7Uj7fY9TXG4qROMJ/uqHmxmKJ/mPd55LdTjEWUvm8ZZzl/LdP+5lV9fMzdeQK4Ww+F8OvBO4RESecf+unOlOtHVEeNXKRkIBGbf41FjrBgTOyGJZr2muZ/uIQmJ3bthPQ005b1jbwtIFlbS5IuBZtOtOnJ/Rh0RSuWfjfl55amPKVz9WH3d19fLRe57jnKXz+MzVU/dlLp1fRW04RGtH95ildpc3VHPTK1dw7+b2VN0YSJtvN5zdjdNcX8G8qjLafPwQY4kkWw+NLjsBpEawbknVqO9Otc0kVeUhasIhunqiOV8THvuO9vP4rqO8Zb1TQyipsPVQJDVieeTo62x4LpYndh8F4Oyl87j+3CU8sr2TQ93ZM7B+9NQ+th/u4TNXneF78hRwDIPeEU/J3QOx1MC3XBhZUvrODfupCYe46szF460GwA3nLaVnMM4vW7PPm9DWEeHMJfXuU1R+LP5/+PlWntn/El++/ixOTjM6PnrFaVSWBfnCz2Y8X8U3My78qvoHVRVVPUtVz3H/xk4xyQM9gzH2HOlj7dJ5rFxUm7NF0NbezcmNNVn91Ktb6hlyg43gjCh9eGsn161bQnkowJrm+pT17uWEr2muZ2tamtzvd3TR0T3IDeuXsrAmTHN9Rdankr5onL/4702UhwL8+43rsgZbcyUQEFY119HWESHiVubMxvsvPoWm2jC3/LQtZf31Rce3+EXEPf6Jf4i7unoZimfPTvJGsLa6lueWESmPM0mTW7+orb2bU5r8XRPp3L1pPyJw/blLUje5tvbu1Ijl1T4tfoA/7nSEf3VzHW9Zv5SkOr7+kRzvG+Krv36BC09eyOWrT/B/sAzHW9Kvx8jgaJegH7xrq3sgRmQwxi+eP8jrz16ckVY6FhesWMiyBVVZn2rSr4k1LdlHek+V+59u5wd/epH3vnIFrxtxo2qoCfOhy07lke1d/Hbb4TG2UFjm5Mjd4TIAzoWxpSOSU/5tW8fYefIpN4R7sd27+QDxpPIWdxTo6uY69h7t53jfENsO9qQuzsFYkt1uPvldG/czv6qMy1Y52SOrmutH3ZxUlU/e+zy7unr55tvW0jxvYh+tX9a0ODeiY31DY/6ga8IhPvG603n2QHdKXLyaL2P5+MERsu2Heiaccs+z0kYGdlPbaXZ+0PuO9dMbjftyieSDxtowXZEorR3d4/YVRrvrEknl7o0HeNWpjTTPq2RxfQXzq8pobY+MGrE8Ht7Aqcd3HUnNsby8oZo/W7GAOzfsHzXK+asPbac3Gudzr189yo02ESsX1VIWlAwr2qnFPxnhH56h7YFnOxiIJbjhPH8ZfIGAcMN5S3li9zFeHDGYL/2aWNNcz47O3gkni8+FbYcifOLe5zh/xQI+fsXpWZd518uWc1JjNV/42dainF5yTgp/yh/bXMealnqO9g1xyGfFyq6eKIcig2MGElcsrKa6PJi6mdy5MTNY5fmAf/JMO0OJJKvcPoAjDEd7ozy05TDXrVuSsuDXtNSx+0hfyocO8P3H9/LTZzv48GtP4+WnjM44mQqrm50bUWdPdFxL7g3ntLB22Ty++OB2egZjWcv7jt62a/12jj13LjjnqKo8yIoxctFXt9RzOBLld9s7U9stBE11FWw/3MPhSHTCa2Lkk85jLzixn7e6Pm0nHlBP28HujNIQE+GdoxeP9md8D289fyn7jvXzxJ6jqbYtHRF+9OQ+3nnBiRkpmX4pDwVYuSgznTaSY2XOkf3uHohx14b9nLaoNlXu2g9vWreEgAxnv3mkZ4Otbq4jkdRx52rOhchgjL/8n83UVZRx69vXjjlSvDwU4LNXr2LPkT6++8c907Lv6WROCn9bx3AZgJGBwonXzZ5i6OG5Slrbu9n44nF2d2UGqzwL7sfuI+qalnpOaqgmHArQ2h7hvqfbiSU0Ixd8TXN9RnbCpheP8fc/38plZzTxl6/OfQzCRKQ/zYxnyQUCwi2vX82R3ii3/nbn8ExO4zyqr2nOzFEfiy0dEVaN47f3tnPnhv2UBYVTF81sYNejqTacSkkc6ykw/ZpI584N+1lYXZ6RnrnajQc8vf+476eY9HOUvs7r1iymtiLEXe61pqp8/oE26ivL+NvLVvo7wCx47jrvKXnSFr+7zlN7jvHsgW7ect7SnJ5ATqiv4KLTmrh744GM0cSt7RHKgsLKRbVpRtXU/fyqykfvfpZ9x/r5txvXTVgJ9qLTmrj09Ca++dudeS/fnStzVPi7U8JxxmJnJKRfP6B3Aa0aJ3VwdXM9Ww5GuOOpfaOCVY21YRbVhdl2qIfq8iArFlYTCgY4Y7EjDHdu2M/aZU7swSOVDdMe4UhvlL/64WZa5lfy1beck5cRgic1VFNR5lwa9RPUXzl76TzefO4Sbv/jntTNs3qM4C7Acs/6HSeukkzqqFINI/G+/22Heli5qHZa4huTIb1MhJ9rwnO7dPVE+c3Ww1y3zklN9FjTUkcsoezu8hfYhcyBU+nrVJQFecM5Lfyi9RDd/TF+8fwhntxzjA+/9rRJBWPT+3isb4iDbuB40sFdd507N+yjPBjgjWtbct7GDectpbMnyqMvdKXa2jq6WbmolvJQgCXzK6mrCOWcuZeNbz+2m1+1HeaTrzud85Yv8LXOZ65eRTSe4EsPbp/y/qeTOSf8XhkAz2KvKg9xcmNNThb/sgVV4z7arm6uo38owf1Pt2cNVnn7PmPxcCG1NS11bNh7jB2dvdywPjOdbVFdmIXV5Tx74CU+8KOneak/xrduPHdSj9d+CAUDqbEAfgblfOyK06kIBfnWo87EJuNZ/CnrdxwLbO/RPvqGEllr53jUVpSxfKFTfXSm8/fT8XL5T1xYNa7V610Te1x/9H1PO7GfG0akLqY/Sfo9rvRzNDIYfMN5SxmKJ7lz4z7+8RdbOWNxHW87f2oj4VelTYgzGEsQjSdzmm/Xo6Y8hIhTHfQ1qxexwGdl1XQuOb2Jhprh1FVncGVkRHG5el+ZZOPxp11H+dKD27jqrMXc9IoVvtdb0VDNe16xgns2HeCZ/S9NqQ/TyZwT/mxlANY0+4/8t7ZHJnwE9yz0pJIK6mZ87v6g14wogJVUpyri1WdnjiIVEVa31HPf0+38afdR/uGNZ45rXU4H3jH6ubk01ob54KWnMhhzHrfHC+6CI25bD0bGLLaVKlUwgcXr3RgmO2J3OvAe9yfq6/BTW7cT+9mwn3XL5nFKU6af/cQFVamsKL/HVVEWpDwUGDXHsreNVYvr+OKD22l/aYBbXr9qymmvZyyuJSDOefIGjk3GCAkEJHWzHGns+KUsGOBN61p4eFsnnT2DHOwe5FjfUObvu6WOrWOk0/rhUPcgH7hjMysaqvnim87KOSD+gUtOpXFEBlyhKWitnnzzzYd38NNnMyfV7nX90OmW1ZqWeu5/poPLvvYoE53Sfcf6R1lpIzmlqYbyUIAVC6s5Z+noYmaeYGVUnXTbrj5rcdZ0yDXNdTz2Qhdv/7NlExanmg48IfM7DP/dFy7njg372N3VN25wF5xj/d7je3nN1x7NKkLH+4coDwYm9Nuvaa7n588dLFhgF4bLNkyUduldE1/42Ra+/vAOdnf18aU3nTVqOe+JaNvByKgRy+NRX1nmui1Hf59vPX8pn/1JG1edtZg/Oyn3ekYj8Z6Sb//jHn7yTDuQe7kGj/rKMmrCIV4xhQSFt5y3lG8/tps33PrH1BN0RlXRlnqG4kle+y+PpQZ15cLx/iH6hxL8+OYLJlX6oSYc4uNXnM5H7n6WS776yIRlNEbyj9ed6du15JeSFv7G2nBW8bhmflXGj+rKMxfT2t6dmmpuPFY112VUhcxGWTDAZ646g1Maa7L+EF95agM3vWIFr101nEN9xuI6/uLVJ/P2MR7D33TuEuJJ5cOvnXxQLhdet2axW4XTn6iWhwL86w3n8NgLXRPWxL/09CauW9cybord2qXzJ/yBvHFtCy8NDPnuYz44ubGGv7zoZK6bYMR0WTDAxy4/jc3ugLfzly/g9WNcR++/+BQOdw/mZFl++DUrWbZw9MQ74GS/7O7qy1pbabJ84NJTedAdPLVu2XxedvLkbigfuOQUGmrCU4pVndxYw4dfs5KtbimQi05r5Mw04b9oZRNvWreEgdjkpgAVhLedv2zU01kuXLe2hX3H+tk5QTZbNipzGGDnF5kN9aPXr1+vGzduLHQ3DMMwZhUisklVR9V8n3M+fsMwjLmOCb9hGMYcw4TfMAxjjmHCbxiGMccw4TcMw5hjmPAbhmHMMUz4DcMw5hgm/IZhGHOMWTGAS0S6gBcnuXoDcGQau1NI7FiKl1I6HjuW4mQyx3KiqjaObJwVwj8VRGRjtpFrsxE7luKllI7HjqU4mc5jMVePYRjGHMOE3zAMY44xF4T/tkJ3YBqxYyleSul47FiKk2k7lpL38RuGYRiZzAWL3zAMw0jDhN8wDGOOMSuFX0RuF5FOEWlNaztbRP4kIs+LyAMiUpf22SdFZKeIbBeRy9Par3DbdorIJ2b6ONw++D4WEXmNiGxy2zeJyCVp65zrtu8UkW9IrhODzvCxpH2+TER6ReQjaW2z6ry4n53lftbmfl7hts+q8yIiZSLyfbd9q4h8Mm2dYjgvS0XkdyKyxf2uP+S2LxCRh0Rkh/t/vtsu7ve+U0SeE5F1adt6t7v8DhF59yw4lhvdY3heRB4XkbPTtpXbuVHVWfcHvApYB7SmtW0AXu2+fg/wBff1KuBZIAysAHYBQfdvF3ASUO4us6rIj2Ut0Oy+XgO0p63zFHABIMAvgdcV87GkfX4PcDfwEff9bDwvIeA54Gz3/UIgOBvPC/B24Mfu6ypgL7C8iM7LYmCd+7oWeMH9jX8J+ITb/gngi+7rK93vXdzz8KTbvgDY7f6f776eX+THcqHXR+B1aceS87mZlRa/qj4GHBvRvBJ4zH39EPAm9/W1OBdyVFX3ADuB892/naq6W1WHgB+7y84ouRyLqj6tqt7s8W1ApYiERWQxUKeqT6hzJfwAeEP+e59JjucFEXkDsAfnWDxm3XkBXgs8p6rPuuseVdXELD0vClSLSAioBIaACMVzXg6q6mb3dQ+wFWhx+/J9d7HvM/w9Xwv8QB2eAOa55+Vy4CFVPaaqx3G+gytm8FByPhZVfdztK8ATgDfRc87nZlYK/xi0MXywbwaWuq9bgP1pyx1w28ZqLwbGOpZ03gRsVtUoTr8PpH1W9MciIjXAx4HPj1h+Np6XlYCKyK9EZLOIfMxtn3XnBecJrA84COwDvqKqxyjC8yIiy3Gegp8EFqnqQfejQ8Ai9/Ws+P37PJZ0bsJ5koFJHEspCf97gL8SkU04j01DBe7PVBj3WERkNfBF4H0F6FuujHUstwD/oqq9herYJBjrWELAK4Ab3f9vFJFLC9NF34x1LOcDCaAZxzX6YRE5qTBdHBvXcPhf4G9UNZL+mft0NWvy1HM9FhG5GEf4Pz7ZfYYmu2KxoarbcB65EZGVwFXuR+1kWsxL3DbGaS8o4xwLIrIEuA94l6rucpvbGX7sg9lxLH8GXC8iXwLmAUkRGQQ2MfvOywHgMVU94n72Cxyf+v8w+87L24EHVTUGdIrIH4H1OBZlUZwXESnDEcofquq9bvNhEVmsqgddV06n2z7W778duGhE+yP57Hc2cjwWROQs4Ds4saKjbvN4GpedmQxmTHNgZDmZwaom938Ax5f6Hvf9ajKDu7txgiEh9/UKhgMiq4v8WOa5/bwuyzZGBhGvLOZjGbHOLQwHd2fjeZkPbMYJhoaA3wBXzcbzgmNFftd9XQ1sAc4qlvPifo8/AP51RPuXyQyIfsl9fRWZwd2n3PYFOPGl+e7fHmBBkR/LMpwY5YUjls/53Mz4BThNX9gdOD7IGI61dRPwIZyo+AvAP+OOSnaX/zRO1Hs7aVkVOBH/F9zPPl3sxwJ8Bsf/+kzan/cDXg+0usdya/rxF+OxjFjvFlzhn43nxV3+HTh+81bvhzobzwtQg5Nl1YYj+h8tsvPyChzXx3Npv4ErcTKpHgZ24Nx4F7jLC/Bvbp+fB9anbes9OEK6E/jzWXAs3wGOpy27cbLnxko2GIZhzDFKKbhrGIZh+MCE3zAMY45hwm8YhjHHMOE3DMOYY5jwG4ZhzDFM+A0jDRFZKCLPuH+HRKTdfd0rIv9e6P4ZxnRg6ZyGMQYicgvQq6pfKXRfDGM6MYvfMHwgIheJyM/c17e4Net/LyIvish1IvIlt076g+4wfK8W/6PizJ3wK3f4vWEUHBN+w5gcJwOXANfg1OT5naqeCQwAV7ni/03gelU9F7gd+IdCddYw0imZIm2GMcP8UlVjIvI8Tu2nB93253Hq4pyGM1nOQ+JMuhXEKZtgGAXHhN8wJkcUQFWTIhLT4WBZEud3JUCbqr6sUB00jLEwV49h5IftQKOIvAxSc9muLnCfDAMw4TeMvKDOFHjXA18UkWdxqileWNheGYaDpXMahmHMMcziNwzDmGOY8BuGYcwxTPgNwzDmGCb8hmEYcwwTfsMwjDmGCb9hGMYcw4TfMAxjjvH/A5dUFs1l18IsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2ERBabgyT-U"
      },
      "source": [
        "We could also make this a bar plot, rather than a line plot:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4kse5nXyWeA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "fc52ea87-ad9b-4eb7-dac8-a49cf47d7249"
      },
      "source": [
        "plt.bar(freq_df['time'], freq_df['id'])\n",
        "plt.ylabel('Number of earthquakes')\n",
        "plt.xlabel('Time')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Time')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3D6fLBRxNBk"
      },
      "source": [
        "### Summary statistics through time\n",
        "\n",
        "We might also want to look at how the mean or median of a parameter changes through time. To show an example, here I'll plot the mean depth of earthquakes through time:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vpuuEctIui_P"
      },
      "source": [
        "# get the years from the dataframe\n",
        "y = df.index.year"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcO369D7xaoS"
      },
      "source": [
        "# group the dataframe by the year, and then get the mean depth in each year\n",
        "depth_df = df.groupby(y)[['mag', 'depth']].mean().reset_index()"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZItJbNpxlVM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "cffb8e66-e4da-45eb-ec51-000573f0e157"
      },
      "source": [
        "depth_df"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>mag</th>\n",
              "      <th>depth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1900</td>\n",
              "      <td>4.50</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1902</td>\n",
              "      <td>5.40</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1903</td>\n",
              "      <td>5.80</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1904</td>\n",
              "      <td>4.50</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1906</td>\n",
              "      <td>7.90</td>\n",
              "      <td>11.7000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>2007</td>\n",
              "      <td>5.45</td>\n",
              "      <td>9.7410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>2011</td>\n",
              "      <td>4.57</td>\n",
              "      <td>7.5805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2014</td>\n",
              "      <td>6.02</td>\n",
              "      <td>11.1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2017</td>\n",
              "      <td>4.58</td>\n",
              "      <td>6.3100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2019</td>\n",
              "      <td>4.71</td>\n",
              "      <td>10.1000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    time   mag    depth\n",
              "0   1900  4.50      NaN\n",
              "1   1902  5.40      NaN\n",
              "2   1903  5.80      NaN\n",
              "3   1904  4.50      NaN\n",
              "4   1906  7.90  11.7000\n",
              "..   ...   ...      ...\n",
              "76  2007  5.45   9.7410\n",
              "77  2011  4.57   7.5805\n",
              "78  2014  6.02  11.1200\n",
              "79  2017  4.58   6.3100\n",
              "80  2019  4.71  10.1000\n",
              "\n",
              "[81 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2j9QVq0LxnYM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "16781f9a-22b8-45fe-fe97-79df6efbc8cb"
      },
      "source": [
        "plt.scatter(depth_df['time'], depth_df['depth'], c='cyan', edgecolor='black', s=60)\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Mean depth (km)')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Mean depth (km)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5fEBwEn8VRH"
      },
      "source": [
        "### Exercise 2: Temporal analysis\r\n",
        "In this section, we've explored some plotting techniques to analyse the temporal distribution of earthquakes. Try the following:\r\n",
        "\r\n",
        "1. Modify the plot of the number of earthquakes through time to make it look nicer. You could change the line/bar colours, change the font size, etc. Save it as a PNG file. \r\n",
        "\r\n",
        "* You can see the matplotlib documentation on [line plots](https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.plot.html) or [bar plots](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.bar.html) for help. Remember to look at the examples at the bottom of these pages: they show you nice examples of different plotting styles with code.\r\n",
        "\r\n",
        "\r\n",
        "2. Try to plot some other summary statistics through time. For example, you could:\r\n",
        "  * Make a plot of the median depth of each year through time.\r\n",
        "  * Make a plot of the mean magnitude of each year through time.\r\n",
        "  * Make a plot of the maximum magnitude of each year through time. \r\n",
        "  * Make sure to save these plots as PNG files. You can make these same figures to explore your own dataset for your project."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "nqHurqVk8Yom",
        "outputId": "ae03ac79-b96f-45be-a34a-7a14d6780475"
      },
      "source": [
        "# median depth through time\r\n",
        "depth_df = df.groupby(y)['depth'].median().reset_index()\r\n",
        "plt.scatter(depth_df['time'], depth_df['depth'], c='cyan', edgecolor='black', s=60)\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Median depth (km)')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Median depth (km)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "3KEJt8qJ8sLS",
        "outputId": "8170ef80-92a5-4919-9e27-b314ea391cea"
      },
      "source": [
        "# mean magnitude through time\r\n",
        "mag_df = df.groupby(y)['mag'].mean().reset_index()\r\n",
        "plt.scatter(mag_df['time'], mag_df['mag'], c='cyan', edgecolor='black', s=60)\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Mean magnitude')"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Mean magnitude')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "MsyC3FNF8-cH",
        "outputId": "19ede70b-997e-407c-cbe8-6dba3068dce7"
      },
      "source": [
        "# maximum magnitude through time\r\n",
        "mag_df = df.groupby(y)['mag'].max().reset_index()\r\n",
        "plt.scatter(mag_df['time'], mag_df['mag'], c='cyan', edgecolor='black', s=60)\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Maximum magnitude')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Maximum magnitude')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtXFvnV39Ffj"
      },
      "source": [
        "_You can see that the plots of mean and maximum magnitude look very similar (this is to be expected). The depth data don’t seem to show any trend through time, and there are less data points, especially for earlier records. This is probably because older records were unable to work out earthquake depths due to technology issues (see Week 1 lecture)._"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtBhGahe0ily"
      },
      "source": [
        "\n",
        "3. Think about what all these plots are telling you. Are there particular years in which there are more earthquakes? Can you identify which year had the highest magnitude earthquake? \n",
        "\n",
        "_Look at the plot showing the number of earthquakes through time. You should see that 1989 had significantly more earthquakes than any other year (16 compared to between 2 and 6 for all other years!)._\n",
        "\n",
        "_The plot of maximum magnitude by year shows that 1906 had the highest magnitude earthquake (M8)._\n",
        "\n",
        "4. From the plots you've made so far, choose two of the largest earthquakes. Do some research and find out some more information about these earthquakes. If they are large events, you should be able to find reports on them by Googling the year in which they occurred.\n",
        "\n",
        "* Were there any fatalities resulting from these earthquakes?\n",
        "* What was the economic damage?\n",
        "\n",
        "_The two largest earthquakes are both very famous events. The largest one in 1906 is the Great San Francisco Earthquake that devastated much of San Francisco city centre: https://earthquake.usgs.gov/earthquakes/events/1906calif/18april/_\n",
        "\n",
        "_The second largest one is the Loma Prieta earthquake which occurred in 1989. You can read more about it here: https://pubs.usgs.gov/pp/pp1550/pp1550c/pp1550c.pdf_\n",
        "\n",
        "5. As well as magnitude, earthquake depth is important for seismic hazard. Shallow earthquakes are often the most damaging, as the seismic waves have less distance through the Earth's crust to travel before reaching infrastructure, and therefore dissipate less energy. Looking at the plots of earthquake depth, do you see any trend in earthquake depth through time? \n",
        "\n",
        "_As discussed above, there doesn’t appear to be any trends in earthquake depth through time (we wouldn’t expect there to be)._\n",
        "\n",
        "6. Modify your plot of the mean magnitude through time so that the points are coloured by earthquake depth. From this plot, which years would you expect the most damage from earthquake events to occur in?\n",
        "\n",
        "_Your plot might look like the one below. We would expect the most damage to occur from the highest magnitude earthquakes, but also from the shallowest ones. So the 1906 earthquake would be damaging (highest magnitude), but potentially also the early 1980s (high magnitude, relatively shallow)._"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xs2it1PKynTz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "173f776b-8d73-460a-f3bc-9d3c3fbb83ec"
      },
      "source": [
        "# answer to question 6\r\n",
        "# group the dataframe by the year, and then get the mean magnitude depth in each year\r\n",
        "y = df.index.year\r\n",
        "summary_df = df.groupby(y)[['mag', 'depth']].mean().reset_index()\r\n",
        "\r\n",
        "# import cm so we can change the colourmap\r\n",
        "import matplotlib.cm as cm\r\n",
        "\r\n",
        "# set up a figure\r\n",
        "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10,4))\r\n",
        "#add gridlines\r\n",
        "plt.grid(color='0.8', linestyle='--', which='both')\r\n",
        "\r\n",
        "#define the colourmap you want (reds)\r\n",
        "cmap = cm.Reds\r\n",
        "\r\n",
        "# make a scatter plot of\r\n",
        "cbar = plt.scatter(summary_df['time'], summary_df['mag'], c=summary_df['depth'], cmap=cmap, edgecolor='black', s=60)\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Mean magnitude')\r\n",
        "plt.title('Magnitude and depth through time')\r\n",
        "\r\n",
        "# add a colourbar. You can change the label, orientation, etc.\r\n",
        "plt.colorbar(cbar, cmap=cmap, label='Earthquake depth (km)', orientation='vertical')\r\n",
        "plt.savefig('mean_mag_depth.png')"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYlOR9Xh2uU1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}