diff --git a/README.md b/README.md index 400c0ac..1812e8b 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,43 @@ # GeoSMART Curriculum Jupyter Book (ESS 469/569) -[![Deploy](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml) -[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-book) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-book/HEAD?urlpath=lab) +[![Deploy](https://github.com/geo-smart/mlgeo-instructor/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-instructor/actions/workflows/deploy.yaml) +[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-instructor) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-instructor/HEAD?urlpath=lab) [![GeoSMART Library Badge](book/img/curricula_badge.svg)](https://geo-smart.github.io/curriculum) +[![Student Version](book/img/student_version_badge.svg)](https://geo-smart.github.io/mlgeo-book/) -## About +## Repository Overview -This repository stores configuration for GeoSMART curriculum content, specifically the student version of the book. This version of the book should never be directly edited, as the student version is automatically generated on push. \ No newline at end of file +This repository stores configuration for GeoSMART curriculum content, specifically the teacher version of the book. Only this version of the book should ever be edited, as the student version is automatically generated on push by github actions. + +## Making Changes + +Edit the book content by modifying the `_config.yml`, `_toc.yml` and `*.ipynb` files in the `book` directory. The book is hosted on Github Pages and will be automatically updated on push, and the student book will also be created automatically on push. + +Making changes requires that you set up a conda environment and build locally before making sure that it will build with github actions. We accepted rendered notebooks, but some oddities, such as kernels different than python, will make it crash. So we recommend that contributors first build the book with the added notebooks. + +```sh + conda env create -f ./conda/environment.yml + conda activate curriculum_book + +``` + +To modify the exact differences between this book and the student book, edit `.github/workflows/clean_book.py`. When you push, a github action will clone the repo and run this python file which modifies certain parts of `*.ipynb` file contents, then pushes to the student repo. To edit the student repo's README, edit `STUDENT_README.md`. The Github Actions workflow also automatically replaces `README.md` with `STUDENT_README.md` in the student repo. + +### `Student Response Sections` + +One modifications made by the `clean_book.py` workflow is to clear sections marked for student response. Code cells marked for student response may contain code in the teacher version of the book, but will have their code removed and replaced with a TODO comment in the student version. + +To mark a code cell to be cleared, insert a markdown cell directly preceding it with the following content: + +````markdown +```{admonition} Student response section +This section is left for the student to complete. +``` +```` + +## Serving Locally + +Activate the `curriculum_book` conda environment (or any conda environment that has the necessary jupyter book dependencies). Navigate to the root folder of the curriculum book repository in anaconda prompt, then run `python server.py`. + +On startup, the server will run `jb build book` to build all changes to the notebook and create the compiled HTML. The server code can take a `--no-build` flag (or `--nb` shorthand) if you don't want to build any changes you've made to the notebooks. In the case that you don't want to build changes made to the notebooks, you can just run `python serer.py --nb` from any terminal with python installed. diff --git a/STUDENT_README.md b/STUDENT_README.md new file mode 100644 index 0000000..400c0ac --- /dev/null +++ b/STUDENT_README.md @@ -0,0 +1,10 @@ +# GeoSMART Curriculum Jupyter Book (ESS 469/569) + +[![Deploy](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml) +[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-book) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-book/HEAD?urlpath=lab) +[![GeoSMART Library Badge](book/img/curricula_badge.svg)](https://geo-smart.github.io/curriculum) + +## About + +This repository stores configuration for GeoSMART curriculum content, specifically the student version of the book. This version of the book should never be directly edited, as the student version is automatically generated on push. \ No newline at end of file diff --git a/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb b/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb index f6117ca..908efd0 100644 --- a/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb +++ b/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb @@ -1,574804 +1,574809 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "50e82750", - "metadata": {}, - "source": [ - "# 2.3 Pandas\n", - "\n", - "This section aims to provide new skills in python to handle structured, tabular data. \n", - "\n", - "Learning outcome:\n", - "- Manipulation of data frames (describing, filtering, ...) \n", - "- Learn about Lambda functions\n", - "- Intro to datetime objects\n", - "- Plotting data from data frames (histograms and maps)\n", - "- Introduction to Plotly\n", - "- Introduction to CSV & Parquet\n", - "\n", - "This tutorial can be offered in a 2-hour course. Sections are labeled as Level 1, 2, 3 in Section 1, 2, 3, and instructors may choose to leave higher levels for asynchronous, self-guided learning.\n", - "\n", - "\n", - "We will work on several structured data sets: sensor metadata, seismic data product (earthquake catalog).\n", - "\n", - "First, we import all the modules we need:" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "50e82750", + "metadata": {}, + "source": [ + "# 2.3 Pandas\n", + "\n", + "This section aims to provide new skills in python to handle structured, tabular data. \n", + "\n", + "Learning outcome:\n", + "- Manipulation of data frames (describing, filtering, ...) \n", + "- Learn about Lambda functions\n", + "- Intro to datetime objects\n", + "- Plotting data from data frames (histograms and maps)\n", + "- Introduction to Plotly\n", + "- Introduction to CSV & Parquet\n", + "\n", + "This tutorial can be offered in a 2-hour course. Sections are labeled as Level 1, 2, 3 in Section 1, 2, 3, and instructors may choose to leave higher levels for asynchronous, self-guided learning.\n", + "\n", + "\n", + "We will work on several structured data sets: sensor metadata, seismic data product (earthquake catalog).\n", + "\n", + "First, we import all the modules we need:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "28d926f9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import io\n", + "import requests\n", + "import matplotlib.pyplot as plt\n", + "from datetime import datetime, timedelta\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ebf112a", + "metadata": {}, + "source": [ + "This notebook uses ``plotly`` to make interactive plots" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3bf66863", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 35, - "id": "28d926f9", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import io\n", - "import requests\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime, timedelta\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: plotly in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (5.17.0)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (8.2.3)\n", + "Requirement already satisfied: packaging in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (23.2)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install plotly\n", + "import plotly.express as px\n", + "import plotly.io as pio\n", + "# pio.renderers.default = 'vscode' # writes as standalone html, \n", + "# pio.renderers.default = 'iframe' # writes files as standalone html, \n", + "# pio.renderers.default = 'png' # writes files as standalone png, \n", + "# try notebook, jupyterlab, png, vscode, iframe\n" + ] + }, + { + "cell_type": "markdown", + "id": "3f1829a3", + "metadata": {}, + "source": [ + "# 1 Pandas Fundamentals\n", + "\n", + "## 1.1 Basics\n", + "\n", + "Pandas are composed of ``Series`` and ``DataFrame``. ``Series`` are columns with attributes or keys. The ``DataFrame`` is a multi-dimensional table made up of ``Series``.\n", + "\n", + "We can create a DataFrame composed of series from scratch using Python dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3cdfc90e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "2ebf112a", - "metadata": {}, - "source": [ - "This notebook uses ``plotly`` to make interactive plots" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " temperature precipitation\n", + "0 36 3\n", + "1 37 1\n", + "2 30 0\n", + "3 50 0\n" + ] + } + ], + "source": [ + "data = {\n", + " 'temperature' : [36,37,30,50],\n", + " 'precipitation':[3,1,0,0]\n", + "}\n", + "my_pd = pd.DataFrame(data)\n", + "print(my_pd)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8e041ce5", + "metadata": {}, + "source": [ + "Each (key,value) item in the dataframe corresponds to a value in ``data``. To get the keys of the dataframe, type:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "fb8d9c69", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 36, - "id": "3bf66863", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: plotly in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (5.17.0)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (8.2.3)\n", - "Requirement already satisfied: packaging in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (23.2)\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install plotly\n", - "import plotly.express as px\n", - "import plotly.io as pio\n", - "# pio.renderers.default = 'vscode' # writes as standalone html, \n", - "# pio.renderers.default = 'iframe' # writes files as standalone html, \n", - "# pio.renderers.default = 'png' # writes files as standalone png, \n", - "# try notebook, jupyterlab, png, vscode, iframe\n" + "data": { + "text/plain": [ + "Index(['temperature', 'precipitation'], dtype='object')" ] - }, + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_pd.keys()" + ] + }, + { + "cell_type": "markdown", + "id": "ff7935a4", + "metadata": {}, + "source": [ + "get a specific ``Series`` (different from the array)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "c5d734fd", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "3f1829a3", - "metadata": {}, - "source": [ - "# 1 Pandas Fundamentals\n", - "\n", - "## 1.1 Basics\n", - "\n", - "Pandas are composed of ``Series`` and ``DataFrame``. ``Series`` are columns with attributes or keys. The ``DataFrame`` is a multi-dimensional table made up of ``Series``.\n", - "\n", - "We can create a DataFrame composed of series from scratch using Python dictionary:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 36\n", + "1 37\n", + "2 30\n", + "3 50\n", + "Name: temperature, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "print(my_pd.temperature[:])\n", + "print(type(my_pd.temperature[:]))" + ] + }, + { + "cell_type": "markdown", + "id": "1f5f2426", + "metadata": {}, + "source": [ + "to get the _value_ of a specific key (e.g., temperature), at a specific index (e.g., 2) type:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "2ab0ad9d", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "id": "3cdfc90e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " temperature precipitation\n", - "0 36 3\n", - "1 37 1\n", - "2 30 0\n", - "3 50 0\n" - ] - } - ], - "source": [ - "data = {\n", - " 'temperature' : [36,37,30,50],\n", - " 'precipitation':[3,1,0,0]\n", - "}\n", - "my_pd = pd.DataFrame(data)\n", - "print(my_pd)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n", + "\n" + ] + } + ], + "source": [ + "print(my_pd.temperature[2])\n", + "print(type(my_pd.temperature[2]))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9fca0cf5", + "metadata": {}, + "source": [ + "## 1.2 Reading a DataFrame from a CSV file\n", + "\n", + "We can read a pandas directly from a standard file. We will download a catalog of earthquakes" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f029ed84", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "8e041ce5", - "metadata": {}, - "source": [ - "Each (key,value) item in the dataframe corresponds to a value in ``data``. To get the keys of the dataframe, type:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-10-07 05:13:23-- https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 129629 (127K) [text/plain]\n", + "Saving to: ‘Global_Quakes_IRIS.csv.1’\n", + "\n", + "Global_Quakes_IRIS. 100%[===================>] 126.59K --.-KB/s in 0.05s \n", + "\n", + "2024-10-07 05:13:23 (2.35 MB/s) - ‘Global_Quakes_IRIS.csv.1’ saved [129629/129629]\n", + "\n" + ] + } + ], + "source": [ + "fname = \"\"\n", + "gist_dir = \"\"\n", + "!wget \"https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\"\n", + "# !wget \"https://github.com/UW-MLGEO/MLGeo-dataset/blob/main/data/Global_Quakes_IRIS.csv\"\n", + "ff = \"Global_Quakes_IRIS.csv\"\n", + "quake = pd.read_csv(ff)" + ] + }, + { + "cell_type": "markdown", + "id": "62e31c2c", + "metadata": {}, + "source": [ + "Now you use the ``head`` function to display what is in the file" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "ed565400", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 38, - "id": "fb8d9c69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['temperature', 'precipitation'], dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS
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" ], - "source": [ - "my_pd.keys()" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", + "\n", + " description \n", + "0 VANUATU ISLANDS \n", + "1 NEAR EAST COAST OF HONSHU \n", + "2 SOUTH OF MARIANA ISLANDS \n", + "3 NORTHERN CHILE \n", + "4 NEAR COAST OF CENTRAL CHILE " ] - }, + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# enter answer here\n", + "quake.head()" + ] + }, + { + "cell_type": "markdown", + "id": "73bfcd15", + "metadata": {}, + "source": [ + "Display the depth using two ways to use the pandas object" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "9b01ce51", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "ff7935a4", - "metadata": {}, - "source": [ - "get a specific ``Series`` (different from the array)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64\n", + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64\n" + ] + } + ], + "source": [ + "print(quake.depth)\n", + "print(quake['depth'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9c623644", + "metadata": {}, + "source": [ + "Calculate basic statistic of the data using the function ``describe``." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "2b9f083e", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 39, - "id": "c5d734fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 36\n", - "1 37\n", - "2 30\n", - "3 50\n", - "Name: temperature, dtype: int64\n", - "\n" - ] - } + "data": { + "text/html": [ + "
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max74.391800179.998100685500.0000009.100000
\n", + "
" ], - "source": [ - "print(my_pd.temperature[:])\n", - "print(type(my_pd.temperature[:]))" - ] - }, - { - "cell_type": "markdown", - "id": "1f5f2426", - "metadata": {}, - "source": [ - "to get the _value_ of a specific key (e.g., temperature), at a specific index (e.g., 2) type:" + "text/plain": [ + " latitude longitude depth magnitude\n", + "count 1785.000000 1785.000000 1785.000000 1785.000000\n", + "mean 0.840700 40.608674 82773.187675 6.382403\n", + "std 30.579308 125.558363 146988.302031 0.429012\n", + "min -69.782500 -179.957200 0.000000 6.000000\n", + "25% -19.905100 -73.832200 12000.000000 6.100000\n", + "50% -4.478900 113.077800 24400.000000 6.300000\n", + "75% 27.161700 145.305700 58000.000000 6.600000\n", + "max 74.391800 179.998100 685500.000000 9.100000" ] - }, + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quake.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "8b54218c", + "metadata": {}, + "source": [ + "Calculate mean and median of specific ``Series``, for example depth." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "83f21ca3", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 40, - "id": "2ab0ad9d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "30\n", - "\n" - ] - } - ], - "source": [ - "print(my_pd.temperature[2])\n", - "print(type(my_pd.temperature[2]))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "82773.18767507003\n", + "24400.0\n" + ] + } + ], + "source": [ + "# answer it here\n", + "print(quake.depth.mean())\n", + "print(quake.depth.median())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "591f8d33", + "metadata": {}, + "source": [ + "## 1.3 Manipulating Pandas with Python\n", + "\n", + "### Classic functions\n", + "We will now practice how to modify the content of the DataFrame using functions. We will take the example where we want to change the depth values from meters to kilometers. First we can define this operation as a function" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "5b4f94b4", + "metadata": {}, + "outputs": [], + "source": [ + "# this function converts a value in meters to a value in kilometers\n", + "m2km = 1000 # this is defined as a global variable\n", + "def meters2kilometers(x):\n", + " return x/m2km\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "16cb70b9", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "9fca0cf5", - "metadata": {}, - "source": [ - "## 1.2 Reading a DataFrame from a CSV file\n", - "\n", - "We can read a pandas directly from a standard file. We will download a catalog of earthquakes" + "data": { + "text/plain": [ + "34.4" ] - }, + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now test it using the first element of the quake DataFrame\n", + "meters2kilometers(quake.depth[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7a360cc5", + "metadata": {}, + "outputs": [], + "source": [ + "# this function converts a value in meters to a value in kilometers\n", + "def meters2kilometers2(x):\n", + " m2km = 1000 # this is defined as a global variable\n", + " return x/m2km" + ] + }, + { + "cell_type": "markdown", + "id": "aa52810b", + "metadata": {}, + "source": [ + "Let's define another function that uses a local instead of global variable" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "b01a33fe", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 41, - "id": "f029ed84", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-10-07 05:13:23-- https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 129629 (127K) [text/plain]\n", - "Saving to: ‘Global_Quakes_IRIS.csv.1’\n", - "\n", - "Global_Quakes_IRIS. 100%[===================>] 126.59K --.-KB/s in 0.05s \n", - "\n", - "2024-10-07 05:13:23 (2.35 MB/s) - ‘Global_Quakes_IRIS.csv.1’ saved [129629/129629]\n", - "\n" - ] - } + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_km
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS34.4
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU30.1
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS16.9
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE109.4
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE25.7
\n", + "
" ], - "source": [ - "fname = \"\"\n", - "gist_dir = \"\"\n", - "!wget \"https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\"\n", - "# !wget \"https://github.com/UW-MLGEO/MLGeo-dataset/blob/main/data/Global_Quakes_IRIS.csv\"\n", - "ff = \"Global_Quakes_IRIS.csv\"\n", - "quake = pd.read_csv(ff)" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", + "\n", + " description depth_km \n", + "0 VANUATU ISLANDS 34.4 \n", + "1 NEAR EAST COAST OF HONSHU 30.1 \n", + "2 SOUTH OF MARIANA ISLANDS 16.9 \n", + "3 NORTHERN CHILE 109.4 \n", + "4 NEAR COAST OF CENTRAL CHILE 25.7 " ] - }, + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Apply the meters2kilometers2 function to the 'depth' column and add it as a new column 'depth_km' to the quake DataFrame\n", + "quake['depth_km'] = quake['depth'].apply(meters2kilometers2)\n", + "\n", + "# Display the first few rows to verify the new column\n", + "quake.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f6db26f1", + "metadata": {}, + "source": [ + "### Lambda functions\n", + "We now discuss the **lambda** functions. \n", + "* Lambda functions are small, anonymous functions in Python.\n", + "* They are used for quick, simple operations without needing to define a full function using def.\n", + "* In geoscience, where we deal with large datasets (e.g., climate data, seismic measurements), lambda functions allow us to process data efficiently.\n", + "* Lambda functions in Pandas allow you to quickly transform, filter, or process this data with minimal code.\n", + "* Please read additional tutorials from [RealPython](!https://realpython.com/python-lambda/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "a2713d55", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "62e31c2c", - "metadata": {}, - "source": [ - "Now you use the ``head`` function to display what is in the file" + "data": { + "text/plain": [ + "(x)>" ] - }, + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lambda x: x * 1.2" + ] + }, + { + "cell_type": "markdown", + "id": "d3ad802b", + "metadata": {}, + "source": [ + "This lambda function multiplies any input ``x`` by 1.2, which could be useful for tasks like converting units (e.g., from km to m)." + ] + }, + { + "cell_type": "markdown", + "id": "8820efb8", + "metadata": {}, + "source": [ + "Below is an example on how to use lambda in a pandas dataframe. \n", + "**Example 1**:\n", + "The lambda function lambda ``x: (x - 32) * 5 / 9`` converts Fahrenheit to Celsius for each value in the Temperature_F column." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "cafff8c2", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 42, - "id": "ed565400", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescription
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE
\n", - "
" - ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", - "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", - "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", - "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", - "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", - "\n", - " description \n", - "0 VANUATU ISLANDS \n", - "1 NEAR EAST COAST OF HONSHU \n", - "2 SOUTH OF MARIANA ISLANDS \n", - "3 NORTHERN CHILE \n", - "4 NEAR COAST OF CENTRAL CHILE " - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# enter answer here\n", - "quake.head()" + "name": "stdout", + "output_type": "stream", + "text": [ + " Temperature_F Temperature_C\n", + "0 32 0.000000\n", + "1 50 10.000000\n", + "2 77 25.000000\n", + "3 100 37.777778\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Sample DataFrame\n", + "data = {'Temperature_F': [32, 50, 77, 100]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Convert Fahrenheit to Celsius\n", + "df['Temperature_C'] = df['Temperature_F'].apply(lambda x: (x - 32) * 5 / 9)\n", + "\n", + "print(df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "cbb26a36", + "metadata": {}, + "outputs": [], + "source": [ + "# Now the equivalent in lambda is:\n", + "lambda_meters2kilometers = lambda x:x/1000\n", + "# x is the variable" + ] + }, + { + "cell_type": "markdown", + "id": "e604fca3", + "metadata": {}, + "source": [ + "Now apply the function to the entire series\n", + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "7d94f6a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 34.40\n", + "1 30.10\n", + "2 16.90\n", + "3 109.40\n", + "4 25.70\n", + " ... \n", + "1780 10.00\n", + "1781 105.00\n", + "1782 608.51\n", + "1783 10.00\n", + "1784 35.44\n", + "Name: depth, Length: 1785, dtype: float64" ] - }, + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply it to the entire series\n", + "lambda_meters2kilometers(quake.depth)" + ] + }, + { + "cell_type": "markdown", + "id": "cef03d96", + "metadata": {}, + "source": [ + "Lambda functions can take several inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "caad68a3", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "73bfcd15", - "metadata": {}, - "source": [ - "Display the depth using two ways to use the pandas object" + "data": { + "text/plain": [ + "1" ] - }, + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can add several variables into lambda functions\n", + "remove_anything = lambda x,y:x-y\n", + "remove_anything(3,2)" + ] + }, + { + "cell_type": "markdown", + "id": "a6da00e2", + "metadata": {}, + "source": [ + "This did not affect the values of the DataFrame, check it:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "40367bfb", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 43, - "id": "9b01ce51", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64\n", - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64\n" - ] - } - ], - "source": [ - "print(quake.depth)\n", - "print(quake['depth'])" + "data": { + "text/plain": [ + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64" ] - }, + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quake.depth" + ] + }, + { + "cell_type": "markdown", + "id": "6347a1ec", + "metadata": {}, + "source": [ + "Instead, you could overwrite ``quake.depth=X``. Try two approaches but just do it once! You can use python functions ``map`` (a function to apply functions that is generic to Python) and ``apply`` (a function to apply functions that is specific to Pandas)." + ] + }, + { + "cell_type": "markdown", + "id": "ade5723d", + "metadata": {}, + "source": [ + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "0d45bc9b", + "metadata": {}, + "source": [ + "Try ``map`` to apply a ``lambda`` function that rescale depth from meters to kilometers." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "8a1da18f", + "metadata": {}, + "outputs": [], + "source": [ + "#type answer below\n", + "quake.depth=quake.depth.map(lambda x:x/1000)" + ] + }, + { + "cell_type": "markdown", + "id": "9b11b4cb", + "metadata": {}, + "source": [ + "Discuss in class: What happened to the depth field?\n", + "\n", + "What happened to the original data frame?" + ] + }, + { + "cell_type": "markdown", + "id": "a520ada6", + "metadata": {}, + "source": [ + "Try ``apply`` to apply a ``lambda`` function that rescale depth from meters to kilometers." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "ec3420af", + "metadata": {}, + "outputs": [], + "source": [ + "# or like this\n", + "quake.depth=quake.depth.apply(lambda x:x/1000) " + ] + }, + { + "cell_type": "markdown", + "id": "fa66b4e8", + "metadata": {}, + "source": [ + "Plot a histogram of the depth distributions using matplotlib function ``hist``." + ] + }, + { + "cell_type": "markdown", + "id": "ade5723d", + "metadata": {}, + "source": [ + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "ae368fd4", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "9c623644", - "metadata": {}, - "source": [ - "Calculate basic statistic of the data using the function ``describe``." + "data": { + "image/png": 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" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# answer here\n", + "plt.hist(quake.depth,100)\n", + "plt.grid(True)\n", + "plt.xlabel('Quake depth (km)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8bb58e3c", + "metadata": {}, + "source": [ + "You can use the interactive plotting package Plotly. First we will show a histogram of the event depth using the function ``histogram``." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "5f07dc52", + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 44, - "id": "2b9f083e", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/html": [ - "
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"showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "width": 600, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "depth" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "count" + } } - ], - "source": [ - "quake.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "8b54218c", - "metadata": {}, - "source": [ - "Calculate mean and median of specific ``Series``, for example depth." - ] - }, + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.histogram(quake, #specify what dataframe to use\n", + " x=\"depth\", #specify the variable for the histogram \n", + " nbins=50, #number of bins for the histogram \n", + " height=400, #dimensions of the figure\n", + " width=600);\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "60a133d4", + "metadata": {}, + "source": [ + "### Example 2: Conditional Logic for Earthquake Magnitude \n", + "You can use lambda functions to add conditional logic, such as classifying earthquake magnitudes into categories.\n", + "\n", + "Scenario: You have a column Magnitude containing seismic event magnitudes, and you want to classify events as \"Minor\", \"Moderate\", or \"Severe\"." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "7f7520de", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 45, - "id": "83f21ca3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "82773.18767507003\n", - "24400.0\n" - ] - } + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategory
02010-07-02 06:04:03.570-13.6098166.65410.03446.3VANUATU ISLANDS34.4Moderate
12010-07-04 21:55:52.37039.6611142.57920.03016.3NEAR EAST COAST OF HONSHU30.1Moderate
22010-07-10 11:43:33.00011.1664146.08230.01696.3SOUTH OF MARIANA ISLANDS16.9Moderate
32010-07-12 00:11:20.060-22.2789-68.31590.10946.2NORTHERN CHILE109.4Moderate
42010-07-14 08:32:21.850-38.0635-73.46490.02576.6NEAR COAST OF CENTRAL CHILE25.7Severe
\n", + "
" ], - "source": [ - "# answer it here\n", - "print(quake.depth.mean())\n", - "print(quake.depth.median())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "591f8d33", - "metadata": {}, - "source": [ - "## 1.3 Manipulating Pandas with Python\n", - "\n", - "### Classic functions\n", - "We will now practice how to modify the content of the DataFrame using functions. We will take the example where we want to change the depth values from meters to kilometers. First we can define this operation as a function" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.0344 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 0.0301 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 0.0169 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 0.1094 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 0.0257 6.6 \n", + "\n", + " description depth_km Category \n", + "0 VANUATU ISLANDS 34.4 Moderate \n", + "1 NEAR EAST COAST OF HONSHU 30.1 Moderate \n", + "2 SOUTH OF MARIANA ISLANDS 16.9 Moderate \n", + "3 NORTHERN CHILE 109.4 Moderate \n", + "4 NEAR COAST OF CENTRAL CHILE 25.7 Severe " ] - }, + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Classify earthquake magnitudes\n", + "quake['Category'] = quake['magnitude'].apply(lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe'))\n", + "quake.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8be50d1c", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "b87c6c42", + "metadata": {}, + "source": [ + "We will now make a new plot of the location of the earthquakes. We will use Plotly tool. \n", + "\n", + "The markersize will be scaled with the earthquake magnitude. To do so, we add a ``marker_size`` series in the DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "d9cb41c3", + "metadata": {}, + "outputs": [], + "source": [ + "quake['marker_size'] = quake['magnitude'].apply(lambda x: np.fix(np.exp(x))) # add marker size as exp(mag)\n", + "quake['magnitude_bin'] = quake['magnitude'].apply(lambda x: 0.5*np.fix(2*x)) # add marker size as exp(mag)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "6c7befa4", + "metadata": {}, + "outputs": [], + "source": [ + "# another way to do it\n", + "quake['marker_size'] = np.fix(np.exp(quake['magnitude'])) # add marker size as exp(mag)\n", + "quake['magnitude_bin'] = 0.5*np.fix(2*quake['magnitude']) # add marker size as exp(mag)" + ] + }, + { + "cell_type": "markdown", + "id": "2fed2184", + "metadata": {}, + "source": [ + "### 1.4 Intermediate Manipulation with Pandas\n", + "\n", + "You can also apply lambda functions to work with multiple columns, which is common in geoscientific datasets, where you might have spatial or temporal data.\n", + "\n", + "**Example 3: Calculating an Index Based on Multiple Measurements**\n", + "Scenario: You have rainfall (``Rainfall_mm``) and evaporation (``Evaporation_mm``) data, and you want to calculate the ``Net Water Balance ``for each record." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "7931923f", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 46, - "id": "5b4f94b4", - "metadata": {}, - "outputs": [], - "source": [ - "# this function converts a value in meters to a value in kilometers\n", - "m2km = 1000 # this is defined as a global variable\n", - "def meters2kilometers(x):\n", - " return x/m2km\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " Rainfall_mm Evaporation_mm Net_Water_Balance\n", + "0 100 60 40\n", + "1 80 70 10\n", + "2 120 65 55\n" + ] + } + ], + "source": [ + "data = {'Rainfall_mm': [100, 80, 120], 'Evaporation_mm': [60, 70, 65]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Calculate Net Water Balance\n", + "df['Net_Water_Balance'] = df.apply(lambda row: row['Rainfall_mm'] - row['Evaporation_mm'], axis=1)\n", + "\n", + "print(df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b5b64a6b", + "metadata": {}, + "source": [ + "In this example, ``lambda row: row['Rainfall_mm'] - row['Evaporation_mm']`` calculates the net water balance by subtracting evaporation from rainfall for each record.\n", + "\n", + "### 1.5 Advanced: Time Series Data Manipulation with Lambda Functions\n", + "Geoscientists frequently work with time series data (e.g., climate data). Lambda functions can be used for efficient data transformations within time series.\n", + "\n", + "**Example 4: Applying a Rolling Window Calculation**\n", + "Scenario: Suppose you have daily temperature data, and you want to calculate a 7-day rolling average." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "6933e64a", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 47, - "id": "16cb70b9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34.4" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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" ], - "source": [ - "# now test it using the first element of the quake DataFrame\n", - "meters2kilometers(quake.depth[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7a360cc5", - "metadata": {}, - "outputs": [], - "source": [ - "# this function converts a value in meters to a value in kilometers\n", - "def meters2kilometers2(x):\n", - " m2km = 1000 # this is defined as a global variable\n", - " return x/m2km" + "text/plain": [ + " Date Temperature_C 7_day_avg\n", + "0 2023-09-01 20 NaN\n", + "1 2023-09-02 22 NaN\n", + "2 2023-09-03 23 21.666667\n", + "3 2023-09-04 21 22.000000\n", + "4 2023-09-05 19 21.000000" ] - }, + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sample daily temperature data\n", + "data = {'Date': pd.date_range(start='2023-09-01', periods=10, freq='D'),\n", + " 'Temperature_C': [20, 22, 23, 21, 19, 24, 25, 26, 22, 20]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Calculate 7-day rolling average using lambda\n", + "df['7_day_avg'] = df['Temperature_C'].rolling(window=3).apply(lambda x: x.mean())\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "e3d3328a", + "metadata": {}, + "source": [ + "Here, ``lambda x: x.mean()`` calculates the rolling mean over a 3-day window for temperature data, which is essential in climate analysis for smoothing short-term fluctuations.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e01cf834", + "metadata": {}, + "source": [ + "### 1.6 Aggregate function\n", + "The ``agg`` is a powerful method in Pandas that allows you to perform multiple operations on DataFrames and Series. You can apply one or more aggregation functions such as ``sum``, ``mean``, ``min``, ``max``, etc., on different columns or groups of data." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "aea0ee4e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "aa52810b", - "metadata": {}, - "source": [ - "Let's define another function that uses a local instead of global variable" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "sum 10.0\n", + "mean 2.5\n", + "Name: A, dtype: float64\n" + ] + } + ], + "source": [ + "# Sample DataFrame\n", + "data = {\n", + " 'A': [1, 2, 3, 4],\n", + " 'B': [5, 6, 7, 8]\n", + "}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Apply aggregation functions\n", + "result = df['A'].agg(['sum', 'mean'])\n", + "print(result)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f2664926", + "metadata": {}, + "source": [ + "#### Aggregating Multiple Columns with Multiple Functions\n", + "You can pass a dictionary to agg where the keys are column names and the values are the functions to be applied." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "9df9cde2", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 49, - "id": "b01a33fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescriptiondepth_km
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS34.4
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU30.1
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS16.9
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE109.4
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE25.7
\n", - "
" - ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", - "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", - "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", - "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", - "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", - "\n", - " description depth_km \n", - "0 VANUATU ISLANDS 34.4 \n", - "1 NEAR EAST COAST OF HONSHU 30.1 \n", - "2 SOUTH OF MARIANA ISLANDS 16.9 \n", - "3 NORTHERN CHILE 109.4 \n", - "4 NEAR COAST OF CENTRAL CHILE 25.7 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Apply the meters2kilometers2 function to the 'depth' column and add it as a new column 'depth_km' to the quake DataFrame\n", - "quake['depth_km'] = quake['depth'].apply(meters2kilometers2)\n", - "\n", - "# Display the first few rows to verify the new column\n", - "quake.head()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " A B\n", + "sum 10.0 NaN\n", + "mean 2.5 NaN\n", + "min NaN 5.0\n", + "max NaN 8.0\n" + ] + } + ], + "source": [ + "# Apply different functions to different columns\n", + "result = df.agg({'A': ['sum', 'mean'], 'B': ['min', 'max']})\n", + "print(result)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5ccea12a", + "metadata": {}, + "source": [ + "In this example, column 'A' gets ``sum`` and ``mean``, while column 'B' gets ``min`` and ``max``." + ] + }, + { + "cell_type": "markdown", + "id": "6b9e44e0", + "metadata": {}, + "source": [ + "You may also use costum functions" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "79ff0c94", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "f6db26f1", - "metadata": {}, - "source": [ - "### Lambda functions\n", - "We now discuss the **lambda** functions. \n", - "* Lambda functions are small, anonymous functions in Python.\n", - "* They are used for quick, simple operations without needing to define a full function using def.\n", - "* In geoscience, where we deal with large datasets (e.g., climate data, seismic measurements), lambda functions allow us to process data efficiently.\n", - "* Lambda functions in Pandas allow you to quickly transform, filter, or process this data with minimal code.\n", - "* Please read additional tutorials from [RealPython](!https://realpython.com/python-lambda/).\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " A B\n", + "mean 2.5 NaN\n", + "data_range 3.0 3.0\n" + ] + } + ], + "source": [ + "# Custom function to calculate range (max - min)\n", + "def data_range(x):\n", + " return x.max() - x.min()\n", + "\n", + "# Apply custom function\n", + "result = df.agg({'A': ['mean', data_range], 'B': data_range})\n", + "print(result)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94abaccd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f004df73", + "metadata": {}, + "source": [ + "## 2.4 Mapping using Plotly\n", + "\n", + "Now we will plot the earthquakes locations on a map using the Plotly package. More tutorials on [Plotly](https://plotly.com/). The input of the function is self-explanatory and typical of Python's function. The code [documentation](https://plotly.com/python/scatter-plots-on-maps/) of Plotly scatter_geo lists the variables." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "ff53045a", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 50, - "id": "a2713d55", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/plain": [ - "(x)>" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" + "customdata": [ + [ + "VANUATU ISLANDS", + 6.3, + 0.0344 + ], + [ + "NEAR EAST COAST OF HONSHU", + 6.3, + 0.030100000000000002 + ], + [ + "SOUTH OF MARIANA ISLANDS", + 6.3, + 0.0169 + ], + [ + "NORTHERN CHILE", + 6.2, + 0.10940000000000001 + ], + [ + "NEAR COAST OF CENTRAL CHILE", + 6.6, + 0.0257 + ], + [ + "FOX ISLANDS", + 6.7, + 0.0215 + ], + [ + "NEW BRITAIN REGION", + 6.9, + 0.0458 + ], + [ + "NEW BRITAIN 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+ "NEAR EAST COAST OF HONSHU", + "SULAWESI", + "SOUTH OF PANAMA", + "OFF EAST COAST OF HONSHU", + "LOYALTY ISLANDS", + "COSTA RICA", + "NEAR EAST COAST OF HONSHU", + "HINDU KUSH REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "SOLOMON ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "WEST OF MACQUARIE ISLAND", + "SOUTH ISLAND", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "CHILE-BOLIVIA BORDER REGION", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FOX ISLANDS", + "SANTA CRUZ ISLANDS", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEGROS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "ALASKA PENINSULA", + "TAJIKISTAN", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEW IRELAND REGION", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NEAR S. COAST OF HONSHU", + "KURIL ISLANDS", + "ASCENSION ISLAND REGION", + "CERAM SEA", + "OFF EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SUNDA STRAIT", + "PERU-BRAZIL BORDER REGION", + "VANUATU ISLANDS", + "BANDA SEA", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "SOUTH SANDWICH ISLANDS REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "NORTHERN SUMATRA", + "AUCKLAND ISLANDS", + "VANCOUVER ISLAND", + "NEAR ISLANDS", + "EAST OF NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SIKKIM", + "TONGA ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "SOUTH OF BALI", + "EASTERN NEW GUINEA REG.", + "SOUTHEASTERN SIBERIA", + "NEW BRITAIN REGION", + "HOKKAIDO", + "KERMADEC ISLANDS REGION", + "TURKEY", + "TURKEY", + "FIJI ISLANDS REGION", + "NEAR COAST OF PERU", + "REVILLA GIGEDO ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "NICARAGUA", + "NORTHEAST OF TAIWAN", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHERN MOLUCCA SEA", + "OFF COAST OF ECUADOR", + "CENTRAL BOLIVIA", + "NEAR EAST COAST OF HONSHU", + "HOKKAIDO", + "NEW IRELAND REGION", + "LUZON", + "NEAR COAST OF NORTHERN CHILE", + "GUERRERO", + "SOUTH SANDWICH ISLANDS REGION", + "MINAHASSA PENINSULA", + "EASTERN NEW GUINEA REG.", + "TONGA ISLANDS", + "SOUTHWESTERN SIBERIA", + "SOUTHEAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH SHETLAND ISLANDS", + "SOUTH SHETLAND ISLANDS", + "NEAR COAST OF CHIAPAS", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF PERU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "NEGROS", + "NEGROS", + "FIJI ISLANDS REGION", + "SOLOMON ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTHWESTERN SIBERIA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEW BRITAIN REGION", + "IRIAN JAYA", + "NEAR COAST OF GUERRERO", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN EAST PACIFIC RISE", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF GUERRERO", + "NEW IRELAND REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "NORTH INDIAN OCEAN", + "OFF W COAST OF NORTHERN SUMATRA", + "OFF COAST OF OREGON", + "MICHOACAN", + "BAJA CALIFORNIA", + "BAJA CALIFORNIA", + "DRAKE PASSAGE", + "VANUATU ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF CENTRAL CHILE", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "IRIAN JAYA REGION", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS REGION", + "TONGA ISLANDS", + "NEAR COAST OF CHIAPAS", + "PERU-BOLIVIA BORDER REGION", + "OFF COAST OF SOUTHERN CHILE", + "NORTHERN ITALY", + "OFF EAST COAST OF HONSHU", + "HOKKAIDO", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "GREENLAND SEA", + "BONIN ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "SALTA PROVINCE", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "OFF EAST COAST OF HONSHU", + "CHILE-ARGENTINA BORDER REGION", + "SOUTHERN PERU", + "DODECANESE ISLANDS", + "LUZON", + "NEAR EAST COAST OF HONSHU", + "NEAR ISLANDS", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF KAMCHATKA", + "NORTHERN XINJIANG", + "COOK STRAIT", + "VANUATU ISLANDS", + "KURIL ISLANDS", + "KURIL ISLANDS", + "NORTHERN SUMATRA", + "SOLOMON ISLANDS", + "MAURITIUS - REUNION REGION", + "NEW IRELAND REGION", + "PERU-BRAZIL BORDER REGION", + "NEW IRELAND REGION", + "FOX ISLANDS", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "XIZANG", + "SEA OF OKHOTSK", + "SULAWESI", + "NEAR N COAST OF NEW GUINEA", + "NORTHERN MOLUCCA SEA", + "OFF COAST OF CENTRAL AMERICA", + "JAN MAYEN ISLAND REGION", + "PHILIPPINE ISLANDS REGION", + "SOUTH OF JAVA", + "SANTA CRUZ ISLANDS", + "COSTA RICA", + "IRIAN JAYA REGION", + "KURIL ISLANDS", + "SOUTHERN SUMATRA", + "BAJA CALIFORNIA", + "ANDREANOF ISLANDS", + "COLOMBIA", + "OFF EAST COAST OF HONSHU", + "GULF OF CALIFORNIA", + "BANDA SEA", + "WEST OF MACQUARIE ISLAND", + "IRIAN JAYA REGION", + "CELEBES SEA", + "VANUATU ISLANDS", + "COSTA RICA", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "MINDANAO", + "NEAR COAST OF CHIAPAS", + "VANCOUVER ISLAND", + "CENTRAL PERU", + "MYANMAR", + "NEAR COAST OF CHIAPAS", + "GULF OF ALASKA", + "OFF COAST OF SOUTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "GUERRERO", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "NEAR N COAST OF NEW GUINEA", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NORTH ISLAND", + "BANDA SEA", + "NORTHERN MOLUCCA SEA", + "OFF W. COAST OF BAJA CALIFORNIA", + "NEAR ISLANDS", + "NEW IRELAND REGION", + "MINAHASSA PENINSULA", + "VANUATU ISLANDS", + "SOUTHEASTERN ALASKA", + "PACIFIC-ANTARCTIC RIDGE", + "NORTHERN SUMATRA", + "LAKE ISSYK-KUL REGION", + "CENTRAL CHILE", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "HOKKAIDO", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "COLOMBIA", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "EASTERN SIBERIA", + "MINDANAO", + "SANTIAGO DEL ESTERO PROV.", + "KURIL ISLANDS", + "KURIL ISLANDS", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "EAST OF KURIL ISLANDS", + "VANUATU ISLANDS REGION", + "GUATEMALA", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "E. RUSSIA-N.E. CHINA BORDER REG.", + "IRIAN JAYA", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "SOLOMON ISLANDS", + "SOUTHERN IRAN", + "NEAR N COAST OF NEW GUINEA", + "KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "SICHUAN", + "BANDA SEA", + "KURIL ISLANDS", + "SOUTHEAST OF HONSHU", + "MICHOACAN", + "NEW IRELAND REGION", + "KERMADEC ISLANDS REGION", + "SOUTHERN IRAN", + "TONGA ISLANDS", + "MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "OFF COAST OF SOUTHERN CHILE", + "OFF EAST COAST OF KAMCHATKA", + "OFF EAST COAST OF KAMCHATKA", + "SOUTH OF FIJI ISLANDS", + "TONGA ISLANDS", + "SEA OF OKHOTSK", + "SEA OF OKHOTSK", + "TAIWAN", + "SANTA CRUZ ISLANDS", + "SOUTH OF JAVA", + "SOUTH OF KERMADEC ISLANDS", + "CRETE", + "NEAR COAST OF NICARAGUA", + "CRETE", + "NORTHERN MID-ATLANTIC RIDGE", + "NORTHERN SUMATRA", + "SOLOMON ISLANDS", + "SOUTHERN SUMATRA", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN PERU", + "COOK STRAIT", + "GANSU", + "PRINCE EDWARD ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "BANDA SEA", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF NORTHERN PERU", + "SOUTH OF PANAMA", + "COOK STRAIT", + "SOUTHWEST INDIAN RIDGE", + "NEAR COAST OF GUERRERO", + "KERMADEC ISLANDS REGION", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "BANDA SEA", + "QUEEN CHARLOTTE ISLANDS REGION", + "SOUTHEAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "PHILIPPINE ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "CENTRAL EAST PACIFIC RISE", + "ANDREANOF ISLANDS", + "FLORES SEA", + "PAKISTAN", + "SOUTHERN EAST PACIFIC RISE", + "NEAR COAST OF PERU", + "PAKISTAN", + "KERMADEC ISLANDS", + "SEA OF OKHOTSK", + "MID-INDIAN RIDGE", + "SOUTH OF MARIANA ISLANDS", + "WEST CHILE RISE", + "SAKHALIN", + "KERMADEC ISLANDS", + "NEAR COAST OF VENEZUELA", + "CRETE", + "MINDANAO", + "SOLOMON ISLANDS", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "OFF EAST COAST OF HONSHU", + "BALLENY ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "TAIWAN", + "NEAR COAST OF CENTRAL CHILE", + "EASTER ISLAND REGION", + "TONGA ISLANDS REGION", + "CELEBES SEA", + "NEAR EAST COAST OF KAMCHATKA", + "SCOTIA SEA", + "SCOTIA SEA", + "SCOTIA SEA", + "HALMAHERA", + "MARIANA ISLANDS", + "FIJI ISLANDS REGION", + "KURIL ISLANDS", + "SOUTH ATLANTIC OCEAN", + "BANDA SEA", + "NORTHERN SUMATRA", + "KURIL ISLANDS", + "MARIANA ISLANDS REGION", + "VANUATU ISLANDS", + "PUERTO RICO REGION", + "NORTH ISLAND", + "TONGA ISLANDS", + "JAVA", + "GREECE", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH OF KERMADEC ISLANDS", + "GREECE", + "VANUATU ISLANDS", + "XIZANG", + "NORTH ATLANTIC OCEAN", + "FOX ISLANDS", + "NEAR COAST OF NICARAGUA", + "RYUKYU ISLANDS", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "OFF COAST OF NORTHERN CALIFORNIA", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "NEAR COAST OF PERU", + "NEAR COAST OF NORTHERN PERU", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NICOBAR ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "SANTA CRUZ ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "OFF COAST OF NORTHERN CHILE", + "SOUTH OF PANAMA", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "NICARAGUA", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR COAST OF NICARAGUA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "BOUVET ISLAND REGION", + "BALLENY ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "GUERRERO", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "VANCOUVER ISLAND", + "TONGA ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR S. COAST OF HONSHU", + "THAILAND", + "WEST CHILE RISE", + "SOLOMON ISLANDS", + "GUERRERO", + "TONGA ISLANDS", + "GUERRERO", + "SOUTHERN EAST PACIFIC RISE", + "SOUTH OF PANAMA", + "E. CAROLINE ISLANDS", + "E. CAROLINE ISLANDS", + "NEGROS", + "OFF W COAST OF NORTHERN SUMATRA", + "BAY OF BENGAL", + "AEGEAN SEA", + "OFF COAST OF JALISCO", + "SOUTH INDIAN OCEAN", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "VOLCANO ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "TONGA ISLANDS", + "BONIN ISLANDS", + "BALLENY ISLANDS REGION", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "NEW BRITAIN REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "MINDANAO", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "TONGA ISLANDS", + "TONGA ISLANDS", + "OWEN FRACTURE ZONE REGION", + "KURIL ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEASTERN ALASKA", + "NORTHERN MID-ATLANTIC RIDGE", + "OAXACA", + "BISMARCK SEA", + "BISMARCK SEA", + "E. CAROLINE ISLANDS", + "XIZANG", + "YUNNAN", + "BANDA SEA", + "BANDA SEA", + "HOKKAIDO", + "HOKKAIDO", + "IRAN-IRAQ BORDER REGION", + "IRAN-IRAQ BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN CALIFORNIA", + "CENTRAL PERU", + "CENTRAL PERU", + "EASTER ISLAND REGION", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "OFF COAST OF JALISCO", + "SOUTHERN MOLUCCA SEA", + "MARIANA ISLANDS", + "JUJUY PROVINCE", + "SOLOMON ISLANDS", + "SOUTHERN ALASKA", + "NEW BRITAIN REGION", + "YUNNAN", + "GULF OF CALIFORNIA", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHERN EAST PACIFIC RISE", + "HOKKAIDO", + "OFF COAST OF CENTRAL AMERICA", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "FIJI ISLANDS REGION", + "BALLENY ISLANDS REGION", + "MID-INDIAN RIDGE", + "NEW BRITAIN REGION", + "PRIMOR'YE", + "FIJI ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "OFF E. COAST OF N. ISLAND", + "PRINCE EDWARD ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "SICHUAN", + "EASTERN HONSHU", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "SOUTH OF PANAMA", + "BANDA SEA", + "SOLOMON ISLANDS", + "NEAR COAST OF GUATEMALA", + "SOUTH OF PANAMA", + "TAIWAN REGION", + "PHILIPPINE ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "NORTHERN MOLUCCA SEA", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "MINDANAO", + "FIJI ISLANDS REGION", + "SOUTHEAST OF EASTER ISLAND", + "SOUTH OF PANAMA", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "ADMIRALTY ISLANDS REGION", + "MENDOZA PROVINCE", + "JUJUY PROVINCE", + "REYKJANES RIDGE", + "TAIWAN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS REGION", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "OFF COAST OF JALISCO", + "FLORES SEA", + "SOUTHERN SUMATRA", + "MID-INDIAN RIDGE", + "NORTHERN COLOMBIA", + "MINAHASSA PENINSULA", + "NORTHERN MOLUCCA SEA", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "NORTHERN CHILE", + "NEW BRITAIN REGION", + "SAMOA ISLANDS REGION", + "SAMOA ISLANDS REGION", + "SAMOA ISLANDS REGION", + "NEW BRITAIN REGION", + "TONGA ISLANDS", + "CRETE", + "FIJI ISLANDS REGION", + "TAIWAN REGION", + "TAIWAN REGION", + "SANTA CRUZ ISLANDS", + "SOUTH ISLAND", + "QUEEN CHARLOTTE ISLANDS REGION", + "NEPAL", + "NEPAL", + "NEPAL", + "NEPAL", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "MOROCCO", + "SOLOMON ISLANDS", + "NEPAL", + "NEPAL", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN SUMATRA", + "PACIFIC-ANTARCTIC RIDGE", + "TONGA ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTHERN MID-ATLANTIC RIDGE", + "TONGA ISLANDS", + "ALASKA PENINSULA", + "BONIN ISLANDS", + "TONGA ISLANDS", + "SOUTHEAST OF HONSHU", + "BORNEO", + "HOKKAIDO", + "NORTHERN CHILE", + "SAMOA ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTHERN MID-ATLANTIC RIDGE", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "BONIN ISLANDS", + "NEW BRITAIN REGION", + "SOUTHERN XINJIANG", + "LEYTE", + "EAST OF KURIL ISLANDS", + "SOLOMON ISLANDS", + "NORTH ATLANTIC OCEAN", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "IRIAN JAYA", + "SOUTHERN ALASKA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "MID-INDIAN RIDGE", + "SOLOMON ISLANDS", + "KERMADEC ISLANDS REGION", + "SOUTHEAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "FOX ISLANDS", + "GULF OF CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "IRIAN JAYA REGION", + "IRIAN JAYA REGION", + "NEAR COAST OF CENTRAL CHILE", + "JUJUY PROVINCE", + "PACIFIC-ANTARCTIC RIDGE", + "EAST OF KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "BOUVET ISLAND REGION", + "PRINCE EDWARD ISLANDS REGION", + "PRINCE EDWARD ISLANDS REGION", + "HINDU KUSH REGION", + "TIMOR REGION", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NICOBAR ISLANDS", + "ANDREANOF ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHWEST OF RYUKYU ISLANDS", + "GREECE", + "SOLOMON ISLANDS", + "BANDA SEA", + "MARIANA ISLANDS", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "NEAR COAST OF NORTHERN CHILE", + "SOUTHEAST INDIAN RIDGE", + "TAJIKISTAN", + "BANDA SEA", + "FIJI ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "BORNEO", + "SOUTHERN EAST PACIFIC RISE", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "MYANMAR-INDIA BORDER REGION", + "MYANMAR-INDIA BORDER REGION", + "PACIFIC-ANTARCTIC RIDGE", + "TALAUD ISLANDS", + "HOKKAIDO", + "SOUTHERN BOLIVIA", + "HOKKAIDO", + "OFF COAST OF JALISCO", + "SOUTHERN ALASKA", + "STRAIT OF GIBRALTAR", + "KAMCHATKA PENINSULA", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "TAIWAN", + "SOLOMON ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SUMBA REGION", + "SOUTHERN EAST PACIFIC RISE", + "HALMAHERA", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "SOUTHWEST OF SUMATRA", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "LEEWARD ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "NEAR N COAST OF NEW GUINEA", + "ALASKA PENINSULA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "JAVA", + "VANUATU ISLANDS", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "MYANMAR-INDIA BORDER REGION", + "MINDANAO", + "KYUSHU", + "KYUSHU", + "VANUATU ISLANDS", + "OFF COAST OF CHIAPAS", + "KYUSHU", + "NEAR COAST OF ECUADOR", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF CHIAPAS", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "NORTHERN EAST PACIFIC RISE", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NORTHERN TERRITORY", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "TAIWAN REGION", + "SOUTHERN SUMATRA", + "BANDA SEA", + "KERMADEC ISLANDS", + "OFF COAST OF JALISCO", + "NORTHERN MOLUCCA SEA", + "SOUTH OF SUMBAWA", + "NICARAGUA", + "SOLOMON ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "LOYALTY ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "NEW IRELAND REGION", + "TAJIKISTAN-XINJIANG BORDER REG.", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "NEAR COAST OF ECUADOR", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "ADMIRALTY ISLANDS REGION", + "MARIANA ISLANDS", + "JUJUY PROVINCE", + "VOLCANO ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH GEORGIA ISLAND REGION", + "FLORES SEA", + "CENTRAL ITALY", + "MYANMAR", + "NORTH OF ASCENSION ISLAND", + "NEW IRELAND REGION", + "OFF E. COAST OF N. ISLAND", + "OFF E. COAST OF N. ISLAND", + "KOMANDORSKIYE OSTROVA REGION", + "MACQUARIE ISLAND REGION", + "NORTHERN PERU", + "NORTHERN COLOMBIA", + "SOLOMON ISLANDS", + "NEAR NORTH COAST OF IRIAN JAYA", + "SOUTHEAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "MINDANAO", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "JAVA SEA", + "WESTERN HONSHU", + "TONGA ISLANDS", + "CENTRAL ITALY", + "CENTRAL ITALY", + "CHILE-ARGENTINA BORDER REGION", + "NEAR EAST COAST OF HONSHU", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SAN JUAN PROVINCE", + "NEAR EAST COAST OF HONSHU", + "OFF COAST OF CENTRAL AMERICA", + "SOUTHERN XINJIANG", + "SOUTHERN PERU", + "NEAR ISLANDS", + "BANDA SEA", + "NORTHERN SUMATRA", + "OFF COAST OF NORTHERN CALIFORNIA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "MARIANA ISLANDS REGION", + "NEW IRELAND REGION", + "NEW IRELAND REGION", + "SOLOMON ISLANDS", + "W. CAROLINE ISLANDS", + "PERU-BRAZIL BORDER REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "NEW IRELAND REGION", + "SOUTHERN CHILE", + "SUMBAWA REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF FIJI ISLANDS", + "QUEEN ELIZABETH ISLANDS", + "CELEBES SEA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTHWESTERN PAKISTAN", + "MINDANAO", + "JUJUY PROVINCE", + "SOUTHERN BOLIVIA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "NEW BRITAIN REGION", + "NICOBAR ISLANDS", + "SOLOMON ISLANDS", + "NEAR ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "BOTSWANA", + "NORTHERN AND CENTRAL IRAN", + "CHILE-ARGENTINA BORDER REGION", + "FIJI ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "SOUTHEASTERN ALASKA", + "TAJIKISTAN", + "ANDREANOF ISLANDS", + "RYUKYU ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CENTRAL AMERICA", + "NEW BRITAIN REGION", + "NEGROS", + "SULAWESI", + "NEAR ISLANDS", + "AEGEAN SEA", + "GUATEMALA", + "KERMADEC ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF GUATEMALA", + "FIJI ISLANDS REGION", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "LEYTE", + "AUCKLAND ISLANDS", + "NEW IRELAND REGION", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "NEAR COAST OF PERU", + "MAURITIUS - REUNION REGION", + "DODECANESE ISLANDS", + "SOUTHEAST OF RYUKYU ISLANDS", + "NORTH ATLANTIC OCEAN", + "GANSU", + "NORTHERN XINJIANG", + "MINDORO", + "SOUTHERN SUMATRA", + "NORTH OF ASCENSION ISLAND", + "FIJI ISLANDS REGION", + "ADMIRALTY ISLANDS REGION", + "SOUTHERN SUMATRA", + "NORTH KOREA", + "SOUTH SANDWICH ISLANDS REGION", + "BONIN ISLANDS", + "NEAR COAST OF CHIAPAS", + "CENTRAL MEXICO", + "AUCKLAND ISLANDS", + "OFF EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "OAXACA", + "SOUTH OF FIJI ISLANDS", + "OFF EAST COAST OF HONSHU", + "TONGA ISLANDS", + "BALLENY ISLANDS REGION", + "RAT ISLANDS", + "NORTHERN CHILE", + "BOUVET ISLAND REGION", + "TONGA ISLANDS", + "BANDA SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SERAM", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "TONGA ISLANDS", + "NEW GUINEA", + "SOUTHEAST OF HONSHU", + "ASCENSION ISLAND REGION", + "IRAN-IRAQ BORDER REGION", + "COSTA RICA", + "EASTERN XIZANG-INDIA BORDER REG.", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "SAMOA ISLANDS REGION", + "NEW IRELAND REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "NORTHERN AND CENTRAL IRAN", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF ECUADOR", + "W. CAROLINE ISLANDS", + "W. CAROLINE ISLANDS", + "W. CAROLINE ISLANDS", + "NORTHERN AND CENTRAL IRAN", + "NORTHERN AND CENTRAL IRAN", + "BOUVET ISLAND REGION", + "JAVA", + "NORTH OF HONDURAS", + "MYANMAR", + "NEAR COAST OF PERU", + "GULF OF CALIFORNIA", + "NORTHERN CHILE", + "JAVA", + "GULF OF ALASKA", + "HOKKAIDO", + "KOMANDORSKIYE OSTROVA REGION", + "NEAR N COAST OF NEW GUINEA", + "SOUTHWEST OF AFRICA", + "HINDU KUSH REGION", + "FIJI ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "TAIWAN", + "TAIWAN", + "SOUTH OF MARIANA ISLANDS", + "OAXACA", + "NEW GUINEA", + "NEW GUINEA", + "CERAM SEA", + "NEW GUINEA", + "WEST OF MACQUARIE ISLAND", + "NEW GUINEA", + "NEW GUINEA", + "NEW GUINEA", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "SOUTHEAST INDIAN RIDGE", + "BANDA SEA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTHERN BOLIVIA", + "MINDANAO", + "NEW GUINEA", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN MOLUCCA SEA", + "PRINCE EDWARD ISLANDS REGION", + "EASTER ISLAND REGION", + "HAWAII", + "LUZON", + "NEW BRITAIN REGION", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "SOUTH OF KERMADEC ISLANDS", + "VANUATU ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "KERMADEC ISLANDS", + "VANUATU ISLANDS", + "EASTERN GULF OF ADEN", + "EASTERN GULF OF ADEN", + "SANTA CRUZ ISLANDS", + "NEW BRITAIN REGION", + "FLORES SEA", + "SUMBAWA REGION", + "SUMBAWA REGION", + "NORTHERN ALASKA", + "NORTHERN ALASKA", + "SOUTH SANDWICH ISLANDS REGION", + "ANDREANOF ISLANDS", + "VOLCANO ISLANDS", + "FLORES SEA", + "COSTA RICA", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "SUMBAWA REGION", + "FIJI ISLANDS REGION", + "SUMBAWA REGION", + "NEAR COAST OF VENEZUELA", + "VANUATU ISLANDS", + "OFF COAST OF OREGON", + "ANDREANOF ISLANDS", + "PERU-BRAZIL BORDER REGION", + "WESTERN IRAN", + "TIMOR REGION", + "MARIANA ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "HOKKAIDO", + "FIJI ISLANDS", + "ECUADOR", + "MINDANAO", + "SOLOMON ISLANDS", + "KERMADEC ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTHWEST INDIAN RIDGE", + "MINAHASSA PENINSULA", + "MINAHASSA PENINSULA", + "FIJI ISLANDS REGION", + "SUMBA REGION", + "KURIL ISLANDS", + "BALI SEA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "KURIL ISLANDS", + "NORTHWEST OF KURIL ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "IONIAN SEA", + "OFF COAST OF CENTRAL AMERICA", + "DRAKE PASSAGE", + "NORTH ISLAND", + "NORTHERN CHILE", + "MINDANAO", + "JAN MAYEN ISLAND REGION", + "TONGA ISLANDS", + "NORTH ATLANTIC OCEAN", + "NEAR EAST COAST OF KAMCHATKA", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN EAST PACIFIC RISE", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "CARIBBEAN SEA", + "IRAN-IRAQ BORDER REGION", + "SOUTHERN ALASKA", + "BANDA SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "IRIAN JAYA", + "SOUTHEAST OF EASTER ISLAND", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "KOMANDORSKIYE OSTROVA REGION", + "MINDANAO", + "ALASKA PENINSULA", + "WESTERN BRAZIL", + "NORTHERN MOLUCCA SEA", + "KYUSHU", + "VANUATU ISLANDS", + "BISMARCK SEA", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SUMBA REGION", + "SUMBA REGION", + "PRINCE EDWARD ISLANDS REGION", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "MARIANA ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "NEW IRELAND REGION", + "PERU-ECUADOR BORDER REGION", + "CENTRAL PERU", + "SOUTH OF KERMADEC ISLANDS", + "PHILIPPINE ISLANDS REGION", + "FIJI ISLANDS REGION", + "D'ENTRECASTEAUX ISLANDS REGION", + "CENTRAL BOLIVIA", + "VANUATU ISLANDS", + "COLOMBIA", + "NORTHERN MOLUCCA SEA", + "EAST OF KURIL ISLANDS", + "NEW BRITAIN REGION", + "NEAR COAST OF ECUADOR", + "MAURITIUS - REUNION REGION", + "RAT ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "BANDA SEA", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "SULAWESI", + "TAIWAN", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "LUZON", + "SOUTH SANDWICH ISLANDS REGION", + "SAMAR", + "SOUTH OF FIJI ISLANDS", + "CARLSBERG RIDGE", + "SOLOMON ISLANDS", + "EASTERN NEW GUINEA REG.", + "KYUSHU", + "PANAMA-COSTA RICA BORDER REGION", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "ANDREANOF ISLANDS", + "NORTHERN PERU", + "EL SALVADOR", + "FIJI ISLANDS REGION", + "MINDANAO", + "TONGA ISLANDS", + "SOUTHEAST OF HONSHU", + "OFF COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS", + "NEAR WEST COAST OF HONSHU", + "KERMADEC ISLANDS", + "IRIAN JAYA", + "KERMADEC ISLANDS", + "IRIAN JAYA", + "BANDA SEA", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "PANAMA-COSTA RICA BORDER REGION", + "KERMADEC ISLANDS REGION", + "MARIANA ISLANDS", + "VANUATU ISLANDS", + "QUEEN CHARLOTTE ISLANDS REGION", + "CENTRAL CALIFORNIA", + "CENTRAL CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "SOLOMON ISLANDS", + "NORTHWEST OF RYUKYU ISLANDS", + "WESTERN AUSTRALIA", + "HALMAHERA", + "NEW BRITAIN REGION", + "BALLENY ISLANDS REGION", + "PHILIPPINE ISLANDS REGION", + "NEAR S. COAST OF HONSHU", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHWEST OF SUMATRA", + "NEAR EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF OREGON", + "FIJI ISLANDS REGION", + "JAVA", + "MONA PASSAGE", + "SOUTHWEST INDIAN RIDGE", + "SERAM", + "CENTRAL CHILE", + "KERMADEC ISLANDS", + "MINDANAO", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "VANUATU ISLANDS", + "NORTH OF ASCENSION ISLAND", + "MINDANAO", + "MINDANAO", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "TONGA ISLANDS", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "OFF COAST OF CHIAPAS", + "SEA OF OKHOTSK", + "LAOS", + "IRIAN JAYA REGION", + "ANDREANOF ISLANDS", + "ALBANIA", + "CRETE", + "ANDREANOF ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "MINDANAO", + "HINDU KUSH REGION", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "SANTIAGO DEL ESTERO PROV.", + "COLOMBIA", + "VANCOUVER ISLAND", + "SOUTHERN EAST PACIFIC RISE", + "NORTHERN SUMATRA", + "PUERTO RICO REGION", + "NEW BRITAIN REGION", + "EASTERN SIBERIA", + "NEAR NORTH COAST OF IRIAN JAYA", + "SOUTHERN XINJIANG", + "MINAHASSA PENINSULA", + "SOUTH SANDWICH ISLANDS REGION", + "ANDREANOF ISLANDS", + "TURKEY", + "ANDREANOF ISLANDS", + "SOLOMON ISLANDS", + "CUBA REGION", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "JAVA", + "MINDANAO", + "SOUTH SANDWICH ISLANDS REGION", + "NEW BRITAIN REGION", + "KURIL ISLANDS", + "TURKEY-IRAN BORDER REGION", + "KERMADEC ISLANDS REGION", + "SAMOA ISLANDS REGION", + "VANUATU ISLANDS", + "SOUTH OF BALI", + "CENTRAL EAST PACIFIC RISE", + "EAST OF KURIL ISLANDS", + "MINDANAO", + "WESTERN IDAHO", + "NORTHERN MOLUCCA SEA", + "MID-INDIAN RIDGE", + "KERMADEC ISLANDS REGION", + "NORTH OF HONDURAS", + "BONIN ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "CRETE", + "BANDA SEA", + "SOLOMON ISLANDS", + "SANTA CRUZ ISLANDS", + "NEVADA", + "OFF COAST OF CENTRAL MEXICO", + "VANUATU ISLANDS", + "SOUTHERN PERU", + "NORTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "HALMAHERA", + "SOUTHERN MID-ATLANTIC RIDGE", + "RYUKYU ISLANDS", + "MARIANA ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "ICELAND REGION", + "MINAHASSA PENINSULA", + "NEAR COAST OF OAXACA", + "XIZANG", + "W. CAROLINE ISLANDS", + "JAVA SEA", + "EASTERN NEW GUINEA REG.", + "ANDAMAN ISLANDS", + "SAMOA ISLANDS REGION", + "FIJI ISLANDS REGION", + "ALASKA PENINSULA", + "SOUTH OF ALASKA", + "XIZANG", + "SOUTH SANDWICH ISLANDS REGION", + "ALASKA PENINSULA", + "MINDANAO", + "BISMARCK SEA", + "VANUATU ISLANDS", + "PRINCE EDWARD ISLANDS REGION", + "TANZANIA", + "SAMAR", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "BANDA SEA", + "ASCENSION ISLAND REGION", + "COSTA RICA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "CHAGOS ARCHIPELAGO REGION", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "VANUATU ISLANDS", + "CENTRAL MID-ATLANTIC RIDGE", + "MINDANAO", + "VANUATU ISLANDS", + "NORTHERN CHILE", + "NEAR EAST COAST OF HONSHU", + "KAMCHATKA PENINSULA", + "FIJI ISLANDS", + "CENTRAL MID-ATLANTIC RIDGE", + "SOUTH OF AFRICA", + "TONGA ISLANDS", + "NEW BRITAIN REGION", + "FIJI ISLANDS REGION", + "EASTERN NEW GUINEA REG.", + "SOUTH OF ALASKA", + "SOUTH OF FIJI ISLANDS", + "DODECANESE ISLANDS", + "OFF COAST OF SOUTHERN CHILE", + "SOUTH SHETLAND ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "MINDANAO", + "OFF COAST OF CENTRAL CHILE", + "PRIMOR'YE", + "CHILE-ARGENTINA BORDER REGION", + "FOX ISLANDS", + "BALLENY ISLANDS REGION", + "NORTHERN CHILE", + "TAIWAN REGION", + "CHILE-BOLIVIA BORDER REGION", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "W. CAROLINE ISLANDS", + "MINDORO", + "OFF COAST OF CENTRAL CHILE", + "NORTHWESTERN BALKAN REGION", + "ANDREANOF ISLANDS", + "KERMADEC ISLANDS REGION", + "MINAHASSA PENINSULA", + "KERMADEC ISLANDS", + "VANUATU ISLANDS", + "SALTA PROVINCE", + "VANUATU ISLANDS", + "RUSSIA-MONGOLIA BORDER REGION", + "SULAWESI", + "SAN JUAN PROVINCE", + "PHILIPPINE ISLANDS REGION", + "SOUTH SHETLAND ISLANDS", + "WEST CHILE RISE", + "MINDANAO", + "BISMARCK SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWEST OF SUMATRA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "TONGA ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "GREECE", + "OFF E. COAST OF N. ISLAND", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF KAMCHATKA", + "NORTHERN ALGERIA", + "NORTHERN MID-ATLANTIC RIDGE", + "WEST OF MACQUARIE ISLAND", + "NEAR EAST COAST OF HONSHU", + "KERMADEC ISLANDS", + "FIJI ISLANDS REGION", + "EAST OF SOUTH SANDWICH ISLANDS", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "JAVA", + "CELEBES SEA", + "NEAR N COAST OF NEW GUINEA", + "OFF W COAST OF NORTHERN SUMATRA", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NORTHEASTERN INDIA", + "KERMADEC ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "WEST OF MACQUARIE ISLAND", + "FIJI ISLANDS REGION", + "NEAR COAST OF GUATEMALA", + "MAURITIUS - REUNION REGION", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTHERN EAST PACIFIC RISE", + "YUNNAN", + "QINGHAI", + "FIJI ISLANDS REGION", + "CENTRAL ALASKA", + "NORTHERN MOLUCCA SEA", + "KERMADEC ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "CALIFORNIA-NEVADA BORDER REGION", + "SOUTH OF PANAMA", + "BISMARCK SEA", + "SOUTH OF PANAMA", + "MINDORO", + "KERMADEC ISLANDS REGION", + "MINAHASSA PENINSULA", + "ALASKA PENINSULA", + "PERU-ECUADOR BORDER REGION", + "NICOBAR ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH 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"name": "stdout", - "output_type": "stream", - "text": [ - " Temperature_F Temperature_C\n", - "0 32 0.000000\n", - "1 50 10.000000\n", - "2 77 25.000000\n", - "3 100 37.777778\n" + ], + "layout": { + "coloraxis": { + "cmax": 9, + "cmin": 6, + "colorbar": { + "title": { + "text": "magnitude" + } + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Sample DataFrame\n", - "data = {'Temperature_F': [32, 50, 77, 100]}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Convert Fahrenheit to Celsius\n", - "df['Temperature_C'] = df['Temperature_F'].apply(lambda x: (x - 32) * 5 / 9)\n", - "\n", - "print(df)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "cbb26a36", - "metadata": {}, - "outputs": [], - "source": [ - "# Now the equivalent in lambda is:\n", - "lambda_meters2kilometers = lambda x:x/1000\n", - "# x is the variable" - ] - }, - { - "cell_type": "markdown", - "id": "e604fca3", - "metadata": {}, - "source": [ - "Now apply the function to the entire series\n", - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "7d94f6a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 34.40\n", - "1 30.10\n", - "2 16.90\n", - "3 109.40\n", - "4 25.70\n", - " ... \n", - "1780 10.00\n", - "1781 105.00\n", - "1782 608.51\n", - "1783 10.00\n", - "1784 35.44\n", - "Name: depth, Length: 1785, dtype: float64" - ] + ] + }, + "geo": { + "center": {}, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, 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"autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# you can add several variables into lambda functions\n", - "remove_anything = lambda x,y:x-y\n", - "remove_anything(3,2)" - ] - }, - { - "cell_type": "markdown", - "id": "a6da00e2", - "metadata": {}, - "source": [ - "This did not affect the values of the DataFrame, check it:" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "40367bfb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64" + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ 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Try two approaches but just do it once! You can use python functions ``map`` (a function to apply functions that is generic to Python) and ``apply`` (a function to apply functions that is specific to Pandas)." - ] - }, - { - "cell_type": "markdown", - "id": "ade5723d", - "metadata": {}, - "source": [ - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "0d45bc9b", - "metadata": {}, - "source": [ - "Try ``map`` to apply a ``lambda`` function that rescale depth from meters to kilometers." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "8a1da18f", - "metadata": {}, - "outputs": [], - "source": [ - "# implement answer here" - ] - }, - { - "cell_type": "markdown", - "id": "9b11b4cb", - "metadata": {}, - "source": [ - "Discuss in class: What happened to the depth field?\n", - "\n", - "What happened to the original data frame?" - ] - }, - { - "cell_type": "markdown", - "id": "a520ada6", - "metadata": {}, - "source": [ - "Try ``apply`` to apply a ``lambda`` function that rescale depth from meters to kilometers." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "ec3420af", - "metadata": {}, - "outputs": [], - "source": [ - "# or like this\n", - "quake.depth=quake.depth.apply(lambda x:x/1000) " - ] - }, - { - "cell_type": "markdown", - "id": "fa66b4e8", - "metadata": {}, - "source": [ - "Plot a histogram of the depth distributions using matplotlib function ``hist``." - ] - }, - { - "cell_type": "markdown", - "id": "ade5723d", - "metadata": {}, - "source": [ - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "ae368fd4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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T3377bZ/bzz//vPr06aPi4mLdcMMNMsZo1apVWrRokaZMmSJJeuGFF5SYmKicnBzNmjVLp06d0rp16/Tiiy8qPT1dkvTSSy8pJSVFW7Zs0YQJE4J0aAAAoL3yq6Cc69SpU5Kknj17SpJKSkpUVlamzMxM75yoqCiNHTtWRUVFmjVrloqLi+V2u33mJCcna/jw4SoqKmq2oLhcLrlcLu/tyspKSZLb7Zbb7W7JITTRuL2ztxsVZi76cR1Fczl0NmTgQQ4e5OBBDh7kEFgG/sx1GGO++1/fZhhjNHnyZFVUVGj79u2SpKKiIl1//fX68ssvlZyc7J377//+7zp69Kg2b96snJwc/fKXv/QpHJKUmZmpQYMG6emnn26yL6fTqezs7CbjOTk5io2NDWT5AACgldXU1GjatGk6deqU4uPjLzg34DMo9913n/7xj39ox44dTe5zOBw+t40xTcbOdaE5Cxcu1Ny5c723KysrlZKSoszMzO88QH+53W4VFBQoIyNDERERkqThzs3f+bgDzo51aaq5HDobMvAgBw9y8CAHD3IILIPGKyAXI6CCcv/99+vNN9/Utm3b1K9fP+94UlKSJKmsrEx9+/b1jpeXlysxMdE7p66uThUVFerRo4fPnDFjxjS7v6ioKEVFRTUZj4iICNkT4+xtu+ovXK4a53dEocy4vSADD3LwIAcPcvAgB/8y8Ccrv97FY4zRfffdp9dff13vvPOOBg0a5HP/oEGDlJSUpIKCAu9YXV2dCgsLveUjLS1NERERPnNKS0t14MCB8xYUAADQufh1BuXee+9VTk6O/vKXvyguLk5lZWWSpO7duysmJkYOh0NZWVlasmSJUlNTlZqaqiVLlig2NlbTpk3zzp05c6YefPBBJSQkqGfPnpo3b56uuuoq77t6AABA5+ZXQVm7dq0kady4cT7jzz//vGbMmCFJmj9/vmprazV79mxVVFRo1KhRys/PV1xcnHf+ypUrFR4erqlTp6q2tlbjx4/X+vXrFRYW1rKjAQAAHYJfBeVi3vDjcDjkdDrldDrPOyc6OlqrV6/W6tWr/dk9AADoJPgsHgAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrhLf1AjqKgQveajL2+Z9uaoOVAADQ/nEGBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh78kG0Ln/nVZ/rIsAAAXhzMoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACsQ0EBAADWoaAAAADrUFAAAIB1KCgAAMA6FBQAAGAdCgoAALAOBQUAAFiHggIAAKxDQQEAANahoAAAAOv4XVC2bdumW265RcnJyXI4HHrjjTd87jfGyOl0Kjk5WTExMRo3bpwOHjzoM8flcun+++9Xr1691LVrV02aNEnHjx9v0YEAAICOw++Ccvr0aV1zzTVas2ZNs/cvW7ZMK1as0Jo1a7R7924lJSUpIyNDVVVV3jlZWVnKy8tTbm6uduzYoerqat18882qr68P/EgAAECHEe7vAyZOnKiJEyc2e58xRqtWrdKiRYs0ZcoUSdILL7ygxMRE5eTkaNasWTp16pTWrVunF198Uenp6ZKkl156SSkpKdqyZYsmTJjQgsMBAAAdgd8F5UJKSkpUVlamzMxM71hUVJTGjh2roqIizZo1S8XFxXK73T5zkpOTNXz4cBUVFTVbUFwul1wul/d2ZWWlJMntdsvtdgfzELzbO3u7UWEmqNtuD5rLobMhAw9y8CAHD3LwIIfAMvBnblALSllZmSQpMTHRZzwxMVFHjx71zomMjFSPHj2azGl8/LmWLl2q7OzsJuP5+fmKjY0NxtKbKCgo8P73suuCs82NGzcGZ0Ot6OwcOisy8CAHD3LwIAcPcvAvg5qamoueG9SC0sjhcPjcNsY0GTvXheYsXLhQc+fO9d6urKxUSkqKMjMzFR8f3/IFn8XtdqugoEAZGRmKiIiQJA13bg7Ktg8428/lq+Zy6GzIwIMcPMjBgxw8yCGwDBqvgFyMoBaUpKQkSZ6zJH379vWOl5eXe8+qJCUlqa6uThUVFT5nUcrLyzVmzJhmtxsVFaWoqKgm4xERESF7Ypy9bVf9hcuVP9tsb0KZcXtBBh7k4EEOHuTgQQ7+ZeBPVkH9OyiDBg1SUlKSz+meuro6FRYWestHWlqaIiIifOaUlpbqwIED5y0oAACgc/H7DEp1dbWOHDnivV1SUqJ9+/apZ8+e6t+/v7KysrRkyRKlpqYqNTVVS5YsUWxsrKZNmyZJ6t69u2bOnKkHH3xQCQkJ6tmzp+bNm6errrrK+64eAADQufldUPbs2aMf/ehH3tuNrw256667tH79es2fP1+1tbWaPXu2KioqNGrUKOXn5ysuLs77mJUrVyo8PFxTp05VbW2txo8fr/Xr1yssLCwIhwQAANo7vwvKuHHjZMz533brcDjkdDrldDrPOyc6OlqrV6/W6tWr/d09AADoBELyLh40b+CCt5qMff6nm9pgJQAA2I0PCwQAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHT7NuI2d+wnHfLoxAACcQQEAABaioAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArENBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYJ7ytF2Cr4c7NctU72noZAAB0SpxBAQAA1qGgAAAA61BQAACAdSgoAADAOhQUAABgHQoKAACwDgUFAABYh4ICAACswx9qs8zABW81Gfv8Tze1wUoAAGg7nEEBAADW4QxKO3DuWRXOqAAAOjrOoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBQAAWIeCAgAArMPfQYHf+Gu3AIBQ4wwKAACwDgUFAABYh0s8+E7NXdIBACCUKCgdBJ/XAwDoSLjEAwAArMMZlHaISy4AgI6OgoKg4BITACCYuMQDAACsQ0EBAADW4RJPB3Wxf+31fK9niQozWnadNNy5WZIj2Ms7r0AuFfGXbQGg46GgdCJt/eLajvA6FcoQ0HF0hJ9JHRkFBSERyjLU1kULQMfELyB2oaCgQ+I3I6B9su1792LWczG/NLX1cbRHFBS0mYv5pm7NsyXn7qvxdTgt3Y7ED6fOgP/fv1uwvp/bssQ07rutXqfXmbRpQXnyySf16KOPqrS0VFdeeaVWrVqlH/7wh225JHQibVl+QvkD1bbfQGE3ni+wVZsVlFdeeUVZWVl68skndf311+vpp5/WxIkTdejQIfXv37+tloUOqiVlZLhzs1z15/8NKZBtX8xv2/xGfmHBOq0eaM6h+v89mIJVPs7eTqBnFgNl25nWQAWyxs7+/d5mBWXFihWaOXOm7r77bknSqlWrtHnzZq1du1ZLly5tq2UBbSZY/+AFy7mnskMpWNf5O4tQFp1Ac+ZMDIKtTQpKXV2diouLtWDBAp/xzMxMFRUVNZnvcrnkcrm8t0+dOiVJOnHihNxud1DX5na7VVNTo3B3F9U3dN7riuENRjU1DZ06BxszGDzv//ncvphv4G+//TagfYWfOe353//N4dtvv1VERERA27rYfTU69zilwH5YNXfso5b+7Tu329z+o7oY/W5Eg65d9LpcDY6Asj/3OM+3r+9ysWs+d15zeTS3pgvu+zzPh1D9f2ir1vj5EMhzQ5J2LRzv92PO/b64mO00/nvpz8+GqqoqSZIx5rsnmzbw5ZdfGknmvffe8xl/+OGHzZAhQ5rMX7x4sZHEF1988cUXX3x1gK9jx459Z1do00LrcPi2TmNMkzFJWrhwoebOneu93dDQoBMnTighIaHZ+S1RWVmplJQUHTt2TPHx8UHddntCDmTQiBw8yMGDHDzIIbAMjDGqqqpScnLyd85tk4LSq1cvhYWFqayszGe8vLxciYmJTeZHRUUpKirKZ+ySSy4J5RIVHx/faZ90ZyMHMmhEDh7k4EEOHuTgfwbdu3e/qHlt8mGBkZGRSktLU0FBgc94QUGBxowZ0xZLAgAAFmmzSzxz587VHXfcoZEjR2r06NF65pln9MUXX+iee+5pqyUBAABLtFlBuf322/Xtt9/qP//zP1VaWqrhw4dr48aNGjBgQFstSZLnctLixYubXFLqbMiBDBqRgwc5eJCDBzmEPgOHMRfzXh8AAIDW0yavQQEAALgQCgoAALAOBQUAAFiHggIAAKzTKQvKk08+qUGDBik6OlppaWnavn37BecXFhYqLS1N0dHRuuyyy/TUU0+10kpDx58MSktLNW3aNA0dOlRdunRRVlZW6y00xPzJ4fXXX1dGRoZ69+6t+Ph4jR49Wps3b27F1YaOPzns2LFD119/vRISEhQTE6MrrrhCK1eubMXVho6/PxsavffeewoPD9e1114b2gW2En9y2Lp1qxwOR5Ovjz76qBVXHHz+PhdcLpcWLVqkAQMGKCoqSpdffrmee+65Vlpt6PiTw4wZM5p9Llx55ZWB7TwoH67TjuTm5pqIiAjz7LPPmkOHDpk5c+aYrl27mqNHjzY7/7PPPjOxsbFmzpw55tChQ+bZZ581ERER5s9//nMrrzx4/M2gpKTE/Md//Id54YUXzLXXXmvmzJnTugsOEX9zmDNnjnnkkUfMf//3f5vDhw+bhQsXmoiICPPBBx+08sqDy98cPvjgA5OTk2MOHDhgSkpKzIsvvmhiY2PN008/3corDy5/c2h08uRJc9lll5nMzExzzTXXtM5iQ8jfHN59910jyXz88cemtLTU+3XmzJlWXnnwBPJcmDRpkhk1apQpKCgwJSUlZteuXU0+b6698TeHkydP+jwHjh07Znr27GkWL14c0P47XUG57rrrzD333OMzdsUVV5gFCxY0O3/+/Pnmiiuu8BmbNWuW+cEPfhCyNYaavxmcbezYsR2moLQkh0bDhg0z2dnZwV5aqwpGDj/72c/ML37xi2AvrVUFmsPtt99ufve735nFixd3iILibw6NBaWioqIVVtc6/M1g06ZNpnv37ubbb79tjeW1mpb+bMjLyzMOh8N8/vnnAe2/U13iqaurU3FxsTIzM33GMzMzVVRU1Oxj3n///SbzJ0yYoD179sjtdodsraESSAYdUTByaGhoUFVVlXr27BmKJbaKYOSwd+9eFRUVaezYsaFYYqsINIfnn39en376qRYvXhzqJbaKljwfRowYob59+2r8+PF69913Q7nMkAokgzfffFMjR47UsmXLdOmll2rIkCGaN2+eamtrW2PJIRGMnw3r1q1Tenp6wH+AtU0/zbi1ffPNN6qvr2/ygYSJiYlNPriwUVlZWbPzz5w5o2+++UZ9+/YN2XpDIZAMOqJg5LB8+XKdPn1aU6dODcUSW0VLcujXr5/+53/+R2fOnJHT6dTdd98dyqWGVCA5fPLJJ1qwYIG2b9+u8PCO8aM0kBz69u2rZ555RmlpaXK5XHrxxRc1fvx4bd26VTfccENrLDuoAsngs88+044dOxQdHa28vDx98803mj17tk6cONFuX4fS0p+RpaWl2rRpk3JycgJeQ8f4rvKTw+HwuW2MaTL2XfObG29P/M2gowo0hw0bNsjpdOovf/mL+vTpE6rltZpActi+fbuqq6u1c+dOLViwQIMHD9bPf/7zUC4z5C42h/r6ek2bNk3Z2dkaMmRIay2v1fjzfBg6dKiGDh3qvT169GgdO3ZM//Vf/9UuC0ojfzJoaGiQw+HQyy+/7P2k3hUrVujWW2/VE088oZiYmJCvN1QC/Rm5fv16XXLJJfrpT38a8L47VUHp1auXwsLCmrS/8vLyJi2xUVJSUrPzw8PDlZCQELK1hkogGXRELcnhlVde0cyZM/Xqq68qPT09lMsMuZbkMGjQIEnSVVddpa+//lpOp7PdFhR/c6iqqtKePXu0d+9e3XfffZI8/0gZYxQeHq78/HzdeOONrbL2YArWz4cf/OAHeumll4K9vFYRSAZ9+/bVpZde6i0nkvS9731PxhgdP35cqampIV1zKLTkuWCM0XPPPac77rhDkZGRAa+hU70GJTIyUmlpaSooKPAZLygo0JgxY5p9zOjRo5vMz8/P18iRIxURERGytYZKIBl0RIHmsGHDBs2YMUM5OTm66aabQr3MkAvW88EYI5fLFezltRp/c4iPj9f+/fu1b98+79c999yjoUOHat++fRo1alRrLT2ogvV82Lt3b7u7/N0okAyuv/56ffXVV6qurvaOHT58WF26dFG/fv1Cut5QaclzobCwUEeOHNHMmTNbtoiAXlrbjjW+bWrdunXm0KFDJisry3Tt2tX7KuMFCxaYO+64wzu/8W3GDzzwgDl06JBZt25dh3mb8cVmYIwxe/fuNXv37jVpaWlm2rRpZu/evebgwYNtsfyg8TeHnJwcEx4ebp544gmft9KdPHmyrQ4hKPzNYc2aNebNN980hw8fNocPHzbPPfeciY+PN4sWLWqrQwiKQL4vztZR3sXjbw4rV640eXl55vDhw+bAgQNmwYIFRpJ57bXX2uoQWszfDKqqqky/fv3Mrbfeag4ePGgKCwtNamqqufvuu9vqEIIi0O+JX/ziF2bUqFEt3n+nKyjGGPPEE0+YAQMGmMjISPP973/fFBYWeu+76667zNixY33mb9261YwYMcJERkaagQMHmrVr17byioPP3wwkNfkaMGBA6y46BPzJYezYsc3mcNddd7X+woPMnxwef/xxc+WVV5rY2FgTHx9vRowYYZ588klTX1/fBisPLn+/L87WUQqKMf7l8Mgjj5jLL7/cREdHmx49eph//dd/NW+99VYbrDq4/H0ufPjhhyY9Pd3ExMSYfv36mblz55qamppWXnXw+ZvDyZMnTUxMjHnmmWdavG+HMf/7ik8AAABLdKrXoAAAgPaBggIAAKxDQQEAANahoAAAAOtQUAAAgHUoKAAAwDoUFAAAYB0KCgAAsA4FBYCX0+nUtddeG/L9OBwOvfHGGyHfz9atW+VwOHTy5Em/H7tu3TplZmZ6b8+YMaNFn8zaHJfLpf79+6u4uDio2wU6AgoKYLljx45p5syZSk5OVmRkpAYMGKA5c+bo22+/beulWWXcuHHKysoKyrZcLpf+8Ic/6Pe//31Qtnc+UVFRmjdvnh566KGQ7gdojygogMU+++wzjRw5UocPH9aGDRt05MgRPfXUU/rb3/6m0aNH68SJE229xA7ptddeU7du3fTDH/4w5PuaPn26tm/frg8//DDk+wLaEwoKYLF7771XkZGRys/P19ixY9W/f39NnDhRW7Zs0ZdffqlFixZ55zZ32eSSSy7R+vXrvbcfeughDRkyRLGxsbrsssv0+9//Xm63+7z7Lykp0eDBg/Wb3/xGDQ0Nqqur0/z583XppZeqa9euGjVqlLZu3XrBY/jkk090ww03KDo6WsOGDWvy8e2S9OWXX+r2229Xjx49lJCQoMmTJ+vzzz/33t94eSU7O1t9+vRRfHy8Zs2apbq6Ou/9hYWFeuyxx+RwOORwOHweX1xcrJEjRyo2NlZjxozRxx9/fME15+bmatKkSRecU1xcrD59+ujhhx+W9H+Xx5577jn1799f3bp1029+8xvV19dr2bJlSkpK8pnfKCEhQWPGjNGGDRsuuD+gs6GgAJY6ceKENm/erNmzZysmJsbnvqSkJE2fPl2vvPKK/Pm8z7i4OK1fv16HDh3SY489pmeffVYrV65sdu6BAwd0/fXX67bbbtPatWvVpUsX/fKXv9R7772n3Nxc/eMf/9Btt92mH//4x/rkk0+a3UZDQ4OmTJmisLAw7dy5U0899VSTyxk1NTX60Y9+pG7dumnbtm3asWOHunXrph//+MfeAiJJf/vb3/Thhx/q3Xff1YYNG5SXl6fs7GxJ0mOPPabRo0fr17/+tUpLS1VaWqqUlBTvYxctWqTly5drz549Cg8P169+9asL5rR9+3aNHDnyvPdv3bpV48ePV3Z2tk9J/PTTT7Vp0ya9/fbb2rBhg5577jnddNNNOn78uAoLC/XII4/od7/7nXbu3Omzveuuu07bt2+/4JqATqfFn4cMICR27txpJJm8vLxm71+xYoWRZL7++mtjjGl2bvfu3c3zzz9/3n0sW7bMpKWleW8vXrzYXHPNNaaoqMj07NnTPProo977jhw5YhwOh/nyyy99tjF+/HizcOHCZre/efNmExYWZo4dO+Yd27Rpk89a161bZ4YOHWoaGhq8c1wul4mJiTGbN282xng+1r1nz57m9OnT3jlr16413bp1M/X19cYYY8aOHWvmzJnjs/93333XSDJbtmzxjr311ltGkqmtrW12zRUVFUaS2bZtm8/4XXfdZSZPnmzeeOMNExcXZ3JycnzuX7x4sYmNjTWVlZXesQkTJpiBAwd612iMMUOHDjVLly71eexjjz1mBg4c2Ox6gM4qvA27EYAWMP975iQyMvKiH/PnP/9Zq1at0pEjR1RdXa0zZ84oPj7eZ84XX3yh9PR0/fGPf9QDDzzgHf/ggw9kjNGQIUN85rtcLiUkJDS7vw8//FD9+/dXv379vGOjR4/2mVNcXKwjR44oLi7OZ/yf//ynPv30U+/ta665RrGxsT7bqa6u1rFjxzRgwIALHvfVV1/t/e++fftKksrLy9W/f/8mc2trayVJ0dHRTe7btWuX/vrXv+rVV1/Vz372syb3Dxw40Oc4EhMTFRYWpi5duviMlZeX+zwuJiZGNTU1FzwGoLOhoACWGjx4sBwOhw4dOtTs21s/+ugj9e7dW5dccokkz2tQzDmXe85+fcnOnTv1b//2b8rOztaECRPUvXt35ebmavny5T6P6d27t5KTk5Wbm6uZM2d6C0xDQ4PCwsJUXFyssLAwn8d069at2WM4dz2N6zxbQ0OD0tLS9PLLLzeZ27t372a3e6HtNSciIqLJ/IaGhmbnJiQkyOFwqKKiosl9l19+uRISEryXbs4th2fvp3FfzY2du+8TJ05c1LECnQmvQQEslZCQoIyMDD355JPe3+oblZWV6eWXX9aMGTO8Y71791Zpaan39ieffOLzW/l7772nAQMGaNGiRRo5cqRSU1N19OjRJvuNiYnRX//6V0VHR2vChAmqqqqSJI0YMUL19fUqLy/X4MGDfb6SkpKaPYZhw4bpiy++0FdffeUde//9933mfP/739cnn3yiPn36NNlu9+7dvfP+/ve/++Swc+dOdevWzXt2JjIyUvX19efN82JFRkZq2LBhOnToUJP7evXqpXfeeUeffvqpbr/99gu+wNgfBw4c0IgRI4KyLaCjoKAAFluzZo1cLpcmTJigbdu26dixY3r77beVkZGhIUOG6A9/+IN37o033qg1a9bogw8+0J49e3TPPff4/PY+ePBgffHFF8rNzdWnn36qxx9/XHl5ec3ut2vXrnrrrbcUHh6uiRMnqrq6WkOGDNH06dN155136vXXX1dJSYl2796tRx55RBs3bmx2O+np6Ro6dKjuvPNO/f3vf9f27dt9XlQqed5m26tXL02ePFnbt29XSUmJCgsLNWfOHB0/ftw7r66uTjNnztShQ4e0adMmLV68WPfdd5/38snAgQO1a9cuff755/rmm2/Oe4bkYkyYMEE7duxo9r4+ffronXfe0UcffaSf//znOnPmTMD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0, - 1 - ], - "title": { - "text": "depth" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "count" - } - } - } - } + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.histogram(quake, #specify what dataframe to use\n", - " x=\"depth\", #specify the variable for the histogram \n", - " nbins=50, #number of bins for the histogram \n", - " height=400, #dimensions of the figure\n", - " width=600);\n", - "fig.show()" - ] - }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "width": 600 + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_geo(quake,\n", + " lat='latitude',lon='longitude', \n", + " range_color=(6,9),\n", + " height=600, width=600,\n", + " size='marker_size', color='magnitude',\n", + " hover_name=\"description\",\n", + " hover_data=['description','magnitude','depth']);\n", + "fig.update_geos(resolution=110, showcountries=True)\n", + "fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "7a9e55fb", + "metadata": {}, + "source": [ + "The lambda function ``lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe')`` classifies earthquake magnitudes based on their values." + ] + }, + { + "cell_type": "markdown", + "id": "69b7c97f", + "metadata": {}, + "source": [ + "The data was sorted by time. We now want to sort and show the data instead by magnitude. We use the pandas function ``sort`` to create a new DataFrame with sorted values." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "7738e4d0", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "60a133d4", - "metadata": {}, - "source": [ - "### Example 2: Conditional Logic for Earthquake Magnitude \n", - "You can use lambda functions to add conditional logic, such as classifying earthquake magnitudes into categories.\n", - "\n", - "Scenario: You have a column Magnitude containing seismic event magnitudes, and you want to classify events as \"Minor\", \"Moderate\", or \"Severe\"." + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategorymarker_sizemagnitude_bin
02010-07-02 06:04:03.570-13.6098166.65410.034406.3VANUATU ISLANDS34.40Moderate544.06.0
11122017-08-31 17:06:55.750-1.159099.68810.043146.3SOUTHERN SUMATRA43.14Moderate544.06.0
11112017-08-27 04:17:51.010-1.4513148.08030.008006.3ADMIRALTY ISLANDS REGION8.00Moderate544.06.0
11102017-08-19 02:00:52.550-17.9609-178.84060.544006.4FIJI ISLANDS REGION544.00Moderate601.06.0
11082017-08-13 03:08:10.560-3.7682101.62280.031006.4SOUTHERN SUMATRA31.00Moderate601.06.0
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" + ], + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.03440 6.3 \n", + "1112 2017-08-31 17:06:55.750 -1.1590 99.6881 0.04314 6.3 \n", + "1111 2017-08-27 04:17:51.010 -1.4513 148.0803 0.00800 6.3 \n", + "1110 2017-08-19 02:00:52.550 -17.9609 -178.8406 0.54400 6.4 \n", + "1108 2017-08-13 03:08:10.560 -3.7682 101.6228 0.03100 6.4 \n", + "\n", + " description depth_km Category marker_size magnitude_bin \n", + "0 VANUATU ISLANDS 34.40 Moderate 544.0 6.0 \n", + "1112 SOUTHERN SUMATRA 43.14 Moderate 544.0 6.0 \n", + "1111 ADMIRALTY ISLANDS REGION 8.00 Moderate 544.0 6.0 \n", + "1110 FIJI ISLANDS REGION 544.00 Moderate 601.0 6.0 \n", + "1108 SOUTHERN SUMATRA 31.00 Moderate 601.0 6.0 " ] - }, + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quakes2plot=quake.sort_values(by='magnitude_bin')\n", + "quakes2plot.head()" + ] + }, + { + "cell_type": "markdown", + "id": "dcf7472d", + "metadata": {}, + "source": [ + "Now we will plot again using Plotly" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "c21ce4ea", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 60, - "id": "7f7520de", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategory
02010-07-02 06:04:03.570-13.6098166.65410.03446.3VANUATU ISLANDS34.4Moderate
12010-07-04 21:55:52.37039.6611142.57920.03016.3NEAR EAST COAST OF HONSHU30.1Moderate
22010-07-10 11:43:33.00011.1664146.08230.01696.3SOUTH OF MARIANA ISLANDS16.9Moderate
32010-07-12 00:11:20.060-22.2789-68.31590.10946.2NORTHERN CHILE109.4Moderate
42010-07-14 08:32:21.850-38.0635-73.46490.02576.6NEAR COAST OF CENTRAL CHILE25.7Severe
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ISLAND", + "CENTRAL ITALY", + "FLORES SEA", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "VOLCANO ISLANDS", + "JUJUY PROVINCE", + "ADMIRALTY ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CHIAPAS", + "VANUATU ISLANDS", + "KYUSHU", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "SOUTHERN EAST PACIFIC RISE", + "BORNEO", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "MARIANA ISLANDS", + "SOUTHEASTERN AFGHANISTAN", + "OFF COAST OF CENTRAL CHILE", + "PRINCE EDWARD ISLANDS REGION", + "PRINCE EDWARD ISLANDS REGION", + "BOUVET ISLAND REGION", + "TONGA ISLANDS", + "EAST OF KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "JUJUY PROVINCE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "NEAR COAST OF CENTRAL CHILE", + "PACIFIC-ANTARCTIC RIDGE", + "SOUTHERN BOLIVIA", + "KYUSHU", + "MINDANAO", + "JAVA", + "ALASKA PENINSULA", + "NEAR N COAST OF NEW GUINEA", + "NEAR EAST COAST OF KAMCHATKA", + "LEEWARD ISLANDS", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "HALMAHERA", + "SOUTHERN EAST PACIFIC RISE", + "SUMBA REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOLOMON ISLANDS", + "TAIWAN", + "KERMADEC ISLANDS REGION", + "BALLENY ISLANDS REGION", + "STRAIT OF GIBRALTAR", + "HOKKAIDO", + "FIJI ISLANDS REGION", + "SOUTHEAST OF EASTER ISLAND", + "TONGA ISLANDS", + "NEAR N COAST OF NEW GUINEA", + "CELEBES SEA", + "JAVA", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "FIJI ISLANDS REGION", + "WEST OF MACQUARIE ISLAND", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF W COAST OF NORTHERN SUMATRA", + "NORTHERN ALGERIA", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NORTHEASTERN INDIA", + "SOUTH SANDWICH ISLANDS REGION", + "NICOBAR ISLANDS", + "PERU-ECUADOR BORDER REGION", + "MINAHASSA PENINSULA", + "KERMADEC ISLANDS REGION", + "BISMARCK SEA", + "SOUTH OF PANAMA", + "CALIFORNIA-NEVADA BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "CENTRAL ALASKA", + "YUNNAN", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "NEAR COAST OF GUATEMALA", + "FIJI ISLANDS REGION", + "WEST OF MACQUARIE ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "GREECE", + "W. CAROLINE ISLANDS", + "NEAR EAST COAST OF HONSHU", + "MINDANAO", + "CHILE-BOLIVIA BORDER REGION", + "TAIWAN REGION", + "NORTHERN CHILE", + "BALLENY ISLANDS REGION", + "FOX ISLANDS", + "CHILE-ARGENTINA BORDER REGION", + "PRIMOR'YE", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "FIJI ISLANDS REGION", + "TONGA ISLANDS", + "SOUTH SHETLAND ISLANDS", + "OFF COAST OF SOUTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "EASTERN NEW GUINEA REG.", + "FIJI ISLANDS REGION", + "MINDORO", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHWESTERN BALKAN REGION", + "KERMADEC ISLANDS REGION", + "FIJI ISLANDS REGION", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWEST OF SUMATRA", + "SOUTHEAST OF LOYALTY ISLANDS", + "BISMARCK SEA", + "MINDANAO", + "SAN JUAN PROVINCE", + "SULAWESI", + "VANUATU ISLANDS", + "SALTA PROVINCE", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "MINAHASSA PENINSULA", + "ANDREANOF ISLANDS", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "KURIL ISLANDS", + "LUZON", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHERN SUMATRA", + "JUJUY PROVINCE", + "BALLENY ISLANDS REGION", + "GUATEMALA", + "SOUTH OF MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "CENTRAL MID-ATLANTIC RIDGE", + "BANDA SEA", + "SOUTH OF PANAMA", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "FOX ISLANDS", + "TALAUD ISLANDS", + "SHIKOKU", + "SOLOMON ISLANDS", + "SOUTHEAST INDIAN RIDGE", + "SOUTH OF FIJI ISLANDS", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "RAT ISLANDS", + "WEST OF MACQUARIE ISLAND", + "TONGA ISLANDS", + "TIMOR REGION", + "SOUTHERN EAST PACIFIC RISE", + "OFF EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "LUZON", + "BISMARCK SEA", + "TAIWAN REGION", + "PHILIPPINE ISLANDS REGION", + "BISMARCK SEA", + "MINDANAO", + "MINDANAO", + "VANUATU ISLANDS", + "NEW BRITAIN REGION", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "SOUTH OF KERMADEC ISLANDS", + "NEAR COAST OF NICARAGUA", + "BANDA SEA", + "SOUTHERN MOLUCCA SEA", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH SANDWICH ISLANDS REGION", + "TAIWAN", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "SOLOMON ISLANDS", + "CRETE", + "HAWAII", + "SOUTH SANDWICH ISLANDS REGION", + "MID-INDIAN RIDGE", + "SEA OF JAPAN", + "CRETE", + "ANDREANOF ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "KURIL ISLANDS", + "CHILE-ARGENTINA BORDER REGION", + "FIJI ISLANDS REGION", + "KERMADEC ISLANDS", + "NEAR COAST OF NICARAGUA", + "SOUTH SANDWICH ISLANDS REGION", + "IRIAN JAYA REGION", + "SOUTHERN IRAN", + "TAIWAN REGION", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "OFF COAST OF JALISCO", + "NEAR COAST OF NORTHERN CALIF.", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS", + "PRINCE EDWARD ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "RYUKYU ISLANDS", + "NORTH OF HALMAHERA", + "EASTER ISLAND REGION", + "NEW IRELAND REGION", + "NEW IRELAND REGION", + "SOUTHEAST OF HONSHU", + "MYANMAR-INDIA BORDER REGION", + "SANTA CRUZ ISLANDS", + "NEW IRELAND REGION", + "SOUTH INDIAN OCEAN", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS", + "CENTRAL CALIFORNIA", + "QUEEN CHARLOTTE ISLANDS REGION", + "VANUATU ISLANDS", + "MARIANA ISLANDS", + "KERMADEC ISLANDS REGION", + "PANAMA-COSTA RICA BORDER REGION", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "SOLOMON ISLANDS", + "IRIAN JAYA", + "IRIAN JAYA", + "KERMADEC ISLANDS", + "NEAR WEST COAST OF HONSHU", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "TONGA ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "SOUTHEAST OF HONSHU", + "KERMADEC ISLANDS", + "NORTHWEST OF RYUKYU ISLANDS", + "NEW BRITAIN REGION", + "BALLENY ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH SANDWICH ISLANDS REGION", + "NORTH OF ASCENSION ISLAND", + "VANUATU ISLANDS", + "MINDANAO", + "MINDANAO", + "KERMADEC ISLANDS", + "CENTRAL CHILE", + "SOUTHWEST INDIAN RIDGE", + "MONA PASSAGE", + "JAVA", + "OFF COAST OF OREGON", + "VANUATU ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN EAST PACIFIC RISE", + "NEAR S. COAST OF HONSHU", + "PHILIPPINE ISLANDS REGION", + "TONGA ISLANDS", + "MINDANAO", + "FIJI ISLANDS REGION", + "ANDREANOF ISLANDS", + "CENTRAL BOLIVIA", + "D'ENTRECASTEAUX ISLANDS REGION", + "FIJI ISLANDS REGION", + "PHILIPPINE ISLANDS REGION", + "SOUTH OF KERMADEC ISLANDS", + "NEW IRELAND REGION", + "NORTHERN MID-ATLANTIC RIDGE", + "MARIANA ISLANDS", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "FIJI ISLANDS REGION", + "SOLOMON ISLANDS", + "SUMBA REGION", + "SUMBA REGION", + "VANUATU ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "ALASKA PENINSULA", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "COLOMBIA", + "EAST OF KURIL ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "PANAMA-COSTA RICA BORDER REGION", + "KYUSHU", + "SOLOMON ISLANDS", + "CARLSBERG RIDGE", + "SOUTH OF FIJI ISLANDS", + "SAMAR", + "SOUTH SANDWICH ISLANDS REGION", + "LUZON", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "BANDA SEA", + "SOUTH SANDWICH ISLANDS REGION", + "RAT ISLANDS", + "MAURITIUS - REUNION REGION", + "NEAR COAST OF ECUADOR", + "NEW BRITAIN REGION", + "NORTHERN MOLUCCA SEA", + "SOUTH OF AFRICA", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "SAMOA ISLANDS REGION", + "ANDAMAN ISLANDS", + "W. CAROLINE ISLANDS", + "XIZANG", + "MINAHASSA PENINSULA", + "ICELAND REGION", + "MARIANA ISLANDS", + "SOUTHERN MID-ATLANTIC RIDGE", + "FIJI ISLANDS REGION", + "HALMAHERA", + "SOUTHERN PERU", + "VANUATU ISLANDS", + "OFF COAST OF CENTRAL MEXICO", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NORTH OF HONDURAS", + "KERMADEC ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF ALASKA", + "XIZANG", + "SOUTH SANDWICH ISLANDS REGION", + "KAMCHATKA PENINSULA", + "NEAR EAST COAST OF HONSHU", + "NORTHERN CHILE", + "VANUATU ISLANDS", + "MINDANAO", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "CHAGOS ARCHIPELAGO REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "COSTA RICA", + "ASCENSION ISLAND REGION", + "TANZANIA", + "PRINCE EDWARD ISLANDS REGION", + "VANUATU ISLANDS", + "BISMARCK SEA", + "MINDANAO", + "ALASKA PENINSULA", + "MID-INDIAN RIDGE", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "CENTRAL EAST PACIFIC RISE", + "VANCOUVER ISLAND", + "COLOMBIA", + "SANTIAGO DEL ESTERO PROV.", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "HINDU KUSH REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "ANDREANOF ISLANDS", + "CRETE", + "ALBANIA", + "ANDREANOF ISLANDS", + "IRIAN JAYA REGION", + "LAOS", + "SEA OF OKHOTSK", + "OFF COAST OF CHIAPAS", + "NORTHERN MOLUCCA SEA", + "SOUTHERN EAST PACIFIC RISE", + "VANUATU ISLANDS", + "NORTHERN SUMATRA", + "NEW BRITAIN REGION", + "SOUTH OF BALI", + "VANUATU ISLANDS", + "SAMOA ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "TURKEY-IRAN BORDER REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "MINDANAO", + "JAVA", + "SOLOMON ISLANDS", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "MINAHASSA PENINSULA", + "SOUTHERN XINJIANG", + "NEAR NORTH COAST OF IRIAN JAYA", + "EASTERN SIBERIA", + "PUERTO RICO REGION", + "MID-INDIAN RIDGE", + "BANDA SEA", + "PHILIPPINE ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "EAST OF KURIL ISLANDS", + "VANUATU ISLANDS REGION", + "GUATEMALA", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "E. RUSSIA-N.E. CHINA BORDER REG.", + "SULAWESI", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "EAST OF KURIL ISLANDS", + "BANDA SEA", + "KURIL ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "OFF EAST COAST OF KAMCHATKA", + "OFF EAST COAST OF KAMCHATKA", + "OFF COAST OF SOUTHERN CHILE", + "NEAR EAST COAST OF HONSHU", + "COSTA RICA", + "KYUSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN IRAN", + "KERMADEC ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "MICHOACAN", + "SOUTHEAST OF HONSHU", + "TONGA ISLANDS", + "CRETE", + "NEAR EAST COAST OF HONSHU", + "CENTRAL MID-ATLANTIC RIDGE", + "MINAHASSA PENINSULA", + "TAJIKISTAN", + "PACIFIC-ANTARCTIC RIDGE", + "NORTHERN SUMATRA", + "LAKE ISSYK-KUL REGION", + "ALASKA PENINSULA", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "SANTA CRUZ ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "KURIL ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "SANTIAGO DEL ESTERO PROV.", + "MINDANAO", + "WEST OF MACQUARIE ISLAND", + "HINDU KUSH REGION", + "SOUTH ISLAND", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "IRIAN JAYA REGION", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "TAIWAN", + "NEAR EAST COAST OF HONSHU", + "EASTERN HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEGROS", + "SOUTHWEST INDIAN RIDGE", + "NEAR COAST OF GUERRERO", + "KERMADEC ISLANDS REGION", + "ANDREANOF ISLANDS", + "NEAR EAST COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "FLORES SEA", + "ANDREANOF ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF NORTHERN PERU", + "CENTRAL EAST PACIFIC RISE", + "NEAR EAST COAST OF HONSHU", + "PHILIPPINE ISLANDS REGION", + "NORTHERN MID-ATLANTIC RIDGE", + "NEAR EAST COAST OF HONSHU", + "ANDREANOF ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF CHIAPAS", + "KERMADEC ISLANDS REGION", + "NEAR WEST COAST OF HONSHU", + "BANDA SEA", + "SOUTHERN SUMATRA", + "SOLOMON ISLANDS", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF HONSHU", + "CRETE", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF NICARAGUA", + "SOUTH OF KERMADEC ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "CRETE", + "SOLOMON ISLANDS", + "PHILIPPINE ISLANDS REGION", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "GANSU", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "EASTERN HONSHU", + "SOUTHERN PERU", + "OFF EAST COAST OF HONSHU", + "PRINCE EDWARD ISLANDS REGION", + "NEW IRELAND REGION", + "NEAR ISLANDS", + "OFF W. COAST OF BAJA CALIFORNIA", + "NORTHERN ITALY", + "NEAR COAST OF NORTHERN CHILE", + "LUZON", + "OFF EAST COAST OF HONSHU", + "HOKKAIDO", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "GREENLAND SEA", + "BONIN ISLANDS", + "NEW IRELAND REGION", + "SALTA PROVINCE", + "HOKKAIDO", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "CHILE-ARGENTINA BORDER REGION", + "SOUTHERN PERU", + "KURIL ISLANDS", + "NICARAGUA", + "VANUATU ISLANDS", + "COOK STRAIT", + "NORTHERN XINJIANG", + "NEAR EAST COAST OF KAMCHATKA", + "OFF COAST OF SOUTHERN CHILE", + "NORTHERN SUMATRA", + "NEAR ISLANDS", + "NEAR EAST COAST OF HONSHU", + "LUZON", + "SOUTHERN MOLUCCA SEA", + "DODECANESE ISLANDS", + "OFF COAST OF ECUADOR", + "SOUTHERN EAST PACIFIC RISE", + "PERU-BOLIVIA BORDER REGION", + "NEAR COAST OF CHIAPAS", + "KERMADEC ISLANDS REGION", + "IRIAN JAYA", + "SOUTH SANDWICH ISLANDS REGION", + "NEW BRITAIN REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CHIAPAS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF PERU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "SANTIAGO DEL ESTERO PROV.", + "KURIL ISLANDS", + "SOUTH SHETLAND ISLANDS", + "NEAR EAST COAST OF HONSHU", + "IRIAN JAYA REGION", + "EAST OF SOUTH SANDWICH ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "VANUATU ISLANDS", + "DRAKE PASSAGE", + "NORTHERN EAST PACIFIC RISE", + "MINAHASSA PENINSULA", + "BAJA CALIFORNIA", + "OFF COAST OF OREGON", + "NORTH INDIAN OCEAN", + "NEW IRELAND REGION", + "NEAR COAST OF GUERRERO", + "SANTA CRUZ ISLANDS", + "TONGA ISLANDS", + "SOUTHERN EAST PACIFIC RISE", + "NORTHERN SUMATRA", + "REVILLA GIGEDO ISLANDS REGION", + "FIJI ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "CERAM SEA", + "CELEBES SEA", + "ASCENSION ISLAND REGION", + "VANUATU ISLANDS", + "COSTA RICA", + "KURIL ISLANDS", + "NEAR S. COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "MINDANAO", + "VANCOUVER ISLAND", + "CENTRAL PERU", + "VANUATU ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "GULF OF ALASKA", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NORTHERN MOLUCCA SEA", + "NEW IRELAND REGION", + "GULF OF CALIFORNIA", + "NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR N COAST OF NEW GUINEA", + "NEW BRITAIN REGION", + "NEAR EAST COAST OF HONSHU", + "GUERRERO", + "NEAR COAST OF CENTRAL CHILE", + "OFF COAST OF SOUTHERN CHILE", + "VANUATU ISLANDS", + "BANDA SEA", + "SUNDA STRAIT", + "VANUATU ISLANDS", + "BALLENY ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "SULAWESI", + "SOUTH OF BALI", + "SOUTHEASTERN SIBERIA", + "NEW BRITAIN REGION", + "SOUTH OF KERMADEC ISLANDS", + "HOKKAIDO", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "TURKEY", + "FOX ISLANDS", + "FIJI ISLANDS REGION", + "NEW IRELAND REGION", + "PERU-BRAZIL BORDER REGION", + "XIZANG", + "PACIFIC-ANTARCTIC RIDGE", + "TONGA ISLANDS", + "SOUTH OF JAVA", + "GULF OF CALIFORNIA", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "BAJA CALIFORNIA", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "OFF EAST COAST OF HONSHU", + "SOUTHERN SUMATRA", + "AUCKLAND ISLANDS", + "IRIAN JAYA REGION", + "NEAR ISLANDS", + "SANTA CRUZ ISLANDS", + "EAST OF NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "KURIL ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTHERN EAST PACIFIC RISE", + "TONGA ISLANDS", + "IRIAN JAYA REGION", + "E. CAROLINE ISLANDS", + "E. CAROLINE ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "NEGROS", + "OFF W COAST OF NORTHERN SUMATRA", + "BAY OF BENGAL", + "SOUTHERN SUMATRA", + "OFF COAST OF JALISCO", + "VANUATU ISLANDS", + "HALMAHERA", + "GUERRERO", + "ANDREANOF ISLANDS", + "SOUTHWESTERN RYUKYU ISL.", + "IRIAN JAYA REGION", + "ARU ISLANDS REGION", + "RAT ISLANDS", + "RAT ISLANDS", + "HINDU KUSH REGION", + "RAT ISLANDS", + "RAT ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "VOLCANO ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "ANDREANOF ISLANDS", + "TONGA ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "NEW BRITAIN REGION", + "JAN MAYEN ISLAND REGION", + "SOUTHERN IRAN", + "NORTHERN SUMATRA", + "TAJIKISTAN", + "SOLOMON ISLANDS", + "NORTHERN SUMATRA", + "SOUTHERN SUMATRA", + "LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTH OF FIJI ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "BALLENY ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "NEAR ISLANDS", + "BONIN ISLANDS", + "SOLOMON ISLANDS", + "BANDA SEA", + "OFF EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "NEAR S. COAST OF HONSHU", + "THAILAND", + "WEST CHILE RISE", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "BONIN ISLANDS", + "BALLENY ISLANDS REGION", + "TONGA ISLANDS", + "HOKKAIDO", + "HOKKAIDO", + "IRAN-IRAQ BORDER REGION", + "IRAN-IRAQ BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "ANDREANOF ISLANDS", + "NORTHERN CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "OFF EAST COAST OF KAMCHATKA", + "EASTER ISLAND REGION", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "OFF COAST OF JALISCO", + "SOUTHERN MOLUCCA SEA", + "NORTH OF HALMAHERA", + "JUJUY PROVINCE", + "SOLOMON ISLANDS", + "SOUTHERN ALASKA", + "NEW BRITAIN REGION", + "YUNNAN", + "GULF OF CALIFORNIA", + "NEW BRITAIN REGION", + "FOX ISLANDS", + "HOKKAIDO", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "NORTHERN CHILE", + "SOUTH OF MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "BANDA SEA", + "TONGA ISLANDS", + "BANDA SEA", + "XIZANG", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF COAST OF JALISCO", + "KERMADEC ISLANDS REGION", + "NORTHERN SUMATRA", + "NEW BRITAIN REGION", + "SOLOMON ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH OF MARIANA ISLANDS", + "FIJI ISLANDS REGION", + "MAURITIUS - REUNION REGION", + "VANUATU ISLANDS", + "NEW BRITAIN REGION", + "VANUATU ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "MINDANAO", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "TONGA ISLANDS", + "KURIL ISLANDS", + "TONGA ISLANDS", + "OWEN FRACTURE ZONE REGION", + "KURIL ISLANDS", + "SOUTHEASTERN ALASKA", + "NORTHERN MID-ATLANTIC RIDGE", + "OAXACA", + "BISMARCK SEA", + "BISMARCK SEA", + "YUNNAN", + "MYANMAR-INDIA BORDER REGION", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS REGION", + "EASTER ISLAND REGION", + "OAXACA", + "VANUATU ISLANDS", + "SOUTH ISLAND", + "EASTER ISLAND REGION", + "NEAR EAST COAST OF HONSHU", + "TAIWAN", + "NEAR EAST COAST OF KAMCHATKA", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF PERU", + "PUERTO RICO REGION", + "OFF COAST OF CENTRAL CHILE", + "BALLENY ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "NICOBAR ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "CELEBES SEA", + "NEAR EAST COAST OF HONSHU", + "SCOTIA SEA", + "OFF EAST COAST OF HONSHU", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "NORTH ISLAND", + "TONGA ISLANDS", + "JAVA", + "NEAR EAST COAST OF HONSHU", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "GREECE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "GREECE", + "KURIL ISLANDS", + "NEAR EAST COAST OF HONSHU", + "MARIANA ISLANDS", + "HALMAHERA", + "OFF EAST COAST OF HONSHU", + "FOX ISLANDS", + "NEAR COAST OF NICARAGUA", + "NORTHERN CHILE", + "NEAR COAST OF CHIAPAS", + "SOUTH OF KERMADEC ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF NORTHERN PERU", + "MARIANA ISLANDS REGION", + "KERMADEC ISLANDS", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "WEST CHILE RISE", + "NEAR COAST OF VENEZUELA", + "SOLOMON ISLANDS", + "NICARAGUA", + "NEAR COAST OF NORTHERN CHILE", + "SULAWESI", + "KERMADEC ISLANDS", + "OFF EAST COAST OF HONSHU", + "MID-INDIAN RIDGE", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "OFF EAST COAST OF HONSHU", + "MACQUARIE ISLAND REGION", + "OFF COAST OF CENTRAL CHILE", + "NORTHERN MOLUCCA SEA", + "TONGA ISLANDS", + "PERU-BRAZIL BORDER REGION", + "KERMADEC ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "FIJI ISLANDS REGION", + "MINDANAO", + "FOX ISLANDS", + "MINDANAO", + "JUJUY PROVINCE", + "NEAR COAST OF CENTRAL CHILE", + "NORTHEAST OF TAIWAN", + "SOUTH OF MARIANA ISLANDS", + "SOUTHERN SUMATRA", + "LUZON", + "OFF EAST COAST OF HONSHU", + "WESTERN AUSTRALIA", + "GUERRERO", + "KERMADEC ISLANDS", + "EASTERN NEW GUINEA REG.", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "WEST CHILE RISE", + "SOUTH SHETLAND ISLANDS", + "MINDANAO", + "EAST OF SOUTH SANDWICH ISLANDS", + "NEAR EAST COAST OF HONSHU", + "RUSSIA-MONGOLIA BORDER REGION", + "MINDANAO", + "NEAR COAST OF NICARAGUA", + "SOUTHEAST OF LOYALTY ISLANDS", + "TONGA ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "SOUTHWESTERN SIBERIA", + "NEAR COAST OF CENTRAL CHILE", + "SERAM", + "VANUATU ISLANDS", + "OFF EAST COAST OF KAMCHATKA", + "SOUTHEAST OF HONSHU", + "NEW BRITAIN REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "TAIWAN", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "SOUTH SHETLAND ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTHWEST OF SUMATRA", + "CENTRAL BOLIVIA", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NEVADA", + "ALASKA PENINSULA", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHEAST OF RYUKYU ISLANDS", + "SOUTHEAST OF RYUKYU ISLANDS", + "SANTA CRUZ ISLANDS", + "BANDA SEA", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN IRAN", + "CENTRAL MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "SOUTHEAST OF HONSHU", + "NEW BRITAIN REGION", + "CRETE", + "LOYALTY ISLANDS", + "NEGROS", + "NORTHERN CHILE", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SAMAR", + "SOUTHERN SUMATRA", + "NEAR COAST OF NICARAGUA", + "CELEBES SEA", + "EAST OF KURIL ISLANDS", + "SOUTHERN SUMATRA", + "CHILE-BOLIVIA BORDER REGION", + "JAVA SEA", + "VANUATU ISLANDS", + "CELEBES SEA", + "NEAR EAST COAST OF HONSHU", + "BANDA SEA", + "VANUATU ISLANDS REGION", + "LOYALTY ISLANDS", + "RYUKYU ISLANDS", + "ALASKA PENINSULA", + "BONIN ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "WESTERN IDAHO", + "BONIN ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHERN SUMATRA", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "VANCOUVER ISLAND", + "SOUTHERN EAST PACIFIC RISE", + "MYANMAR", + "OFF W COAST OF NORTHERN SUMATRA", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "SIKKIM", + "KERMADEC ISLANDS REGION", + "MAURITIUS - REUNION REGION", + "ANDREANOF ISLANDS", + "EASTERN NEW GUINEA REG.", + "NORTHERN PERU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "BALI SEA", + "FOX ISLANDS", + "BANDA SEA", + "CENTRAL MID-ATLANTIC RIDGE", + "WEST OF MACQUARIE ISLAND", + "SOUTHEAST INDIAN RIDGE", + "MINDORO", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "NEAR N COAST OF NEW GUINEA", + "CHIAPAS", + "CENTRAL MID-ATLANTIC RIDGE", + "SOUTH OF JAVA", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "QINGHAI", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "TURKEY", + "CYPRUS REGION", + "FOX ISLANDS", + "FOX ISLANDS", + "SUNDA STRAIT", + "BONIN ISLANDS", + "NEW BRITAIN REGION", + "NEGROS", + "FIJI ISLANDS REGION", + "SCOTIA SEA", + "NEAR EAST COAST OF KAMCHATKA", + "NEAR COAST OF CENTRAL CHILE", + "EAST OF SOUTH SANDWICH ISLANDS", + "GULF OF CALIFORNIA", + "SOLOMON ISLANDS", + "MYANMAR", + "NEW IRELAND REGION", + "CRETE", + "SAKHALIN", + "SEA OF OKHOTSK", + "PAKISTAN", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "SOUTH ATLANTIC OCEAN", + "BANDA SEA", + "VANUATU ISLANDS", + "SOUTHERN SUMATRA", + "OFF COAST OF JALISCO", + "NEAR COAST OF NORTHERN CHILE", + "OFF COAST OF NORTHERN CALIFORNIA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "JAVA SEA", + "MYANMAR-INDIA BORDER REGION", + "NORTH ATLANTIC OCEAN", + "XIZANG", + "VANUATU ISLANDS", + "NORTHERN EAST PACIFIC RISE", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "FIJI ISLANDS REGION", + "RYUKYU ISLANDS", + "CENTRAL ITALY", + "SOUTH ISLAND", + "SOUTH ISLAND", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "NORTHERN MID-ATLANTIC RIDGE", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SULAWESI", + "NEAR ISLANDS", + "NEW BRITAIN REGION", + "SOUTH OF JAVA", + "NEAR COAST OF GUATEMALA", + "SEA OF OKHOTSK", + "LEYTE", + "AUCKLAND ISLANDS", + "DODECANESE ISLANDS", + "MARIANA ISLANDS", + "GANSU", + "GUATEMALA", + "HOKKAIDO", + "BOTSWANA", + "COOK STRAIT", + "NEAR EAST COAST OF HONSHU", + "ANDREANOF ISLANDS", + "SOUTHEAST OF HONSHU", + "NORTHERN SUMATRA", + "OFF COAST OF NORTHERN CALIFORNIA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "BANDA SEA", + "COOK STRAIT", + "SOUTH OF PANAMA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "MINDANAO", + "SOUTHERN BOLIVIA", + "SOUTH OF FIJI ISLANDS", + "BANDA SEA", + "NEW IRELAND REGION", + "NEAR COAST OF NORTHERN CHILE", + "MYANMAR-INDIA BORDER REGION", + "NEPAL", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEAR COAST OF CHIAPAS", + "NEAR EAST COAST OF HONSHU", + "PACIFIC-ANTARCTIC RIDGE", + "SANTA CRUZ ISLANDS REGION", + "SOLOMON ISLANDS", + "ALASKA PENINSULA", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN MID-ATLANTIC RIDGE", + "BONIN ISLANDS", + "KERMADEC ISLANDS", + "SOLOMON ISLANDS", + "NEPAL", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "SOUTHERN EAST PACIFIC RISE", + "SOLOMON ISLANDS", + "SOUTH OF PANAMA", + "NORTH ATLANTIC OCEAN", + "MARIANA ISLANDS", + "VANUATU ISLANDS", + "CENTRAL PERU", + "JUJUY PROVINCE", + "CENTRAL PERU", + "OFF EAST COAST OF HONSHU", + "E. CAROLINE ISLANDS", + "FIJI ISLANDS REGION", + "SOUTH OF PANAMA", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "KERMADEC ISLANDS", + "TIMOR REGION", + "NEAR COAST OF CENTRAL CHILE", + "NICOBAR ISLANDS", + "ANDREANOF ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHWEST OF RYUKYU ISLANDS", + "SOLOMON ISLANDS", + "GREECE", + "NEAR COAST OF NICARAGUA", + "PERU-BRAZIL BORDER REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "NEAR COAST OF CHIAPAS", + "NEAR COAST OF NORTHERN CHILE", + "MYANMAR-INDIA BORDER REGION", + "SOLOMON ISLANDS", + "TALAUD ISLANDS", + "BOUVET ISLAND REGION", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTH INDIAN OCEAN", + "AEGEAN SEA", + "GULF OF CALIFORNIA", + "SOUTH OF PANAMA", + "SOLOMON ISLANDS", + "GUERRERO", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH OF FIJI ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "VANCOUVER ISLAND", + "IRIAN JAYA REGION", + "NEAR COAST OF CENTRAL CHILE", + "NORTH OF ASCENSION ISLAND", + "SOUTHERN XINJIANG", + "NORTHWEST OF KURIL ISLANDS", + "HAWAII", + "SOUTHERN BOLIVIA", + "NEW BRITAIN REGION", + "MAURITIUS - REUNION REGION", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW IRELAND REGION", + "NEW GUINEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEW IRELAND REGION", + "VANUATU ISLANDS", + "NORTHERN MOLUCCA SEA", + "CENTRAL CHILE", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "PRINCE EDWARD ISLANDS REGION", + "HOKKAIDO", + "SANTIAGO DEL ESTERO PROV.", + "NEAR COAST OF CHIAPAS", + "TONGA ISLANDS", + "IRIAN JAYA REGION", + "WESTERN BRAZIL", + "JAN MAYEN ISLAND REGION", + "KURIL ISLANDS", + "MYANMAR", + "HOKKAIDO", + "SOLOMON ISLANDS", + "ANDREANOF ISLANDS", + "KERMADEC ISLANDS REGION", + "SICHUAN", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "JAN MAYEN ISLAND REGION", + "NORTHERN MOLUCCA SEA", + "SUMBAWA REGION", + "KURIL ISLANDS", + "FIJI ISLANDS REGION", + "VANCOUVER ISLAND", + "WEST OF MACQUARIE ISLAND", + "IRIAN JAYA REGION", + "VANCOUVER ISLAND", + "FLORES SEA", + "VANCOUVER ISLAND", + "IONIAN SEA", + "ANDREANOF ISLANDS", + "SUMBAWA REGION", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHWEST OF AFRICA", + "EL SALVADOR", + "RAT ISLANDS", + "BOUVET ISLAND REGION", + "EASTERN NEW GUINEA REG.", + "SANTA CRUZ ISLANDS", + "BANDA SEA", + "EASTERN SIBERIA", + "CENTRAL MID-ATLANTIC RIDGE", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "KURIL ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "TONGA ISLANDS", + "KURIL ISLANDS", + "COSTA RICA", + "NEW GUINEA", + "SANTA CRUZ ISLANDS", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWESTERN SIBERIA", + "NEAR N COAST OF NEW GUINEA", + "BOUVET ISLAND REGION", + "JAVA", + "SOLOMON ISLANDS", + "SANTA CRUZ ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "MICHOACAN", + "SULAWESI", + "SOUTH SANDWICH ISLANDS REGION", + "BONIN ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEAST INDIAN RIDGE", + "GUERRERO", + "NORTHERN MOLUCCA SEA", + "VANUATU ISLANDS", + "OFF COAST OF CENTRAL AMERICA", + "NEW BRITAIN REGION", + "VANUATU ISLANDS REGION", + "SOLOMON ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "NEAR COAST OF CENTRAL CHILE", + "VANUATU ISLANDS REGION", + "NEW BRITAIN REGION", + "OFF COAST OF CENTRAL AMERICA", + "CENTRAL PERU", + "FIJI ISLANDS REGION", + "SOUTHWESTERN PAKISTAN", + "KERMADEC ISLANDS", + "BAJA CALIFORNIA", + "OFF EAST COAST OF HONSHU", + "IRIAN JAYA", + "IRIAN JAYA REGION", + "MINDANAO", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF GUERRERO", + "EASTERN NEW GUINEA REG.", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "ECUADOR", + "SOUTH ISLAND", + "CENTRAL PERU", + "SOLOMON ISLANDS", + "NEPAL", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "TURKEY", + "NORTHERN MOLUCCA SEA", + "SOUTHERN EAST PACIFIC RISE", + "EASTERN NEW GUINEA REG.", + "CENTRAL CALIFORNIA", + "NEAR COAST OF PERU", + "FLORES SEA", + "FLORES SEA", + "MINDANAO", + "SOUTHERN ALASKA", + "BANDA SEA", + "NEAR COAST OF CENTRAL CHILE", + "REYKJANES RIDGE", + "HALMAHERA", + "NEAR COAST OF CENTRAL CHILE", + "TAJIKISTAN", + "HAITI REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "OFF COAST OF CENTRAL AMERICA", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "NEAR COAST OF PERU", + "KERMADEC ISLANDS", + "OFF E. COAST OF N. ISLAND", + "NORTH OF ASCENSION ISLAND", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "SOUTHEAST OF LOYALTY ISLANDS", + "OAXACA", + "BANDA SEA", + "SOUTH OF KERMADEC ISLANDS", + "PHILIPPINE ISLANDS REGION", + "CENTRAL MEXICO", + "KURIL ISLANDS", + "NEAR EAST COAST OF HONSHU", + "IRIAN JAYA", + "SOUTH OF FIJI ISLANDS", + "DODECANESE ISLANDS", + "IRAN-IRAQ BORDER REGION", + "FOX ISLANDS", + "NEW IRELAND REGION", + "LOYALTY ISLANDS", + "COLOMBIA", + "CELEBES SEA", + "SANTA CRUZ ISLANDS", + "EASTERN NEW GUINEA REG.", + "ANDREANOF ISLANDS", + "NEAR COAST OF OAXACA", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF PERU", + "QINGHAI", + "KAMCHATKA PENINSULA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "OFF COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "KYUSHU", + "SOUTHERN ALASKA", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "KURIL ISLANDS", + "NEAR COAST OF VENEZUELA", + "VANUATU ISLANDS", + "COLOMBIA", + "NEAR COAST OF CHIAPAS", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "NEAR EAST COAST OF HONSHU", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "NEPAL", + "SOUTHERN IRAN", + "SOLOMON ISLANDS", + "COSTA RICA", + "KOMANDORSKIYE OSTROVA REGION", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "ALASKA PENINSULA", + "MINDANAO", + "PERU-BRAZIL BORDER REGION", + "SOUTHERN CHILE", + "MINDANAO", + "PHILIPPINE ISLANDS REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "MINAHASSA PENINSULA", + "SOLOMON ISLANDS", + "NEAR COAST OF GUERRERO", + "SOUTHWEST OF SUMATRA", + "SOUTH OF ALASKA", + "SOUTHEAST OF LOYALTY ISLANDS", + "EAST OF KURIL ISLANDS", + "SCOTIA SEA", + "QUEEN CHARLOTTE ISLANDS REGION", + "PRIMOR'YE", + "MARIANA ISLANDS", + "SEA OF OKHOTSK", + "SOUTHERN SUMATRA", + "NORTHERN PERU", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR EAST COAST OF 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lat='latitude',lon='longitude', \n", + " range_color=(6,9),\n", + " height=600, width=600,\n", + " size='marker_size', color='magnitude',\n", + " hover_name=\"description\",\n", + " hover_data=['description','magnitude','depth']);\n", + "fig.update_geos(resolution=110, showcountries=True)\n", + "# fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "3355fe19", + "metadata": {}, + "source": [ + "# 3 Create a Pandas from a generic text file.\n", + "\n", + "The python package pandas is very useful to read csv files, but also many text files that are more or less formatted as one observation per row and one column for each feature.\n", + "\n", + "As an example, we are going to look at the list of seismic stations from the Northern California seismic network, available [here](http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day):\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "ef9f302d", + "metadata": {}, + "outputs": [], + "source": [ + "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "3a60ef42", + "metadata": {}, + "outputs": [], + "source": [ + "# this gets the file linked in the URL page and convert it to a string\n", + "s = requests.get(url).content " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "9edd0bf5", + "metadata": {}, + "outputs": [], + "source": [ + "# this will convert the string, decode it , and make it a table\n", + "data = pd.read_csv(io.StringIO(s.decode('utf-8')), header=None, skiprows=2, sep='\\s+', usecols=list(range(0, 13)))\n", + "# because columns/keys were not assigned, assign them now\n", + "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" + ] + }, + { + "cell_type": "markdown", + "id": "b2def912", + "metadata": {}, + "source": [ + "Let us look at the data. They are now stored into a pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "448b69ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.0
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.0
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3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.0
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.0
\n", + "
" ], - "source": [ - "# Classify earthquake magnitudes\n", - "quake['Category'] = quake['magnitude'].apply(lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe'))\n", - "quake.head()\n" + "text/plain": [ + " station network channel location rate start_time \\\n", + "0 AAR NC EHZ -- 0.0 1976/07/20,17:38:00 \n", + "1 AAR NC EHZ -- 0.0 1977/12/01,22:37:00 \n", + "2 AAR NC EHZ -- 0.0 1981/06/12,19:15:00 \n", + "3 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", + "4 AAR NC EHZ -- 100.0 1985/11/03,22:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip azimuth \n", + "0 1977/12/01,22:37:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 " ] - }, - { - "cell_type": "markdown", - "id": "8be50d1c", - "metadata": {}, - "source": [] - }, + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "d05055aa", + "metadata": {}, + "source": [ + "We can output the first element of the DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "51971cde", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "b87c6c42", - "metadata": {}, - "source": [ - "We will now make a new plot of the location of the earthquakes. We will use Plotly tool. \n", - "\n", - "The markersize will be scaled with the earthquake magnitude. To do so, we add a ``marker_size`` series in the DataFrame" + "data": { + "text/plain": [ + "station AAR\n", + "network NC\n", + "channel EHZ\n", + "location --\n", + "rate 0.0\n", + "start_time 1976/07/20,17:38:00\n", + "end_time 1977/12/01,22:37:00\n", + "latitude 39.27594\n", + "longitude -121.02696\n", + "elevation 911.0\n", + "depth 0.0\n", + "dip -90.0\n", + "azimuth 0.0\n", + "Name: 0, dtype: object" ] - }, + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "d9bb3b35", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 61, - "id": "d9cb41c3", - "metadata": {}, - "outputs": [], - "source": [ - "quake['marker_size'] = quake['magnitude'].apply(lambda x: np.fix(np.exp(x))) # add marker size as exp(mag)\n", - "quake['magnitude_bin'] = quake['magnitude'].apply(lambda x: 0.5*np.fix(2*x)) # add marker size as exp(mag)" + "data": { + "text/plain": [ + "station object\n", + "network object\n", + "channel object\n", + "location object\n", + "rate float64\n", + "start_time object\n", + "end_time object\n", + "latitude float64\n", + "longitude float64\n", + "elevation float64\n", + "depth float64\n", + "dip float64\n", + "azimuth float64\n", + "dtype: object" ] - }, + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# display the type of each column\n", + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "b3cc09c6", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 62, - "id": "6c7befa4", - "metadata": {}, - "outputs": [], - "source": [ - "# another way to do it\n", - "quake['marker_size'] = np.fix(np.exp(quake['magnitude'])) # add marker size as exp(mag)\n", - "quake['magnitude_bin'] = 0.5*np.fix(2*quake['magnitude']) # add marker size as exp(mag)" + "data": { + "text/plain": [ + "0 AAR\n", + "1 AAR\n", + "2 AAR\n", + "3 AAR\n", + "4 AAR\n", + " ... \n", + "29434 WMP\n", + "29435 WMP\n", + "29436 WMP\n", + "29437 WSL\n", + "29438 WWVB\n", + "Name: station, Length: 29439, dtype: object" ] - }, + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[:, 0]" + ] + }, + { + "cell_type": "markdown", + "id": "9afc9c2f", + "metadata": {}, + "source": [ + "The ``start_time`` and ``end_time`` are stored as ``object`` or ``string``, convert these columns to datetime objects. With a too-simple prompt, CoPilot may fail at converting:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "08156920", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "2fed2184", - "metadata": {}, - "source": [ - "### 1.4 Intermediate Manipulation with Pandas\n", - "\n", - "You can also apply lambda functions to work with multiple columns, which is common in geoscientific datasets, where you might have spatial or temporal data.\n", - "\n", - "**Example 3: Calculating an Index Based on Multiple Measurements**\n", - "Scenario: You have rainfall (``Rainfall_mm``) and evaporation (``Evaporation_mm``) data, and you want to calculate the ``Net Water Balance ``for each record." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/js/lzmy975n0l5bjbmr9db291m00000gn/T/ipykernel_26544/3238846712.py:2: UserWarning:\n", + "\n", + "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 63, - "id": "7931923f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Rainfall_mm Evaporation_mm Net_Water_Balance\n", - "0 100 60 40\n", - "1 80 70 10\n", - "2 120 65 55\n" - ] - } + "ename": "DateParseError", + "evalue": "Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mDateParseError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# convert start_time and end_time to datetime\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstart_time\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:1108\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1106\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mtz_localize(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, ABCSeries):\n\u001b[0;32m-> 1108\u001b[0m cache_array \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_listlike\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cache_array\u001b[38;5;241m.\u001b[39mempty:\n\u001b[1;32m 1110\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:254\u001b[0m, in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m 252\u001b[0m unique_dates \u001b[38;5;241m=\u001b[39m unique(arg)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_dates) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(arg):\n\u001b[0;32m--> 254\u001b[0m cache_dates \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43munique_dates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# GH#45319\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:490\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m--> 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mobjects_to_datetime64ns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 502\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py:2346\u001b[0m, in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, allow_object)\u001b[0m\n\u001b[1;32m 2343\u001b[0m \u001b[38;5;66;03m# if str-dtype, convert\u001b[39;00m\n\u001b[1;32m 2344\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(data, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mobject_)\n\u001b[0;32m-> 2346\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mtslib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_to_datetime\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2347\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2348\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2349\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2351\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2355\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;66;03m# Return i8 values to denote unix timestamps\u001b[39;00m\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi8\u001b[39m\u001b[38;5;124m\"\u001b[39m), tz_parsed\n", + "File \u001b[0;32mtslib.pyx:403\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mtslib.pyx:552\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mtslib.pyx:517\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mconversion.pyx:546\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.convert_str_to_tsobject\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mparsing.pyx:331\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mparsing.pyx:660\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.dateutil_parse\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mDateParseError\u001b[0m: Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0" + ] + } + ], + "source": [ + "# convert start_time and end_time to datetime\n", + "data['start_time'] = pd.to_datetime(data['start_time'])\n", + "data['end_time'] = pd.to_datetime(data['end_time'])" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "b8bcf253", + "metadata": {}, + "outputs": [], + "source": [ + "data['startdate'] = pd.to_datetime(data['start_time'], format='%Y/%m/%d,%H:%M:%S')" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "da911e32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:00
\n", + "
" ], - "source": [ - "data = {'Rainfall_mm': [100, 80, 120], 'Evaporation_mm': [60, 70, 65]}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Calculate Net Water Balance\n", - "df['Net_Water_Balance'] = df.apply(lambda row: row['Rainfall_mm'] - row['Evaporation_mm'], axis=1)\n", - "\n", - "print(df)\n" + "text/plain": [ + " station network channel location rate start_time \\\n", + "0 AAR NC EHZ -- 0.0 1976/07/20,17:38:00 \n", + "1 AAR NC EHZ -- 0.0 1977/12/01,22:37:00 \n", + "2 AAR NC EHZ -- 0.0 1981/06/12,19:15:00 \n", + "3 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", + "4 AAR NC EHZ -- 100.0 1985/11/03,22:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip azimuth \\\n", + "0 1977/12/01,22:37:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "\n", + " startdate \n", + "0 1976-07-20 17:38:00 \n", + "1 1977-12-01 22:37:00 \n", + "2 1981-06-12 19:15:00 \n", + "3 1984-01-01 00:00:00 \n", + "4 1985-11-03 22:00:00 " ] - }, + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "4a2f3dc4", + "metadata": {}, + "outputs": [], + "source": [ + "# do the same for end times\n", + "# Avoid 'OutOfBoundsDatetime' error with year 3000\n", + "enddate = data['end_time'].str.replace('3000', '2026')\n", + "enddate = pd.to_datetime(enddate, format='%Y/%m/%d,%H:%M:%S')\n", + "data['enddate'] = enddate" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "4c3af296", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "b5b64a6b", - "metadata": {}, - "source": [ - "In this example, ``lambda row: row['Rainfall_mm'] - row['Evaporation_mm']`` calculates the net water balance by subtracting evaporation from rainfall for each record.\n", - "\n", - "### 1.5 Advanced: Time Series Data Manipulation with Lambda Functions\n", - "Geoscientists frequently work with time series data (e.g., climate data). Lambda functions can be used for efficient data transformations within time series.\n", - "\n", - "**Example 4: Applying a Rolling Window Calculation**\n", - "Scenario: Suppose you have daily temperature data, and you want to calculate a 7-day rolling average." + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdateenddate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:001977-12-01 22:37:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:001981-06-12 19:15:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:001984-01-01 00:00:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:001985-11-03 22:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:001987-03-10 23:15:00
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average using lambda\n", - "df['7_day_avg'] = df['Temperature_C'].rolling(window=3).apply(lambda x: x.mean())\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "e3d3328a", - "metadata": {}, - "source": [ - "Here, ``lambda x: x.mean()`` calculates the rolling mean over a 3-day window for temperature data, which is essential in climate analysis for smoothing short-term fluctuations.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "e01cf834", - "metadata": {}, - "source": [ - "### 1.6 Aggregate function\n", - "The ``agg`` is a powerful method in Pandas that allows you to perform multiple operations on DataFrames and Series. You can apply one or more aggregation functions such as ``sum``, ``mean``, ``min``, ``max``, etc., on different columns or groups of data." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "aea0ee4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sum 10.0\n", - "mean 2.5\n", - "Name: A, dtype: float64\n" + ], + "layout": { + "geo": { + "center": {}, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 ] - } - ], - "source": [ - "# Sample DataFrame\n", - "data = {\n", - " 'A': [1, 2, 3, 4],\n", - " 'B': [5, 6, 7, 8]\n", - "}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Apply aggregation functions\n", - "result = df['A'].agg(['sum', 'mean'])\n", - "print(result)\n" - ] - }, - { - "cell_type": "markdown", - "id": "f2664926", - "metadata": {}, - "source": [ - "#### Aggregating Multiple Columns with Multiple Functions\n", - "You can pass a dictionary to agg where the keys are column names and the values are the functions to be applied." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "9df9cde2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B\n", - "sum 10.0 NaN\n", - "mean 2.5 NaN\n", - "min NaN 5.0\n", - "max NaN 8.0\n" - ] - } - ], - "source": [ - "# Apply different functions to different columns\n", - "result = df.agg({'A': ['sum', 'mean'], 'B': ['min', 'max']})\n", - "print(result)\n" - ] - }, - { - "cell_type": "markdown", - "id": "5ccea12a", - "metadata": {}, - "source": [ - "In this example, column 'A' gets ``sum`` and ``mean``, while column 'B' gets ``min`` and ``max``." - ] - }, - { - "cell_type": "markdown", - "id": "6b9e44e0", - "metadata": {}, - "source": [ - "You may also use costum functions" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "79ff0c94", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B\n", - "mean 2.5 NaN\n", - "data_range 3.0 3.0\n" + }, + "resolution": 110, + "showcountries": true + }, + "height": 600, + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": 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"type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } ] - } - ], - "source": [ - "# Custom function to calculate range (max - min)\n", - "def data_range(x):\n", - " return x.max() - x.min()\n", - "\n", - "# Apply custom function\n", - "result = df.agg({'A': ['mean', data_range], 'B': data_range})\n", - "print(result)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94abaccd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f004df73", - "metadata": {}, - "source": [ - "## 2.4 Mapping using Plotly\n", - "\n", - "Now we will plot the earthquakes locations on a map using the Plotly package. More tutorials on [Plotly](https://plotly.com/). The input of the function is self-explanatory and typical of Python's function. The code [documentation](https://plotly.com/python/scatter-plots-on-maps/) of Plotly scatter_geo lists the variables." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "ff53045a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - "VANUATU ISLANDS", - 6.3, - 0.0344 - ], - [ - "NEAR EAST COAST OF HONSHU", - 6.3, - 0.030100000000000002 - ], - [ - "SOUTH OF MARIANA ISLANDS", - 6.3, - 0.0169 - ], - [ - "NORTHERN CHILE", - 6.2, - 0.10940000000000001 - ], - [ - "NEAR COAST OF CENTRAL CHILE", - 6.6, - 0.0257 - ], - [ - "FOX ISLANDS", - 6.7, - 0.0215 - ], - [ - "NEW BRITAIN REGION", - 6.9, - 0.0458 - ], - [ - "NEW BRITAIN REGION", - 7.3, - 0.0431 - ], - [ - "FOX ISLANDS", - 6, - 0.0356 - ], - [ - "NEW BRITAIN REGION", - 6.3, - 0.041100000000000005 - ], - [ - "NORTH OF HALMAHERA", - 6.1, - 0.1119 - ], - [ - "MINDANAO", - 7.3, - 0.6102000000000001 - 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magnitude=%{marker.color}
depth=%{customdata[2]}", - "hovertext": [ - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF MARIANA ISLANDS", - "NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "FOX ISLANDS", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "FOX ISLANDS", - "NEW BRITAIN REGION", - "NORTH OF HALMAHERA", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "OFF EAST COAST OF KAMCHATKA", - "NORTHERN MOLUCCA SEA", - "EASTERN NEW GUINEA REG.", - "ANDREANOF ISLANDS", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "VANUATU ISLANDS", - "ECUADOR", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "NEW BRITAIN REGION", - "MAURITIUS - REUNION REGION", - "FIJI ISLANDS REGION", - "SOUTH OF MARIANA ISLANDS", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "OFF COAST OF JALISCO", - "ANDREANOF ISLANDS", - "SOUTH ISLAND", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "HINDU KUSH REGION", - "ARU ISLANDS REGION", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "SOUTHWESTERN RYUKYU ISL.", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "HALMAHERA", - "GULF OF CALIFORNIA", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "PACIFIC-ANTARCTIC RIDGE", - "IRIAN JAYA REGION", - "TONGA ISLANDS", - "SOUTHEAST INDIAN RIDGE", - "PACIFIC-ANTARCTIC RIDGE", - "NEW BRITAIN REGION", - "BONIN ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "SOUTHERN IRAN", - "BONIN ISLANDS", - "BONIN ISLANDS", - "NEAR ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "VANUATU ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "BONIN ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SOUTHERN SUMATRA", - "NORTHERN SUMATRA", - "SOUTHWESTERN PAKISTAN", - "TAJIKISTAN", - "NORTHERN SUMATRA", - "SOUTHERN IRAN", - "JAN MAYEN ISLAND REGION", - "TONGA ISLANDS", - "MYANMAR-INDIA BORDER REGION", - "SOLOMON ISLANDS", - "CELEBES SEA", - "CELEBES SEA", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SULAWESI", - "NEAR EAST COAST OF KAMCHATKA", - "SOUTH OF FIJI ISLANDS", - "SOUTH ISLAND", - "OAXACA", - "EASTER ISLAND REGION", - "NORTHERN CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "BALI SEA", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "EASTERN HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR WEST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "EASTERN HONSHU", - "OFF EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MYANMAR", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "CRETE", - "FIJI ISLANDS REGION", - "SOUTH OF JAVA", - "CHIAPAS", - "NEAR EAST COAST OF HONSHU", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF KERMADEC ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SULAWESI", - "SOUTH OF PANAMA", - "OFF EAST COAST OF HONSHU", - "LOYALTY ISLANDS", - "COSTA RICA", - "NEAR EAST COAST OF HONSHU", - "HINDU KUSH REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "SOLOMON ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "WEST OF MACQUARIE ISLAND", - "SOUTH ISLAND", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "CHILE-BOLIVIA BORDER REGION", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FOX ISLANDS", - "SANTA CRUZ ISLANDS", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEGROS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "ALASKA PENINSULA", - "TAJIKISTAN", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW IRELAND REGION", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NEAR S. COAST OF HONSHU", - "KURIL ISLANDS", - "ASCENSION ISLAND REGION", - "CERAM SEA", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SUNDA STRAIT", - "PERU-BRAZIL BORDER REGION", - "VANUATU ISLANDS", - "BANDA SEA", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "NORTHERN SUMATRA", - "AUCKLAND ISLANDS", - "VANCOUVER ISLAND", - "NEAR ISLANDS", - "EAST OF NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SIKKIM", - "TONGA ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF BALI", - "EASTERN NEW GUINEA REG.", - "SOUTHEASTERN SIBERIA", - "NEW BRITAIN REGION", - "HOKKAIDO", - "KERMADEC ISLANDS REGION", - "TURKEY", - "TURKEY", - "FIJI ISLANDS REGION", - "NEAR COAST OF PERU", - "REVILLA GIGEDO ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "NICARAGUA", - "NORTHEAST OF TAIWAN", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHERN MOLUCCA SEA", - "OFF COAST OF ECUADOR", - "CENTRAL BOLIVIA", - "NEAR EAST COAST OF HONSHU", - "HOKKAIDO", - "NEW IRELAND REGION", - "LUZON", - "NEAR COAST OF NORTHERN CHILE", - "GUERRERO", - "SOUTH SANDWICH ISLANDS REGION", - "MINAHASSA PENINSULA", - "EASTERN NEW GUINEA REG.", - "TONGA ISLANDS", - "SOUTHWESTERN SIBERIA", - "SOUTHEAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH SHETLAND ISLANDS", - "SOUTH SHETLAND ISLANDS", - "NEAR COAST OF CHIAPAS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF PERU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "NEGROS", - "NEGROS", - "FIJI ISLANDS REGION", - "SOLOMON ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHWESTERN SIBERIA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "IRIAN JAYA", - "NEAR COAST OF GUERRERO", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN EAST PACIFIC RISE", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF GUERRERO", - "NEW IRELAND REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "NORTH INDIAN OCEAN", - "OFF W COAST OF NORTHERN SUMATRA", - "OFF COAST OF OREGON", - "MICHOACAN", - "BAJA CALIFORNIA", - "BAJA CALIFORNIA", - "DRAKE PASSAGE", - "VANUATU ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF CENTRAL CHILE", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS REGION", - "TONGA ISLANDS", - "NEAR COAST OF CHIAPAS", - "PERU-BOLIVIA BORDER REGION", - "OFF COAST OF SOUTHERN CHILE", - "NORTHERN ITALY", - "OFF EAST COAST OF HONSHU", - "HOKKAIDO", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "GREENLAND SEA", - "BONIN ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "SALTA PROVINCE", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "OFF EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "SOUTHERN PERU", - "DODECANESE ISLANDS", - "LUZON", - "NEAR EAST COAST OF HONSHU", - "NEAR ISLANDS", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF KAMCHATKA", - "NORTHERN XINJIANG", - "COOK STRAIT", - "VANUATU ISLANDS", - "KURIL ISLANDS", - "KURIL ISLANDS", - "NORTHERN SUMATRA", - "SOLOMON ISLANDS", - "MAURITIUS - REUNION REGION", - "NEW IRELAND REGION", - "PERU-BRAZIL BORDER REGION", - "NEW IRELAND REGION", - "FOX ISLANDS", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "XIZANG", - "SEA OF OKHOTSK", - "SULAWESI", - "NEAR N COAST OF NEW GUINEA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF CENTRAL AMERICA", - "JAN MAYEN ISLAND REGION", - "PHILIPPINE ISLANDS REGION", - "SOUTH OF JAVA", - "SANTA CRUZ ISLANDS", - "COSTA RICA", - "IRIAN JAYA REGION", - "KURIL ISLANDS", - "SOUTHERN SUMATRA", - "BAJA CALIFORNIA", - "ANDREANOF ISLANDS", - "COLOMBIA", - "OFF EAST COAST OF HONSHU", - "GULF OF CALIFORNIA", - "BANDA SEA", - "WEST OF MACQUARIE ISLAND", - "IRIAN JAYA REGION", - "CELEBES SEA", - "VANUATU ISLANDS", - "COSTA RICA", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "MINDANAO", - "NEAR COAST OF CHIAPAS", - "VANCOUVER ISLAND", - "CENTRAL PERU", - "MYANMAR", - "NEAR COAST OF CHIAPAS", - "GULF OF ALASKA", - "OFF COAST OF SOUTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "GUERRERO", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "NEAR N COAST OF NEW GUINEA", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NORTH ISLAND", - "BANDA SEA", - "NORTHERN MOLUCCA SEA", - "OFF W. COAST OF BAJA CALIFORNIA", - "NEAR ISLANDS", - "NEW IRELAND REGION", - "MINAHASSA PENINSULA", - "VANUATU ISLANDS", - "SOUTHEASTERN ALASKA", - "PACIFIC-ANTARCTIC RIDGE", - "NORTHERN SUMATRA", - "LAKE ISSYK-KUL REGION", - "CENTRAL CHILE", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "HOKKAIDO", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "COLOMBIA", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "EASTERN SIBERIA", - "MINDANAO", - "SANTIAGO DEL ESTERO PROV.", - "KURIL ISLANDS", - "KURIL ISLANDS", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "EAST OF KURIL ISLANDS", - "VANUATU ISLANDS REGION", - "GUATEMALA", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "E. RUSSIA-N.E. CHINA BORDER REG.", - "IRIAN JAYA", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "SOUTHERN IRAN", - "NEAR N COAST OF NEW GUINEA", - "KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "SICHUAN", - "BANDA SEA", - "KURIL ISLANDS", - "SOUTHEAST OF HONSHU", - "MICHOACAN", - "NEW IRELAND REGION", - "KERMADEC ISLANDS REGION", - "SOUTHERN IRAN", - "TONGA ISLANDS", - "MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "OFF COAST OF SOUTHERN CHILE", - "OFF EAST COAST OF KAMCHATKA", - "OFF EAST COAST OF KAMCHATKA", - "SOUTH OF FIJI ISLANDS", - "TONGA ISLANDS", - "SEA OF OKHOTSK", - "SEA OF OKHOTSK", - "TAIWAN", - "SANTA CRUZ ISLANDS", - "SOUTH OF JAVA", - "SOUTH OF KERMADEC ISLANDS", - "CRETE", - "NEAR COAST OF NICARAGUA", - "CRETE", - "NORTHERN MID-ATLANTIC RIDGE", - "NORTHERN SUMATRA", - "SOLOMON ISLANDS", - "SOUTHERN SUMATRA", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN PERU", - "COOK STRAIT", - "GANSU", - "PRINCE EDWARD ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "BANDA SEA", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF NORTHERN PERU", - "SOUTH OF PANAMA", - "COOK STRAIT", - "SOUTHWEST INDIAN RIDGE", - "NEAR COAST OF GUERRERO", - "KERMADEC ISLANDS REGION", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "BANDA SEA", - "QUEEN CHARLOTTE ISLANDS REGION", - "SOUTHEAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "PHILIPPINE ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "CENTRAL EAST PACIFIC RISE", - "ANDREANOF ISLANDS", - "FLORES SEA", - "PAKISTAN", - "SOUTHERN EAST PACIFIC RISE", - "NEAR COAST OF PERU", - "PAKISTAN", - "KERMADEC ISLANDS", - "SEA OF OKHOTSK", - "MID-INDIAN RIDGE", - "SOUTH OF MARIANA ISLANDS", - "WEST CHILE RISE", - "SAKHALIN", - "KERMADEC ISLANDS", - "NEAR COAST OF VENEZUELA", - "CRETE", - "MINDANAO", - "SOLOMON ISLANDS", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "OFF EAST COAST OF HONSHU", - "BALLENY ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "TAIWAN", - "NEAR COAST OF CENTRAL CHILE", - "EASTER ISLAND REGION", - "TONGA ISLANDS REGION", - "CELEBES SEA", - "NEAR EAST COAST OF KAMCHATKA", - "SCOTIA SEA", - "SCOTIA SEA", - "SCOTIA SEA", - "HALMAHERA", - "MARIANA ISLANDS", - "FIJI ISLANDS REGION", - "KURIL ISLANDS", - "SOUTH ATLANTIC OCEAN", - "BANDA SEA", - "NORTHERN SUMATRA", - "KURIL ISLANDS", - "MARIANA ISLANDS REGION", - "VANUATU ISLANDS", - "PUERTO RICO REGION", - "NORTH ISLAND", - "TONGA ISLANDS", - "JAVA", - "GREECE", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH OF KERMADEC ISLANDS", - "GREECE", - "VANUATU ISLANDS", - "XIZANG", - "NORTH ATLANTIC OCEAN", - "FOX ISLANDS", - "NEAR COAST OF NICARAGUA", - "RYUKYU ISLANDS", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "OFF COAST OF NORTHERN CALIFORNIA", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "NEAR COAST OF PERU", - "NEAR COAST OF NORTHERN PERU", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NICOBAR ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "SANTA CRUZ ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "OFF COAST OF NORTHERN CHILE", - "SOUTH OF PANAMA", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "NICARAGUA", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR COAST OF NICARAGUA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "BOUVET ISLAND REGION", - "BALLENY ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "GUERRERO", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "VANCOUVER ISLAND", - "TONGA ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR S. COAST OF HONSHU", - "THAILAND", - "WEST CHILE RISE", - "SOLOMON ISLANDS", - "GUERRERO", - "TONGA ISLANDS", - "GUERRERO", - "SOUTHERN EAST PACIFIC RISE", - "SOUTH OF PANAMA", - "E. CAROLINE ISLANDS", - "E. CAROLINE ISLANDS", - "NEGROS", - "OFF W COAST OF NORTHERN SUMATRA", - "BAY OF BENGAL", - "AEGEAN SEA", - "OFF COAST OF JALISCO", - "SOUTH INDIAN OCEAN", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "VOLCANO ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "TONGA ISLANDS", - "BONIN ISLANDS", - "BALLENY ISLANDS REGION", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "NEW BRITAIN REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MINDANAO", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "TONGA ISLANDS", - "TONGA ISLANDS", - "OWEN FRACTURE ZONE REGION", - "KURIL ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEASTERN ALASKA", - "NORTHERN MID-ATLANTIC RIDGE", - "OAXACA", - "BISMARCK SEA", - "BISMARCK SEA", - "E. CAROLINE ISLANDS", - "XIZANG", - "YUNNAN", - "BANDA SEA", - "BANDA SEA", - "HOKKAIDO", - "HOKKAIDO", - "IRAN-IRAQ BORDER REGION", - "IRAN-IRAQ BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN CALIFORNIA", - "CENTRAL PERU", - "CENTRAL PERU", - "EASTER ISLAND REGION", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "OFF COAST OF JALISCO", - "SOUTHERN MOLUCCA SEA", - "MARIANA ISLANDS", - "JUJUY PROVINCE", - "SOLOMON ISLANDS", - "SOUTHERN ALASKA", - "NEW BRITAIN REGION", - "YUNNAN", - "GULF OF CALIFORNIA", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHERN EAST PACIFIC RISE", - "HOKKAIDO", - "OFF COAST OF CENTRAL AMERICA", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "FIJI ISLANDS REGION", - "BALLENY ISLANDS REGION", - "MID-INDIAN RIDGE", - "NEW BRITAIN REGION", - "PRIMOR'YE", - "FIJI ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "OFF E. COAST OF N. ISLAND", - "PRINCE EDWARD ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "SICHUAN", - "EASTERN HONSHU", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "SOUTH OF PANAMA", - "BANDA SEA", - "SOLOMON ISLANDS", - "NEAR COAST OF GUATEMALA", - "SOUTH OF PANAMA", - "TAIWAN REGION", - "PHILIPPINE ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "NORTHERN MOLUCCA SEA", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "MINDANAO", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "SOUTH OF PANAMA", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "ADMIRALTY ISLANDS REGION", - "MENDOZA PROVINCE", - "JUJUY PROVINCE", - "REYKJANES RIDGE", - "TAIWAN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF COAST OF JALISCO", - "FLORES SEA", - "SOUTHERN SUMATRA", - "MID-INDIAN RIDGE", - "NORTHERN COLOMBIA", - "MINAHASSA PENINSULA", - "NORTHERN MOLUCCA SEA", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "NORTHERN CHILE", - "NEW BRITAIN REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NEW BRITAIN REGION", - "TONGA ISLANDS", - "CRETE", - "FIJI ISLANDS REGION", - "TAIWAN REGION", - "TAIWAN REGION", - "SANTA CRUZ ISLANDS", - "SOUTH ISLAND", - "QUEEN CHARLOTTE ISLANDS REGION", - "NEPAL", - "NEPAL", - "NEPAL", - "NEPAL", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "MOROCCO", - "SOLOMON ISLANDS", - "NEPAL", - "NEPAL", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN SUMATRA", - "PACIFIC-ANTARCTIC RIDGE", - "TONGA ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "TONGA ISLANDS", - "ALASKA PENINSULA", - "BONIN ISLANDS", - "TONGA ISLANDS", - "SOUTHEAST OF HONSHU", - "BORNEO", - "HOKKAIDO", - "NORTHERN CHILE", - "SAMOA ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTHERN MID-ATLANTIC RIDGE", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "BONIN ISLANDS", - "NEW BRITAIN REGION", - "SOUTHERN XINJIANG", - "LEYTE", - "EAST OF KURIL ISLANDS", - "SOLOMON ISLANDS", - "NORTH ATLANTIC OCEAN", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "IRIAN JAYA", - "SOUTHERN ALASKA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "MID-INDIAN RIDGE", - "SOLOMON ISLANDS", - "KERMADEC ISLANDS REGION", - "SOUTHEAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "FOX ISLANDS", - "GULF OF CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "NEAR COAST OF CENTRAL CHILE", - "JUJUY PROVINCE", - "PACIFIC-ANTARCTIC RIDGE", - "EAST OF KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "BOUVET ISLAND REGION", - "PRINCE EDWARD ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "HINDU KUSH REGION", - "TIMOR REGION", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NICOBAR ISLANDS", - "ANDREANOF ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHWEST OF RYUKYU ISLANDS", - "GREECE", - "SOLOMON ISLANDS", - "BANDA SEA", - "MARIANA ISLANDS", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "NEAR COAST OF NORTHERN CHILE", - "SOUTHEAST INDIAN RIDGE", - "TAJIKISTAN", - "BANDA SEA", - "FIJI ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "BORNEO", - "SOUTHERN EAST PACIFIC RISE", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "MYANMAR-INDIA BORDER REGION", - "MYANMAR-INDIA BORDER REGION", - "PACIFIC-ANTARCTIC RIDGE", - "TALAUD ISLANDS", - "HOKKAIDO", - "SOUTHERN BOLIVIA", - "HOKKAIDO", - "OFF COAST OF JALISCO", - "SOUTHERN ALASKA", - "STRAIT OF GIBRALTAR", - "KAMCHATKA PENINSULA", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "TAIWAN", - "SOLOMON ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SUMBA REGION", - "SOUTHERN EAST PACIFIC RISE", - "HALMAHERA", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "SOUTHWEST OF SUMATRA", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "LEEWARD ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "NEAR N COAST OF NEW GUINEA", - "ALASKA PENINSULA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "JAVA", - "VANUATU ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "MYANMAR-INDIA BORDER REGION", - "MINDANAO", - "KYUSHU", - "KYUSHU", - "VANUATU ISLANDS", - "OFF COAST OF CHIAPAS", - "KYUSHU", - "NEAR COAST OF ECUADOR", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "NORTHERN EAST PACIFIC RISE", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NORTHERN TERRITORY", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "TAIWAN REGION", - "SOUTHERN SUMATRA", - "BANDA SEA", - "KERMADEC ISLANDS", - "OFF COAST OF JALISCO", - "NORTHERN MOLUCCA SEA", - "SOUTH OF SUMBAWA", - "NICARAGUA", - "SOLOMON ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "LOYALTY ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "NEW IRELAND REGION", - "TAJIKISTAN-XINJIANG BORDER REG.", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "NEAR COAST OF ECUADOR", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "ADMIRALTY ISLANDS REGION", - "MARIANA ISLANDS", - "JUJUY PROVINCE", - "VOLCANO ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH GEORGIA ISLAND REGION", - "FLORES SEA", - "CENTRAL ITALY", - "MYANMAR", - "NORTH OF ASCENSION ISLAND", - "NEW IRELAND REGION", - "OFF E. COAST OF N. ISLAND", - "OFF E. COAST OF N. ISLAND", - "KOMANDORSKIYE OSTROVA REGION", - "MACQUARIE ISLAND REGION", - "NORTHERN PERU", - "NORTHERN COLOMBIA", - "SOLOMON ISLANDS", - "NEAR NORTH COAST OF IRIAN JAYA", - "SOUTHEAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MINDANAO", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "JAVA SEA", - "WESTERN HONSHU", - "TONGA ISLANDS", - "CENTRAL ITALY", - "CENTRAL ITALY", - "CHILE-ARGENTINA BORDER REGION", - "NEAR EAST COAST OF HONSHU", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SAN JUAN PROVINCE", - "NEAR EAST COAST OF HONSHU", - "OFF COAST OF CENTRAL AMERICA", - "SOUTHERN XINJIANG", - "SOUTHERN PERU", - "NEAR ISLANDS", - "BANDA SEA", - "NORTHERN SUMATRA", - "OFF COAST OF NORTHERN CALIFORNIA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "MARIANA ISLANDS REGION", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "SOLOMON ISLANDS", - "W. CAROLINE ISLANDS", - "PERU-BRAZIL BORDER REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEW IRELAND REGION", - "SOUTHERN CHILE", - "SUMBAWA REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF FIJI ISLANDS", - "QUEEN ELIZABETH ISLANDS", - "CELEBES SEA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTHWESTERN PAKISTAN", - "MINDANAO", - "JUJUY PROVINCE", - "SOUTHERN BOLIVIA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "NEW BRITAIN REGION", - "NICOBAR ISLANDS", - "SOLOMON ISLANDS", - "NEAR ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "BOTSWANA", - "NORTHERN AND CENTRAL IRAN", - "CHILE-ARGENTINA BORDER REGION", - "FIJI ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "SOUTHEASTERN ALASKA", - "TAJIKISTAN", - "ANDREANOF ISLANDS", - "RYUKYU ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CENTRAL AMERICA", - "NEW BRITAIN REGION", - "NEGROS", - "SULAWESI", - "NEAR ISLANDS", - "AEGEAN SEA", - "GUATEMALA", - "KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF GUATEMALA", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "LEYTE", - "AUCKLAND ISLANDS", - "NEW IRELAND REGION", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "NEAR COAST OF PERU", - "MAURITIUS - REUNION REGION", - "DODECANESE ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "NORTH ATLANTIC OCEAN", - "GANSU", - "NORTHERN XINJIANG", - "MINDORO", - "SOUTHERN SUMATRA", - "NORTH OF ASCENSION ISLAND", - "FIJI ISLANDS REGION", - "ADMIRALTY ISLANDS REGION", - "SOUTHERN SUMATRA", - "NORTH KOREA", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "NEAR COAST OF CHIAPAS", - "CENTRAL MEXICO", - "AUCKLAND ISLANDS", - "OFF EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "OAXACA", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "TONGA ISLANDS", - "BALLENY ISLANDS REGION", - "RAT ISLANDS", - "NORTHERN CHILE", - "BOUVET ISLAND REGION", - "TONGA ISLANDS", - "BANDA SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SERAM", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "TONGA ISLANDS", - "NEW GUINEA", - "SOUTHEAST OF HONSHU", - "ASCENSION ISLAND REGION", - "IRAN-IRAQ BORDER REGION", - "COSTA RICA", - "EASTERN XIZANG-INDIA BORDER REG.", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SAMOA ISLANDS REGION", - "NEW IRELAND REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "NORTHERN AND CENTRAL IRAN", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF ECUADOR", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "NORTHERN AND CENTRAL IRAN", - "NORTHERN AND CENTRAL IRAN", - "BOUVET ISLAND REGION", - "JAVA", - "NORTH OF HONDURAS", - "MYANMAR", - "NEAR COAST OF PERU", - "GULF OF CALIFORNIA", - "NORTHERN CHILE", - "JAVA", - "GULF OF ALASKA", - "HOKKAIDO", - "KOMANDORSKIYE OSTROVA REGION", - "NEAR N COAST OF NEW GUINEA", - "SOUTHWEST OF AFRICA", - "HINDU KUSH REGION", - "FIJI ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "TAIWAN", - "TAIWAN", - "SOUTH OF MARIANA ISLANDS", - "OAXACA", - "NEW GUINEA", - "NEW GUINEA", - "CERAM SEA", - "NEW GUINEA", - "WEST OF MACQUARIE ISLAND", - "NEW GUINEA", - "NEW GUINEA", - "NEW GUINEA", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "SOUTHEAST INDIAN RIDGE", - "BANDA SEA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTHERN BOLIVIA", - "MINDANAO", - "NEW GUINEA", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "PRINCE EDWARD ISLANDS REGION", - "EASTER ISLAND REGION", - "HAWAII", - "LUZON", - "NEW BRITAIN REGION", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "SOUTH OF KERMADEC ISLANDS", - "VANUATU ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "KERMADEC ISLANDS", - "VANUATU ISLANDS", - "EASTERN GULF OF ADEN", - "EASTERN GULF OF ADEN", - "SANTA CRUZ ISLANDS", - "NEW BRITAIN REGION", - "FLORES SEA", - "SUMBAWA REGION", - "SUMBAWA REGION", - "NORTHERN ALASKA", - "NORTHERN ALASKA", - "SOUTH SANDWICH ISLANDS REGION", - "ANDREANOF ISLANDS", - "VOLCANO ISLANDS", - "FLORES SEA", - "COSTA RICA", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "SUMBAWA REGION", - "FIJI ISLANDS REGION", - "SUMBAWA REGION", - "NEAR COAST OF VENEZUELA", - "VANUATU ISLANDS", - "OFF COAST OF OREGON", - "ANDREANOF ISLANDS", - "PERU-BRAZIL BORDER REGION", - "WESTERN IRAN", - "TIMOR REGION", - "MARIANA ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "HOKKAIDO", - "FIJI ISLANDS", - "ECUADOR", - "MINDANAO", - "SOLOMON ISLANDS", - "KERMADEC ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHWEST INDIAN RIDGE", - "MINAHASSA PENINSULA", - "MINAHASSA PENINSULA", - "FIJI ISLANDS REGION", - "SUMBA REGION", - "KURIL ISLANDS", - "BALI SEA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "NORTHWEST OF KURIL ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "IONIAN SEA", - "OFF COAST OF CENTRAL AMERICA", - "DRAKE PASSAGE", - "NORTH ISLAND", - "NORTHERN CHILE", - "MINDANAO", - "JAN MAYEN ISLAND REGION", - "TONGA ISLANDS", - "NORTH ATLANTIC OCEAN", - "NEAR EAST COAST OF KAMCHATKA", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN EAST PACIFIC RISE", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "CARIBBEAN SEA", - "IRAN-IRAQ BORDER REGION", - "SOUTHERN ALASKA", - "BANDA SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "IRIAN JAYA", - "SOUTHEAST OF EASTER ISLAND", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "KOMANDORSKIYE OSTROVA REGION", - "MINDANAO", - "ALASKA PENINSULA", - "WESTERN BRAZIL", - "NORTHERN MOLUCCA SEA", - "KYUSHU", - "VANUATU ISLANDS", - "BISMARCK SEA", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SUMBA REGION", - "SUMBA REGION", - "PRINCE EDWARD ISLANDS REGION", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "MARIANA ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "NEW IRELAND REGION", - "PERU-ECUADOR BORDER REGION", - "CENTRAL PERU", - "SOUTH OF KERMADEC ISLANDS", - "PHILIPPINE ISLANDS REGION", - "FIJI ISLANDS REGION", - "D'ENTRECASTEAUX ISLANDS REGION", - "CENTRAL BOLIVIA", - "VANUATU ISLANDS", - "COLOMBIA", - "NORTHERN MOLUCCA SEA", - "EAST OF KURIL ISLANDS", - "NEW BRITAIN REGION", - "NEAR COAST OF ECUADOR", - "MAURITIUS - REUNION REGION", - "RAT ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "BANDA SEA", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "SULAWESI", - "TAIWAN", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "LUZON", - "SOUTH SANDWICH ISLANDS REGION", - "SAMAR", - "SOUTH OF FIJI ISLANDS", - "CARLSBERG RIDGE", - "SOLOMON ISLANDS", - "EASTERN NEW GUINEA REG.", - "KYUSHU", - "PANAMA-COSTA RICA BORDER REGION", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "ANDREANOF ISLANDS", - "NORTHERN PERU", - "EL SALVADOR", - "FIJI ISLANDS REGION", - "MINDANAO", - "TONGA ISLANDS", - "SOUTHEAST OF HONSHU", - "OFF COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR WEST COAST OF HONSHU", - "KERMADEC ISLANDS", - "IRIAN JAYA", - "KERMADEC ISLANDS", - "IRIAN JAYA", - "BANDA SEA", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "PANAMA-COSTA RICA BORDER REGION", - "KERMADEC ISLANDS REGION", - "MARIANA ISLANDS", - "VANUATU ISLANDS", - "QUEEN CHARLOTTE ISLANDS REGION", - "CENTRAL CALIFORNIA", - "CENTRAL CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "SOLOMON ISLANDS", - "NORTHWEST OF RYUKYU ISLANDS", - "WESTERN AUSTRALIA", - "HALMAHERA", - "NEW BRITAIN REGION", - "BALLENY ISLANDS REGION", - "PHILIPPINE ISLANDS REGION", - "NEAR S. COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHWEST OF SUMATRA", - "NEAR EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF OREGON", - "FIJI ISLANDS REGION", - "JAVA", - "MONA PASSAGE", - "SOUTHWEST INDIAN RIDGE", - "SERAM", - "CENTRAL CHILE", - "KERMADEC ISLANDS", - "MINDANAO", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "VANUATU ISLANDS", - "NORTH OF ASCENSION ISLAND", - "MINDANAO", - "MINDANAO", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF CHIAPAS", - "SEA OF OKHOTSK", - "LAOS", - "IRIAN JAYA REGION", - "ANDREANOF ISLANDS", - "ALBANIA", - "CRETE", - "ANDREANOF ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "MINDANAO", - "HINDU KUSH REGION", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "SANTIAGO DEL ESTERO PROV.", - "COLOMBIA", - "VANCOUVER ISLAND", - "SOUTHERN EAST PACIFIC RISE", - "NORTHERN SUMATRA", - "PUERTO RICO REGION", - "NEW BRITAIN REGION", - "EASTERN SIBERIA", - "NEAR NORTH COAST OF IRIAN JAYA", - "SOUTHERN XINJIANG", - "MINAHASSA PENINSULA", - "SOUTH SANDWICH ISLANDS REGION", - "ANDREANOF ISLANDS", - "TURKEY", - "ANDREANOF ISLANDS", - "SOLOMON ISLANDS", - "CUBA REGION", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "JAVA", - "MINDANAO", - "SOUTH SANDWICH ISLANDS REGION", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "TURKEY-IRAN BORDER REGION", - "KERMADEC ISLANDS REGION", - "SAMOA ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTH OF BALI", - "CENTRAL EAST PACIFIC RISE", - "EAST OF KURIL ISLANDS", - "MINDANAO", - "WESTERN IDAHO", - "NORTHERN MOLUCCA SEA", - "MID-INDIAN RIDGE", - "KERMADEC ISLANDS REGION", - "NORTH OF HONDURAS", - "BONIN ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "CRETE", - "BANDA SEA", - "SOLOMON ISLANDS", - "SANTA CRUZ ISLANDS", - "NEVADA", - "OFF COAST OF CENTRAL MEXICO", - "VANUATU ISLANDS", - "SOUTHERN PERU", - "NORTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "HALMAHERA", - "SOUTHERN MID-ATLANTIC RIDGE", - "RYUKYU ISLANDS", - "MARIANA ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "ICELAND REGION", - "MINAHASSA PENINSULA", - "NEAR COAST OF OAXACA", - "XIZANG", - "W. CAROLINE ISLANDS", - "JAVA SEA", - "EASTERN NEW GUINEA REG.", - "ANDAMAN ISLANDS", - "SAMOA ISLANDS REGION", - "FIJI ISLANDS REGION", - "ALASKA PENINSULA", - "SOUTH OF ALASKA", - "XIZANG", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "MINDANAO", - "BISMARCK SEA", - "VANUATU ISLANDS", - "PRINCE EDWARD ISLANDS REGION", - "TANZANIA", - "SAMAR", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "BANDA SEA", - "ASCENSION ISLAND REGION", - "COSTA RICA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "CHAGOS ARCHIPELAGO REGION", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "VANUATU ISLANDS", - "CENTRAL MID-ATLANTIC RIDGE", - "MINDANAO", - "VANUATU ISLANDS", - "NORTHERN CHILE", - "NEAR EAST COAST OF HONSHU", - "KAMCHATKA PENINSULA", - "FIJI ISLANDS", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF AFRICA", - "TONGA ISLANDS", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "EASTERN NEW GUINEA REG.", - "SOUTH OF ALASKA", - "SOUTH OF FIJI ISLANDS", - "DODECANESE ISLANDS", - "OFF COAST OF SOUTHERN CHILE", - "SOUTH SHETLAND ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "MINDANAO", - "OFF COAST OF CENTRAL CHILE", - "PRIMOR'YE", - "CHILE-ARGENTINA BORDER REGION", - "FOX ISLANDS", - "BALLENY ISLANDS REGION", - "NORTHERN CHILE", - "TAIWAN REGION", - "CHILE-BOLIVIA BORDER REGION", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "W. CAROLINE ISLANDS", - "MINDORO", - "OFF COAST OF CENTRAL CHILE", - "NORTHWESTERN BALKAN REGION", - "ANDREANOF ISLANDS", - "KERMADEC ISLANDS REGION", - "MINAHASSA PENINSULA", - "KERMADEC ISLANDS", - "VANUATU ISLANDS", - "SALTA PROVINCE", - "VANUATU ISLANDS", - "RUSSIA-MONGOLIA BORDER REGION", - "SULAWESI", - "SAN JUAN PROVINCE", - "PHILIPPINE ISLANDS REGION", - "SOUTH SHETLAND ISLANDS", - "WEST CHILE RISE", - "MINDANAO", - "BISMARCK SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWEST OF SUMATRA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "TONGA ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "GREECE", - "OFF E. COAST OF N. ISLAND", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF KAMCHATKA", - "NORTHERN ALGERIA", - "NORTHERN MID-ATLANTIC RIDGE", - "WEST OF MACQUARIE ISLAND", - "NEAR EAST COAST OF HONSHU", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "EAST OF SOUTH SANDWICH ISLANDS", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "JAVA", - "CELEBES SEA", - "NEAR N COAST OF NEW GUINEA", - "OFF W COAST OF NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NORTHEASTERN INDIA", - "KERMADEC ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "WEST OF MACQUARIE ISLAND", - "FIJI ISLANDS REGION", - "NEAR COAST OF GUATEMALA", - "MAURITIUS - REUNION REGION", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTHERN EAST PACIFIC RISE", - "YUNNAN", - "QINGHAI", - "FIJI ISLANDS REGION", - "CENTRAL ALASKA", - "NORTHERN MOLUCCA SEA", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "CALIFORNIA-NEVADA BORDER REGION", - "SOUTH OF PANAMA", - "BISMARCK SEA", - "SOUTH OF PANAMA", - "MINDORO", - "KERMADEC ISLANDS REGION", - "MINAHASSA PENINSULA", - "ALASKA PENINSULA", - "PERU-ECUADOR BORDER REGION", - "NICOBAR ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "HAITI REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF GUERRERO", - "CHILE-ARGENTINA BORDER REGION", - "KURIL ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF NICARAGUA", - "ANDREANOF ISLANDS", - "CRETE", - "EAST OF KURIL ISLANDS", - "SEA OF JAPAN", - "VANUATU ISLANDS REGION", - "MID-INDIAN RIDGE", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS REGION", - "HAWAII", - "ALASKA PENINSULA", - "CRETE", - "SOLOMON ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "TAIWAN", - "SOUTH SANDWICH ISLANDS REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTHERN MOLUCCA SEA", - "NEAR COAST OF NICARAGUA", - "SOUTHEAST OF RYUKYU ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "IRIAN JAYA REGION", - "SOUTHERN IRAN", - "SOUTHERN IRAN", - "SOUTH INDIAN OCEAN", - "NEW IRELAND REGION", - "SANTA CRUZ ISLANDS", - "MYANMAR-INDIA BORDER REGION", - "NORTHERN PERU", - "SOUTHEAST OF HONSHU", - "SOUTHEAST OF HONSHU", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "EASTER ISLAND REGION", - "NORTH OF HALMAHERA", - "RYUKYU ISLANDS", - "WEST OF MACQUARIE ISLAND", - "FLORES SEA", - "SOUTH SANDWICH ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "FIJI ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF NORTHERN CALIF.", - "OFF COAST OF JALISCO", - "FIJI ISLANDS REGION", - "BANDA SEA", - "VANUATU ISLANDS", - "TAIWAN REGION", - "BANDA SEA", - "NEAR COAST OF NICARAGUA", - "QINGHAI", - "SOUTH OF KERMADEC ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "CYPRUS REGION", - "FOX ISLANDS", - "FOX ISLANDS", - "SUNDA STRAIT", - "SOLOMON ISLANDS", - "SHIKOKU", - "TALAUD ISLANDS", - "FOX ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH OF PANAMA", - "KERMADEC ISLANDS REGION", - "BANDA SEA", - "NORTHERN PERU", - "SOUTHEAST INDIAN RIDGE", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF MARIANA ISLANDS", - "GUATEMALA", - "SOUTH OF FIJI ISLANDS", - "BALLENY ISLANDS REGION", - "JUJUY PROVINCE", - "NORTHERN SUMATRA", - "KERMADEC ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "LUZON", - "LUZON", - "SOUTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "TAIWAN", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "MINDANAO", - "MINDANAO", - "NEAR COAST OF NICARAGUA", - "BISMARCK SEA", - "PHILIPPINE ISLANDS REGION", - "TAIWAN REGION", - "BISMARCK SEA", - "JUJUY PROVINCE", - "MACQUARIE ISLAND REGION", - "LUZON", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "CENTRAL PERU", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "TIMOR REGION", - "TONGA ISLANDS", - "WEST OF MACQUARIE ISLAND", - "RAT ISLANDS", - "PERU-BRAZIL BORDER REGION", - "SOUTHEASTERN AFGHANISTAN", - "PHILIPPINE ISLANDS REGION" - ], - "lat": [ - -13.6098, - 39.6611, - 11.1664, - -22.2789, - -38.0635, - 52.7536, - -5.9807, - -6.0444, - 52.8204, - -5.9808, - 3.0029, - 6.7113, - 6.4232, - 6.7403, - 6.1711, - 6.5583, - 52.4218, - 1.2978, - -5.5179, - 51.3848, - -5.8249, - 46.0031, - -17.5345, - -1.2779, - 12.4748, - 12.3454, - 12.2011, - -5.7825, - -17.8076, - -20.8416, - 12.247, - -6.5338, - 2.1984, - 18.5693, - 51.6273, - -43.3608, - -17.4526, - -15.8867, - -20.7545, - -37.0545, - 36.5352, - -5.2995, - -4.9188, - -4.9863, - 24.2175, - 51.3459, - 51.4225, - 2.7637, - 24.7878, - -3.5248, - -3.0065, - -57.116, - -4.6494, - -20.519, - -45.5992, - -54.8356, - -6.0155, - 28.3941, - -15.8273, - -6.1002, - -56.4318, - -6.5265, - -7.2664, - 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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategorymarker_sizemagnitude_bin
02010-07-02 06:04:03.570-13.6098166.65410.034406.3VANUATU ISLANDS34.40Moderate544.06.0
11122017-08-31 17:06:55.750-1.159099.68810.043146.3SOUTHERN SUMATRA43.14Moderate544.06.0
11112017-08-27 04:17:51.010-1.4513148.08030.008006.3ADMIRALTY ISLANDS REGION8.00Moderate544.06.0
11102017-08-19 02:00:52.550-17.9609-178.84060.544006.4FIJI ISLANDS REGION544.00Moderate601.06.0
11082017-08-13 03:08:10.560-3.7682101.62280.031006.4SOUTHERN SUMATRA31.00Moderate601.06.0
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" + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.03440 6.3 \n", - "1112 2017-08-31 17:06:55.750 -1.1590 99.6881 0.04314 6.3 \n", - "1111 2017-08-27 04:17:51.010 -1.4513 148.0803 0.00800 6.3 \n", - "1110 2017-08-19 02:00:52.550 -17.9609 -178.8406 0.54400 6.4 \n", - "1108 2017-08-13 03:08:10.560 -3.7682 101.6228 0.03100 6.4 \n", - "\n", - " description depth_km Category marker_size magnitude_bin \n", - "0 VANUATU ISLANDS 34.40 Moderate 544.0 6.0 \n", - "1112 SOUTHERN SUMATRA 43.14 Moderate 544.0 6.0 \n", - "1111 ADMIRALTY ISLANDS REGION 8.00 Moderate 544.0 6.0 \n", - "1110 FIJI ISLANDS REGION 544.00 Moderate 601.0 6.0 \n", - "1108 SOUTHERN SUMATRA 31.00 Moderate 601.0 6.0 " + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "quakes2plot=quake.sort_values(by='magnitude_bin')\n", - "quakes2plot.head()" - ] - }, - { - "cell_type": "markdown", - "id": "dcf7472d", - "metadata": {}, - "source": [ - "Now we will plot again using Plotly" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "c21ce4ea", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - "VANUATU ISLANDS", - 6.3, - 0.0344 - ], - [ - "SOUTHERN SUMATRA", - 6.3, - 0.04314 - ], - [ - "ADMIRALTY ISLANDS REGION", - 6.3, - 0.008 - ], - [ - "FIJI ISLANDS REGION", - 6.4, - 0.544 - ], - [ - "SOUTHERN SUMATRA", - 6.4, - 0.031 - ], - [ - "MINDORO", - 6.2, - 0.172 - ], - [ - "NORTHERN XINJIANG", - 6.3, - 0.02 - ], - [ - "NORTH ATLANTIC OCEAN", - 6, - 0.01 - ], - [ - "SOUTHEAST OF RYUKYU ISLANDS", - 6, - 0.012 - ], - [ - "NORTH KOREA", - 6.3, - 0 - ], - [ - "MAURITIUS - REUNION REGION", - 6, - 0.01 - ], - [ - "KOMANDORSKIYE OSTROVA REGION", - 6.3, - 0.01 - ], - [ - "NEW IRELAND REGION", - 6.4, - 0.03478 - ], - [ - "NEAR COAST OF ECUADOR", - 6, - 0.013 - ], - [ - "KERMADEC ISLANDS REGION", - 6, - 0.41461000000000003 - ], - [ - "KERMADEC ISLANDS", - 6, - 0.019 - ], - [ - "FIJI ISLANDS REGION", - 6.2, - 0.038270000000000005 - ], - [ - "SOUTH OF FIJI ISLANDS", - 6.1, - 0.511 - ], - [ - "KERMADEC ISLANDS", - 6, - 0.034 - ], - [ - "NEAR COAST OF PERU", - 6.4, - 0.04492 - ], - [ - "SOUTH SANDWICH ISLANDS REGION", - 6, - 0.035 - ], - [ - "BONIN ISLANDS", - 6.1, - 0.451 - ], - [ - "AUCKLAND ISLANDS", - 6.1, - 0.01 - ], - [ - "NEW IRELAND REGION", - 6, - 0.052 - ], - [ - "SAMOA ISLANDS REGION", - 6, - 0.006 - ], - [ - "LOYALTY ISLANDS", - 6, - 0.01 - ], - [ - "LOYALTY ISLANDS", - 6.3, - 0.004 - ], - [ - "EASTERN XIZANG-INDIA BORDER REG.", - 6.4, - 0.008 - ], - [ - "ASCENSION ISLAND REGION", - 6.1, - 0.01 - ], - [ - "SOUTHEAST OF HONSHU", - 6, - 0.012 - ], - [ - "LOYALTY ISLANDS", - 6, - 0.01 - ], - [ - "LOYALTY ISLANDS", - 6.1, - 0.01 - ], - [ - "SERAM", - 6.1, - 0.01847 - ], - [ - "TONGA ISLANDS", - 6.1, - 0.01 - ], - [ - "NORTHERN CHILE", - 6.3, - 0.08243 - ], - [ - "BALLENY ISLANDS REGION", - 6.3, - 0.01 - ], - [ - "TONGA ISLANDS", - 6.1, - 0.01 - ], - [ - "OFF EAST COAST OF HONSHU", - 6.2, - 0.009 - ], - [ - "SOUTH OF FIJI ISLANDS", - 6.4, - 0.09806999999999999 - ], - [ - "OAXACA", - 6.1, - 0.00962 - ], - [ - "VANUATU ISLANDS", - 6.4, - 0.197 - ], - [ - "OFF EAST COAST OF HONSHU", - 6.1, - 0.011 - ], - [ - "AEGEAN SEA", - 6.3, - 0.012 - ], - [ - "NEGROS", - 6, - 0.533 - ], - [ - "NEW BRITAIN REGION", - 6.2, - 0.01 - ], - [ - "OFF COAST OF CENTRAL AMERICA", - 6.2, - 0.01 - ], - [ - "W. 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marker_size=%{marker.size}
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depth=%{customdata[2]}", - "hovertext": [ - "VANUATU ISLANDS", - "SOUTHERN SUMATRA", - "ADMIRALTY ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTHERN SUMATRA", - "MINDORO", - "NORTHERN XINJIANG", - "NORTH ATLANTIC OCEAN", - "SOUTHEAST OF RYUKYU ISLANDS", - "NORTH KOREA", - "MAURITIUS - REUNION REGION", - "KOMANDORSKIYE OSTROVA REGION", - "NEW IRELAND REGION", - "NEAR COAST OF ECUADOR", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS", - "NEAR COAST OF PERU", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "AUCKLAND ISLANDS", - "NEW IRELAND REGION", - "SAMOA ISLANDS REGION", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "EASTERN XIZANG-INDIA BORDER REG.", - "ASCENSION ISLAND REGION", - "SOUTHEAST OF HONSHU", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SERAM", - "TONGA ISLANDS", - "NORTHERN CHILE", - "BALLENY ISLANDS REGION", - "TONGA ISLANDS", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "OAXACA", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "AEGEAN SEA", - "NEGROS", - "NEW BRITAIN REGION", - "OFF COAST OF CENTRAL AMERICA", - "W. CAROLINE ISLANDS", - "SOLOMON ISLANDS", - "NEW IRELAND REGION", - "MARIANA ISLANDS REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEAR ISLANDS", - "SOUTHERN PERU", - "SAN JUAN PROVINCE", - "SOUTH ISLAND", - "SOUTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "CENTRAL ITALY", - "TONGA ISLANDS", - "WESTERN HONSHU", - "NEW BRITAIN REGION", - "TONGA ISLANDS", - "MINDANAO", - "PERU-BRAZIL BORDER REGION", - "NORTHERN AND CENTRAL IRAN", - "SOLOMON ISLANDS", - "SUMBAWA REGION", - "RYUKYU ISLANDS", - "ANDREANOF ISLANDS", - "TAJIKISTAN", - "SOUTHEASTERN ALASKA", - "SOUTHEASTERN ALASKA", - "OFF COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "CHILE-ARGENTINA BORDER REGION", - "NORTHERN AND CENTRAL IRAN", - "NEAR ISLANDS", - "SOLOMON ISLANDS", - "NICOBAR ISLANDS", - "NEW BRITAIN REGION", - "SOLOMON ISLANDS", - "JUJUY PROVINCE", - "SOUTHWESTERN PAKISTAN", - "SOLOMON ISLANDS", - "QUEEN ELIZABETH ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEW IRELAND REGION", - "OFF EAST COAST OF HONSHU", - "EASTERN NEW GUINEA REG.", - "W. CAROLINE ISLANDS", - "MINAHASSA PENINSULA", - "SOUTHWEST INDIAN RIDGE", - "SOUTHEAST OF LOYALTY ISLANDS", - "MINDANAO", - "ECUADOR", - "MARIANA ISLANDS", - "TIMOR REGION", - "WESTERN IRAN", - "SUMBA REGION", - "ANDREANOF ISLANDS", - "SUMBAWA REGION", - "FIJI ISLANDS REGION", - "COSTA RICA", - "VOLCANO ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN ALASKA", - "NORTHERN ALASKA", - "SUMBAWA REGION", - "OFF COAST OF OREGON", - "KURIL ISLANDS", - "BALI SEA", - "NEW BRITAIN REGION", - "PACIFIC-ANTARCTIC RIDGE", - "SOUTHEAST OF LOYALTY ISLANDS", - "BANDA SEA", - "IRAN-IRAQ BORDER REGION", - "CARIBBEAN SEA", - "SOLOMON ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR EAST COAST OF KAMCHATKA", - "NORTH ATLANTIC OCEAN", - "TONGA ISLANDS", - "MINDANAO", - "NORTHERN CHILE", - "NORTH ISLAND", - "DRAKE PASSAGE", - "OFF COAST OF CENTRAL AMERICA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEW BRITAIN REGION", - "FLORES SEA", - "NEW BRITAIN REGION", - "SANTA CRUZ ISLANDS", - "EASTERN GULF OF ADEN", - "CERAM SEA", - "NEW GUINEA", - "SOUTH OF MARIANA ISLANDS", - "TAIWAN", - "TAIWAN", - "PACIFIC-ANTARCTIC RIDGE", - "FIJI ISLANDS REGION", - "HINDU KUSH REGION", - "NEAR N COAST OF NEW GUINEA", - "KOMANDORSKIYE OSTROVA REGION", - "HOKKAIDO", - "JAVA", - "NORTHERN CHILE", - "GULF OF CALIFORNIA", - "MYANMAR", - "NORTHERN AND CENTRAL IRAN", - "NORTHERN AND CENTRAL IRAN", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "NEW GUINEA", - "NEAR COAST OF ECUADOR", - "WEST OF MACQUARIE ISLAND", - "NEW GUINEA", - "EASTERN GULF OF ADEN", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "VANUATU ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "NEW BRITAIN REGION", - "LUZON", - "EASTER ISLAND REGION", - "PRINCE EDWARD ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "NEAR COAST OF CENTRAL CHILE", - "NEW GUINEA", - "MINDANAO", - "SOUTH OF FIJI ISLANDS", - "BANDA SEA", - "SOUTHEAST INDIAN RIDGE", - "NEW BRITAIN REGION", - "NEW GUINEA", - "IRIAN JAYA", - "SOUTHEAST OF HONSHU", - "SOLOMON ISLANDS", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NORTHERN CHILE", - "HOKKAIDO", - "BORNEO", - "SOUTHERN XINJIANG", - "SOUTHEAST OF HONSHU", - "TONGA ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "TONGA ISLANDS", - "SOUTHERN SUMATRA", - "NEPAL", - "MOROCCO", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "LEYTE", - "EAST OF KURIL ISLANDS", - "SOUTHERN ALASKA", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEW BRITAIN REGION", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "FOX ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHEAST OF HONSHU", - "KERMADEC ISLANDS REGION", - "SOLOMON ISLANDS", - "MID-INDIAN RIDGE", - "SOUTH OF FIJI ISLANDS", - "NEPAL", - "QUEEN CHARLOTTE ISLANDS REGION", - "SOUTH ISLAND", - "SANTA CRUZ ISLANDS", - "MENDOZA PROVINCE", - "ADMIRALTY ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "FIJI ISLANDS REGION", - "MINDANAO", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "NORTHERN MOLUCCA SEA", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "TAIWAN REGION", - "NEAR COAST OF GUATEMALA", - "BANDA SEA", - "SOUTH OF PANAMA", - "EASTERN HONSHU", - "SICHUAN", - "PRINCE EDWARD ISLANDS REGION", - "TAIWAN REGION", - "OFF COAST OF CENTRAL CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "TAIWAN REGION", - "TAIWAN REGION", - "CRETE", - "TONGA ISLANDS", - "NEW BRITAIN REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NORTHERN CHILE", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "MINAHASSA PENINSULA", - "NORTHERN COLOMBIA", - "MID-INDIAN RIDGE", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "NEAR NORTH COAST OF IRIAN JAYA", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "NICARAGUA", - "SOUTH OF SUMBAWA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF JALISCO", - "NORTHERN MID-ATLANTIC RIDGE", - "KERMADEC ISLANDS", - "TAIWAN REGION", - "FIJI ISLANDS REGION", - "NORTHERN TERRITORY", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "BANDA SEA", - "NEW IRELAND REGION", - "TAJIKISTAN-XINJIANG BORDER REG.", - "VANUATU ISLANDS", - "NORTHERN COLOMBIA", - "NORTHERN PERU", - "MACQUARIE ISLAND REGION", - "KOMANDORSKIYE OSTROVA REGION", - "OFF E. COAST OF N. ISLAND", - "CENTRAL ITALY", - "FLORES SEA", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "VOLCANO ISLANDS", - "JUJUY PROVINCE", - "ADMIRALTY ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CHIAPAS", - "VANUATU ISLANDS", - "KYUSHU", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "SOUTHERN EAST PACIFIC RISE", - "BORNEO", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "MARIANA ISLANDS", - "SOUTHEASTERN AFGHANISTAN", - "OFF COAST OF CENTRAL CHILE", - "PRINCE EDWARD ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "BOUVET ISLAND REGION", - "TONGA ISLANDS", - "EAST OF KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "JUJUY PROVINCE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "NEAR COAST OF CENTRAL CHILE", - "PACIFIC-ANTARCTIC RIDGE", - "SOUTHERN BOLIVIA", - "KYUSHU", - "MINDANAO", - "JAVA", - "ALASKA PENINSULA", - "NEAR N COAST OF NEW GUINEA", - "NEAR EAST COAST OF KAMCHATKA", - "LEEWARD ISLANDS", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "HALMAHERA", - "SOUTHERN EAST PACIFIC RISE", - "SUMBA REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOLOMON ISLANDS", - "TAIWAN", - "KERMADEC ISLANDS REGION", - "BALLENY ISLANDS REGION", - "STRAIT OF GIBRALTAR", - "HOKKAIDO", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "TONGA ISLANDS", - "NEAR N COAST OF NEW GUINEA", - "CELEBES SEA", - "JAVA", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "FIJI ISLANDS REGION", - "WEST OF MACQUARIE ISLAND", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF W COAST OF NORTHERN SUMATRA", - "NORTHERN ALGERIA", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NORTHEASTERN INDIA", - "SOUTH SANDWICH ISLANDS REGION", - "NICOBAR ISLANDS", - "PERU-ECUADOR BORDER REGION", - "MINAHASSA PENINSULA", - "KERMADEC ISLANDS REGION", - "BISMARCK SEA", - "SOUTH OF PANAMA", - "CALIFORNIA-NEVADA BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "CENTRAL ALASKA", - "YUNNAN", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "NEAR COAST OF GUATEMALA", - "FIJI ISLANDS REGION", - "WEST OF MACQUARIE ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "GREECE", - "W. CAROLINE ISLANDS", - "NEAR EAST COAST OF HONSHU", - "MINDANAO", - "CHILE-BOLIVIA BORDER REGION", - "TAIWAN REGION", - "NORTHERN CHILE", - "BALLENY ISLANDS REGION", - "FOX ISLANDS", - "CHILE-ARGENTINA BORDER REGION", - "PRIMOR'YE", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "SOUTH SHETLAND ISLANDS", - "OFF COAST OF SOUTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "EASTERN NEW GUINEA REG.", - "FIJI ISLANDS REGION", - "MINDORO", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHWESTERN BALKAN REGION", - "KERMADEC ISLANDS REGION", - "FIJI ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWEST OF SUMATRA", - "SOUTHEAST OF LOYALTY ISLANDS", - "BISMARCK SEA", - "MINDANAO", - "SAN JUAN PROVINCE", - "SULAWESI", - "VANUATU ISLANDS", - "SALTA PROVINCE", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "MINAHASSA PENINSULA", - "ANDREANOF ISLANDS", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "LUZON", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN SUMATRA", - "JUJUY PROVINCE", - "BALLENY ISLANDS REGION", - "GUATEMALA", - "SOUTH OF MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "CENTRAL MID-ATLANTIC RIDGE", - "BANDA SEA", - "SOUTH OF PANAMA", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "FOX ISLANDS", - "TALAUD ISLANDS", - "SHIKOKU", - "SOLOMON ISLANDS", - "SOUTHEAST INDIAN RIDGE", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "RAT ISLANDS", - "WEST OF MACQUARIE ISLAND", - "TONGA ISLANDS", - "TIMOR REGION", - "SOUTHERN EAST PACIFIC RISE", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "LUZON", - "BISMARCK SEA", - "TAIWAN REGION", - "PHILIPPINE ISLANDS REGION", - "BISMARCK SEA", - "MINDANAO", - "MINDANAO", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "SOUTH OF KERMADEC ISLANDS", - "NEAR COAST OF NICARAGUA", - "BANDA SEA", - "SOUTHERN MOLUCCA SEA", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH SANDWICH ISLANDS REGION", - "TAIWAN", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "CRETE", - "HAWAII", - "SOUTH SANDWICH ISLANDS REGION", - "MID-INDIAN RIDGE", - "SEA OF JAPAN", - "CRETE", - "ANDREANOF ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "KURIL ISLANDS", - "CHILE-ARGENTINA BORDER REGION", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR COAST OF NICARAGUA", - "SOUTH SANDWICH ISLANDS REGION", - "IRIAN JAYA REGION", - "SOUTHERN IRAN", - "TAIWAN REGION", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "OFF COAST OF JALISCO", - "NEAR COAST OF NORTHERN CALIF.", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS", - "PRINCE EDWARD ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "RYUKYU ISLANDS", - "NORTH OF HALMAHERA", - "EASTER ISLAND REGION", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "SOUTHEAST OF HONSHU", - "MYANMAR-INDIA BORDER REGION", - "SANTA CRUZ ISLANDS", - "NEW IRELAND REGION", - "SOUTH INDIAN OCEAN", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS", - "CENTRAL CALIFORNIA", - "QUEEN CHARLOTTE ISLANDS REGION", - "VANUATU ISLANDS", - "MARIANA ISLANDS", - "KERMADEC ISLANDS REGION", - "PANAMA-COSTA RICA BORDER REGION", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "SOLOMON ISLANDS", - "IRIAN JAYA", - "IRIAN JAYA", - "KERMADEC ISLANDS", - "NEAR WEST COAST OF HONSHU", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "TONGA ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SOUTHEAST OF HONSHU", - "KERMADEC ISLANDS", - "NORTHWEST OF RYUKYU ISLANDS", - "NEW BRITAIN REGION", - "BALLENY ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "NORTH OF ASCENSION ISLAND", - "VANUATU ISLANDS", - "MINDANAO", - "MINDANAO", - "KERMADEC ISLANDS", - "CENTRAL CHILE", - "SOUTHWEST INDIAN RIDGE", - "MONA PASSAGE", - "JAVA", - "OFF COAST OF OREGON", - "VANUATU ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN EAST PACIFIC RISE", - "NEAR S. COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "TONGA ISLANDS", - "MINDANAO", - "FIJI ISLANDS REGION", - "ANDREANOF ISLANDS", - "CENTRAL BOLIVIA", - "D'ENTRECASTEAUX ISLANDS REGION", - "FIJI ISLANDS REGION", - "PHILIPPINE ISLANDS REGION", - "SOUTH OF KERMADEC ISLANDS", - "NEW IRELAND REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "MARIANA ISLANDS", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "FIJI ISLANDS REGION", - "SOLOMON ISLANDS", - "SUMBA REGION", - "SUMBA REGION", - "VANUATU ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "ALASKA PENINSULA", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "COLOMBIA", - "EAST OF KURIL ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "PANAMA-COSTA RICA BORDER REGION", - "KYUSHU", - "SOLOMON ISLANDS", - "CARLSBERG RIDGE", - "SOUTH OF FIJI ISLANDS", - "SAMAR", - "SOUTH SANDWICH ISLANDS REGION", - "LUZON", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "BANDA SEA", - "SOUTH SANDWICH ISLANDS REGION", - "RAT ISLANDS", - "MAURITIUS - REUNION REGION", - "NEAR COAST OF ECUADOR", - "NEW BRITAIN REGION", - "NORTHERN MOLUCCA SEA", - "SOUTH OF AFRICA", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "SAMOA ISLANDS REGION", - "ANDAMAN ISLANDS", - "W. CAROLINE ISLANDS", - "XIZANG", - "MINAHASSA PENINSULA", - "ICELAND REGION", - "MARIANA ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "FIJI ISLANDS REGION", - "HALMAHERA", - "SOUTHERN PERU", - "VANUATU ISLANDS", - "OFF COAST OF CENTRAL MEXICO", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NORTH OF HONDURAS", - "KERMADEC ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF ALASKA", - "XIZANG", - "SOUTH SANDWICH ISLANDS REGION", - "KAMCHATKA PENINSULA", - "NEAR EAST COAST OF HONSHU", - "NORTHERN CHILE", - "VANUATU ISLANDS", - "MINDANAO", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "CHAGOS ARCHIPELAGO REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "COSTA RICA", - "ASCENSION ISLAND REGION", - "TANZANIA", - "PRINCE EDWARD ISLANDS REGION", - "VANUATU ISLANDS", - "BISMARCK SEA", - "MINDANAO", - "ALASKA PENINSULA", - "MID-INDIAN RIDGE", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "CENTRAL EAST PACIFIC RISE", - "VANCOUVER ISLAND", - "COLOMBIA", - "SANTIAGO DEL ESTERO PROV.", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "HINDU KUSH REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "ANDREANOF ISLANDS", - "CRETE", - "ALBANIA", - "ANDREANOF ISLANDS", - "IRIAN JAYA REGION", - "LAOS", - "SEA OF OKHOTSK", - "OFF COAST OF CHIAPAS", - "NORTHERN MOLUCCA SEA", - "SOUTHERN EAST PACIFIC RISE", - "VANUATU ISLANDS", - "NORTHERN SUMATRA", - "NEW BRITAIN REGION", - "SOUTH OF BALI", - "VANUATU ISLANDS", - "SAMOA ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "TURKEY-IRAN BORDER REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "MINDANAO", - "JAVA", - "SOLOMON ISLANDS", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "MINAHASSA PENINSULA", - "SOUTHERN XINJIANG", - "NEAR NORTH COAST OF IRIAN JAYA", - "EASTERN SIBERIA", - "PUERTO RICO REGION", - "MID-INDIAN RIDGE", - "BANDA SEA", - "PHILIPPINE ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "EAST OF KURIL ISLANDS", - "VANUATU ISLANDS REGION", - "GUATEMALA", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "E. RUSSIA-N.E. CHINA BORDER REG.", - "SULAWESI", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "EAST OF KURIL ISLANDS", - "BANDA SEA", - "KURIL ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "OFF EAST COAST OF KAMCHATKA", - "OFF EAST COAST OF KAMCHATKA", - "OFF COAST OF SOUTHERN CHILE", - "NEAR EAST COAST OF HONSHU", - "COSTA RICA", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN IRAN", - "KERMADEC ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "MICHOACAN", - "SOUTHEAST OF HONSHU", - "TONGA ISLANDS", - "CRETE", - "NEAR EAST COAST OF HONSHU", - "CENTRAL MID-ATLANTIC RIDGE", - "MINAHASSA PENINSULA", - "TAJIKISTAN", - "PACIFIC-ANTARCTIC RIDGE", - "NORTHERN SUMATRA", - "LAKE ISSYK-KUL REGION", - "ALASKA PENINSULA", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SANTA CRUZ ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "KURIL ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "SANTIAGO DEL ESTERO PROV.", - "MINDANAO", - "WEST OF MACQUARIE ISLAND", - "HINDU KUSH REGION", - "SOUTH ISLAND", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "IRIAN JAYA REGION", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "TAIWAN", - "NEAR EAST COAST OF HONSHU", - "EASTERN HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEGROS", - "SOUTHWEST INDIAN RIDGE", - "NEAR COAST OF GUERRERO", - "KERMADEC ISLANDS REGION", - "ANDREANOF ISLANDS", - "NEAR EAST COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "FLORES SEA", - "ANDREANOF ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF NORTHERN PERU", - "CENTRAL EAST PACIFIC RISE", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "NEAR EAST COAST OF HONSHU", - "ANDREANOF ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF CHIAPAS", - "KERMADEC ISLANDS REGION", - "NEAR WEST COAST OF HONSHU", - "BANDA SEA", - "SOUTHERN SUMATRA", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "CRETE", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF NICARAGUA", - "SOUTH OF KERMADEC ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "CRETE", - "SOLOMON ISLANDS", - "PHILIPPINE ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "GANSU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "EASTERN HONSHU", - "SOUTHERN PERU", - "OFF EAST COAST OF HONSHU", - "PRINCE EDWARD ISLANDS REGION", - "NEW IRELAND REGION", - "NEAR ISLANDS", - "OFF W. COAST OF BAJA CALIFORNIA", - "NORTHERN ITALY", - "NEAR COAST OF NORTHERN CHILE", - "LUZON", - "OFF EAST COAST OF HONSHU", - "HOKKAIDO", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "GREENLAND SEA", - "BONIN ISLANDS", - "NEW IRELAND REGION", - "SALTA PROVINCE", - "HOKKAIDO", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "SOUTHERN PERU", - "KURIL ISLANDS", - "NICARAGUA", - "VANUATU ISLANDS", - "COOK STRAIT", - "NORTHERN XINJIANG", - "NEAR EAST COAST OF KAMCHATKA", - "OFF COAST OF SOUTHERN CHILE", - "NORTHERN SUMATRA", - "NEAR ISLANDS", - "NEAR EAST COAST OF HONSHU", - "LUZON", - "SOUTHERN MOLUCCA SEA", - "DODECANESE ISLANDS", - "OFF COAST OF ECUADOR", - "SOUTHERN EAST PACIFIC RISE", - "PERU-BOLIVIA BORDER REGION", - "NEAR COAST OF CHIAPAS", - "KERMADEC ISLANDS REGION", - "IRIAN JAYA", - "SOUTH SANDWICH ISLANDS REGION", - "NEW BRITAIN REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CHIAPAS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF PERU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "SANTIAGO DEL ESTERO PROV.", - "KURIL ISLANDS", - "SOUTH SHETLAND ISLANDS", - "NEAR EAST COAST OF HONSHU", - "IRIAN JAYA REGION", - "EAST OF SOUTH SANDWICH ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "VANUATU ISLANDS", - "DRAKE PASSAGE", - "NORTHERN EAST PACIFIC RISE", - "MINAHASSA PENINSULA", - "BAJA CALIFORNIA", - "OFF COAST OF OREGON", - "NORTH INDIAN OCEAN", - "NEW IRELAND REGION", - "NEAR COAST OF GUERRERO", - "SANTA CRUZ ISLANDS", - "TONGA ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "NORTHERN SUMATRA", - "REVILLA GIGEDO ISLANDS REGION", - "FIJI ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "CERAM SEA", - "CELEBES SEA", - "ASCENSION ISLAND REGION", - "VANUATU ISLANDS", - "COSTA RICA", - "KURIL ISLANDS", - "NEAR S. COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "MINDANAO", - "VANCOUVER ISLAND", - "CENTRAL PERU", - "VANUATU ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "GULF OF ALASKA", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NORTHERN MOLUCCA SEA", - "NEW IRELAND REGION", - "GULF OF CALIFORNIA", - "NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR N COAST OF NEW GUINEA", - "NEW BRITAIN REGION", - "NEAR EAST COAST OF HONSHU", - "GUERRERO", - "NEAR COAST OF CENTRAL CHILE", - "OFF COAST OF SOUTHERN CHILE", - "VANUATU ISLANDS", - "BANDA SEA", - "SUNDA STRAIT", - "VANUATU ISLANDS", - "BALLENY ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "SULAWESI", - "SOUTH OF BALI", - "SOUTHEASTERN SIBERIA", - "NEW BRITAIN REGION", - "SOUTH OF KERMADEC ISLANDS", - "HOKKAIDO", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "TURKEY", - "FOX ISLANDS", - "FIJI ISLANDS REGION", - "NEW IRELAND REGION", - "PERU-BRAZIL BORDER REGION", - "XIZANG", - "PACIFIC-ANTARCTIC RIDGE", - "TONGA ISLANDS", - "SOUTH OF JAVA", - "GULF OF CALIFORNIA", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "BAJA CALIFORNIA", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "OFF EAST COAST OF HONSHU", - "SOUTHERN SUMATRA", - "AUCKLAND ISLANDS", - "IRIAN JAYA REGION", - "NEAR ISLANDS", - "SANTA CRUZ ISLANDS", - "EAST OF NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "KURIL ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTHERN EAST PACIFIC RISE", - "TONGA ISLANDS", - "IRIAN JAYA REGION", - "E. CAROLINE ISLANDS", - "E. CAROLINE ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "NEGROS", - "OFF W COAST OF NORTHERN SUMATRA", - "BAY OF BENGAL", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "VANUATU ISLANDS", - "HALMAHERA", - "GUERRERO", - "ANDREANOF ISLANDS", - "SOUTHWESTERN RYUKYU ISL.", - "IRIAN JAYA REGION", - "ARU ISLANDS REGION", - "RAT ISLANDS", - "RAT ISLANDS", - "HINDU KUSH REGION", - "RAT ISLANDS", - "RAT ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "VOLCANO ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "ANDREANOF ISLANDS", - "TONGA ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "NEW BRITAIN REGION", - "JAN MAYEN ISLAND REGION", - "SOUTHERN IRAN", - "NORTHERN SUMATRA", - "TAJIKISTAN", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "SOUTHERN SUMATRA", - "LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "BALLENY ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "NEAR ISLANDS", - "BONIN ISLANDS", - "SOLOMON ISLANDS", - "BANDA SEA", - "OFF EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "NEAR S. COAST OF HONSHU", - "THAILAND", - "WEST CHILE RISE", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "BONIN ISLANDS", - "BALLENY ISLANDS REGION", - "TONGA ISLANDS", - "HOKKAIDO", - "HOKKAIDO", - "IRAN-IRAQ BORDER REGION", - "IRAN-IRAQ BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "ANDREANOF ISLANDS", - "NORTHERN CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "OFF EAST COAST OF KAMCHATKA", - "EASTER ISLAND REGION", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "OFF COAST OF JALISCO", - "SOUTHERN MOLUCCA SEA", - "NORTH OF HALMAHERA", - "JUJUY PROVINCE", - "SOLOMON ISLANDS", - "SOUTHERN ALASKA", - "NEW BRITAIN REGION", - "YUNNAN", - "GULF OF CALIFORNIA", - "NEW BRITAIN REGION", - "FOX ISLANDS", - "HOKKAIDO", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "NORTHERN CHILE", - "SOUTH OF MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "BANDA SEA", - "TONGA ISLANDS", - "BANDA SEA", - "XIZANG", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF COAST OF JALISCO", - "KERMADEC ISLANDS REGION", - "NORTHERN SUMATRA", - "NEW BRITAIN REGION", - "SOLOMON ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH OF MARIANA ISLANDS", - "FIJI ISLANDS REGION", - "MAURITIUS - REUNION REGION", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "MINDANAO", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "TONGA ISLANDS", - "KURIL ISLANDS", - "TONGA ISLANDS", - "OWEN FRACTURE ZONE REGION", - "KURIL ISLANDS", - "SOUTHEASTERN ALASKA", - "NORTHERN MID-ATLANTIC RIDGE", - "OAXACA", - "BISMARCK SEA", - "BISMARCK SEA", - "YUNNAN", - "MYANMAR-INDIA BORDER REGION", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS REGION", - "EASTER ISLAND REGION", - "OAXACA", - "VANUATU ISLANDS", - "SOUTH ISLAND", - "EASTER ISLAND REGION", - "NEAR EAST COAST OF HONSHU", - "TAIWAN", - "NEAR EAST COAST OF KAMCHATKA", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF PERU", - "PUERTO RICO REGION", - "OFF COAST OF CENTRAL CHILE", - "BALLENY ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "NICOBAR ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "CELEBES SEA", - "NEAR EAST COAST OF HONSHU", - "SCOTIA SEA", - "OFF EAST COAST OF HONSHU", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "NORTH ISLAND", - "TONGA ISLANDS", - "JAVA", - "NEAR EAST COAST OF HONSHU", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "GREECE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "GREECE", - "KURIL ISLANDS", - "NEAR EAST COAST OF HONSHU", - "MARIANA ISLANDS", - "HALMAHERA", - "OFF EAST COAST OF HONSHU", - "FOX ISLANDS", - "NEAR COAST OF NICARAGUA", - "NORTHERN CHILE", - "NEAR COAST OF CHIAPAS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF NORTHERN PERU", - "MARIANA ISLANDS REGION", - "KERMADEC ISLANDS", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "WEST CHILE RISE", - "NEAR COAST OF VENEZUELA", - "SOLOMON ISLANDS", - "NICARAGUA", - "NEAR COAST OF NORTHERN CHILE", - "SULAWESI", - "KERMADEC ISLANDS", - "OFF EAST COAST OF HONSHU", - "MID-INDIAN RIDGE", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "MACQUARIE ISLAND REGION", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "TONGA ISLANDS", - "PERU-BRAZIL BORDER REGION", - "KERMADEC ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "FIJI ISLANDS REGION", - "MINDANAO", - "FOX ISLANDS", - "MINDANAO", - "JUJUY PROVINCE", - "NEAR COAST OF CENTRAL CHILE", - "NORTHEAST OF TAIWAN", - "SOUTH OF MARIANA ISLANDS", - "SOUTHERN SUMATRA", - "LUZON", - "OFF EAST COAST OF HONSHU", - "WESTERN AUSTRALIA", - "GUERRERO", - "KERMADEC ISLANDS", - "EASTERN NEW GUINEA REG.", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "WEST CHILE RISE", - "SOUTH SHETLAND ISLANDS", - "MINDANAO", - "EAST OF SOUTH SANDWICH ISLANDS", - "NEAR EAST COAST OF HONSHU", - "RUSSIA-MONGOLIA BORDER REGION", - "MINDANAO", - "NEAR COAST OF NICARAGUA", - "SOUTHEAST OF LOYALTY ISLANDS", - "TONGA ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "SOUTHWESTERN SIBERIA", - "NEAR COAST OF CENTRAL CHILE", - "SERAM", - "VANUATU ISLANDS", - "OFF EAST COAST OF KAMCHATKA", - "SOUTHEAST OF HONSHU", - "NEW BRITAIN REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "TAIWAN", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "SOUTH SHETLAND ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTHWEST OF SUMATRA", - "CENTRAL BOLIVIA", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NEVADA", - "ALASKA PENINSULA", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHEAST OF RYUKYU ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "SANTA CRUZ ISLANDS", - "BANDA SEA", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN IRAN", - "CENTRAL MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "SOUTHEAST OF HONSHU", - "NEW BRITAIN REGION", - "CRETE", - "LOYALTY ISLANDS", - "NEGROS", - "NORTHERN CHILE", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SAMAR", - "SOUTHERN SUMATRA", - "NEAR COAST OF NICARAGUA", - "CELEBES SEA", - "EAST OF KURIL ISLANDS", - "SOUTHERN SUMATRA", - "CHILE-BOLIVIA BORDER REGION", - "JAVA SEA", - "VANUATU ISLANDS", - "CELEBES SEA", - "NEAR EAST COAST OF HONSHU", - "BANDA SEA", - "VANUATU ISLANDS REGION", - "LOYALTY ISLANDS", - "RYUKYU ISLANDS", - "ALASKA PENINSULA", - "BONIN ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "WESTERN IDAHO", - "BONIN ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "VANCOUVER ISLAND", - "SOUTHERN EAST PACIFIC RISE", - "MYANMAR", - "OFF W COAST OF NORTHERN SUMATRA", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "SIKKIM", - "KERMADEC ISLANDS REGION", - "MAURITIUS - REUNION REGION", - "ANDREANOF ISLANDS", - "EASTERN NEW GUINEA REG.", - "NORTHERN PERU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "BALI SEA", - "FOX ISLANDS", - "BANDA SEA", - "CENTRAL MID-ATLANTIC RIDGE", - "WEST OF MACQUARIE ISLAND", - "SOUTHEAST INDIAN RIDGE", - "MINDORO", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "NEAR N COAST OF NEW GUINEA", - "CHIAPAS", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF JAVA", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "QINGHAI", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "TURKEY", - "CYPRUS REGION", - "FOX ISLANDS", - "FOX ISLANDS", - "SUNDA STRAIT", - "BONIN ISLANDS", - "NEW BRITAIN REGION", - "NEGROS", - "FIJI ISLANDS REGION", - "SCOTIA SEA", - "NEAR EAST COAST OF KAMCHATKA", - "NEAR COAST OF CENTRAL CHILE", - "EAST OF SOUTH SANDWICH ISLANDS", - "GULF OF CALIFORNIA", - "SOLOMON ISLANDS", - "MYANMAR", - "NEW IRELAND REGION", - "CRETE", - "SAKHALIN", - "SEA OF OKHOTSK", - "PAKISTAN", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH ATLANTIC OCEAN", - "BANDA SEA", - "VANUATU ISLANDS", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "NEAR COAST OF NORTHERN CHILE", - "OFF COAST OF NORTHERN CALIFORNIA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "JAVA SEA", - "MYANMAR-INDIA BORDER REGION", - "NORTH ATLANTIC OCEAN", - "XIZANG", - "VANUATU ISLANDS", - "NORTHERN EAST PACIFIC RISE", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "FIJI ISLANDS REGION", - "RYUKYU ISLANDS", - "CENTRAL ITALY", - "SOUTH ISLAND", - "SOUTH ISLAND", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "NORTHERN MID-ATLANTIC RIDGE", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SULAWESI", - "NEAR ISLANDS", - "NEW BRITAIN REGION", - "SOUTH OF JAVA", - "NEAR COAST OF GUATEMALA", - "SEA OF OKHOTSK", - "LEYTE", - "AUCKLAND ISLANDS", - "DODECANESE ISLANDS", - "MARIANA ISLANDS", - "GANSU", - "GUATEMALA", - "HOKKAIDO", - "BOTSWANA", - "COOK STRAIT", - "NEAR EAST COAST OF HONSHU", - "ANDREANOF ISLANDS", - "SOUTHEAST OF HONSHU", - "NORTHERN SUMATRA", - "OFF COAST OF NORTHERN CALIFORNIA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "BANDA SEA", - "COOK STRAIT", - "SOUTH OF PANAMA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "MINDANAO", - "SOUTHERN BOLIVIA", - "SOUTH OF FIJI ISLANDS", - "BANDA SEA", - "NEW IRELAND REGION", - "NEAR COAST OF NORTHERN CHILE", - "MYANMAR-INDIA BORDER REGION", - "NEPAL", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEAR COAST OF CHIAPAS", - "NEAR EAST COAST OF HONSHU", - "PACIFIC-ANTARCTIC RIDGE", - "SANTA CRUZ ISLANDS REGION", - "SOLOMON ISLANDS", - "ALASKA PENINSULA", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN MID-ATLANTIC RIDGE", - "BONIN ISLANDS", - "KERMADEC ISLANDS", - "SOLOMON ISLANDS", - "NEPAL", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "SOUTHERN EAST PACIFIC RISE", - "SOLOMON ISLANDS", - "SOUTH OF PANAMA", - "NORTH ATLANTIC OCEAN", - "MARIANA ISLANDS", - "VANUATU ISLANDS", - "CENTRAL PERU", - "JUJUY PROVINCE", - "CENTRAL PERU", - "OFF EAST COAST OF HONSHU", - "E. CAROLINE ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF PANAMA", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "KERMADEC ISLANDS", - "TIMOR REGION", - "NEAR COAST OF CENTRAL CHILE", - "NICOBAR ISLANDS", - "ANDREANOF ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHWEST OF RYUKYU ISLANDS", - "SOLOMON ISLANDS", - "GREECE", - "NEAR COAST OF NICARAGUA", - "PERU-BRAZIL BORDER REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF NORTHERN CHILE", - "MYANMAR-INDIA BORDER REGION", - "SOLOMON ISLANDS", - "TALAUD ISLANDS", - "BOUVET ISLAND REGION", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTH INDIAN OCEAN", - "AEGEAN SEA", - "GULF OF CALIFORNIA", - "SOUTH OF PANAMA", - "SOLOMON ISLANDS", - "GUERRERO", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH OF FIJI ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "VANCOUVER ISLAND", - "IRIAN JAYA REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTH OF ASCENSION ISLAND", - "SOUTHERN XINJIANG", - "NORTHWEST OF KURIL ISLANDS", - "HAWAII", - "SOUTHERN BOLIVIA", - "NEW BRITAIN REGION", - "MAURITIUS - REUNION REGION", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW IRELAND REGION", - "NEW GUINEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEW IRELAND REGION", - "VANUATU ISLANDS", - "NORTHERN MOLUCCA SEA", - "CENTRAL CHILE", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "PRINCE EDWARD ISLANDS REGION", - "HOKKAIDO", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CHIAPAS", - "TONGA ISLANDS", - "IRIAN JAYA REGION", - "WESTERN BRAZIL", - "JAN MAYEN ISLAND REGION", - "KURIL ISLANDS", - "MYANMAR", - "HOKKAIDO", - "SOLOMON ISLANDS", - "ANDREANOF ISLANDS", - "KERMADEC ISLANDS REGION", - "SICHUAN", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "JAN MAYEN ISLAND REGION", - "NORTHERN MOLUCCA SEA", - "SUMBAWA REGION", - "KURIL ISLANDS", - "FIJI ISLANDS REGION", - "VANCOUVER ISLAND", - "WEST OF MACQUARIE ISLAND", - "IRIAN JAYA REGION", - "VANCOUVER ISLAND", - "FLORES SEA", - "VANCOUVER ISLAND", - "IONIAN SEA", - "ANDREANOF ISLANDS", - "SUMBAWA REGION", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHWEST OF AFRICA", - "EL SALVADOR", - "RAT ISLANDS", - "BOUVET ISLAND REGION", - "EASTERN NEW GUINEA REG.", - "SANTA CRUZ ISLANDS", - "BANDA SEA", - "EASTERN SIBERIA", - "CENTRAL MID-ATLANTIC RIDGE", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "KURIL ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "TONGA ISLANDS", - "KURIL ISLANDS", - "COSTA RICA", - "NEW GUINEA", - "SANTA CRUZ ISLANDS", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWESTERN SIBERIA", - "NEAR N COAST OF NEW GUINEA", - "BOUVET ISLAND REGION", - "JAVA", - "SOLOMON ISLANDS", - "SANTA CRUZ ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "MICHOACAN", - "SULAWESI", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST INDIAN RIDGE", - "GUERRERO", - "NORTHERN MOLUCCA SEA", - "VANUATU ISLANDS", - "OFF COAST OF CENTRAL AMERICA", - "NEW BRITAIN REGION", - "VANUATU ISLANDS REGION", - "SOLOMON ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CENTRAL CHILE", - "VANUATU ISLANDS REGION", - "NEW BRITAIN REGION", - "OFF COAST OF CENTRAL AMERICA", - "CENTRAL PERU", - "FIJI ISLANDS REGION", - "SOUTHWESTERN PAKISTAN", - "KERMADEC ISLANDS", - "BAJA CALIFORNIA", - "OFF EAST COAST OF HONSHU", - "IRIAN JAYA", - "IRIAN JAYA REGION", - "MINDANAO", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF GUERRERO", - "EASTERN NEW GUINEA REG.", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "ECUADOR", - "SOUTH ISLAND", - "CENTRAL PERU", - "SOLOMON ISLANDS", - "NEPAL", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "TURKEY", - "NORTHERN MOLUCCA SEA", - "SOUTHERN EAST PACIFIC RISE", - "EASTERN NEW GUINEA REG.", - "CENTRAL CALIFORNIA", - "NEAR COAST OF PERU", - "FLORES SEA", - "FLORES SEA", - "MINDANAO", - "SOUTHERN ALASKA", - "BANDA SEA", - "NEAR COAST OF CENTRAL CHILE", - "REYKJANES RIDGE", - "HALMAHERA", - "NEAR COAST OF CENTRAL CHILE", - "TAJIKISTAN", - "HAITI REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "OFF COAST OF CENTRAL AMERICA", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "NEAR COAST OF PERU", - "KERMADEC ISLANDS", - "OFF E. COAST OF N. ISLAND", - "NORTH OF ASCENSION ISLAND", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "SOUTHEAST OF LOYALTY ISLANDS", - "OAXACA", - "BANDA SEA", - "SOUTH OF KERMADEC ISLANDS", - "PHILIPPINE ISLANDS REGION", - "CENTRAL MEXICO", - "KURIL ISLANDS", - "NEAR EAST COAST OF HONSHU", - "IRIAN JAYA", - "SOUTH OF FIJI ISLANDS", - "DODECANESE ISLANDS", - "IRAN-IRAQ BORDER REGION", - "FOX ISLANDS", - "NEW IRELAND REGION", - "LOYALTY ISLANDS", - "COLOMBIA", - "CELEBES SEA", - "SANTA CRUZ ISLANDS", - "EASTERN NEW GUINEA REG.", - "ANDREANOF ISLANDS", - "NEAR COAST OF OAXACA", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF PERU", - "QINGHAI", - "KAMCHATKA PENINSULA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "OFF COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "KYUSHU", - "SOUTHERN ALASKA", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "NEAR COAST OF VENEZUELA", - "VANUATU ISLANDS", - "COLOMBIA", - "NEAR COAST OF CHIAPAS", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "NEAR EAST COAST OF HONSHU", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "NEPAL", - "SOUTHERN IRAN", - "SOLOMON ISLANDS", - "COSTA RICA", - "KOMANDORSKIYE OSTROVA REGION", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "ALASKA PENINSULA", - "MINDANAO", - "PERU-BRAZIL BORDER REGION", - "SOUTHERN CHILE", - "MINDANAO", - "PHILIPPINE ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "MINAHASSA PENINSULA", - "SOLOMON ISLANDS", - "NEAR COAST OF GUERRERO", - "SOUTHWEST OF SUMATRA", - "SOUTH OF ALASKA", - "SOUTHEAST OF LOYALTY ISLANDS", - "EAST OF KURIL ISLANDS", - "SCOTIA SEA", - "QUEEN CHARLOTTE ISLANDS REGION", - "PRIMOR'YE", - "MARIANA ISLANDS", - "SEA OF OKHOTSK", - "SOUTHERN SUMATRA", - "NORTHERN PERU", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "SOUTHEASTERN ALASKA", - "NEAR COAST OF ECUADOR", - "NEW GUINEA", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS", - "RAT ISLANDS", - "SANTA CRUZ ISLANDS", - "PERU-ECUADOR BORDER REGION", - "BONIN ISLANDS", - "GULF OF ALASKA", - "PAKISTAN", - "SOUTH ISLAND", - "CUBA REGION", - "PERU-BRAZIL BORDER REGION", - "KERMADEC ISLANDS REGION", - "HINDU KUSH REGION", - "NEW BRITAIN REGION", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CHIAPAS", - "NORTHERN PERU", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "OFF W COAST OF NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SEA OF OKHOTSK", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU" - ], - "lat": [ - -13.6098, - -1.159, - -1.4513, - -17.9609, - -3.7682, - 13.9987, - 44.3014, - 13.396, - 26.8975, - 41.3343, - -17.4154, - 54.5825, - -4.7722, - -0.2882, - -31.106, - -30.3103, - -19.6664, - -24.0934, - -30.5139, - -16.4206, - -57.7886, - 27.7829, - -50.371, - 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"# fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "3355fe19", - "metadata": {}, - "source": [ - "# 3 Create a Pandas from a generic text file.\n", - "\n", - "The python package pandas is very useful to read csv files, but also many text files that are more or less formatted as one observation per row and one column for each feature.\n", - "\n", - "As an example, we are going to look at the list of seismic stations from the Northern California seismic network, available [here](http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day):\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "ef9f302d", - "metadata": {}, - "outputs": [], - "source": [ - "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "3a60ef42", - "metadata": {}, - "outputs": [], - "source": [ - "# this gets the file linked in the URL page and convert it to a string\n", - "s = requests.get(url).content " - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "9edd0bf5", - "metadata": {}, - "outputs": [], - "source": [ - "# this will convert the string, decode it , and make it a table\n", - "data = pd.read_csv(io.StringIO(s.decode('utf-8')), header=None, skiprows=2, sep='\\s+', usecols=list(range(0, 13)))\n", - "# because columns/keys were not assigned, assign them now\n", - "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" - ] - }, - { - "cell_type": "markdown", - "id": "b2def912", - "metadata": {}, - "source": [ - "Let us look at the data. They are now stored into a pandas dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "448b69ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.0
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2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.0
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.0
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDateParseError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# convert start_time and end_time to datetime\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstart_time\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:1108\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1106\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mtz_localize(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, ABCSeries):\n\u001b[0;32m-> 1108\u001b[0m cache_array \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_listlike\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cache_array\u001b[38;5;241m.\u001b[39mempty:\n\u001b[1;32m 1110\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:254\u001b[0m, in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m 252\u001b[0m unique_dates \u001b[38;5;241m=\u001b[39m unique(arg)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_dates) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(arg):\n\u001b[0;32m--> 254\u001b[0m cache_dates \u001b[38;5;241m=\u001b[39m 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\u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m--> 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mobjects_to_datetime64ns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 502\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py:2346\u001b[0m, in 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\u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2351\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2355\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;66;03m# Return i8 values to denote unix timestamps\u001b[39;00m\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi8\u001b[39m\u001b[38;5;124m\"\u001b[39m), tz_parsed\n", - "File \u001b[0;32mtslib.pyx:403\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mtslib.pyx:552\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mtslib.pyx:517\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mconversion.pyx:546\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.convert_str_to_tsobject\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsing.pyx:331\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsing.pyx:660\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.dateutil_parse\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mDateParseError\u001b[0m: Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0" + "mapbox": { + "center": { + "lat": 38.04061885424097, + "lon": -121.71164549237406 + }, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 ] - } - ], - "source": [ - "# convert start_time and end_time to datetime\n", - "data['start_time'] = pd.to_datetime(data['start_time'])\n", - "data['end_time'] = pd.to_datetime(data['end_time'])" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "b8bcf253", - "metadata": {}, - "outputs": [], - "source": [ - "data['startdate'] = pd.to_datetime(data['start_time'], format='%Y/%m/%d,%H:%M:%S')" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "da911e32", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:00
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0.0 \n", - "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "\n", - " startdate \n", - "0 1976-07-20 17:38:00 \n", - "1 1977-12-01 22:37:00 \n", - "2 1981-06-12 19:15:00 \n", - "3 1984-01-01 00:00:00 \n", - "4 1985-11-03 22:00:00 " - ] + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 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0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:001977-12-01 22:37:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:001981-06-12 19:15:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:001984-01-01 00:00:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:001985-11-03 22:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:001987-03-10 23:15:00
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"ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Northern California Seismic Network" - }, - "width": 500 - } - } + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.scatter_mapbox(data,\n", - " lat='latitude',lon='longitude', \n", - " range_color=(6,9),mapbox_style=\"carto-positron\",\n", - " height=600, width=500,\n", - " hover_name=\"station\",\n", - " hover_data=['network','station','channel','rate']);\n", - "fig.update_layout(title=\"Northern California Seismic Network\")\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "373751b7", - "metadata": {}, - "source": [ - "# 4: Exercise\n", - "We will now practice on manipulating pandas. This exercise pulls station metadata from a URL and students are expected to practice on specific tasks.\n", - "\n", - "Download data from the NCEDC URL" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "18059dae", - "metadata": {}, - "outputs": [], - "source": [ - "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" - ] - }, - { - "cell_type": "markdown", - "id": "ceba5236", - "metadata": {}, - "source": [ - "The column names are shown at the top of the text file, make a list of strings of these names in order to rename the columns once the dataframe is made" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "db521722", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59c9c285", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here\n", - "# request the data from the URL and use the IO package\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f67ba2d0", - "metadata": {}, - "outputs": [], - "source": [ - "# assign the column names\n", - "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" - ] - }, - { - "cell_type": "markdown", - "id": "2e81e4b9", - "metadata": {}, - "source": [ - "Find the row of channel KCPB" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d28d4e95", - "metadata": {}, - "outputs": [], - "source": [ - "# find the row of station KCPB" - ] - }, - { - "cell_type": "markdown", - "id": "40806ebd", - "metadata": {}, - "source": [ - "Now select two stations of your choice using ``|``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb5561d5", - "metadata": {}, - "outputs": [], - "source": [ - "# answer below" - ] - }, - { - "cell_type": "markdown", - "id": "479e019a", - "metadata": {}, - "source": [ - "Now select a given station and a specific channel code, example is ``KCPB`` and channel code ``HNZ``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebba2b90", - "metadata": {}, - "outputs": [], - "source": [ - "# Select two stations, use the typical \"AND\" " - ] - }, - { - "cell_type": "markdown", - "id": "bc0abb79", - "metadata": {}, - "source": [ - "You may also choose the pandas function ``isin`` to select rows that have a given attribute that belongs to a list. For instance, use ``isin`` to select all rows that have the key ``station`` within a list ``['KCBP','KHBB' ]``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "561aecf9", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "9133ae6b", - "metadata": {}, - "source": [ - "**Q** Use panda native functions to calculate how many unique sites (station names) there are in the network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f235cec3", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f5d93eb9", - "metadata": {}, - "source": [ - "**Q** Use pandas native functions to select the unique set of channel codes that end with ``Z``: this will tell you of how many types digitized data the seismic network manages." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7a13877", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "c801ed50", - "metadata": {}, - "source": [ - "**Q** What station names has the most number of channels? __hint__ you may use ``value_counts()``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "031731dd", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "4edf820a", - "metadata": {}, - "source": [ - "**Q** What is the maximum difference in elevation between the stations using lambda functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19f7184c", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "141ac2c1", - "metadata": {}, - "source": [ - "Here, pandas does not recognize the start_time and end_time columns as a datetime format, so we cannot use datetime operations on them. We first need to convert these columns into a datetime format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02663a77", - "metadata": {}, - "outputs": [], - "source": [ - "# answer here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afaf1851", - "metadata": {}, - "outputs": [], - "source": [ - "# Transform column from string into datetime format" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "bae00773", - "metadata": {}, - "outputs": [], - "source": [ - "# do the same for end times" - ] - }, - { - "cell_type": "markdown", - "id": "f00ce758", - "metadata": {}, - "source": [ - "We can now look when each seismic station was installed using ``groupby`` and sorting by the earliest deployment (i.e., the minimum of the start_time)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0cc2a86", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "5183304a", - "metadata": {}, - "source": [ - "Select the stations that were deployed first and recovered last using ``agg`` and lambda functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e7b38a0", - "metadata": {}, - "outputs": [], - "source": [ - "# answer here" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c5c38796", - "metadata": {}, - "source": [ - "# 4. CSV vs Parquet\n", - "\n", - "Parquet is a compressed data format that stores and compresses the columns. It is fast for I/O and compact format.\n", - "\n", - "Save ``data`` into a CSV file:" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "f20f5de3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "195 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", - "-rw-r--r--@ 1 marinedenolle staff 4.2M Oct 7 05:15 my_metadata.csv\n" - ] - } - ], - "source": [ - "%timeit data.to_csv(\"my_metadata.csv\")\n", - "!ls -lh my_metadata.csv" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "4b5dd078", - "metadata": {}, - "source": [ - "Try and save in Parquet and compare time and memory." - ] - }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Northern California Seismic Network" + }, + "width": 500 + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_mapbox(data,\n", + " lat='latitude',lon='longitude', \n", + " range_color=(6,9),mapbox_style=\"carto-positron\",\n", + " height=600, width=500,\n", + " hover_name=\"station\",\n", + " hover_data=['network','station','channel','rate']);\n", + "fig.update_layout(title=\"Northern California Seismic Network\")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "373751b7", + "metadata": {}, + "source": [ + "# 4: Exercise\n", + "We will now practice on manipulating pandas. This exercise pulls station metadata from a URL and students are expected to practice on specific tasks.\n", + "\n", + "Download data from the NCEDC URL" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "18059dae", + "metadata": {}, + "outputs": [], + "source": [ + "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" + ] + }, + { + "cell_type": "markdown", + "id": "ceba5236", + "metadata": {}, + "source": [ + "The column names are shown at the top of the text file, make a list of strings of these names in order to rename the columns once the dataframe is made" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "db521722", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59c9c285", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here\n", + "# request the data from the URL and use the IO package\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f67ba2d0", + "metadata": {}, + "outputs": [], + "source": [ + "# assign the column names\n", + "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" + ] + }, + { + "cell_type": "markdown", + "id": "2e81e4b9", + "metadata": {}, + "source": [ + "Find the row of channel KCPB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d28d4e95", + "metadata": {}, + "outputs": [], + "source": [ + "# find the row of station KCPB" + ] + }, + { + "cell_type": "markdown", + "id": "40806ebd", + "metadata": {}, + "source": [ + "Now select two stations of your choice using ``|``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb5561d5", + "metadata": {}, + "outputs": [], + "source": [ + "# answer below" + ] + }, + { + "cell_type": "markdown", + "id": "479e019a", + "metadata": {}, + "source": [ + "Now select a given station and a specific channel code, example is ``KCPB`` and channel code ``HNZ``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebba2b90", + "metadata": {}, + "outputs": [], + "source": [ + "# Select two stations, use the typical \"AND\" " + ] + }, + { + "cell_type": "markdown", + "id": "bc0abb79", + "metadata": {}, + "source": [ + "You may also choose the pandas function ``isin`` to select rows that have a given attribute that belongs to a list. For instance, use ``isin`` to select all rows that have the key ``station`` within a list ``['KCBP','KHBB' ]``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "561aecf9", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "9133ae6b", + "metadata": {}, + "source": [ + "**Q** Use panda native functions to calculate how many unique sites (station names) there are in the network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f235cec3", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f5d93eb9", + "metadata": {}, + "source": [ + "**Q** Use pandas native functions to select the unique set of channel codes that end with ``Z``: this will tell you of how many types digitized data the seismic network manages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7a13877", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "c801ed50", + "metadata": {}, + "source": [ + "**Q** What station names has the most number of channels? __hint__ you may use ``value_counts()``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "031731dd", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "4edf820a", + "metadata": {}, + "source": [ + "**Q** What is the maximum difference in elevation between the stations using lambda functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19f7184c", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "141ac2c1", + "metadata": {}, + "source": [ + "Here, pandas does not recognize the start_time and end_time columns as a datetime format, so we cannot use datetime operations on them. We first need to convert these columns into a datetime format:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02663a77", + "metadata": {}, + "outputs": [], + "source": [ + "# answer here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afaf1851", + "metadata": {}, + "outputs": [], + "source": [ + "# Transform column from string into datetime format" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bae00773", + "metadata": {}, + "outputs": [], + "source": [ + "# do the same for end times" + ] + }, + { + "cell_type": "markdown", + "id": "f00ce758", + "metadata": {}, + "source": [ + "We can now look when each seismic station was installed using ``groupby`` and sorting by the earliest deployment (i.e., the minimum of the start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0cc2a86", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "5183304a", + "metadata": {}, + "source": [ + "Select the stations that were deployed first and recovered last using ``agg`` and lambda functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e7b38a0", + "metadata": {}, + "outputs": [], + "source": [ + "# answer here" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c5c38796", + "metadata": {}, + "source": [ + "# 4. CSV vs Parquet\n", + "\n", + "Parquet is a compressed data format that stores and compresses the columns. It is fast for I/O and compact format.\n", + "\n", + "Save ``data`` into a CSV file:" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "f20f5de3", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 89, - "id": "0952eb9f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "25.2 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", - "-rw-r--r--@ 1 marinedenolle staff 591K Oct 7 05:15 my_metadata.pq\n" - ] - } - ], - "source": [ - "%timeit data.to_parquet(\"my_metadata.pq\")\n", - "!ls -lh my_metadata.pq" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "195 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", + "-rw-r--r--@ 1 marinedenolle staff 4.2M Oct 7 05:15 my_metadata.csv\n" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "mlgeo", - "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": "3.9.18" + ], + "source": [ + "%timeit data.to_csv(\"my_metadata.csv\")\n", + "!ls -lh my_metadata.csv" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4b5dd078", + "metadata": {}, + "source": [ + "Try and save in Parquet and compare time and memory." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "0952eb9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25.2 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", + "-rw-r--r--@ 1 marinedenolle staff 591K Oct 7 05:15 my_metadata.pq\n" + ] } + ], + "source": [ + "%timeit data.to_parquet(\"my_metadata.pq\")\n", + "!ls -lh my_metadata.pq" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlgeo", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-images-idx3-ubyte b/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-images-idx3-ubyte new file mode 100644 index 0000000..37bac79 Binary files /dev/null and b/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-images-idx3-ubyte differ diff --git a/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-images-idx3-ubyte.gz b/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-images-idx3-ubyte.gz new file mode 100644 index 0000000..667844f Binary files /dev/null and b/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-images-idx3-ubyte.gz differ diff --git a/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-labels-idx1-ubyte b/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-labels-idx1-ubyte new file mode 100644 index 0000000..2195a4d Binary files /dev/null and b/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-labels-idx1-ubyte differ diff --git a/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz b/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz new file mode 100644 index 0000000..abdddb8 Binary files /dev/null and b/book/Chapter4-DeepLearning/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz differ diff --git a/book/Chapter4-DeepLearning/FashionMNIST/raw/train-images-idx3-ubyte b/book/Chapter4-DeepLearning/FashionMNIST/raw/train-images-idx3-ubyte new file mode 100644 index 0000000..ff2f5a9 Binary files /dev/null and b/book/Chapter4-DeepLearning/FashionMNIST/raw/train-images-idx3-ubyte differ diff --git a/book/Chapter4-DeepLearning/FashionMNIST/raw/train-images-idx3-ubyte.gz b/book/Chapter4-DeepLearning/FashionMNIST/raw/train-images-idx3-ubyte.gz new file mode 100644 index 0000000..e6ee0e3 Binary files /dev/null and b/book/Chapter4-DeepLearning/FashionMNIST/raw/train-images-idx3-ubyte.gz differ diff --git a/book/Chapter4-DeepLearning/FashionMNIST/raw/train-labels-idx1-ubyte b/book/Chapter4-DeepLearning/FashionMNIST/raw/train-labels-idx1-ubyte new file mode 100644 index 0000000..30424ca Binary files /dev/null and b/book/Chapter4-DeepLearning/FashionMNIST/raw/train-labels-idx1-ubyte differ diff --git a/book/Chapter4-DeepLearning/FashionMNIST/raw/train-labels-idx1-ubyte.gz b/book/Chapter4-DeepLearning/FashionMNIST/raw/train-labels-idx1-ubyte.gz new file mode 100644 index 0000000..9c4aae2 Binary files /dev/null and b/book/Chapter4-DeepLearning/FashionMNIST/raw/train-labels-idx1-ubyte.gz differ diff --git a/book/Chapter4-DeepLearning/mlgeo_4.4_RNN.ipynb b/book/Chapter4-DeepLearning/mlgeo_4.4_RNN.ipynb index 0bca5f6..8a53dad 100644 --- a/book/Chapter4-DeepLearning/mlgeo_4.4_RNN.ipynb +++ b/book/Chapter4-DeepLearning/mlgeo_4.4_RNN.ipynb @@ -6,7 +6,8 @@ "source": [ "# 4.4 Recurrent Neural Networks: Processing sequences\n", "\n", - "They are used for time series predictions. Regular dense networks can also do it, and CNNs can also work for very long time series.\n", + "They are used for time series ***forecast***. Regular dense networks can also do it, and CNNs can also work for very long time series.\n", + "\n", "A recurrent neuron receives an input *and* the output from the neuron at the previous time step. Because each neuron learns from the previous time step, it has *memory*; but these simple cells have relatively short memory (10 cells about).\n", "RNNs take in a sequence and output a sequence.\n", "\n", @@ -15,34 +16,40 @@ "\n", "From D2DL: example of an RNN with a hidden state. \n", "The RNN takes the multiplication of the weights and the data plus other weights with the hidden states to the next layer.\n", + "\n", + "\n", "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import tensorflow as tf\n", - "from tensorflow import keras" + "import torch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "In time series forecasting, the goal is to predict future values based on historical data. Two important concepts in this process are context data and prediction length.\n", + "\n", + "1. **Context Data** refers to the historical data used by the forecasting model to make predictions. This data provides the context or background information needed to understand the patterns, trends, and seasonality in the time series. The length of the context data is often referred to as the look-back window or input window.\n", + "2. **Prediction length** refers to the number of future time steps the model aims to predict. This is also known as the ***forecast horizon***. The number of future time steps the model predicts. For example, if the forecast horizon is 5, the model predicts the next 5 time steps.\n", + "\n", "**Synthetic data**\n", "\n", - "Let's create a synthetic time series" + "Let's create a synthetic time series. We will overlap 2 frequencies with noise." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,12 +59,19 @@ " y = 0.5*np.sin( (t-off1)*(f1*10+10) ) # first wave\n", " y += 0.5*np.sin( (t-off2)*(f2*20+20) ) # second wave\n", " y += 0.3* (np.random.rand(batch_size,n_steps)-0.5) # noise\n", - " return y[...,np.newaxis].astype(np.float32) \n", - "# here we added a dimension to the output time series because most ML algorithms can be multidimensional, but here we are just doing a single time series.\n", - "\n", - "# we generate 10k time series of 51 points.\n", - "n_steps=50\n", - "y = generate_time_series(10000,n_steps+1)" + " return y.astype(np.float32) " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# we generate 10k time series of context+prediction_horizon points.\n", + "context=50\n", + "prediction_horizon=10\n", + "data = generate_time_series(10000,context+prediction_horizon)" ] }, { @@ -66,29 +80,57 @@ "metadata": {}, "outputs": [], "source": [ - "plt.plot(y[5000,:]);plt.grid(True)" + "# plot some of the time series in a 2x3 grid\n", + "plt.figure(figsize=(6,4))\n", + "for i in range(6):\n", + " plt.subplot(2,3,i+1)\n", + " # plot context data\n", + " plt.plot(range(context),data[i,:context],'b-',label='context')\n", + " # plot prediction horizon\n", + " plt.plot(range(context,context+prediction_horizon),data[i,context:],'r-',label='prediction')\n", + " plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**train-validation-test split**\n", - "\n", - "In forecasting problem, we do not want to shuffle the training and test set since we want to make sure test is a prediction from the past (training).\n", + "## Design a vanilla RNN\n", "\n", - "The training data are time series of 50 points, the \"label\" or \"model output\" is the **last value** of the time series." + "The RNN will take context" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "x_train,y_train = y[:7000,:n_steps],y[:7000,-1] \n", - "x_val,y_val = y[7000:9000,:n_steps],y[7000:9000,-1] \n", - "x_test,y_test = y[9000:,:n_steps],y[9000:,-1] " + "class VanillaRNN(torch.nn.Module):\n", + " def __init__(self, input_size, hidden_size, output_size):\n", + " super(VanillaRNN, self).__init__()\n", + " self.rnn = torch.nn.RNN(input_size, hidden_size, batch_first=True) # RNN layer, batch_size is the first dimension\n", + " self.fc = torch.nn.Linear(hidden_size, output_size) # Fully connected layer\n", + " \n", + " def forward(self, x):\n", + " out, _ = self.rnn(x) # out: tensor of shape (batch_size, seq_length, hidden_size)\n", + " out = self.fc(out[:, -1, :]) # out: tensor of shape (batch_size, output_size)\n", + " return out\n", + "\n", + "def create_rnn_and_data(time_series, context, prediction_horizon):\n", + " input_size = time_series.shape[-1]\n", + " hidden_size = 20 # You can adjust this value\n", + " output_size = prediction_horizon\n", + " \n", + " model = VanillaRNN(input_size, hidden_size, output_size)\n", + " \n", + " # Prepare the input and target data\n", + " x = time_series[:, :context,np.newaxis ] # Add a dummy dimension for the input size\n", + " y = time_series[:, context:context + prediction_horizon]\n", + " \n", + " return model, x, y" ] }, { @@ -97,29 +139,91 @@ "metadata": {}, "outputs": [], "source": [ - "plt.plot(np.arange(n_steps),x_train[5000,:],'b')\n", - "plt.plot(51,y_train[5000],'r+')\n", - "plt.grid(True)" + "model, x, y = create_rnn_and_data(data, context, prediction_horizon)\n", + "\n", + "# Print model summary\n", + "print(model)" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ + "Now prepare the training function " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def train(model, x_train, y_train, x_val, y_val, n_epochs=100, lr=0.001):\n", + "\n", + " '''\n", + " Function to train the model.\n", "\n", - "We can use the past values to predict the state, called *naive forecasting*:\n", - "```python\n", - "y_pred=x_val[:,-1]\n", - "```\n", + " Parameters:\n", + " model: torch.nn.Module\n", + " The model to train\n", + " x_train: np.ndarray\n", + " The input data for training (context)\n", + " y_train: np.ndarray\n", + " The target data for training (prediction horizon)\n", + " x_val: np.ndarray\n", + " The input data for validation (context)\n", + " y_val: np.ndarray\n", + " The target data for validation (prediction horizon)\n", + " n_epochs: int\n", + " The number of epochs\n", + " lr: float\n", + " The learning rate\n", "\n", - "Or we can use a fully connected network and predict the value as a MLP regression:\n", + " Returns:\n", + " train_losses: list\n", + " The training losses for each epoch\n", + " val_losses: list\n", + " The validation losses for each epoch \n", + " \n", + " \n", + " '''\n", "\n", - "```python\n", - "model.keras.models.Sequential([keras.layers.Flatten(input_shape=[50,1]),\n", - " keras.layers.Dense(1)])\n", - "```\n", - "They don't do too bad in this problem. The simple block is a ``simpleRNN``. The simplest recurrent neuron is a ``SimpleRNN(1,input_shape=[None,1])`` that takes any input scalar since it can process any number of time steps. The default activation function is ``tanh``. To return a time series, and not its final output, you need to set ``return_sequences=True``. It turns out that the simplest and single recurrent neuron won't work. So we stack several simpleRNNs." + " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + " loss_fn = torch.nn.MSELoss()\n", + " train_losses = []\n", + " val_losses = []\n", + " \n", + " for epoch in range(n_epochs):\n", + " model.train()\n", + " optimizer.zero_grad()\n", + " y_pred = model(torch.from_numpy(x_train).float())\n", + " print(f\"Shape of outputs: {y_pred.shape}\")\n", + " print(f\"Shape of y_train: {y_train.shape}\")\n", + "\n", + " loss = loss_fn(y_pred, torch.from_numpy(y_train).float())\n", + " train_losses.append(loss.item())\n", + " loss.backward()\n", + " optimizer.step()\n", + " \n", + " if epoch % 10 == 0:\n", + " model.eval()\n", + " with torch.no_grad():\n", + " y_val_pred = model(torch.from_numpy(x_val).float())\n", + " print(f\"Shape of val_outputs: {y_val_pred.shape}\")\n", + " print(f\"Shape of y_val: {y_val.shape}\")\n", + " \n", + " val_loss = loss_fn(y_val_pred, torch.from_numpy(y_val).float())\n", + " val_losses.append(val_loss.item())\n", + " print(f'Epoch {epoch}, Loss {loss.item():.6f}, Val loss {val_loss.item():.6f}')\n", + " \n", + " return train_losses, val_losses\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Train the VanillaRNN by first splitting the data between training and validation data" ] }, { @@ -128,12 +232,7 @@ "metadata": {}, "outputs": [], "source": [ - "model=keras.models.Sequential([\n", - " keras.layers.SimpleRNN(20,input_shape=[None,1],return_sequences=True),\n", - " keras.layers.SimpleRNN(20,return_sequences=True),\n", - " keras.layers.SimpleRNN(1)\n", - " ])\n", - "model.summary()" + "y.shape" ] }, { @@ -142,8 +241,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.compile(optimizer='adam',loss='mse',metrics=['mse'])\n", - "history=model.fit(x_train,y_train,validation_data=(x_val,y_val), epochs=20, batch_size=128) " + "prediction_horizon, context" ] }, { @@ -152,9 +250,19 @@ "metadata": {}, "outputs": [], "source": [ - "pd.DataFrame(history.history).plot(figsize=(8,5))\n", - "plt.grid(True)\n", - "plt.xlabel('epochs')" + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# split y into training and validation sets\n", + "n_train = 7000\n", + "x_train, y_train = data[:n_train, :context], data[:n_train, context:context + prediction_horizon]\n", + "x_val, y_val = data[n_train:, :context], data[n_train:, context:context + prediction_horizon]" ] }, { @@ -163,12 +271,18 @@ "metadata": {}, "outputs": [], "source": [ - "model=keras.models.Sequential([\n", - " keras.layers.SimpleRNN(20,input_shape=[None,1],return_sequences=True),\n", - " keras.layers.SimpleRNN(20),\n", - " keras.layers.Dense(1)\n", - " ])\n", - "model.summary()" + "x_train.shape, y_train.shape, x_val.shape, y_val.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# remove the last dimnsion of y_train and y_val\n", + "y_train = y_train.squeeze()\n", + "y_val = y_val.squeeze()" ] }, { @@ -177,8 +291,7 @@ "metadata": {}, "outputs": [], "source": [ - "model.compile(optimizer='adam',loss='mse',metrics=['mse'])\n", - "history=model.fit(x_train,y_train,validation_data=(x_val,y_val), epochs=20, batch_size=128) " + "train_losses, val_losses = train(model, x_train, y_train, x_val, y_val, n_epochs=100, lr=0.001)" ] }, { @@ -187,11 +300,56 @@ "metadata": {}, "outputs": [], "source": [ - "pd.DataFrame(history.history).plot(figsize=(8,5))\n", + "# plot the loss curves\n", + "plt.plot(train_losses, label='train')\n", + "plt.plot(val_losses, label='val')\n", + "plt.legend()\n", "plt.grid(True)\n", - "plt.xlabel('epochs')" + "plt.yscale('log')\n", + "\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_val.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# now show examples on the validation set with the ground truth and the prediction\n", + "# add a dimension to x_val\n", + "# x_val = x_val[...,np.newaxis]\n", + "model.eval()\n", + "with torch.no_grad():\n", + " y_val_pred = model(torch.from_numpy(x_val).float()).numpy()\n", + "\n", + "# plot some of the time series in a 2x3 grid\n", + "plt.figure(figsize=(6,4))\n", + "for i in range(6):\n", + " plt.subplot(2,3,i+1)\n", + " plt.plot(np.concatenate([x_val[i],y_val[i]]), label='GT')\n", + " plt.plot(np.concatenate([x_val[i],y_val_pred[i,np.newaxis].T]), label='Prediction')\n", + " plt.legend()\n", + " plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -341,7 +499,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.5", + "display_name": "mlgeo", "language": "python", "name": "python3" }, @@ -355,12 +513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" - }, - "vscode": { - "interpreter": { - "hash": "bf1c1ab31e530e60b58e3d6ad0457a0c579c03efa8f6c28b6cdd125835b5a825" - } + "version": "3.9.18" } }, "nbformat": 4, diff --git a/book/Chapter4-DeepLearning/mlgeo_4.6_AutoEncoder.ipynb b/book/Chapter4-DeepLearning/mlgeo_4.6_AutoEncoder.ipynb index 66bfa02..c647c11 100644 --- a/book/Chapter4-DeepLearning/mlgeo_4.6_AutoEncoder.ipynb +++ b/book/Chapter4-DeepLearning/mlgeo_4.6_AutoEncoder.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 1, "id": "fd035ac6-55ed-4cb2-aa54-4c42ded3b602", "metadata": {}, "outputs": [], @@ -59,10 +59,92 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 2, "id": "bb21fc37-6e62-41bb-820a-4ff575e8e1c5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./FashionMNIST/raw/train-images-idx3-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 26421880/26421880 [00:02<00:00, 10417165.17it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./FashionMNIST/raw/train-images-idx3-ubyte.gz to ./FashionMNIST/raw\n", + "\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./FashionMNIST/raw/train-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 29515/29515 [00:00<00:00, 175578.14it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./FashionMNIST/raw/train-labels-idx1-ubyte.gz to ./FashionMNIST/raw\n", + "\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4422102/4422102 [00:01<00:00, 3538808.13it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./FashionMNIST/raw/t10k-images-idx3-ubyte.gz to ./FashionMNIST/raw\n", + "\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n", + "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5148/5148 [00:00<00:00, 8317518.10it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting ./FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to ./FashionMNIST/raw\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "dataset = datasets.FashionMNIST(root=\"./\",download=True,\n", "transform=Compose([ToTensor(),Normalize([0.5],[0.5])]))\n", @@ -75,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 3, "id": "6dc978de", "metadata": {}, "outputs": [ @@ -83,7 +165,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", "torch.Size([50, 1, 28, 28])\n" ] } @@ -105,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 4, "id": "26d4fa44-54b2-4e74-973a-65631213306d", "metadata": {}, "outputs": [ @@ -140,7 +221,7 @@ "==========================================================================================" ] }, - "execution_count": 188, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -203,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 5, "id": "869b2658", "metadata": {}, "outputs": [], @@ -303,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 6, "id": "e6277b83", "metadata": {}, "outputs": [ @@ -311,11 +392,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[Epoch 1] train loss: 0.596 - val loss: 0.594\n", - "[Epoch 2] train loss: 0.596 - val loss: 0.593\n", - "[Epoch 3] train loss: 0.595 - val loss: 0.593\n", - "[Epoch 4] train loss: 0.595 - val loss: 0.593\n", - "[Epoch 5] train loss: 0.595 - val loss: 0.593\n" + "[Epoch 1] train loss: 0.617 - val loss: 0.603\n", + "[Epoch 2] train loss: 0.599 - val loss: 0.599\n", + "[Epoch 3] train loss: 0.596 - val loss: 0.597\n", + "[Epoch 4] train loss: 0.595 - val loss: 0.597\n", + "[Epoch 5] train loss: 0.595 - val loss: 0.596\n" ] } ], @@ -333,23 +414,23 @@ }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 7, "id": "adced269", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 192, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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Oh4PBgwezdOlS3+eff/45t912G6mpqVgsFhYvXnzOMQzD4KmnniIlJYXo6GhycnLYvXt3W8rvsprWg3ptbSFujyYMi4hIeAk41Lz55ptMnz6dmTNnsnHjRkaPHs2ECRMoLS31297lcnHTTTdx4MABFi1aREFBAfPmzSMtLc3XpqamhtGjRzNnzpwWv/f3v/89//M//8PcuXNZu3YtsbGxTJgwgdpaXUpprVtGptAzJpIjFbV8stP/+RIREQlVFiPAe3yzsrK48soreeGFFwDweDykp6fzs5/9jCeeeOKc9nPnzuXZZ59l586dREZGXrggi4V3332XyZMn+7YZhkFqair/8R//wc9//nMAKioqSEpKYv78+dx5550XPG5lZSXx8fFUVFQQFxfXyt6Gn9lLd/Di5/u4dnAfXvnBOLPLEREROa9Afn8HNFLjcrnIy8sjJyfn9AGsVnJycli9erXffd577z2ys7PJzc0lKSmJjIwMZs2ahdvtbvX37t+/H6fT2ex74+PjycrKavF76+rqqKysbPYSuDurHxYLrNp1lIPHaswuR0REpN0EFGrKyspwu90kJSU1256UlITT6fS7z759+1i0aBFut5ulS5fy5JNP8sc//pFnnnmm1d/bdOxAvnf27NnEx8f7Xunp6a3+vnDWv3cs11zWB/DOrREREQkXHX73k8fjITExkZdeeonMzEymTJnCr371K+bOnduh3ztjxgwqKip8r6Ii3fHTpGnC8Fsbiqitb/2ImYiISDALKNQkJCRgs9koKSlptr2kpITk5GS/+6SkpDB48GBsNptv27Bhw3A6nbhcrlZ9b9OxA/leh8NBXFxcs5d4XT80kbQe0Zw4Wc/SLcVmlyMiItIuAgo1drudzMxMVq5c6dvm8XhYuXIl2dnZfvcZP348e/bswePx+Lbt2rWLlJQU7HZ7q773kksuITk5udn3VlZWsnbt2ha/V1pms1q4u2k9KD1hWEREwkTAl5+mT5/OvHnzeOWVV9ixYwdTp06lpqaGBx98EID777+fGTNm+NpPnTqV48ePM23aNHbt2sWSJUuYNWsWubm5vjbV1dXk5+eTn58PeCcG5+fnU1jonfNhsVh49NFHeeaZZ3jvvffYsmUL999/P6mpqc3ukpLW+94V6UTaLGwqLGfr4QqzyxEREbloEYHuMGXKFI4ePcpTTz2F0+lkzJgxLF++3DeJt7CwEKv1dFZKT09nxYoVPPbYY4waNYq0tDSmTZvG448/7muzYcMGrr/+et/fp0+fDsADDzzA/PnzAfjlL39JTU0NDz/8MOXl5Xzta19j+fLlREVFtanjXV2f7g4mZqTw/ldHWLj2ILNvH2V2SSIiIhcl4OfUhCo9p+Zca/cdY8pLa4iOtLH2VzcSF3Xh5wiJiIh0pg57To2El3GX9GJwUjdO1bv5R94hs8sRERG5KAo1XZjFYvGt3r1gbSFdZNBORETClEJNF/fty9OIsdvYU1rNmn3HzS5HRESkzRRqurjuUZFMvty7uOiCtbq9W0REQpdCjXBvlvcS1IqtTkorteq5iIiEJoUaYXhqHJn9e9LgMXhzvZaTEBGR0KRQI8Dp9aBeW1dIg9tzgdYiIiLBR6FGAJg0MplesXaKK2r5ZGep2eWIiIgETKFGAHBE2PjeFemA1oMSEZHQpFAjPvdk9cNigX/tLuNAWY3Z5YiIiAREoUZ80nvFcN3gPgAs1O3dIiISYhRqpJmmJwy/nXeI2nq3ydWIiIi0nkKNNHPdkETSekRTfrKeDzYXm12OiIhIqynUSDM2q4W7s/oBsEAThkVEJIQo1Mg5plyZTqTNQn5ROVsOVZhdjoiISKso1Mg5Ero5mJSRAmi0RkREQodCjfh1X7Z3wvA/vzpMxal6k6sRERG5MIUa8euK/j0ZktSd2noP7+QdMrscERGRC1KoEb8sFgv3No7WLFh7EMMwTK5IRETk/BRqpEXfvjyNWLuNfUdrWL33mNnliIiInJdCjbSomyOCb49NA7yjNSIiIsFMoUbOq+kJwyu2lVBSWWtyNSIiIi1TqJHzGpocx5UDeuL2GLyxrsjsckRERFqkUCMX1DRa8/q6QhrcHpOrERER8U+hRi5oYkYyvWPtOCtr+XhHqdnliIiI+KVQIxfkiLAx5cp0QE8YFhGR4KVQI61y17h+WCzw7z1l7DtabXY5IiIi51CokVZJ7xXDDUMSAVi4ttDkakRERM6lUCOt1jRheFHeIU653CZXIyIi0pxCjbTaNYP7kN4rmopT9by/+YjZ5YiIiDSjUCOtZrNauHucd7RmoSYMi4hIkFGokYB874q+2G1WvjpUweZD5WaXIyIi4qNQIwHp3c3BLSOTAd3eLSIiwUWhRgJ2X7b3EtR7Xx2h4mS9ydWIiIh4KdRIwMb268nQ5O7U1ntYtPGQ2eWIiIgACjXSBhaLxTdas3DNQQzDMLkiERERhRppo8lj0ujmiGBfWQ1f7j1mdjkiIiIKNdI2sY4Ibh+bBsCrqzVhWEREzKdQI23W9IThj3aU4KyoNbkaERHp6hRqpM0GJ3Vn3CW9cHsMXl+n9aBERMRcCjVyUZpGa95YX0i922NyNSIi0pUp1MhFmTgimYRudkoq6/h4e4nZ5YiISBemUCMXxR5hZcqV6QAsWKsJwyIiYh6FGrlod43rh9UCX+w5xt6j1WaXIyIiXZRCjVy0vj1juGFoIgAL12jCsIiImEOhRtpF04ThRXlFnHK5Ta5GRES6IoUaaRfXXNaHfr1iqKxt4P2vjphdjoiIdEEKNdIurFYL92T1A+DVNZowLCIinU+hRtrNd69Ixx5hZcvhCr4qKje7HBER6WIUaqTd9Iq1842RKYBGa0REpPMp1Ei7uqdxwvD7Xx2h/KTL5GpERKQrUaiRdjW2Xw+Gp8RR1+BhUd4hs8sREZEuRKFG2pXFYvHd3r1gzUE8HsPkikREpKtQqJF2960xqXR3RHDg2Em+2FtmdjkiItJFKNRIu4t1RHD72DQAXl2tCcMiItI5FGqkQzRdgvp4RwnFFadMrkZERLoChRrpEJcldSfrkl54DHh9rdaDEhGRjqdQIx3mvmzvaM3r64uod3tMrkZERMKdQo10mJuHJ5PQzcHRqjo+3FZidjkiIhLmFGqkw9gjrNw1Lh3w3t4tIiLSkRRqpEPdNa4fVgus3neMPaVVZpcjIiJhTKFGOlRqj2huHJYEwII1mjAsIiIdR6FGOtx9jbd3v5N3iJOuBpOrERGRcNWmUDNnzhwGDBhAVFQUWVlZrFu37rzty8vLyc3NJSUlBYfDweDBg1m6dGlAx7zuuuuwWCzNXj/5yU/aUr50sq8NSqB/7xiq6hp4L/+I2eWIiEiYCjjUvPnmm0yfPp2ZM2eyceNGRo8ezYQJEygtLfXb3uVycdNNN3HgwAEWLVpEQUEB8+bNIy0tLeBjPvTQQxQXF/tev//97wMtX0xgtVq4N8s7WvPqmoMYhtaDEhGR9hdwqHnuued46KGHePDBBxk+fDhz584lJiaGl19+2W/7l19+mePHj7N48WLGjx/PgAEDuPbaaxk9enTAx4yJiSE5Odn3iouLC7R8Mcl3Mvtij7Cy7Ugl+UXlZpcjIiJhKKBQ43K5yMvLIycn5/QBrFZycnJYvXq1333ee+89srOzyc3NJSkpiYyMDGbNmoXb7Q74mAsXLiQhIYGMjAxmzJjByZMnAylfTNQz1s5to1IB72iNiIhIe4sIpHFZWRlut5ukpKRm25OSkti5c6ffffbt28cnn3zCPffcw9KlS9mzZw8//elPqa+vZ+bMma0+5t13303//v1JTU1l8+bNPP744xQUFPCPf/zD7/fW1dVRV1fn+3tlZWUgXZUOcO9V/Xhn4yE+2FzMk7cOp2es3eySREQkjAQUatrC4/GQmJjISy+9hM1mIzMzk8OHD/Pss88yc+bMVh/n4Ycf9v155MiRpKSkcOONN7J3714GDhx4TvvZs2fzm9/8pl36IO1jTHoPMtLi2Hq4krfzinj4mnPPm4iISFsFdPkpISEBm81GSUnzR96XlJSQnJzsd5+UlBQGDx6MzWbzbRs2bBhOpxOXy9WmYwJkZWUBsGfPHr+fz5gxg4qKCt+rqKioVX2UjmOxnJ4wvHBtIR6PJgyLiEj7CSjU2O12MjMzWblypW+bx+Nh5cqVZGdn+91n/Pjx7NmzB4/n9IKGu3btIiUlBbvd3qZjAuTn5wPe0OSPw+EgLi6u2UvM980xqXSPiuDgsZP8a0+Z2eWIiEgYCfjup+nTpzNv3jxeeeUVduzYwdSpU6mpqeHBBx8E4P7772fGjBm+9lOnTuX48eNMmzaNXbt2sWTJEmbNmkVubm6rj7l3716efvpp8vLyOHDgAO+99x73338/11xzDaNGjbrYfwPpRDH2CO4Y2xfQelAiItK+Ap5TM2XKFI4ePcpTTz2F0+lkzJgxLF++3DfRt7CwEKv1dFZKT09nxYoVPPbYY4waNYq0tDSmTZvG448/3upj2u12Pv74Y/70pz9RU1NDeno6d9xxB7/+9a8vtv9ignuv6s/8Lw+wckcJh8tPkdYj2uySREQkDFiMLvIktMrKSuLj46moqNClqCBw10trWL3vGD+7YRD/cfMQs8sREZEgFcjvb639JKa4L9s7Yfj1dUW4GjwXaC0iInJhCjViipuGJ5HY3UFZdR0fbneaXY6IiIQBhRoxRaTNyp3j+gHw6mpNGBYRkYunUCOmuWtcOjarhbX7j7O7pMrsckREJMQp1IhpUuKjyRmWCOj2bhERuXgKNWKqe6/yThj+x8bD1NQ1mFyNiIiEMoUaMdX4gQlckhBLVV0D/8w/YnY5IiISwhRqxFRWq4V7srwThhesOUgXeWySiIh0AIUaMd13MvviiLCyvbiSjYXlZpcjIiIhSqFGTNcjxs5to1MBWKgJwyIi0kYKNRIU7mucMPzB5mKO17hMrkZEREKRQo0EhdHpPRiZFo/L7eGtDUVmlyMiIiFIoUaCRtNozcK1B/F4NGFYREQCo1AjQeO20anERUVQdPwUq3YfNbscEREJMQo1EjSi7Ta+k5kOaMKwiIgETqFGgso9V3mfWbNyZymHTpw0uRoREQklCjXt4fBGaKgzu4qwMLBPN8YP6o1hwOvrCs0uR0REQohCzcWqcsL/fgvmfg0OfGF2NWHh3izvhOE31xfhavCYXI2IiIQKhZqLdXw/RDigbBfMvwUW58LJ42ZXFdJyhieRFOegrNrF8m1Os8sREZEQoVBzsfpnwyPrIfNB79/zF8ALV0D+66B1jNok0mblzisb14NarQnDIiLSOgo17SG6J9z2J/jBh5A4HE4eg8U/gVdug7LdZlcXku4a1w+b1cK6A8cpcFaZXY6IiIQAhZr21C8Lfvw55PwnRETDgX/BX6+Gz36nicQBSo6P4ubhSYB39W4REZELUahpb7ZI+Npj8NPVMCgH3C74bLY33Oz/l9nVhZR7G58w/O6mw1TXNZhcjYiIBDuFmo7S6xK4ZxF85+/QLQmO7YFXvgHvToWaY2ZXFxKuHtibS/vEUl3XwOJNh80uR0REgpxCTUeyWCDjdshdB1f+CLDAV6/BC5mwaYEmEl+AxWLhnsbbuxesOYihfy8RETkPhZrOEN0Dbv0j/PAjSMqAUyfgn7kw/xtwdJfZ1QW174ztS1SklZ3OKvIOnjC7HBERCWIKNZ0p/Up4+DO46WmIjIGD//bOtfnk/0J9rdnVBaX4mEi+OToV0IRhERE5P4WazmaLhPH/H/x0DVx2M3jq4fPfe8PNvs/Mri4o3XfVAACWbnFyrFp3kYmIiH8KNWbp2R/ufgu++wp0S4bje73LLfzjYag+anZ1QWVk33hG943H5fbw1oZDZpcjIiJBSqHGTBYLjJgMj6yDcQ8DFtj8pveJxBv/Fzxa96hJ0+3dC9cexO3RhGERETmXQk0wiIqHW56FH62E5JFQWw7v/cy7llTpTrOrCwq3jU4lPjqSQydO8fkujWSJiMi5FGqCSd9MeOgzuPn/eicSF672rv698mmoP2V2daaKirTx3cy+ALyqCcMiIuKHQk2wsUXA1Y94n20zeJJ3IvG//gB/yYY9K82uzlT3NF6C+rSglKLjJ02uRkREgo1CTbDqkQ53vQ5TFkD3VDixHxbcDu/8CKpLza7OFJckxPL1yxIwDHhtXaHZ5YiISJBRqAlmFgsMu807kThrKlissOVt70TiDX/vkhOJm54w/Nb6Iuoa3CZXIyIiwUShJhQ4usOk33knEqeMhtoK+OBR+PtEKNludnWdKmdYIslxURyrcbF8q9PsckREJIgo1ISStLHwo09g4u/A3g2K1sKLX4eP/xNcXWOOSYTNyl3j+gF6wrCIiDSnUBNqbBFw1VTIXQtDvwGeBvj38/CXq2D3x2ZX1ynuHJdOhNXC+gMn2OmsNLscEREJEgo1oSq+L9y5EO58DeLSoPwgLLwD3n4QqsL7skxSXBQ3j0gCNFojIiKnKdSEuqG3ekdtrsr1TiTe9g94YRys/1tYTyRuesLwuxsPU13XYHI1IiISDBRqwoGjO0ycBQ99CqmXQ10FLPkPePlmcG41u7oOkX1pbwb2iaXG5ebdTYfNLkdERIKAQk04SR3jvUNq0u/B3h0OrYcXr4EPnwRXjdnVtSuLxeIbrVmw+iCGofWgRES6OoWacGO1QdaPvc+2GfZNMNzw5f/AnKtg14dmV9eubh/bl+hIGwUlVWw4eMLsckRExGQKNeEqLhWmvAp3vQHx6VBRCK99F956ACqLza6uXcRHR/KtMakAvLpaE4ZFRLo6hZpwN2QS/HQNXP0zsNhg+2KYMw7WzQNP6D+Rt+kS1LKtxZRV15lcjYiImEmhpitwdIObn4GHP4O0TKirhKU/h7/lQPFms6u7KBlp8YxJ70G92+DN9UVmlyMiIiZSqOlKUkbBDz+CW/4Ajjg4shFeug5W/Arqqs2urs2aRmteW1uI26MJwyIiXZVCTVdjtcG4hyB3HYz4tnci8eoXYE4WFCwzu7o2+caoFHrERHK4/BSfFXTNFcxFREShpuuKS4Hvzoe734Ye/aDyELx+J7x5L1SE1nNfoiJtfDezLwCv6gnDIiJdlkJNVzf4ZvjpWhj/qHci8Y73vROJ18wNqYnE92R5L0Gt2nWUwmNdY3FPERFpTqFGwB4DN/0Gfvw59L0SXNWw/HH4241wJN/s6lplQEIsX78sAcOAhes0WiMi0hUp1MhpyRnwgw/h1ufAEQ9HNsG862H5DKirMru6C7qvccLw2xsOUVsfOqNMIiLSPhRqpDmrFa78ITyyHjLuAMMDa/7inUi8c4nZ1Z3XDUMTSYmP4niNi2Vbw+MBgyIi0noKNeJf9yT4zstw7zvQoz9UHoY37obX74aKQ2ZX51eEzcrd4/oBsGBNocnViIhIZ1OokfMblON9IvHXpoM1AgqWwAvjYPVfwN1gdnXnmDIunQirhbyDJ9h+pNLsckREpBMp1MiF2WMgZyb8+F+QfhXU18CKGd75Noc3ml1dM4ndo5iQkQzAgrWaMCwi0pUo1EjrJQ2HB5fBbf8NUfHg3Oy9Q2rZ41AbPKMiTROGF286TFVtvcnViIhIZ1GokcBYrZD5fXhkA4z8nnci8dq53mfbbH8PDPOXKci6pBeXJXbjpMvNu5tC60GCIiLSdgo10jbdEuGOeXDfu9DzEqgqhrfug9fvgnJzJ+laLBbfelCvrj6IEQRBS0REOp5CjVycgTfAT1fDNb8AayTsWua9/fvLP5s6kfjbY9OIjrSxu7SadfuPm1aHiIh0HoUauXiR0XDDr+En/4Z+V0P9Sfjw1zDvOjiUZ0pJcVGRTL48DdB6UCIiXYVCjbSfxKHw/SXwzRcgqgc4t3gnEi/5OdRWdHo5917lfWbNim1OSqtqO/37RUSkcynUSPuyWmHsfd6JxKPuBAxYP8/7bJtt73bqROIRqfGM7deDerfBW+uLOu17RUTEHAo10jG69YHbX4T734NeA6HaCW9/H177HpzovMtBTROGX1tbiNujCcMiIuGsTaFmzpw5DBgwgKioKLKysli3bt1525eXl5Obm0tKSgoOh4PBgwezdOnSgI5ZW1tLbm4uvXv3plu3btxxxx2UlJS0pXzpTJdeC1O/hGsfB5sddn/onUj8xX+Du+OfIXPLyBR6xkRypKKWT3aWdvj3iYiIeQIONW+++SbTp09n5syZbNy4kdGjRzNhwgRKS/3/wnC5XNx0000cOHCARYsWUVBQwLx580hLSwvomI899hjvv/8+b7/9NqtWreLIkSPcfvvtbeiydLrIKLj+/8BPvoD+X4OGU/DRU/DitVB0/kB8saIibXzvinQAFmjCsIhIWLMYAT7EIysriyuvvJIXXngBAI/HQ3p6Oj/72c944oknzmk/d+5cnn32WXbu3ElkZGSbjllRUUGfPn147bXX+M53vgPAzp07GTZsGKtXr+aqq666YN2VlZXEx8dTUVFBXFxcIF2W9mQYkP+a9+6oU8cBC1zxA7jxKYju0SFfefBYDdf94TMMA1b94jr6947tkO8REZH2F8jv74BGalwuF3l5eeTk5Jw+gNVKTk4Oq1ev9rvPe++9R3Z2Nrm5uSQlJZGRkcGsWbNwu92tPmZeXh719fXN2gwdOpR+/fq1+L11dXVUVlY2e0kQsFjg8nu8E4nH3AMYsOH/eZ9IvPWdDplI3L93LNdc1gfwzq0REZHwFFCoKSsrw+12k5SU1Gx7UlISTqfT7z779u1j0aJFuN1uli5dypNPPskf//hHnnnmmVYf0+l0Yrfb6dGjR6u/d/bs2cTHx/te6enpgXRVOlpsb5j8F3jgA+h9GVSXwKIfwII74Pj+dv+6pvWg3tpQRG29u92PLyIi5uvwu588Hg+JiYm89NJLZGZmMmXKFH71q18xd+7cDv3eGTNmUFFR4XsVFemW3qB0yddh6hdw3f/xTiTeuxL+chX867l2nUh8/dBE0npEc+JkPUu3FLfbcUVEJHgEFGoSEhKw2Wzn3HVUUlJCcnKy331SUlIYPHgwNpvNt23YsGE4nU5cLlerjpmcnIzL5aK8vLzV3+twOIiLi2v2kiAV4YDrHoepq+GSa6ChFlb+BuZ+HQrXtMtX2KwW7s7yPoxPTxgWEQlPAYUau91OZmYmK1eu9G3zeDysXLmS7Oxsv/uMHz+ePXv24PF4fNt27dpFSkoKdru9VcfMzMwkMjKyWZuCggIKCwtb/F4JQQmDvM+1+faLENMbju6AlyfA+9Pg1ImLPvz3rkgn0mZhU2E5Ww93/hOORUSkYwV8+Wn69OnMmzePV155hR07djB16lRqamp48MEHAbj//vuZMWOGr/3UqVM5fvw406ZNY9euXSxZsoRZs2aRm5vb6mPGx8fzwx/+kOnTp/Ppp5+Sl5fHgw8+SHZ2dqvufJIQYrHA6Du9E4kvv8+7LW8+vHAlbH77oiYS9+nuYGJGCgAL12q0RkQk3EQEusOUKVM4evQoTz31FE6nkzFjxrB8+XLfRN/CwkKs1tNZKT09nRUrVvDYY48xatQo0tLSmDZtGo8//nirjwnw/PPPY7VaueOOO6irq2PChAn85S9/uZi+SzCL6QXfegFG3wUfPAZlBfCPH0H+QvjGc9Dr0jYd9t6sfrz/1REWbzrCjFuGERfl/zEDIiISegJ+Tk2o0nNqQliDC778b1j1LLjrICIKrvk5XD0NIuwBHcowDCb86XN2lVTzn7cN5/vjL+mgokVEpD102HNqREwRYYdrfgE/XQ2XXuedSPzJMzD3a3Dwy4AOZbFYfOtBLVhbSBfJ9CIiXYJCjYSO3gPhvsVw+zyI7eO9JPX3SfDPR+Dk8VYf5tuXpxFjt7GntJo1+1q/n4iIBDeFGgktFguM+h7kroOxD3i3bXrVO5H4qzdbNZG4e1Qkky/3rj22QBOGRUTChkKNhKaYXvDN/4EHl0OfYXCyDN59GP73W3Bs7wV3vzfLewlqxVYnpZW1HV2tiIh0AoUaCW39s+HHn3sXxIyIgv2r4C/ZsOr30FDX4m7DU+PI7N+TBo/BG+v1tGkRkXCgUCOhL8IOX/8P70TigTd675D69P/CX8fDgX+3uFvTelCvryukwe1psZ2IiIQGhRoJH70uhXvfgTv+H8QmwrHdMP9WWJwLNcfOaT5pZDK9Yu0UV9SycmepCQWLiEh7UqiR8GKxwMjvwCPr4YofeLflL4AXroD815pNJHZE2PjeFd7V2xdoPSgRkZCnUCPhKboHfON5+OFHkDgCTh2HxVPhldugbLev2T1Z/bBY4F+7y9hfVmNevSIictEUaiS8pY+DH6+CnN9ARDQc+Bf89Wr4dDbU15LeK4brBvcB4MG/r+O5DwvYUVyph/KJiIQgLZMgXceJA7Dk57DnI+/few+CbzzPJttI7v3bWmpcbl/TAb1jmDQyhUkZyYxMi8disZhTs4hIFxfI72+FGulaDAO2L4Zlj0N1iXfb6LuovGYmKwvdLN3iZNWuo7gaTt8NldYjmkkZyUwamcLl6T2wWhVwREQ6i0KNHwo10kxtBaz8Laz/f4AB0T1h2G2QOIJTvYayqrwP7++u45OdpZyqPz2CkxwXxcSMZCZmJHPlgF7YFHBERDqUQo0fCjXi16EN8P40KNl67mfdknEnDqcwYgBfVCXx7pGebKlLwkUkAAnd7Nw8IplJGclcdWlvIm2aoiYi0t4UavxQqJEWuRugYCkUfwWl270Bp7zQb1OPxcZRe1821aWxpb4vBUY6O41+VEelcNPwZG4ZmcLVg3rjiLB1cidERMKTQo0fCjUSkNpKOLrTG3BKtp8OO7UVfptXGdEUGOkUeNI5YOtP3IAxDB+TzfiMgURFKuCIiLSVQo0fCjVy0QwDKo+cDjiNYcc4WoDFU+93l2KjN8e6DSIqbRR9h2QS1XcU9L7Mu7SDiIhckEKNHwo10mEaXHBsjzfgOLdScfArLKXbiHeV+G3usURgJFyGLTkDEodD0gjvKy7N+0RkERHxUajxQ6FGOptx6gR7t25g77Y11B7aSqprH0MsRcRZTvnfISre+/TjpOGNYScDEodBlP73KiJdl0KNHwo1YibDMNheXMnyLcVs2LyZ6BMFDLUUMtRaxFBLEQOtR7DRwkrh8f0aR3POCDu9B4EtonM7ISJiAoUaPxRqJJjsLqli2VYnS7cUs9NZhZ16BlqOMNRaxPU9ShkbVUxK3T5s1cX+D2CzQ8KQM8JO4yWs7sm6hCUiYUWhxg+FGglW+8tqWLa1mOVbnWw+1PzuqmvTI/huegXj40rpWbnLO0m5dAe4qv0fLLpn42Wr4d6wk5QBfYaCo1sn9EREpP0p1PihUCOhoOj4SVZs847gbCwsb/bZ6L7xTMxIYdKIRAbYyhrvwmq8E6t0u3eystHCJayeA84NO70uBatuNxeR4KZQ44dCjYQaZ0Uty7cWs2yrk3UHjnPmT+qwlDhuyUhm0shkBiV2926sPwVHCxrDzjbvq3T76TWuzhYRBX2GnBF2Gi9hdUvs+M6JiLSSQo0fCjUSyo5W1fHhdifLtjhZve8Ybs/pH9vLErv5Ftwcmtz93BXFa8pOB5ymsHN0J9Sf9P9lMQmnA05T2OkzFOwxHdhDERH/FGr8UKiRcHGixsVH20tYtrWYf+8po959+kd4QO8YJo1MYVJGMiPT4s8NOE08bjhx4Nywc3wf4O8/CRbv5aqzw07PS8CqNa9EpOMo1PihUCPhqOJUPZ/sLGHZFierdh2lruH0nJq0HtGNIzjJXJ7eE2trVhR3nWxcHmJb8ycnnyzz3z4yxjuK0xR2kkZ478SK7d1OPRSRrk6hxg+FGgl3NXUNfFpQyrItTj7ZWcqperfvs6Q4BxNHeC9RXTmgF7bWBJwzVZeeuw7W0QJoqPXfvltS8xGdpBHeW9Ajoy6ihyLSFSnU+KFQI13JKZebVbuOsnxrMR/vKKW6rsH3WUI3OzePSGZSRjJXXdqbSFsbLx953N7LVWeHnRMH/Le32KD3wNOjOUmNgSe+ny5hiUiLFGr8UKiRrqquwc0Xe8pYusXJR9tLqDh1evHNHjGR3DQsiVtGpnD1oN44ItrhFu+6av8rnJ864b+9vZt3OQhf2GkMPNE9L74WEQl5CjV+KNSIQL3bw+q9x1i21cmH25wcq3H5PuvuiCBneBITM5K5dnAfoiLb8Rk2hgFVTihtnJBcst3756MF4Hb536d76rlPTE4YrBXORboYhRo/FGpEmnN7DNbtP+57Fk5pVZ3vsxi7jeuHJjIpI5nrhyQS6+igdabc9XBs77lhp7zQf3trBPS+7NywE99Xy0OIhCmFGj8UakRa5vEYbCo6wdItTpZvdXK4/PRK4o4IK9cO7sMtI1O4YVgicVGRHV9QbaV3OYizw05thf/2jnhIuAxiekN0D4jqceH3yGgFIZEQoFDjh0KNSOsYhsHmQxUs2+pk2dZiDh47/ZA+u83K1y5LYGJGMjcPT6JHTCdeCjIMqDx8OuA0hZ2yXeCpv/D+Z7PZ/YedqPgLByJ7rAKRSCdRqPFDoUYkcIZhsKO4imWNl6j2lJ5eSDPCaiF7YG8mZaRw84gkEro5zCmywQXHdnsvY506AbXlcKrc+15bcfrPZ74b7paP1xrWiFaMBsX7/8zRXYFIJAAKNX4o1IhcvN0lVY0jOE52FFf6tlstMO6SXkzKSGFiRjJJcUH8PBrD8K5y7i/stPhecfrPbRkVOpPF1rrRIH8jR4443f4uXY5CjR8KNSLt60BZje8S1eZDzee6ZPbvyaSMZCZmJNO3ZxitGWUY3jWzAgpEZ7y3dKdXa1ms3mDT2kB05rsjTquyS0hSqPFDoUak4xQdP8mKbd4RnLyDzZ9HM7pvPBMzvOtRDUiINanCIFF/qu2BqKWnN7eapTEQtXBZrMX3nt7RIgUiMYlCjR8KNSKdw1lRy4ptTpZuKWb9geOcsaA4w1LimJSRzC0jkxmU2N28IkNRfe355wmd772lFdkD4Yg763KYv0toPf3PLbJ1wh1zErYUavxQqBHpfGXVdXy4zbui+Jd7j+E+I+EMSuzGLRnJTMxIYVhK95ZXFJeL1+A6/zyh8727qv0eMiD2bq28wyy+MTzFnX63d9c8oi5OocYPhRoRc52ocfHRjhKWb3Xyr91HqXef/k/PgN4xTMxI4ZaRyYxMi1fACSbu+hZGh06cJxg1bq+rbOmoAbB47xg7O+z4ez/fZ7p8FrIUavxQqBEJHpW19Xyyo5SlW4pZtesodQ0e32dpPaKZlJHMpJHJXJ7eE2ugK4pL8HA3eIPN2bfatzhiVOFtX1vpfb/YidVnsnc7T+jp7n2AY4uhqXEEydZBT9aW81Ko8UOhRiQ41dQ18GlBKcu2Ovl0ZyknXaefIZMU52DiiGQmjUzhygG9sCngdC31tWeEnIrTYafZe9V5PqtshwnWZ4iMaSEQxfm/dOYvGGntsoAp1PihUCMS/Grr3azadZRlW4pZuaOUqroG32cJ3ezcPCKZSRnJXHVpbyJtmmchrdDgagw5FS0EopY+O+O9PSZaN4mIOuuSWffGP59vpOiszyOD+DlQHUChxg+FGpHQUtfg5ss9x1i6pZiPdpRQfvL0Q+96xERy07AkbhmZwtWDeuOI0HwJ6UDuem8AahZ+qloxinTGe3tMuG5is58ViAIYKfIFo9BZ+0yhxg+FGpHQVe/2sGbfMZZtdbJiq5NjNafnWnR3RHDjsERuHpHMsJQ40ntGE6FRHAk2HncLgagVI0VN73VVQDv9yrZGnDUBO4CRoqYwZe/WKcFIocYPhRqR8OD2GKw/cJxlW4pZvs1JSWVds88jbRb6947l0oRYBiZ2870PTOhGfIyelyIhzOPxjvj4DT6tDUaVYHgu/F2tYbGeO8m616UweU77HL+RQo0fCjUi4cfjMdhUdIJlW5x8ufcY+8qqqa1v+T/YvWPtDOzTjUv7xHJpn9jGP3fT6I50HYYBrho/gacVl9DO/LOnwf/x+wyF3LXtWrJCjR8KNSLhz+MxKK6sZd/RavaWVrOvrIa9R6vZd7SG4oqW74LR6I5IAAzDu+SHv5EimwOG3tKuX6dQ44dCjUjXVlPXwP7GkLP3aA37GsOORndEgptCjR8KNSLiT9Pozt7Sam/Q0eiOSFBRqPFDoUZEAuVvdGfv0Rr2a3RHpNMo1PihUCMi7eXs0Z29jZexNLoj0v4UavxQqBGRzqDRHZH2pVDjh0KNiJjJ4zE4UnHKOzn5jNGdvaU1OCs1uiPSEoUaPxRqRCRYnT260zRRWaM7Igo1finUiEioOXN0Z+8Zt6BrdEe6EoUaPxRqRCScVNc1sL8p5JwZeo5WU9fQutGd06M8Gt2R4KVQ44dCjYh0Bf5Gd5reWzO6M7Ax5Gh0R4KFQo0fCjUi0tU1G90prWZvWY1GdyToKdT4oVAjIuKfRnckmCnU+KFQIyISOH+jO3tLq9lfVqPRHekUCjV+KNSIiLSfptGd0w8YbJqorNEdaV8KNX4o1IiIdI62ju50j4qgZ4ydnjGR9Gj2bqdnbGTjZ3Z6xETSM9b7eXSkDYvF0om9k84WyO/viLZ8wZw5c3j22WdxOp2MHj2aP//5z4wbN85v2/nz5/Pggw822+ZwOKitPZ3kS0pKePzxx/nwww8pLy/nmmuu4c9//jOXXXaZr811113HqlWrmh3nxz/+MXPnzm1LF0REpIN0c0Qwsm88I/vGN9vu8RgcLj/FvjL/oztVtQ1U1TZQeLz132WPsNIzJvJ02Imx+wJR07Zesc23xUVHYrMqCIWjgEPNm2++yfTp05k7dy5ZWVn86U9/YsKECRQUFJCYmOh3n7i4OAoKCnx/PzNVG4bB5MmTiYyM5J///CdxcXE899xz5OTksH37dmJjY31tH3roIX7729/6/h4TExNo+SIiYhKr1UJ6rxjSe8Vw7eA+zT6rrmvAWVFL+UkXJ07Wc6LGxYnGP3u3nfln73u928DV4KGkso6SyrpW12GxQHz02UEo8qxRonNHh6Iibe39TyLtLOBQ89xzz/HQQw/5Rl/mzp3LkiVLePnll3niiSf87mOxWEhOTvb72e7du1mzZg1bt25lxIgRAPz1r38lOTmZ119/nR/96Ee+tjExMS0eR0REQlc3RwSDEru1ur1hGNS43JyocVF+sr4x9Hj/fLzGdTocnTz9efnJeqrrGjAMKD9ZT/nJ+oBqjI60nQ49sZFnjQid/nPTpbEeMXbioiJ0eawTBRRqXC4XeXl5zJgxw7fNarWSk5PD6tWrW9yvurqa/v374/F4GDt2LLNmzfIFmLo6b7qOiopqdkyHw8G///3vZqFm4cKFLFiwgOTkZG677TaefPLJFkdr6urqfMcG7zU5EREJDxaLhW6OCLo5Ikjv1fr9XA0eyk81Bp2a5qM/J066ztrmbVd+qh63x+BUvZtTFW6OVLQ8EfpsNquFHtGR514aiz13dKhX47Ye0XbsEbpDrC0CCjVlZWW43W6SkpKabU9KSmLnzp1+9xkyZAgvv/wyo0aNoqKigj/84Q9cffXVbNu2jb59+zJ06FD69evHjBkzePHFF4mNjeX555/n0KFDFBcX+45z9913079/f1JTU9m8eTOPP/44BQUF/OMf//D7vbNnz+Y3v/lNIN0TEZEwZ4+wktg9isTuURdu3MjjMaiqazhr9MfFiZrTgei4n22n6t24PQbHalwcq3EBNa3+zm6OiPNcGmsKRXZ6nTFxOtauSdMB3f105MgR0tLS+PLLL8nOzvZt/+Uvf8mqVatYu3btBY9RX1/PsGHDuOuuu3j66acByMvL44c//CFfffUVNpuNnJwcrFYrhmGwbNkyv8f55JNPuPHGG9mzZw8DBw4853N/IzXp6em6+0lERDpFbb37nEtjvvczRoSOn/FZxal62npPcqTNctZdY/4nTnsvj3m3xUdHBv1zgzrs7qeEhARsNhslJSXNtpeUlLR6rktkZCSXX345e/bs8W3LzMwkPz+fiooKXC4Xffr0ISsriyuuuKLF42RlZQG0GGocDgcOh6NVNYmIiLS3qEgbyfE2kuNbPyrk9hhUnqr3OzG62baa02HpxMl6XA0e6t0GR6vqOFrV+knTAHFREb6Rn7PvJDtz4nTTnWQ9Y+xE24Nz0nRAocZut5OZmcnKlSuZPHkyAB6Ph5UrV/LII4+06hhut5stW7Zwyy23nPNZfLz39r/du3ezYcMG30iOP/n5+QCkpKQE0gUREZGgZbNavCMpsfZW72MY3vk+TXeNlZ95ieysydK+0aEaF1W1DQBU1jZQWdvAwWMnW/2djghr8/DTOHG6b89ofnrdoID73V4Cvvtp+vTpPPDAA1xxxRWMGzeOP/3pT9TU1Pjuhrr//vtJS0tj9uzZAPz2t7/lqquuYtCgQZSXl/Pss89y8ODBZhOA3377bfr06UO/fv3YsmUL06ZNY/Lkydx8880A7N27l9dee41bbrmF3r17s3nzZh577DGuueYaRo0a1R7/DiIiIiHJYrEQY48gxh5BWo/oVu/X4PZQfqq+2W30Z4eflm6lr2vw4KysPefp0Zf2iQ2tUDNlyhSOHj3KU089hdPpZMyYMSxfvtw3ebiwsBCr9fT1uRMnTvDQQw/hdDrp2bMnmZmZfPnllwwfPtzXpri4mOnTp1NSUkJKSgr3338/Tz75pO9zu93Oxx9/7AtQ6enp3HHHHfz617++mL6LiIh0WRE2KwndHCR0a/1UDcMwqK5rODf8NM4R6h7Vpmf6thstkyAiIiJBK5Df38E95VlERESklRRqREREJCwo1IiIiEhYUKgRERGRsKBQIyIiImFBoUZERETCgkKNiIiIhAWFGhEREQkLCjUiIiISFhRqREREJCwo1IiIiEhYUKgRERGRsKBQIyIiImHB3DXCO1HTYuSVlZUmVyIiIiKt1fR7u+n3+Pl0mVBTVVUFQHp6usmViIiISKCqqqqIj48/bxuL0ZroEwY8Hg9Hjhyhe/fuWCyWdj12ZWUl6enpFBUVERcX167HDgbh3j8I/z6qf6Ev3Puo/oW+juqjYRhUVVWRmpqK1Xr+WTNdZqTGarXSt2/fDv2OuLi4sP0fK4R//yD8+6j+hb5w76P6F/o6oo8XGqFpoonCIiIiEhYUakRERCQsKNS0A4fDwcyZM3E4HGaX0iHCvX8Q/n1U/0JfuPdR/Qt9wdDHLjNRWERERMKbRmpEREQkLCjUiIiISFhQqBEREZGwoFAjIiIiYUGhppXmzJnDgAEDiIqKIisri3Xr1p23/dtvv83QoUOJiopi5MiRLF26tJMqbZtA+jd//nwsFkuzV1RUVCdWG5jPP/+c2267jdTUVCwWC4sXL77gPp999hljx47F4XAwaNAg5s+f3+F1XoxA+/jZZ5+dcw4tFgtOp7NzCg7A7NmzufLKK+nevTuJiYlMnjyZgoKCC+4XSj+DbeljKP0c/vWvf2XUqFG+h7JlZ2ezbNmy8+4TSucPAu9jKJ0/f373u99hsVh49NFHz9uus8+jQk0rvPnmm0yfPp2ZM2eyceNGRo8ezYQJEygtLfXb/ssvv+Suu+7ihz/8IZs2bWLy5MlMnjyZrVu3dnLlrRNo/8D7xMji4mLf6+DBg51YcWBqamoYPXo0c+bMaVX7/fv3c+utt3L99deTn5/Po48+yo9+9CNWrFjRwZW2XaB9bFJQUNDsPCYmJnZQhW23atUqcnNzWbNmDR999BH19fXcfPPN1NTUtLhPqP0MtqWPEDo/h3379uV3v/sdeXl5bNiwgRtuuIFvfetbbNu2zW/7UDt/EHgfIXTO39nWr1/Piy++yKhRo87bzpTzaMgFjRs3zsjNzfX93e12G6mpqcbs2bP9tv/e975n3Hrrrc22ZWVlGT/+8Y87tM62CrR/f//73434+PhOqq59Aca777573ja//OUvjREjRjTbNmXKFGPChAkdWFn7aU0fP/30UwMwTpw40Sk1tafS0lIDMFatWtVim1D7GTxba/oYyj+HhmEYPXv2NP72t7/5/SzUz1+T8/UxVM9fVVWVcdlllxkfffSRce211xrTpk1rsa0Z51EjNRfgcrnIy8sjJyfHt81qtZKTk8Pq1av97rN69epm7QEmTJjQYnsztaV/ANXV1fTv35/09PQL/r+RUBNK5+9ijRkzhpSUFG666Sa++OILs8tplYqKCgB69erVYptQP4et6SOE5s+h2+3mjTfeoKamhuzsbL9tQv38taaPEJrnLzc3l1tvvfWc8+OPGedRoeYCysrKcLvdJCUlNduelJTU4vwDp9MZUHsztaV/Q4YM4eWXX+af//wnCxYswOPxcPXVV3Po0KHOKLnDtXT+KisrOXXqlElVta+UlBTmzp3LO++8wzvvvEN6ejrXXXcdGzduNLu08/J4PDz66KOMHz+ejIyMFtuF0s/g2Vrbx1D7OdyyZQvdunXD4XDwk5/8hHfffZfhw4f7bRuq5y+QPoba+QN444032LhxI7Nnz25VezPOY5dZpVvaT3Z2drP/93H11VczbNgwXnzxRZ5++mkTK5PWGjJkCEOGDPH9/eqrr2bv3r08//zzvPrqqyZWdn65ubls3bqVf//732aX0mFa28dQ+zkcMmQI+fn5VFRUsGjRIh544AFWrVrV4i/9UBRIH0Pt/BUVFTFt2jQ++uijoJ7QrFBzAQkJCdhsNkpKSpptLykpITk52e8+ycnJAbU3U1v6d7bIyEguv/xy9uzZ0xEldrqWzl9cXBzR0dEmVdXxxo0bF9Rh4ZFHHuGDDz7g888/p2/fvudtG0o/g2cKpI9nC/afQ7vdzqBBgwDIzMxk/fr1/Pd//zcvvvjiOW1D9fwF0sezBfv5y8vLo7S0lLFjx/q2ud1uPv/8c1544QXq6uqw2WzN9jHjPOry0wXY7XYyMzNZuXKlb5vH42HlypUtXivNzs5u1h7go48+Ou+1VbO0pX9nc7vdbNmyhZSUlI4qs1OF0vlrT/n5+UF5Dg3D4JFHHuHdd9/lk08+4ZJLLrngPqF2DtvSx7OF2s+hx+Ohrq7O72ehdv5acr4+ni3Yz9+NN97Ili1byM/P972uuOIK7rnnHvLz888JNGDSeeywKchh5I033jAcDocxf/58Y/v27cbDDz9s9OjRw3A6nYZhGMZ9991nPPHEE772X3zxhREREWH84Q9/MHbs2GHMnDnTiIyMNLZs2WJWF84r0P795je/MVasWGHs3bvXyMvLM+68804jKirK2LZtm1ldOK+qqipj06ZNxqZNmwzAeO6554xNmzYZBw8eNAzDMJ544gnjvvvu87Xft2+fERMTY/ziF78wduzYYcyZM8ew2WzG8uXLzerCBQXax+eff95YvHixsXv3bmPLli3GtGnTDKvVanz88cdmdaFFU6dONeLj443PPvvMKC4u9r1OnjzpaxPqP4Nt6WMo/Rw+8cQTxqpVq4z9+/cbmzdvNp544gnDYrEYH374oWEYoX/+DCPwPobS+WvJ2Xc/BcN5VKhppT//+c9Gv379DLvdbowbN85Ys2aN77Nrr73WeOCBB5q1f+utt4zBgwcbdrvdGDFihLFkyZJOrjgwgfTv0Ucf9bVNSkoybrnlFmPjxo0mVN06Tbcvn/1q6tMDDzxgXHvttefsM2bMGMNutxuXXnqp8fe//73T6w5EoH38r//6L2PgwIFGVFSU0atXL+O6664zPvnkE3OKvwB//QKanZNQ/xlsSx9D6efwBz/4gdG/f3/Dbrcbffr0MW688UbfL3vDCP3zZxiB9zGUzl9Lzg41wXAeLYZhGB03DiQiIiLSOTSnRkRERMKCQo2IiIiEBYUaERERCQsKNSIiIhIWFGpEREQkLCjUiIiISFhQqBEREZGwoFAjIiIiYUGhRkRERMKCQo2IiIiEBYUaERERCQsKNSIiIhIW/n8iGbhZPHwoZgAAAABJRU5ErkJggg==", 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" ] @@ -373,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 8, "id": "19e8d636-2c7f-445d-9f49-b23ffe4f0edb", "metadata": {}, "outputs": [], @@ -395,13 +476,13 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 9, "id": "39cb28fe-5a60-490b-9af8-20f08a0af970", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -434,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 10, "id": "128be833", "metadata": {}, "outputs": [], @@ -469,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 11, "id": "98c8ffa5", "metadata": {}, "outputs": [ @@ -503,7 +584,7 @@ "==========================================================================================" ] }, - "execution_count": 196, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -517,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 12, "id": "aa84d294", "metadata": {}, "outputs": [ @@ -525,11 +606,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "[Epoch 1] train loss: 0.609 - val loss: 0.574\n", - "[Epoch 2] train loss: 0.574 - val loss: 0.571\n", - "[Epoch 3] train loss: 0.572 - val loss: 0.570\n", - "[Epoch 4] train loss: 0.571 - val loss: 0.569\n", - "[Epoch 5] train loss: 0.571 - val loss: 0.569\n" + "[Epoch 1] train loss: 0.611 - val loss: 0.578\n", + "[Epoch 2] train loss: 0.574 - val loss: 0.574\n", + "[Epoch 3] train loss: 0.572 - val loss: 0.573\n", + "[Epoch 4] train loss: 0.571 - val loss: 0.573\n", + "[Epoch 5] train loss: 0.571 - val loss: 0.572\n" ] } ], @@ -539,13 +620,13 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 13, "id": "7bc48a2c-cecf-437b-964c-41143fddbc6a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -576,29 +657,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "2e412be4-9164-47ac-8e10-df57f9073391", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_15\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "encoder_cnn (Sequential) (None, 3, 3, 64) 23296 \n", - "_________________________________________________________________\n", - "decoder_cnn (Sequential) (None, 28, 28) 23233 \n", - "=================================================================\n", - "Total params: 46,529\n", - "Trainable params: 46,529\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], + "outputs": [], "source": [ "\n", "# Define the CNN autoencoder model in PyTorch\n", @@ -608,7 +670,7 @@ "\n", " # Encoder\n", " self.encoder = nn.Sequential(\n", - " nn.DropOut(0.2), # Dropout layer to mimic noisy data\n", + " nn.Dropout(0.2), # Dropout layer to mimic noisy data\n", " nn.Conv2d(1, 16, kernel_size=3, stride=2, padding=1),\n", " nn.ReLU(),\n", " nn.Conv2d(16, 32, kernel_size=3, stride=2, padding=1),\n", @@ -629,6 +691,17 @@ " return x" ] }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1a2f6474", + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate the model\n", + "model_denoised_CNNAE = DenoiseCNNAE()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -639,51 +712,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/20\n", - "1719/1719 [==============================] - 25s 14ms/step - loss: 0.2551 - val_loss: 0.2533\n", - "Epoch 2/20\n", - "1719/1719 [==============================] - 26s 15ms/step - loss: 0.2565 - val_loss: 0.2543\n", - "Epoch 3/20\n", - "1719/1719 [==============================] - 26s 15ms/step - loss: 0.2554 - val_loss: 0.2525\n", - "Epoch 4/20\n", - "1719/1719 [==============================] - 25s 14ms/step - loss: 0.2551 - val_loss: 0.2531\n", - "Epoch 5/20\n", - "1719/1719 [==============================] - 26s 15ms/step - loss: 0.2557 - val_loss: 0.2524\n", - "Epoch 6/20\n", - "1719/1719 [==============================] - 27s 16ms/step - loss: 0.2550 - val_loss: 0.2526\n", - "Epoch 7/20\n", - "1719/1719 [==============================] - 26s 15ms/step - loss: 0.2552 - val_loss: 0.2539\n", - "Epoch 8/20\n", - "1719/1719 [==============================] - 26s 15ms/step - loss: 0.2549 - val_loss: 0.2521\n", - "Epoch 9/20\n", - "1719/1719 [==============================] - 26s 15ms/step - loss: 0.2558 - val_loss: 0.2527\n", - "Epoch 10/20\n", - "1719/1719 [==============================] - 25s 15ms/step - loss: 0.2552 - val_loss: 0.2524\n", - "Epoch 11/20\n", - "1719/1719 [==============================] - 25s 15ms/step - loss: 0.2548 - val_loss: 0.2525\n", - "Epoch 12/20\n", - "1719/1719 [==============================] - 25s 15ms/step - loss: 0.2564 - val_loss: 0.2523\n", - "Epoch 13/20\n", - "1719/1719 [==============================] - 23s 13ms/step - loss: 0.2556 - val_loss: 0.2522\n", - "Epoch 14/20\n", - "1719/1719 [==============================] - 23s 13ms/step - loss: 0.2550 - val_loss: 0.2520\n", - "Epoch 15/20\n", - "1719/1719 [==============================] - 23s 13ms/step - loss: 0.2550 - val_loss: 0.2524\n", - "Epoch 16/20\n", - "1719/1719 [==============================] - 23s 13ms/step - loss: 0.2553 - val_loss: 0.2526\n", - "Epoch 17/20\n", - "1719/1719 [==============================] - 23s 13ms/step - loss: 0.2541 - val_loss: 0.2533\n", - "Epoch 18/20\n", - "1719/1719 [==============================] - 23s 14ms/step - loss: 0.2550 - val_loss: 0.2520\n", - "Epoch 19/20\n", - "1719/1719 [==============================] - 22s 13ms/step - loss: 0.2555 - val_loss: 0.2519\n", - "Epoch 20/20\n", - "1719/1719 [==============================] - 23s 13ms/step - loss: 0.2550 - val_loss: 0.2540\n" + "[Epoch 1] train loss: 0.612 - val loss: 0.585\n" ] } ], "source": [ - "(loss, loss_val) = train(DenoiseCNNAE, 30,loaded_train, loaded_test)" + "(loss, loss_val) = train(model_denoised_CNNAE, 30,loaded_train, loaded_test)" ] }, { diff --git a/book/Chapter4-DeepLearning/mlgeo_4.7_PINN.ipynb b/book/Chapter4-DeepLearning/mlgeo_4.7_PINN.ipynb index 3b39e77..5b0d107 100644 --- a/book/Chapter4-DeepLearning/mlgeo_4.7_PINN.ipynb +++ b/book/Chapter4-DeepLearning/mlgeo_4.7_PINN.ipynb @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -113,7 +113,7 @@ "Text(0.5, 0, 'Time (s)')" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -264,28 +264,38 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0/20000, loss: 4713.87\n", - "Epoch 2000/20000, loss: 2509.96\n", - "Epoch 4000/20000, loss: 2140.79\n", - "Epoch 6000/20000, loss: 724.98\n", - "Epoch 8000/20000, loss: 87.96\n", - "Epoch 10000/20000, loss: 1.94\n", - "Epoch 12000/20000, loss: 0.40\n", - "Epoch 14000/20000, loss: 0.35\n", - "Epoch 16000/20000, loss: 0.34\n", - "Epoch 18000/20000, loss: 0.33\n" + "Epoch 0/2000, loss: 4816.01\n", + "Epoch 200/2000, loss: 2511.37\n", + "Epoch 400/2000, loss: 2074.74\n", + "Epoch 600/2000, loss: 613.42\n", + "Epoch 800/2000, loss: 95.27\n", + "Epoch 1000/2000, loss: 25.84\n", + "Epoch 1200/2000, loss: 8.47\n", + "Epoch 1400/2000, loss: 3.51\n", + "Epoch 1600/2000, loss: 2.66\n", + "Epoch 1800/2000, loss: 2.31\n" ] }, { "data": { - "image/png": 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", + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", "text/plain": [ "
" ] @@ -295,17 +305,51 @@ } ], "source": [ - "net = Net(1,1, loss2=None, epochs=20000, lr=1e-5).to(DEVICE)\n", + "net = Net(1,1, loss2=None, epochs=2000, lr=1e-4).to(DEVICE)\n", "\n", "losses = net.fit(t, T)\n", "\n", "plt.plot(losses)\n", - "plt.yscale('log')" + "# plt.yscale('log')" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "prediction_temp_baseline = net.predict(times)\n", + "\n", + "plt.plot(times, prediction_temp_baseline, alpha=0.8)\n", + "plt.plot(t, T, 'o')\n", + "plt.legend(['Prediction', 'Training data'])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -316,28 +360,28 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0/20000, loss: 11220.99\n", - "Epoch 2000/20000, loss: 3922.64\n", - "Epoch 4000/20000, loss: 2426.29\n", - "Epoch 6000/20000, loss: 1615.85\n", - "Epoch 8000/20000, loss: 1198.53\n", - "Epoch 10000/20000, loss: 1020.56\n", - "Epoch 12000/20000, loss: 923.80\n", - "Epoch 14000/20000, loss: 848.24\n", - "Epoch 16000/20000, loss: 783.72\n", - "Epoch 18000/20000, loss: 719.29\n" + "Epoch 0/20000, loss: 11694.52\n", + "Epoch 2000/20000, loss: 4116.99\n", + "Epoch 4000/20000, loss: 2410.78\n", + "Epoch 6000/20000, loss: 1415.76\n", + "Epoch 8000/20000, loss: 904.16\n", + "Epoch 10000/20000, loss: 697.12\n", + "Epoch 12000/20000, loss: 616.28\n", + "Epoch 14000/20000, loss: 566.81\n", + "Epoch 16000/20000, loss: 523.32\n", + "Epoch 18000/20000, loss: 484.77\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -357,7 +401,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -366,13 +410,13 @@ "Text(0.5, 0, 'Time (s)')" ] }, - "execution_count": 8, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -384,13 +428,13 @@ "source": [ "predsreg = netreg.predict(times)\n", "\n", - "preds = net.predict(times)\n", + "prediction_temp_regularized = netreg.predict(times)\n", "plt.plot(times, temps, alpha=0.8)\n", "plt.plot(t, T, 'o')\n", - "plt.plot(times, preds, alpha=0.8)\n", - "plt.plot(times, predsreg, alpha=0.8)\n", + "plt.plot(times, prediction_temp_regularized, alpha=0.8)\n", + "plt.plot(times, prediction_temp_baseline, alpha=0.8)\n", "\n", - "plt.legend(labels=['Equation','Training data', 'Network', 'L2 Network'])\n", + "plt.legend(labels=['Equation','Training data', 'Regularized', 'Baseline'])\n", "plt.ylabel('Temperature (C)')\n", "plt.xlabel('Time (s)')" ] @@ -404,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -418,28 +462,28 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0/30000, loss: 4776.84\n", - "Epoch 3000/30000, loss: 2437.13\n", - "Epoch 6000/30000, loss: 556.53\n", - "Epoch 9000/30000, loss: 2.05\n", - "Epoch 12000/30000, loss: 1.41\n", - "Epoch 15000/30000, loss: 1.39\n", - "Epoch 18000/30000, loss: 1.36\n", - "Epoch 21000/30000, loss: 1.36\n", - "Epoch 24000/30000, loss: 1.33\n", - "Epoch 27000/30000, loss: 1.33\n" + "Epoch 0/30000, loss: 4392.25\n", + "Epoch 3000/30000, loss: 1.61\n", + "Epoch 6000/30000, loss: 2.39\n", + "Epoch 9000/30000, loss: 1.37\n", + "Epoch 12000/30000, loss: 4.35\n", + "Epoch 15000/30000, loss: 1.74\n", + "Epoch 18000/30000, loss: 1.40\n", + "Epoch 21000/30000, loss: 2.01\n", + "Epoch 24000/30000, loss: 1.89\n", + "Epoch 27000/30000, loss: 1.84\n" ] }, { "data": { - "image/png": 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JRW2rGlmMDQBwDsIJIi5ztEd5maMkSXtoPQEAnINwAkvMnZAuSdp5rMHSOgAA0YdwAkvM7RkUu5tBsQCAc9gmnLAIW2yZw4wdAMAF2CacsAhbbOkdFHu84YzqWjosrgYAEE1sE04QW1ISEzRt7GhJ0i4GxQIA+iCcwDKFPeud7KoknAAAziKcwDK9XTu7jzdYWwgAIKoQTmCZ3hk7O481yhhjcTUAgGhBOIFlZuWmyemQTjV3qLqJQbEAgADCCSwzyuPS5dkpkqRdLMYGAOhBOIGl5rLeCQDgHIQTWKr3CsVMJwYA9CKcwFKFwZaTBgbFAgAkEU5gsfycFCW4HGpo69Kx02esLgcAEAVsE064tk5s8rpdumJ8qiTGnQAAAmwTTri2TuzqXYyNGTsAAMlG4QSxixk7AIC+CCew3NyeGTt7jjfK72dQLADEO8IJLHfZuGQlJjjV3NGtirpWq8sBAFiMcALLuV1Ozcpl3AkAIIBwgqhwdlAs404AIN4RThAVCvMC4WQ34QQA4h7hBFFhzoR0SdKeE43q9vmtLQYAYCnCCaLCtKzRSva61d7l16FaBsUCQDwjnCAqOJ0OXZ6dLEnaV9VscTUAACsRThA18nNSJEnvEE4AIK4RThA18rMD4WR/NeEEAOIZ4QRR4/KelpP9tJwAQFwjnCBq9LacHK1vU1tnt8XVAACsQjhB1BiT7FVWsleS9E51i8XVAACsQjhBVMnPCczYYVAsAMQv24STkpISFRQUaOHChVaXgjDKz06VxKBYAIhntgknxcXFKi8v15YtW6wuBWHU23LCoFgAiF+2CSeID/k5tJwAQLwjnCCqXDYu0HJyqrlD9a2dFlcDALAC4QRRZbTXrbzMUZLo2gGAeEU4QdTpHRT7Dl07ABCXCCeIOr2DYrkAIADEJ8IJos7lPSvF0nICAPGJcIKoM7Nnxs47Vc0yxlhcDQAg0ggniDpTs0bL7XSouaNbJxrbrS4HABBhhBNEHY/bqeljWcYeAOIV4QRR6fKcwLgTBsUCQPwhnCAq5Wf3tJwwKBYA4g7hBFEpuIw9LScAEHcIJ4hK+T3TiQ+calG3z29xNQCASCKcICpNzBilJI9Lnd1+Ha5rs7ocAEAEEU4QlZxOhy5jMTYAiEuEE0St3kGxzNgBgPhCOEHUyu+zUiwAIH4QThC1egfF7qdbBwDiCuEEUevynm6dI3Wtau/yWVwNACBSCCeIWmNTvEpJdMtvpMN1rVaXAwCIkIiHk8rKSl133XUqKCjQ3Llz9eSTT0a6BNiEw+HQjHGB1pMDNS0WVwMAiJSIhxO3262f/vSnKi8v1wsvvKCvfOUram3lr2IMbEbPBQAP1vAZAYB44Y70C44fP17jx4+XJOXk5CgrK0v19fUaPXp0pEuBDUzvbTk5RcsJAMSLYbecbNiwQTfffLNyc3PlcDi0du3a87YpKSnRlClTlJiYqMWLF2vz5s0D7mvbtm3y+XzKy8sbduGID70tJ3TrAED8GHY4aW1tVWFhoUpKSgZ8/oknntDKlSu1atUqbd++XYWFhVq6dKlqamr6bVdfX6/Pfvaz+vWvf31plSMu9I45OXSqRT6/sbgaAEAkDLtbZ9myZVq2bNkFn3/ooYe0YsUK3XnnnZKkRx55RM8++6wee+wx3XfffZKkjo4O3Xrrrbrvvvv03ve+d9DX6+joUEdHR/B+U1PTcEuGjeVlJsnjcqqj268TDWeUl5lkdUkAgDAL6YDYzs5Obdu2TUVFRWdfwOlUUVGRNm7cKEkyxuhzn/ucPvCBD+gzn/nMRfe5evVqpaWlBW90AcUXl9OhqVmB8Uh07QBAfAhpOKmtrZXP51N2dna/x7Ozs1VVVSVJeuONN/TEE09o7dq1mjdvnubNm6fdu3dfcJ/333+/Ghsbg7fKyspQlgwbYDoxAMSXiM/Wufrqq+X3+4e8vdfrldfrDWNFiHbTCScAEFdC2nKSlZUll8ul6urqfo9XV1crJycnlC+FONLbcnKQ6cQAEBdCGk48Ho/mz5+v0tLS4GN+v1+lpaVasmTJiPZdUlKigoICLVy4cKRlwmamj+0Zc3KqRcYwYwcAYt2wu3VaWlp04MCB4P2KigqVlZUpMzNTkyZN0sqVK7V8+XItWLBAixYt0k9/+lO1trYGZ+9cquLiYhUXF6upqUlpaWkj2hfsZfrYZDkcUkNbl+paO5WVTDcfAMSyYYeTrVu36vrrrw/eX7lypSRp+fLlWrNmjW6//XadOnVKDz74oKqqqjRv3jytW7fuvEGywFAlJrg0MWOUKuvP6EBNC+EEAGLcsMPJddddd9Gm9XvuuUf33HPPJRcFnGvG2GRV1p/RwVMtes+0MVaXAwAIo4hf+A+4FNNZxh4A4oZtwgkDYuMba50AQPywTTgpLi5WeXm5tmzZYnUpsEBwOjHhBABinm3CCeJbb7fOicZ2tXZ0W1wNACCcCCewhYzRHo0Z7ZEkHTrVanE1AIBwIpzANoLL2J9qtrgSAEA4EU5gGwyKBYD4YJtwwmwdzBjbOyiWbh0AiGW2CSfM1sHZbh1aTgAgltkmnAC93TqHa1vV5fNbXA0AIFwIJ7CN3LREJXlc6vYbHalrs7ocAECYEE5gGw6HI7jeyUG6dgAgZhFOYCvTx46WxIwdAIhltgknzNaBxDL2ABAPbBNOmK0Dqc9aJ3TrAEDMsk04AaT+LSfGGIurAQCEA+EEtjIpc7RcTodaO32qamq3uhwAQBgQTmArHrdTk8ckSWJQLADEKsIJbKd3GXvCCQDEJsIJbCc47oRBsQAQkwgnsJ3ptJwAQEyzTThhnRP0Ck4n5urEABCTbBNOWOcEvXqvTlzb0qHGti6LqwEAhJptwgnQK9nr1vi0REksxgYAsYhwAlsKXgCQcScAEHMIJ7AllrEHgNhFOIEtTR/HjB0AiFWEE9hS70JsrHUCALGHcAJbmj5utCSpsr5N7V0+i6sBAIQS4QS2NDbZq9REt/xGqqhlvRMAiCW2CScswoa+HA5Hn8XY6NoBgFhim3DCImw4F9fYAYDYZJtwApyLa+wAQGwinMC26NYBgNhEOIFt9YaTQ7Wt8vmNxdUAAEKFcALbmpiRJI/bqc5uv46fPmN1OQCAECGcwLZcToemZQXWOzlwqtniagAAoUI4ga1NGxsIJxW1bRZXAgAIFcIJbG1qVm84YVAsAMQKwglsbcqYQDg5TMsJAMQMwgls7Wy3DkvYA0CsIJzA1qZmBaYTH284wwUAASBG2CaccG0dDCQjKUGpiW5J0pE6unYAIBbYJpxwbR0MxOFwaGrPMvYMigWA2GCbcAJcSO9aJ4cYdwIAMYFwAts7O2OHcAIAsYBwAtubyowdAIgphBPY3rQswgkAxBLCCWxvSk84qW3pVFN7l8XVAABGinAC20v2ujU2xSuJcScAEAsIJ4gJU+naAYCYQThBTJg6hnACALGCcIKYwIwdAIgdhBPEBLp1ACB2EE4QE/qGE2OMxdUAAEaCcIKYMCkzSQ6H1NzerbrWTqvLAQCMAOEEMSExwaUJ6aMk0bUDAHZHOEHMYNwJAMQG24STkpISFRQUaOHChVaXgijVG04OnSKcAICd2SacFBcXq7y8XFu2bLG6FESpacFw0mJxJQCAkbBNOAEuZvq4ZEnSQcIJANga4QQxY/rYQDg5UtemLp/f4moAAJeKcIKYkZOaqFEJLnX7jSrr26wuBwBwiQgniBlOp0PTepaxP8igWACwLcIJYkpv1w7jTgDAvggniCm9LSfM2AEA+yKcIKacbTmhWwcA7IpwgpjSG04O1LRwAUAAsCnCCWJK7yqxjWe6VM8FAAHAlggniCmjPGcvAHiIa+wAgC0RThBzgivF1jAoFgDsiHCCmNN7jR2mEwOAPRFOEHN6W064OjEA2BPhBDFn+lhaTgDAzggniDm904mP1repo9tncTUAgOEinCDmjEvxKtnrlt8ErlAMALAXwglijsPhCHbtsIw9ANgP4QQxqbdr591qwgkA2A3hBDHpsuwUSdI7rHUCALZDOEFMys8JtJy8U9VscSUAgOEinCAmXd7TcnLwVIs6u/0WVwMAGA5Lwsltt92mjIwMfeITn7Di5REHJqSPUrLXrW6/UQXX2AEAW7EknHz5y1/W73//eyteGnHC4XDo8uxA187+arp2AMBOLAkn1113nVJSUqx4acSR/JyeQbGMOwEAWxl2ONmwYYNuvvlm5ebmyuFwaO3atedtU1JSoilTpigxMVGLFy/W5s2bQ1ErMCy9405oOQEAexl2OGltbVVhYaFKSkoGfP6JJ57QypUrtWrVKm3fvl2FhYVaunSpampqRlwsMBz5PeHkxfJqiysBAAyHe7jfsGzZMi1btuyCzz/00ENasWKF7rzzTknSI488omeffVaPPfaY7rvvvmEX2NHRoY6OjuD9pqamYe8D8Wnm+NTg16dbO5Ux2mNhNQCAoQrpmJPOzk5t27ZNRUVFZ1/A6VRRUZE2btx4SftcvXq10tLSgre8vLxQlYsYl9knjByuY8YOANhFSMNJbW2tfD6fsrOz+z2enZ2tqqqq4P2ioiL9wz/8g5577jlNnDhx0OBy//33q7GxMXirrKwMZcmIcYunZkoS04kBwEaG3a0TCi+99NKQt/V6vfJ6vWGsBrHssuxkbaqo17ssYw8AthHSlpOsrCy5XC5VV/cfgFhdXa2cnJxQvhQwJPk5gXEn+04yVgkA7CKk4cTj8Wj+/PkqLS0NPub3+1VaWqolS5aMaN8lJSUqKCjQwoULR1om4kjB+MCMnXLCCQDYxrC7dVpaWnTgwIHg/YqKCpWVlSkzM1OTJk3SypUrtXz5ci1YsECLFi3ST3/6U7W2tgZn71yq4uJiFRcXq6mpSWlpaSPaF+JHfk6qHA6puqlDdS0dGpNMFyEARLthh5OtW7fq+uuvD95fuXKlJGn58uVas2aNbr/9dp06dUoPPvigqqqqNG/ePK1bt+68QbJAJCR73ZqcmaTDdW3ae7JZV19GOAGAaOcwxhirixiO3paTxsZGpaamXvwbEPf+9Q/b9NzuKj3woZm669rpVpcDAHFpOOdvS66tA0RSQc9ibG+fYNwJANiBbcIJA2JxqWZPCIxR2nWs0eJKAABDYZtwUlxcrPLycm3ZssXqUmAzcyemSwosxNZ4psvaYgAAF2WbcAJcqszRHuVljpIk7ab1BACiHuEEceGKnsXYfrn+wEW2BABYjXCCuJCY4JIkvXmwzuJKAAAXY5twwoBYjMRtV04Ifm2z2fMAEHdsE04YEIuRWDAlI/j1qeYOCysBAFyMbcIJMBIpiQmaNna0JGn3cQbFAkA0I5wgblyZF2g9KatssLYQAMCgCCeIG1dOSpdEOAGAaEc4QdyYl5cuKRBO/H4GxQJAtCKcIG7k56TI63aqub1bh+tarS4HAHABtgknTCXGSCW4nCrIDSzGxnV2ACB62SacMJUYoVDYc52dnccaLK0DAHBhtgknQCjM6blC8dvHmyyuBABwIYQTxJXZPeFk8+F6BsUCQJQinCCuTO9ZiE2SXt5XY2El9rDneKP2sGgdgAgjnCCuuF1nP/K7GHcyqPYunz7y89f1kZ+/rrbO7pDt93dvHtZv36gI2f4AxB7CCeLOXddOkySdaum0uJLodqbTF/y6uT004aSlo1ur/vq2vvu3cjWe6QrJPgHEHtuEE6YSI1SumhRYxn7bkXqLK4luDkfo99nV7Q9+3dnnawDoyzbhhKnECJWFPVcofqe6RTXN7RZXAwA4l23CCRAqY5K9mjsxMGvn1f2nLK4mejl0tunEMLEJQAQRThCXrrt8rCTpG/+zy+JK7MGIdAIgcggniEs3zsqRJPmNtK+KBdkG1GfMSahaTog4sa/L5+83mBq4FIQTxKVZPdfYkaTdXGcHCJn3/+gVXfHgupBOP0f8IZwgLjkcDt193XRJ0nrGnURMGCYAIcqcaAwMMt97stniSmBnhBPErff3jDvZfvS0DCM+z9N3KnG8H51Dp1pU3cTMLiBSCCeIW70XATzZ2K6v/nmnxdVEH1o5Ak41d+gDP3lVi39QanUpQNywTThhETaE2mivW1dOSpckPbXjuLXFRCGHo+9U4vhtO3m3hu4JINJsE05YhA3h8PM7rgx+fehUi4WVRLc4zia4ROFYYRjxwzbhBAiHiRlJKrpinCTp0dcOWVwNLkV9a6fKKhusLgNACBFOEPe+8P7ArJ3/2Xac5ez7sEtXznt+UKpbS97QW4fqwvMC9jgMQEwhnCDuLZicoasmpavT59czO05YXQ6GqdMXuIDgq+8wJTya0KuDkSCcIO45HA4tmjpGknSi8YzF1QCDO95wRo9uOKSm9i6rSwHCxm11AUA0yEr2SJJqWzotriR69O3NYPn66PGxX76h6qYO7T7eqJ/1GdAdbbr9/LRx6Wg5ASRlJXslSbXNHRZXEj9CfTHBeOlGqG4KfEZfP1BrcSWD44rfGAnCCSApOzVRknSSbp0BhSpI9A0Qe45zTaNY1uX3W10CbIxwAkjKyxwlKdCf76M5OiJ8nLtGJF5aihCfCCeApPFpo+R2OtTlM1xDpUffcSY2mVVsO+v31+i+/9mlM50+q0sBogrhBJDkcjqCA/h+vYHF2M4VqmzSdz+h/svfjiuSfu63W/T4lko98upBq0sBooptwgnX1kGkrHnzsNUlxAU7holwYawT0J9twgnX1gEiLMxdOXbpKrJJmUBMsU04AcJt2ewcq0uIWqFayp7GktCh5QmxjHAC9FgwJdPqEgAAIpwAQZMyk6wuYUS+/PgOffzhN8MyFTocXRuh/sufFUmjDD8OjADhBOixYHJG8Ov2LvtN7Xym7IS2HTmtnccaQrK/vguvHa1vC9E+w+f3bx4Jy347u88uyOIPUwBy0OEF9EM4AXqkJyUoJTFwuakjdaE5GVshHANNdx8L/WquoW45OROmQNl33ZtOVo4DIoJwAvRwOByamZMiSXojyq9bMphwDJS0y8ya+BLdrS18ZDAShBOgj5sLcyVJv1x/QM1xfkn6fivEhuHaOnYJPDYpE4gphBOgj08unKSpWaNV29KpOd95QU1hDiitHd2qbYnslZD/uOmoiv+wvd9YiouxS5AIh2h975H+3AxFqKacA4QToA+P26l/v2128P7c77yg+tZOdYVprMG8772gBd9/SXUhPNFcrLH/gad369ndJ/XU9mMhe82h6rd8fXT3SgCwEOEEOMd7p2f1u3/Vv72oDz70alim6Hb5AvvcdTx0A04dQzzrN7d3D3mfYZlKHOVjJnBWZX2b7n9qlw7UtFhdCuIE4QQYwM5VN/a7f7iuLazN6EM5TTe1d+mRVw/q2OnIzCTqG0jC0VwfqnEsscDqVqQun1//9dYRHTw1cPj4l99t1Z82V+q2X74x6H7o1UGoEE6AAaSNStD3Pjqr32P+S/zNa4zR//9EmX5W+u4FtxlKa8c3n96jHz6/T7f98s1Bt2to6xx2jZFCW8nArD6pr3njsL69do9u+MmrAz6/v7pZ0vBa24CRIJwAF/DZJVO08oOXB+9/4uGNl7SfrUdO6+kdx/XQi++MqJ7e6c2nmgdvwbHLNGhOdNFj65F6q0sA+iGcAIP40gdmBL8+3nBG31q7Wy0d3er2+fVSefWQWinOdF58cTArWhQu1ljTtysnHH/Z//db4VnRNdQi0f1kdbdOqNCrg1AhnACDcDgc2vPdpcH7//3WURX95FX9+rVD+pffb9Xtv3rrovsYyi/skZ6cwj2F81K7tM7Vdy/hGGAcbpfyc/L7TdRPsQ1HedH+nhHdbBNOSkpKVFBQoIULF1pdCuJMstet7d/+YPB+VVO7frRuv6SzffGDGcov6aHMXBlsP32fcobhz/Bnyk6EfJ92PHUNN1D5/EY3/+J1ferRTXF3st54qM7qEmBjtgknxcXFKi8v15YtW6wuBXEoc7RHZQ9+8OIbDiAiLSf9djayfQ3keMOZkO/TLufqvnVuPDi8E25FbYvePtGkjYfqBn2/L+2tucTqotee401WlwAbs004AayWnuTp14IyZEM4CbcNYVxKKF9Pklo7Bn/N8Kxt0mf/dkknfYykJ2qwb43G1V4vhR1/pohOhBNgGDJHe/T6N67v99izu04O+j19Z9e0dQ48Q+WZsuMDPl7TfPaKuK2DBJhLOSk88urBYX/PSJkLfB0Nmtu7LjoTaiQ4cQNDRzgBhmliRpJ2f+fsIm3Ff9yuKfc9q4IH1w24DH13nz+3e1eEPddAA05/uf6AFv17qX7VEyIGuxbOpXTrnOkKYWvNEPU9Qe86FrpVcUNhznde0MJ/f0mNbeG5nlI0R5Njp0PfbQeMBOEEuAQpiQna92836WNXTQg+1tbp0/zvv3Tets4+YeFCAyoH+qO6d9Dt6uf3XbSevt8fqmXhwzKDI/S7DLnBBjn7/Jd+jSWrG078g/RJlZ8MzfgQO/x8YQ+EE+ASJSa49NA/zlPaqIR+j0+571ltO1Kvtw7V6XO/3azNh88ucHW0fuCl50c6rbbvWhzR3H0QxaUFOc/Jdn1L/tvOwbvwBhOq6diX4sf/u08L//0lnQjDwOa+7PDzhT0QToAR2rnqRmWnevs99vGHN+qTv35L6/ef0lPbz44n6TvmpG+IeKG8ekQ19D0p/GVb5K82PFTnnvij0WAzpxrPhKfLJ9xKXjmoutZOlbxyQJL01PZj+sjPXwv5dZpCuWDdb9+o0NodA4/FQuwjnAAhsOmBIr208tqLbrf18Ong1+H6K7OuNXqvrROt+gbF865z1Oe54bZ+9N1XNCw617sGzso/79Se40367t/KQ7r/UH2mj9S16rt/K9dXnigLzQ5hO4QTIERmjEvR4R9+WI/801UX3Kbv9XVCeaoKz/iQcFyJODr1zQ3tgwwUHu5x7htzfFHQ53Fu7rrQ7DGrNYRpUDLsg3AChNhNs8fr8A8/rF986sqBn//pBv2s9F21hvDEEInrv4RCFJyfB9S3VeNvO/uvhtu35OEultd3td7BBqRGyrmrB4djNeFQ6BvkonkMFcLHbXUBQKz6yNxcfWRurqqb2rX4B6XBx/dVNWtfVfN5VyneduS0ZoxNVlpSwrm7uii7/P6OphD1t50n1NzerbdPNGrBlIzg4+1d/Wfk9D22VU3tGo6+5/63DtXrptk5l1RrqJSf6D8rp6Pr0mcfDSRUn8NzLzoZpRkKYUQ4AcIsOzVRh3/4YUnS6dZOPbmtUr95rUI15yz49fGH35QUWOhtfFpiv+ce3XCo3/09xxuVmOBUgitwG2wNlEsWjhwRJdnE5zf60p92BO//YdPR4NdP7ziu/3v7vH7b9jp0qvWSXzMaVoHtO3NsoPtSIBicN+5miMIRPn3GyGnJdbtDy+83evtEk2aOT1GCi06LiyGcABGUMdqju66drhXXTNORujZ9/S+7gieI8WmJOtnYrvrWTtWfM6j135/b2+/+R37++qCvU/jdF+R0SG6XUy6HQy5n4HbuoMxbfvG6nL3P99nO6XSoraN/t9P9T+1SR7dfLodDbpdTCS6HElxOuV0OJTidcjodamzrVFZyYOaSw6FgeEpwOeRyOs9bgfWPm47K5/fL5XTK4+65uZzyuAPbJzgDr9X7Gm6XQwkuh9xOZ7DWvtq7fHL3PG5MIAs5emrpe8IdzliL7hGsbdL3eL+0t1r/9J7Jl7yvSPH5jdwua8NA3+6mzm6/Orv9Wr//lK7LH6vRXrcq69u081iDPjxnfL+f66ADm8Oguqld41K8qqw/o5REtzJGe7T7WKMmZoxSxmhPv21/8coBPfTiO7p9QZ7+zyfm6kTDGTW0dakgN1UHapqVkzZKyV63/H4j5wDT2npDozFGe082a9rY0UpMcIX9PVqFcAJYwOFwaErWaP35i0v6Pd7S0a2jdW2qajqjito2/dvfA7Mpbrtygs50+rTu7argtulJCerq9qvLZ9Tp638CHeqU1+Gs0vqnzZVD3naoHnh6d0j3N/Pb6wZ93uEIhJWLDf8o/sN2Jbgccjoceuqc6az/Z92+YGhyOc+Gpd4AluByyOMOhLKK2rMtLev3n9KbB2uDwerc88/BUy1yOQKv6XBIzp7A6OwJVk7HpY0RGe5Yl6qmdk3MSBr260ih69ZxO8+2LLxYXq0N75zSUzuOK8Hl0JwJadp+tEGS9MfpR5WbPuqC0+fHpniV5HHJGCnJ41LqqAQdq2/TicZ2jRntUXJi4BSY4HKqpb1bbpej32q5KYluNbcHgqzDIS2akqlNFfXKSvZoUmZSsI5etxTm6q89Y5b+658Xye106o5H3+q3zRNbK5WbPkr/96VAt+4/zJ+oJ7cdU1ayV1fPGKO1ZSf0n5+cp/dMG6M7Hn1LY5O9umpyhh5eH1gp+vu3zta31u6R2+nQuBSvTjS2B2v95oeu0LSxyfrOX9/WN5bN1PLHNuuWwlzduzRfyx/brNkT0vTXnSf040/M1ZLpY/Tau7W6/6ndujw7WU1nujXa69Ij/zRfe6ua9ZE54wcMSZHiMDYbbdTU1KS0tDQ1NjYqNTXV6nKAqGCMkc9v1NLRrcYzXeryGRlj1OUz8vc81+03cjsDJ85HNxzSlZMzNCE9UT5/4K9ln9/IZ4z8Pdt63E4ZY/Tlx8uUnerVPy2eLKczcOLs9hl1+/zq8ht1dfvV2unTy/uqdX3+uOBfrH6/UZc/EJ58Pf8aE2hB6PXBgmy5nQ51+03wL+ROn19dvsD23T6/uv1GXT5/4DX9Zx/39byvC10SIF54eroIHD3hxdkTbBySOrr96jinyy8nNbHf2JnURLea2vu3JE0ekyS/Cfy8jDn/qtR5maOCQaTvGaSpvSt4MpcCJ/N+HGdbsnqDVnA/MsGvdx1rtOTyCjjrGzfN1N3XTQ/pPodz/iacALC93iDW6fMHQlNPOHO7AgHLqGeNkp6uHmOkitpW/WrDQXlcTrV0dGvBlEz9rPRdvf/ysbouf6w6uv3B1orvPxvoVpuYMUo3FuQEQ5LPHwhNXf6zAayrN1x1G3X4/NpZ2RCs87Jxyer2BwKj3xhV1vf/K10mMMYi8PzZ0BkFE30QZxZOydCTX3xvSPdJOAGAGNM3qPj8RjXN7fK4A60mvUHG9DzXG268bqeSPC6N8ri0dscJzRiXrCSPS4kJTr15sE6TMpM0KTNJTodDlafb9PT247pj8aSeLidHsBvM4XDovzYeUX5OsuZPzgx2SfW2kvW2hvS698ldKsxL0/X54wK1Sz1jgExwLNBA40P67ud/367W33ae0BfeP021zZ36n+3Ru/LxcDkdZ7sWPS5nsFu27+NXz8jS6wdqJQW6p3rHa2UkJeh0iNaBudC+Fk3J1PdunaWZOaE9xxJOAABAVBnO+Zv5TAAAIKoQTgAAQFQhnAAAgKhiSTj5+9//rvz8fF122WX6zW9+Y0UJAAAgSkV8Ebbu7m6tXLlSr7zyitLS0jR//nzddtttGjNmTKRLAQAAUSjiLSebN2/WrFmzNGHCBCUnJ2vZsmV64YUXIl0GAACIUsMOJxs2bNDNN9+s3NxcORwOrV279rxtSkpKNGXKFCUmJmrx4sXavHlz8LkTJ05owoQJwfsTJkzQ8ePHz9sHAACIT8MOJ62trSosLFRJScmAzz/xxBNauXKlVq1ape3bt6uwsFBLly5VTU3NiIsFAACxb9jhZNmyZfr+97+v2267bcDnH3roIa1YsUJ33nmnCgoK9MgjjygpKUmPPfaYJCk3N7dfS8nx48eVm5t7wdfr6OhQU1NTvxsAAIhdIR1z0tnZqW3btqmoqOjsCzidKioq0saNGyVJixYt0p49e3T8+HG1tLTo+eef19KlSy+4z9WrVystLS14y8vLC2XJAAAgyoQ0nNTW1srn8yk7O7vf49nZ2aqqClzq3e126yc/+Ymuv/56zZs3T1/96lcHnalz//33q7GxMXirrAz9ZdsBAED0iPhUYkm65ZZbdMsttwxpW6/XK6/XG+aKAABAtAhpy0lWVpZcLpeqq6v7PV5dXa2cnJxQvhQAAIhRIW058Xg8mj9/vkpLS3XrrbdKkvx+v0pLS3XPPfeMaN8lJSUqKSlRd3e3JDEwFgAAG+k9bxtjLr6xGabm5mazY8cOs2PHDiPJPPTQQ2bHjh3myJEjxhhjHn/8ceP1es2aNWtMeXm5ueuuu0x6erqpqqoa7ksNqLKy0kjixo0bN27cuNnwVllZedFzvcOYoUSYs9avX6/rr7/+vMeXL1+uNWvWSJJ+8Ytf6Mc//rGqqqo0b948/exnP9PixYuH8zIX5Pf7deLECaWkpMjhcIRkn72ampqUl5enyspKpaamhnTfsYZjNXQcq6HjWA0dx2roOFZDF85jZYxRc3OzcnNz5XQOPqpk2OEkljU1NSktLU2NjY18gC+CYzV0HKuh41gNHcdq6DhWQxctx8qSqxIDAABcCOEEAABEFcJJH16vV6tWrWJdlSHgWA0dx2roOFZDx7EaOo7V0EXLsWLMCQAAiCq0nAAAgKhCOAEAAFGFcAIAAKIK4QQAAEQVwkmPkpISTZkyRYmJiVq8eLE2b95sdUlh9Z3vfEcOh6PfbebMmcHn29vbVVxcrDFjxig5OVkf//jHz7ug49GjR/XhD39YSUlJGjdunO69997gtY96rV+/XldddZW8Xq9mzJgRXEU4mm3YsEE333yzcnNz5XA4tHbt2n7PG2P04IMPavz48Ro1apSKior07rvv9tumvr5en/70p5Wamqr09HT98z//s1paWvpts2vXLl1zzTVKTExUXl6efvSjH51Xy5NPPqmZM2cqMTFRc+bM0XPPPRfy9ztSFzten/vc5877rN100039tomH47V69WotXLhQKSkpGjdunG699Vbt37+/3zaR/H8Xzb/zhnKsrrvuuvM+V1/84hf7bRMPx0qSHn74Yc2dO1epqalKTU3VkiVL9Pzzzweft+XnKiQXvLG5xx9/3Hg8HvPYY4+Zt99+26xYscKkp6eb6upqq0sLm1WrVplZs2aZkydPBm+nTp0KPv/FL37R5OXlmdLSUrN161bznve8x7z3ve8NPt/d3W1mz55tioqKzI4dO8xzzz1nsrKyzP333x/c5tChQyYpKcmsXLnSlJeXm5///OfG5XKZdevWRfS9Dtdzzz1nvvnNb5qnnnrKSDJPP/10v+d/+MMfmrS0NLN27Vqzc+dOc8stt5ipU6eaM2fOBLe56aabTGFhoXnrrbfMa6+9ZmbMmGHuuOOO4PONjY0mOzvbfPrTnzZ79uwxf/rTn8yoUaPMr371q+A2b7zxhnG5XOZHP/qRKS8vN9/61rdMQkKC2b17d9iPwXBc7HgtX77c3HTTTf0+a/X19f22iYfjtXTpUvPb3/7W7Nmzx5SVlZkPfehDZtKkSaalpSW4TaT+30X777yhHKv3v//9ZsWKFf0+V42NjcHn4+VYGWPMX//6V/Pss8+ad955x+zfv9888MADJiEhwezZs8cYY8/PFeHEGLNo0SJTXFwcvO/z+Uxubq5ZvXq1hVWF16pVq0xhYeGAzzU0NJiEhATz5JNPBh/bu3evkWQ2btxojAmckJxOZ78LOj788MMmNTXVdHR0GGOM+frXv25mzZrVb9+33367Wbp0aYjfTfice7L1+/0mJyfH/PjHPw4+1tDQYLxer/nTn/5kjDGmvLzcSDJbtmwJbvP8888bh8Nhjh8/bowx5pe//KXJyMgIHitjjPnGN75h8vPzg/f/8R//0Xz4wx/uV8/ixYvNF77whZC+x1C6UDj56Ec/esHvidfjVVNTYySZV1991RgT2f93dvudd+6xMiYQTr785S9f8Hvi9Vj1ysjIML/5zW9s+7mK+26dzs5Obdu2TUVFRcHHnE6nioqKtHHjRgsrC793331Xubm5mjZtmj796U/r6NGjkqRt27apq6ur3zGZOXOmJk2aFDwmGzdu1Jw5c5SdnR3cZunSpWpqatLbb78d3KbvPnq3sfNxraioUFVVVb/3lZaWpsWLF/c7Nunp6VqwYEFwm6KiIjmdTm3atCm4zbXXXiuPxxPcZunSpdq/f79Onz4d3CZWjt/69es1btw45efn6+6771ZdXV3wuXg9Xo2NjZKkzMxMSZH7f2fH33nnHqtef/jDH5SVlaXZs2fr/vvvV1tbW/C5eD1WPp9Pjz/+uFpbW7VkyRLbfq7cw/6OGFNbWyufz9fvhyJJ2dnZ2rdvn0VVhd/ixYu1Zs0a5efn6+TJk/rud7+ra665Rnv27FFVVZU8Ho/S09P7fU92draqqqokSVVVVQMes97nBtumqalJZ86c0ahRo8L07sKn970N9L76vu9x48b1e97tdiszM7PfNlOnTj1vH73PZWRkXPD49e7DLm666SZ97GMf09SpU3Xw4EE98MADWrZsmTZu3CiXyxWXx8vv9+srX/mK3ve+92n27NmSFLH/d6dPn7bV77yBjpUkfepTn9LkyZOVm5urXbt26Rvf+Ib279+vp556SlL8Havdu3dryZIlam9vV3Jysp5++mkVFBSorKzMlp+ruA8n8WrZsmXBr+fOnavFixdr8uTJ+vOf/2zL0IDo9clPfjL49Zw5czR37lxNnz5d69ev1w033GBhZdYpLi7Wnj179Prrr1tdStS70LG66667gl/PmTNH48eP1w033KCDBw9q+vTpkS7Tcvn5+SorK1NjY6P+8pe/aPny5Xr11VetLuuSxX23TlZWllwu13kjl6urq5WTk2NRVZGXnp6uyy+/XAcOHFBOTo46OzvV0NDQb5u+xyQnJ2fAY9b73GDbpKam2jYA9b63wT4vOTk5qqmp6fd8d3e36uvrQ3L87P65nDZtmrKysnTgwAFJ8Xe87rnnHv3973/XK6+8ookTJwYfj9T/Ozv9zrvQsRrI4sWLJanf5yqejpXH49GMGTM0f/58rV69WoWFhfrP//xP236u4j6ceDwezZ8/X6WlpcHH/H6/SktLtWTJEgsri6yWlhYdPHhQ48eP1/z585WQkNDvmOzfv19Hjx4NHpMlS5Zo9+7d/U4qL774olJTU1VQUBDcpu8+erex83GdOnWqcnJy+r2vpqYmbdq0qd+xaWho0LZt24LbvPzyy/L7/cFfoEuWLNGGDRvU1dUV3ObFF19Ufn6+MjIygtvE2vGTpGPHjqmurk7jx4+XFD/Hyxije+65R08//bRefvnl87qpIvX/zg6/8y52rAZSVlYmSf0+V/FwrC7E7/ero6PDvp+rYQ+hjUGPP/648Xq9Zs2aNaa8vNzcddddJj09vd/I5Vjz1a9+1axfv95UVFSYN954wxQVFZmsrCxTU1NjjAlMPZs0aZJ5+eWXzdatW82SJUvMkiVLgt/fO/XsxhtvNGVlZWbdunVm7NixA049u/fee83evXtNSUmJLaYSNzc3mx07dpgdO3YYSeahhx4yO3bsMEeOHDHGBKYSp6enm2eeecbs2rXLfPSjHx1wKvGVV15pNm3aZF5//XVz2WWX9Zsa29DQYLKzs81nPvMZs2fPHvP444+bpKSk86bGut1u8x//8R9m7969ZtWqVVE1NbbXYMerubnZfO1rXzMbN240FRUV5qWXXjJXXXWVueyyy0x7e3twH/FwvO6++26TlpZm1q9f32/6a1tbW3CbSP2/i/bfeRc7VgcOHDDf+973zNatW01FRYV55plnzLRp08y1114b3Ee8HCtjjLnvvvvMq6++aioqKsyuXbvMfffdZxwOh3nhhReMMfb8XBFOevz85z83kyZNMh6PxyxatMi89dZbVpcUVrfffrsZP3688Xg8ZsKECeb22283Bw4cCD5/5swZ86//+q8mIyPDJCUlmdtuu82cPHmy3z4OHz5sli1bZkaNGmWysrLMV7/6VdPV1dVvm1deecXMmzfPeDweM23aNPPb3/42Em9vRF555RUj6bzb8uXLjTGB6cTf/va3TXZ2tvF6veaGG24w+/fv77ePuro6c8cdd5jk5GSTmppq7rzzTtPc3Nxvm507d5qrr77aeL1eM2HCBPPDH/7wvFr+/Oc/m8svv9x4PB4za9Ys8+yzz4btfV+qwY5XW1ubufHGG83YsWNNQkKCmTx5slmxYsV5v6zi4XgNdIwk9fs/Ecn/d9H8O+9ix+ro0aPm2muvNZmZmcbr9ZoZM2aYe++9t986J8bEx7EyxpjPf/7zZvLkycbj8ZixY8eaG264IRhMjLHn58phjDHDb28BAAAIj7gfcwIAAKIL4QQAAEQVwgkAAIgqhBMAABBVCCcAACCqEE4AAEBUIZwAAICoQjgBAABRhXACAACiCuEEAABEFcIJAACIKoQTAAAQVf4fHqGL0mT+MR4AAAAASUVORK5CYII=", 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", 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" ] @@ -449,9 +493,9 @@ } ], "source": [ - "net = Net(1,1, loss2=physics_loss, epochs=30000, loss2_weight=1, lr=1e-5).to(DEVICE)\n", + "net_PINN = Net(1,1, loss2=physics_loss, epochs=10000, loss2_weight=1, lr=1e-4).to(DEVICE)\n", "\n", - "losses = net.fit(t, T)\n", + "losses = net_PINN.fit(t, T)\n", "plt.plot(losses)\n", "plt.yscale('log')\n" ] diff --git a/book/Chapter4-DeepLearning/stacked_ae_checkpoint/checkpoint.pt b/book/Chapter4-DeepLearning/stacked_ae_checkpoint/checkpoint.pt new file mode 100644 index 0000000..ff60114 Binary files /dev/null and b/book/Chapter4-DeepLearning/stacked_ae_checkpoint/checkpoint.pt differ