From a45374e7c1ff58bc7c690c5a6e8b4365beb4b9bf Mon Sep 17 00:00:00 2001 From: geo-smart Date: Wed, 18 Oct 2023 17:54:31 +0000 Subject: [PATCH] update student version with curriculum book changes --- README.md | 35 +- STUDENT_README.md | 10 + .../1.1_open_reproducible_science.md | 8 +- .../1.2_jupyter_environment.md | 1 - .../1.3_python_environment.md | 10 +- .../2.3_pandas_rendered.ipynb | 349827 ++++++++------- .../2.6_resampling.ipynb | 305 +- book/img/Open-Science.svg | 1 + book/img/YouTube-Logo.wine.svg | 1 + book/reference/glossary.md | 10 +- 10 files changed, 175100 insertions(+), 175108 deletions(-) create mode 100644 STUDENT_README.md create mode 100644 book/img/Open-Science.svg create mode 100644 book/img/YouTube-Logo.wine.svg diff --git a/README.md b/README.md index 400c0ac..079cd9f 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,35 @@ # 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. + +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/Chapter1-GettingStarted/1.1_open_reproducible_science.md b/book/Chapter1-GettingStarted/1.1_open_reproducible_science.md index 725064c..f45ca76 100644 --- a/book/Chapter1-GettingStarted/1.1_open_reproducible_science.md +++ b/book/Chapter1-GettingStarted/1.1_open_reproducible_science.md @@ -1,7 +1,9 @@ # 1.1 Open Reproducible Science -We strive to promote open and reproducible science in our broad fields of geosciences. It is particularly important +We should strive to promote open and reproducible science in our work. -[![Lecture Slides](../../img/Google_Slides_Logo.svg)](https://docs.google.com/presentation/d/1SCrx65-Q_CB8JRMN81Uk-QniPvPE-wozRD7YDDQEapc/edit?usp=sharing) +To that end, please review the following presentation: -[![YouTube video](../../img/YouTube-Logo.wine.svg)](https://youtu.be/WeZ2vJxBuTg?si=aG1IuBNhCQnb1ljv) +[![Lecture Slides](../img/Open-Science.svg)](https://docs.google.com/presentation/d/1SCrx65-Q_CB8JRMN81Uk-QniPvPE-wozRD7YDDQEapc/edit?usp=sharing) + +[![Presentation recording](https://img.youtube.com/vi/WeZ2vJxBuTg/maxresdefault.jpg)](https://www.youtube.com/watch?v=WeZ2vJxBuTg) \ No newline at end of file diff --git a/book/Chapter1-GettingStarted/1.2_jupyter_environment.md b/book/Chapter1-GettingStarted/1.2_jupyter_environment.md index e42dbb2..b0b82d1 100644 --- a/book/Chapter1-GettingStarted/1.2_jupyter_environment.md +++ b/book/Chapter1-GettingStarted/1.2_jupyter_environment.md @@ -18,7 +18,6 @@ Jupyter Lab is an expanded version of the browser interface that includes **note ## Jupyter Hub Is a Jupyter Lab launched by a given hardware/computer. Users may see where the computer is located in the browser address. The user may not know the type of hardware used. One exception is the selection of types of instances in public clouds. - ## Markdown ### What is Markdown? diff --git a/book/Chapter1-GettingStarted/1.3_python_environment.md b/book/Chapter1-GettingStarted/1.3_python_environment.md index f11ed6d..ca53316 100644 --- a/book/Chapter1-GettingStarted/1.3_python_environment.md +++ b/book/Chapter1-GettingStarted/1.3_python_environment.md @@ -1,16 +1,16 @@ # 1.3 Python Ecosystem ## What do you need to run your Python code? -- Python. Most packages work with Python3. Your laptop may already come with one. We recommend downloading Anaconda, which comes with a version of python, and aliasing $python$ to the binary executable python specific to conda. +- **Python.** Most packages work with Python3. Your laptop may already come with Python preinstalled. +We recommend downloading Anaconda, which comes with a version of python, and aliasing $python$ to the binary executable python specific to conda. -- A code: Some files with .py extension - -- Dependencies: specific modules that contains many functions related to each other (e.g. numpy, scipy, pandas, scikit-learn). Often these modules are packages imported in the python code as +- **Some code:** Files with .py extension +- **Dependencies:** Specific *modules* that contain many functions related to each other (e.g. numpy, scipy, pandas, scikit-learn). Often these modules are packages imported in the python code as import numpy as np -- To run a python code with the proper dependencies, you need to define its environment: Version of Python + Some packages + Version of packages +To run Python code with the proper dependencies, you need to define an **environment**: Version of Python + Some packages + Version of packages. ## What are virtual environments? A Python virtual environment is an isolated working copy of a specific version of Python interpreter together with specific versions of a number of external libraries installed into that virtual environment. diff --git a/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb b/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb index e7fde30..10349a6 100644 --- a/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb +++ b/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb @@ -1,175072 +1,175077 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "id": "50e82750", + "metadata": {}, + "source": [ + "# 2.4 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": 1, + "id": "28d926f9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import io\n", + "import requests\n", + "import time\n", + "import matplotlib.pyplot as plt\n", + "from datetime import datetime, timedelta\n", + "\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": 2, + "id": "3cdfc90e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "50e82750", - "metadata": {}, - "source": [ - "# 2.4 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:" - ] - }, + "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": 3, + "id": "fb8d9c69", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 1, - "id": "28d926f9", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import io\n", - "import requests\n", - "import time\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime, timedelta\n", - "\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": 3, + "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": 4, + "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": 5, + "id": "2ab0ad9d", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "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" - ] - } + "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. Here you will read a catalog of earthquakes." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f029ed84", + "metadata": {}, + "outputs": [], + "source": [ + "quake = pd.read_csv(\"Global_Quakes_IRIS.csv\")" + ] + }, + { + "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": 7, + "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
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" ], - "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)" + "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": 7, + "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": 8, + "id": "9b01ce51", + "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": [ + "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": 9, + "id": "2b9f083e", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "id": "fb8d9c69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['temperature', 'precipitation'], dtype='object')" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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count1785.0000001785.0000001785.0000001785.000000
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std30.579308125.558363146988.3020310.429012
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" ], - "source": [ - "my_pd.keys()" + "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": 9, + "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": 10, + "id": "83f21ca3", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "ff7935a4", - "metadata": {}, - "source": [ - "get a specific ``Series`` (different from the array)" - ] - }, + "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())\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "591f8d33", + "metadata": {}, + "source": [ + "## 1.3 Basic Python 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": 11, + "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": 12, + "id": "16cb70b9", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "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" - ] - } - ], - "source": [ - "print(my_pd.temperature[:])\n", - "print(type(my_pd.temperature[:]))" + "data": { + "text/plain": [ + "34.4" ] - }, + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now test it using the first element of the quake DataFrame\n", + "meters2kilometers(quake.depth[0])" + ] + }, + { + "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": 13, + "id": "d8e21ea1", + "metadata": {}, + "outputs": [], + "source": [ + "def meters2kilometers2(x):\n", + " m2km2=1000\n", + " return x/m2km2\n", + "# m2km2 is a local variable and cannot be called outside of the function. Prove it next by inquiring its value in the next cell." + ] + }, + { + "cell_type": "markdown", + "id": "f6db26f1", + "metadata": {}, + "source": [ + "We now discuss the **lambda** functions." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a2713d55", + "metadata": {}, + "outputs": [ { - "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:" + "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": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now we apply it on the entire Series\n", + "meters2kilometers(quake.depth)" + ] + }, + { + "cell_type": "markdown", + "id": "5b296ab5", + "metadata": {}, + "source": [ + "We can also define this very basic function as a **lambda** function. There are several ways of doing an operation on all rows of a column. The first option is to use the map function.\n", + "\n", + "If you are not familiar with lambda function in Python, please read additional tutorials [here](!https://realpython.com/python-lambda/).\n", + "\n", + "We will practice a bit lambda functions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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": "code", + "execution_count": 16, + "id": "7d94f6a8", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "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]))" + "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": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply it to the entire series\n", + "lambda_meters2kilometers(quake.depth)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "caad68a3", + "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. Here you will read a catalog of earthquakes." + "data": { + "text/plain": [ + "1" ] - }, + }, + "execution_count": 17, + "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": 18, + "id": "40367bfb", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 6, - "id": "f029ed84", - "metadata": {}, - "outputs": [], - "source": [ - "quake = pd.read_csv(\"Global_Quakes_IRIS.csv\")" + "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": 18, + "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": 19, + "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": 20, + "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": 21, + "id": "ae368fd4", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "62e31c2c", - "metadata": {}, - "source": [ - "Now you use the ``head`` function to display what is in the file" - ] - }, + "ename": "NameError", + "evalue": "name 'plt' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/marinedenolle/Dropbox/CLASSES/ESS490/curriculum-book/book/Chapter2-DataManipulation/2.4_pandas_rendered.ipynb Cell 43\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m \u001b[39m# answer here\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m plt\u001b[39m.\u001b[39mhist(quake\u001b[39m.\u001b[39mdepth,\u001b[39m100\u001b[39m)\n\u001b[1;32m 3\u001b[0m plt\u001b[39m.\u001b[39mgrid(\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m 4\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mQuake depth (km)\u001b[39m\u001b[39m'\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined" + ] + } + ], + "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": null, + "id": "5f07dc52", + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "id": "ed565400", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "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
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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 " + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": 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"text": "depth" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "count" + } } - ], - "source": [ - "# enter answer here\n", - "quake.head()" - ] - }, + } + } + }, + "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": "28a15457", + "metadata": {}, + "source": [ + "Alternatively, you can try another interactive plotting package Bokeh. Here is the same plot as above but with Bokeh." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d8f738c6", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "73bfcd15", - "metadata": {}, - "source": [ - "Display the depth using two ways to use the pandas object" + "data": { + "text/html": [ + "
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\n" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 8, - "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": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? 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density=True, bins=50)\n", + "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#636EFA\", fill_color=\"#636EFA\")\n", + "\n", + "show(p)" + ] + }, + { + "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": null, + "id": "d9cb41c3", + "metadata": {}, + "outputs": [], + "source": [ + "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)" + ] + }, + { + "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": null, + "id": "ff53045a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/html": [ - "
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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, + 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"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": "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": null, + "id": "7738e4d0", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 10, - "id": "83f21ca3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "82773.18767507003\n", - "24400.0\n" - ] - } + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptionmarker_sizemagnitude bin
02010-07-02 06:04:03.570-13.6098166.65410.034406.3VANUATU ISLANDS544.06.0
11122017-08-31 17:06:55.750-1.159099.68810.043146.3SOUTHERN SUMATRA544.06.0
11112017-08-27 04:17:51.010-1.4513148.08030.008006.3ADMIRALTY ISLANDS REGION544.06.0
11102017-08-19 02:00:52.550-17.9609-178.84060.544006.4FIJI ISLANDS REGION601.06.0
11082017-08-13 03:08:10.560-3.7682101.62280.031006.4SOUTHERN SUMATRA601.06.0
\n", + "
" ], - "source": [ - "# answer it here\n", - "print(quake.depth.mean())\n", - "print(quake.depth.median())\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "591f8d33", - "metadata": {}, - "source": [ - "## 1.3 Basic Python 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": 11, - "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" + "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 marker_size magnitude bin \n", + "0 VANUATU ISLANDS 544.0 6.0 \n", + "1112 SOUTHERN SUMATRA 544.0 6.0 \n", + "1111 ADMIRALTY ISLANDS REGION 544.0 6.0 \n", + "1110 FIJI ISLANDS REGION 601.0 6.0 \n", + "1108 SOUTHERN SUMATRA 601.0 6.0 " ] - }, + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quakes2plot=quake.sort_values(by='magnitude bin')\n", + "\n", + "quakes2plot.head()" + ] + }, + { + "cell_type": "markdown", + "id": "dcf7472d", + "metadata": {}, + "source": [ + "Now we will plot again using Plotly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c21ce4ea", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "id": "16cb70b9", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/plain": [ - "34.4" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "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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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 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"execution_count": 13, - "id": "d8e21ea1", - "metadata": {}, - "outputs": [], - "source": [ - "def meters2kilometers2(x):\n", - " m2km2=1000\n", - " return x/m2km2\n", - "# m2km2 is a local variable and cannot be called outside of the function. Prove it next by inquiring its value in the next cell." - ] - }, - { - "cell_type": "markdown", - "id": "f6db26f1", - "metadata": {}, - "source": [ - "We now discuss the **lambda** functions." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a2713d55", - "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" - ] + ], + "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" + ], + [ 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function. There are several ways of doing an operation on all rows of a column. The first option is to use the map function.\n", - "\n", - "If you are not familiar with lambda function in Python, please read additional tutorials [here](!https://realpython.com/python-lambda/).\n", - "\n", - "We will practice a bit lambda functions" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "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": "code", - "execution_count": 16, - "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" - ] + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# apply it to the entire series\n", - "lambda_meters2kilometers(quake.depth)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "caad68a3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" ] - }, - "execution_count": 17, - "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": 18, - "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" + ], + "sequential": [ + [ + 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" ] - }, - "execution_count": 18, - "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": 19, - "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": 20, - "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": 21, - "id": "ae368fd4", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'plt' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/marinedenolle/Dropbox/CLASSES/ESS490/curriculum-book/book/Chapter2-DataManipulation/2.4_pandas_rendered.ipynb Cell 43\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m \u001b[39m# answer here\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m plt\u001b[39m.\u001b[39mhist(quake\u001b[39m.\u001b[39mdepth,\u001b[39m100\u001b[39m)\n\u001b[1;32m 3\u001b[0m plt\u001b[39m.\u001b[39mgrid(\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m 4\u001b[0m plt\u001b[39m.\u001b[39mxlabel(\u001b[39m'\u001b[39m\u001b[39mQuake depth (km)\u001b[39m\u001b[39m'\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined" - ] - } - ], - "source": [ - "# implement answer here" - ] - }, - { - "cell_type": "markdown", - "id": "8bb58e3c", - "metadata": {}, - "source": [ - "You can use the interactive plotting package Plotly. 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0, - 1 - ], - "title": { - "text": "depth" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "count" - } - } - } - } - }, - "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": "28a15457", - "metadata": {}, - "source": [ - "Alternatively, you can try another interactive plotting package Bokeh. Here is the same plot as above but with Bokeh." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8f738c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - " \n", - " Loading BokehJS ...\n", - "
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
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  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
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Application\",\"version\":\"2.4.3\"}};\n const render_items = [{\"docid\":\"e7e44371-be1a-48f5-8736-3d0c6025edb0\",\"root_ids\":[\"1003\"],\"roots\":{\"1003\":\"3007c08c-7640-47af-acd1-a299f8d583fd\"}}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", - "application/vnd.bokehjs_exec.v0+json": "" + "hoverlabel": { + "align": "left" }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "id": "1003" - } + "hovermode": "closest", + "mapbox": { + "style": "light" }, - "output_type": "display_data" - } - ], - "source": [ - "p = figure(plot_width=600, plot_height=400)\n", - "\n", - "hist, edges = np.histogram(quake.depth, density=True, bins=50)\n", - "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#636EFA\", fill_color=\"#636EFA\")\n", - "\n", - "show(p)" - ] - }, - { - "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": null, - "id": "d9cb41c3", - "metadata": {}, - "outputs": [], - "source": [ - "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)" - ] - }, - { - "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": null, - "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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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", - 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timelatitudelongitudedepthmagnitudedescriptionmarker_sizemagnitude bin
02010-07-02 06:04:03.570-13.6098166.65410.034406.3VANUATU ISLANDS544.06.0
11122017-08-31 17:06:55.750-1.159099.68810.043146.3SOUTHERN SUMATRA544.06.0
11112017-08-27 04:17:51.010-1.4513148.08030.008006.3ADMIRALTY ISLANDS REGION544.06.0
11102017-08-19 02:00:52.550-17.9609-178.84060.544006.4FIJI ISLANDS REGION601.06.0
11082017-08-13 03:08:10.560-3.7682101.62280.031006.4SOUTHERN SUMATRA601.06.0
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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 marker_size magnitude bin \n", - "0 VANUATU ISLANDS 544.0 6.0 \n", - "1112 SOUTHERN SUMATRA 544.0 6.0 \n", - "1111 ADMIRALTY ISLANDS REGION 544.0 6.0 \n", - "1110 FIJI ISLANDS REGION 601.0 6.0 \n", - "1108 SOUTHERN SUMATRA 601.0 6.0 " - ] + "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" + } }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "quakes2plot=quake.sort_values(by='magnitude bin')\n", - "\n", - "quakes2plot.head()" - ] - }, - { - "cell_type": "markdown", - "id": "dcf7472d", - "metadata": {}, - "source": [ - "Now we will plot again using Plotly" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "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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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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"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": null, - "id": "9edd0bf5", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# 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. 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" - ], - "text/plain": [ - " station network channel location rate start_time \\\n", - "0 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", - "1 AAR NC EHZ -- 100.0 1987/05/01,00:00:00 \n", - "2 AAR NC SHZ -- 20.0 1994/11/28,00:00:00 \n", - "3 AAS NC EHZ -- 100.0 1984/11/27,18:45:00 \n", - "4 AAS NC EHZ -- 100.0 1987/05/01,00:00:00 \n", - "\n", - " end_time latitude longitude elevation depth dip azimuth \n", - "0 1987/05/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "1 2006/01/04,19:19:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "2 2006/01/04,19:19:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "3 1987/05/01,00:00:00 38.43014 -121.10959 31.0 0.0 -90.0 0.0 \n", - "4 2021/08/13,16:50:00 38.43014 -121.10959 31.0 0.0 -90.0 0.0 " - ] + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } }, - "execution_count": 32, - "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": null, - "id": "51971cde", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "station AAR\n", - "network NC\n", - "channel EHZ\n", - "location --\n", - "rate 100.0\n", - "start_time 1984/01/01,00:00:00\n", - "end_time 1987/05/01,00:00:00\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" - ] + "title": { + "x": 0.05 }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.iloc[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3cc09c6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 AAR\n", - "1 AAR\n", - "2 AAR\n", - "3 AAS\n", - "4 AAS\n", - " ... \n", - "7151 WMP\n", - "7152 WMP\n", - "7153 WMP\n", - "7154 WMP\n", - "7155 WWVB\n", - "Name: station, Length: 7156, dtype: object" - ] + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.iloc[:, 0]" - ] - }, - { - "cell_type": "markdown", - "id": "a201c0e9", - "metadata": {}, - "source": [ - "The DataFrame may have bad values. A typical data cleaning involves removing Nan and Zeros for instance." - ] - }, - { - "cell_type": "markdown", - "id": "fc8219f0", - "metadata": {}, - "source": [ - "Use Plotly to map the stations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "78c75b19", - "metadata": {}, - "outputs": [], - "source": [ - "data.dropna(inplace=True)\n", - "data=data[data.longitude!=0]" - ] - }, + "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(quakes2plot,\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" + ] + }, + { + "cell_type": "markdown", + "id": "3355fe19", + "metadata": {}, + "source": [ + "# 2 Create a Pandas from a 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": null, + "id": "ef9f302d", + "metadata": {}, + "outputs": [], + "source": [ + "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "id": "9edd0bf5", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# 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": null, + "id": "448b69ab", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "7f3533db", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - "NC", - "AAR", - "EHZ", - 100 - ], - [ - "NC", - "AAR", - "EHZ", - 100 - ], - [ - "NC", - "AAR", - "SHZ", - 20 - ], - [ - "NC", - "AAS", - "EHZ", - 100 - ], - [ - "NC", - "AAS", - "EHZ", - 100 - ], - [ - "NC", - "AAS", - "SHZ", - 20 - ], - [ - "NC", - "ABJ", - "EHZ", - 100 - ], - [ - "NC", - "ABJ", - "EHZ", - 100 - ], - [ - "NC", - "ABJ", - "EHZ", - 100 - ], - [ - "NC", - "ABJ", - "SHZ", - 20 - ], - [ - "NC", - "ABR", - "EHZ", - 100 - ], - [ - "NC", - "ABR", - "EHZ", - 100 - ], - [ - "NC", - "ABR", - "SHZ", - 20 - ], - [ - "NC", - "ADW", - "EHZ", - 100 - ], - [ - "NC", - "ADW", - 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network=%{customdata[0]}
station=%{customdata[1]}
channel=%{customdata[2]}
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"CMI", - "CMI", - "CMJ", - "CMJ", - "CMJ", - "CMK", - "CMK", - "CMK", - "CMK", - "CMK", - "CMK", - "CML", - "CML", - "CML", - "CML", - "CML", - "CML", - "CML", - "CMLP", - "CMLP", - "CMLP", - "CMLP", - "CMM", - "CMM", - "CMM", - "CMM", - "CMM", - "CMM", - "CMM", - "CMN", - "CMN", - "CMN", - "CMN", - "CMN", - "CMN", - "CMO", - "CMO", - "CMO", - "CMO", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMP", - "CMP", - "CMP", - "CMP", - "CMP", - "CMR", - "CMR", - "CMR", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "COS", - "COS", - "COS", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPL", - "CPL", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPP", - "CPP", - "CPP", - "CPP", - "CPP", - "CRA", - "CRA", - "CRA", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRP", - "CRP", - "CRP", - "CRP", - "CRP", - "CRP", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSC", - "CSC", - "CSH", - "CSH", - "CSH", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSP", - "CSP", - "CSP", - "CSP", - "CSP", - "CSPB", - "CSPB", - "CSPB", - "CSPB", - "CST", - "CST", - "CST", - "CSTB", - "CSTB", - "CSTC", - "CSTC", - "CSTC", - "CSU", - "CSU", - "CSU", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSV", - "CSV", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTH", - "CTH", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CVA", - "CVA", - "CVA", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "DBOG", - "DBOG", - "DBOG", - "DBOG", - "DBOG", - "EXSG", - "EXSG", - "EXSG", - "EXSG", - "EXSG", - "EXSG", - "G001", - "G001", - "G001", - "G002", - "G002", - "G002", - "G003", - "G003", - "G003", - "G004", - "G004", - "G004", - "G005", - "G005", - "G005", - "G006", - "G006", - "G006", - "G007", - "G007", - "G007", - "GAC", - "GAC", - "GAC", - "GAR", - "GAR", - "GAR", - "GAX", - "GAX", - "GAX", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GBB", - "GBB", - "GBD", - "GBD", - "GBD", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBM", - "GBM", - "GBM", - "GCB", - "GCB", - "GCB", - "GCK", - "GCK", - "GCM", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCR", - "GCR", - "GCR", - "GCR", - "GCS", - "GCS", - "GCS", - "GCV", - "GCV", - "GCV", - "GCV", - "GCV", - "GCV", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCW", - "GCW", - "GCW", - "GCW", - "GCW", - "GDC", - "GDC", - "GDC", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDX", - "GDX", - "GDX", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GFC", - "GFC", - "GFC", - "GFC", - "GFC", - "GFC", - "GGL", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGPB", - "GGPB", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGU", - "GGU", - "GGU", - "GHC", - "GHC", - "GHC", - "GHG", - "GHG", - "GHG", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHL", - "GHL", - "GHL", - "GHM", - "GHM", - "GHM", - "GHO", - "GHO", - "GHO", - "GHO", - "GHO", - "GHO", - "GHV", - "GHV", - "GHV", - "GHV", - "GMC", - "GMC", - "GMC", - "GMK", - "GMK", - "GMK", - "GMM", - "GMO", - "GMO", - "GMR", - "GMR", - "GMR", - "GMR", - "GMR", - "GMR", - "GMR", - "GMR", - "GNA", - "GNA", - "GNA", - "GNA", - "GNA", - "GNA", - "GNA", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GPM", - "GPM", - "GPM", - "GPM", - "GPM", - "GPM", - "GPM", - "GPM", - "GRO", - "GRO", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GRT", - "GRT", - "GRT", - "GRT", - "GRT", - "GRT", - "GRT", - "GSG", - "GSG", - "GSG", - "GSG", - "GSG", - "GSM", - "GSN", - "GSN", - "GSN", - "GSN", - "GSN", - "GSN", - "GSN", - "GSN", - "GSS", - "GSS", - "GSS", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GTC", - "GTC", - "GTC", - "GTC", - "GTC", - "GTS", - "GTS", - "GTS", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVV", - "GVV", - "GVV", - "GWK", - "GWK", - "GWK", - "GWK", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "H001", - "H001", - "H001", - "H002", - "H002", - "H002", - "HAZ", - "HAZ", - "HAZ", - "HAZ", - "HAZ", - "HAZ", - "HAZ", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HCA", - "HCA", - "HCA", - "HCA", - "HCA", - "HCAB", - "HCAB", - "HCAB", - "HCAB", - "HCB", - "HCB", - "HCB", - "HCBB", - "HCBB", - "HCBB", - "HCBB", - "HCO", - "HCO", - "HCO", - "HCO", - "HCO", - "HCO", - "HCO", - "HCP", - "HCP", - "HCR", - "HCR", - "HCR", - "HCRB", - "HCRB", - "HCRB", - "HCRB", - "HDL", - "HDL", - "HDL", - "HDL", - "HDL", - "HER", - "HER", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HFE", - "HFE", - "HFE", - "HFE", - "HFE", - "HFH", - "HFH", - "HFM", - "HFM", - "HFM", - "HFM", - "HFM", - "HFM", - "HFP", - "HFP", - "HFP", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGW", - "HGW", - "HGW", - "HJG", - "HJG", - "HJG", - "HJG", - "HJG", - "HJS", - "HJS", - "HJS", - "HJS", - "HJS", - "HJS", - "HJS", - "HKR", - "HKR", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLP", - "HLP", - "HLP", - "HLP", - "HLP", - "HLP", - "HLT", - "HLT", - "HLT", - "HLT", - "HLT", - "HLT", - "HLT", - "HMO", - "HMO", - "HMO", - "HMO", - "HMO", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HOR", - "HOR", - "HOR", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPH", - "HPH", - "HPH", - "HPH", - "HPH", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPR", - "HPR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HSF", - "HSF", - "HSF", - "HSF", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSL", - "HSL", - "HSL", - "HSL", - "HSP", - "HSP", - "HSP", - "HTU", - "HTU", - "HTU", - "HTU", - "J001", - "J001", - "J001", - "J002", - "J002", - "J002", - "J003", - "J003", - "J003", - "J004", - "J004", - "J004", - "J005", - "J005", - "J005", - "J006", - "J006", - "J006", - "J007", - "J007", - "J007", - "J008", - "J008", - "J008", - "J009", - "J009", - "J009", - "J010", - "J010", - "J010", - "J011", - "J011", - "J011", - "J012", - "J012", - "J012", - "J013", - "J013", - "J013", - "J014", - "J014", - "J014", - "J015", - "J015", - "J015", - "J016", - "J016", - "J016", - "J017", - "J017", - "J017", - "J018", - "J018", - "J018", - "J019", - "J019", - "J019", - "J020", - "J020", - "J020", - "J021", - "J021", - "J021", - "J022", - "J022", - "J022", - "J023", - "J023", - "J023", - "J024", - "J024", - "J024", - "J025", - "J025", - "J025", - "J026", - "J026", - "J026", - "J027", - "J027", - "J027", - "J028", - "J028", - "J028", - "J029", - "J029", - "J029", - "J030", - "J030", - "J030", - "J031", - "J031", - "J031", - "J032", - "J032", - "J032", - "J033", - "J033", - "J033", - "J034", - "J034", - "J034", - "J035", - "J035", - "J035", - "J036", - "J036", - "J036", - "J037", - "J037", - "J037", - "J038", - "J038", - "J038", - "J039", - "J039", - "J039", - "J040", - "J040", - "J040", - "J041", - "J041", - "J041", - "J042", - "J042", - "J042", - "J043", - "J043", - "J043", - "J044", - "J044", - "J044", - "J045", - "J045", - "J045", - "J046", - "J046", - "J046", - "J047", - "J047", - "J047", - "J048", - "J048", - "J048", - "J048", - "J048", - "J048", - "J049", - "J049", - "J049", - "J050", - "J050", - "J050", - "J051", - "J051", - "J051", - "J052", - "J052", - "J052", - "J053", - "J053", - "J053", - "J054", - "J054", - "J054", - "J055", - "J055", - "J055", - "J056", - "J056", - "J056", - "J057", - "J057", - "J057", - "J058", - "J058", - "J058", - "J059", - "J059", - "J059", - "J060", - "J060", - "J060", - "J061", - "J061", - "J061", - "J062", - "J062", - "J062", - "J063", - "J063", - "J063", - "J064", - "J064", - "J064", - "J065", - "J065", - "J065", - "J066", - "J066", - "J066", - "J067", - "J067", - "J067", - "J068", - "J068", - "J068", - "JAL", - "JAL", - "JAL", - "JAR", - "JAR", - "JAR", - "JAR", - "JBC", - "JBC", - "JBC", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBM", - "JBM", - "JBM", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBN", - "JBN", - "JBN", - "JBN", - "JBN", - "JBN", - "JBN", - "JBN", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBZ", - "JBZ", - "JBZ", - "JCB", - 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"KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KOM", - "KPP", - "KPP", - "KPP", - "KPP", - "KPP", - "KPP", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPPB", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KPR", - "KRK", - "KRK", - "KRK", - "KRK", - "KRK", - "KRK", - "KRK", - "KRK", - "KRK", - "KRK", - "KRM", - "KRM", - "KRM", - "KRM", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KSC", - "KSC", - "KSC", - "KSC", - "KSM", - "KSM", - "KSM", - "KSM", - "KSM", - "KSM", - "KSM", - "KSP", - "KSP", - "KSP", - "KSX", - "KSX", - "KSX", - "KSX", - "KSX", - "KSX", - "KSX", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KTD", - "KTD", - "KTD", - "KTD", - "KTD", - "KTD", - "KTR", - "KTR", - "KTR", - "KTR", - "L001", - "L001", - "L001", - "L002", - "L002", - "L002", - "L003", - "L003", - "L003", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAC", - "LAC", - "LAC", - "LAM", - "LAM", - "LAM", - "LAMB", - "LAS", - "LAS", - "LAS", - "LAS", - "LAS", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LBA", - "LBC", - "LBC", - "LBC", - "LBC", - "LBC", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBF", - "LBF", - "LBF", - "LBF", - "LBF", - "LBF", - "LBG", - "LBG", - "LBK", - "LBK", - "LBK", - "LBK", - "LBK", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBM", - "LBM", - "LBM", - "LBM", - "LBM", - "LBO", - "LBO", - "LBO", - "LBO", - "LBO", - "LBO", - "LBO", - "LBP", - "LBP", - "LBP", - "LBP", - "LBP", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LCA", - "LCA", - "LCF", - "LCF", - "LCF", - "LCF", - "LCF", - "LCF", - "LCF", - "LCF", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCS", - "LCS", - "LCS", - "LCSB", - "LCSB", - "LDB", - "LDB", - "LDB", - "LDB", - "LDB", - "LDB", - "LDB", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LGB", - "LGB", - "LGB", - "LGB", - "LGB", - "LGB", - "LGB", - "LGH", - "LGM", - "LGM", - "LGM", - "LGM", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGP", - "LGP", - "LGP", - "LGP", - "LGP", - "LGR", - "LGR", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LHC", - "LHC", - "LHC", - "LHC", - "LHC", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHH", - "LHK", - "LHK", - "LHK", - "LHM", - "LHM", - "LHM", - "LHO", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMD", - "LMD", - "LMD", - "LME", - "LME", - "LME", - "LME", - "LME", - "LME", - "LME", - "LME", - "LME", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMI", - "LMI", - "LMI", - "LMI", - "LMI", - "LMI", - "LMM", - "LMP", - "LMP", - "LMP", - "LMP", - "LMP", - "LMP", - "LMP", - "LMP", - "LMZ", - "LMZ", - "LMZ", - "LOM", - "LPD", - "LPD", - "LPD", - "LPG", - "LPG", - "LPG", - "LPG", - "LPK", - "LPK", - "LPK", - "LPK", - "LRB", - "LRB", - "LRB", - "LRB", - "LRCG", - "LRCG", - "LRCG", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRS", - "LRS", - "LRS", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSF", - "LSF", - "LSF", - "LSF", - "LSF", - "LSF", - "LSF", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSI", - "LSI", - "LSI", - "LSI", - "LSL", - "LSL", - "LSL", - "LSL", - "LSL", - "LSM", - "LSM", - "LSM", - "LSR", - "LSR", - "LSR", - "LSR", - "LSS", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTI", - "LTI", - "LTI", - "LTI", - "LTI", - "LTN", - "LTN", - "LTN", - "LVR", - "LVR", - "LVR", - "LVR", - "LWD", - "LWH", - "LWH", - "LWH", - "LWH", - "LWH", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "MAT", - "MAT", - "MBE", - "MBE", - "MBE", - "MBO", - "MBO", - "MBO", - "MBO", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBU", - "MBU", - "MBUB", - "MBUB", - "MBUB", - "MBUB", - "MBUB", - "MBUB", - "MBY", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCD", - "MCD", - "MCD", - "MCD", - "MCH", - "MCL", - "MCL", - "MCL", - "MCM", - "MCM", - "MCM", - "MCM", - "MCN", - "MCO", - "MCO", - "MCO", - "MCO", - "MCR", - "MCR", - "MCS", - "MCS", - "MCS", - "MCU", - "MCU", - "MCU", - "MCU", - "MCU", - "MCU", - "MCUB", - "MCUB", - "MCV", - "MCV", - "MCW", - "MCY", - "MCY", - "MCY", - "MCY", - "MCY", - "MCY", - "MDC", - "MDC", - "MDC", - "MDC", - "MDC", - "MDH", - "MDH", - "MDH", - "MDH", - "MDH", - "MDH", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDW", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MFB", - "MFB", - "MFB", - "MGP", - "MGP", - "MGP", - "MGP", - "MGP", - "MGPB", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MHD", - "MHD", - "MHD", - "MHD", - "MHD", - "MILG", - "MILG", - "MILG", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MLC", - "MLC", - "MLC", - "MLC", - "MLH", - "MLH", - "MLH", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLK", - "MLK", - "MLK", - "MLK", - "MLK", - "MLL", - "MLM", - "MLM", - "MLM", - "MLM", - "MLR", - "MLR", - "MLR", - "MMI", - "MMI", - "MMI", - "MMI", - "MMI", - "MMI", - "MML", - "MML", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMP", - "MMP", - "MMP", - "MMS", - "MMS", - "MMS", - "MMS", - "MMT", - "MMT", - "MMW", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MNH", - "MNH", - "MNH", - "MNHB", - "MNHB", - "MNHB", - "MNP", - "MNP", - "MNP", - "MNP", - "MOG", - "MOG", - "MOY", - "MOY", - "MOY", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPR", - "MPR", - "MPR", - "MPR", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ2P", - "MQ2P", - "MQ2P", - "MQ2P", - "MQ2P", - "MQ2P", - "MQ3P", - "MQ3P", - "MQ3P", - "MQ3P", - "MQ3P", - "MQ3P", - "MRC", - "MRC", - "MRC", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRF", - "MRF", - "MRF", - "MRF", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRS", - "MRS", - "MRS", - "MSK", - "MSK", - "MSK", - "MSK", - "MSL", - "MSL", - "MSL", - "MSL", - "MSL", - "MSL", - "MST", - "MST", - "MST", - "MSV", - "MSV", - "MSVB", - "MSVB", - "MSVB", - "MSVB", - "MSVB", - "MSVB", - "MTC", - "MTC", - "MTC", - "MTC", - "MTC", - "MTC", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTU", - "MTU", - "MTU", - "MTU", - "MTU", - "MWB", - "MWB", - "MWB", - "MWB", - "MYL", - "MYL", - "MYL", - "MYM", - "N001", - "N001", - "N001", - "N002", - "N002", - "N002", - "N003", - "N003", - "N003", - "N004", - "N004", - "N004", - "N005", - "N005", - "N005", - "N006", - "N006", - "N006", - "N007", - "N007", - "N007", - "N008", - "N008", - "N008", - "N009", - "N009", - "N009", - "N010", - "N010", - "N010", - "N011", - "N011", - "N011", - "N012", - "N012", - "N012", - "N013", - "N013", - "N013", - "N014", - "N014", - "N014", - "N015", - "N015", - "N015", - "N016", - "N016", - "N016", - "N017", - "N017", - "N017", - "N018", - "N018", - "N018", - "N019", - "N019", - "N019", - "N019B", - "N019B", - "N019B", - "N020", - "N020", - "N020", - "N021", - "N021", - "N021", - "N022", - "N022", - "N022", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAP", - "NAP", - "NAP", - "NAPB", - "NAPB", - "NAPC", - "NAPC", - "NAPC", - "NAPC", - "NAPC", - "NAPC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBP", - "NBP", - "NBP", - "NBP", - "NBP", - "NBP", - 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"NSP", - "NSP", - "NSP", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTA", - "NTAB", - "NTAB", - "NTAB", - "NTAB", - "NTAB", - "NTAC", - "NTAC", - "NTAC", - "NTAC", - "NTAC", - "NTAC", - "NTAC", - "NTAC", - "NTAC", - "NTB", - "NTB", - "NTB", - "NTO", - "NTO", - "NTO", - "NTO", - "NTO", - "NTO", - "NTO", - "NTO", - "NTO", - "NTR", - "NTR", - "NTR", - "NTR", - "NTR", - "NTR", - "NTR", - "NTY", - "NTY", - "NTY", - "NTYB", - "NTYB", - "NTYB", - "NTYB", - "NTYB", - "NTYB", - "NTYB", - "NTYB", - "NTYB", - "NVA", - "NVA", - "NVA", - "NVC", - "NVC", - "NVC", - "NVC", - "NVC", - "NVC", - "NVE", - "NVE", - "NVE", - "NVE", - "NVE", - "NVE", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWL", - "NWR", - "NWR", - "NWR", - "NWR", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "NWS", - "O001", - "O001", - "O001", - "OBH", - "OBH", - "OBH", - "OBH", - "OBH", - "OBH", - "OBHB", - "OBHB", - "OBHB", - "OBHB", - "OBHB", - "OBHB", - "OBHB", - "OBHB", - "OBHB", - "OBHB", - "OBHB", - "OCH", - "OCH", - "OCM", - "OCM", - "OCM", - "OCR", - "OCR", - "OGO", - "OGO", - "OGO", - "OGO", - "OGO", - "OHC", - "OHC", - "OHC", - "OHC", - "ORA", - "ORA", - "ORA", - "ORA", - "ORA", - "ORA", - "ORD", - "ORD", - "ORD", - "OST", - "OST", - "OST", - "OSU", - "OSU", - "OSU", - "OWY", - "OWY", - "OWY", - "OWY", - "OWY", - "OWY", - "PAB", - "PAB", - "PAB", - "PABB", - "PABB", - "PABB", - "PABB", - "PAD", - "PAD", - "PAD", - "PAD", - "PAD", - "PADB", - "PADB", - "PADB", - "PAG", - "PAG", - "PAG", - "PAG", - "PAG", - "PAG", - "PAG", - "PAG", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAGB", - "PAN", - "PAN", - "PAN", - "PAN", - "PAN", - "PAN", - "PAN", - "PAN", - "PAN", - "PAN", - "PAP", - "PAP", - "PAP", - "PAP", - "PAP", - "PAP", - "PAP", - "PAR", - "PAR", - "PAR", - "PAV", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBC", - "PBI", - "PBI", - "PBI", - "PBI", - "PBI", - "PBI", - "PBI", - "PBI", - "PBI", - "PBIB", - "PBIB", - "PBIB", - "PBIB", - "PBIB", - "PBM", - "PBM", - "PBM", - "PBM", - "PBM", - "PBM", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBO", - "PBP", - "PBP", - "PBP", - "PBP", - "PBR", - "PBS", - "PBS", - "PBW", - "PBW", - "PBW", - "PBW", - "PBW", - "PBW", - "PBW", - "PBY", - "PCA", - "PCA", - "PCA", - "PCA", - "PCA", - "PCB", - "PCB", - "PCB", - "PCB", - "PCB", - "PCB", - "PCB", - 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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
0AARNCEHZ--100.01984/01/01,00:00:001987/05/01,00:00:0039.27594-121.02696911.00.0-90.00.0
1AARNCEHZ--100.01987/05/01,00:00:002006/01/04,19:19:0039.27594-121.02696911.00.0-90.00.0
2AARNCSHZ--20.01994/11/28,00:00:002006/01/04,19:19:0039.27594-121.02696911.00.0-90.00.0
3AASNCEHZ--100.01984/11/27,18:45:001987/05/01,00:00:0038.43014-121.1095931.00.0-90.00.0
4AASNCEHZ--100.01987/05/01,00:00:002021/08/13,16:50:0038.43014-121.1095931.00.0-90.00.0
\n", + "
" ], - "source": [ - "fig = px.scatter_geo(data,\n", - " lat='latitude',lon='longitude', \n", - " range_color=(6,9),\n", - " height=600, width=600,\n", - " hover_name=\"station\",\n", - " hover_data=['network','station','channel','rate']);\n", - "fig.update_geos(resolution=110, showcountries=True)\n" + "text/plain": [ + " station network channel location rate start_time \\\n", + "0 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", + "1 AAR NC EHZ -- 100.0 1987/05/01,00:00:00 \n", + "2 AAR NC SHZ -- 20.0 1994/11/28,00:00:00 \n", + "3 AAS NC EHZ -- 100.0 1984/11/27,18:45:00 \n", + "4 AAS NC EHZ -- 100.0 1987/05/01,00:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip azimuth \n", + "0 1987/05/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "1 2006/01/04,19:19:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "2 2006/01/04,19:19:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "3 1987/05/01,00:00:00 38.43014 -121.10959 31.0 0.0 -90.0 0.0 \n", + "4 2021/08/13,16:50:00 38.43014 -121.10959 31.0 0.0 -90.0 0.0 " ] - 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"BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJF", - "BJO", - "BJO", - "BJO", - "BJO", - "BJO", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BJOB", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BKC", - "BLR", - "BLR", - "BLR", - "BLR", - "BLR", - "BLR", - "BLR", - "BMS", - "BMS", - "BMS", - "BMS", - "BMS", - "BMS", - "BPC", - "BPC", - "BPC", - "BPC", - "BPC", - "BPC", - "BPC", - "BPC", - "BPE", - "BPE", - "BPE", - "BPE", - "BPE", - "BPE", - "BPF", - "BPF", - "BPF", - "BPF", - "BPF", - "BPI", - "BPI", - "BPI", - "BPI", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPL", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPO", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPOB", - "BPP", - "BPR", - "BPR", - "BPR", - "BPR", - "BPRB", - "BPRB", - "BPRB", - "BPRB", - "BPS", - "BPS", - "BPS", - "BPS", - "BPS", - "BPS", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BPV", - "BRM", - "BRM", - "BRM", - "BRV", - "BRV", - "BRV", - "BRV", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSC", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSG", - "BSL", - "BSL", - "BSL", - "BSL", - "BSM", - "BSM", - "BSM", - "BSM", - "BSM", - "BSM", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BSR", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTI", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BTV", - "BVL", - "BVL", - "BVL", - "BVLB", - "BVY", - "BVY", - "BVY", - "C001", - "C001", - "C001", - "C002", - "C002", - "C002", - "C003", - "C003", - "C003", - "C004", - "C004", - "C004", - "C005", - "C005", - "C005", - "C006", - "C006", - "C006", - "C007", - "C007", - "C007", - "C008", - "C008", - "C008", - "C008", - "C008", - "C008", - "C009", - "C009", - "C009", - "C010", - "C010", - "C010", - "C011", - "C011", - "C011", - "C012", - "C012", - "C012", - "C013", - "C013", - "C013", - "C014", - "C014", - "C014", - "C015", - "C015", - "C015", - "C016", - "C016", - "C016", - "C017", - "C017", - "C017", - "C018", - "C018", - "C018", - "C019", - "C019", - "C019", - "C020", - "C020", - "C020", - "C021", - "C021", - "C021", - "C022", - "C022", - "C022", - "C023", - "C023", - "C023", - "C024", - "C024", - "C024", - "C025", - "C025", - "C025", - "C026", - "C026", - "C026", - "C027", - "C027", - "C027", - "C028", - "C028", - "C028", - "C029", - "C029", - "C029", - "C030", - "C030", - "C030", - "C031", - "C031", - "C031", - "C032", - "C032", - "C032", - "C033", - "C033", - "C033", - "C034", - "C034", - "C034", - "C035", - "C035", - "C035", - "C036", - "C036", - "C036", - "C037", - "C037", - "C037", - "C038", - "C038", - "C038", - "C039", - "C039", - "C039", - "C040", - "C040", - "C040", - "C041", - "C041", - "C041", - "C042", - "C042", - "C042", - "C043", - "C043", - "C043", - "C044", - "C044", - "C044", - "C045", - "C045", - "C045", - "C046", - "C046", - "C046", - "C047", - "C047", - "C047", - "C048", - "C048", - "C048", - "C049", - "C049", - "C049", - "C050", - "C050", - "C050", - "C051", - "C051", - "C051", - "C052", - "C052", - "C052", - "C053", - "C053", - "C053", - "C054", - "C054", - "C054", - "C055", - "C055", - "C055", - "C056", - "C056", - "C056", - "C057", - "C057", - "C057", - "C058", - "C058", - "C058", - "C059", - "C059", - "C059", - "C060", - "C060", - "C060", - "C061", - "C061", - "C061", - "C062", - "C062", - "C062", - "C063", - "C063", - "C063", - "C064", - "C064", - "C064", - "C065", - "C065", - "C065", - "C066", - "C066", - "C066", - "C067", - "C067", - "C067", - "C068", - "C068", - "C068", - "C069", - "C069", - "C069", - "C070", - "C070", - "C070", - "C071", - "C071", - "C071", - "C072", - "C072", - "C072", - "C073", - "C073", - "C073", - "CAC", - "CAC", - "CAC", - "CAC", - "CAC", - "CAD", - "CAD", - "CAD", - "CAD", - "CAD", - "CAD", - "CADB", - "CADB", - "CADB", - "CADB", - "CADB", - "CADB", - "CADB", - "CADB", - "CADB", - "CAG", - "CAG", - "CAG", - "CAG", - "CAG", - "CAG", - "CAG", - "CAG", - "CAG", - "CAI", - "CAI", - "CAI", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAL", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAO", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CAP", - "CBK", - "CBK", - "CBLG", - "CBLG", - "CBLG", - "CBLG", - "CBP", - "CBP", - "CBP", - "CBP", - "CBP", - "CBP", - "CBP", - "CBP", - "CBP", - "CBP", - "CBP", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBR", - "CBS", - "CBS", - "CBS", - "CBSB", - "CBSB", - "CBSB", - "CBSB", - "CBW", - "CBW", - "CBW", - "CBW", - "CBW", - "CBW", - "CBW", - "CBW", - "CBZ", - "CBZ", - "CBZ", - "CBZ", - "CBZ", - "CBZ", - "CBZ", - "CBZ", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCH1", - "CCO", - "CCO", - "CCO", - "CCO", - "CCO", - "CCO", - "CCO", - "CCO", - "CCO", - "CCO", - "CCO", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCOB", - "CCY", - "CCY", - "CCY", - "CCY", - "CCY", - "CDA", - "CDA", - "CDA", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDG", - "CDO", - "CDO", - "CDO", - "CDOB", - "CDOB", - "CDOB", - "CDOB", - "CDOB", - "CDOB", - "CDOB", - "CDU", - "CDU", - "CDU", - "CDU", - "CDU", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CDV", - "CGP", - "CGP", - "CGP", - "CGP", - "CGP", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CGP1", - "CHMG", - "CHMG", - "CHMG", - "CHMG", - "CHMG", - "CHMG", - "CHR", - "CHR", - "CHR", - "CHR", - "CHR", - "CLC", - "CLC", - "CLC", - "CLCB", - "CLCB", - "CLCB", - "CLCB", - "CLCB", - "CLCB", - "CLCB", - "CLCB", - "CLCB", - "CMC", - "CMC", - "CMC", - "CMC", - "CMC", - "CMC", - "CMC", - "CMC", - "CMC", - "CMH", - "CMH", - "CMH", - "CMI", - "CMI", - "CMI", - "CMJ", - "CMJ", - "CMJ", - "CMK", - "CMK", - "CMK", - "CMK", - "CMK", - "CMK", - "CML", - "CML", - "CML", - "CML", - "CML", - "CML", - "CML", - "CMLP", - "CMLP", - "CMLP", - "CMLP", - "CMM", - "CMM", - "CMM", - "CMM", - "CMM", - "CMM", - "CMM", - "CMN", - "CMN", - "CMN", - "CMN", - "CMN", - "CMN", - "CMO", - "CMO", - "CMO", - "CMO", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMOB", - "CMP", - "CMP", - "CMP", - "CMP", - "CMP", - "CMR", - "CMR", - "CMR", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CMW1", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "CNI", - "COS", - "COS", - "COS", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPI", - "CPL", - "CPL", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPM", - "CPP", - "CPP", - "CPP", - "CPP", - "CPP", - "CRA", - "CRA", - "CRA", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRH", - "CRP", - "CRP", - "CRP", - "CRP", - "CRP", - "CRP", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CRPB", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSA", - "CSC", - "CSC", - "CSH", - "CSH", - "CSH", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSL", - "CSP", - "CSP", - "CSP", - "CSP", - "CSP", - "CSPB", - "CSPB", - "CSPB", - "CSPB", - "CST", - "CST", - "CST", - "CSTB", - "CSTB", - "CSTC", - "CSTC", - "CSTC", - "CSU", - "CSU", - "CSU", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSU1", - "CSV", - "CSV", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTA", - "CTH", - "CTH", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CUSLD", - "CVA", - "CVA", - "CVA", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVL", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CVP", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYB", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "CYD1", - "DBOG", - "DBOG", - "DBOG", - "DBOG", - "DBOG", - "EXSG", - "EXSG", - "EXSG", - "EXSG", - "EXSG", - "EXSG", - "G001", - "G001", - "G001", - "G002", - "G002", - "G002", - "G003", - "G003", - "G003", - "G004", - "G004", - "G004", - "G005", - "G005", - "G005", - "G006", - "G006", - "G006", - "G007", - "G007", - "G007", - "GAC", - "GAC", - "GAC", - "GAR", - "GAR", - "GAR", - "GAX", - "GAX", - "GAX", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GAXB", - "GBB", - "GBB", - "GBD", - "GBD", - "GBD", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBG", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBL", - "GBM", - "GBM", - "GBM", - "GCB", - "GCB", - "GCB", - "GCK", - "GCK", - "GCM", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCPN", - "GCR", - "GCR", - "GCR", - "GCR", - "GCS", - "GCS", - "GCS", - "GCV", - "GCV", - "GCV", - "GCV", - "GCV", - "GCV", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCVB", - "GCW", - "GCW", - "GCW", - "GCW", - "GCW", - "GDC", - "GDC", - "GDC", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDCB", - "GDX", - "GDX", - "GDX", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GDXB", - "GFC", - "GFC", - "GFC", - "GFC", - "GFC", - "GFC", - "GGL", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGP", - "GGPB", - "GGPB", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGPC", - "GGU", - "GGU", - "GGU", - "GHC", - "GHC", - "GHC", - "GHG", - "GHG", - "GHG", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHGB", - "GHL", - "GHL", - "GHL", - "GHM", - "GHM", - "GHM", - "GHO", - "GHO", - "GHO", - "GHO", - "GHO", - "GHO", - "GHV", - "GHV", - "GHV", - "GHV", - "GMC", - "GMC", - "GMC", - "GMK", - "GMK", - "GMK", - "GMM", - "GMO", - "GMO", - "GMR", - "GMR", - "GMR", - "GMR", - "GMR", - "GMR", - "GMR", - "GMR", - "GNA", - "GNA", - "GNA", - "GNA", - "GNA", - "GNA", - "GNA", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GNR", - "GPM", - "GPM", - "GPM", - "GPM", - "GPM", - "GPM", - "GPM", - "GPM", - "GRO", - "GRO", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GROB", - "GRT", - "GRT", - "GRT", - "GRT", - "GRT", - "GRT", - "GRT", - "GSG", - "GSG", - "GSG", - "GSG", - "GSG", - "GSM", - "GSN", - "GSN", - "GSN", - "GSN", - "GSN", - "GSN", - "GSN", - "GSN", - "GSS", - "GSS", - "GSS", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GSX", - "GTC", - "GTC", - "GTC", - "GTC", - "GTC", - "GTS", - "GTS", - "GTS", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVA", - "GVV", - "GVV", - "GVV", - "GWK", - "GWK", - "GWK", - "GWK", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWR", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "GWW", - "H001", - "H001", - "H001", - "H002", - "H002", - "H002", - "HAZ", - "HAZ", - "HAZ", - "HAZ", - "HAZ", - "HAZ", - "HAZ", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBC", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBH", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HBT", - "HCA", - "HCA", - "HCA", - "HCA", - "HCA", - "HCAB", - "HCAB", - "HCAB", - "HCAB", - "HCB", - "HCB", - "HCB", - "HCBB", - "HCBB", - "HCBB", - "HCBB", - "HCO", - "HCO", - "HCO", - "HCO", - "HCO", - "HCO", - "HCO", - "HCP", - "HCP", - "HCR", - "HCR", - "HCR", - "HCRB", - "HCRB", - "HCRB", - "HCRB", - "HDL", - "HDL", - "HDL", - "HDL", - "HDL", - "HER", - "HER", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HES", - "HFE", - "HFE", - "HFE", - "HFE", - "HFE", - "HFH", - "HFH", - "HFM", - "HFM", - "HFM", - "HFM", - "HFM", - "HFM", - "HFP", - "HFP", - "HFP", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGS", - "HGW", - "HGW", - "HGW", - "HJG", - "HJG", - "HJG", - "HJG", - "HJG", - "HJS", - "HJS", - "HJS", - "HJS", - "HJS", - "HJS", - "HJS", - "HKR", - "HKR", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLB", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLO", - "HLP", - "HLP", - "HLP", - "HLP", - "HLP", - "HLP", - "HLT", - "HLT", - "HLT", - "HLT", - "HLT", - "HLT", - "HLT", - "HMO", - "HMO", - "HMO", - "HMO", - "HMO", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMOB", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMP", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HMS", - "HOR", - "HOR", - "HOR", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPC", - "HPH", - "HPH", - "HPH", - "HPH", - "HPH", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPL", - "HPR", - "HPR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HQR", - "HSF", - "HSF", - "HSF", - "HSF", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSFB", - "HSL", - "HSL", - "HSL", - "HSL", - "HSP", - "HSP", - "HSP", - "HTU", - "HTU", - "HTU", - "HTU", - "J001", - "J001", - "J001", - "J002", - "J002", - "J002", - "J003", - "J003", - "J003", - "J004", - "J004", - "J004", - "J005", - "J005", - "J005", - "J006", - "J006", - "J006", - "J007", - "J007", - "J007", - "J008", - "J008", - "J008", - "J009", - "J009", - "J009", - "J010", - "J010", - "J010", - "J011", - "J011", - "J011", - "J012", - "J012", - "J012", - "J013", - "J013", - "J013", - "J014", - "J014", - "J014", - "J015", - "J015", - "J015", - "J016", - "J016", - "J016", - "J017", - "J017", - "J017", - "J018", - "J018", - "J018", - "J019", - "J019", - "J019", - "J020", - "J020", - "J020", - "J021", - "J021", - "J021", - "J022", - "J022", - "J022", - "J023", - "J023", - "J023", - "J024", - "J024", - "J024", - "J025", - "J025", - "J025", - "J026", - "J026", - "J026", - "J027", - "J027", - "J027", - "J028", - "J028", - "J028", - "J029", - "J029", - "J029", - "J030", - "J030", - "J030", - "J031", - "J031", - "J031", - "J032", - "J032", - "J032", - "J033", - "J033", - "J033", - "J034", - "J034", - "J034", - "J035", - "J035", - "J035", - "J036", - "J036", - "J036", - "J037", - "J037", - "J037", - "J038", - "J038", - "J038", - "J039", - "J039", - "J039", - "J040", - "J040", - "J040", - "J041", - "J041", - "J041", - "J042", - "J042", - "J042", - "J043", - "J043", - "J043", - "J044", - "J044", - "J044", - "J045", - "J045", - "J045", - "J046", - "J046", - "J046", - "J047", - "J047", - "J047", - "J048", - "J048", - "J048", - "J048", - "J048", - "J048", - "J049", - "J049", - "J049", - "J050", - "J050", - "J050", - "J051", - "J051", - "J051", - "J052", - "J052", - "J052", - "J053", - "J053", - "J053", - "J054", - "J054", - "J054", - "J055", - "J055", - "J055", - "J056", - "J056", - "J056", - "J057", - "J057", - "J057", - "J058", - "J058", - "J058", - "J059", - "J059", - "J059", - "J060", - "J060", - "J060", - "J061", - "J061", - "J061", - "J062", - "J062", - "J062", - "J063", - "J063", - "J063", - "J064", - "J064", - "J064", - "J065", - "J065", - "J065", - "J066", - "J066", - "J066", - "J067", - "J067", - "J067", - "J068", - "J068", - "J068", - "JAL", - "JAL", - "JAL", - "JAR", - "JAR", - "JAR", - "JAR", - "JBC", - "JBC", - "JBC", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBG", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBL", - "JBM", - "JBM", - "JBM", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBMB", - "JBN", - "JBN", - "JBN", - "JBN", - "JBN", - "JBN", - "JBN", - "JBN", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBNB", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBR", - "JBZ", - "JBZ", - "JBZ", - "JCB", - "JCB", - "JCB", - "JCB", - "JCB", - "JCB", - "JCB", - "JCB", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCH", - "JCP", - "JCP", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JCPB", - "JEC", - "JEC", - "JEC", - "JEC", - "JEC", - "JECB", - "JECB", - "JECB", - "JECB", - "JECB", - "JECB", - "JECB", - "JECB", - "JEG", - "JEG", - "JEG", - "JEG", - "JEG", - "JEL", - "JEL", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JELB", - "JFP", - "JFP", - "JFP", - "JFP", - "JFP", - "JGR", - "JGR", - "JGR", - "JGR", - "JGR", - "JHL", - "JHL", - "JHL", - "JHP", - "JHP", - "JHP", - "JHU", - "JHU", - "JHU", - "JHU", - "JHU", - "JJO", - 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"KBN", - "KBN", - "KBN", - "KBN", - "KBN", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBO", - "KBR", - "KBR", - "KBR", - "KBS", - "KBS", - "KBS", - "KBS", - "KBS", - "KBS", - "KBS", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KBU", - "KCO", - "KCO", - "KCO", - "KCO", - "KCO", - "KCO", - "KCO", - "KCO", - "KCO", - "KCO", - "KCP", - "KCP", - "KCP", - "KCP", - "KCP", - "KCP", - "KCP", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCPB", - "KCR", - "KCR", - "KCR", - "KCR", - "KCR", - "KCR", - "KCR", - "KCR", - "KCR", - "KCR", - "KCR", - "KCR", - "KCS", - "KCS", - "KCS", - "KCS", - "KCS", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCSB", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KCT", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KEB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFB", - "KFP", - "KFP", - "KFP", - "KFP", - "KFP", - "KFP", - "KFP", - "KFP", - "KFP", - "KFP", - "KGM", - "KGM", - "KGM", - "KHB", - "KHB", - "KHB", - "KHB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - "KHBB", - 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"KKP", - "KKP", - "KKP", - "KMP", - "KMP", - "KMP", - "KMP", - "KMP", - "KMP", - "KMP", - "KMP", - "KMP", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMPB", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - "KMR", - 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"KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRMB", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KRP", - "KSC", - "KSC", - "KSC", - "KSC", - "KSM", - "KSM", - "KSM", - "KSM", - "KSM", - "KSM", - "KSM", - "KSP", - "KSP", - "KSP", - "KSX", - "KSX", - "KSX", - "KSX", - "KSX", - "KSX", - "KSX", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KSXB", - "KTD", - "KTD", - "KTD", - "KTD", - "KTD", - "KTD", - "KTR", - "KTR", - "KTR", - "KTR", - "L001", - "L001", - "L001", - "L002", - "L002", - "L002", - "L003", - "L003", - "L003", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAB", - "LAC", - "LAC", - "LAC", - "LAM", - "LAM", - "LAM", - "LAMB", - "LAS", - "LAS", - "LAS", - "LAS", - "LAS", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LASH", - "LBA", - "LBC", - "LBC", - "LBC", - "LBC", - "LBC", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBE", - "LBF", - "LBF", - "LBF", - "LBF", - "LBF", - "LBF", - "LBG", - "LBG", - "LBK", - "LBK", - "LBK", - "LBK", - "LBK", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBL", - "LBM", - "LBM", - "LBM", - "LBM", - "LBM", - "LBO", - "LBO", - "LBO", - "LBO", - "LBO", - "LBO", - "LBO", - "LBP", - "LBP", - "LBP", - "LBP", - "LBP", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBR", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LBU", - "LCA", - "LCA", - "LCF", - "LCF", - "LCF", - "LCF", - "LCF", - "LCF", - "LCF", - "LCF", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCM", - "LCS", - "LCS", - "LCS", - "LCSB", - "LCSB", - "LDB", - "LDB", - "LDB", - "LDB", - "LDB", - "LDB", - "LDB", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDH", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LDL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LEL", - "LGB", - "LGB", - "LGB", - "LGB", - "LGB", - "LGB", - "LGB", - "LGH", - "LGM", - "LGM", - "LGM", - "LGM", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGO", - "LGP", - "LGP", - "LGP", - "LGP", - "LGP", - "LGR", - "LGR", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LGY", - "LHC", - "LHC", - "LHC", - "LHC", - "LHC", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHE", - "LHH", - "LHK", - "LHK", - "LHK", - "LHM", - "LHM", - "LHM", - "LHO", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMC", - "LMD", - "LMD", - "LMD", - "LME", - "LME", - "LME", - "LME", - "LME", - "LME", - "LME", - "LME", - "LME", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMH", - "LMI", - "LMI", - "LMI", - "LMI", - "LMI", - "LMI", - "LMM", - "LMP", - "LMP", - "LMP", - "LMP", - "LMP", - "LMP", - "LMP", - "LMP", - "LMZ", - "LMZ", - "LMZ", - "LOM", - "LPD", - "LPD", - "LPD", - "LPG", - "LPG", - "LPG", - "LPG", - "LPK", - "LPK", - "LPK", - "LPK", - "LRB", - "LRB", - "LRB", - "LRB", - "LRCG", - "LRCG", - "LRCG", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRD", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRR", - "LRS", - "LRS", - "LRS", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSB", - "LSF", - "LSF", - "LSF", - "LSF", - "LSF", - "LSF", - "LSF", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSH", - "LSI", - "LSI", - "LSI", - "LSI", - "LSL", - "LSL", - "LSL", - "LSL", - "LSL", - "LSM", - "LSM", - "LSM", - "LSR", - "LSR", - "LSR", - "LSR", - "LSS", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTC", - "LTI", - "LTI", - "LTI", - "LTI", - "LTI", - "LTN", - "LTN", - "LTN", - "LVR", - "LVR", - "LVR", - "LVR", - "LWD", - "LWH", - "LWH", - "LWH", - "LWH", - "LWH", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "LWHB", - "MAT", - "MAT", - "MBE", - "MBE", - "MBE", - "MBO", - "MBO", - "MBO", - "MBO", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBS1", - "MBU", - "MBU", - "MBUB", - "MBUB", - "MBUB", - "MBUB", - "MBUB", - "MBUB", - "MBY", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCB", - "MCD", - "MCD", - "MCD", - "MCD", - "MCH", - "MCL", - "MCL", - "MCL", - "MCM", - "MCM", - "MCM", - "MCM", - "MCN", - "MCO", - "MCO", - "MCO", - "MCO", - "MCR", - "MCR", - "MCS", - "MCS", - "MCS", - "MCU", - "MCU", - "MCU", - "MCU", - "MCU", - "MCU", - "MCUB", - "MCUB", - "MCV", - "MCV", - "MCW", - "MCY", - "MCY", - "MCY", - "MCY", - "MCY", - "MCY", - "MDC", - "MDC", - "MDC", - "MDC", - "MDC", - "MDH", - "MDH", - "MDH", - "MDH", - "MDH", - "MDH", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDH1", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDP1", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDPB", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDR", - "MDW", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MDY", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MEM", - "MFB", - "MFB", - "MFB", - "MGP", - "MGP", - "MGP", - "MGP", - "MGP", - "MGPB", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MGS", - "MHD", - "MHD", - "MHD", - "MHD", - "MHD", - "MILG", - "MILG", - "MILG", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MINS", - "MLC", - "MLC", - "MLC", - "MLC", - "MLH", - "MLH", - "MLH", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLI", - "MLK", - "MLK", - "MLK", - "MLK", - "MLK", - "MLL", - "MLM", - "MLM", - "MLM", - "MLM", - "MLR", - "MLR", - "MLR", - "MMI", - "MMI", - "MMI", - "MMI", - "MMI", - "MMI", - "MML", - "MML", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMLB", - "MMP", - "MMP", - "MMP", - "MMS", - "MMS", - "MMS", - "MMS", - "MMT", - "MMT", - "MMW", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MMX1", - "MNH", - "MNH", - "MNH", - "MNHB", - "MNHB", - "MNHB", - "MNP", - "MNP", - "MNP", - "MNP", - "MOG", - "MOG", - "MOY", - "MOY", - "MOY", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPARD", - "MPR", - "MPR", - "MPR", - "MPR", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ1P", - "MQ2P", - "MQ2P", - "MQ2P", - "MQ2P", - "MQ2P", - "MQ2P", - "MQ3P", - "MQ3P", - "MQ3P", - "MQ3P", - "MQ3P", - "MQ3P", - "MRC", - "MRC", - "MRC", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRD", - "MRF", - "MRF", - "MRF", - "MRF", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRH", - "MRS", - "MRS", - "MRS", - "MSK", - "MSK", - "MSK", - "MSK", - "MSL", - "MSL", - "MSL", - "MSL", - "MSL", - "MSL", - "MST", - "MST", - "MST", - "MSV", - "MSV", - "MSVB", - "MSVB", - "MSVB", - "MSVB", - "MSVB", - "MSVB", - "MTC", - "MTC", - "MTC", - "MTC", - "MTC", - "MTC", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTL", - "MTU", - "MTU", - "MTU", - "MTU", - "MTU", - "MWB", - "MWB", - "MWB", - "MWB", - "MYL", - "MYL", - "MYL", - "MYM", - "N001", - "N001", - "N001", - "N002", - "N002", - "N002", - "N003", - "N003", - "N003", - "N004", - "N004", - "N004", - "N005", - "N005", - "N005", - "N006", - "N006", - "N006", - "N007", - "N007", - "N007", - "N008", - "N008", - "N008", - "N009", - "N009", - "N009", - "N010", - "N010", - "N010", - "N011", - "N011", - "N011", - "N012", - "N012", - "N012", - "N013", - "N013", - "N013", - "N014", - "N014", - "N014", - "N015", - "N015", - "N015", - "N016", - "N016", - "N016", - "N017", - "N017", - "N017", - "N018", - "N018", - "N018", - "N019", - "N019", - "N019", - "N019B", - "N019B", - "N019B", - "N020", - "N020", - "N020", - "N021", - "N021", - "N021", - "N022", - "N022", - "N022", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAD", - "NAP", - "NAP", - "NAP", - "NAPB", - "NAPB", - "NAPC", - "NAPC", - "NAPC", - "NAPC", - "NAPC", - "NAPC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBC", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBO", - "NBP", - "NBP", - "NBP", - "NBP", - "NBP", - "NBP", - "NBP", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBPB", - "NBR", - "NBR", - "NBR", - "NBRB", - "NBRB", - "NBRB", - "NBRB", - "NBRB", - "NCC", - "NCC", - "NCC", - "NCC", - "NCC", - "NCC", - "NCC", - "NCC", - "NCF", - "NCF", - "NCF", - "NCP", - "NCP", - "NCP", - "NCP", - "NCT", - "NCT", - "NCT", - "NCT", - "NDH", - "NDH", - "NDH", - "NEA", - "NEA", - "NEA", - "NEA", - "NEA", - "NEH", - "NEH", - "NEH", - "NEH", - "NEH", - "NEH", - "NFI", - "NFI", - "NFI", - "NFR", - "NFR", - "NFR", - "NFR", - "NFR", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFRB", - "NFV", - "NFV", - "NFV", - "NFV", - "NFV", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - "NFVB", - 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"cell_type": "markdown", - "id": "373751b7", - "metadata": {}, - "source": [ - "# 3: Manupulating Pandas \n", - "We can filter the data with the value taken by a given column:" + "data": { + "text/plain": [ + "0 AAR\n", + "1 AAR\n", + "2 AAR\n", + "3 AAS\n", + "4 AAS\n", + " ... \n", + "7151 WMP\n", + "7152 WMP\n", + "7153 WMP\n", + "7154 WMP\n", + "7155 WWVB\n", + "Name: station, Length: 7156, dtype: object" ] - }, + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[:, 0]" + ] + }, + { + "cell_type": "markdown", + "id": "a201c0e9", + "metadata": {}, + "source": [ + "The DataFrame may have bad values. A typical data cleaning involves removing Nan and Zeros for instance." + ] + }, + { + "cell_type": "markdown", + "id": "fc8219f0", + "metadata": {}, + "source": [ + "Use Plotly to map the stations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78c75b19", + "metadata": {}, + "outputs": [], + "source": [ + "data.dropna(inplace=True)\n", + "data=data[data.longitude!=0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f3533db", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "d28d4e95", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/html": [ - "
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3409KCPBNCBHE--50.02002/01/24,23:50:002002/10/16,23:59:0039.68631-123.582421261.00.00.090.0
3410KCPBNCBHE--20.02002/10/17,00:00:002006/01/24,18:00:0039.68631-123.582421261.00.00.090.0
3411KCPBNCBHN--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
..........................................
3480KCPBNCMNE--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.00.090.0
3481KCPBNCMNN--10.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
3482KCPBNCMNN--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.00.00.0
3483KCPBNCMNZ--10.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.0-90.00.0
3484KCPBNCMNZ--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.0-90.00.0
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78 rows × 13 columns

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" - ], - "text/plain": [ - " station network channel location rate start_time \\\n", - "3407 KCPB NC BHE -- 50.0 1999/08/03,00:00:00 \n", - "3408 KCPB NC BHE -- 50.0 2000/06/06,16:00:00 \n", - "3409 KCPB NC BHE -- 50.0 2002/01/24,23:50:00 \n", - "3410 KCPB NC BHE -- 20.0 2002/10/17,00:00:00 \n", - "3411 KCPB NC BHN -- 50.0 1999/08/03,00:00:00 \n", - "... ... ... ... ... ... ... \n", - "3480 KCPB NC MNE -- 10.0 2000/06/06,16:00:00 \n", - "3481 KCPB NC MNN -- 10.0 1999/08/03,00:00:00 \n", - "3482 KCPB NC MNN -- 10.0 2000/06/06,16:00:00 \n", - "3483 KCPB NC MNZ -- 10.0 1999/08/03,00:00:00 \n", - "3484 KCPB NC MNZ -- 10.0 2000/06/06,16:00:00 \n", - "\n", - " end_time latitude longitude elevation depth dip \\\n", - "3407 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", - "3408 2002/01/24,23:50:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", - "3409 2002/10/16,23:59:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", - "3410 2006/01/24,18:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", - "3411 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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
3407KCPBNCBHE--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.090.0
3408KCPBNCBHE--50.02000/06/06,16:00:002002/01/24,23:50:0039.68631-123.582421261.00.00.090.0
3409KCPBNCBHE--50.02002/01/24,23:50:002002/10/16,23:59:0039.68631-123.582421261.00.00.090.0
3410KCPBNCBHE--20.02002/10/17,00:00:002006/01/24,18:00:0039.68631-123.582421261.00.00.090.0
3411KCPBNCBHN--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
..........................................
3666KHBBNCLHZ--1.02016/04/28,16:56:002022/08/09,18:00:0040.65990-123.219661864.00.0-90.00.0
3667KHBBNCLHZ--1.02022/08/09,18:00:003000/01/01,00:00:0040.65990-123.219661864.00.0-90.00.0
3668KHBBNCLNE--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.00.090.0
3669KHBBNCLNN--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.00.00.0
3670KHBBNCLNZ--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.0-90.00.0
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3411KCPBNCBHN--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
..........................................
3666KHBBNCLHZ--1.02016/04/28,16:56:002022/08/09,18:00:0040.65990-123.219661864.00.0-90.00.0
3667KHBBNCLHZ--1.02022/08/09,18:00:003000/01/01,00:00:0040.65990-123.219661864.00.0-90.00.0
3668KHBBNCLNE--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.00.090.0
3669KHBBNCLNN--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.00.00.0
3670KHBBNCLNZ--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.0-90.00.0
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3407KCPBNCBHE--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.090.0
3408KCPBNCBHE--50.02000/06/06,16:00:002002/01/24,23:50:0039.68631-123.582421261.00.00.090.0
3409KCPBNCBHE--50.02002/01/24,23:50:002002/10/16,23:59:0039.68631-123.582421261.00.00.090.0
3410KCPBNCBHE--20.02002/10/17,00:00:002006/01/24,18:00:0039.68631-123.582421261.00.00.090.0
3411KCPBNCBHN--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
..........................................
3480KCPBNCMNE--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.00.090.0
3481KCPBNCMNN--10.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
3482KCPBNCMNN--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.00.00.0
3483KCPBNCMNZ--10.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.0-90.00.0
3484KCPBNCMNZ--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.0-90.00.0
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78 rows × 13 columns

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"execution_count": null, - "id": "51dd7dc7", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 7136.000000\n", - "mean 665.036617\n", - "std 670.177475\n", - "min -1388.000000\n", - "25% 164.000000\n", - "50% 446.000000\n", - "75% 925.000000\n", - "max 3680.000000\n", - "Name: elevation, dtype: float64" - ] + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.elevation.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "1aa0ad85", - "metadata": {}, - "source": [ - "We can perform standard operations on the whole data set:" - ] - }, + "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(data,\n", + " lat='latitude',lon='longitude', \n", + " range_color=(6,9),\n", + " height=600, width=600,\n", + " hover_name=\"station\",\n", + " hover_data=['network','station','channel','rate']);\n", + "fig.update_geos(resolution=110, showcountries=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "819da3ef", + "metadata": {}, + "outputs": [ { - 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"cell_type": "code", - "execution_count": null, - "id": "51c58c07", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['EHZ', 'SHZ', 'HHE', 'HHN', 'HHZ', 'HNE', 'HNN', 'HNZ', 'LHE',\n", - " 'LHN', 'LHZ', 'ACE', 'GAN', 'GEL', 'GLA', 'GLO', 'GNS', 'GPL',\n", - " 'LCE', 'LCQ', 'VCO', 'VDT', 'VEC', 'VEI', 'VPB', 'ELE', 'ELN',\n", - " 'ELZ', 'SLE', 'SLN', 'SLZ', 'LCL', 'LOG', 'OCF', 'VEA', 'VEP',\n", - " 'VFP', 'VKI', 'GST', 'EHE', 'EHN', 'BNE', 'BNN', 'BN1', 'BN2',\n", - " 'BN3', 'BV1', 'EP1', 'EP2', 'EP3', 'HDO', 'HN1', 'HN2', 'HN3',\n", - " 'HV1', 'SP2', 'SP3', 'BNZ', 'LDO', 'HJ2', 'HJ3', 'HJZ', 'SHE',\n", - " 'SHN', 'BHE', 'BHN', 'BHZ', 'LNE', 'LNN', 'LNZ', 'MHE', 'MHN',\n", - " 'MHZ', 'MNE', 'MNN', 'MNZ', 'HH2', 'HH3', 'LH2', 'LH3', 'SP1',\n", - " 'DP1', 'DP2', 'DP3', 'HLE', 'HLN', 'HLZ', 'XNE', 'XNN', 'XNZ',\n", - " 'EH1', 'VM1', 'VM2', 'VM3'], dtype=object)" - ] + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + 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"data.station.value_counts()" - ] - }, - { - "cell_type": "markdown", - "id": "8ff6d551", - "metadata": {}, - "source": [ - "The second option is to use the apply function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "610141ba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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7136 rows × 13 columns

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" - ], - "text/plain": [ - " station network channel location rate start_time \\\n", - "0 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", - "1 AAR NC EHZ -- 100.0 1987/05/01,00:00:00 \n", - "2 AAR NC SHZ -- 20.0 1994/11/28,00:00:00 \n", - "3 AAS NC EHZ -- 100.0 1984/11/27,18:45:00 \n", - "4 AAS NC EHZ -- 100.0 1987/05/01,00:00:00 \n", - "... ... ... ... ... ... ... \n", - "7150 WMP NC SHE -- 20.0 1995/07/02,12:00:00 \n", - "7151 WMP NC SHN -- 20.0 1995/07/02,12:00:00 \n", - "7152 WMP NC SHZ -- 20.0 1995/03/02,19:00:00 \n", - "7153 WMP NC SHZ -- 20.0 1995/07/02,12:00:00 \n", - "7154 WMP NC SHZ 10 20.0 1995/07/02,12:00:00 \n", - "\n", - " end_time latitude longitude elevation depth dip \\\n", - "0 1987/05/01,00:00:00 39.27594 -121.02696 154.125816 0.0 -90.0 \n", - "1 2006/01/04,19:19:00 39.27594 -121.02696 154.125816 0.0 -90.0 \n", - "2 2006/01/04,19:19:00 39.27594 -121.02696 154.125816 0.0 -90.0 \n", - "3 1987/05/01,00:00:00 38.43014 -121.10959 -725.874184 0.0 -90.0 \n", - "4 2021/08/13,16:50:00 38.43014 -121.10959 -725.874184 0.0 -90.0 \n", - "... ... ... ... ... ... ... \n", - "7150 2002/05/08,22:30:00 35.64059 -118.78570 321.125816 0.0 0.0 \n", - "7151 2002/05/08,22:30:00 35.64059 -118.78570 321.125816 0.0 0.0 \n", - "7152 1995/07/02,12:00:00 35.64059 -118.78570 321.125816 0.0 -90.0 \n", - "7153 2002/05/08,22:30:00 35.64059 -118.78570 321.125816 0.0 -90.0 \n", - "7154 1999/05/11,23:59:00 35.64059 -118.78570 321.125816 0.0 -90.0 \n", - "\n", - " azimuth \n", - "0 0.0 \n", - "1 0.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 \n", - "... ... \n", - "7150 90.0 \n", - "7151 0.0 \n", - "7152 0.0 \n", - "7153 0.0 \n", - "7154 0.0 \n", - "\n", - "[7136 rows x 13 columns]" - ] + "hoverlabel": { + "align": "left" }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_elevation_mean=data.elevation.unique().mean()\n", - "def remean_elevation(row):\n", - " row.elevation = row.elevation - data_elevation_mean\n", - " return row\n", - "data.apply(remean_elevation, axis='columns')" - ] - }, - { - "cell_type": "markdown", - "id": "4193d83c", - "metadata": {}, - "source": [ - "We can also carry out simple operations on columns, provided they make sense." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e02b7ee4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 NC - AAR\n", - "1 NC - AAR\n", - "2 NC - AAR\n", - "3 NC - AAS\n", - "4 NC - AAS\n", - " ... \n", - "7150 NC - WMP\n", - "7151 NC - WMP\n", - "7152 NC - WMP\n", - "7153 NC - WMP\n", - "7154 NC - WMP\n", - "Length: 7136, dtype: object" - ] + "hovermode": "closest", + "mapbox": { + "style": "light" }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.network + ' - ' + data.station" - ] - }, - { - "cell_type": "markdown", - "id": "c6e15985", - "metadata": {}, - "source": [ - "A useful feature is to group the rows depending on the value of a categorical variable, and then apply the same operation to all the groups. For instance, I want to know how many times each station appears in the file:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cdf44701", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "station\n", - "AAR 3\n", - "AAS 3\n", - "ABJ 4\n", - "ABR 3\n", - "ADW 3\n", - " ..\n", - "VCL 2\n", - "VRC 4\n", - "VSP 2\n", - "VWB 1\n", - "WMP 10\n", - "Name: station, Length: 905, dtype: int64" - ] + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.groupby('station').station.count()" - ] - }, - { - "cell_type": "markdown", - "id": "9ab5d024", - "metadata": {}, - "source": [ - "We can have access to the data type of each column:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1cc71f38", - "metadata": {}, - "outputs": [ - { - "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" - ] + "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" + } }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.dtypes" - ] - }, + "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": [ + "# 3: Manupulating Pandas \n", + "We can filter the data with the value taken by a given column:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d28d4e95", + "metadata": {}, + "outputs": [ { - "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. 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3480KCPBNCMNE--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.00.090.0
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78 rows × 13 columns

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" + ], + "text/plain": [ + " station network channel location rate start_time \\\n", + "3407 KCPB NC BHE -- 50.0 1999/08/03,00:00:00 \n", + "3408 KCPB NC BHE -- 50.0 2000/06/06,16:00:00 \n", + "3409 KCPB NC BHE -- 50.0 2002/01/24,23:50:00 \n", + "3410 KCPB NC BHE -- 20.0 2002/10/17,00:00:00 \n", + "3411 KCPB NC BHN -- 50.0 1999/08/03,00:00:00 \n", + "... ... ... ... ... ... ... \n", + "3480 KCPB NC MNE -- 10.0 2000/06/06,16:00:00 \n", + "3481 KCPB NC MNN -- 10.0 1999/08/03,00:00:00 \n", + "3482 KCPB NC MNN -- 10.0 2000/06/06,16:00:00 \n", + "3483 KCPB NC MNZ -- 10.0 1999/08/03,00:00:00 \n", + "3484 KCPB NC MNZ -- 10.0 2000/06/06,16:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip \\\n", + "3407 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3408 2002/01/24,23:50:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3409 2002/10/16,23:59:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3410 2006/01/24,18:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3411 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "... ... ... ... ... ... ... \n", + "3480 2000/07/12,00:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3481 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3482 2000/07/12,00:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3483 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "3484 2000/07/12,00:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "\n", + " azimuth \n", + "3407 90.0 \n", + "3408 90.0 \n", + "3409 90.0 \n", + "3410 90.0 \n", + "3411 0.0 \n", + "... ... \n", + "3480 90.0 \n", + "3481 0.0 \n", + "3482 0.0 \n", + "3483 0.0 \n", + "3484 0.0 \n", + "\n", + "[78 rows x 13 columns]" ] - }, + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.loc[data.station=='KCPB']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb5561d5", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "02663a77", - "metadata": {}, - "outputs": [], - "source": [ - "data.start_time.values()" + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
3407KCPBNCBHE--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.090.0
3408KCPBNCBHE--50.02000/06/06,16:00:002002/01/24,23:50:0039.68631-123.582421261.00.00.090.0
3409KCPBNCBHE--50.02002/01/24,23:50:002002/10/16,23:59:0039.68631-123.582421261.00.00.090.0
3410KCPBNCBHE--20.02002/10/17,00:00:002006/01/24,18:00:0039.68631-123.582421261.00.00.090.0
3411KCPBNCBHN--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
..........................................
3666KHBBNCLHZ--1.02016/04/28,16:56:002022/08/09,18:00:0040.65990-123.219661864.00.0-90.00.0
3667KHBBNCLHZ--1.02022/08/09,18:00:003000/01/01,00:00:0040.65990-123.219661864.00.0-90.00.0
3668KHBBNCLNE--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.00.090.0
3669KHBBNCLNN--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.00.00.0
3670KHBBNCLNZ--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.0-90.00.0
\n", + "

123 rows × 13 columns

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" + ], + "text/plain": [ + " station network channel location rate start_time \\\n", + "3407 KCPB NC BHE -- 50.0 1999/08/03,00:00:00 \n", + "3408 KCPB NC BHE -- 50.0 2000/06/06,16:00:00 \n", + "3409 KCPB NC BHE -- 50.0 2002/01/24,23:50:00 \n", + "3410 KCPB NC BHE -- 20.0 2002/10/17,00:00:00 \n", + "3411 KCPB NC BHN -- 50.0 1999/08/03,00:00:00 \n", + "... ... ... ... ... ... ... \n", + "3666 KHBB NC LHZ -- 1.0 2016/04/28,16:56:00 \n", + "3667 KHBB NC LHZ -- 1.0 2022/08/09,18:00:00 \n", + "3668 KHBB NC LNE -- 1.0 2015/10/29,21:18:00 \n", + "3669 KHBB NC LNN -- 1.0 2015/10/29,21:18:00 \n", + "3670 KHBB NC LNZ -- 1.0 2015/10/29,21:18:00 \n", + "\n", + " end_time latitude longitude elevation depth dip \\\n", + "3407 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3408 2002/01/24,23:50:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3409 2002/10/16,23:59:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3410 2006/01/24,18:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3411 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "... ... ... ... ... ... ... \n", + "3666 2022/08/09,18:00:00 40.65990 -123.21966 1864.0 0.0 -90.0 \n", + "3667 3000/01/01,00:00:00 40.65990 -123.21966 1864.0 0.0 -90.0 \n", + "3668 2016/04/28,16:56:00 40.65990 -123.21966 1864.0 0.0 0.0 \n", + "3669 2016/04/28,16:56:00 40.65990 -123.21966 1864.0 0.0 0.0 \n", + "3670 2016/04/28,16:56:00 40.65990 -123.21966 1864.0 0.0 -90.0 \n", + "\n", + " azimuth \n", + "3407 90.0 \n", + "3408 90.0 \n", + "3409 90.0 \n", + "3410 90.0 \n", + "3411 0.0 \n", + "... ... \n", + "3666 0.0 \n", + "3667 0.0 \n", + "3668 90.0 \n", + "3669 0.0 \n", + "3670 0.0 \n", + "\n", + "[123 rows x 13 columns]" ] - }, + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select two stations, use the typical \"OR\" |\n", + "data.loc[(data.station=='KCPB') | (data.station=='KHBB')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebba2b90", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "62d6937c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
3457KCPBNCHNZ--100.02002/10/17,00:00:002006/10/18,00:08:0039.68631-123.582421261.00.0-90.00.0
3458KCPBNCHNZ--100.02006/10/18,00:08:002010/11/01,22:00:0039.68631-123.582421261.00.0-90.00.0
3459KCPBNCHNZ--100.02010/11/01,22:00:002011/07/13,00:00:0039.68631-123.582421261.00.0-90.00.0
3460KCPBNCHNZ--100.02011/07/13,00:00:002011/09/07,19:00:0039.68631-123.582421261.00.0-90.00.0
3461KCPBNCHNZ--100.02011/09/07,19:00:002015/10/29,18:00:0039.68631-123.582421261.00.0-90.00.0
3462KCPBNCHNZ--100.02015/10/29,18:00:002016/04/28,16:39:0039.68631-123.582421261.00.0-90.00.0
3463KCPBNCHNZ--100.02016/04/28,16:39:003000/01/01,00:00:0039.68631-123.582421261.00.0-90.00.0
\n", + "
" ], - "source": [ - "type(data['start_time'][0])" + "text/plain": [ + " station network channel location rate start_time \\\n", + "3457 KCPB NC HNZ -- 100.0 2002/10/17,00:00:00 \n", + "3458 KCPB NC HNZ -- 100.0 2006/10/18,00:08:00 \n", + "3459 KCPB NC HNZ -- 100.0 2010/11/01,22:00:00 \n", + "3460 KCPB NC HNZ -- 100.0 2011/07/13,00:00:00 \n", + "3461 KCPB NC HNZ -- 100.0 2011/09/07,19:00:00 \n", + "3462 KCPB NC HNZ -- 100.0 2015/10/29,18:00:00 \n", + "3463 KCPB NC HNZ -- 100.0 2016/04/28,16:39:00 \n", + "\n", + " end_time latitude longitude elevation depth dip \\\n", + "3457 2006/10/18,00:08:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "3458 2010/11/01,22:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "3459 2011/07/13,00:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "3460 2011/09/07,19:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "3461 2015/10/29,18:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "3462 2016/04/28,16:39:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "3463 3000/01/01,00:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "\n", + " azimuth \n", + "3457 0.0 \n", + "3458 0.0 \n", + "3459 0.0 \n", + "3460 0.0 \n", + "3461 0.0 \n", + "3462 0.0 \n", + "3463 0.0 " ] - }, + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select two stations, use the typical \"AND\" &\n", + "data.loc[(data.station=='KCPB') & (data.channel=='HNZ')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "561aecf9", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "afaf1851", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1984-01-01 00:00:00\n", - "1 1987-05-01 00:00:00\n", - "2 1994-11-28 00:00:00\n", - "3 1984-11-27 18:45:00\n", - "4 1987-05-01 00:00:00\n", - " ... \n", - "7150 1995-07-02 12:00:00\n", - "7151 1995-07-02 12:00:00\n", - "7152 1995-03-02 19:00:00\n", - "7153 1995-07-02 12:00:00\n", - "7154 1995-07-02 12:00:00\n", - "Name: start_time, Length: 7136, dtype: datetime64[ns]\n" - ] - }, - { - "data": { - "text/plain": [ - "pandas._libs.tslibs.timestamps.Timestamp" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
3407KCPBNCBHE--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.090.0
3408KCPBNCBHE--50.02000/06/06,16:00:002002/01/24,23:50:0039.68631-123.582421261.00.00.090.0
3409KCPBNCBHE--50.02002/01/24,23:50:002002/10/16,23:59:0039.68631-123.582421261.00.00.090.0
3410KCPBNCBHE--20.02002/10/17,00:00:002006/01/24,18:00:0039.68631-123.582421261.00.00.090.0
3411KCPBNCBHN--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
..........................................
3666KHBBNCLHZ--1.02016/04/28,16:56:002022/08/09,18:00:0040.65990-123.219661864.00.0-90.00.0
3667KHBBNCLHZ--1.02022/08/09,18:00:003000/01/01,00:00:0040.65990-123.219661864.00.0-90.00.0
3668KHBBNCLNE--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.00.090.0
3669KHBBNCLNN--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.00.00.0
3670KHBBNCLNZ--1.02015/10/29,21:18:002016/04/28,16:56:0040.65990-123.219661864.00.0-90.00.0
\n", + "

123 rows × 13 columns

\n", + "
" ], - "source": [ - "# Transform column from string into datetime format\n", - "startdate = pd.to_datetime(data['start_time'], format='%Y/%m/%d,%H:%M:%S')\n", - "data['start_time'] = startdate\n", - "print(data['start_time'] )\n", - "type(data['start_time'][0])" + "text/plain": [ + " station network channel location rate start_time \\\n", + "3407 KCPB NC BHE -- 50.0 1999/08/03,00:00:00 \n", + "3408 KCPB NC BHE -- 50.0 2000/06/06,16:00:00 \n", + "3409 KCPB NC BHE -- 50.0 2002/01/24,23:50:00 \n", + "3410 KCPB NC BHE -- 20.0 2002/10/17,00:00:00 \n", + "3411 KCPB NC BHN -- 50.0 1999/08/03,00:00:00 \n", + "... ... ... ... ... ... ... \n", + "3666 KHBB NC LHZ -- 1.0 2016/04/28,16:56:00 \n", + "3667 KHBB NC LHZ -- 1.0 2022/08/09,18:00:00 \n", + "3668 KHBB NC LNE -- 1.0 2015/10/29,21:18:00 \n", + "3669 KHBB NC LNN -- 1.0 2015/10/29,21:18:00 \n", + "3670 KHBB NC LNZ -- 1.0 2015/10/29,21:18:00 \n", + "\n", + " end_time latitude longitude elevation depth dip \\\n", + "3407 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3408 2002/01/24,23:50:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3409 2002/10/16,23:59:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3410 2006/01/24,18:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3411 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "... ... ... ... ... ... ... \n", + "3666 2022/08/09,18:00:00 40.65990 -123.21966 1864.0 0.0 -90.0 \n", + "3667 3000/01/01,00:00:00 40.65990 -123.21966 1864.0 0.0 -90.0 \n", + "3668 2016/04/28,16:56:00 40.65990 -123.21966 1864.0 0.0 0.0 \n", + "3669 2016/04/28,16:56:00 40.65990 -123.21966 1864.0 0.0 0.0 \n", + "3670 2016/04/28,16:56:00 40.65990 -123.21966 1864.0 0.0 -90.0 \n", + "\n", + " azimuth \n", + "3407 90.0 \n", + "3408 90.0 \n", + "3409 90.0 \n", + "3410 90.0 \n", + "3411 0.0 \n", + "... ... \n", + "3666 0.0 \n", + "3667 0.0 \n", + "3668 90.0 \n", + "3669 0.0 \n", + "3670 0.0 \n", + "\n", + "[123 rows x 13 columns]" ] - }, + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# or like this\n", + "data.loc[data.station.isin(['KCPB', 'KHBB'])]" + ] + }, + { + "cell_type": "markdown", + "id": "590a68f6", + "metadata": {}, + "source": [ + "We can filter the data with the value taken by a given column:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f75f626c", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "78278bab", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1987/05/01,00:00:00\n", - "1 2006/01/04,19:19:00\n", - "2 2006/01/04,19:19:00\n", - "3 1987/05/01,00:00:00\n", - "4 2021/08/13,16:50:00\n", - " ... \n", - "7150 2002/05/08,22:30:00\n", - "7151 2002/05/08,22:30:00\n", - "7152 1995/07/02,12:00:00\n", - "7153 2002/05/08,22:30:00\n", - "7154 1999/05/11,23:59:00\n", - "Name: end_time, Length: 7136, dtype: object\n" - ] - } + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
3407KCPBNCBHE--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.090.0
3408KCPBNCBHE--50.02000/06/06,16:00:002002/01/24,23:50:0039.68631-123.582421261.00.00.090.0
3409KCPBNCBHE--50.02002/01/24,23:50:002002/10/16,23:59:0039.68631-123.582421261.00.00.090.0
3410KCPBNCBHE--20.02002/10/17,00:00:002006/01/24,18:00:0039.68631-123.582421261.00.00.090.0
3411KCPBNCBHN--50.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
..........................................
3480KCPBNCMNE--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.00.090.0
3481KCPBNCMNN--10.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.00.00.0
3482KCPBNCMNN--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.00.00.0
3483KCPBNCMNZ--10.01999/08/03,00:00:002000/06/06,16:00:0039.68631-123.582421261.00.0-90.00.0
3484KCPBNCMNZ--10.02000/06/06,16:00:002000/07/12,00:00:0039.68631-123.582421261.00.0-90.00.0
\n", + "

78 rows × 13 columns

\n", + "
" ], - "source": [ - "print(data['end_time'])" + "text/plain": [ + " station network channel location rate start_time \\\n", + "3407 KCPB NC BHE -- 50.0 1999/08/03,00:00:00 \n", + "3408 KCPB NC BHE -- 50.0 2000/06/06,16:00:00 \n", + "3409 KCPB NC BHE -- 50.0 2002/01/24,23:50:00 \n", + "3410 KCPB NC BHE -- 20.0 2002/10/17,00:00:00 \n", + "3411 KCPB NC BHN -- 50.0 1999/08/03,00:00:00 \n", + "... ... ... ... ... ... ... \n", + "3480 KCPB NC MNE -- 10.0 2000/06/06,16:00:00 \n", + "3481 KCPB NC MNN -- 10.0 1999/08/03,00:00:00 \n", + "3482 KCPB NC MNN -- 10.0 2000/06/06,16:00:00 \n", + "3483 KCPB NC MNZ -- 10.0 1999/08/03,00:00:00 \n", + "3484 KCPB NC MNZ -- 10.0 2000/06/06,16:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip \\\n", + "3407 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3408 2002/01/24,23:50:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3409 2002/10/16,23:59:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3410 2006/01/24,18:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3411 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "... ... ... ... ... ... ... \n", + "3480 2000/07/12,00:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3481 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3482 2000/07/12,00:00:00 39.68631 -123.58242 1261.0 0.0 0.0 \n", + "3483 2000/06/06,16:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "3484 2000/07/12,00:00:00 39.68631 -123.58242 1261.0 0.0 -90.0 \n", + "\n", + " azimuth \n", + "3407 90.0 \n", + "3408 90.0 \n", + "3409 90.0 \n", + "3410 90.0 \n", + "3411 0.0 \n", + "... ... \n", + "3480 90.0 \n", + "3481 0.0 \n", + "3482 0.0 \n", + "3483 0.0 \n", + "3484 0.0 \n", + "\n", + "[78 rows x 13 columns]" ] - }, + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.loc[data.station=='KCPB']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c7707667", + "metadata": {}, + "source": [ + "We can access a brief summary of the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4a33d52", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "bae00773", - "metadata": {}, - "outputs": [], - "source": [ - "# do the same for end times\n", - "# Avoid 'OutOfBoundsDatetime' error with year 3000\n", - "enddate = data['end_time'].str.replace('3000', '2025')\n", - "enddate = pd.to_datetime(enddate, format='%Y/%m/%d,%H:%M:%S')\n", - "data['end_time'] = enddate" + "data": { + "text/plain": [ + "count 7136\n", + "unique 905\n", + "top KCPB\n", + "freq 78\n", + "Name: station, dtype: object" ] - }, + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.station.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51dd7dc7", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "f00ce758", - "metadata": {}, - "source": [ - "We can now look when each seismic station was installed:" + "data": { + "text/plain": [ + "count 7136.000000\n", + "mean 665.036617\n", + "std 670.177475\n", + "min -1388.000000\n", + "25% 164.000000\n", + "50% 446.000000\n", + "75% 925.000000\n", + "max 3680.000000\n", + "Name: elevation, dtype: float64" ] - }, + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.elevation.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "1aa0ad85", + "metadata": {}, + "source": [ + "We can perform standard operations on the whole data set:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b787869", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "a0cc2a86", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "station\n", - "AAR 1984-01-01 00:00:00\n", - "AAS 1984-11-27 18:45:00\n", - "ABJ 1984-01-01 00:00:00\n", - "ABR 1984-01-01 00:00:00\n", - "ADW 1984-01-01 00:00:00\n", - " ... \n", - "VCL 1984-01-01 00:00:00\n", - "VRC 1993-09-23 22:20:00\n", - "VSP 1993-09-24 22:05:00\n", - "VWB 1985-01-01 00:00:00\n", - "WMP 1995-03-02 19:00:00\n", - "Length: 905, dtype: datetime64[ns]" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.groupby('station').apply(lambda df: df.start_time.min())" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\otodo\\AppData\\Local\\Temp\\ipykernel_2672\\531903386.py:1: FutureWarning:\n", + "\n", + "The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n", + "\n" + ] }, { - "cell_type": "markdown", - "id": "f1d61170", - "metadata": {}, - "source": [ - "The ``agg`` function allows to carry out several operations to each group of rows:" + "data": { + "text/plain": [ + "rate 87.692383\n", + "latitude 38.053684\n", + "longitude -121.828840\n", + "elevation 665.036617\n", + "depth 15.064938\n", + "dip -41.758688\n", + "azimuth 17.735987\n", + "dtype: float64" ] - }, + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.mean()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f5d93eb9", + "metadata": {}, + "source": [ + "In the case of a categorical variable, we can get the list of possible values that this variable can take:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51c58c07", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "fd80a620", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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minmax
station
AAR911.0911.0
AAS31.031.0
ABJ434.0434.0
ABR-1.0-1.0
ADW228.0228.0
.........
VCL2048.02048.0
VRC1666.01666.0
VSP1545.01545.0
VWB1736.01736.0
WMP1078.01078.0
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905 rows × 2 columns

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" - ], - "text/plain": [ - " min max\n", - "station \n", - "AAR 911.0 911.0\n", - "AAS 31.0 31.0\n", - "ABJ 434.0 434.0\n", - "ABR -1.0 -1.0\n", - "ADW 228.0 228.0\n", - "... ... ...\n", - "VCL 2048.0 2048.0\n", - "VRC 1666.0 1666.0\n", - "VSP 1545.0 1545.0\n", - "VWB 1736.0 1736.0\n", - "WMP 1078.0 1078.0\n", - "\n", - "[905 rows x 2 columns]" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.groupby(['station']).elevation.agg(['min', 'max'])" + "data": { + "text/plain": [ + "array(['EHZ', 'SHZ', 'HHE', 'HHN', 'HHZ', 'HNE', 'HNN', 'HNZ', 'LHE',\n", + " 'LHN', 'LHZ', 'ACE', 'GAN', 'GEL', 'GLA', 'GLO', 'GNS', 'GPL',\n", + " 'LCE', 'LCQ', 'VCO', 'VDT', 'VEC', 'VEI', 'VPB', 'ELE', 'ELN',\n", + " 'ELZ', 'SLE', 'SLN', 'SLZ', 'LCL', 'LOG', 'OCF', 'VEA', 'VEP',\n", + " 'VFP', 'VKI', 'GST', 'EHE', 'EHN', 'BNE', 'BNN', 'BN1', 'BN2',\n", + " 'BN3', 'BV1', 'EP1', 'EP2', 'EP3', 'HDO', 'HN1', 'HN2', 'HN3',\n", + " 'HV1', 'SP2', 'SP3', 'BNZ', 'LDO', 'HJ2', 'HJ3', 'HJZ', 'SHE',\n", + " 'SHN', 'BHE', 'BHN', 'BHZ', 'LNE', 'LNN', 'LNZ', 'MHE', 'MHN',\n", + " 'MHZ', 'MNE', 'MNN', 'MNZ', 'HH2', 'HH3', 'LH2', 'LH3', 'SP1',\n", + " 'DP1', 'DP2', 'DP3', 'HLE', 'HLN', 'HLZ', 'XNE', 'XNN', 'XNZ',\n", + " 'EH1', 'VM1', 'VM2', 'VM3'], dtype=object)" ] - }, + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.channel.unique()" + ] + }, + { + "cell_type": "markdown", + "id": "bf4ec82e", + "metadata": {}, + "source": [ + "and get the number of times that each value is taken:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "031731dd", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "5183304a", - "metadata": {}, - "source": [ - "Select the stations that were deployed first and recovered last" + "data": { + "text/plain": [ + "KCPB 78\n", + "JSGB 73\n", + "KMPB 72\n", + "KHMB 63\n", + "CCH1 60\n", + " ..\n", + "LAMB 1\n", + "LBA 1\n", + "PBY 1\n", + "MCW 1\n", + "MMW 1\n", + "Name: station, Length: 905, dtype: int64" ] - }, + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.station.value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "8ff6d551", + "metadata": {}, + "source": [ + "The second option is to use the apply function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "610141ba", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "7e7b38a0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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start_timeend_time
station
AAR1984-01-01 00:00:002006-01-04 19:19:00
AAS1984-11-27 18:45:002021-08-13 16:50:00
ABJ1984-01-01 00:00:002019-06-26 19:17:00
ABR1984-01-01 00:00:001997-08-04 21:02:00
ADW1984-01-01 00:00:002006-04-20 01:08:00
.........
VCL1984-01-01 00:00:001985-03-25 21:30:00
VRC1993-09-23 22:20:002001-07-12 16:50:00
VSP1993-09-24 22:05:002001-07-12 16:50:00
VWB1985-01-01 00:00:001985-03-25 19:00:00
WMP1995-03-02 19:00:002002-05-08 22:30:00
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905 rows × 2 columns

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3AASNCEHZ--100.01984/11/27,18:45:001987/05/01,00:00:0038.43014-121.10959-725.8741840.0-90.00.0
4AASNCEHZ--100.01987/05/01,00:00:002021/08/13,16:50:0038.43014-121.10959-725.8741840.0-90.00.0
..........................................
7150WMPNCSHE--20.01995/07/02,12:00:002002/05/08,22:30:0035.64059-118.78570321.1258160.00.090.0
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7154WMPNCSHZ1020.01995/07/02,12:00:001999/05/11,23:59:0035.64059-118.78570321.1258160.0-90.00.0
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7136 rows × 13 columns

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" ], - "source": [ - "data.groupby(['station']).agg({'start_time':lambda x: min(x), 'end_time':lambda x: max(x)})" + "text/plain": [ + " station network channel location rate start_time \\\n", + "0 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", + "1 AAR NC EHZ -- 100.0 1987/05/01,00:00:00 \n", + "2 AAR NC SHZ -- 20.0 1994/11/28,00:00:00 \n", + "3 AAS NC EHZ -- 100.0 1984/11/27,18:45:00 \n", + "4 AAS NC EHZ -- 100.0 1987/05/01,00:00:00 \n", + "... ... ... ... ... ... ... \n", + "7150 WMP NC SHE -- 20.0 1995/07/02,12:00:00 \n", + "7151 WMP NC SHN -- 20.0 1995/07/02,12:00:00 \n", + "7152 WMP NC SHZ -- 20.0 1995/03/02,19:00:00 \n", + "7153 WMP NC SHZ -- 20.0 1995/07/02,12:00:00 \n", + "7154 WMP NC SHZ 10 20.0 1995/07/02,12:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip \\\n", + "0 1987/05/01,00:00:00 39.27594 -121.02696 154.125816 0.0 -90.0 \n", + "1 2006/01/04,19:19:00 39.27594 -121.02696 154.125816 0.0 -90.0 \n", + "2 2006/01/04,19:19:00 39.27594 -121.02696 154.125816 0.0 -90.0 \n", + "3 1987/05/01,00:00:00 38.43014 -121.10959 -725.874184 0.0 -90.0 \n", + "4 2021/08/13,16:50:00 38.43014 -121.10959 -725.874184 0.0 -90.0 \n", + "... ... ... ... ... ... ... \n", + "7150 2002/05/08,22:30:00 35.64059 -118.78570 321.125816 0.0 0.0 \n", + "7151 2002/05/08,22:30:00 35.64059 -118.78570 321.125816 0.0 0.0 \n", + "7152 1995/07/02,12:00:00 35.64059 -118.78570 321.125816 0.0 -90.0 \n", + "7153 2002/05/08,22:30:00 35.64059 -118.78570 321.125816 0.0 -90.0 \n", + "7154 1999/05/11,23:59:00 35.64059 -118.78570 321.125816 0.0 -90.0 \n", + "\n", + " azimuth \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "7150 90.0 \n", + "7151 0.0 \n", + "7152 0.0 \n", + "7153 0.0 \n", + "7154 0.0 \n", + "\n", + "[7136 rows x 13 columns]" ] - }, + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_elevation_mean=data.elevation.unique().mean()\n", + "def remean_elevation(row):\n", + " row.elevation = row.elevation - data_elevation_mean\n", + " return row\n", + "data.apply(remean_elevation, axis='columns')" + ] + }, + { + "cell_type": "markdown", + "id": "4193d83c", + "metadata": {}, + "source": [ + "We can also carry out simple operations on columns, provided they make sense." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e02b7ee4", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "28ec3c28", - "metadata": {}, - "source": [ - "We can also make groups by selecting the values of two categorical variables:" + "data": { + "text/plain": [ + "0 NC - AAR\n", + "1 NC - AAR\n", + "2 NC - AAR\n", + "3 NC - AAS\n", + "4 NC - AAS\n", + " ... \n", + "7150 NC - WMP\n", + "7151 NC - WMP\n", + "7152 NC - WMP\n", + "7153 NC - WMP\n", + "7154 NC - WMP\n", + "Length: 7136, dtype: object" ] - }, + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.network + ' - ' + data.station" + ] + }, + { + "cell_type": "markdown", + "id": "c6e15985", + "metadata": {}, + "source": [ + "A useful feature is to group the rows depending on the value of a categorical variable, and then apply the same operation to all the groups. For instance, I want to know how many times each station appears in the file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdf44701", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "b5aacd39", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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............
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" - ], - "text/plain": [ - " start_time end_time\n", - "station channel \n", - "AAR EHZ 1984-01-01 00:00:00 2006-01-04 19:19:00\n", - " SHZ 1994-11-28 00:00:00 2006-01-04 19:19:00\n", - "AAS EHZ 1984-11-27 18:45:00 2021-08-13 16:50:00\n", - " SHZ 1994-11-28 00:00:00 2006-01-24 18:00:00\n", - "ABJ EHZ 1984-01-01 00:00:00 2019-06-26 19:17:00" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_grouped.head()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1984-01-01 00:00:00\n", + "1 1987-05-01 00:00:00\n", + "2 1994-11-28 00:00:00\n", + "3 1984-11-27 18:45:00\n", + "4 1987-05-01 00:00:00\n", + " ... \n", + "7150 1995-07-02 12:00:00\n", + "7151 1995-07-02 12:00:00\n", + "7152 1995-03-02 19:00:00\n", + "7153 1995-07-02 12:00:00\n", + "7154 1995-07-02 12:00:00\n", + "Name: start_time, Length: 7136, dtype: datetime64[ns]\n" + ] }, { - "cell_type": "markdown", - "id": "755f691b", - "metadata": {}, - "source": [ - "When we select only some rows, the index is not automatically reset to start at 0. We can do it manually. Many functions in pandas have also an option to reset the index, and option to transform the dataframe in place, instead of saving the results in another variable." + "data": { + "text/plain": [ + "pandas._libs.tslibs.timestamps.Timestamp" ] - }, + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Transform column from string into datetime format\n", + "startdate = pd.to_datetime(data['start_time'], format='%Y/%m/%d,%H:%M:%S')\n", + "data['start_time'] = startdate\n", + "print(data['start_time'] )\n", + "type(data['start_time'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78278bab", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "e41c6f08", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stationchannelstart_timeend_time
0AAREHZ1984-01-01 00:00:002006-01-04 19:19:00
1AARSHZ1994-11-28 00:00:002006-01-04 19:19:00
2AASEHZ1984-11-27 18:45:002021-08-13 16:50:00
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4ABJEHZ1984-01-01 00:00:002019-06-26 19:17:00
...............
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start_timeend_time
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AAREHZ1984-01-012006-01-04 19:19:00
LWHEHZ1984-01-012020-03-17 20:50:00
LWDEHZ1984-01-011986-09-25 21:30:00
LTNEHZ1984-01-011988-07-26 23:50:00
............
BTIVCO2022-09-282025-01-01 00:00:00
VDT2022-09-282025-01-01 00:00:00
VEC2022-09-282025-01-01 00:00:00
GEL2022-09-282025-01-01 00:00:00
VPB2022-09-282025-01-01 00:00:00
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" ], - "source": [ - "data_grouped.sort_values(by='start_time')" + "text/plain": [ + " min max\n", + "station \n", + "AAR 911.0 911.0\n", + "AAS 31.0 31.0\n", + "ABJ 434.0 434.0\n", + "ABR -1.0 -1.0\n", + "ADW 228.0 228.0\n", + "... ... ...\n", + "VCL 2048.0 2048.0\n", + "VRC 1666.0 1666.0\n", + "VSP 1545.0 1545.0\n", + "VWB 1736.0 1736.0\n", + "WMP 1078.0 1078.0\n", + "\n", + "[905 rows x 2 columns]" ] - }, + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['station']).elevation.agg(['min', 'max'])" + ] + }, + { + "cell_type": "markdown", + "id": "5183304a", + "metadata": {}, + "source": [ + "Select the stations that were deployed first and recovered last" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e7b38a0", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "29c0b9b5", - "metadata": {}, - "source": [ - "We can apply the sorting to several columns:" + "data": { + "text/html": [ + "
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AAS1984-11-27 18:45:002021-08-13 16:50:00
ABJ1984-01-01 00:00:002019-06-26 19:17:00
ABR1984-01-01 00:00:001997-08-04 21:02:00
ADW1984-01-01 00:00:002006-04-20 01:08:00
.........
VCL1984-01-01 00:00:001985-03-25 21:30:00
VRC1993-09-23 22:20:002001-07-12 16:50:00
VSP1993-09-24 22:05:002001-07-12 16:50:00
VWB1985-01-01 00:00:001985-03-25 19:00:00
WMP1995-03-02 19:00:002002-05-08 22:30:00
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" + ], + "text/plain": [ + " start_time end_time\n", + "station \n", + "AAR 1984-01-01 00:00:00 2006-01-04 19:19:00\n", + "AAS 1984-11-27 18:45:00 2021-08-13 16:50:00\n", + "ABJ 1984-01-01 00:00:00 2019-06-26 19:17:00\n", + "ABR 1984-01-01 00:00:00 1997-08-04 21:02:00\n", + "ADW 1984-01-01 00:00:00 2006-04-20 01:08:00\n", + "... ... ...\n", + "VCL 1984-01-01 00:00:00 1985-03-25 21:30:00\n", + "VRC 1993-09-23 22:20:00 2001-07-12 16:50:00\n", + "VSP 1993-09-24 22:05:00 2001-07-12 16:50:00\n", + "VWB 1985-01-01 00:00:00 1985-03-25 19:00:00\n", + "WMP 1995-03-02 19:00:00 2002-05-08 22:30:00\n", + "\n", + "[905 rows x 2 columns]" ] - }, + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['station']).agg({'start_time':lambda x: min(x), 'end_time':lambda x: max(x)})" + ] + }, + { + "cell_type": "markdown", + "id": "28ec3c28", + "metadata": {}, + "source": [ + "We can also make groups by selecting the values of two categorical variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5aacd39", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "5cf427b0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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start_timeend_time
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PG1HV11983-06-271993-11-10 18:00:00
PGCEHZ1984-01-011984-04-12 18:00:00
AFOEHZ1984-01-011984-04-23 00:00:00
MCHEHZ1984-01-011984-08-01 19:31:00
MCNEHZ1984-01-011984-09-22 16:45:00
............
BTIVCO2022-09-282025-01-01 00:00:00
VDT2022-09-282025-01-01 00:00:00
VEC2022-09-282025-01-01 00:00:00
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stationchannel
AAREHZ1984-01-01 00:00:002006-01-04 19:19:00
SHZ1994-11-28 00:00:002006-01-04 19:19:00
AASEHZ1984-11-27 18:45:002021-08-13 16:50:00
SHZ1994-11-28 00:00:002006-01-24 18:00:00
ABJEHZ1984-01-01 00:00:002019-06-26 19:17:00
............
WMPEHN1995-07-02 12:00:002002-05-08 22:30:00
EHZ1995-03-02 19:00:002002-05-08 22:30:00
SHE1995-07-02 12:00:002002-05-08 22:30:00
SHN1995-07-02 12:00:002002-05-08 22:30:00
SHZ1995-03-02 19:00:002002-05-08 22:30:00
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4289 rows × 2 columns

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" ], - "source": [ - "data_grouped.sort_values(by=['start_time', 'end_time'])" + "text/plain": [ + " start_time end_time\n", + "station channel \n", + "AAR EHZ 1984-01-01 00:00:00 2006-01-04 19:19:00\n", + " SHZ 1994-11-28 00:00:00 2006-01-04 19:19:00\n", + "AAS EHZ 1984-11-27 18:45:00 2021-08-13 16:50:00\n", + " SHZ 1994-11-28 00:00:00 2006-01-24 18:00:00\n", + "ABJ EHZ 1984-01-01 00:00:00 2019-06-26 19:17:00\n", + "... ... ...\n", + "WMP EHN 1995-07-02 12:00:00 2002-05-08 22:30:00\n", + " EHZ 1995-03-02 19:00:00 2002-05-08 22:30:00\n", + " SHE 1995-07-02 12:00:00 2002-05-08 22:30:00\n", + " SHN 1995-07-02 12:00:00 2002-05-08 22:30:00\n", + " SHZ 1995-03-02 19:00:00 2002-05-08 22:30:00\n", + "\n", + "[4289 rows x 2 columns]" ] - }, + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['station', 'channel']).agg({'start_time':lambda x: min(x), 'end_time':lambda x: max(x)})" + ] + }, + { + "cell_type": "markdown", + "id": "9eaaa490", + "metadata": {}, + "source": [ + "Previously, we just printed the output, but we can also store it in a new variable:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14adf61e", + "metadata": {}, + "outputs": [], + "source": [ + "data_grouped = data.groupby(['station', 'channel']).agg({'start_time':lambda x: min(x), 'end_time':lambda x: max(x)})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dcccafe", + "metadata": {}, + "outputs": [ { - "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:" + "data": { + "text/html": [ + "
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" ], - "source": [ - "%timeit data.to_csv(\"my_metadata.csv\")\n", - "!ls -lh my_metadata.csv" + "text/plain": [ + " station channel start_time end_time\n", + "0 AAR EHZ 1984-01-01 00:00:00 2006-01-04 19:19:00\n", + "1 AAR SHZ 1994-11-28 00:00:00 2006-01-04 19:19:00\n", + "2 AAS EHZ 1984-11-27 18:45:00 2021-08-13 16:50:00\n", + "3 AAS SHZ 1994-11-28 00:00:00 2006-01-24 18:00:00\n", + "4 ABJ EHZ 1984-01-01 00:00:00 2019-06-26 19:17:00\n", + "... ... ... ... ...\n", + "4284 WMP EHN 1995-07-02 12:00:00 2002-05-08 22:30:00\n", + "4285 WMP EHZ 1995-03-02 19:00:00 2002-05-08 22:30:00\n", + "4286 WMP SHE 1995-07-02 12:00:00 2002-05-08 22:30:00\n", + "4287 WMP SHN 1995-07-02 12:00:00 2002-05-08 22:30:00\n", + "4288 WMP SHZ 1995-03-02 19:00:00 2002-05-08 22:30:00\n", + "\n", + "[4289 rows x 4 columns]" ] - }, + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_grouped.reset_index()" + ] + }, + { + "cell_type": "markdown", + "id": "48a33bd8", + "metadata": {}, + "source": [ + "It is also possible to sort the dataset by value." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5d91ecb", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "4b5dd078", - "metadata": {}, - "source": [ - "Try and save in Parquet and compare time and memory." + "data": { + "text/html": [ + "
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............
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" + ], + "text/plain": [ + " start_time end_time\n", + "station channel \n", + "PG1 HV1 1983-06-27 1993-11-10 18:00:00\n", + "PGC EHZ 1984-01-01 1984-04-12 18:00:00\n", + "AFO EHZ 1984-01-01 1984-04-23 00:00:00\n", + "MCH EHZ 1984-01-01 1984-08-01 19:31:00\n", + "MCN EHZ 1984-01-01 1984-09-22 16:45:00\n", + "... ... ...\n", + "BTI VCO 2022-09-28 2025-01-01 00:00:00\n", + " VDT 2022-09-28 2025-01-01 00:00:00\n", + " VEC 2022-09-28 2025-01-01 00:00:00\n", + " VEI 2022-09-28 2025-01-01 00:00:00\n", + " VPB 2022-09-28 2025-01-01 00:00:00\n", + "\n", + "[4289 rows x 2 columns]" ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "data_grouped.sort_values(by=['start_time', 'end_time'])" + ] + }, + { + "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": null, + "id": "f20f5de3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "66.6 ms ± 4.45 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + ] }, - "vscode": { - "interpreter": { - "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" - } + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'ls' is not recognized as an internal or external command,\n", + "operable program or batch file.\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." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0952eb9f", + "metadata": {}, + "outputs": [], + "source": [ + "%timeit data.to_parquet(\"my_metadata.pq\")\n", + "!ls -lh my_metadata.pq" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "vscode": { + "interpreter": { + "hash": "c446eef832ec964573dc49f36fd16bdbed40cbfbefbf557bc2dc78d9e7968689" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/Chapter2-DataManipulation/2.6_resampling.ipynb b/book/Chapter2-DataManipulation/2.6_resampling.ipynb index 556096e..dfc1b33 100644 --- a/book/Chapter2-DataManipulation/2.6_resampling.ipynb +++ b/book/Chapter2-DataManipulation/2.6_resampling.ipynb @@ -7,22 +7,26 @@ "source": [ "# 2.6 Resampling Methods \n", "\n", + "Data have natural variability, which we often have to account for. \n", "\n", + "Among other applications (e.g., uncertainity propagation or assigning confidence intervals to some statistic), **resampling methods** can help us explore this varaibility. \n", "\n", - "Data has natural variability that often needs to be accounted for in statistical analysis. There are several strategies to map the data variability into the data-inferred model using resampling methods. \n", - "Resampling methods involve repeatedly drawing samples from a training set and re-fitting a model. The collection of best-fit models provides confidence in model fitting.\n", + "Broadly, resampling refers to any technique where we repeatedly draw observations from some sample.\n", "\n", - "The lecture introduces resampling techniques in several ways. We explore correlation in toy data sets (Level 1) and use resampling techniques for model inference of a linear regression (Level 2)\n", + "\n", "\n", - "* **Level 1**: Resampling for Robust Data Correlation\n", - "* **Level 2**: Resampling for Robust Model Inference\n", + "The lecture introduces resampling techniques in several ways. We explore various fundamental examples of resampling (Level 1) and demonstrate how resampling can be used for model inference of a linear regression (Level 2).\n", "\n", - "First of all, import some useful modules:\n" + "First of all, import some useful modules:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,11 +39,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# Random generator \n", + "# We define a random generator\n", "rng = np.random.default_rng()" ] }, @@ -47,25 +51,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Level 1: Resampling for Robust Data Correlation\n", + "## 1. Examples of resampling techniques (Level 1)\n", "\n", "### 1.1 Randomization\n", "\n", - "Let's build 2 simple random data sets\n", - "\n" + "As we introduced in class: \n", + "\n", + "Given two datasets, $A$ and $B$, and a parameter, $x$, we can randomly assign observations to either $A$ or $B$, calculate some statistic (e.g., $x_A - x_B$), and repeat to build a distribution of that statistic." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The means of A and B are, 5.320848054363878 and 5.409122094748068, respectively.\n", - "The difference of means is, -0.0882740403841904.\n" + "The means of A and B are, 4.8190425043357745 and 5.7062434298834965, respectively.\n", + "The difference of means is, -0.887200925547722.\n" ] } ], @@ -91,52 +96,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Let's plot the data to see what it looks like" + "Now, we will want to resample." ] }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'B')" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(A,B,'o')\n", - "plt.grid(True)\n", - "plt.xlabel('A')\n", - "plt.ylabel('B')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data does not look correlated." - ] - }, - { - "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -146,27 +111,17 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(200,)\n" - ] - } - ], + "outputs": [], "source": [ - "\n", "# Next, we create an array that will store the difference of means\n", "array_of_diffs = np.zeros([number_runs, 1])\n", - "\n", "# To ease computational burden, let's declare some variables\n", "# A combined list of A and B\n", "combined = np.concatenate((A, B))\n", "# Length of A and B\n", - "length_A = len(A)\n" + "length_A = len(A)" ] }, { @@ -178,12 +133,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "\n", - "# Okay, now, for each run:\n", + "# For each run:\n", "for i in range(number_runs):\n", " # Let us shuffle the combined list\n", " # Note that shuffle works in place!\n", @@ -193,17 +147,17 @@ " new_A = combined[0:length_A]\n", " new_B = combined[length_A:len(combined)]\n", " # Calculate and store a difference of means\n", - " array_of_diffs[i] = np.mean(new_A) - np.mean(new_B)\n" + " array_of_diffs[i] = np.mean(new_A) - np.mean(new_B)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -213,10 +167,10 @@ } ], "source": [ - "# Now, plot up the array of diffs\n", + "# Plot up the array of diffs\n", "plt.hist(array_of_diffs, color='black')\n", "\n", - "# Now, given an alpha of 0.05, can we accept or reject the null hypothesis of no difference in means?\n", + "# Given an alpha of 0.05, can we accept or reject the null hypothesis of no difference in means?\n", "\n", "alpha = 0.05\n", "lower_critical_value = np.quantile(array_of_diffs, alpha/2)\n", @@ -224,10 +178,11 @@ "\n", "# Plot the critical values and the observed value\n", "plt.axvline(x = diff_means, color='r')\n", - "plt.axvline(x = lower_critical_value)\n", - "plt.axvline(x = upper_critical_value)\n", - "plt.legend(['Observed difference of means', 'Lower critical value', 'Upper critical value'])\n", - "plt.grid()\n", + "plt.axvline(x = lower_critical_value, color='g')\n", + "plt.axvline(x = upper_critical_value, color='b')\n", + "plt.xlabel('Difference of means')\n", + "plt.ylabel('Count')\n", + "plt.legend(['Observed difference of means', 'Lower critical value', 'Upper critical value'], loc='center left', bbox_to_anchor=(1, 0.5))\n", "# Show your plot\n", "plt.show()" ] @@ -238,26 +193,21 @@ "source": [ "### 1.2 Bootstrapping\n", "\n", - "* **Data Reuse**: Bootstrapping involves resampling from the original dataset itself. It repeatedly draws random samples with replacement from the observed data. This means that each resampled dataset can contain duplicate observations from the original data.\n", - "\n", - "* **Estimation**: Bootstrapping is primarily used for estimating statistical properties of the sample, such as the mean, variance, or confidence intervals, without making strong assumptions about the underlying distribution.\n", + "When bootstrapping, you repeatedly draw observations, **with replacement**, from a dataset. Since you are drawing with replacement, your newly resampled dataset likely will contain duplicate observations from the original sample.\n", "\n", - "* **Correlated Data**: Bootstrapping can be less suitable when dealing with highly correlated data because it assumes that each observation is independent. Correlations may not be adequately preserved in the resampled datasets, potentially leading to biased results.\n", + "Bootstrapping does not make strong assumptions about the underlying distribution of the dataset. \n", "\n", - "* **Applications**: Bootstrapping is often employed when you want to estimate sampling variability, assess confidence intervals, and make inferences about the population from a single dataset, especially when parametric assumptions are questionable.\n", - "\n", - "\n", - "We will first create correlated data using the numpy function ``random.multivariate_normal`` with [docs here](https://numpy.org/doc/stable/reference/random/generated/numpy.random.multivariate_normal.html). The mean of both data will be zero and their covariance is -0.75. \n" + "We will first create correlated data using the numpy function ``random.multivariate_normal`` ([docs here](https://numpy.org/doc/stable/reference/random/generated/numpy.random.multivariate_normal.html)). The mean of both data will be zero and their covariance is -0.75. \n" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# We begin with a dataset where two variables are strongly correlated\n", - "correlated_data= np.random.multivariate_normal([0, 0], [[1, -.75], [-.75, 1]], 1000)\n" + "correlated_data= np.random.multivariate_normal([0, 0], [[1, -.75], [-.75, 1]], 1000)" ] }, { @@ -269,12 +219,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -285,9 +235,9 @@ ], "source": [ "plt.scatter(correlated_data[:, 0], correlated_data[:, 1], marker='x', c='black')\n", - "plt.grid()\n", "plt.xlabel('X')\n", - "plt.xlabel('Y')\n", + "plt.ylabel('Y')\n", + "plt.legend(['Observation'])\n", "plt.show()" ] }, @@ -295,24 +245,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also verify that the Pearson correlation coefficient, which is equal to the ratio of the data covariance with each data variance, using the numpy function ``corrcoeff``." + "We can verify that the Pearson [correlation coefficient](https://en.wikipedia.org/wiki/Correlation_coefficient) is close to our target using the numpy function ``corrcoef``." ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The correlation coefficient is: -0.753056949521871.\n" + "The correlation coefficient is: -0.7490650993380316.\n" ] } ], "source": [ - "\n", "# The correlation matrix of the first and second columns of correlated_data\n", "correlation_matrix = np.corrcoef(correlated_data[:, 0], correlated_data[:, 1])\n", "\n", @@ -324,33 +273,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We are now going to resample the data and calculate again the Pearson coefficient. We will take a subset of the data" + "We now take a subset of the data --- think of the subset as a *sample of the true population*." ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "nsubset=10" + "nsubset=10\n", + "subset = rng.choice(correlated_data, size=nsubset, replace=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can calculate the correlation coefficent of this sample." ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The correlation coefficient is: -0.5069452099352522.\n" + "The correlation coefficient of our sample is: -0.8238774989356271.\n" ] }, { "data": { - "image/png": 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V6QMIFxcXXb1KuNbxAACwE4mNEOHyPYQQ8oknngj1+XYXIu1KWIFAvQ3cYL64JOhGyUUJSp7lfs+FrOLi8doJYhgAED9iI0RqpZOFSFwNRJSQie5t0Cf0qiRWk9egmUSpzgkqJ9Z/n8vlYhmq6aTwIACgvYAQiTlcl1OFMhATExOuMtdmETWJlMuhoP081tbWPAmsUXp71JMw/Uqq8Yio44pTVZSUnZMwDQBoPyBEYg4390VKr1EfHx9vydNq1LJars26nthZLBZlJpNxqmu47TXy6TyMRyRqjsjKygrb5C0OIkQR5xJyAEDnAiHSBuiTcGdnZ9nqjFY9rYYNZdCnbrrQ4ykUCnJjY4MN0fg9ndcrfBXGGIfxHpiqZhqZmFuPcxDHpnoAgM4GQsRA3PIywngSWkmYUEalUpHj4+Oe46AeBOXpiPJ0Xq/8hrDhiVKp5Lu9fD4v9+zZ4+kjwpUq1+tYgj6Xz+eN3WP16zluYwYAAJ0NhAhDXBP3uATPOIiQsE/RyiCaxMX6+rrrnFbjaaklv6Gejc82Nzc983do7svi4iK7z9Uei9/naGjPlGfECcA4XWMAgM4FQoQhjol7psTH2dnZuqy/Wg9QWM9Ftec07NN5vfIbGuEJqyWpN8qx+H0uTIM1rscLckQAAI0GQsRAnBL3wlSb1EK1HqAoBraabUR9Oufez1Ub0f1vhlerEcduEkym8A+XPOsnQholvusp9OIWPgUAVAeEiA9xcFNzI+wLhat9OGgCq8nYRt1GFCPEGVhlHDgDWyqVQg+Cq1YI6h6UkZGRWITYqjGaJm9QkLAxJcT6Xc/NCEfWcxtxDZ8CAKIDIRJAqxP3uBH26sZLE1i56bFhnwpNrdfVz9zwNvpZtV3dONDtNyJpVD9eztBWO/Ol0U/bQeunU4h10eB3foJKhP2uZ32f6M/6MVdzDuoZ8oxj+BQAUB0QIj7EwSMipZSbm5tyfHzcs+1KpSJHR0dlIpHw9BEJa/iDWq8nk0ljtYUOZxw4kWMybtw+VRPK8MuPCONZafTTdtD6bdt2TSHm5tTQ483n854+JVzTNO77bfY5qGfIM07hUwBA9UCIGIjbTY57gqaGn3otojwVhmm9HuXJUjeQIyMjvl4JkzGL4pGI0tcjjKBs9NN2WI+GbduyWCzK6elpadu2S4yUy2W2pFt9zu8chLmeG3kO6inw4/KwAACoHggRhma4fZvRgEsfsGZaf72TYU0hEq4rbD3c52Ge3kdGRiKF2BotRP3Wn8lknO+OXotKjExPT7OhGyHcs2wUtCGeZVns9Wzbtid/p5HnoJ4hz1aHTwEAtQEhwtBq13zU9XOGnxqzMOuvd8M03Tg02rPkJ+zW19edXhpRnpob/bRtWn+xWAzdg0T/zrjvvFKpyEOHDsl0Os1O/1WhoMnJSc81F+YccOdevcaJ6mq/jyjnEB4RANoHCBEDjUxWbITHhZvyGnb9YVuvVxOe0UVNs41FrU/1jXraVtcXt37u+gpKxA3qPPvVr36VbZu/vr4uLcvy/Y6DEly5yikaTqKiV0+wRo4IAABCpEU0KmmPPhlTwxNkqMK2Xo96XDTRMozBrSdhBJ/fhF+/ypVa0A01971x59vkZfITmqZkZJUPREUBd1xBHgfuHJfLZU+uS7lc9sxMUiXnrayaQS8SAFoPhEgLqYdbOShHJGi2iVpHlNbrJnTjoBItc7kcG0IwGdx6QY2wHu5QE37T6bRcXl725MwMDg66Klfq+bTNGWqu6sUUnqEeET1/p1gsus6rnmOyvLzMrofrQxNWLHPv446PmyKtb6uZfUTQiwSAeAAh0mJqcf2HeSoMmvZazzCRurGrbqa6wU0mk3J4eNi1TyaPRL2oVCrskLpSqSTT6bSzX6r8WYkQtX96cicVbNX2bYkqRDhDT/eDC4kUi0Vne7QaJ5PJyN/7vd9zXRPcmIBa2tJzXjm1mDrdVioVubGxEWooH/1MLd4M9CIBIB5AiLSQWj0iUfpRmNZf76fCzc1NOTEx4XhSqCHKZrNyYGDAEQBRhEgtRscUPqCCKJlMyrm5Odf+JhIJl9FUSZ+mvi22bbMJn9yxhA3NRDGWURud+V1z1VwXnKgOO5W5VZ4J5JkA0HogRFpEvW6AYWaOhMkRqVecXO9tMj4+Lm3b9nhmstksG5rhthdkqNR2/J6mg8IH+mJZlufJvVQqOcJO95RQj4Opjb3ehTZMsmpUI+13XWWzWde2jh8/7nvNRbkuwnpEuO202jOByhsAWguESAtoZcOsZt/Y8/m8nJub8wiRXC7nyT0xPf1yuSdqno0+4t7UVn5zc9PV4ZUay97eXs++cUaomtwOBScoOAOoJ42q0JIpobZUKoWqsBkcHJSJRMJjbPUE1loblPnliPiJnlZ7JtCLBIDWASHSAtqtT0k1HhPOGOqLqQMoZxDp7zOZjMzlco6xU4mwpoZf+XzeCRfpnWNPnDjhMdAmIaL2w+RJMTWQU+eJHh8VALSShBreWr5H3bD29/c729DLsqNWRvkdl1/VTJTvuJmeCXhEAGgtECItohFlg5zrn1tnlPWHCYuYZtHMzc15DHw1T8t0e1xOxdramut1KlJUropu9IMWXVTQ87m1tSUHBgY8hp6WtAaJM0586N6JjY2NqjxbJhFIw036OkyJokFE7SMSVUA12jPRak8MAABCpGNolJfFL8yjh0UoXKdWZdxNXoUwN36ucZsp34OGcej+JJNJ+cgjj7jem81mjWEW/dwWi0WZSqVcn0+lUrJYLAaea3re9ONWAwJt25ZTU1Oe3JYwDcDo+1UXVpXXQtvG03XXmggatbNqFC9aI8VAq0OYAICrQIh0CM0aUqY/NXJt4HURohJT6fu2trYCS4v99oNb9PWdPn1aTk5OupImTR4RGj7QE0/1Xhx60qfpOE3nenFx0XPc1IuQy+Vc1TecqAtK+qQt/lVvEbX/elVOLSHAenn1GuGZCNq/zc1N9BEBIAZAiHQQjXQzm55W9XDC3Nycy/OwtLTE5gfopb1B+8gdm8p7MAmRZDLplAqn02mZy+U8OSJdXV0ym826DL9eiks9FSYRxHl+TMfBhZf09XN5FUGijSsLpsKPlnPX+qRfTw9cI0S0af/Ud0mTmOmog6AKLgBA/YEQ6TAa6d42xe+5bfp1/ZyYmPB0cuXmoCg4Q7W1teURIsqA09blQgjZ3d0thbjaF0T3ZqRSKTkwMOCEQug2lQhRBu3MmTOhhMjy8nLgd6OXNOdyOU+jN87zFPSdKi+AbsQXFxedRm16PkfURmKm70Q/xrDioRFhRW7/KpWKnJiYcHnx9IZs8IAA0HwgRDqQeiX86Qmautigpab6Nufm5jzrUu3e6ZC1ME/ruqGieRZKZNDwCO2Malr0Shm/mTM0R8MvNCSEkMPDw0ajzokp3TtCE22pqKOiLayXi/vOqLemUnl7xpCecBzGMHNeKq7jKz0HpnXVI8yjr0MXfqdPn3Z565RQRk4IAK0FQqRNCdPIrBaPCDX+QcPS6O9N2zTlL+jGwrIstgpHPbmXSiWPlyCVSnmEBfUy0E6uJjHi1+E1TCmyWtLptKu9Onc+6XnRBdzi4mJVw+1McMm9aj3Ly8vGIXRhcl1M56Yes2SiYjq/3N9DtcIOANAYIETakDA33UwmIxcXF6u+yVLhYCox5UpQ/W7suujQ31ssFuX4+Dg7CVZ9dnJyUk5NTXn2RQ+5qHLacrksn376aaNwCDt8j0sYpYaNbt+UI+L3xK7vTz0Mu0lA6dU0+vdHryFOVOlEEU75fN4JBZlm9VSTm+EXKtJzh2gflbBivRHl9gCAq0CItCH1bCLlh16iqzfCWlhYYMfI+23TFOJRXVG546LNyQqFgqvTqKkUeHBw0ONx4ZbZ2dlIs2r8vCp6/5KwngRdAFChVm2oQzf++hwdXbRxvwszB8jkEeGEqWrFTxvM6deM+n01npOgc0pFh5607Be+bOUsHAB2AhAibQqXjKj3i9DfW+3N3a/vRZAHg9um/gQ9MjLiypvQEzpp7gi3Hf2JV/X4GBwcDCzb5Xqg6OhJjtyivAdhznWjhSQ370d5kPRzpYQUV4EU5EELU9ZNr5n19XWXsFWvU8+W2p9qczX8vEDVJP+avi/u+JFbAkB1QIi0MUHJiJRa3Md+ya9RXdZBT9CmJE7uuHTjzXkpTPkjdOGElL7PepWP3ll1cXHR97gppm6kU1NTjniiQqZYLMrBwUFPZQ/dlt5VV62flgVzwweF8Cb8+l1HdLtBxpkuc3NzHkGn/xv2+wjCLy9Gb2w3NzcXKnzpJ7qQWwJAbUCItDmNboldz3LgME/Q6vUwzc6KxaLLC0TXZxIfyWRSzs/Ps9sNqpqJKpT8RFqpVHJN6a1UKk7zNToMUHmdEomEPHToEFvZQvNmqLChDdpowm42m3V5QLhZO0Hfs1+4gsunSSaTcmFhweiRoO8L8lD54SfO9bwnNbE5rGejnn8LAIC3gRBpYxp9Y6znU2CYJ2i/0Ie+vXK57HgpqAigXUS7uro8Rm5pacnZrm3bcmxszDcngSsdDhM6ippXYDo/6+vrxsoWuh9cH5agc5vL5VjBRtenkkv1Kb+bm5vy/Pnz7JwbJWQymYzMZrOu/Z+fn/dtDDc7Oxv6mtLhwpX0vBSLRZenKGq5spSY0gtAI4AQaVMa7Squd1w8jGEeGRnxGErTcW1ubso9e/a4KkrUNtTI+2uuuca1vmw26xgaJUQmJyc9RpbbdzU3JUwyrf5elYyrjpWGeVQFid69NchjRLdXLBZdg/3096gmcrqX6Ytf/KLz/66uLplMJl05I4lEQu7atUsODg7KTCbjdJpdW1uTu3fv9lTzlMtlZxtdXV3O/3O5nEuMXH/99UYhYtt2qEodnbDXK0105tbhdx3AIwJAY4AQaUOakTwX9Yk+TK6I33vW19edRme6S11/+lfGRK8o0XNGTLkPutGup5jSJ83qVUf0ZxUimpqacsIyg4ODnr4nyWRSjoyMsEnD9Cmf6+eimpWtrq56PBHUG0LPFfUk0ddzuZxcX193CZpkMinPnDkjK5WKXFpacr1OQzEm74u+r8qTElWMNLqyBTkiADQOCJE2pFnlhGETUWvdH3qTT6fTMpVKGRNYM5kMO5lWGQfd2Pb29roMIB1MV01SZNTkXBpa0Q2u+teyLJdx1yf7quM7efKkJyygCzK9LFWtX/e0qM8kEgmnE61faEx5SdR6dGExODjIdi2llTJhRAgVjKbzbPoOqm1XHwSqZgBoLBAibUqcGizVcqPWQxhnz571lNwWCgV57tw5OTQ05FkX5y7nnrx1Q5dIJJxkxTBUe74rlYocHh52bfvhhx927c/w8LDHG6Tv++OPP86WnKomcKYKFCps9O+G5tiEHein3r+2tsZOIeZap+shN10o6iLMsqzQOTuURvX0QB8RABoLhAioCzRJlMtRKBaLrDFXN3nLsuTY2JgnxJBMJuXw8LBjYIeGhjzGgOtLohvJhx9+mPUyNHooG+cRMS0qtyXofdSrE1ZEcH1B6L4vLi6GFiLZbJYtA1bnma7bJI5Mx2lZltGrIWXt3olaBGVchD8AnQaECKgZZag5o6he40pMFapPBq0+4fIdOIPKldJalhU4+I5rm26iWuMXtiKIGvig9/T19XkEiN/n1HZpqIMaTj3Zth7LiRMn2OPu7+938l9yuZxnv3O5XCjPQrX5GvBsABBPYiNELly4IP/lv/yXTqMofXprEBAirYMaav1JWf0cpsRUf8/x48dZQ63njdBt0e3bti1PnDhhNM5RkgyjGj9dvMzPz4fydvgtN998sxwbG3OJC1o+zC2PP/64y8DqBtdPLEXZX85DotanKnLW1tZcnjNuArKepGryNlRTwYJcDwDiSWyEyH/7b/9NfvrTn5Zf+tKXIETaEM4zQQ2MXv1iMuamzpy6UNG7hKpqGF2YcPtEn9ijhGdotQ01fnpPCvV+v2oWaqxPnDjhEgG5XM414VgtAwMDgS3rufWfOnWKTe49d+4cu77+/n5Py/egpVAoyEcffdT1Wj6fdyYq0+94bW3N2P2WEwgmL0U1PT1Q/RIdhKVAo4mNEHFtaIcIkU76Aw8jRNT7gp5k9cqP48ePs14Tff16iGhqakoeOnSIbSGuxIFfUiTXMl3fNzUIkFuPquIICs/orx84cMAoOOj8nCiLLr7W1takbduyu7tbJpNJefvtt8tMJiMty5Kjo6Ns6bNpSSQSrm61arEsS66vr7NN4LjjoZ6qoBLrWnp6oB9IeBDOAs2gbYXI66+/Lre3t52lXC63lRDppD/wMKEZakz8nmS55E7dq+DX/p02HysWi67S1rm5OdfP6+vrcnNz05hAq0IF9Pj0cEJQi3jVop1OKdaraJRXgPMyPP74457X9Tk3uijzC9dwycBqv5aXl+XGxoargqe3tzeUKKHn4eTJk54wHGf8OU+Z3jPF7+9D/b4arwY6pIYD4SzQDNpWiDz44IPsDbFdhEhc/8Cr8dKESVblchL0p1GuAZj+FG9Zlqfhl/q8noQZdH65cfTqs+o40um0XF5e9oQTqIH2S3zlGq9VKhVP7srp06flwsKCy+g//PDDzjZp9RAnWtSiQiqmahrdu3TmzBnPQD/6Ocuy5E033RQoRNT59LuO/Yw/zR2p9XsN+ruBRyQaCGeBRtO2QqTdPSJSxu8PvFovTaVSkcvLy8by3aGhIVksFn2PV59vws1UsSzLMcKWZbkmp1LBUyqV5ObmZuCxjI+Ps0/eepgpkUgEVrT4eUT0/eAMoTLia2trrAfi1KlTnn0y7YvyNCwvL/u+J5vNykwm48zb4QRkkPhQ+5FIJDzD6ug1w1XmUGE2PT3Nerr06y7MNTo1NeUaKEihDdb0QYmc8AFXgXgDjaRthYhOO+aISBmvP/Bqnjb10AP9HJ3Cqnf35NadyWTYKbZKGFBPSDqddhprUYOpcg6mp6fZsAs9VuWh0T0CNASkG3xdkKieHn7t7unP1BDm83n5+OOPu7ZfLBbljTfeyAqdM2fOBAqDdDrtiD6/Jmn79+93/q9EnS4CdA8GXfRE4GuuuUaura15DDg3i0c3/nqSMSeG6HUXNNV4cnKS/RuiYT96rXGVW+0SFm0mCGeBRgEhEgPi9AdeTZkqDafoXTXV64cOHTL2EVFi4NChQ+yTbLlclmtra3JqasqZx0INovo5nU67KjSU2PATIy+++KIcGRlh81IWFhbkgQMHXK/39vZ6jCWtmtGf2J3tv/mmfOmP/kj+v93d8rAQMj805ORC0JwN1SfEz/vgt/T397sSZIM+k0gkXDNhOBFg2g86zC6RSLB9QMKIWz2BVS/D5pqxqXVzwse0Pa6tPQ0LqhECnODe6cTpgQl0HrERIleuXJHPP/+8fP7556UQQn7uc5+Tzz//vNzc3Az1+XYVInH8A4+6T35eBfXE6zf0TnlVuNkv1DWvPBxchU42m/WUiQa58fP5vNy1axdbzaKX1Pp5BOj69Zb1hw4dkr/S2ytf7+uTUghnea2nR/6rH4dglBgJasIWZslkMnJ1dVVOT08bE2PpcvDgQZeHRs8RMQkZ+noikXC1kde9QX7fATX+JvGTTCadsA/1ZJlChX5iWoX9uPdw3yeIXwgZdB6xESLPPvsse8O77777Qn2+HYVInP/Ao3ppahFU1YSETGEDkygwrTdMP47u7m6PQZ6dnTXuH339fULIt368UCGiXnsfETZ06m2URSX20hybYrEoz507F6nfyMTEhCyVSi4xoEJgTz/9NJskm0qlHK+RmprrF5rSvRh6KEVvNa/OO/Uc6bk9nLgNcz3G8SEgbsQ1qR50FrERIrXSbkIkzn/gYW/QusHRxcHi4qJn3WE6ZQaJMm7/TIJJr5KhCa6FwtWOp37G2lS6qp7STU/mW1tbMj80JLcYEULFyKXubpn/8TA/anwXFhaM81z0/eCOJZ/Py1KpJCcmJnyPL5PJuNZRKpU8vVhKpZKrksi02LbtfN4vmVQ/V+o6CvJ06YP99G3o11YYMR2nsGgc6aQ2AyC+QIi0iFr/wNXNmwt30Nej3iDCCoJKpeLkbOgJf/RpmrbsDjquqE+xem6Bes0UhtHXy3VK9TP2lmW5QjZqW+VyWZZKJc8xvfj5z7MCRF/mf/M3XdsbHh6WxWIxlBBRLdx1Qz0xMSErlYosFouePBfO46B/t6oXCz0mmnysLyqPox5zefTvNZvNso3duOtifHxcbm5uwiNSRzqp8SKIJxAiLaSWSaAqB0Dvg6GXpkZ5WoliREqlkhPbp90x9UqT/v5+j1Dx8/T4PaHS/Qtq964bE70qZHZ21lPx47fQc33q1CmX50EJGnqut7a25K9reSGm5YOMODC1WE8mk/Lxxx93iQ4qjtT+qGsrTN4JzcEwXW9UtHHniyaUVpPwrP+e847o58iUGD06Ouqbs8Rdj3EKizYDiAsQJyBE2hB646Zuaq5jZpTwThQvDeeqpxUs+uthEgGDnlC5xmm64eKqHrj1qrwK3VNiMtS0soSKAN3oqRBDoVCQh0OIECmEPKwZWLrtdDrNDvNbWFhgW9fTc0vFot9y8803s6ES9X8a3snlckYPS9RW/kHXnV/5sDpXtOmdX+iGK9H1e089w6JxM/oIt4C4ASHSpnAJl0Ex9DAE3TRpb46tLXOvCk6UhBUhQSEhUx4C7VnBdXItFArsMDkqOEyNy2zb9nz22LFjnoZoGxsbb1ehDA3J1/v6fHNENoWQ3cSwfuELX3AZ2rNnz8pSqeTyEGQyGVkqldjeH/p3eejQISfPxXT8VEDoRoqWZycSCWPOjF41Uy6X5enTp333z++68xOPnNilHqHx8XFfIzs5OelbSl4vQxz1Wm0Wcc5PAzsTCJE2xi9pM0iE1NLKXb956WEEFY4JmwhYbSO1oP03rVcfXKeMrPI6cI3M/BaVb6Jm2+zevVtmMhk5ODgoP7xvX6iqGSoKbrvtNplIJOTw8LArvKbEyNjYmFxdXQ30OOjt5TmPmS5aqaeLept0wamfn76+PmlZltNFlctvCSuM/US2Og5OMKr1h7k2Gu2lqNZ71yx2emgKxAsIkTbH5L4OevqsxjVriuVz3Uap0QsyRKb9obkJXKOsMIm8+nppZ1O6UK/B2tqab6KoXmY7MzMjBwcHHcNiWZbrqf19QshXurtdQmSTESFUjBw8eJANr62vr0vLslxGbGVlhW1VT43h8vKy833k83k5OjrKDphTXU719XM5K7pnJZVKyfn5ede5SyaTnkol/TumRlgfoKj6vNDpvqo9vb4/s7Ozxmu+2dSSz9QskKwL4gKESANp9FNXtR6RWlyzeqzdFMqgRjTM0xbXe0KJCNq5lO6Dae4N29lUuqs+VCkvNQp6zoFfPoRpoVVE1Itw+k//VP7rvj75QXE1J6TbZx2FQsE17I4mZdK8HLUtPY+jUCg4JbdqmBwNR5w7d87VxEyJxomJCed1vYU7t8zOznoE2/XXX++6BriEUnVtcUKRiqdcLue09VcCUW/1r19zantBf1/NyNuopsKr3gQdp963BeXLoBVAiDSIRieE1ZojUotrlqtoUEZcfxrmhteFcUVXK5ZMn6NzRvT94gxuGM/Ivn37XD93d3cbRQM9T5x44fqZmMScPpNHT14eGxtzSqtpWKBUKjkeJr0p2NzcnPNzJpORlmXJkydP+h6vnkSrH6c+kVidk8nJSd/QGZ3Eq3sTdOGbSCRcVUOJREL+yZ/8ia8XrZnJmn4PC402+lG62tLrHh4R0GwgRBpEIxPC6lU1U4trVk9EVG7xYrHoPLGm02nX7JioN/lqxRL3OW4eDn2/blDn5uZ8h8aZFjXRVRlPLqShdyhVpa967srMzAz7/RSLRd8+HJZledz/xWLRVYKrPE3c1N2ox63P3xHianjOZPympqZcZc5B37HJmNMeKPPz8x6RR699eu2VSqVAAaT/3dTiJQnTBbgR+N2DdM8ackRAK4EQaSCNSgirZx+RajpLbm3xDcLoDT2XyzlPvpSoN/SoZaB+JbtqQB23Dd34ZrNZJxekt7fXFaLp6ekJbaR10ZFMJllxwoVCMpmMnJmZCf39mAy2ZVmuwX40bKXPollZWZELCwuu12i4JcpiWZYjmui+5fN5V8O0MN+xfp3mcjmX8C4UCvLo0aOu86wEJycw9P1ZXFx0zbyh2zYJ6DDhHc4zFracvR5w9yAuvMe9F1UzoFlAiDSYRiWE1aOzajX7pt/ATYmI9eyPEEYscW5o/XMjIyO+A9KiVMpEWVQFDA29ZLNZY5hGz1uJ8v1wBlsXQspA64KD236UJZvNenJqUqmUy/Amk0k5Pj7uKV31+46561R1VuV+RwWXn8AwdYoNY5wrlYqcnJw0dvJV1U1DpH0/PQ962KyRRp87R3p4T38v+oiAZgIh0gTiOM+iGm9NK/oPhBVL+r5xVTt6B1HuM34JmqYeGkEiRAghh4aGXPkNNKeBvldV2wwMDDjb0ytPVKglzLkyldFy/UT0n7mQi1r0yqHu7m45Pz9vTF6mx6ob4fPnzxsTOWn4qFAoyNOnTzuhP/Ue/e9LlT3r27csy5UbxL1H7Ydt23JxcdEluum1Q5vF5XI55/vgPCDU6OsJrLZt19Xom7w03ByoODVZAzsbCJEGE8cSuWoFRaOS/Ew3T3pTDyOWoibwmvqiBFWLqCWbzRpbsVPDRhuR0bkp+XxeLi0teapAhoaGnNcSiYQzN4bum2pqxh27nqfiJzb00lgqLEzHZhJkYaYHU7FVKBTk8vKyE75Kp9NydnbWMfD6ueKqZuh4AXpMeqKtOpagjrp6fo3att4kTe8snE6n5eLiIju5WM/p0fNV6ilCuL9P7ppu9T0IAAqESANpVI5IrdQiKOpd9mjaF3qjV+Pl6f6ZxBKtjqE3XT2fgLrY9XUEtRYPs8zMzLBueGrc1PHqJZSnT592zfHhnrZpIjAVlul0WlqW5fHucIPjhHg7yVg/ZiUquM9QwbF//36PMEkmk/Kxxx7zDXWpfB3qQVGdZPXOqarFvqkXB91P/V8qRIJCb5yXSghh9Eb5DQHkBEiUv5Vq/s7CzOxpZn4KAGGBEGkQcW+jHJf5F34lnDS2b3qi5PqIjIyMuIyCCoWF8dpE8Yj4LbZtO+Jnenra2K+B85jl83l55swZozdIiQ3ds5PL5RzDqMSBMtqmmTOFQkEuLS35HjNXytvV1eWbSxI0SDCXy3kScYV422tExcCxY8ecZGIlStfX19n8GVoGnUwmXaLOL9SkyoA5saKH7TKZjCtso3+GDgCshloeFHTPGNc8LS73IAAUECINAoOlwuPnOTI1fTKJJS72r/fcUJ/T16GLEFNCqZ8xo0/46qlfJWb6CQB13LTM+Mknn2TzXPQcCSndlSF0f2ZnZ12Joup3t912G3ueFhcXPaEiFXagiajd3d3ykUceqVmwqf08deqUxxPCnftcLud8b7rgPH36tCs0Nz4+LkulUtXCsr+/39cLo17X82L8hEgtownCCghTxViYSiAAWgGESAOJi9ehHahHLo1+o45SkqnH/Lncg6Blfn4+0BWu5xGobW1tXZ3zsry87BIMDzzwgGc7lmU5eRVKPKh1Dg4OsgacrjOXyznNzfTXt7a25OrqqqfsWC0HDhww5ohks1l2IF3QcvDgQWlZlseg6w3U6Pe4teXt/WJZllPOfu7cObm5uSml9IZQrrvuOtfnfvu3f9v18/79+x0PjC5AuHEG3DFxAjrKw0mtYd0wCfK4B4G4ACECYkMt1UXcU6QuLlSIh3uyVOWYqsKhWCzK8fFxT6jDb1HrUmIknU67WpQXi0U5PT0tLctyhQm6urqchNHR0VHftvKJREJ+/vOfZ8UGFU/675WwoBUetDU83c+VlRVj2GVlZUU++uijrtd6e3ud7ZoGBqZSqZrLo2nVDU2kzWazzvbz+bxcWFhwJYOaQiimRe9RohYVEjMJD65Hh2mOThhPR7XiPI4J8gD4ASECYkGtN88wFQN6bF9/rxrbrgzB5uam84R9/vx5efvtt7O5Dw899JDHzV0qleTIyIirdJNOw41iGE3vN/2cTqdZ4aRCDWo/S6WSXFtbkxsbG2z4iFs4DwDNh6Gihy5BOSN04TwhfmXBtm3LpaUlxxujz9xRzf1OnDjh+pzeqO2GG25wBI4qcdbPn/od7SxsmgekVzbp13kYT0dUcR7XBHkA/IAQAS2nXjfPMGXAYUWOWtfm5qbc2Nhw5Z7oAkBVdehzTfRKi3K57GkiFqVL6/79+10/9/X1ed7T3d1t9N7QlujLy8syk8m4KpL0hFp923qy5/79+x3jrMSAXl7b39/PelfClPrqx8W9rtadTqdd61RVSep7O3jwYKDwSyQScnR01JWnw4mera2rXWnHxsbYUQrqelOt7PXrMqzo5lrw+123cU+QbyQIg7c3ECKgpTTr5hnlyZK20B8fH3eV31KvAw2h6M3S9GPIZDJyYGCg6q6lpiXq+hKJhHz88cdduRxKRHCVNSbB0NXV5ZTU0tlCVJgEhWuClq6uLpf44cTI4OCgx1vS19dnTBKm54H7WYV2aP8SU65RpVJx5vdEHbCnX48zMzOua0efFB0kzpWHi9tWqVSSy8vLvvvZzqAwoP2BEAEtxXQTCXOTD0vUsA+ttFDGQA93qNdpcqiaoaJvW88ryOVynjBBVAOtG/tqPp9KpYx9N9Sie2HUQruXquPPZDJybGzM6ULKnXc/T4hfHo7egdZPVHR3d8tsNusRViq/xnTOVOdVtc+010vUgXjqCZ0T2uvr655rgk4r9psUzYlz+jek9y5RZfCZTEYuLy97qsQ6wUjvZE9QpwAhAlqO7lalN9b19fWabp5Rwz5q29TIm4w0NSymAYNcmakylEGCwSQChHCXlqp8hqheh6C8Db/1qd9Roba6uurKq9na8rZfD0r6VbNpOC+HEjtBfU9M+5nL5eTIyIixskeF0NT3v7Cw4LmW8vm8HB4eli+++KLnWuO8IPrUY72qqa+vz7WPtOuraVK0fv1HmbLbqUYauTHtDYQIiB30xkpnfOg3m42NDV8xEuVJiXuC9fM46OWZJi8N1+m1lkV5FNS/hYK7a2zY3Ava8rwe+6UqRMKeP1NuTCqVkmfPnvV8rqurSw4MDDj5LFz32wcffJBdJ9e2fWBgwPWz+n2hcLW/y7XXXusRAnRa8e7du53yYHpN6ZU6Kll5bW2N7ZMyPj4ul5aWPN+DPttGv679hjfqhlif7dOpRhrVQu0LhAiIJTQ5VD0p0psn7VpqEiNhY8ebm5uu99EbmslzEaa0mK4nisEPO1wvkUjIJ554wtV0a3BwUKbT6UDviJ9IMDUSM3locrmcXF5edipE9Cfx3t7ewHAKJ5L0tuyqzwnXQ8RvUd8h9TzoQoQOoDt79qzzut+0YuUx4YQt10+GK8tWAk6v0Jmbm4scLjEZYt0rQ3+nexzbnTgOGAXBQIiA2BJ2bgwtudUplUqeEkopr4oU5VHRPScq0dHP2AU9aXGTfU09KIQQvr1DhOBLWlOplOzv73cqZfTyVf0zuvEP2qZauru75R/8wR94Oq5SQ59Op2Umk5Grq6tyY2MjVOgkmUzKhx9+2ChG1Pay2axrKq76f1dXl+zr6/McByfk1GuWZXm8Qepf27adpE59P48fP85ei37ehTDjAkw9S1Rn2KgigZuyq0S2LnaUuG/3HBEFPCLtC4QIiDX6zfP48eOuG7+K6XOubHVjGh8fd7nRTVN3qffilltuMT7F05JN02CzSqXi9K9QT6R+wmZ+fl6eOnWK/d0NN9xgNKy6wLAsS46NjbG9PLLZbGhvizrOqGW2yWRS9vf3y9HR0VB5MGGWVCol+/r6ZC6Xc4mhbDYrb7jhBvZYOY8LnShsyjWhlSqnTp1ipxWfOnUqdFmtaYAiXS9Ndj558iSbqBomQdvU2t2U66T3W2nn8lfkiLQ3ECIgtnA3Vv3GT5uE6Y2lqLCYmJgITO7zm6YqhLtdu3ofbVimKJfLslgsynw+Lw8dOiQPHTrErpeKgq6uLle3UNOSzWZd3gJ9sW1bHjt2zGNwTJNl/Zbbb79dfuELX6hKPHR1dfluy+St0StdaDjo+uuvd0RHf38/a8zDLmoOEOexsG1bjo+Py0Kh4PHYPPzww7JQKBgHK+rXr8kjMjs768oZSSQScnh42OOlUR66IM+FXtVDDTGdmaR7d5LJpFxaWmrr8ldUzbQ/ECIdQCc289FvIsePH/fcyBV6uSPNJ+EaTnHrpzdu3YhTAWLbtrQsS+7atUsmEglHENCSVdu2XX0n9MZj/f39Rm9BmNwJtegeFpPHJUx+it5lVB1TqVSSo6OjgZ/nvDZCXBVbtbR25zw4yhOi7+vtt98eKtykhgGa8oDm5uaMAth0TrmwjFoHl7OjerdwgjKZTLpa/pvKhhWlUontc6Ln0nCN+NRn6DbazZCjj0j7AyHS5nTiHyGXX8HF0GkohvNmUBHCuWY5j4u+Dsuy5MLCgqs5Vi6Xc/I9aEVGMpn0TN89deqUpwcGffLllnQ6LY8dO+YxHLrBNFV+UOPIbcfkTVH7p7qL0lbwQWEWta9RQj9+YuPhhx+OlOCbSCTkpz71qVDr1s8FJyo++9nPul67//77PZ8tFApybm7O8cgp7wWtmlHCVf2ffke2bcv5+Xl50003ses+ceKEK2zj1/fGJERoXpLuKaM5IvT6bsfQRic+jO0kIETanE50S1JxpSemzs7OuuL49Aap55OEuYlyMfxMJuMJw0xMTDg3a/3GzeUoKCNDQyJhQi9CXA1r6AbU5FWwLMszbE0t1ZTmzs3NOW3t1c1bDQQ0TeSlRp1rO6+WAwcO1Dz4LorI6O/vD2wcp/fwCPKE0OWxxx6ThUJBTkxMuMKDY2NjcmpqivU06B6zXC5nFND0Z1XdYvo7Xl5elkNDQ6yQsCxLnjlzJrBqBsmeoFVAiHQAnZiopapauOM4f/6886RHyyU5I0JDODqmzzz55JOyXC67vCzJZFJubGy4nq78cgDoE30ymZTnzp0zJi4mEglf0dDd3W0UAarqgj75UhHlt/T29nq8KJZluRqSKZaXlz2GX589o35vCo/QY9Q/qwsDtdBkWdMgP1NZMVc27Ldftm3LmZkZ13Y++tGPut5LvVDqfYVCwdMWXlVrcd7KcrksV1dXZS6XcwSJX4fb2dnZUJ5PlRytCwldzOv3B5pwrV+j1ZQRAxAVCJEOIezTTDu5MLmbr975VPdOcEZG704ppft8qW6Z9DN6nomplNIkLvTt+yXeUsOpG1saEqFJh4lEwhUSUq8NDg7K6elpuby8HOgRoQaYChJqUFXeix4GMnkj9u/fH7hdv0RWk6gwvT+RSIQKB+nn9sEHH3REDi0PpotKRNa3d9999znr0ruh6onLtEycvjY+Pu4KHXLhR7Wo+TdhRPnc3Jzrs6dPnw70mNKma1yokiZ6N4p2ui+B+gMh0kEENfNpx3wS/Qald+409X04ceIEWwapr0Odi0ql4nqqpWLO1PQpTJ+IEydOeDxWdIha0GJZlienQ1V8qKZvu3fvdgy1bdvy/PnzoUMMVCjRkAEVN3p+SNQSYNPvEomE0TNS74U7Buq54YQOnVbMtdDnjm10dDRwJIGp0ksPIw0NDbnCgyqBVe3TzMyM6zrhhISa/2P6m+e+cz0Eyg10rCfteF8C9QVCpEMI4xHplHwS3Ztxyy23eAyrlN4JptyQMP3Gp+eZmDozcp0zOaOqqm70c768vGx8ys9ms3JxcdE5Rt0bQfepXC7LF1980fXEnM/n5cjIiK8XgQohdW5o2TGXUNvX18c2VhPiqidD7zmSyWTk008/7ZscK8TVHBv62Ww265scm0wmPSET9TlT63i6n/Q7C0rCpf1pdKHwq7/6q+y+BQ2pU7+jSa4nT55kt03FcTKZdH3PdKGzh3QPlxrmx1Eul13es7BVZ/X0YHTKfQlUD4RIBxAlR6RT8klMFS96GMY0kI67kfqJOfp+euNUia1nzpwxPv3rvUb0Hif652grc87jwoWqgtz7Qlz1zqjf0yTNiYkJubm56RgQlXypG959+/YZvSE33XSTvPnmmz3CYGNjIzBJt6+vz3UOVH+NXC7Hbi+Xy8nTp0/7rtNP9FADaxI66t/R0dFQ51b/fvRZL2pGDr2OTOE6+j0VCgVX0nM+n/eUslMBrJeaB5X+qn2ioSJ6HNwohUZ4MDrlvgSqA0KkzanmaaJTsuO5xDqOqF0p9RthPp93GlwpUaJuxEtLSy5Dq0p4dXe3aSicenLVe00MDQ3JhYUFx5BkMhmXl0RVDOnfP1c5RL9jPcRw9uxZ16wdOpMkatXNo48+6jKKqnlWUChn//79jkCg4ZBisejydtHf17KkUilXWENf9Am49N9CoSAfeOCBQBGjFprLpM80klJ6cjoKhYKsVCqe6/HcuXOeRFS1cOE7tY2wwqBSqXj2RXne9L+fRnkwOuW+BKIDIdLmVPt00u7Doep50wpzY9V7klQqFU9nTuWRUSEO+lmV8Kcn21Kjq9an54/QJ2ou3MTtJ10OHDjAelbS6bTT2psKHPoEXs3ClaSGWfr6+lwVO9ysIb16KJvN+goDbqFhDG6hiZujo6OuBOFjx46xx2bKdaFhufPnz7u+87W1NY+44EI76u/Xb9yB7nXSw3fVtIf3+3tqlAej3e9LoDogRDqAqPHadn/yqPdNMIyYo70i9G1mMhk2Dk/LKvVZN36lnTRmn0gkjG3raSUP19CNeg/0XIj9+/fL1dVVZ7t6qXJYo869d2VlxRM+CCtglDBSITW6Dc4bEnV+TldXlxP60ddHW+erhODNzU05MTER6ImxLMuYb6KE5PT0tLQsyzOcUF83vSbU36/fAMig8J3pmudCRIWCt0lbGDFSj/tIu9+XQPXEToj84R/+oczn8zKTyciJiQn5ta99LdTndrIQiUK7x2Ib5RYOI+ZMN0rT4Dv62aBt6tun3hbbtl1hGRVC4Wbn0GV4eNjXgKoKjyCPilquu+46z2ucJ6CaxmXZbNZ58qdGN5/PG40tTZzt7u6WR48eDS2idPGiT+RV5131/OB6oxQKBfn44487+2Y6Ztu25blz51zvM1XpKCFEr2Ga2Konk9q27QgbPXxn+numwpuG4miZMO1JUigUPCXIinp5MNr9vlQNKFl+m1gJkaeeekqmUil58uRJ+dJLL8lf//Vfl7t373Y9TZqAEAmmE7LTW13q1wjXsepcqveg4JJVueMulUpybGwssEMnt5w8edJliPxyTNLptHEabyKRCOy86rf09vbK1dVVubX1dlty1QjO1GKeiolsNhuqiRu35HI5JwdjfHzc2X4mk5Ff+tKXAufB+Ak+VfGUTCZ9W/vT8ms1dE8lENPvh4Zt6PH29/c74Tv692zbtiyVSq7rRV+n8vaNjo6yw/aUKNH/rqr1YASV5HPHEff7UlRafR+LG7ESIj/1Uz8l77//ftdrt912m/zkJz8Z+FkIkWA65eJv1ZNEo1zHfrNCdAOsd4rVe0HoT5NhF1UhwiVEnjhxwmX0dC9IIpGQCwsL8uzZs57PHjhwIHToJJVKOZ6JdDotb7vtNmeYnd/nqAgyeRv8tql60Sjj79cf5sYbb/SEsBKJhLzmmmuM61f/Vx4HP8+Jyo1Rhp9LcFUUi0WZyWRkV1eXtCzL5Zmj18Xk5KQslUpGEZHP5125L7T6bH193dU63hTOCevBCNukUG873w73pSh0wkNhPYmNEHnjjTecqZiU//gf/6P8p//0n3re//rrr8vt7W1nKZfLECIhgDuwOhrpOi6Xyy7DZ9u2XFlZYbt9UiOhG0y/gWf6wnUw1Q1sUOdV3Yiura3Ja6+91vV6NpuVBw8ejCSKhLhawsuJgY997GPGz1QzcG9gYIAtcfXrmLtv3z42v4R6i/QeJd3d3XJhYUFubW0ZE1v17qZqf/z+Zr/61a+6PDjqGqDXhjLwSpBw17T+3aprnFZtKU9FLUbU9NlisWgsNe7U+9JODEeZiI0QeeWVV6QQXlf37/7u78rh4WHP+x988EH2jxlCBNSbZjy9+D2Fc51iaZ6A6mWiDIXaZ7W+KCET5aovFK6WJlNDlM1mnX2wLEvOzs66yo/p+2tZBgYGjFUz1Sx+HhKV+2H6XqnQMAmd/v5+3060aunq6jK2sOeEQFhDpF87pl4iej8bKb2Cy1RhpfJlpKzdswoD/DZI0L1K7ITIxYsXXa//zu/8jrz11ls974dHBDQLkzuZls7qLbyreYLb2tryeEFU1UylUpGjo6MeA0EFA9d0KpfL+XpGqPFT3hZa1aMM2aFDh5z15HI5Z6bN6uqqk0NBE0xXVlYCu5aaFmUsTY22wiym+TF+21PfATWKi4uLjmHmjqe/v985L4lEQt5+++2e90VtY69PlabXG4cqF9cFhF9zM91rop87fT1R96fe5cKdDEqWYyREooZmdJAjAhqJXuFChYk+kZd7IgxblaMbz2w262lcRhfqGVHlnBsbG1JKr7v7s5/9LOsd0Ie26b1FbNuWq6urTvkpLXHd2Nhw7XMikZDj4+O+Lez9FpUjos4t12graOnr65PLy8tyYWEhVLjmwIEDnmZzuockyNNjWZZ8/PHHWe9TLpdjW8+bBFa1M6IWFxfZ9VHhQb9T+r3lcjmjcNO9KPUCBhiCTBEbISLl1WTVj3zkI67Xbr/9diSrglihGyyVJMiFatQsmEOHDl29oX/721I++6yUZ85I+eyzcv3iRWlZlhwbGzN6LlSPEq5XCHXlr66uyj179nhmkti27dukTAkYldx46NAhJ2lT9w6oUlFqoIrFossA61OCo3gE0uk0W7kRVdCk02lpWZan7bxpOX/+vOOB0T0SQfk2ypDTCc7qPAR5ZWzb9ngghoaGPL1lgkKD1OPhJ2y2trZc3yH9HtfW1jzikZuTVA9ggBGiosRKiKjy3VOnTsmXXnpJ/sZv/IbcvXu3/Ju/+ZvAz0KIgGZCbyIqR4Nzfefzeblr1y4phJDvE0K+kkhIKYSzbP34dd0w0ERBIa4mO+qNsOiytLQkNzY2XGGW22+/XWYyGdnf32/0TigBQZ+QE4kE22OECiO1b7lcTp48eZI11F1dXa4+G3S5/vrrXT/39vZ6EmJNVTxBCz0/piF9QgjZ09PjeExURYtt266x95wnKplMsuKqmmRZJUJyuZyrVwknhkxGi4oQ27Y9QxL10BPXhVVPmKafDTOvptq/nZ1qgFE14yZWQkTKqw3NbrzxRplOp+XExIS8cOFCqM9BiIBmw7nsadWCuqEkk0n5PiHkWz9eqBBRr73PYDS4p1eu14dKJqRPtYlEgk2O7Ovr84gA3ZjSVuO6C31kZMSVG6IW7ul/165drv4USvxwwkUPD1Q7V8YvYZQTAvq+0EnNm5ubcmRkhO06q4ucVColb7jhhsj7Ozg46GraZpp4y4lCXYSYmqupihxTF9alpSWnBDyXy7kao3FltdUCA3yVTmmlUC9iJ0SqBUIEtALdSOdyOc8T3trKinwlkfCIECpGXk2lZF5zyUt59aY0MDDglIZysXw6y2Zzc1PeeuutrMFLJpOu7p2mRe9qynkEFhYWPN1GOS9BIpGQ8/PzcmxsTGYyGTk4OChXV1fl1NSUtG1bLiwsSMuyZCaTkVNTU3J1dbWmxmhq0cWWaUmn0/L3f//3XcemutbShN3R0VFPXgW3pFIpY9M3ek6oQLzhhhscMaTCZCZDpF9vMzMzntlFypukCywqTpTXjYpl9bop9KM3RqsGGOC3QSuFt4EQAaBKuCdU/Ulza2vrak6IQYTQ5fKf/zm7HdVmXDeCNByUz+dlsVg0VpnooZChoSG2v0c+n5enTp3yzEHRPQK5XM7j4hfCG55QFRfKsKsbLO0kWy6XnYZb6+vrrCeEe5JPJpPyscce87zfr8vr0aNHjZ4WWjWkG/d8Pi/Pnz/Pzp2hHqgwIkr1E9HPn2VZjnEOOyMqmUzKpaUlWSqV5MTEhCe/xhRuoWKDto83zUuqpziAAQY6ECIAVAEX59aFgpMkeOZMKCEiz5wxbq9cLrPrD5uwqC8nT55kBUuhUDAmtaonbWpATZ1TE4mEK9HR1NLb1NTKJHCUsU+n03J5eVlKKeXy8nJoL4pK3DVVD+n7p4c+uNBLlPLi7u5ux9jrIbbTp0+Hvt5mZ2c9fWW4QXWcx47zRKj+MZwIgDgAjQZCBICIcIbUL18krEdEPvuscZuqnbcuGmg+yvT0tDxz5kwogxhkPE2t0gcGBlzCY35+Xo6MjHje98gjj/g+TXMVOer4wjQgow22TImW+vHQ7+Xo0aOsCONKdrmQjGo1oI4hSqKqaganfwdc/xD9eqPzZrgqKroOU06JaRu0B021HotGeTuc9b75pqvqTL75JoRSBwAhAkBE9Dg3fZJXRovOjjl7+rTcEt5EVbW8Ja5W05QN1WG614OrMiiVSvKpp56qSmAEDWKjP9OZLktLS04OC/fZtbU1XyPhF9rSW6T7GdNSqSTPnz9vrLCxLEuura3JwcFBj5jTz4EuLE3v7+3tdd4XtnGaabu0a66pmdn09LQzlI8KJt2rMjIy4ql2ilKZUksOR9jPqmF+HNz1otZ7fzYrfzgw4Prb+eHAgLyfTG4G7QmECABVoJ7QuIF1XKfLoKqZDySTbDJgmCoD5Va3bVvu2rVLJpNJ+eijj7KG79prr3X1u1CGMKoBzeVyjnGnr1PPgBIj3HlT6E24lNAZHBxkk19TqZSrlb3yFGUyGXns2DF2X0dHR+Xy8rK0LEvedNNNrv2jYkefgKuO03QOBgcH5ezsbNXVPUJcHSioJ4xyVSOVSkVubGx4mteZEomrrUxpxBwZ/VrVxZTCJHTK5bK8P5v1/fu5P5vt+EqbTgZCBIAaME3OLRaLjhHr6uqSqVRKfnjfPs8T3SuJhPxAMil/8id/suqnTNWES+3DY489ZvRyqC6pqqS2ml4dNDmTvj4wMOARJ4lEwun0ynmSdI9DKpXyrXjp7u42Dv3z+0yqu1seFkL+UleXPCyE7BZCnj17VhaLRVd1Ca0q4pqNcQPvgrbt934qPvyqZfTvnH5W/5cz9rR7b5RtRO3zEfRZ2qAutNB58035w4EBX4/iDwcGroZtQFsCIQJAjeit1Gm+Qy6Xk6urq07FyNa3vy1/cWBA/v9uvlleOXdOlv7iL5yunjrlcllubm46n+V+r6pN/MIceuVLoVCQpVJJLiwsuBIcV1ZWQomSVCrF5kyMj487+6LEyO7du+Xm5qazvzTEZOoZEpRvkUwm5dzcnLOuwcFB38+8T1xtHOcSgN3d8ld6e+X09LR8+umnWcGmV7Uobw3tHFuPhU5U1kMTXM7F+fPnPdvP5/NyZGTEeT2fzzsCUBcfjZ4FE/TZyEKnDjlWIN5AiABQB0xCwFQqqcSA38yafD4v9+zZw+YNcE+23LRa3YWveo6USiX25k8boiWTSTZ0oYyxfqz0SbZcLsuvfvWrjgih+029GHSibzKZZBuwqaoQLkxSKFxtba+3V6ciJMilv7GxETioLpvNujw9JuFT7fRhKhwUtIxYhaMqlYqcmpryfM9zc3OubR86dIjNFYkSvqhlFkzQZyMJnTpUnYF4AyECOpJW9CrgSiXpUx/t16ASOfUy0enpaVksFtm8gSA3dqVS8VSwKANAwzibm5tsyIfuTzKZdIUoVPjCL7GV5oP4PdlzIRm/kmEqqHTBMDs76+qfQZduIQKThN/o75frFy/6Cgh1bGESUy3Lkh/96EeNv+eanSWTSblr1y6P4CyVSs72MpmMM9OI2wfbtl1CybKsmlqnN9IjYvp7MQodeEQ6HggR0HFwRlYZQs6TEEaYhBnDzpVK6gbOVJ1BxQsN81BvRpAbm3pcOAMQ5Pan5215eVlOTU3JQuHqUD9l7IeHh43eANVUTeUBTE9Py1KpJDc3Nz1P9nqS6tDQkOvnm266Sdq2LYeHh11JpLoRTqVSbNWOEEIeDmO8hJA//ePjsW3bOKBP5TfoybnVLrlcztMLRAkIFarTBVYul5MzMzMuz9SBAwdc52RwcNBzzdUiQuqdI+JXWmxcN3JEOh4IEdBxcNNx9U6ZypMQJnkvKGGUlnnqN189z4D2nrBtWy4uLhrnf0S5addiPPRjpR4WNUFY99CYFjo9V53viYkJx6OSyWTk8vKyMZ+Firhbb73VM6NGCG+ORiqVcjU8U69/MKQQ+eCPj2t+fp71NigvjGVZdWk/r64L3ctE9/2hhx5yiRQuFGbqQDs3N+d6LUpIxa/yhYYVdVGu8pXCJKLqCatB1yqqZjofCBHQkei9N6iB4QbT+cXP/W7OVETo+SDcYDfl5dDFh24AFxcXXfuju7EXFxdD7V+1+QHc+QxKZKVhh2Qy6RyTHjbQk2fp61w5K116e3tD9z05HFKIKI+IydOjex30pZqpu/Sz8/Pz0rZtVuRwIkWJI72HSC6XkxMTE75esSBMortSqTjjA/QOrOo6m5ycdLxoppwmun/qIYE+ENBrVXnw0Eek84EQAR2LXwJpVI+Bn8chk8l4mmzp+QQDAwOupmf6E65u6Og6ueNQOQNShivxnZqaYvuUqP4UYVp7Ly0tGQ2lSRDQUJPelZUzsOqJOWzSp2VZMp1Oy3Q67QmrhMkRubJvn+zXhvep/abeLD23Qw035MRm1CWbzTrhJf38/Pqv/zrr+aDnVS2Dg4Ou4Xn18IrRa0Gty9S9VSVg+wl6FaajnkoaplTeSRXqVAIDnVU7GwgR0NGYEkireVo0hUjUU52C3rRzuZxjHCzL8h1Pb1mWXFxcdHkPqMEZHBx0GUcqfvxyWEqlkpycnPTkzNBBaar0Vu2/bgjK5TJrdE+ePOl7TGrJ5/Py0KFDnqd+NRFY/Tw2NubqwWISO/S7W1tbk6dOnWK7oAZVzbzf4M1YWVlhW6jTc88lz/qJjTBTecOui14zesWM3livnl6xeob/OE9evfcZtAcQIqBj4YSDcTBdSMJk+useClNYo7+/37U/6XRajo2NeUIZyvgoQ2VZlifXxQ9Tzszg4KCrOZmaCqvnvGxsbMgXX3xR7tq1izWeYUITurE0GWD9nAwNDRk7v87OzspKpSIPHTrkykvRPRVcH5FyV5cjQnK5nEdwcB4HfX+jDLsTQrClyabFT7TQ85VOp6Vt23JpacnVWI8mDOuiMgwmYcs1kYuaDKuvrx7iBrQ3ECKgI+FucEFJoVHWGfR5/UauJxAeOHDAVY2iXqcGdWBgwGOQ1Kj2MEm2pn3Xc2Zooid92lZP/YVCwenEqvaBqy7xEyT6NvVZMvq21Wfm5+d9S3u/8IUvuH5WJcTz8/PufRNXc0Y++ON/u8XbHgVTYzX6feTzeadNfJTj1pfe3t6qm6ENDQ25ckb6+vqcEutCoSCXlpakbdtyfHzcFaJR3gduxgv1Tqjfc/1LaGfWWsW837VZD3ED2g8IEdBxhJmOq5fJ1pIjEqWkkRo4OrqdVpjQ1x5++GHX59SoenWcUWLjppyZ/v5+j3GkIoQaZPWvKSShG2W9EZifp0FfTp486REhXIIr/XlwcFDOzc2FNvb79u1zHfPMzIzr96lUyhXqOH36tGf7fi3pTUstnVnVOUin07K/v991Hc/NzbkqlPzyiKjgsCzLaZ5Hc3RolRMVOPUWDbU0TwPtD4QI6Dj0m26YxmHVVs0EfV5/z8zMDNv9lHZWNQmGKDd9k2s9KGdGLcePH3clJ05MTBgrfkyGVj2tq4RSdf5175CpdwfnCTEJmaD8i6BFD2+oRRliPWSli5+w2zF5ZqKIkIWFBde+cP1q1Ou00y2Xj0E/R/u1BFU5VZPwbbpGueudemNA5wMhAjoSvR+GXyv1oBBHtaPRTQJGL7ucm5vzbFN/DxUGQTd90/5y8f2gTqFqHdRocHNm9HWur6+7chNWV1fl0NAQO2SP+zztsTE8POwytirMoxJfM5mMPHv2LOtlMIVNdOFy4sQJj8dMGWOu/PvkyZOhhEMikZB79uyRQvC5Kls/fj3MdyHE263b9aROvanbwMCAXF9fd67DSqUi19fXnXOfz+c94crBwUHXPphm6uhl6tUkllJvDPXo0H3KZDJydXW16R2SQfOBEAE7glpbvlfzeU4QcE9/emvvMDNjwnpwlKudC01x4RXdO6GHgrgnb93Y53I5TzdX1X6edpalnUWV6FAChLaVV3NY/J6ct7a2fCtYwiaWcoLHZIRNSbTcElS9c9/evYG5JolEQp4/f971XZv61aiQkrpmVMhvdHSUDcOFDZdls1m5vr7uDFtU+2DbtpycnAwtDPRriZaq09dVMm6UB4BWjHcAtQEhAkADoTdF/elxbm7O1dxJr7BReST0M/qoeNNNt1gsOk/JemfOwcFB+eSTT7oMUiKR8HSBVZ9VT9Z0Roxt2y4hoZJNabUNNR4bGxuuPJOFhQU2b4a+TsNoynhwuQScp0dfCoUCK0aOHTvmOoalpSVW8HR1dUnLsnwFJT2XrmRgEdzP5P+77rrAME0qlfKEK7a2toyhLbUvVIhxHo6VlRXPefVbn/qep6amnFCibdvOz2HRryca6lHhIe568vPCVOu9bBYQSTwQIgA0Aa6EVo+P60/jSgDoN17VgCyoCyZndAYHBz2eg+7ubjkyMuJ6sqZueeVVoftJf6aVPufPn3fta6lUchZaIZTP5+Utt9ziMpSqYyc1GPqTN1eOTfMZbNuWx48fD2VQl5aWpJTSNUMmk8nIUqnkMczHjh1zpuPS/RgYGGDLmm3bdpJYDxsEiL4cDiEEqHegXC6H7mNCvydunUFhoQceeMDzedu25cLCgm/rdw59AKIuIpXg08NPaiSCX4iyGV2GqyXuIqmVQIgA0AToTcjUUXJyclKOjY35ttHWh/VxN10a2tHFCH16TiaTcmlpSZ47d85TvTM9PS3Pnz/vyikYHx935YyoY9C9NPR4xsfHnS6xdCAe3SeVEEqPV386pAarUCjIxcVFT+KoCkWECTPQ8BH1qGQyGZeY0j9D99+yrFClu1Fm3nDfGScAyuWyfPLJJ12v79+/PzAEpXtIgqqQ6NLT0+P6+ejRoy5hGTaJWjfGuujTPT9Re5dwFW6655HSKE+E7v3Qk+bVMcZBJLUaCBEAmkSYjpKmp0tTK3Z6k9bLiv2MimVZjsclbDKv3oeC3mj1m7kagsblAeiJuNmAgWWlUsnlwucqPoQQ8syZM67XTCEL9bp+vmzbdg3ky+fz8tFHH3V9tr+/X05MTMhcLucxzJzXQIjoHpGgkuV0Oi2PHTvmek0JohtuuCFQGAkh5MGDB9lQnBBCZnt75bsSCflBIeQ9XV2BISMavgtCv/Zp3hNddKEYtXeJqWRe9ZnR31dvT0SYhPFMJhPo4dkpQIgA0AKi9CWJMv2XGn2/slKahKq20YjYtf40y1VicMmIdJ9MQmRpacllpA+QmTGDg4NGzwgdyEefsKm3Q5Us9/f3e7wepg6pyWRSPvDAA57Xw+SIlLu6ZJJU8vT19YUWlSo8E7XTq/7+rq6uwMoeIYT8t//23/peS2GuCT2sx3loVN5ItY0IubyXZoVrwg7KjHI8nQyECAANxM/A03JKvxtSmPwS3bCqn02x/yhPsbUeK/dEq4wtndBL3fGlUsk1yZWGZlROgxInukGlIoQauJtuusnV0p5+Zm5uzjWQjX433d3dvn1K/v2///eBDcqCqmY+oH0+kUh4KoW4bSjvTpg8j6Dl/w7Yx/cZPleNEfWrDDMJSFPvEu66M3lE1Oea0Ure72Gj3t1pTbRLciyECAANIkxy2sjISKgbEr2pZTIZV2Mvrvwyk8l4+kIIcfXpOWpcvx7HynUfHRkZcRlQlSiqe3loLo1+nKp6h762uLjoSowtFApyeXlZTk1NyVwux+Z1qLyctbU1Z0oxZyy5pb+/31hBQ7vDvk8Ieamryz3zprtb3rd3r+dzXV1dcnV11fXdDw0Nsdug10K1HVvDeG02hbsB28MPP+zyIEW5llQ5N3ftc8JVD9XoniuTpyOZTMqRkRFXJVqtIioKppLzes7rMdFOybEQIgA0iDAZ/FFujKaGZNzTv26UstmsUxVDG4bl8/m6uKSjuqK5/aXtxPUbtnqi1A0ULW9WSz6fl6urq67kWrp/akmn065Ot93d3XJwcNB1c15bWwuVkMq9R3k1FhYWnO+Cdlbl8i/0CiB1Hh977DHjtgcGBuTIyIiryVvYRR37YYMA0ZfD2mdPnTrlErZhryVapq5f+ybBqedaFApX287TvCqumkt/nRM/jcTUzbjRnpk4VxDpQIgA0ED83LPVuIr1mxpXBUGNgzKSuVzOddNWE2f37NkjNzc3G3asukExPbEnk0k5MzPjOQd+/Tr0c0j7muTzeSeEpVhfX3dtv6+vL7A1elCfjiBxMj8/LwuFgjx48GCgcNm3bx+bP/PQQw8FbiuRSHiOLYyAUqXOUSp76Hweet0dOnTI8SbpcAnQ3LWvi8/FxUXXz/TvghuJkM/nPY3z1GfW1tZi4RHRhVUjhUGUXLRWAiECQIMJileHvSGFNcqqg2YymXQ9Iav+HzTsUS+PSJh9DFteq+eL6LNpuPJWZfD1c0uPrVQqybGxMc/n9QTap59+2snBiTJHhooMXQSECfGYSmq5xdR+XYiroaKw+SLVeERs23Y1gksmk/Ls2bNycnIyMAxQKpVCec6iGmruujP9ban29s3OEaEl5yYPTyNCJUHnJg5AiADQBHRPxsjISKTYLfdk4xemyOfz8uTJk64mZXoVQtibkZPw9uabUj77rJRnzlz998032YS3xcVF137pE2y/9KUvuX6v5z7kcjnn+Lmn2MHBQU9bcxreUeEqeg7V+ZuamvJM0OXWoU8NjrJcf/318ujRo57XC4WCPHHihG/iq/JocW3b9XNg8tSE8YR0dXXJVHf322EiEaKyp7tb2to5SSaTcnx83FWq7SeuS6WSMW9BdVqlU4OD/i78/sZU2KUVIQrTNvV+OHqfkUbla8R9ujGECAANhnsiUd1KObieHFzIgoujc30ZakmOUwlv92ez8ocDAy7j9INsVv5Kb6/LONC23VRo0Hk3ahKvWkyD1RYWFlwiJJvNuhJ0dWNtWZbz+1wu52oYpUJSAwMDxv4ZJ0+erFp8BC35fF6ura0Zt60bd5PXKJFIsIm/QYsSJ+pfrkz3O0LIHzFihFbN6KLw9OnTrjbvNMHY5HXgKjloqbYe3lF/D36G2u+pvxVJm3FKFIVHpIlAiIA4Uo8YrX5To+vUS1lVroT+9FPtE1G5XJb3Z7O+ZZ2/0tvrhDKoAU2lUo5YsCzL1Y58YGDA9USvhzNol1DVATaoZHllZYWdX0LLcNV7ufBFIpEwhpX8vBh+y8GDBx1R1SiRE7Ts27fPqSxZWFiQv7xnj+/3+R3t9U0h5L/q6mJDRrr49RO9plLSSqUix8bGXLN86N+PaaAenQhtyk0K2ra6xpvRWbUZ29RBjkiTgRABcaOeLuEwXVn1HAlqLII8IsYb45tvyh8ODPi67F9JJOT6xYvs9vv7+z1P8JZlydHRUTaEwOVWcN0wuSZuqjqCC2voXhc9yTfsooa+Ba2fHk+h4B24t5cp2TUtYbql9vb2+h6Lk/fy7W/LVxKJwDLde8TVkM3d4u2SXVPIR21XdevVRe/c3JxcW1tz9YWhnD9/3nVtcHkjqrSb/j1MT0+7rm190jQV51xX4k4HVTMtAEIExI1GuGf91rm0tOS6oXNVK9RLoXtY2Im+zz4bKonx/8lmXdUKXOhEGeyFhQVjMuX111/v+vnEiRPO8dEqCWp86HZNo+5N3VBNIuXYsWPs60tLS3J6epoVI8pQhxE3Yft9ZLPZmhuVqe3Nzs7KX9TCa6blMHNcQfueSqVc5eRUqKRSKSck55ekqq4d/dqllUzqWlDGNJPJyIGBATk+Pu4ZHDk4OCgPHTok8/l8bHpmNIs4hYeCgBABoIE0wj3LrVOfvUIrZHSDYWqGxiYTnjkTuqxTrV99Vq92UU/2tm2HLolVSa7qxjkxMRHYN6Lapl7qs+l0mhUzN9xwg1xfX3cSDoeHh9n9XV1dlX/yJ3/CHmNXV5fLsPt5Rnp7e53vhn5Gr4aKsoQt0/3mQw/5Vjfp4kjfH9VoTm/Bb5qL5DeSQF0D+nVPc3+6urpcLf5VyCiXy7kEUBye/ptJHMJDYYAQAaADMLmqqbcgnU47Xgout0Rv/FUoFOTf/dmfRX6CXllZYRPkTGENkyCg7d/pMdFGZVJ6G8T5eSRM4QX1NK+MoUnM0P3itvPkk086T+b6sYapZuEW+v1ls1m5vLwsi8Vi4LnkjPvhkEJEPvusLBaLbHJtIpHwiCw9/MZ5ctLpNDvZmHrmTB1VTU/3em8YtehdhU3zjEA8gBABoEPgkvfojbhYLIbKoHf1XBgakt+/4YbAHJFubVv0yVd/MqYLbY4V1piYZosE9SfxWw4cOMCKEKcjqiYi1M/6jJdUKlWTR4ZbVKk37RYbph/LysqKp/w5TCv3N/r7ZeXv/15OTEz4HksqlXJNjzUtah0qz8OUOO0nRPzyHXRBposkToTEyRsAIEQA6Ej8qmTCVNDQ6pOggW3vE+a4vjIAMzMzrJHiBEpvb68nEZXrLaGj9y+pZqEhhnQ67WqHz+37wMAAK0ioEX744Ydr2ieVhEmNMc3FmZmZMZZAczNogr7PX+ntlefOnQuV67KwsBBKBAYJ4aBhd3rJul4d47fo04Hjlh8BYiREfud3fkfecccd8tprr5U9PT2RPw8hAsBV/LweYXsKlMtll2F9nxDy+zfc4DJcajw8FQl6+azy0OhG+vrrr/dt9/77v//7rtcWFxcjH3OYRX96ptN5VVgiyCCr49/a2nLlKURduCm/an/Ud0QH8SWTSTk7O8u20adeAvr+G2+80fk+9T4iryQSTl+Yzc1NOTo66lrH5z73OVYkBQmCRCIh19bWfEtJ6bWgBKDuDdO7AutiRbWr564nVXkVx4oRECMh8pnPfEZ+7nOfkx//+MchRACoEr+bPTdEztRTgHvKtQYG5L/u65MfFFerZN7xkz8pLcuSy8vLrn1QyZzj4+NydXXVtR4/V//Ro0eNRt+vAdz6+rqTtJjP5+Utt9ziMkJ+3Uz1/enq6nIESDqdDpXXkc1mHcFlmpArhHuWjC6A6P7RhFa9yqlUKsnx8XH2PNHJyoODgy7vDm17/wd/8AdXhY94ewDfLw4MyK1vf9vVPExtV9+WKSnY77ulCdJ6aIV6nPr7+5330d4zqVTKObe6qO3v72c9KnT+jhJtceyhAWIkRBRPPPEEhAgAVRB2Am7QHA+uYZr+9Lm1tSU3Nzc9I9gV6+vrcmhoyHl6zWQyTiUDF+oYGRmRlUpFzs/Puwze3Nyca4S73lOEegiUWNFHvtu27fFU9Pb2ugwnFRx6bkrQohs527blo48+6nmf6p+Sz+fl1taWfPrpp9n1LS0tOf1QJicnnR4xtLxar0hSnWQXFhac4x8cHHSJCLVdKaU8ceKE6/O0TFrKtz1b6XRafvGLX3S9Vx3ryMgIu/9+5b1cvsb58+dlOp2W6XTa8SypY9CH+XHXYyqVciXV0jk8/f39TR90B6LTtkLk9ddfl9vb285SLpchRMCOxq9vQNg5HtxQMm6Muam5Gl0fFSB0Eq6+vmPHjjlP4TQHYnx8XFYqFU84QnlGqMFSr9NzoDp+TkxMBM5u0Z/aw7ZR5wbwDQ8Ps69TsaTQ596cPn3a+R1NqDRNr6UiR5U2LywseH6fTCbl6Oio3NzcdJ03uszPz0sp3R4mVc6sG3IqeLjzoTxu9Lwnk0mX94w24hsbG3OJTPqd08/TkJN+jtPptCN06DHoyaw0J6pdyls7nbYVIg8++CB7Y4AQATsZvxsrN8dDoW66fu3k1ULFjO5R0ce2UwGid4DVn1DptpWoUNDqD5WXQMMx1LhXKhWnk2a5XJabm5tOabPJ00HzPLh5OKbloYceMv5O95ToU4E5QUAFHWcIdW8VDa9Rsabn2KjXR0ZGnO8nmUx6PDePPvooG3oxbYsTHLS6Sa+80RNPafm4ft3qowo4YWdZlvNdWZYlNzY2XOfK1IG3VTNoAE9DhYhJLNDl61//uusz8IgA0FqizPCQkn9C58achxmK5iek6FMuNUhcC3jdiKj16t4YtahkWBrG6u/vDyzFNeW06AmS1PiPj4/LYrHo8uacPHnS1ahNeXPoMejdRPVmb2GEExUraluPP/6472fod2nyTnFJyrqHiwoG6qWg66fdc4OOSV0zKieJm1jtlxNFm+GZrucwCa3wqtROQ4XId77zHfmXf/mXvss//uM/uj6DHBEAWk/UORVc+KbaHBU/OCER1oj4GTfdMHHzecIuqo093a5lWbKrq0umUik5Pz/PejFGRkY8+S30GLimddQY6zkhnAgZHR11CR21jk984hOe91Oxo6hUKk6y7MTEhMvIcoKAnnPLsjwN0rhrYXx83CXKTNUwNMQSZmI1d33oYiTqUDh4VepD24ZmdCBEAPBS7dNalBusycDr7nzOsHHrM8FtRw8R+FUBccaf/pvP553k22KxaKxOMS20EsayLI9XidvO/Py8az9Onjzpep9+riqViiyVSi4DqjrohqnwsW1bnjt3zng+6TIzM2OclmsaIhcUTtIX1eODO0+6YPIToNw+1nL9hk1obafBcnEmNkJkc3NTPv/88/I//+f/LPfs2SOff/55+fzzz8srV66E+jyECABuan1a40QMnQKs38Rt23bliOg3dpovohPkwvYrS9bFgp4zwjUC040cNf4bGxuyXC6zyZh+C92PdDrt6jqqby+fz8u5uTnfY+AMIZeMG1YkDQ0NyT179kjLspwcn6A8DD3sVS2mkJieR0NFGvf9qEXvr8IRRYSHafJnwu/aDCtodjqxESL33Xcfe6E+++yzoT4PIQKAm3o/renChq5fhQump6dlsVj0JIVGubFXcxx0US3R1fvUfqvERi7ZVoUDaIks9Yjo3h1qED/3uc+5vBp6lUbQPhcKBY8gMJ0v/Vz4CQl9P0+dOuUklWYyGXnmzBnW40P7udA8kLDXCO1Foo5ZF6d9fX2e/BuaJ7SxscGKR1qtw4Wvwu4fpRaPiN86/HrfIHfETWyESK1AiADgpZ5Pa7oRLBaLbM4CN/W3lidDP8/O2tqasVRW7UulUnHyF5So0EfKUwOqXqN5CsViUZZKJbmwsGAM16j94EQYXbf+9E09AmHOFxfGCCtGaI6GKZSjh4zy+XwoQ6++JzqYUG80lkwmnfOkl0jPzc35rksXc6pcOWwOBncd6eeS5ulEvWb171UXxPr3h9yRt4EQAaBJtCq7vh5PfNy6CgVvuS51p6tQTT3c1KYnWfp0zJXK6iWr3D5w554arWKxKCcnJ2Umk5GWZTkTjYeHh13nNJPJsH03bNt2+nzQahlOONEGbn7nSx9mF2Xp6+vziJBkMinn5+ddU46feOIJ1tCbrlUuBGbqysrtOw1HqbJr3bui70OUvxtdSNM8G128RvUYBuUvIXfEHwgRAJpAq7Pra4mB65iETb3KIcMQJu9DX8KW+ipoPgx9qrcsS546dcq3+2oul5PZbNYlSvT5NTQxVQghn3zySdeTvul8mfJXHnnkkVBChGtDPzc35yqx3r17txNa0EtiC4WCnJqaYnvS0DCMyVNl27bLM3Ps2LHA0uV6Qa9FleTrJxjC7EOY/CXkjvgDIQJAE2hldn09PSIKTtg0U2yZkjZV3oTJW1DtudfzHIL6i/j9Xv3OsizjPBgl6vTzpapmuPbqJu9DmIWG13p7e13zbdT5OnfunPMevwoo02A6fTtBicOt/nsI420J83eNFvPBQIgA0CRakV1f722qlusmj4hyp+ufoZU2lFpCUnpCpMk1zhn6as7D1pZ3inCYJZfLyYGBAWewXSKRcAmQ3t5etmSXdrtV/1c5OaaBdGrJ5/OuPA+/fdOPSYVp6Pk5evSo8/vBwcHAxnam87S4uBhKQJqqhuqFLqQXFxcbVuaui8ZavJGdCoQIAE2kEd4JE42ompmYmDAadK7JVRQvSa05NFyJaLVlsnT/6T4tLi56tqFXyejL7OysLJfL8umnn/YVBqa+IbTtPfXKqOF8ek5KMpl0zZtJJpNsOCao+RkVI3Q5d+6cr8D1awaXz+fl8vKyr4BstAjhtpnJZFxN+Oh7bduWk5OTkcrcFVw3YHhEvECIANBk6pmv4Ue9QyUbGxuuZEOVb0ETJ5PJpGveR1gxVCqVatpXzrgkEgm2kyhd1NTfMOePK0MVQsiBgQFfMaIn0poW7jrQz9/y8rIT9lAhHj0MNDw87Mq5OHXqVKgwDZfT8R/+w39wvXb06FHfc05FiFqfPrtGCSVTFREd/Kefi1rDepyAoiLN1BE4k8kY5zRF2RZyRHggRABoIs30iEhZ30odvbcG5xFRE3MpYW7ItXhv/BIQ6b5yoQq/Phl0n/T5KLrR1qtQEomEPHHiRKS8DeX1CDp/MzMzvjkotm07Ja96ufH+/fuNn00mk54W7Nw++k1SVueIig6TZ2V0dNRVVk33Q08qNs2TiXIdm64xfdJvLpdzzVVS5zRKvgo6rkYDQgSAJtEJT0hBOSJRPBb6MVdzfrgbvr6PppbuXLUEt990siz9nG3brrbunCA4ceKE5/V0Ou0SA9ls1pXzESTmuEUZTyrI9OZfKm8jm80axUgikWDzO6677jrHE6MSVU1epnQ6LW+77TY2GZWWPCcSCZe4y2azrMeNG6inn5cwnr2gfjQmkcgl5QbR6iq5dgNCBIAm0GlPSNWEl8J8JqrHKMwNf3R0lK1c0JMlTef+/PnzrGdgYWHBY9D7+/tdhlzfbldXl8tboAxxKpVyvU7DW6bzxxlL3TukSoHX19d9K38SiYTzs6nRmenY9ETTZDIpl5eXPeXctEeKft7U/uthvtOnT8vBwUFWFND8i7B/O0H5HLpHiMsbCQum8oYHQgSAJtBJT0jVhJfCfEbduDnBElS54HfD39zcNFYuhD33MzMzrs8fP37c5Sk5cOCAXF1ddQbdUYPW39/vKdPV8yfo7/SEX9P5U14EvYKFHpNexUS9C7qgyOVyvoPzEomEnJ+f94gRrpGcEgam710/n+r7qFQqcmFhQaYTCXlYCPlBIeRhIeQQqe7RhU+9klu5qqhahAgID4QIAE2iE56QqgmfhPkMN+KePgWb8gPC7rNf5UKUgXv6kk6nPcaqVCo5lSypVEoWi0VZLBZdT/bUA0H3KShHRA8rqRCJ7hnQ29XTdeqVP2ESaanweOqpp+TY2Jivl4l+V2Fb2quRAfdns/LKvn1SCuEsr3R3yw/v28eKKC7HJ+rfmikRWffCgMYAIQIACEU14aWwn9nY2PAkB1aTMKgbILod1XLer4W6bqT0/Tx+/LjLSM3MzHj2qVQqOQbTsixnG0Ft2YOqZkyTaW3blsPDw6w3RYUvgkbec7ks1Cug/r9r1y65ubkpK5WKnJubY/eftmf36++ii1LbtuWv9PbKt4SQbxERIn/881tCyPv27vWIGu4aiOJ91DvnVnvtgeqBEAEAhKKa8FLYz5RKpZqNQZjpwJOTk/LQoUNs9079GHQRxSVnJhIJzxN5pVKRTz31lKs7KTe5V1+4c6Qfk97ELZ/Py127djnHYxI+lmXJ5eVlo2cqqArHtm156623yosXL7rOFbf/nHdL5YhQIba0tORaV/ePPR+6CKFiZFMI2R1wzqIKZiocqfej1vJdEB4IEQBAaKoJL4X5TD1CM2GmA+stxlWFCmek/NrI0xwHavxpifPo6Cg7E0b3DtBQhUmMmM6f7klSx0hzKFR1ijK2VKgtLS057+vq6vKUsap1Dw4OSsuy5PT0tCwWiy6Bp4fa1tfXXV4UZdyLxaKr+RoVlltbW/Jf9/WxAkRfDourOTp+5yxKCLFSqcipqSnflvVTU1NtETptVyBEAACxQM1R4Vz+5XJZlkqlyAPI9OnAXGWJHh7QjRQ19tSI69Uc5XLZ1ZMimUz6hjxMs1aiVE/pvV38FjofRhlY2hyNJtQqD4lt267jpOJEXx89h/39/U5Le12sqARbXVgWP/3pUELkgyHPWZQE6XpN+AXVASECAIgFJqNqSoI0EbVnCWeoKarSJJPJyGKx6FoHNcqLi4vGpFK6DA8Ps4bUNOjOD24KsUn0qNbq9NjVMZ8/f97xClCP0cjIiBNiymQycnV1lQ27cOLGsixPFYraHjfV9xcHBkIJkZ/5sWAKc134lYx3UiVbuwMhAgCIBbo3wa8sNIgwPUv099DyUC6vgMtR4aotCgW+i6taVF8Pun6alxLV8AU1O1PeDT1Ekc/nXV1S1fa5KiPbth0RViqVZKlUYsMfujiJco7D5ojkh4Zc29nY2GDPWZAg7bTePu0MhAgAoOVwT/aFgrdRlqkdu0J1Vb354EFXH4qbDx50PYVzRkp/yg/b+VY3tnTIHl1OnDhhTJY0JUKGFSbz8/NGIdLb28u2Uef6lahzGLbvCnceaQM1/Xe5XM6YxBumaub93d2udZs8FmFzRKopRwf1B0IEANByTImhYQwnXcfExIR8f3e33NIM2daPjdjExIQr2ZIzhpyHgxokKg5Mhtivm6sqw6VJujT3RF93UHhAn5ViEiOcZ4br4Mqtzy8nQxdiIyMjrKhTP6fTaU/bfJpcfH82K1/XElc3yfen90fR8zv0ainlyTF5Oqpp0AfqC4QIACAWUIPCeRmCPAMbGxvy/T927ZueqP9Vd7eTfGl6Mh4YGGC9AfR9evWILmhMvTKUSFHdQLkSY27dflVHpsocv5JcOplYz53hvCf6MamQDmfE6Xb15Fj1uj6tmHZWLZfLUr75pnzx8593PFrdhmuAy/PQK7CokDOJO/16W1xcbPvmg+0EhAgAIFZU+4Ra+fu/l3+bSvnmGLyaSsn/a3LSmKBo27arxFQ3ppznhK5Lz3PRc0F0g84ZaVqZEnTcygtEjT/9PCdGTEP/9MZedD30s8vLy2zCqt73RZ0n3eOUy+U8HVLDJBNz58KU50Gn+upCzq9pnVpMicudnMTays7PECIAgNhQU8z+2WdDVV1cOXeOveFSQaAMpkkcqDACVw6qKn/0UJI6tvHxcVd3V86AhxVf1CPCledy3hBTEvDi4qKnsRcXduGaz3Hb5FrYr62tGfNwOMFXKLiH5flVuFRzzfgl3NJ94t7bSUmsra4gghABAMSCmqsYzpwJJUTkmTOhts0Zc2qYTE+QqvcId9NWT5amRE4uXOEHbcZFK2CkvOoVoL1BdEGgiyI/D5BaVEiJDs/jvB6WZclbbrnF9VmaxEsH1dF1DQ4OuoSK2k81Rdh0DVTjRfO73rguv52cxNrqCiIIEQBALKj5qSykR0Q++2zobXO5A/XCVNoaxSNSqVTk5OQkG0ZYW1tzvBJ647ZC4e0Be1ypcxgvAw1/0H0uFotssi5N4lUdbaV0z3rJZDJybGyMTY4N6rMSpmRbP3d+1xvtiRLlO2lXWllBBCECAIgNtcSpw+SI/G0qJSt///ehth2lM2fU/TX1/gj79K22yz3Jlstlef78+cBcFWrc9X3gclS4p2PO+Pv1g6E9TfRzrbaZz+dd3hO6D6ZzWnVeUcD3p08qDuOlamdaVUEEIQIAiBXVGvcwVTPv7+5mS1Z1wjwdVuvB0ZNeubkstKmYbrRVgqoKb3DrU0/yyWRSrq2tsdvnclXUesIka9LKHk7ERO0HYzKCxWJRlkol9pool8tyeXnZeBy1GNGdWtYb1bNUDyBEAACxoZbwjF8fEa4PhYmw8fKNjY3IcfUwg/kKhYIslUrGpFfqcaBiRA+TWJZlbAC3sbHBJoDqosZk/P1Kl5WIidIPRglMLhQ2OTnJCqOtrS2XiKN5J7XmNnBC1C9htlNKeuERqREIEQDan1qT5sJ2VvUjihiKGlc39b2gPTmmp6dlqVRiEztNZcArKyuRkl1rEXxhviMlYnRhkc1mXfN66OdMibMmjxEVXpyXpdpqD+749DlI9BrslJJe5IjUAQgRADqDetwQa3UvRwkPRX2KDLvuoPPAeRyiPMlWGwILK2K40A0VE/oxmprAcaXU9Gc/z081ngru+PQRBMqr0yklvaiaqRMQIgB0DrW4iFvhXm5UXD3oWLjKm2Y8yQaJGC5041cSqzdX049dFyOcqGn08dH94aqQOO9Xu3RnRR+ROgEhAkBnUY1xb4V7udHCx3Qe/Pqc6EazmU/qYftz0NyOiYmJQCOoV7CYrolGGvmw33WrDXs1oLNqHYAQAaBzqMa4t8K93GjhYzoPNCzT1dUlE4mEZ4bO2tqaTCaTcs+ePXJzc7NORxxMkBHmclnCeFg4ETM4OOhJYG20kQ8jkFsd6mg3IEQAALGiWuPe7KfQRhsbv/OgQhmqi6n6P21RT/tyNNvgmYQF5xUJEmz6Z7LZrKt9vBIjfue9Xk/7QQKZbkffn6BW9TsZCBEAQGyoR9VMs9zLjRQ+fudBGTNVKeLXRySswWvGedPDRWGalumD+FToiXaNVeLENCiwXt9TkEDm5g9xwoVWQTWDdshVgRABAMSGdoutN+omH3Qe8vm8q016LXkqzTjnVFipfddDTIWCt427amHP9RHRxYjpmOvhuQpbssyJodnZWdc+zs3NVX0eo9Iuf08QIgCAWNEOT3DNIOp5qLZypxn5DNQg0rbyVIyYBttVKhVjZ9W5ublQx1xrLk81JcuFgrujbCMSmYNol1wVCBEAAGhzaq3caUa1ERVWNPlUz59Qk4SDMA3d29raYoVareeomv4vatFn7jRTjLSyUVlYIEQAAKCNqZehaVb/FeVdoKEMmgNi23ZguKBYLDp5MHpPEr91NGuOiu6pmZ2dlVJ6vyuTp0fK+nr/WtFbJwoQIgAAECOihGTq7XqPaqirCaPRfdZLedXP3D7TicN6Auv6+rrHQ6LPymmWMaYJxdx21H5MTU3JycnJpuVvtGKYXVhiIUS+/e1vyw996EMyn8/La665RhYKBfmZz3xGvvHGG6HXASECAGh3oiYX1jMZMaqhrmXbpuZmtDLGtC1uUGAymZQjIyNOKXMmk3GFd5oVnohSGUTnCTU6fwMekRB8+ctflv/m3/wbubi4KL/1rW/J+fl52dfXJ//Tf/pPodcBIQIAaHeq8XDUI7k3jKHWt0P3leZ1hDGkUYUIN7W4XC47Tdv0z1MRUq3XKOp5rWY7zRBIyBGpgc9+9rPy4MGDod8PIQIA6ASabTjCGFBacqt3MlWCIpPJyMXFxcB9rTY043de/EIO1XhumvUZ/bjq7a1A1UyNfPrTn5bveMc7jL9//fXX5fb2trOUy2UIEQBAR9BMV3oYA6qX1qr3FYtFdhhdoXC1TJfzxtSSrGqqSAk6T83wblSzHUWj8jfQR6QG/vqv/1ped9118uTJk8b3PPjgg6xbD0IEANAJNDO5MIwB1Y3w4uKiU7mSzWZd+6pKVU1GTi/f1ate/Mp39fPSKM9RK/JKGiE626EvT0OFiEks0OXrX/+66zOvvPKKvPnmm+WHP/xh33XDIwIA6FTimlzI7RfnlVA/c16DWsIFJo/I2tpaqHVENcqN/h7aIX+jGTRUiHznO9+Rf/mXf+m7/OM//qPz/ldeeUUODw/LI0eOyLfeeqthBwIAAHElyDipRE2OZjzh6h4J1WY9mUzK48ePOyKECgRKPXIp8vm8HBkZcQkeXdDUK4ejUZ6pdsnfaAaxCc1cunRJ3nLLLfKDH/ygfPPNNyN/HkIEgPhRi1u4HVzK9SaMceLmrtD3NDLmXw+PiJT1ydmoVCqedvFqnY2oaqm3R6Rd8jeaQSyEiArH/PRP/7S8dOmS/Nu//VtnCQuECADxopYb7U69SQcdt23b7GTdZjxF1zNHJCrNmqCrVwQ1OmyyE8U2RyyEyBNPPGHMIQkLhAgA8aIW1/NOdlsHGSd9sFoz8gpM30c1VTPV4ndeSqWSLJVKxn2n+xHGyxHl+oOYqJ1YCJF6ACECQPyo5akSiXxmmp3MavJIVNNHpFn7pvaP85YE5X2EXefm5uaO9NzVGwgRAEBDqcVoxrV6JA40e3ZIPTur1pOo3rOw11QYT8dO9tzVEwgRAEDDqcVoxnlYV6uIg0CLUx5PWO9ZI7xs8NzVDoQIAKChwCNSX+Jk+OKUHxF0rTTSe4HrtDYgRAAADQM5IvUFoQB//LxnjfbgwHNXPRAiAICGgKqZ+mMyprSnhm5Md0rlRhivRKM8OPCI1AaECACgIaCPSGPQjSk9V3rJ7E45V630nsFzVzsQIgCAhoHOqo2n2d6juH0vrfSewXNXH6LY724BAAAR6OnpEZZlsb+zLEv09PQ05LM7CcuyxHPPPScKhYJ4+eWXxd133y0uXrwo7r77bvHyyy+LQqEgnnvuOeO5jML29ra49957xeHDh0W5XHb9rlwui8OHD4t7771XbG9v17ytsOzdu1f09fU5xzk0NCSEEGJoaMg5L319fWLv3r0dtW3F9va2uHTpEvu7S5cuNfW7aAZdUkrZ6p0w8dprr4menh6xvb0trrvuulbvDgAANJVyueyID4VuIGvl0qVL4vDhwy6BMzQ05Np2oVAQFy5cqIvwEeKqob1y5Qq7vkuXLjlGPug9jRKuYfavkdu+9957xeXLlz3fs/pO+vr6xDPPPBNr4R7FfsMjAgAAMWVoaEjMzMy4XpuZmambCBGiud4XIcJ7YNS+mfa5kUa4lZ67K1euiMuXLzvfhTpHVBhevnxZXLlypWH70GwgRAAAIKaUy2Vx5MgR12tHjhzxGPBaoWGHl19+Wbzzne/0eEjqRSsMbTuFOpotDGNBwzNWagDJqgCAnUorKjea1TejmcfWrtVa7V4+jKoZAABoY1pRudFsw9es7bVzFUw7N1RD1QwAALQxza7c0BNTV1ZWXKGBeoeChGhO/osQ0UMdcQnjhAnLxS2sVDVNEEZVA48IAGCn0qzeHq3yGMTRAxOXMA7d10wmI3O5nLRt27XPcQ4rSQmPCAAAtD3NqtxoRd+MuHpg4lCxcunSJWdbtm2LbDYrXn31VSGEELZti5dfflncdddd4q677uqcCpomCKOqgUcEAAAaTzM7q8bdA9Pq9u66V4buj23bMpfLyUwmE/vkVSSrAgAAiCWtCH9EFRetrljRhWGr96caothvdFYFAADQVJrZubTazrEXL14U73znO52fV1ZWxJ133lmXfaqGuO1PEOisCgAAILY0s3NpNTkwzWokF5a47U+9gRABAADQsfT09IhnnnlGXLhwwVMaPDQ0JC5cuOCa29KKRFo/4rY/jQBCBAAAQEcT1gNDK1aUB+XOO+/09CEx9RmpN3Hbn0YBIQIAAAbi0twKNIdWlDK30/40CiSrAgAAQ6eMYwfRaGYibTvuT1iQrAoAADUSh+ZWO5VWeqKamUjbjvvTCCBEAACAYUeOY48ByhN1+PBhTyJmuVwWhw8fFvfeey/CYh0EhAgAABigsfiXX35ZvPOd7/T0owD1BZ6onQeECAAA+NCsKbHgKvBE7TwgRAAAwIdObyYVR5rhiUJFVHyAEAEAAAM7oZlUXGmkJwp5KPECQgQAABh2SjOpuNJITxTyUOIFhAgAADDslGZScaTRnijkocQLNDQDAAAD7dpMqp2pZlputd8TXacCFVH1AQ3NAACgDuyEZlJxI6onqpZ8D1RExQMIEQAAALEh6rTcWvI9UBEVDyBEAAAAxIoonqhq8z1QERUfkCMCAACg7YmS71FNHgqIBnJEAAAA7Cii5HugIipewCMCAACg7YlaAYOKqMYSG4/Iz//8zwvbtsU111wjBgYGxJEjR8Srr77ayE0CAADYYVST74GKqPjQUCFyzz33iD//8z8X3/zmN8WXvvQl8a1vfUu8//3vb+QmAQAA7CDQAbf9STZy5b/5m7/p/P/GG28Un/zkJ8Uv/MIviB/+8IcilUo1ctMAAAB2ACrfQwjB5nvcfffdyPeIOQ0VIpTvfe974sknnxR33nmnUYS88cYb4o033nB+fu2115q1ewAAANoQ1XeEy/dQfUeQ7xFvGl4184lPfELs3r1b3HDDDWJra0vMz88b3/vII4+Inp4eZ0F3OwAAAEEg36O9iSxEHnroIdHV1eW7fOMb33De/1u/9Vvi+eefF1/5yldEIpEQv/zLvyxMhToPPPCA2N7edhY0lAEAAAA6m8jlu9/97nfFd7/7Xd/35PN5cc0113hev3TpkhgaGhIXL14Ud9xxR+C2UL4LAAAA1JdmlC5Hsd+Rc0R6e3tFb29vVTumNA/NAwEAAABAc1BDAi9fvuzpsaLKoPv6+lzzfBpNw3JENjY2xBe+8AXxwgsviM3NTfHss8+KX/qlXxI33XRTKG8IAAAAAOpLLUMCG0XDhMi1114rZmdnxbve9S5x6623ig996ENidHRUXLhwQWQymUZtFgAAAAAGqh0S2EjQ4h0AAADYYURtiR+V2LR4BwAAAED8iDIksNFAiAAAAAA7jHK5LI4cOeJ67ciRIy1pmwEhAgAAAOwgqhkS2EggRAAAAIAdQhyHBDZt1gwAAAAAWkschwSiagYAAADYQbR9Z1UAAAAAtC9qsCxHM/uHKJAjAgAAAICWASECAAAAgJYBIQIAAACAlgEhAgAAAICWASECAAAAgJYBIQIAAACAlgEhAgAAAICWASECAAAANInt7W1j+/RLly6J7e3tJu9R64EQAQAAAJrA9va2uPfee8Xhw4c9g+XK5bI4fPiwuPfee3ecGIEQAQAAAJrAlStXxOXLlz1Tbuk03MuXL4srV660eE+bC4QIAAAA0AQsy/JMub148aJnGm4r2qy3Egy9AwAAAJoI9YAolAhR03DbnSj2Gx4RAAAAoIkMDQ2JmZkZ12szMzMdI0KiAiECAAAANJFyuSyOHDnieu3IkSOeBNadAoQIAAAA0CRoWKZQKIiVlRVXzshOFCMQIgAAAEATuHTpkicx9c477/QksJr6jHQqyVbvAAAAALAT2Lt3r+jr6xNCCFdi6tDQkHjuuefE3XffLfr6+sTevXtbuZtNB1UzAAAAQJPY3t4WV65cYUt0L126JPbu3St6enpasGf1JYr9hkcEAAAAaBI9PT1GobHT+ocokCMCAAAAgJYBIQIAAACAlgEhAgAAAICWASECAAAAgJYBIQIAAACAlgEhAgAAAICWASECAAAAgJYBIQIAAACAlgEhAgAAAICWEevOqqr7/GuvvdbiPQEAAABAWJTdDjNFJtZC5MqVK0II4QwGAgAAAED7cOXKlcDZObEeevejH/1IvPrqq0JKKWzbFuVyGcPvquC1114TQ0NDOH9VgvNXGzh/tYHzVxs4f7VR7fmTUoorV66IXC4nurv9s0Bi7RHp7u4WlmU5Lp7rrrsOF1IN4PzVBs5fbeD81QbOX23g/NVGNecv7BRhJKsCAAAAoGVAiAAAAACgZbSFEMlkMuLBBx8UmUym1bvSluD81QbOX23g/NUGzl9t4PzVRjPOX6yTVQEAAADQ2bSFRwQAAAAAnQmECAAAAABaBoQIAAAAAFoGhAgAAAAAWkbbCZGf//mfF7Zti2uuuUYMDAyII0eOiFdffbXVu9UW/M3f/I348Ic/LA4ePCiuvfZacdNNN4kHH3xQ/OAHP2j1rrUNv/u7vyvuvPNOsWvXLnH99de3enfagj/6oz8SBw8eFNdcc414xzveIf77f//vrd6ltuBrX/ua+Lmf+zmRy+VEV1eXePrpp1u9S23FI488IqampsTevXtFX1+f+IVf+AXxzW9+s9W71Tb88R//sRgbG3Mamd1xxx3iy1/+ckO21XZC5J577hF//ud/Lr75zW+KL33pS+Jb3/qWeP/739/q3WoL/uqv/kr86Ec/El/84hdFqVQSv/d7vyeOHz8uPvWpT7V619qGH/zgB+IDH/iA+MhHPtLqXWkL/uzP/kz8xm/8hvj0pz8tnn/+efFP/sk/Ee95z3vE1tZWq3ct9nz/+98Xhw4dEl/4whdavSttyYULF8RHP/pRsba2JpaWlsSbb74p3v3ud4vvf//7rd61tsCyLPHoo4+Kb3zjG+Ib3/iG+Omf/mnx3ve+V5RKpfpvTLY58/PzsqurS/7gBz9o9a60JZ/97GflwYMHW70bbccTTzwhe3p6Wr0bseenfuqn5P333+967bbbbpOf/OQnW7RH7YkQQs7NzbV6N9qay5cvSyGEvHDhQqt3pW3Zt2+ffPzxx+u+3rbziFC+973viSeffFLceeedIpVKtXp32pLt7W2xf//+Vu8G6EB+8IMfiP/xP/6HePe73+16/d3vfre4ePFii/YK7FS2t7eFEAL3uyp46623xFNPPSW+//3vizvuuKPu629LIfKJT3xC7N69W9xwww1ia2tLzM/Pt3qX2pJvfetb4vOf/7y4//77W70roAP57ne/K9566y2RzWZdr2ezWfG///f/btFegZ2IlFJ8/OMfF3fddZcYHR1t9e60DS+++KLYs2ePyGQy4v777xdzc3PiJ37iJ+q+nVgIkYceekh0dXX5Lt/4xjec9//Wb/2WeP7558VXvvIVkUgkxC//8i8LuYMbxEY9f0II8eqrr4p7771XfOADHxD/7t/9uxbteTyo5vyB8HR1dbl+llJ6XgOgkXzsYx8Tf/EXfyHOnj3b6l1pK2699VbxwgsviLW1NfGRj3xE3HfffeKll16q+3aSdV9jFXzsYx8TH/zgB33fk8/nnf/39vaK3t5eMTw8LG6//XYxNDQk1tbWGuIyageinr9XX31V3HPPPeKOO+4QJ06caPDexZ+o5w+Eo7e3VyQSCY/34/Llyx4vCQCN4td+7dfEwsKC+NrXviYsy2r17rQV6XRa3HzzzUIIISYnJ8XXv/51cezYMfHFL36xrtuJhRBRwqIalCfkjTfeqOcutRVRzt8rr7wi7rnnHvGOd7xDPPHEE6K7OxZOsZZSy/UHzKTTafGOd7xDLC0tife9733O60tLS+K9731vC/cM7ASklOLXfu3XxNzcnHjuuefEwYMHW71LbY+UsiG2NhZCJCwbGxtiY2ND3HXXXWLfvn3i5ZdfFp/5zGfETTfdtGO9IVF49dVXxd133y1s2xb/5b/8F/Gd73zH+V1/f38L96x92NraEt/73vfE1taWeOutt8QLL7wghBDi5ptvFnv27GntzsWQj3/84+LIkSNicnLS8cBtbW0hLykE//AP/yD++q//2vn529/+tnjhhRfE/v37hW3bLdyz9uCjH/2oOHPmjJifnxd79+51PHM9PT3i2muvbfHexZ9PfepT4j3veY8YGhoSV65cEU899ZR47rnnxDPPPFP/jdW9DqeB/MVf/IW855575P79+2Umk5H5fF7ef//98tKlS63etbbgiSeekEIIdgHhuO+++9jz9+yzz7Z612LLH/7hH8obb7xRptNpOTExgfLJkDz77LPstXbfffe1etfaAtO97oknnmj1rrUFH/rQh5y/2wMHDsh3vetd8itf+UpDttUl5Q7O8gQAAABAS0GCAAAAAABaBoQIAAAAAFoGhAgAAAAAWgaECAAAAABaBoQIAAAAAFoGhAgAAAAAWgaECAAAAABaBoQIAAAAAFoGhAgAAAAAWgaECAAAAABaBoQIAAAAAFoGhAgAAAAAWsb/H1uyHoblIk7RAAAAAElFTkSuQmCC", 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", 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" ] @@ -360,16 +317,15 @@ } ], "source": [ - "# Next, let's take some subset of the correlated data\n", - "\n", - "subset = rng.choice(correlated_data, size=nsubset, replace=False)\n", + "# Report the correlation coefficient of the subset\n", + "print('The correlation coefficient of our sample is: ' + str(np.corrcoef(subset[:,0], subset[:, 1])[0,1]) + '.')\n", "\n", - "# Report the correlation coefficient of the subsets\n", - "print('The correlation coefficient is: ' + str(np.corrcoef(subset[:,0], subset[:, 1])[0,1]) + '.')\n", - "\n", - "# Plot up the original data vs. the samples we ended up taking\n", + "# Plot up both the original data and our sample\n", "plt.scatter(correlated_data[:, 0], correlated_data[:, 1], marker='x', c='black')\n", "plt.scatter(subset[:,0], subset[:, 1], c='red')\n", + "plt.xlabel('X')\n", + "plt.ylabel('Y')\n", + "plt.legend(['True population', 'Subset'])\n", "plt.show()" ] }, @@ -377,26 +333,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will resample ``number_runs`` times the data." + "Now, we will bootstrap. Once again, we define a number of runs. " ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "number_runs =1000" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each time we resample, we will calculate and store the correlation coefficent of the resampled data." + ] + }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -410,6 +373,7 @@ "corr_coef_collector = np.zeros([number_runs, 1])\n", "\n", "# Let's also get the length of the subset\n", + "# When bootstrapping, the size of your resampled dataset should match the size of your original sample!\n", "length_sub = len(subset)\n", "\n", "# Now, for each run\n", @@ -421,10 +385,12 @@ "\n", "# Plot up the corr_coef_collector values\n", "plt.hist(corr_coef_collector, color='black')\n", + "plt.xlabel('Correlation coefficient')\n", + "plt.ylabel('Count')\n", "\n", "# Show the true correlation coefficient\n", "plt.axvline(x=correlation_matrix[0,1], color='red')\n", - "plt.axvline(x=np.median(corr_coef_collector), color='green')\n", + "plt.axvline(x=np.median(corr_coef_collector), color='blue')\n", "plt.legend(['True correlation coefficient', 'Median correlation coefficient'])\n", "plt.show()" ] @@ -433,43 +399,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We see that the median Pearson coefficient is not exactly the true Pearson coefficient. Why is that? How much data was selected in the subset? What happens if you increase the sibset to its max?" + "We see that the median Pearson coefficient is not exactly the true Pearson coefficient. Why is that?\n", + "\n", + "What happens if you increase the size of the subset (e.g., your sample of the true population)?" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "### 1.3 Monte Carlo\n", + "\n", + "Named after the casino in Monaco, Monte Carlo methods involve *simulating new data* based on a known (or assumed!) statistical model. Unlike the previous two examples, we do not take draws from an existing sample. \n", + "\n", + "Monte Carlo techniques have many applications. For example, you might see Monte Carlo methods used for probabilistic risk assessment, uncertainty propagation in models, and evaluating complex systems' performance. It is especially valuable when dealing with complex geospatial, environmental, or engineering models, where the relationships among variables are not easily captured by simple parametric methods." + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1.3 Monte Carlo Resampling\n", - "\n", - "* **Data Simulation**: Monte Carlo resampling involves *simulating new data* based on a known or assumed statistical model. It generates *synthetic data points* according to the specified distribution and correlation structure. It does not draw from the original dataset.\n", - "\n", - "* **Estimation**: Monte Carlo resampling is used for *simulating a wide range of possible scenarios* and assessing complex systems or models. It's particularly useful when dealing with intricate, non-linear relationships, and it can estimate a variety of statistical properties, including those involving complex simulations and models.\n", - "\n", - "* **Correlated Data**: Monte Carlo resampling is *well-suited to handling correlated data* as it explicitly incorporates the correlation structure specified in the simulation model. This makes it more adaptable to scenarios with intricate dependencies.\n", + "Here, we demonstrate Monte Carlo sampling by trying to estimate $\\pi$.\n", "\n", - "* **Applications**: Monte Carlo resampling is often employed for probabilistic risk assessment, uncertainty propagation in models, and evaluating complex systems' performance. It is especially valuable when dealing with complex geospatial, environmental, or engineering models, where the relationships among variables are not easily captured by simple parametric methods.\n", + "We begin with a central conceit: that the ratio of the area of a circle to the area of the square is $\\pi/4$.\n", "\n", - "We will test Monte Carlo sampling by trying to estimate $\\pi$. The ratio of the area of a circle to the area of the square is $\\pi/4$. How can we use this model to estimate a value for $\\pi$?\n", - "\n", - "The ratio of the area of a circle to the area of the square is pi/4. How can we use this knowledge to estimate a value for pi?\n" + "We then imagine a circle of radius 1 that is inscribed within a square with sides going from -1 to 1. " ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "# Let's imagine a circle of radius 1 that is inscribed within a square with sides going from -1 to 1. \n", - "\n", "# We're going to draw a (x,y) point from a *uniform* distribution and then determine whether that point sits within the circle. If it does, we append it to two lists: points within the circle and points within the square. If not, we append it only to a list of points inside the square\n", "in_circle = np.empty([0,2])\n", "in_square = np.empty([0,2])\n", @@ -492,12 +454,12 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -510,9 +472,11 @@ "# We can visualize what we just did\n", "plt.scatter(in_square[:,0], in_square[:,1], marker='x', c='black')\n", "plt.scatter(in_circle[:,0], in_circle[:,1], marker='o', c='red')\n", + "plt.xlabel('X coordinate')\n", + "plt.ylabel('Y coordinate')\n", + "plt.legend(['Points in square', 'Points in circle'], loc='center left', bbox_to_anchor=(1, 0.5))\n", "ax = plt.gca()\n", "ax.set_aspect('equal', adjustable='box')\n", - "plt.legend(['Points in square', 'Points in circle'])\n", "plt.show()" ] }, @@ -525,14 +489,14 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "We estimate the value of pi to be: 3.096.\n" + "We estimate the value of pi to be: 3.12.\n" ] } ], @@ -548,34 +512,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now with a few runs, we do not get a good answer. In fact, a Monte Carlo approach may need more runs to converge. Students should explore how to estimate $\\pi$ for an increasing number of runs and evaluate when and how the calulation converges." + "Now with a few runs, we do not get a good answer. \n", + "\n", + "In fact, a Monte Carlo approach needs more runs to **converge**. \n", + "\n", + "You should explore how changing the number of runs changes your estimate of $\\pi$ (and how your calculation converges)." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Level 2: Resampling for Robust Model Inference\n", + "## Using resampling for robust model inference (Level 2)\n", "\n", "### 2.1 Plate Motion - Geodetic Data\n", - "For this tutorial, we use a GPS time series in the Pacific Northwest and explore the long term motions due to the Cascadia subduction zone. We will download GPS time series from the University of Nevada - Reno, [data center](!http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html).\n", - "\n" + "\n", + "For this tutorial, we use a GPS time series in the Pacific Northwest and explore the long term motions that are due to the Cascadia subduction zone. \n", + "\n", + "We will download GPS time series from the University of Nevada - Reno [data center](!http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html).\n" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ - "\n", + "# The station designation\n", "sta=\"P395\"\n", "file_url=\"http://geodesy.unr.edu/gps_timeseries/tenv/IGS14/\"+ sta + \".tenv\"\n", "r = requests.get(file_url).text.splitlines() # download, read text, split lines into a list\n", @@ -592,12 +555,12 @@ "# # errors\n", " se.append(float(crap[10])*1000)\n", " sn.append(float(crap[11])*1000)\n", - " sv.append(float(crap[12])*1000)\n" + " sv.append(float(crap[12])*1000)" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -700,26 +663,24 @@ "max 2023.726200 3.927000 3.157000 28.663000" ] }, - "execution_count": 54, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\n", - " # make dataframe\n", + "# We now make a data frame\n", "crap={'station':sta,'date':date,'date_year':date_year,'east':ue,'north':un,'up':uv}\n", "if len(df)==0:\n", " df = pd.DataFrame(crap, columns = ['station', 'date','date_year','east','north','up'])\n", "else:\n", " df=pd.concat([df,pd.DataFrame(crap, columns = ['station', 'date','date_year','east','north','up'])])\n", - "df.describe()\n", - "\n" + "df.describe()" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -728,13 +689,13 @@ "Text(0.5, 0, 'Time (years)')" ] }, - "execution_count": 55, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -757,14 +718,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2.2 Linear Regression\n", + "## 2.2 Linear regression\n", "\n", - "There is a clean **linear trend** in the horizontal position data over the course of the past 16 years. Obviously we could fit the data as such:\n", + "There is a clean **linear trend** in the horizontal position data over the course of the past 16 years. We can fit the data using:\n", "\n", "$E(t) = V_e t + u_e,$\\\n", "$N(t) = V_n t + u_n,$\n", "\n", - "where $t$ is time, and we want to regress the data to find the coefficients $V_e$, $u_e$, $V_n$, $u_n$. The displacements are mostly westward, so we will just focus on the East $E(t)$ component for this exercise. The coefficients $u_e$ and $u_n$ are the intercept at $t=0$. They are not zero in this case because $t$ starts in 2006. The coefficients $V_e$ and $V_n$ have the dimension of velocities:\n", + "where $t$ is time. We want to regress the data to find the coefficients $V_e$, $u_e$, $V_n$, $u_n$. The displacements are mostly westward, so we will just focus on the East $E(t)$ component for this exercise. The coefficients $u_e$ and $u_n$ are the intercept at $t=0$. They are not zero in this case because $t$ starts in 2006. The coefficients $V_e$ and $V_n$ have the dimension of velocities:\n", "\n", "$V_e \\sim E(t) / t$ , $V_n \\sim N(t) / t$ ,\n", "\n", @@ -777,6 +738,7 @@ "where $\\hat{x}$ is the mean of $x$. When fitting the regression, we predict the values $x_{pred}$. The residuals are the differences between the data and the predicted values: $e = x - x_{pred} $. $R^2$ or coefficient of determination is:\n", "\n", "$R^2 = 1 - \\text{Var}(x-x_{pred}) /\\text{Var}(x) = 1 - \\text{Var}(e) /\\text{Var}(x) $\n", + "\n", "The smaller the error, the \"better\" the fit (we will discuss later that a fit can be too good!), the closer $R^2$ is to one." ] }, @@ -1327,11 +1289,6 @@ "# the test error is the average of the mean-square-errors\n", "print(\"mean MSE for training set : %4.2f and the validation set: %4.2f\"%(np.mean(mse_train),np.mean(mse_val)))" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { @@ -1350,7 +1307,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.10.8" }, "orig_nbformat": 4, "vscode": { diff --git a/book/img/Open-Science.svg b/book/img/Open-Science.svg new file mode 100644 index 0000000..8652fb7 --- /dev/null +++ b/book/img/Open-Science.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/book/img/YouTube-Logo.wine.svg b/book/img/YouTube-Logo.wine.svg new file mode 100644 index 0000000..d3fd48e --- /dev/null +++ b/book/img/YouTube-Logo.wine.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/book/reference/glossary.md b/book/reference/glossary.md index b0ec482..8d2f4d7 100644 --- a/book/reference/glossary.md +++ b/book/reference/glossary.md @@ -1,21 +1,13 @@ # Glossaries ## Definitions -```{glossary} - -Node: refers to computing unit a single CPU with multiple cores, a dedicated memory, and a dedicated storage. Terminology used in HPC. - -Instance: refers to a computing unit, like a node. Terminology used in Cloud. -Core: a processing unit of a CPU or GPU that performs the tasks. -``` +See https://github.com/UW-ESS-DS/Glossary for definitions, which we will eventually incorporate into this document. ## Tools and Technology (general) - ```{glossary} - [Binder](https://mybinder.org) A free, public service for running reproducible interactive computing environments. Binder is a 100% open source infrastructure that is run by