From 54a3ecdfbbd0ff05734b04f3a3d930c2e75cf97f Mon Sep 17 00:00:00 2001 From: geo-smart Date: Wed, 25 Oct 2023 05:42:34 +0000 Subject: [PATCH] update student version with curriculum book changes --- README.md | 35 +- STUDENT_README.md | 10 + .../2.10_dim_reduc.ipynb | 903 - .../2.10_dimensionality_reduction.ipynb | 1054 + .../2.3_pandas_rendered.ipynb | 349827 ++++++++------- .../2.7_data_spectral_transforms.ipynb | 16 +- 6 files changed, 176024 insertions(+), 175821 deletions(-) create mode 100644 STUDENT_README.md delete mode 100644 book/Chapter2-DataManipulation/2.10_dim_reduc.ipynb create mode 100644 book/Chapter2-DataManipulation/2.10_dimensionality_reduction.ipynb 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/Chapter2-DataManipulation/2.10_dim_reduc.ipynb b/book/Chapter2-DataManipulation/2.10_dim_reduc.ipynb deleted file mode 100644 index fc6f886..0000000 --- a/book/Chapter2-DataManipulation/2.10_dim_reduc.ipynb +++ /dev/null @@ -1,903 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "059aa832-245c-4923-b168-e2add62b0222", - "metadata": {}, - "source": [ - "# 2.9 Dimensionality Reduction\n", - "\n", - "Ideally, one would not need to extract or select features in the input data. However, reducing the dimensionality as a separate pre-processing step may be advantageous:\n", - "\n", - "\n", - "1. The complexity of the algorithm depends on the number of input dimensions and size of the data.\n", - "2. If some features are unecessary, not extracting them saves computing time\n", - "3. Simpler models are more robust on small datasets\n", - "4. Fewer features lead to a better understanding of the data.\n", - "5. Visualization is easier in fewer dimensions.\n", - "\n", - "\n", - "Feature *selection* finds the dimensions that explain the data without loss of information and ends with a smaller dimensionality of the input data. A *forward selection* approach starts with one variable that decreases the error the most and adds one by one. A *backward selection* starts with all variables and removes them one by one.\n", - "\n", - "Feature *extraction* finds a new set of dimensions as a combination of the original dimensions. They can be supervised or unsupervised depending on the output information. Examples are **Principal Component Analysis**, **Independent Component Analysis** **Linear Discriminant Analysis**\n", - "\n", - "\n", - "## Principal Component Analysis\n", - "\n", - "PCA is an unsupervised learning method that finds the mapping from the input to a lower dimensional space with minimum loss of information. First, we will download again our GPS time series from Oregon." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "54bbe9a7-e64a-4d71-bdd9-36503a419179", - "metadata": {}, - "source": [ - "\n", - "\n", - "PCA identifies the axis that accounts for the largest amount of variance in the data. Let's $\\mathbf{Y} = \\mathbf{y}_1,\\cdots,\\mathbf{y}_n $ be the data, measured $n$ times over multiple fields of measurements (the length of $\\mathbf{y})$. Each **column** of $\\mathbf{Y}$ is different measurements for single experiments (e.g., single time stamps). Each **row** of $ \\mathbf{Y} $ is the same measurement repeated over several experiments (or time). The steps to PCA are:\n", - "1. Center and standardize the data by subtracting the mean of each row of $\\mathbf{Y} $ (all measurements at a single experiment or time stamp)\n", - "2. Calculate the covariance matrix of the de-meaned data $\\mathbf{C} = \\frac{1}{n-1} \\mathbf{Y}^{\\ast}\\mathbf{Y}$. By definition, the covariance matrix is positive symmetric, and thus can be diagonalized.\n", - "3. Calculate the Singular Value Decomposition (SVD) of the covariance matrix $\\mathbf{C} $.\n", - "\n", - "\n", - "PCA uses the *(Singular Value Decomposition (SVD)* to decompose the data covariance matrix $\\mathbf{C} $:\n", - "\n", - "$\\mathbf{X} = \\mathbf{U} \\Sigma \\mathbf{V}^T ,$\n", - "\n", - "where $\\mathbf{V}^T$ contains the eigenvectors, or principal components. The *PCs* are normalized, centered around zero.\n", - "\n", - "The *1st principal component* eigenvector has the highest eigenvalue in the direction that has the highest variance.\n", - "\n", - "We will start simple with 10000 data points measured for 2 variables." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "881389fd-3239-4d3a-b073-ae0723a5624e", - "metadata": {}, - "outputs": [], - "source": [ - "# Import useful modules\n", - "import requests, zipfile, io, gzip, glob, os\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import numpy.linalg as ln\n", - "import pandas as pd\n", - "from matplotlib import cm\n", - "%matplotlib inline\n", - "from sklearn.decomposition import PCA" - ] - }, - { - "cell_type": "markdown", - "id": "fb512ee3", - "metadata": {}, - "source": [ - "We will start with fake data, a 2D cloud point of 10,000 measurements over 2 variables." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a89058bc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2, 10000)\n" - ] - }, - { - "data": { - "text/plain": [ - "(-6.0, 8.0)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# create fake data\n", - "xC = np.array([2, 1]) # Center of data (mean)\n", - "sig = np.array([2, 0.5]) # Principal axes\n", - "theta = np.pi/3 # Rotate cloud by pi/3\n", - "R = np.array([[np.cos(theta), -np.sin(theta)], # Rotation matrix\n", - " [np.sin(theta), np.cos(theta)]])\n", - "nPoints = 10000 # Create 10,000 points\n", - "\n", - "# create the cloud of points by multiplying (np.matmul is also called by @)\n", - "X = R @ np.diag(sig) @ np.random.randn(2,nPoints) + np.diag(xC) @ np.ones((2,nPoints))\n", - "# print the shape of the array\n", - "print(X.shape)\n", - "# plot the data\n", - "fig = plt.figure()\n", - "ax1 = fig.add_subplot(111)\n", - "ax1.plot(X[0,:],X[1,:], '.', color='k')\n", - "ax1.grid()\n", - "plt.xlim((-6, 8))\n", - "plt.ylim((-6,8))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7c8c0714", - "metadata": {}, - "source": [ - "### 1. Subtract the mean of the data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e5d1d348", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(2, 10000)\n" - ] - } - ], - "source": [ - "## Remove the mean of the data\n", - "Xavg = np.mean(X,axis=1) # Compute mean\n", - "B = X - np.tile(Xavg,(nPoints,1)).T # Mean-subtracted data\n", - "print(B.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "b679f852", - "metadata": {}, - "source": [ - "### 2. SVD of the covariance matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0e2f63a6", - "metadata": {}, - "outputs": [], - "source": [ - "# Find principal components (SVD): \n", - "# use the option full_matrices =0 will calculate the covariance of B\n", - "U, S, VT = np.linalg.svd(B/np.sqrt(nPoints),full_matrices=0)" - ] - }, - { - "cell_type": "markdown", - "id": "be99f580", - "metadata": {}, - "source": [ - "Plot the results" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "419efef0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "fig = plt.figure()\n", - "ax2 = fig.add_subplot(111)\n", - "ax2.plot(X[0,:],X[1,:], '.', color='k') # Plot data to overlay PCA\n", - "ax2.grid()\n", - "plt.xlim((-6, 8))\n", - "plt.ylim((-6,8))\n", - "\n", - "theta = 2 * np.pi * np.arange(0,1,0.01)\n", - "\n", - "# 1-std confidence interval\n", - "Xstd = U @ np.diag(S) @ np.array([np.cos(theta),np.sin(theta)])\n", - "\n", - "ax2.plot(Xavg[0] + Xstd[0,:], Xavg[1] + Xstd[1,:],'-',color='r',linewidth=2)\n", - "ax2.plot(Xavg[0] + 2*Xstd[0,:], Xavg[1] + 2*Xstd[1,:],'-',color='r',linewidth=2)\n", - "ax2.plot(Xavg[0] + 3*Xstd[0,:], Xavg[1] + 3*Xstd[1,:],'-',color='r',linewidth=2)\n", - "\n", - "# Plot principal components U[:,0]S[0] and U[:,1]S[1]\n", - "ax2.plot(np.array([Xavg[0], Xavg[0]+U[0,0]*S[0]]),\n", - " np.array([Xavg[1], Xavg[1]+U[1,0]*S[0]]),'-',color='cyan',linewidth=3)\n", - "ax2.plot(np.array([Xavg[0], Xavg[0]+U[0,1]*S[1]]),\n", - " np.array([Xavg[1], Xavg[1]+U[1,1]*S[1]]),'-',color='cyan',linewidth=3)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "6bf37758", - "metadata": {}, - "source": [ - "Now let's work on the 3D component of the surface motions recorded at a GPS data. The 3D components are the displacements in Earth, North, Vertical/Upward. the 3 fields are measured daily over years." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "3ff7e067-1e70-407a-9a65-463d84333559", - "metadata": {}, - "outputs": [], - "source": [ - "# Download data and save into a pandas.\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", - "ue=[];un=[];uv=[];se=[];sn=[];sv=[];date=[];date_year=[];df=[]\n", - "for iday in r: # this loops through the days of data\n", - " crap=iday.split()\n", - " if len(crap)<10:\n", - " continue\n", - " date.append((crap[1]))\n", - " date_year.append(float(crap[2]))\n", - " ue.append(float(crap[6])*1000)\n", - " un.append(float(crap[7])*1000)\n", - " uv.append(float(crap[8])*1000)\n", - "# Extract the three data dimensions\n", - "E = np.asarray(ue)# East component\n", - "N = np.asarray(un)# North component\n", - "U = np.asarray(uv)# Vertical component" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c4af647d-efb8-460b-b92f-f164a8ad26ec", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "# plot the data\n", - "fig=plt.figure(figsize=(11,8))\n", - "ax=fig.add_subplot(projection='3d')\n", - "ax.scatter(E,N,U);ax.grid(True)\n", - "ax.set_xlabel('East motions (mm)')\n", - "ax.set_ylabel('North motions (mm)')\n", - "ax.set_zlabel('Vertical motions (mm)')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 217, - "id": "81ddd943-7cdb-4fed-8ced-552b2bfa5b59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(6086,)\n", - "(3, 6086)\n" - ] - } - ], - "source": [ - "# Let's organize our GPS data in a matrix\n", - "X = np.vstack([E,N,U])\n", - "print(U.shape)\n", - "print(X.shape)\n", - "nPoints=U.shape[0]\n", - "{\n", - " \"tags\": [\n", - " \"hide-output\"\n", - " ]\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 218, - "id": "bb6d9182", - "metadata": {}, - "outputs": [], - "source": [ - "## Remove the mean of the data\n", - "Xavg = np.mean(X,axis=1) # Compute mean\n", - "B = X - np.tile(Xavg,(nPoints,1)).T # Mean-subtracted data" - ] - }, - { - "cell_type": "code", - "execution_count": 219, - "id": "916ebb13", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[35.32745808 5.43102533 1.79619328]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Find principal components (SVD): \n", - "# use the option full_matrices =0 will calculate the covariance of B\n", - "U1, S, VT = np.linalg.svd(B/np.sqrt(nPoints),full_matrices=0)\n", - "print(S)\n", - "plt.plot(S);plt.grid(True)\n", - "plt.xticks(range(0,3));plt.title('Eigen values of the 3D motions');plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "31e70fc6", - "metadata": {}, - "source": [ - "There are 3 components of motion, but the majority of the motion is explained in 1 direction." - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "id": "1e976d56", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-0.88025917 0.04281483 -0.47255761]\n", - " [-0.47388028 -0.02872139 0.88012076]\n", - " [ 0.0241097 0.9986701 0.04557137]]\n" - ] - } - ], - "source": [ - "print(U1)" - ] - }, - { - "cell_type": "markdown", - "id": "2ee93bc0", - "metadata": {}, - "source": [ - "The first eigenvector that explains most of the variance is oriented in the East direction. " - ] - }, - { - "cell_type": "markdown", - "id": "ad0bcba6", - "metadata": {}, - "source": [ - "Now let's calculate the PCA values by removing the means of the rows of the data matrix. To remove a vector of values from matrices, we have to subtract 2 matrices. So we build an array of " - ] - }, - { - "cell_type": "markdown", - "id": "843d2a1b-1ae2-48f7-92bc-7ef27fec2769", - "metadata": {}, - "source": [ - "We will use the ``sklearn`` package. The model ``PCA`` deals with the data transformation (removing mean and standardizing), calculate the covariance, and the SVD.\n", - "\n", - "**WARNING:** Numpy and sklean do not organize the data in the same dimension. \n", - "* Using sklearn, organize the data for each field as a column.\n", - "* Using numpy, organize the data for each field as a row.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 271, - "id": "e00fa19e-43ef-4466-821b-40f3180b3768", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PCA(n_components=3)\n" - ] - } - ], - "source": [ - "from sklearn.decomposition import PCA # this is the SKLEAN model\n", - "pca=PCA(n_components=3).fit(X.transpose())# retain all 3 components\n", - "print(pca)" - ] - }, - { - "cell_type": "code", - "execution_count": 272, - "id": "d54c0de2-d376-4c3f-bfb6-7776ad850388", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-0.88025917 -0.47388028 0.0241097 ]\n", - " [-0.04281483 0.02872139 -0.9986701 ]\n", - " [ 0.47255761 -0.88012076 -0.04557137]]\n" - ] - } - ], - "source": [ - "# The 3 PCs\n", - "print(pca.components_)" - ] - }, - { - "cell_type": "code", - "execution_count": 273, - "id": "5a84699a-cbf1-46a6-91ea-21a265f24512", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1248.23439358 29.50088343 3.22684049]\n" - ] - } - ], - "source": [ - "# The 3 PCs' explained variance\n", - "print(pca.explained_variance_)" - ] - }, - { - "cell_type": "markdown", - "id": "e3a01eb4", - "metadata": {}, - "source": [ - "Now you see that most of the variance is explained by the first PC. What azimuth is the direction of the first PC?" - ] - }, - { - "cell_type": "code", - "execution_count": 274, - "id": "1785f2cb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "direction of the plate 241.70456397212095 degrees from North\n" - ] - } - ], - "source": [ - "\n", - "import math\n", - "azimuth=math.degrees(math.atan2(pca.components_[0][0],pca.components_[0][1]))\n", - "if azimuth <0:azimuth+=360\n", - "print(\"direction of the plate \",azimuth,\" degrees from North\")" - ] - }, - { - "cell_type": "code", - "execution_count": 275, - "id": "242e1397-c23d-4b84-9480-f375a3d3e8ad", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.97445067 0.02303025 0.00251908]\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Variance explained with cumulated PCs')" - ] - }, - "execution_count": 275, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# The 3 PCs' explained variance ratio: how much of the variance is explained by each component\n", - "print(pca.explained_variance_ratio_)\n", - "fig,ax=plt.subplots(2,1,figsize=(11,8))\n", - "ax[0].plot(pca.explained_variance_ratio_);ax[0].set_xticks(range(0,3))\n", - "ax[0].set_xlabel('Number of dimensions')\n", - "ax[0].set_ylabel('Explained variance ')\n", - "ax[0].set_title('Variance explained with each PC')\n", - "ax[1].plot(np.cumsum(pca.explained_variance_ratio_))\n", - "ax[1].set_xlabel('Number of dimensions')\n", - "ax[1].set_ylabel('Explained variance ')\n", - "ax[1].set_title('Variance explained with cumulated PCs')" - ] - }, - { - "cell_type": "markdown", - "id": "958de9fc-ef8e-4d87-875a-a4cd9e43d0a9", - "metadata": {}, - "source": [ - "Now we see that 97% of the variance is explained by the first PC. We can reduce the dimension of our system by only working with a single dimension. Another way to choose the number of dimensions is to select the minimum of PCs that would explain 95% of the variance." - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "id": "e7c09351-6f23-4215-b5a5-6d1724e3884d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "minimum dimension size to explain 95% of the variance 1\n" - ] - } - ], - "source": [ - "d = np.argmax(np.cumsum(pca.explained_variance_ratio_)>=0.95) +1\n", - "print(\"minimum dimension size to explain 95% of the variance \",d)" - ] - }, - { - "cell_type": "code", - "execution_count": 277, - "id": "fb2b0a52-c599-41ca-84d7-96b68823085f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "original shape: (3, 6086)\n", - "transformed shape: (3, 1)\n" - ] - } - ], - "source": [ - "pca = PCA(n_components=d).fit(X)\n", - "X_pca = pca.transform(X)\n", - "print(\"original shape: \", X.shape)\n", - "print(\"transformed shape:\", X_pca.shape)" - ] - }, - { - "cell_type": "markdown", - "id": "e28f15e6", - "metadata": {}, - "source": [ - "Inverse Transform back to the original data, but with only the first PC" - ] - }, - { - "cell_type": "code", - "execution_count": 285, - "id": "f5024fcd-922c-43a1-a673-84951f8cbe98", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(3, 6086)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Find the azimuth of the displacement vector\n", - "X_new = pca.inverse_transform(X_pca)\n", - "print(X_new.shape)\n", - "plt.scatter(X[0,:], X[1,:], alpha=1)\n", - "plt.scatter(X_new[0,:], X_new[1,:], alpha=0.2)\n", - "plt.legend(['Original data','Reconstructed Data'])\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c9d04dd8-de1c-47e9-afc7-43ee6d85622c", - "metadata": {}, - "source": [ - "*SVD* can be computationally intensive for larger dimensions. It is recommended to use a **randomized PCA** to approximate the first principal components. Scikit-learn automatically switches to randomized PCA in either the following happens: data size > 500, number of vectors is > 500 and the number of PCs selected is less than 80% of either one.\n", - "\n", - "## Multi-sensors PCA\n", - "\n", - "We will now explore the cases of single motion component (vertical), but multiple sensors. We will download California GPS stations" - ] - }, - { - "cell_type": "code", - "execution_count": 289, - "id": "4514f692", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stationlatitudelongitudeelevation
0AB0152.21-174.2025.48
1AB0252.97-168.85192.80
2AB0463.66-170.57136.55
3AB0654.89-163.42500.43
4AB0755.35-160.4889.57
\n", - "
" - ], - "text/plain": [ - " station latitude longitude elevation\n", - "0 AB01 52.21 -174.20 25.48\n", - "1 AB02 52.97 -168.85 192.80\n", - "2 AB04 63.66 -170.57 136.55\n", - "3 AB06 54.89 -163.42 500.43\n", - "4 AB07 55.35 -160.48 89.57" - ] - }, - "execution_count": 289, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gps_loc = pd.read_csv('gps_locations.csv')\n", - "gps_loc.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0eb78d64", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "647aa4ae-0c00-47ae-8f15-e85c8e4aa327", - "metadata": {}, - "source": [ - "## Independent Component Analysis\n", - "\n", - "ICA is used to estimate sources given noisy measurements. It is frequently used in Geodesy to isolate contributions from earthquakes and hydrology. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7e0d7c0-9003-4dff-879a-231802865b3f", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy import signal\n", - "from sklearn.decomposition import FastICA\n", - "\n", - "# Generate sample data\n", - "np.random.seed(0)\n", - "n_samples = 2000\n", - "time = np.linspace(0, 8, n_samples)\n", - "\n", - "# create 3 source signals\n", - "s1 = np.sin(2 * time) # Signal 1 : sinusoidal signal\n", - "s2 = np.sign(np.sin(3 * time)) # Signal 2 : square signal\n", - "s3 = signal.sawtooth(2 * np.pi * time) # Signal 3: saw tooth signal\n", - "\n", - "\n", - "S = np.c_[s1, s2, s3]\n", - "S += 0.2 * np.random.normal(size=S.shape) # Add noise\n", - "S /= S.std(axis=0) # Standardize data\n", - "\n", - "print(S)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59882032-8754-401f-a9f2-3868aa66bab8", - "metadata": {}, - "outputs": [], - "source": [ - "# Mix data\n", - "# create 3 signals at 3 receivers:\n", - "A = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]]) # Mixing matrix\n", - "X = np.dot(S, A.T) # Generate observations\n", - "\n", - "\n", - "# Compute ICA\n", - "ica = FastICA(n_components=3)\n", - "S_ = ica.fit_transform(X) # Reconstruct signals\n", - "A_ = ica.mixing_ # Get estimated mixing matrix\n", - "print(A_,A)\n", - "# For comparison, compute PCA\n", - "pca = PCA(n_components=3)\n", - "H = pca.fit_transform(X) # Reconstruct signals based on orthogonal components\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4fc634c8-f44b-4383-9004-7a4199a8e32f", - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(11,8))\n", - "models = [X, S, S_, H]\n", - "names = ['Observations (mixed signal)',\n", - " 'True Sources',\n", - " 'ICA recovered signals', \n", - " 'PCA recovered signals']\n", - "colors = ['red', 'steelblue', 'orange']\n", - "for ii, (model, name) in enumerate(zip(models, names), 1):\n", - " plt.subplot(4, 1, ii)\n", - " plt.title(name)\n", - " for sig, color in zip(model.T, colors):\n", - " plt.plot(sig, color=color)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ee662218-0920-4a44-9bcf-64af0bf6675f", - "metadata": {}, - "source": [ - "## Other Techniques\n", - "\n", - "\n", - "1. Random Projections\n", - "https://scikit-learn.org/stable/modules/random_projection.html\n", - "2. Multidimensional Scaling\n", - "3. Isomap\n", - "4. t-Distributed stochastic neighbor embedding\n", - "5. Linear discriminant analysis" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.5 64-bit", - "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": "3.10.5" - }, - "vscode": { - "interpreter": { - "hash": "af2b86a0d97d2bdb49befe19981ba48b79a904c391b62d75845b127da778abba" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/book/Chapter2-DataManipulation/2.10_dimensionality_reduction.ipynb b/book/Chapter2-DataManipulation/2.10_dimensionality_reduction.ipynb new file mode 100644 index 0000000..767fdb6 --- /dev/null +++ b/book/Chapter2-DataManipulation/2.10_dimensionality_reduction.ipynb @@ -0,0 +1,1054 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "059aa832-245c-4923-b168-e2add62b0222", + "metadata": {}, + "source": [ + "# 2.10 Dimensionality Reduction\n", + "\n", + "Ideally, one would not need to extract or select features in the input data. However, reducing the dimensionality as a separate pre-processing step may be advantageous:\n", + "\n", + "1. The complexity of the algorithm depends on the number of input dimensions and size of the data.\n", + "2. If some features are unecessary, not extracting them saves computing time\n", + "3. Simpler models are more robust on small datasets\n", + "4. Fewer features lead to a better understanding of the data.\n", + "5. Visualization is easier in fewer dimensions.\n", + "\n", + "\n", + "Feature *selection* finds the dimensions that explain the data without loss of information and ends with a smaller dimensionality of the input data. A *forward selection* approach starts with one variable that decreases the error the most and adds one by one. A *backward selection* starts with all variables and removes them one by one.\n", + "\n", + "Feature *extraction* finds a new set of dimensions as a combination of the original dimensions. They can be supervised or unsupervised depending on the output information. Examples are **Principal Component Analysis**, **Independent Component Analysis** **Linear Discriminant Analysis**\n", + "\n", + "\n", + "## Principal Component Analysis\n", + "\n", + "PCA is an unsupervised learning method that finds the mapping from the input to a lower dimensional space with minimum loss of information. First, we will download again our GPS time series from Oregon." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "881389fd-3239-4d3a-b073-ae0723a5624e", + "metadata": {}, + "outputs": [], + "source": [ + "# Import useful modules\n", + "import requests, zipfile, io, gzip, glob, os\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import numpy.linalg as ln\n", + "import pandas as pd\n", + "import matplotlib\n", + "from matplotlib import cm\n", + "%matplotlib inline\n", + "from sklearn.decomposition import PCA\n", + "from sklearn import datasets" + ] + }, + { + "cell_type": "markdown", + "id": "c45a194d", + "metadata": {}, + "source": [ + "## 2.10.1 Feature selection via parameter exploration" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ba8e35ae", + "metadata": {}, + "outputs": [], + "source": [ + "# Heatmap functions\n", + "def heatmap(data, row_labels, col_labels, ax=None,\n", + " cbar_kw=None, cbarlabel=\"\", **kwargs):\n", + " \"\"\"\n", + " Create a heatmap from a numpy array and two lists of labels.\n", + "\n", + " Parameters\n", + " ----------\n", + " data\n", + " A 2D numpy array of shape (M, N).\n", + " row_labels\n", + " A list or array of length M with the labels for the rows.\n", + " col_labels\n", + " A list or array of length N with the labels for the columns.\n", + " ax\n", + " A `matplotlib.axes.Axes` instance to which the heatmap is plotted. If\n", + " not provided, use current axes or create a new one. Optional.\n", + " cbar_kw\n", + " A dictionary with arguments to `matplotlib.Figure.colorbar`. Optional.\n", + " cbarlabel\n", + " The label for the colorbar. Optional.\n", + " **kwargs\n", + " All other arguments are forwarded to `imshow`.\n", + " \"\"\"\n", + "\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " if cbar_kw is None:\n", + " cbar_kw = {}\n", + "\n", + " # Plot the heatmap\n", + " im = ax.imshow(data, **kwargs)\n", + "\n", + " # Create colorbar\n", + " cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)\n", + " cbar.ax.set_ylabel(cbarlabel, rotation=-90, va=\"bottom\")\n", + "\n", + " # Show all ticks and label them with the respective list entries.\n", + " ax.set_xticks(np.arange(data.shape[1]), labels=col_labels)\n", + " ax.set_yticks(np.arange(data.shape[0]), labels=row_labels)\n", + "\n", + " # Let the horizontal axes labeling appear on top.\n", + " ax.tick_params(top=True, bottom=False,\n", + " labeltop=True, labelbottom=False)\n", + "\n", + " # Rotate the tick labels and set their alignment.\n", + " plt.setp(ax.get_xticklabels(), rotation=-30, ha=\"right\",\n", + " rotation_mode=\"anchor\")\n", + "\n", + " # Turn spines off and create white grid.\n", + " ax.spines[:].set_visible(False)\n", + "\n", + " ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)\n", + " ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)\n", + " ax.grid(which=\"minor\", color=\"w\", linestyle='-', linewidth=3)\n", + " ax.tick_params(which=\"minor\", bottom=False, left=False)\n", + "\n", + " return im, cbar\n", + "\n", + "def annotate_heatmap(im, data=None, valfmt=\"{x:.2f}\",\n", + " textcolors=(\"black\", \"white\"),\n", + " threshold=None, **textkw):\n", + " \"\"\"\n", + " A function to annotate a heatmap.\n", + "\n", + " Parameters\n", + " ----------\n", + " im\n", + " The AxesImage to be labeled.\n", + " data\n", + " Data used to annotate. If None, the image's data is used. Optional.\n", + " valfmt\n", + " The format of the annotations inside the heatmap. This should either\n", + " use the string format method, e.g. \"$ {x:.2f}\", or be a\n", + " `matplotlib.ticker.Formatter`. Optional.\n", + " textcolors\n", + " A pair of colors. The first is used for values below a threshold,\n", + " the second for those above. Optional.\n", + " threshold\n", + " Value in data units according to which the colors from textcolors are\n", + " applied. If None (the default) uses the middle of the colormap as\n", + " separation. Optional.\n", + " **kwargs\n", + " All other arguments are forwarded to each call to `text` used to create\n", + " the text labels.\n", + " \"\"\"\n", + "\n", + " if not isinstance(data, (list, np.ndarray)):\n", + " data = im.get_array()\n", + "\n", + " # Normalize the threshold to the images color range.\n", + " if threshold is not None:\n", + " threshold = im.norm(threshold)\n", + " else:\n", + " threshold = im.norm(data.max())/2.\n", + "\n", + " # Set default alignment to center, but allow it to be\n", + " # overwritten by textkw.\n", + " kw = dict(horizontalalignment=\"center\",\n", + " verticalalignment=\"center\")\n", + " kw.update(textkw)\n", + "\n", + " # Get the formatter in case a string is supplied\n", + " if isinstance(valfmt, str):\n", + " valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)\n", + "\n", + " # Loop over the data and create a `Text` for each \"pixel\".\n", + " # Change the text's color depending on the data.\n", + " texts = []\n", + " for i in range(data.shape[0]):\n", + " for j in range(data.shape[1]):\n", + " kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])\n", + " text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)\n", + " texts.append(text)\n", + "\n", + " return texts" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "09ee7a7b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's load in the iris dataset\n", + "iris = datasets.load_iris()\n", + "\n", + "# Convert iris to a pandas dataframe...\n", + "irisDF = pd.DataFrame(data=iris.data, \n", + " columns=iris.feature_names)\n", + "\n", + "# Now, plot up sepal length vs width, color-coded by target (or species)\n", + "\n", + "scatter = plt.scatter(irisDF['sepal length (cm)'], irisDF['sepal width (cm)'], c=iris.target)\n", + "plt.xlabel('sepal length (cm)')\n", + "plt.ylabel('sepal width (cm)')\n", + "plt.legend(scatter.legend_elements()[0], iris.target_names, title=\"Classes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "7fe2fc5e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Now, how might we reduce these dimensions? \n", + "# One way is by looking at how variables are correlated\n", + "# Calculate the correlation coefficients for all variables\n", + "allCorr = irisDF.corr()\n", + "\n", + "im, _ = heatmap(allCorr, irisDF.head(), irisDF.head(),\n", + " cmap=\"PuOr\", vmin=-1, vmax=1,\n", + " cbarlabel=\"correlation coeff.\")\n", + "\n", + "annotate_heatmap(im, size=7)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "7a1682e7", + "metadata": {}, + "source": [ + "Now, which dimensions might we remove?" + ] + }, + { + "cell_type": "markdown", + "id": "b5717c84", + "metadata": {}, + "source": [ + "## 2.10.2 Feature extraction via PCA" + ] + }, + { + "cell_type": "markdown", + "id": "9a7c7775", + "metadata": {}, + "source": [ + "Principal Component Analysis (**PCA**) identifies the axis that accounts for the largest amount of variance in the data. \n", + "\n", + "Let: $\\mathbf{Y} = \\mathbf{y}_1,\\cdots,\\mathbf{y}_n $ be the data, measured $n$ times over multiple fields of measurements (the length of $\\mathbf{y})$. \n", + "\n", + "Each **column** of $\\mathbf{Y}$ represents a unique observation. Each **row** of $ \\mathbf{Y} $ represents a single parameter. \n", + "\n", + "To undertake PCA, we will\n", + "\n", + "1. Center and standardize the data by subtracting the mean of each row of $\\mathbf{Y} $.\n", + "2. Calculate the covariance matrix of the de-meaned data $\\mathbf{C} = \\frac{1}{n-1} \\mathbf{Y}^{\\ast}\\mathbf{Y}$. By definition, the covariance matrix is positive symmetric, and thus can be diagonalized.\n", + "3. Calculate the *Singular Value Decomposition (SVD)* of the covariance matrix $\\mathbf{C} $. \n", + "\n", + "As the name implies, SVD decomposes the data covariance matrix $\\mathbf{C} $ into 3 terms:\n", + "\n", + "$\\mathbf{X} = \\mathbf{U} \\Sigma \\mathbf{V}^T ,$\n", + "\n", + "where $\\mathbf{V}^T$ contains the eigenvectors, or principal components. \n", + "\n", + "Principal components are normalized, centered around zero.\n", + "\n", + "The *1st principal component* eigenvector has the highest eigenvalue in the direction that has the highest variance.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "490d730c", + "metadata": {}, + "source": [ + "To demonstrate the application of PCA, we will start with some toy data: a two-dimensional (2D) point cloud made up of 10,000 observations, each with two parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "a89058bc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Generating the toy data\n", + "xC = np.array([2, 1]) # Center of data (mean)\n", + "sig = np.array([2, 0.5]) # Principal axes\n", + "theta = np.pi/3 # Rotate cloud by pi/3\n", + "R = np.array([[np.cos(theta), -np.sin(theta)], # Rotation matrix\n", + " [np.sin(theta), np.cos(theta)]])\n", + "nPoints = 10000 # Create 10,000 points\n", + "\n", + "# create the cloud of points by multiplying (np.matmul is also called by @)\n", + "X = R @ np.diag(sig) @ np.random.randn(2,nPoints) + np.diag(xC) @ np.ones((2,nPoints))\n", + "# plot the data\n", + "fig = plt.figure()\n", + "ax1 = fig.add_subplot(111)\n", + "ax1.plot(X[0,:],X[1,:], '.', color='k', alpha=0.125)\n", + "ax1.grid()\n", + "plt.xlim((-6, 8))\n", + "plt.ylim((-6,8))\n", + "ax1.set_aspect('equal')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "7c8c0714", + "metadata": {}, + "source": [ + "### *Step one: subtract the mean*" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "e5d1d348", + "metadata": {}, + "outputs": [], + "source": [ + "## Remove the mean of the data\n", + "Xavg = np.mean(X, axis=1) # Compute mean\n", + "B = X - np.tile(Xavg,(nPoints,1)).T # Mean-subtracted data" + ] + }, + { + "cell_type": "markdown", + "id": "b679f852", + "metadata": {}, + "source": [ + "### *Step two: Determine the SVD of the covariance matrix*" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "0e2f63a6", + "metadata": {}, + "outputs": [], + "source": [ + "# Find principal components (SVD): \n", + "# use the option full_matrices =0 will calculate the covariance of B\n", + "U, S, VT = np.linalg.svd(B/np.sqrt(nPoints),full_matrices=0)" + ] + }, + { + "cell_type": "markdown", + "id": "be99f580", + "metadata": {}, + "source": [ + "### *Step three: explore the outcomes*" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "419efef0", + "metadata": {}, + "outputs": [ + { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "fig = plt.figure()\n", + "ax2 = fig.add_subplot(111)\n", + "ax2.plot(X[0,:],X[1,:], '.', color='k', alpha=0.125) # Plot data to overlay PCA\n", + "ax2.grid()\n", + "plt.xlim((-6, 8))\n", + "plt.ylim((-6,8))\n", + "ax2.set_aspect('equal')\n", + "\n", + "theta = 2 * np.pi * np.arange(0,1,0.01)\n", + "\n", + "# 1-std confidence interval\n", + "Xstd = U @ np.diag(S) @ np.array([np.cos(theta),np.sin(theta)])\n", + "\n", + "ax2.plot(Xavg[0] + Xstd[0,:], Xavg[1] + Xstd[1,:],'-',color='r',linewidth=2)\n", + "ax2.plot(Xavg[0] + 2*Xstd[0,:], Xavg[1] + 2*Xstd[1,:],'-',color='r',linewidth=2)\n", + "ax2.plot(Xavg[0] + 3*Xstd[0,:], Xavg[1] + 3*Xstd[1,:],'-',color='r',linewidth=2)\n", + "\n", + "# Plot principal components U[:,0]S[0] and U[:,1]S[1]\n", + "ax2.plot(np.array([Xavg[0], Xavg[0]+U[0,0]*S[0]]),\n", + " np.array([Xavg[1], Xavg[1]+U[1,0]*S[0]]),'-',color='cyan',linewidth=2)\n", + "ax2.plot(np.array([Xavg[0], Xavg[0]+U[0,1]*S[1]]),\n", + " np.array([Xavg[1], Xavg[1]+U[1,1]*S[1]]),'-',color='white',linewidth=2)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6bf37758", + "metadata": {}, + "source": [ + "## 2.10.3 PCA on 3D data.\n", + "\n", + "Now let's work on the 3D component of the surface motions recorded at a GPS data. \n", + "\n", + "Our 3 parameters are displacements in: East, North, and Vertical. These measurements are made daily for many years." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "3ff7e067-1e70-407a-9a65-463d84333559", + "metadata": {}, + "outputs": [], + "source": [ + "# Download data and save into a pandas.\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", + "ue=[];un=[];uv=[];se=[];sn=[];sv=[];date=[];date_year=[];df=[]\n", + "for iday in r: # this loops through the days of data\n", + " crap=iday.split()\n", + " if len(crap)<10:\n", + " continue\n", + " date.append((crap[1]))\n", + " date_year.append(float(crap[2]))\n", + " ue.append(float(crap[6])*1000)\n", + " un.append(float(crap[7])*1000)\n", + " uv.append(float(crap[8])*1000)\n", + "# Extract the three data dimensions\n", + "E = np.asarray(ue)# East component\n", + "N = np.asarray(un)# North component\n", + "U = np.asarray(uv)# Vertical component" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "c4af647d-efb8-460b-b92f-f164a8ad26ec", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the data\n", + "fig=plt.figure(figsize=(11,8))\n", + "ax=fig.add_subplot(projection='3d')\n", + "ax.scatter(E,N,U);ax.grid(True)\n", + "ax.set_xlabel('East motions (mm)')\n", + "ax.set_ylabel('North motions (mm)')\n", + "ax.set_zlabel('Vertical motions (mm)')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "81ddd943-7cdb-4fed-8ced-552b2bfa5b59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(6341,)\n", + "(3, 6341)\n" + ] + }, + { + "data": { + "text/plain": [ + "{'tags': ['hide-output']}" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's organize our GPS data in a matrix\n", + "X = np.vstack([E,N,U])\n", + "print(U.shape)\n", + "print(X.shape)\n", + "nPoints=U.shape[0]\n", + "{\n", + " \"tags\": [\n", + " \"hide-output\"\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "bb6d9182", + "metadata": {}, + "outputs": [], + "source": [ + "## Remove the mean of the data\n", + "Xavg = np.mean(X,axis=1) # Compute mean\n", + "B = X - np.tile(Xavg,(nPoints,1)).T # Mean-subtracted data" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "916ebb13", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[36.90832086 5.4954325 1.81382108]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Find principal components (SVD): \n", + "# use the option full_matrices =0 will calculate the covariance of B\n", + "U1, S, VT = np.linalg.svd(B/np.sqrt(nPoints),full_matrices=0)\n", + "print(S)\n", + "plt.plot(S);plt.grid(True)\n", + "plt.xticks(range(0,3));plt.title('Eigen values of the 3D motions');plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "31e70fc6", + "metadata": {}, + "source": [ + "There are 3 components of motion, but the majority of the motion is explained in 1 direction." + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "id": "1e976d56", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.88025917 0.04281483 -0.47255761]\n", + " [-0.47388028 -0.02872139 0.88012076]\n", + " [ 0.0241097 0.9986701 0.04557137]]\n" + ] + } + ], + "source": [ + "print(U1)" + ] + }, + { + "cell_type": "markdown", + "id": "2ee93bc0", + "metadata": {}, + "source": [ + "The first eigenvector that explains most of the variance is oriented in the East direction. " + ] + }, + { + "cell_type": "markdown", + "id": "ad0bcba6", + "metadata": {}, + "source": [ + "Now let's calculate the PCA values by removing the means of the rows of the data matrix. To remove a vector of values from matrices, we have to subtract 2 matrices. So we build an array of " + ] + }, + { + "cell_type": "markdown", + "id": "843d2a1b-1ae2-48f7-92bc-7ef27fec2769", + "metadata": {}, + "source": [ + "We will use the ``sklearn`` package. The model ``PCA`` deals with the data transformation (removing mean and standardizing), calculate the covariance, and the SVD.\n", + "\n", + "**WARNING:** Numpy and sklean do not organize the data in the same dimension. \n", + "* Using sklearn, organize the data for each field as a column.\n", + "* Using numpy, organize the data for each field as a row.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "id": "e00fa19e-43ef-4466-821b-40f3180b3768", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PCA(n_components=3)\n" + ] + } + ], + "source": [ + "from sklearn.decomposition import PCA # this is the SKLEAN model\n", + "pca=PCA(n_components=3).fit(X.transpose())# retain all 3 components\n", + "print(pca)" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "id": "d54c0de2-d376-4c3f-bfb6-7776ad850388", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.88025917 -0.47388028 0.0241097 ]\n", + " [-0.04281483 0.02872139 -0.9986701 ]\n", + " [ 0.47255761 -0.88012076 -0.04557137]]\n" + ] + } + ], + "source": [ + "# The 3 PCs\n", + "print(pca.components_)" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "id": "5a84699a-cbf1-46a6-91ea-21a265f24512", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1248.23439358 29.50088343 3.22684049]\n" + ] + } + ], + "source": [ + "# The 3 PCs' explained variance\n", + "print(pca.explained_variance_)" + ] + }, + { + "cell_type": "markdown", + "id": "e3a01eb4", + "metadata": {}, + "source": [ + "Now you see that most of the variance is explained by the first PC. What azimuth is the direction of the first PC?" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "1785f2cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "direction of the plate 241.70456397212095 degrees from North\n" + ] + } + ], + "source": [ + "\n", + "import math\n", + "azimuth=math.degrees(math.atan2(pca.components_[0][0],pca.components_[0][1]))\n", + "if azimuth <0:azimuth+=360\n", + "print(\"direction of the plate \",azimuth,\" degrees from North\")" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "id": "242e1397-c23d-4b84-9480-f375a3d3e8ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.97445067 0.02303025 0.00251908]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Variance explained with cumulated PCs')" + ] + }, + "execution_count": 275, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# The 3 PCs' explained variance ratio: how much of the variance is explained by each component\n", + "print(pca.explained_variance_ratio_)\n", + "fig,ax=plt.subplots(2,1,figsize=(11,8))\n", + "ax[0].plot(pca.explained_variance_ratio_);ax[0].set_xticks(range(0,3))\n", + "ax[0].set_xlabel('Number of dimensions')\n", + "ax[0].set_ylabel('Explained variance ')\n", + "ax[0].set_title('Variance explained with each PC')\n", + "ax[1].plot(np.cumsum(pca.explained_variance_ratio_))\n", + "ax[1].set_xlabel('Number of dimensions')\n", + "ax[1].set_ylabel('Explained variance ')\n", + "ax[1].set_title('Variance explained with cumulated PCs')" + ] + }, + { + "cell_type": "markdown", + "id": "958de9fc-ef8e-4d87-875a-a4cd9e43d0a9", + "metadata": {}, + "source": [ + "Now we see that 97% of the variance is explained by the first PC. We can reduce the dimension of our system by only working with a single dimension. Another way to choose the number of dimensions is to select the minimum of PCs that would explain 95% of the variance." + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "e7c09351-6f23-4215-b5a5-6d1724e3884d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "minimum dimension size to explain 95% of the variance 1\n" + ] + } + ], + "source": [ + "d = np.argmax(np.cumsum(pca.explained_variance_ratio_)>=0.95) +1\n", + "print(\"minimum dimension size to explain 95% of the variance \",d)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "fb2b0a52-c599-41ca-84d7-96b68823085f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original shape: (3, 6086)\n", + "transformed shape: (3, 1)\n" + ] + } + ], + "source": [ + "pca = PCA(n_components=d).fit(X)\n", + "X_pca = pca.transform(X)\n", + "print(\"original shape: \", X.shape)\n", + "print(\"transformed shape:\", X_pca.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "e28f15e6", + "metadata": {}, + "source": [ + "Inverse Transform back to the original data, but with only the first PC" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "f5024fcd-922c-43a1-a673-84951f8cbe98", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 6086)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Find the azimuth of the displacement vector\n", + "X_new = pca.inverse_transform(X_pca)\n", + "print(X_new.shape)\n", + "plt.scatter(X[0,:], X[1,:], alpha=1)\n", + "plt.scatter(X_new[0,:], X_new[1,:], alpha=0.2)\n", + "plt.legend(['Original data','Reconstructed Data'])\n", + "plt.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c9d04dd8-de1c-47e9-afc7-43ee6d85622c", + "metadata": {}, + "source": [ + "*SVD* can be computationally intensive for larger dimensions.\n", + "\n", + "It is recommended to use a **randomized PCA** to approximate the first principal components. Scikit-learn automatically switches to randomized PCA in either the following happens: data size > 500, number of vectors is > 500 and the number of PCs selected is less than 80% of either one.\n" + ] + }, + { + "cell_type": "markdown", + "id": "647aa4ae-0c00-47ae-8f15-e85c8e4aa327", + "metadata": {}, + "source": [ + "## Independent Component Analysis\n", + "\n", + "ICA is used to estimate sources given noisy measurements. It is frequently used in Geodesy to isolate contributions from earthquakes and hydrology. " + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "d7e0d7c0-9003-4dff-879a-231802865b3f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.495126 0.07841108 -1.31840023]\n", + " [ 0.64019598 1.34570272 -1.94657351]\n", + " [ 0.28913069 0.9500949 -1.646886 ]\n", + " ...\n", + " [-0.38561943 -0.71624672 1.34043406]\n", + " [-0.50777458 -1.24052539 1.74176784]\n", + " [-0.5550078 -0.90265774 -1.54534953]]\n" + ] + } + ], + "source": [ + "from scipy import signal\n", + "from sklearn.decomposition import FastICA\n", + "\n", + "# Generate sample data\n", + "np.random.seed(0)\n", + "n_samples = 2000\n", + "time = np.linspace(0, 8, n_samples)\n", + "\n", + "# create 3 source signals\n", + "s1 = np.sin(2 * time) # Signal 1 : sinusoidal signal\n", + "s2 = np.sign(np.sin(3 * time)) # Signal 2 : square signal\n", + "s3 = signal.sawtooth(2 * np.pi * time) # Signal 3: saw tooth signal\n", + "\n", + "\n", + "S = np.c_[s1, s2, s3]\n", + "S += 0.2 * np.random.normal(size=S.shape) # Add noise\n", + "S /= S.std(axis=0) # Standardize data\n", + "\n", + "print(S)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "59882032-8754-401f-a9f2-3868aa66bab8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.01396018 1.02993002 -0.94915864]\n", + " [ 1.9826966 0.98533885 -0.47232592]\n", + " [ 0.99746591 2.05242661 -1.37841317]] [[1. 1. 1. ]\n", + " [0.5 2. 1. ]\n", + " [1.5 1. 2. ]]\n" + ] + } + ], + "source": [ + "# Mix data\n", + "# create 3 signals at 3 receivers:\n", + "A = np.array([[1, 1, 1], [0.5, 2, 1.0], [1.5, 1.0, 2.0]]) # Mixing matrix\n", + "X = np.dot(S, A.T) # Generate observations\n", + "# Compute ICA\n", + "ica = FastICA(n_components=3)\n", + "S_ = ica.fit_transform(X) # Reconstruct signals\n", + "A_ = ica.mixing_ # Get estimated mixing matrix\n", + "print(A_,A)\n", + "# For comparison, compute PCA\n", + "pca = PCA(n_components=3)\n", + "H = pca.fit_transform(X) # Reconstruct signals based on orthogonal components\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "4fc634c8-f44b-4383-9004-7a4199a8e32f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(11,8))\n", + "models = [X, S, S_, H]\n", + "names = ['Observations (mixed signal)',\n", + " 'True Sources',\n", + " 'ICA recovered signals', \n", + " 'PCA recovered signals']\n", + "colors = ['red', 'steelblue', 'orange']\n", + "for ii, (model, name) in enumerate(zip(models, names), 1):\n", + " plt.subplot(4, 1, ii)\n", + " plt.title(name)\n", + " for sig, color in zip(model.T, colors):\n", + " plt.plot(sig, color=color)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ee662218-0920-4a44-9bcf-64af0bf6675f", + "metadata": {}, + "source": [ + "## Other Techniques\n", + "\n", + "\n", + "1. Random Projections\n", + "https://scikit-learn.org/stable/modules/random_projection.html\n", + "2. Multidimensional Scaling\n", + "3. Isomap\n", + "4. t-Distributed stochastic neighbor embedding\n", + "5. Linear discriminant analysis" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.5 64-bit", + "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": "3.10.8" + }, + "vscode": { + "interpreter": { + "hash": "af2b86a0d97d2bdb49befe19981ba48b79a904c391b62d75845b127da778abba" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} 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
\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)" + "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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latitudelongitudedepthmagnitude
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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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"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": [ + "
\n", + " \n", + " Loading BokehJS ...\n", + "
\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? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\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 \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
  • \\n\"+\n \"
  • use INLINE resources instead, as so:
  • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", + "application/vnd.bokehjs_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bokeh.plotting import figure, show, output_notebook\n", + "output_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6beb321c", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "9c623644", - "metadata": {}, - "source": [ - "Calculate basic statistic of the data using the function ``describe``." + "data": { + "text/html": [ + "\n", + "
\n" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 9, - "id": "2b9f083e", - "metadata": {}, - "outputs": [ + "data": { + "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = 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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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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 \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
  • \\n\"+\n \"
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\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\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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...............
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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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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
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VPB2022-09-282025-01-01 00:00:00
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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", + "AAR EHZ 1984-01-01 2006-01-04 19:19:00\n", + "LWH EHZ 1984-01-01 2020-03-17 20:50:00\n", + "LWD EHZ 1984-01-01 1986-09-25 21:30:00\n", + "LTN EHZ 1984-01-01 1988-07-26 23:50:00\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", + " GEL 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": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_grouped.sort_values(by='start_time')" + ] + }, + { + "cell_type": "markdown", + "id": "29c0b9b5", + "metadata": {}, + "source": [ + "We can apply the sorting to several columns:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cf427b0", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "0952eb9f", - "metadata": {}, - "outputs": [], - "source": [ - "%timeit data.to_parquet(\"my_metadata.pq\")\n", - "!ls -lh my_metadata.pq" + "data": { + "text/html": [ + "
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start_timeend_time
stationchannel
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
VEI2022-09-282025-01-01 00:00:00
VPB2022-09-282025-01-01 00:00:00
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4289 rows × 2 columns

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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/2.7_data_spectral_transforms.ipynb b/book/Chapter2-DataManipulation/2.7_data_spectral_transforms.ipynb index 7ab46cf..2532495 100644 --- a/book/Chapter2-DataManipulation/2.7_data_spectral_transforms.ipynb +++ b/book/Chapter2-DataManipulation/2.7_data_spectral_transforms.ipynb @@ -29,7 +29,19 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'obspy'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/workspaces/mlgeo-instructor/book/Chapter2-DataManipulation/2.7_data_spectral_transforms.ipynb Cell 2\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 8\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mscipy\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39msignal\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msignal\u001b[39;00m\n\u001b[1;32m 11\u001b[0m \u001b[39m# seismic python toolbox\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mobspy\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mobspy\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mclients\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mfdsn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mclient\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mfdsn\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mobspy\u001b[39;00m \u001b[39mimport\u001b[39;00m UTCDateTime \n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'obspy'" + ] + } + ], "source": [ "# Import modules for seismic data and feature extraction\n", "\n", @@ -1128,7 +1140,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.8" }, "orig_nbformat": 4, "vscode": {