diff --git a/README.md b/README.md index 400c0ac..1812e8b 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,43 @@ # GeoSMART Curriculum Jupyter Book (ESS 469/569) -[![Deploy](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml) -[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-book) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-book/HEAD?urlpath=lab) +[![Deploy](https://github.com/geo-smart/mlgeo-instructor/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-instructor/actions/workflows/deploy.yaml) +[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-instructor) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-instructor/HEAD?urlpath=lab) [![GeoSMART Library Badge](book/img/curricula_badge.svg)](https://geo-smart.github.io/curriculum) +[![Student Version](book/img/student_version_badge.svg)](https://geo-smart.github.io/mlgeo-book/) -## About +## Repository Overview -This repository stores configuration for GeoSMART curriculum content, specifically the student version of the book. This version of the book should never be directly edited, as the student version is automatically generated on push. \ No newline at end of file +This repository stores configuration for GeoSMART curriculum content, specifically the teacher version of the book. Only this version of the book should ever be edited, as the student version is automatically generated on push by github actions. + +## Making Changes + +Edit the book content by modifying the `_config.yml`, `_toc.yml` and `*.ipynb` files in the `book` directory. The book is hosted on Github Pages and will be automatically updated on push, and the student book will also be created automatically on push. + +Making changes requires that you set up a conda environment and build locally before making sure that it will build with github actions. We accepted rendered notebooks, but some oddities, such as kernels different than python, will make it crash. So we recommend that contributors first build the book with the added notebooks. + +```sh + conda env create -f ./conda/environment.yml + conda activate curriculum_book + +``` + +To modify the exact differences between this book and the student book, edit `.github/workflows/clean_book.py`. When you push, a github action will clone the repo and run this python file which modifies certain parts of `*.ipynb` file contents, then pushes to the student repo. To edit the student repo's README, edit `STUDENT_README.md`. The Github Actions workflow also automatically replaces `README.md` with `STUDENT_README.md` in the student repo. + +### `Student Response Sections` + +One modifications made by the `clean_book.py` workflow is to clear sections marked for student response. Code cells marked for student response may contain code in the teacher version of the book, but will have their code removed and replaced with a TODO comment in the student version. + +To mark a code cell to be cleared, insert a markdown cell directly preceding it with the following content: + +````markdown +```{admonition} Student response section +This section is left for the student to complete. +``` +```` + +## Serving Locally + +Activate the `curriculum_book` conda environment (or any conda environment that has the necessary jupyter book dependencies). Navigate to the root folder of the curriculum book repository in anaconda prompt, then run `python server.py`. + +On startup, the server will run `jb build book` to build all changes to the notebook and create the compiled HTML. The server code can take a `--no-build` flag (or `--nb` shorthand) if you don't want to build any changes you've made to the notebooks. In the case that you don't want to build changes made to the notebooks, you can just run `python serer.py --nb` from any terminal with python installed. diff --git a/STUDENT_README.md b/STUDENT_README.md new file mode 100644 index 0000000..400c0ac --- /dev/null +++ b/STUDENT_README.md @@ -0,0 +1,10 @@ +# GeoSMART Curriculum Jupyter Book (ESS 469/569) + +[![Deploy](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml/badge.svg)](https://github.com/geo-smart/mlgeo-book/actions/workflows/deploy.yaml) +[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://geo-smart.github.io/mlgeo-book) +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/geo-smart/mlgeo-book/HEAD?urlpath=lab) +[![GeoSMART Library Badge](book/img/curricula_badge.svg)](https://geo-smart.github.io/curriculum) + +## About + +This repository stores configuration for GeoSMART curriculum content, specifically the student version of the book. This version of the book should never be directly edited, as the student version is automatically generated on push. \ No newline at end of file diff --git a/book/Chapter2-DataManipulation/2.11_feature_engineering.ipynb b/book/Chapter2-DataManipulation/2.11_feature_engineering.ipynb index 8d513d5..fc02573 100644 --- a/book/Chapter2-DataManipulation/2.11_feature_engineering.ipynb +++ b/book/Chapter2-DataManipulation/2.11_feature_engineering.ipynb @@ -5,25 +5,357 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2.10 Feature engineering\n", + "# 2.10 Feature Engineering\n", "\n", + "## 1. Introduction\n", + "The process of feature engineering involves manipulating, transforming, and selecting raw data into features that can be used in statistical analysis and prediction.\n", "\n", - "The process of feature engineering is of manipulating, transforming, and selecting raw data into features that can be used in statistical analysis of prediction.\n", + "Key types of features include:\n", "\n", - "* statistical features\n", - "* temporal features\n", - "* spectral features (Fourier and Wavelet transforms)\n", + "### Statistical features\n", "\n", - "This lecture will demonstrate how to automatically extract features from a popular (but simple) Python package ``tsfel`` to extract common features of time series. We will take the example of seismic waveforms recorded in the Pacific Northwest. The Pacific Northwest data detect and labels seismic waveforms for event of various origins: earthquake, explosions (mostly quarry blasts), and surface events (usually avalanches and landslides), but also seismic noise (ambient Earth vibrations in between).\n", + "Statistical features summarize the distribution of data values, providing insights into the central tendency, dispersion, and shape of the dataset.\n", "\n", - "We will explore how these features vary among the four categories, or classes of seismic events.\n", + "* **Mean**: Average value of the data points.\n", + "* **Variance/Standard Deviation**: Measure of the spread of the data.\n", + "* **Skewness**: Asymmetry in the distribution of values.\n", + "* **Kurtosis**: \"Tailedness\" of the distribution.\n", + "* **Percentiles** : Threshold values for different segments of the distribution.\n", "\n", - "[Level 1]" + "### Temporal Features\n", + "Temporal features focus on the time-dependent patterns within the series.\n", + "\n", + "* **Autocorrelation**: Correlation of a time series with a lagged version of itself.\n", + "* **Trend**: Long-term increase or decrease in the data.\n", + "* **Seasonality**: Regular patterns that repeat over a specific period.\n", + "* **Change Points**: Locations where statistical properties of the series change significantly.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autocorrelation of the seasonal component is 0.66\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Create a time series with trend and seasonality\n", + "t = np.arange(0, 365*3)\n", + "seasonal_series = np.random.randn(3*365) + np.linspace(0, 10, 3*365) + \\\n", + " 2* np.sin(2 * np.pi * np.linspace(0, 3, 3*365))\n", + "\n", + "# fit a trend using least squares regression\n", + "t = np.arange(len(seasonal_series))\n", + "trend = np.polyfit(t, seasonal_series, 1)[0] * t + np.polyfit(t, seasonal_series, 1)[1]\n", + "# calculate the seasonal component by subtracting the trend\n", + "seasonal = seasonal_series - trend\n", + "# calculate the autocorrelation of the seasonal component\n", + "autocorr = np.corrcoef(seasonal[:-1], seasonal[1:])[0, 1]\n", + "print(f'The autocorrelation of the seasonal component is {autocorr:.2f}')\n", + "\n", + "# Plot the original time series, trend and seasonal components\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(seasonal_series, label='Original Time Series')\n", + "plt.plot(trend, label='Trend')\n", + "plt.plot(seasonal, label='Seasonal')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatial features\n", + "\n", + "For geospatial data, spatial features capture the relationships between different locations in an image or dataset.\n", + "\n", + "* **Texture**: Patterns in the local intensity variations (e.g., smooth, rough).\n", + "* **Spatial Correlation**: Measures how similar nearby locations are in terms of intensity values. \n" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, '2D Spatially Correlated Noise')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.ndimage import gaussian_filter\n", + "from scipy.fft import fft2, ifft2, fftshift\n", + "\n", + "# Generate a 2D grid (e.g., geospatial data, such as a topographic map)\n", + "n = 100 # Size of the grid\n", + "x = np.linspace(0, 10, n)\n", + "y = np.linspace(0, 10, n)\n", + "X, Y = np.meshgrid(x, y)\n", + "\n", + "# Generate 2D white noise\n", + "white_noise_2d = np.random.normal(0, 1, (n, n))\n", + "\n", + "# Generate spatially correlated noise using a Gaussian filter\n", + "spatially_correlated_noise = gaussian_filter(white_noise_2d, sigma=3)\n", + "\n", + "# Plot the synthetic 2D white noise, correlated noise, and autocorrelation function\n", + "plt.figure(figsize=(18, 6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.imshow(white_noise_2d, extent=[0, 10, 0, 10], cmap='viridis')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.colorbar(label='Amplitude')\n", + "plt.title('2D White Noise')\n", + "\n", + "# Spatially correlated noise\n", + "plt.subplot(1, 2, 2)\n", + "plt.imshow(spatially_correlated_noise, extent=[0, 10, 0, 10], cmap='viridis')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.colorbar(label='Amplitude')\n", + "plt.title('2D Spatially Correlated Noise')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "spatial correlation of the white noise" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Compute the 2D Fourier Transform of the spatially correlated noise\n", + "fft_white_noise = fft2(white_noise_2d)\n", + "\n", + "# Compute the power spectrum\n", + "power_spectrum = np.abs(fft_white_noise) ** 2\n", + "\n", + "# Compute the inverse 2D Fourier Transform of the power spectrum to get the autocorrelation function\n", + "autocorrelation_white = np.real(ifft2(power_spectrum))\n", + "\n", + "# Shift the zero-frequency component to the center of the spectrum\n", + "autocorrelation_white = fftshift(autocorrelation_white)\n", + "\n", + "# Normalize the autocorrelation function\n", + "autocorrelation_white /= autocorrelation_white.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "spatial correlation of the correlated noise" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the 2D Fourier Transform of the spatially correlated noise\n", + "fft_noise = fft2(spatially_correlated_noise)\n", + "\n", + "# Compute the power spectrum\n", + "power_spectrum = np.abs(fft_noise) ** 2\n", + "\n", + "# Compute the inverse 2D Fourier Transform of the power spectrum to get the autocorrelation function\n", + "autocorrelation = np.real(ifft2(power_spectrum))\n", + "\n", + "# Shift the zero-frequency component to the center of the spectrum\n", + "autocorrelation = fftshift(autocorrelation)\n", + "\n", + "# Normalize the autocorrelation function\n", + "autocorrelation /= autocorrelation.max()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Plot the synthetic 2D white noise, correlated noise, and autocorrelation function\n", + "plt.figure(figsize=(11, 6))\n", + "\n", + "# White noise (unfiltered)\n", + "plt.subplot(2, 2, 1)\n", + "plt.imshow(white_noise_2d, extent=[0, 10, 0, 10], cmap='viridis')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.colorbar(label='Amplitude')\n", + "plt.title('2D White Noise')\n", + "\n", + "\n", + "plt.subplot(2, 2, 2)\n", + "plt.imshow(autocorrelation_white, extent=[0, 10, 0, 10], cmap='viridis')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.colorbar(label='Amplitude')\n", + "plt.title('2D White Noise Autocorrelation')\n", + "\n", + "\n", + "# Spatially correlated noise\n", + "plt.subplot(2, 2, 3)\n", + "plt.imshow(spatially_correlated_noise, extent=[0, 10, 0, 10], cmap='viridis')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.colorbar(label='Amplitude')\n", + "plt.title('2D Spatially Correlated Noise')\n", + "\n", + "# Autocorrelation function\n", + "plt.subplot(2, 2, 4)\n", + "plt.imshow(autocorrelation, extent=[-5, 5, -5, 5], cmap='viridis')\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.colorbar(label='Normalized Amplitude')\n", + "plt.title('Autocorrelation Function')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate the feature \"correlation length\" of the two images. The correlation length is often measured as the distance at which the autocorelation function decays to 1/e of its maximum value." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated correlation length for white noise: 0.07 and for spatially correlated noise: 0.56\n" + ] + } + ], + "source": [ + "\n", + "center = n // 2\n", + "# calculate correlation length for the white noise\n", + "autocorr_center = autocorrelation_white[center, center:]\n", + "distances = np.linspace(0, 5, center)\n", + "correlation_length_white = np.interp(1/np.e, autocorr_center[::-1], distances[::-1])\n", + "\n", + "# calculate correlation length for the spatially correlated noise \n", + "autocorr_center = autocorrelation[center, center:]\n", + "distances = np.linspace(0, 5, center)\n", + "correlation_length = np.interp(1/np.e, autocorr_center[::-1], distances[::-1])\n", + "\n", + "print(f'Estimated correlation length for white noise: {correlation_length_white:.2f} and for spatially correlated noise: {correlation_length:.2f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fractal Features\n", + "Fractal features describe the self-similarity or complexity of the data at different scales. A common fractal feature is the Fractal Dimension, which quantifies the complexity of a dataset, especially useful in understanding geospatial structures and irregular patterns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 2. Seismic Time Series Data Example\n", + "This lecture will demonstrate how to automatically extract features using a popular and simple Python package, `tsfel`, to extract common time series features. We will apply this to the example of seismic waveforms recorded in the Pacific Northwest.\n", + "\n", + "An example will follow.\n", + "The Pacific Northwest data includes labeled seismic waveforms for events of various origins:\n", + "\n", + "- **Earthquakes**\n", + "- **Explosions** (mostly quarry blasts)\n", + "- **Surface events** (such as avalanches and landslides)\n", + "- **Seismic noise** (ambient Earth vibrations between events)\n", + "\n", + "We will explore how these features vary across the four categories, or classes, of seismic events." + ] + }, + { + "cell_type": "code", + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -38,19 +370,26 @@ "import h5py # for reading .h5 files" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We download 2 files from the class storage: a CSV file with the waveforms themselves as an HDF5 file and their associated metadata as a CSV file:" + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Collecting wget\n", - " Using cached wget-3.2-py3-none-any.whl\n", - "Installing collected packages: wget\n", - "Successfully installed wget-3.2\n" + "Requirement already satisfied: wget in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (3.2)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], @@ -60,32 +399,25 @@ "import wget" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We download 2 files from the class storage: a CSV file with the waveforms themselves as an HDF5 file and their associated metadata as a CSV file:" - ] - }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'miniPNW_waveforms (1).hdf5'" + "'miniPNW_waveforms.hdf5'" ] }, - "execution_count": 3, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "wget.download(\"https://www.dropbox.com/s/f0e1ywupdbuv3l3/miniPNW_metadata.csv?dl=1\")\n", - "wget.download(\"https://www.dropbox.com/s/0ffh4r23mitn2dz/miniPNW_waveforms.hdf5?dl=1\")" + "wget.download(\"https://dasway.ess.washington.edu/shared/niyiyu/PNW-ML/miniPNW_metadata.csv\")\n", + "wget.download(\"https://dasway.ess.washington.edu/shared/niyiyu/PNW-ML/miniPNW_waveforms.hdf5\")" ] }, { @@ -99,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -325,15 +657,17 @@ "[5 rows x 37 columns]" ] }, - "execution_count": 4, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# plot the time series\n", + "import pandas as pd\n", + "# Handle irregular rows by skipping them\n", "df = pd.read_csv(\"miniPNW_metadata.csv\")\n", - "df.head()\n" + "# Display the first few rows of the DataFrame\n", + "df.head()" ] }, { @@ -345,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -355,7 +689,7 @@ " 'surface_event'], dtype=object)" ] }, - "execution_count": 5, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -373,11 +707,26 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "source_type\n", + "earthquake 500\n", + "explosion 500\n", + "surface_event 500\n", + "sonic_boom 126\n", + "thunder 94\n", + "Name: count, dtype: int64\n" + ] + } + ], "source": [ - "labels=df['source_type']" + "labels=df['source_type']\n", + "print(labels.value_counts())" ] }, { @@ -412,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -423,13 +772,13 @@ " )" ] }, - "execution_count": 7, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -456,13 +805,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "f = h5py.File(\"miniPNW_waveforms.hdf5\", \"r\")" ] }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "chan_list = ['N','E','Z']" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -472,13 +830,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "def read_data(tn,f):\n", + " \"\"\"\n", + " Function to read the waveform data from the .h5 file\n", + " tn: trace_name of the waveform\n", + " f: file object of the .h5 file\n", + " \"\"\"\n", " bucket, narray = tn.split('$') # split the string of trace_name into bucket and narray\n", - " x, y, z = iter([int(i) for i in narray.split(',:')]) # split thenarray into x, y, z\n", + " x, y, z = iter([int(i) for i in narray.split(',:')]) # split the narray into x, y, z\n", " data = f['/data/%s' % bucket][x, :y, :z] # read the data as a 3D array\n", " return data" ] @@ -492,7 +855,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -501,31 +864,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(3, 15001)\n" + "The first dimension of the data is 3\n", + "The second dimension of the data is 15001\n" ] }, { "data": { + "image/png": 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", "text/plain": [ - "[]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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"metadata": {}, "outputs": [], "source": [ @@ -569,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -577,32 +946,37 @@ "output_type": "stream", "text": [ "Requirement already satisfied: tsfel in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (0.1.6)\n", - "Requirement already satisfied: setuptools>=47.1.1 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from tsfel) (65.5.1)\n", - "Requirement already satisfied: ipython>=7.4.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from tsfel) (8.6.0)\n", - "Requirement already satisfied: scipy>=1.7.3 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from tsfel) (1.9.3)\n", + "Requirement already satisfied: ipython>=7.4.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from tsfel) (8.16.1)\n", + "Requirement already satisfied: 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stack-data->ipython>=7.4.0->tsfel) (0.2.2)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], @@ -620,11 +994,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 85, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'spectral': {'FFT mean coefficient': {'complexity': 'constant', 'description': 'Computes the mean value of each spectrogram frequency.', 'function': 'tsfel.fft_mean_coeff', 'parameters': {'fs': 100, 'nfreq': 256}, 'n_features': 'nfreq', 'use': 'yes'}, 'Fundamental frequency': {'complexity': 'log', 'description': 'Computes the fundamental frequency.', 'function': 'tsfel.fundamental_frequency', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Human range energy': {'complexity': 'log', 'description': 'Computes the human range energy ratio given by the ratio between the energy in frequency 0.6-2.5Hz and the whole energy band.', 'function': 'tsfel.human_range_energy', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes', 'tag': 'inertial'}, 'LPCC': {'complexity': 'log', 'description': 'Computes the linear prediction cepstral coefficients.', 'function': 'tsfel.lpcc', 'parameters': {'n_coeff': 12}, 'n_features': 'n_coeff', 'use': 'yes', 'tag': 'audio'}, 'MFCC': {'complexity': 'constant', 'description': 'Computes the MEL cepstral coefficients.', 'function': 'tsfel.mfcc', 'parameters': {'cep_lifter': 22, 'fs': 100, 'nfft': 512, 'nfilt': 40, 'num_ceps': 12, 'pre_emphasis': 0.97}, 'n_features': 'num_ceps', 'use': 'yes', 'tag': ['audio', 'emg']}, 'Max power spectrum': {'complexity': 'log', 'description': 'Computes the maximum power spectrum density.', 'function': 'tsfel.max_power_spectrum', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Maximum frequency': {'complexity': 'log', 'description': 'Computes the maximum frequency.', 'function': 'tsfel.max_frequency', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Median frequency': {'complexity': 'log', 'description': 'Computes the median frequency.', 'function': 'tsfel.median_frequency', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Power bandwidth': {'complexity': 'log', 'description': 'Computes power spectrum density bandwidth of the signal.', 'function': 'tsfel.power_bandwidth', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral centroid': {'complexity': 'linear', 'description': 'Computes the barycenter of the spectrum.', 'function': 'tsfel.spectral_centroid', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes', 'tag': 'audio'}, 'Spectral decrease': {'complexity': 'log', 'description': 'Computes the amount of decreasing of the spectra amplitude.', 'function': 'tsfel.spectral_decrease', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral distance': {'complexity': 'log', 'description': 'Computes the signal spectral distance.', 'function': 'tsfel.spectral_distance', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral entropy': {'complexity': 'log', 'description': 'Computes the spectral entropy of the signal based on Fourier transform.', 'function': 'tsfel.spectral_entropy', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes', 'tag': 'eeg'}, 'Spectral kurtosis': {'complexity': 'linear', 'description': 'Computes the flatness of a distribution around its mean value.', 'function': 'tsfel.spectral_kurtosis', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral positive turning points': {'complexity': 'log', 'description': 'Computes number of positive turning points of the fft magnitude signal', 'function': 'tsfel.spectral_positive_turning', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral roll-off': {'complexity': 'log', 'description': 'Computes the frequency where 95% of the signal magnitude is contained below of this value.', 'function': 'tsfel.spectral_roll_off', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes', 'tag': 'audio'}, 'Spectral roll-on': {'complexity': 'log', 'description': 'Computes the frequency where 5% of the signal magnitude is contained below of this value.', 'function': 'tsfel.spectral_roll_on', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral skewness': {'complexity': 'linear', 'description': 'Computes the asymmetry of a distribution around its mean value.', 'function': 'tsfel.spectral_skewness', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral slope': {'complexity': 'log', 'description': 'Computes the spectral slope, obtained by linear regression of the spectral amplitude.', 'function': 'tsfel.spectral_slope', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral spread': {'complexity': 'linear', 'description': 'Computes the spread of the spectrum around its mean value.', 'function': 'tsfel.spectral_spread', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Spectral variation': {'complexity': 'log', 'description': 'Computes the amount of variation of the spectrum along time.', 'function': 'tsfel.spectral_variation', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Wavelet absolute mean': {'complexity': 'linear', 'description': 'Computes CWT absolute mean value of each wavelet scale.', 'function': 'tsfel.wavelet_abs_mean', 'parameters': {'function': 'scipy.signal.ricker', 'widths': 'np.arange(1,10)'}, 'n_features': 'widths', 'use': 'yes', 'tag': ['eeg', 'ecg']}, 'Wavelet energy': {'complexity': 'linear', 'description': 'Computes CWT energy of each wavelet scale.', 'function': 'tsfel.wavelet_energy', 'parameters': {'function': 'scipy.signal.ricker', 'widths': 'np.arange(1,10)'}, 'n_features': 'widths', 'use': 'yes', 'tag': 'eeg'}, 'Wavelet entropy': {'complexity': 'linear', 'description': 'Computes CWT entropy of the signal.', 'function': 'tsfel.wavelet_entropy', 'parameters': {'function': 'scipy.signal.ricker', 'widths': 'np.arange(1,10)'}, 'n_features': 1, 'use': 'yes', 'tag': 'eeg'}, 'Wavelet standard deviation': {'complexity': 'linear', 'description': 'Computes CWT std value of each wavelet scale.', 'function': 'tsfel.wavelet_std', 'parameters': {'function': 'scipy.signal.ricker', 'widths': 'np.arange(1,10)'}, 'n_features': 'widths', 'use': 'yes', 'tag': 'eeg'}, 'Wavelet variance': {'complexity': 'linear', 'description': 'Computes CWT variance value of each wavelet scale.', 'function': 'tsfel.wavelet_var', 'parameters': {'function': 'scipy.signal.ricker', 'widths': 'np.arange(1,10)'}, 'n_features': 'widths', 'use': 'yes', 'tag': 'eeg'}}, 'statistical': {'Absolute energy': {'complexity': 'log', 'description': 'Computes the absolute energy of the signal.', 'function': 'tsfel.abs_energy', 'parameters': '', 'n_features': 1, 'use': 'yes', 'tag': 'audio'}, 'Average power': {'complexity': 'constant', 'description': 'Computes the average power of the signal.', 'function': 'tsfel.average_power', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes', 'tag': 'audio'}, 'ECDF': {'complexity': 'log', 'description': 'Computes the values of ECDF (empirical cumulative distribution function) along the time axis.', 'function': 'tsfel.ecdf', 'parameters': {'d': 10}, 'n_features': 'd', 'use': 'yes'}, 'ECDF Percentile': {'complexity': 'log', 'description': 'Determines the percentile value of the ECDF.', 'function': 'tsfel.ecdf_percentile', 'parameters': {'percentile': '[0.2, 0.8]'}, 'n_features': 'percentile', 'use': 'yes'}, 'ECDF Percentile Count': {'complexity': 'log', 'description': 'Determines the cumulative sum of samples that are less than the percentile.', 'function': 'tsfel.ecdf_percentile_count', 'parameters': {'percentile': '[0.2, 0.8]'}, 'n_features': 'percentile', 'use': 'yes'}, 'Entropy': {'complexity': 'log', 'description': 'Computes the entropy of the signal using the Shannon Entropy.', 'function': 'tsfel.entropy', 'parameters': {'prob': 'standard'}, 'n_features': 1, 'use': 'yes', 'tag': 'eeg'}, 'Histogram': {'complexity': 'log', 'description': 'Computes histogram of the signal.', 'function': 'tsfel.hist', 'parameters': {'nbins': 10, 'r': 1}, 'n_features': 'nbins', 'use': 'yes'}, 'Interquartile range': {'complexity': 'constant', 'description': 'Computes interquartile range of the signal.', 'function': 'tsfel.interq_range', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Kurtosis': {'complexity': 'constant', 'description': 'Computes kurtosis of the signal.', 'function': 'tsfel.kurtosis', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Max': {'complexity': 'constant', 'description': 'Computes the maximum value of the signal.', 'function': 'tsfel.calc_max', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Mean': {'complexity': 'constant', 'description': 'Computes the mean value of the signal.', 'function': 'tsfel.calc_mean', 'parameters': '', 'n_features': 1, 'use': 'yes', 'tag': 'inertial'}, 'Mean absolute deviation': {'complexity': 'log', 'description': 'Computes mean absolute deviation of the signal.', 'function': 'tsfel.mean_abs_deviation', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Median': {'complexity': 'constant', 'description': 'Computes median of the signal.', 'function': 'tsfel.calc_median', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Median absolute deviation': {'complexity': 'constant', 'description': 'Computes median absolute deviation of the signal.', 'function': 'tsfel.median_abs_deviation', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Min': {'complexity': 'constant', 'description': 'Computes the minimum value of the signal.', 'function': 'tsfel.calc_min', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Peak to peak distance': {'complexity': 'constant', 'description': 'Computes the peak to peak distance.', 'function': 'tsfel.pk_pk_distance', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Root mean square': {'complexity': 'constant', 'description': 'Computes root mean square of the signal.', 'function': 'tsfel.rms', 'parameters': '', 'n_features': 1, 'use': 'yes', 'tag': ['emg', 'inertial']}, 'Skewness': {'complexity': 'constant', 'description': 'Computes skewness of the signal.', 'function': 'tsfel.skewness', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Standard deviation': {'complexity': 'constant', 'description': 'Computes standard deviation of the signal.', 'function': 'tsfel.calc_std', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Variance': {'complexity': 'constant', 'description': 'Computes variance of the signal.', 'function': 'tsfel.calc_var', 'parameters': '', 'n_features': 1, 'use': 'yes'}}, 'temporal': {'Area under the curve': {'complexity': 'log', 'description': 'Computes the area under the curve of the signal computed with trapezoid rule.', 'function': 'tsfel.auc', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Autocorrelation': {'complexity': 'constant', 'description': 'Computes autocorrelation of the signal.', 'function': 'tsfel.autocorr', 'parameters': '', 'n_features': 1, 'use': 'yes', 'tag': 'inertial'}, 'Centroid': {'complexity': 'constant', 'description': 'Computes the centroid along the time axis.', 'function': 'tsfel.calc_centroid', 'parameters': {'fs': 100}, 'n_features': 1, 'use': 'yes'}, 'Mean absolute diff': {'complexity': 'constant', 'description': 'Computes mean absolute differences of the signal.', 'function': 'tsfel.mean_abs_diff', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Mean diff': {'complexity': 'constant', 'description': 'Computes mean of differences of the signal.', 'function': 'tsfel.mean_diff', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Median absolute diff': {'complexity': 'constant', 'description': 'Computes median absolute differences of the signal.', 'function': 'tsfel.median_abs_diff', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Median diff': {'complexity': 'constant', 'description': 'Computes median of differences of the signal.', 'function': 'tsfel.median_diff', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Negative turning points': {'complexity': 'constant', 'description': 'Computes number of negative turning points of the signal.', 'function': 'tsfel.negative_turning', 'parameters': '', 'n_features': 1, 'use': 'yes', 'tag': 'emg'}, 'Neighbourhood peaks': {'complexity': 'constant', 'description': 'Computes the number of peaks from a defined neighbourhood of the signal.', 'function': 'tsfel.neighbourhood_peaks', 'parameters': {'n': 10}, 'n_features': 1, 'use': 'yes'}, 'Positive turning points': {'complexity': 'constant', 'description': 'Computes number of positive turning points of the signal.', 'function': 'tsfel.positive_turning', 'parameters': '', 'n_features': 1, 'use': 'yes', 'tag': 'emg'}, 'Signal distance': {'complexity': 'constant', 'description': 'Computes signal traveled distance.', 'function': 'tsfel.distance', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Slope': {'complexity': 'log', 'description': 'Computes the slope of the signal by fitting a linear equation to the observed data.', 'function': 'tsfel.slope', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Sum absolute diff': {'complexity': 'constant', 'description': 'Computes sum of absolute differences of the signal.', 'function': 'tsfel.sum_abs_diff', 'parameters': '', 'n_features': 1, 'use': 'yes'}, 'Zero crossing rate': {'complexity': 'constant', 'description': 'Computes Zero-crossing rate of the signal.', 'function': 'tsfel.zero_cross', 'parameters': '', 'n_features': 1, 'use': 'yes', 'tag': ['audio', 'emg']}}}\n" + ] + } + ], "source": [ - "cfg = tsfel.get_features_by_domain() " + "cfg = tsfel.get_features_by_domain() \n", + "print(cfg)" ] }, { @@ -644,24 +1027,1793 @@ } ], "source": [ - "Z.shape" + "\n", + "print(f'We have a total of {Z.shape[0]} data sample and each has {Z.shape[1]} data poins')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting features from sample 0/1720\n", + "Extracting features from sample 1/1720\n", + "Extracting features from sample 2/1720\n", + "Extracting features from sample 3/1720\n", + "Extracting features from sample 4/1720\n", + "Extracting features from sample 5/1720\n", + "Extracting features from sample 6/1720\n", + "Extracting features from sample 7/1720\n", + "Extracting features from sample 8/1720\n", + "Extracting features from sample 9/1720\n", + "Extracting features from sample 10/1720\n", + "Extracting features from sample 11/1720\n", + "Extracting features from sample 12/1720\n", + "Extracting features from sample 13/1720\n", + "Extracting features from sample 14/1720\n", + "Extracting features from sample 15/1720\n", + "Extracting features from sample 16/1720\n", + "Extracting features from sample 17/1720\n", + "Extracting features from sample 18/1720\n", + "Extracting features from sample 19/1720\n", + "Extracting features from sample 20/1720\n", + "Extracting features from sample 21/1720\n", + "Extracting features from sample 22/1720\n", + "Extracting features from sample 23/1720\n", + "Extracting features from sample 24/1720\n", + "Extracting features from sample 25/1720\n", + "Extracting features from sample 26/1720\n", + "Extracting features from sample 27/1720\n", + "Extracting features from sample 28/1720\n", + "Extracting features from sample 29/1720\n", + "Extracting features from sample 30/1720\n", + "Extracting features from sample 31/1720\n", + "Extracting features from sample 32/1720\n", + "Extracting features from sample 33/1720\n", + "Extracting features from sample 34/1720\n", + "Extracting features from sample 35/1720\n", + "Extracting features from sample 36/1720\n", + "Extracting features from sample 37/1720\n", + "Extracting features from sample 38/1720\n", + "Extracting features from sample 39/1720\n", + "Extracting features from sample 40/1720\n", + "Extracting features from sample 41/1720\n", + "Extracting features from sample 42/1720\n", + "Extracting features from sample 43/1720\n", + "Extracting features from sample 44/1720\n", + "Extracting features from sample 45/1720\n", + "Extracting features from sample 46/1720\n", + "Extracting features from sample 47/1720\n", + "Extracting features from sample 48/1720\n", + "Extracting features from sample 49/1720\n", + "Extracting features from sample 50/1720\n", + "Extracting features from sample 51/1720\n", + "Extracting features from sample 52/1720\n", + "Extracting features from sample 53/1720\n", + "Extracting features from sample 54/1720\n", + "Extracting features from sample 55/1720\n", + "Extracting features from sample 56/1720\n", + "Extracting features from sample 57/1720\n", + "Extracting features from sample 58/1720\n", + "Extracting features from sample 59/1720\n", + "Extracting features from sample 60/1720\n", + "Extracting features from sample 61/1720\n", + "Extracting features from sample 62/1720\n", + "Extracting features from sample 63/1720\n", + "Extracting features from sample 64/1720\n", + "Extracting features from sample 65/1720\n", + "Extracting features from sample 66/1720\n", + "Extracting features from sample 67/1720\n", + "Extracting features from sample 68/1720\n", + "Extracting features from sample 69/1720\n", + "Extracting features from sample 70/1720\n", + "Extracting features from sample 71/1720\n", + "Extracting features from sample 72/1720\n", + "Extracting features from sample 73/1720\n", + "Extracting features from sample 74/1720\n", + "Extracting features from sample 75/1720\n", + "Extracting features from sample 76/1720\n", + "Extracting features from sample 77/1720\n", + "Extracting features from sample 78/1720\n", + "Extracting features from sample 79/1720\n", + "Extracting features from sample 80/1720\n", + "Extracting features from sample 81/1720\n", + "Extracting features from sample 82/1720\n", + "Extracting features from sample 83/1720\n", + "Extracting features from sample 84/1720\n", + "Extracting features from sample 85/1720\n", + "Extracting features from sample 86/1720\n", + "Extracting features from sample 87/1720\n", + "Extracting features from sample 88/1720\n", + "Extracting features from sample 89/1720\n", + "Extracting features from sample 90/1720\n", + "Extracting features from sample 91/1720\n", + "Extracting features from sample 92/1720\n", + "Extracting features from sample 93/1720\n", + "Extracting features from sample 94/1720\n", + "Extracting features from sample 95/1720\n", + "Extracting features from sample 96/1720\n", + "Extracting features from sample 97/1720\n", + "Extracting features from sample 98/1720\n", + "Extracting features from sample 99/1720\n", + "Extracting features from sample 100/1720\n", + "Extracting features from sample 101/1720\n", + "Extracting features from sample 102/1720\n", + "Extracting features from sample 103/1720\n", + "Extracting features from sample 104/1720\n", + "Extracting features from sample 105/1720\n", + "Extracting features from sample 106/1720\n", + "Extracting features from sample 107/1720\n", + "Extracting features from sample 108/1720\n", + "Extracting features from sample 109/1720\n", + "Extracting features from sample 110/1720\n", + "Extracting features from sample 111/1720\n", + "Extracting features from sample 112/1720\n", + "Extracting features from sample 113/1720\n", + "Extracting features from sample 114/1720\n", + "Extracting features from sample 115/1720\n", + "Extracting features from sample 116/1720\n", + "Extracting features from sample 117/1720\n", + "Extracting features from sample 118/1720\n", + "Extracting features from sample 119/1720\n", + "Extracting features from sample 120/1720\n", + "Extracting features from sample 121/1720\n", + "Extracting features from sample 122/1720\n", + "Extracting features from sample 123/1720\n", + "Extracting features from sample 124/1720\n", + "Extracting features from sample 125/1720\n", + "Extracting features from sample 126/1720\n", + "Extracting features from sample 127/1720\n", + "Extracting features from sample 128/1720\n", + "Extracting features from sample 129/1720\n", + "Extracting features from sample 130/1720\n", + "Extracting features from sample 131/1720\n", + "Extracting features from sample 132/1720\n", + "Extracting features from sample 133/1720\n", + "Extracting features from sample 134/1720\n", + "Extracting features from sample 135/1720\n", + "Extracting features from sample 136/1720\n", + "Extracting features from sample 137/1720\n", + "Extracting features from sample 138/1720\n", + "Extracting features from sample 139/1720\n", + "Extracting features from sample 140/1720\n", + "Extracting features from sample 141/1720\n", + "Extracting features from sample 142/1720\n", + "Extracting features from sample 143/1720\n", + "Extracting features from sample 144/1720\n", + "Extracting features from sample 145/1720\n", + "Extracting features from sample 146/1720\n", + "Extracting features from sample 147/1720\n", + "Extracting features from sample 148/1720\n", + "Extracting features from sample 149/1720\n", + "Extracting features from sample 150/1720\n", + "Extracting features from sample 151/1720\n", + "Extracting features from sample 152/1720\n", + "Extracting features from sample 153/1720\n", + "Extracting features from sample 154/1720\n", + "Extracting features from sample 155/1720\n", + "Extracting features from sample 156/1720\n", + "Extracting features from sample 157/1720\n", + "Extracting features from sample 158/1720\n", + "Extracting features from sample 159/1720\n", + "Extracting features from sample 160/1720\n", + "Extracting features from sample 161/1720\n", + "Extracting features from sample 162/1720\n", + "Extracting features from sample 163/1720\n", + "Extracting features from sample 164/1720\n", + "Extracting features from sample 165/1720\n", + "Extracting features from sample 166/1720\n", + "Extracting features from sample 167/1720\n", + "Extracting features from sample 168/1720\n", + "Extracting features from sample 169/1720\n", + "Extracting features from sample 170/1720\n", + "Extracting features from sample 171/1720\n", + "Extracting features from sample 172/1720\n", + "Extracting features from sample 173/1720\n", + "Extracting features from sample 174/1720\n", + "Extracting features from sample 175/1720\n", + "Extracting features from sample 176/1720\n", + "Extracting features from sample 177/1720\n", + "Extracting features from sample 178/1720\n", + "Extracting features from sample 179/1720\n", + "Extracting features from sample 180/1720\n", + "Extracting features from sample 181/1720\n", + "Extracting features from sample 182/1720\n", + "Extracting features from sample 183/1720\n", + "Extracting features from sample 184/1720\n", + "Extracting features from sample 185/1720\n", + "Extracting features from sample 186/1720\n", + "Extracting features from sample 187/1720\n", + "Extracting features from sample 188/1720\n", + "Extracting features from sample 189/1720\n", + "Extracting features from sample 190/1720\n", + "Extracting features from sample 191/1720\n", + "Extracting features from sample 192/1720\n", + "Extracting features from sample 193/1720\n", + "Extracting features from sample 194/1720\n", + "Extracting features from sample 195/1720\n", + "Extracting features from sample 196/1720\n", + "Extracting features from sample 197/1720\n", + "Extracting features from sample 198/1720\n", + "Extracting features from sample 199/1720\n", + "Extracting features from sample 200/1720\n", + "Extracting features from sample 201/1720\n", + "Extracting features from sample 202/1720\n", + "Extracting features from sample 203/1720\n", + "Extracting features from sample 204/1720\n", + "Extracting features from sample 205/1720\n", + "Extracting features from sample 206/1720\n", + "Extracting features from sample 207/1720\n", + "Extracting features from sample 208/1720\n", + "Extracting features from sample 209/1720\n", + "Extracting features from sample 210/1720\n", + "Extracting features from sample 211/1720\n", + "Extracting features from sample 212/1720\n", + "Extracting features from sample 213/1720\n", + "Extracting features from sample 214/1720\n", + "Extracting features from sample 215/1720\n", + "Extracting features from sample 216/1720\n", + "Extracting features from sample 217/1720\n", + "Extracting features from sample 218/1720\n", + "Extracting features from sample 219/1720\n", + "Extracting features from sample 220/1720\n", + "Extracting features from sample 221/1720\n", + "Extracting features from sample 222/1720\n", + "Extracting features from sample 223/1720\n", + "Extracting features from sample 224/1720\n", + "Extracting features from sample 225/1720\n", + "Extracting features from sample 226/1720\n", + "Extracting features from sample 227/1720\n", + "Extracting features from sample 228/1720\n", + "Extracting features from sample 229/1720\n", + "Extracting features from sample 230/1720\n", + "Extracting features from sample 231/1720\n", + "Extracting features from sample 232/1720\n", + "Extracting features from sample 233/1720\n", + "Extracting features from sample 234/1720\n", + "Extracting features from sample 235/1720\n", + "Extracting features from sample 236/1720\n", + "Extracting features from sample 237/1720\n", + "Extracting features from sample 238/1720\n", + "Extracting features from sample 239/1720\n", + "Extracting features from sample 240/1720\n", + "Extracting features from sample 241/1720\n", + "Extracting features from sample 242/1720\n", + "Extracting features from sample 243/1720\n", + "Extracting features from sample 244/1720\n", + "Extracting features from sample 245/1720\n", + "Extracting features from sample 246/1720\n", + "Extracting features from sample 247/1720\n", + "Extracting features from sample 248/1720\n", + "Extracting features from sample 249/1720\n", + "Extracting features from sample 250/1720\n", + "Extracting features from sample 251/1720\n", + "Extracting features from sample 252/1720\n", + "Extracting features from sample 253/1720\n", + "Extracting features from sample 254/1720\n", + "Extracting features from sample 255/1720\n", + "Extracting features from sample 256/1720\n", + "Extracting features from sample 257/1720\n", + "Extracting features from sample 258/1720\n", + "Extracting features from sample 259/1720\n", + "Extracting features from sample 260/1720\n", + "Extracting features from sample 261/1720\n", + "Extracting features from sample 262/1720\n", + "Extracting features from sample 263/1720\n", + "Extracting features from sample 264/1720\n", + "Extracting features from sample 265/1720\n", + "Extracting features from sample 266/1720\n", + "Extracting features from sample 267/1720\n", + "Extracting features from sample 268/1720\n", + "Extracting features from sample 269/1720\n", + "Extracting features from sample 270/1720\n", + "Extracting features from sample 271/1720\n", + "Extracting features from sample 272/1720\n", + "Extracting features from sample 273/1720\n", + "Extracting features from sample 274/1720\n", + "Extracting features from sample 275/1720\n", + "Extracting features from sample 276/1720\n", + "Extracting features from sample 277/1720\n", + "Extracting features from sample 278/1720\n", + "Extracting features from sample 279/1720\n", + "Extracting features from sample 280/1720\n", + "Extracting features from sample 281/1720\n", + "Extracting features from sample 282/1720\n", + "Extracting features from sample 283/1720\n", + "Extracting features from sample 284/1720\n", + "Extracting features from sample 285/1720\n", + "Extracting features from sample 286/1720\n", + "Extracting features from sample 287/1720\n", + "Extracting features from sample 288/1720\n", + "Extracting features from sample 289/1720\n", + "Extracting features from sample 290/1720\n", + "Extracting features from sample 291/1720\n", + "Extracting features from sample 292/1720\n", + "Extracting features from sample 293/1720\n", + "Extracting features from sample 294/1720\n", + "Extracting features from sample 295/1720\n", + "Extracting features from sample 296/1720\n", + "Extracting features from sample 297/1720\n", + "Extracting features from sample 298/1720\n", + "Extracting features from sample 299/1720\n", + "Extracting features from sample 300/1720\n", + "Extracting features from sample 301/1720\n", + "Extracting features from sample 302/1720\n", + "Extracting features from sample 303/1720\n", + "Extracting features from sample 304/1720\n", + "Extracting features from sample 305/1720\n", + "Extracting features from sample 306/1720\n", + "Extracting features from sample 307/1720\n", + "Extracting features from sample 308/1720\n", + "Extracting features from sample 309/1720\n", + "Extracting features from sample 310/1720\n", + "Extracting features from sample 311/1720\n", + "Extracting features from sample 312/1720\n", + "Extracting features from sample 313/1720\n", + "Extracting features from sample 314/1720\n", + "Extracting features from sample 315/1720\n", + "Extracting features from sample 316/1720\n", + "Extracting features from sample 317/1720\n", + "Extracting features from sample 318/1720\n", + "Extracting features from sample 319/1720\n", + "Extracting features from sample 320/1720\n", + "Extracting features from sample 321/1720\n", + "Extracting features from sample 322/1720\n", + "Extracting features from sample 323/1720\n", + "Extracting features from sample 324/1720\n", + "Extracting features from sample 325/1720\n", + "Extracting features from sample 326/1720\n", + "Extracting features from sample 327/1720\n", + "Extracting features from sample 328/1720\n", + "Extracting features from sample 329/1720\n", + "Extracting features from sample 330/1720\n", + "Extracting features from sample 331/1720\n", + "Extracting features from sample 332/1720\n", + "Extracting features from sample 333/1720\n", + "Extracting features from sample 334/1720\n", + "Extracting features from sample 335/1720\n", + "Extracting features from sample 336/1720\n", + "Extracting features from sample 337/1720\n", + "Extracting features from sample 338/1720\n", + "Extracting features from sample 339/1720\n", + "Extracting features from sample 340/1720\n", + "Extracting features from sample 341/1720\n", + "Extracting features from sample 342/1720\n", + "Extracting features from sample 343/1720\n", + "Extracting features from sample 344/1720\n", + "Extracting features from sample 345/1720\n", + "Extracting features from sample 346/1720\n", + "Extracting features from sample 347/1720\n", + "Extracting features from sample 348/1720\n", + "Extracting features from sample 349/1720\n", + "Extracting features from sample 350/1720\n", + "Extracting features from sample 351/1720\n", + "Extracting features from sample 352/1720\n", + "Extracting features from sample 353/1720\n", + "Extracting features from sample 354/1720\n", + "Extracting features from sample 355/1720\n", + "Extracting features from sample 356/1720\n", + "Extracting features from sample 357/1720\n", + "Extracting features from sample 358/1720\n", + "Extracting features from sample 359/1720\n", + "Extracting features from sample 360/1720\n", + "Extracting features from sample 361/1720\n", + "Extracting features from sample 362/1720\n", + "Extracting features from sample 363/1720\n", + "Extracting features from sample 364/1720\n", + "Extracting features from sample 365/1720\n", + "Extracting features from sample 366/1720\n", + "Extracting features from sample 367/1720\n", + "Extracting features from sample 368/1720\n", + "Extracting features from sample 369/1720\n", + "Extracting features from sample 370/1720\n", + "Extracting features from sample 371/1720\n", + "Extracting features from sample 372/1720\n", + "Extracting features from sample 373/1720\n", + "Extracting features from sample 374/1720\n", + "Extracting features from sample 375/1720\n", + "Extracting features from sample 376/1720\n", + "Extracting features from sample 377/1720\n", + "Extracting features from sample 378/1720\n", + "Extracting features from sample 379/1720\n", + "Extracting features from sample 380/1720\n", + "Extracting features from sample 381/1720\n", + "Extracting features from sample 382/1720\n", + "Extracting features from sample 383/1720\n", + "Extracting features from sample 384/1720\n", + "Extracting features from sample 385/1720\n", + "Extracting features from sample 386/1720\n", + "Extracting features from sample 387/1720\n", + "Extracting features from sample 388/1720\n", + "Extracting features from sample 389/1720\n", + "Extracting features from sample 390/1720\n", + "Extracting features from sample 391/1720\n", + "Extracting features from sample 392/1720\n", + "Extracting features from sample 393/1720\n", + "Extracting features from sample 394/1720\n", + "Extracting features from sample 395/1720\n", + "Extracting features from sample 396/1720\n", + "Extracting features from sample 397/1720\n", + "Extracting features from sample 398/1720\n", + "Extracting features from sample 399/1720\n", + "Extracting features from sample 400/1720\n", + "Extracting features from sample 401/1720\n", + "Extracting features from sample 402/1720\n", + "Extracting features from sample 403/1720\n", + "Extracting features from sample 404/1720\n", + "Extracting features from sample 405/1720\n", + "Extracting features from sample 406/1720\n", + "Extracting features from sample 407/1720\n", + "Extracting features from sample 408/1720\n", + "Extracting features from sample 409/1720\n", + "Extracting features from sample 410/1720\n", + "Extracting features from sample 411/1720\n", + "Extracting features from sample 412/1720\n", + "Extracting features from sample 413/1720\n", + "Extracting features from sample 414/1720\n", + "Extracting features from sample 415/1720\n", + "Extracting features from sample 416/1720\n", + "Extracting features from sample 417/1720\n", + "Extracting features from sample 418/1720\n", + "Extracting features from sample 419/1720\n", + "Extracting features from sample 420/1720\n", + "Extracting features from sample 421/1720\n", + "Extracting features from sample 422/1720\n", + "Extracting features from sample 423/1720\n", + "Extracting features from sample 424/1720\n", + "Extracting features from sample 425/1720\n", + "Extracting features from sample 426/1720\n", + "Extracting features from sample 427/1720\n", + "Extracting features from sample 428/1720\n", + "Extracting features from sample 429/1720\n", + "Extracting features from sample 430/1720\n", + "Extracting features from sample 431/1720\n", + "Extracting features from sample 432/1720\n", + "Extracting features from sample 433/1720\n", + "Extracting features from sample 434/1720\n", + "Extracting features from sample 435/1720\n", + "Extracting features from sample 436/1720\n", + "Extracting features from sample 437/1720\n", + "Extracting features from sample 438/1720\n", + "Extracting features from sample 439/1720\n", + "Extracting features from sample 440/1720\n", + "Extracting features from sample 441/1720\n", + "Extracting features from sample 442/1720\n", + "Extracting features from sample 443/1720\n", + "Extracting features from sample 444/1720\n", + "Extracting features from sample 445/1720\n", + "Extracting features from sample 446/1720\n", + "Extracting features from sample 447/1720\n", + "Extracting features from sample 448/1720\n", + "Extracting features from sample 449/1720\n", + "Extracting features from sample 450/1720\n", + "Extracting features from sample 451/1720\n", + "Extracting features from sample 452/1720\n", + "Extracting features from sample 453/1720\n", + "Extracting features from sample 454/1720\n", + "Extracting features from sample 455/1720\n", + "Extracting features from sample 456/1720\n", + "Extracting features from sample 457/1720\n", + "Extracting features from sample 458/1720\n", + "Extracting features from sample 459/1720\n", + "Extracting features from sample 460/1720\n", + "Extracting features from sample 461/1720\n", + "Extracting features from sample 462/1720\n", + "Extracting features from sample 463/1720\n", + "Extracting features from sample 464/1720\n", + "Extracting features from sample 465/1720\n", + "Extracting features from sample 466/1720\n", + "Extracting features from sample 467/1720\n", + "Extracting features from sample 468/1720\n", + "Extracting features from sample 469/1720\n", + "Extracting features from sample 470/1720\n", + "Extracting features from sample 471/1720\n", + "Extracting features from sample 472/1720\n", + "Extracting features from sample 473/1720\n", + "Extracting features from sample 474/1720\n", + "Extracting features from sample 475/1720\n", + "Extracting features from sample 476/1720\n", + "Extracting features from sample 477/1720\n", + "Extracting features from sample 478/1720\n", + "Extracting features from sample 479/1720\n", + "Extracting features from sample 480/1720\n", + "Extracting features from sample 481/1720\n", + "Extracting features from sample 482/1720\n", + "Extracting features from sample 483/1720\n", + "Extracting features from sample 484/1720\n", + "Extracting features from sample 485/1720\n", + "Extracting features from sample 486/1720\n", + "Extracting features from sample 487/1720\n", + "Extracting features from sample 488/1720\n", + "Extracting features from sample 489/1720\n", + "Extracting features from sample 490/1720\n", + "Extracting features from sample 491/1720\n", + "Extracting features from sample 492/1720\n", + "Extracting features from sample 493/1720\n", + "Extracting features from sample 494/1720\n", + "Extracting features from sample 495/1720\n", + "Extracting features from sample 496/1720\n", + "Extracting features from sample 497/1720\n", + "Extracting features from sample 498/1720\n", + "Extracting features from sample 499/1720\n", + "Extracting features from sample 500/1720\n", + "Extracting features from sample 501/1720\n", + "Extracting features from sample 502/1720\n", + "Extracting features from sample 503/1720\n", + "Extracting features from sample 504/1720\n", + "Extracting features from sample 505/1720\n", + "Extracting features from sample 506/1720\n", + "Extracting features from sample 507/1720\n", + "Extracting features from sample 508/1720\n", + "Extracting features from sample 509/1720\n", + "Extracting features from sample 510/1720\n", + "Extracting features from sample 511/1720\n", + "Extracting features from sample 512/1720\n", + "Extracting features from sample 513/1720\n", + "Extracting features from sample 514/1720\n", + "Extracting features from sample 515/1720\n", + "Extracting features from sample 516/1720\n", + "Extracting features from sample 517/1720\n", + "Extracting features from sample 518/1720\n", + "Extracting features from sample 519/1720\n", + "Extracting features from sample 520/1720\n", + "Extracting features from sample 521/1720\n", + "Extracting features from sample 522/1720\n", + "Extracting features from sample 523/1720\n", + "Extracting features from sample 524/1720\n", + "Extracting features from sample 525/1720\n", + "Extracting features from sample 526/1720\n", + "Extracting features from sample 527/1720\n", + "Extracting features from sample 528/1720\n", + "Extracting features from sample 529/1720\n", + "Extracting features from sample 530/1720\n", + "Extracting features from sample 531/1720\n", + "Extracting features from sample 532/1720\n", + "Extracting features from sample 533/1720\n", + "Extracting features from sample 534/1720\n", + "Extracting features from sample 535/1720\n", + "Extracting features from sample 536/1720\n", + "Extracting features from sample 537/1720\n", + "Extracting features from sample 538/1720\n", + "Extracting features from sample 539/1720\n", + "Extracting features from sample 540/1720\n", + "Extracting features from sample 541/1720\n", + "Extracting features from sample 542/1720\n", + "Extracting features from sample 543/1720\n", + "Extracting features from sample 544/1720\n", + "Extracting features from sample 545/1720\n", + "Extracting features from sample 546/1720\n", + "Extracting features from sample 547/1720\n", + "Extracting features from sample 548/1720\n", + "Extracting features from sample 549/1720\n", + "Extracting features from sample 550/1720\n", + "Extracting features from sample 551/1720\n", + "Extracting features from sample 552/1720\n", + "Extracting features from sample 553/1720\n", + "Extracting features from sample 554/1720\n", + "Extracting features from sample 555/1720\n", + "Extracting features from sample 556/1720\n", + "Extracting features from sample 557/1720\n", + "Extracting features from sample 558/1720\n", + "Extracting features from sample 559/1720\n", + "Extracting features from sample 560/1720\n", + "Extracting features from sample 561/1720\n", + "Extracting features from sample 562/1720\n", + "Extracting features from sample 563/1720\n", + "Extracting features from sample 564/1720\n", + "Extracting features from sample 565/1720\n", + "Extracting features from sample 566/1720\n", + "Extracting features from sample 567/1720\n", + "Extracting features from sample 568/1720\n", + "Extracting features from sample 569/1720\n", + "Extracting features from sample 570/1720\n", + "Extracting features from sample 571/1720\n", + "Extracting features from sample 572/1720\n", + "Extracting features from sample 573/1720\n", + "Extracting features from sample 574/1720\n", + "Extracting features from sample 575/1720\n", + "Extracting features from sample 576/1720\n", + "Extracting features from sample 577/1720\n", + "Extracting features from sample 578/1720\n", + "Extracting features from sample 579/1720\n", + "Extracting features from sample 580/1720\n", + "Extracting features from sample 581/1720\n", + "Extracting features from sample 582/1720\n", + "Extracting features from sample 583/1720\n", + "Extracting features from sample 584/1720\n", + "Extracting features from sample 585/1720\n", + "Extracting features from sample 586/1720\n", + "Extracting features from sample 587/1720\n", + "Extracting features from sample 588/1720\n", + "Extracting features from sample 589/1720\n", + "Extracting features from sample 590/1720\n", + "Extracting features from sample 591/1720\n", + "Extracting features from sample 592/1720\n", + "Extracting features from sample 593/1720\n", + "Extracting features from sample 594/1720\n", + "Extracting features from sample 595/1720\n", + "Extracting features from sample 596/1720\n", + "Extracting features from sample 597/1720\n", + "Extracting features from sample 598/1720\n", + "Extracting features from sample 599/1720\n", + "Extracting features from sample 600/1720\n", + "Extracting features from sample 601/1720\n", + "Extracting features from sample 602/1720\n", + "Extracting features from sample 603/1720\n", + "Extracting features from sample 604/1720\n", + "Extracting features from sample 605/1720\n", + "Extracting features from sample 606/1720\n", + "Extracting features from sample 607/1720\n", + "Extracting features from sample 608/1720\n", + "Extracting features from sample 609/1720\n", + "Extracting features from sample 610/1720\n", + "Extracting features from sample 611/1720\n", + "Extracting features from sample 612/1720\n", + "Extracting features from sample 613/1720\n", + "Extracting features from sample 614/1720\n", + "Extracting features from sample 615/1720\n", + "Extracting features from sample 616/1720\n", + "Extracting features from sample 617/1720\n", + "Extracting features from sample 618/1720\n", + "Extracting features from sample 619/1720\n", + "Extracting features from sample 620/1720\n", + "Extracting features from sample 621/1720\n", + "Extracting features from sample 622/1720\n", + "Extracting features from sample 623/1720\n", + "Extracting features from sample 624/1720\n", + "Extracting features from sample 625/1720\n", + "Extracting features from sample 626/1720\n", + "Extracting features from sample 627/1720\n", + "Extracting features from sample 628/1720\n", + "Extracting features from sample 629/1720\n", + "Extracting features from sample 630/1720\n", + "Extracting features from sample 631/1720\n", + "Extracting features from sample 632/1720\n", + "Extracting features from sample 633/1720\n", + "Extracting features from sample 634/1720\n", + "Extracting features from sample 635/1720\n", + "Extracting features from sample 636/1720\n", + "Extracting features from sample 637/1720\n", + "Extracting features from sample 638/1720\n", + "Extracting features from sample 639/1720\n", + "Extracting features from sample 640/1720\n", + "Extracting features from sample 641/1720\n", + "Extracting features from sample 642/1720\n", + "Extracting features from sample 643/1720\n", + "Extracting features from sample 644/1720\n", + "Extracting features from sample 645/1720\n", + "Extracting features from sample 646/1720\n", + "Extracting features from sample 647/1720\n", + "Extracting features from sample 648/1720\n", + "Extracting features from sample 649/1720\n", + "Extracting features from sample 650/1720\n", + "Extracting features from sample 651/1720\n", + "Extracting features from sample 652/1720\n", + "Extracting features from sample 653/1720\n", + "Extracting features from sample 654/1720\n", + "Extracting features from sample 655/1720\n", + "Extracting features from sample 656/1720\n", + "Extracting features from sample 657/1720\n", + "Extracting features from sample 658/1720\n", + "Extracting features from sample 659/1720\n", + "Extracting features from sample 660/1720\n", + "Extracting features from sample 661/1720\n", + "Extracting features from sample 662/1720\n", + "Extracting features from sample 663/1720\n", + "Extracting features from sample 664/1720\n", + "Extracting features from sample 665/1720\n", + "Extracting features from sample 666/1720\n", + "Extracting features from sample 667/1720\n", + "Extracting features from sample 668/1720\n", + "Extracting features from sample 669/1720\n", + "Extracting features from sample 670/1720\n", + "Extracting features from sample 671/1720\n", + "Extracting features from sample 672/1720\n", + "Extracting features from sample 673/1720\n", + "Extracting features from sample 674/1720\n", + "Extracting features from sample 675/1720\n", + "Extracting features from sample 676/1720\n", + "Extracting features from sample 677/1720\n", + "Extracting features from sample 678/1720\n", + "Extracting features from sample 679/1720\n", + "Extracting features from sample 680/1720\n", + "Extracting features from sample 681/1720\n", + "Extracting features from sample 682/1720\n", + "Extracting features from sample 683/1720\n", + "Extracting features from sample 684/1720\n", + "Extracting features from sample 685/1720\n", + "Extracting features from sample 686/1720\n", + "Extracting features from sample 687/1720\n", + "Extracting features from sample 688/1720\n", + "Extracting features from sample 689/1720\n", + "Extracting features from sample 690/1720\n", + "Extracting features from sample 691/1720\n", + "Extracting features from sample 692/1720\n", + "Extracting features from sample 693/1720\n", + "Extracting features from sample 694/1720\n", + "Extracting features from sample 695/1720\n", + "Extracting features from sample 696/1720\n", + "Extracting features from sample 697/1720\n", + "Extracting features from sample 698/1720\n", + "Extracting features from sample 699/1720\n", + "Extracting features from sample 700/1720\n", + "Extracting features from sample 701/1720\n", + "Extracting features from sample 702/1720\n", + "Extracting features from sample 703/1720\n", + "Extracting features from sample 704/1720\n", + "Extracting features from sample 705/1720\n", + "Extracting features from sample 706/1720\n", + "Extracting features from sample 707/1720\n", + "Extracting features from sample 708/1720\n", + "Extracting features from sample 709/1720\n", + "Extracting features from sample 710/1720\n", + "Extracting features from sample 711/1720\n", + "Extracting features from sample 712/1720\n", + "Extracting features from sample 713/1720\n", + "Extracting features from sample 714/1720\n", + "Extracting features from sample 715/1720\n", + "Extracting features from sample 716/1720\n", + "Extracting features from sample 717/1720\n", + "Extracting features from sample 718/1720\n", + "Extracting features from sample 719/1720\n", + "Extracting features from sample 720/1720\n", + "Extracting features from sample 721/1720\n", + "Extracting features from sample 722/1720\n", + "Extracting features from sample 723/1720\n", + "Extracting features from sample 724/1720\n", + "Extracting features from sample 725/1720\n", + "Extracting features from sample 726/1720\n", + "Extracting features from sample 727/1720\n", + "Extracting features from sample 728/1720\n", + "Extracting features from sample 729/1720\n", + "Extracting features from sample 730/1720\n", + "Extracting features from sample 731/1720\n", + "Extracting features from sample 732/1720\n", + "Extracting features from sample 733/1720\n", + "Extracting features from sample 734/1720\n", + "Extracting features from sample 735/1720\n", + "Extracting features from sample 736/1720\n", + "Extracting features from sample 737/1720\n", + "Extracting features from sample 738/1720\n", + "Extracting features from sample 739/1720\n", + "Extracting features from sample 740/1720\n", + "Extracting features from sample 741/1720\n", + "Extracting features from sample 742/1720\n", + "Extracting features from sample 743/1720\n", + "Extracting features from sample 744/1720\n", + "Extracting features from sample 745/1720\n", + "Extracting features from sample 746/1720\n", + "Extracting features from sample 747/1720\n", + "Extracting features from sample 748/1720\n", + "Extracting features from sample 749/1720\n", + "Extracting features from sample 750/1720\n", + "Extracting features from sample 751/1720\n", + "Extracting features from sample 752/1720\n", + "Extracting features from sample 753/1720\n", + "Extracting features from sample 754/1720\n", + "Extracting features from sample 755/1720\n", + "Extracting features from sample 756/1720\n", + "Extracting features from sample 757/1720\n", + "Extracting features from sample 758/1720\n", + "Extracting features from sample 759/1720\n", + "Extracting features from sample 760/1720\n", + "Extracting features from sample 761/1720\n", + "Extracting features from sample 762/1720\n", + "Extracting features from sample 763/1720\n", + "Extracting features from sample 764/1720\n", + "Extracting features from sample 765/1720\n", + "Extracting features from sample 766/1720\n", + "Extracting features from sample 767/1720\n", + "Extracting features from sample 768/1720\n", + "Extracting features from sample 769/1720\n", + "Extracting features from sample 770/1720\n", + "Extracting features from sample 771/1720\n", + "Extracting features from sample 772/1720\n", + "Extracting features from sample 773/1720\n", + "Extracting features from sample 774/1720\n", + "Extracting features from sample 775/1720\n", + "Extracting features from sample 776/1720\n", + "Extracting features from sample 777/1720\n", + "Extracting features from sample 778/1720\n", + "Extracting features from sample 779/1720\n", + "Extracting features from sample 780/1720\n", + "Extracting features from sample 781/1720\n", + "Extracting features from sample 782/1720\n", + "Extracting features from sample 783/1720\n", + "Extracting features from sample 784/1720\n", + "Extracting features from sample 785/1720\n", + "Extracting features from sample 786/1720\n", + "Extracting features from sample 787/1720\n", + "Extracting features from sample 788/1720\n", + "Extracting features from sample 789/1720\n", + "Extracting features from sample 790/1720\n", + "Extracting features from sample 791/1720\n", + "Extracting features from sample 792/1720\n", + "Extracting features from sample 793/1720\n", + "Extracting features from sample 794/1720\n", + "Extracting features from sample 795/1720\n", + "Extracting features from sample 796/1720\n", + "Extracting features from sample 797/1720\n", + "Extracting features from sample 798/1720\n", + "Extracting features from sample 799/1720\n", + "Extracting features from sample 800/1720\n", + "Extracting features from sample 801/1720\n", + "Extracting features from sample 802/1720\n", + "Extracting features from sample 803/1720\n", + "Extracting features from sample 804/1720\n", + "Extracting features from sample 805/1720\n", + "Extracting features from sample 806/1720\n", + "Extracting features from sample 807/1720\n", + "Extracting features from sample 808/1720\n", + "Extracting features from sample 809/1720\n", + "Extracting features from sample 810/1720\n", + "Extracting features from sample 811/1720\n", + "Extracting features from sample 812/1720\n", + "Extracting features from sample 813/1720\n", + "Extracting features from sample 814/1720\n", + "Extracting features from sample 815/1720\n", + "Extracting features from sample 816/1720\n", + "Extracting features from sample 817/1720\n", + "Extracting features from sample 818/1720\n", + "Extracting features from sample 819/1720\n", + "Extracting features from sample 820/1720\n", + "Extracting features from sample 821/1720\n", + "Extracting features from sample 822/1720\n", + "Extracting features from sample 823/1720\n", + "Extracting features from sample 824/1720\n", + "Extracting features from sample 825/1720\n", + "Extracting features from sample 826/1720\n", + "Extracting features from sample 827/1720\n", + "Extracting features from sample 828/1720\n", + "Extracting features from sample 829/1720\n", + "Extracting features from sample 830/1720\n", + "Extracting features from sample 831/1720\n", + "Extracting features from sample 832/1720\n", + "Extracting features from sample 833/1720\n", + "Extracting features from sample 834/1720\n", + "Extracting features from sample 835/1720\n", + "Extracting features from sample 836/1720\n", + "Extracting features from sample 837/1720\n", + "Extracting features from sample 838/1720\n", + "Extracting features from sample 839/1720\n", + "Extracting features from sample 840/1720\n", + "Extracting features from sample 841/1720\n", + "Extracting features from sample 842/1720\n", + "Extracting features from sample 843/1720\n", + "Extracting features from sample 844/1720\n", + "Extracting features from sample 845/1720\n", + "Extracting features from sample 846/1720\n", + "Extracting features from sample 847/1720\n", + "Extracting features from sample 848/1720\n", + "Extracting features from sample 849/1720\n", + "Extracting features from sample 850/1720\n", + "Extracting features from sample 851/1720\n", + "Extracting features from sample 852/1720\n", + "Extracting features from sample 853/1720\n", + "Extracting features from sample 854/1720\n", + "Extracting features from sample 855/1720\n", + "Extracting features from sample 856/1720\n", + "Extracting features from sample 857/1720\n", + "Extracting features from sample 858/1720\n", + "Extracting features from sample 859/1720\n", + "Extracting features from sample 860/1720\n", + "Extracting features from sample 861/1720\n", + "Extracting features from sample 862/1720\n", + "Extracting features from sample 863/1720\n", + "Extracting features from sample 864/1720\n", + "Extracting features from sample 865/1720\n", + "Extracting features from sample 866/1720\n", + "Extracting features from sample 867/1720\n", + "Extracting features from sample 868/1720\n", + "Extracting features from sample 869/1720\n", + "Extracting features from sample 870/1720\n", + "Extracting features from sample 871/1720\n", + "Extracting features from sample 872/1720\n", + "Extracting features from sample 873/1720\n", + "Extracting features from sample 874/1720\n", + "Extracting features from sample 875/1720\n", + "Extracting features from sample 876/1720\n", + "Extracting features from sample 877/1720\n", + "Extracting features from sample 878/1720\n", + "Extracting features from sample 879/1720\n", + "Extracting features from sample 880/1720\n", + "Extracting features from sample 881/1720\n", + "Extracting features from sample 882/1720\n", + "Extracting features from sample 883/1720\n", + "Extracting features from sample 884/1720\n", + "Extracting features from sample 885/1720\n", + "Extracting features from sample 886/1720\n", + "Extracting features from sample 887/1720\n", + "Extracting features from sample 888/1720\n", + "Extracting features from sample 889/1720\n", + "Extracting features from sample 890/1720\n", + "Extracting features from sample 891/1720\n", + "Extracting features from sample 892/1720\n", + "Extracting features from sample 893/1720\n", + "Extracting features from sample 894/1720\n", + "Extracting features from sample 895/1720\n", + "Extracting features from sample 896/1720\n", + "Extracting features from sample 897/1720\n", + "Extracting features from sample 898/1720\n", + "Extracting features from sample 899/1720\n", + "Extracting features from sample 900/1720\n", + "Extracting features from sample 901/1720\n", + "Extracting features from sample 902/1720\n", + "Extracting features from sample 903/1720\n", + "Extracting features from sample 904/1720\n", + "Extracting features from sample 905/1720\n", + "Extracting features from sample 906/1720\n", + "Extracting features from sample 907/1720\n", + "Extracting features from sample 908/1720\n", + "Extracting features from sample 909/1720\n", + "Extracting features from sample 910/1720\n", + "Extracting features from sample 911/1720\n", + "Extracting features from sample 912/1720\n", + "Extracting features from sample 913/1720\n", + "Extracting features from sample 914/1720\n", + "Extracting features from sample 915/1720\n", + "Extracting features from sample 916/1720\n", + "Extracting features from sample 917/1720\n", + "Extracting features from sample 918/1720\n", + "Extracting features from sample 919/1720\n", + "Extracting features from sample 920/1720\n", + "Extracting features from sample 921/1720\n", + "Extracting features from sample 922/1720\n", + "Extracting features from sample 923/1720\n", + "Extracting features from sample 924/1720\n", + "Extracting features from sample 925/1720\n", + "Extracting features from sample 926/1720\n", + "Extracting features from sample 927/1720\n", + "Extracting features from sample 928/1720\n", + "Extracting features from sample 929/1720\n", + "Extracting features from sample 930/1720\n", + "Extracting features from sample 931/1720\n", + "Extracting features from sample 932/1720\n", + "Extracting features from sample 933/1720\n", + "Extracting features from sample 934/1720\n", + "Extracting features from sample 935/1720\n", + "Extracting features from sample 936/1720\n", + "Extracting features from sample 937/1720\n", + "Extracting features from sample 938/1720\n", + "Extracting features from sample 939/1720\n", + "Extracting features from sample 940/1720\n", + "Extracting features from sample 941/1720\n", + "Extracting features from sample 942/1720\n", + "Extracting features from sample 943/1720\n", + "Extracting features from sample 944/1720\n", + "Extracting features from sample 945/1720\n", + "Extracting features from sample 946/1720\n", + "Extracting features from sample 947/1720\n", + "Extracting features from sample 948/1720\n", + "Extracting features from sample 949/1720\n", + "Extracting features from sample 950/1720\n", + "Extracting features from sample 951/1720\n", + "Extracting features from sample 952/1720\n", + "Extracting features from sample 953/1720\n", + "Extracting features from sample 954/1720\n", + "Extracting features from sample 955/1720\n", + "Extracting features from sample 956/1720\n", + "Extracting features from sample 957/1720\n", + "Extracting features from sample 958/1720\n", + "Extracting features from sample 959/1720\n", + "Extracting features from sample 960/1720\n", + "Extracting features from sample 961/1720\n", + "Extracting features from sample 962/1720\n", + "Extracting features from sample 963/1720\n", + "Extracting features from sample 964/1720\n", + "Extracting features from sample 965/1720\n", + "Extracting features from sample 966/1720\n", + "Extracting features from sample 967/1720\n", + "Extracting features from sample 968/1720\n", + "Extracting features from sample 969/1720\n", + "Extracting features from sample 970/1720\n", + "Extracting features from sample 971/1720\n", + "Extracting features from sample 972/1720\n", + "Extracting features from sample 973/1720\n", + "Extracting features from sample 974/1720\n", + "Extracting features from sample 975/1720\n", + "Extracting features from sample 976/1720\n", + "Extracting features from sample 977/1720\n", + "Extracting features from sample 978/1720\n", + "Extracting features from sample 979/1720\n", + "Extracting features from sample 980/1720\n", + "Extracting features from sample 981/1720\n", + "Extracting features from sample 982/1720\n", + "Extracting features from sample 983/1720\n", + "Extracting features from sample 984/1720\n", + "Extracting features from sample 985/1720\n", + "Extracting features from sample 986/1720\n", + "Extracting features from sample 987/1720\n", + "Extracting features from sample 988/1720\n", + "Extracting features from sample 989/1720\n", + "Extracting features from sample 990/1720\n", + "Extracting features from sample 991/1720\n", + "Extracting features from sample 992/1720\n", + "Extracting features from sample 993/1720\n", + "Extracting features from sample 994/1720\n", + "Extracting features from sample 995/1720\n", + "Extracting features from sample 996/1720\n", + "Extracting features from sample 997/1720\n", + "Extracting features from sample 998/1720\n", + "Extracting features from sample 999/1720\n", + "Extracting features from sample 1000/1720\n", + "Extracting features from sample 1001/1720\n", + "Extracting features from sample 1002/1720\n", + "Extracting features from sample 1003/1720\n", + "Extracting features from sample 1004/1720\n", + "Extracting features from sample 1005/1720\n", + "Extracting features from sample 1006/1720\n", + "Extracting features from sample 1007/1720\n", + "Extracting features from sample 1008/1720\n", + "Extracting features from sample 1009/1720\n", + "Extracting features from sample 1010/1720\n", + "Extracting features from sample 1011/1720\n", + "Extracting features from sample 1012/1720\n", + "Extracting features from sample 1013/1720\n", + "Extracting features from sample 1014/1720\n", + "Extracting features from sample 1015/1720\n", + "Extracting features from sample 1016/1720\n", + "Extracting features from sample 1017/1720\n", + "Extracting features from sample 1018/1720\n", + "Extracting features from sample 1019/1720\n", + "Extracting features from sample 1020/1720\n", + "Extracting features from sample 1021/1720\n", + "Extracting features from sample 1022/1720\n", + "Extracting features from sample 1023/1720\n", + "Extracting features from sample 1024/1720\n", + "Extracting features from sample 1025/1720\n", + "Extracting features from sample 1026/1720\n", + "Extracting features from sample 1027/1720\n", + "Extracting features from sample 1028/1720\n", + "Extracting features from sample 1029/1720\n", + "Extracting features from sample 1030/1720\n", + "Extracting features from sample 1031/1720\n", + "Extracting features from sample 1032/1720\n", + "Extracting features from sample 1033/1720\n", + "Extracting features from sample 1034/1720\n", + "Extracting features from sample 1035/1720\n", + "Extracting features from sample 1036/1720\n", + "Extracting features from sample 1037/1720\n", + "Extracting features from sample 1038/1720\n", + "Extracting features from sample 1039/1720\n", + "Extracting features from sample 1040/1720\n", + "Extracting features from sample 1041/1720\n", + "Extracting features from sample 1042/1720\n", + "Extracting features from sample 1043/1720\n", + "Extracting features from sample 1044/1720\n", + "Extracting features from sample 1045/1720\n", + "Extracting features from sample 1046/1720\n", + "Extracting features from sample 1047/1720\n", + "Extracting features from sample 1048/1720\n", + "Extracting features from sample 1049/1720\n", + "Extracting features from sample 1050/1720\n", + "Extracting features from sample 1051/1720\n", + "Extracting features from sample 1052/1720\n", + "Extracting features from sample 1053/1720\n", + "Extracting features from sample 1054/1720\n", + "Extracting features from sample 1055/1720\n", + "Extracting features from sample 1056/1720\n", + "Extracting features from sample 1057/1720\n", + "Extracting features from sample 1058/1720\n", + "Extracting features from sample 1059/1720\n", + "Extracting features from sample 1060/1720\n", + "Extracting features from sample 1061/1720\n", + "Extracting features from sample 1062/1720\n", + "Extracting features from sample 1063/1720\n", + "Extracting features from sample 1064/1720\n", + "Extracting features from sample 1065/1720\n", + "Extracting features from sample 1066/1720\n", + "Extracting features from sample 1067/1720\n", + "Extracting features from sample 1068/1720\n", + "Extracting features from sample 1069/1720\n", + "Extracting features from sample 1070/1720\n", + "Extracting features from sample 1071/1720\n", + "Extracting features from sample 1072/1720\n", + "Extracting features from sample 1073/1720\n", + "Extracting features from sample 1074/1720\n", + "Extracting features from sample 1075/1720\n", + "Extracting features from sample 1076/1720\n", + "Extracting features from sample 1077/1720\n", + "Extracting features from sample 1078/1720\n", + "Extracting features from sample 1079/1720\n", + "Extracting features from sample 1080/1720\n", + "Extracting features from sample 1081/1720\n", + "Extracting features from sample 1082/1720\n", + "Extracting features from sample 1083/1720\n", + "Extracting features from sample 1084/1720\n", + "Extracting features from sample 1085/1720\n", + "Extracting features from sample 1086/1720\n", + "Extracting features from sample 1087/1720\n", + "Extracting features from sample 1088/1720\n", + "Extracting features from sample 1089/1720\n", + "Extracting features from sample 1090/1720\n", + "Extracting features from sample 1091/1720\n", + "Extracting features from sample 1092/1720\n", + "Extracting features from sample 1093/1720\n", + "Extracting features from sample 1094/1720\n", + "Extracting features from sample 1095/1720\n", + "Extracting features from sample 1096/1720\n", + "Extracting features from sample 1097/1720\n", + "Extracting features from sample 1098/1720\n", + "Extracting features from sample 1099/1720\n", + "Extracting features from sample 1100/1720\n", + "Extracting features from sample 1101/1720\n", + "Extracting features from sample 1102/1720\n", + "Extracting features from sample 1103/1720\n", + "Extracting features from sample 1104/1720\n", + "Extracting features from sample 1105/1720\n", + "Extracting features from sample 1106/1720\n", + "Extracting features from sample 1107/1720\n", + "Extracting features from sample 1108/1720\n", + "Extracting features from sample 1109/1720\n", + "Extracting features from sample 1110/1720\n", + "Extracting features from sample 1111/1720\n", + "Extracting features from sample 1112/1720\n", + "Extracting features from sample 1113/1720\n", + "Extracting features from sample 1114/1720\n", + "Extracting features from sample 1115/1720\n", + "Extracting features from sample 1116/1720\n", + "Extracting features from sample 1117/1720\n", + "Extracting features from sample 1118/1720\n", + "Extracting features from sample 1119/1720\n", + "Extracting features from sample 1120/1720\n", + "Extracting features from sample 1121/1720\n", + "Extracting features from sample 1122/1720\n", + "Extracting features from sample 1123/1720\n", + "Extracting features from sample 1124/1720\n", + "Extracting features from sample 1125/1720\n", + "Extracting features from sample 1126/1720\n", + "Extracting features from sample 1127/1720\n", + "Extracting features from sample 1128/1720\n", + "Extracting features from sample 1129/1720\n", + "Extracting features from sample 1130/1720\n", + "Extracting features from sample 1131/1720\n", + "Extracting features from sample 1132/1720\n", + "Extracting features from sample 1133/1720\n", + "Extracting features from sample 1134/1720\n", + "Extracting features from sample 1135/1720\n", + "Extracting features from sample 1136/1720\n", + "Extracting features from sample 1137/1720\n", + "Extracting features from sample 1138/1720\n", + "Extracting features from sample 1139/1720\n", + "Extracting features from sample 1140/1720\n", + "Extracting features from sample 1141/1720\n", + "Extracting features from sample 1142/1720\n", + "Extracting features from sample 1143/1720\n", + "Extracting features from sample 1144/1720\n", + "Extracting features from sample 1145/1720\n", + "Extracting features from sample 1146/1720\n", + "Extracting features from sample 1147/1720\n", + "Extracting features from sample 1148/1720\n", + "Extracting features from sample 1149/1720\n", + "Extracting features from sample 1150/1720\n", + "Extracting features from sample 1151/1720\n", + "Extracting features from sample 1152/1720\n", + "Extracting features from sample 1153/1720\n", + "Extracting features from sample 1154/1720\n", + "Extracting features from sample 1155/1720\n", + "Extracting features from sample 1156/1720\n", + "Extracting features from sample 1157/1720\n", + "Extracting features from sample 1158/1720\n", + "Extracting features from sample 1159/1720\n", + "Extracting features from sample 1160/1720\n", + "Extracting features from sample 1161/1720\n", + "Extracting features from sample 1162/1720\n", + "Extracting features from sample 1163/1720\n", + "Extracting features from sample 1164/1720\n", + "Extracting features from sample 1165/1720\n", + "Extracting features from sample 1166/1720\n", + "Extracting features from sample 1167/1720\n", + "Extracting features from sample 1168/1720\n", + "Extracting features from sample 1169/1720\n", + "Extracting features from sample 1170/1720\n", + "Extracting features from sample 1171/1720\n", + "Extracting features from sample 1172/1720\n", + "Extracting features from sample 1173/1720\n", + "Extracting features from sample 1174/1720\n", + "Extracting features from sample 1175/1720\n", + "Extracting features from sample 1176/1720\n", + "Extracting features from sample 1177/1720\n", + "Extracting features from sample 1178/1720\n", + "Extracting features from sample 1179/1720\n", + "Extracting features from sample 1180/1720\n", + "Extracting features from sample 1181/1720\n", + "Extracting features from sample 1182/1720\n", + "Extracting features from sample 1183/1720\n", + "Extracting features from sample 1184/1720\n", + "Extracting features from sample 1185/1720\n", + "Extracting features from sample 1186/1720\n", + "Extracting features from sample 1187/1720\n", + "Extracting features from sample 1188/1720\n", + "Extracting features from sample 1189/1720\n", + "Extracting features from sample 1190/1720\n", + "Extracting features from sample 1191/1720\n", + "Extracting features from sample 1192/1720\n", + "Extracting features from sample 1193/1720\n", + "Extracting features from sample 1194/1720\n", + "Extracting features from sample 1195/1720\n", + "Extracting features from sample 1196/1720\n", + "Extracting features from sample 1197/1720\n", + "Extracting features from sample 1198/1720\n", + "Extracting features from sample 1199/1720\n", + "Extracting features from sample 1200/1720\n", + "Extracting features from sample 1201/1720\n", + "Extracting features from sample 1202/1720\n", + "Extracting features from sample 1203/1720\n", + "Extracting features from sample 1204/1720\n", + "Extracting features from sample 1205/1720\n", + "Extracting features from sample 1206/1720\n", + "Extracting features from sample 1207/1720\n", + "Extracting features from sample 1208/1720\n", + "Extracting features from sample 1209/1720\n", + "Extracting features from sample 1210/1720\n", + "Extracting features from sample 1211/1720\n", + "Extracting features from sample 1212/1720\n", + "Extracting features from sample 1213/1720\n", + "Extracting features from sample 1214/1720\n", + "Extracting features from sample 1215/1720\n", + "Extracting features from sample 1216/1720\n", + "Extracting features from sample 1217/1720\n", + "Extracting features from sample 1218/1720\n", + "Extracting features from sample 1219/1720\n", + "Extracting features from sample 1220/1720\n", + "Extracting features from sample 1221/1720\n", + "Extracting features from sample 1222/1720\n", + "Extracting features from sample 1223/1720\n", + "Extracting features from sample 1224/1720\n", + "Extracting features from sample 1225/1720\n", + "Extracting features from sample 1226/1720\n", + "Extracting features from sample 1227/1720\n", + "Extracting features from sample 1228/1720\n", + "Extracting features from sample 1229/1720\n", + "Extracting features from sample 1230/1720\n", + "Extracting features from sample 1231/1720\n", + "Extracting features from sample 1232/1720\n", + "Extracting features from sample 1233/1720\n", + "Extracting features from sample 1234/1720\n", + "Extracting features from sample 1235/1720\n", + "Extracting features from sample 1236/1720\n", + "Extracting features from sample 1237/1720\n", + "Extracting features from sample 1238/1720\n", + "Extracting features from sample 1239/1720\n", + "Extracting features from sample 1240/1720\n", + "Extracting features from sample 1241/1720\n", + "Extracting features from sample 1242/1720\n", + "Extracting features from sample 1243/1720\n", + "Extracting features from sample 1244/1720\n", + "Extracting features from sample 1245/1720\n", + "Extracting features from sample 1246/1720\n", + "Extracting features from sample 1247/1720\n", + "Extracting features from sample 1248/1720\n", + "Extracting features from sample 1249/1720\n", + "Extracting features from sample 1250/1720\n", + "Extracting features from sample 1251/1720\n", + "Extracting features from sample 1252/1720\n", + "Extracting features from sample 1253/1720\n", + "Extracting features from sample 1254/1720\n", + "Extracting features from sample 1255/1720\n", + "Extracting features from sample 1256/1720\n", + "Extracting features from sample 1257/1720\n", + "Extracting features from sample 1258/1720\n", + "Extracting features from sample 1259/1720\n", + "Extracting features from sample 1260/1720\n", + "Extracting features from sample 1261/1720\n", + "Extracting features from sample 1262/1720\n", + "Extracting features from sample 1263/1720\n", + "Extracting features from sample 1264/1720\n", + "Extracting features from sample 1265/1720\n", + "Extracting features from sample 1266/1720\n", + "Extracting features from sample 1267/1720\n", + "Extracting features from sample 1268/1720\n", + "Extracting features from sample 1269/1720\n", + "Extracting features from sample 1270/1720\n", + "Extracting features from sample 1271/1720\n", + "Extracting features from sample 1272/1720\n", + "Extracting features from sample 1273/1720\n", + "Extracting features from sample 1274/1720\n", + "Extracting features from sample 1275/1720\n", + "Extracting features from sample 1276/1720\n", + "Extracting features from sample 1277/1720\n", + "Extracting features from sample 1278/1720\n", + "Extracting features from sample 1279/1720\n", + "Extracting features from sample 1280/1720\n", + "Extracting features from sample 1281/1720\n", + "Extracting features from sample 1282/1720\n", + "Extracting features from sample 1283/1720\n", + "Extracting features from sample 1284/1720\n", + "Extracting features from sample 1285/1720\n", + "Extracting features from sample 1286/1720\n", + "Extracting features from sample 1287/1720\n", + "Extracting features from sample 1288/1720\n", + "Extracting features from sample 1289/1720\n", + "Extracting features from sample 1290/1720\n", + "Extracting features from sample 1291/1720\n", + "Extracting features from sample 1292/1720\n", + "Extracting features from sample 1293/1720\n", + "Extracting features from sample 1294/1720\n", + "Extracting features from sample 1295/1720\n", + "Extracting features from sample 1296/1720\n", + "Extracting features from sample 1297/1720\n", + "Extracting features from sample 1298/1720\n", + "Extracting features from sample 1299/1720\n", + "Extracting features from sample 1300/1720\n", + "Extracting features from sample 1301/1720\n", + "Extracting features from sample 1302/1720\n", + "Extracting features from sample 1303/1720\n", + "Extracting features from sample 1304/1720\n", + "Extracting features from sample 1305/1720\n", + "Extracting features from sample 1306/1720\n", + "Extracting features from sample 1307/1720\n", + "Extracting features from sample 1308/1720\n", + "Extracting features from sample 1309/1720\n", + "Extracting features from sample 1310/1720\n", + "Extracting features from sample 1311/1720\n", + "Extracting features from sample 1312/1720\n", + "Extracting features from sample 1313/1720\n", + "Extracting features from sample 1314/1720\n", + "Extracting features from sample 1315/1720\n", + "Extracting features from sample 1316/1720\n", + "Extracting features from sample 1317/1720\n", + "Extracting features from sample 1318/1720\n", + "Extracting features from sample 1319/1720\n", + "Extracting features from sample 1320/1720\n", + "Extracting features from sample 1321/1720\n", + "Extracting features from sample 1322/1720\n", + "Extracting features from sample 1323/1720\n", + "Extracting features from sample 1324/1720\n", + "Extracting features from sample 1325/1720\n", + "Extracting features from sample 1326/1720\n", + "Extracting features from sample 1327/1720\n", + "Extracting features from sample 1328/1720\n", + "Extracting features from sample 1329/1720\n", + "Extracting features from sample 1330/1720\n", + "Extracting features from sample 1331/1720\n", + "Extracting features from sample 1332/1720\n", + "Extracting features from sample 1333/1720\n", + "Extracting features from sample 1334/1720\n", + "Extracting features from sample 1335/1720\n", + "Extracting features from sample 1336/1720\n", + "Extracting features from sample 1337/1720\n", + "Extracting features from sample 1338/1720\n", + "Extracting features from sample 1339/1720\n", + "Extracting features from sample 1340/1720\n", + "Extracting features from sample 1341/1720\n", + "Extracting features from sample 1342/1720\n", + "Extracting features from sample 1343/1720\n", + "Extracting features from sample 1344/1720\n", + "Extracting features from sample 1345/1720\n", + "Extracting features from sample 1346/1720\n", + "Extracting features from sample 1347/1720\n", + "Extracting features from sample 1348/1720\n", + "Extracting features from sample 1349/1720\n", + "Extracting features from sample 1350/1720\n", + "Extracting features from sample 1351/1720\n", + "Extracting features from sample 1352/1720\n", + "Extracting features from sample 1353/1720\n", + "Extracting features from sample 1354/1720\n", + "Extracting features from sample 1355/1720\n", + "Extracting features from sample 1356/1720\n", + "Extracting features from sample 1357/1720\n", + "Extracting features from sample 1358/1720\n", + "Extracting features from sample 1359/1720\n", + "Extracting features from sample 1360/1720\n", + "Extracting features from sample 1361/1720\n", + "Extracting features from sample 1362/1720\n", + "Extracting features from sample 1363/1720\n", + "Extracting features from sample 1364/1720\n", + "Extracting features from sample 1365/1720\n", + "Extracting features from sample 1366/1720\n", + "Extracting features from sample 1367/1720\n", + "Extracting features from sample 1368/1720\n", + "Extracting features from sample 1369/1720\n", + "Extracting features from sample 1370/1720\n", + "Extracting features from sample 1371/1720\n", + "Extracting features from sample 1372/1720\n", + "Extracting features from sample 1373/1720\n", + "Extracting features from sample 1374/1720\n", + "Extracting features from sample 1375/1720\n", + "Extracting features from sample 1376/1720\n", + "Extracting features from sample 1377/1720\n", + "Extracting features from sample 1378/1720\n", + "Extracting features from sample 1379/1720\n", + "Extracting features from sample 1380/1720\n", + "Extracting features from sample 1381/1720\n", + "Extracting features from sample 1382/1720\n", + "Extracting features from sample 1383/1720\n", + "Extracting features from sample 1384/1720\n", + "Extracting features from sample 1385/1720\n", + "Extracting features from sample 1386/1720\n", + "Extracting features from sample 1387/1720\n", + "Extracting features from sample 1388/1720\n", + "Extracting features from sample 1389/1720\n", + "Extracting features from sample 1390/1720\n", + "Extracting features from sample 1391/1720\n", + "Extracting features from sample 1392/1720\n", + "Extracting features from sample 1393/1720\n", + "Extracting features from sample 1394/1720\n", + "Extracting features from sample 1395/1720\n", + "Extracting features from sample 1396/1720\n", + "Extracting features from sample 1397/1720\n", + "Extracting features from sample 1398/1720\n", + "Extracting features from sample 1399/1720\n", + "Extracting features from sample 1400/1720\n", + "Extracting features from sample 1401/1720\n", + "Extracting features from sample 1402/1720\n", + "Extracting features from sample 1403/1720\n", + "Extracting features from sample 1404/1720\n", + "Extracting features from sample 1405/1720\n", + "Extracting features from sample 1406/1720\n", + "Extracting features from sample 1407/1720\n", + "Extracting features from sample 1408/1720\n", + "Extracting features from sample 1409/1720\n", + "Extracting features from sample 1410/1720\n", + "Extracting features from sample 1411/1720\n", + "Extracting features from sample 1412/1720\n", + "Extracting features from sample 1413/1720\n", + "Extracting features from sample 1414/1720\n", + "Extracting features from sample 1415/1720\n", + "Extracting features from sample 1416/1720\n", + "Extracting features from sample 1417/1720\n", + "Extracting features from sample 1418/1720\n", + "Extracting features from sample 1419/1720\n", + "Extracting features from sample 1420/1720\n", + "Extracting features from sample 1421/1720\n", + "Extracting features from sample 1422/1720\n", + "Extracting features from sample 1423/1720\n", + "Extracting features from sample 1424/1720\n", + "Extracting features from sample 1425/1720\n", + "Extracting features from sample 1426/1720\n", + "Extracting features from sample 1427/1720\n", + "Extracting features from sample 1428/1720\n", + "Extracting features from sample 1429/1720\n", + "Extracting features from sample 1430/1720\n", + "Extracting features from sample 1431/1720\n", + "Extracting features from sample 1432/1720\n", + "Extracting features from sample 1433/1720\n", + "Extracting features from sample 1434/1720\n", + "Extracting features from sample 1435/1720\n", + "Extracting features from sample 1436/1720\n", + "Extracting features from sample 1437/1720\n", + "Extracting features from sample 1438/1720\n", + "Extracting features from sample 1439/1720\n", + "Extracting features from sample 1440/1720\n", + "Extracting features from sample 1441/1720\n", + "Extracting features from sample 1442/1720\n", + "Extracting features from sample 1443/1720\n", + "Extracting features from sample 1444/1720\n", + "Extracting features from sample 1445/1720\n", + "Extracting features from sample 1446/1720\n", + "Extracting features from sample 1447/1720\n", + "Extracting features from sample 1448/1720\n", + "Extracting features from sample 1449/1720\n", + "Extracting features from sample 1450/1720\n", + "Extracting features from sample 1451/1720\n", + "Extracting features from sample 1452/1720\n", + "Extracting features from sample 1453/1720\n", + "Extracting features from sample 1454/1720\n", + "Extracting features from sample 1455/1720\n", + "Extracting features from sample 1456/1720\n", + "Extracting features from sample 1457/1720\n", + "Extracting features from sample 1458/1720\n", + "Extracting features from sample 1459/1720\n", + "Extracting features from sample 1460/1720\n", + "Extracting features from sample 1461/1720\n", + "Extracting features from sample 1462/1720\n", + "Extracting features from sample 1463/1720\n", + "Extracting features from sample 1464/1720\n", + "Extracting features from sample 1465/1720\n", + "Extracting features from sample 1466/1720\n", + "Extracting features from sample 1467/1720\n", + "Extracting features from sample 1468/1720\n", + "Extracting features from sample 1469/1720\n", + "Extracting features from sample 1470/1720\n", + "Extracting features from sample 1471/1720\n", + "Extracting features from sample 1472/1720\n", + "Extracting features from sample 1473/1720\n", + "Extracting features from sample 1474/1720\n", + "Extracting features from sample 1475/1720\n", + "Extracting features from sample 1476/1720\n", + "Extracting features from sample 1477/1720\n", + "Extracting features from sample 1478/1720\n", + "Extracting features from sample 1479/1720\n", + "Extracting features from sample 1480/1720\n", + "Extracting features from sample 1481/1720\n", + "Extracting features from sample 1482/1720\n", + "Extracting features from sample 1483/1720\n", + "Extracting features from sample 1484/1720\n", + "Extracting features from sample 1485/1720\n", + "Extracting features from sample 1486/1720\n", + "Extracting features from sample 1487/1720\n", + "Extracting features from sample 1488/1720\n", + "Extracting features from sample 1489/1720\n", + "Extracting features from sample 1490/1720\n", + "Extracting features from sample 1491/1720\n", + "Extracting features from sample 1492/1720\n", + "Extracting features from sample 1493/1720\n", + "Extracting features from sample 1494/1720\n", + "Extracting features from sample 1495/1720\n", + "Extracting features from sample 1496/1720\n", + "Extracting features from sample 1497/1720\n", + "Extracting features from sample 1498/1720\n", + "Extracting features from sample 1499/1720\n", + "Extracting features from sample 1500/1720\n", + "Extracting features from sample 1501/1720\n", + "Extracting features from sample 1502/1720\n", + "Extracting features from sample 1503/1720\n", + "Extracting features from sample 1504/1720\n", + "Extracting features from sample 1505/1720\n", + "Extracting features from sample 1506/1720\n", + "Extracting features from sample 1507/1720\n", + "Extracting features from sample 1508/1720\n", + "Extracting features from sample 1509/1720\n", + "Extracting features from sample 1510/1720\n", + "Extracting features from sample 1511/1720\n", + "Extracting features from sample 1512/1720\n", + "Extracting features from sample 1513/1720\n", + "Extracting features from sample 1514/1720\n", + "Extracting features from sample 1515/1720\n", + "Extracting features from sample 1516/1720\n", + "Extracting features from sample 1517/1720\n", + "Extracting features from sample 1518/1720\n", + "Extracting features from sample 1519/1720\n", + "Extracting features from sample 1520/1720\n", + "Extracting features from sample 1521/1720\n", + "Extracting features from sample 1522/1720\n", + "Extracting features from sample 1523/1720\n", + "Extracting features from sample 1524/1720\n", + "Extracting features from sample 1525/1720\n", + "Extracting features from sample 1526/1720\n", + "Extracting features from sample 1527/1720\n", + "Extracting features from sample 1528/1720\n", + "Extracting features from sample 1529/1720\n", + "Extracting features from sample 1530/1720\n", + "Extracting features from sample 1531/1720\n", + "Extracting features from sample 1532/1720\n", + "Extracting features from sample 1533/1720\n", + "Extracting features from sample 1534/1720\n", + "Extracting features from sample 1535/1720\n", + "Extracting features from sample 1536/1720\n", + "Extracting features from sample 1537/1720\n", + "Extracting features from sample 1538/1720\n", + "Extracting features from sample 1539/1720\n", + "Extracting features from sample 1540/1720\n", + "Extracting features from sample 1541/1720\n", + "Extracting features from sample 1542/1720\n", + "Extracting features from sample 1543/1720\n", + "Extracting features from sample 1544/1720\n", + "Extracting features from sample 1545/1720\n", + "Extracting features from sample 1546/1720\n", + "Extracting features from sample 1547/1720\n", + "Extracting features from sample 1548/1720\n", + "Extracting features from sample 1549/1720\n", + "Extracting features from sample 1550/1720\n", + "Extracting features from sample 1551/1720\n", + "Extracting features from sample 1552/1720\n", + "Extracting features from sample 1553/1720\n", + "Extracting features from sample 1554/1720\n", + "Extracting features from sample 1555/1720\n", + "Extracting features from sample 1556/1720\n", + "Extracting features from sample 1557/1720\n", + "Extracting features from sample 1558/1720\n", + "Extracting features from sample 1559/1720\n", + "Extracting features from sample 1560/1720\n", + "Extracting features from sample 1561/1720\n", + "Extracting features from sample 1562/1720\n", + "Extracting features from sample 1563/1720\n", + "Extracting features from sample 1564/1720\n", + "Extracting features from sample 1565/1720\n", + "Extracting features from sample 1566/1720\n", + "Extracting features from sample 1567/1720\n", + "Extracting features from sample 1568/1720\n", + "Extracting features from sample 1569/1720\n", + "Extracting features from sample 1570/1720\n", + "Extracting features from sample 1571/1720\n", + "Extracting features from sample 1572/1720\n", + "Extracting features from sample 1573/1720\n", + "Extracting features from sample 1574/1720\n", + "Extracting features from sample 1575/1720\n", + "Extracting features from sample 1576/1720\n", + "Extracting features from sample 1577/1720\n", + "Extracting features from sample 1578/1720\n", + "Extracting features from sample 1579/1720\n", + "Extracting features from sample 1580/1720\n", + "Extracting features from sample 1581/1720\n", + "Extracting features from sample 1582/1720\n", + "Extracting features from sample 1583/1720\n", + "Extracting features from sample 1584/1720\n", + "Extracting features from sample 1585/1720\n", + "Extracting features from sample 1586/1720\n", + "Extracting features from sample 1587/1720\n", + "Extracting features from sample 1588/1720\n", + "Extracting features from sample 1589/1720\n", + "Extracting features from sample 1590/1720\n", + "Extracting features from sample 1591/1720\n", + "Extracting features from sample 1592/1720\n", + "Extracting features from sample 1593/1720\n", + "Extracting features from sample 1594/1720\n", + "Extracting features from sample 1595/1720\n", + "Extracting features from sample 1596/1720\n", + "Extracting features from sample 1597/1720\n", + "Extracting features from sample 1598/1720\n", + "Extracting features from sample 1599/1720\n", + "Extracting features 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from sample {i}/{Z.shape[0]}')\n", " if i==0:\n", - " X=tsfel.time_series_features_extractor(cfg, Z[i,:],fs=100.,)\n", + " X=tsfel.time_series_features_extractor(cfg, Z[i,:],fs=100., verbose = 0)\n", " X['source_type']=df.iloc[i]['source_type']\n", " else:\n", - " XX = tsfel.time_series_features_extractor(cfg, Z[i,:],fs=100.,)\n", + " XX = tsfel.time_series_features_extractor(cfg, Z[i,:],fs=100., verbose = 0)\n", " XX['source_type']=df.iloc[i]['source_type']\n", - " X=pd.concat([XX,X],axis=0,ignore_index=True)\n" + " X=pd.concat([XX,X],axis=0,ignore_index=True)\n", + "\n", + "\n", + "# change the column names to remove the prefix 0_\n", + "X.columns = X.columns.str.replace('0_', '')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a general code, you may package this into a function. " + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_features(Z):\n", + " \"\"\"\n", + " Function to calculate features from the data\n", + " Z: 2D array of seismic data data\n", + "\n", + " Returns:\n", + " X: DataFrame of the features\n", + " \"\"\"\n", + " for i in range(Z.shape[0]):\n", + " print(f'Extracting features from sample {i}/{Z.shape[0]}')\n", + " if i==0:\n", + " X=tsfel.time_series_features_extractor(cfg, Z[i,:],fs=100., verbose = 0)\n", + " X['source_type']=df.iloc[i]['source_type']\n", + " else:\n", + " XX = tsfel.time_series_features_extractor(cfg, Z[i,:],fs=100., verbose = 0)\n", + " XX['source_type']=df.iloc[i]['source_type']\n", + " X=pd.concat([XX,X],axis=0,ignore_index=True)\n", + "\n", + " # change the column names to remove the prefix 0_\n", + " X.columns = X.columns.str.replace('0_', '')\n", + "\n", + " return X" ] }, { @@ -669,6 +2821,248 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "# Calculate features from the seismic data and time it\n", + "import time\n", + "start = time.time()\n", + "X = calculate_features(Z)\n", + "end = time.time()\n", + "print(f'Time taken to calculate features: {end - start:.2f} seconds')" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0_Absolute energy 0_Area under the curve 0_Autocorrelation \\\n", + "0 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "1 2.325603e+09 5.627850e+04 2.325603e+09 \n", + "2 8.705101e+11 7.477590e+05 8.705101e+11 \n", + "3 9.606099e+11 1.168706e+06 9.606099e+11 \n", + "4 3.595338e+11 7.343233e+05 3.595338e+11 \n", + "\n", + " 0_Average power 0_Centroid 0_ECDF Percentile Count_0 \\\n", + "0 0.000000e+00 0.000000 0.0 \n", + "1 1.550402e+07 76.418530 3000.0 \n", + "2 5.803400e+09 74.967691 3000.0 \n", + "3 6.404066e+09 74.660640 3000.0 \n", + "4 2.396892e+09 74.976185 3000.0 \n", + "\n", + " 0_ECDF Percentile Count_1 0_ECDF Percentile_0 0_ECDF Percentile_1 \\\n", + "0 0.0 0.000000 0.000000 \n", + "1 12000.0 292.999790 450.250905 \n", + "2 12000.0 2434.988468 5493.021391 \n", + "3 12000.0 -9228.169002 -6414.699254 \n", + "4 12000.0 -4927.503218 -4863.251218 \n", + "\n", + " 0_ECDF_0 ... 0_Wavelet variance_1 0_Wavelet variance_2 \\\n", + "0 0.000067 ... 0.000000e+00 0.000000e+00 \n", + "1 0.000067 ... 6.678689e+02 1.055538e+03 \n", + "2 0.000067 ... 9.263918e+07 2.386667e+08 \n", + "3 0.000067 ... 2.431281e+06 7.334504e+06 \n", + "4 0.000067 ... 4.896885e+03 1.023186e+04 \n", + "\n", + " 0_Wavelet variance_3 0_Wavelet variance_4 0_Wavelet variance_5 \\\n", + "0 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "1 2.530908e+03 5.790352e+03 1.165462e+04 \n", + "2 2.936940e+08 2.466776e+08 1.717919e+08 \n", + "3 1.268369e+07 1.649068e+07 1.778231e+07 \n", + "4 1.774939e+04 2.750474e+04 3.936635e+04 \n", + "\n", + " 0_Wavelet variance_6 0_Wavelet variance_7 0_Wavelet variance_8 \\\n", + "0 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "1 2.118641e+04 3.548474e+04 5.549514e+04 \n", + "2 1.109657e+08 6.954319e+07 4.344068e+07 \n", + "3 1.683463e+07 1.469724e+07 1.235497e+07 \n", + "4 5.330725e+04 6.934682e+04 8.750847e+04 \n", + "\n", + " 0_Zero crossing rate source_type \n", + "0 0.0 surface_event \n", + "1 28.0 surface_event \n", + "2 258.0 surface_event \n", + "3 16.0 surface_event \n", + "4 0.0 surface_event \n", + "\n", + "[5 rows x 390 columns]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "X.head()" ] @@ -677,9 +3071,74 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exploring the feature space\n", + "### New DataFrame Clean up\n", + "\n", + "Removing the samples with NaN or Infinity\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "no of samples in the dataframe:1720\n", + "no of samples in the new dataframe:1717\n" + ] + } + ], + "source": [ + "print(f'no of samples in the dataframe:{X.shape[0]}')\n", + "# Replace infinities with NaN\n", + "new_X = X.replace([np.inf, -np.inf], np.nan, inplace= False)\n", + "\n", + "# Drop rows with NaN values\n", + "new_X = new_X.dropna(inplace= False)\n", + "print(f'no of samples in the new dataframe:{new_X.shape[0]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explore correlation among features\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Compute pairwise correlation of columns\n", + "corr_matrix = new_X.drop('source_type',axis=1).corr().abs()\n", + "\n", + "# Create a mask to only show the lower triangle of the matrix (since it's mirrored around its \n", + "# top-left to bottom-right diagonal)\n", + "mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n", + "\n", + "# Draw the heatmap with the mask\n", + "plt.figure(figsize=(15, 10))\n", + "sns.heatmap(corr_matrix, mask=mask, cmap='coolwarm', vmax=1, vmin=-1, center=0, square=True, linewidths=.5, \n", + " #cbar_kws={\"shrink\": .5}, \n", + " annot= False)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploring the feature space for classification purpose\n", "\n", - "Here we will plot distributions of features among the four classes. And we will explore what features are most correlated with each others in each of the categories.\n" + "Here we will plot distributions of certain features among the four classes. And we will explore what features are most correlated with each others in each of the categories.\n" ] }, { @@ -688,7 +3147,34 @@ "metadata": {}, "outputs": [], "source": [ - "plt.hist(np.log10(X['0_Wavelet variance_1']));" + "def plot_feature_histograms(new_X, feature):\n", + " # Get unique source types\n", + " source_types = new_X['source_type'].unique()\n", + "\n", + " # Define colorblind-friendly palette\n", + " colors = sns.color_palette('colorblind')\n", + "\n", + " for i, source_type in enumerate(source_types):\n", + " # Select data for this source type\n", + " data = new_X[new_X['source_type'] == source_type][feature]\n", + " \n", + " # Plot histogram for this source type\n", + " plt.hist(np.log10(data), color=colors[i % len(colors)], alpha=0.5, label=source_type)\n", + "\n", + " # Add title\n", + " plt.title(f'np.log10({feature})')\n", + " \n", + " # Add x-axis label\n", + " plt.xlabel(f'np.log10({feature})')\n", + " \n", + " # Add y-axis label\n", + " plt.ylabel('Count')\n", + " \n", + " # Add legend\n", + " plt.legend()\n", + "\n", + " # Show plot\n", + " plt.show()\n" ] }, { @@ -697,17 +3183,32 @@ "metadata": {}, "outputs": [], "source": [ - "plt.matshow(X.drop('source_type',axis=1).corr(),cmap='seismic',vmin=-1,vmax=1)" + "\n", + "feature = '0_Area under the curve'\n", + "plot_feature_histograms(new_X, feature)\n", + "\n", + "feature = '0_Kurtosis'\n", + "plot_feature_histograms(new_X, feature)\n", + "\n", + "feature = '0_Spectral variation'\n", + "plot_feature_histograms(new_X, feature)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now calculate features for 100 events of each category and find features that differ from each other.\n", + "### Student exercise\n", "\n", - "You may 1) calculate all features, 2) plot histograms/distributions of each feature and overlay each source-specific feature, 3) report on the ones that look different between each class and propose a workflow to classify between event types." + "1. Which of the features will be best to classify among the event type classes?" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb b/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb index f6117ca..908efd0 100644 --- a/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb +++ b/book/Chapter2-DataManipulation/2.3_pandas_rendered.ipynb @@ -1,574804 +1,574809 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "50e82750", - "metadata": {}, - "source": [ - "# 2.3 Pandas\n", - "\n", - "This section aims to provide new skills in python to handle structured, tabular data. \n", - "\n", - "Learning outcome:\n", - "- Manipulation of data frames (describing, filtering, ...) \n", - "- Learn about Lambda functions\n", - "- Intro to datetime objects\n", - "- Plotting data from data frames (histograms and maps)\n", - "- Introduction to Plotly\n", - "- Introduction to CSV & Parquet\n", - "\n", - "This tutorial can be offered in a 2-hour course. Sections are labeled as Level 1, 2, 3 in Section 1, 2, 3, and instructors may choose to leave higher levels for asynchronous, self-guided learning.\n", - "\n", - "\n", - "We will work on several structured data sets: sensor metadata, seismic data product (earthquake catalog).\n", - "\n", - "First, we import all the modules we need:" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "50e82750", + "metadata": {}, + "source": [ + "# 2.3 Pandas\n", + "\n", + "This section aims to provide new skills in python to handle structured, tabular data. \n", + "\n", + "Learning outcome:\n", + "- Manipulation of data frames (describing, filtering, ...) \n", + "- Learn about Lambda functions\n", + "- Intro to datetime objects\n", + "- Plotting data from data frames (histograms and maps)\n", + "- Introduction to Plotly\n", + "- Introduction to CSV & Parquet\n", + "\n", + "This tutorial can be offered in a 2-hour course. Sections are labeled as Level 1, 2, 3 in Section 1, 2, 3, and instructors may choose to leave higher levels for asynchronous, self-guided learning.\n", + "\n", + "\n", + "We will work on several structured data sets: sensor metadata, seismic data product (earthquake catalog).\n", + "\n", + "First, we import all the modules we need:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "28d926f9", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import io\n", + "import requests\n", + "import matplotlib.pyplot as plt\n", + "from datetime import datetime, timedelta\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ebf112a", + "metadata": {}, + "source": [ + "This notebook uses ``plotly`` to make interactive plots" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "3bf66863", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 35, - "id": "28d926f9", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import io\n", - "import requests\n", - "import matplotlib.pyplot as plt\n", - "from datetime import datetime, timedelta\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: plotly in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (5.17.0)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (8.2.3)\n", + "Requirement already satisfied: packaging in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (23.2)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install plotly\n", + "import plotly.express as px\n", + "import plotly.io as pio\n", + "# pio.renderers.default = 'vscode' # writes as standalone html, \n", + "# pio.renderers.default = 'iframe' # writes files as standalone html, \n", + "# pio.renderers.default = 'png' # writes files as standalone png, \n", + "# try notebook, jupyterlab, png, vscode, iframe\n" + ] + }, + { + "cell_type": "markdown", + "id": "3f1829a3", + "metadata": {}, + "source": [ + "# 1 Pandas Fundamentals\n", + "\n", + "## 1.1 Basics\n", + "\n", + "Pandas are composed of ``Series`` and ``DataFrame``. ``Series`` are columns with attributes or keys. The ``DataFrame`` is a multi-dimensional table made up of ``Series``.\n", + "\n", + "We can create a DataFrame composed of series from scratch using Python dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "3cdfc90e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "2ebf112a", - "metadata": {}, - "source": [ - "This notebook uses ``plotly`` to make interactive plots" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " temperature precipitation\n", + "0 36 3\n", + "1 37 1\n", + "2 30 0\n", + "3 50 0\n" + ] + } + ], + "source": [ + "data = {\n", + " 'temperature' : [36,37,30,50],\n", + " 'precipitation':[3,1,0,0]\n", + "}\n", + "my_pd = pd.DataFrame(data)\n", + "print(my_pd)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8e041ce5", + "metadata": {}, + "source": [ + "Each (key,value) item in the dataframe corresponds to a value in ``data``. To get the keys of the dataframe, type:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "fb8d9c69", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 36, - "id": "3bf66863", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: plotly in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (5.17.0)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (8.2.3)\n", - "Requirement already satisfied: packaging in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from plotly) (23.2)\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" - ] - } - ], - "source": [ - "!pip install plotly\n", - "import plotly.express as px\n", - "import plotly.io as pio\n", - "# pio.renderers.default = 'vscode' # writes as standalone html, \n", - "# pio.renderers.default = 'iframe' # writes files as standalone html, \n", - "# pio.renderers.default = 'png' # writes files as standalone png, \n", - "# try notebook, jupyterlab, png, vscode, iframe\n" + "data": { + "text/plain": [ + "Index(['temperature', 'precipitation'], dtype='object')" ] - }, + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_pd.keys()" + ] + }, + { + "cell_type": "markdown", + "id": "ff7935a4", + "metadata": {}, + "source": [ + "get a specific ``Series`` (different from the array)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "c5d734fd", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "3f1829a3", - "metadata": {}, - "source": [ - "# 1 Pandas Fundamentals\n", - "\n", - "## 1.1 Basics\n", - "\n", - "Pandas are composed of ``Series`` and ``DataFrame``. ``Series`` are columns with attributes or keys. The ``DataFrame`` is a multi-dimensional table made up of ``Series``.\n", - "\n", - "We can create a DataFrame composed of series from scratch using Python dictionary:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 36\n", + "1 37\n", + "2 30\n", + "3 50\n", + "Name: temperature, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "print(my_pd.temperature[:])\n", + "print(type(my_pd.temperature[:]))" + ] + }, + { + "cell_type": "markdown", + "id": "1f5f2426", + "metadata": {}, + "source": [ + "to get the _value_ of a specific key (e.g., temperature), at a specific index (e.g., 2) type:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "2ab0ad9d", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 37, - "id": "3cdfc90e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " temperature precipitation\n", - "0 36 3\n", - "1 37 1\n", - "2 30 0\n", - "3 50 0\n" - ] - } - ], - "source": [ - "data = {\n", - " 'temperature' : [36,37,30,50],\n", - " 'precipitation':[3,1,0,0]\n", - "}\n", - "my_pd = pd.DataFrame(data)\n", - "print(my_pd)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n", + "\n" + ] + } + ], + "source": [ + "print(my_pd.temperature[2])\n", + "print(type(my_pd.temperature[2]))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9fca0cf5", + "metadata": {}, + "source": [ + "## 1.2 Reading a DataFrame from a CSV file\n", + "\n", + "We can read a pandas directly from a standard file. We will download a catalog of earthquakes" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f029ed84", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "8e041ce5", - "metadata": {}, - "source": [ - "Each (key,value) item in the dataframe corresponds to a value in ``data``. To get the keys of the dataframe, type:" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "--2024-10-07 05:13:23-- https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 129629 (127K) [text/plain]\n", + "Saving to: ‘Global_Quakes_IRIS.csv.1’\n", + "\n", + "Global_Quakes_IRIS. 100%[===================>] 126.59K --.-KB/s in 0.05s \n", + "\n", + "2024-10-07 05:13:23 (2.35 MB/s) - ‘Global_Quakes_IRIS.csv.1’ saved [129629/129629]\n", + "\n" + ] + } + ], + "source": [ + "fname = \"\"\n", + "gist_dir = \"\"\n", + "!wget \"https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\"\n", + "# !wget \"https://github.com/UW-MLGEO/MLGeo-dataset/blob/main/data/Global_Quakes_IRIS.csv\"\n", + "ff = \"Global_Quakes_IRIS.csv\"\n", + "quake = pd.read_csv(ff)" + ] + }, + { + "cell_type": "markdown", + "id": "62e31c2c", + "metadata": {}, + "source": [ + "Now you use the ``head`` function to display what is in the file" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "ed565400", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 38, - "id": "fb8d9c69", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['temperature', 'precipitation'], dtype='object')" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE
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" ], - "source": [ - "my_pd.keys()" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", + "\n", + " description \n", + "0 VANUATU ISLANDS \n", + "1 NEAR EAST COAST OF HONSHU \n", + "2 SOUTH OF MARIANA ISLANDS \n", + "3 NORTHERN CHILE \n", + "4 NEAR COAST OF CENTRAL CHILE " ] - }, + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# enter answer here\n", + "quake.head()" + ] + }, + { + "cell_type": "markdown", + "id": "73bfcd15", + "metadata": {}, + "source": [ + "Display the depth using two ways to use the pandas object" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "9b01ce51", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "ff7935a4", - "metadata": {}, - "source": [ - "get a specific ``Series`` (different from the array)" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64\n", + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64\n" + ] + } + ], + "source": [ + "print(quake.depth)\n", + "print(quake['depth'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9c623644", + "metadata": {}, + "source": [ + "Calculate basic statistic of the data using the function ``describe``." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "2b9f083e", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 39, - "id": "c5d734fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 36\n", - "1 37\n", - "2 30\n", - "3 50\n", - "Name: temperature, dtype: int64\n", - "\n" - ] - } + "data": { + "text/html": [ + "
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\n", + "
" ], - "source": [ - "print(my_pd.temperature[:])\n", - "print(type(my_pd.temperature[:]))" - ] - }, - { - "cell_type": "markdown", - "id": "1f5f2426", - "metadata": {}, - "source": [ - "to get the _value_ of a specific key (e.g., temperature), at a specific index (e.g., 2) type:" + "text/plain": [ + " latitude longitude depth magnitude\n", + "count 1785.000000 1785.000000 1785.000000 1785.000000\n", + "mean 0.840700 40.608674 82773.187675 6.382403\n", + "std 30.579308 125.558363 146988.302031 0.429012\n", + "min -69.782500 -179.957200 0.000000 6.000000\n", + "25% -19.905100 -73.832200 12000.000000 6.100000\n", + "50% -4.478900 113.077800 24400.000000 6.300000\n", + "75% 27.161700 145.305700 58000.000000 6.600000\n", + "max 74.391800 179.998100 685500.000000 9.100000" ] - }, + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quake.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "8b54218c", + "metadata": {}, + "source": [ + "Calculate mean and median of specific ``Series``, for example depth." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "83f21ca3", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 40, - "id": "2ab0ad9d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "30\n", - "\n" - ] - } - ], - "source": [ - "print(my_pd.temperature[2])\n", - "print(type(my_pd.temperature[2]))" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "82773.18767507003\n", + "24400.0\n" + ] + } + ], + "source": [ + "# answer it here\n", + "print(quake.depth.mean())\n", + "print(quake.depth.median())" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "591f8d33", + "metadata": {}, + "source": [ + "## 1.3 Manipulating Pandas with Python\n", + "\n", + "### Classic functions\n", + "We will now practice how to modify the content of the DataFrame using functions. We will take the example where we want to change the depth values from meters to kilometers. First we can define this operation as a function" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "5b4f94b4", + "metadata": {}, + "outputs": [], + "source": [ + "# this function converts a value in meters to a value in kilometers\n", + "m2km = 1000 # this is defined as a global variable\n", + "def meters2kilometers(x):\n", + " return x/m2km\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "16cb70b9", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "9fca0cf5", - "metadata": {}, - "source": [ - "## 1.2 Reading a DataFrame from a CSV file\n", - "\n", - "We can read a pandas directly from a standard file. We will download a catalog of earthquakes" + "data": { + "text/plain": [ + "34.4" ] - }, + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now test it using the first element of the quake DataFrame\n", + "meters2kilometers(quake.depth[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7a360cc5", + "metadata": {}, + "outputs": [], + "source": [ + "# this function converts a value in meters to a value in kilometers\n", + "def meters2kilometers2(x):\n", + " m2km = 1000 # this is defined as a global variable\n", + " return x/m2km" + ] + }, + { + "cell_type": "markdown", + "id": "aa52810b", + "metadata": {}, + "source": [ + "Let's define another function that uses a local instead of global variable" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "b01a33fe", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 41, - "id": "f029ed84", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2024-10-07 05:13:23-- https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 129629 (127K) [text/plain]\n", - "Saving to: ‘Global_Quakes_IRIS.csv.1’\n", - "\n", - "Global_Quakes_IRIS. 100%[===================>] 126.59K --.-KB/s in 0.05s \n", - "\n", - "2024-10-07 05:13:23 (2.35 MB/s) - ‘Global_Quakes_IRIS.csv.1’ saved [129629/129629]\n", - "\n" - ] - } + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_km
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS34.4
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU30.1
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS16.9
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE109.4
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE25.7
\n", + "
" ], - "source": [ - "fname = \"\"\n", - "gist_dir = \"\"\n", - "!wget \"https://raw.githubusercontent.com/UW-MLGEO/MLGeo-dataset/refs/heads/main/data/Global_Quakes_IRIS.csv\"\n", - "# !wget \"https://github.com/UW-MLGEO/MLGeo-dataset/blob/main/data/Global_Quakes_IRIS.csv\"\n", - "ff = \"Global_Quakes_IRIS.csv\"\n", - "quake = pd.read_csv(ff)" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", + "\n", + " description depth_km \n", + "0 VANUATU ISLANDS 34.4 \n", + "1 NEAR EAST COAST OF HONSHU 30.1 \n", + "2 SOUTH OF MARIANA ISLANDS 16.9 \n", + "3 NORTHERN CHILE 109.4 \n", + "4 NEAR COAST OF CENTRAL CHILE 25.7 " ] - }, + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Apply the meters2kilometers2 function to the 'depth' column and add it as a new column 'depth_km' to the quake DataFrame\n", + "quake['depth_km'] = quake['depth'].apply(meters2kilometers2)\n", + "\n", + "# Display the first few rows to verify the new column\n", + "quake.head()" + ] + }, + { + "cell_type": "markdown", + "id": "f6db26f1", + "metadata": {}, + "source": [ + "### Lambda functions\n", + "We now discuss the **lambda** functions. \n", + "* Lambda functions are small, anonymous functions in Python.\n", + "* They are used for quick, simple operations without needing to define a full function using def.\n", + "* In geoscience, where we deal with large datasets (e.g., climate data, seismic measurements), lambda functions allow us to process data efficiently.\n", + "* Lambda functions in Pandas allow you to quickly transform, filter, or process this data with minimal code.\n", + "* Please read additional tutorials from [RealPython](!https://realpython.com/python-lambda/).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "a2713d55", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "62e31c2c", - "metadata": {}, - "source": [ - "Now you use the ``head`` function to display what is in the file" + "data": { + "text/plain": [ + "(x)>" ] - }, + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lambda x: x * 1.2" + ] + }, + { + "cell_type": "markdown", + "id": "d3ad802b", + "metadata": {}, + "source": [ + "This lambda function multiplies any input ``x`` by 1.2, which could be useful for tasks like converting units (e.g., from km to m)." + ] + }, + { + "cell_type": "markdown", + "id": "8820efb8", + "metadata": {}, + "source": [ + "Below is an example on how to use lambda in a pandas dataframe. \n", + "**Example 1**:\n", + "The lambda function lambda ``x: (x - 32) * 5 / 9`` converts Fahrenheit to Celsius for each value in the Temperature_F column." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "cafff8c2", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 42, - "id": "ed565400", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescription
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE
\n", - "
" - ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", - "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", - "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", - "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", - "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", - "\n", - " description \n", - "0 VANUATU ISLANDS \n", - "1 NEAR EAST COAST OF HONSHU \n", - "2 SOUTH OF MARIANA ISLANDS \n", - "3 NORTHERN CHILE \n", - "4 NEAR COAST OF CENTRAL CHILE " - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# enter answer here\n", - "quake.head()" + "name": "stdout", + "output_type": "stream", + "text": [ + " Temperature_F Temperature_C\n", + "0 32 0.000000\n", + "1 50 10.000000\n", + "2 77 25.000000\n", + "3 100 37.777778\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Sample DataFrame\n", + "data = {'Temperature_F': [32, 50, 77, 100]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Convert Fahrenheit to Celsius\n", + "df['Temperature_C'] = df['Temperature_F'].apply(lambda x: (x - 32) * 5 / 9)\n", + "\n", + "print(df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "cbb26a36", + "metadata": {}, + "outputs": [], + "source": [ + "# Now the equivalent in lambda is:\n", + "lambda_meters2kilometers = lambda x:x/1000\n", + "# x is the variable" + ] + }, + { + "cell_type": "markdown", + "id": "e604fca3", + "metadata": {}, + "source": [ + "Now apply the function to the entire series\n", + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "7d94f6a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 34.40\n", + "1 30.10\n", + "2 16.90\n", + "3 109.40\n", + "4 25.70\n", + " ... \n", + "1780 10.00\n", + "1781 105.00\n", + "1782 608.51\n", + "1783 10.00\n", + "1784 35.44\n", + "Name: depth, Length: 1785, dtype: float64" ] - }, + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply it to the entire series\n", + "lambda_meters2kilometers(quake.depth)" + ] + }, + { + "cell_type": "markdown", + "id": "cef03d96", + "metadata": {}, + "source": [ + "Lambda functions can take several inputs" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "caad68a3", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "73bfcd15", - "metadata": {}, - "source": [ - "Display the depth using two ways to use the pandas object" + "data": { + "text/plain": [ + "1" ] - }, + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you can add several variables into lambda functions\n", + "remove_anything = lambda x,y:x-y\n", + "remove_anything(3,2)" + ] + }, + { + "cell_type": "markdown", + "id": "a6da00e2", + "metadata": {}, + "source": [ + "This did not affect the values of the DataFrame, check it:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "40367bfb", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 43, - "id": "9b01ce51", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64\n", - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64\n" - ] - } - ], - "source": [ - "print(quake.depth)\n", - "print(quake['depth'])" + "data": { + "text/plain": [ + "0 34400.0\n", + "1 30100.0\n", + "2 16900.0\n", + "3 109400.0\n", + "4 25700.0\n", + " ... \n", + "1780 10000.0\n", + "1781 105000.0\n", + "1782 608510.0\n", + "1783 10000.0\n", + "1784 35440.0\n", + "Name: depth, Length: 1785, dtype: float64" ] - }, + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quake.depth" + ] + }, + { + "cell_type": "markdown", + "id": "6347a1ec", + "metadata": {}, + "source": [ + "Instead, you could overwrite ``quake.depth=X``. Try two approaches but just do it once! You can use python functions ``map`` (a function to apply functions that is generic to Python) and ``apply`` (a function to apply functions that is specific to Pandas)." + ] + }, + { + "cell_type": "markdown", + "id": "ade5723d", + "metadata": {}, + "source": [ + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "0d45bc9b", + "metadata": {}, + "source": [ + "Try ``map`` to apply a ``lambda`` function that rescale depth from meters to kilometers." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "8a1da18f", + "metadata": {}, + "outputs": [], + "source": [ + "#type answer below\n", + "quake.depth=quake.depth.map(lambda x:x/1000)" + ] + }, + { + "cell_type": "markdown", + "id": "9b11b4cb", + "metadata": {}, + "source": [ + "Discuss in class: What happened to the depth field?\n", + "\n", + "What happened to the original data frame?" + ] + }, + { + "cell_type": "markdown", + "id": "a520ada6", + "metadata": {}, + "source": [ + "Try ``apply`` to apply a ``lambda`` function that rescale depth from meters to kilometers." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "ec3420af", + "metadata": {}, + "outputs": [], + "source": [ + "# or like this\n", + "quake.depth=quake.depth.apply(lambda x:x/1000) " + ] + }, + { + "cell_type": "markdown", + "id": "fa66b4e8", + "metadata": {}, + "source": [ + "Plot a histogram of the depth distributions using matplotlib function ``hist``." + ] + }, + { + "cell_type": "markdown", + "id": "ade5723d", + "metadata": {}, + "source": [ + "```{admonition} Student response section\n", + "This section is left for the student to complete.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "ae368fd4", + "metadata": {}, + "outputs": [ { - "attachments": {}, - "cell_type": "markdown", - "id": "9c623644", - "metadata": {}, - "source": [ - "Calculate basic statistic of the data using the function ``describe``." + "data": { + "image/png": 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" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# answer here\n", + "plt.hist(quake.depth,100)\n", + "plt.grid(True)\n", + "plt.xlabel('Quake depth (km)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8bb58e3c", + "metadata": {}, + "source": [ + "You can use the interactive plotting package Plotly. First we will show a histogram of the event depth using the function ``histogram``." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "5f07dc52", + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 44, - "id": "2b9f083e", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/html": [ - "
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"showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "width": 600, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "depth" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "count" + } } - ], - "source": [ - "quake.describe()" - ] - }, - { - "cell_type": "markdown", - "id": "8b54218c", - "metadata": {}, - "source": [ - "Calculate mean and median of specific ``Series``, for example depth." - ] - }, + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.histogram(quake, #specify what dataframe to use\n", + " x=\"depth\", #specify the variable for the histogram \n", + " nbins=50, #number of bins for the histogram \n", + " height=400, #dimensions of the figure\n", + " width=600);\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "60a133d4", + "metadata": {}, + "source": [ + "### Example 2: Conditional Logic for Earthquake Magnitude \n", + "You can use lambda functions to add conditional logic, such as classifying earthquake magnitudes into categories.\n", + "\n", + "Scenario: You have a column Magnitude containing seismic event magnitudes, and you want to classify events as \"Minor\", \"Moderate\", or \"Severe\"." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "7f7520de", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 45, - "id": "83f21ca3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "82773.18767507003\n", - "24400.0\n" - ] - } + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategory
02010-07-02 06:04:03.570-13.6098166.65410.03446.3VANUATU ISLANDS34.4Moderate
12010-07-04 21:55:52.37039.6611142.57920.03016.3NEAR EAST COAST OF HONSHU30.1Moderate
22010-07-10 11:43:33.00011.1664146.08230.01696.3SOUTH OF MARIANA ISLANDS16.9Moderate
32010-07-12 00:11:20.060-22.2789-68.31590.10946.2NORTHERN CHILE109.4Moderate
42010-07-14 08:32:21.850-38.0635-73.46490.02576.6NEAR COAST OF CENTRAL CHILE25.7Severe
\n", + "
" ], - "source": [ - "# answer it here\n", - "print(quake.depth.mean())\n", - "print(quake.depth.median())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "591f8d33", - "metadata": {}, - "source": [ - "## 1.3 Manipulating Pandas with Python\n", - "\n", - "### Classic functions\n", - "We will now practice how to modify the content of the DataFrame using functions. We will take the example where we want to change the depth values from meters to kilometers. First we can define this operation as a function" + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.0344 6.3 \n", + "1 2010-07-04 21:55:52.370 39.6611 142.5792 0.0301 6.3 \n", + "2 2010-07-10 11:43:33.000 11.1664 146.0823 0.0169 6.3 \n", + "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 0.1094 6.2 \n", + "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 0.0257 6.6 \n", + "\n", + " description depth_km Category \n", + "0 VANUATU ISLANDS 34.4 Moderate \n", + "1 NEAR EAST COAST OF HONSHU 30.1 Moderate \n", + "2 SOUTH OF MARIANA ISLANDS 16.9 Moderate \n", + "3 NORTHERN CHILE 109.4 Moderate \n", + "4 NEAR COAST OF CENTRAL CHILE 25.7 Severe " ] - }, + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Classify earthquake magnitudes\n", + "quake['Category'] = quake['magnitude'].apply(lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe'))\n", + "quake.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8be50d1c", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "b87c6c42", + "metadata": {}, + "source": [ + "We will now make a new plot of the location of the earthquakes. We will use Plotly tool. \n", + "\n", + "The markersize will be scaled with the earthquake magnitude. To do so, we add a ``marker_size`` series in the DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "d9cb41c3", + "metadata": {}, + "outputs": [], + "source": [ + "quake['marker_size'] = quake['magnitude'].apply(lambda x: np.fix(np.exp(x))) # add marker size as exp(mag)\n", + "quake['magnitude_bin'] = quake['magnitude'].apply(lambda x: 0.5*np.fix(2*x)) # add marker size as exp(mag)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "6c7befa4", + "metadata": {}, + "outputs": [], + "source": [ + "# another way to do it\n", + "quake['marker_size'] = np.fix(np.exp(quake['magnitude'])) # add marker size as exp(mag)\n", + "quake['magnitude_bin'] = 0.5*np.fix(2*quake['magnitude']) # add marker size as exp(mag)" + ] + }, + { + "cell_type": "markdown", + "id": "2fed2184", + "metadata": {}, + "source": [ + "### 1.4 Intermediate Manipulation with Pandas\n", + "\n", + "You can also apply lambda functions to work with multiple columns, which is common in geoscientific datasets, where you might have spatial or temporal data.\n", + "\n", + "**Example 3: Calculating an Index Based on Multiple Measurements**\n", + "Scenario: You have rainfall (``Rainfall_mm``) and evaporation (``Evaporation_mm``) data, and you want to calculate the ``Net Water Balance ``for each record." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "7931923f", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 46, - "id": "5b4f94b4", - "metadata": {}, - "outputs": [], - "source": [ - "# this function converts a value in meters to a value in kilometers\n", - "m2km = 1000 # this is defined as a global variable\n", - "def meters2kilometers(x):\n", - " return x/m2km\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " Rainfall_mm Evaporation_mm Net_Water_Balance\n", + "0 100 60 40\n", + "1 80 70 10\n", + "2 120 65 55\n" + ] + } + ], + "source": [ + "data = {'Rainfall_mm': [100, 80, 120], 'Evaporation_mm': [60, 70, 65]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Calculate Net Water Balance\n", + "df['Net_Water_Balance'] = df.apply(lambda row: row['Rainfall_mm'] - row['Evaporation_mm'], axis=1)\n", + "\n", + "print(df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b5b64a6b", + "metadata": {}, + "source": [ + "In this example, ``lambda row: row['Rainfall_mm'] - row['Evaporation_mm']`` calculates the net water balance by subtracting evaporation from rainfall for each record.\n", + "\n", + "### 1.5 Advanced: Time Series Data Manipulation with Lambda Functions\n", + "Geoscientists frequently work with time series data (e.g., climate data). Lambda functions can be used for efficient data transformations within time series.\n", + "\n", + "**Example 4: Applying a Rolling Window Calculation**\n", + "Scenario: Suppose you have daily temperature data, and you want to calculate a 7-day rolling average." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "6933e64a", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 47, - "id": "16cb70b9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34.4" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } + "data": { + "text/html": [ + "
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" ], - "source": [ - "# now test it using the first element of the quake DataFrame\n", - "meters2kilometers(quake.depth[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "7a360cc5", - "metadata": {}, - "outputs": [], - "source": [ - "# this function converts a value in meters to a value in kilometers\n", - "def meters2kilometers2(x):\n", - " m2km = 1000 # this is defined as a global variable\n", - " return x/m2km" + "text/plain": [ + " Date Temperature_C 7_day_avg\n", + "0 2023-09-01 20 NaN\n", + "1 2023-09-02 22 NaN\n", + "2 2023-09-03 23 21.666667\n", + "3 2023-09-04 21 22.000000\n", + "4 2023-09-05 19 21.000000" ] - }, + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sample daily temperature data\n", + "data = {'Date': pd.date_range(start='2023-09-01', periods=10, freq='D'),\n", + " 'Temperature_C': [20, 22, 23, 21, 19, 24, 25, 26, 22, 20]}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Calculate 7-day rolling average using lambda\n", + "df['7_day_avg'] = df['Temperature_C'].rolling(window=3).apply(lambda x: x.mean())\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "e3d3328a", + "metadata": {}, + "source": [ + "Here, ``lambda x: x.mean()`` calculates the rolling mean over a 3-day window for temperature data, which is essential in climate analysis for smoothing short-term fluctuations.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "e01cf834", + "metadata": {}, + "source": [ + "### 1.6 Aggregate function\n", + "The ``agg`` is a powerful method in Pandas that allows you to perform multiple operations on DataFrames and Series. You can apply one or more aggregation functions such as ``sum``, ``mean``, ``min``, ``max``, etc., on different columns or groups of data." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "aea0ee4e", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "aa52810b", - "metadata": {}, - "source": [ - "Let's define another function that uses a local instead of global variable" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "sum 10.0\n", + "mean 2.5\n", + "Name: A, dtype: float64\n" + ] + } + ], + "source": [ + "# Sample DataFrame\n", + "data = {\n", + " 'A': [1, 2, 3, 4],\n", + " 'B': [5, 6, 7, 8]\n", + "}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Apply aggregation functions\n", + "result = df['A'].agg(['sum', 'mean'])\n", + "print(result)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f2664926", + "metadata": {}, + "source": [ + "#### Aggregating Multiple Columns with Multiple Functions\n", + "You can pass a dictionary to agg where the keys are column names and the values are the functions to be applied." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "9df9cde2", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 49, - "id": "b01a33fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescriptiondepth_km
02010-07-02 06:04:03.570-13.6098166.654134400.06.3VANUATU ISLANDS34.4
12010-07-04 21:55:52.37039.6611142.579230100.06.3NEAR EAST COAST OF HONSHU30.1
22010-07-10 11:43:33.00011.1664146.082316900.06.3SOUTH OF MARIANA ISLANDS16.9
32010-07-12 00:11:20.060-22.2789-68.3159109400.06.2NORTHERN CHILE109.4
42010-07-14 08:32:21.850-38.0635-73.464925700.06.6NEAR COAST OF CENTRAL CHILE25.7
\n", - "
" - ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 34400.0 6.3 \n", - "1 2010-07-04 21:55:52.370 39.6611 142.5792 30100.0 6.3 \n", - "2 2010-07-10 11:43:33.000 11.1664 146.0823 16900.0 6.3 \n", - "3 2010-07-12 00:11:20.060 -22.2789 -68.3159 109400.0 6.2 \n", - "4 2010-07-14 08:32:21.850 -38.0635 -73.4649 25700.0 6.6 \n", - "\n", - " description depth_km \n", - "0 VANUATU ISLANDS 34.4 \n", - "1 NEAR EAST COAST OF HONSHU 30.1 \n", - "2 SOUTH OF MARIANA ISLANDS 16.9 \n", - "3 NORTHERN CHILE 109.4 \n", - "4 NEAR COAST OF CENTRAL CHILE 25.7 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Apply the meters2kilometers2 function to the 'depth' column and add it as a new column 'depth_km' to the quake DataFrame\n", - "quake['depth_km'] = quake['depth'].apply(meters2kilometers2)\n", - "\n", - "# Display the first few rows to verify the new column\n", - "quake.head()" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " A B\n", + "sum 10.0 NaN\n", + "mean 2.5 NaN\n", + "min NaN 5.0\n", + "max NaN 8.0\n" + ] + } + ], + "source": [ + "# Apply different functions to different columns\n", + "result = df.agg({'A': ['sum', 'mean'], 'B': ['min', 'max']})\n", + "print(result)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5ccea12a", + "metadata": {}, + "source": [ + "In this example, column 'A' gets ``sum`` and ``mean``, while column 'B' gets ``min`` and ``max``." + ] + }, + { + "cell_type": "markdown", + "id": "6b9e44e0", + "metadata": {}, + "source": [ + "You may also use costum functions" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "79ff0c94", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "f6db26f1", - "metadata": {}, - "source": [ - "### Lambda functions\n", - "We now discuss the **lambda** functions. \n", - "* Lambda functions are small, anonymous functions in Python.\n", - "* They are used for quick, simple operations without needing to define a full function using def.\n", - "* In geoscience, where we deal with large datasets (e.g., climate data, seismic measurements), lambda functions allow us to process data efficiently.\n", - "* Lambda functions in Pandas allow you to quickly transform, filter, or process this data with minimal code.\n", - "* Please read additional tutorials from [RealPython](!https://realpython.com/python-lambda/).\n" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + " A B\n", + "mean 2.5 NaN\n", + "data_range 3.0 3.0\n" + ] + } + ], + "source": [ + "# Custom function to calculate range (max - min)\n", + "def data_range(x):\n", + " return x.max() - x.min()\n", + "\n", + "# Apply custom function\n", + "result = df.agg({'A': ['mean', data_range], 'B': data_range})\n", + "print(result)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94abaccd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f004df73", + "metadata": {}, + "source": [ + "## 2.4 Mapping using Plotly\n", + "\n", + "Now we will plot the earthquakes locations on a map using the Plotly package. More tutorials on [Plotly](https://plotly.com/). The input of the function is self-explanatory and typical of Python's function. The code [documentation](https://plotly.com/python/scatter-plots-on-maps/) of Plotly scatter_geo lists the variables." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "ff53045a", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 50, - "id": "a2713d55", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/plain": [ - "(x)>" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" + "customdata": [ + [ + "VANUATU ISLANDS", + 6.3, + 0.0344 + ], + [ + "NEAR EAST COAST OF HONSHU", + 6.3, + 0.030100000000000002 + ], + [ + "SOUTH OF MARIANA ISLANDS", + 6.3, + 0.0169 + ], + [ + "NORTHERN CHILE", + 6.2, + 0.10940000000000001 + ], + [ + "NEAR COAST OF CENTRAL CHILE", + 6.6, + 0.0257 + ], + [ + "FOX ISLANDS", + 6.7, + 0.0215 + ], + [ + "NEW BRITAIN REGION", + 6.9, + 0.0458 + ], + [ + "NEW BRITAIN 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+ "NEAR EAST COAST OF HONSHU", + "SULAWESI", + "SOUTH OF PANAMA", + "OFF EAST COAST OF HONSHU", + "LOYALTY ISLANDS", + "COSTA RICA", + "NEAR EAST COAST OF HONSHU", + "HINDU KUSH REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "SOLOMON ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "WEST OF MACQUARIE ISLAND", + "SOUTH ISLAND", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "CHILE-BOLIVIA BORDER REGION", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FOX ISLANDS", + "SANTA CRUZ ISLANDS", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEGROS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "ALASKA PENINSULA", + "TAJIKISTAN", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEW IRELAND REGION", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NEAR S. COAST OF HONSHU", + "KURIL ISLANDS", + "ASCENSION ISLAND REGION", + "CERAM SEA", + "OFF EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SUNDA STRAIT", + "PERU-BRAZIL BORDER REGION", + "VANUATU ISLANDS", + "BANDA SEA", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "SOUTH SANDWICH ISLANDS REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "NORTHERN SUMATRA", + "AUCKLAND ISLANDS", + "VANCOUVER ISLAND", + "NEAR ISLANDS", + "EAST OF NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SIKKIM", + "TONGA ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "SOUTH OF BALI", + "EASTERN NEW GUINEA REG.", + "SOUTHEASTERN SIBERIA", + "NEW BRITAIN REGION", + "HOKKAIDO", + "KERMADEC ISLANDS REGION", + "TURKEY", + "TURKEY", + "FIJI ISLANDS REGION", + "NEAR COAST OF PERU", + "REVILLA GIGEDO ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "NICARAGUA", + "NORTHEAST OF TAIWAN", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHERN MOLUCCA SEA", + "OFF COAST OF ECUADOR", + "CENTRAL BOLIVIA", + "NEAR EAST COAST OF HONSHU", + "HOKKAIDO", + "NEW IRELAND REGION", + "LUZON", + "NEAR COAST OF NORTHERN CHILE", + "GUERRERO", + "SOUTH SANDWICH ISLANDS REGION", + "MINAHASSA PENINSULA", + "EASTERN NEW GUINEA REG.", + "TONGA ISLANDS", + "SOUTHWESTERN SIBERIA", + "SOUTHEAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH SHETLAND ISLANDS", + "SOUTH SHETLAND ISLANDS", + "NEAR COAST OF CHIAPAS", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF PERU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "NEGROS", + "NEGROS", + "FIJI ISLANDS REGION", + "SOLOMON ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTHWESTERN SIBERIA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEW BRITAIN REGION", + "IRIAN JAYA", + "NEAR COAST OF GUERRERO", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN EAST PACIFIC RISE", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF GUERRERO", + "NEW IRELAND REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "NORTH INDIAN OCEAN", + "OFF W COAST OF NORTHERN SUMATRA", + "OFF COAST OF OREGON", + "MICHOACAN", + "BAJA CALIFORNIA", + "BAJA CALIFORNIA", + "DRAKE PASSAGE", + "VANUATU ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF CENTRAL CHILE", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "IRIAN JAYA REGION", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS REGION", + "TONGA ISLANDS", + "NEAR COAST OF CHIAPAS", + "PERU-BOLIVIA BORDER REGION", + "OFF COAST OF SOUTHERN CHILE", + "NORTHERN ITALY", + "OFF EAST COAST OF HONSHU", + "HOKKAIDO", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "GREENLAND SEA", + "BONIN ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "SALTA PROVINCE", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "OFF EAST COAST OF HONSHU", + "CHILE-ARGENTINA BORDER REGION", + "SOUTHERN PERU", + "DODECANESE ISLANDS", + "LUZON", + "NEAR EAST COAST OF HONSHU", + "NEAR ISLANDS", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF KAMCHATKA", + "NORTHERN XINJIANG", + "COOK STRAIT", + "VANUATU ISLANDS", + "KURIL ISLANDS", + "KURIL ISLANDS", + "NORTHERN SUMATRA", + "SOLOMON ISLANDS", + "MAURITIUS - REUNION REGION", + "NEW IRELAND REGION", + "PERU-BRAZIL BORDER REGION", + "NEW IRELAND REGION", + "FOX ISLANDS", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "XIZANG", + "SEA OF OKHOTSK", + "SULAWESI", + "NEAR N COAST OF NEW GUINEA", + "NORTHERN MOLUCCA SEA", + "OFF COAST OF CENTRAL AMERICA", + "JAN MAYEN ISLAND REGION", + "PHILIPPINE ISLANDS REGION", + "SOUTH OF JAVA", + "SANTA CRUZ ISLANDS", + "COSTA RICA", + "IRIAN JAYA REGION", + "KURIL ISLANDS", + "SOUTHERN SUMATRA", + "BAJA CALIFORNIA", + "ANDREANOF ISLANDS", + "COLOMBIA", + "OFF EAST COAST OF HONSHU", + "GULF OF CALIFORNIA", + "BANDA SEA", + "WEST OF MACQUARIE ISLAND", + "IRIAN JAYA REGION", + "CELEBES SEA", + "VANUATU ISLANDS", + "COSTA RICA", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "MINDANAO", + "NEAR COAST OF CHIAPAS", + "VANCOUVER ISLAND", + "CENTRAL PERU", + "MYANMAR", + "NEAR COAST OF CHIAPAS", + "GULF OF ALASKA", + "OFF COAST OF SOUTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "GUERRERO", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "NEAR N COAST OF NEW GUINEA", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NORTH ISLAND", + "BANDA SEA", + "NORTHERN MOLUCCA SEA", + "OFF W. COAST OF BAJA CALIFORNIA", + "NEAR ISLANDS", + "NEW IRELAND REGION", + "MINAHASSA PENINSULA", + "VANUATU ISLANDS", + "SOUTHEASTERN ALASKA", + "PACIFIC-ANTARCTIC RIDGE", + "NORTHERN SUMATRA", + "LAKE ISSYK-KUL REGION", + "CENTRAL CHILE", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "HOKKAIDO", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "COLOMBIA", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "EASTERN SIBERIA", + "MINDANAO", + "SANTIAGO DEL ESTERO PROV.", + "KURIL ISLANDS", + "KURIL ISLANDS", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "EAST OF KURIL ISLANDS", + "VANUATU ISLANDS REGION", + "GUATEMALA", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "E. RUSSIA-N.E. CHINA BORDER REG.", + "IRIAN JAYA", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "SOLOMON ISLANDS", + "SOUTHERN IRAN", + "NEAR N COAST OF NEW GUINEA", + "KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "SICHUAN", + "BANDA SEA", + "KURIL ISLANDS", + "SOUTHEAST OF HONSHU", + "MICHOACAN", + "NEW IRELAND REGION", + "KERMADEC ISLANDS REGION", + "SOUTHERN IRAN", + "TONGA ISLANDS", + "MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "OFF COAST OF SOUTHERN CHILE", + "OFF EAST COAST OF KAMCHATKA", + "OFF EAST COAST OF KAMCHATKA", + "SOUTH OF FIJI ISLANDS", + "TONGA ISLANDS", + "SEA OF OKHOTSK", + "SEA OF OKHOTSK", + "TAIWAN", + "SANTA CRUZ ISLANDS", + "SOUTH OF JAVA", + "SOUTH OF KERMADEC ISLANDS", + "CRETE", + "NEAR COAST OF NICARAGUA", + "CRETE", + "NORTHERN MID-ATLANTIC RIDGE", + "NORTHERN SUMATRA", + "SOLOMON ISLANDS", + "SOUTHERN SUMATRA", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN PERU", + "COOK STRAIT", + "GANSU", + "PRINCE EDWARD ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "BANDA SEA", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF NORTHERN PERU", + "SOUTH OF PANAMA", + "COOK STRAIT", + "SOUTHWEST INDIAN RIDGE", + "NEAR COAST OF GUERRERO", + "KERMADEC ISLANDS REGION", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "BANDA SEA", + "QUEEN CHARLOTTE ISLANDS REGION", + "SOUTHEAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "PHILIPPINE ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "CENTRAL EAST PACIFIC RISE", + "ANDREANOF ISLANDS", + "FLORES SEA", + "PAKISTAN", + "SOUTHERN EAST PACIFIC RISE", + "NEAR COAST OF PERU", + "PAKISTAN", + "KERMADEC ISLANDS", + "SEA OF OKHOTSK", + "MID-INDIAN RIDGE", + "SOUTH OF MARIANA ISLANDS", + "WEST CHILE RISE", + "SAKHALIN", + "KERMADEC ISLANDS", + "NEAR COAST OF VENEZUELA", + "CRETE", + "MINDANAO", + "SOLOMON ISLANDS", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "OFF EAST COAST OF HONSHU", + "BALLENY ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "TAIWAN", + "NEAR COAST OF CENTRAL CHILE", + "EASTER ISLAND REGION", + "TONGA ISLANDS REGION", + "CELEBES SEA", + "NEAR EAST COAST OF KAMCHATKA", + "SCOTIA SEA", + "SCOTIA SEA", + "SCOTIA SEA", + "HALMAHERA", + "MARIANA ISLANDS", + "FIJI ISLANDS REGION", + "KURIL ISLANDS", + "SOUTH ATLANTIC OCEAN", + "BANDA SEA", + "NORTHERN SUMATRA", + "KURIL ISLANDS", + "MARIANA ISLANDS REGION", + "VANUATU ISLANDS", + "PUERTO RICO REGION", + "NORTH ISLAND", + "TONGA ISLANDS", + "JAVA", + "GREECE", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH OF KERMADEC ISLANDS", + "GREECE", + "VANUATU ISLANDS", + "XIZANG", + "NORTH ATLANTIC OCEAN", + "FOX ISLANDS", + "NEAR COAST OF NICARAGUA", + "RYUKYU ISLANDS", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "OFF COAST OF NORTHERN CALIFORNIA", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "NEAR COAST OF PERU", + "NEAR COAST OF NORTHERN PERU", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NICOBAR ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "SANTA CRUZ ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "OFF COAST OF NORTHERN CHILE", + "SOUTH OF PANAMA", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "NICARAGUA", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR COAST OF NICARAGUA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "BOUVET ISLAND REGION", + "BALLENY ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "GUERRERO", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "VANCOUVER ISLAND", + "TONGA ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR S. COAST OF HONSHU", + "THAILAND", + "WEST CHILE RISE", + "SOLOMON ISLANDS", + "GUERRERO", + "TONGA ISLANDS", + "GUERRERO", + "SOUTHERN EAST PACIFIC RISE", + "SOUTH OF PANAMA", + "E. CAROLINE ISLANDS", + "E. CAROLINE ISLANDS", + "NEGROS", + "OFF W COAST OF NORTHERN SUMATRA", + "BAY OF BENGAL", + "AEGEAN SEA", + "OFF COAST OF JALISCO", + "SOUTH INDIAN OCEAN", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "RAT ISLANDS", + "VOLCANO ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "TONGA ISLANDS", + "BONIN ISLANDS", + "BALLENY ISLANDS REGION", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "NEW BRITAIN REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "MINDANAO", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "TONGA ISLANDS", + "TONGA ISLANDS", + "OWEN FRACTURE ZONE REGION", + "KURIL ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEASTERN ALASKA", + "NORTHERN MID-ATLANTIC RIDGE", + "OAXACA", + "BISMARCK SEA", + "BISMARCK SEA", + "E. CAROLINE ISLANDS", + "XIZANG", + "YUNNAN", + "BANDA SEA", + "BANDA SEA", + "HOKKAIDO", + "HOKKAIDO", + "IRAN-IRAQ BORDER REGION", + "IRAN-IRAQ BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN CALIFORNIA", + "CENTRAL PERU", + "CENTRAL PERU", + "EASTER ISLAND REGION", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "OFF COAST OF JALISCO", + "SOUTHERN MOLUCCA SEA", + "MARIANA ISLANDS", + "JUJUY PROVINCE", + "SOLOMON ISLANDS", + "SOUTHERN ALASKA", + "NEW BRITAIN REGION", + "YUNNAN", + "GULF OF CALIFORNIA", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHERN EAST PACIFIC RISE", + "HOKKAIDO", + "OFF COAST OF CENTRAL AMERICA", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "FIJI ISLANDS REGION", + "BALLENY ISLANDS REGION", + "MID-INDIAN RIDGE", + "NEW BRITAIN REGION", + "PRIMOR'YE", + "FIJI ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "OFF E. COAST OF N. ISLAND", + "PRINCE EDWARD ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "SICHUAN", + "EASTERN HONSHU", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "SOUTH OF PANAMA", + "BANDA SEA", + "SOLOMON ISLANDS", + "NEAR COAST OF GUATEMALA", + "SOUTH OF PANAMA", + "TAIWAN REGION", + "PHILIPPINE ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "NORTHERN MOLUCCA SEA", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "MINDANAO", + "FIJI ISLANDS REGION", + "SOUTHEAST OF EASTER ISLAND", + "SOUTH OF PANAMA", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "ADMIRALTY ISLANDS REGION", + "MENDOZA PROVINCE", + "JUJUY PROVINCE", + "REYKJANES RIDGE", + "TAIWAN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS REGION", + "VANUATU ISLANDS", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "OFF COAST OF JALISCO", + "FLORES SEA", + "SOUTHERN SUMATRA", + "MID-INDIAN RIDGE", + "NORTHERN COLOMBIA", + "MINAHASSA PENINSULA", + "NORTHERN MOLUCCA SEA", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "NORTHERN CHILE", + "NEW BRITAIN REGION", + "SAMOA ISLANDS REGION", + "SAMOA ISLANDS REGION", + "SAMOA ISLANDS REGION", + "NEW BRITAIN REGION", + "TONGA ISLANDS", + "CRETE", + "FIJI ISLANDS REGION", + "TAIWAN REGION", + "TAIWAN REGION", + "SANTA CRUZ ISLANDS", + "SOUTH ISLAND", + "QUEEN CHARLOTTE ISLANDS REGION", + "NEPAL", + "NEPAL", + "NEPAL", + "NEPAL", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "MOROCCO", + "SOLOMON ISLANDS", + "NEPAL", + "NEPAL", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN SUMATRA", + "PACIFIC-ANTARCTIC RIDGE", + "TONGA ISLANDS", + "SANTA CRUZ ISLANDS REGION", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTHERN MID-ATLANTIC RIDGE", + "TONGA ISLANDS", + "ALASKA PENINSULA", + "BONIN ISLANDS", + "TONGA ISLANDS", + "SOUTHEAST OF HONSHU", + "BORNEO", + "HOKKAIDO", + "NORTHERN CHILE", + "SAMOA ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTHERN MID-ATLANTIC RIDGE", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "BONIN ISLANDS", + "NEW BRITAIN REGION", + "SOUTHERN XINJIANG", + "LEYTE", + "EAST OF KURIL ISLANDS", + "SOLOMON ISLANDS", + "NORTH ATLANTIC OCEAN", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "IRIAN JAYA", + "SOUTHERN ALASKA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "MID-INDIAN RIDGE", + "SOLOMON ISLANDS", + "KERMADEC ISLANDS REGION", + "SOUTHEAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "FOX ISLANDS", + "GULF OF CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "IRIAN JAYA REGION", + "IRIAN JAYA REGION", + "NEAR COAST OF CENTRAL CHILE", + "JUJUY PROVINCE", + "PACIFIC-ANTARCTIC RIDGE", + "EAST OF KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "BOUVET ISLAND REGION", + "PRINCE EDWARD ISLANDS REGION", + "PRINCE EDWARD ISLANDS REGION", + "HINDU KUSH REGION", + "TIMOR REGION", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NICOBAR ISLANDS", + "ANDREANOF ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHWEST OF RYUKYU ISLANDS", + "GREECE", + "SOLOMON ISLANDS", + "BANDA SEA", + "MARIANA ISLANDS", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "NEAR COAST OF NORTHERN CHILE", + "SOUTHEAST INDIAN RIDGE", + "TAJIKISTAN", + "BANDA SEA", + "FIJI ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "BORNEO", + "SOUTHERN EAST PACIFIC RISE", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "MYANMAR-INDIA BORDER REGION", + "MYANMAR-INDIA BORDER REGION", + "PACIFIC-ANTARCTIC RIDGE", + "TALAUD ISLANDS", + "HOKKAIDO", + "SOUTHERN BOLIVIA", + "HOKKAIDO", + "OFF COAST OF JALISCO", + "SOUTHERN ALASKA", + "STRAIT OF GIBRALTAR", + "KAMCHATKA PENINSULA", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "TAIWAN", + "SOLOMON ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SUMBA REGION", + "SOUTHERN EAST PACIFIC RISE", + "HALMAHERA", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "SOUTHWEST OF SUMATRA", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "LEEWARD ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "NEAR N COAST OF NEW GUINEA", + "ALASKA PENINSULA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "JAVA", + "VANUATU ISLANDS", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "MYANMAR-INDIA BORDER REGION", + "MINDANAO", + "KYUSHU", + "KYUSHU", + "VANUATU ISLANDS", + "OFF COAST OF CHIAPAS", + "KYUSHU", + "NEAR COAST OF ECUADOR", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF CHIAPAS", + "NEAR COAST OF CHIAPAS", + "VANUATU ISLANDS", + "NORTHERN EAST PACIFIC RISE", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "NORTHERN TERRITORY", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "TAIWAN REGION", + "SOUTHERN SUMATRA", + "BANDA SEA", + "KERMADEC ISLANDS", + "OFF COAST OF JALISCO", + "NORTHERN MOLUCCA SEA", + "SOUTH OF SUMBAWA", + "NICARAGUA", + "SOLOMON ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "LOYALTY ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "NEW IRELAND REGION", + "TAJIKISTAN-XINJIANG BORDER REG.", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "NEAR COAST OF ECUADOR", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "ADMIRALTY ISLANDS REGION", + "MARIANA ISLANDS", + "JUJUY PROVINCE", + "VOLCANO ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH GEORGIA ISLAND REGION", + "FLORES SEA", + "CENTRAL ITALY", + "MYANMAR", + "NORTH OF ASCENSION ISLAND", + "NEW IRELAND REGION", + "OFF E. COAST OF N. ISLAND", + "OFF E. COAST OF N. ISLAND", + "KOMANDORSKIYE OSTROVA REGION", + "MACQUARIE ISLAND REGION", + "NORTHERN PERU", + "NORTHERN COLOMBIA", + "SOLOMON ISLANDS", + "NEAR NORTH COAST OF IRIAN JAYA", + "SOUTHEAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "MINDANAO", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "JAVA SEA", + "WESTERN HONSHU", + "TONGA ISLANDS", + "CENTRAL ITALY", + "CENTRAL ITALY", + "CHILE-ARGENTINA BORDER REGION", + "NEAR EAST COAST OF HONSHU", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SOUTH ISLAND", + "SAN JUAN PROVINCE", + "NEAR EAST COAST OF HONSHU", + "OFF COAST OF CENTRAL AMERICA", + "SOUTHERN XINJIANG", + "SOUTHERN PERU", + "NEAR ISLANDS", + "BANDA SEA", + "NORTHERN SUMATRA", + "OFF COAST OF NORTHERN CALIFORNIA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "MARIANA ISLANDS REGION", + "NEW IRELAND REGION", + "NEW IRELAND REGION", + "SOLOMON ISLANDS", + "W. CAROLINE ISLANDS", + "PERU-BRAZIL BORDER REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "NEW IRELAND REGION", + "SOUTHERN CHILE", + "SUMBAWA REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF FIJI ISLANDS", + "QUEEN ELIZABETH ISLANDS", + "CELEBES SEA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTHWESTERN PAKISTAN", + "MINDANAO", + "JUJUY PROVINCE", + "SOUTHERN BOLIVIA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "NEW BRITAIN REGION", + "NICOBAR ISLANDS", + "SOLOMON ISLANDS", + "NEAR ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "BOTSWANA", + "NORTHERN AND CENTRAL IRAN", + "CHILE-ARGENTINA BORDER REGION", + "FIJI ISLANDS REGION", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "SOUTHEASTERN ALASKA", + "TAJIKISTAN", + "ANDREANOF ISLANDS", + "RYUKYU ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CENTRAL AMERICA", + "NEW BRITAIN REGION", + "NEGROS", + "SULAWESI", + "NEAR ISLANDS", + "AEGEAN SEA", + "GUATEMALA", + "KERMADEC ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF GUATEMALA", + "FIJI ISLANDS REGION", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "LEYTE", + "AUCKLAND ISLANDS", + "NEW IRELAND REGION", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "NEAR COAST OF PERU", + "MAURITIUS - REUNION REGION", + "DODECANESE ISLANDS", + "SOUTHEAST OF RYUKYU ISLANDS", + "NORTH ATLANTIC OCEAN", + "GANSU", + "NORTHERN XINJIANG", + "MINDORO", + "SOUTHERN SUMATRA", + "NORTH OF ASCENSION ISLAND", + "FIJI ISLANDS REGION", + "ADMIRALTY ISLANDS REGION", + "SOUTHERN SUMATRA", + "NORTH KOREA", + "SOUTH SANDWICH ISLANDS REGION", + "BONIN ISLANDS", + "NEAR COAST OF CHIAPAS", + "CENTRAL MEXICO", + "AUCKLAND ISLANDS", + "OFF EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "OAXACA", + "SOUTH OF FIJI ISLANDS", + "OFF EAST COAST OF HONSHU", + "TONGA ISLANDS", + "BALLENY ISLANDS REGION", + "RAT ISLANDS", + "NORTHERN CHILE", + "BOUVET ISLAND REGION", + "TONGA ISLANDS", + "BANDA SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SERAM", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "TONGA ISLANDS", + "NEW GUINEA", + "SOUTHEAST OF HONSHU", + "ASCENSION ISLAND REGION", + "IRAN-IRAQ BORDER REGION", + "COSTA RICA", + "EASTERN XIZANG-INDIA BORDER REG.", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "SAMOA ISLANDS REGION", + "NEW IRELAND REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "NORTHERN AND CENTRAL IRAN", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF ECUADOR", + "W. CAROLINE ISLANDS", + "W. CAROLINE ISLANDS", + "W. CAROLINE ISLANDS", + "NORTHERN AND CENTRAL IRAN", + "NORTHERN AND CENTRAL IRAN", + "BOUVET ISLAND REGION", + "JAVA", + "NORTH OF HONDURAS", + "MYANMAR", + "NEAR COAST OF PERU", + "GULF OF CALIFORNIA", + "NORTHERN CHILE", + "JAVA", + "GULF OF ALASKA", + "HOKKAIDO", + "KOMANDORSKIYE OSTROVA REGION", + "NEAR N COAST OF NEW GUINEA", + "SOUTHWEST OF AFRICA", + "HINDU KUSH REGION", + "FIJI ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "TAIWAN", + "TAIWAN", + "SOUTH OF MARIANA ISLANDS", + "OAXACA", + "NEW GUINEA", + "NEW GUINEA", + "CERAM SEA", + "NEW GUINEA", + "WEST OF MACQUARIE ISLAND", + "NEW GUINEA", + "NEW GUINEA", + "NEW GUINEA", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "SOUTHEAST INDIAN RIDGE", + "BANDA SEA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTHERN BOLIVIA", + "MINDANAO", + "NEW GUINEA", + "NEAR COAST OF CENTRAL CHILE", + "NORTHERN MOLUCCA SEA", + "PRINCE EDWARD ISLANDS REGION", + "EASTER ISLAND REGION", + "HAWAII", + "LUZON", + "NEW BRITAIN REGION", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "SOUTH OF KERMADEC ISLANDS", + "VANUATU ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "KERMADEC ISLANDS", + "VANUATU ISLANDS", + "EASTERN GULF OF ADEN", + "EASTERN GULF OF ADEN", + "SANTA CRUZ ISLANDS", + "NEW BRITAIN REGION", + "FLORES SEA", + "SUMBAWA REGION", + "SUMBAWA REGION", + "NORTHERN ALASKA", + "NORTHERN ALASKA", + "SOUTH SANDWICH ISLANDS REGION", + "ANDREANOF ISLANDS", + "VOLCANO ISLANDS", + "FLORES SEA", + "COSTA RICA", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "SUMBAWA REGION", + "FIJI ISLANDS REGION", + "SUMBAWA REGION", + "NEAR COAST OF VENEZUELA", + "VANUATU ISLANDS", + "OFF COAST OF OREGON", + "ANDREANOF ISLANDS", + "PERU-BRAZIL BORDER REGION", + "WESTERN IRAN", + "TIMOR REGION", + "MARIANA ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "HOKKAIDO", + "FIJI ISLANDS", + "ECUADOR", + "MINDANAO", + "SOLOMON ISLANDS", + "KERMADEC ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTHWEST INDIAN RIDGE", + "MINAHASSA PENINSULA", + "MINAHASSA PENINSULA", + "FIJI ISLANDS REGION", + "SUMBA REGION", + "KURIL ISLANDS", + "BALI SEA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "KURIL ISLANDS", + "NORTHWEST OF KURIL ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "IONIAN SEA", + "OFF COAST OF CENTRAL AMERICA", + "DRAKE PASSAGE", + "NORTH ISLAND", + "NORTHERN CHILE", + "MINDANAO", + "JAN MAYEN ISLAND REGION", + "TONGA ISLANDS", + "NORTH ATLANTIC OCEAN", + "NEAR EAST COAST OF KAMCHATKA", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN EAST PACIFIC RISE", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "CARIBBEAN SEA", + "IRAN-IRAQ BORDER REGION", + "SOUTHERN ALASKA", + "BANDA SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "PACIFIC-ANTARCTIC RIDGE", + "IRIAN JAYA", + "SOUTHEAST OF EASTER ISLAND", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "KOMANDORSKIYE OSTROVA REGION", + "MINDANAO", + "ALASKA PENINSULA", + "WESTERN BRAZIL", + "NORTHERN MOLUCCA SEA", + "KYUSHU", + "VANUATU ISLANDS", + "BISMARCK SEA", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SUMBA REGION", + "SUMBA REGION", + "PRINCE EDWARD ISLANDS REGION", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "MARIANA ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "NEW IRELAND REGION", + "PERU-ECUADOR BORDER REGION", + "CENTRAL PERU", + "SOUTH OF KERMADEC ISLANDS", + "PHILIPPINE ISLANDS REGION", + "FIJI ISLANDS REGION", + "D'ENTRECASTEAUX ISLANDS REGION", + "CENTRAL BOLIVIA", + "VANUATU ISLANDS", + "COLOMBIA", + "NORTHERN MOLUCCA SEA", + "EAST OF KURIL ISLANDS", + "NEW BRITAIN REGION", + "NEAR COAST OF ECUADOR", + "MAURITIUS - REUNION REGION", + "RAT ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "BANDA SEA", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "SULAWESI", + "TAIWAN", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "LUZON", + "SOUTH SANDWICH ISLANDS REGION", + "SAMAR", + "SOUTH OF FIJI ISLANDS", + "CARLSBERG RIDGE", + "SOLOMON ISLANDS", + "EASTERN NEW GUINEA REG.", + "KYUSHU", + "PANAMA-COSTA RICA BORDER REGION", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "ANDREANOF ISLANDS", + "NORTHERN PERU", + "EL SALVADOR", + "FIJI ISLANDS REGION", + "MINDANAO", + "TONGA ISLANDS", + "SOUTHEAST OF HONSHU", + "OFF COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS", + "NEAR WEST COAST OF HONSHU", + "KERMADEC ISLANDS", + "IRIAN JAYA", + "KERMADEC ISLANDS", + "IRIAN JAYA", + "BANDA SEA", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "PANAMA-COSTA RICA BORDER REGION", + "KERMADEC ISLANDS REGION", + "MARIANA ISLANDS", + "VANUATU ISLANDS", + "QUEEN CHARLOTTE ISLANDS REGION", + "CENTRAL CALIFORNIA", + "CENTRAL CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "SOLOMON ISLANDS", + "NORTHWEST OF RYUKYU ISLANDS", + "WESTERN AUSTRALIA", + "HALMAHERA", + "NEW BRITAIN REGION", + "BALLENY ISLANDS REGION", + "PHILIPPINE ISLANDS REGION", + "NEAR S. COAST OF HONSHU", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHERN EAST PACIFIC RISE", + "SOUTHWEST OF SUMATRA", + "NEAR EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF OREGON", + "FIJI ISLANDS REGION", + "JAVA", + "MONA PASSAGE", + "SOUTHWEST INDIAN RIDGE", + "SERAM", + "CENTRAL CHILE", + "KERMADEC ISLANDS", + "MINDANAO", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "VANUATU ISLANDS", + "NORTH OF ASCENSION ISLAND", + "MINDANAO", + "MINDANAO", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "TONGA ISLANDS", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "OFF COAST OF CHIAPAS", + "SEA OF OKHOTSK", + "LAOS", + "IRIAN JAYA REGION", + "ANDREANOF ISLANDS", + "ALBANIA", + "CRETE", + "ANDREANOF ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "MINDANAO", + "HINDU KUSH REGION", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "SANTIAGO DEL ESTERO PROV.", + "COLOMBIA", + "VANCOUVER ISLAND", + "SOUTHERN EAST PACIFIC RISE", + "NORTHERN SUMATRA", + "PUERTO RICO REGION", + "NEW BRITAIN REGION", + "EASTERN SIBERIA", + "NEAR NORTH COAST OF IRIAN JAYA", + "SOUTHERN XINJIANG", + "MINAHASSA PENINSULA", + "SOUTH SANDWICH ISLANDS REGION", + "ANDREANOF ISLANDS", + "TURKEY", + "ANDREANOF ISLANDS", + "SOLOMON ISLANDS", + "CUBA REGION", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "JAVA", + "MINDANAO", + "SOUTH SANDWICH ISLANDS REGION", + "NEW BRITAIN REGION", + "KURIL ISLANDS", + "TURKEY-IRAN BORDER REGION", + "KERMADEC ISLANDS REGION", + "SAMOA ISLANDS REGION", + "VANUATU ISLANDS", + "SOUTH OF BALI", + "CENTRAL EAST PACIFIC RISE", + "EAST OF KURIL ISLANDS", + "MINDANAO", + "WESTERN IDAHO", + "NORTHERN MOLUCCA SEA", + "MID-INDIAN RIDGE", + "KERMADEC ISLANDS REGION", + "NORTH OF HONDURAS", + "BONIN ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "CRETE", + "BANDA SEA", + "SOLOMON ISLANDS", + "SANTA CRUZ ISLANDS", + "NEVADA", + "OFF COAST OF CENTRAL MEXICO", + "VANUATU ISLANDS", + "SOUTHERN PERU", + "NORTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "HALMAHERA", + "SOUTHERN MID-ATLANTIC RIDGE", + "RYUKYU ISLANDS", + "MARIANA ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "ICELAND REGION", + "MINAHASSA PENINSULA", + "NEAR COAST OF OAXACA", + "XIZANG", + "W. CAROLINE ISLANDS", + "JAVA SEA", + "EASTERN NEW GUINEA REG.", + "ANDAMAN ISLANDS", + "SAMOA ISLANDS REGION", + "FIJI ISLANDS REGION", + "ALASKA PENINSULA", + "SOUTH OF ALASKA", + "XIZANG", + "SOUTH SANDWICH ISLANDS REGION", + "ALASKA PENINSULA", + "MINDANAO", + "BISMARCK SEA", + "VANUATU ISLANDS", + "PRINCE EDWARD ISLANDS REGION", + "TANZANIA", + "SAMAR", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "BANDA SEA", + "ASCENSION ISLAND REGION", + "COSTA RICA", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "CENTRAL MID-ATLANTIC RIDGE", + "CHAGOS ARCHIPELAGO REGION", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF CENTRAL CHILE", + "VANUATU ISLANDS", + "CENTRAL MID-ATLANTIC RIDGE", + "MINDANAO", + "VANUATU ISLANDS", + "NORTHERN CHILE", + "NEAR EAST COAST OF HONSHU", + "KAMCHATKA PENINSULA", + "FIJI ISLANDS", + "CENTRAL MID-ATLANTIC RIDGE", + "SOUTH OF AFRICA", + "TONGA ISLANDS", + "NEW BRITAIN REGION", + "FIJI ISLANDS REGION", + "EASTERN NEW GUINEA REG.", + "SOUTH OF ALASKA", + "SOUTH OF FIJI ISLANDS", + "DODECANESE ISLANDS", + "OFF COAST OF SOUTHERN CHILE", + "SOUTH SHETLAND ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "MINDANAO", + "OFF COAST OF CENTRAL CHILE", + "PRIMOR'YE", + "CHILE-ARGENTINA BORDER REGION", + "FOX ISLANDS", + "BALLENY ISLANDS REGION", + "NORTHERN CHILE", + "TAIWAN REGION", + "CHILE-BOLIVIA BORDER REGION", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "W. CAROLINE ISLANDS", + "MINDORO", + "OFF COAST OF CENTRAL CHILE", + "NORTHWESTERN BALKAN REGION", + "ANDREANOF ISLANDS", + "KERMADEC ISLANDS REGION", + "MINAHASSA PENINSULA", + "KERMADEC ISLANDS", + "VANUATU ISLANDS", + "SALTA PROVINCE", + "VANUATU ISLANDS", + "RUSSIA-MONGOLIA BORDER REGION", + "SULAWESI", + "SAN JUAN PROVINCE", + "PHILIPPINE ISLANDS REGION", + "SOUTH SHETLAND ISLANDS", + "WEST CHILE RISE", + "MINDANAO", + "BISMARCK SEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWEST OF SUMATRA", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "TONGA ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "GREECE", + "OFF E. COAST OF N. ISLAND", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF KAMCHATKA", + "NORTHERN ALGERIA", + "NORTHERN MID-ATLANTIC RIDGE", + "WEST OF MACQUARIE ISLAND", + "NEAR EAST COAST OF HONSHU", + "KERMADEC ISLANDS", + "FIJI ISLANDS REGION", + "EAST OF SOUTH SANDWICH ISLANDS", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "JAVA", + "CELEBES SEA", + "NEAR N COAST OF NEW GUINEA", + "OFF W COAST OF NORTHERN SUMATRA", + "FIJI ISLANDS REGION", + "FIJI ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NORTHEASTERN INDIA", + "KERMADEC ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "WEST OF MACQUARIE ISLAND", + "FIJI ISLANDS REGION", + "NEAR COAST OF GUATEMALA", + "MAURITIUS - REUNION REGION", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTHERN EAST PACIFIC RISE", + "YUNNAN", + "QINGHAI", + "FIJI ISLANDS REGION", + "CENTRAL ALASKA", + "NORTHERN MOLUCCA SEA", + "KERMADEC ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "CALIFORNIA-NEVADA BORDER REGION", + "SOUTH OF PANAMA", + "BISMARCK SEA", + "SOUTH OF PANAMA", + "MINDORO", + "KERMADEC ISLANDS REGION", + "MINAHASSA PENINSULA", + "ALASKA PENINSULA", + "PERU-ECUADOR BORDER REGION", + "NICOBAR ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH 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"name": "stdout", - "output_type": "stream", - "text": [ - " Temperature_F Temperature_C\n", - "0 32 0.000000\n", - "1 50 10.000000\n", - "2 77 25.000000\n", - "3 100 37.777778\n" + ], + "layout": { + "coloraxis": { + "cmax": 9, + "cmin": 6, + "colorbar": { + "title": { + "text": "magnitude" + } + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# Sample DataFrame\n", - "data = {'Temperature_F': [32, 50, 77, 100]}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Convert Fahrenheit to Celsius\n", - "df['Temperature_C'] = df['Temperature_F'].apply(lambda x: (x - 32) * 5 / 9)\n", - "\n", - "print(df)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "cbb26a36", - "metadata": {}, - "outputs": [], - "source": [ - "# Now the equivalent in lambda is:\n", - "lambda_meters2kilometers = lambda x:x/1000\n", - "# x is the variable" - ] - }, - { - "cell_type": "markdown", - "id": "e604fca3", - "metadata": {}, - "source": [ - "Now apply the function to the entire series\n", - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "7d94f6a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 34.40\n", - "1 30.10\n", - "2 16.90\n", - "3 109.40\n", - "4 25.70\n", - " ... \n", - "1780 10.00\n", - "1781 105.00\n", - "1782 608.51\n", - "1783 10.00\n", - "1784 35.44\n", - "Name: depth, Length: 1785, dtype: float64" - ] + ] + }, + "geo": { + "center": {}, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, 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"autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# you can add several variables into lambda functions\n", - "remove_anything = lambda x,y:x-y\n", - "remove_anything(3,2)" - ] - }, - { - "cell_type": "markdown", - "id": "a6da00e2", - "metadata": {}, - "source": [ - "This did not affect the values of the DataFrame, check it:" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "40367bfb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 34400.0\n", - "1 30100.0\n", - "2 16900.0\n", - "3 109400.0\n", - "4 25700.0\n", - " ... \n", - "1780 10000.0\n", - "1781 105000.0\n", - "1782 608510.0\n", - "1783 10000.0\n", - "1784 35440.0\n", - "Name: depth, Length: 1785, dtype: float64" + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ 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Try two approaches but just do it once! You can use python functions ``map`` (a function to apply functions that is generic to Python) and ``apply`` (a function to apply functions that is specific to Pandas)." - ] - }, - { - "cell_type": "markdown", - "id": "ade5723d", - "metadata": {}, - "source": [ - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "0d45bc9b", - "metadata": {}, - "source": [ - "Try ``map`` to apply a ``lambda`` function that rescale depth from meters to kilometers." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "8a1da18f", - "metadata": {}, - "outputs": [], - "source": [ - "# implement answer here" - ] - }, - { - "cell_type": "markdown", - "id": "9b11b4cb", - "metadata": {}, - "source": [ - "Discuss in class: What happened to the depth field?\n", - "\n", - "What happened to the original data frame?" - ] - }, - { - "cell_type": "markdown", - "id": "a520ada6", - "metadata": {}, - "source": [ - "Try ``apply`` to apply a ``lambda`` function that rescale depth from meters to kilometers." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "ec3420af", - "metadata": {}, - "outputs": [], - "source": [ - "# or like this\n", - "quake.depth=quake.depth.apply(lambda x:x/1000) " - ] - }, - { - "cell_type": "markdown", - "id": "fa66b4e8", - "metadata": {}, - "source": [ - "Plot a histogram of the depth distributions using matplotlib function ``hist``." - ] - }, - { - "cell_type": "markdown", - "id": "ade5723d", - "metadata": {}, - "source": [ - "```{admonition} Student response section\n", - "This section is left for the student to complete.\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "ae368fd4", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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0, - 1 - ], - "title": { - "text": "depth" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "count" - } - } - } - } + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.histogram(quake, #specify what dataframe to use\n", - " x=\"depth\", #specify the variable for the histogram \n", - " nbins=50, #number of bins for the histogram \n", - " height=400, #dimensions of the figure\n", - " width=600);\n", - "fig.show()" - ] - }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "width": 600 + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_geo(quake,\n", + " lat='latitude',lon='longitude', \n", + " range_color=(6,9),\n", + " height=600, width=600,\n", + " size='marker_size', color='magnitude',\n", + " hover_name=\"description\",\n", + " hover_data=['description','magnitude','depth']);\n", + "fig.update_geos(resolution=110, showcountries=True)\n", + "fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "7a9e55fb", + "metadata": {}, + "source": [ + "The lambda function ``lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe')`` classifies earthquake magnitudes based on their values." + ] + }, + { + "cell_type": "markdown", + "id": "69b7c97f", + "metadata": {}, + "source": [ + "The data was sorted by time. We now want to sort and show the data instead by magnitude. We use the pandas function ``sort`` to create a new DataFrame with sorted values." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "7738e4d0", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "60a133d4", - "metadata": {}, - "source": [ - "### Example 2: Conditional Logic for Earthquake Magnitude \n", - "You can use lambda functions to add conditional logic, such as classifying earthquake magnitudes into categories.\n", - "\n", - "Scenario: You have a column Magnitude containing seismic event magnitudes, and you want to classify events as \"Minor\", \"Moderate\", or \"Severe\"." + "data": { + "text/html": [ + "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategorymarker_sizemagnitude_bin
02010-07-02 06:04:03.570-13.6098166.65410.034406.3VANUATU ISLANDS34.40Moderate544.06.0
11122017-08-31 17:06:55.750-1.159099.68810.043146.3SOUTHERN SUMATRA43.14Moderate544.06.0
11112017-08-27 04:17:51.010-1.4513148.08030.008006.3ADMIRALTY ISLANDS REGION8.00Moderate544.06.0
11102017-08-19 02:00:52.550-17.9609-178.84060.544006.4FIJI ISLANDS REGION544.00Moderate601.06.0
11082017-08-13 03:08:10.560-3.7682101.62280.031006.4SOUTHERN SUMATRA31.00Moderate601.06.0
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" + ], + "text/plain": [ + " time latitude longitude depth magnitude \\\n", + "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.03440 6.3 \n", + "1112 2017-08-31 17:06:55.750 -1.1590 99.6881 0.04314 6.3 \n", + "1111 2017-08-27 04:17:51.010 -1.4513 148.0803 0.00800 6.3 \n", + "1110 2017-08-19 02:00:52.550 -17.9609 -178.8406 0.54400 6.4 \n", + "1108 2017-08-13 03:08:10.560 -3.7682 101.6228 0.03100 6.4 \n", + "\n", + " description depth_km Category marker_size magnitude_bin \n", + "0 VANUATU ISLANDS 34.40 Moderate 544.0 6.0 \n", + "1112 SOUTHERN SUMATRA 43.14 Moderate 544.0 6.0 \n", + "1111 ADMIRALTY ISLANDS REGION 8.00 Moderate 544.0 6.0 \n", + "1110 FIJI ISLANDS REGION 544.00 Moderate 601.0 6.0 \n", + "1108 SOUTHERN SUMATRA 31.00 Moderate 601.0 6.0 " ] - }, + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "quakes2plot=quake.sort_values(by='magnitude_bin')\n", + "quakes2plot.head()" + ] + }, + { + "cell_type": "markdown", + "id": "dcf7472d", + "metadata": {}, + "source": [ + "Now we will plot again using Plotly" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "c21ce4ea", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 60, - "id": "7f7520de", - "metadata": {}, - "outputs": [ + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ { - "data": { - "text/html": [ - "
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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategory
02010-07-02 06:04:03.570-13.6098166.65410.03446.3VANUATU ISLANDS34.4Moderate
12010-07-04 21:55:52.37039.6611142.57920.03016.3NEAR EAST COAST OF HONSHU30.1Moderate
22010-07-10 11:43:33.00011.1664146.08230.01696.3SOUTH OF MARIANA ISLANDS16.9Moderate
32010-07-12 00:11:20.060-22.2789-68.31590.10946.2NORTHERN CHILE109.4Moderate
42010-07-14 08:32:21.850-38.0635-73.46490.02576.6NEAR COAST OF CENTRAL CHILE25.7Severe
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ISLAND", + "CENTRAL ITALY", + "FLORES SEA", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "VOLCANO ISLANDS", + "JUJUY PROVINCE", + "ADMIRALTY ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "KERMADEC ISLANDS REGION", + "NEAR COAST OF ECUADOR", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF COAST OF CHIAPAS", + "VANUATU ISLANDS", + "KYUSHU", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "SOUTHERN EAST PACIFIC RISE", + "BORNEO", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "MARIANA ISLANDS", + "SOUTHEASTERN AFGHANISTAN", + "OFF COAST OF CENTRAL CHILE", + "PRINCE EDWARD ISLANDS REGION", + "PRINCE EDWARD ISLANDS REGION", + "BOUVET ISLAND REGION", + "TONGA ISLANDS", + "EAST OF KURIL ISLANDS", + "EAST OF KURIL ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "JUJUY PROVINCE", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "NEAR COAST OF CENTRAL CHILE", + "PACIFIC-ANTARCTIC RIDGE", + "SOUTHERN BOLIVIA", + "KYUSHU", + "MINDANAO", + "JAVA", + "ALASKA PENINSULA", + "NEAR N COAST OF NEW GUINEA", + "NEAR EAST COAST OF KAMCHATKA", + "LEEWARD ISLANDS", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "HALMAHERA", + "SOUTHERN EAST PACIFIC RISE", + "SUMBA REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOLOMON ISLANDS", + "TAIWAN", + "KERMADEC ISLANDS REGION", + "BALLENY ISLANDS REGION", + "STRAIT OF GIBRALTAR", + "HOKKAIDO", + "FIJI ISLANDS REGION", + "SOUTHEAST OF EASTER ISLAND", + "TONGA ISLANDS", + "NEAR N COAST OF NEW GUINEA", + "CELEBES SEA", + "JAVA", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "FIJI ISLANDS REGION", + "WEST OF MACQUARIE ISLAND", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF W COAST OF NORTHERN SUMATRA", + "NORTHERN ALGERIA", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "NORTHEASTERN INDIA", + "SOUTH SANDWICH ISLANDS REGION", + "NICOBAR ISLANDS", + "PERU-ECUADOR BORDER REGION", + "MINAHASSA PENINSULA", + "KERMADEC ISLANDS REGION", + "BISMARCK SEA", + "SOUTH OF PANAMA", + "CALIFORNIA-NEVADA BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "FIJI ISLANDS REGION", + "NORTHERN MOLUCCA SEA", + "CENTRAL ALASKA", + "YUNNAN", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "NEAR COAST OF GUATEMALA", + "FIJI ISLANDS REGION", + "WEST OF MACQUARIE ISLAND", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "GREECE", + "W. CAROLINE ISLANDS", + "NEAR EAST COAST OF HONSHU", + "MINDANAO", + "CHILE-BOLIVIA BORDER REGION", + "TAIWAN REGION", + "NORTHERN CHILE", + "BALLENY ISLANDS REGION", + "FOX ISLANDS", + "CHILE-ARGENTINA BORDER REGION", + "PRIMOR'YE", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "FIJI ISLANDS REGION", + "TONGA ISLANDS", + "SOUTH SHETLAND ISLANDS", + "OFF COAST OF SOUTHERN CHILE", + "SOUTH OF FIJI ISLANDS", + "EASTERN NEW GUINEA REG.", + "FIJI ISLANDS REGION", + "MINDORO", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHWESTERN BALKAN REGION", + "KERMADEC ISLANDS REGION", + "FIJI ISLANDS REGION", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWEST OF SUMATRA", + "SOUTHEAST OF LOYALTY ISLANDS", + "BISMARCK SEA", + "MINDANAO", + "SAN JUAN PROVINCE", + "SULAWESI", + "VANUATU ISLANDS", + "SALTA PROVINCE", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "MINAHASSA PENINSULA", + "ANDREANOF ISLANDS", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "KURIL ISLANDS", + "LUZON", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHERN SUMATRA", + "JUJUY PROVINCE", + "BALLENY ISLANDS REGION", + "GUATEMALA", + "SOUTH OF MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "CENTRAL MID-ATLANTIC RIDGE", + "BANDA SEA", + "SOUTH OF PANAMA", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "FOX ISLANDS", + "TALAUD ISLANDS", + "SHIKOKU", + "SOLOMON ISLANDS", + "SOUTHEAST INDIAN RIDGE", + "SOUTH OF FIJI ISLANDS", + "KERMADEC ISLANDS REGION", + "VANUATU ISLANDS", + "RAT ISLANDS", + "WEST OF MACQUARIE ISLAND", + "TONGA ISLANDS", + "TIMOR REGION", + "SOUTHERN EAST PACIFIC RISE", + "OFF EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "LUZON", + "BISMARCK SEA", + "TAIWAN REGION", + "PHILIPPINE ISLANDS REGION", + "BISMARCK SEA", + "MINDANAO", + "MINDANAO", + "VANUATU ISLANDS", + "NEW BRITAIN REGION", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NORTHERN MID-ATLANTIC RIDGE", + "SOUTH OF KERMADEC ISLANDS", + "NEAR COAST OF NICARAGUA", + "BANDA SEA", + "SOUTHERN MOLUCCA SEA", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH SANDWICH ISLANDS REGION", + "TAIWAN", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "SOLOMON ISLANDS", + "CRETE", + "HAWAII", + "SOUTH SANDWICH ISLANDS REGION", + "MID-INDIAN RIDGE", + "SEA OF JAPAN", + "CRETE", + "ANDREANOF ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "KURIL ISLANDS", + "CHILE-ARGENTINA BORDER REGION", + "FIJI ISLANDS REGION", + "KERMADEC ISLANDS", + "NEAR COAST OF NICARAGUA", + "SOUTH SANDWICH ISLANDS REGION", + "IRIAN JAYA REGION", + "SOUTHERN IRAN", + "TAIWAN REGION", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "OFF COAST OF JALISCO", + "NEAR COAST OF NORTHERN CALIF.", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS", + "PRINCE EDWARD ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "RYUKYU ISLANDS", + "NORTH OF HALMAHERA", + "EASTER ISLAND REGION", + "NEW IRELAND REGION", + "NEW IRELAND REGION", + "SOUTHEAST OF HONSHU", + "MYANMAR-INDIA BORDER REGION", + "SANTA CRUZ ISLANDS", + "NEW IRELAND REGION", + "SOUTH INDIAN OCEAN", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS", + "CENTRAL CALIFORNIA", + "QUEEN CHARLOTTE ISLANDS REGION", + "VANUATU ISLANDS", + "MARIANA ISLANDS", + "KERMADEC ISLANDS REGION", + "PANAMA-COSTA RICA BORDER REGION", + "KOMANDORSKIYE OSTROVA REGION", + "KOMANDORSKIYE OSTROVA REGION", + "SOLOMON ISLANDS", + "IRIAN JAYA", + "IRIAN JAYA", + "KERMADEC ISLANDS", + "NEAR WEST COAST OF HONSHU", + "KERMADEC ISLANDS", + "KERMADEC ISLANDS REGION", + "TONGA ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "SOUTHEAST OF HONSHU", + "KERMADEC ISLANDS", + "NORTHWEST OF RYUKYU ISLANDS", + "NEW BRITAIN REGION", + "BALLENY ISLANDS REGION", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH SANDWICH ISLANDS REGION", + "NORTH OF ASCENSION ISLAND", + "VANUATU ISLANDS", + "MINDANAO", + "MINDANAO", + "KERMADEC ISLANDS", + "CENTRAL CHILE", + "SOUTHWEST INDIAN RIDGE", + "MONA PASSAGE", + "JAVA", + "OFF COAST OF OREGON", + "VANUATU ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN EAST PACIFIC RISE", + "NEAR S. COAST OF HONSHU", + "PHILIPPINE ISLANDS REGION", + "TONGA ISLANDS", + "MINDANAO", + "FIJI ISLANDS REGION", + "ANDREANOF ISLANDS", + "CENTRAL BOLIVIA", + "D'ENTRECASTEAUX ISLANDS REGION", + "FIJI ISLANDS REGION", + "PHILIPPINE ISLANDS REGION", + "SOUTH OF KERMADEC ISLANDS", + "NEW IRELAND REGION", + "NORTHERN MID-ATLANTIC RIDGE", + "MARIANA ISLANDS", + "SOUTHERN SUMATRA", + "SOUTHERN SUMATRA", + "FIJI ISLANDS REGION", + "SOLOMON ISLANDS", + "SUMBA REGION", + "SUMBA REGION", + "VANUATU ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "ALASKA PENINSULA", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "EAST OF SOUTH SANDWICH ISLANDS", + "COLOMBIA", + "EAST OF KURIL ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "PANAMA-COSTA RICA BORDER REGION", + "KYUSHU", + "SOLOMON ISLANDS", + "CARLSBERG RIDGE", + "SOUTH OF FIJI ISLANDS", + "SAMAR", + "SOUTH SANDWICH ISLANDS REGION", + "LUZON", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "BANDA SEA", + "SOUTH SANDWICH ISLANDS REGION", + "RAT ISLANDS", + "MAURITIUS - REUNION REGION", + "NEAR COAST OF ECUADOR", + "NEW BRITAIN REGION", + "NORTHERN MOLUCCA SEA", + "SOUTH OF AFRICA", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "SAMOA ISLANDS REGION", + "ANDAMAN ISLANDS", + "W. CAROLINE ISLANDS", + "XIZANG", + "MINAHASSA PENINSULA", + "ICELAND REGION", + "MARIANA ISLANDS", + "SOUTHERN MID-ATLANTIC RIDGE", + "FIJI ISLANDS REGION", + "HALMAHERA", + "SOUTHERN PERU", + "VANUATU ISLANDS", + "OFF COAST OF CENTRAL MEXICO", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NORTH OF HONDURAS", + "KERMADEC ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "SOUTH OF ALASKA", + "XIZANG", + "SOUTH SANDWICH ISLANDS REGION", + "KAMCHATKA PENINSULA", + "NEAR EAST COAST OF HONSHU", + "NORTHERN CHILE", + "VANUATU ISLANDS", + "MINDANAO", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "CHAGOS ARCHIPELAGO REGION", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "COSTA RICA", + "ASCENSION ISLAND REGION", + "TANZANIA", + "PRINCE EDWARD ISLANDS REGION", + "VANUATU ISLANDS", + "BISMARCK SEA", + "MINDANAO", + "ALASKA PENINSULA", + "MID-INDIAN RIDGE", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "CENTRAL EAST PACIFIC RISE", + "VANCOUVER ISLAND", + "COLOMBIA", + "SANTIAGO DEL ESTERO PROV.", + "VANCOUVER ISLAND", + "VANCOUVER ISLAND", + "HINDU KUSH REGION", + "VANUATU ISLANDS", + "TONGA ISLANDS", + "VANUATU ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "ANDREANOF ISLANDS", + "CRETE", + "ALBANIA", + "ANDREANOF ISLANDS", + "IRIAN JAYA REGION", + "LAOS", + "SEA OF OKHOTSK", + "OFF COAST OF CHIAPAS", + "NORTHERN MOLUCCA SEA", + "SOUTHERN EAST PACIFIC RISE", + "VANUATU ISLANDS", + "NORTHERN SUMATRA", + "NEW BRITAIN REGION", + "SOUTH OF BALI", + "VANUATU ISLANDS", + "SAMOA ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "TURKEY-IRAN BORDER REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "MINDANAO", + "JAVA", + "SOLOMON ISLANDS", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "ANDREANOF ISLANDS", + "ANDREANOF ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "MINAHASSA PENINSULA", + "SOUTHERN XINJIANG", + "NEAR NORTH COAST OF IRIAN JAYA", + "EASTERN SIBERIA", + "PUERTO RICO REGION", + "MID-INDIAN RIDGE", + "BANDA SEA", + "PHILIPPINE ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "EAST OF KURIL ISLANDS", + "VANUATU ISLANDS REGION", + "GUATEMALA", + "TAIWAN", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "E. RUSSIA-N.E. CHINA BORDER REG.", + "SULAWESI", + "SOUTHERN IRAN", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "EAST OF KURIL ISLANDS", + "BANDA SEA", + "KURIL ISLANDS", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "OFF EAST COAST OF KAMCHATKA", + "OFF EAST COAST OF KAMCHATKA", + "OFF COAST OF SOUTHERN CHILE", + "NEAR EAST COAST OF HONSHU", + "COSTA RICA", + "KYUSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTHERN IRAN", + "KERMADEC ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "MICHOACAN", + "SOUTHEAST OF HONSHU", + "TONGA ISLANDS", + "CRETE", + "NEAR EAST COAST OF HONSHU", + "CENTRAL MID-ATLANTIC RIDGE", + "MINAHASSA PENINSULA", + "TAJIKISTAN", + "PACIFIC-ANTARCTIC RIDGE", + "NORTHERN SUMATRA", + "LAKE ISSYK-KUL REGION", + "ALASKA PENINSULA", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "SANTA CRUZ ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "SANTA CRUZ ISLANDS", + "KURIL ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "SANTIAGO DEL ESTERO PROV.", + "MINDANAO", + "WEST OF MACQUARIE ISLAND", + "HINDU KUSH REGION", + "SOUTH ISLAND", + "NORTHERN MOLUCCA SEA", + "NEW BRITAIN REGION", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "SANTA CRUZ ISLANDS", + "IRIAN JAYA REGION", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "TAIWAN", + "NEAR EAST COAST OF HONSHU", + "EASTERN HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEGROS", + "SOUTHWEST INDIAN RIDGE", + "NEAR COAST OF GUERRERO", + "KERMADEC ISLANDS REGION", + "ANDREANOF ISLANDS", + "NEAR EAST COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "FLORES SEA", + "ANDREANOF ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF NORTHERN PERU", + "CENTRAL EAST PACIFIC RISE", + "NEAR EAST COAST OF HONSHU", + "PHILIPPINE ISLANDS REGION", + "NORTHERN MID-ATLANTIC RIDGE", + "NEAR EAST COAST OF HONSHU", + "ANDREANOF ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF CHIAPAS", + "KERMADEC ISLANDS REGION", + "NEAR WEST COAST OF HONSHU", + "BANDA SEA", + "SOUTHERN SUMATRA", + "SOLOMON ISLANDS", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF HONSHU", + "CRETE", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF NICARAGUA", + "SOUTH OF KERMADEC ISLANDS", + "NEAR EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "CRETE", + "SOLOMON ISLANDS", + "PHILIPPINE ISLANDS REGION", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "VANUATU ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "GANSU", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "EASTERN HONSHU", + "SOUTHERN PERU", + "OFF EAST COAST OF HONSHU", + "PRINCE EDWARD ISLANDS REGION", + "NEW IRELAND REGION", + "NEAR ISLANDS", + "OFF W. COAST OF BAJA CALIFORNIA", + "NORTHERN ITALY", + "NEAR COAST OF NORTHERN CHILE", + "LUZON", + "OFF EAST COAST OF HONSHU", + "HOKKAIDO", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "GREENLAND SEA", + "BONIN ISLANDS", + "NEW IRELAND REGION", + "SALTA PROVINCE", + "HOKKAIDO", + "SOUTH OF PANAMA", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "CHILE-ARGENTINA BORDER REGION", + "SOUTHERN PERU", + "KURIL ISLANDS", + "NICARAGUA", + "VANUATU ISLANDS", + "COOK STRAIT", + "NORTHERN XINJIANG", + "NEAR EAST COAST OF KAMCHATKA", + "OFF COAST OF SOUTHERN CHILE", + "NORTHERN SUMATRA", + "NEAR ISLANDS", + "NEAR EAST COAST OF HONSHU", + "LUZON", + "SOUTHERN MOLUCCA SEA", + "DODECANESE ISLANDS", + "OFF COAST OF ECUADOR", + "SOUTHERN EAST PACIFIC RISE", + "PERU-BOLIVIA BORDER REGION", + "NEAR COAST OF CHIAPAS", + "KERMADEC ISLANDS REGION", + "IRIAN JAYA", + "SOUTH SANDWICH ISLANDS REGION", + "NEW BRITAIN REGION", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CHIAPAS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF PERU", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "SANTIAGO DEL ESTERO PROV.", + "KURIL ISLANDS", + "SOUTH SHETLAND ISLANDS", + "NEAR EAST COAST OF HONSHU", + "IRIAN JAYA REGION", + "EAST OF SOUTH SANDWICH ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "OFF W COAST OF NORTHERN SUMATRA", + "VANUATU ISLANDS", + "DRAKE PASSAGE", + "NORTHERN EAST PACIFIC RISE", + "MINAHASSA PENINSULA", + "BAJA CALIFORNIA", + "OFF COAST OF OREGON", + "NORTH INDIAN OCEAN", + "NEW IRELAND REGION", + "NEAR COAST OF GUERRERO", + "SANTA CRUZ ISLANDS", + "TONGA ISLANDS", + "SOUTHERN EAST PACIFIC RISE", + "NORTHERN SUMATRA", + "REVILLA GIGEDO ISLANDS REGION", + "FIJI ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "CERAM SEA", + "CELEBES SEA", + "ASCENSION ISLAND REGION", + "VANUATU ISLANDS", + "COSTA RICA", + "KURIL ISLANDS", + "NEAR S. COAST OF HONSHU", + "QUEEN CHARLOTTE ISLANDS REGION", + "QUEEN CHARLOTTE ISLANDS REGION", + "MINDANAO", + "VANCOUVER ISLAND", + "CENTRAL PERU", + "VANUATU ISLANDS REGION", + "NEAR COAST OF CHIAPAS", + "GULF OF ALASKA", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "NORTHERN MOLUCCA SEA", + "NEW IRELAND REGION", + "GULF OF CALIFORNIA", + "NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR N COAST OF NEW GUINEA", + "NEW BRITAIN REGION", + "NEAR EAST COAST OF HONSHU", + "GUERRERO", + "NEAR COAST OF CENTRAL CHILE", + "OFF COAST OF SOUTHERN CHILE", + "VANUATU ISLANDS", + "BANDA SEA", + "SUNDA STRAIT", + "VANUATU ISLANDS", + "BALLENY ISLANDS REGION", + "NEAR N COAST OF NEW GUINEA", + "SULAWESI", + "SOUTH OF BALI", + "SOUTHEASTERN SIBERIA", + "NEW BRITAIN REGION", + "SOUTH OF KERMADEC ISLANDS", + "HOKKAIDO", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "TURKEY", + "FOX ISLANDS", + "FIJI ISLANDS REGION", + "NEW IRELAND REGION", + "PERU-BRAZIL BORDER REGION", + "XIZANG", + "PACIFIC-ANTARCTIC RIDGE", + "TONGA ISLANDS", + "SOUTH OF JAVA", + "GULF OF CALIFORNIA", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS", + "BAJA CALIFORNIA", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "OFF EAST COAST OF HONSHU", + "SOUTHERN SUMATRA", + "AUCKLAND ISLANDS", + "IRIAN JAYA REGION", + "NEAR ISLANDS", + "SANTA CRUZ ISLANDS", + "EAST OF NORTH ISLAND", + "NEAR EAST COAST OF HONSHU", + "KURIL ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SOUTHERN EAST PACIFIC RISE", + "TONGA ISLANDS", + "IRIAN JAYA REGION", + "E. CAROLINE ISLANDS", + "E. CAROLINE ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "NEGROS", + "OFF W COAST OF NORTHERN SUMATRA", + "BAY OF BENGAL", + "SOUTHERN SUMATRA", + "OFF COAST OF JALISCO", + "VANUATU ISLANDS", + "HALMAHERA", + "GUERRERO", + "ANDREANOF ISLANDS", + "SOUTHWESTERN RYUKYU ISL.", + "IRIAN JAYA REGION", + "ARU ISLANDS REGION", + "RAT ISLANDS", + "RAT ISLANDS", + "HINDU KUSH REGION", + "RAT ISLANDS", + "RAT ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "VOLCANO ISLANDS", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "ANDREANOF ISLANDS", + "TONGA ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "NEW BRITAIN REGION", + "JAN MAYEN ISLAND REGION", + "SOUTHERN IRAN", + "NORTHERN SUMATRA", + "TAJIKISTAN", + "SOLOMON ISLANDS", + "NORTHERN SUMATRA", + "SOUTHERN SUMATRA", + "LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "VANUATU ISLANDS", + "SOUTH OF FIJI ISLANDS", + "PACIFIC-ANTARCTIC RIDGE", + "BALLENY ISLANDS REGION", + "SANTA CRUZ ISLANDS REGION", + "NEAR ISLANDS", + "BONIN ISLANDS", + "SOLOMON ISLANDS", + "BANDA SEA", + "OFF EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS", + "FIJI ISLANDS REGION", + "SOUTH OF FIJI ISLANDS", + "NEAR S. COAST OF HONSHU", + "THAILAND", + "WEST CHILE RISE", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "BONIN ISLANDS", + "BALLENY ISLANDS REGION", + "TONGA ISLANDS", + "HOKKAIDO", + "HOKKAIDO", + "IRAN-IRAQ BORDER REGION", + "IRAN-IRAQ BORDER REGION", + "NEAR COAST OF CENTRAL CHILE", + "ANDREANOF ISLANDS", + "NORTHERN CALIFORNIA", + "NORTHERN MOLUCCA SEA", + "OFF EAST COAST OF KAMCHATKA", + "EASTER ISLAND REGION", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "OFF COAST OF JALISCO", + "SOUTHERN MOLUCCA SEA", + "NORTH OF HALMAHERA", + "JUJUY PROVINCE", + "SOLOMON ISLANDS", + "SOUTHERN ALASKA", + "NEW BRITAIN REGION", + "YUNNAN", + "GULF OF CALIFORNIA", + "NEW BRITAIN REGION", + "FOX ISLANDS", + "HOKKAIDO", + "TONGA ISLANDS", + "EASTER ISLAND REGION", + "NORTHERN CHILE", + "SOUTH OF MARIANA ISLANDS", + "NEAR EAST COAST OF HONSHU", + "BANDA SEA", + "TONGA ISLANDS", + "BANDA SEA", + "XIZANG", + "BALLENY ISLANDS REGION", + "KERMADEC ISLANDS REGION", + "OFF COAST OF JALISCO", + "KERMADEC ISLANDS REGION", + "NORTHERN SUMATRA", + "NEW BRITAIN REGION", + "SOLOMON ISLANDS", + "OFF W COAST OF NORTHERN SUMATRA", + "SOUTH OF MARIANA ISLANDS", + "FIJI ISLANDS REGION", + "MAURITIUS - REUNION REGION", + "VANUATU ISLANDS", + "NEW BRITAIN REGION", + "VANUATU ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "MINDANAO", + "MINDANAO", + "SOUTHEASTERN ALASKA", + "TONGA ISLANDS", + "KURIL ISLANDS", + "TONGA ISLANDS", + "OWEN FRACTURE ZONE REGION", + "KURIL ISLANDS", + "SOUTHEASTERN ALASKA", + "NORTHERN MID-ATLANTIC RIDGE", + "OAXACA", + "BISMARCK SEA", + "BISMARCK SEA", + "YUNNAN", + "MYANMAR-INDIA BORDER REGION", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "TONGA ISLANDS REGION", + "EASTER ISLAND REGION", + "OAXACA", + "VANUATU ISLANDS", + "SOUTH ISLAND", + "EASTER ISLAND REGION", + "NEAR EAST COAST OF HONSHU", + "TAIWAN", + "NEAR EAST COAST OF KAMCHATKA", + "EASTERN NEW GUINEA REG.", + "EAST OF SOUTH SANDWICH ISLANDS", + "BISMARCK SEA", + "KYUSHU", + "NEAR EAST COAST OF HONSHU", + "NEAR COAST OF PERU", + "PUERTO RICO REGION", + "OFF COAST OF CENTRAL CHILE", + "BALLENY ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "NICOBAR ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "CELEBES SEA", + "NEAR EAST COAST OF HONSHU", + "SCOTIA SEA", + "OFF EAST COAST OF HONSHU", + "KURIL ISLANDS", + "NEW BRITAIN REGION", + "NORTH ISLAND", + "TONGA ISLANDS", + "JAVA", + "NEAR EAST COAST OF HONSHU", + "NORTHERN SUMATRA", + "NEAR EAST COAST OF HONSHU", + "SOLOMON ISLANDS", + "GREECE", + "NEAR COAST OF NORTHERN CHILE", + "NEAR COAST OF NORTHERN CHILE", + "GREECE", + "KURIL ISLANDS", + "NEAR EAST COAST OF HONSHU", + "MARIANA ISLANDS", + "HALMAHERA", + "OFF EAST COAST OF HONSHU", + "FOX ISLANDS", + "NEAR COAST OF NICARAGUA", + "NORTHERN CHILE", + "NEAR COAST OF CHIAPAS", + "SOUTH OF KERMADEC ISLANDS", + "SOUTH OF FIJI ISLANDS", + "NEAR COAST OF NORTHERN PERU", + "MARIANA ISLANDS REGION", + "KERMADEC ISLANDS", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF PANAMA", + "NEAR COAST OF CENTRAL CHILE", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF CENTRAL CHILE", + "TONGA ISLANDS", + "SOUTH OF MARIANA ISLANDS", + "WEST CHILE RISE", + "NEAR COAST OF VENEZUELA", + "SOLOMON ISLANDS", + "NICARAGUA", + "NEAR COAST OF NORTHERN CHILE", + "SULAWESI", + "KERMADEC ISLANDS", + "OFF EAST COAST OF HONSHU", + "MID-INDIAN RIDGE", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "OFF EAST COAST OF HONSHU", + "MACQUARIE ISLAND REGION", + "OFF COAST OF CENTRAL CHILE", + "NORTHERN MOLUCCA SEA", + "TONGA ISLANDS", + "PERU-BRAZIL BORDER REGION", + "KERMADEC ISLANDS", + "OFF EAST COAST OF HONSHU", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "FIJI ISLANDS REGION", + "MINDANAO", + "FOX ISLANDS", + "MINDANAO", + "JUJUY PROVINCE", + "NEAR COAST OF CENTRAL CHILE", + "NORTHEAST OF TAIWAN", + "SOUTH OF MARIANA ISLANDS", + "SOUTHERN SUMATRA", + "LUZON", + "OFF EAST COAST OF HONSHU", + "WESTERN AUSTRALIA", + "GUERRERO", + "KERMADEC ISLANDS", + "EASTERN NEW GUINEA REG.", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "WEST CHILE RISE", + "SOUTH SHETLAND ISLANDS", + "MINDANAO", + "EAST OF SOUTH SANDWICH ISLANDS", + "NEAR EAST COAST OF HONSHU", + "RUSSIA-MONGOLIA BORDER REGION", + "MINDANAO", + "NEAR COAST OF NICARAGUA", + "SOUTHEAST OF LOYALTY ISLANDS", + "TONGA ISLANDS REGION", + "NEAR EAST COAST OF HONSHU", + "SOUTHWESTERN SIBERIA", + "NEAR COAST OF CENTRAL CHILE", + "SERAM", + "VANUATU ISLANDS", + "OFF EAST COAST OF KAMCHATKA", + "SOUTHEAST OF HONSHU", + "NEW BRITAIN REGION", + "OFF EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "TAIWAN", + "NORTHERN MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "SOUTH SHETLAND ISLANDS", + "NEAR EAST COAST OF HONSHU", + "SOUTHWEST OF SUMATRA", + "CENTRAL BOLIVIA", + "SOUTH SANDWICH ISLANDS REGION", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NEVADA", + "ALASKA PENINSULA", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHEAST OF RYUKYU ISLANDS", + "SOUTHEAST OF RYUKYU ISLANDS", + "SANTA CRUZ ISLANDS", + "BANDA SEA", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN IRAN", + "CENTRAL MID-ATLANTIC RIDGE", + "OFF COAST OF CENTRAL CHILE", + "NEAR COAST OF NORTHERN CHILE", + "SOLOMON ISLANDS", + "NEAR COAST OF NORTHERN CHILE", + "SOUTHEAST OF HONSHU", + "NEW BRITAIN REGION", + "CRETE", + "LOYALTY ISLANDS", + "NEGROS", + "NORTHERN CHILE", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SAMAR", + "SOUTHERN SUMATRA", + "NEAR COAST OF NICARAGUA", + "CELEBES SEA", + "EAST OF KURIL ISLANDS", + "SOUTHERN SUMATRA", + "CHILE-BOLIVIA BORDER REGION", + "JAVA SEA", + "VANUATU ISLANDS", + "CELEBES SEA", + "NEAR EAST COAST OF HONSHU", + "BANDA SEA", + "VANUATU ISLANDS REGION", + "LOYALTY ISLANDS", + "RYUKYU ISLANDS", + "ALASKA PENINSULA", + "BONIN ISLANDS", + "SOUTH OF KERMADEC ISLANDS", + "WESTERN IDAHO", + "BONIN ISLANDS", + "SOUTH OF FIJI ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "NORTHERN SUMATRA", + "FIJI ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "VANCOUVER ISLAND", + "SOUTHERN EAST PACIFIC RISE", + "MYANMAR", + "OFF W COAST OF NORTHERN SUMATRA", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "SIKKIM", + "KERMADEC ISLANDS REGION", + "MAURITIUS - REUNION REGION", + "ANDREANOF ISLANDS", + "EASTERN NEW GUINEA REG.", + "NORTHERN PERU", + "NEAR EAST COAST OF HONSHU", + "NEAR EAST COAST OF HONSHU", + "SOUTH OF FIJI ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "BALI SEA", + "FOX ISLANDS", + "BANDA SEA", + "CENTRAL MID-ATLANTIC RIDGE", + "WEST OF MACQUARIE ISLAND", + "SOUTHEAST INDIAN RIDGE", + "MINDORO", + "SOUTH OF PANAMA", + "NEAR EAST COAST OF HONSHU", + "NEAR N COAST OF NEW GUINEA", + "CHIAPAS", + "CENTRAL MID-ATLANTIC RIDGE", + "SOUTH OF JAVA", + "GULF OF CALIFORNIA", + "SOUTH OF FIJI ISLANDS", + "QINGHAI", + "VANUATU ISLANDS", + "KERMADEC ISLANDS", + "TURKEY", + "CYPRUS REGION", + "FOX ISLANDS", + "FOX ISLANDS", + "SUNDA STRAIT", + "BONIN ISLANDS", + "NEW BRITAIN REGION", + "NEGROS", + "FIJI ISLANDS REGION", + "SCOTIA SEA", + "NEAR EAST COAST OF KAMCHATKA", + "NEAR COAST OF CENTRAL CHILE", + "EAST OF SOUTH SANDWICH ISLANDS", + "GULF OF CALIFORNIA", + "SOLOMON ISLANDS", + "MYANMAR", + "NEW IRELAND REGION", + "CRETE", + "SAKHALIN", + "SEA OF OKHOTSK", + "PAKISTAN", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "SOUTH ATLANTIC OCEAN", + "BANDA SEA", + "VANUATU ISLANDS", + "SOUTHERN SUMATRA", + "OFF COAST OF JALISCO", + "NEAR COAST OF NORTHERN CHILE", + "OFF COAST OF NORTHERN CALIFORNIA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "AFGHANISTAN-TAJIKISTAN BORD REG.", + "JAVA SEA", + "MYANMAR-INDIA BORDER REGION", + "NORTH ATLANTIC OCEAN", + "XIZANG", + "VANUATU ISLANDS", + "NORTHERN EAST PACIFIC RISE", + "NEAR COAST OF ECUADOR", + "NEAR COAST OF ECUADOR", + "FIJI ISLANDS REGION", + "RYUKYU ISLANDS", + "CENTRAL ITALY", + "SOUTH ISLAND", + "SOUTH ISLAND", + "OFF COAST OF CENTRAL CHILE", + "MINDANAO", + "NORTHERN MID-ATLANTIC RIDGE", + "VANUATU ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SULAWESI", + "NEAR ISLANDS", + "NEW BRITAIN REGION", + "SOUTH OF JAVA", + "NEAR COAST OF GUATEMALA", + "SEA OF OKHOTSK", + "LEYTE", + "AUCKLAND ISLANDS", + "DODECANESE ISLANDS", + "MARIANA ISLANDS", + "GANSU", + "GUATEMALA", + "HOKKAIDO", + "BOTSWANA", + "COOK STRAIT", + "NEAR EAST COAST OF HONSHU", + "ANDREANOF ISLANDS", + "SOUTHEAST OF HONSHU", + "NORTHERN SUMATRA", + "OFF COAST OF NORTHERN CALIFORNIA", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF KAMCHATKA", + "BANDA SEA", + "COOK STRAIT", + "SOUTH OF PANAMA", + "SOUTH OF FIJI ISLANDS", + "SOLOMON ISLANDS", + "MINDANAO", + "SOUTHERN BOLIVIA", + "SOUTH OF FIJI ISLANDS", + "BANDA SEA", + "NEW IRELAND REGION", + "NEAR COAST OF NORTHERN CHILE", + "MYANMAR-INDIA BORDER REGION", + "NEPAL", + "NEW BRITAIN REGION", + "NEW BRITAIN REGION", + "NEAR COAST OF CHIAPAS", + "NEAR EAST COAST OF HONSHU", + "PACIFIC-ANTARCTIC RIDGE", + "SANTA CRUZ ISLANDS REGION", + "SOLOMON ISLANDS", + "ALASKA PENINSULA", + "TONGA ISLANDS", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHERN MID-ATLANTIC RIDGE", + "BONIN ISLANDS", + "KERMADEC ISLANDS", + "SOLOMON ISLANDS", + "NEPAL", + "OFF EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "FIJI ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "NORTHERN MOLUCCA SEA", + "NORTHERN MOLUCCA SEA", + "MINDANAO", + "SOUTHERN EAST PACIFIC RISE", + "SOLOMON ISLANDS", + "SOUTH OF PANAMA", + "NORTH ATLANTIC OCEAN", + "MARIANA ISLANDS", + "VANUATU ISLANDS", + "CENTRAL PERU", + "JUJUY PROVINCE", + "CENTRAL PERU", + "OFF EAST COAST OF HONSHU", + "E. CAROLINE ISLANDS", + "FIJI ISLANDS REGION", + "SOUTH OF PANAMA", + "SANTA CRUZ ISLANDS", + "FOX ISLANDS", + "KERMADEC ISLANDS", + "TIMOR REGION", + "NEAR COAST OF CENTRAL CHILE", + "NICOBAR ISLANDS", + "ANDREANOF ISLANDS", + "OFF COAST OF CENTRAL CHILE", + "OFF COAST OF CENTRAL CHILE", + "NORTHWEST OF RYUKYU ISLANDS", + "SOLOMON ISLANDS", + "GREECE", + "NEAR COAST OF NICARAGUA", + "PERU-BRAZIL BORDER REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "NEAR COAST OF CHIAPAS", + "NEAR COAST OF NORTHERN CHILE", + "MYANMAR-INDIA BORDER REGION", + "SOLOMON ISLANDS", + "TALAUD ISLANDS", + "BOUVET ISLAND REGION", + "IRIAN JAYA REGION", + "KERMADEC ISLANDS", + "SOLOMON ISLANDS", + "SOLOMON ISLANDS", + "SOUTH INDIAN OCEAN", + "AEGEAN SEA", + "GULF OF CALIFORNIA", + "SOUTH OF PANAMA", + "SOLOMON ISLANDS", + "GUERRERO", + "NEAR COAST OF CENTRAL CHILE", + "NEAR COAST OF CENTRAL CHILE", + "SOUTH OF FIJI ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "VANCOUVER ISLAND", + "IRIAN JAYA REGION", + "NEAR COAST OF CENTRAL CHILE", + "NORTH OF ASCENSION ISLAND", + "SOUTHERN XINJIANG", + "NORTHWEST OF KURIL ISLANDS", + "HAWAII", + "SOUTHERN BOLIVIA", + "NEW BRITAIN REGION", + "MAURITIUS - REUNION REGION", + "SOLOMON ISLANDS", + "FIJI ISLANDS REGION", + "NEW BRITAIN REGION", + "NEW IRELAND REGION", + "NEW GUINEA", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEW IRELAND REGION", + "VANUATU ISLANDS", + "NORTHERN MOLUCCA SEA", + "CENTRAL CHILE", + "VANUATU ISLANDS", + "NEAR COAST OF CENTRAL CHILE", + "PRINCE EDWARD ISLANDS REGION", + "HOKKAIDO", + "SANTIAGO DEL ESTERO PROV.", + "NEAR COAST OF CHIAPAS", + "TONGA ISLANDS", + "IRIAN JAYA REGION", + "WESTERN BRAZIL", + "JAN MAYEN ISLAND REGION", + "KURIL ISLANDS", + "MYANMAR", + "HOKKAIDO", + "SOLOMON ISLANDS", + "ANDREANOF ISLANDS", + "KERMADEC ISLANDS REGION", + "SICHUAN", + "SOUTH OF FIJI ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "JAN MAYEN ISLAND REGION", + "NORTHERN MOLUCCA SEA", + "SUMBAWA REGION", + "KURIL ISLANDS", + "FIJI ISLANDS REGION", + "VANCOUVER ISLAND", + "WEST OF MACQUARIE ISLAND", + "IRIAN JAYA REGION", + "VANCOUVER ISLAND", + "FLORES SEA", + "VANCOUVER ISLAND", + "IONIAN SEA", + "ANDREANOF ISLANDS", + "SUMBAWA REGION", + "ARMENIA-AZERBAIJAN-IRAN BORD REG", + "EASTERN NEW GUINEA REG.", + "NEAR COAST OF CENTRAL CHILE", + "SOUTHWEST OF AFRICA", + "EL SALVADOR", + "RAT ISLANDS", + "BOUVET ISLAND REGION", + "EASTERN NEW GUINEA REG.", + "SANTA CRUZ ISLANDS", + "BANDA SEA", + "EASTERN SIBERIA", + "CENTRAL MID-ATLANTIC RIDGE", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "KURIL ISLANDS", + "LOYALTY ISLANDS", + "LOYALTY ISLANDS", + "TONGA ISLANDS", + "KURIL ISLANDS", + "COSTA RICA", + "NEW GUINEA", + "SANTA CRUZ ISLANDS", + "NEW BRITAIN REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "SOUTHWESTERN SIBERIA", + "NEAR N COAST OF NEW GUINEA", + "BOUVET ISLAND REGION", + "JAVA", + "SOLOMON ISLANDS", + "SANTA CRUZ ISLANDS", + "WESTERN INDIAN-ANTARCTIC RIDGE", + "MICHOACAN", + "SULAWESI", + "SOUTH SANDWICH ISLANDS REGION", + "BONIN ISLANDS", + "VANUATU ISLANDS", + "FIJI ISLANDS REGION", + "SOUTHEAST INDIAN RIDGE", + "GUERRERO", + "NORTHERN MOLUCCA SEA", + "VANUATU ISLANDS", + "OFF COAST OF CENTRAL AMERICA", + "NEW BRITAIN REGION", + "VANUATU ISLANDS REGION", + "SOLOMON ISLANDS", + "SANTIAGO DEL ESTERO PROV.", + "NEAR COAST OF CENTRAL CHILE", + "VANUATU ISLANDS REGION", + "NEW BRITAIN REGION", + "OFF COAST OF CENTRAL AMERICA", + "CENTRAL PERU", + "FIJI ISLANDS REGION", + "SOUTHWESTERN PAKISTAN", + "KERMADEC ISLANDS", + "BAJA CALIFORNIA", + "OFF EAST COAST OF HONSHU", + "IRIAN JAYA", + "IRIAN JAYA REGION", + "MINDANAO", + "OFF W COAST OF NORTHERN SUMATRA", + "NEAR COAST OF GUERRERO", + "EASTERN NEW GUINEA REG.", + "KOMANDORSKIYE OSTROVA REGION", + "VANUATU ISLANDS", + "ECUADOR", + "SOUTH ISLAND", + "CENTRAL PERU", + "SOLOMON ISLANDS", + "NEPAL", + "SOLOMON ISLANDS", + "NEAR EAST COAST OF HONSHU", + "KERMADEC ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTHEAST OF LOYALTY ISLANDS", + "TURKEY", + "NORTHERN MOLUCCA SEA", + "SOUTHERN EAST PACIFIC RISE", + "EASTERN NEW GUINEA REG.", + "CENTRAL CALIFORNIA", + "NEAR COAST OF PERU", + "FLORES SEA", + "FLORES SEA", + "MINDANAO", + "SOUTHERN ALASKA", + "BANDA SEA", + "NEAR COAST OF CENTRAL CHILE", + "REYKJANES RIDGE", + "HALMAHERA", + "NEAR COAST OF CENTRAL CHILE", + "TAJIKISTAN", + "HAITI REGION", + "SOLOMON ISLANDS", + "BANDA SEA", + "OFF COAST OF CENTRAL AMERICA", + "SANTA CRUZ ISLANDS", + "NEAR EAST COAST OF HONSHU", + "OFF EAST COAST OF HONSHU", + "SANTA CRUZ ISLANDS REGION", + "OFF E. COAST OF N. ISLAND", + "NEAR COAST OF PERU", + "KERMADEC ISLANDS", + "OFF E. COAST OF N. ISLAND", + "NORTH OF ASCENSION ISLAND", + "MINDANAO", + "NEAR EAST COAST OF HONSHU", + "SOUTH GEORGIA ISLAND REGION", + "OFF EAST COAST OF HONSHU", + "SOUTHEAST OF LOYALTY ISLANDS", + "OAXACA", + "BANDA SEA", + "SOUTH OF KERMADEC ISLANDS", + "PHILIPPINE ISLANDS REGION", + "CENTRAL MEXICO", + "KURIL ISLANDS", + "NEAR EAST COAST OF HONSHU", + "IRIAN JAYA", + "SOUTH OF FIJI ISLANDS", + "DODECANESE ISLANDS", + "IRAN-IRAQ BORDER REGION", + "FOX ISLANDS", + "NEW IRELAND REGION", + "LOYALTY ISLANDS", + "COLOMBIA", + "CELEBES SEA", + "SANTA CRUZ ISLANDS", + "EASTERN NEW GUINEA REG.", + "ANDREANOF ISLANDS", + "NEAR COAST OF OAXACA", + "SOUTH SANDWICH ISLANDS REGION", + "OFF EAST COAST OF HONSHU", + "NEAR COAST OF PERU", + "QINGHAI", + "KAMCHATKA PENINSULA", + "VANUATU ISLANDS", + "VANUATU ISLANDS", + "SOUTHEAST OF LOYALTY ISLANDS", + "OFF COAST OF NORTHERN CHILE", + "VANUATU ISLANDS", + "KYUSHU", + "SOUTHERN ALASKA", + "SOUTH SANDWICH ISLANDS REGION", + "SOUTH SANDWICH ISLANDS REGION", + "KURIL ISLANDS", + "NEAR COAST OF VENEZUELA", + "VANUATU ISLANDS", + "COLOMBIA", + "NEAR COAST OF CHIAPAS", + "PERU-BRAZIL BORDER REGION", + "PERU-BRAZIL BORDER REGION", + "NEAR EAST COAST OF HONSHU", + "NORTH OF HONDURAS", + "SOLOMON ISLANDS", + "NEPAL", + "SOUTHERN IRAN", + "SOLOMON ISLANDS", + "COSTA RICA", + "KOMANDORSKIYE OSTROVA REGION", + "NEW IRELAND REGION", + "NEW BRITAIN REGION", + "ALASKA PENINSULA", + "MINDANAO", + "PERU-BRAZIL BORDER REGION", + "SOUTHERN CHILE", + "MINDANAO", + "PHILIPPINE ISLANDS REGION", + "NEW BRITAIN REGION", + "SOUTH SANDWICH ISLANDS REGION", + "NEAR COAST OF NORTHERN CHILE", + "MINAHASSA PENINSULA", + "SOLOMON ISLANDS", + "NEAR COAST OF GUERRERO", + "SOUTHWEST OF SUMATRA", + "SOUTH OF ALASKA", + "SOUTHEAST OF LOYALTY ISLANDS", + "EAST OF KURIL ISLANDS", + "SCOTIA SEA", + "QUEEN CHARLOTTE ISLANDS REGION", + "PRIMOR'YE", + "MARIANA ISLANDS", + "SEA OF OKHOTSK", + "SOUTHERN SUMATRA", + "NORTHERN PERU", + "SOUTHEAST OF LOYALTY ISLANDS", + "NEAR EAST COAST OF 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lat='latitude',lon='longitude', \n", + " range_color=(6,9),\n", + " height=600, width=600,\n", + " size='marker_size', color='magnitude',\n", + " hover_name=\"description\",\n", + " hover_data=['description','magnitude','depth']);\n", + "fig.update_geos(resolution=110, showcountries=True)\n", + "# fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "3355fe19", + "metadata": {}, + "source": [ + "# 3 Create a Pandas from a generic text file.\n", + "\n", + "The python package pandas is very useful to read csv files, but also many text files that are more or less formatted as one observation per row and one column for each feature.\n", + "\n", + "As an example, we are going to look at the list of seismic stations from the Northern California seismic network, available [here](http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day):\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "ef9f302d", + "metadata": {}, + "outputs": [], + "source": [ + "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "3a60ef42", + "metadata": {}, + "outputs": [], + "source": [ + "# this gets the file linked in the URL page and convert it to a string\n", + "s = requests.get(url).content " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "9edd0bf5", + "metadata": {}, + "outputs": [], + "source": [ + "# this will convert the string, decode it , and make it a table\n", + "data = pd.read_csv(io.StringIO(s.decode('utf-8')), header=None, skiprows=2, sep='\\s+', usecols=list(range(0, 13)))\n", + "# because columns/keys were not assigned, assign them now\n", + "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" + ] + }, + { + "cell_type": "markdown", + "id": "b2def912", + "metadata": {}, + "source": [ + "Let us look at the data. They are now stored into a pandas dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "448b69ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuth
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.0
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.0
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3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.0
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.0
\n", + "
" ], - "source": [ - "# Classify earthquake magnitudes\n", - "quake['Category'] = quake['magnitude'].apply(lambda x: 'Minor' if x < 4.0 else ('Moderate' if x < 6.5 else 'Severe'))\n", - "quake.head()\n" + "text/plain": [ + " station network channel location rate start_time \\\n", + "0 AAR NC EHZ -- 0.0 1976/07/20,17:38:00 \n", + "1 AAR NC EHZ -- 0.0 1977/12/01,22:37:00 \n", + "2 AAR NC EHZ -- 0.0 1981/06/12,19:15:00 \n", + "3 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", + "4 AAR NC EHZ -- 100.0 1985/11/03,22:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip azimuth \n", + "0 1977/12/01,22:37:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 " ] - }, - { - "cell_type": "markdown", - "id": "8be50d1c", - "metadata": {}, - "source": [] - }, + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "d05055aa", + "metadata": {}, + "source": [ + "We can output the first element of the DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "51971cde", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "b87c6c42", - "metadata": {}, - "source": [ - "We will now make a new plot of the location of the earthquakes. We will use Plotly tool. \n", - "\n", - "The markersize will be scaled with the earthquake magnitude. To do so, we add a ``marker_size`` series in the DataFrame" + "data": { + "text/plain": [ + "station AAR\n", + "network NC\n", + "channel EHZ\n", + "location --\n", + "rate 0.0\n", + "start_time 1976/07/20,17:38:00\n", + "end_time 1977/12/01,22:37:00\n", + "latitude 39.27594\n", + "longitude -121.02696\n", + "elevation 911.0\n", + "depth 0.0\n", + "dip -90.0\n", + "azimuth 0.0\n", + "Name: 0, dtype: object" ] - }, + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "d9bb3b35", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 61, - "id": "d9cb41c3", - "metadata": {}, - "outputs": [], - "source": [ - "quake['marker_size'] = quake['magnitude'].apply(lambda x: np.fix(np.exp(x))) # add marker size as exp(mag)\n", - "quake['magnitude_bin'] = quake['magnitude'].apply(lambda x: 0.5*np.fix(2*x)) # add marker size as exp(mag)" + "data": { + "text/plain": [ + "station object\n", + "network object\n", + "channel object\n", + "location object\n", + "rate float64\n", + "start_time object\n", + "end_time object\n", + "latitude float64\n", + "longitude float64\n", + "elevation float64\n", + "depth float64\n", + "dip float64\n", + "azimuth float64\n", + "dtype: object" ] - }, + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# display the type of each column\n", + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "b3cc09c6", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 62, - "id": "6c7befa4", - "metadata": {}, - "outputs": [], - "source": [ - "# another way to do it\n", - "quake['marker_size'] = np.fix(np.exp(quake['magnitude'])) # add marker size as exp(mag)\n", - "quake['magnitude_bin'] = 0.5*np.fix(2*quake['magnitude']) # add marker size as exp(mag)" + "data": { + "text/plain": [ + "0 AAR\n", + "1 AAR\n", + "2 AAR\n", + "3 AAR\n", + "4 AAR\n", + " ... \n", + "29434 WMP\n", + "29435 WMP\n", + "29436 WMP\n", + "29437 WSL\n", + "29438 WWVB\n", + "Name: station, Length: 29439, dtype: object" ] - }, + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.iloc[:, 0]" + ] + }, + { + "cell_type": "markdown", + "id": "9afc9c2f", + "metadata": {}, + "source": [ + "The ``start_time`` and ``end_time`` are stored as ``object`` or ``string``, convert these columns to datetime objects. With a too-simple prompt, CoPilot may fail at converting:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "08156920", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "2fed2184", - "metadata": {}, - "source": [ - "### 1.4 Intermediate Manipulation with Pandas\n", - "\n", - "You can also apply lambda functions to work with multiple columns, which is common in geoscientific datasets, where you might have spatial or temporal data.\n", - "\n", - "**Example 3: Calculating an Index Based on Multiple Measurements**\n", - "Scenario: You have rainfall (``Rainfall_mm``) and evaporation (``Evaporation_mm``) data, and you want to calculate the ``Net Water Balance ``for each record." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/js/lzmy975n0l5bjbmr9db291m00000gn/T/ipykernel_26544/3238846712.py:2: UserWarning:\n", + "\n", + "Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", + "\n" + ] }, { - "cell_type": "code", - "execution_count": 63, - "id": "7931923f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Rainfall_mm Evaporation_mm Net_Water_Balance\n", - "0 100 60 40\n", - "1 80 70 10\n", - "2 120 65 55\n" - ] - } + "ename": "DateParseError", + "evalue": "Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mDateParseError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# convert start_time and end_time to datetime\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstart_time\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:1108\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1106\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mtz_localize(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, ABCSeries):\n\u001b[0;32m-> 1108\u001b[0m cache_array \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_listlike\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cache_array\u001b[38;5;241m.\u001b[39mempty:\n\u001b[1;32m 1110\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:254\u001b[0m, in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m 252\u001b[0m unique_dates \u001b[38;5;241m=\u001b[39m unique(arg)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_dates) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(arg):\n\u001b[0;32m--> 254\u001b[0m cache_dates \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43munique_dates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# GH#45319\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:490\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m--> 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mobjects_to_datetime64ns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 502\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n", + "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py:2346\u001b[0m, in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, allow_object)\u001b[0m\n\u001b[1;32m 2343\u001b[0m \u001b[38;5;66;03m# if str-dtype, convert\u001b[39;00m\n\u001b[1;32m 2344\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(data, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mobject_)\n\u001b[0;32m-> 2346\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mtslib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_to_datetime\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2347\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2348\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2349\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2351\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2355\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;66;03m# Return i8 values to denote unix timestamps\u001b[39;00m\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi8\u001b[39m\u001b[38;5;124m\"\u001b[39m), tz_parsed\n", + "File \u001b[0;32mtslib.pyx:403\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mtslib.pyx:552\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mtslib.pyx:517\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mconversion.pyx:546\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.convert_str_to_tsobject\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mparsing.pyx:331\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mparsing.pyx:660\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.dateutil_parse\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mDateParseError\u001b[0m: Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0" + ] + } + ], + "source": [ + "# convert start_time and end_time to datetime\n", + "data['start_time'] = pd.to_datetime(data['start_time'])\n", + "data['end_time'] = pd.to_datetime(data['end_time'])" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "b8bcf253", + "metadata": {}, + "outputs": [], + "source": [ + "data['startdate'] = pd.to_datetime(data['start_time'], format='%Y/%m/%d,%H:%M:%S')" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "da911e32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:00
\n", + "
" ], - "source": [ - "data = {'Rainfall_mm': [100, 80, 120], 'Evaporation_mm': [60, 70, 65]}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Calculate Net Water Balance\n", - "df['Net_Water_Balance'] = df.apply(lambda row: row['Rainfall_mm'] - row['Evaporation_mm'], axis=1)\n", - "\n", - "print(df)\n" + "text/plain": [ + " station network channel location rate start_time \\\n", + "0 AAR NC EHZ -- 0.0 1976/07/20,17:38:00 \n", + "1 AAR NC EHZ -- 0.0 1977/12/01,22:37:00 \n", + "2 AAR NC EHZ -- 0.0 1981/06/12,19:15:00 \n", + "3 AAR NC EHZ -- 100.0 1984/01/01,00:00:00 \n", + "4 AAR NC EHZ -- 100.0 1985/11/03,22:00:00 \n", + "\n", + " end_time latitude longitude elevation depth dip azimuth \\\n", + "0 1977/12/01,22:37:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", + "\n", + " startdate \n", + "0 1976-07-20 17:38:00 \n", + "1 1977-12-01 22:37:00 \n", + "2 1981-06-12 19:15:00 \n", + "3 1984-01-01 00:00:00 \n", + "4 1985-11-03 22:00:00 " ] - }, + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "4a2f3dc4", + "metadata": {}, + "outputs": [], + "source": [ + "# do the same for end times\n", + "# Avoid 'OutOfBoundsDatetime' error with year 3000\n", + "enddate = data['end_time'].str.replace('3000', '2026')\n", + "enddate = pd.to_datetime(enddate, format='%Y/%m/%d,%H:%M:%S')\n", + "data['enddate'] = enddate" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "4c3af296", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "b5b64a6b", - "metadata": {}, - "source": [ - "In this example, ``lambda row: row['Rainfall_mm'] - row['Evaporation_mm']`` calculates the net water balance by subtracting evaporation from rainfall for each record.\n", - "\n", - "### 1.5 Advanced: Time Series Data Manipulation with Lambda Functions\n", - "Geoscientists frequently work with time series data (e.g., climate data). Lambda functions can be used for efficient data transformations within time series.\n", - "\n", - "**Example 4: Applying a Rolling Window Calculation**\n", - "Scenario: Suppose you have daily temperature data, and you want to calculate a 7-day rolling average." + "data": { + "text/html": [ + "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdateenddate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:001977-12-01 22:37:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:001981-06-12 19:15:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:001984-01-01 00:00:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:001985-11-03 22:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:001987-03-10 23:15:00
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average using lambda\n", - "df['7_day_avg'] = df['Temperature_C'].rolling(window=3).apply(lambda x: x.mean())\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "id": "e3d3328a", - "metadata": {}, - "source": [ - "Here, ``lambda x: x.mean()`` calculates the rolling mean over a 3-day window for temperature data, which is essential in climate analysis for smoothing short-term fluctuations.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "e01cf834", - "metadata": {}, - "source": [ - "### 1.6 Aggregate function\n", - "The ``agg`` is a powerful method in Pandas that allows you to perform multiple operations on DataFrames and Series. You can apply one or more aggregation functions such as ``sum``, ``mean``, ``min``, ``max``, etc., on different columns or groups of data." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "aea0ee4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sum 10.0\n", - "mean 2.5\n", - "Name: A, dtype: float64\n" + ], + "layout": { + "geo": { + "center": {}, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 ] - } - ], - "source": [ - "# Sample DataFrame\n", - "data = {\n", - " 'A': [1, 2, 3, 4],\n", - " 'B': [5, 6, 7, 8]\n", - "}\n", - "df = pd.DataFrame(data)\n", - "\n", - "# Apply aggregation functions\n", - "result = df['A'].agg(['sum', 'mean'])\n", - "print(result)\n" - ] - }, - { - "cell_type": "markdown", - "id": "f2664926", - "metadata": {}, - "source": [ - "#### Aggregating Multiple Columns with Multiple Functions\n", - "You can pass a dictionary to agg where the keys are column names and the values are the functions to be applied." - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "9df9cde2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B\n", - "sum 10.0 NaN\n", - "mean 2.5 NaN\n", - "min NaN 5.0\n", - "max NaN 8.0\n" - ] - } - ], - "source": [ - "# Apply different functions to different columns\n", - "result = df.agg({'A': ['sum', 'mean'], 'B': ['min', 'max']})\n", - "print(result)\n" - ] - }, - { - "cell_type": "markdown", - "id": "5ccea12a", - "metadata": {}, - "source": [ - "In this example, column 'A' gets ``sum`` and ``mean``, while column 'B' gets ``min`` and ``max``." - ] - }, - { - "cell_type": "markdown", - "id": "6b9e44e0", - "metadata": {}, - "source": [ - "You may also use costum functions" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "79ff0c94", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " A B\n", - "mean 2.5 NaN\n", - "data_range 3.0 3.0\n" + }, + "resolution": 110, + "showcountries": true + }, + "height": 600, + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": 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"type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } ] - } - ], - "source": [ - "# Custom function to calculate range (max - min)\n", - "def data_range(x):\n", - " return x.max() - x.min()\n", - "\n", - "# Apply custom function\n", - "result = df.agg({'A': ['mean', data_range], 'B': data_range})\n", - "print(result)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "94abaccd", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f004df73", - "metadata": {}, - "source": [ - "## 2.4 Mapping using Plotly\n", - "\n", - "Now we will plot the earthquakes locations on a map using the Plotly package. More tutorials on [Plotly](https://plotly.com/). The input of the function is self-explanatory and typical of Python's function. The code [documentation](https://plotly.com/python/scatter-plots-on-maps/) of Plotly scatter_geo lists the variables." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "ff53045a", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - "VANUATU ISLANDS", - 6.3, - 0.0344 - ], - [ - "NEAR EAST COAST OF HONSHU", - 6.3, - 0.030100000000000002 - ], - [ - "SOUTH OF MARIANA ISLANDS", - 6.3, - 0.0169 - ], - [ - "NORTHERN CHILE", - 6.2, - 0.10940000000000001 - ], - [ - "NEAR COAST OF CENTRAL CHILE", - 6.6, - 0.0257 - ], - [ - "FOX ISLANDS", - 6.7, - 0.0215 - ], - [ - "NEW BRITAIN REGION", - 6.9, - 0.0458 - ], - [ - "NEW BRITAIN REGION", - 7.3, - 0.0431 - ], - [ - "FOX ISLANDS", - 6, - 0.0356 - ], - [ - "NEW BRITAIN REGION", - 6.3, - 0.041100000000000005 - ], - [ - "NORTH OF HALMAHERA", - 6.1, - 0.1119 - ], - [ - "MINDANAO", - 7.3, - 0.6102000000000001 - 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magnitude=%{marker.color}
depth=%{customdata[2]}", - "hovertext": [ - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF MARIANA ISLANDS", - "NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "FOX ISLANDS", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "FOX ISLANDS", - "NEW BRITAIN REGION", - "NORTH OF HALMAHERA", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "MINDANAO", - "OFF EAST COAST OF KAMCHATKA", - "NORTHERN MOLUCCA SEA", - "EASTERN NEW GUINEA REG.", - "ANDREANOF ISLANDS", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "VANUATU ISLANDS", - "ECUADOR", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "NEW BRITAIN REGION", - "MAURITIUS - REUNION REGION", - "FIJI ISLANDS REGION", - "SOUTH OF MARIANA ISLANDS", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "OFF COAST OF JALISCO", - "ANDREANOF ISLANDS", - "SOUTH ISLAND", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "HINDU KUSH REGION", - "ARU ISLANDS REGION", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "SOUTHWESTERN RYUKYU ISL.", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "HALMAHERA", - "GULF OF CALIFORNIA", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "PACIFIC-ANTARCTIC RIDGE", - "IRIAN JAYA REGION", - "TONGA ISLANDS", - "SOUTHEAST INDIAN RIDGE", - "PACIFIC-ANTARCTIC RIDGE", - "NEW BRITAIN REGION", - "BONIN ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "SOUTHERN IRAN", - "BONIN ISLANDS", - "BONIN ISLANDS", - "NEAR ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "VANUATU ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "BONIN ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SOUTHERN SUMATRA", - "NORTHERN SUMATRA", - "SOUTHWESTERN PAKISTAN", - "TAJIKISTAN", - "NORTHERN SUMATRA", - "SOUTHERN IRAN", - "JAN MAYEN ISLAND REGION", - "TONGA ISLANDS", - "MYANMAR-INDIA BORDER REGION", - "SOLOMON ISLANDS", - "CELEBES SEA", - "CELEBES SEA", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SULAWESI", - "NEAR EAST COAST OF KAMCHATKA", - "SOUTH OF FIJI ISLANDS", - "SOUTH ISLAND", - "OAXACA", - "EASTER ISLAND REGION", - "NORTHERN CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "BALI SEA", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "EASTERN HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR WEST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "EASTERN HONSHU", - "OFF EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MYANMAR", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "CRETE", - "FIJI ISLANDS REGION", - "SOUTH OF JAVA", - "CHIAPAS", - "NEAR EAST COAST OF HONSHU", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF KERMADEC ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SULAWESI", - "SOUTH OF PANAMA", - "OFF EAST COAST OF HONSHU", - "LOYALTY ISLANDS", - "COSTA RICA", - "NEAR EAST COAST OF HONSHU", - "HINDU KUSH REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "SOLOMON ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "WEST OF MACQUARIE ISLAND", - "SOUTH ISLAND", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "CHILE-BOLIVIA BORDER REGION", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FOX ISLANDS", - "SANTA CRUZ ISLANDS", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEGROS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "ALASKA PENINSULA", - "TAJIKISTAN", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW IRELAND REGION", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NEAR S. COAST OF HONSHU", - "KURIL ISLANDS", - "ASCENSION ISLAND REGION", - "CERAM SEA", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SUNDA STRAIT", - "PERU-BRAZIL BORDER REGION", - "VANUATU ISLANDS", - "BANDA SEA", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "NORTHERN SUMATRA", - "AUCKLAND ISLANDS", - "VANCOUVER ISLAND", - "NEAR ISLANDS", - "EAST OF NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SIKKIM", - "TONGA ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF BALI", - "EASTERN NEW GUINEA REG.", - "SOUTHEASTERN SIBERIA", - "NEW BRITAIN REGION", - "HOKKAIDO", - "KERMADEC ISLANDS REGION", - "TURKEY", - "TURKEY", - "FIJI ISLANDS REGION", - "NEAR COAST OF PERU", - "REVILLA GIGEDO ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "NICARAGUA", - "NORTHEAST OF TAIWAN", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHERN MOLUCCA SEA", - "OFF COAST OF ECUADOR", - "CENTRAL BOLIVIA", - "NEAR EAST COAST OF HONSHU", - "HOKKAIDO", - "NEW IRELAND REGION", - "LUZON", - "NEAR COAST OF NORTHERN CHILE", - "GUERRERO", - "SOUTH SANDWICH ISLANDS REGION", - "MINAHASSA PENINSULA", - "EASTERN NEW GUINEA REG.", - "TONGA ISLANDS", - "SOUTHWESTERN SIBERIA", - "SOUTHEAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH SHETLAND ISLANDS", - "SOUTH SHETLAND ISLANDS", - "NEAR COAST OF CHIAPAS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF PERU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "NEGROS", - "NEGROS", - "FIJI ISLANDS REGION", - "SOLOMON ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHWESTERN SIBERIA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "IRIAN JAYA", - "NEAR COAST OF GUERRERO", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN EAST PACIFIC RISE", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF GUERRERO", - "NEW IRELAND REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "NORTH INDIAN OCEAN", - "OFF W COAST OF NORTHERN SUMATRA", - "OFF COAST OF OREGON", - "MICHOACAN", - "BAJA CALIFORNIA", - "BAJA CALIFORNIA", - "DRAKE PASSAGE", - "VANUATU ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF CENTRAL CHILE", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS REGION", - "TONGA ISLANDS", - "NEAR COAST OF CHIAPAS", - "PERU-BOLIVIA BORDER REGION", - "OFF COAST OF SOUTHERN CHILE", - "NORTHERN ITALY", - "OFF EAST COAST OF HONSHU", - "HOKKAIDO", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "GREENLAND SEA", - "BONIN ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "SALTA PROVINCE", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "OFF EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "SOUTHERN PERU", - "DODECANESE ISLANDS", - "LUZON", - "NEAR EAST COAST OF HONSHU", - "NEAR ISLANDS", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF KAMCHATKA", - "NORTHERN XINJIANG", - "COOK STRAIT", - "VANUATU ISLANDS", - "KURIL ISLANDS", - "KURIL ISLANDS", - "NORTHERN SUMATRA", - "SOLOMON ISLANDS", - "MAURITIUS - REUNION REGION", - "NEW IRELAND REGION", - "PERU-BRAZIL BORDER REGION", - "NEW IRELAND REGION", - "FOX ISLANDS", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "XIZANG", - "SEA OF OKHOTSK", - "SULAWESI", - "NEAR N COAST OF NEW GUINEA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF CENTRAL AMERICA", - "JAN MAYEN ISLAND REGION", - "PHILIPPINE ISLANDS REGION", - "SOUTH OF JAVA", - "SANTA CRUZ ISLANDS", - "COSTA RICA", - "IRIAN JAYA REGION", - "KURIL ISLANDS", - "SOUTHERN SUMATRA", - "BAJA CALIFORNIA", - "ANDREANOF ISLANDS", - "COLOMBIA", - "OFF EAST COAST OF HONSHU", - "GULF OF CALIFORNIA", - "BANDA SEA", - "WEST OF MACQUARIE ISLAND", - "IRIAN JAYA REGION", - "CELEBES SEA", - "VANUATU ISLANDS", - "COSTA RICA", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "MINDANAO", - "NEAR COAST OF CHIAPAS", - "VANCOUVER ISLAND", - "CENTRAL PERU", - "MYANMAR", - "NEAR COAST OF CHIAPAS", - "GULF OF ALASKA", - "OFF COAST OF SOUTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "GUERRERO", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "NEAR N COAST OF NEW GUINEA", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NORTH ISLAND", - "BANDA SEA", - "NORTHERN MOLUCCA SEA", - "OFF W. COAST OF BAJA CALIFORNIA", - "NEAR ISLANDS", - "NEW IRELAND REGION", - "MINAHASSA PENINSULA", - "VANUATU ISLANDS", - "SOUTHEASTERN ALASKA", - "PACIFIC-ANTARCTIC RIDGE", - "NORTHERN SUMATRA", - "LAKE ISSYK-KUL REGION", - "CENTRAL CHILE", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "HOKKAIDO", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "COLOMBIA", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "EASTERN SIBERIA", - "MINDANAO", - "SANTIAGO DEL ESTERO PROV.", - "KURIL ISLANDS", - "KURIL ISLANDS", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "EAST OF KURIL ISLANDS", - "VANUATU ISLANDS REGION", - "GUATEMALA", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "E. RUSSIA-N.E. CHINA BORDER REG.", - "IRIAN JAYA", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "SOUTHERN IRAN", - "NEAR N COAST OF NEW GUINEA", - "KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "SICHUAN", - "BANDA SEA", - "KURIL ISLANDS", - "SOUTHEAST OF HONSHU", - "MICHOACAN", - "NEW IRELAND REGION", - "KERMADEC ISLANDS REGION", - "SOUTHERN IRAN", - "TONGA ISLANDS", - "MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "OFF COAST OF SOUTHERN CHILE", - "OFF EAST COAST OF KAMCHATKA", - "OFF EAST COAST OF KAMCHATKA", - "SOUTH OF FIJI ISLANDS", - "TONGA ISLANDS", - "SEA OF OKHOTSK", - "SEA OF OKHOTSK", - "TAIWAN", - "SANTA CRUZ ISLANDS", - "SOUTH OF JAVA", - "SOUTH OF KERMADEC ISLANDS", - "CRETE", - "NEAR COAST OF NICARAGUA", - "CRETE", - "NORTHERN MID-ATLANTIC RIDGE", - "NORTHERN SUMATRA", - "SOLOMON ISLANDS", - "SOUTHERN SUMATRA", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN PERU", - "COOK STRAIT", - "GANSU", - "PRINCE EDWARD ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "BANDA SEA", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF NORTHERN PERU", - "SOUTH OF PANAMA", - "COOK STRAIT", - "SOUTHWEST INDIAN RIDGE", - "NEAR COAST OF GUERRERO", - "KERMADEC ISLANDS REGION", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "BANDA SEA", - "QUEEN CHARLOTTE ISLANDS REGION", - "SOUTHEAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "PHILIPPINE ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "CENTRAL EAST PACIFIC RISE", - "ANDREANOF ISLANDS", - "FLORES SEA", - "PAKISTAN", - "SOUTHERN EAST PACIFIC RISE", - "NEAR COAST OF PERU", - "PAKISTAN", - "KERMADEC ISLANDS", - "SEA OF OKHOTSK", - "MID-INDIAN RIDGE", - "SOUTH OF MARIANA ISLANDS", - "WEST CHILE RISE", - "SAKHALIN", - "KERMADEC ISLANDS", - "NEAR COAST OF VENEZUELA", - "CRETE", - "MINDANAO", - "SOLOMON ISLANDS", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "OFF EAST COAST OF HONSHU", - "BALLENY ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "TAIWAN", - "NEAR COAST OF CENTRAL CHILE", - "EASTER ISLAND REGION", - "TONGA ISLANDS REGION", - "CELEBES SEA", - "NEAR EAST COAST OF KAMCHATKA", - "SCOTIA SEA", - "SCOTIA SEA", - "SCOTIA SEA", - "HALMAHERA", - "MARIANA ISLANDS", - "FIJI ISLANDS REGION", - "KURIL ISLANDS", - "SOUTH ATLANTIC OCEAN", - "BANDA SEA", - "NORTHERN SUMATRA", - "KURIL ISLANDS", - "MARIANA ISLANDS REGION", - "VANUATU ISLANDS", - "PUERTO RICO REGION", - "NORTH ISLAND", - "TONGA ISLANDS", - "JAVA", - "GREECE", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH OF KERMADEC ISLANDS", - "GREECE", - "VANUATU ISLANDS", - "XIZANG", - "NORTH ATLANTIC OCEAN", - "FOX ISLANDS", - "NEAR COAST OF NICARAGUA", - "RYUKYU ISLANDS", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "OFF COAST OF NORTHERN CALIFORNIA", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "NEAR COAST OF PERU", - "NEAR COAST OF NORTHERN PERU", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NICOBAR ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "SANTA CRUZ ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "OFF COAST OF NORTHERN CHILE", - "SOUTH OF PANAMA", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "NICARAGUA", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR COAST OF NICARAGUA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "BOUVET ISLAND REGION", - "BALLENY ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "GUERRERO", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "VANCOUVER ISLAND", - "TONGA ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR S. COAST OF HONSHU", - "THAILAND", - "WEST CHILE RISE", - "SOLOMON ISLANDS", - "GUERRERO", - "TONGA ISLANDS", - "GUERRERO", - "SOUTHERN EAST PACIFIC RISE", - "SOUTH OF PANAMA", - "E. CAROLINE ISLANDS", - "E. CAROLINE ISLANDS", - "NEGROS", - "OFF W COAST OF NORTHERN SUMATRA", - "BAY OF BENGAL", - "AEGEAN SEA", - "OFF COAST OF JALISCO", - "SOUTH INDIAN OCEAN", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "RAT ISLANDS", - "VOLCANO ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "TONGA ISLANDS", - "BONIN ISLANDS", - "BALLENY ISLANDS REGION", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "NEW BRITAIN REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MINDANAO", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "TONGA ISLANDS", - "TONGA ISLANDS", - "OWEN FRACTURE ZONE REGION", - "KURIL ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEASTERN ALASKA", - "NORTHERN MID-ATLANTIC RIDGE", - "OAXACA", - "BISMARCK SEA", - "BISMARCK SEA", - "E. CAROLINE ISLANDS", - "XIZANG", - "YUNNAN", - "BANDA SEA", - "BANDA SEA", - "HOKKAIDO", - "HOKKAIDO", - "IRAN-IRAQ BORDER REGION", - "IRAN-IRAQ BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN CALIFORNIA", - "CENTRAL PERU", - "CENTRAL PERU", - "EASTER ISLAND REGION", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "OFF COAST OF JALISCO", - "SOUTHERN MOLUCCA SEA", - "MARIANA ISLANDS", - "JUJUY PROVINCE", - "SOLOMON ISLANDS", - "SOUTHERN ALASKA", - "NEW BRITAIN REGION", - "YUNNAN", - "GULF OF CALIFORNIA", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHERN EAST PACIFIC RISE", - "HOKKAIDO", - "OFF COAST OF CENTRAL AMERICA", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "FIJI ISLANDS REGION", - "BALLENY ISLANDS REGION", - "MID-INDIAN RIDGE", - "NEW BRITAIN REGION", - "PRIMOR'YE", - "FIJI ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "OFF E. COAST OF N. ISLAND", - "PRINCE EDWARD ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "SICHUAN", - "EASTERN HONSHU", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "SOUTH OF PANAMA", - "BANDA SEA", - "SOLOMON ISLANDS", - "NEAR COAST OF GUATEMALA", - "SOUTH OF PANAMA", - "TAIWAN REGION", - "PHILIPPINE ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "NORTHERN MOLUCCA SEA", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "MINDANAO", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "SOUTH OF PANAMA", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "ADMIRALTY ISLANDS REGION", - "MENDOZA PROVINCE", - "JUJUY PROVINCE", - "REYKJANES RIDGE", - "TAIWAN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF COAST OF JALISCO", - "FLORES SEA", - "SOUTHERN SUMATRA", - "MID-INDIAN RIDGE", - "NORTHERN COLOMBIA", - "MINAHASSA PENINSULA", - "NORTHERN MOLUCCA SEA", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "NORTHERN CHILE", - "NEW BRITAIN REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NEW BRITAIN REGION", - "TONGA ISLANDS", - "CRETE", - "FIJI ISLANDS REGION", - "TAIWAN REGION", - "TAIWAN REGION", - "SANTA CRUZ ISLANDS", - "SOUTH ISLAND", - "QUEEN CHARLOTTE ISLANDS REGION", - "NEPAL", - "NEPAL", - "NEPAL", - "NEPAL", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "MOROCCO", - "SOLOMON ISLANDS", - "NEPAL", - "NEPAL", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN SUMATRA", - "PACIFIC-ANTARCTIC RIDGE", - "TONGA ISLANDS", - "SANTA CRUZ ISLANDS REGION", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "TONGA ISLANDS", - "ALASKA PENINSULA", - "BONIN ISLANDS", - "TONGA ISLANDS", - "SOUTHEAST OF HONSHU", - "BORNEO", - "HOKKAIDO", - "NORTHERN CHILE", - "SAMOA ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTHERN MID-ATLANTIC RIDGE", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "BONIN ISLANDS", - "NEW BRITAIN REGION", - "SOUTHERN XINJIANG", - "LEYTE", - "EAST OF KURIL ISLANDS", - "SOLOMON ISLANDS", - "NORTH ATLANTIC OCEAN", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "IRIAN JAYA", - "SOUTHERN ALASKA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "MID-INDIAN RIDGE", - "SOLOMON ISLANDS", - "KERMADEC ISLANDS REGION", - "SOUTHEAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "FOX ISLANDS", - "GULF OF CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "IRIAN JAYA REGION", - "IRIAN JAYA REGION", - "NEAR COAST OF CENTRAL CHILE", - "JUJUY PROVINCE", - "PACIFIC-ANTARCTIC RIDGE", - "EAST OF KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "BOUVET ISLAND REGION", - "PRINCE EDWARD ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "HINDU KUSH REGION", - "TIMOR REGION", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NICOBAR ISLANDS", - "ANDREANOF ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHWEST OF RYUKYU ISLANDS", - "GREECE", - "SOLOMON ISLANDS", - "BANDA SEA", - "MARIANA ISLANDS", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "NEAR COAST OF NORTHERN CHILE", - "SOUTHEAST INDIAN RIDGE", - "TAJIKISTAN", - "BANDA SEA", - "FIJI ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "BORNEO", - "SOUTHERN EAST PACIFIC RISE", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "MYANMAR-INDIA BORDER REGION", - "MYANMAR-INDIA BORDER REGION", - "PACIFIC-ANTARCTIC RIDGE", - "TALAUD ISLANDS", - "HOKKAIDO", - "SOUTHERN BOLIVIA", - "HOKKAIDO", - "OFF COAST OF JALISCO", - "SOUTHERN ALASKA", - "STRAIT OF GIBRALTAR", - "KAMCHATKA PENINSULA", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "TAIWAN", - "SOLOMON ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SUMBA REGION", - "SOUTHERN EAST PACIFIC RISE", - "HALMAHERA", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "SOUTHWEST OF SUMATRA", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "LEEWARD ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "NEAR N COAST OF NEW GUINEA", - "ALASKA PENINSULA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "JAVA", - "VANUATU ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "MYANMAR-INDIA BORDER REGION", - "MINDANAO", - "KYUSHU", - "KYUSHU", - "VANUATU ISLANDS", - "OFF COAST OF CHIAPAS", - "KYUSHU", - "NEAR COAST OF ECUADOR", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF CHIAPAS", - "VANUATU ISLANDS", - "NORTHERN EAST PACIFIC RISE", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NORTHERN TERRITORY", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "TAIWAN REGION", - "SOUTHERN SUMATRA", - "BANDA SEA", - "KERMADEC ISLANDS", - "OFF COAST OF JALISCO", - "NORTHERN MOLUCCA SEA", - "SOUTH OF SUMBAWA", - "NICARAGUA", - "SOLOMON ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "LOYALTY ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "NEW IRELAND REGION", - "TAJIKISTAN-XINJIANG BORDER REG.", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "NEAR COAST OF ECUADOR", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "ADMIRALTY ISLANDS REGION", - "MARIANA ISLANDS", - "JUJUY PROVINCE", - "VOLCANO ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH GEORGIA ISLAND REGION", - "FLORES SEA", - "CENTRAL ITALY", - "MYANMAR", - "NORTH OF ASCENSION ISLAND", - "NEW IRELAND REGION", - "OFF E. COAST OF N. ISLAND", - "OFF E. COAST OF N. ISLAND", - "KOMANDORSKIYE OSTROVA REGION", - "MACQUARIE ISLAND REGION", - "NORTHERN PERU", - "NORTHERN COLOMBIA", - "SOLOMON ISLANDS", - "NEAR NORTH COAST OF IRIAN JAYA", - "SOUTHEAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "MINDANAO", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "JAVA SEA", - "WESTERN HONSHU", - "TONGA ISLANDS", - "CENTRAL ITALY", - "CENTRAL ITALY", - "CHILE-ARGENTINA BORDER REGION", - "NEAR EAST COAST OF HONSHU", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SOUTH ISLAND", - "SAN JUAN PROVINCE", - "NEAR EAST COAST OF HONSHU", - "OFF COAST OF CENTRAL AMERICA", - "SOUTHERN XINJIANG", - "SOUTHERN PERU", - "NEAR ISLANDS", - "BANDA SEA", - "NORTHERN SUMATRA", - "OFF COAST OF NORTHERN CALIFORNIA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "MARIANA ISLANDS REGION", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "SOLOMON ISLANDS", - "W. CAROLINE ISLANDS", - "PERU-BRAZIL BORDER REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEW IRELAND REGION", - "SOUTHERN CHILE", - "SUMBAWA REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF FIJI ISLANDS", - "QUEEN ELIZABETH ISLANDS", - "CELEBES SEA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTHWESTERN PAKISTAN", - "MINDANAO", - "JUJUY PROVINCE", - "SOUTHERN BOLIVIA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "NEW BRITAIN REGION", - "NICOBAR ISLANDS", - "SOLOMON ISLANDS", - "NEAR ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "BOTSWANA", - "NORTHERN AND CENTRAL IRAN", - "CHILE-ARGENTINA BORDER REGION", - "FIJI ISLANDS REGION", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "SOUTHEASTERN ALASKA", - "TAJIKISTAN", - "ANDREANOF ISLANDS", - "RYUKYU ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CENTRAL AMERICA", - "NEW BRITAIN REGION", - "NEGROS", - "SULAWESI", - "NEAR ISLANDS", - "AEGEAN SEA", - "GUATEMALA", - "KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF GUATEMALA", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "LEYTE", - "AUCKLAND ISLANDS", - "NEW IRELAND REGION", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "NEAR COAST OF PERU", - "MAURITIUS - REUNION REGION", - "DODECANESE ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "NORTH ATLANTIC OCEAN", - "GANSU", - "NORTHERN XINJIANG", - "MINDORO", - "SOUTHERN SUMATRA", - "NORTH OF ASCENSION ISLAND", - "FIJI ISLANDS REGION", - "ADMIRALTY ISLANDS REGION", - "SOUTHERN SUMATRA", - "NORTH KOREA", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "NEAR COAST OF CHIAPAS", - "CENTRAL MEXICO", - "AUCKLAND ISLANDS", - "OFF EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "OAXACA", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "TONGA ISLANDS", - "BALLENY ISLANDS REGION", - "RAT ISLANDS", - "NORTHERN CHILE", - "BOUVET ISLAND REGION", - "TONGA ISLANDS", - "BANDA SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SERAM", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "TONGA ISLANDS", - "NEW GUINEA", - "SOUTHEAST OF HONSHU", - "ASCENSION ISLAND REGION", - "IRAN-IRAQ BORDER REGION", - "COSTA RICA", - "EASTERN XIZANG-INDIA BORDER REG.", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SAMOA ISLANDS REGION", - "NEW IRELAND REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "NORTHERN AND CENTRAL IRAN", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF ECUADOR", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "NORTHERN AND CENTRAL IRAN", - "NORTHERN AND CENTRAL IRAN", - "BOUVET ISLAND REGION", - "JAVA", - "NORTH OF HONDURAS", - "MYANMAR", - "NEAR COAST OF PERU", - "GULF OF CALIFORNIA", - "NORTHERN CHILE", - "JAVA", - "GULF OF ALASKA", - "HOKKAIDO", - "KOMANDORSKIYE OSTROVA REGION", - "NEAR N COAST OF NEW GUINEA", - "SOUTHWEST OF AFRICA", - "HINDU KUSH REGION", - "FIJI ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "TAIWAN", - "TAIWAN", - "SOUTH OF MARIANA ISLANDS", - "OAXACA", - "NEW GUINEA", - "NEW GUINEA", - "CERAM SEA", - "NEW GUINEA", - "WEST OF MACQUARIE ISLAND", - "NEW GUINEA", - "NEW GUINEA", - "NEW GUINEA", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "SOUTHEAST INDIAN RIDGE", - "BANDA SEA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTHERN BOLIVIA", - "MINDANAO", - "NEW GUINEA", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "PRINCE EDWARD ISLANDS REGION", - "EASTER ISLAND REGION", - "HAWAII", - "LUZON", - "NEW BRITAIN REGION", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "SOUTH OF KERMADEC ISLANDS", - "VANUATU ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "KERMADEC ISLANDS", - "VANUATU ISLANDS", - "EASTERN GULF OF ADEN", - "EASTERN GULF OF ADEN", - "SANTA CRUZ ISLANDS", - "NEW BRITAIN REGION", - "FLORES SEA", - "SUMBAWA REGION", - "SUMBAWA REGION", - "NORTHERN ALASKA", - "NORTHERN ALASKA", - "SOUTH SANDWICH ISLANDS REGION", - "ANDREANOF ISLANDS", - "VOLCANO ISLANDS", - "FLORES SEA", - "COSTA RICA", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "SUMBAWA REGION", - "FIJI ISLANDS REGION", - "SUMBAWA REGION", - "NEAR COAST OF VENEZUELA", - "VANUATU ISLANDS", - "OFF COAST OF OREGON", - "ANDREANOF ISLANDS", - "PERU-BRAZIL BORDER REGION", - "WESTERN IRAN", - "TIMOR REGION", - "MARIANA ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "HOKKAIDO", - "FIJI ISLANDS", - "ECUADOR", - "MINDANAO", - "SOLOMON ISLANDS", - "KERMADEC ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHWEST INDIAN RIDGE", - "MINAHASSA PENINSULA", - "MINAHASSA PENINSULA", - "FIJI ISLANDS REGION", - "SUMBA REGION", - "KURIL ISLANDS", - "BALI SEA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "NORTHWEST OF KURIL ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "IONIAN SEA", - "OFF COAST OF CENTRAL AMERICA", - "DRAKE PASSAGE", - "NORTH ISLAND", - "NORTHERN CHILE", - "MINDANAO", - "JAN MAYEN ISLAND REGION", - "TONGA ISLANDS", - "NORTH ATLANTIC OCEAN", - "NEAR EAST COAST OF KAMCHATKA", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN EAST PACIFIC RISE", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "CARIBBEAN SEA", - "IRAN-IRAQ BORDER REGION", - "SOUTHERN ALASKA", - "BANDA SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "PACIFIC-ANTARCTIC RIDGE", - "IRIAN JAYA", - "SOUTHEAST OF EASTER ISLAND", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "KOMANDORSKIYE OSTROVA REGION", - "MINDANAO", - "ALASKA PENINSULA", - "WESTERN BRAZIL", - "NORTHERN MOLUCCA SEA", - "KYUSHU", - "VANUATU ISLANDS", - "BISMARCK SEA", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SUMBA REGION", - "SUMBA REGION", - "PRINCE EDWARD ISLANDS REGION", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "MARIANA ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "NEW IRELAND REGION", - "PERU-ECUADOR BORDER REGION", - "CENTRAL PERU", - "SOUTH OF KERMADEC ISLANDS", - "PHILIPPINE ISLANDS REGION", - "FIJI ISLANDS REGION", - "D'ENTRECASTEAUX ISLANDS REGION", - "CENTRAL BOLIVIA", - "VANUATU ISLANDS", - "COLOMBIA", - "NORTHERN MOLUCCA SEA", - "EAST OF KURIL ISLANDS", - "NEW BRITAIN REGION", - "NEAR COAST OF ECUADOR", - "MAURITIUS - REUNION REGION", - "RAT ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "BANDA SEA", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "SULAWESI", - "TAIWAN", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "LUZON", - "SOUTH SANDWICH ISLANDS REGION", - "SAMAR", - "SOUTH OF FIJI ISLANDS", - "CARLSBERG RIDGE", - "SOLOMON ISLANDS", - "EASTERN NEW GUINEA REG.", - "KYUSHU", - "PANAMA-COSTA RICA BORDER REGION", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "ANDREANOF ISLANDS", - "NORTHERN PERU", - "EL SALVADOR", - "FIJI ISLANDS REGION", - "MINDANAO", - "TONGA ISLANDS", - "SOUTHEAST OF HONSHU", - "OFF COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR WEST COAST OF HONSHU", - "KERMADEC ISLANDS", - "IRIAN JAYA", - "KERMADEC ISLANDS", - "IRIAN JAYA", - "BANDA SEA", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "PANAMA-COSTA RICA BORDER REGION", - "KERMADEC ISLANDS REGION", - "MARIANA ISLANDS", - "VANUATU ISLANDS", - "QUEEN CHARLOTTE ISLANDS REGION", - "CENTRAL CALIFORNIA", - "CENTRAL CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "SOLOMON ISLANDS", - "NORTHWEST OF RYUKYU ISLANDS", - "WESTERN AUSTRALIA", - "HALMAHERA", - "NEW BRITAIN REGION", - "BALLENY ISLANDS REGION", - "PHILIPPINE ISLANDS REGION", - "NEAR S. COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHERN EAST PACIFIC RISE", - "SOUTHWEST OF SUMATRA", - "NEAR EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF OREGON", - "FIJI ISLANDS REGION", - "JAVA", - "MONA PASSAGE", - "SOUTHWEST INDIAN RIDGE", - "SERAM", - "CENTRAL CHILE", - "KERMADEC ISLANDS", - "MINDANAO", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "VANUATU ISLANDS", - "NORTH OF ASCENSION ISLAND", - "MINDANAO", - "MINDANAO", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF CHIAPAS", - "SEA OF OKHOTSK", - "LAOS", - "IRIAN JAYA REGION", - "ANDREANOF ISLANDS", - "ALBANIA", - "CRETE", - "ANDREANOF ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "MINDANAO", - "HINDU KUSH REGION", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "SANTIAGO DEL ESTERO PROV.", - "COLOMBIA", - "VANCOUVER ISLAND", - "SOUTHERN EAST PACIFIC RISE", - "NORTHERN SUMATRA", - "PUERTO RICO REGION", - "NEW BRITAIN REGION", - "EASTERN SIBERIA", - "NEAR NORTH COAST OF IRIAN JAYA", - "SOUTHERN XINJIANG", - "MINAHASSA PENINSULA", - "SOUTH SANDWICH ISLANDS REGION", - "ANDREANOF ISLANDS", - "TURKEY", - "ANDREANOF ISLANDS", - "SOLOMON ISLANDS", - "CUBA REGION", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "JAVA", - "MINDANAO", - "SOUTH SANDWICH ISLANDS REGION", - "NEW BRITAIN REGION", - "KURIL ISLANDS", - "TURKEY-IRAN BORDER REGION", - "KERMADEC ISLANDS REGION", - "SAMOA ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTH OF BALI", - "CENTRAL EAST PACIFIC RISE", - "EAST OF KURIL ISLANDS", - "MINDANAO", - "WESTERN IDAHO", - "NORTHERN MOLUCCA SEA", - "MID-INDIAN RIDGE", - "KERMADEC ISLANDS REGION", - "NORTH OF HONDURAS", - "BONIN ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "CRETE", - "BANDA SEA", - "SOLOMON ISLANDS", - "SANTA CRUZ ISLANDS", - "NEVADA", - "OFF COAST OF CENTRAL MEXICO", - "VANUATU ISLANDS", - "SOUTHERN PERU", - "NORTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "HALMAHERA", - "SOUTHERN MID-ATLANTIC RIDGE", - "RYUKYU ISLANDS", - "MARIANA ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "ICELAND REGION", - "MINAHASSA PENINSULA", - "NEAR COAST OF OAXACA", - "XIZANG", - "W. CAROLINE ISLANDS", - "JAVA SEA", - "EASTERN NEW GUINEA REG.", - "ANDAMAN ISLANDS", - "SAMOA ISLANDS REGION", - "FIJI ISLANDS REGION", - "ALASKA PENINSULA", - "SOUTH OF ALASKA", - "XIZANG", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "MINDANAO", - "BISMARCK SEA", - "VANUATU ISLANDS", - "PRINCE EDWARD ISLANDS REGION", - "TANZANIA", - "SAMAR", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "BANDA SEA", - "ASCENSION ISLAND REGION", - "COSTA RICA", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "CENTRAL MID-ATLANTIC RIDGE", - "CHAGOS ARCHIPELAGO REGION", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CENTRAL CHILE", - "VANUATU ISLANDS", - "CENTRAL MID-ATLANTIC RIDGE", - "MINDANAO", - "VANUATU ISLANDS", - "NORTHERN CHILE", - "NEAR EAST COAST OF HONSHU", - "KAMCHATKA PENINSULA", - "FIJI ISLANDS", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF AFRICA", - "TONGA ISLANDS", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "EASTERN NEW GUINEA REG.", - "SOUTH OF ALASKA", - "SOUTH OF FIJI ISLANDS", - "DODECANESE ISLANDS", - "OFF COAST OF SOUTHERN CHILE", - "SOUTH SHETLAND ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "MINDANAO", - "OFF COAST OF CENTRAL CHILE", - "PRIMOR'YE", - "CHILE-ARGENTINA BORDER REGION", - "FOX ISLANDS", - "BALLENY ISLANDS REGION", - "NORTHERN CHILE", - "TAIWAN REGION", - "CHILE-BOLIVIA BORDER REGION", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "W. CAROLINE ISLANDS", - "MINDORO", - "OFF COAST OF CENTRAL CHILE", - "NORTHWESTERN BALKAN REGION", - "ANDREANOF ISLANDS", - "KERMADEC ISLANDS REGION", - "MINAHASSA PENINSULA", - "KERMADEC ISLANDS", - "VANUATU ISLANDS", - "SALTA PROVINCE", - "VANUATU ISLANDS", - "RUSSIA-MONGOLIA BORDER REGION", - "SULAWESI", - "SAN JUAN PROVINCE", - "PHILIPPINE ISLANDS REGION", - "SOUTH SHETLAND ISLANDS", - "WEST CHILE RISE", - "MINDANAO", - "BISMARCK SEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWEST OF SUMATRA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "TONGA ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "GREECE", - "OFF E. COAST OF N. ISLAND", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF KAMCHATKA", - "NORTHERN ALGERIA", - "NORTHERN MID-ATLANTIC RIDGE", - "WEST OF MACQUARIE ISLAND", - "NEAR EAST COAST OF HONSHU", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "EAST OF SOUTH SANDWICH ISLANDS", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "JAVA", - "CELEBES SEA", - "NEAR N COAST OF NEW GUINEA", - "OFF W COAST OF NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "FIJI ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NORTHEASTERN INDIA", - "KERMADEC ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "WEST OF MACQUARIE ISLAND", - "FIJI ISLANDS REGION", - "NEAR COAST OF GUATEMALA", - "MAURITIUS - REUNION REGION", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTHERN EAST PACIFIC RISE", - "YUNNAN", - "QINGHAI", - "FIJI ISLANDS REGION", - "CENTRAL ALASKA", - "NORTHERN MOLUCCA SEA", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "CALIFORNIA-NEVADA BORDER REGION", - "SOUTH OF PANAMA", - "BISMARCK SEA", - "SOUTH OF PANAMA", - "MINDORO", - "KERMADEC ISLANDS REGION", - "MINAHASSA PENINSULA", - "ALASKA PENINSULA", - "PERU-ECUADOR BORDER REGION", - "NICOBAR ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "HAITI REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF GUERRERO", - "CHILE-ARGENTINA BORDER REGION", - "KURIL ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF NICARAGUA", - "ANDREANOF ISLANDS", - "CRETE", - "EAST OF KURIL ISLANDS", - "SEA OF JAPAN", - "VANUATU ISLANDS REGION", - "MID-INDIAN RIDGE", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS REGION", - "HAWAII", - "ALASKA PENINSULA", - "CRETE", - "SOLOMON ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "TAIWAN", - "SOUTH SANDWICH ISLANDS REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTHERN MOLUCCA SEA", - "NEAR COAST OF NICARAGUA", - "SOUTHEAST OF RYUKYU ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "IRIAN JAYA REGION", - "SOUTHERN IRAN", - "SOUTHERN IRAN", - "SOUTH INDIAN OCEAN", - "NEW IRELAND REGION", - "SANTA CRUZ ISLANDS", - "MYANMAR-INDIA BORDER REGION", - "NORTHERN PERU", - "SOUTHEAST OF HONSHU", - "SOUTHEAST OF HONSHU", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "EASTER ISLAND REGION", - "NORTH OF HALMAHERA", - "RYUKYU ISLANDS", - "WEST OF MACQUARIE ISLAND", - "FLORES SEA", - "SOUTH SANDWICH ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "FIJI ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF NORTHERN CALIF.", - "OFF COAST OF JALISCO", - "FIJI ISLANDS REGION", - "BANDA SEA", - "VANUATU ISLANDS", - "TAIWAN REGION", - "BANDA SEA", - "NEAR COAST OF NICARAGUA", - "QINGHAI", - "SOUTH OF KERMADEC ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "CYPRUS REGION", - "FOX ISLANDS", - "FOX ISLANDS", - "SUNDA STRAIT", - "SOLOMON ISLANDS", - "SHIKOKU", - "TALAUD ISLANDS", - "FOX ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH OF PANAMA", - "KERMADEC ISLANDS REGION", - "BANDA SEA", - "NORTHERN PERU", - "SOUTHEAST INDIAN RIDGE", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF MARIANA ISLANDS", - "GUATEMALA", - "SOUTH OF FIJI ISLANDS", - "BALLENY ISLANDS REGION", - "JUJUY PROVINCE", - "NORTHERN SUMATRA", - "KERMADEC ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "LUZON", - "LUZON", - "SOUTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "TAIWAN", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "MINDANAO", - "MINDANAO", - "NEAR COAST OF NICARAGUA", - "BISMARCK SEA", - "PHILIPPINE ISLANDS REGION", - "TAIWAN REGION", - "BISMARCK SEA", - "JUJUY PROVINCE", - "MACQUARIE ISLAND REGION", - "LUZON", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "CENTRAL PERU", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "TIMOR REGION", - "TONGA ISLANDS", - "WEST OF MACQUARIE ISLAND", - "RAT ISLANDS", - "PERU-BRAZIL BORDER REGION", - "SOUTHEASTERN AFGHANISTAN", - "PHILIPPINE ISLANDS REGION" - ], - "lat": [ - -13.6098, - 39.6611, - 11.1664, - -22.2789, - -38.0635, - 52.7536, - -5.9807, - -6.0444, - 52.8204, - -5.9808, - 3.0029, - 6.7113, - 6.4232, - 6.7403, - 6.1711, - 6.5583, - 52.4218, - 1.2978, - -5.5179, - 51.3848, - -5.8249, - 46.0031, - -17.5345, - -1.2779, - 12.4748, - 12.3454, - 12.2011, - -5.7825, - -17.8076, - -20.8416, - 12.247, - -6.5338, - 2.1984, - 18.5693, - 51.6273, - -43.3608, - -17.4526, - -15.8867, - -20.7545, - -37.0545, - 36.5352, - -5.2995, - -4.9188, - -4.9863, - 24.2175, - 51.3459, - 51.4225, - 2.7637, - 24.7878, - -3.5248, - -3.0065, - -57.116, - -4.6494, - -20.519, - -45.5992, - -54.8356, - -6.0155, - 28.3941, - -15.8273, - -6.1002, - -56.4318, - -6.5265, - -7.2664, - 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timelatitudelongitudedepthmagnitudedescriptiondepth_kmCategorymarker_sizemagnitude_bin
02010-07-02 06:04:03.570-13.6098166.65410.034406.3VANUATU ISLANDS34.40Moderate544.06.0
11122017-08-31 17:06:55.750-1.159099.68810.043146.3SOUTHERN SUMATRA43.14Moderate544.06.0
11112017-08-27 04:17:51.010-1.4513148.08030.008006.3ADMIRALTY ISLANDS REGION8.00Moderate544.06.0
11102017-08-19 02:00:52.550-17.9609-178.84060.544006.4FIJI ISLANDS REGION544.00Moderate601.06.0
11082017-08-13 03:08:10.560-3.7682101.62280.031006.4SOUTHERN SUMATRA31.00Moderate601.06.0
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" + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" ], - "text/plain": [ - " time latitude longitude depth magnitude \\\n", - "0 2010-07-02 06:04:03.570 -13.6098 166.6541 0.03440 6.3 \n", - "1112 2017-08-31 17:06:55.750 -1.1590 99.6881 0.04314 6.3 \n", - "1111 2017-08-27 04:17:51.010 -1.4513 148.0803 0.00800 6.3 \n", - "1110 2017-08-19 02:00:52.550 -17.9609 -178.8406 0.54400 6.4 \n", - "1108 2017-08-13 03:08:10.560 -3.7682 101.6228 0.03100 6.4 \n", - "\n", - " description depth_km Category marker_size magnitude_bin \n", - "0 VANUATU ISLANDS 34.40 Moderate 544.0 6.0 \n", - "1112 SOUTHERN SUMATRA 43.14 Moderate 544.0 6.0 \n", - "1111 ADMIRALTY ISLANDS REGION 8.00 Moderate 544.0 6.0 \n", - "1110 FIJI ISLANDS REGION 544.00 Moderate 601.0 6.0 \n", - "1108 SOUTHERN SUMATRA 31.00 Moderate 601.0 6.0 " + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "quakes2plot=quake.sort_values(by='magnitude_bin')\n", - "quakes2plot.head()" - ] - }, - { - "cell_type": "markdown", - "id": "dcf7472d", - "metadata": {}, - "source": [ - "Now we will plot again using Plotly" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "c21ce4ea", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - "VANUATU ISLANDS", - 6.3, - 0.0344 - ], - [ - "SOUTHERN SUMATRA", - 6.3, - 0.04314 - ], - [ - "ADMIRALTY ISLANDS REGION", - 6.3, - 0.008 - ], - [ - "FIJI ISLANDS REGION", - 6.4, - 0.544 - ], - [ - "SOUTHERN SUMATRA", - 6.4, - 0.031 - ], - [ - "MINDORO", - 6.2, - 0.172 - ], - [ - "NORTHERN XINJIANG", - 6.3, - 0.02 - ], - [ - "NORTH ATLANTIC OCEAN", - 6, - 0.01 - ], - [ - "SOUTHEAST OF RYUKYU ISLANDS", - 6, - 0.012 - ], - [ - "NORTH KOREA", - 6.3, - 0 - ], - [ - "MAURITIUS - REUNION REGION", - 6, - 0.01 - ], - [ - "KOMANDORSKIYE OSTROVA REGION", - 6.3, - 0.01 - ], - [ - "NEW IRELAND REGION", - 6.4, - 0.03478 - ], - [ - "NEAR COAST OF ECUADOR", - 6, - 0.013 - ], - [ - "KERMADEC ISLANDS REGION", - 6, - 0.41461000000000003 - ], - [ - "KERMADEC ISLANDS", - 6, - 0.019 - ], - [ - "FIJI ISLANDS REGION", - 6.2, - 0.038270000000000005 - ], - [ - "SOUTH OF FIJI ISLANDS", - 6.1, - 0.511 - ], - [ - "KERMADEC ISLANDS", - 6, - 0.034 - ], - [ - "NEAR COAST OF PERU", - 6.4, - 0.04492 - ], - [ - "SOUTH SANDWICH ISLANDS REGION", - 6, - 0.035 - ], - [ - "BONIN ISLANDS", - 6.1, - 0.451 - ], - [ - "AUCKLAND ISLANDS", - 6.1, - 0.01 - ], - [ - "NEW IRELAND REGION", - 6, - 0.052 - ], - [ - "SAMOA ISLANDS REGION", - 6, - 0.006 - ], - [ - "LOYALTY ISLANDS", - 6, - 0.01 - ], - [ - "LOYALTY ISLANDS", - 6.3, - 0.004 - ], - [ - "EASTERN XIZANG-INDIA BORDER REG.", - 6.4, - 0.008 - ], - [ - "ASCENSION ISLAND REGION", - 6.1, - 0.01 - ], - [ - "SOUTHEAST OF HONSHU", - 6, - 0.012 - ], - [ - "LOYALTY ISLANDS", - 6, - 0.01 - ], - [ - "LOYALTY ISLANDS", - 6.1, - 0.01 - ], - [ - "SERAM", - 6.1, - 0.01847 - ], - [ - "TONGA ISLANDS", - 6.1, - 0.01 - ], - [ - "NORTHERN CHILE", - 6.3, - 0.08243 - ], - [ - "BALLENY ISLANDS REGION", - 6.3, - 0.01 - ], - [ - "TONGA ISLANDS", - 6.1, - 0.01 - ], - [ - "OFF EAST COAST OF HONSHU", - 6.2, - 0.009 - ], - [ - "SOUTH OF FIJI ISLANDS", - 6.4, - 0.09806999999999999 - ], - [ - "OAXACA", - 6.1, - 0.00962 - ], - [ - "VANUATU ISLANDS", - 6.4, - 0.197 - ], - [ - "OFF EAST COAST OF HONSHU", - 6.1, - 0.011 - ], - [ - "AEGEAN SEA", - 6.3, - 0.012 - ], - [ - "NEGROS", - 6, - 0.533 - ], - [ - "NEW BRITAIN REGION", - 6.2, - 0.01 - ], - [ - "OFF COAST OF CENTRAL AMERICA", - 6.2, - 0.01 - ], - [ - "W. 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marker_size=%{marker.size}
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depth=%{customdata[2]}", - "hovertext": [ - "VANUATU ISLANDS", - "SOUTHERN SUMATRA", - "ADMIRALTY ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTHERN SUMATRA", - "MINDORO", - "NORTHERN XINJIANG", - "NORTH ATLANTIC OCEAN", - "SOUTHEAST OF RYUKYU ISLANDS", - "NORTH KOREA", - "MAURITIUS - REUNION REGION", - "KOMANDORSKIYE OSTROVA REGION", - "NEW IRELAND REGION", - "NEAR COAST OF ECUADOR", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS", - "NEAR COAST OF PERU", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "AUCKLAND ISLANDS", - "NEW IRELAND REGION", - "SAMOA ISLANDS REGION", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "EASTERN XIZANG-INDIA BORDER REG.", - "ASCENSION ISLAND REGION", - "SOUTHEAST OF HONSHU", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "SERAM", - "TONGA ISLANDS", - "NORTHERN CHILE", - "BALLENY ISLANDS REGION", - "TONGA ISLANDS", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "OAXACA", - "VANUATU ISLANDS", - "OFF EAST COAST OF HONSHU", - "AEGEAN SEA", - "NEGROS", - "NEW BRITAIN REGION", - "OFF COAST OF CENTRAL AMERICA", - "W. CAROLINE ISLANDS", - "SOLOMON ISLANDS", - "NEW IRELAND REGION", - "MARIANA ISLANDS REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEAR ISLANDS", - "SOUTHERN PERU", - "SAN JUAN PROVINCE", - "SOUTH ISLAND", - "SOUTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "CENTRAL ITALY", - "TONGA ISLANDS", - "WESTERN HONSHU", - "NEW BRITAIN REGION", - "TONGA ISLANDS", - "MINDANAO", - "PERU-BRAZIL BORDER REGION", - "NORTHERN AND CENTRAL IRAN", - "SOLOMON ISLANDS", - "SUMBAWA REGION", - "RYUKYU ISLANDS", - "ANDREANOF ISLANDS", - "TAJIKISTAN", - "SOUTHEASTERN ALASKA", - "SOUTHEASTERN ALASKA", - "OFF COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "CHILE-ARGENTINA BORDER REGION", - "NORTHERN AND CENTRAL IRAN", - "NEAR ISLANDS", - "SOLOMON ISLANDS", - "NICOBAR ISLANDS", - "NEW BRITAIN REGION", - "SOLOMON ISLANDS", - "JUJUY PROVINCE", - "SOUTHWESTERN PAKISTAN", - "SOLOMON ISLANDS", - "QUEEN ELIZABETH ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEW IRELAND REGION", - "OFF EAST COAST OF HONSHU", - "EASTERN NEW GUINEA REG.", - "W. CAROLINE ISLANDS", - "MINAHASSA PENINSULA", - "SOUTHWEST INDIAN RIDGE", - "SOUTHEAST OF LOYALTY ISLANDS", - "MINDANAO", - "ECUADOR", - "MARIANA ISLANDS", - "TIMOR REGION", - "WESTERN IRAN", - "SUMBA REGION", - "ANDREANOF ISLANDS", - "SUMBAWA REGION", - "FIJI ISLANDS REGION", - "COSTA RICA", - "VOLCANO ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN ALASKA", - "NORTHERN ALASKA", - "SUMBAWA REGION", - "OFF COAST OF OREGON", - "KURIL ISLANDS", - "BALI SEA", - "NEW BRITAIN REGION", - "PACIFIC-ANTARCTIC RIDGE", - "SOUTHEAST OF LOYALTY ISLANDS", - "BANDA SEA", - "IRAN-IRAQ BORDER REGION", - "CARIBBEAN SEA", - "SOLOMON ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR EAST COAST OF KAMCHATKA", - "NORTH ATLANTIC OCEAN", - "TONGA ISLANDS", - "MINDANAO", - "NORTHERN CHILE", - "NORTH ISLAND", - "DRAKE PASSAGE", - "OFF COAST OF CENTRAL AMERICA", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEW BRITAIN REGION", - "FLORES SEA", - "NEW BRITAIN REGION", - "SANTA CRUZ ISLANDS", - "EASTERN GULF OF ADEN", - "CERAM SEA", - "NEW GUINEA", - "SOUTH OF MARIANA ISLANDS", - "TAIWAN", - "TAIWAN", - "PACIFIC-ANTARCTIC RIDGE", - "FIJI ISLANDS REGION", - "HINDU KUSH REGION", - "NEAR N COAST OF NEW GUINEA", - "KOMANDORSKIYE OSTROVA REGION", - "HOKKAIDO", - "JAVA", - "NORTHERN CHILE", - "GULF OF CALIFORNIA", - "MYANMAR", - "NORTHERN AND CENTRAL IRAN", - "NORTHERN AND CENTRAL IRAN", - "W. CAROLINE ISLANDS", - "W. CAROLINE ISLANDS", - "NEW GUINEA", - "NEAR COAST OF ECUADOR", - "WEST OF MACQUARIE ISLAND", - "NEW GUINEA", - "EASTERN GULF OF ADEN", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "VANUATU ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "NEW BRITAIN REGION", - "LUZON", - "EASTER ISLAND REGION", - "PRINCE EDWARD ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "NEAR COAST OF CENTRAL CHILE", - "NEW GUINEA", - "MINDANAO", - "SOUTH OF FIJI ISLANDS", - "BANDA SEA", - "SOUTHEAST INDIAN RIDGE", - "NEW BRITAIN REGION", - "NEW GUINEA", - "IRIAN JAYA", - "SOUTHEAST OF HONSHU", - "SOLOMON ISLANDS", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NORTHERN CHILE", - "HOKKAIDO", - "BORNEO", - "SOUTHERN XINJIANG", - "SOUTHEAST OF HONSHU", - "TONGA ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "TONGA ISLANDS", - "SOUTHERN SUMATRA", - "NEPAL", - "MOROCCO", - "NEW BRITAIN REGION", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "LEYTE", - "EAST OF KURIL ISLANDS", - "SOUTHERN ALASKA", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "NEW BRITAIN REGION", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "FOX ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTHEAST OF HONSHU", - "KERMADEC ISLANDS REGION", - "SOLOMON ISLANDS", - "MID-INDIAN RIDGE", - "SOUTH OF FIJI ISLANDS", - "NEPAL", - "QUEEN CHARLOTTE ISLANDS REGION", - "SOUTH ISLAND", - "SANTA CRUZ ISLANDS", - "MENDOZA PROVINCE", - "ADMIRALTY ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "FIJI ISLANDS REGION", - "MINDANAO", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "NORTHERN MOLUCCA SEA", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "TAIWAN REGION", - "NEAR COAST OF GUATEMALA", - "BANDA SEA", - "SOUTH OF PANAMA", - "EASTERN HONSHU", - "SICHUAN", - "PRINCE EDWARD ISLANDS REGION", - "TAIWAN REGION", - "OFF COAST OF CENTRAL CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "VANUATU ISLANDS", - "TAIWAN REGION", - "TAIWAN REGION", - "CRETE", - "TONGA ISLANDS", - "NEW BRITAIN REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "SAMOA ISLANDS REGION", - "NORTHERN CHILE", - "FIJI ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "MINAHASSA PENINSULA", - "NORTHERN COLOMBIA", - "MID-INDIAN RIDGE", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "NEAR NORTH COAST OF IRIAN JAYA", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "LOYALTY ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "NICARAGUA", - "SOUTH OF SUMBAWA", - "NORTHERN MOLUCCA SEA", - "OFF COAST OF JALISCO", - "NORTHERN MID-ATLANTIC RIDGE", - "KERMADEC ISLANDS", - "TAIWAN REGION", - "FIJI ISLANDS REGION", - "NORTHERN TERRITORY", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "BANDA SEA", - "NEW IRELAND REGION", - "TAJIKISTAN-XINJIANG BORDER REG.", - "VANUATU ISLANDS", - "NORTHERN COLOMBIA", - "NORTHERN PERU", - "MACQUARIE ISLAND REGION", - "KOMANDORSKIYE OSTROVA REGION", - "OFF E. COAST OF N. ISLAND", - "CENTRAL ITALY", - "FLORES SEA", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "VOLCANO ISLANDS", - "JUJUY PROVINCE", - "ADMIRALTY ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "KERMADEC ISLANDS REGION", - "NEAR COAST OF ECUADOR", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF COAST OF CHIAPAS", - "VANUATU ISLANDS", - "KYUSHU", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "SOUTHERN EAST PACIFIC RISE", - "BORNEO", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "MARIANA ISLANDS", - "SOUTHEASTERN AFGHANISTAN", - "OFF COAST OF CENTRAL CHILE", - "PRINCE EDWARD ISLANDS REGION", - "PRINCE EDWARD ISLANDS REGION", - "BOUVET ISLAND REGION", - "TONGA ISLANDS", - "EAST OF KURIL ISLANDS", - "EAST OF KURIL ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "JUJUY PROVINCE", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "NEAR COAST OF CENTRAL CHILE", - "PACIFIC-ANTARCTIC RIDGE", - "SOUTHERN BOLIVIA", - "KYUSHU", - "MINDANAO", - "JAVA", - "ALASKA PENINSULA", - "NEAR N COAST OF NEW GUINEA", - "NEAR EAST COAST OF KAMCHATKA", - "LEEWARD ISLANDS", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "HALMAHERA", - "SOUTHERN EAST PACIFIC RISE", - "SUMBA REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOLOMON ISLANDS", - "TAIWAN", - "KERMADEC ISLANDS REGION", - "BALLENY ISLANDS REGION", - "STRAIT OF GIBRALTAR", - "HOKKAIDO", - "FIJI ISLANDS REGION", - "SOUTHEAST OF EASTER ISLAND", - "TONGA ISLANDS", - "NEAR N COAST OF NEW GUINEA", - "CELEBES SEA", - "JAVA", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "FIJI ISLANDS REGION", - "WEST OF MACQUARIE ISLAND", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF W COAST OF NORTHERN SUMATRA", - "NORTHERN ALGERIA", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "NORTHEASTERN INDIA", - "SOUTH SANDWICH ISLANDS REGION", - "NICOBAR ISLANDS", - "PERU-ECUADOR BORDER REGION", - "MINAHASSA PENINSULA", - "KERMADEC ISLANDS REGION", - "BISMARCK SEA", - "SOUTH OF PANAMA", - "CALIFORNIA-NEVADA BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "FIJI ISLANDS REGION", - "NORTHERN MOLUCCA SEA", - "CENTRAL ALASKA", - "YUNNAN", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "NEAR COAST OF GUATEMALA", - "FIJI ISLANDS REGION", - "WEST OF MACQUARIE ISLAND", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "GREECE", - "W. CAROLINE ISLANDS", - "NEAR EAST COAST OF HONSHU", - "MINDANAO", - "CHILE-BOLIVIA BORDER REGION", - "TAIWAN REGION", - "NORTHERN CHILE", - "BALLENY ISLANDS REGION", - "FOX ISLANDS", - "CHILE-ARGENTINA BORDER REGION", - "PRIMOR'YE", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "FIJI ISLANDS REGION", - "TONGA ISLANDS", - "SOUTH SHETLAND ISLANDS", - "OFF COAST OF SOUTHERN CHILE", - "SOUTH OF FIJI ISLANDS", - "EASTERN NEW GUINEA REG.", - "FIJI ISLANDS REGION", - "MINDORO", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHWESTERN BALKAN REGION", - "KERMADEC ISLANDS REGION", - "FIJI ISLANDS REGION", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWEST OF SUMATRA", - "SOUTHEAST OF LOYALTY ISLANDS", - "BISMARCK SEA", - "MINDANAO", - "SAN JUAN PROVINCE", - "SULAWESI", - "VANUATU ISLANDS", - "SALTA PROVINCE", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "MINAHASSA PENINSULA", - "ANDREANOF ISLANDS", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "LUZON", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN SUMATRA", - "JUJUY PROVINCE", - "BALLENY ISLANDS REGION", - "GUATEMALA", - "SOUTH OF MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "CENTRAL MID-ATLANTIC RIDGE", - "BANDA SEA", - "SOUTH OF PANAMA", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "FOX ISLANDS", - "TALAUD ISLANDS", - "SHIKOKU", - "SOLOMON ISLANDS", - "SOUTHEAST INDIAN RIDGE", - "SOUTH OF FIJI ISLANDS", - "KERMADEC ISLANDS REGION", - "VANUATU ISLANDS", - "RAT ISLANDS", - "WEST OF MACQUARIE ISLAND", - "TONGA ISLANDS", - "TIMOR REGION", - "SOUTHERN EAST PACIFIC RISE", - "OFF EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "LUZON", - "BISMARCK SEA", - "TAIWAN REGION", - "PHILIPPINE ISLANDS REGION", - "BISMARCK SEA", - "MINDANAO", - "MINDANAO", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NORTHERN MID-ATLANTIC RIDGE", - "SOUTH OF KERMADEC ISLANDS", - "NEAR COAST OF NICARAGUA", - "BANDA SEA", - "SOUTHERN MOLUCCA SEA", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH SANDWICH ISLANDS REGION", - "TAIWAN", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "SOLOMON ISLANDS", - "CRETE", - "HAWAII", - "SOUTH SANDWICH ISLANDS REGION", - "MID-INDIAN RIDGE", - "SEA OF JAPAN", - "CRETE", - "ANDREANOF ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "KURIL ISLANDS", - "CHILE-ARGENTINA BORDER REGION", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR COAST OF NICARAGUA", - "SOUTH SANDWICH ISLANDS REGION", - "IRIAN JAYA REGION", - "SOUTHERN IRAN", - "TAIWAN REGION", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "OFF COAST OF JALISCO", - "NEAR COAST OF NORTHERN CALIF.", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS", - "PRINCE EDWARD ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "RYUKYU ISLANDS", - "NORTH OF HALMAHERA", - "EASTER ISLAND REGION", - "NEW IRELAND REGION", - "NEW IRELAND REGION", - "SOUTHEAST OF HONSHU", - "MYANMAR-INDIA BORDER REGION", - "SANTA CRUZ ISLANDS", - "NEW IRELAND REGION", - "SOUTH INDIAN OCEAN", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS", - "CENTRAL CALIFORNIA", - "QUEEN CHARLOTTE ISLANDS REGION", - "VANUATU ISLANDS", - "MARIANA ISLANDS", - "KERMADEC ISLANDS REGION", - "PANAMA-COSTA RICA BORDER REGION", - "KOMANDORSKIYE OSTROVA REGION", - "KOMANDORSKIYE OSTROVA REGION", - "SOLOMON ISLANDS", - "IRIAN JAYA", - "IRIAN JAYA", - "KERMADEC ISLANDS", - "NEAR WEST COAST OF HONSHU", - "KERMADEC ISLANDS", - "KERMADEC ISLANDS REGION", - "TONGA ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SOUTHEAST OF HONSHU", - "KERMADEC ISLANDS", - "NORTHWEST OF RYUKYU ISLANDS", - "NEW BRITAIN REGION", - "BALLENY ISLANDS REGION", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH SANDWICH ISLANDS REGION", - "NORTH OF ASCENSION ISLAND", - "VANUATU ISLANDS", - "MINDANAO", - "MINDANAO", - "KERMADEC ISLANDS", - "CENTRAL CHILE", - "SOUTHWEST INDIAN RIDGE", - "MONA PASSAGE", - "JAVA", - "OFF COAST OF OREGON", - "VANUATU ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN EAST PACIFIC RISE", - "NEAR S. COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "TONGA ISLANDS", - "MINDANAO", - "FIJI ISLANDS REGION", - "ANDREANOF ISLANDS", - "CENTRAL BOLIVIA", - "D'ENTRECASTEAUX ISLANDS REGION", - "FIJI ISLANDS REGION", - "PHILIPPINE ISLANDS REGION", - "SOUTH OF KERMADEC ISLANDS", - "NEW IRELAND REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "MARIANA ISLANDS", - "SOUTHERN SUMATRA", - "SOUTHERN SUMATRA", - "FIJI ISLANDS REGION", - "SOLOMON ISLANDS", - "SUMBA REGION", - "SUMBA REGION", - "VANUATU ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "ALASKA PENINSULA", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "EAST OF SOUTH SANDWICH ISLANDS", - "COLOMBIA", - "EAST OF KURIL ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "PANAMA-COSTA RICA BORDER REGION", - "KYUSHU", - "SOLOMON ISLANDS", - "CARLSBERG RIDGE", - "SOUTH OF FIJI ISLANDS", - "SAMAR", - "SOUTH SANDWICH ISLANDS REGION", - "LUZON", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "BANDA SEA", - "SOUTH SANDWICH ISLANDS REGION", - "RAT ISLANDS", - "MAURITIUS - REUNION REGION", - "NEAR COAST OF ECUADOR", - "NEW BRITAIN REGION", - "NORTHERN MOLUCCA SEA", - "SOUTH OF AFRICA", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "SAMOA ISLANDS REGION", - "ANDAMAN ISLANDS", - "W. CAROLINE ISLANDS", - "XIZANG", - "MINAHASSA PENINSULA", - "ICELAND REGION", - "MARIANA ISLANDS", - "SOUTHERN MID-ATLANTIC RIDGE", - "FIJI ISLANDS REGION", - "HALMAHERA", - "SOUTHERN PERU", - "VANUATU ISLANDS", - "OFF COAST OF CENTRAL MEXICO", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NORTH OF HONDURAS", - "KERMADEC ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "SOUTH OF ALASKA", - "XIZANG", - "SOUTH SANDWICH ISLANDS REGION", - "KAMCHATKA PENINSULA", - "NEAR EAST COAST OF HONSHU", - "NORTHERN CHILE", - "VANUATU ISLANDS", - "MINDANAO", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "CHAGOS ARCHIPELAGO REGION", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "COSTA RICA", - "ASCENSION ISLAND REGION", - "TANZANIA", - "PRINCE EDWARD ISLANDS REGION", - "VANUATU ISLANDS", - "BISMARCK SEA", - "MINDANAO", - "ALASKA PENINSULA", - "MID-INDIAN RIDGE", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "CENTRAL EAST PACIFIC RISE", - "VANCOUVER ISLAND", - "COLOMBIA", - "SANTIAGO DEL ESTERO PROV.", - "VANCOUVER ISLAND", - "VANCOUVER ISLAND", - "HINDU KUSH REGION", - "VANUATU ISLANDS", - "TONGA ISLANDS", - "VANUATU ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "ANDREANOF ISLANDS", - "CRETE", - "ALBANIA", - "ANDREANOF ISLANDS", - "IRIAN JAYA REGION", - "LAOS", - "SEA OF OKHOTSK", - "OFF COAST OF CHIAPAS", - "NORTHERN MOLUCCA SEA", - "SOUTHERN EAST PACIFIC RISE", - "VANUATU ISLANDS", - "NORTHERN SUMATRA", - "NEW BRITAIN REGION", - "SOUTH OF BALI", - "VANUATU ISLANDS", - "SAMOA ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "TURKEY-IRAN BORDER REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "MINDANAO", - "JAVA", - "SOLOMON ISLANDS", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "ANDREANOF ISLANDS", - "ANDREANOF ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "MINAHASSA PENINSULA", - "SOUTHERN XINJIANG", - "NEAR NORTH COAST OF IRIAN JAYA", - "EASTERN SIBERIA", - "PUERTO RICO REGION", - "MID-INDIAN RIDGE", - "BANDA SEA", - "PHILIPPINE ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "EAST OF KURIL ISLANDS", - "VANUATU ISLANDS REGION", - "GUATEMALA", - "TAIWAN", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "E. RUSSIA-N.E. CHINA BORDER REG.", - "SULAWESI", - "SOUTHERN IRAN", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "EAST OF KURIL ISLANDS", - "BANDA SEA", - "KURIL ISLANDS", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "OFF EAST COAST OF KAMCHATKA", - "OFF EAST COAST OF KAMCHATKA", - "OFF COAST OF SOUTHERN CHILE", - "NEAR EAST COAST OF HONSHU", - "COSTA RICA", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTHERN IRAN", - "KERMADEC ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "MICHOACAN", - "SOUTHEAST OF HONSHU", - "TONGA ISLANDS", - "CRETE", - "NEAR EAST COAST OF HONSHU", - "CENTRAL MID-ATLANTIC RIDGE", - "MINAHASSA PENINSULA", - "TAJIKISTAN", - "PACIFIC-ANTARCTIC RIDGE", - "NORTHERN SUMATRA", - "LAKE ISSYK-KUL REGION", - "ALASKA PENINSULA", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "SANTA CRUZ ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "SANTA CRUZ ISLANDS", - "KURIL ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "SANTIAGO DEL ESTERO PROV.", - "MINDANAO", - "WEST OF MACQUARIE ISLAND", - "HINDU KUSH REGION", - "SOUTH ISLAND", - "NORTHERN MOLUCCA SEA", - "NEW BRITAIN REGION", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "SANTA CRUZ ISLANDS", - "IRIAN JAYA REGION", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "TAIWAN", - "NEAR EAST COAST OF HONSHU", - "EASTERN HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEGROS", - "SOUTHWEST INDIAN RIDGE", - "NEAR COAST OF GUERRERO", - "KERMADEC ISLANDS REGION", - "ANDREANOF ISLANDS", - "NEAR EAST COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "FLORES SEA", - "ANDREANOF ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF NORTHERN PERU", - "CENTRAL EAST PACIFIC RISE", - "NEAR EAST COAST OF HONSHU", - "PHILIPPINE ISLANDS REGION", - "NORTHERN MID-ATLANTIC RIDGE", - "NEAR EAST COAST OF HONSHU", - "ANDREANOF ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF CHIAPAS", - "KERMADEC ISLANDS REGION", - "NEAR WEST COAST OF HONSHU", - "BANDA SEA", - "SOUTHERN SUMATRA", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "CRETE", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF NICARAGUA", - "SOUTH OF KERMADEC ISLANDS", - "NEAR EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "CRETE", - "SOLOMON ISLANDS", - "PHILIPPINE ISLANDS REGION", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "VANUATU ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "GANSU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "EASTERN HONSHU", - "SOUTHERN PERU", - "OFF EAST COAST OF HONSHU", - "PRINCE EDWARD ISLANDS REGION", - "NEW IRELAND REGION", - "NEAR ISLANDS", - "OFF W. COAST OF BAJA CALIFORNIA", - "NORTHERN ITALY", - "NEAR COAST OF NORTHERN CHILE", - "LUZON", - "OFF EAST COAST OF HONSHU", - "HOKKAIDO", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "GREENLAND SEA", - "BONIN ISLANDS", - "NEW IRELAND REGION", - "SALTA PROVINCE", - "HOKKAIDO", - "SOUTH OF PANAMA", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "CHILE-ARGENTINA BORDER REGION", - "SOUTHERN PERU", - "KURIL ISLANDS", - "NICARAGUA", - "VANUATU ISLANDS", - "COOK STRAIT", - "NORTHERN XINJIANG", - "NEAR EAST COAST OF KAMCHATKA", - "OFF COAST OF SOUTHERN CHILE", - "NORTHERN SUMATRA", - "NEAR ISLANDS", - "NEAR EAST COAST OF HONSHU", - "LUZON", - "SOUTHERN MOLUCCA SEA", - "DODECANESE ISLANDS", - "OFF COAST OF ECUADOR", - "SOUTHERN EAST PACIFIC RISE", - "PERU-BOLIVIA BORDER REGION", - "NEAR COAST OF CHIAPAS", - "KERMADEC ISLANDS REGION", - "IRIAN JAYA", - "SOUTH SANDWICH ISLANDS REGION", - "NEW BRITAIN REGION", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CHIAPAS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF PERU", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "SANTIAGO DEL ESTERO PROV.", - "KURIL ISLANDS", - "SOUTH SHETLAND ISLANDS", - "NEAR EAST COAST OF HONSHU", - "IRIAN JAYA REGION", - "EAST OF SOUTH SANDWICH ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "OFF W COAST OF NORTHERN SUMATRA", - "VANUATU ISLANDS", - "DRAKE PASSAGE", - "NORTHERN EAST PACIFIC RISE", - "MINAHASSA PENINSULA", - "BAJA CALIFORNIA", - "OFF COAST OF OREGON", - "NORTH INDIAN OCEAN", - "NEW IRELAND REGION", - "NEAR COAST OF GUERRERO", - "SANTA CRUZ ISLANDS", - "TONGA ISLANDS", - "SOUTHERN EAST PACIFIC RISE", - "NORTHERN SUMATRA", - "REVILLA GIGEDO ISLANDS REGION", - "FIJI ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "CERAM SEA", - "CELEBES SEA", - "ASCENSION ISLAND REGION", - "VANUATU ISLANDS", - "COSTA RICA", - "KURIL ISLANDS", - "NEAR S. COAST OF HONSHU", - "QUEEN CHARLOTTE ISLANDS REGION", - "QUEEN CHARLOTTE ISLANDS REGION", - "MINDANAO", - "VANCOUVER ISLAND", - "CENTRAL PERU", - "VANUATU ISLANDS REGION", - "NEAR COAST OF CHIAPAS", - "GULF OF ALASKA", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "NORTHERN MOLUCCA SEA", - "NEW IRELAND REGION", - "GULF OF CALIFORNIA", - "NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR N COAST OF NEW GUINEA", - "NEW BRITAIN REGION", - "NEAR EAST COAST OF HONSHU", - "GUERRERO", - "NEAR COAST OF CENTRAL CHILE", - "OFF COAST OF SOUTHERN CHILE", - "VANUATU ISLANDS", - "BANDA SEA", - "SUNDA STRAIT", - "VANUATU ISLANDS", - "BALLENY ISLANDS REGION", - "NEAR N COAST OF NEW GUINEA", - "SULAWESI", - "SOUTH OF BALI", - "SOUTHEASTERN SIBERIA", - "NEW BRITAIN REGION", - "SOUTH OF KERMADEC ISLANDS", - "HOKKAIDO", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "TURKEY", - "FOX ISLANDS", - "FIJI ISLANDS REGION", - "NEW IRELAND REGION", - "PERU-BRAZIL BORDER REGION", - "XIZANG", - "PACIFIC-ANTARCTIC RIDGE", - "TONGA ISLANDS", - "SOUTH OF JAVA", - "GULF OF CALIFORNIA", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS", - "BAJA CALIFORNIA", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "OFF EAST COAST OF HONSHU", - "SOUTHERN SUMATRA", - "AUCKLAND ISLANDS", - "IRIAN JAYA REGION", - "NEAR ISLANDS", - "SANTA CRUZ ISLANDS", - "EAST OF NORTH ISLAND", - "NEAR EAST COAST OF HONSHU", - "KURIL ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SOUTHERN EAST PACIFIC RISE", - "TONGA ISLANDS", - "IRIAN JAYA REGION", - "E. CAROLINE ISLANDS", - "E. CAROLINE ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "NEGROS", - "OFF W COAST OF NORTHERN SUMATRA", - "BAY OF BENGAL", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "VANUATU ISLANDS", - "HALMAHERA", - "GUERRERO", - "ANDREANOF ISLANDS", - "SOUTHWESTERN RYUKYU ISL.", - "IRIAN JAYA REGION", - "ARU ISLANDS REGION", - "RAT ISLANDS", - "RAT ISLANDS", - "HINDU KUSH REGION", - "RAT ISLANDS", - "RAT ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "VOLCANO ISLANDS", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "ANDREANOF ISLANDS", - "TONGA ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "NEW BRITAIN REGION", - "JAN MAYEN ISLAND REGION", - "SOUTHERN IRAN", - "NORTHERN SUMATRA", - "TAJIKISTAN", - "SOLOMON ISLANDS", - "NORTHERN SUMATRA", - "SOUTHERN SUMATRA", - "LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "VANUATU ISLANDS", - "SOUTH OF FIJI ISLANDS", - "PACIFIC-ANTARCTIC RIDGE", - "BALLENY ISLANDS REGION", - "SANTA CRUZ ISLANDS REGION", - "NEAR ISLANDS", - "BONIN ISLANDS", - "SOLOMON ISLANDS", - "BANDA SEA", - "OFF EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF FIJI ISLANDS", - "NEAR S. COAST OF HONSHU", - "THAILAND", - "WEST CHILE RISE", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "BONIN ISLANDS", - "BALLENY ISLANDS REGION", - "TONGA ISLANDS", - "HOKKAIDO", - "HOKKAIDO", - "IRAN-IRAQ BORDER REGION", - "IRAN-IRAQ BORDER REGION", - "NEAR COAST OF CENTRAL CHILE", - "ANDREANOF ISLANDS", - "NORTHERN CALIFORNIA", - "NORTHERN MOLUCCA SEA", - "OFF EAST COAST OF KAMCHATKA", - "EASTER ISLAND REGION", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "OFF COAST OF JALISCO", - "SOUTHERN MOLUCCA SEA", - "NORTH OF HALMAHERA", - "JUJUY PROVINCE", - "SOLOMON ISLANDS", - "SOUTHERN ALASKA", - "NEW BRITAIN REGION", - "YUNNAN", - "GULF OF CALIFORNIA", - "NEW BRITAIN REGION", - "FOX ISLANDS", - "HOKKAIDO", - "TONGA ISLANDS", - "EASTER ISLAND REGION", - "NORTHERN CHILE", - "SOUTH OF MARIANA ISLANDS", - "NEAR EAST COAST OF HONSHU", - "BANDA SEA", - "TONGA ISLANDS", - "BANDA SEA", - "XIZANG", - "BALLENY ISLANDS REGION", - "KERMADEC ISLANDS REGION", - "OFF COAST OF JALISCO", - "KERMADEC ISLANDS REGION", - "NORTHERN SUMATRA", - "NEW BRITAIN REGION", - "SOLOMON ISLANDS", - "OFF W COAST OF NORTHERN SUMATRA", - "SOUTH OF MARIANA ISLANDS", - "FIJI ISLANDS REGION", - "MAURITIUS - REUNION REGION", - "VANUATU ISLANDS", - "NEW BRITAIN REGION", - "VANUATU ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "MINDANAO", - "MINDANAO", - "SOUTHEASTERN ALASKA", - "TONGA ISLANDS", - "KURIL ISLANDS", - "TONGA ISLANDS", - "OWEN FRACTURE ZONE REGION", - "KURIL ISLANDS", - "SOUTHEASTERN ALASKA", - "NORTHERN MID-ATLANTIC RIDGE", - "OAXACA", - "BISMARCK SEA", - "BISMARCK SEA", - "YUNNAN", - "MYANMAR-INDIA BORDER REGION", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "TONGA ISLANDS REGION", - "EASTER ISLAND REGION", - "OAXACA", - "VANUATU ISLANDS", - "SOUTH ISLAND", - "EASTER ISLAND REGION", - "NEAR EAST COAST OF HONSHU", - "TAIWAN", - "NEAR EAST COAST OF KAMCHATKA", - "EASTERN NEW GUINEA REG.", - "EAST OF SOUTH SANDWICH ISLANDS", - "BISMARCK SEA", - "KYUSHU", - "NEAR EAST COAST OF HONSHU", - "NEAR COAST OF PERU", - "PUERTO RICO REGION", - "OFF COAST OF CENTRAL CHILE", - "BALLENY ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "NICOBAR ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "CELEBES SEA", - "NEAR EAST COAST OF HONSHU", - "SCOTIA SEA", - "OFF EAST COAST OF HONSHU", - "KURIL ISLANDS", - "NEW BRITAIN REGION", - "NORTH ISLAND", - "TONGA ISLANDS", - "JAVA", - "NEAR EAST COAST OF HONSHU", - "NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "GREECE", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF NORTHERN CHILE", - "GREECE", - "KURIL ISLANDS", - "NEAR EAST COAST OF HONSHU", - "MARIANA ISLANDS", - "HALMAHERA", - "OFF EAST COAST OF HONSHU", - "FOX ISLANDS", - "NEAR COAST OF NICARAGUA", - "NORTHERN CHILE", - "NEAR COAST OF CHIAPAS", - "SOUTH OF KERMADEC ISLANDS", - "SOUTH OF FIJI ISLANDS", - "NEAR COAST OF NORTHERN PERU", - "MARIANA ISLANDS REGION", - "KERMADEC ISLANDS", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF PANAMA", - "NEAR COAST OF CENTRAL CHILE", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF CENTRAL CHILE", - "TONGA ISLANDS", - "SOUTH OF MARIANA ISLANDS", - "WEST CHILE RISE", - "NEAR COAST OF VENEZUELA", - "SOLOMON ISLANDS", - "NICARAGUA", - "NEAR COAST OF NORTHERN CHILE", - "SULAWESI", - "KERMADEC ISLANDS", - "OFF EAST COAST OF HONSHU", - "MID-INDIAN RIDGE", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "OFF EAST COAST OF HONSHU", - "MACQUARIE ISLAND REGION", - "OFF COAST OF CENTRAL CHILE", - "NORTHERN MOLUCCA SEA", - "TONGA ISLANDS", - "PERU-BRAZIL BORDER REGION", - "KERMADEC ISLANDS", - "OFF EAST COAST OF HONSHU", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "FIJI ISLANDS REGION", - "MINDANAO", - "FOX ISLANDS", - "MINDANAO", - "JUJUY PROVINCE", - "NEAR COAST OF CENTRAL CHILE", - "NORTHEAST OF TAIWAN", - "SOUTH OF MARIANA ISLANDS", - "SOUTHERN SUMATRA", - "LUZON", - "OFF EAST COAST OF HONSHU", - "WESTERN AUSTRALIA", - "GUERRERO", - "KERMADEC ISLANDS", - "EASTERN NEW GUINEA REG.", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "WEST CHILE RISE", - "SOUTH SHETLAND ISLANDS", - "MINDANAO", - "EAST OF SOUTH SANDWICH ISLANDS", - "NEAR EAST COAST OF HONSHU", - "RUSSIA-MONGOLIA BORDER REGION", - "MINDANAO", - "NEAR COAST OF NICARAGUA", - "SOUTHEAST OF LOYALTY ISLANDS", - "TONGA ISLANDS REGION", - "NEAR EAST COAST OF HONSHU", - "SOUTHWESTERN SIBERIA", - "NEAR COAST OF CENTRAL CHILE", - "SERAM", - "VANUATU ISLANDS", - "OFF EAST COAST OF KAMCHATKA", - "SOUTHEAST OF HONSHU", - "NEW BRITAIN REGION", - "OFF EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "TAIWAN", - "NORTHERN MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "SOUTH SHETLAND ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOUTHWEST OF SUMATRA", - "CENTRAL BOLIVIA", - "SOUTH SANDWICH ISLANDS REGION", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NEVADA", - "ALASKA PENINSULA", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHEAST OF RYUKYU ISLANDS", - "SOUTHEAST OF RYUKYU ISLANDS", - "SANTA CRUZ ISLANDS", - "BANDA SEA", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN IRAN", - "CENTRAL MID-ATLANTIC RIDGE", - "OFF COAST OF CENTRAL CHILE", - "NEAR COAST OF NORTHERN CHILE", - "SOLOMON ISLANDS", - "NEAR COAST OF NORTHERN CHILE", - "SOUTHEAST OF HONSHU", - "NEW BRITAIN REGION", - "CRETE", - "LOYALTY ISLANDS", - "NEGROS", - "NORTHERN CHILE", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SAMAR", - "SOUTHERN SUMATRA", - "NEAR COAST OF NICARAGUA", - "CELEBES SEA", - "EAST OF KURIL ISLANDS", - "SOUTHERN SUMATRA", - "CHILE-BOLIVIA BORDER REGION", - "JAVA SEA", - "VANUATU ISLANDS", - "CELEBES SEA", - "NEAR EAST COAST OF HONSHU", - "BANDA SEA", - "VANUATU ISLANDS REGION", - "LOYALTY ISLANDS", - "RYUKYU ISLANDS", - "ALASKA PENINSULA", - "BONIN ISLANDS", - "SOUTH OF KERMADEC ISLANDS", - "WESTERN IDAHO", - "BONIN ISLANDS", - "SOUTH OF FIJI ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "VANCOUVER ISLAND", - "SOUTHERN EAST PACIFIC RISE", - "MYANMAR", - "OFF W COAST OF NORTHERN SUMATRA", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "SIKKIM", - "KERMADEC ISLANDS REGION", - "MAURITIUS - REUNION REGION", - "ANDREANOF ISLANDS", - "EASTERN NEW GUINEA REG.", - "NORTHERN PERU", - "NEAR EAST COAST OF HONSHU", - "NEAR EAST COAST OF HONSHU", - "SOUTH OF FIJI ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "BALI SEA", - "FOX ISLANDS", - "BANDA SEA", - "CENTRAL MID-ATLANTIC RIDGE", - "WEST OF MACQUARIE ISLAND", - "SOUTHEAST INDIAN RIDGE", - "MINDORO", - "SOUTH OF PANAMA", - "NEAR EAST COAST OF HONSHU", - "NEAR N COAST OF NEW GUINEA", - "CHIAPAS", - "CENTRAL MID-ATLANTIC RIDGE", - "SOUTH OF JAVA", - "GULF OF CALIFORNIA", - "SOUTH OF FIJI ISLANDS", - "QINGHAI", - "VANUATU ISLANDS", - "KERMADEC ISLANDS", - "TURKEY", - "CYPRUS REGION", - "FOX ISLANDS", - "FOX ISLANDS", - "SUNDA STRAIT", - "BONIN ISLANDS", - "NEW BRITAIN REGION", - "NEGROS", - "FIJI ISLANDS REGION", - "SCOTIA SEA", - "NEAR EAST COAST OF KAMCHATKA", - "NEAR COAST OF CENTRAL CHILE", - "EAST OF SOUTH SANDWICH ISLANDS", - "GULF OF CALIFORNIA", - "SOLOMON ISLANDS", - "MYANMAR", - "NEW IRELAND REGION", - "CRETE", - "SAKHALIN", - "SEA OF OKHOTSK", - "PAKISTAN", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH ATLANTIC OCEAN", - "BANDA SEA", - "VANUATU ISLANDS", - "SOUTHERN SUMATRA", - "OFF COAST OF JALISCO", - "NEAR COAST OF NORTHERN CHILE", - "OFF COAST OF NORTHERN CALIFORNIA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "AFGHANISTAN-TAJIKISTAN BORD REG.", - "JAVA SEA", - "MYANMAR-INDIA BORDER REGION", - "NORTH ATLANTIC OCEAN", - "XIZANG", - "VANUATU ISLANDS", - "NORTHERN EAST PACIFIC RISE", - "NEAR COAST OF ECUADOR", - "NEAR COAST OF ECUADOR", - "FIJI ISLANDS REGION", - "RYUKYU ISLANDS", - "CENTRAL ITALY", - "SOUTH ISLAND", - "SOUTH ISLAND", - "OFF COAST OF CENTRAL CHILE", - "MINDANAO", - "NORTHERN MID-ATLANTIC RIDGE", - "VANUATU ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SULAWESI", - "NEAR ISLANDS", - "NEW BRITAIN REGION", - "SOUTH OF JAVA", - "NEAR COAST OF GUATEMALA", - "SEA OF OKHOTSK", - "LEYTE", - "AUCKLAND ISLANDS", - "DODECANESE ISLANDS", - "MARIANA ISLANDS", - "GANSU", - "GUATEMALA", - "HOKKAIDO", - "BOTSWANA", - "COOK STRAIT", - "NEAR EAST COAST OF HONSHU", - "ANDREANOF ISLANDS", - "SOUTHEAST OF HONSHU", - "NORTHERN SUMATRA", - "OFF COAST OF NORTHERN CALIFORNIA", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF KAMCHATKA", - "BANDA SEA", - "COOK STRAIT", - "SOUTH OF PANAMA", - "SOUTH OF FIJI ISLANDS", - "SOLOMON ISLANDS", - "MINDANAO", - "SOUTHERN BOLIVIA", - "SOUTH OF FIJI ISLANDS", - "BANDA SEA", - "NEW IRELAND REGION", - "NEAR COAST OF NORTHERN CHILE", - "MYANMAR-INDIA BORDER REGION", - "NEPAL", - "NEW BRITAIN REGION", - "NEW BRITAIN REGION", - "NEAR COAST OF CHIAPAS", - "NEAR EAST COAST OF HONSHU", - "PACIFIC-ANTARCTIC RIDGE", - "SANTA CRUZ ISLANDS REGION", - "SOLOMON ISLANDS", - "ALASKA PENINSULA", - "TONGA ISLANDS", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHERN MID-ATLANTIC RIDGE", - "BONIN ISLANDS", - "KERMADEC ISLANDS", - "SOLOMON ISLANDS", - "NEPAL", - "OFF EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "NORTHERN MOLUCCA SEA", - "NORTHERN MOLUCCA SEA", - "MINDANAO", - "SOUTHERN EAST PACIFIC RISE", - "SOLOMON ISLANDS", - "SOUTH OF PANAMA", - "NORTH ATLANTIC OCEAN", - "MARIANA ISLANDS", - "VANUATU ISLANDS", - "CENTRAL PERU", - "JUJUY PROVINCE", - "CENTRAL PERU", - "OFF EAST COAST OF HONSHU", - "E. CAROLINE ISLANDS", - "FIJI ISLANDS REGION", - "SOUTH OF PANAMA", - "SANTA CRUZ ISLANDS", - "FOX ISLANDS", - "KERMADEC ISLANDS", - "TIMOR REGION", - "NEAR COAST OF CENTRAL CHILE", - "NICOBAR ISLANDS", - "ANDREANOF ISLANDS", - "OFF COAST OF CENTRAL CHILE", - "OFF COAST OF CENTRAL CHILE", - "NORTHWEST OF RYUKYU ISLANDS", - "SOLOMON ISLANDS", - "GREECE", - "NEAR COAST OF NICARAGUA", - "PERU-BRAZIL BORDER REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "NEAR COAST OF CHIAPAS", - "NEAR COAST OF NORTHERN CHILE", - "MYANMAR-INDIA BORDER REGION", - "SOLOMON ISLANDS", - "TALAUD ISLANDS", - "BOUVET ISLAND REGION", - "IRIAN JAYA REGION", - "KERMADEC ISLANDS", - "SOLOMON ISLANDS", - "SOLOMON ISLANDS", - "SOUTH INDIAN OCEAN", - "AEGEAN SEA", - "GULF OF CALIFORNIA", - "SOUTH OF PANAMA", - "SOLOMON ISLANDS", - "GUERRERO", - "NEAR COAST OF CENTRAL CHILE", - "NEAR COAST OF CENTRAL CHILE", - "SOUTH OF FIJI ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "VANCOUVER ISLAND", - "IRIAN JAYA REGION", - "NEAR COAST OF CENTRAL CHILE", - "NORTH OF ASCENSION ISLAND", - "SOUTHERN XINJIANG", - "NORTHWEST OF KURIL ISLANDS", - "HAWAII", - "SOUTHERN BOLIVIA", - "NEW BRITAIN REGION", - "MAURITIUS - REUNION REGION", - "SOLOMON ISLANDS", - "FIJI ISLANDS REGION", - "NEW BRITAIN REGION", - "NEW IRELAND REGION", - "NEW GUINEA", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEW IRELAND REGION", - "VANUATU ISLANDS", - "NORTHERN MOLUCCA SEA", - "CENTRAL CHILE", - "VANUATU ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "PRINCE EDWARD ISLANDS REGION", - "HOKKAIDO", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CHIAPAS", - "TONGA ISLANDS", - "IRIAN JAYA REGION", - "WESTERN BRAZIL", - "JAN MAYEN ISLAND REGION", - "KURIL ISLANDS", - "MYANMAR", - "HOKKAIDO", - "SOLOMON ISLANDS", - "ANDREANOF ISLANDS", - "KERMADEC ISLANDS REGION", - "SICHUAN", - "SOUTH OF FIJI ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "JAN MAYEN ISLAND REGION", - "NORTHERN MOLUCCA SEA", - "SUMBAWA REGION", - "KURIL ISLANDS", - "FIJI ISLANDS REGION", - "VANCOUVER ISLAND", - "WEST OF MACQUARIE ISLAND", - "IRIAN JAYA REGION", - "VANCOUVER ISLAND", - "FLORES SEA", - "VANCOUVER ISLAND", - "IONIAN SEA", - "ANDREANOF ISLANDS", - "SUMBAWA REGION", - "ARMENIA-AZERBAIJAN-IRAN BORD REG", - "EASTERN NEW GUINEA REG.", - "NEAR COAST OF CENTRAL CHILE", - "SOUTHWEST OF AFRICA", - "EL SALVADOR", - "RAT ISLANDS", - "BOUVET ISLAND REGION", - "EASTERN NEW GUINEA REG.", - "SANTA CRUZ ISLANDS", - "BANDA SEA", - "EASTERN SIBERIA", - "CENTRAL MID-ATLANTIC RIDGE", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "KURIL ISLANDS", - "LOYALTY ISLANDS", - "LOYALTY ISLANDS", - "TONGA ISLANDS", - "KURIL ISLANDS", - "COSTA RICA", - "NEW GUINEA", - "SANTA CRUZ ISLANDS", - "NEW BRITAIN REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "SOUTHWESTERN SIBERIA", - "NEAR N COAST OF NEW GUINEA", - "BOUVET ISLAND REGION", - "JAVA", - "SOLOMON ISLANDS", - "SANTA CRUZ ISLANDS", - "WESTERN INDIAN-ANTARCTIC RIDGE", - "MICHOACAN", - "SULAWESI", - "SOUTH SANDWICH ISLANDS REGION", - "BONIN ISLANDS", - "VANUATU ISLANDS", - "FIJI ISLANDS REGION", - "SOUTHEAST INDIAN RIDGE", - "GUERRERO", - "NORTHERN MOLUCCA SEA", - "VANUATU ISLANDS", - "OFF COAST OF CENTRAL AMERICA", - "NEW BRITAIN REGION", - "VANUATU ISLANDS REGION", - "SOLOMON ISLANDS", - "SANTIAGO DEL ESTERO PROV.", - "NEAR COAST OF CENTRAL CHILE", - "VANUATU ISLANDS REGION", - "NEW BRITAIN REGION", - "OFF COAST OF CENTRAL AMERICA", - "CENTRAL PERU", - "FIJI ISLANDS REGION", - "SOUTHWESTERN PAKISTAN", - "KERMADEC ISLANDS", - "BAJA CALIFORNIA", - "OFF EAST COAST OF HONSHU", - "IRIAN JAYA", - "IRIAN JAYA REGION", - "MINDANAO", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR COAST OF GUERRERO", - "EASTERN NEW GUINEA REG.", - "KOMANDORSKIYE OSTROVA REGION", - "VANUATU ISLANDS", - "ECUADOR", - "SOUTH ISLAND", - "CENTRAL PERU", - "SOLOMON ISLANDS", - "NEPAL", - "SOLOMON ISLANDS", - "NEAR EAST COAST OF HONSHU", - "KERMADEC ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTHEAST OF LOYALTY ISLANDS", - "TURKEY", - "NORTHERN MOLUCCA SEA", - "SOUTHERN EAST PACIFIC RISE", - "EASTERN NEW GUINEA REG.", - "CENTRAL CALIFORNIA", - "NEAR COAST OF PERU", - "FLORES SEA", - "FLORES SEA", - "MINDANAO", - "SOUTHERN ALASKA", - "BANDA SEA", - "NEAR COAST OF CENTRAL CHILE", - "REYKJANES RIDGE", - "HALMAHERA", - "NEAR COAST OF CENTRAL CHILE", - "TAJIKISTAN", - "HAITI REGION", - "SOLOMON ISLANDS", - "BANDA SEA", - "OFF COAST OF CENTRAL AMERICA", - "SANTA CRUZ ISLANDS", - "NEAR EAST COAST OF HONSHU", - "OFF EAST COAST OF HONSHU", - "SANTA CRUZ ISLANDS REGION", - "OFF E. COAST OF N. ISLAND", - "NEAR COAST OF PERU", - "KERMADEC ISLANDS", - "OFF E. COAST OF N. ISLAND", - "NORTH OF ASCENSION ISLAND", - "MINDANAO", - "NEAR EAST COAST OF HONSHU", - "SOUTH GEORGIA ISLAND REGION", - "OFF EAST COAST OF HONSHU", - "SOUTHEAST OF LOYALTY ISLANDS", - "OAXACA", - "BANDA SEA", - "SOUTH OF KERMADEC ISLANDS", - "PHILIPPINE ISLANDS REGION", - "CENTRAL MEXICO", - "KURIL ISLANDS", - "NEAR EAST COAST OF HONSHU", - "IRIAN JAYA", - "SOUTH OF FIJI ISLANDS", - "DODECANESE ISLANDS", - "IRAN-IRAQ BORDER REGION", - "FOX ISLANDS", - "NEW IRELAND REGION", - "LOYALTY ISLANDS", - "COLOMBIA", - "CELEBES SEA", - "SANTA CRUZ ISLANDS", - "EASTERN NEW GUINEA REG.", - "ANDREANOF ISLANDS", - "NEAR COAST OF OAXACA", - "SOUTH SANDWICH ISLANDS REGION", - "OFF EAST COAST OF HONSHU", - "NEAR COAST OF PERU", - "QINGHAI", - "KAMCHATKA PENINSULA", - "VANUATU ISLANDS", - "VANUATU ISLANDS", - "SOUTHEAST OF LOYALTY ISLANDS", - "OFF COAST OF NORTHERN CHILE", - "VANUATU ISLANDS", - "KYUSHU", - "SOUTHERN ALASKA", - "SOUTH SANDWICH ISLANDS REGION", - "SOUTH SANDWICH ISLANDS REGION", - "KURIL ISLANDS", - "NEAR COAST OF VENEZUELA", - "VANUATU ISLANDS", - "COLOMBIA", - "NEAR COAST OF CHIAPAS", - "PERU-BRAZIL BORDER REGION", - "PERU-BRAZIL BORDER REGION", - "NEAR EAST COAST OF HONSHU", - "NORTH OF HONDURAS", - "SOLOMON ISLANDS", - "NEPAL", - "SOUTHERN IRAN", - "SOLOMON ISLANDS", - "COSTA RICA", - "KOMANDORSKIYE OSTROVA REGION", - "NEW IRELAND REGION", - "NEW BRITAIN REGION", - "ALASKA PENINSULA", - "MINDANAO", - "PERU-BRAZIL BORDER REGION", - "SOUTHERN CHILE", - "MINDANAO", - "PHILIPPINE ISLANDS REGION", - "NEW BRITAIN REGION", - "SOUTH SANDWICH ISLANDS REGION", - "NEAR COAST OF NORTHERN CHILE", - "MINAHASSA PENINSULA", - "SOLOMON ISLANDS", - "NEAR COAST OF GUERRERO", - "SOUTHWEST OF SUMATRA", - "SOUTH OF ALASKA", - "SOUTHEAST OF LOYALTY ISLANDS", - "EAST OF KURIL ISLANDS", - "SCOTIA SEA", - "QUEEN CHARLOTTE ISLANDS REGION", - "PRIMOR'YE", - "MARIANA ISLANDS", - "SEA OF OKHOTSK", - "SOUTHERN SUMATRA", - "NORTHERN PERU", - "SOUTHEAST OF LOYALTY ISLANDS", - "NEAR EAST COAST OF HONSHU", - "SOLOMON ISLANDS", - "SOUTHEASTERN ALASKA", - "NEAR COAST OF ECUADOR", - "NEW GUINEA", - "OFF EAST COAST OF HONSHU", - "FIJI ISLANDS", - "RAT ISLANDS", - "SANTA CRUZ ISLANDS", - "PERU-ECUADOR BORDER REGION", - "BONIN ISLANDS", - "GULF OF ALASKA", - "PAKISTAN", - "SOUTH ISLAND", - "CUBA REGION", - "PERU-BRAZIL BORDER REGION", - "KERMADEC ISLANDS REGION", - "HINDU KUSH REGION", - "NEW BRITAIN REGION", - "NEAR COAST OF NORTHERN CHILE", - "NEAR COAST OF CHIAPAS", - "NORTHERN PERU", - "SOUTH SANDWICH ISLANDS REGION", - "ALASKA PENINSULA", - "OFF W COAST OF NORTHERN SUMATRA", - "FIJI ISLANDS REGION", - "KERMADEC ISLANDS", - "NEAR COAST OF CENTRAL CHILE", - "SEA OF OKHOTSK", - "OFF W COAST OF NORTHERN SUMATRA", - "NEAR EAST COAST OF HONSHU" - ], - "lat": [ - -13.6098, - -1.159, - -1.4513, - -17.9609, - -3.7682, - 13.9987, - 44.3014, - 13.396, - 26.8975, - 41.3343, - -17.4154, - 54.5825, - -4.7722, - -0.2882, - -31.106, - -30.3103, - -19.6664, - -24.0934, - -30.5139, - -16.4206, - -57.7886, - 27.7829, - -50.371, - 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"# fig.update_geos(resolution=110, showcountries=True,projection_type=\"orthographic\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "3355fe19", - "metadata": {}, - "source": [ - "# 3 Create a Pandas from a generic text file.\n", - "\n", - "The python package pandas is very useful to read csv files, but also many text files that are more or less formatted as one observation per row and one column for each feature.\n", - "\n", - "As an example, we are going to look at the list of seismic stations from the Northern California seismic network, available [here](http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day):\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "ef9f302d", - "metadata": {}, - "outputs": [], - "source": [ - "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "3a60ef42", - "metadata": {}, - "outputs": [], - "source": [ - "# this gets the file linked in the URL page and convert it to a string\n", - "s = requests.get(url).content " - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "id": "9edd0bf5", - "metadata": {}, - "outputs": [], - "source": [ - "# this will convert the string, decode it , and make it a table\n", - "data = pd.read_csv(io.StringIO(s.decode('utf-8')), header=None, skiprows=2, sep='\\s+', usecols=list(range(0, 13)))\n", - "# because columns/keys were not assigned, assign them now\n", - "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" - ] - }, - { - "cell_type": "markdown", - "id": "b2def912", - "metadata": {}, - "source": [ - "Let us look at the data. They are now stored into a pandas dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "448b69ab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.0
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2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.0
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.0
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mDateParseError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# convert start_time and end_time to datetime\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstart_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstart_time\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:1108\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1106\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mtz_localize(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutc\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1107\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, ABCSeries):\n\u001b[0;32m-> 1108\u001b[0m cache_array \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_listlike\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m cache_array\u001b[38;5;241m.\u001b[39mempty:\n\u001b[1;32m 1110\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/tools/datetimes.py:254\u001b[0m, in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m 252\u001b[0m unique_dates \u001b[38;5;241m=\u001b[39m unique(arg)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_dates) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(arg):\n\u001b[0;32m--> 254\u001b[0m cache_dates \u001b[38;5;241m=\u001b[39m 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\u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m--> 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mobjects_to_datetime64ns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 502\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n", - "File \u001b[0;32m~/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages/pandas/core/arrays/datetimes.py:2346\u001b[0m, in 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\u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2351\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2355\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;66;03m# Return i8 values to denote unix timestamps\u001b[39;00m\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi8\u001b[39m\u001b[38;5;124m\"\u001b[39m), tz_parsed\n", - "File \u001b[0;32mtslib.pyx:403\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mtslib.pyx:552\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mtslib.pyx:517\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mconversion.pyx:546\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.convert_str_to_tsobject\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsing.pyx:331\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.parse_datetime_string\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mparsing.pyx:660\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.parsing.dateutil_parse\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mDateParseError\u001b[0m: Unknown datetime string format, unable to parse: 1976/07/20,17:38:00, at position 0" + "mapbox": { + "center": { + "lat": 38.04061885424097, + "lon": -121.71164549237406 + }, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 ] - } - ], - "source": [ - "# convert start_time and end_time to datetime\n", - "data['start_time'] = pd.to_datetime(data['start_time'])\n", - "data['end_time'] = pd.to_datetime(data['end_time'])" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "b8bcf253", - "metadata": {}, - "outputs": [], - "source": [ - "data['startdate'] = pd.to_datetime(data['start_time'], format='%Y/%m/%d,%H:%M:%S')" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "da911e32", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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stationnetworkchannellocationratestart_timeend_timelatitudelongitudeelevationdepthdipazimuthstartdate
0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:00
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0.0 \n", - "1 1981/06/12,19:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "2 1984/01/01,00:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "3 1985/11/03,22:00:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "4 1987/03/10,23:15:00 39.27594 -121.02696 911.0 0.0 -90.0 0.0 \n", - "\n", - " startdate \n", - "0 1976-07-20 17:38:00 \n", - "1 1977-12-01 22:37:00 \n", - "2 1981-06-12 19:15:00 \n", - "3 1984-01-01 00:00:00 \n", - "4 1985-11-03 22:00:00 " - ] + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 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0AARNCEHZ--0.01976/07/20,17:38:001977/12/01,22:37:0039.27594-121.02696911.00.0-90.00.01976-07-20 17:38:001977-12-01 22:37:00
1AARNCEHZ--0.01977/12/01,22:37:001981/06/12,19:15:0039.27594-121.02696911.00.0-90.00.01977-12-01 22:37:001981-06-12 19:15:00
2AARNCEHZ--0.01981/06/12,19:15:001984/01/01,00:00:0039.27594-121.02696911.00.0-90.00.01981-06-12 19:15:001984-01-01 00:00:00
3AARNCEHZ--100.01984/01/01,00:00:001985/11/03,22:00:0039.27594-121.02696911.00.0-90.00.01984-01-01 00:00:001985-11-03 22:00:00
4AARNCEHZ--100.01985/11/03,22:00:001987/03/10,23:15:0039.27594-121.02696911.00.0-90.00.01985-11-03 22:00:001987-03-10 23:15:00
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"ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Northern California Seismic Network" - }, - "width": 500 - } - } + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = px.scatter_mapbox(data,\n", - " lat='latitude',lon='longitude', \n", - " range_color=(6,9),mapbox_style=\"carto-positron\",\n", - " height=600, width=500,\n", - " hover_name=\"station\",\n", - " hover_data=['network','station','channel','rate']);\n", - "fig.update_layout(title=\"Northern California Seismic Network\")\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "373751b7", - "metadata": {}, - "source": [ - "# 4: Exercise\n", - "We will now practice on manipulating pandas. This exercise pulls station metadata from a URL and students are expected to practice on specific tasks.\n", - "\n", - "Download data from the NCEDC URL" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "18059dae", - "metadata": {}, - "outputs": [], - "source": [ - "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" - ] - }, - { - "cell_type": "markdown", - "id": "ceba5236", - "metadata": {}, - "source": [ - "The column names are shown at the top of the text file, make a list of strings of these names in order to rename the columns once the dataframe is made" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "db521722", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59c9c285", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here\n", - "# request the data from the URL and use the IO package\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f67ba2d0", - "metadata": {}, - "outputs": [], - "source": [ - "# assign the column names\n", - "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" - ] - }, - { - "cell_type": "markdown", - "id": "2e81e4b9", - "metadata": {}, - "source": [ - "Find the row of channel KCPB" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d28d4e95", - "metadata": {}, - "outputs": [], - "source": [ - "# find the row of station KCPB" - ] - }, - { - "cell_type": "markdown", - "id": "40806ebd", - "metadata": {}, - "source": [ - "Now select two stations of your choice using ``|``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb5561d5", - "metadata": {}, - "outputs": [], - "source": [ - "# answer below" - ] - }, - { - "cell_type": "markdown", - "id": "479e019a", - "metadata": {}, - "source": [ - "Now select a given station and a specific channel code, example is ``KCPB`` and channel code ``HNZ``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ebba2b90", - "metadata": {}, - "outputs": [], - "source": [ - "# Select two stations, use the typical \"AND\" " - ] - }, - { - "cell_type": "markdown", - "id": "bc0abb79", - "metadata": {}, - "source": [ - "You may also choose the pandas function ``isin`` to select rows that have a given attribute that belongs to a list. For instance, use ``isin`` to select all rows that have the key ``station`` within a list ``['KCBP','KHBB' ]``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "561aecf9", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "9133ae6b", - "metadata": {}, - "source": [ - "**Q** Use panda native functions to calculate how many unique sites (station names) there are in the network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f235cec3", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "f5d93eb9", - "metadata": {}, - "source": [ - "**Q** Use pandas native functions to select the unique set of channel codes that end with ``Z``: this will tell you of how many types digitized data the seismic network manages." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d7a13877", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "c801ed50", - "metadata": {}, - "source": [ - "**Q** What station names has the most number of channels? __hint__ you may use ``value_counts()``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "031731dd", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "4edf820a", - "metadata": {}, - "source": [ - "**Q** What is the maximum difference in elevation between the stations using lambda functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19f7184c", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "141ac2c1", - "metadata": {}, - "source": [ - "Here, pandas does not recognize the start_time and end_time columns as a datetime format, so we cannot use datetime operations on them. We first need to convert these columns into a datetime format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02663a77", - "metadata": {}, - "outputs": [], - "source": [ - "# answer here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "afaf1851", - "metadata": {}, - "outputs": [], - "source": [ - "# Transform column from string into datetime format" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "bae00773", - "metadata": {}, - "outputs": [], - "source": [ - "# do the same for end times" - ] - }, - { - "cell_type": "markdown", - "id": "f00ce758", - "metadata": {}, - "source": [ - "We can now look when each seismic station was installed using ``groupby`` and sorting by the earliest deployment (i.e., the minimum of the start_time)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0cc2a86", - "metadata": {}, - "outputs": [], - "source": [ - "# students answer here" - ] - }, - { - "cell_type": "markdown", - "id": "5183304a", - "metadata": {}, - "source": [ - "Select the stations that were deployed first and recovered last using ``agg`` and lambda functions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7e7b38a0", - "metadata": {}, - "outputs": [], - "source": [ - "# answer here" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c5c38796", - "metadata": {}, - "source": [ - "# 4. CSV vs Parquet\n", - "\n", - "Parquet is a compressed data format that stores and compresses the columns. It is fast for I/O and compact format.\n", - "\n", - "Save ``data`` into a CSV file:" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "f20f5de3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "195 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", - "-rw-r--r--@ 1 marinedenolle staff 4.2M Oct 7 05:15 my_metadata.csv\n" - ] - } - ], - "source": [ - "%timeit data.to_csv(\"my_metadata.csv\")\n", - "!ls -lh my_metadata.csv" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "4b5dd078", - "metadata": {}, - "source": [ - "Try and save in Parquet and compare time and memory." - ] - }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Northern California Seismic Network" + }, + "width": 500 + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = px.scatter_mapbox(data,\n", + " lat='latitude',lon='longitude', \n", + " range_color=(6,9),mapbox_style=\"carto-positron\",\n", + " height=600, width=500,\n", + " hover_name=\"station\",\n", + " hover_data=['network','station','channel','rate']);\n", + "fig.update_layout(title=\"Northern California Seismic Network\")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "373751b7", + "metadata": {}, + "source": [ + "# 4: Exercise\n", + "We will now practice on manipulating pandas. This exercise pulls station metadata from a URL and students are expected to practice on specific tasks.\n", + "\n", + "Download data from the NCEDC URL" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "18059dae", + "metadata": {}, + "outputs": [], + "source": [ + "url = 'http://ncedc.org/ftp/pub/doc/NC.info/NC.channel.summary.day'" + ] + }, + { + "cell_type": "markdown", + "id": "ceba5236", + "metadata": {}, + "source": [ + "The column names are shown at the top of the text file, make a list of strings of these names in order to rename the columns once the dataframe is made" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "db521722", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59c9c285", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here\n", + "# request the data from the URL and use the IO package\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f67ba2d0", + "metadata": {}, + "outputs": [], + "source": [ + "# assign the column names\n", + "data.columns = ['station', 'network', 'channel', 'location', 'rate', 'start_time', 'end_time', 'latitude', 'longitude', 'elevation', 'depth', 'dip', 'azimuth']" + ] + }, + { + "cell_type": "markdown", + "id": "2e81e4b9", + "metadata": {}, + "source": [ + "Find the row of channel KCPB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d28d4e95", + "metadata": {}, + "outputs": [], + "source": [ + "# find the row of station KCPB" + ] + }, + { + "cell_type": "markdown", + "id": "40806ebd", + "metadata": {}, + "source": [ + "Now select two stations of your choice using ``|``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb5561d5", + "metadata": {}, + "outputs": [], + "source": [ + "# answer below" + ] + }, + { + "cell_type": "markdown", + "id": "479e019a", + "metadata": {}, + "source": [ + "Now select a given station and a specific channel code, example is ``KCPB`` and channel code ``HNZ``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebba2b90", + "metadata": {}, + "outputs": [], + "source": [ + "# Select two stations, use the typical \"AND\" " + ] + }, + { + "cell_type": "markdown", + "id": "bc0abb79", + "metadata": {}, + "source": [ + "You may also choose the pandas function ``isin`` to select rows that have a given attribute that belongs to a list. For instance, use ``isin`` to select all rows that have the key ``station`` within a list ``['KCBP','KHBB' ]``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "561aecf9", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "9133ae6b", + "metadata": {}, + "source": [ + "**Q** Use panda native functions to calculate how many unique sites (station names) there are in the network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f235cec3", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f5d93eb9", + "metadata": {}, + "source": [ + "**Q** Use pandas native functions to select the unique set of channel codes that end with ``Z``: this will tell you of how many types digitized data the seismic network manages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7a13877", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "c801ed50", + "metadata": {}, + "source": [ + "**Q** What station names has the most number of channels? __hint__ you may use ``value_counts()``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "031731dd", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "4edf820a", + "metadata": {}, + "source": [ + "**Q** What is the maximum difference in elevation between the stations using lambda functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19f7184c", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "141ac2c1", + "metadata": {}, + "source": [ + "Here, pandas does not recognize the start_time and end_time columns as a datetime format, so we cannot use datetime operations on them. We first need to convert these columns into a datetime format:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02663a77", + "metadata": {}, + "outputs": [], + "source": [ + "# answer here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afaf1851", + "metadata": {}, + "outputs": [], + "source": [ + "# Transform column from string into datetime format" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "bae00773", + "metadata": {}, + "outputs": [], + "source": [ + "# do the same for end times" + ] + }, + { + "cell_type": "markdown", + "id": "f00ce758", + "metadata": {}, + "source": [ + "We can now look when each seismic station was installed using ``groupby`` and sorting by the earliest deployment (i.e., the minimum of the start_time)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0cc2a86", + "metadata": {}, + "outputs": [], + "source": [ + "# students answer here" + ] + }, + { + "cell_type": "markdown", + "id": "5183304a", + "metadata": {}, + "source": [ + "Select the stations that were deployed first and recovered last using ``agg`` and lambda functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e7b38a0", + "metadata": {}, + "outputs": [], + "source": [ + "# answer here" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c5c38796", + "metadata": {}, + "source": [ + "# 4. CSV vs Parquet\n", + "\n", + "Parquet is a compressed data format that stores and compresses the columns. It is fast for I/O and compact format.\n", + "\n", + "Save ``data`` into a CSV file:" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "f20f5de3", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 89, - "id": "0952eb9f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "25.2 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", - "-rw-r--r--@ 1 marinedenolle staff 591K Oct 7 05:15 my_metadata.pq\n" - ] - } - ], - "source": [ - "%timeit data.to_parquet(\"my_metadata.pq\")\n", - "!ls -lh my_metadata.pq" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "195 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n", + "-rw-r--r--@ 1 marinedenolle staff 4.2M Oct 7 05:15 my_metadata.csv\n" + ] } - ], - "metadata": { - "kernelspec": { - "display_name": "mlgeo", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + ], + "source": [ + "%timeit data.to_csv(\"my_metadata.csv\")\n", + "!ls -lh my_metadata.csv" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4b5dd078", + "metadata": {}, + "source": [ + "Try and save in Parquet and compare time and memory." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "0952eb9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25.2 ms ± 10.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n", + "-rw-r--r--@ 1 marinedenolle staff 591K Oct 7 05:15 my_metadata.pq\n" + ] } + ], + "source": [ + "%timeit data.to_parquet(\"my_metadata.pq\")\n", + "!ls -lh my_metadata.pq" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlgeo", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/book/Chapter2-DataManipulation/2.9_filtering_data.ipynb b/book/Chapter2-DataManipulation/2.9_filtering_data.ipynb index 97a6157..a26b980 100644 --- a/book/Chapter2-DataManipulation/2.9_filtering_data.ipynb +++ b/book/Chapter2-DataManipulation/2.9_filtering_data.ipynb @@ -1,13 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 2.9 Filtering Data\n", - "\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -51,12 +43,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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fp08fAKWuCzdu3KBz584UKlQIfX19DA0N6d+/PwkJCTx48ACAsmXLYmNjw8SJE1mxYgU+Pj6p9pXU/a5Xr17s2LGDly9fqnW8vvjiC65cuaJ0GzFihFrbSMujR4+4f/++4lgkP17t27fn9evXqbrkdO/eXen+0aNHiY+Pp3///krPNzExwdXVVdFtydfXl1evXtGnTx+lX8udnJwUrY2Zad++Pc+ePWPPnj2MHz+eKlWqsHfvXjp37swPP/ygWO/AgQNUrVqVmjVrKsXUpk2bVF2pVHmP01OoUCHOnj3LlStXmDNnDl26dOHBgwdMmjSJatWq8f79e5VeV1pSHueMhIeHM3HiRMqWLYuBgQEGBgZYWFgQERHBvXv3FOsdPnyYZs2aUalSpXS3pc6xU9eBAwdo1qwZRYsWVdp2u3btAHnrdXLt27dXasFMijuptTXl8mfPniktr1KlCjVq1FBa1qdPH0JDQ7l+/XqWXm/z5s2xsbFJ9dpOnTpFy5Ytsba2VnyOpk6dSmBgYKquiZpgY2OTqjUgu+/dq1evFC0w6jp16hQtWrSgePHiSssHDhxIZGRkqiImnTt3VrpfvXp1AEWX1YsXLxITE0OvXr2U1mvQoEGqFuqEhASl15vZ+Mro6GhOnjxJ165dMTMzS3Xei46O5uLFi0rPSe/7KJPJaN++veK+gYEBZcuWxcHBgVq1aimWFyxYkCJFiqTZJTcrsnoeTh53p06dlJZVr15dKb7Tp09jaWmZ6r1K+luZliJFiqj9900QQIypEoQsCwoKQpKkNMfHFC1aFPg0hiZpzMCJEyd49OgR/v7+iqTq0qVLhIeHc+LECUqXLk2pUqUA2LhxI4aGhkq35AwMDChUqJDSMnt7e6X9Pnv2jMaNG/Py5Uv++usvxcVzUp/7qKgoQD5A19PTk5o1a/LLL79QpUoVihYtyrRp0xTjcpo0acLevXsVf/QcHR2pWrUq27ZtU+l4WVtbU7duXaVb0nHKjqRxNOPHj091vJKStpSJQcr3LGkb9erVS7WNf/75R/H8pOOadJyTS2tZekxNTfnyyy+ZN28enp6ePHr0iMqVK7N06VK8vb0VMd2+fTtVPJaWlkiSpIhJ1fc4M3Xr1mXixIn8+++/vHr1ih9//BF/f/9sFatIb+xYWvr06cOSJUv47rvvOHr0KJcvX+bKlSvY2toqvYZ3794pun2mR9VjlxVv3rxh//79qbZdpUoVIPVnrWDBgkr3jYyMMlyeNLA/SUaftaTPo7qvN6335fLly7Ru3RqA1atXc/78ea5cucLkyZMB1T9H6kgrjuy+d0kFD7IiMDBQpfN5kpTn36RukEnHKml9Ozu7VNtMuaxMmTJKr3fGjBmZxhofH8/ixYtTHaukBEmV9x3kFVhTHjMjI6NUn9Gk5Sk/o1mV1fNwRnEbGxsrxRcYGJjm8c/ofG1iYqKVz7uQ/4kxVYKQRTY2Nujp6fH69etUj7169QpAMQ7EyMiIL774ghMnTuDo6Ii9vT3VqlWjdOnSgHwg8cmTJ+nYsaNiG506deLKlSvp7j8+Pp7AwEClP+xJg2+Tlu3du5eIiAh2796Nk5OTYr205gWqVq0a27dvR5Ikbt++zYYNG5gxYwampqb8/PPPAHTp0oUuXboQExPDxYsXcXd3p0+fPpQsWZKGDRuqdNw0LekYT5o0iW7duqW5ToUKFZTupxyTkbSNnTt3Kh2nlJKOa1qDnNMb+KyKEiVKMGTIEMaOHYu3tzdVqlShcOHCmJqapjsmKilmdd5jVRkaGjJt2jT+/PNP7t69q1hubGycahA4pL7YTKLquKWQkBAOHDjAtGnTFJ81gJiYGD58+KC0rq2trVLxlLSoeuyyonDhwlSvXp1Zs2al+bgmfihILqPPWtLnUd3Xm9b7sn37dgwNDTlw4IDSherevXtVjjXpeSk/I+klQmnFkd33rnDhwqk+M6oqVKiQSudzdbYHKBVQSRIQEKDUWrV//36l45bZ58jGxkbRK2LkyJFprpP0A12S3FbNLqvnYXUUKlSIy5cvp1qe0fn6w4cP2TpHCJ8vkVQJQhaZm5vj7OzM7t27mT9/vqIkcWJiIps3b8bR0VFpIHLLli2ZNGkSlpaWii5+5ubmNGjQgMWLF/Pq1SvFcpD/MUj5S2hKW7ZsYfTo0Yr7W7duBT7NZ5L0Ryv5QHJJkli9enW625TJZNSoUYM///yTDRs2KLoYJWdsbIyrqysFChTg6NGj3LhxQ+tJVcpfgZNUqFCBcuXKcevWLWbPnp2lbbdp0wYDAwMeP36cYZe1ChUq4ODgwLZt2xg3bpzi+D59+hQvL69ML4TCwsKQyWRK3UWTJHVxS9pGx44dmT17NoUKFUp1cZRcVt7j5F6/fp3mL9gp4wF55bfbt28rrXfq1CnCw8NV2ld6ZDIZkiSlKniwZs0aEhISlJa1a9eOv//+G19f31TJchJVj11WdOzYkUOHDlGmTJk0u9Bpmre3N7du3VLqArh161YsLS2pXbu2Iqbsvt6k8tpJBRZA/l37+++/U61rbGyc5i/5SUnC7du3ld6blMU3MpLd11KxYkW1EsHkWrRowZ49e3j16pXS537Tpk2YmZmpXVbf2dkZY2Nj/vnnH6UffC5evMjTp0+Vkqpq1aqluY30zntmZmY0a9aMGzduUL16dUVLZ16m6nlYHc2aNWPHjh38999/Sl0Ak/5WpuXJkydUrVpVI/sXPi8iqRKETJw6dSpViWCQj5Vwd3enVatWNGvWjPHjx2NkZMSyZcu4e/cu27ZtU/olrkWLFiQkJHDy5EmlsrQtW7Zk2rRpyGQytaoNGRkZsWDBAsLDw6lXr56i+l+7du0UZatbtWqFkZERX3/9NRMmTCA6Oprly5cTFBSktK0DBw6wbNkyvvzyS0qXLo0kSezevZvg4GBatWoFwNSpU3nx4gUtWrTA0dGR4OBg/vrrLwwNDXNkvqEyZcpgamrKli1bqFSpEhYWFhQtWpSiRYuycuVK2rVrR5s2bRg4cCDFihXjw4cP3Lt3j+vXr/Pvv/9muO2SJUsyY8YMJk+ezJMnT2jbti02Nja8efOGy5cvY25urqjY99tvv/Hdd9/RtWtXvv/+e4KDgxWV2TLj6+tLmzZt+Oqrr3B1dcXBwYGgoCAOHjzIqlWraNq0qWJs1tixY9m1axdNmjThxx9/pHr16iQmJvLs2TOOHTvGTz/9hLOzs8rvcXratGmDo6MjnTp1omLFiiQmJnLz5k0WLFiAhYWF0qTM/fr149dff2Xq1Km4urri4+PDkiVLsLa2Vmlf6bGysqJJkybMmzePwoULU7JkSTw9PVm7di0FChRQWnfGjBkcPnyYJk2a8Msvv1CtWjWCg4M5cuQI48aNo2LFiiofu6yYMWMGx48fx8XFhdGjR1OhQgWio6Px9/fn0KFDrFixItPuieooWrQonTt3xs3NDQcHBzZv3szx48f5/fffFXM6aeL1dujQgT/++IM+ffowZMgQAgMDmT9/fpqV/ZJatf/55x9Kly6NiYkJ1apVo169elSoUIHx48cTHx+PjY0Ne/bs4dy5cyq/3uy+lqZNm7Ju3ToePHiQZnW9lOOMkri6ujJt2jTFmLmpU6dSsGBBtmzZwsGDB5k7d67an/OCBQsybtw43N3dsbGxoWvXrrx48YLp06fj4OCgUuVTS0tLnJyc2LdvHy1atKBgwYKK78hff/3FF198QePGjRk+fDglS5YkLCyMR48esX//fpUrs+YWqp6H1dG/f3/+/PNP+vfvz6xZsyhXrhyHDh3i6NGjaa4fGBjIw4cPGTVqlCZekvC50VGBDEHI9ZIqFKV3S6pwdfbsWal58+aSubm5ZGpqKjVo0EDav39/qu0lJiYqqr69fPlSsfz8+fMSINWuXVvl2AYMGCCZm5tLt2/flpo2bSqZmppKBQsWlIYPHy6Fh4crrbt//36pRo0akomJiVSsWDHpf//7n3T48GGlalz379+Xvv76a6lMmTKSqampZG1tLdWvX1/asGGDYjsHDhyQ2rVrJxUrVkwyMjKSihQpIrVv3146e/ZspvGmrKCW1uvJrPqfJEnStm3bpIoVK0qGhoapKmLdunVL6tWrl1SkSBHJ0NBQsre3l5o3by6tWLFCsU5mFR337t0rNWvWTLKyspKMjY0lJycnqUePHtKJEyeU1luzZo1Urlw5ycjISCpfvry0bt26NF9DSkFBQdLMmTOl5s2bK46jubm5VLNmTWnmzJlSZGSk0vrh4eHSlClTpAoVKkhGRkaStbW1VK1aNenHH3+UAgICFOup8h6n559//pH69OkjlStXTrKwsJAMDQ2lEiVKSP369UtVYS8mJkaaMGGCVLx4ccnU1FRydXWVbt68mW71v7SOc3pV2F68eCF1795dsrGxkSwtLaW2bdtKd+/eTfNz8Pz5c2nw4MGSvb29ZGhoKBUtWlTq1auX9ObNG7WPXWbS+uy+e/dOGj16tFSqVCnJ0NBQKliwoFSnTh1p8uTJiu9fUvW/efPmqfT60zpmSfveuXOnVKVKFcnIyEgqWbKk9Mcff6SKU9XXC0gjR45M87WuW7dOqlChgmRsbCyVLl1acnd3l9auXZuqop+/v7/UunVrydLSUgKUPvcPHjyQWrduLVlZWUm2trbSqFGjpIMHD6ZZ/a9KlSppxpGd9y4kJESysLCQ5s6dq7Q86bind0uK7c6dO1KnTp0ka2trycjISKpRo0aqCnHpvYdJ73ny9RMTE6WZM2dKjo6OkpGRkVS9enXpwIEDUo0aNaSuXbtm+FqSnDhxQqpVq5ZkbGwsAUrfBz8/P2nw4MFSsWLFJENDQ8nW1lZycXGRZs6cmWm8kvTpb0lK6b0/mZ3LU8qo+l92zsPpxT1t2jQp5aVt0rnFwsJCsrS0lLp37y55eXmlWf1v7dq1kqGhoVrnCEFIIpOkTGYZFQQh1xk4cCA7d+7MdrcrQRByr5IlS1K1alUOHDig61DylFGjRnHy5Em8vb1z3TgiAD8/PypWrMi0adP45ZdfdB2OkEzjxo0pUaIEW7Zs0XUoQh4kqv8JgiAIgpBvTJkyhZcvX7Jr1y5dh8KtW7f4+eef+e+///Dw8GDlypW0bNkSKysrvv32W12HJyRz5swZrly5wm+//abrUIQ8SoypEgRBEAQh37Czs2PLli0qjyvUJnNzc65evcratWsJDg7G2tqapk2bMmvWrDRLfQu6ExgYyKZNmxRVeQVBXaL7nyAIgiAIgiAIQjaI7n+CIAiCIAiCIAjZIJIqQRAEQRAEQRCEbBBJlSAIgiAIgiAIQjaIQhUpJCYm8urVKywtLXNlKVZBEARBEARBEHKGJEmEhYVRtGjRDCftFklVCq9evaJ48eK6DkMQBEEQBEEQhFzi+fPnODo6pvu4SKpSsLS0BOQHzsrKSsfRCIIgCIIgCIKgK6GhoRQvXlyRI6RHJFUpJHX5s7KyEkmVIAiCIAiCIAiZDgsShSoEQRAEQRAEQRCyQSRVgiAIgiAIgiAI2SCSKkEQBEEQBEEQhGzIM2Oq3NzcmD59utIyOzs7AgICAHm5w+nTp7Nq1SqCgoJwdnZm6dKlVKlSReOxJCQkEBcXp/HtCoIgZJe+vj4GBgZiSghBEARByEF5JqkCqFKlCidOnFDc19fXV/x77ty5/PHHH2zYsIHy5cszc+ZMWrVqha+vb6bVOtQRHh7OixcvkCRJY9sUBEHQJDMzMxwcHDAyMtJ1KIIgCILwWchTSZWBgQH29vaplkuSxMKFC5k8eTLdunUDYOPGjdjZ2bF161aGDh2qkf0nJCTw4sULzMzMsLW1Fb8EC4KQq0iSRGxsLO/evcPPz49y5cplOFGhIAiCIAiakaeSqocPH1K0aFGMjY1xdnZm9uzZlC5dGj8/PwICAmjdurViXWNjY1xdXfHy8sowqYqJiSEmJkZxPzQ0NN114+LikCQJW1tbTE1NNfOiBEEQNMjU1BRDQ0OePn1KbGwsJiYmug5JEARBEPK9PPMTprOzM5s2beLo0aOsXr2agIAAXFxcCAwMVIyrsrOzU3pO8jFX6XF3d8fa2lpxK168eKaxiBYqQRByM9E6JQiCIAg5K8/85W3Xrh3du3enWrVqtGzZkoMHDwLybn5JUiY7kiRlmgBNmjSJkJAQxe358+eaD14QBEEQBEEQhHwrzyRVKZmbm1OtWjUePnyoGGeVslXq7du3qVqvUjI2NsbKykrpJgiCIAiCIAh5QVxCItefBRGfkKjrUD5reTapiomJ4d69ezg4OFCqVCns7e05fvy44vHY2Fg8PT1xcXHRYZSCIAiCIAiCoD3T/vOm2zIvZh26B8Djd+E8/xCp46g+P3kmqRo/fjyenp74+flx6dIlevToQWhoKAMGDEAmkzF27Fhmz57Nnj17uHv3LgMHDsTMzIw+ffroOnSdGzhwIDKZDJlMhoGBASVKlGD48OEEBQXlaBxPnjzh66+/pmjRopiYmODo6EiXLl148OCBYp3Tp0/TrFkzChYsiJmZGeXKlWPAgAHEx8crvY70boIgCIIgCJ+TrZeeAbD+vD+h0XG0WOBJ47mnxfQ/OSzPVP978eIFX3/9Ne/fv8fW1pYGDRpw8eJFnJycAJgwYQJRUVGMGDFCMfnvsWPHNDpHVV7Wtm1b1q9fT3x8PD4+PgwePJjg4GC2bduWI/uPjY2lVatWVKxYkd27d+Pg4MCLFy84dOgQISEhAHh7e9OuXTtGjx7N4sWLMTU15eHDh+zcuZPExET++usv5syZo9img4MD69evp23btjnyGgRBEARBEHKz18HRin9LEojfm3NOnkmqtm/fnuHjMpkMNzc33NzcciYg5IUwouIScmx/yZka6qvVMmNsbKwYe+bo6Ejv3r3ZsGGD4vGEhASGDBnCqVOnCAgIoESJEowYMYIxY8YAcOfOHWrUqMHbt28pXLgwQUFBFCpUiO7du/Pvv/8C8kqK//33HxcuXEi1fx8fH548ecKpU6cUibCTkxONGjVSrHP8+HEcHByYO3euYlmZMmUUSZORkRHW1tZK2y1QoECac5cJgiAIgiDkV4HhMaw884TutR2Vlv918lPvn0RJQg+RVeWUPJNU5UZRcQlUnnpUJ/v2mdEGM6OsvX1PnjzhyJEjGBoaKpYlJibi6OjIjh07KFy4MF5eXgwZMgQHBwd69epF1apVKVSoEJ6ennTv3p0zZ85QqFAhzpw5o9iGh4cHrq6uae7T1tYWPT09du7cydixY9HX10+1jr29Pa9fv+bMmTM0adIkS69NEARBEAQhv5u46w4n7r1h1ZknSssP3flUtE10/stZeWZMlZA9Bw4cwMLCAlNTU8qUKYOPjw8TJ05UPG5oaMj06dOpV68epUqVom/fvgwcOJAdO3YA8pbAJk2a4OHhAcgTqAEDBpCYmIiPjw/x8fF4eXnRtGnTNPdfrFgxFi1axNSpU7GxsaF58+b89ttvPHny6WTQs2dPvv76a1xdXXFwcKBr164sWbIkwwmZBUEQBEEQPje3XgRnuk6iGFOVo0RLVTaYGurjM6ONzvatjmbNmrF8+XIiIyNZs2YNDx48YNSoUUrrrFixgjVr1vD06VOioqKIjY2lZs2aisebNm3KqlWrAPD09OS3337Dz88PT09PQkJCiIqKUurOl9LIkSPp378/p0+f5tKlS/z777/Mnj2b//77j1atWqGvr8/69euZOXMmp06d4uLFi8yaNYvff/+dy5cv4+DgoNZrFgRBEARB+FztvfGSoMg4hrmW0XUonwXRUpUNMpkMMyMDndzUrXRnbm5O2bJlqV69OosWLSImJobp06crHt+xYwc//vgjgwcP5tixY9y8eZNBgwYRGxurWKdp06Z4e3vz6NEj7t69S+PGjXF1dcXT0xMPDw/q1KmTaWEQS0tLOnfuzKxZs7h16xaNGzdm5syZSusUK1aMfv36sXTpUnx8fIiOjmbFihVqvV5BEARBEITP2cRdd5hz+D73XosePzlBJFWfqWnTpjF//nxevXoFwNmzZ3FxcWHEiBHUqlWLsmXL8vjxY6XnJI2rmjlzJjVq1MDKykopqUpvPFV6ZDIZFStWJCIiIt11bGxscHBwyHAdQRAEQRAEIW2rU4y7ErRDJFWfqaZNm1KlShVmz54NQNmyZbl69SpHjx7lwYMH/Prrr1y5ckXpOUnjqjZv3qwYO1W9enViY2M5efJkuuOpAG7evEmXLl3YuXMnPj4+PHr0iLVr17Ju3Tq6dOkCwMqVKxk+fDjHjh3j8ePHeHt7M3HiRLy9venUqZNWjoMgCIIgCEJeo05/pd03XmotDuETkVR9xsaNG8fq1at5/vw5w4YNo1u3bvTu3RtnZ2cCAwMZMWJEquc0a9aMhIQERQIlk8lo3LgxAF988UW6+3J0dKRkyZJMnz4dZ2dnateuzV9//cX06dOZPHkyAPXr1yc8PJxhw4ZRpUoVXF1duXjxInv37lW7FUwQBEEQBEEQcopMEtMtKwkNDcXa2pqQkBCsrKyUHouOjsbPz49SpUphYmKiowgFQRAyJs5VgiAI+Vu9WSd4Fxaj8vr+czpoMZr8LaPcIDnRUiUIgiAIgiAIudyr4Ci8X4XoOgwhHaKkuiAIgiAIgiDkYu/DY3CZcwqAsxOaqTWmSsgZoqVKEARBEARBEHKpa0+DqDvzhOK+aK3KnURSJQiCIAiCIAi51JBNV5XuJyRmfVvhMfHExmdjA0K6RFIlCIIgCIIgCLlUyiQoQZKQZaH/X1h0HFWnHeWL309pKDIhOZFUCYIgCIIgCLmGJEl8t/EKQzZdRRSpJtWkVImJElk5LLdfyLsNvlWjaqCgOlGoQhAEQRAEQcg1AiNiOXHvLQDBkXHYmBvpOCLdStkolZCYtURTFLfQLtFSJQiCIAiCIOQa+sn6tkXExrP09CN8XoXqMKKcl7yFLmWrVFaTKpFVaZdoqRIEQRAEQRByDT29T1f/S08/Ytvl58w76vvZTGD7/EMkvVZeYKBLSfwDIwmLiVd6PKtjqvSSPUmSJGRZ2YiQLtFSJQjJbNiwgQIFCijuu7m5UbNmTZ3Fk5aBAwfy5ZdfZrpev379mD17tvYDErQq5fvdo0cP/vjjD90FJAiCoG3JGmJuPAvWWRi6MveoL69DonE/fJ9tl5+lenzPjZe8CVVvXFR0XAJH7gYo7me5tUtIl0iqPgOqXoR/Ljw8PJDJZKluU6ZMoXfv3jx48CDd5+aVY3n79m0OHjzIqFGjFMuaNm3K2LFjdRdUOlImsrqW3ucj+W3Dhg06i2/q1KnMmjWL0NDPqyuMIAifj8Rk/d3iP8OL/4TEjEueX/b7oPY2B2+4wgYv/0/7EAVANE50/xM+W76+vlhZWSnuW1hYYGpqiqmpqdb3HRsbi5GR9gbeLlmyhJ49e2Jpaam1feQ2CQkJyGQy9PSy91uRi4sLr1+/VtwfM2YMoaGhrF+/XrHM2tpa4/tVVfXq1SlZsiRbtmxh+PDhObJPQRCEnJT8cl+0qGiG1+NApfuZ5G1CFoiWquyQJIiI0M1Ng78weHp6Ur9+fYyNjXFwcODnn38mPl7ef3f//v0UKFCAxI/fvps3byKTyfjf//6neP7QoUP5+uuv091+Uhe6lStXUrx4cczMzOjZsyfBwcGKda5cuUKrVq0oXLgw1tbWuLq6cv369VTbKVGiBMbGxhQtWpTRo0crHlu2bBnlypXDxMQEOzs7evTokenrLlKkCPb29oqbhYVFhq0mbm5ubNy4kX379ilaLDw8PAB4+fIlvXv3xsbGhkKFCtGlSxf8/f0Vz01q4XJ3d6do0aKUL19epeclJCQwbtw4ChQoQKFChZgwYUKm5WUTExP5999/6dy5c6bHILldu3ZRpUoVjI2NKVmyJAsWLFB6vGTJksyePZvBgwdjaWlJiRIlWLVqldI6Xl5e1KxZExMTE+rWrcvevXuRyWTcvHkzzX16eHgwaNAgQkJCFMfUzc0NkCeeEyZMoFixYpibm+Ps7Kw43vCphevAgQNUrlwZY2Njnj59SsmSJZk5cyb9+/fHwsICJycn9u3bx7t37+jSpQsWFhZUq1aNq1evphmTkZGR0ufC1NQUY2Njxf0jR47g4OCQar+qxnv06FEqVaqEhYUFbdu2VUrgVH2/O3fuzLZt2zJ4NwVBEPIuSaml6tPVv9/7CF2Ek+NkOVBRQrRUaZ5IqrIjMhIsLHRzi4zUyEt4+fIl7du3p169ety6dYvly5ezdu1aZs6cCUCTJk0ICwvjxo0bgDwBK1y4MJ6enopteHh44OrqmuF+Hj16xI4dO9i/fz9Hjhzh5s2bjBw5UvF4WFgYAwYM4OzZs1y8eJFy5crRvn17wsLCANi5cyd//vknK1eu5OHDh+zdu5dq1aoBcPXqVUaPHs2MGTPw9fXlyJEjNGnSRCPHJ7nx48fTq1cvxYXw69evcXFxITIykmbNmmFhYcGZM2c4d+6c4oI5NjZW8fyTJ09y7949jh8/zoEDB1R63oIFC1i3bh1r167l3LlzfPjwgT179mQY5+3btwkODqZu3boqv7Zr167Rq1cvvvrqK+7cuYObmxu//vprqm5uCxYsoG7duty4cYMRI0YwfPhw7t+/D8jfw06dOlGtWjWuX7/Ob7/9xsSJEzPcr4uLCwsXLsTKykpxTMePHw/AoEGDOH/+PNu3b+f27dv07NmTtm3b8vDhQ8XzIyMjcXd3Z82aNXh7e1OkSBEA/vzzTxo1asSNGzfo0KED/fr1o3///nzzzTdcv36dsmXL0r9//yzPf5LWflWNd/78+fz999+cOXOGZ8+eKV5v0vFV5f2uX78+ly9fJiZGzDUiCEL+k/zMHJ/w6V6z+R5cexqU8wHlQ6IFUPNE97/P3LJlyyhevDhLlixBJpNRsWJFXr16xcSJE5k6dSrW1tbUrFkTDw8P6tSpg4eHBz/++CPTp08nLCyMiIgIHjx4QNOmTTPcT3R0NBs3bsTR0RGAxYsX06FDBxYsWIC9vT3NmzdXWn/lypXY2Njg6elJx44defbsGfb29rRs2RJDQ0NKlChB/fr1AXj27Bnm5uZ07NgRS0tLnJycqFWrVqavPSmWJE+fPs1w/aTugTExMdjb2yuWb968GT09PdasWaOopLN+/XoKFCiAh4cHrVu3BsDc3Jw1a9Youv2tW7cu0+ctXLiQSZMm0b17dwBWrFjB0aNHM4zT398ffX19RYKhij/++IMWLVrw66+/AlC+fHl8fHyYN28eAwcOVKzXvn17RowYAcDEiRP5888/8fDwoGLFimzZsgWZTMbq1asxMTGhcuXKvHz5ku+//z7d/RoZGWFtbY1MJlM6po8fP2bbtm28ePGCokWLAvKk9siRI6xfv15RgCMuLo5ly5ZRo0YNpe22b9+eoUOHAvIxSMuXL6devXr07NlTEXvDhg158+aN0n5VlXK/6sS7YsUKypQpA8APP/zAjBkzFNtV9f0uVqwYMTExBAQE4OTkpHb8giAIuVnyMVVxCcr91Lov92J40zJMbFsxp8PKVxJFUqVxoqUqO8zMIDxcNzczM428hHv37tGwYUOlspqNGjUiPDycFy9eAPICBx4eHkiSxNmzZ+nSpQtVq1bl3LlznD59Gjs7OypWlJ/cLCwsFLdhw4YptlmiRAmlJKZhw4YkJibi6+sLwNu3bxk2bBjly5fH2toaa2trwsPDefZMXvWmZ8+eREVFUbp0ab7//nv27Nmj6KLYqlUrnJycKF26NP369WPLli1EqtCSd/bsWW7evKm42djYZOkYXrt2jUePHmFpaal47QULFiQ6OprHjx8r1qtWrZrSOKrMnhcSEsLr169p2LCh4jkGBgaZtkBFRUVhbGysVqnUe/fu0ahRI6VljRo14uHDhyQkJCiWVa9eXfHvpETo7Vv5BI2+vr5Ur14dExMTxTpJiW+SKlWqKF5ru3bt0o3n+vXrSJJE+fLllT5Tnp6eSsfUyMhIKaa04rSzswNQtGwmX5YUu7pS7lfVeM3MzBQJFYCDg4MiBnXe76Rxf6p8zgVBEPKcZNf7cQmpL/6Xezxmx9XnORhQzrn3OpSDd15nvmI2ie5/midaqrJDJgNzc11HkS1pzVOQ1CUqaXnTpk1Zu3Ytt27dQk9Pj8qVK+Pq6oqnpydBQUFKXf+Sj51JXgQipaRtJ/1/4MCBvHv3joULF+Lk5ISxsTENGzZUdIMrXrw4vr6+HD9+nBMnTjBixAjmzZuHp6cnlpaWXL9+HQ8PD44dO8bUqVNxc3PjypUrGVaVK1WqlEaqziUmJlKnTh22bNmS6jFbW1vFv81TfFZUfZ66ChcuTGRkpFrFMDL6HCRnaGiodF8mkynG26myjUOHDhEXFweQYUGQxMRE9PX1uXbtGvr6+kqPWVhYKP5tamqaZvKYPM6kx9NalpjFkbop96tqvGkdv6x0QfzwQV75KTufE0EQhNxEkiTmHvXFqaAZzSp+6mkRn5D2eXrCztv0qls8p8LLMe3+Opsj+3kaGIGNmRH6emKuKk0RLVWfucqVK+Pl5aV0Yefl5YWlpSXFihUDPo2rWrhwIa6urshkMlxdXfHw8Eg1nqps2bKKW/LuZ8+ePePVq1eK+xcuXEBPT09RsOHs2bOMHj2a9u3bK4olvH//XilWU1NTOnfuzKJFi/Dw8ODChQvcuXMHkP+i37JlS+bOncvt27fx9/fn1KlTGj9eRkZGSi03ALVr1+bhw4cUKVJE6fWXLVtWqUpcSpk9z9raGgcHBy5evKh4Tnx8PNeuXcswxqR5tXx8fFR+XZUrV+bcuXNKy7y8vChfvnyqJCE9FStW5Pbt20rjfFIWg3ByclK8xqTPV1rHtFatWiQkJPD27dtUxyYr3fW0TRPxqvN+3717F0dHRwoXLqyx1yAIgqBLt1+EsNzjMT/vvqPc/U/FbmpxCYlcehJIdFxC5isLdF9+gRFbMr6eENQjkqrPREhIiFJXt5s3b/Ls2TNGjBjB8+fPGTVqFPfv32ffvn1MmzaNcePGKUpEJ42r2rx5s2LsVJMmTbh+/bpK46kATExMGDBgALdu3VIkUL169VJccJYtW5a///6be/fucenSJfr27avUkrFhwwbWrl3L3bt3efLkCX///TempqY4OTlx4MABFi1axM2bN3n69CmbNm0iMTGRChUqaPw4lixZktu3b+Pr68v79++Ji4ujb9++FC5cmC5dunD27Fn8/Pzw9PRkzJgxii6UaVHleWPGjGHOnDns2bOH+/fvM2LECKWqiWmxtbWldu3aqZIkgHfv3qX6HAQEBPDTTz9x8uRJfvvtNx48eMDGjRtZsmSJUhGFzPTp04fExESGDBnCvXv3OHr0KPPnzwfIsCtiyZIlCQ8P5+TJk7x//57IyEjKly9P37596d+/P7t378bPz48rV67w+++/c+jQIZVjyimailfV9/vs2bOKsXqCIAj5QXhMvOLfyRvw02upSun3w/fpveoi4/+9penQ8q2j3m90HUK+IpKqz4SHhwe1atVSuk2dOpVixYpx6NAhLl++TI0aNRg2bBjffvstU6ZMUXp+s2bNSEhIUCRQNjY2VK5cGVtbWypVqpTp/suWLUu3bt1o3749rVu3pmrVqixbtkzx+Lp16wgKCqJWrVr069eP0aNHK7V0FShQgNWrV9OoUSOqV6/OyZMn2b9/P4UKFaJAgQLs3r2b5s2bU6lSJVasWMG2bduoUqWKZg5eMt9//z0VKlSgbt262Nracv78eczMzDhz5gwlSpSgW7duVKpUicGDBxMVFZVhF0hVnvfTTz/Rv39/Bg4cSMOGDbG0tKRr166ZxjlkyJA0uxVu3bo11edgxYoV1K5dmx07drB9+3aqVq3K1KlTmTFjhlKRisxYWVmxf/9+bt68Sc2aNZk8eTJTp04FUBpnlZKLiwvDhg2jd+/e2NraMnfuXEBetKN///789NNPVKhQgc6dO3Pp0iWKF8+d3T00Ea8q73d0dDR79uzJsACIIAhCXpO8G1rytilV6ymsOecHwIHb2h+PlJ+8DI7ibWi0rsPIF2RSVmsK51OhoaFYW1sTEhKS6oI4OjoaPz8/SpUqleFFoqDMzc2NvXv3pjtXkaB50dHRVKhQge3btysVPshpW7ZsUcxDlROTKn8Oli5dyr59+zh27Fi664hzlSAIec1V/w/0WHEBgA7VHTioQnLkP6cDANefBdFtmVeq5XlRyZ8P6mS/T2a3R0+Mr0pTRrlBcqJQhSDkQyYmJmzatCnVuDRt27RpE6VLl6ZYsWLcunWLiRMn0qtXL5FQaZChoSGLFy/WdRiCIAgalfyCXpWEKklUbIJSQiVkTXyihJFIqrJFJFWCkE9lNiGzNgQEBDB16lQCAgJwcHCgZ8+ezJo1K8fjyM+GDBmi6xAEQRA0ziCLF/SRsfGZr5RHbL/8TGf7ThQd17JNjKkStM7NzU10/ftMTJgwAX9/f0X3sz///BMzDc2pJgiCkN+FRMXpOgSd0VNjbsUkASHRaRZC8nr8nifvwjURVo6ae9RXZ/tOEJMBZ5tIqgRBEARBEHTsmHcANaYfw/3wPV2HohNZmS+pgftJ5qRxvPqsvkTzBZ6aCCvPKFEwez9gismAsy/PJlXu7u7IZDLGjh2rWCZJEm5ubhQtWhRTU1OaNm2Kt7e3xvctansIgpCbiXOUIOQ90/fL5xZc6flEx5HoRlZPWzuupj91SV6TnRFNBvrZGw91+v7bbD1fyKNJ1ZUrV1i1ahXVq1dXWj537lz++OMPlixZwpUrV7C3t6dVq1aEhYVpZL9Jk6DGxsZqZHuCIAjaEBkZCciLWgiCkPsdufual8FRivtLTz/izosQHUaU8yTEj0HZYaiXvUv6Mdtv4vc+QkPRfJ7UKlQhSRKenp6cPXsWf39/IiMjsbW1pVatWrRs2TJH5o8JDw+nb9++rF69mpkzZyrFtnDhQiZPnky3bt0A2LhxI3Z2dmzdupWhQ4dme98GBgaYmZnx7t07DA0NFZPjCoIg5AaSJBEZGcnbt28pUKCA4ocgQcgLbjwLwtLEgLJFLHUdSo4btvm60v15R32Zd9Q3T5cGV5c2GtjnH/VlfJsKmt9wLpTdliqAd2ExlCpsroFoPk8qJVVRUVH8+eefLFu2jMDAQGrUqEGxYsUwNTXl0aNH7N27l++//57WrVszdepUGjRooLWAR44cSYcOHWjZsqVSUuXn50dAQACtW7dWLDM2NsbV1RUvL690k6qYmBhiYmIU90NDQ9Pdt0wmw8HBAT8/P54+faqBVyMIgqB5BQoUwN7eXtdhCILK3oRG0/VjWezPKZEQPtFGUrXk9COaVSxCHScbzW88lzHQz/4P/ebG4oe47FApqSpfvjzOzs6sWLGCNm3apNml5OnTp2zdupXevXszZcoUvv/+e40Hu337dq5fv86VK1dSPRYQEACAnZ2d0nI7O7sMEyB3d3emT5+ucgxGRkaUK1dOdAEUBCFXMjQ0FC1UQp7zNDBS1yEIOvD8QyS7rr+gVgkbrY3p6b7c67NI1A01MMeUGI6bPSolVYcPH6Zq1aoZruPk5MSkSZP46aeftNKK8/z5c8aMGcOxY8cwMTFJd72UpTUlSUqz3GaSSZMmMW7cOMX90NDQTLsx6unpZRiDIAiCIAiqE8VVPk9dl3nxPjwm8xWFTJUqbE7X2sWYts+bDYPq883aS2muZ5gQh3lsFIkyPWL0DYkxNFY8JsqqZ49KSVVmCVVySS05mnbt2jXevn1LnTp1FMsSEhI4c+YMS5YswddXXts/adLRJG/fvk3VepWcsbExxsbG6T4uCIIgCIJ2fa6XcpIk8eu+u7oOQ2dEQiWXkCjxISKWwIjMe0F9Xb8E25JNEjy3e3UuPglkcodKFDAzonfd4p+6AkoSVd48pvXDi9R74UO598+wjQxW2l6IsTmvrGx5WLgEhfWaQzMXaNAALD+/sY3ZpXKhiocPHzJ16lRWrlyJlZWV0mMhISEMHz6cmTNnUrp0aY0HCdCiRQvu3LmjtGzQoEFUrFiRiRMnUrp0aezt7Tl+/Di1atUC5FX6PD09+f3337USkyAIgiAI2Zf4mbZUTfvPm80Xn6X7+PvwGApbiB9+87t+ay/h9ThQpXX7OisnVU0r2tKr3qceVgb6epCQQGcfD4Zf3Emld/4Zbs86JgLrdxHy9e6dAXdATw9q1wZXV2jSBBo3Bpv8Py4tu1ROqubNm0fx4sVTJVQA1tbWFC9enHnz5rF8+XKNBpjE0tIyVYuZubk5hQoVUiwfO3Yss2fPply5cpQrV47Zs2djZmZGnz59tBKTIAiCIAjZlzynyqzbfn5x7ekHNl3IeLjEv1dfMLxpmRyKSNAVVROqDYPqUcbWQmmZqWGKMbSXL8O337LorrwFNEbfkBNl63OuZC1u25clsJAdb/XNkAHmsVEUCf+AY8gbKr3z53uLEAr63AI/P7h6VX5bsEC+3SpVoFEjcHGR/79MGfgMvqfqUDmpOnPmDH///Xe6j/fq1UvnycuECROIiopixIgRBAUF4ezszLFjx7AUTZiCIAiCkGspJ1Wfx7XaoPWpi26l9PuR+/k2qUoU43fUVtHeSum70aGaA5YmH4vHJSaCmxvMmgWJiQSbWLCqfjc212pPqMmnRMzUUJ/EuAQAQk0sCDWx4FHhEniUqYfrkAY0KF0Inj+HM2fkN09P8PUFb2/5bdUq+YaKFIGaNaFyZXnCVbkylCgB9vZgoNaMTfmGyq/66dOnFClSJN3HCxcuzPPnzzUSlKo8PDyU7stkMtzc3HBzc8vROARBEARByLrkE78mShJ65P+sKjI2QaX1FhzzpXONopSzy18/EMeLpEpt+noy9JJlVaNbfKxhEBoK33wD+/fL7/ftS9NCHQg2taJYAVNCk00sndEPFopuuMWLQ9++8hvA27fg5SW/nT8vb8F6+xaOHZPfktPTAzs7cHAAa2v52Kykm7GxPOHS15ffkv87WWCSJBGfKGHYsoW8ZSyPUDmpsra25vHjxzg5OaX5+KNHj9LsGigIgiAIgpCR5NfXn8u1tqqtcYtPPWLxqUf5riy4qDSnPgM9GakqpwcHQ5s28m5/JiawZg307UvwzwcBKG9nwfTOVfhu01WADH+ueBeWTuGQIkXgyy/lN4DoaLhx41Prlbc33L8Pr15BQgK8fi2/ZZEMMASCE6BAfkyqmjRpwuLFi2nevHmajy9atIjGjRtrLDBBEARBED4PyQtVSJ9tLUD1PA2M4LcD9xjetDR1nArqOhy1xScm6jqEPMdAX7mlShb8Afr1kLccFSwIhw9D/fqpnlc+WStnRuMVx2y/SZeaxTIPxMQEGjaU35JLSIB37+DlSwgIkLeghYXJb6GhEBsrXychAeLjP/07IVmrrSQpCnHo6Rehd+bR5BoqJ1WTJk2iYcOG9OjRgwkTJlChQgUA7t+/z9y5czl69CheXl5aC1QQBEEQhHwqxZiqz4EMGdkpJj9q2w1uvwjhxL03ebIVK0rF7o+acP7RexqVLZxj+9MWfT2ZooXTMCEOp+/6yROqQoXg5EmoUSPTbWi1Y62+vnxMlb19tjYz6WMr2/D6eWs8oZ6qK9aqVYudO3dy5swZGjZsSMGCBSlYsCAuLi6cPXuWHTt2ULt2bW3GKgiCIAhCPpRyTFV+FhIVx+9H7hOboF5LzcHbn7pTRcbGc/tFiKZDy1ETd93OsX31XZP2RLi5wbyj91Var4CZIaaG+shkMhb0qM7hu39jfP6sfKzSiRMZJlRKjVN5aLiifh6rWKNWeY6OHTvy9OlTjhw5wqNHj5AkifLly9O6dWvMzMy0FaMgCIIgCPnQD1uvEx4TzzfOn8Zr5/ehNtP3e7P7+ku1nzdy63U6VJe3SLX/66ymw8pxp33f6TqEXGHp6ccqrbdnRCNF173uZ/6FI7vlLUP//iuvwqeivJSm6KUaQJa7qV3z0NTUlK5du2ojFkEQBEEQPhORsfEc+Nj60rS8rWJ5fm+puv40KMvP/W7jVWZ3rYp/YKQGIxLyAoOkBOPiRZg4Uf7vv/6SF6nIp/JaS5XK3f8ALly4wJEjR5Dy+QlPEARBEATtCo+JV/w7+S/S+f0SIyEbL/DEvTfUn31Sg9F8Pu69DtV1CKkcuatmhbygIPjqK3mRh169YMQIlZ6WPDfJbd+vx+/C6bbsPKd936Z6TF+tLEX3VA535syZjBkzhhUrVtCzZ09txiQIgiAIQj4XFv0pqYpL+HSld/1Z1lty8oKEhFx2VZvH9XEuodJ67XJhl8lhm6+rvK6ZkT6MHAlPn0KZMrB6tUp1+W0tjSlgZqS4b2iQ8aW/JEn4vAolTs0xf1k1ausNrj8LVkyGHR33qYBJXuv+p3JStWrVKlauXMm///7L/v37CQwM1GZcgiAIgiDkYxHJWqo2XfBX/HvQ+iu8SjZZaX6TnZYqITWDPHbhra4CZobM7VGdQiePwLZt8sl1t22DTOaGXdO/Lq0r2/Fzu0pYGBuwb2QjDoz6Av1MjtcGL3/aLzrLz7vuaPJlpOtDRKzS/UdvwxX/LmBqlHL1XE3lpKpEiRJcvXqVmzdvYmJiIib6FQRBEAQhy96Hf5po9GmKMUL+7yNyOpwco5fHxonoUuNymZdBz+/Hs11VB3qVsYDhw+UL/vc/qFcv0+e1rGzHqv51KWguT0xqFC9A1WLW2FkZZ/i8OYfl1Qh3XX+RvcBVlHJeuuQtZHltfKXKSdW6devYsWMHI0eOZPv27RgaGmozLkEQBEEQ8qmVno8ZvOGqrsPIUXdfhvDNmku8DonW+LZXn3mi8W1qy/MPkUiSRCWHzH+cH9msrOLfgxuVSnOdzFpe8rqExET46Sd4/RoqVAA3t2xtb2mf2jhYm6T7eEx8sqQmh0txBkXEstHLX3E/IY+VAlU5qSpfvjzHjx/n8uXLtGvXTpsxCYIgCEK+c9Q7gCGbrhISGafrUHTO/XDGc/PkrUup1OITEhm84QqLTj5ULOuxwotzj95rZX+zDt3TynY1bdMFfxrPPc30/T5UcrDMdH1D/U8JU3VH6zTXUSepWnLqYaruZrldsfs3Yf16+fipdevAJP2ESBVOhcw5M6FZmo/VKF5A6X7HxeeytS9VJG+MGr7lGntvvlLcj8+vSZUgCIIgCFk39O9rHPN5w4LjvroOReskSeLsw3e8CZW3ysQnJPLTjluM3HJdpQHweazXTyon7r3h1P23/HH8gWJZdJx2B/57aSlh06RZB+XJ3wYvf5VKyxvofbpMTS95Uiepmn/sAbV/O84JnzcqP0enJInOm/6Q/3vwYHBx0chm0ytVHhuv/Bn1eR2K23/eXPX/oJH9piX5V/3iE+X9JCTmTLEMTVEpqdq+fbvKG3z+/Dnnz5/PckCCIAiCkJ8Fhsd+/H8MXZac4+9kRRryi9O+b+m39jLOs0+y6YI/sw/dZ9f1Fxy885rdKozVCIqM5eDt18TEJ2S6bm4UFZfzcfdZc4khm67m6mlvkrc8qDLXlkGylip9PRk2kSG4+N+k9YML1H55D+P42CwVqvhuU97oetrO9zylHt4GMzP47TeNbVdPT8b/2lRItTytsvMbvPzpseKCxvatjrzWUqXS5L/Lly/Hzc2NQYMG0blzZypVqqT0eEhICOfPn2fz5s2cOHGCtWvXaiVYQRAEQcgrImLiMTHUT/eX9MWnHnHrRQi3XoTQr2HJnA1Oy848+NRqMnWft9JjwSp0fxy17QYAQ5uUZlL7SpmsnfvlVJfPYz5v8H0TRkX73FlMTN0xMob6ehQyN6Ki92Va/jCPtmfOoJesbSPKwBi/W+045NSGx4WLazpcnboyvjGFnEfL70yYAA4OGt3+yGZlmXdU963mGf0G0LKSXc4FogEqtVR5enoyf/58Tp06RdWqVbGysqJcuXJUq1YNR0dHChUqxLfffkvJkiW5e/cunTp10nbcgiAIgpBrvQ+Pocq0o3y5NP2eG1GxebMVRhUZtZYYqjGj5/5brzJfKRdK/vKjYhOoMeNYju07KEJ3Y/ai4xLoteKCUrfHJEtPP1J7e4bhoVy+v5Et/0zB6Iwnekg8sSnKTYfyvDMvgGl8DJVP7OXI+h8Ye24LBgnxmW80F4hLSOSYd0CG6xTYsBq9J0/kydT48TkUmS6kf64ob5f5uLvcRKWWKoCOHTvSsWNHAgMDOXfuHP7+/kRFRVG4cGFq1apFrVq10NMTQ7QEQRAE4dS9twDceRnCuYfv2eDl9+nBjw1X+flPZkbtETMO+Ki8HVk+KJf94E1Yju7v69UX8Z/TIUf3mWT/rVdc9v/AZf8PjGtVXukxdVpFXMoUwuhtACW/bIvs7l35l2XECBrF1+aldRH5SpJErVe+/PnwACUveTD2/DZqv7zPyC9/JszYXJMvS+NGbLnO8QzGdVlHhWEwe5b8zm+/gXnufj3ZkYt7q6pN5aQqSaFChejSpYs2YhEEQRCEfCH53CvfrL2Uzlp5P2FIj6bml8kHORVdMmitzG9i4jVTWGBL++LQpC+ypJaanTvBxYWXPx/8tJJMxo1iFTk4qDO+C1cz58gimvjf4J+tP/P1V7MJMc19rRz/XHnG9ivPufEsOMP1RnltRxYUBNWqwcCBORKbruS1uagyko9/JxMEQRAE3cho6EhSnpAfEob0qFDgTyV59Rjlo+vEHGceE4msY0d5QlW6NJw/n3HVO5mM/yq70rPP72BvT+W3fmz41w3zmIwLYeiioMfEXXcyTahKBL3m21uH5Hfmzwd9fe0HloPeh8dw5O5r4hMS2XPjBUH5aIoJkVQJgiAIgoZldL2W1KUtj+YLKtHUBassXx+l/Cfb77oksebUYrh5E4oUgRMnoFTak/6m5G1fFk6cIMjEklqvfVm673f0EtMftxiXkDsz3wmeG5HFxUGbNtC6tVb31btuzhf36LLkPMM2X2f1WT9+/OdWju9fm0RSJQiCIAgaJqlweZlXW2FUoalGgLx6jLLy8gc3Ui15yNWy+cZ/c+MQDW+fBSMj+O8/lRKqHnUcgY+V4qpUoX+vGUQZGNPU7xrjz/6d7vNUmS8tp9V+cY+OvufkY8jmzdP6/nLy+yVJEoHhMbwMjgLkk6HnNyKpEgRBEAQNy7Cl6uP/9fJqxqACjY2p0shWcr8WFYswtVNlDo9prFjWuFxhHUaU88q9e8qU0x+n5Pn9d3B2TnfdYgVMmdKhEsv61sbOyoT7v7Vldf86ANxxKMeEdvJS5CMu7qTd/XNpbiMht/XRlCSmnF4j//fgwfLxVFqWk6cg98P3qTPzhOJ+Ljv6GpHlpCo2NhZfX1/i4/NG+UpBEARByCkZXTAkXcjk54RBU3N25ofqf5k5/3NzVvevCygn2kv61M7yNpeefsS1p0HZji27ouMSWHjiAXdehGS4nl5iAnMPL8IkPpabVRvCmDEZrm9pYsB3jUvTvpp87iYTQ32lz8r+yq6srN8NgDlHFuMQ+i7VNqRc1lDV3vc8tV/5IpmZwYwZObTXnPt+rTrzJMf2pStqJ1WRkZF8++23mJmZUaVKFZ49ewbA6NGjmTNnjsYDFARBEIQ8R4VfwfNzwqCpMVWhUXlzELuqr99IX4+i1ibofZwgupiNqeIxKxO1CzQrzDvqS/flXll+flalfNUrPZ+w8MRDOi1Ju7UoSZ9bR6n12pcwI1NWD5iUbhPK6v51qVLUisVf10p3W0s/JqPzmvTnpkN5rGMi+OPgH6nGV2m76lxgeAzj/rnJpSeBma5rFB/Hzx7rAZBpYaLf9KhzCnobGq3Znee2lkINUDupmjRpErdu3cLDwwMTExPF8pYtW/LPP/9oNDhBEARByEu8X4Vw6M5rlbq25OOcSmMXrJF5dIJkVV/94bGNlZJrC2MDLkxqztUpLZHJZHSoljMX15qS8m33fpVxCxWAbfgHfj23CYD5TfoTUqBIuuu2qmzHwdGNKZfBpLAdqjtw/ufmNK1alLtzlxFhaELDZ3f4/soepfUSJIkpe+/w9aqLJGiqaTWZ6ft92H3jJb1XXcx03X7XD1Ai5A1vLArm6ES/emqcg9ovOsuas0/YfPGpRvad2REf0NBJI/vJSWonVXv37mXJkiV88cUXSieCypUr8/jxY40GJwiCIAh5SYdF5xix5XqGZZMVJdXzcQdATV2jRsUl8PxDxqWx8zLDNGaAdrA2pbCFMQB/9K7BnhEZlBPP5VT5GEz22oxxZDi37Mvxd6329NfAxXSxAqasGVCP6k3rML3FEADGnd1MmffPFeskShKbLz7jwpNArXSVfBoYodJ61lFhjPbaDsD8xt/k6ES/6pyD3ofHMvPgPabsvauRIh9h0WkPH2pYuhBflC3MtE5Vsr2PnKZ2UvXu3TuKFEn9K0JERES+7sogCIIgCKq6HxCW6Tr56U/mrIM+9FzhRezHyV812bWq54oLGttWbpPZZ8DYQJ9aJWxyJhgNSNntMbNukBXe+dP55nEACqxextZhjWhdxV5j8ejJZOyo3opTpetinBDP70cWIfs4mCp565Q6LTaqUvUbMNprO9YxEdyzLYnF999qPhAtSJQkPkTEEh2n3JKsTrdfv/dpJ51/9q7J5u+cFV1i8xK1k6p69epx8OCnGa2TEqnVq1fTsGFDzUUmCIIgCHlUWHT6Y4Hy4zxVq8/6ccU/iJP33gCarewVEBqd4fHMlVQ8AKkuHBMS4OpVWLsWFi+GffvgwwfNx5dDMrvGnnR6PXqSBD164NSxJQ1KF9Lo/mUy+X8mtxlJuJEpdV/eo991+TXsvdehivUM9bVfDPuHrddTLXMKeqWIZ3azwQxpXk7rcSSX1R923oXFUPu34zSee1qxLDougRYLPFO9zv9uvWLJqYcqb9vUMO9Odqz2KEh3d3fatm2Lj48P8fHx/PXXX3h7e3PhwgU8PT21EaMgCIIg5CkvgqLSfSypFSc/tVQlGb7lOtM7V+Hg7dca3e6obTfYMKi+RreZGyhyqsREeSLl7g5+fsorGRiwqJwLCxp/w1ObojkeozpS5lAZ5VSN/G/S1O8a8foGGLi7ayWepGqKr61smeM6kJnHlzPhzCZOlnVm8IarivW0kVSlTCgPpPGdmOC5EaPEeDxL1eZsqdrYmBlpPI6MZPUU5PVYXnzjXVgMV/w/UK9kQTwfvOPJ+wievI9gSZ9P647edkOtbRsb5t3ZntSO3MXFBS8vLyIjIylTpgzHjh3Dzs6OCxcuUKdOHW3EKAiCIAj5xr6br4hLSMy381RN+89b49v08E1dEjs3U2XyZ/j4q3xAALRsCUOGyBMqKyto3Rq6d4fKlSE+ns73znBk3Si+vnkkT1VNS7cbqCQxwXMjAOdb9YSyZbWy/+TfsS212nHZsTIWsVHMOrpU6TimMbQt25J/BtIqhFH7xT06+J4nQabHrGaDAXlp+JyUfNjO3B7VVX5e8m5+PVdcwOvRe419LI0NPpOkKi4ujkGDBmFmZsbGjRu5e/cuPj4+bN68mWo5MEmZIAiCIOQ2x33e8O/V55mvmIz3q9D81f9PUFthC2MKvHoGLi5w+jSYmcGCBfD6NRw9Cjt3grc3XL/OOacamMbH4H50CdNOrlKMC8qMup/L7Ep5YZ3ehbar33VqBDwk0tCYo12/01o8yXtXSjI9JrUdRYy+AU39rvGlj0emcWpKRKxyUQaZlMjUU6sA+LdaSx7YltRuACroVbe4yuv+duCe0v3Tvm/RVKffvFyfQa2kytDQkD179mS+oiAIgiB8Jr7fdJX/7bzNiyDVq9RN3+/NSs/8PxmmJj3/EKmV0te6MqKcCbRoIW+dKl0arl+HcePkyVVytWrRr/dvzHEdCMCga/uZfWSJSpnA/3be1kLkqkszQkli1Mdqd1tqtiPCSnuFOFJeoD8uVJy/Gsn7pk09uZpCEcGAduasSr7JiBjlpOpLbw9qvn5IuJEpCxr3A6B1ZTuNx5CZlPmLqvlMeIrXExufqJHE1CgPt1JBFrr/de3alb1792ohlIwtX76c6tWrY2VlhZWVFQ0bNuTw4cOKxyVJws3NjaJFi2JqakrTpk3x9tZ8FwRBEARBSJL8Ij+9EsFpyajk+uesS830xww1nnua4Zuv5WA0WZfZBaZZbBQdJw+BZ8+gXDnw8oIKFdLfnkyPFQ16MLbjTyTI9Pj69jHGnt+q4ag1L61qcA2e36Huy3vE6Buyul5XrTbYplVAblX9bvgUKUXBqFCmnZS3FiVmv0J4hpKfJ0xjo5nouQGApQ178c5Cd9UdU5ZUH986/c9gRmITEjXSTmWQByv+Jad2UlW2bFl+++03evTogbu7O4sWLVK6aYujoyNz5szh6tWrXL16lebNm9OlSxdF4jR37lz++OMPlixZwpUrV7C3t6dVq1aEhWVe1lYQBEEQsiL5fC15/YIgN6hWzDrDx4/5vMmhSLRrxvEVFHl8D4oUgSNHwC7jVor6pQoCENP7a35tPRyAsee30ckndxUIS35hHRYdx9mH71Ot84PXPwD8U701by0LabRSZEppjVuM1zdgQrsxJMj06HzvDC0eXaLTknMam9Q2SfJ8MnnSNuzSLuzDP/Dc2o51dbtodJ/qSnl4snoOi4lL1EhrX14/h6pd/W/NmjUUKFCAa9euce2a8i9GMpmM0aNHayy45Dp16qR0f9asWSxfvpyLFy9SuXJlFi5cyOTJk+nWrRsAGzduxM7Ojq1btzJ06FCtxCQIgiB83mLikyVVOVCWOT+q7mjN7RchQN6/qEoSEpV+Cfgu3qfpcfckiXp66P37r7zrXyY2DqqP75swajha865LFbZ0DqSvx3bcjy7B264MTwo5ajL8LEveMjVp951Uj9d+eY8vnt4iTk+fFQ26az2e9IrB3LUvy+r6XRl2aRczjy7jcvGqTNl7l971imulEmBSwuEQ+o4hl3cDMLvpIGIMPlX700XH1pRHJ6uvPSZBM93/cqK0vTapHb2fn1+6tydPcqZ/eEJCAtu3byciIoKGDRvi5+dHQEAArVu3VqxjbGyMq6srXl5eGW4rJiaG0NBQpZsgCIIgqOJ9eIzi3/klIchJE9pWoK5TQcV9VRLT+wG5++/0jWdBuB++n+ZjRcIC+e3YcgAu9hkOTZqotE1TI31qFi+ATCajiKUJV78bx4US1bCIjWLZXneM4nPfPF5plRBPaqXaVbUFr6yKAOoVSNCkPxv1wc/GAYfwQH72WA9Ax0XnNLb95DnG4lOPAJh+YiWm8TFccqzC4QqNNLYvTelZN2vJeWy8Zrr/fXZJlS7duXMHCwsLjI2NGTZsGHv27KFy5coEBAQAYJei+dzOzk7xWHrc3d2xtrZW3IoX182XWxAEIa/xfx/BH8d8CYnMfRd0OeFDRCwtFnzqfpWHKl3nGiUKKhdlqFsy8/ElbRee5UNErLZCUtvL4CjmHrlP6z898XsfwaKT6U90OvXkaqxiI7npUJ4LXw/P8j7dutbkmvtS4gvbUvH9U8ZkML6qz+qLLDzxIMv70pQqAY9o/uQqCTI9ljfooVjuXKpgBs/KnvgMBkvFGBozqe0oAPrePILzszv4vtHckJHkrXa7rr+g9YMLtH54kTg9fXkXzhStaLr4SSbl5NOWJoZZ2k5CopTm+Dl15fVCFWp3/xs8eHCGj69bty7LwWSmQoUK3Lx5k+DgYHbt2sWAAQOUJhxOWeVFkqRMSzNOmjSJcePGKe6HhoaKxEoQBEEFQ/++hu+bMG69CGHj4Pw3MWtmTt1/q3Q/4TPNqrJzMaUvkyldW1a0t1LpeZP33GH5N7ljbsxGc04p/t1svodi/FNKTR9fpaPvOeJlevzS5gea6Wd9TiJrM0N+6NMYTFdCt24Mu7SLo+UbctuhfKp1vR4H4vU4kLEtUz+maRl9FH64sAOA/yo1UZrEWJsltDOrFnmxRHW21mhLn1tHmHNkER0Gaqc2gEVMJNOPrwDkhTJyQwl10Fwid+r+W4pYGmd7O3m9sV/tlDAoKEjp9vbtW06dOsXu3bsJDg7WQoifGBkZUbZsWerWrYu7uzs1atTgr7/+wt7eHiBVq9Tbt29TtV6lZGxsrKgomHQTBEEQMpf0q67ng3dExMTj9p83l/0+6DiqnJO86x/Aj//czPK2NPErr66oG7qDtYni32ldUBcyN0q1LKU7L0PU22kOioxNXQXSKD6O6SfkF9Xr63bGx660Zlo2u3blv0pN0JcSmX9wYYbdABN1WI6+3LuntHvgRSIyljbspfSYNi+kVcnX3JsN4rVFIUoFvWbaiVUa2e+ve+9yP+BTq9cEz404hAfiX8CBRS5faWQfmtCttry7Xw3HjAvEqGL7FeU50Z4FRrL2nJ9a28jrE6KrnVTt2bNH6XbgwAGePHnCV199RYMGDbQRY7okSSImJoZSpUphb2/P8ePHFY/Fxsbi6emJi4tLjsYkCIKQn90PCGXQ+svceaF8Ubvo5EM2ePnTa+UFHUWW81L+Cn7zebDGtpWXZKfqV1oX1Ab6mV9Y5ebjlZBGj7M+Nw/jFBzAW3MbFn6cJ0lTL+HA97/wzqwA5QOf8e3VvemuFxajesl/TUtqpTpcwYVHhUsoPabNlqoytha0q2qf4Tphxub82OknEpHR+85x+OefbO/372SVBJs+vkL/GwcBmNxmJDGG2W/R0ZQK9pZcndKSXcM1f63cZuEZfjvgo9Zz8nhOpZkxVXp6evz444/8+eefmthcmn755RfOnj2Lv78/d+7cYfLkyXh4eNC3b19kMhljx45l9uzZ7Nmzh7t37zJw4EDMzMzo06eP1mISBEH43Hy74Sqnfd/x5bLzSssfv4tQazu+AWFExyVoMrQcp8kL+6DIOIJy0Tghdah7GJJfN+nrpZwpBwz0Mr80eR0SzZzD99WacDmnpEwULWIiFZPdLvyiDxHG8nFkkobqvUVaFmBW828BGOW1naKhb9NcLy6tbE/D0npNpT68pOP9swD4DR2j9RiSk8lkKnUTvViiOkuSWtCGDAENFV4rFBHMvEN/AbCuTmfOl6ypke1qUmELY41XLo1PSCQqC+f3z66lKj2PHz8mPl57v4K8efOGfv36UaFCBVq0aMGlS5c4cuQIrVq1AmDChAmMHTuWESNGULduXV6+fMmxY8ewtLTUWkyCIAifm5fBUUDqhOLEPdXnDzru84Y2C8/QbVnG1VlzO03My5Kk3qwT1PrtOBE6bE3IinMP3/PsQ9YTGz2ZLFVxCkMVWqoAVng+pv/ay1net7Z4v1KuTjjk0i4KRYXyuGAx/qn+qUqxpj4+MhnsrdyUS45VMIuL4deTa9JcT5OfV3UMv/gv+lIiJ8rU44cfe+okBlX89UUfrharBKGh0Lmz/P/ZoJ+YwML987GNDOZ+YSd+bzpQM4HmAc6zT2bpeZnNU5fbqV2oInlRB5B3wXv9+jUHDx5kwIABGgsspbVr12b4uEwmw83NDTc3N63FIAiC8Dk7cjd1iWR1JBUP+veqvO+9z+vcXRo7M9oYo+L3PoKqeeTCYsfV50zYeVvt5yXv7mVjbkQNR2uW9a1NlaLyMc3q/Gr+5L16LaSaltlYONvwIL772CVvbpMBJOh9Kk6h0XF0MhlTWw/n4PrRtHvgheuTa3iWVm6hyYmcKuU+HEPe0NX7NABLXL6ipfZDyJSNmSFBaVQsTdDTZ2SXiVzcMwmZtzf06QP79kEWC4pMObWGxk9vEmFowujO/1Oakyq/C8xiq/u0TlU0HEnOUrul6saNG0q327flJ9QFCxawcOFCTccnCIIg5BLDNl/P8nN/P3KfRnNOEZiiuENeFB2XQN2ZJ1j0ce4ZTcqJLlqasP/WqywlVEnm96zB6OZlFXMvta/mgFMhcwD+6FVDU2FqXWaJyuCr+zCLi+GmQ3mOlm+o9FinGkXTeZZ6krpM+dqWZEOdTgC4nViRqmhFToxDS7mHYRd3YpiYwJmStbhZtILW95+eoa7yCZbPTmiW4XpvLAuz/n9/gokJHDwI334LCep3Y/v28h4GXdsPwLiO43JNtb/cbFTzslibZa2ke26hdkvV6dOntRGHIAiCkM8kJkqKeVCWezwGYI2a1aByo00X/FNV/tOUiJi8Mc5M3apeyTkGvqTHzuPg4QHD3oClJdSqBb17Q9u2VHcswKVfWmS5C1FOyqhLnWVMBH1vHAJgsUtvxSj8zd86U8rWnGIFTDUSQ/JhKAu/6Evne2coFfSab6/uZXmDT93tcrr7n0PoO3rdlhcQW+LSW7H8wqTm/H3hKcs+nhNywqR2lZjUrpJK6+42KMrgLVugVy/YuFF+gFetAkMVL/gXLuTX0/LeVXOb9OdoeVEw7XOhdktV8+bN0yydHhoaSvPmzTURkyAIgpCHXfX/wKYL/tSccYy7KUpfS1Ler/CUVtchTflm7SVO+6ZdaCA3ycrluWlsNDOOLWfbHwNhzhy4eBH8/OD2bfnFa/v2UL8+3LqVq6v7JZdRlP2uH8QqNhLfwiU4VaaeYnm9UjYaS6gAjJJ1lww3NsO96SBAXrTCPvT9p1hz+JAOvbQLo8R4LpSoxuXiVRXLHaxNqe5YIGeDUYOeTAbdusGWLaCnBxs2QOvW8PbT9/LU/TfMPXJfuQtwTAyMGgU//gjA4oa9WdYg/TFkKSe6zetFGrIrP7x6tZMqDw8PYmNT95WMjo7m7NmzGglKEARByLt6rLjA1H3ehEbH03HxOaWxI3n5ukGSpBwpJJGd+a5ywqvgKO69Um88nGPIGw5sHEv/GwfRS0yUJ1B//w1eXrB/v/xi1Nwcrl6F+vWx376Jyg6qzRsZG6+7LpPptf4Yx8Uw6Op/AKxw7oEk+3S5pa/hL8Ev7ZVbYPZUacaVYpUxi4vhF491mcaqSUm7KBIWyNe3jgKwyOVrre9Xk26/CCE4Mlbecrp3L1hYyFtVK1WCZcsgKorBG66yzOMxB+68lr/ow4dJrF0bliwB5C1UCxp/k+EJL/kjdlbGqd5HXTkw6otUy8yNsj5R9edE5e5/SWOnAHx8fJQm2k1ISODIkSMUK1ZMs9EJgiDo0L3XodhbmWCjwmSk+d2HbJT79nocqPh3Hs6pGLP9Jv/deqX1/YREaa8lTBNc5pxSa/1SH16yfdsk7MI/QLFi8l/+W6YoWdCxI0yeLC9n/d9/6A0fxgE3N0pTN9PtrzvvxzDXMmrFlFXhMfFs9PKnbVV7ythapNv60/PuSWwjg3lhVYQuc8ZRJzyeKXvvAvIy8ppUoqCZ8gKZDLdWQ9m/YSyd751ha822XCxRXWPzYmUkqaT60Mu7MU6I47JjZS6UqAaAo43mWue07bjPG3rWLQ6dOslbVPv2hVu3YORImDyZVbYVeWVZmErXDeDhTfD3Rw+ItCmE2YZ1LPNSLwm5OKmFVufrUkdaf+/0tDlDcz6icktVzZo1qVWrFjKZjObNm1OzZk3FrU6dOsycOZOpU6dqM1ZBEIQc4/MqlHZ/naXurBO6DiVX+GX3nSw/N/n4o1xy3ZAlqiZUtpbZm9xTR5WvtaJwRBD/HZyJXfgHfAuXgEuXUidUSezs5C0DH68l9Nzc+Pbynkz3MefwfQJCojUXdAbcD91j3lFfWizwBNJ+r/QTExhyaRcAq+p3xcbanIr2n6Z30fTFc1qb87Yrw5Za7QCYdmIV+okJWm+pOn3/LXOP+GIbHkTfm4eBj61UMhn9Gzqx/4fULSC6MKNL1UzXUUp8q1SBK1dg0SIoWRKCg2n98CIDrx+g3PG94O9PpLEpq+t9iUu/xQwPzbyBYf3AekrvW25JqFKytTTm72/ra/yHgPxK5aTKz8+Px48fI0kSly9fxs/PT3F7+fIloaGhDB48WJuxCoIg5Bivx/KxCHllbIe2XX8WlOXnJv+DnHqq1/xnXKvyug4hVzBMiGPV7plYvnrO0wL29PlqtrylKiMyGUyfDu7uAPx6ei0d753JdF9j/7mhiZAzddVf+XuQVqLS4f5ZSoS8IdDUih3VW31cT3sxJb8gH9SopOLfCxp/Q5CJJZXe+fPNjUOaLeGehkEbrgAw5PIuTOJjuV60Auc+TnbbuJxtrmnxV6Xqor6ejNchUXy/6ar8b4GhobyL6qNHcPEiM5p/z+KGvbkybALs30/dEZuY1fw7gk2tOHw3INPtN6tYhI7V5XFULaZaN9eckvwMfWKcK43L2Wq8y2raO877fxtU7v7n5OQEQGJi3ij3KgiCkB3PszGhaX7gGxBGMRtTLIzlfyayM4g6+R/kfPB3M1Pl7cSk8wDjzm6h9itfYi2tGNhzOoHmBVR/8sSJ8OYNLFzI3MN/4VvYiYe2Tumu7qPmGC9NSZWmSBLDL+4EYH3dzkQbmiCTgYO1SY7E07KSHevP+wMQbGrFPNf+zD66lJ/Obubt2wlQRLufzaKhb+l//QAAfzXqo/jC62ri4azSk8n4edcdPB+847jPG/zndJA/oK9PQMUarKvXBYBpnSpTr1EpIs8dVHsfM7pUwblUQVpUstNk6BqVdL7Oie5/9lY58x3RJrVLqifx8fHh2bNnqYpWdO7cOdtBCYIg6NrGC091HYLOXHoSSO9VF7GzMubSLy057fuWgNCsd6/SU2qpyt+tVb+0r6iRrjKB4TEUssheN0JdcvG/ydCPXeAuT56LX5CaY65lMpg/n7N7PGj89CYr9rrTuf8fRBibpbm6saFuBtKnTBaaPrlKpXf+hBuZsql2R0B+gV68oBmr+tWhkEXOttZsr96ar28eodqbxyTOdoOtm7S6vx/PbsU4QV7xz7NUbcXylK1k5ewstBpHdv267y7B6VT5HP/vLcW/9WQytVsAk7qCmhkZyMdt5QHabKla1a8O5x+9p2ddR63tI6eoXf3vyZMn1KhRg6pVq9KhQwe+/PJLvvzyS7p27UrXrl21EaMgCEKOUnd8xlX/D/x14iHxeWTi1swkdV95ExpDTHwCg9Zfydb23iRLyA7cea0YzJ4ftavqgCZ+1O26zCv7G9ERk7hofj+yGD0k/Lv35WXzdlnbkL4+Yzr/j9cWhSjz4QWTP879kxZjA7UvZ7Ik+bXl8w+RqcZUjbj4LwBbarYj1ESeOCRVMWxdxZ46TgW1HmPyUt2JevpMazkMgALb/obLl7W23wrv/Ol+Vz632BzXgUoHK2X3xzK2Fmz93pkT45poLZ7sSC+hAnj8Llzxbz2Z+l07d4/I3fNWpfVytDmmqnUVe6Z3qYqhfs58h7VJ7VcwZswYSpUqxZs3bzAzM8Pb25szZ85Qt25dPDw8tBCiIAhCzmr0u2rVzSJi4rn1PJgeKy7w54kHbLv8jIdvwvJV18Ghf1/L9jam7vNW/PvJuwiOer/J9jZzK0nSzHwzzz5E5tkkfZTXPxQPeYNUvDglN6zI1rY+mFnzY6efAOhz6yhNnqT9eTTRQUtV47mnlcq5133hTf0XPsToG7C2rrx7WIdqDjleOa1QirFL1x0rsauqfB7R9wO/R0pIPcH0s8BIXNxPZmtS5wmeG9FD4mCFRtwqWkHpMdM03h+XMoUpq+XuiJn5X5sKma+UQvJxtrIstFSZGWW5k1iOsDL5FF/SHGiaTqqqFJX/0PBHrxoa3a6uqZ1UXbhwgRkzZmBra4uenh56enp88cUXuLu7M3r0aG3EKAiCkKNULU7Rddl5uiw9r7h//Vkwrf48Q+O5p7UVWo7z8H2n6xDyFAmJeA1VJVh06hGBySon5gaZXUCWff+M7z9W7JMtXgwWFtnq7vlHrxpU7dOF9XU6AfD74UVYRYenWi+nWqpSio77lKAM+ziWaneV5ry1LKSTeAz19Vg7oB5Vi1kpTTA8x3UQYUamFL53mwe/L0n1vFmHfHgVEs1vB3yytuODB2nx+ApxevosaNwv1cNNyttmbbtalpVkPHmXTz2ZLEdK1eckSxNDNn/rzNbvnRXHR9NJVcfqRXkwsx3dauf9Ln/JqX0WSkhIwMJC3qRduHBhXr2Sl5h1cnLC19dXs9EJgiDkEg/ehPE6JCrFMuWLu4dvw3IyJCEXMjc2ICYudUtAViw6+ZBR23Kmqp2qMruA/PXUGowS4zle1hm6dMn2/rrVdmRKx8rMbTIAPxsHHMIDmei5IdV6ukqqklR450/Lx1dIRMZK5+6fHsihRqqhTUrTqrIddZ1sqFzUigOjGlO/1Keuhu8sbFjYqA8ATnOny4uAJJOtKqdRUfDxR/V1dbvwpJDyhbJbp8q5tiS3QRbieh/+qZbAad+3VPz1sMrP7VDNQe396cIX5QrjUqaw4r6mK5qWLGSm1E01v1D7FVWtWlUxEbCzszNz587l/PnzzJgxg9KlS2s8QEEQBF17HRJF6z/P0NBd3i1w382XNF/gkWq9uy91U4FM0L6/L6pWuKSwhTGxGuy2l3zi5NwgoypuLv43cfW7TqyeAb81/06xvJgGJn2NMjJhYrsxAHx98yg1Xyn/iKvtC7Tk3fySSzocSa1Uhyo0wr/gp6IcOZVKTGpfidX96yp1NUzZYrqxTid8ipTCJCQIvv9ecxOi/f47PHlCgEVBFrl8lerh3DoHE6hfMOPiE+Xv43GfNyq3VLWsVIQFebS7W6caRbk4qYXGtte2qr3GtpWbqH0WmjJliqKs+syZM3n69CmNGzfm0KFDLFq0SOMBCoIg6Nr9AOUWqDHbb/LkXUSGz9H2nDBCzvp1791M12lVWV4aObO3vmmF3NkVKiPRcQlc9f/Azmsv0nxcJiUy5/IWALbUasczm0+/yLuUKcTk9pXYOLh+tmK4XLwqu6o2Rw+JmceWoZf4qUXQ2EB7Y6r83kdQfsphpuxNPQF2giThGBxAp49zaa1o0ENrcagr5TkoXt+AHzv+RIy+AezfD2uTF/7IYuJz4wbMng3Ab82/T7M6Y27MqfaObMT8njWUWmNU8fBN1nsjNChdSCdj/zTFXoPTAuTmRDs71E6q2rRpQ7du3QAoXbo0Pj4+vH//nrdv39K8eXONBygIgqBryU//cSq2Qoic6vOTNKjbpUwhGpZOe0zN7K7V+OurWmptNyw6/UpkOWXo39foseICk3anTiwA2t8/Twm/e4QbmbKkYW+lx2QyGd83KY2rBsbVuDcdRIixOVXfPOabG4cUy7XZUrXc4xEAmy8+S/XYXyce8P2VPRhIiZwpWYu79mWVHi9jm7tKh/valmR+4/7yO2PHgrd3hutnKDoavvkG4uLgyy85WPELjcSYE2oWL0CPOuqN5wmLjlO5xTot4m9C/qfWWSg+Ph4DAwPu3lX+xa5gwYL5NusUBEFIfn6LVnG8jPj7+fkx1Jd/Tgz09dg2pAF1nGxSrdPHuQTWpoZqbffh29SFGXJSQqKE54P0C5boJSbw09m/AVhVv5t6k/yq6b25DfNcBwAw/szf2IYHAdodU5VRoY3z57z56tYxAJanaKUa0qQ0w5uW0VpcmUnvHLS2Xhdo3hwiIuDLLyE4OGutST/+CD4+YGcHq1al2ySVH64OJUmi1R9nUo2jFYTk1DoLGRgY4OTkREIa5TgFQRDyg7TGTiTvRnPk4xxOmRHd/1STG1phNMUgxTwr61s70ufmYaadWMnMo0sZemknXL+u9nY1UaI9Ow7cfpXh4+18vSgd9AqpYEHW1PtS6/FsrdGGW/blsIqN5GfP9YB2W6oyOvyDr+7DOCGO60UrcKFEdcXyphVs+aV9Jd1290rnFJSopw/bt4OTEzx6BD17YhgXm/bKH0XExCsXs1i2DFasQJLJiFuzFmwzaIXMBz+6T9h5O1sToAufhyyNqZo0aRIfPnzQRjyCIOjYy+AoRm69zvVnQboORSfWn089T0t03KdE6387b6u0nfxWZldbqrkdY9/Nl7oOQyMUc/G8fQuDB2NVoTSzjy5l0LX9fHPzMJM8NkCdOuDsTO2X91Terr6OL0qTVztLRZIUE95Ko0YRaZT9ohSZSdTT59fWw0lERve7p6j7wlurTcPJD3/yVmur6HC+uXEQgKUNeymt2LKSnfYC0gCpcGHYswfMzODECYYsnYRhQto/cLwNi6bKtKN8veqifMGOHYpqf783GUCb++acyaAlM++nVPBvOmMJBSE5tZOqRYsWcfbsWYoWLUqFChWoXbu20k0QhLxt7PYbHLz9mm7LvBTLomIT8Hr8Ps9ORqqOG8+CUy2LiVe/dV4SHQBVpmqimtuNblEOPD2hcmVYvx4SEqBhQ453GsBfLl9xtFwDMDKCy5fZvfl//M9zo1KxhfTo5eLKw038rlPl7RMiDE3QGzVKsVzdLo7quu1Qnu01WgPw27HlSPFx3H4RzH+3Mm5Vy5q004IB1/ZjGRvFPduSnCpTT+mxCva6ndQWMj4HXfb7ALVqyQtWGBtT88YZNv/zKzaRIanWPXwn4ONzAmHlSujTBxIS8G3fkxXO3XnyPoL+6y6nu6/ONYtm/8UI+UbKyanzE7Wndf7yyy+1EIYgCLmF3/vUVe2G/H2Vsw/fM7pFOY3PV5HbBEWm/lU+IiYLSZXIqVSWrTlychHbo/vh668hPh6qV4cVK6BhQ87svasY4O7/Y134+WfYsIGRF/+lbOBzfugykTj99JOQ3PxZSmql2lajDd8V+lSc4+jYJlrf97wm/Wnn60Wld/64HN1B5ztNAahob0l5O80lNWk1FJrFRjH46n8ALGvQE0mmnPnWK1kw9ZNykaDIj61SzZvDf/8R1aUbzs/vcnj9KGgog169QF/e8hqXkEixkLdM8lgP98/KnzdoEGf6/QxHH2a6LysT7SbYeYX4oQ2+rl+cUc3L6ToMrVE7qZo2bZo24hAEIZdI6/r27MP3AGy++DRfJ1Xrz/txyS911+Zf9qRd8SwjuflCODM53dssPyRVTR9fhT9myROqXr1gwwYwlXeFU7qYsrOD9esZHViYeYf+os3Diyzd9zsjM0isUs43lNPSGx9Y++U9Gjy/S6yeAWvqdeU7YNdwF8Ki4zRafjk9QWbWzHPtz+yjS2nzzzJ+H1KD9+Y2hEXHa3Q/aX0dvr55BJvoMPxsHFJVvdP1RMRJMjoHBYRE0WXJOYa6lqF969bMnbaeb+b9SJkPL+UtURMmQOPGYGlJ6ws3GHT3GvpSorzZ1N0d/vc/Es48ybkXkw/k5b8JSU6Mc6XlH55Zeq6DtQmzu1bL14XtsvTNDw4OZs2aNUpjq65fv87Ll/mjX7wgfM4yKrCQf0+FctP3+2hsW//dEufDz0W5d09Zts9dXlr6q69g61ZFQpWe4St/5bvuvxKjb0jrhxeZfnxlulddCYm5s9vt8I8T3u6p0owAK/l8P3WcbGhaoUiOxbC9emtu25fFKjZS3pKCvLS9+6F7/LzrtkYKxqS8BjSLjWLYpV0ArHDuIS/8kExuSaoy4rbfh1svQhixRV44JcCxNO0HLWbBF32JKWADL17Atm2wahUl7lxBX0rknFMNuHJFnnDJZBlOBC3kT2WLZG2KAD/39lyY1CJfJ1SQhaTq9u3blC9fnt9//5358+cTHBwMwJ49e5g0aZKm4xMEIYdl9KN4Pj8fatSv+7Ix/4sOzdjvw/rz/mo/r17J1OXDPwfmMZGs2OuOWVwMtGgBmzYpuk0lSevas5KDFWdL1Wb4l5NIREafW0fof/1AmvuIT0j7S/kmNDrNapU5ofw7f1o9ukQiMlY6d8+x/aY8ByXq6fNrK+WiFYmSxMozT9h+5TmPM5mkW6V9Jvs5SQYMvLYf28hg/As4sKtqi1TrW+aS7m7q5DwyGcQYGLG40ddU/3YtA3pO5/qIiTBtGv9rNxrXIav45qtZULs2MfEJrDrzmAcBWZ8IV8g/VJnMPL8nU0nUTqrGjRvHwIEDefjwISYmn5r327Vrx5kzZzQanCAIOS/jX3Y/jxOjJsTGJ+a5bm3RcQmsS6P6YWaMDPT4o1dNzQeUB8w4sYIyH17w2qKQ/Jd9w9QX1I3LFU73+afK1se96SAApp5cTa2X91Otk1b3v4dvwnCefZL2i85mI/qsG/qxpeZI+YY8KaTeJKpZUcnBCoDWlVNX1btVtAL/VG8FyItWxMd+qmIXHpP9roDJrwctIsMUrVR/NO5LvL7aoyhyTFbH8MQYGOFZug6bGvdmY+uB/Fu9NU9tPhWbGLzhCrMP3WfvTW0UBcm/8tZfA9V990Vp7v/WNs3HWlYqQh/nEjkcke6onVRduXKFoUOHplperFgxAgJUm79FEITcS/To0JwLjwN1HYJastqdZ/uQBhQvaJatfV98EsiWS0+ztY2c1vTxVbrfPUWCTA+3vr+mO1dPmyr2rB9Uj4uTUrdqAKyu35X/KjXBQEpk4YH5mMdEKj2eVlJ14PZrAB7pYGJgx5A3dPGRj6tY3qBnjuxz0+D6TO9chbk9aqT5+FzXAQSbWFDpnT+F/16rWK7qZN2q6n5qG1YxEdwv7MT+StovxpEd2T2X7735imn/Kbe477jynPOPVD+v2VoaZy+IHFCzeIEc2U9+/duqrydLdz62NQPqMbtrtRyOSHfUTqpMTEwIDQ1NtdzX1xfbjCZ/EwQhT0h+3vdN0b0jP7fga2Oy3vhcOhZG0zQxOe1Xqy4yec9drvjnjjkQXwZH8c2aS5y+/zbNx81jIpl1dCkAp9t+zf9mDE53WzKZjGYViqRfvEEmY0rrEbywKoJTcADTT6xUevhtaDRN553mj2O+imW6vD777vIeDKREzjrV5I6DvJJX84raHUdla2nMAJeS6ZZqDzKzZl6T/gAU+8OdwhHyefaiNJBUJX2+i4a+pYunvNrhgib9UlX8y8tkKvZCmLBL9ekPutUqxp4RLlkNKcdsGFQv85WEdBnq5+MLAzWpfUbo0qULM2bMIC5O3rwuk8l49uwZP//8M92751y/akHQtJCoOJ68y/lffXOb5K0VbRae+TThYz43bsctjW9TE8lGTspqXqmnwZd5wucNa8/56XxOtEm773Du0XsGbbjCtsvPUj3+09nNFAt7R2LJUrT8dyVli6hfwjv5xUioiQVjO/1EgkyPHndPyqsJfrTo1EP8AyNZdOpR1l6MBhWKCOar28cAWNZQ3kplaWLAsr66n6dyW4023LYvi0FYKD97bADA4/5bjX2Wfjm9HpO4GC47VuZ4WWeNbFOb1Po6a+FUNaxpGRxtsteCnRMKmBkxtWNlre/Hzir3t9plhZ2V9it95hVqJ1Xz58/n3bt3FClShKioKFxdXSlbtiyWlpbMmjVLGzEKQo5wnn2C5gs8efjm8xx8Gx2XwEYvfyJjlX/ZvfDkU1ePvJUiqGfPDc1X68trSVVCFrMqTb7OlWee8NsBH7amkcjkpHdhMYp/T9qtXFK/dOAL+t04CEDskqVgbp6lfWz+1hl7KxNW968LwFXHKqyr2xmAmceWYhYbBUBASLTS8w7efs1WHXWVHHhtPybxsdx0KMeFEtUB+KJs4XS7/+SkRD19prYaDkCPuydp8uQaGy88ZWU2S3/LZNDw6W063j8r7+rZcliGzfZ57Guv9Fn/XA1qVJIDo77IfMUs+u6LUnSpWUxr29eVv76qme2u3/mJ2kmVlZUV586dY9euXcyZM4cffviBQ4cO4enpiXkW/7AIQm4QHSf/NfPco/c6jkQ3Fp96mKr/fEp57WJBFZIkae2iQpMtODlByuIP+hl9LmqVKJClbXq/TN3NPCdl9N5N8liPYWICJ8rUg9ats7wP59KFuPhLC1olK77wxxff8NzaDsfQd4w7uxmAuBTV/0Zuvc778NSTVGuDJEmKFkyLmEgGfKxQuLxBT8UbnxsSqiQ3i1bgeR95V8x5h/+iQFQou669yNY2DeLimH58BQBHXDrjY1c623HmBFV/I6k36wQHP47R+1zJZDKqFrPW2vandKyMfl77g5CO5Oer5IniyGZldBFOrpLlDsHNmzdn/PjxTJgwgZYtW2oyJkHQqfw6mDQzl55kPpZF1X73ecmCYw+oN+uEVratl8f+iGa1UEVGLVUV7dXvFge6T+DT23+DZ7dp9egS8TI93JulP44qq6KMTJjSegQAg67tp2qAcpe/W8+DNb7P9DwNjKDerBOs8HwMQJ+bh7GKieBRQUeOlWugWM/EMGfHFu0c1pACZumXLb83djKPCjpiF/6BWUeXkt1po5puWUz5wGe8MyvA3x2+zd7GcpRu/5jlrbOf9vRr4KTrEDTKKJ0v1PjWFXI4ktwnS6eakydP0rFjR8qUKUPZsmXp2LEjJ05o56JEEHLa5zihoSRJXH0alOl6ur7Q1YYlp7U3TiWvHS5tJFVZnZ9E15+1NF+TJDH5lLyy3Naa7XhcqLhW9u1Zug6edVqiLyUy4/hyZMmaELssPa+VfaZl5sF7vA+PJTAiFuP4WL67sheAFQ16KBVpMDbI2ZaquiULsn5g+sUF4oxNGdtpPHF6+nTwPc+XXnuzvrPz53HZI59U+Je2PxBmrr3WDG1a2a8OVYpa0TcHy1vr+jucG/i5t+e3L6vqOgzNSufPxOcyF1VG1E6qlixZQtu2bbG0tGTMmDGMHj0aKysr2rdvz5IlS7QRoyAIWqZKQpUkJj6BC48DdTbpqCY8fBPGXyceamQOm4zkvZaqzNdJWempasAj7H75CWrU4Mribzi96nsW/zeXlg8voZeYgHEWmwl0/Qc6rb23eHyZam8eE25kysIv+sjX01KYqzuPINzIlNqvfOl297R2dpKJmGTf8e53T1IkIohXloXZV9lVab2BLiVzOLLUXSKTi09M5K59WX53HQDA0D1L4HTmx/Dg7dcc8042Nczr19CrFzJJYmfVFhwv1yBPJQrJfyNpU8Weg6Mb07qKve4C+gzp+jymDcObyrv5da+t/fnp8hq1Z61zd3fnzz//5IcfflAsGz16NI0aNWLWrFlKyzXJ3d2d3bt3c//+fUxNTXFxceH333+nQoVPzY2SJDF9+nRWrVpFUFAQzs7OLF26lCpVqmglJkHILz5EqDY+43VINBWmHFHcXzewLmZGBpQqbJ6nKgC1+lM+UXlghHYHaCflVLHxiel2mchNVCkrf3hME1r+4UmBqFBmHltOx/ufJp+1BWwjgykV9JpO987wxLEcUrNVrM9CLLq+FEl1MSRJ/OC1A4C/a3Xgg5m8xUJbrTR9uzZg8eneTPLYwM+e6zlWvgFhxjk7bjnuY1Kln5jAsIs7AVhVvxtx+vKud43LFWZlvzqYGeX8BLgZVfRLmnR7Tb2uVHnzhK4+HtC9O5w6BTVrpvmcoIhYRm69DsDDWe0wjI2RP+fVK94XL41bS/n8nHmt+ExK2pg6Qvi8VC1mjff0NpgZpT73Xf6lBfVnn9RBVLmD2n/lQ0NDads29czJrVu3TnP+Kk3x9PRk5MiRXLx4kePHjxMfH0/r1q2JiIhQrDN37lz++OMPlixZwpUrV7C3t6dVq1aEhX2e1dyErPk8u/9l7XmDN1zlq1UXcc6jJ9GbOTA+ZYXnY8pPOYzX49xfAEXVlqrKb55wcP0YOt4/SyIywrv1hD17aDdoEX16z2Rl/W5QoAClXzykdKeW9L1xSPvBa1jKa2eXp7eo9dqXaAMj1tbrorX9Ghvo4fVzc9pVc2Bd3S48LlgM24hgRp/fprV9pidpnrVO985QIuQN782s2V7jU2EOmUymk4QKIC6DD+uqpGp/Mhk/tx2Fb6mqEBQErVrBzZtpPic0Ok7x7zXHvHnQoDlcuAAFCrDz1yWEG8srnBmo0PqcW/KutI5QbioqIuRd5sYGabbCFbEyobTt51u0Tu2kqnPnzuzZsyfV8n379tGpUyeNBJWWI0eOMHDgQKpUqUKNGjVYv349z54949q1a4D815eFCxcyefJkunXrRtWqVdm4cSORkZFs3bpVa3EJ+c9nmFN9tm6/CNHq9hMlmHP4PpC6LHduFBOf+USpxrdvsnPnFIqFveOJTVE6DfiTdyvXw5dfcq9IabxK1pQXcPD1hZ49kSUkMOvYMn78WMlOVbpqEXgRFElsfGKqlrIfLshbqbbVaMN7cxssjA2Y2726xvevJ5NRtIApAHH6hsxoMQSQlzIv8/65xveXkbgECZmUyIgL8glv19XtQrThpxZpXeYODUoXTPfi7X6ySctjDI3p0eVXpLp14f17aNQItm1LdaJPytGKhr6l4bfdKX/7Iglm5vDffwQVK6lYz1A/97c4Z6R+yYI5uLdckl3msA7VHXQdgk4Vscyf83GpQu2fmCpVqsSsWbPw8PCgYcOGAFy8eJHz58/z008/sWjRIsW6o0eP1lykKYSEyC+GChaUnyD8/PwICAigdbLytsbGxri6uuLl5cXQoUPT3E5MTAwxMZ+6AGmztU3IGz63nEqSJIIjc6Y88+cmIdmv6bmtBfSK/weWnX7Erx0rM2XvXcWFfEacgl5RpFs/9MJD+VC9Dl2aTyDM2Dzt8uNFisA//xBcrjIFZk9njNd2YgyMWNawl0rx6SKnuvb0A92XX6C6o7XSxXPtl/dweXabWD0DVtXvRpeaRfmzV80cGTPnWboOx8vWp9Wjy7idWEm/3r/l2MGJS0ik9cOLlA98RqixOX/X7qD0uC6HDBob6HNynCubLz3j1713M1w3zNicyyu3U3/SCGTHjkGfPrBhA4wfD40bg7Exek/9GX1+G0Mu78YiNoogE0u85q7mlVQMiU/XCI/z0ATxdZxsOHX/rdKynBznmVta7IScNa9HDSbuus33TfLG1AOapHZStXbtWmxsbPDx8cHHx0exvECBAqxdu1ZxXyaTaS2pkiSJcePG8cUXX1C1qryqSkCAfHCpnZ2d0rp2dnY8fZr+JInu7u5Mnz5dK3EKuZskSdx6EULZIhZYGBskW67DoHLYrefBOVpN7HOTPJFKzGV1PXquuADAaV9PxbLk34OULGIiWbPrN/SCg6BePcL+2UfYSvkYlHRblWQyIv83keVeT5nksYEJZzbx3NqO/SkKHaRFFy1Vu67LJ4C+/SKEeiVtFMtHfmyl2l21OWXrVGJ212o5enH6W/PvaeJ3g8ZPb9L2gRdHKjTS6v4kSeJ1SDTeL0Nw//jaN9bumGpMl64H4ctkMgxVfB9677jP2nnraNFwNfGzZmNw7BgcOwZ6emBkhFN0NOM+rnvDoQKjukzgxXMLeH5P6Zf39OYHW/FNbYZtvp7dl6RR3zUuhZmRPo3L2epk/2mNucnt1g+qx5aLTzlx723mKwtpKl7QjK3fN8h8xXxI7aTKz89PG3Go5YcffuD27ducO3cu1WMpT/KSJGV44p80aRLjxo1T3A8NDaV4ce2UyRVylwO3XzNq2w3K2Jpz8qemiuVSPmyreh0ShSSRqjVCJFTaFRb9qbqgtisNakJGMc4+uoRygc9JcHBAf98+9E0+lZbO6NpaTyZjpXMPbCJDGXZ5N3MPL+JJIUe87XLfRJHJx8tc8ZdXxKzy5jEtHl8hQabH8gY9mNaoJOYZJJ+aVK6IBQ/fhvPMxoGV9bsx+sI//HpyDZ6l6hBlpL3CMN9tvMrJ+29p/vgK1QMeEWlozPq6nVOtlxuKW35Zqxj/XH3OjWfBma574lEgdSdMpsuronx7dR99X1xF791biI5G0tfnctFKbKnZlv2VmiiVjH+rwuTgbavmvi5fxgb6DGpUSif7blvFHgfrzFu/c5tmFYrQrEIRSv58UOXnlC5szpP3n8b354KvhaAjea5z8KhRo/jvv/84ffo0jo6fyjna28vLhCa1WCV5+/Ztqtar5IyNjbGyslK6CZ+HfTdfAfD4XYTS8vzWUhUbn0hD91O4zDml0pgZQTWqDFhPnlTl1RL0e0a4cMbpLZ3vnSFepkfo39vBwQEDvU9/PpJalca0KAfAtE6VPz32cbW5rgPwLFUb0/gYlu11xzwmMsP96qIRRD+N93TEx5aaAxUb89SmaI4WZtg1wkXx72UNe/LCqgjFwt4x2mu7Vvd78v5bZFIi/zuzCYCNtTspqh0q0/3lo4mhPntGNFJpglUzIwOQgX/BYvzaegSvff3g1St48gTfh6/o3WcO/1VuqpRQqUJ0c0ttiOvn0/WrV73iDEnW1c3aNP2JqYX8Te2kSpIk/v33X0aMGEGPHj3o1q2b0k1bJEnihx9+YPfu3Zw6dYpSpZR/fSlVqhT29vYcP35csSw2NhZPT09cXFxSbk4Q0v1DmN9KzkYka30Ijcr9rSV5hSoD1qf/551q2dvQaM48eJdnPmeVZRE4Th4PwNKGvUl0dgaUE5CkpGpsy3Jc/qWF0q/jSY8l6ukzqvMEoos64hQcwK+n1mS4X5kOLthTJspl3j+nna8XAEsb9gTAuZR2B/onbym3Mvl0cRZtaKIo6/3dlT1aL1rR8d5ZKr3zJ9TIjBXO3dNcp0RBM63GoI46TjaZrrP2nB8Hbr9S3I+KSwAHByhVigTjrA+uv+PWBoB2VeU/7g5tkvtaYXNaHjm9aYSeDBysP7Ucj2tVnvolCzKvh+YL2Qi5m9pJ1ZgxY+jXrx9+fn5YWFhgbW2tdNOWkSNHsnnzZrZu3YqlpSUBAQEEBAQQFRUFyLv9jR07ltmzZ7Nnzx7u3r3LwIEDMTMzo0+fPlqLS8i7PpcfF9P623b3ZQijtt3I8Vjyk5ST4KYlLFlCm1S0ooH7Sfqvu5xn+uwb/fwzekEfuGtXhsUuvRWfp7Ra6mQyGUVSzFeWfGxUqIkFD+cuIREZX90+RusHF9Ldb07++h8RE/8x0VVePuLSv+ghcaxcAx7YlqSCnWWOjyOq7PCp98SJcs6cKFMPw8QEZpxYrrUrV4OEeMadk1drXOXcjRBTyzTX+7FVOa3sPyu61Cyq0nqT93wqahEV+6n1OKuHsq6TjWIs4uKva3FiXBP6OpfI2sbykbw4niqr9GTKPwEVsjBmx7CG9KwrhpJ8btTux7B582Z2795N+/bttRFPupYvXw5A06ZNlZavX7+egQMHAjBhwgSioqIYMWKEYvLfY8eOYWmZ9h8E4fOW/NroRdCnrkj57Re2kKhP86/8sPU6i/vU4qtVF/PEGJ/cTN3SygkfP1hJBQHPPnxHq8rpd03ODeo9v4ts6xakj/P9xOsbKL4f+smSyowqG+qnSELCnBuxyrkbwy7tYuaxZVwsUY1QE4tUz8vJ1GXI31c5/yhQaZljcABdvD0AWPKxYmFO5FMpW+jaVLHH5/WnqrRuLYfyxdNbNHp6m073zqhU9ENdvW8fo1TQa96bWbOubvpzclma5J5uTllJdqPjEzh05zV2ViYq/UiSlrYfW6cADPT1KFtEXG8MblSKSg75dyjFjqENmX/Ml8t+HwB5UtW9jiMbLzylWYUiOo5O0CW1W6qsra0pXTrn+8pKkpTmLSmhAvlJ1c3NjdevXxMdHY2np6eiOqAgpJT84qXzkk8FG1SZADWvOPPgHc3meyjuX/L7wNS93p9tQiVJEgPWXdbIttS9hkvIYx8s/cQEZp9aBUD84G+5a18WAGND+Z+N5MlSRklVyuEpMpmMP774hscFHSkSEcQEz41pPi8nL9hTJlQAwy7twkBK5EzJWtx2KA/obu6s5F4UsGdpA3lXxCmn12IVrdkS3wc87vDTxznFFrt8RaRR3is2oKr7AWGM2HKd7su98tV5X9emJhtTmV8YGegxoW0FetRxpF5JG6UkXE8mP1+d+sk1X752QXVqJ1Vubm5Mnz5d0e1OEPKKhERJqVhA8uujDxGfyuTmp+p/f554kGrZsw8ZFwjIqmUej7SyXU0KiYrD88E7XYcB5P7upz3unKBcwBOwscFwjjt/fVWTP3rVUIzzST6mKqNy8SlbqmQyiDUw5Jc2IwH45uZhar+4l+p55sa66z5UJCyQnnfk43OXJptXS5s5VdJA9ykdKyktT2ufK5178LigI3bhH5h2YmWW9xkQEs2Fx58SyoREiQ/jJlEwKpT7hZ3YXCtne6TkNL9kRYqyOo+co03+TToFubvT23BramtGNC3L/J41kMlkSj+wJLWS6nqKAUH31E6qevbsSVBQEEWKFKFatWrUrl1b6SYIuVWbhWeoM/M40XHyCnjpnf8+RMTyNDAi7QfzmLReYmyCdqrQzT3iq5Xt6lp6n5NiKkyWm5HcnLobx8cy5vw2+Z1ff4XChelSsxjdan+quGqUrPujrWX6g/xTtu4k3b9Uohr/VGsFgPvRxRgmxCmtp8vJkr+/sgfjhHiuFKvMpeKfejtos6VqUruKXPqlBX2dlavYpfkdNjBkfPuxJMj06O59GvbsydI+G7if5OvVFxWJVcLVq/S9eRgAt1ZDSdDL3+NilOeRU+/zVqyAKT+2LE+bKvaZr5zLbP3Omd5ivI/KLIwNME0xRszE8NP93DC1gJA7qD2mauDAgVy7do1vvvkGOzs7kZkLudqRuwEYG+rRrEIRHr2Vd5N58CaM6o4F0q0utunCUzZdeMqlX1pgZ6W9uWByQlrfT11erOpSfEIih+8GZL5iBr6uX4K2Ve35+4I/UztW4fzj9/gGhLHBy18zQeYSfW8cpmjYe6Lti2IyfHia6+jpybgyuSXxiYmpLjiSS/kRTH5/drPBtHh8mQrvnzH46j5WOvdQPPYmNIaY+ASMDf7f3n2HN1V+ARz/Jt27lE4olFH23pQ9C8gUBBQUkCF7yBIQlCGCCogs+anIcDCUIRsR2XtV9h5lb8oqXbm/P0pD05m0WS3n8zx9aHLXyU1C77nv+57XvBf2OV5EaBOLWdXa6wRsyosnlUpl0P83R3MXZW6VNvTd9wf07AnVqkEa04ekZe+lB4QEumLbtStqRcOaojXZlzftymXLe4dk6FjWJDZRE2usgUlVrcLeDGxgPYU6DFEt2Jtqwd7EKQp/Hr5u6XCyJJdE/+eZcyJwYd0MTqrWrVvHpk2bqFGjhiniEcJoIl7E0OvXw0D8wNIE+pZqPnEjIusnVSk8l9XG9hjLT7suM3nDGYO3s1Orta17k1qXAqB2YR8A8uaMr/JVISgH49ac4v6ztCcJXXnU+i9gXKJe0Gdf/NxM4f2GUtgx9e9AWi1UCZLO/ZT4cYSTG1/W7cq0dd8ycPdiVherzS33+HM7b9dl/j1zl61D62TgVWRc10OrcY6J4ph/MNvzJ+l9YYGbiGkd8rvqHah38SDF7l3hReu2OG//F2wNn0dLURSuDRhOnpMnuO/swecNe6W7TYUg05aWN4fEjfaGtlSl1eU1qxjUoJAkVRnklGi+OmsYaymsg8Hd//LkySMT5IosISru9US3n648rv094f+/9P4fjInTcOjKQyKjs+6EuSm9xudRWff1ZMamkxlrpSrs70r14Jy0L+kNmzbBzJnxP+vWwdOnADQvk4uDn9ZPd18fL/1P+7slGwzTuoD88PBqvF9EcClHLu62fi/Tx0p8wfFVm1KUzu1BmTye2udWlKjH/sASOMdE8dmWH3W2vXzfvN1w3V8+o/ORtQDMrtou2RfI2m5IR9va0a/FJzyzd8J5z04YPjxD+8l7cCe5fpwFwKhG/VKZ6Df7ictES1WFfOnPi2XtMpsMDGlY2EiRZD3VCubU/l4ur6flAhFWxeCkaurUqQwfPpwrV66YIBwhjCdxi9SDRIUoAAYsPsraY7fS3L7Xr0d4Z+5eui08aJL4zCGlVrn0WlOyq4xePsQ9juC3y2v4qk9DaNwYBgyI/2nWLL67Vd++cO8eKpXK4IaM+bsvM3DJUbO3HsalktG5RL2gx4H48Tnf1uiI2s7wVo+kEiciRf3dsbVR81ff6nzd5lX3MpWKMaG9iVWpaXJuD3UuHtLZfttZ883n1fnwGtyjnnMuZ17+LlwVAHdHy96RTq+L/UXvPAxp+nH8g2+/hTlzDNp/voc3aD55CDaKhsWlQ/m7cNbv1qevxEV7OhlYFbRFGf3mxbJmvm4O+LlnfNLj/vWzZvdHY2hWOoCRTYrybfsyFPWXhgYRz+Ck6v3332fr1q0ULFgQNzc3vLy8dH6EdYuIjOHjpWFWUwHNlJREF46Jq/tdefCc1f/d1Hs/ey4mL7ecZVjZnXVLysgFcZmbZ1k0vTt8+WV8q1RgILRuDW3aQP78EBkZfxFbrBisWmXw6R635hR/hd1k86nMjfUyVGpJ3Hv/bcQj6jkXvQJZW6ymUcbMprqPRE+f88mnnQ9p3D9zcYh5nfhP/+d8pmPQh0vUC7od+gsgfpLjV7XgE+ef1vp12lS4GtOrv2pV7NsXfv5Zr+0CI+7w69IxOD57wuFcRfm8Ycrj57KrfZceZnjb7NDly9ZGzc7h9VjTT4ZzJJUwuXm+nM4pLlepVPSsXZC3ywWmuFy8mQy+DTl9+nQThCHMZdrfZ1l59AYrj97gyuSmlg7HpFK7+R9joup31iijf/bbVgjkj2zW197Qa6B6Fw7w/aovcYiLhQIFYOpUaNEC1K/uRSkKbNsGgwbBsWPw9tv0rPsh31duY3Bsz8zcJTOlYiX2sTF0P7gKgLlV2qCo1EYfQpT4qEkvSr+r/h7NT+8g6PFteu//k+k1OgLw6IVuK7OpdDq6Ds+Xz7joFci6oq8vMtOK2ZpMr96BQRV8YMYM6NYNLl2CcePAJpVCH4cPs/S3EeR+eo8HuYLo9fanRNtaz2S+1i7pWMGsyt5WjW0GJj7OzpP7AvzVrzpztl5kaKMilg5FZCEGJ1WdO3c2RRzCTG48fnPmF0uti5NIn62NwY3YVs+QVpc6Fw8yd+WX2Gti4xOpRYvAI8k4E5UK6taFQ4dgyBCYOZNPts5HFRfHnERzG1mjlFqqWp/Ygv+zh9x082ZViTqA8ZOIxHP6ONrpfsaeOzgzoX4P5vw1md77/mRlibpczZGLqw9ecO7OUwr7uRk1lsScol/S/VW3x1kh7dAkKiWuURSGNy7CjC3n+byF+Sf21PstUKniu/85OsLXX8PEibB5M0yZAjVqvN7Ro0fw3XcweTK5o6K46JWbtV/O595p08xfZ04qVfy9DndHW568NO0E59kkpwIMrwjr7WrPwq6VTBSNdSiRy4PZHWWaIGGYDF05Xbx4kdGjR/Pee+9x9258f/eNGzdy8uRJowYnRGakNhg/cbGA7C6j18S22emK4RV9X1GRe1eY/ddX2GtiWVukBixfnjyhSszOLr51YPJkAIbvWMT7R9ebJDZjSVq5TK2Jo+eB5QD8VOltYmziWyyM9THYNrQOa/vXwNv19fiN0OL+hBTISfNEY1PWF6nOjnzlcIiLYfzmudq+d/svma4LbpxGoWPYenJGPuGKZwCri9fWWa4o0KdOMCfGNqJELusu4HD0egR89RUsXAhubnDgANSqBUFBEBoKISEQEBDfghUVxebgyrz9wVQivDJWit3abB1Sh7HNi3Pg0wYmP1Z2mk7G0EqGU9qWwdctvipos9IBANrvcfXgnKluJ0R2Z3BStX37dkqVKsX+/ftZsWIFz57Fz/1z7NgxPv/8c6MHKERGfbn+tKVDsDh9Wxrer5pX53FGrxceWHERDH1ek/vLZ/y4fAIuMS/ZFVSGQc2H6l+i+pNPeP7JKADGb/mBKuHH09nAcpK24jY5u4f8j27xyNGNxWUaaZ831nVjPm8XSubWTUjsbdUs/qgqM98rR41gb+0BP2vYiygbW2pfPkLjc3sAOHT1ERszOcdYajQREfTaH59Qzg5pm2zC24S7+JZqva1VyEfvdd+eE3++LjR+mx2rd0KPHuDkBNeuxbda7dsHUVFQujQsW0aP1mN44uiabeauy+ftQpfq+XUmZhXpM/T9T5hSAmB6+7L8/XEtZrxblgOj6rOoaxVjhydElmHwX4kRI0bwxRdfsHnzZuzt7bXP161bl7179xo1OGFc287e5Z/T5qukZWmZneg1O9D3orhhcX+jHK/CF/+w9pj+RUDMSZ/5ySb8/T15I+4Q7uFHv5afEGtjWA9pl0lfwHvvoY6LZfZfkwl4knpBGAXLXcj+tu9qokAU+uz7A4AFFZoTaf96Xio7MyUSAR6vj3nFKzdzX00C/NmWH3GOjuSvsJv0+vUwF+89M+px9158wLaPPsH7RQQXvXKzskS9ZOuMbVHCqMc0VMncHjrz7OmjwbQddNp4nUOjJsPdu7BlC/zyCyxdCmfOwH//Qdu22v8gsktSJTLG0K7yiVvpbG3UFPZzQ6VS4evumG3GmgmREQb/xTx+/Dhvv/12sud9fHx48CALV0l7A3SZr1sa/HlULM+iTNvvXGQNalXSCZIzrt/vR4mN03Dx3jOdCozWrunpnbQ8vZ1YlZr+LYbz2CkDA7FVKvjpJ+LKlsX7RQRT132LSrG+wihTN5/T/l778hFK3L3EcztHFlRorrOem6N5ChckbQWaU7Ut4R5+5Hp6nwG7F2ufv/HIuGNCB3y7gWp/LQJgSs0PkiXRszuU573KeVPa1Kwq5/dieW/9Eqsxq05of//vegS4ukK9evD++9CuHTtUXgxacpSIFzHa9RbtvZrSrsQbwpD/p71d7dNfSYg3lMFJlaenJ7duJZ/f5+jRo+TOndsoQQnzKPH5Jkp+vonoWOu76BPGoU/rDMR3E6yc//WUCJkZL+DlYs/AJWHUn7qdpQevZXg/xpbW3dgcLyKYsPl7AGaHtOe/XJmo+OTsjM2yZbywc6Ba+DG6H1iV7iaWHJ7R+1Ur1e9lGxPh5EYBHxftMjfHzM9TpY8czrrJW5SdA2Mb9ASg26G/KHQv/qJ/n5HHVvXfswSXmJeEBRRmQ5HqyZZ7uVjPBWSFIP2mLPklUSvkhbtPky3v9PMBVoXd5Ju/zxgcQ81C3rg52vJt+zIGb2tuGwfVtHQIWYYhBXGz0H0yIcxO76Rqx44dxMTE0KFDBz755BNu376NSqVCo9Gwe/duhg4dSqdOnUwZqzCRh8/NU7LYnCRRjKfvxbo+6/WsXUCvfRXxc2Pd8fgbL3O3X9QvAHNI42Jg6M5f8Ip8wvU8hZhZrX3mj1WoEOPr9QBg2I5FFL53JXk4VnBxUv7GaapeO0G02pZ5FVsBsPDDytrl5kqqetUpmOy5f4Mr83ehqthp4vhi8/egKMzZlrnPk84d+WPH6BC2AYCvandJ8UtgyS6axrD4QPxNjQfPojh89ZHOspuPXxq0r7YVAvmlWxX++yw0S8zNU9TfnQUfZu8KdcaSx8sp/ZVekfFqQqRO76Sqbt26PHr0iIkTJ5I3b15y587Ns2fPKF68OLVq1aJatWqMHj3alLEKA0W8iNGrWT8bFTHSmr31gqVDsAr6tjglLWihUkG7ioEU8HHhv89CWdYzhGGh+rXe7E3UmmBNFbJSa6kqefsC74VtAsDjp7kGj6NKzZIyjdgcXBl7TSyTN85M1g3QEuNYtp29S89fDmkfJ7RSrShZj9vu3tQI9iaPlzO/96jC0o+q4mBrngso91S6GY6r/xGRtg5UuXaCt09uzdQxXkTHUuubrQxZ9l98ubM+fbBVNGwoXI29QaVT3CZxtcKsat+lB4RM/pc23+9hbyYmMs/9qhy+OguNmalTxJdFXSunv+IbLsDDSa9xe/m9XfjfBxXMEJEQWZPeSVXCxbmdnR2//fYb586dY9myZfz666+cOXOGX375BZvUJhkUZvUyJo6DVx5SZvzf9Pv9aLrrW9F1r9EktJS86fR9a5MOLra3UfP1O2XYMrg2Hs52VM7vha2Nmp3D6xp2fCv6bCW9Uw+gUjSM3/w9ahTo2BG30OSFCjJqSc8QxjTsw1N7J8rfPEvHoxt0lie0IphTl/kH2XTyDgCF712h4YUDaFDxvyq6ExZXK+hNlQKWL418w8NX23I4auvPuL/MeKGK9cdvc+1hJMuPXI+fd2z3bp7bOTK+fo9UtzHl3Fjm8u4P+7Qt99vOZaxQUaeQIHrWSt6amBW4mqm1NaurnN8LN4f4c1XYzzXZ8obF/dg6tE6yKp5CiNcMGlOV+K5zwYIFeeedd2jXrh2FChUyemAiY6JjNRQds5G2c+MrMeqTXOg77iYryY7zLGXEqVtP9Fov4WwNb1yEQr6u9KodfwGVtKUp8eSt+jD25LGGunD3GZfSqBgXem4f5W+eJdbZJX7CVKBsHk/t8npFfTN87FK5Pbjt7s03teK7RQ/fvhC/p/dTXNcSp6n3vj8B2FCkGpe9LD8etkWi+aoS+7Hy21zwCsTnxWPGvZq76mVMHPeeZqx8v+/TByhDhgAwo/q73HLXv2R5lpfBxtHxLUviZJ81b5oa46vl6WzHoq6VqRiUwwh7s14r+lSjbYVA5nVO3m1S/qQKkT6DbuGMGTMGZ2fnNNeZNm1apgISmfPFulMGb2NNrQnGYumLeWuh74VnQj/5PnWC6VMnONX1DO3OZ8l34UV0LA2mbQfg/MQmyVdQFAbsWQLA9U49yJcr/qJ+Re9qRMdpUKtU2Nlk/BUktP79Wu4t3j65jXK3zjL2nx/o/faoDO8zo2b9e15nioHAx7dpfnoHEF9pL4ElvzbT25dl9X/Jy/HH2Ngxokl/lvw+krdPbYOffqLGnfzcfxbFnhH1yOWpf6KvUjRMWT8d1cOHUL48P1dsacRXYP1iU5kQPTszVhfkWoV9uP8sikMptHhnF4X83PimbcpFSOoXyx4TRAthSgYlVcePH9eZmyopaxo/8aaS0rjxZK4Mw3g4vRrTotHA9u2wfj1cuAA2NlCsGDRrBpUrG3zVbcn/EhIXYDmSwoVQ/YsHtKXEr3fuSb5Xz6vVKhzVmb8rn/DaNWobRjbux9oFA2lybg91Lh5kW0HzDqCf8vc5nccfHViJraJhR75ynPRPPYk2p7TG6hwKLMGUWp0YsX0B9O9PQPvJ3A8oxO4L92lbMY9e+1cB3Q+sotaVo0TaOuD022/ELLCiQipmMG/XZe3v+pbRLuqftbtASq+FzOlbtyDl8uTIVKu9EG8Kg5KqlStX4usrX6ysZvbWC3RIY64Va6hCZmxZaTC1NfB2dYAdO6B/fzh2LPkKX3wB1arBnDkG7deSXUsT3+S5/SRJpTNFof+rVqpfyjelmJfxxw/ZJDr+Gd/8/FyxJR8dXMn4zXMJzVuKl3aOaWxtOt7PH9Hu+GYA5oS0TWdt6/G/Kq2peP0kDS4e5Oc/x/HO+1+jUulf2tt/1xZabV8AwMR63fiiaFEgaydVRf3dOHM7edl0fWw9m/rE1ImtH5C1S5OXyOWOWgWZaaRL+CYb0iqaXRQLcKdBcWmlEkIfeo+pklYo6xeTymQT32w6y5A//kt1u6w0QWtqIqPjGLw0jA2vxpBlotdWip68jEl/pSyqdiFvnL6ZDHXqxCdU7u7QtSvMmgXffQfvvgv29rBnD1SoQLeDq/TOxNOaG8rUEn8E7JJMLlv78hHK3jpPpK0DP1Z6m1wexk9wknZBnV6jAzfdvMkbcYe+e/8w+vH01fXQXzjGRnMkVxH25SllsThSsrx3NXrUzM+2oXWSLVNUagY1H8bN/EXwefGYX5aOwfVmuH473rWLyqP6YqNoWFw6lF/LNuHfM3eMG7wZrO1fQ+fx82jTT96e1W9QqVSqFMcIGboPgCr5vRjdtNgbUap9xnvl6FItH01KBlg6FCGyDIOr/wnrNWRZ6onTrvMpD5CHDI9dtioL915hxdEb9P7tCABHwh8bdf8xWWjeq32XHtDnt8PcSdo6kxJFYdDqmTB6dHyi1KULXLoE8+ZB374wYAAsXgyXL0ObNhAXx5h/f2LcP3P1Sqwu3H1GxAvLJKSJkxqdpCpRK9VvZRvzwMWTQiao8pb0YvSFvRPj6n8EQM/9yyn44HX1P3O16LlFPef9I+sBmFO1XbL+mcG+yat+mVOFoBx82rQ4zg4pd7985uBMy7dGc9XTn7wRd6jVpSWrf1iV9mds9Wpo1Ai7F8/ZFVSGz0J7g0pF1wWHUt/GSpXM7UGVRJN0a7LOf0uWlcmv1zsV4uflUqlUdK9ZgDpFsn+PnRZlcjG2RQnpSi+EAfROqubPn4+Hh5TStGYpDfJOoE7jnY7VKFk+aX6UePxMuPEHEmels/PuD/tYf/w2o1YcT3fd4TsWUm71b/EX13PmwPz5kDOFrnC5csEff8C0aWhQ0fnIOj7b8qNeidXa46l/Lk0pcb5w/dEL7e8h4ceoeOM0ioMDu1p14as2pmutKeDtovN4U+EQ/i1QEXtNLBP+/t7sfW87HV6Le/QLznrnZUuw7t32SvlyMETPuchMLa1CM/dcc9Cuw2RO++TD+eF93urVht1tu8P9JDeObt+G3r2hZUt48YId+crRvc0YYmxSnhMrq0h8aiwx11lWlPjT1L1GfoO3H2ol3wshhHXTO6nq3LkzDg5ZfyLEN5VNGhcpjafv4IN5B8wYjfHZ277+KLees8fo+4+MjuPkzYgslXyGP3yR5vKWJ7fS51VZbX78Mf4CNC0qFXz8MZ80GQBA18Or+ejAinTjsIaS/ePWvK6KOXD3YgBUPXqw4NNWtK+U+njDzEr2aVGp+LxhL17a2lMt/BgtTsdXJ3z0IjrZtsYQGR2n/cy6RT2nx8GVAMwOaYei0v3v/4cPKuLqYB1z+qRXvfOOmzdtO37N2qI1sVU0vLV+EQQGQt260KED1KoV/3juXAB+qfI23d75zGLj2ExFkir9FPR53QJraHdGtUr374sQQqRG/qd4Q6R1kfL0ZSy7LqTePdDanbvzlJn/XjDpMd6ft5+mM3ax/vjt9Fe2EmmNZyp87wpfbZwJwN53e0K3bnrvt/CIAazsNAyAT7YvpOWdtFvELNV7JKULzsrXTlD12gmi1bYwfLgFooJrnv7MDImf0HbMvz/h/vKZTtJnLCduRFDss418uuoEAN0OrsLz5TPO5czL2qLJiw/YGHsgYiboE8kzB2f6tfyEHq1Hc8w/GKKiYNu2+O6qO3dCXByEhMC//zKmTrcs30KVElNXSJ/dobxpD2Amebyc+bNXCP8Mrp3sb6Glu7wKIbIPSareEE+jTD+g2VIGLD5q8mNcfRDf6vPLvismP5axpJZT2cbFMm3dtzjGRrMtfwVcv5pk0H571CrA2wu+YknpUGwUDZP/+JKAJ6lXErNUjZuULjj7744fS/VH6QaQR79S3KbwY+XWXPQKxOf5Y4bs/AXQ7aJoDN9tOQ/A7/vD8Yh8SteDfwHwbY0OaFIoGZ9VS09vLlSVVp2/hVOn0Pw0j9NDP+fR3Hlw9mx8cZW6dS0dosmYuqGqaensU6SgYj4vgn1ddW7y+Lo5sG5AjdQ3EkIIA0hSJbSyUte2xJ6+NF/CuO/SwyxznlLrGtRv71JK3rlIrGcOgtcspVTeHIbvXKXis4a9CQsojNPzp2w/uYBJrUpkMmLjup9k4uNyN85Q82oYMWobvq9qnlLiqX1Wom3tGB0a393ygyPrKXXrPGczWBo79WO//v2jAytwj37BaZ98bCxSLcX1s/KAdBsbNRQrxh+lG9DEphLlLvux6IE919LpApvVZZX/i6xJ4paqIv5uONhmfk46IYSADCZVjx8/5qeffmLkyJE8fPgQgCNHjnDjxg2jBif0t/XM3Uzvw9RdSUxhzX83ufE40qzHPHrtsVmPl1EpJVUFHlynz6ty3rZzvyewRMEM7z/a1o5BzYYQ4+SM/Y7thKxamOJ6lhhTFadRaDl7t85zCRX/Vpaoy3UP88y7MqhBYQDalA9MtmxvUBlWFa+NGoWJf89m8O+HjXyRHL8v/yf36XpoNQDTar6fbCxVAtu0qtlYuYTP2I5zr7sxf/bXSRpP38G+Sw8sFZbJyZgqwyW+d/BVm9KWC0QIke0Y/Ff02LFjFC5cmK+++oopU6bw+PFjIH5i4JEjRxo7PqGnDxcczPQ+dpzTbzJIa/Hftcf0z0TXv2/eif+DGmrgxIZxWST7THa9pSh8tuVH7DWx/FugIrRvn6n9/9krhJZt66Ce/i0Aead9qVMmXMsCDSDRSUrgl7p1nnqXDhGnUjM7pJ3Z4mhVLjd7RtTTftaSmli3O08cXCh9+wIt9q/hv+sRRjt2wvs/fMdCnGKjOBBYnM3BVVJcd3GPqlbVUmVr4PiuhMYHJUlpkOfRcSw7lMJnMpvIIv8VWZXEc24mTObbokwuS4UjhMhGDE6qBg8eTJcuXTh//jyOjq8rKTVp0oQdO3YYNTihn3m7LhtlPx8uOMiV+8+Nsi9zuHT/Waa2b1sxD+e+aEKLsob9Qc0qN4evP9Jtwat/8QB1Lh8mWm3L+Po9Mr3/ivm8+LhhYWx69IAmTVDHRDNx0+xkJyi9Sm6mkPTiuv/epQD8Vbw2V3OY9wIql6dTqhXH7rnm4JtaHwAwbPsirp64wN8njVMMRQHK3DxL65NbAZhQr0eqA9xCCqZQRt+C3BwNKyqRkBCm9N3M6Pe1WIB7xjY0I2mpMly5vJ7Jnpvargxr+tVg7vsVqBCUg53Ds+84PCGE6RicVB08eJCePXsmez537tzcvp11KqNlJxPWGq9y2NUsNAbBGNcT9rZqg7unDVpi+sIYxmYbF8un/84DYF6lVlzxym28nSfMceXsTNVrJ2h7fLPuYuDivWc0nLadv8LM00U48R38YncvEXp+HxpUZm2lSmpZz5AUn/+tbBP+8y+Ee/QLlKFD+eiXw2lO1q23uFjG/fM/AJaXrMfxgEKZ36cZ9amjf9fUhNbjlP5PMDTx8HCyY2D9QszvUin9lS1McirD1S7sw5yO5flncG3tc3Y2akoFetC4pD/Le1cjj5czhf3iqwLWK2qersJCiKzP4KTK0dGRJ0+eJHv+7Nmz+Pj4GCWo1OzYsYPmzZuTK1cuVCoVq1at0lmuKApjx44lV65cODk5UadOHU6ePGnSmLIbtQrGrj7J2NXWf94GL/svw9smvltpaEPKzYiXGT6uqd19knJsbU5socCjmzx1z8GskHY0NLDLY7ry5YPx4wH4dOvP5Hz+WLto1Mrj1J+6nfN3nzFwSZhxj5uKxBfS/fbEt1KtK1qDizktV/Gvcn4vfu9RhaL+bvSvF6x9XqO2YXRoHzSoaHVqOzUvH+G/648zfbwG/yyj7K1zPLF35qtanTO9P3MzJF+IitUQHatJ1kIJhneRa1shkI8bFsbfw/rntGr2qjpfqdweFo4k61CpVLxVKiDdUuq/dqvC6KbFmNq2jJkiE0JkdQYnVS1btmT8+PHExMQA8f9BhYeHM2LECNq0aWP0ABN7/vw5ZcqUYdasWSku//rrr5k2bRqzZs3i4MGD+Pv707BhQ54+NW5VLWvy2/6rRt1fRGQMC/ZcYcGeKzx6bpoJSa1BSIHX3Z2y093ekSuSzxllHxvDgFelxB1Gf8rUbjWZ3r6s8Q8+cCAn/Ari+fIZn26dp306Ksn4JnNQXh2y0L2rND0bX7BiVrXMjSEzhmoFvdk4qBblg3QrLh4PKMTCCs0AmLZuGi6PMtlSdfkybVbET3w7ue6H3HWzru59pnD5/vMUv8tr/rtp0H6saWxZej5rXpypbcuwsGtlo+97XueKRt9nVuLr7kj3mgXwcM5+85sJIUzD4KRqypQp3Lt3D19fXyIjI6lduzbBwcG4ubkxceJEU8So1aRJE7744gtat26dbJmiKEyfPp1PP/2U1q1bU7JkSRYuXMiLFy/4/fffTRqXud17GkVkdBy3IiL5dOUJkx0nNpuMgrZLYdB74taptCbJzWoupTAmrkPYBnI/vceTnH7Y9+9L45L+uDjYGv/gtraMbNQPDSpan9xK1fBjxj+GnhLe0357lwGwoXA1zvrk0y4vZOEJP21SaB6dXLsLp33y4fP8MQ2/+Dh+8tqMiImBjh1xiH7J/jwlWVymUSajzRoUFKNU+rO3zTpVEJ3tbWlTIRAvF3uj7vfnLhWpV9TXqPsUQojszuC/Hu7u7uzatYvly5czefJk+vXrx/r169m+fTsuLi6miFEvly9f5vbt24SGhmqfc3BwoHbt2uzZsyfV7aKionjy5InOjzW7HfGSShP/oeqkLTw3wYS+iS/2UupKkxVVDPJKc3mcxvCWlD8OXUNRFC7ee8bLmAxe/JqAvY3uV9op+iV998UnFie7DQBH03ZpOh5QiF/LvQXAhL+/xy4uxqTHS41GUSjw4DrNzuwEkrdSfWPhLj0pFe+IsnOgb8sRPLdzJPfRffDxxxlrRh01Cvbu5YmDC0PfGpRqCfXsZt/FBzwxwpx1zvYmuOGQxdQr6qdTJU8IIUT6MvzXtl69egwdOpThw4fToEEDY8aUIQlFMvz8dMeK+Pn5pVlAY9KkSXh4eGh/8uSx3JgLfSTciY2IjMHGBPPK6FQps+KcypD5fFK88xwbB+HhcOIENq/mWkts1ydpV38a9ucxRq86Qf2p22k9J/Wk3dwc7HRfa7vjm/F5/phwDz8qjhtilhim1PqAe86eFHpwje4HV5nlmEmt/e8mffcuxUbRsDm4Mif9dIselM3jaZG4EqT21b2UM5Bhbw2KfzBzJnzzjd77PH/nKV+2HgJTpgAw7K2BXPP0z2SklmNoPjl2TeYK9nxUqwD5cjrToUreTO3H1OoWiW9BcnfUTf7mda5Ibk8nyicaL1o9WP9un0X93Whcwp8hDQsbJU4hhHjTGHxVPmDAAGbMmJHs+VmzZjFo0CBjxJQpSe+uKYqS5h23kSNHEhERof25ds265zRJ/FJS6kKUWbsvvB7LYcU5lUFzRbkmuvgIfHybiZtm0bdDDQgKglKlaNGgDH//1IeP9i/ns9qBfN2mNIE5nNPd72/7wwE4dct6WjcdEiWQNpo4bVLzQ5U22Dk6mCWGJ46ufFm3KwADdi8hMOKOWY6b2MKFm2l1ajsAM6u9q7MsaWueJdilEcP6ojU40G9U/INPPoHJk9PMMCJexNB94UFm9pjA8FXTAZgZ0p5NhasZM+Rsb9Rbxdg2rC4eTtY9hqZrjfzMeK8cf39cW+f5+sX82D2iHlUSjRftV1f/io9qlYq5H1Sgf/2sVSVSCCGshcFXF8uXL6d69erJnq9WrRp//vmnUYLKCH//+DuySVul7t69m6z1KjEHBwfc3d11fqyZ2sTd8xbtfV34IuE67trDF3y7+RwPrahwhb7jvSrn9wIFVIqG3vv+YOuPPekYthHnJ4/Bzg684rsGFn4Qzqht8+n6UTPaPbD+yoepSTzI/q0zu8gTcYcHTu78UbK+WeNYWaIu+/KUxCk2is//+cGsxwbov2cJNoqGfwpW4liA9d15d0hn3M7p93rw5ONh8Q9GjoTu3eFZyvOyfbvxFIV/nsWMNd9gq2hYVqoBU2u+b+yQhZWws1HTokwuvaoTGlJzQyoICiFE5hicVD148AAPj+T/+bq7u3P/vhHmVsmg/Pnz4+/vz+bNr+fIiY6OZvv27VSrln3u2OoUWDBxIYlPVx6n+cxdtJy9m++2nOfjpWEmPZ4hDJl7xi76JT+s+IJPti/EThPHjnzlWDrpZ3jxAh48YMnGMIY3HsBVT3+4fh2aNIFJk7JkWcAczq8GrCsKPQ+sAGBhhebkz+NtluN/VKtA/C8qFaND+xCjtqHhhf00OL9fu87D59Gcv2O6ipy71u6i5atWqu+qd0i23BrGCqZXDEGtghEV2jOhXnc0qODnn6FECfj+e7h3L36lFy9g1Sq6DOvI8B2LAJhfoTmfNBmASs+raU9nO9pVDMzUazGHGe+Vs3QIWZK+lQz71CnIp82KmTgaIYTI3gxOqoKDg9m4cWOy5zds2ECBAgWMElRqnj17RlhYGGFhYUB8cYqwsDDCw8NRqVQMGjSIL7/8kpUrV3LixAm6dOmCs7MzHTokv7DKqhJPVGvqpGrLmbscvxGhbaHaa4TKWsaib0uVXXQUvacMpOGFA7y0tWfoW4Po1G48rm81Btv4boGRru4sKxNKo66zoE+f+A1HjYovC57FEquEqn7Vr/5HyTsXeWHnwKLyTZnWrqxZjj+ySVFal4+fWPiCd15+rPw2AGP/+R9O0fFzaJWfsJmG3+7gcgqVCjPrwt1n3P9ktHYslbVOeOtga5P2CioVMXEK8yq1ouO7E4kKyB0/BrBPH/D1BTc3cHGBt98m36WTPLN3YliTAYyr/xGKSq33dNaHRzfk63esfx6eFmVyGb3C3ZtA32ITwxsXxd3Rurs9CiGEtTO4zNHgwYPp168f9+7do169egBs2bKFqVOnMn36dGPHp+PQoUPUrfu6gMDgwYMB6Ny5MwsWLGD48OFERkbSp08fHj16RJUqVfj7779xc3MzaVzmlPjGY8Nvd5j34FaUXzx4ln5XRJWioc+CCRQ5dYin9k50e+dzGvVux2R7G94q9XoAf0KrwUs7R5g6G4oVg/796XFwFc/snfmuhn5J+eGrjzh2/TFdquWzWOWsPw9fB6Dn/uUALC0dymMnd3K4mOeCSaVSkdfr9Xi0mSHv0uLUdgKf3KX/3iV8XbuLdtmRq4/I723ciqEPDobR4lT892J6olaqsnk8Cbv2GLCOPDm97n9xcRqc7OITr71BpZk4bSXj7+6Nb7H677/XXQHz5uWn3JWYW6EV911ez32l7/0Wa56TKWmLYkoVE0XarPjtFUKIbMfgpKpr165ERUUxceJEJkyYAEC+fPn4/vvv6dSpk9EDTKxOnTppVn1TqVSMHTuWsWPHmjQOSzL0uqJ1+dysOHLDKMe2hm5TCab+fTbddQbtWkz1I/+CnR092ozhQJ6SDPBzo0Yh3a5wbcoH8lfYTWoX9ol/ol8/0Ghg4EA+3v07j3MHsTB/8nGESbX5Pr4KYICHI41LBhj+ooyk+J1L1LpylDiVmnkVWwLgbGe+MtGJW1Mj7R0Z16AnP674gh4HVrKiRD0ueMdXVzPFNXLBqRNQo7CpUFVO+gdrn09IUKxFet3/YjUKjokqOcY6OMKAAfE/ERFw9y54eoK3N1+MXG/iaK2DrWQIBksrES3q78aZ26brhiuEEG+aDJXB6t27N9evX+fOnTs8efKES5cumTyhEvEMbQGpEWy8sTTWcIc/wd2nUWkurxp+jP57lsQ/+Okn9uUtDaDTipLA0c6GZT1D6Fv39UU4AwbAiBEAjF0zneJ3Lukd28V7xu/WZoiPDsS3Uq0rWoPrr0pqO9qbr+Jd0o/o5kJV2RxcGTtNHBM2f2+yD5Jmy794b/2bWJWar2t3TjMmS0uvpSomTsE2UYVAnVPm4QGFChGdIyebT981+Ng5nONbLTtaeenwpBqVSL3gkEiZtO4JIYT5ZOpKy8fHB1dXV2PFIvRg6B9JY/5NtaKcKs1uLR6RT/l2zVTUKKyv3AQ6dWLjoJr83r0KeXOmXypd64svoHFjVJGRLFj/Nc7RkXptpjHxWLfEx1l8IJzTt56w+8J9Qr/dTmDEHZqdjp/w9n+VW2vXNWcZ8ZTemnENehJp60BI+HFandoWv54xr/c0Gl4Oiu8O/HvZJlzMqTvfXOJjWcPnOL0xVXeevORpOhPZTtt8jh6LDhl87P2jGrD0o6p83ryEwduaU1F/3W7bAxtYXxVHa5fWVIaV86c9KboQQgjDZKhP0J9//smyZcsIDw8nOlp3bMuRI0eMEphImSXvOxoy4a6pqdI4E59unUfAswdc9MrNN2/14S2gqH8GSuXb2MDvv0OZMvheu8anW+fxaaN+Ka5a8+t/Dd9/Jn216Qz/267bgvb5wVXYKhp2BpXV6f5mqTFeCa57+DGzWnuG71jEp//O49+CldJ8Dw22aBHOJ/7jqb0T0/UcA2dJdjZpv/YFe64keSb5dy9h/Jyh7G3VOnMZWauWZXLz6HkMFYLix4ql17onkkt8E+7jBoX59p9zABwa3YDf9oVbKiwhhMiWDP4rNWPGDD788EN8fX05evQolStXJmfOnFy6dIkmTZqYIkbxysuYOIt2Y0p6WXf1wXM2HL9lkWQrocpdUlXCj9Pu+D8ADGsyCF//TF485sgBCxYA0DFsI3UvHkxxtWsPX7dimetsJE2oPCOf0P7Y3wD8UKV1SpuYRWqf0R8rv80Fr0B8Xjxm6I5fjHfA+/dRhg4FYFa19jx0Tj7lg1ETOCMwNMndeOI2E9aeSlLx03pucpiCWq2ia438lMnjCZi2C2exAOuenzCjEidV7k6v/8/0dLKzui6xQgiR1RmcVM2ZM4cffviBWbNmYW9vz/Dhw9m8eTMDBgwgIiLCFDEK4ktFFx2zkaF//GfpULRqf7ON3r8dYdPJO2Y/dsV8OZI9Zx8bw8RNswHYH/oOeZvVZ2o7I5SLrlcPPv4YgK82zMD9ZcqTsCYwZA4tY3r/6HqcY6I45ZufnfksN69PaglDjI0dY0J7A/Gxeh07bJwDDh2K6sEDTvvkY17FVskW96lTMMtfQD56EcO8XZf5K+x10Rkrajg2CzsTdWHtWbsAP3epaJJ9W1riFlFn+9ddTtUqa7vNIIQQWZ/Bf6XCw8O1k+k6OTnx9Gl89aAPPviAxYsXGzc6oTVvV3yrxKMXMQC4v3xG6xNbmLRhBr8tGcXaBQNZ+vsIJm+YQZvjW3CLii+WoELF121Kk8fLia/blM5UDKldxB0Jf5Sp/Roeh5LiHF2djqwh+OF17rl4cn3YZ0x/txyBOQwYQ5WWL7/koldufJ8/Mm4ri5E4xETR5fAaAOZWaaNzW3/UW0XNGktaCczeoDIsL1EXNQoVR/SBhw8zd7B162DhQjQqFaMa9SPWJnkL5uCG2WcsztUHL7S/v2E5FXY2ahZ1rUwBH+OV4f+oVgFGNilGgIeT0fZpTRInor7ujtrfVar4lkAhhBDGY/CYKn9/fx48eEBQUBBBQUHs27ePMmXKcPnyZasac5P9xP8BzPvoFgP3LKbZ6Z04xMUkW6vKtRO8e+xvIv92YHGZRtiHTqBVvdK0qxQ/cH/48mNmjdrYDl99RM9fDiWbqNIj8in99ywF4Otanajk6WncAzs6Mjq0L4uXjOL9o+tZXrIe/+UqkuKqlvgavHNiC94vIrju7sv6IjW0z28aVIsi/uadpy1xUYw8Xk46XSMBxjbsRYUbZ8h3+waXmr5Dgd3/pD2iPjW3bkGXLgDMr9CCo7lTTh5tzVikw9QiY+IAOHjloXZS7jdJrcI+1C/qy6V7lzO9L09nO0a9VcwIUVkv20QtVbk9nZjWrgzO9rYWH2MphBDZkcFXG/Xq1WPNmvg74t26dePjjz+mYcOGtG/fnrffftvoAYp49tGRjN7yI1t+6kWbE//iEBfDaZ98zK3ShkHNhtDlnc/p33wYc6q+w/mceXCKjaLr4dU0bFkTslELYs9fDnP/WTSX7uuWLe+7dxkeUc857ZOP5SXrm2ROm71BpbWtLF9umo2NJi7F9cydU6k1cXQ/uBKAeZVa6rTW5M5h/jvw7SvloYifG33qFKR0oGey5U8dXOjbagRRNnYU2LcVRo82/CDR0US/0w7u3+eUb36+SjSpcHqs8ebP6Kb6Xdz/sCO+xbrt3L2mDMeqZfbtq13Yh2GNirC6b430V87iErdUqVXQunwgjUvGT7MgeZUQQhiXwS1VP/zwAxqNBoBevXrh5eXFrl27aN68Ob169TJ6gAI4cID+g9vhfesqANvyV2B6jQ6EBRRO9pdxq0N9vq7VmRpXwhixfQEl71yEDh1g2zaYPdsCwRtXdGzyRCbw8W06H4lP9CfX+RCN2gYbE3Vt+bJuN+pfPEiJu5focngN8yq1Sr6SmS/aG5/bS/5Ht3js6MrS0qE6y8xZSj2Bm6Mdmz6uBUD/xUdTXOekX0E+bdSXKeunw6RJPPHywW3IoHTvoEfHajh76wklPx2I/Z5dPLV3on+L4UTb2qW5nbVN/gvwftW8/PqqAlvSlte0WGNSaE6JX33twj5sP3fPoO09nOx056TLxuzUqcx1hvUVbxFCiKxOryuu1q1b8+TJEwB+/fVX4uJeX9i2a9eOGTNmMGDAAOzt7U0T5Zvs55+hZk28b13llmtOurwzli7txhGWq4hOQlU5nxeHRjfgn8G1QaViV/5ytOw0jTM9P47vWvXDD9CyJU7RLwFoUz7QUq/I6D7e/TsOcbHsDCrL9vzlAUyWVD1w8WTyq1aRQbt+w/fpg2Tr7Dh/n1/2XTXJ8ZNRFHrt/xOAheWb8cJet2UqvdLdppbW0f8s1YCpNToC4D5sMH93G57u/gb9fohjLd5D9csi4tQ29G05ItmcVCkZ06y49ndrSUkSX9QaMmdQZnKqlCa/zmoSv/76xXwtF4iVck1UGTVx97+4JB8caakSQgjj0iupWrt2Lc+fx3e3+vDDD6XKnznExsLAgdCtG0RHc6pqfRp1m822gqlXqfJ2dcDf4/Vg5Di1DWd7DYEVK8DJCdav538rJ2IfG0PD4r4s713NHK/EqJJeT+Z5fJuWJ7cB8E3tTtorBVN0/0uwtEwoRwOK4BYdyeit85ItD7v2mDGrTvD7/nAGLw3j2sMXKezFOELCj1H69gUibR1YWKF5suWWHjuR3vX/zGrv8kv1dwBoNH8KdO0Kr27gJHP3Lu+M60vHsI3EqdQMbzyAHQUq6BVHHitMJhK/Nfm8Xdg2tA6DGhRKd7u+v2d8LsDv3i2b4W2tRVCiCbwNnQwdsn8y0aVaPmoEezOhZQmd7n9Ji/tInQohhDAuvbr/FS1alJEjR1K3bl0URWHZsmW4u6c8r0enTp2MGuAba/lymDEj/vexY/mjTGueZHSyxpYt4Z9/IDSUWleOMnP1V5QZ9Q8vNcYL12ySXKX33vcntoqGbfkrcCzgdZW3HM6mazVVVGpGh/Zm9aLBtDi9gyWlQ9mTr2yy9UatPA7AubtPWdu/pkli6b0vvpVqWekGKc7PZGnpdlVTqRhTowvhdu6M3DYf9fz5sH59/A2F5s0hIACuX0dZsQJl5kzqPXpElI0dg5sOZl0x05xTc0l6TZvP2wUvl/Q/txtO3M7wMXN7Zv0qdx2q5OXm40hqFfYh3IQ3LLIqFwdbfu1eBdD9/kn3PyGEMC29kqq5c+cyePBg1q1bh0qlYvTo0SneAVepVJJUGUu7drB9OzRoAK1bo6w+maHd1Cv6qntMtWqwejXKW2/R6Pw+mDSW8BHjjBiw+fk/uc87ryb6nVmtvc4yQ7pT6atFmVys/u8mACf9g/m1XBM6H1nH+M1zadJ1JjE2KY+LOXc77XmtMqrEnYvUunKUWJWaHytZZ5EYfbuq/VilNf/lKsyyfT/ApUswalT8zyuqVz+nffIxqPlQzvrkM3lMptawuD8L917Fw+n158Y2I1UQDZENrqPtbNSMfFW1L+Ce4d+tbHAK9Jb477STvU2SZeaORgghsje9kqpq1aqxb98+ANRqNefOncPXV/qym5RKBXPmsOX0HV78dzNDfwBHNy2GW+IB8PXqoVq0CNq3hylT8C5WAvAxWsjmkPh6uOeB5dhrYtmbtxSHA1+PmalawMsk3d6+e7esNqkCmFrzA946s5vgh9fpfnAV31dtm/KGJrp46bl/OQDritbkuqe/aQ6SSYnHd6TnQJ6SMP80/PZb/M/+/fDsGXh4sM0rmBUl67G2aA00ausrOpERNQp5s6pvdfIl6s5ma4IxcA2K+fHP6fgJurNb60QBH1dLh2D1vmhVkvvPoiiY5FzVKOQNWKaYjRBCZEcG/W8aGxtLp06diIqKMlU8IhGNRqHbwkP0X3yUB88Mn5PGLqU/lu3aaVsAnPv2ZlVtd73LOVvSi+hY5my7wLOoWAC8nz/ivf82ATAzRLeVKoV5gY0iaaL2xNGVL+t2BaD/niXkenI35e1MEMueNTtpemYXAP+r0sYERzCOoY1SnssrVfb28OGH8d1Vnz6F6Gh4/Jgu7caxunjtbJNQJSibxxPPRF1VjT0WsH3FPDqTP0vrxJvn/apBDGqQfALsov7ubP64Fgc/bWCBqIQQIvsxKKmytbVl+fLlOtX/hOloEvVTehyZfKLfDJswARo3hpcvKTu8D90r5TLevk3k283n+HrjWe3j7gdW4hgbzZFcRdgTVEZn3cR3/k1tZYm67M9TEueYKD7b8mOK65jiQvb+J6OxUTRsKlSVU34FjH8AI/Fxc8jwttcevmD3VeMVxQn2jb9TX7eI9bbOGrtrooKi06UwO+dUns52fN2mNN+8U1r7XFAK/xe0qZB9Kp9mViE/Nzyc9S/nL4QQInUGt/vXr1+fbdu2mSAUkdSNx5Ha39Mb8K8YUiharYYFC8DXF06cgE8+yWCE5nPo6iPt756RT/jg6HogvnpcQtbyR68Q3quch1FvmbHlTaViTMNexKrUND63lzoXDyVfxdiXsidP0uz0TgCm1+hg3H1bkZpfb6XjT/s5dOWhUfb3W/cqjGxSlG/blzXK/kzB2I2sigI2iboUZqRaXlahVqloVymPNnkGCMrpov29fF5PtgypTc1C1ptUCyGEyLoMnvy3SZMmjBw5khMnTlChQgVcXFx0lrdo0cJowb3p2v9vX6a2T/P6yc8P5s+Hpk1hxgxqtc2pd3lqS7BJ9GI+PLQal5iXnPQtwNYCr0vMV8rnRaV8xi9Qkdh375Zl4JIwnefO+eTj54ot+ejgSsb9M5fQvLOJsst4C016lPHjUaOwoXA1Tvum3kr1bfsyqS6zZneevOT0rddl1Q8nSqgzw8/dkZ61CxplX6YSpzFuSU4F3S6F2Tin0t54ik3U/zdxtUNbG3WycUVCCCGEsRicVPXu3RuAadOmJVumUqmka6AR3X7y8vXvES/TWDOD3noL+vWDWbP4ctMsQrvNSTZ5rLVIuMPuFvWcDw+vAV5V/DPzVWLLsrmTJVUA31V/jxantxP0+Da99//J9FeT2oKRQzx6FNWyZdpjpsbf3ZG3y2XNbk41v9pKdNzr5MJUY+SsUYQxu/kS31KVOKlKOldRdhQT+/qz42D7ujNGuuX9hRBCiEwwuPufRqNJ9UcSKtM5f9c0ZbmZNAny5SPwyT2G7VhkmmMYQUJi8sGRdbhHPed8zjxsKhxikVg6Vsmb7LnnDs5MqNcDiJ87Ku+jW9plRsupFAXN4MEArC5WizO++VNf1egdycwncUIFWfu1GCqni3FbOOM0Gp0xVdk5qUp4ZYk/P4kTSsmphBBCmJLUUs0mElf6q1vEBxu1iqalAtLf0NUV/vc/ADofWcu8Qsa9U24sNmoVTtEv6X5wFQCzQtqhqCzz8f2iVUn2jqyX7Pl1RWuwM6gsDnExjPtnrvYqztDy7oqiEBuXQjewNWtQb9tGlI0dX9XukuY+/D2ss8UxIxIXKDGUmwEl3a1By7K56FW7ICVzpzy5uqFi4hQc7V9/T5LOVZSdJCRNMXGvs6fEXz2NZFVCCCFMyOArjvHjx6e5/LPPPstwMCJjXB1s+aJVSe3jn7tUIjpOg4OtnhdQoaHQpQvqBQuoP3UUdk2/THUi28v3n5PfW3ccnTm61ahVKjqEbcAr8glXPANYW6yWyY+ZGpVKRYCHE0s/qkr7H/YlXsDnDXux8ed+1L10mEbn97KpcLVkLVUvY+I4Gv6YSvlyYJtC2fvevx5hz8X77PykHh5Odtx8HMk3q48xadwQHIGfKrXihkfK88S1KJOLJy9jGN+iZIrLrd2Oc/eMtq/xLUu8nvw6i7C1UTOiSVG+XK9w4saT9DdIR8L/A8t6hhAbp9Gdty6bSfh/KLVxab2sfDydEEKIrM3gpGrlypU6j2NiYrh8+TK2trYULFhQkioz+617FaoHe+s8p1Kp9E+oEkydChs2wOnTdPdNfSLbpjN2cmp844yGm2G2MS/peWAFAHOqtiXOCuYrckmhFeRSzkB+qNKafnuX8dk/P7I7qCw46rY69P3tCFvO3KVv3YIMa1Q02T42nrwd/++JW7SvlJehf/xHlQUzcLx0gXsunqlPMgxUzu/F+1WDMvfCjGzu++Xp9esRvdbt9PMBox23U0g+o+3L3GLjjHOjIuZVi2fl/KYt4GJN6hTxpYC3C+Xy5tB5PrSEdU6QLYQQInswuP/U0aNHdX5OnDjBrVu3qF+/Ph9//LEpYhRpsDHWZKFeXjBlCpD2RLYvopOPmzO0e1tG1Nq1Ft/nj7jh5sPKknVNfrzMmBXSjmsefuR+eo+Jm2YnG8yx5Uz8uV2452qa+0nYTH3yJL33/QHA2Po9eeaQ+jxc1tjBqXHJAP7sZZnxb1lV4q5qzUoH0KRkxhICYyVnWUHCK3W0s2HLkNpMbVdGxlEJIYQwG6MMSnF3d2f8+PGMGTPGGLsTBrCzMWJC07Ej1KqV5kS2AD/suGi8Y+ojOpoWG34BYG7VNql2TTS31Ob8eWnnyMBmQ4lVqWl5ejutj27SLkvcVTL9uceA6GhGL/8Ge00sm4OrsK5oDWOEbnbmSLyzk6QFJTI6v1TSoh9vCvm8CSGEMDejjfR//PgxERERxtqd0JON2ojFGlQqmD0bxcaGxuf2MijmQoqrfbn+jPGOqY+FC/F+dIc7rl4sKx1q3mOnIfF12+IeVXWWHQksxtRaHwAwcsMc2L+fy/efU2niP3rvX1GAYcMoev0sjx1dGR3aO8tONGSsBlV9lcnjad4DGllcooR7eKOiGS4hWSbQw0gRZQEp3KPIol8XIYQQWZDBY6pmzJih81hRFG7dusUvv/xC48bmH2vzprM19tVqyZKoBg2CqVN5/7epfP/+d0TZ2qe5SZxG4drDF+TxSr1bWobFxMSXfQd+qNxaG0tuTydqBHtz9Nojzt0xUbl5AxTySz6p6Nwqbahw/RQNLh6E5s3537AfuP/sdStbej2T8m1YAa++b4ObDuaOm3c6W4BDCoUvrIHRuqnqqUs16xpXZihNopaqvDmd9cqpLk96i/wj12sfD2tUhA+r5zN+cFbm6zalGfPXCeZ+kHzycun+J4QQwlwMTqq+/fZbncdqtRofHx86d+7MyJEjjRaY0I9JLlY//xwWL8b75jV67l/OjDQmmQWYt+sy83ZdZlaHcjQrnSvTh99x7h5bz95lRJOiOPz+O1y+zGO3HPxW9nXS3ikkiJ61C9J4+o5MHy+jEl+w2ahULPiwEl3mH3y9XKVmQIvhLF8ykmK3LjDk6z7saT2e8BwBybZPqs7Fg1RdORGAn6q349/gynrF5GBnnUmVnZmTvYx2l7MWhnb/m/h2SZ0ub/a2avrWDTZJbNamXaU8tKkQaPbEXQghhEjM4KTq8uXLpohDZJBRx1QlcHODadPg3Xfps+8PVpaoyzXP9AfK/7DjklGSqoQKcIFu9nSbGJ9YrG3wHi/tHLXrWMMN6MTFBGxsVJTKnbyr1Qt7Jzq/PYb9f0/A58IF/vxtOL1ajeJIYLFU99v6xBa+2jADtSaOc/WbM7HC+3rHZHDVRzMxd1KV1cUlybjTyxGTzknnYPtmnW9JqIQQQlhapv/yXr16lVOnTqFJZW4QkTFJ71SnxqhjqhJr146HVWrgGBvN5//8L9ni93/an+w5Y1/WeK5ZCefPg5cXf9d628h7z7ykLVWptSbcdctJrWbjuBhQEN/nj1j2+yd8+u9POD9+wL5LD16vePkyMe++x7R132KniWNdyTo0LdfVoEmOrbelyvQXvZXzvS4bntVbqjRJvv/pvRrVqzWGNSoCxHeJE0IIIYT56H0FtnDhQqZPn67z3EcffUSBAgUoVaoUJUuW5Nq1a8aO74117s7TVJdVyvd6/pUAD8dU18sUlQrPn39AY2tHg4sHaXRuj87iXRfup7iNsdjGxVJnyZz4Bx9/TKSj7nithISmfaU8AJS1QGECnZYqtQp1GnfLrzl40KL9JFaUqIutoqHHwVXsn90J21o10bz9NlSsCAULYrd0CXEqNd9Ve5d+bw02uNJhUX+3DL8eUzJLS1Wi05/Vk6qkldDTq2aXkHf3rRvMiXGNaJKk5UoIIYQQpqX3lc7cuXPx8HjdvWnjxo3Mnz+fRYsWcfDgQTw9PRk3bpxJgnwTpdV9x0atIuyzhhwe3QBHO9N191IXL4Z6+DAAxm7+H65RL9Jc35iXsW2P/0POm1fB2xsGDkw2/kh51QGwc0g+lvUM4bfuVYx4dP0kDkmtUqXbBem5gzODmw2hyztjCQsohK2ioeKN06hXrYLDh0FR2J6/PG9/MIVva75vUAvVzuF1Wdu/BgEeThl7MSZma4aWqsRHcLK3zhY7fSU9Xem3VL3mmsKk1G+qsnk9LR2CEEKIN4Tef33PnTtHxYoVtY//+usvWrRoQceOHQH48ssv+fDDD40fYQbMmTOHb775hlu3blGiRAmmT59OzZo1LR2WQRzSSJbUKhWezmlX5DOa0aOJ/PV3AsKvMGTnL4xr0DPVVY3VOOAY85KBu38HYEPLbhR5qeLQ1UcprqtWq6ic3yvFZaaWrKVKzx6w2wpWZFvBigQ9ukmZW+eZVCc3LoEB3Chens6/ns1QLHm8nMmToS3Nw9ZU3VRTUbuwr1mPZ2xDGxXh4JVHdKmWD0i5papbjfzM2xU/xjWrt8yZStNSAUS301A60NPSoQghhMjm9L7SiYyMxN3dXft4z5491KpVS/u4QIEC3L5927jRZcDSpUsZNGgQn376KUePHqVmzZo0adKE8PBwS4dmkLRaqsx6AeXkxOUJUwHofHgtpW+dS3VVY0XV+cha/J895Lq7LwM9q1Jv6vZk61hDqeTEk/eqVYa/L1dz5GJ18drsbfwua4rUoHoGE6qswNyFBLJ64YLAHM7sHlGPHrUKACnfsEj8lORUKVOpVLQuH0iwb/IpD4QQQghj0jupCgoK4vDhwwDcv3+fkydPUqNGDe3y27dv63QPtJRp06bRrVs3unfvTrFixZg+fTp58uTh+++/t3RoBkkrqRoSWtiMkcCzmnVYUaIuahQmbZyFjSYuxfXSG/ehl3v36LP3DwCm1exItG3KY4rqF7N8S0TiWgIqPbr/pab7okP0X3zUSFFZpzetGp2xpffRkpYqIYQQwrL0vtLp1KkTffv2ZcKECbRt25aiRYtSocLryRb37NlDyZIlTRKkvqKjozl8+DChoaE6z4eGhrJnz54Ut4mKiuLJkyc6P9YgrdLY5fLmSHWZKahVMLFuNx45ulHi7iW6H1yZ4noqIOzaY3adT6GIhb5GjcIj6jknfQuwqnidVFcr6u+e6jJzSVqh0UYubFPlaGfD0NDC9K1bkJqF0p/EWOhSpdAObC+JqhBCCGE19P6r/Mknn9C9e3dWrFiBo6Mjf/zxh87y3bt38957aU8Sa2r3798nLi4OPz8/nef9/PxS7Zo4adIkPDw8tD958ljHyBRzlKDWl1qt4oGLJ1/W7QrA4J2/UvRu8vnKVCpoNXs378/bz50nLw0/0IEDMG8eAJ817IVGnXJi6eVipvFkBpKcKm396hViWKOifPduOT6oGoSboy1ftCqJm6NxCitYQY9Qk0nps1U9+HVyKi1VQgghhGXpnVSp1WomTJjA0aNH2bBhA8WK6U5e+scff9CtWzejB5gRSbuhKYqSate0kSNHEhERof2xlrLwKpWKYgHu5HDW7f42r3PFVLYwnYRqYn+UasDm4Co4xMXy7dqpOMRG66yX+G767QgDk6rYWOjbFxSF5SXrcTiweKqrKtYwoAqoGJSDUrk9aFU2fsJjo3R/fAN4udgzoVVJjo9txPtVg/ilm/krN2Y1KX20CiUaJ5TFh5AJIYQQWV62qr3r7e2NjY1Nslapu3fvJmu9SuDg4ICDg4M5wjPY2v41iNVoKDJ6o/a5+sVSfh2mpG1JUKkY0bg/5X4+Q7F7Vxi6YxET63V/vWKiCzuD056vv4ZDh8DDg8m1064iaR0pFdjaqFnTv0b6K4o0uRuppcpqPhgmkTxr8nV3ZErbMtjZqLA1xzxgQgghhEhVtvpLbG9vT4UKFdi8ebPO85s3b6ZatWoWiirjbNSqNMdWmYu74+vWsgcunnzSZAAAPQ6uovHZ3dpliS/7NIa0Jh0/DmPHxv8+cyb3XNMeM9ajZgH99y2snrEq9SnZOKtK7RS9UyGQlmVzmzcYIYQQQiSTrZIqgMGDB/PTTz/x888/c/r0aT7++GPCw8Pp1auXpUPLslwcbBnWqIj28ZbgKvxYqRUAU9d9S6F7VwHdhgK9c6qnT6FdO4iJgZYtiX2vQ5qrz+5Qnt61CxoQvcjuAnM4UbWAF0E5XSwdislIz1IhhBDCumW7pKp9+/ZMnz6d8ePHU7ZsWXbs2MH69esJCgqydGhZWt+6wTqPJ9f5kN1BpXGJecnPy8fj9/R+ku5XCoqicPn+89THQGk00KULnDkDuXOzZdAEio/9O804KgTlQC0DSEQiP3WuyJKPQow2T5o1Sqn6nxBCCCGsR7ZLqgD69OnDlStXiIqK4vDhwzqTFAvjiFPb0L/FJ1zxDCBPxB1+XTqGR+E3tMs1Cszdfom6U7bxxbrTOttqNApbTt3mRe++sGIF2NvD8uV02xhOdKwmzeNaU1VEYRmty+l2d4uJzb7d/hJIS5UQQghh3TI0Qvz69eusXr2a8PBwoqN1K8BNmzbNKIEJ6/fQ2YP33/2CZb99QqEH1/jf3IF82HYsV3PkQlHgq41nAJi36zJjmr2u5rfiUDgRfQdS/9Bf8U/MmwdVqsDKdeke007m5nkjlcnjSS4PR4Y2KsLjFzGsOJo4gY9PqrJz4iEl04UQQgjrZnBStWXLFlq0aEH+/Pk5e/YsJUuW5MqVKyiKQvny5U0Ro7Bi1z38eL/9Fyxa9hkFHt3kr0WDGdOwNxpNKmWyb92iSM/3KRW2K/7xnDnw/vt6H89eqpwBEFIgJ9UK5mTq5nOEFjd/RUhjSq9r29r+NSjq76atcHck/JF22XuV81A60MOk8QkhhBBCpMfgK9SRI0cyZMgQTpw4gaOjI8uXL+fatWvUrl2btm3bmiJG8YqLveUrAabkUs5A3v5gKmEBhfB8+YyZa74hsHlDWp7cSu6Iu7i/fBZfLn3UKChalFJhu4iysWNA82HQuzeKotB27h69jmUr46kY3bQY379fnr51g1nZpxozO5SzdEiZ4mCX9n9DJXN76JQMT/wJmNiqlHZ+sHcq5Hm1vrvRY7Q0a53wWgghhBDxDG6pOn36NIsXL47f2NaWyMhIXF1dGT9+PC1btqR3795GD1LE83azzvm0AO655qBtx6/pt2cZvfb/SeDpML47HfZ6he9e/3qlQAl61+7Jad8CzAAu3X/OwSuPku4yRcYqv52VdQrJh/2rbpDl8qZdfj4r8HN3ZGD9Qny35bxe6yfuCpe4V1zl/F7sHF4XP3dHY4docd1r5ufY9Qj+OX3H0qEIIYQQIgUGt1S5uLgQFRUFQK5cubh48aJ22f37940XmdCa3r4suT2dmNPRurtXxtjY8W3NjtToNY/FoZ047ZOPaPWrvN3TE5o1gxUrmDJ2Pqd9X881FafRv9CASsaWpDpnUVb2ccPCeq+b+COQ9POQx8tZm3BmJ872tvzUuaKlwxBCCCFEKgxuqapatSq7d++mePHiNG3alCFDhnD8+HFWrFhB1apVTRHjG69Vudy0Kpd1Jvi85+rFL291Z2S5dqg1cdhqNJyb0kq7XPPbYZ31DZoo2Mot7x3Cjzsus/HkbZMd400qWvBRreQTPWfHlih9/dkrhM/+Osm4liUsHYoQQgghEjE4qZo2bRrPnj0DYOzYsTx79oylS5cSHBzMt99+a/QAhfXwdnXg/rMovdZNSJM0ahui1TbM332Zv8JusrBrZdYf1004slFORYUgL55VidMmVeNalOB5dCxfbzxrtGO8QTlViiUs/NwdWdi1Mq4O1jnG0JQq5vNi/cCalg5DCCGEEEkYnFQVKPD6zrGzszNz5swxakDCeq3tX4Nlh64xbfM5g7cdt+YUAD/suJhsWXZKqgCdyY47V8sHYOSk6g3KqlJRu7CPpUMQQgghhNDK0OCDx48f89NPPzFy5EgePnwIwJEjR7hx40Y6W4qszN/DkQH1C+m17ulbT1J8fvbWFJIq0s+q3B1t+biB/uNuRDYh+aMQQgghsgCDW6qOHTtGgwYN8PDw4MqVK/To0QMvLy9WrlzJ1atXWbRokSniFFYkX05nrjx4YZR9KYrCiRsRaa6zuEdVquT3Qp0dKzQY6MyExpYOQQghhBBCJGFwS9XgwYPp0qUL58+fx9Hx9YDxJk2asGPHDqMGJ6zT6v41jLav8hM288ny42muE1IwpyRUxM9T5mj3Zo0jSm9iYCGEEEIIa2BwUnXw4EF69uyZ7PncuXNz+7bpKp4J6+HuaGe0fT16EWO0fVmLlDozvm2E6o3T2pfN9D6smQwVE0IIIURWZXBS5ejoyJMnycfLnD17Fh8fGTwuROncHoDufFJT25ahkK9rpvZbzN89U9tbu5QKlkiiJYQQQoiswOCkqmXLlowfP56YmPgWBpVKRXh4OCNGjKBNmzZGD1C82Wa8V87SIRgsp6sDBz6tz7GxjbTPqdUqFnatbNB+vny7lM7jNzHBeANfshBCCCGyIIOTqilTpnDv3j18fX2JjIykdu3aBAcH4+bmxsSJE00Ro3iDtSiTy9IhZIivmyOuDrp1YBxsDfu6JU2i3sRxZW/aGDIhhBBCZE0GV/9zd3dn165d/Pvvvxw5cgSNRkP58uVp0KCBKeITVqpmIW92nr9v6TCyFEMThKQ5lL+7Y8orZkMjmxRl/YnbfFg9n6VDEUIIIYRIl8FJVYJ69epRr149Y8YispD5XSrx4Hk05+884/15+y0dTpZgeEuVblZlk81bqkY3LcYX604zpGFhetYuSM/aBS0dkhBCCCGEXvS+ytu/fz8bNmzQeW7RokXkz58fX19fPvroI6KiooweoLBOtjZq/NwdqVHI29KhZBm2Nhmaazt+22yeUAF0r1mAA6Pq01/PCaaFEEIIIayF3ld5Y8eO5dixY9rHx48fp1u3bjRo0IARI0awZs0aJk2aZJIgxZujSUl/S4dgNiVypV3NL3Ea9aYUqfB9g7o4CiGEECL70DupCgsLo379+trHS5YsoUqVKvz4448MHjyYGTNmsGzZMpMEKd4c1YO934ixQ20rBDK0UZFUl9vZqHS6/yXtCiiEEEIIIayH3knVo0eP8PPz0z7evn07jRs31j6uVKkS165dM2504o2jUsH375fHyc6Gz5oVt3Q4JpPT1YE6hVOf1+3Y5410W6pMH5IQQgghhMggvZMqPz8/Ll++DEB0dDRHjhwhJCREu/zp06fY2dkZP0LxRlGrVJTLm4PjY0PpWiO/pcMxmehYTZqtT072NiSeC1caqoQQQgghrJfeSVXjxo0ZMWIEO3fuZOTIkTg7O1OzZk3t8mPHjlGwoFTrEplj8yp7yExRh6wgKjbOoPVV0lYlhBBCCGG19C6p/sUXX9C6dWtq166Nq6srCxcuxN7eXrv8559/JjQ01CRBijeDr5sDzcoEWDoMs/B0NqxVt1Sgh4kiEUIIIYQQmaV3UuXj48POnTuJiIjA1dUVGxvdiUz/+OMPXF1djR6geHNsHVoHZ/sMT52WJczpWJ7VYTcNnoNp5nvlTBSREEIIIYTILIOvYD08Ur5j7uXllelgxJvN3sDJcbOit0oF8FYp/VrjEnf483sDKiIKIYQQQmRV2f8qVmQZdtl8HJWhlPRXEUIIIYQQVkCuYoWwoA5V8lo6BCGEEEIIkUnZewCLEFZubPMSNCsdQDF/d5rN3MWNx5GWDkkIIYQQQhhIkiphcW0rBNKnbrClw7AIe1s11Qp6WzoMIYQQQgiRCZJUiUzLl9OZKw9eZGjbpR9VpUqBnEaOSAghhBBCCPORMVUi0+Z1qUT9or6s7FPN4G0L+bmZIKLsoUZwfAuWt6uDhSMRQgghhBBpkZYqkWkFfVyZ16VShra1UanSX+kN5e/hyKHRDXB1kK+pEEIIIYQ1yzItVRMnTqRatWo4Ozvj6emZ4jrh4eE0b94cFxcXvL29GTBgANHR0eYNVBhElWU+gaaXUn7p7eqAo51N8gVCCCGEEMJqZJlb4NHR0bRt25aQkBDmzZuXbHlcXBxNmzbFx8eHXbt28eDBAzp37oyiKMycOdMCEQt9SEvVa4pMTCWEEEIIkSVlmaRq3LhxACxYsCDF5X///TenTp3i2rVr5MqVC4CpU6fSpUsXJk6ciLu7u7lCFYCtWkWsJv0sQS1JlRBCCCGEyOKyTeervXv3UrJkSW1CBdCoUSOioqI4fPhwqttFRUXx5MkTnR+ReSoV+LilX2BBnW0+gUIIIYQQ4k2VbS5pb9++jZ+fn85zOXLkwN7entu3b6e63aRJk/Dw8ND+5MmTx9ShvjHmvl8+3XWkpeq1Qn6ulg5BCCGEEEJkgEWTqrFjx6JSqdL8OXTokN77U6Vwga4oSorPJxg5ciQRERHan2vXrmXotYjkUuv9VzEoh/Z3GVP12tdtStO2QiB/9a1u6VCEEEIIIYQBLDqmql+/frz77rtprpMvXz699uXv78/+/ft1nnv06BExMTHJWrASc3BwwMFB5gEyFjsbFTFxCkX93XGwTTlnT1zNTnKq13zdHfmmbRlLhyGEEEIIIQxk0aTK29sbb29vo+wrJCSEiRMncuvWLQICAoD44hUODg5UqFDBKMcQ6VvTvwbzdl5mYINC5PZ04t1KeVhyULf1L29OZ7gQ/3tarYhCCCGEEEJkBVlmTFV4eDhhYWGEh4cTFxdHWFgYYWFhPHv2DIDQ0FCKFy/OBx98wNGjR9myZQtDhw6lR48eUvnPjIr6u/NN2zIE5nBGpVIxuU1pneUD6gXTtFSAhaITQgghhBDC+LJMUvXZZ59Rrlw5Pv/8c549e0a5cuUoV66cdsyVjY0N69atw9HRkerVq9OuXTtatWrFlClTLBy5SGxwaBFLhyCEEEIIIYRRZZl5qhYsWJDqHFUJ8ubNy9q1a80TkMgwmeRWCCGEEEJkJ1mmpUpkH4528rETQgghhBDZR5ZpqRLZR4WgHLQqm4v83jIvkxBCCCGEyPokqRJmp1KpmP5uOUuHIYQQQgghhFFIPywhhBBCCCGEyARJqoQQQgghhBAiEySpEkIIIYQQQohMkKRKCCGEEEIIITJBkiohhBBCCCGEyARJqoQQQgghhBAiEySpEkIIIYQQQohMkKRKCCGEEEIIITJBkiohhBBCCCGEyARJqoQQQgghhBAiEySpEkIIIYQQQohMkKRKCCGEEEIIITJBkiphcvY28R+zfDmdLRyJEEIIIYQQxidJlTC5VX2r07RUAPM/rGzpUIQQQgghhDA6W0sHILK/4rncmd2xvKXDEEIIIYQQwiSkpUoIIYQQQgghMkGSKiGEEEIIIYTIBEmqhBBCCCGEECITJKkSQgghhBBCiEyQpEoIIYQQQgghMkGSKiGEEEIIIYTIBEmqhBBCCCGEECITJKkSQgghhBBCiEyQpEoIIYQQQgghMkGSKiGEEEIIIYTIBEmqhBBCCCGEECITbC0dgLVRFAWAJ0+eWDgSIYQQQgghhCUl5AQJOUJqJKlK4unTpwDkyZPHwpEIIYQQQgghrMHTp0/x8PBIdblKSS/tesNoNBpu3ryJm5sbKpXKorE8efKEPHnycO3aNdzd3S0ay5tIzr/lyXtgWXL+LU/eA8uT98Cy5Pxb3pv+HiiKwtOnT8mVKxdqdeojp6SlKgm1Wk1gYKClw9Dh7u7+Rn6IrYWcf8uT98Cy5PxbnrwHlifvgWXJ+be8N/k9SKuFKoEUqhBCCCGEEEKITJCkSgghhBBCCCEyQZIqK+bg4MDnn3+Og4ODpUN5I8n5tzx5DyxLzr/lyXtgefIeWJacf8uT90A/UqhCCCGEEEIIITJBWqqEEEIIIYQQIhMkqRJCCCGEEEKITJCkSgghhBBCCCEyQZIqIYQQQgghhMgESaqs1Jw5c8ifPz+Ojo5UqFCBnTt3WjqkbGHs2LGoVCqdH39/f+1yRVEYO3YsuXLlwsnJiTp16nDy5EmdfURFRdG/f3+8vb1xcXGhRYsWXL9+3dwvJcvYsWMHzZs3J1euXKhUKlatWqWz3Fjn/NGjR3zwwQd4eHjg4eHBBx98wOPHj0386qxfeue/S5cuyb4TVatW1VlHzn/GTZo0iUqVKuHm5oavry+tWrXi7NmzOuvId8C09HkP5HtgOt9//z2lS5fWThwbEhLChg0btMvl82966b0H8vk3DkmqrNDSpUsZNGgQn376KUePHqVmzZo0adKE8PBwS4eWLZQoUYJbt25pf44fP65d9vXXXzNt2jRmzZrFwYMH8ff3p2HDhjx9+lS7zqBBg1i5ciVLlixh165dPHv2jGbNmhEXF2eJl2P1nj9/TpkyZZg1a1aKy411zjt06EBYWBgbN25k48aNhIWF8cEHH5j89Vm79M4/QOPGjXW+E+vXr9dZLuc/47Zv307fvn3Zt28fmzdvJjY2ltDQUJ4/f65dR74DpqXPewDyPTCVwMBAJk+ezKFDhzh06BD16tWjZcuW2sRJPv+ml957APL5NwpFWJ3KlSsrvXr10nmuaNGiyogRIywUUfbx+eefK2XKlElxmUajUfz9/ZXJkydrn3v58qXi4eGhzJ07V1EURXn8+LFiZ2enLFmyRLvOjRs3FLVarWzcuNGksWcHgLJy5UrtY2Od81OnTimAsm/fPu06e/fuVQDlzJkzJn5VWUfS868oitK5c2elZcuWqW4j59+47t69qwDK9u3bFUWR74AlJH0PFEW+B+aWI0cO5aeffpLPvwUlvAeKIp9/Y5GWKisTHR3N4cOHCQ0N1Xk+NDSUPXv2WCiq7OX8+fPkypWL/Pnz8+6773Lp0iUALl++zO3bt3XOvYODA7Vr19ae+8OHDxMTE6OzTq5cuShZsqS8PxlgrHO+d+9ePDw8qFKlinadqlWr4uHhIe+LHrZt24avry+FCxemR48e3L17V7tMzr9xRUREAODl5QXId8ASkr4HCeR7YHpxcXEsWbKE58+fExISIp9/C0j6HiSQz3/m2Vo6AKHr/v37xMXF4efnp/O8n58ft2/ftlBU2UeVKlVYtGgRhQsX5s6dO3zxxRdUq1aNkydPas9vSuf+6tWrANy+fRt7e3ty5MiRbB15fwxnrHN++/ZtfH19k+3f19dX3pd0NGnShLZt2xIUFMTly5cZM2YM9erV4/Dhwzg4OMj5NyJFURg8eDA1atSgZMmSgHwHzC2l9wDke2Bqx48fJyQkhJcvX+Lq6srKlSspXry49mJbPv+ml9p7APL5NxZJqqyUSqXSeawoSrLnhOGaNGmi/b1UqVKEhIRQsGBBFi5cqB2UmZFzL+9P5hjjnKe0vrwv6Wvfvr3295IlS1KxYkWCgoJYt24drVu3TnU7Of+G69evH8eOHWPXrl3Jlsl3wDxSew/ke2BaRYoUISwsjMePH7N8+XI6d+7M9u3btcvl8296qb0HxYsXl8+/kUj3Pyvj7e2NjY1Nsqz+7t27ye7kiMxzcXGhVKlSnD9/XlsFMK1z7+/vT3R0NI8ePUp1HaE/Y51zf39/7ty5k2z/9+7dk/fFQAEBAQQFBXH+/HlAzr+x9O/fn9WrV7N161YCAwO1z8t3wHxSew9SIt8D47K3tyc4OJiKFSsyadIkypQpw3fffSeffzNK7T1IiXz+M0aSKitjb29PhQoV2Lx5s87zmzdvplq1ahaKKvuKiori9OnTBAQEkD9/fvz9/XXOfXR0NNu3b9ee+woVKmBnZ6ezzq1btzhx4oS8PxlgrHMeEhJCREQEBw4c0K6zf/9+IiIi5H0x0IMHD7h27RoBAQGAnP/MUhSFfv36sWLFCv7991/y58+vs1y+A6aX3nuQEvkemJaiKERFRcnn34IS3oOUyOc/g8xXE0Poa8mSJYqdnZ0yb9485dSpU8qgQYMUFxcX5cqVK5YOLcsbMmSIsm3bNuXSpUvKvn37lGbNmilubm7aczt58mTFw8NDWbFihXL8+HHlvffeUwICApQnT55o99GrVy8lMDBQ+eeff5QjR44o9erVU8qUKaPExsZa6mVZtadPnypHjx5Vjh49qgDKtGnTlKNHjypXr15VFMV457xx48ZK6dKllb179yp79+5VSpUqpTRr1szsr9fapHX+nz59qgwZMkTZs2ePcvnyZWXr1q1KSEiIkjt3bjn/RtK7d2/Fw8ND2bZtm3Lr1i3tz4sXL7TryHfAtNJ7D+R7YFojR45UduzYoVy+fFk5duyYMmrUKEWtVit///23oijy+TeHtN4D+fwbjyRVVmr27NlKUFCQYm9vr5QvX16n9KvIuPbt2ysBAQGKnZ2dkitXLqV169bKyZMntcs1Go3y+eefK/7+/oqDg4NSq1Yt5fjx4zr7iIyMVPr166d4eXkpTk5OSrNmzZTw8HBzv5QsY+vWrQqQ7Kdz586KohjvnD948EDp2LGj4ubmpri5uSkdO3ZUHj16ZKZXab3SOv8vXrxQQkNDFR8fH8XOzk7Jmzev0rlz52TnVs5/xqV07gFl/vz52nXkO2Ba6b0H8j0wra5du2qvZ3x8fJT69etrEypFkc+/OaT1Hsjn33hUiqIo5msXE0IIIYQQQojsRcZUCSGEEEIIIUQmSFIlhBBCCCGEEJkgSZUQQgghhBBCZIIkVUIIIYQQQgiRCZJUCSGEEEIIIUQmSFIlhBBCCCGEEJkgSZUQQgghhBBCZIIkVUIIIYQQQgiRCZJUCSGEsEpjx46lbNmyFjv+mDFj+Oijj9Jcp06dOgwaNMg8AQGVKlVixYoVZjueEEII/UhSJYQQwuxUKlWaP126dGHo0KFs2bLFIvHduXOH7777jlGjRlnk+KkZM2YMI0aMQKPRWDoUIYQQiUhSJYQQwuxu3bql/Zk+fTru7u46z3333Xe4urqSM2dOi8Q3b948QkJCyJcvn0WOn5qmTZsSERHBpk2bLB2KEEKIRCSpEkIIYXb+/v7aHw8PD1QqVbLnknb/69KlC61ateLLL7/Ez88PT09Pxo0bR2xsLMOGDcPLy4vAwEB+/vlnnWPduHGD9u3bkyNHDnLmzEnLli25cuVKmvEtWbKEFi1a6Dz3/PlzOnXqhKurKwEBAUydOjXZdr/++isVK1bEzc0Nf39/OnTowN27dwFQFIXg4GCmTJmis82JEydQq9VcvHgRiO/2mDdvXhwcHMiVKxcDBgzQrmtjY8Nbb73F4sWL0z3HQgghzEeSKiGEEFnGv//+y82bN9mxYwfTpk1j7NixNGvWjBw5crB//3569epFr169uHbtGgAvXrygbt26uLq6smPHDnbt2oWrqyuNGzcmOjo6xWM8evSIEydOULFiRZ3nhw0bxtatW1m5ciV///0327Zt4/DhwzrrREdHM2HCBP777z9WrVrF5cuX6dKlCxDf5bFr167Mnz9fZ5uff/6ZmjVrUrBgQf7880++/fZb/ve//3H+/HlWrVpFqVKldNavXLkyO3fuzMxpFEIIYWyKEEIIYUHz589XPDw8kj3/+eefK2XKlNE+7ty5sxIUFKTExcVpnytSpIhSs2ZN7ePY2FjFxcVFWbx4saIoijJv3jylSJEiikaj0a4TFRWlODk5KZs2bUoxnqNHjyqAEh4ern3u6dOnir29vbJkyRLtcw8ePFCcnJyUgQMHpvraDhw4oADK06dPFUVRlJs3byo2NjbK/v37FUVRlOjoaMXHx0dZsGCBoiiKMnXqVKVw4cJKdHR0qvv866+/FLVarXMehBBCWJa0VAkhhMgySpQogVr9+k+Xn5+fTkuOjY0NOXPm1Ha5O3z4MBcuXMDNzQ1XV1dcXV3x8vLi5cuX2u52SUVGRgLg6Oiofe7ixYtER0cTEhKifc7Ly4siRYrobHv06FFatmxJUFAQbm5u1KlTB4Dw8HAAAgICaNq0qbaL4tq1a3n58iVt27YFoG3btkRGRlKgQAF69OjBypUriY2N1TmGk5MTGo2GqKgo/U+cEEIIk5KkSgghRJZhZ2en81ilUqX4XEJ1PI1GQ4UKFQgLC9P5OXfuHB06dEjxGN7e3kB8N8AEiqKkG9vz588JDQ3F1dWVX3/9lYMHD7Jy5UoAna6G3bt3Z8mSJURGRjJ//nzat2+Ps7MzAHny5OHs2bPMnj0bJycn+vTpQ61atYiJidFu//DhQ5ydnXFycko3JiGEEOYhSZUQQohsq3z58pw/fx5fX1+Cg4N1fjw8PFLcpmDBgri7u3Pq1Cntc8HBwdjZ2bFv3z7tc48ePeLcuXPax2fOnOH+/ftMnjyZmjVrUrRoUW2LWWJvvfUWLi4ufP/992zYsIGuXbvqLHdycqJFixbMmDGDbdu2sXfvXo4fP65dfuLECcqXL5/hcyKEEML4JKkSQgiRbXXs2BFvb29atmzJzp07uXz5Mtu3b2fgwIFcv349xW3UajUNGjRg165d2udcXV3p1q0bw4YNY8uWLZw4cYIuXbrodEXMmzcv9vb2zJw5k0uXLrF69WomTJiQbP82NjZ06dKFkSNHEhwcrNOlcMGCBcybN48TJ05w6dIlfvnlF5ycnAgKCtKus3PnTkJDQ41xeoQQQhiJJFVCCCGyLWdnZ3bs2EHevHlp3bo1xYoVo2vXrkRGRuLu7p7qdh999BFLlizRmWT3m2++oVatWrRo0YIGDRpQo0YNKlSooF3u4+PDggUL+OOPPyhevDiTJ09OVj49Qbdu3YiOjk7WSuXp6cmPP/5I9erVKV26NFu2bGHNmjXa+bpu3LjBnj17+PDDDzNzWoQQQhiZStGno7gQQgjxBlEUhapVqzJo0CDee+89o+9/9+7d1KlTh+vXr+Pn56f3dsOGDSMiIoIffvjB6DEJIYTIOGmpEkIIIZJQqVT88MMPySrvZVZUVBQXLlxgzJgxtGvXzqCECsDX1zfFLoVCCCEsS1qqhBBCCDNZsGAB3bp1o2zZsqxevZrcuXNbOiQhhBBGIEmVEEIIIYQQQmSCdP8TQgghhBBCiEyQpEoIIYQQQgghMkGSKiGEEEIIIYTIBEmqhBBCCCGEECITJKkSQgghhBBCiEyQpEoIIYQQQgghMkGSKiGEEEIIIYTIBEmqhBBCCCGEECIT/g/vw7bbP9s2LgAAAABJRU5ErkJggg==", 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", 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", 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" ] @@ -92,21 +84,22 @@ "import pandas as pd\n", "\n", "# Example: Sea surface temperature time series (synthetic data for simplicity)\n", - "# Generate a synthetic SST time series with a trend, seasonal cycle, and noise\n", + "# Generate a synthetic climate time series with a trend, seasonal cycle, and noise\n", "np.random.seed(42)\n", + "\n", "time = np.arange(0, 365 * 10, 1) # 10 years of daily data\n", "trend = 0.01 * time # Long-term warming trend\n", "seasonal = 10 * np.sin(2 * np.pi * time / 365) # Seasonal cycle\n", "noise = np.random.normal(0, 0.5, len(time)) # Random noise\n", "\n", - "sst = trend + seasonal + noise\n", + "clima = trend + seasonal + 5*noise\n", "\n", "# Plot the raw data\n", "plt.figure(figsize=(10, 4))\n", - "plt.plot(time, sst, label='Raw SST')\n", + "plt.plot(time, clima, label='Raw Data')\n", "plt.xlabel('Time (days)')\n", - "plt.ylabel('Sea Surface Temperature (°C)')\n", - "plt.title('Raw Sea Surface Temperature Data')\n", + "plt.ylabel('Time Series (°C)')\n", + "plt.title('Climate Time Series Data')\n", "plt.legend()\n", "plt.show()\n", "\n", @@ -121,15 +114,15 @@ "cutoff = 1 / 365 # Cutoff frequency (1 cycle per year, isolating long-term trend)\n", "fs = 1 # Sampling frequency (1 sample per day)\n", "b, a = butter_lowpass(cutoff, fs)\n", - "sst_lowpass = filtfilt(b, a, sst)\n", + "clima_lowpass = filtfilt(b, a, clima)\n", "\n", "# Plot the filtered data (long-term trend)\n", "plt.figure(figsize=(10, 4))\n", - "plt.plot(time, sst, label='Raw SST')\n", - "plt.plot(time, sst_lowpass, label='Low-pass Filtered (Long-term Trend)', color='red')\n", + "plt.plot(time, clima, label='Raw Clima')\n", + "plt.plot(time, clima_lowpass, label='Low-pass Filtered (Long-term Trend)', color='red')\n", "plt.xlabel('Time (days)')\n", - "plt.ylabel('Sea Surface Temperature (°C)')\n", - "plt.title('Low-pass Filtered Sea Surface Temperature (Long-term Trend)')\n", + "plt.ylabel('Clima (°C)')\n", + "plt.title('Low-pass Filtered Climate Data (Long-term Trend)')\n", "plt.legend()\n", "plt.show()\n", "\n", @@ -143,15 +136,15 @@ "# Apply high-pass filter\n", "cutoff = 1 / 30 # Cutoff frequency (1 cycle per month, isolating short-term variations)\n", "b, a = butter_highpass(cutoff, fs)\n", - "sst_highpass = filtfilt(b, a, sst)\n", + "clima_highpass = filtfilt(b, a, clima)\n", "\n", "# Plot the filtered data (short-term fluctuations)\n", "plt.figure(figsize=(10, 4))\n", - "plt.plot(time, sst, label='Raw SST')\n", - "plt.plot(time, sst_highpass, label='High-pass Filtered (Short-term Fluctuations)', color='green')\n", + "plt.plot(time, clima, label='Raw SST')\n", + "plt.plot(time, clima_highpass, label='High-pass Filtered (Short-term Fluctuations)', color='green')\n", "plt.xlabel('Time (days)')\n", - "plt.ylabel('Sea Surface Temperature (°C)')\n", - "plt.title('High-pass Filtered Sea Surface Temperature (Short-term Fluctuations)')\n", + "plt.ylabel('Climate Data (°C)')\n", + "plt.title('High-pass Filtered Climate Data (Short-term Fluctuations)')\n", "plt.legend()\n", "plt.show()" ] @@ -160,7 +153,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Advantages of Filtering SST Data:\n", + "#### Advantages of Filtering CLimate Data:\n", "- **Long-Term Climate Analysis**: A low-pass filter isolates long-term trends, such as global warming, making it easier to quantify the rate of temperature increase.\n", "- **Seasonal and Short-Term Variability**: High-pass filters isolate short-term changes, which are crucial for studying seasonal cycles or local events like storms and oceanic oscillations.\n", "- **Data Smoothing**: Filters help smooth noisy data, making it easier to identify underlying patterns, which is important for both analysis and visualization.\n", @@ -178,7 +171,50 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: obspy in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (1.4.0)\n", + "Requirement already satisfied: numpy>=1.20 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from obspy) (1.26.0)\n", + "Requirement already satisfied: scipy>=1.7 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from obspy) (1.11.3)\n", + "Requirement already satisfied: matplotlib>=3.3 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from obspy) (3.8.0)\n", + "Requirement already satisfied: lxml in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from obspy) (4.9.3)\n", + "Requirement already satisfied: setuptools in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from obspy) (68.2.2)\n", + "Requirement already satisfied: sqlalchemy in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from obspy) (1.4.49)\n", + "Requirement already satisfied: decorator in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from obspy) (5.1.1)\n", + "Requirement already satisfied: requests in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from obspy) (2.31.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (1.3.0)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (0.12.0)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (4.43.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (23.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (2.8.2)\n", + "Requirement already satisfied: importlib-resources>=3.2.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from matplotlib>=3.3->obspy) (6.1.0)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from requests->obspy) (3.3.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from requests->obspy) (3.4)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from requests->obspy) (1.26.20)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from requests->obspy) (2023.7.22)\n", + "Requirement already satisfied: zipp>=3.1.0 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib>=3.3->obspy) (3.17.0)\n", + "Requirement already satisfied: six>=1.5 in /Users/marinedenolle/opt/miniconda3/envs/mlgeo/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib>=3.3->obspy) (1.16.0)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install obspy" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -208,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -217,7 +253,7 @@ "UW.RATT..HHZ | 2021-07-29T04:15:00.000000Z - 2021-07-29T06:14:59.990000Z | 100.0 Hz, 720000 samples" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -244,28 +280,27 @@ " endtime=Tstart, attach_response=True)\n", "# basic pre-processing: merge all data if there is gaps, detrend, taper,\n", "# remove the seismic instrumental response to go from the digitizer units to ground motion (velocity) units.\n", - "\n", "N.merge(); N.detrend(type='linear');N[0].taper(max_percentage=0.05)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0, 0.5, 'Velocity (m/s)')" + "Text(0, 0.5, 'Counts')" ] }, - "execution_count": 4, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -275,7 +310,28 @@ } ], "source": [ - "plt.plot(Z[0].data);plt.plot(N[0].data);plt.grid(True);plt.ylabel('Velocity (m/s)')" + "plt.plot(Z[0].data);plt.grid(True);plt.ylabel('Counts')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(N[0].data);" ] }, { @@ -289,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -309,12 +365,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "\n", - "sos = signal.butter(2,[1./150.,1.], 'bp', fs=fs, output='sos')\n", + "sos = signal.butter(2,[1,10], 'bp', fs=fs, output='sos')\n", "zf = signal.sosfilt(sos, z)\n", "nf = signal.sosfilt(sos, n)\n", "t=np.arange(0.,7200.,1./fs)" @@ -322,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -331,15 +386,15 @@ "Text(0.5, 0, 'Time in seconds')" ] }, - "execution_count": 7, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -347,13 +402,14 @@ } ], "source": [ - "\n", - "fig,ax=plt.subplots(2,1,figsize=(11,8),sharex=True)\n", - "ax[0].plot(t,z[:-1]);ax[0].plot(t,n);ax[0].grid(True)\n", - "ax[0].set_title('Raw data');ax[0].legend(['Earthquake','noise'])\n", - "ax[1].plot(t,zf[:-1]);ax[1].plot(t,nf);ax[1].grid(True)\n", - "ax[1].set_title('filtered data');ax[1].legend(['Earthquake','noise'])\n", - "ax[1].set_xlabel('Time in seconds')" + "fig,axis = plt.subplots(2,1,figsize=(10,6))\n", + "axis[0].plot(t,zf[:-1]);\n", + "axis[1].plot(t,nf);\n", + "axis[0].set_xlim([0,1000])\n", + "axis[1].set_xlim([0,1000])\n", + "axis[0].grid(True);axis[1].grid(True)\n", + "plt.title('filtered data');plt.legend(['Earthquake','noise'])\n", + "plt.xlabel('Time in seconds')" ] }, { @@ -365,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -374,7 +430,7 @@ "(700.0, 1000.0)" ] }, - "execution_count": 8, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" },