{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "81dd17c9-2794-4759-b7e9-eaaa99bc5e1b",
   "metadata": {},
   "source": [
    "# GPS velocities for the Alpine region\n",
    "\n",
    "This is a compilation of 3D GPS velocities for the Alps by\n",
    "[Sánchez et al. (2018)](https://doi.org/10.1594/PANGAEA.886889).\n",
    "The horizontal velocities are reference to the Eurasian frame. \n",
    "All velocity components and even the position have error estimates,\n",
    "which is very useful and rare to find in a lot of datasets.\n",
    "\n",
    "License: CC-BY\n",
    "\n",
    "Here, we download the data from 3 separate files (coordinates, vertical velocity, horizontal velocities) and make sure they are aligned and represent the same stations. There are some mistakes in the station names of horizontal velocity file that we fix manually (verified by the coordinates)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28905da1-9159-477f-a0e0-04f80de6bf89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import verde as vd\n",
    "import pooch\n",
    "import pyproj\n",
    "import pygmt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827d30b5-158e-4fe2-9e16-1de8e7ff894d",
   "metadata": {},
   "source": [
    "## Download the data\n",
    "\n",
    "Use Pooch to download and cache the data files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41d23f77-ad1c-4c8c-b1a5-a7667e461e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "fname_position = pooch.retrieve(\n",
    "    url=\"https://store.pangaea.de/Publications/Sanchez-etal_2018/ALPS2017_NEH.CRD\",\n",
    "    known_hash=\"sha256:24b88a0e5ab6ea93c67424ef52542d8b8a8254a150284e1a54afddbfd93e4399\",\n",
    ")\n",
    "fname_velocity = pooch.retrieve(\n",
    "    url=\"https://store.pangaea.de/Publications/Sanchez-etal_2018/ALPS2017_NEH.VEL\",\n",
    "    known_hash=\"sha256:0f2eff87a39260e2b3218897763dbfecdf0f464bf877bef460eff34a70e00aa7\",\n",
    ")\n",
    "fname_velocity_eurasia = pooch.retrieve(\n",
    "    url=\"https://store.pangaea.de/Publications/Sanchez-etal_2018/ALPS2017_REP.VEL\",\n",
    "    known_hash=\"sha256:578677246230e893c828205391d262da4af39bb24a8ca66ff5a95a88c71fe509\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10bb9087-c3d1-43e8-a11a-2891767df9e1",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "These data are in a strange format and getting pandas to read it would be more work than parsing it by hand. So that's what we're going to do.\n",
    "\n",
    "First, the horizontal velocities since there are less points and we will only want the vertical and positions of these stations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18e2f731-8ce1-4449-b8d2-3ade1696a66a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>velocity_east_mmyr</th>\n",
       "      <th>velocity_north_mmyr</th>\n",
       "      <th>velocity_east_error_mmyr</th>\n",
       "      <th>velocity_north_error_mmyr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACOM</th>\n",
       "      <td>ACOM</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFAL</th>\n",
       "      <td>AFAL</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGDE</th>\n",
       "      <td>AGDE</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGNE</th>\n",
       "      <td>AGNE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIGL</th>\n",
       "      <td>AIGL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WLBH</th>\n",
       "      <td>WLBH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WTZR</th>\n",
       "      <td>WTZR</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZADA</th>\n",
       "      <td>ZADA</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIMM</th>\n",
       "      <td>ZIMM</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZOUF</th>\n",
       "      <td>ZOUF</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     station_id  velocity_east_mmyr  velocity_north_mmyr  \\\n",
       "ACOM       ACOM                 0.2                  1.2   \n",
       "AFAL       AFAL                -0.7                  0.9   \n",
       "AGDE       AGDE                -0.2                 -0.2   \n",
       "AGNE       AGNE                 0.0                 -0.2   \n",
       "AIGL       AIGL                 0.0                  0.1   \n",
       "...         ...                 ...                  ...   \n",
       "WLBH       WLBH                 0.0                 -0.2   \n",
       "WTZR       WTZR                 0.1                  0.2   \n",
       "ZADA       ZADA                 0.2                  3.1   \n",
       "ZIMM       ZIMM                -0.1                  0.4   \n",
       "ZOUF       ZOUF                 0.1                  1.0   \n",
       "\n",
       "      velocity_east_error_mmyr  velocity_north_error_mmyr  \n",
       "ACOM                       0.1                        0.1  \n",
       "AFAL                       0.1                        0.2  \n",
       "AGDE                       0.1                        0.3  \n",
       "AGNE                       0.2                        0.6  \n",
       "AIGL                       0.1                        0.5  \n",
       "...                        ...                        ...  \n",
       "WLBH                       0.1                        0.2  \n",
       "WTZR                       0.1                        0.1  \n",
       "ZADA                       0.2                        0.4  \n",
       "ZIMM                       0.1                        0.1  \n",
       "ZOUF                       0.1                        0.1  \n",
       "\n",
       "[186 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station = []\n",
    "velocity_north_mm_yr = []\n",
    "velocity_north_error_mm_yr = []\n",
    "velocity_east_mm_yr = []\n",
    "velocity_east_error_mm_yr = []\n",
    "velocity_up_mm_yr = []\n",
    "velocity_up_error_mm_yr = []\n",
    "with open(fname_velocity_eurasia, encoding=\"latin-1\") as input_file:\n",
    "    for i, line in enumerate(input_file):\n",
    "        if i < 19 or not line.strip():\n",
    "            continue\n",
    "        columns = line.split()\n",
    "        station_id = columns[0]\n",
    "        # Fix these names manually. \n",
    "        # They were confirmed by comparing the coordinates.\n",
    "        if station_id == \"CH1Z\":\n",
    "            station_id = \"CHIZ\"\n",
    "        if station_id == \"IE1G\":\n",
    "            station_id = \"IENG\"\n",
    "        values = columns[3:]\n",
    "        station.append(station_id)\n",
    "        velocity_east_mm_yr.append(1e3 * float(values[0]))\n",
    "        velocity_north_mm_yr.append(1e3 * float(values[1]))\n",
    "        velocity_east_error_mm_yr.append(1e3 * float(values[2]))\n",
    "        velocity_north_error_mm_yr.append(1e3 * float(values[3]))\n",
    "# Merge everything into a DataFrame. \n",
    "# Use the station ID as the index to help us merge the data later.\n",
    "data_horizontal = pd.DataFrame(\n",
    "    data={\n",
    "        \"station_id\": station,\n",
    "        \"velocity_east_mmyr\": velocity_east_mm_yr,\n",
    "        \"velocity_north_mmyr\": velocity_north_mm_yr,\n",
    "        \"velocity_east_error_mmyr\": velocity_east_error_mm_yr,\n",
    "        \"velocity_north_error_mmyr\": velocity_north_error_mm_yr,\n",
    "    },\n",
    "    index=station,\n",
    ")\n",
    "data_horizontal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b023c58-598f-4d79-8805-b69bab6649ec",
   "metadata": {},
   "source": [
    "Now load the position and vertical velocity, keeping only the points that have the horizontal components as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89e94e30-fdcc-47dc-acdd-11f4cb32ef32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>height_m</th>\n",
       "      <th>latitude_error_m</th>\n",
       "      <th>longitude_error_m</th>\n",
       "      <th>height_error_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACOM</th>\n",
       "      <td>ACOM</td>\n",
       "      <td>46.547935</td>\n",
       "      <td>13.514900</td>\n",
       "      <td>1774.682</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFAL</th>\n",
       "      <td>AFAL</td>\n",
       "      <td>46.527144</td>\n",
       "      <td>12.174517</td>\n",
       "      <td>2284.085</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGDE</th>\n",
       "      <td>AGDE</td>\n",
       "      <td>43.296383</td>\n",
       "      <td>3.466427</td>\n",
       "      <td>65.785</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGNE</th>\n",
       "      <td>AGNE</td>\n",
       "      <td>45.467942</td>\n",
       "      <td>7.139620</td>\n",
       "      <td>2354.600</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIGL</th>\n",
       "      <td>AIGL</td>\n",
       "      <td>44.121398</td>\n",
       "      <td>3.581261</td>\n",
       "      <td>1618.764</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WLBH</th>\n",
       "      <td>WLBH</td>\n",
       "      <td>48.415171</td>\n",
       "      <td>7.351299</td>\n",
       "      <td>819.069</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WTZR</th>\n",
       "      <td>WTZR</td>\n",
       "      <td>49.144199</td>\n",
       "      <td>12.878911</td>\n",
       "      <td>666.025</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZADA</th>\n",
       "      <td>ZADA</td>\n",
       "      <td>44.113177</td>\n",
       "      <td>15.227590</td>\n",
       "      <td>64.307</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIMM</th>\n",
       "      <td>ZIMM</td>\n",
       "      <td>46.877098</td>\n",
       "      <td>7.465278</td>\n",
       "      <td>956.341</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZOUF</th>\n",
       "      <td>ZOUF</td>\n",
       "      <td>46.557221</td>\n",
       "      <td>12.973553</td>\n",
       "      <td>1946.508</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     station_id   latitude  longitude  height_m  latitude_error_m  \\\n",
       "ACOM       ACOM  46.547935  13.514900  1774.682            0.0009   \n",
       "AFAL       AFAL  46.527144  12.174517  2284.085            0.0009   \n",
       "AGDE       AGDE  43.296383   3.466427    65.785            0.0018   \n",
       "AGNE       AGNE  45.467942   7.139620  2354.600            0.0036   \n",
       "AIGL       AIGL  44.121398   3.581261  1618.764            0.0009   \n",
       "...         ...        ...        ...       ...               ...   \n",
       "WLBH       WLBH  48.415171   7.351299   819.069            0.0009   \n",
       "WTZR       WTZR  49.144199  12.878911   666.025            0.0005   \n",
       "ZADA       ZADA  44.113177  15.227590    64.307            0.0036   \n",
       "ZIMM       ZIMM  46.877098   7.465278   956.341            0.0009   \n",
       "ZOUF       ZOUF  46.557221  12.973553  1946.508            0.0009   \n",
       "\n",
       "      longitude_error_m  height_error_m  \n",
       "ACOM             0.0005           0.001  \n",
       "AFAL             0.0009           0.001  \n",
       "AGDE             0.0009           0.002  \n",
       "AGNE             0.0009           0.004  \n",
       "AIGL             0.0009           0.002  \n",
       "...                 ...             ...  \n",
       "WLBH             0.0005           0.001  \n",
       "WTZR             0.0005           0.001  \n",
       "ZADA             0.0018           0.004  \n",
       "ZIMM             0.0005           0.001  \n",
       "ZOUF             0.0005           0.001  \n",
       "\n",
       "[186 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station = []\n",
    "latitude = []\n",
    "latitude_error_m = []\n",
    "longitude = []\n",
    "longitude_error_m = []\n",
    "height_m = []\n",
    "height_error_m = []\n",
    "with open(fname_position, encoding=\"latin-1\") as input_file:\n",
    "    for i, line in enumerate(input_file):\n",
    "        if i < 15 or i > 304 or not line.strip():\n",
    "            continue\n",
    "        columns = line.split()\n",
    "        if len(columns) == 12:\n",
    "            station_id = columns[0]\n",
    "            values = columns[2:8]\n",
    "        else:\n",
    "            station_id = columns[0]\n",
    "            values = columns[1:7]\n",
    "        # Only interested in the stations that have horizontal\n",
    "        if station_id not in data_horizontal.station_id:\n",
    "            continue\n",
    "        # Skip repeated stations because it's easier this way\n",
    "        if station_id in station:\n",
    "            continue\n",
    "        values = [float(x) for x in values]\n",
    "        # Make longitude be in [-180, 180] for easier plotting\n",
    "        if values[2] > 300:\n",
    "            values[2] -= 360\n",
    "        station.append(station_id)\n",
    "        latitude.append(values[0])\n",
    "        latitude_error_m.append(values[1])\n",
    "        longitude.append(values[2])\n",
    "        longitude_error_m.append(values[3])\n",
    "        height_m.append(values[4])\n",
    "        height_error_m.append(values[5])\n",
    "# Merge everything into a DataFrame. \n",
    "data_position = pd.DataFrame(\n",
    "    data={\n",
    "        \"station_id\": station,\n",
    "        \"latitude\": latitude,\n",
    "        \"longitude\": longitude,\n",
    "        \"height_m\": height_m,\n",
    "        \"latitude_error_m\": latitude_error_m,\n",
    "        \"longitude_error_m\": longitude_error_m,\n",
    "        \"height_error_m\": height_error_m,\n",
    "    },\n",
    "    index=station,\n",
    ")\n",
    "data_position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "860b3112-46d9-4f87-b673-0090e3298bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>velocity_up_mmyr</th>\n",
       "      <th>velocity_up_error_mmyr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACOM</th>\n",
       "      <td>ACOM</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFAL</th>\n",
       "      <td>AFAL</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGDE</th>\n",
       "      <td>AGDE</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGNE</th>\n",
       "      <td>AGNE</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AIGL</th>\n",
       "      <td>AIGL</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WLBH</th>\n",
       "      <td>WLBH</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WTZR</th>\n",
       "      <td>WTZR</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZADA</th>\n",
       "      <td>ZADA</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIMM</th>\n",
       "      <td>ZIMM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZOUF</th>\n",
       "      <td>ZOUF</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     station_id  velocity_up_mmyr  velocity_up_error_mmyr\n",
       "ACOM       ACOM               1.1                     0.1\n",
       "AFAL       AFAL               1.3                     0.2\n",
       "AGDE       AGDE               0.1                     0.3\n",
       "AGNE       AGNE               1.5                     0.5\n",
       "AIGL       AIGL               0.7                     0.5\n",
       "...         ...               ...                     ...\n",
       "WLBH       WLBH              -2.8                     0.2\n",
       "WTZR       WTZR              -0.1                     0.1\n",
       "ZADA       ZADA              -0.3                     0.4\n",
       "ZIMM       ZIMM               1.0                     0.1\n",
       "ZOUF       ZOUF               1.3                     0.1\n",
       "\n",
       "[186 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station = []\n",
    "velocity_up_mm_yr = []\n",
    "velocity_up_error_mm_yr = []\n",
    "with open(fname_velocity, encoding=\"latin-1\") as input_file:\n",
    "    for i, line in enumerate(input_file):\n",
    "        if i < 15 or i > 303 or not line.strip():\n",
    "            continue\n",
    "        columns = line.split()        \n",
    "        if len(columns) == 12:\n",
    "            station_id = columns[0]\n",
    "            values = columns[6:8]\n",
    "        else:\n",
    "            station_id = columns[0]\n",
    "            values = columns[5:7]\n",
    "        # Only interested in the stations that have horizontal\n",
    "        if station_id not in data_horizontal.station_id:\n",
    "            continue\n",
    "        # Skip repeated stations because it's easier this way\n",
    "        if station_id in station:\n",
    "            continue\n",
    "        station.append(station_id)\n",
    "        velocity_up_mm_yr.append(1e3 * float(values[0]))\n",
    "        velocity_up_error_mm_yr.append(1e3 * float(values[1]))\n",
    "# Merge everything into a DataFrame. \n",
    "data_vertical = pd.DataFrame(\n",
    "    data={\n",
    "        \"station_id\": station,\n",
    "        \"velocity_up_mmyr\": velocity_up_mm_yr,\n",
    "        \"velocity_up_error_mmyr\": velocity_up_error_mm_yr,\n",
    "    },\n",
    "    index=station,\n",
    ")\n",
    "data_vertical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a141be8-a170-4c9a-bca0-f567e01d94b2",
   "metadata": {},
   "source": [
    "Merge all of the DataFrames into a single one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "abd0e357-079f-486d-9352-92f1cdd41845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>velocity_east_mmyr</th>\n",
       "      <th>velocity_north_mmyr</th>\n",
       "      <th>velocity_east_error_mmyr</th>\n",
       "      <th>velocity_north_error_mmyr</th>\n",
       "      <th>velocity_up_mmyr</th>\n",
       "      <th>velocity_up_error_mmyr</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>height_m</th>\n",
       "      <th>latitude_error_m</th>\n",
       "      <th>longitude_error_m</th>\n",
       "      <th>height_error_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACOM</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>46.547935</td>\n",
       "      <td>13.514900</td>\n",
       "      <td>1774.682</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFAL</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>46.527144</td>\n",
       "      <td>12.174517</td>\n",
       "      <td>2284.085</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGDE</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>43.296383</td>\n",
       "      <td>3.466427</td>\n",
       "      <td>65.785</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGNE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.467942</td>\n",
       "      <td>7.139620</td>\n",
       "      <td>2354.600</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AIGL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>44.121398</td>\n",
       "      <td>3.581261</td>\n",
       "      <td>1618.764</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>WLBH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>48.415171</td>\n",
       "      <td>7.351299</td>\n",
       "      <td>819.069</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>WTZR</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>49.144199</td>\n",
       "      <td>12.878911</td>\n",
       "      <td>666.025</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>ZADA</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>44.113177</td>\n",
       "      <td>15.227590</td>\n",
       "      <td>64.307</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>ZIMM</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>46.877098</td>\n",
       "      <td>7.465278</td>\n",
       "      <td>956.341</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>ZOUF</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>46.557221</td>\n",
       "      <td>12.973553</td>\n",
       "      <td>1946.508</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id  velocity_east_mmyr  velocity_north_mmyr  \\\n",
       "0         ACOM                 0.2                  1.2   \n",
       "1         AFAL                -0.7                  0.9   \n",
       "2         AGDE                -0.2                 -0.2   \n",
       "3         AGNE                 0.0                 -0.2   \n",
       "4         AIGL                 0.0                  0.1   \n",
       "..         ...                 ...                  ...   \n",
       "181       WLBH                 0.0                 -0.2   \n",
       "182       WTZR                 0.1                  0.2   \n",
       "183       ZADA                 0.2                  3.1   \n",
       "184       ZIMM                -0.1                  0.4   \n",
       "185       ZOUF                 0.1                  1.0   \n",
       "\n",
       "     velocity_east_error_mmyr  velocity_north_error_mmyr  velocity_up_mmyr  \\\n",
       "0                         0.1                        0.1               1.1   \n",
       "1                         0.1                        0.2               1.3   \n",
       "2                         0.1                        0.3               0.1   \n",
       "3                         0.2                        0.6               1.5   \n",
       "4                         0.1                        0.5               0.7   \n",
       "..                        ...                        ...               ...   \n",
       "181                       0.1                        0.2              -2.8   \n",
       "182                       0.1                        0.1              -0.1   \n",
       "183                       0.2                        0.4              -0.3   \n",
       "184                       0.1                        0.1               1.0   \n",
       "185                       0.1                        0.1               1.3   \n",
       "\n",
       "     velocity_up_error_mmyr   latitude  longitude  height_m  latitude_error_m  \\\n",
       "0                       0.1  46.547935  13.514900  1774.682            0.0009   \n",
       "1                       0.2  46.527144  12.174517  2284.085            0.0009   \n",
       "2                       0.3  43.296383   3.466427    65.785            0.0018   \n",
       "3                       0.5  45.467942   7.139620  2354.600            0.0036   \n",
       "4                       0.5  44.121398   3.581261  1618.764            0.0009   \n",
       "..                      ...        ...        ...       ...               ...   \n",
       "181                     0.2  48.415171   7.351299   819.069            0.0009   \n",
       "182                     0.1  49.144199  12.878911   666.025            0.0005   \n",
       "183                     0.4  44.113177  15.227590    64.307            0.0036   \n",
       "184                     0.1  46.877098   7.465278   956.341            0.0009   \n",
       "185                     0.1  46.557221  12.973553  1946.508            0.0009   \n",
       "\n",
       "     longitude_error_m  height_error_m  \n",
       "0               0.0005           0.001  \n",
       "1               0.0009           0.001  \n",
       "2               0.0009           0.002  \n",
       "3               0.0009           0.004  \n",
       "4               0.0009           0.002  \n",
       "..                 ...             ...  \n",
       "181             0.0005           0.001  \n",
       "182             0.0005           0.001  \n",
       "183             0.0018           0.004  \n",
       "184             0.0005           0.001  \n",
       "185             0.0005           0.001  \n",
       "\n",
       "[186 rows x 13 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(pd.merge(data_horizontal, data_vertical), data_position)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b284b57-cab5-4b9f-9af0-820cb73d43ca",
   "metadata": {},
   "source": [
    "## Plot the data\n",
    "\n",
    "Make a quick plot of the data to see if it looks the same as the paper figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6efecd93-61db-4500-b519-f328abf1cfa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "angle = np.degrees(np.arctan2(data.velocity_north_mmyr, data.velocity_east_mmyr))\n",
    "length = np.hypot(data.velocity_north_mmyr, data.velocity_east_mmyr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a065fd6-2474-43e5-b4cb-553d7424f3ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "region = vd.pad_region(vd.get_region((data.longitude, data.latitude)), pad=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11c18df2-db41-4511-adcd-74be493ff5cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 1000
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pygmt.Figure()\n",
    "with fig.subplot(\n",
    "    nrows=1,\n",
    "    ncols=2,\n",
    "    figsize=(\"35c\", \"15c\"),\n",
    "    sharey=\"l\",  # shared y-axis on the left side\n",
    "    frame=\"WSrt\",\n",
    "):\n",
    "    with fig.set_panel(0):\n",
    "        fig.basemap(region=region, projection=\"M?\", frame=\"af\")\n",
    "        fig.coast(area_thresh=1e4, land=\"#eeeeee\")        \n",
    "        scale_factor = 2 / length.max()\n",
    "        fig.plot(\n",
    "            x=data.longitude,\n",
    "            y=data.latitude,\n",
    "            direction=[angle, length * scale_factor],\n",
    "            style=\"v0.15c+e\",\n",
    "            color=\"blue\",\n",
    "            pen=\"1p,blue\",\n",
    "        )    \n",
    "        # Plot a quiver caption\n",
    "        fig.plot(\n",
    "            x=-4,\n",
    "            y=42,\n",
    "            direction=[[0], [1 * scale_factor]],\n",
    "            style=\"v0.15c+e\",\n",
    "            color=\"blue\",\n",
    "            pen=\"1p,blue\",\n",
    "        )\n",
    "        fig.text(\n",
    "            x=-4,\n",
    "            y=42.2,\n",
    "            text=f\"1 mm/yr\",\n",
    "            justify=\"BL\",\n",
    "            font=\"10p,Helvetica,blue\",\n",
    "        )\n",
    "    with fig.set_panel(1):\n",
    "        fig.basemap(region=region, projection=\"M?\", frame=\"af\")\n",
    "        fig.coast(area_thresh=1e4, land=\"#eeeeee\")\n",
    "        pygmt.makecpt(cmap=\"polar\", series=[data.velocity_up_mmyr.min(), data.velocity_up_mmyr.max()])\n",
    "        fig.plot(\n",
    "            x=data.longitude,\n",
    "            y=data.latitude,\n",
    "            color=data.velocity_up_mmyr,\n",
    "            style=\"c0.2c\",\n",
    "            cmap=True,\n",
    "            pen=\"0.5p,black\",\n",
    "        )\n",
    "        fig.colorbar(\n",
    "            frame='af+l\"vertical velocity [mm/yr]\"',\n",
    "            position=\"jTL+w7c/0.3c+h+o1/1\",\n",
    "        )\n",
    "fig.savefig(\"preview.jpg\", dpi=200)\n",
    "fig.show(width=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6161d222-eaca-494b-a9a1-75a5397ca486",
   "metadata": {},
   "source": [
    "This looks very similar to the plots in [Sánchez et al. (2018)](https://doi.org/10.5194/essd-10-1503-2018)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11b6bd05-72d2-4da5-89a2-13257554938b",
   "metadata": {},
   "source": [
    "## Export\n",
    "\n",
    "Save the data to a compressed CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cb73640-0b2b-4f41-aa58-e253d023ae78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>height_m</th>\n",
       "      <th>velocity_east_mmyr</th>\n",
       "      <th>velocity_north_mmyr</th>\n",
       "      <th>velocity_up_mmyr</th>\n",
       "      <th>longitude_error_m</th>\n",
       "      <th>latitude_error_m</th>\n",
       "      <th>height_error_m</th>\n",
       "      <th>velocity_east_error_mmyr</th>\n",
       "      <th>velocity_north_error_mmyr</th>\n",
       "      <th>velocity_up_error_mmyr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACOM</td>\n",
       "      <td>13.5149004</td>\n",
       "      <td>46.5479352</td>\n",
       "      <td>1774.682</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFAL</td>\n",
       "      <td>12.1745170</td>\n",
       "      <td>46.5271440</td>\n",
       "      <td>2284.085</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGDE</td>\n",
       "      <td>3.4664272</td>\n",
       "      <td>43.2963831</td>\n",
       "      <td>65.785</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGNE</td>\n",
       "      <td>7.1396203</td>\n",
       "      <td>45.4679419</td>\n",
       "      <td>2354.600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AIGL</td>\n",
       "      <td>3.5812609</td>\n",
       "      <td>44.1213985</td>\n",
       "      <td>1618.764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>WLBH</td>\n",
       "      <td>7.3512992</td>\n",
       "      <td>48.4151715</td>\n",
       "      <td>819.069</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>WTZR</td>\n",
       "      <td>12.8789111</td>\n",
       "      <td>49.1441992</td>\n",
       "      <td>666.025</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>ZADA</td>\n",
       "      <td>15.2275901</td>\n",
       "      <td>44.1131766</td>\n",
       "      <td>64.307</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>ZIMM</td>\n",
       "      <td>7.4652781</td>\n",
       "      <td>46.8770983</td>\n",
       "      <td>956.341</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>ZOUF</td>\n",
       "      <td>12.9735532</td>\n",
       "      <td>46.5572211</td>\n",
       "      <td>1946.508</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id   longitude    latitude  height_m velocity_east_mmyr  \\\n",
       "0         ACOM  13.5149004  46.5479352  1774.682                0.2   \n",
       "1         AFAL  12.1745170  46.5271440  2284.085               -0.7   \n",
       "2         AGDE   3.4664272  43.2963831    65.785               -0.2   \n",
       "3         AGNE   7.1396203  45.4679419  2354.600                0.0   \n",
       "4         AIGL   3.5812609  44.1213985  1618.764                0.0   \n",
       "..         ...         ...         ...       ...                ...   \n",
       "181       WLBH   7.3512992  48.4151715   819.069                0.0   \n",
       "182       WTZR  12.8789111  49.1441992   666.025                0.1   \n",
       "183       ZADA  15.2275901  44.1131766    64.307                0.2   \n",
       "184       ZIMM   7.4652781  46.8770983   956.341               -0.1   \n",
       "185       ZOUF  12.9735532  46.5572211  1946.508                0.1   \n",
       "\n",
       "    velocity_north_mmyr velocity_up_mmyr longitude_error_m latitude_error_m  \\\n",
       "0                   1.2              1.1            0.0005           0.0009   \n",
       "1                   0.9              1.3            0.0009           0.0009   \n",
       "2                  -0.2              0.1            0.0009           0.0018   \n",
       "3                  -0.2              1.5            0.0009           0.0036   \n",
       "4                   0.1              0.7            0.0009           0.0009   \n",
       "..                  ...              ...               ...              ...   \n",
       "181                -0.2             -2.8            0.0005           0.0009   \n",
       "182                 0.2             -0.1            0.0005           0.0005   \n",
       "183                 3.1             -0.3            0.0018           0.0036   \n",
       "184                 0.4              1.0            0.0005           0.0009   \n",
       "185                 1.0              1.3            0.0005           0.0009   \n",
       "\n",
       "    height_error_m velocity_east_error_mmyr velocity_north_error_mmyr  \\\n",
       "0            0.001                      0.1                       0.1   \n",
       "1            0.001                      0.1                       0.2   \n",
       "2            0.002                      0.1                       0.3   \n",
       "3            0.004                      0.2                       0.6   \n",
       "4            0.002                      0.1                       0.5   \n",
       "..             ...                      ...                       ...   \n",
       "181          0.001                      0.1                       0.2   \n",
       "182          0.001                      0.1                       0.1   \n",
       "183          0.004                      0.2                       0.4   \n",
       "184          0.001                      0.1                       0.1   \n",
       "185          0.001                      0.1                       0.1   \n",
       "\n",
       "    velocity_up_error_mmyr  \n",
       "0                      0.1  \n",
       "1                      0.2  \n",
       "2                      0.3  \n",
       "3                      0.5  \n",
       "4                      0.5  \n",
       "..                     ...  \n",
       "181                    0.2  \n",
       "182                    0.1  \n",
       "183                    0.4  \n",
       "184                    0.1  \n",
       "185                    0.1  \n",
       "\n",
       "[186 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export = pd.DataFrame({\n",
    "    \"station_id\": data.station_id,\n",
    "    \"longitude\": data.longitude.map(lambda x: \"{:.7f}\".format(x)),\n",
    "    \"latitude\": data.latitude.map(lambda x: \"{:.7f}\".format(x)),    \n",
    "    \"height_m\": data.height_m.map(lambda x: \"{:.3f}\".format(x)),\n",
    "    \"velocity_east_mmyr\": data.velocity_east_mmyr.map(lambda x: \"{:.1f}\".format(x)),\n",
    "    \"velocity_north_mmyr\": data.velocity_north_mmyr.map(lambda x: \"{:.1f}\".format(x)),\n",
    "    \"velocity_up_mmyr\": data.velocity_up_mmyr.map(lambda x: \"{:.1f}\".format(x)),\n",
    "    \"longitude_error_m\": data.longitude_error_m.map(lambda x: \"{:.4f}\".format(x)),\n",
    "    \"latitude_error_m\": data.latitude_error_m.map(lambda x: \"{:.4f}\".format(x)),\n",
    "    \"height_error_m\": data.height_error_m.map(lambda x: \"{:.3f}\".format(x)),\n",
    "    \"velocity_east_error_mmyr\": data.velocity_east_error_mmyr.map(lambda x: \"{:.1f}\".format(x)),\n",
    "    \"velocity_north_error_mmyr\": data.velocity_north_error_mmyr.map(lambda x: \"{:.1f}\".format(x)),\n",
    "    \"velocity_up_error_mmyr\": data.velocity_up_error_mmyr.map(lambda x: \"{:.1f}\".format(x)),\n",
    "})\n",
    "export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "503300b1-61dc-464e-9263-3ceaccdf77dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alps-gps-velocity.csv.xz\n",
      "size: 0.004544 Mb\n",
      "md5:195ee3d88783ce01b6190c2af89f2b14\n",
      "sha256:77f2907c2a019366e5f85de5aafcab2d0e90cc2c378171468a7705cab9938584\n"
     ]
    }
   ],
   "source": [
    "fname = \"alps-gps-velocity.csv.xz\"\n",
    "export.to_csv(fname, index=False)\n",
    "print(fname)\n",
    "print(f\"size: {os.path.getsize(fname) / 1e6} Mb\")\n",
    "for alg in [\"md5\", \"sha256\"]:\n",
    "    print(f\"{alg}:{pooch.file_hash(fname, alg=alg)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd1df7f-e569-435b-9c27-00429f511696",
   "metadata": {},
   "source": [
    "## Read back the data and plot it\n",
    "\n",
    "Verify that the output didn't corrupt anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df2edeb1-1bc5-46a6-b19c-c457cbc8e16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>height_m</th>\n",
       "      <th>velocity_east_mmyr</th>\n",
       "      <th>velocity_north_mmyr</th>\n",
       "      <th>velocity_up_mmyr</th>\n",
       "      <th>longitude_error_m</th>\n",
       "      <th>latitude_error_m</th>\n",
       "      <th>height_error_m</th>\n",
       "      <th>velocity_east_error_mmyr</th>\n",
       "      <th>velocity_north_error_mmyr</th>\n",
       "      <th>velocity_up_error_mmyr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACOM</td>\n",
       "      <td>13.514900</td>\n",
       "      <td>46.547935</td>\n",
       "      <td>1774.682</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFAL</td>\n",
       "      <td>12.174517</td>\n",
       "      <td>46.527144</td>\n",
       "      <td>2284.085</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGDE</td>\n",
       "      <td>3.466427</td>\n",
       "      <td>43.296383</td>\n",
       "      <td>65.785</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGNE</td>\n",
       "      <td>7.139620</td>\n",
       "      <td>45.467942</td>\n",
       "      <td>2354.600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AIGL</td>\n",
       "      <td>3.581261</td>\n",
       "      <td>44.121398</td>\n",
       "      <td>1618.764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>WLBH</td>\n",
       "      <td>7.351299</td>\n",
       "      <td>48.415171</td>\n",
       "      <td>819.069</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>WTZR</td>\n",
       "      <td>12.878911</td>\n",
       "      <td>49.144199</td>\n",
       "      <td>666.025</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>ZADA</td>\n",
       "      <td>15.227590</td>\n",
       "      <td>44.113177</td>\n",
       "      <td>64.307</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>ZIMM</td>\n",
       "      <td>7.465278</td>\n",
       "      <td>46.877098</td>\n",
       "      <td>956.341</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>ZOUF</td>\n",
       "      <td>12.973553</td>\n",
       "      <td>46.557221</td>\n",
       "      <td>1946.508</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    station_id  longitude   latitude  height_m  velocity_east_mmyr  \\\n",
       "0         ACOM  13.514900  46.547935  1774.682                 0.2   \n",
       "1         AFAL  12.174517  46.527144  2284.085                -0.7   \n",
       "2         AGDE   3.466427  43.296383    65.785                -0.2   \n",
       "3         AGNE   7.139620  45.467942  2354.600                 0.0   \n",
       "4         AIGL   3.581261  44.121398  1618.764                 0.0   \n",
       "..         ...        ...        ...       ...                 ...   \n",
       "181       WLBH   7.351299  48.415171   819.069                 0.0   \n",
       "182       WTZR  12.878911  49.144199   666.025                 0.1   \n",
       "183       ZADA  15.227590  44.113177    64.307                 0.2   \n",
       "184       ZIMM   7.465278  46.877098   956.341                -0.1   \n",
       "185       ZOUF  12.973553  46.557221  1946.508                 0.1   \n",
       "\n",
       "     velocity_north_mmyr  velocity_up_mmyr  longitude_error_m  \\\n",
       "0                    1.2               1.1             0.0005   \n",
       "1                    0.9               1.3             0.0009   \n",
       "2                   -0.2               0.1             0.0009   \n",
       "3                   -0.2               1.5             0.0009   \n",
       "4                    0.1               0.7             0.0009   \n",
       "..                   ...               ...                ...   \n",
       "181                 -0.2              -2.8             0.0005   \n",
       "182                  0.2              -0.1             0.0005   \n",
       "183                  3.1              -0.3             0.0018   \n",
       "184                  0.4               1.0             0.0005   \n",
       "185                  1.0               1.3             0.0005   \n",
       "\n",
       "     latitude_error_m  height_error_m  velocity_east_error_mmyr  \\\n",
       "0              0.0009           0.001                       0.1   \n",
       "1              0.0009           0.001                       0.1   \n",
       "2              0.0018           0.002                       0.1   \n",
       "3              0.0036           0.004                       0.2   \n",
       "4              0.0009           0.002                       0.1   \n",
       "..                ...             ...                       ...   \n",
       "181            0.0009           0.001                       0.1   \n",
       "182            0.0005           0.001                       0.1   \n",
       "183            0.0036           0.004                       0.2   \n",
       "184            0.0009           0.001                       0.1   \n",
       "185            0.0009           0.001                       0.1   \n",
       "\n",
       "     velocity_north_error_mmyr  velocity_up_error_mmyr  \n",
       "0                          0.1                     0.1  \n",
       "1                          0.2                     0.2  \n",
       "2                          0.3                     0.3  \n",
       "3                          0.6                     0.5  \n",
       "4                          0.5                     0.5  \n",
       "..                         ...                     ...  \n",
       "181                        0.2                     0.2  \n",
       "182                        0.1                     0.1  \n",
       "183                        0.4                     0.4  \n",
       "184                        0.1                     0.1  \n",
       "185                        0.1                     0.1  \n",
       "\n",
       "[186 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gps = pd.read_csv(fname)\n",
    "gps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "54367143-130a-4944-a1de-009b01035d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "projection = pyproj.Proj(proj=\"merc\", lat_ts=gps.latitude.mean())\n",
    "easting, northing = projection(gps.longitude, gps.latitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83bfa603-e293-4054-9e1c-dfd09abf02da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    1, 2, figsize=(18, 6)\n",
    ")\n",
    "# Plot the horizontal velocity vectors\n",
    "ax = axes[0]\n",
    "ax.set_title(\"Horizontal velocities\")\n",
    "ax.quiver(\n",
    "    easting, northing,\n",
    "    gps.velocity_east_mmyr.values,\n",
    "    gps.velocity_north_mmyr.values,\n",
    "    scale=30,\n",
    "    width=0.002,\n",
    ")\n",
    "ax.set_aspect(\"equal\")\n",
    "ax.set_xlabel(\"easting (m)\")\n",
    "ax.set_ylabel(\"northing (m)\")\n",
    "# Plot the vertical velocity\n",
    "ax = axes[1]\n",
    "ax.set_title(\"Vertical velocity\")\n",
    "maxabs = vd.maxabs(gps.velocity_up_mmyr)\n",
    "tmp = ax.scatter(\n",
    "    easting, northing,\n",
    "    c=gps.velocity_up_mmyr,\n",
    "    s=30,\n",
    "    vmin=-maxabs / 3,\n",
    "    vmax=maxabs / 3,\n",
    "    cmap=\"seismic\",\n",
    ")\n",
    "plt.colorbar(tmp, ax=ax, label=\"mm/year\", pad=0, aspect=50)\n",
    "ax.set_aspect(\"equal\")\n",
    "ax.set_xlabel(\"easting (m)\")\n",
    "ax.set_ylabel(\"northing (m)\")\n",
    "plt.tight_layout(pad=0)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:fatiando-data]",
   "language": "python",
   "name": "conda-env-fatiando-data-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}