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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading file 'bushveld-gravity.csv.xz' from 'doi:10.5281/zenodo.6511942/bushveld-gravity.csv.xz' to '/home/runner/.cache/ensaio/v1'.\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>height_sea_level_m</th>\n",
       "      <th>height_geometric_m</th>\n",
       "      <th>gravity_mgal</th>\n",
       "      <th>gravity_disturbance_mgal</th>\n",
       "      <th>gravity_bouguer_mgal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1192.107148</td>\n",
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       "      <td>12.700307</td>\n",
       "      <td>-130.460126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>978798.55</td>\n",
       "      <td>48.012766</td>\n",
       "      <td>16.602026</td>\n",
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       "    <tr>\n",
       "      <th>3874</th>\n",
       "      <td>31.54832</td>\n",
       "      <td>-23.19333</td>\n",
       "      <td>245.7</td>\n",
       "      <td>257.592670</td>\n",
       "      <td>978803.55</td>\n",
       "      <td>49.161771</td>\n",
       "      <td>22.456674</td>\n",
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       "</table>\n",
       "<p>3877 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude  latitude  height_sea_level_m  height_geometric_m  \\\n",
       "0      25.01500 -26.26334              1230.2         1257.474535   \n",
       "1      25.01932 -26.38713              1297.0         1324.574150   \n",
       "2      25.02499 -26.39667              1304.8         1332.401322   \n",
       "3      25.04500 -26.07668              1165.2         1192.107148   \n",
       "4      25.07668 -26.35001              1262.5         1289.971792   \n",
       "...         ...       ...                 ...                 ...   \n",
       "3872   31.51500 -23.86333               300.5          312.710241   \n",
       "3873   31.52499 -23.30000               280.7          292.686630   \n",
       "3874   31.54832 -23.19333               245.7          257.592670   \n",
       "3875   31.57333 -23.84833               226.8          239.199065   \n",
       "3876   31.37500 -23.00000               285.6          297.165672   \n",
       "\n",
       "      gravity_mgal  gravity_disturbance_mgal  gravity_bouguer_mgal  \n",
       "0        978681.38                 25.081592           -113.259165  \n",
       "1        978669.02                 24.538158           -122.662101  \n",
       "2        978669.28                 26.526960           -121.339321  \n",
       "3        978681.08                 17.954814           -113.817543  \n",
       "4        978665.19                 12.700307           -130.460126  \n",
       "...            ...                       ...                   ...  \n",
       "3872     978776.85                 -4.783965            -39.543608  \n",
       "3873     978798.55                 48.012766             16.602026  \n",
       "3874     978803.55                 49.161771             22.456674  \n",
       "3875     978808.44                  5.116904            -20.419870  \n",
       "3876     978734.77                  5.186926            -25.922627  \n",
       "\n",
       "[3877 rows x 7 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ensaio\n",
    "import pandas as pd\n",
    "\n",
    "fname = ensaio.fetch_bushveld_gravity(version=1)\n",
    "data = pd.read_csv(fname)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d8aafe7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:44:34.267252Z",
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     "shell.execute_reply": "2024-05-03T23:44:34.289358Z"
    }
   },
   "outputs": [],
   "source": [
    "import pyproj\n",
    "\n",
    "projection = pyproj.Proj(proj=\"merc\", lat_ts=data.latitude.values.mean())\n",
    "easting, northing = projection(data.longitude.values, data.latitude.values)\n",
    "\n",
    "coordinates = (easting, northing, data.height_geometric_m.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "347e04ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:44:34.291552Z",
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     "shell.execute_reply": "2024-05-03T23:44:37.572082Z"
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   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>EquivalentSources(damping=1, depth=1000.0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;EquivalentSources<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>EquivalentSources(damping=1, depth=1000.0)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "EquivalentSources(damping=1, depth=1000.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import harmonica as hm\n",
    "\n",
    "eqs_first_guess = hm.EquivalentSources(depth=1e3, damping=1)\n",
    "eqs_first_guess.fit(coordinates, data.gravity_disturbance_mgal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97db4a42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:44:37.576012Z",
     "iopub.status.busy": "2024-05-03T23:44:37.575021Z",
     "iopub.status.idle": "2024-05-03T23:44:41.706063Z",
     "shell.execute_reply": "2024-05-03T23:44:41.705552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8027851941632995"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import verde as vd\n",
    "\n",
    "score_first_guess = np.mean(\n",
    "    vd.cross_val_score(\n",
    "        eqs_first_guess,\n",
    "        coordinates,\n",
    "        data.gravity_disturbance_mgal,\n",
    "    )\n",
    ")\n",
    "score_first_guess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "351e3ea6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:44:41.720744Z",
     "iopub.status.busy": "2024-05-03T23:44:41.720360Z",
     "iopub.status.idle": "2024-05-03T23:44:41.723117Z",
     "shell.execute_reply": "2024-05-03T23:44:41.722681Z"
    }
   },
   "outputs": [],
   "source": [
    "dampings = [0.01, 0.1, 1, 10,]\n",
    "depths = [5e3, 10e3, 20e3, 50e3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d019651c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:44:41.725221Z",
     "iopub.status.busy": "2024-05-03T23:44:41.724893Z",
     "iopub.status.idle": "2024-05-03T23:44:41.728423Z",
     "shell.execute_reply": "2024-05-03T23:44:41.728020Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of combinations: 16\n",
      "Combinations: [{'damping': 0.01, 'depth': 5000.0}, {'damping': 0.01, 'depth': 10000.0}, {'damping': 0.01, 'depth': 20000.0}, {'damping': 0.01, 'depth': 50000.0}, {'damping': 0.1, 'depth': 5000.0}, {'damping': 0.1, 'depth': 10000.0}, {'damping': 0.1, 'depth': 20000.0}, {'damping': 0.1, 'depth': 50000.0}, {'damping': 1, 'depth': 5000.0}, {'damping': 1, 'depth': 10000.0}, {'damping': 1, 'depth': 20000.0}, {'damping': 1, 'depth': 50000.0}, {'damping': 10, 'depth': 5000.0}, {'damping': 10, 'depth': 10000.0}, {'damping': 10, 'depth': 20000.0}, {'damping': 10, 'depth': 50000.0}]\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "parameter_sets = [\n",
    "    dict(damping=combo[0], depth=combo[1])\n",
    "    for combo in itertools.product(dampings, depths)\n",
    "]\n",
    "print(\"Number of combinations:\", len(parameter_sets))\n",
    "print(\"Combinations:\", parameter_sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4775564",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:44:41.730403Z",
     "iopub.status.busy": "2024-05-03T23:44:41.730221Z",
     "iopub.status.idle": "2024-05-03T23:45:42.186728Z",
     "shell.execute_reply": "2024-05-03T23:45:42.186254Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9150928326897354,\n",
       " 0.9142249773025007,\n",
       " 0.9043905752276522,\n",
       " 0.8822529497599797,\n",
       " 0.9158120453861851,\n",
       " 0.9179260104390551,\n",
       " 0.9104311266648011,\n",
       " 0.8751298775892267,\n",
       " 0.9161113383638325,\n",
       " 0.9197261730846333,\n",
       " 0.90702616597728,\n",
       " 0.8619681875562746,\n",
       " 0.9158209704190459,\n",
       " 0.9172918887155358,\n",
       " 0.8946173275890278,\n",
       " 0.8408182949960263]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equivalent_sources = hm.EquivalentSources()\n",
    "\n",
    "scores = []\n",
    "for params in parameter_sets:\n",
    "    equivalent_sources.set_params(**params)\n",
    "    score = np.mean(\n",
    "        vd.cross_val_score(\n",
    "            equivalent_sources,\n",
    "            coordinates,\n",
    "            data.gravity_disturbance_mgal,\n",
    "        )\n",
    "    )\n",
    "    scores.append(score)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c36c361a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:45:42.189971Z",
     "iopub.status.busy": "2024-05-03T23:45:42.189146Z",
     "iopub.status.idle": "2024-05-03T23:45:42.194195Z",
     "shell.execute_reply": "2024-05-03T23:45:42.193771Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.9197261730846333\n",
      "Score with defaults: 0.8027851941632995\n",
      "Best parameters: {'damping': 1, 'depth': 10000.0}\n"
     ]
    }
   ],
   "source": [
    "best = np.argmax(scores)\n",
    "print(\"Best score:\", scores[best])\n",
    "print(\"Score with defaults:\", score_first_guess)\n",
    "print(\"Best parameters:\", parameter_sets[best])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b73dae7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:45:42.197153Z",
     "iopub.status.busy": "2024-05-03T23:45:42.196368Z",
     "iopub.status.idle": "2024-05-03T23:45:43.567532Z",
     "shell.execute_reply": "2024-05-03T23:45:43.566985Z"
    }
   },
   "outputs": [],
   "source": [
    "eqs_best = hm.EquivalentSources(**parameter_sets[best]).fit(\n",
    "    coordinates, data.gravity_disturbance_mgal\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ca3ac90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:45:43.571208Z",
     "iopub.status.busy": "2024-05-03T23:45:43.570344Z",
     "iopub.status.idle": "2024-05-03T23:45:44.891367Z",
     "shell.execute_reply": "2024-05-03T23:45:44.890932Z"
    }
   },
   "outputs": [],
   "source": [
    "# Define grid coordinates\n",
    "region = vd.get_region(coordinates)\n",
    "grid_coords = vd.grid_coordinates(\n",
    "    region=region,\n",
    "    spacing=2e3,\n",
    "    extra_coords=2.5e3,\n",
    ")\n",
    "\n",
    "grid_first_guess = eqs_first_guess.grid(grid_coords)\n",
    "grid = eqs_best.grid(grid_coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd6cd5ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:45:44.893708Z",
     "iopub.status.busy": "2024-05-03T23:45:44.893317Z",
     "iopub.status.idle": "2024-05-03T23:45:45.068272Z",
     "shell.execute_reply": "2024-05-03T23:45:45.067784Z"
    }
   },
   "outputs": [],
   "source": [
    " import pygmt\n",
    "\n",
    " # Needed so that displaying works on jupyter-sphinx and sphinx-gallery at\n",
    " # the same time. Using PYGMT_USE_EXTERNAL_DISPLAY=\"false\" in the Makefile\n",
    " # for sphinx-gallery to work means that fig.show won't display anything here\n",
    " # either.\n",
    " pygmt.set_display(method=\"notebook\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63167a3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-03T23:45:45.070587Z",
     "iopub.status.busy": "2024-05-03T23:45:45.070371Z",
     "iopub.status.idle": "2024-05-03T23:45:45.516118Z",
     "shell.execute_reply": "2024-05-03T23:45:45.515606Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/runner/work/harmonica/harmonica/doc/_build/jupyter_execute/user_guide/equivalent_sources/eqs-parameters-estimation_11_0.png"
      },
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pygmt\n",
    "\n",
    "# Set figure properties\n",
    "w, e, s, n = region\n",
    "fig_height = 10\n",
    "fig_width = fig_height * (e - w) / (n - s)\n",
    "fig_ratio = (n - s) / (fig_height / 100)\n",
    "fig_proj = f\"x1:{fig_ratio}\"\n",
    "\n",
    "maxabs = vd.maxabs(grid_first_guess.scalars, grid.scalars)\n",
    "\n",
    "fig = pygmt.Figure()\n",
    "\n",
    "# Make colormap of data\n",
    "pygmt.makecpt(cmap=\"polar+h0\",series=(-maxabs, maxabs,))\n",
    "\n",
    "title = \"Gravity disturbance with first guess\"\n",
    "\n",
    "fig.grdimage(\n",
    "   projection=fig_proj,\n",
    "   region=region,\n",
    "   frame=[f\"WSne+t{title}\", \"xa100000+a15\", \"ya100000\"],\n",
    "   grid=grid_first_guess.scalars,\n",
    "   cmap=True,\n",
    ")\n",
    "fig.colorbar(cmap=True, frame=[\"a50f25\", \"x+lmGal\"])\n",
    "\n",
    "fig.shift_origin(xshift=fig_width + 1)\n",
    "\n",
    "title = \"Gravity disturbance with best params\"\n",
    "\n",
    "fig.grdimage(\n",
    "   frame=[f\"ESnw+t{title}\", \"xa100000+a15\", \"ya100000\"],\n",
    "   grid=grid.scalars,\n",
    "   cmap=True,\n",
    ")\n",
    "fig.colorbar(cmap=True, frame=[\"a50f25\", \"x+lmGal\"])\n",
    "\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}