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Revert "Update example notebook."
This reverts commit 8ec96f6.
1 parent 1e23b65 commit 57a6c39

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‎notebooks/testing_logratio_transformations.ipynb

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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/root/.cache/pypoetry/virtualenvs/eis-toolkit-QEzTY9B6-py3.10/lib/python3.10/site-packages/geopandas/_compat.py:112: UserWarning: The Shapely GEOS version (3.10.3-CAPI-1.16.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n",
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" warnings.warn(\n"
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"/root/.cache/pypoetry/virtualenvs/eis-toolkit-QEzTY9B6-py3.10/lib/python3.10/site-packages/beartype/_util/hint/pep/utilpeptest.py:347: BeartypeDecorHintPep585DeprecationWarning: PEP 484 type hint typing.Sequence[str] deprecated by PEP 585. This hint is scheduled for removal in the first Python version released after October 5th, 2025. To resolve this, import this hint from \"beartype.typing\" rather than \"typing\". For further commentary and alternatives, see also:\n",
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" https://beartype.readthedocs.io/en/latest/api_roar/#pep-585-deprecations\n",
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" warn(\n"
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]
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}
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],
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "41bfcb78-bdfa-4c03-a9b4-45c2258c60a8",
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"id": "e1bda63b-ab9b-4060-90d5-7520952f2e3a",
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"metadata": {
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"tags": []
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},
@@ -756,6 +757,7 @@
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" <th>Ca_ppm_511</th>\n",
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" <th>Fe_ppm_511</th>\n",
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" <th>Mg_ppm_511</th>\n",
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" <th>residual</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <td>40200.0</td>\n",
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" <td>83200.0</td>\n",
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" <td>17200.0</td>\n",
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" <td>831800.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>14100.0</td>\n",
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" <td>5000.0</td>\n",
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" <td>28300.0</td>\n",
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" <td>7520.0</td>\n",
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" <td>945080.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>7880.0</td>\n",
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" <td>3070.0</td>\n",
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" <td>14500.0</td>\n",
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" <td>4540.0</td>\n",
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" <td>970010.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>7300.0</td>\n",
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" <td>3290.0</td>\n",
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" <td>14600.0</td>\n",
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" <td>3240.0</td>\n",
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" <td>971570.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>12500.0</td>\n",
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" <td>3600.0</td>\n",
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" <td>31500.0</td>\n",
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" <td>8020.0</td>\n",
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" <td>944380.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Al_ppm_511 Ca_ppm_511 Fe_ppm_511 Mg_ppm_511\n",
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"0 27600.0 40200.0 83200.0 17200.0\n",
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"1 14100.0 5000.0 28300.0 7520.0\n",
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"2 7880.0 3070.0 14500.0 4540.0\n",
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"3 7300.0 3290.0 14600.0 3240.0\n",
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"4 12500.0 3600.0 31500.0 8020.0"
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" Al_ppm_511 Ca_ppm_511 Fe_ppm_511 Mg_ppm_511 residual\n",
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"0 27600.0 40200.0 83200.0 17200.0 831800.0\n",
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"1 14100.0 5000.0 28300.0 7520.0 945080.0\n",
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"2 7880.0 3070.0 14500.0 4540.0 970010.0\n",
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"3 7300.0 3290.0 14600.0 3240.0 971570.0\n",
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"4 12500.0 3600.0 31500.0 8020.0 944380.0"
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]
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},
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"execution_count": 21,
@@ -818,12 +825,15 @@
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"df = gpd.read_file(GEOCHEMICAL_DATA, include_fields=elements_to_analyze)\n",
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"df = pd.DataFrame(df.drop(columns='geometry'))\n",
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"\n",
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"# Add a column for the residual\n",
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"\n",
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"df[\"residual\"] = million - np.sum(df, axis=1)\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 24,
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"id": "75728aa4-5b2e-46b6-9511-1250bf4b13ae",
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"metadata": {
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"tags": []
@@ -833,6 +843,7 @@
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"pair_Al_Ca = pairwise_logratio(df, \"Al_ppm_511\", \"Ca_ppm_511\")\n",
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"pair_Fe_Mg = pairwise_logratio(df, \"Fe_ppm_511\", \"Mg_ppm_511\")\n",
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"pair_Mg_Al = pairwise_logratio(df, \"Mg_ppm_511\", \"Al_ppm_511\")\n",
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"pair_Mg_res = pairwise_logratio(df, \"Mg_ppm_511\", \"residual\")\n",
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"\n",
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"df_alr = alr_transform(df)\n",
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"df_alr_Mg = alr_transform(df, \"Mg_ppm_511\")\n",
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 25,
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"id": "e136d05d-671d-420f-95b9-5f350bc7a94c",
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"metadata": {
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"tags": []
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"dtype: float64"
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]
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"execution_count": 23,
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 26,
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"id": "ad352680-433a-4026-b7b5-560b682dfb96",
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"metadata": {
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"tags": []
@@ -906,6 +917,7 @@
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" <th>V1</th>\n",
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" <th>V2</th>\n",
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" <th>V3</th>\n",
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" <th>V4</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <td>0.472906</td>\n",
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" <td>0.848958</td>\n",
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" <td>1.576338</td>\n",
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" <td>3.878683</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0.628609</td>\n",
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" <td>-0.408128</td>\n",
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" <td>1.325296</td>\n",
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" <td>4.833703</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0.551401</td>\n",
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" <td>-0.391249</td>\n",
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" <td>1.161222</td>\n",
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" <td>5.364379</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>0.812301</td>\n",
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" <td>0.015314</td>\n",
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" <td>1.505448</td>\n",
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" <td>5.703340</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0.443790</td>\n",
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" <td>-0.801005</td>\n",
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" <td>1.368049</td>\n",
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" <td>4.768590</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" V1 V2 V3\n",
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"0 0.472906 0.848958 1.576338\n",
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"1 0.628609 -0.408128 1.325296\n",
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"2 0.551401 -0.391249 1.161222\n",
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"3 0.812301 0.015314 1.505448\n",
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"4 0.443790 -0.801005 1.368049"
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" V1 V2 V3 V4\n",
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"0 0.472906 0.848958 1.576338 3.878683\n",
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"1 0.628609 -0.408128 1.325296 4.833703\n",
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"2 0.551401 -0.391249 1.161222 5.364379\n",
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"3 0.812301 0.015314 1.505448 5.703340\n",
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"4 0.443790 -0.801005 1.368049 4.768590"
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]
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},
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"execution_count": 24,
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df_alr_Mg.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b6a1929-51ef-4b7a-8621-f46bbe337e31",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

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