diff --git a/notebooks/testing_logratio_transformations.ipynb b/notebooks/testing_logratio_transformations.ipynb
index f8f62961..19f377aa 100644
--- a/notebooks/testing_logratio_transformations.ipynb
+++ b/notebooks/testing_logratio_transformations.ipynb
@@ -20,9 +20,8 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/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",
- " https://beartype.readthedocs.io/en/latest/api_roar/#pep-585-deprecations\n",
- " warn(\n"
+ "/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",
+ " warnings.warn(\n"
]
}
],
@@ -727,7 +726,7 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "e1bda63b-ab9b-4060-90d5-7520952f2e3a",
+ "id": "41bfcb78-bdfa-4c03-a9b4-45c2258c60a8",
"metadata": {
"tags": []
},
@@ -757,7 +756,6 @@
" | Ca_ppm_511 | \n",
" Fe_ppm_511 | \n",
" Mg_ppm_511 | \n",
- " residual | \n",
" \n",
" \n",
" \n",
@@ -767,7 +765,6 @@
" 40200.0 | \n",
" 83200.0 | \n",
" 17200.0 | \n",
- " 831800.0 | \n",
" \n",
" \n",
" | 1 | \n",
@@ -775,7 +772,6 @@
" 5000.0 | \n",
" 28300.0 | \n",
" 7520.0 | \n",
- " 945080.0 | \n",
"
\n",
" \n",
" | 2 | \n",
@@ -783,7 +779,6 @@
" 3070.0 | \n",
" 14500.0 | \n",
" 4540.0 | \n",
- " 970010.0 | \n",
"
\n",
" \n",
" | 3 | \n",
@@ -791,7 +786,6 @@
" 3290.0 | \n",
" 14600.0 | \n",
" 3240.0 | \n",
- " 971570.0 | \n",
"
\n",
" \n",
" | 4 | \n",
@@ -799,19 +793,18 @@
" 3600.0 | \n",
" 31500.0 | \n",
" 8020.0 | \n",
- " 944380.0 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " Al_ppm_511 Ca_ppm_511 Fe_ppm_511 Mg_ppm_511 residual\n",
- "0 27600.0 40200.0 83200.0 17200.0 831800.0\n",
- "1 14100.0 5000.0 28300.0 7520.0 945080.0\n",
- "2 7880.0 3070.0 14500.0 4540.0 970010.0\n",
- "3 7300.0 3290.0 14600.0 3240.0 971570.0\n",
- "4 12500.0 3600.0 31500.0 8020.0 944380.0"
+ " Al_ppm_511 Ca_ppm_511 Fe_ppm_511 Mg_ppm_511\n",
+ "0 27600.0 40200.0 83200.0 17200.0\n",
+ "1 14100.0 5000.0 28300.0 7520.0\n",
+ "2 7880.0 3070.0 14500.0 4540.0\n",
+ "3 7300.0 3290.0 14600.0 3240.0\n",
+ "4 12500.0 3600.0 31500.0 8020.0"
]
},
"execution_count": 21,
@@ -825,15 +818,12 @@
"df = gpd.read_file(GEOCHEMICAL_DATA, include_fields=elements_to_analyze)\n",
"df = pd.DataFrame(df.drop(columns='geometry'))\n",
"\n",
- "# Add a column for the residual\n",
- "\n",
- "df[\"residual\"] = million - np.sum(df, axis=1)\n",
"df.head()"
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 22,
"id": "75728aa4-5b2e-46b6-9511-1250bf4b13ae",
"metadata": {
"tags": []
@@ -843,7 +833,6 @@
"pair_Al_Ca = pairwise_logratio(df, \"Al_ppm_511\", \"Ca_ppm_511\")\n",
"pair_Fe_Mg = pairwise_logratio(df, \"Fe_ppm_511\", \"Mg_ppm_511\")\n",
"pair_Mg_Al = pairwise_logratio(df, \"Mg_ppm_511\", \"Al_ppm_511\")\n",
- "pair_Mg_res = pairwise_logratio(df, \"Mg_ppm_511\", \"residual\")\n",
"\n",
"df_alr = alr_transform(df)\n",
"df_alr_Mg = alr_transform(df, \"Mg_ppm_511\")\n",
@@ -859,7 +848,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 23,
"id": "e136d05d-671d-420f-95b9-5f350bc7a94c",
"metadata": {
"tags": []
@@ -876,7 +865,7 @@
"dtype: float64"
]
},
- "execution_count": 25,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -887,7 +876,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 24,
"id": "ad352680-433a-4026-b7b5-560b682dfb96",
"metadata": {
"tags": []
@@ -917,7 +906,6 @@
" V1 | \n",
" V2 | \n",
" V3 | \n",
- " V4 | \n",
" \n",
" \n",
" \n",
@@ -926,50 +914,45 @@
" 0.472906 | \n",
" 0.848958 | \n",
" 1.576338 | \n",
- " 3.878683 | \n",
" \n",
" \n",
" | 1 | \n",
" 0.628609 | \n",
" -0.408128 | \n",
" 1.325296 | \n",
- " 4.833703 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.551401 | \n",
" -0.391249 | \n",
" 1.161222 | \n",
- " 5.364379 | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.812301 | \n",
" 0.015314 | \n",
" 1.505448 | \n",
- " 5.703340 | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.443790 | \n",
" -0.801005 | \n",
" 1.368049 | \n",
- " 4.768590 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " V1 V2 V3 V4\n",
- "0 0.472906 0.848958 1.576338 3.878683\n",
- "1 0.628609 -0.408128 1.325296 4.833703\n",
- "2 0.551401 -0.391249 1.161222 5.364379\n",
- "3 0.812301 0.015314 1.505448 5.703340\n",
- "4 0.443790 -0.801005 1.368049 4.768590"
+ " V1 V2 V3\n",
+ "0 0.472906 0.848958 1.576338\n",
+ "1 0.628609 -0.408128 1.325296\n",
+ "2 0.551401 -0.391249 1.161222\n",
+ "3 0.812301 0.015314 1.505448\n",
+ "4 0.443790 -0.801005 1.368049"
]
},
- "execution_count": 26,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -977,14 +960,6 @@
"source": [
"df_alr_Mg.head()"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b6a1929-51ef-4b7a-8621-f46bbe337e31",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {