Fix notebook

GispoCoding · Dec 19, 2024 · ffa84e6 · ffa84e6
1 parent d117ce4
commit ffa84e6
Showing 1 changed file with 12 additions and 33 deletions.
diff --git a/notebooks/testing_logratio_transformations.ipynb b/notebooks/testing_logratio_transformations.ipynb
@@ -680,21 +680,6 @@
     "## Testing with example data"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "f49926c2-f1dd-47e8-a484-f78ce6821904",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Define some constants\n",
-    "\n",
-    "ppm = 1e-6\n",
-    "million = 1e6"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -822,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "id": "75728aa4-5b2e-46b6-9511-1250bf4b13ae",
    "metadata": {
     "tags": []
@@ -838,21 +823,23 @@
     "df_clr = df.copy()\n",
     "df_plr = df.copy()\n",
     "\n",
-    "# As real world geochemical data will often not satisfy sum to a constant, for example 100, a closure needs to be performed\n",
+    "# As real world geochemical data will often not satisfy sum to a constant, a closure needs to be performed by providing the \"scale\" parameter.\n",
+    "# In this example, as the example data are in ppm, let's define a scaling factor 1e6\n",
+    "million = 1e6\n",
     "\n",
-    "df_alr = alr_transform(df_alr, closure_target=100)\n",
-    "df_alr_Mg = alr_transform(df_alr_Mg, denominator_column=\"Mg_ppm_511\", closure_target=100)\n",
-    "df_clr = clr_transform(df_clr, closure_target=100)\n",
-    "df_plr = plr_transform(df_plr, closure_target=100)\n",
+    "df_alr = alr_transform(df_alr, scale=million)\n",
+    "df_alr_Mg = alr_transform(df_alr_Mg, denominator_column=\"Mg_ppm_511\", scale=million)\n",
+    "df_clr = clr_transform(df_clr, scale=million)\n",
+    "df_plr = plr_transform(df_plr, scale=million)\n",
     "\n",
     "df_clr_inv = inverse_clr(df_clr, scale=million)\n",
     "df_alr_inv = inverse_alr(df_alr, \"c\", scale=million)\n",
     "\n",
     "df_ilr_Al_Ca = df.copy()\n",
     "df_ilr_AlCa_FeMg = df.copy()\n",
     "\n",
-    "df_ilr_Al_Ca = single_ilr_transform(df_ilr_Al_Ca, [\"Al_ppm_511\"], [\"Ca_ppm_511\"], closure_target=100)\n",
-    "df_ilr_AlCa_FeMg = single_ilr_transform(df_ilr_AlCa_FeMg, [\"Al_ppm_511\", \"Ca_ppm_511\"], [\"Fe_ppm_511\", \"Mg_ppm_511\"], closure_target=100)"
+    "df_ilr_Al_Ca = single_ilr_transform(df_ilr_Al_Ca, [\"Al_ppm_511\"], [\"Ca_ppm_511\"], scale=million)\n",
+    "df_ilr_AlCa_FeMg = single_ilr_transform(df_ilr_AlCa_FeMg, [\"Al_ppm_511\", \"Ca_ppm_511\"], [\"Fe_ppm_511\", \"Mg_ppm_511\"], scale=million)"
    ]
   },
   {
@@ -969,19 +956,11 @@
    "source": [
     "df_alr_Mg.head()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b6a1929-51ef-4b7a-8621-f46bbe337e31",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "eis-toolkit-l5cKD1lZ-py3.10",
    "language": "python",
    "name": "python3"
   },
@@ -995,7 +974,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,