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Water quality calibration/check_column_names_res1d.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "214f6c0e-b23f-41e9-946d-9151b77d591c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from mikeplus import DataTableAccess\n",
+    "from mikeplus.engines import Engine1D\n",
+    "from mikeio1d import Res1D\n",
+    "from mikeio1d.query import QueryDataStructure\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07c9bb35-946c-4f24-a70b-645452dfa1be",
+   "metadata": {},
+   "source": [
+    "# ST column names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "573590c1-ba7c-45b0-984a-c0fe751a5d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct the file path\n",
+    "result_file_path = r\"path\\example_ST.res1d\"\n",
+    "res1d = Res1D(str(result_file_path))  # Create a Res1D instance with the file\n",
+    "df = res1d.read()  # Read all data from the .res1d file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e022f845-8b2b-4666-a79e-b3a56b95dd75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matching_columns = [col for col in df.columns if \"reach|node|structure_name\" in col]\n",
+    "print(matching_columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0373a6a7-42a1-498a-a4b9-f9bfa2ad16b6",
+   "metadata": {},
+   "source": [
+    "# AD column names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "594eb63b-940e-4300-bf07-f928ed9e2df0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct the file path\n",
+    "result_file_path_ad = r\"path\\exmaple_AD.res1d\"\n",
+    "res1d_ad = Res1D(str(result_file_path_ad))  # Create a Res1D instance with the file\n",
+    "df_ad = res1d_ad.read()  # Read all data from the .res1d file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee6ed680-a425-4b2c-9a4c-9a8fea7a6dc9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matching_columns_ad = [col for col in df_ad.columns if \"reach|node|structure_name\" in col]\n",
+    "print(matching_columns_ad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2cdf6b2-af96-42be-a389-96836c03bfb3",
+   "metadata": {},
+   "source": [
+    "# Catchment column names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcaba177-d464-4368-be7b-5cf293b271d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct the file path\n",
+    "result_file_path_catch = r\"path\\example_Catchment_discharge_quality.res1d\"\n",
+    "res1d_catch = Res1D(str(result_file_path_catch))  # Create a Res1D instance with the file\n",
+    "df_catch = res1d_catch.read()  # Read all data from the .res1d file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4855dd6e-523c-4547-87d5-7806e88afbc9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matching_columns_catch = [col for col in df_catch.columns if \"Catchment_name\" in col]\n",
+    "print(matching_columns_catch)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (Spyder)",
+   "language": "python3",
+   "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": "3.9.21"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}