diff --git a/PV_tool0.4.ipynb b/PV_tool0.4.ipynb
index 48daf88..b9a39d1 100644
--- a/PV_tool0.4.ipynb
+++ b/PV_tool0.4.ipynb
@@ -1,99 +1,266 @@
{
"cells": [
{
- "metadata": {},
"cell_type": "markdown",
+ "id": "5b519e9d-47fb-4aaf-88e6-ea99037b66ce",
+ "metadata": {},
"source": [
"STOWA Proevenverzameling tool v5.0
\n",
"\n",
"Deze Jupyter notebook bevat functies opgenomen in de python package PV-tooling ### voor het opstellen van lokale of regionale proevenverzamelingen voor het bepalen van geotechnische parameters. De methode is ontwikkeld voor het uitvoeren van analyses in relatie tot de geotechnische stabiliteit van dijken, maar kan ook breder worden toegepast. De notebook dient tevens als handleiding om de gebruiker stapsgewijs te ondersteunen bij het opstellen van een proevenverzameling.\n",
"\n",
- "Met de beschikbare functies kunnen zowel gedraineerde als ongedraineerde sterkteparameters worden berekend alsmede enkele samendrukkingsparameters. Van deze parameters worden verwachtingswaarde, karakteristieke waarde en rekenwaarde bepaald.\n",
+ "Met de beschikbare functies kunnen zowel gedraineerde als ongedraineerde sterkteparameters worden berekend alsmede enkele samendrukkingsparameters. Van deze parameters worden verwachtingswaarde, karakteristieke waarde en rekenwaarde bepaald. \n",
"\n",
"De functies zijn opgesteld conform de werkwijze beschreven in [Statistische methoden t.b.v. proevenverzamelingen, DIV, v1.0], zie tevens: https://publicwiki.deltares.nl/spaces/HWBPMacro/pages/217120830/Sterkte+van+grond#Sterktevangrond-150.\n",
- "De tool bevat tevens hulpmiddelen voor het onderscheiden of samenvoegen van groepen in een verzameling op basis van verschillende kenmerken.\n",
+ "De tool bevat tevens hulpmiddelen voor het onderscheiden of samenvoegen van groepen in een verzameling op basis van verschillende kenmerken. \n",
"\n",
"De onderliggende data om een proevenverzameling samen te stellen is beschreven in een vaste structuur. Deze structuur is vastgelegd in een uitwisselformat. Het uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx. De geotechnische laboratoria kennen deze database en kunnen deze database vullen met resultaten van grond- en laboratoriumonderzoek. Op deze wijze ontstaat er uniformering op het gebied van data-uitwisseling en –opslag van proefresultaten.\n",
"\n",
"NB. De verantwoordelijkheid voor het gebruik van deze tools ligt bij de gebruiker."
- ],
- "id": "bee1f1422905e9aa"
+ ]
},
{
+ "cell_type": "markdown",
+ "id": "424ad52e-54df-4a4d-8546-36eb585eb7a8",
"metadata": {},
- "cell_type": "code",
- "outputs": [],
- "execution_count": null,
"source": [
- "# Importeren van benodigde packages\n",
- "# Na het eerte keer installeren van de benodigde packages de kernel handmatig opnieuw optarten via het menu Kernel → Restart\n",
- "\n",
- "import importlib.util\n",
- "\n",
- "# Functie om te controleren of een package is geïnstalleerd\n",
- "def check_package_install(package_name):\n",
- " package_spec = importlib.util.find_spec(package_name)\n",
- " if package_spec is None:\n",
- " print(f\"{package_name} is niet geïnstalleerd. Installatie wordt gestart...\")\n",
- " !pip install {package_name}\n",
- " else:\n",
- " print(f\"{package_name} is al geïnstalleerd.\")\n",
- "\n",
- "# Controleer en installeer indien nodig\n",
- "check_package_install(\"openpyxl\")\n",
- "check_package_install(\"ipyfilechooser\")\n",
- "check_package_install(\"pandas_schema\")\n",
- "check_package_install(\"xlsxwriter\")\n",
- "check_package_install(\"ipywidgets\")\n",
- "check_package_install(\"reportlab\")\n",
- "check_package_install(\"kaleido\")\n",
- "\n",
- "#!pip uninstall C:\\python_cursus\\PVtool2025_github\\dist\\pv_tool-0.1.0-py3-none-any.whl\n",
- "#!pip install --force-reinstall C:\\python_cursus\\PVtool2025_github\\dist\\pv_tool-0.1.3-py3-none-any.whl\n",
- "\n",
- "#!pip install C:\\python_cursus\\PVtool2025_github\\dist\\pv_tool-0.2.0-py3-none-any.whl\n"
- ],
- "id": "9fca9550118698a9"
+ "## Benodigde installaties"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
- "source": "!pip install --force-reinstall ipywidgets",
- "id": "30cf1c2aaee08295"
- },
- {
+ "id": "e3612aae-c928-4b18-b531-328810c8acc1",
"metadata": {},
- "cell_type": "code",
"outputs": [],
- "execution_count": null,
- "source": "!pip install pyarrow",
- "id": "878410f93e074377"
+ "source": [
+ "# Importeren van benodigde package\n",
+ "# Na het eerte keer installeren van de benodigde packages de kernel handmatig opnieuw optarten via het menu Kernel → Restart\n",
+ "\n",
+ "path_to_wheel = r\"C:\\Users\\deenekat7271\\Documents\\GitHub\\pv-tool\\dist\\pv_tool-0.2.7-py3-none-any.whl\"\n",
+ "!pip install \"{path_to_wheel}\""
+ ]
},
{
- "metadata": {},
"cell_type": "markdown",
+ "id": "6ddea397-c025-4f29-93ff-936bc26ff4b7",
+ "metadata": {},
"source": [
"## Stap 1: Inladen van benodigde data\n",
"\n",
"De benodigde data voor het bepalen van geotechnische parameters kan op 3 manieren worden ingeladen in deze notebook.\n",
- "- Op basis van de data in de excel versie van de proevenverzamelingtool (vanaf versie 4.2n of hoger) waarin zowel reeds proevenverzamelingen zijn onderscheiden als geotechnische parameters zijn vastgesteld. NB. de onderscheiden verzamelingen worden als uitgangspunt worden ingeladen, de sterkteparameters worden opnieuw berekend. Onderliggende keuzes zoals het gewenste rekpercentage en de gekozen raaklijnen worden niet ingelezen uit de Excelversie.\n",
+ "- Op basis van de data in de excel versie van de proevenverzamelingtool (vanaf versie 4.2n of hoger) waarin zowel reeds proevenverzamelingen zijn onderscheiden als geotechnische parameters zijn vastgesteld. NB. de onderscheiden verzamelingen worden als uitgangspunt worden ingeladen, de sterkteparameters worden opnieuw berekend. Onderliggende keuzes zoals het gewenste rekpercentage en de gekozen raaklijnen worden niet ingelezen uit de Excelversie. \n",
"- Op basis van het Excel Uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx waarin nog geen verzamelingen onderscheiden zijn.\n",
"\n",
"Zie tevens: https://github.com/kkpdata/Proevenverzamelingentool/\n",
"\n",
- "De proevenverzamelingtool of het uitwisselformat worden omgezet naar het template proevenverzamelingtool 5.0\n",
+ "De proevenverzamelingtool of het uitwisselformat worden omgezet naar het template proevenverzamelingtool 5.0 \n",
"\n",
"- Op basis van het Excel template voor de Proevenverzameling 5.0. Deze bestaat uit de data conform het uitwisselformat 4.2 aangevuld met de benodigde in en uitvoerdata van voorliggende tooling."
- ],
- "id": "4062aa452d2f9e29"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
+ "execution_count": 2,
+ "id": "6763641d-41d8-4046-a14f-745ff77b26d3",
+ "metadata": {},
"outputs": [],
+ "source": [
+ "# all imports\n",
+ "import importlib.util\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "from ipyfilechooser import FileChooser\n",
+ "from pathlib import Path\n",
+ "import os\n",
+ "\n",
+ "from pv_tool.imports.import_data import Dbase\n",
+ "from pv_tool.cphi_analysis.c_phi_analysis import CPhiAnalyse\n",
+ "from pv_tool.cphi_analysis.variables import *\n",
+ "\n",
+ "# widgetfuncties nog verder aanvullen!\n",
+ "from widgetfunctie_cphi import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "27ee1016-292f-43f6-8f49-f5000db7f162",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**openpyxl is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**ipyfilechooser is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**pandas_schema is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**xlsxwriter is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**ipywidgets is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**reportlab is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**reportlab is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**kaleido is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Controleert en installeer indien nodig\n",
+ "packages = ['openpyxl', 'ipyfilechooser', 'pandas_schema', 'xlsxwriter', 'ipywidgets', 'reportlab', 'reportlab', 'kaleido']\n",
+ "for package in packages:\n",
+ " check_package_install(package)"
+ ]
+ },
+ {
+ "cell_type": "code",
"execution_count": null,
+ "id": "05713100-659e-43ce-b3bc-5231e7d28045",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Data inladen\n",
+ "dropdown_template = create_import_dropdown()\n",
+ "file_chooser_import = create_file_chooser()\n",
+ "display(Markdown(\"**Stap 1: Kies Excel template voor uploaden data:**\"))\n",
+ "display(dropdown_template)\n",
+ "display(file_chooser_import)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "229ba24b-906d-49ac-b5a8-213bd644d22b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Stap 1: Kies Excel template voor uploaden data:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c3f3390e98c34aa68058fdc9de33b8ad",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(description='Template:', index=2, layout=Layout(width='400px'), options=('Proevenverzamelingtool 4.2n…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6492a9a343c64aa0b43a82fc83de917c",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "FileChooser(path='C:\\', filename='', title='Selecteer een bestand om te uploaden', show_hidden=False, select_d…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
"source": [
"# Laden van widgets voor uploaden bestanden\n",
"import ipywidgets as widgets\n",
@@ -103,14 +270,14 @@
"# Dropdown widget voor keuze template\n",
"dropdown_template = widgets.Dropdown(\n",
" options=[\n",
- " 'Proevenverzamelingtool 4.2n of hoger',\n",
- " 'STOWA uitwisselingsformat 4.2x',\n",
+ " 'Proevenverzamelingtool 4.2n of hoger', \n",
+ " 'STOWA uitwisselingsformat 4.2x', \n",
" 'Proevenverzamelingtool 5.0'\n",
" ], # De opties in het menu\n",
" value='Proevenverzamelingtool 5.0', # Standaard geselecteerde waarde\n",
" description='Template:',\n",
- " disabled=False,\n",
- " layout=widgets.Layout(width='400px')\n",
+ " disabled=False, \n",
+ " layout=widgets.Layout(width='400px') \n",
")\n",
"\n",
"# Maak een FileChooser-widget\n",
@@ -130,50 +297,91 @@
"display(file_chooser_import)\n",
"\n",
"print()"
- ],
- "id": "2041d8dede6a6e5a"
+ ]
},
{
- "metadata": {},
+ "attachments": {},
"cell_type": "markdown",
+ "id": "49c409f6-3e78-478e-8d55-e1d1a47cb7eb",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true
+ },
"source": [
"## Stap 2a: Importeren inclusief toevoegen/herberekenen analyse kolommen\n",
- "De importfunctie laadt de data in. Daarnaast worden analyse kolommen (ANA) toegevoegd die benodigd zijn voor de functies in \"shansep_analysis\" en \"sutabel_analysis\".\n",
+ "De importfunctie laadt de data in. Daarnaast worden analyse kolommen (ANA) toegevoegd die benodigd zijn voor de functies in \"shansep_analysis\" en \"sutabel_analysis\". \n",
"\n",
- "Hiervoor worden verschillende kolommen aangemaakt:\n",
+ "Hiervoor worden verschillende kolommen aangemaakt: \n",
"ANA_TERREINSPANNING, ANA_TXT_MAX_VERTICALE_CONSOLIDATIE_SPANNING, ANA_DSS_MAX_CONSOLIDATIE_SPANNING, ANA_TXT_CONSOLIDATIE_TYPE_VOORSTEL, ANA_TXT_CONSOLIDATIE_TYPE_HANDMATIG, ANA_TXT_CONSOLIDATIE_TYPE_REKEN, ANA_DSS_CONSOLIDATIE_TYPE_VOORSTEL, ANA_DSS_CONSOLIDATIE_TYPE_HANDMATIG\n",
"ANA_DSS_CONSOLIDATIE_TYPE_REKEN, ANA_GRENSSPANNING_PROEF, ANA_POP_VELD, ANA_POP_VELD_GEMIDDELD, ANA_GRENSSPANNING_VOORSTEL, ANA_GRENSSPANNING_HANDMATIG\n",
"ANA_GRENSSPANNING_REKEN, OCR_TXT, OCR_DSS.\n",
"\n",
"Er moet worden vastgesteld of een proef is uitgevoerd op een normaal geconsolideerd monster (NC) ruim boven de grensspanning of overconsolideerd (OC) bij de geschatte dagelijkse effectieve terreinspanning (OCR>1). De tooling doet hier een voorstel voor door de consolidatiespanning (sigma'vc) te vergelijken met de terreinspanning (sigma'vi). Indien deze niet meer dan +30% afwijkt wordt aangenomen dat het een OC-proef betreft en bij grotere afwijkingen een NC-proef. Het is noodzakelijk om dit voorstel te controleren. In de regel is een OC-proef vrijwel altijd dicht bij de terreinspanning gekozen en ligt een NC-proef er ruim boven, maar hier zijn altijd uitzonderingen op mogelijk.\n",
"\n",
- "Daarnaast is een schatting van de OCR benodigd. Om deze te berekenen is een schatting van de grensspanning benodigd. Indien er op dezelfde regel een resultaat beschikbaar is uit een samendrukkingsproef of CRS-proef wordt deze gebruikt. Indien deze ontbreekt wordt een voorstel gedaan op basis van de gemiddelde POP (POP = grensspanning - effectieve terreinspanning o.b.v. de monsters waar wel een proef is uitgevoerd ) per boring. Indien er bij de betreffende boring geen grensspanning beschikbaar is wordt geen waarde ingevuld. Het is dan niet mogelijk om geautomatiseerd een OCR te schatten.\n",
+ "Daarnaast is een schatting van de OCR benodigd. Om deze te berekenen is een schatting van de grensspanning benodigd. Indien er op dezelfde regel een resultaat beschikbaar is uit een samendrukkingsproef of CRS-proef wordt deze gebruikt. Indien deze ontbreekt wordt een voorstel gedaan op basis van de gemiddelde POP (POP = grensspanning - effectieve terreinspanning o.b.v. de monsters waar wel een proef is uitgevoerd ) per boring. Indien er bij de betreffende boring geen grensspanning beschikbaar is wordt geen waarde ingevuld. Het is dan niet mogelijk om geautomatiseerd een OCR te schatten. \n",
"De data wordt weggeschreven in de velden (ANA_GRENSSPANNING_VOORSTEL). Het is noodzakelijk om de voorgestelde waarden te controleren. Als gebruiker kan je het voorstel overrulen in de kolom (ANA_GRENSSPANNING_HANDMATIG).\n",
"\n",
"NB. Bij gebruik van de bestaande Excel versie van de proevenverzamelingtool zijn de cellen ANA_GRENSSPANNING_HANDMATIG veelal reeds ingevuld. In dat geval worden deze waarden overgenomen.\n"
- ],
- "id": "19b567b87c6ee36"
+ ]
},
{
- "metadata": {},
"cell_type": "markdown",
+ "id": "ae6f0418-c80a-4bef-b7d6-e30530e4ac3d",
+ "metadata": {},
"source": [
- "## Stap 2a: Valideren data\n",
+ "## Stap 2a: Valideren data \n",
"De validatie bestaat uit de volgende controles: voor de verschillende typen proeven – classificatie, CRS-proef, samendrukkingsproef, DSS-proef en triaxiaalproef – wordt per regel gecontroleerd of er een proef is uitgevoerd (data ingevuld) en zo ja, of de velden die in de PV-tool benodigd zijn volledig en correct zijn ingevuld.\n",
"\n",
- "Het resultaat van de validatie wordt opgeslagen in een twee Excel-bestanden Validation_log_##_critical_errors.xlsx en Validation_log_##_warnings.xlsx, waarin per gevalideerde kolom en regel het resultaat wordt weergegeven. Ir is per kolom aangegeven hoeveel fouten zijn aangetroffen. Er is onderscheid gemaakt tussen 'critical errors' en 'warnings'. De critical errors leiden tot fouten wanneer deze data wordt gebruikt bij het bepalen van de parameters. Deze dienen te worden gecorrigeerd of aangevuld. De warnings betreffen aanbevelingen. De resultaten worden weggeschreven in dezelfde directory als de importmap.\n",
+ "Het resultaat van de validatie wordt opgeslagen in een twee Excel-bestanden Validation_log_##_critical_errors.xlsx en Validation_log_##_warnings.xlsx, waarin per gevalideerde kolom en regel het resultaat wordt weergegeven. Ir is per kolom aangegeven hoeveel fouten zijn aangetroffen. Er is onderscheid gemaakt tussen 'critical errors' en 'warnings'. De critical errors leiden tot fouten wanneer deze data wordt gebruikt bij het bepalen van de parameters. Deze dienen te worden gecorrigeerd of aangevuld. De warnings betreffen aanbevelingen. De resultaten worden weggeschreven in dezelfde directory als de importmap. \n",
"\n",
- "Indien de validatie goed is doorlopen worden proevenverzamelingtool (vanaf versie 4.2n of hoger) of Uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx weggeschreven naar het Template_PVtool5_0.xlsx. Indien reeds gebruik is gemaakt van het nieuwe template worden de analyse kolommen opnieuw berekend. Dit is noodzakelijk indien er data gewijzigd is.\n",
+ "Indien de validatie goed is doorlopen worden proevenverzamelingtool (vanaf versie 4.2n of hoger) of Uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx weggeschreven naar het Template_PVtool5_0.xlsx. Indien reeds gebruik is gemaakt van het nieuwe template worden de analyse kolommen opnieuw berekend. Dit is noodzakelijk indien er data gewijzigd is. \n",
"\n",
"Indien er reeds een gevalideerde dataset beschikbaar is conform het Template_PVtool5_0 laadt de data dan in via stap 2b. Dit kost aanzienlijk minder rekentijd bij een grote dataset."
- ],
- "id": "f1f88d13ff53cdf"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
- "execution_count": null,
+ "execution_count": 6,
+ "id": "0afe3ee1-a4bd-4935-8c80-de311f3f0d47",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Je hebt gekozen: Proevenverzamelingtool 4.2n of hoger\n",
+ "Code in script: PV-tool\n",
+ "Geselecteerd bestand: SAFE 2022 Proevenverzameling_tool_v4.2n_test_zonder_functies.xlsm\n",
+ "Geselecteerde directory: C:\\Users\\deenekat7271\\Documents\\GitHub\\pv-tool\\import_files\n",
+ "Volledig pad: C:\\Users\\deenekat7271\\Documents\\GitHub\\pv-tool\\import_files\\SAFE 2022 Proevenverzameling_tool_v4.2n_test_zonder_functies.xlsm\n",
+ "number of critical_errors in validate_kenmerken_boring = 0\n",
+ "number of critical_errors in validate_clas = 0\n",
+ "number of critical_errors in validate_crs = 4\n",
+ "number of critical_errors in validate_samendrukking = 0\n",
+ "number of critical_errors in validate_monster = 0\n",
+ "number of critical_errors in validate_triaxiaal = 28\n",
+ "number of critical_errors in validate_dss = 17\n",
+ "number of warnings in validate_alg = 0\n",
+ "number of warnings in validate_kenmerken_boring = 353\n",
+ "number of warnings in validate_clas = 1843\n",
+ "number of warnings in validate_crs = 76\n",
+ "number of warnings in validate_samendrukking = 246\n",
+ "number of warnings in validate_monster = 0\n",
+ "number of warnings in validate_triaxiaal = 96\n",
+ "number of warnings in validate_dss = 33\n"
+ ]
+ },
+ {
+ "ename": "AttributeError",
+ "evalue": "'Dbase' object has no attribute 'export_dbase_to_excel'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 52\u001b[39m\n\u001b[32m 49\u001b[39m filename_export = file_chooser_import.selected_filename\n\u001b[32m 51\u001b[39m \u001b[38;5;66;03m#Export naar Template_PVtool5_0.xlsx \u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m52\u001b[39m \u001b[43mdbase\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexport_dbase_to_excel\u001b[49m(export_dir=dir2, filename=filename_export)\n",
+ "\u001b[31mAttributeError\u001b[39m: 'Dbase' object has no attribute 'export_dbase_to_excel'"
+ ]
+ }
+ ],
"source": [
"from pv_tool.imports.import_data import Dbase\n",
"from pathlib import Path\n",
@@ -213,10 +421,10 @@
"dir_in = (file_chooser_import.selected)\n",
"dir2 = Path(file_chooser_import.selected_path)\n",
"\n",
- "#starten importeren\n",
+ "#starten importeren \n",
"dbase.import_data(source = template_code, source_dir=dir_in)\n",
"\n",
- "#starten validatie\n",
+ "#starten validatie \n",
"dbase.validate_data(export_path=dir2)\n",
"\n",
"# Bepaal bestandsnaam voor de export van de data, bij PV4.2 of 4.2 uitwisselformat standaard export naar Template_PVtool5_0.xlsx\n",
@@ -225,26 +433,26 @@
"elif dropdown_template.value == 'Proevenverzamelingtool 5.0':\n",
" filename_export = file_chooser_import.selected_filename\n",
"\n",
- "#Export naar Template_PVtool5_0.xlsx\n",
+ "#Export naar Template_PVtool5_0.xlsx \n",
"dbase.export_dbase_to_excel(export_dir=dir2, filename=filename_export)"
- ],
- "id": "f0836be7c7db1a3e"
+ ]
},
{
- "metadata": {},
"cell_type": "markdown",
+ "id": "b72b6fa4-5e92-4b90-a335-099a8058cfdd",
+ "metadata": {},
"source": [
"## Stap 2b: Importeren data zonder validatie of herberekening analysekolommen\n",
"\n",
- "De importfunctie laadt de data conform het Template_PVtool5_0. Deze functie mag alleen gebruikt worden indien er reeds een gevalideerde dataset beschikbaar is conform het Template_PVtool5_0. Na het aanbrengen van wijzigingen in de data altijd stap 2a toepassen voor het importeren. Dit is noodzakelijk omdat de analsyekolommen benodigd voor onderstaande functies opnieuw berekend moeten worden bij aanpassingen aan de data."
- ],
- "id": "7b9ab9203867511f"
+ "De importfunctie laadt de data conform het Template_PVtool5_0. Deze functie mag alleen gebruikt worden indien er reeds een gevalideerde dataset beschikbaar is conform het Template_PVtool5_0. Na het aanbrengen van wijzigingen in de data altijd stap 2a toepassen voor het importeren. Dit is noodzakelijk omdat de analsyekolommen benodigd voor onderstaande functies opnieuw berekend moeten worden bij aanpassingen aan de data. "
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
+ "id": "cfab8ba1-c3c7-4711-85f3-11f26d3db623",
+ "metadata": {},
+ "outputs": [],
"source": [
"from pv_tool.imports.import_data import Dbase\n",
"from pathlib import Path\n",
@@ -280,32 +488,32 @@
"\n",
"# Start importeren zonder validatie\n",
"dbase.import_dbase_short(source=template_code, source_dir=full_path)"
- ],
- "id": "f32dbbcee31ac05b"
+ ]
},
{
- "metadata": {},
"cell_type": "markdown",
+ "id": "5c3a7976-c5b9-479d-8947-5553e7e40b9a",
+ "metadata": {},
"source": [
"## Stap 4a: Bepalen gedraineerde parameters triaxiaalproeven en DSS-proeven op basis van fit (C en Phi)\n",
"\n",
- "In deze stap worden op basis van de data vastgelegd in het template PV-tool 5.0. gedraineerde parameters bepaald. Maak een keuze uit de verzameling waarop de statistische analyse moet plaatsvinden. Als er nog geen aparte verzamelingen onderscheiden zijn in het veld (PV_NAAM) worden alle triaxaalproeven in één verzameling opgenomen.\n",
+ "In deze stap worden op basis van de data vastgelegd in het template PV-tool 5.0. gedraineerde parameters bepaald. Maak een keuze uit de verzameling waarop de statistische analyse moet plaatsvinden. Als er nog geen aparte verzamelingen onderscheiden zijn in het veld (PV_NAAM) worden alle triaxaalproeven in één verzameling opgenomen. \n",
"\n",
"Als gebruiker kunnen handmatig verzamelingen worden opgegeven in de Excel template in het veld PV_NAAM of er kan gebruik worden gemaakt van de functie:\n",
"Definiëren en aanpassen van verzamelingen (stap #). Met behulp van de functie ## kunnen in de grafiek met behulp van de selectietool (should have) ook één of meer proefresultaten worden geselecteerd en worden toegekend aan een andere groep.\n",
"\n",
- "Geef het te hanteren rekpercentage op waarbij de s' en t zijn bepaald: 2%, 5%, 15%, eindrek of bij pieksterkte.\n",
+ "Geef het te hanteren rekpercentage op waarbij de s' en t zijn bepaald: 2%, 5%, 15%, eindrek of bij pieksterkte. \n",
"Indien er eerder parameters zijn vastgesteld worden de gemaakte keuzes voor het % en de raaklijnen ingeladen.\n",
"\n",
"Ook kunnen meerdere verzamelingen getoond worden. NB. Deze extra verzamelingen worden niet meegenomen in de statistische analyse."
- ],
- "id": "6b3e28750d15bed9"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
+ "id": "844c1507-d97a-4bf5-b162-0c8c395845a4",
+ "metadata": {},
+ "outputs": [],
"source": [
"import ipywidgets as widgets\n",
"from IPython.display import display, Markdown, clear_output\n",
@@ -330,9 +538,9 @@
"output_rekpercentage = widgets.Output()\n",
"\n",
"# Lijsten rekpercentage en verzamelingen aanpassen bij wijziging van proeftype txt/dss\n",
- "def update_verzamelings_dropdowns(change):\n",
- " with container_rekpercentage:\n",
- " clear_output();\n",
+ "def update_verzamelings_dropdowns(change): \n",
+ " with container_rekpercentage: \n",
+ " clear_output(); \n",
" (dropdown_verzameling.options, dropdown_verzameling.value, multi_select_verzameling.options, multi_select_verzameling.value, _d) = \\\n",
" (PV_txt_lijst, PV_txt_lijst[0], PV_txt_lijst, [PV_txt_lijst[0]], display(dropdown_rekpercentage_txt)) if dropdown_type_proef.value == 'TXT_CPhi' \\\n",
" else (PV_dss_lijst, PV_dss_lijst[0], PV_dss_lijst, [PV_dss_lijst[0]], display(dropdown_rekpercentage_dss))\n",
@@ -368,14 +576,14 @@
"display(output_rekpercentage)\n",
"display(Markdown(\"**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**\"))\n",
"display(multi_select_verzameling)"
- ],
- "id": "510521965ad86c5e"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
+ "id": "add5e012-d3cd-4e3c-a5d0-8bf1e091bebf",
+ "metadata": {},
+ "outputs": [],
"source": [
"#widget inladen voor opgeven parameters bepalen cohesie en hoek van inwendige wrijving\n",
"\n",
@@ -414,8 +622,8 @@
"#laatste_resultaten = analyse.get_previous_results(path=str(dir2), file_name=filename_export )\n",
"laatste_resultaten = analyse.get_previous_results(path=str(dir2) )\n",
"\n",
- "if (laatste_resultaten is not None and\n",
- " not laatste_resultaten.empty and\n",
+ "if (laatste_resultaten is not None and \n",
+ " not laatste_resultaten.empty and \n",
" 'PV_A1_COH_GEM [kPa]' in laatste_resultaten):\n",
" PV_A1_COH_GEM_handmatig = [laatste_resultaten['PV_A1_COH_GEM [kPa]']]\n",
" PV_A2_PHI_KAR_handmatig = [laatste_resultaten['PV_A2_TAN_PHI_KAR [-]']]\n",
@@ -453,22 +661,24 @@
" dropdown_verzameling,\n",
" gekozen_rekpercentage\n",
")"
- ],
- "id": "4739039b5805463"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
- "source": "print (dir2)",
- "id": "e07888b336325a67"
+ "id": "aa6474ce-ff88-4f15-b0b1-3650f9140927",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print (dir2)"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
+ "id": "98d76aef-5480-47fa-921c-e643799a1b77",
+ "metadata": {},
+ "outputs": [],
"source": [
"#voer C-phi analyse uit met invoer en toon grafieken\n",
"if dropdown_type_proef.value.startswith('TXT'):\n",
@@ -486,14 +696,14 @@
"analyse.show_figure(plot_spanningspaden=True)\n",
"\n",
"analyse.print_short_results()"
- ],
- "id": "52e61c68f0c07459"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
+ "id": "e9b57eef-764e-4a4f-84d3-c185663e395f",
+ "metadata": {},
+ "outputs": [],
"source": [
"# Resultaten opslaan naar: ##_PVtool5_0.xlsx, pdf en exporteer tussenstappen naar excel\n",
"import os\n",
@@ -503,7 +713,7 @@
"os.makedirs(output_path1, exist_ok=True)\n",
"\n",
"# Sla resultaten op als PDF\n",
- "analyse.save_to_pdf(path=output_path1)\n",
+ "analyse.save_to_pdf(path=output_path1) \n",
"\n",
"# schrijf invoer en resultaten naar PV_template, tabblad resultaten c-phi.\n",
"analyse.add_results_to_dbase(path=dir2, file_name=filename_export)\n",
@@ -512,52 +722,52 @@
"bestand_naam = os.path.join(output_path1, \"c_phi_data_geforceerd.xlsx\")\n",
"analyse.cphi_analyses_data_df.to_excel(bestand_naam, index=False)\n",
"\n",
- "#Plotly bestand maken\n",
+ "#Plotly bestand maken \n",
"fig_s_t = analyse.figure # of analyse.fig als die bestaat\n",
"bestandsnaam = dropdown_verzameling.value.replace(' ', '_') + '.html'\n",
"output_path = os.path.join(output_path1, bestandsnaam)\n",
"fig_s_t.write_html(output_path)\n",
"print(f\"Figuur opgeslagen als {output_path}\")\n"
- ],
- "id": "b7f1de4bfeb6f4c0"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
- "source": "",
- "id": "e9f78b1a21eb73b4"
+ "id": "931d2008-a079-4586-ba22-e2a034e40375",
+ "metadata": {},
+ "outputs": [],
+ "source": []
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
- "source": "",
- "id": "1fdc22653874abf8"
+ "id": "bf00389b-3e9c-4c04-880c-31b281915c72",
+ "metadata": {},
+ "outputs": [],
+ "source": []
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
- "source": "",
- "id": "7a27665ca3c54823"
+ "id": "aefecf81-4b57-40f9-b26b-a736db27cae2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
- "source": "",
- "id": "ceabef6342602f20"
+ "id": "12800281-f317-42ed-85e0-5069bfa16c35",
+ "metadata": {},
+ "outputs": [],
+ "source": []
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
+ "id": "6c17d7b6-68c4-4c44-bc9b-a4b54b962c33",
+ "metadata": {},
+ "outputs": [],
"source": [
"# work in progress widget voor Shansep\n",
"\n",
@@ -653,22 +863,22 @@
"# Toon de tabelwidget\n",
"display(Markdown(\"**Opgeven gemiddelde waarden (fit):**\"))\n",
"display(grid)"
- ],
- "id": "66ee44bf82d6c3b8"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
- "source": "",
- "id": "a0eb979d47102481"
+ "id": "b270d822-cd07-480f-a21c-0eb91ab783be",
+ "metadata": {},
+ "outputs": [],
+ "source": []
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
"execution_count": null,
+ "id": "63a7463c-b81d-415b-aec3-d329f2a51945",
+ "metadata": {},
+ "outputs": [],
"source": [
"# Laden van widgets voor keuzes afleiden gedraineerde parameters\n",
"import ipywidgets as widgets\n",
@@ -681,7 +891,7 @@
"PV_txt_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__TRIAXIAAL'], 'PV_NAAM'].dropna().unique().tolist()\n",
"PV_dss_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__DSS'], 'PV_NAAM'].dropna().unique().tolist()\n",
"\n",
- "#### Widgets\n",
+ "#### Widgets \n",
"# # Dropdown widget voor rek%\n",
"dropdown_rekpercentage = widgets.Dropdown(options=['2% rek', '5% rek', '15% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
"\n",
@@ -701,9 +911,8 @@
"display(Markdown(\"**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**\"))\n",
"display(multi_select_verzameling)\n",
"\n",
- "print()\n"
- ],
- "id": "37a0b53deefd214a"
+ "print()"
+ ]
}
],
"metadata": {
@@ -722,7 +931,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.11"
+ "version": "3.13.9"
}
},
"nbformat": 4,
diff --git a/PV_tool0.5 - backup.ipynb b/PV_tool0.5 - backup.ipynb
new file mode 100644
index 0000000..c9d872d
--- /dev/null
+++ b/PV_tool0.5 - backup.ipynb
@@ -0,0 +1,15785 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5b519e9d-47fb-4aaf-88e6-ea99037b66ce",
+ "metadata": {},
+ "source": [
+ "STOWA Proevenverzameling tool v5.0
\n",
+ "\n",
+ "Deze Jupyter notebook bevat functies opgenomen in de python package PV-tooling ### voor het opstellen van lokale of regionale proevenverzamelingen voor het bepalen van geotechnische parameters. De methode is ontwikkeld voor het uitvoeren van analyses in relatie tot de geotechnische stabiliteit van dijken, maar kan ook breder worden toegepast. De notebook dient tevens als handleiding om de gebruiker stapsgewijs te ondersteunen bij het opstellen van een proevenverzameling.\n",
+ "\n",
+ "Met de beschikbare functies kunnen zowel gedraineerde als ongedraineerde sterkteparameters worden berekend alsmede enkele samendrukkingsparameters. Van deze parameters worden verwachtingswaarde, karakteristieke waarde en rekenwaarde bepaald. \n",
+ "\n",
+ "De functies zijn opgesteld conform de werkwijze beschreven in [Statistische methoden t.b.v. proevenverzamelingen, DIV, v1.0], zie tevens: https://publicwiki.deltares.nl/spaces/HWBPMacro/pages/217120830/Sterkte+van+grond#Sterktevangrond-150.\n",
+ "De tool bevat tevens hulpmiddelen voor het onderscheiden of samenvoegen van groepen in een verzameling op basis van verschillende kenmerken. \n",
+ "\n",
+ "De onderliggende data om een proevenverzameling samen te stellen is beschreven in een vaste structuur. Deze structuur is vastgelegd in een uitwisselformat. Het uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx. De geotechnische laboratoria kennen deze database en kunnen deze database vullen met resultaten van grond- en laboratoriumonderzoek. Op deze wijze ontstaat er uniformering op het gebied van data-uitwisseling en –opslag van proefresultaten.\n",
+ "\n",
+ "NB. De verantwoordelijkheid voor het gebruik van deze tools ligt bij de gebruiker."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "424ad52e-54df-4a4d-8546-36eb585eb7a8",
+ "metadata": {},
+ "source": [
+ "## Benodigde installaties"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e3612aae-c928-4b18-b531-328810c8acc1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Importeren van benodigde package\n",
+ "# Na het eerte keer installeren van de benodigde packages de kernel handmatig opnieuw optarten via het menu Kernel → Restart\n",
+ "\n",
+ "path_to_wheel = r\"C:\\Users\\deenekat7271\\Documents\\GitHub\\pv-tool\\dist\\pv_tool-0.2.7-py3-none-any.whl\"\n",
+ "!pip install \"{path_to_wheel}\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6ddea397-c025-4f29-93ff-936bc26ff4b7",
+ "metadata": {},
+ "source": [
+ "## Stap 1: Inladen van benodigde data\n",
+ "\n",
+ "De benodigde data voor het bepalen van geotechnische parameters kan op 3 manieren worden ingeladen in deze notebook.\n",
+ "- Op basis van de data in de excel versie van de proevenverzamelingtool (vanaf versie 4.2n of hoger) waarin zowel reeds proevenverzamelingen zijn onderscheiden als geotechnische parameters zijn vastgesteld. NB. de onderscheiden verzamelingen worden als uitgangspunt worden ingeladen, de sterkteparameters worden opnieuw berekend. Onderliggende keuzes zoals het gewenste rekpercentage en de gekozen raaklijnen worden niet ingelezen uit de Excelversie. \n",
+ "- Op basis van het Excel Uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx waarin nog geen verzamelingen onderscheiden zijn.\n",
+ "\n",
+ "Zie tevens: https://github.com/kkpdata/Proevenverzamelingentool/\n",
+ "\n",
+ "De proevenverzamelingtool of het uitwisselformat worden omgezet naar het template proevenverzamelingtool 5.0 opgenomen in pv_tool\\templates.\n",
+ "\n",
+ "- Op basis van het Excel template voor de Proevenverzameling 5.0. Deze bestaat uit de data conform het uitwisselformat 4.2 aangevuld met de benodigde invoerdata van voorliggende tooling. De uitvoer wordt in het vervolg weggeschreven in 3 aparte tabbladen: [Resultaten c-phi], [Resultaten SHANSEP] en [Resultaten SU-tabel-m]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6763641d-41d8-4046-a14f-745ff77b26d3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# all imports\n",
+ "import importlib.util\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "from ipyfilechooser import FileChooser\n",
+ "from pathlib import Path\n",
+ "import os\n",
+ "\n",
+ "from pv_tool.imports.import_data import Dbase\n",
+ "from pv_tool.cphi_analysis.c_phi_analysis import CPhiAnalyse\n",
+ "from pv_tool.cphi_analysis.variables import *\n",
+ "\n",
+ "# widgetfuncties nog verder aanvullen!\n",
+ "from widgetfunctie_cphi import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "27ee1016-292f-43f6-8f49-f5000db7f162",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**openpyxl is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**ipyfilechooser is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**pandas_schema is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**xlsxwriter is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**ipywidgets is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**reportlab is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**reportlab is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**kaleido is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Controleert en installeer indien nodig\n",
+ "packages = ['openpyxl', 'ipyfilechooser', 'pandas_schema', 'xlsxwriter', 'ipywidgets', 'reportlab', 'reportlab', 'kaleido']\n",
+ "for package in packages:\n",
+ "\n",
+ " check_package_install(package)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "05713100-659e-43ce-b3bc-5231e7d28045",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "229ba24b-906d-49ac-b5a8-213bd644d22b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Stap 1: Kies Excel template voor uploaden data:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(description='Template:', index=2, layout=Layout(width='400px'), options=('Proevenverzamelingtool 4.2n…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2026fbba85bc4ce8a7d9645509b4f122",
+ "version_major": 2,
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+ },
+ "text/plain": [
+ "FileChooser(path='C:\\', filename='', title='Selecteer een bestand om te uploaden', show_hidden=False, select_d…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
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+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Laden van widgets voor uploaden bestanden\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "from ipyfilechooser import FileChooser\n",
+ "\n",
+ "# Dropdown widget voor keuze template\n",
+ "dropdown_template = widgets.Dropdown(\n",
+ " options=[\n",
+ " 'Proevenverzamelingtool 4.2n of hoger', \n",
+ " 'STOWA uitwisselingsformat 4.2x', \n",
+ " 'Proevenverzamelingtool 5.0'\n",
+ " ], # De opties in het menu\n",
+ " value='Proevenverzamelingtool 5.0', # Standaard geselecteerde waarde\n",
+ " description='Template:',\n",
+ " disabled=False, \n",
+ " layout=widgets.Layout(width='400px') \n",
+ ")\n",
+ "\n",
+ "# Maak een FileChooser-widget\n",
+ "file_chooser_import = FileChooser(\n",
+ " '/',\n",
+ " title=\"Selecteer een bestand om te uploaden\",\n",
+ " show_hidden=False # Verberg verborgen bestanden\n",
+ ")\n",
+ "\n",
+ "#Titel\n",
+ "display(Markdown(\"**Stap 1: Kies Excel template voor uploaden data:**\"))\n",
+ "\n",
+ "# Toon de dropdown widget keuze template\n",
+ "display(dropdown_template)\n",
+ "\n",
+ "# Toon widget voor keuze uploaden bestand\n",
+ "display(file_chooser_import)\n",
+ "\n",
+ "print()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27b344b2-e7af-4404-80f4-6ae20e8cc688",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "49c409f6-3e78-478e-8d55-e1d1a47cb7eb",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true
+ },
+ "source": [
+ "## Stap 2a: Importeren inclusief toevoegen/herberekenen analyse kolommen\n",
+ "De importfunctie laadt de data in. Daarnaast worden analyse kolommen (ANA) toegevoegd die benodigd zijn voor de functies in \"shansep_analysis\" en \"sutabel_analysis\". \n",
+ "\n",
+ "Hiervoor worden verschillende kolommen aangemaakt: \n",
+ "ANA_TERREINSPANNING, ANA_TXT_MAX_VERTICALE_CONSOLIDATIE_SPANNING, ANA_DSS_MAX_CONSOLIDATIE_SPANNING, ANA_TXT_CONSOLIDATIE_TYPE_VOORSTEL, ANA_TXT_CONSOLIDATIE_TYPE_HANDMATIG, ANA_TXT_CONSOLIDATIE_TYPE_REKEN, ANA_DSS_CONSOLIDATIE_TYPE_VOORSTEL, ANA_DSS_CONSOLIDATIE_TYPE_HANDMATIG\n",
+ "ANA_DSS_CONSOLIDATIE_TYPE_REKEN, ANA_GRENSSPANNING_PROEF, ANA_POP_VELD, ANA_POP_VELD_GEMIDDELD, ANA_GRENSSPANNING_VOORSTEL, ANA_GRENSSPANNING_HANDMATIG\n",
+ "ANA_GRENSSPANNING_REKEN, OCR_TXT, OCR_DSS.\n",
+ "\n",
+ "Er moet worden vastgesteld of een proef is uitgevoerd op een normaal geconsolideerd monster (NC) ruim boven de grensspanning of overconsolideerd (OC) bij de geschatte dagelijkse effectieve terreinspanning (OCR>1). De tooling doet hier een voorstel voor door de consolidatiespanning (sigma'vc) te vergelijken met de terreinspanning (sigma'vi). Indien deze niet meer dan +30% afwijkt wordt aangenomen dat het een OC-proef betreft en bij grotere afwijkingen een NC-proef. Het is noodzakelijk om dit voorstel te controleren. In de regel is een OC-proef vrijwel altijd dicht bij de terreinspanning gekozen en ligt een NC-proef er ruim boven, maar hier zijn altijd uitzonderingen op mogelijk.\n",
+ "\n",
+ "Daarnaast is een schatting van de OCR benodigd. Om deze te berekenen is een schatting van de grensspanning benodigd. Indien er op dezelfde regel een resultaat beschikbaar is uit een samendrukkingsproef of CRS-proef wordt deze gebruikt. Indien deze ontbreekt wordt een voorstel gedaan op basis van de gemiddelde POP (POP = grensspanning - effectieve terreinspanning o.b.v. de monsters waar wel een proef is uitgevoerd ) per boring. Indien er bij de betreffende boring geen grensspanning beschikbaar is wordt geen waarde ingevuld. Het is dan niet mogelijk om geautomatiseerd een OCR te schatten. \n",
+ "De data wordt weggeschreven in de velden (ANA_GRENSSPANNING_VOORSTEL). Het is noodzakelijk om de voorgestelde waarden te controleren. Als gebruiker kan je het voorstel overrulen in de kolom (ANA_GRENSSPANNING_HANDMATIG).\n",
+ "\n",
+ "NB. Bij gebruik van de bestaande Excel versie van de proevenverzamelingtool zijn de cellen ANA_GRENSSPANNING_HANDMATIG veelal reeds ingevuld. In dat geval worden deze waarden overgenomen.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae6f0418-c80a-4bef-b7d6-e30530e4ac3d",
+ "metadata": {},
+ "source": [
+ "## Stap 2a: Valideren data \n",
+ "De validatie bestaat uit de volgende controles: voor de verschillende typen proeven – classificatie, CRS-proef, samendrukkingsproef, DSS-proef en triaxiaalproef – wordt per regel gecontroleerd of er een proef is uitgevoerd (data ingevuld) en zo ja, of de velden die in de PV-tool benodigd zijn volledig en correct zijn ingevuld.\n",
+ "\n",
+ "Het resultaat van de validatie wordt opgeslagen in een twee Excel-bestanden Validation_log_##_critical_errors.xlsx en Validation_log_##_warnings.xlsx, waarin per gevalideerde kolom en regel het resultaat wordt weergegeven. Ir is per kolom aangegeven hoeveel fouten zijn aangetroffen. Er is onderscheid gemaakt tussen 'critical errors' en 'warnings'. De critical errors leiden tot fouten wanneer deze data wordt gebruikt bij het bepalen van de parameters. Deze dienen te worden gecorrigeerd of aangevuld. De warnings betreffen aanbevelingen. De resultaten worden weggeschreven in dezelfde directory als de importmap. \n",
+ "\n",
+ "Indien de validatie goed is doorlopen worden proevenverzamelingtool (vanaf versie 4.2n of hoger) of Uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx weggeschreven naar het Template_PVtool5_0.xlsx. Indien reeds gebruik is gemaakt van het nieuwe template worden de analyse kolommen opnieuw berekend. Dit is noodzakelijk indien er data gewijzigd is. \n",
+ "\n",
+ "Indien er reeds een gevalideerde dataset beschikbaar is conform het Template_PVtool5_0 laadt de data dan in via stap 2b. Dit kost aanzienlijk minder rekentijd bij een grote dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0afe3ee1-a4bd-4935-8c80-de311f3f0d47",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Je hebt gekozen: Proevenverzamelingtool 5.0\n",
+ "Code in script: Dbase\n",
+ "Geselecteerd bestand: WSRL 2025 PVtool5_0_gevalideerd.xlsx\n",
+ "Geselecteerde directory: C:\\python_cursus\\PVtool2025_github\\import_files\n",
+ "Volledig pad: C:\\python_cursus\\PVtool2025_github\\import_files\\WSRL 2025 PVtool5_0_gevalideerd.xlsx\n",
+ "number of critical_errors in validate_kenmerken_boring = 63\n",
+ "number of critical_errors in validate_clas = 0\n",
+ "number of critical_errors in validate_crs = 3\n",
+ "number of critical_errors in validate_samendrukking = 0\n",
+ "number of critical_errors in validate_monster = 0\n",
+ "number of critical_errors in validate_triaxiaal = 0\n",
+ "number of critical_errors in validate_dss = 0\n",
+ "number of warnings in validate_alg = 922\n",
+ "number of warnings in validate_kenmerken_boring = 643\n",
+ "number of warnings in validate_clas = 2140\n",
+ "number of warnings in validate_crs = 33\n",
+ "number of warnings in validate_samendrukking = 539\n",
+ "number of warnings in validate_monster = 0\n",
+ "number of warnings in validate_triaxiaal = 198\n",
+ "number of warnings in validate_dss = 86\n",
+ "DataFrame naar template geëxporteerd in C:\\python_cursus\\PVtool2025_github\\import_files\\WSRL 2025 PVtool5_0_gevalideerd.xlsx\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pv_tool.imports.import_data import Dbase\n",
+ "from pathlib import Path\n",
+ "dbase = Dbase()\n",
+ "\n",
+ "# Controleer dropdown template en geef de gekozen waarde terug\n",
+ "if dropdown_template.value:\n",
+ " print(f'Je hebt gekozen: {dropdown_template.value}')\n",
+ "else:\n",
+ " print(\"Er is geen template geselecteerd.\")\n",
+ "\n",
+ "# bepaal source voor template\n",
+ "template_mapping = {\n",
+ " \"Proevenverzamelingtool 4.2n of hoger\" : \"PV-tool\",\n",
+ " \"STOWA uitwisselingsformat 4.2x\" : \"Stowa\",\n",
+ " \"Proevenverzamelingtool 5.0\" : \"Dbase\"\n",
+ "}\n",
+ "template_code = template_mapping.get(dropdown_template.value, \"Onbekend template\")\n",
+ "print(f\"Code in script: {template_code}\")\n",
+ "\n",
+ "# Controleer of een bestand is geselecteerd\n",
+ "if file_chooser_import.selected:\n",
+ " # Verkrijg het volledige pad van het geselecteerde bestand\n",
+ " full_path = file_chooser_import.selected\n",
+ " # Verkrijg de directory en bestandsnaam\n",
+ " selected_directory = file_chooser_import.selected_path\n",
+ " filename = file_chooser_import.selected_filename\n",
+ "\n",
+ " # Print de resultaten\n",
+ " print(f\"Geselecteerd bestand: {filename}\")\n",
+ " print(f\"Geselecteerde directory: {selected_directory}\")\n",
+ " print(f\"Volledig pad: {full_path}\")\n",
+ "else:\n",
+ " print(\"Er is geen bestand geselecteerd. Selecteer een bestand voordat je verder gaat.\")\n",
+ "\n",
+ "#import dir en bestandsnaam en export directory\n",
+ "dir_in = (file_chooser_import.selected)\n",
+ "dir2 = Path(file_chooser_import.selected_path)\n",
+ "\n",
+ "#starten importeren \n",
+ "dbase.import_data(source = template_code, source_dir=dir_in)\n",
+ "\n",
+ "#starten validatie \n",
+ "dbase.validate_data(export_path=dir2)\n",
+ "\n",
+ "# Bepaal bestandsnaam voor de export van de data, bij PV4.2 of 4.2 uitwisselformat standaard export naar Template_PVtool5_0.xlsx\n",
+ "if dropdown_template.value in ['Proevenverzamelingtool 4.2n of hoger', 'STOWA uitwisselingsformat 4.2x']:\n",
+ " filename_export = 'Template_PVtool5_0.xlsx'\n",
+ "elif dropdown_template.value == 'Proevenverzamelingtool 5.0':\n",
+ " filename_export = file_chooser_import.selected_filename\n",
+ "\n",
+ "#Export naar Template_PVtool5_0.xlsx \n",
+ "dbase.export_dbase_to_template(export_dir=dir2, export_name=filename_export)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b72b6fa4-5e92-4b90-a335-099a8058cfdd",
+ "metadata": {},
+ "source": [
+ "## Stap 2b: Importeren data zonder validatie of herberekening analysekolommen\n",
+ "\n",
+ "De importfunctie laadt de data conform het Template_PVtool5_0. Deze functie mag alleen gebruikt worden indien er reeds een gevalideerde dataset beschikbaar is conform het Template_PVtool5_0. Na het aanbrengen van wijzigingen in de data altijd stap 2a toepassen voor het importeren. Dit is noodzakelijk omdat de analsyekolommen benodigd voor onderstaande functies opnieuw berekend moeten worden bij aanpassingen aan de data. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "cfab8ba1-c3c7-4711-85f3-11f26d3db623",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Importeren: C:\\python_cursus\\PVtool2025_github\\import_files\\WSRL 2025 PVtool5_0_gevalideerd.xlsx\n"
+ ]
+ },
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#voer C-phi analyse uit met invoer en toon grafieken\n",
+ "if dropdown_type_proef.value.startswith('TXT'):\n",
+ " rekpercentage = dropdown_rekpercentage_txt.value\n",
+ "else:\n",
+ " rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "analyse = CPhiAnalyse(dbase=dbase, investigation_groups=[dropdown_verzameling.value], effective_stress= rekpercentage, analysis_type=dropdown_type_proef.value)\n",
+ "analyse.apply_parameters(cohesie_gem = PV_A1_COH_GEM_handmatig[0], phi_kar = PV_A2_PHI_KAR_handmatig[0], cohesie_kar = PV_A1_COH_KAR_handmatig[0])\n",
+ "analyse.apply_settings(material_factor_cohesion = PV_PARTCOH[0], material_factor_tan_phi = PV_PARTPHI[0], alpha = PV_TYPEVERZAMELING[0])\n",
+ "\n",
+ "#Toon resultaten figuur en tabel met cohesie en hoek van inwendige wrijving\n",
+ "output_df = analyse.get_short_results()\n",
+ "print(output_df)\n",
+ "extra_verzamelingen_tonen = list(multi_select_verzameling.value)\n",
+ "analyse.show_figure(plot_extra_dataset= extra_verzamelingen_tonen )\n",
+ "analyse.show_figure(plot_spanningspaden=True)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e9b57eef-764e-4a4f-84d3-c185663e395f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PDF succesvol opgeslagen op: C:\\python_cursus\\PVtool2025_github\\resultaten/c_phi_pdf_export_TXT_klei_14_16_TXT_CPhi_eindsterkte.pdf\n",
+ "Resultaat toegevoegd aan template in tabblad 'Resultaten c-phi'.\n",
+ "Figuur opgeslagen als C:\\python_cursus\\PVtool2025_github\\resultaten\\TXT_klei_14_16.html\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Resultaten opslaan naar: ##_PVtool5_0.xlsx, pdf en exporteer tussenstappen naar excel\n",
+ "import os\n",
+ "\n",
+ "#directory om resultaten weg te schrijven\n",
+ "output_path1 = os.path.join(os.getcwd(), \"resultaten\")\n",
+ "os.makedirs(output_path1, exist_ok=True)\n",
+ "\n",
+ "# Sla resultaten op als PDF\n",
+ "analyse.save_to_pdf(path=output_path1) \n",
+ "\n",
+ "# schrijf invoer en resultaten naar PV_template, tabblad resultaten c-phi.\n",
+ "analyse.add_results_to_template(path=dir2, export_name=filename_export)\n",
+ "\n",
+ "# Opslaan tussenresultaten naar Excel\n",
+ "bestand_naam = os.path.join(output_path1, \"c_phi_data_geforceerd.xlsx\")\n",
+ "analyse.cphi_analyses_data_df.to_excel(bestand_naam, index=False)\n",
+ "\n",
+ "#Plotly bestand maken \n",
+ "fig_s_t = analyse.figure # of analyse.fig als die bestaat\n",
+ "bestandsnaam = dropdown_verzameling.value.replace(' ', '_') + '.html'\n",
+ "output_path = os.path.join(output_path1, bestandsnaam)\n",
+ "fig_s_t.write_html(output_path)\n",
+ "print(f\"Figuur opgeslagen als {output_path}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "32cf9cd6-1186-4e75-ae7a-b58096a45f75",
+ "metadata": {},
+ "source": [
+ "## Stap 3b: Bepalen gedraineerde parameters triaxiaalproeven en DSS-proeven op basis van methode schematiseringshandleiding (alleen Phi)\n",
+ "\n",
+ "In deze stap worden op basis van de data vastgelegd in het template PV-tool 5.0. de gedraineerde parameters bepaald volgens de methode vastgelegd in de schematiseringshandleiding macrostabiliteit. Maak een keuze uit de verzameling waarop de statistische analyse moet plaatsvinden. Als er nog geen aparte verzamelingen onderscheiden zijn in het veld (PV_NAAM) worden alle triaxaalproeven in één verzameling opgenomen. \n",
+ "\n",
+ "Als gebruiker kunnen handmatig verzamelingen worden opgegeven in de Excel template in het veld PV_NAAM of er kan gebruik worden gemaakt van de functie:\n",
+ "Definiëren en aanpassen van verzamelingen (stap #). Met behulp van de functie ## kunnen in de grafiek met behulp van de selectietool (should have) ook één of meer proefresultaten worden geselecteerd en worden toegekend aan een andere groep.\n",
+ "\n",
+ "Geef het te hanteren rekpercentage op waarbij de s' en t zijn bepaald: 2%, 5%, 15%, eindrek of bij pieksterkte. \n",
+ "Indien er eerder parameters zijn vastgesteld worden de gemaakte keuzes voor het % en de raaklijnen ingeladen.\n",
+ "\n",
+ "Ook kunnen meerdere verzamelingen getoond worden. NB. Deze extra verzamelingen worden niet meegenomen in de statistische analyse."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "57d6cea9-32c3-4a02-b720-b9c4d4717fa6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies type proef:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "baf578a525fd435cb16adf68dd8620e8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('TXT_SH', 'DSS_SH'), value='TXT_SH')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies verzameling voor statistische analyse:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c88aae60428647c29b09f1ae1499bb4d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('geen', 'TXT_testset_klei', 'TXT', 'TXT_klei_17.5_21_ondiep', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies rekpercentage s'en t:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "134a8c1cce224cdd8e5688350ad3c477",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a430f47726ef4a5b9c58a6245e5971d4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9b589733599b4c42aed5edadee252cb5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "SelectMultiple(index=(0,), layout=Layout(height='150px', width='400px'), options=('geen', 'TXT_testset_klei', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown, clear_output\n",
+ "\n",
+ "# Dataframe met proefdata\n",
+ "dbase_df = dbase.dbase_df\n",
+ "\n",
+ "# Lijsten maken met proevenverzamelingen in database onder PVNAAM\n",
+ "PV_txt_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__TRIAXIAAL'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "PV_dss_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__DSS'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "gekozen_rekpercentage = 'eindsterkte'\n",
+ "\n",
+ "# Dropdown widgets\n",
+ "dropdown_type_proef_sh = widgets.Dropdown(options=['TXT_SH', 'DSS_SH'], value='TXT_SH', layout=widgets.Layout(width='400px'))\n",
+ "\n",
+ "\n",
+ "dropdown_rekpercentage_txt = widgets.Dropdown(options=['2% rek', '5% rek', '15% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_rekpercentage_dss = widgets.Dropdown(options=['2% rek', '5% rek', '10% rek', '15% rek', '20% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_verzameling = widgets.Dropdown(options=PV_txt_lijst, value=PV_txt_lijst[0] if PV_txt_lijst else None, layout=widgets.Layout(width='400px'))\n",
+ "multi_select_verzameling = widgets.SelectMultiple(options=PV_txt_lijst, value=[PV_txt_lijst[0]] if PV_txt_lijst else [], layout=widgets.Layout(width='400px', height='150px'))\n",
+ "\n",
+ "# Outputs\n",
+ "container_rekpercentage = widgets.Output()\n",
+ "output_rekpercentage = widgets.Output()\n",
+ "\n",
+ "# Lijsten rekpercentage en verzamelingen aanpassen bij wijziging van proeftype txt/dss\n",
+ "def update_verzamelings_dropdowns(change): \n",
+ " with container_rekpercentage: \n",
+ " clear_output(); \n",
+ " (dropdown_verzameling.options, dropdown_verzameling.value, multi_select_verzameling.options, multi_select_verzameling.value, _d) = \\\n",
+ " (PV_txt_lijst, PV_txt_lijst[0], PV_txt_lijst, [PV_txt_lijst[0]], display(dropdown_rekpercentage_txt)) if dropdown_type_proef.value == 'TXT_SH' \\\n",
+ " else (PV_dss_lijst, PV_dss_lijst[0], PV_dss_lijst, [PV_dss_lijst[0]], display(dropdown_rekpercentage_dss))\n",
+ " update_rekpercentage_output(None)\n",
+ "\n",
+ "def update_rekpercentage_output(change):\n",
+ " global gekozen_rekpercentage\n",
+ " with output_rekpercentage:\n",
+ " clear_output()\n",
+ " if dropdown_type_proef_sh.value == 'TXT_SH':\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_txt.value\n",
+ " else:\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "dropdown_type_proef_sh.observe(update_verzamelings_dropdowns, names='value')\n",
+ "dropdown_rekpercentage_txt.observe(update_rekpercentage_output, names='value')\n",
+ "dropdown_rekpercentage_dss.observe(update_rekpercentage_output, names='value')\n",
+ "\n",
+ "# Initialiseren container met juiste rekpercentage-dropdown\n",
+ "with container_rekpercentage:\n",
+ " clear_output()\n",
+ " display(dropdown_rekpercentage_txt)\n",
+ "with output_rekpercentage:\n",
+ " clear_output()\n",
+ "\n",
+ "# Display widgets\n",
+ "display(Markdown(\"**Kies type proef:**\"))\n",
+ "display(dropdown_type_proef_sh)\n",
+ "display(Markdown(\"**Kies verzameling voor statistische analyse:**\"))\n",
+ "display(dropdown_verzameling)\n",
+ "display(Markdown(\"**Kies rekpercentage s'en t:**\"))\n",
+ "display(container_rekpercentage)\n",
+ "display(output_rekpercentage)\n",
+ "display(Markdown(\"**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**\"))\n",
+ "display(multi_select_verzameling)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aefecf81-4b57-40f9-b26b-a736db27cae2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
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+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Resultaten TXT C-phi analyse (schematiseringshandleiding):\n",
+ " tan phi [-] phi [graden]\n",
+ "Verwachtingswaarde 0.779053 37.920473\n",
+ "Karakteristieke waarde 0.650774 33.055051\n",
+ "Rekenwaarde 0.650774 33.055051\n",
+ "Standaarddeviatie D-stability [-] 3.094335\n"
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+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "TXT_SH analyse met 5% rek op TXT_klei_14_16"
+ },
+ "width": 1280,
+ "xaxis": {
+ "title": {
+ "text": "s' [kPa]"
+ }
+ },
+ "yaxis": {
+ "title": {
+ "text": "t [kPa]"
+ }
+ }
+ }
+ },
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PDF succesvol opgeslagen op: C:\\python_cursus\\PVtool2025_github\\PV-tool\\resultaten/c_phi_pdf_export_TXT_klei_14_16_TXT_SH_5procent_rek.pdf\n",
+ "Resultaat toegevoegd aan template in tabblad 'Resultaten c-phi'.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Analyse hoek van inwendige wrijving uitvoeren methode schematiseringshandleiding\n",
+ "import os\n",
+ "\n",
+ "#directory om resultaten weg te schrijven\n",
+ "output_path1 = os.path.join(os.getcwd(), \"resultaten\")\n",
+ "os.makedirs(output_path1, exist_ok=True)\n",
+ "\n",
+ "#Run analyse bepaling hoek van inwendige wrijving \n",
+ "analyse = CPhiAnalyse(\n",
+ " dbase=dbase,\n",
+ " investigation_groups=[dropdown_verzameling.value],\n",
+ " effective_stress=rekpercentage,\n",
+ " analysis_type=dropdown_type_proef_sh.value\n",
+ ")\n",
+ "\n",
+ "# Pas instellingen toe\n",
+ "analyse.apply_settings(alpha=0.75)\n",
+ "\n",
+ "# Print en exporteer resultaten\n",
+ "print('\\nResultaten TXT C-phi analyse (schematiseringshandleiding):')\n",
+ "print(analyse.get_short_results())\n",
+ "\n",
+ "# Visualisatie\n",
+ "extra_verzamelingen_tonen = list(multi_select_verzameling.value)\n",
+ "analyse.show_figure(plot_extra_dataset = extra_verzamelingen_tonen, plot_spanningspaden= True)\n",
+ "analyse.save_to_pdf(path=str(output_path1))\n",
+ "\n",
+ "# Exporteer de data naar database results\n",
+ "analyse.add_results_to_template(path=dir2, export_name=filename_export)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9776bef6-6ce0-4a70-81a1-ddfb61597b18",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7901fc55-c958-4c9e-9b48-8f5dab183a2e",
+ "metadata": {},
+ "source": [
+ "## Stap 4a: Bepalen ongedraineerde parameters triaxiaalproeven en DSS-proeven op basis van methode Shansep (S, m en POP)\n",
+ "\n",
+ "In deze stap worden op basis van de data vastgelegd in het template PV-tool 5.0. de gedraineerde parameters bepaald volgens de methode vastgelegd in de schematiseringshandleiding macrostabiliteit. Maak een keuze uit de verzameling waarop de statistische analyse moet plaatsvinden. Als er nog geen aparte verzamelingen onderscheiden zijn in het veld (PV_NAAM) worden alle triaxaalproeven in één verzameling opgenomen. \n",
+ "\n",
+ "Als gebruiker kunnen handmatig verzamelingen worden opgegeven in de Excel template in het veld PV_NAAM of er kan gebruik worden gemaakt van de functie:\n",
+ "Definiëren en aanpassen van verzamelingen (stap #). Met behulp van de functie ## kunnen in de grafiek met behulp van de selectietool (should have) ook één of meer proefresultaten worden geselecteerd en worden toegekend aan een andere groep.\n",
+ "\n",
+ "Geef het te hanteren rekpercentage op waarbij de Su is bepaald: 2%, 5%, 15%, eindrek of bij pieksterkte. \n",
+ "Indien er eerder parameters zijn vastgesteld worden de gemaakte keuzes voor het % en de raaklijnen ingeladen.\n",
+ "\n",
+ "Ook kunnen meerdere verzamelingen getoond worden. NB. Deze extra verzamelingen worden niet meegenomen in de statistische analyse."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8bc17b38-c927-4cf2-9485-1e890de1b98c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "7b154111-f216-4a91-8956-575a22011b06",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies type proef:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6315d041777e41b1a1c6e93786bb4fc2",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('TXT_S_POP', 'DSS_S_POP'), value='TXT_S_POP')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies verzameling voor statistische analyse:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c148532534e64f7491e48412420264c8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('geen', 'TXT_testset_klei', 'TXT', 'TXT_klei_17.5_21_ondiep', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies rekpercentage Su:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "516c3d54b93e422b9966cb80d0632aec",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f345e9b2bd5045d6a41be2facfd83d70",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "6deb21b24aa9446bab93a4373816b506",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "SelectMultiple(index=(0,), layout=Layout(height='150px', width='400px'), options=('geen', 'TXT_testset_klei', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown, clear_output\n",
+ "\n",
+ "# Dataframe met proefdata\n",
+ "dbase_df = dbase.dbase_df\n",
+ "\n",
+ "# Lijsten maken met proevenverzamelingen in database onder PVNAAM\n",
+ "PV_txt_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__TRIAXIAAL'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "PV_dss_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__DSS'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "gekozen_rekpercentage = 'eindsterkte'\n",
+ "\n",
+ "# Dropdown widgets\n",
+ "dropdown_type_proef_shansep = widgets.Dropdown(options=['TXT_S_POP', 'DSS_S_POP'], value='TXT_S_POP', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_rekpercentage_txt = widgets.Dropdown(options=['2% rek', '5% rek', '15% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_rekpercentage_dss = widgets.Dropdown(options=['2% rek', '5% rek', '10% rek', '15% rek', '20% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_verzameling = widgets.Dropdown(options=PV_txt_lijst, value=PV_txt_lijst[0] if PV_txt_lijst else None, layout=widgets.Layout(width='400px'))\n",
+ "multi_select_verzameling = widgets.SelectMultiple(options=PV_txt_lijst, value=[PV_txt_lijst[0]] if PV_txt_lijst else [], layout=widgets.Layout(width='400px', height='150px'))\n",
+ "\n",
+ "# Outputs\n",
+ "container_rekpercentage = widgets.Output()\n",
+ "output_rekpercentage = widgets.Output()\n",
+ "\n",
+ "# Lijsten rekpercentage en verzamelingen aanpassen bij wijziging van proeftype txt/dss\n",
+ "def update_verzamelings_dropdowns(change): \n",
+ " with container_rekpercentage: \n",
+ " clear_output(); \n",
+ " (dropdown_verzameling.options, dropdown_verzameling.value, multi_select_verzameling.options, multi_select_verzameling.value, _d) = \\\n",
+ " (PV_txt_lijst, PV_txt_lijst[0], PV_txt_lijst, [PV_txt_lijst[0]], display(dropdown_rekpercentage_txt)) if dropdown_type_proef.value == 'TXT_S_POP' \\\n",
+ " else (PV_dss_lijst, PV_dss_lijst[0], PV_dss_lijst, [PV_dss_lijst[0]], display(dropdown_rekpercentage_dss))\n",
+ " update_rekpercentage_output(None)\n",
+ "\n",
+ "def update_rekpercentage_output(change):\n",
+ " global gekozen_rekpercentage\n",
+ " with output_rekpercentage:\n",
+ " clear_output()\n",
+ " if dropdown_type_proef_shansep.value == 'TXT_S_POP':\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_txt.value\n",
+ " else:\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "dropdown_type_proef_shansep.observe(update_verzamelings_dropdowns, names='value')\n",
+ "dropdown_rekpercentage_txt.observe(update_rekpercentage_output, names='value')\n",
+ "dropdown_rekpercentage_dss.observe(update_rekpercentage_output, names='value')\n",
+ "\n",
+ "# Initialiseren container met juiste rekpercentage-dropdown\n",
+ "with container_rekpercentage:\n",
+ " clear_output()\n",
+ " display(dropdown_rekpercentage_txt)\n",
+ "with output_rekpercentage:\n",
+ " clear_output()\n",
+ "\n",
+ "# Display widgets\n",
+ "display(Markdown(\"**Kies type proef:**\"))\n",
+ "display(dropdown_type_proef_shansep)\n",
+ "display(Markdown(\"**Kies verzameling voor statistische analyse:**\"))\n",
+ "display(dropdown_verzameling)\n",
+ "display(Markdown(\"**Kies rekpercentage Su:**\"))\n",
+ "display(container_rekpercentage)\n",
+ "display(output_rekpercentage)\n",
+ "display(Markdown(\"**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**\"))\n",
+ "display(multi_select_verzameling)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "id": "12800281-f317-42ed-85e0-5069bfa16c35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Input berekenen of uitlezen uit Resultaten SHANSEP voor Shansep Widget met gemiddelde en karakteristieke parameters\n",
+ "\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "from pv_tool.shansep_analysis.shansep_analysis import SHANSEP\n",
+ "from pv_tool.shansep_analysis.variables import *\n",
+ "\n",
+ "dbase_df = dbase.dbase_df\n",
+ "\n",
+ "# Ophalen geselecteerde waarden\n",
+ "verzameling = dropdown_verzameling.value\n",
+ "\n",
+ "if dropdown_type_proef_shansep.value.startswith('TXT'):\n",
+ " rekpercentage = dropdown_rekpercentage_txt.value\n",
+ "else:\n",
+ " rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "# Analyse Shansep parameters\n",
+ "analyse = SHANSEP(\n",
+ " dbase=dbase,\n",
+ " investigation_groups=[dropdown_verzameling.value],\n",
+ " effective_stress=rekpercentage,\n",
+ " analysis_type=dropdown_type_proef_shansep.value\n",
+ ")\n",
+ "\n",
+ "analyse.apply_settings(alpha=0.75)\n",
+ "\n",
+ "analyse.get_shansep_data()\n",
+ "df_gem, df_kar = analyse.get_result_values_shansep()\n",
+ "\n",
+ "# print(analyse.get_short_results())\n",
+ "# print(df_gem)\n",
+ "#print(df_kar)\n",
+ "\n",
+ "# Benaderingswaarden voor het snijpunt y-as, S, m en POP \n",
+ "# Kolom 2: Snijpunt y-as\n",
+ "SNIJPUNT_GEM_BENADERING_R2 = round(analyse.e_a1_oc, 2)\n",
+ "SNIJPUNT_GEM_BENADERING_R3 = None\n",
+ "SNIJPUNT_GEM_BENADERING_R4 = None\n",
+ "SNIJPUNT_GEM_BENADERING_R5 = None\n",
+ "\n",
+ "# Kolom 3: Schuifsterkteratio S\n",
+ "S_GEM_BENADERING_UIT_S_en_POP_R2 = round(analyse.e_a2_oc, 2)\n",
+ "S_GEM_BENADERING_UIT_S_en_m_R3 = round(analyse.exp_e_a1_nc_oc, 2)\n",
+ "S_GEM_BENADERING_R4 = None\n",
+ "S_GEM_BENADERING_UIT_S_OCR1_R5 = round(analyse.exp_gem_ln_su_svc_nc, 2)\n",
+ "\n",
+ "# Kolom 4: Sterktetoename exponent m\n",
+ "m__GEM_BENADERING_R2 = None\n",
+ "m_GEM_BENADERING_uit_S_en_m_R3 = round(analyse.e_a2_nc_oc, 2)\n",
+ "m_GEM_BENADERING_R4 = None\n",
+ "m_GEM_BENADERING_R5 = None\n",
+ "\n",
+ "# Kolom 5: POP\n",
+ "POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R1 = round(analyse.e_a1_oc/analyse.e_a2_oc/analyse.e_a2_nc_oc, 2)\n",
+ "POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R2 = POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R1\n",
+ "POP_GEM_BENADERING_UIT_TXT_R3 = round(analyse.pop_gem_oc, 2)\n",
+ "POP_GEM_BENADERING_R4 = None\n",
+ "\n",
+ "# Ophalen ingevulde handmatige waardes uit Dbase of gelijk aan benadering/default waarden indien deze ontbreekt in dbase\n",
+ "laatste_resultaten = analyse.get_previous_results(path=str(dir2), file_name=str(filename_export))\n",
+ "\n",
+ "if (laatste_resultaten is not None and \n",
+ " not laatste_resultaten.empty and \n",
+ " 'PV_A1_SNIJPUNT_YAS_GEM [-]' in laatste_resultaten):\n",
+ " PV_A1_SNIJPUNT_YAS_GEM_handmatig = [laatste_resultaten['PV_A1_SNIJPUNT_YAS_GEM [-]']]\n",
+ " PV_A2_S_GEM_handmatig = [laatste_resultaten['PV_A2_S_GEM [-]']]\n",
+ " PV_m_GEM_handmatig = [laatste_resultaten['PV_m_GEM [-]']]\n",
+ "\n",
+ "else:\n",
+ " PV_A1_SNIJPUNT_YAS_GEM_handmatig = [SNIJPUNT_GEM_BENADERING_R2]\n",
+ " PV_A2_S_GEM_handmatig = [S_GEM_BENADERING_UIT_S_en_POP_R2]\n",
+ " PV_m_GEM_handmatig = [m_GEM_BENADERING_uit_S_en_m_R3]\n",
+ "\n",
+ "\n",
+ "\n",
+ "# PV_A1_SNIJPUNT_YAS_KAR [-]\t\n",
+ "# PV_A2_S_KAR [-]\t\n",
+ "# PV_m_KAR [-]\t\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 160,
+ "id": "e889f125-5031-420b-a4f9-571957f76242",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "FloatText(value=8.0, layout=Layout(grid_area='widget027', width='180px'))\n",
+ "FloatText(value=0.3, layout=Layout(grid_area='widget028', width='180px'))\n",
+ "FloatText(value=0.7, layout=Layout(grid_area='widget029', width='180px'))\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(PV_A1_SNIJPUNT_YAS_GEM_handmatig)\n",
+ "print(PV_A2_S_GEM_handmatig)\n",
+ "print(PV_m_GEM_handmatig)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "id": "6c17d7b6-68c4-4c44-bc9b-a4b54b962c33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Grid voor Widget gemiddelde shansep waarden.\n",
+ "\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "\n",
+ "## Widget voor gemiddelde parameters benadering en handmatig opgeven waarden\n",
+ "\n",
+ "# Beschrijvingen (eerste kolom)\n",
+ "descriptions = [\n",
+ " \"bepaling S en POP uit triaxiaal- of DSS proeven\",\n",
+ " \"bepaling S en m uit triaxiaal- of DSS proeven\",\n",
+ " \"o.b.v. opgegeven POP bij triaxiaal- of DSS proeven\",\n",
+ " \"bepaling S uit triaxiaal- of DSS proeven OCR=1\"\n",
+ "]\n",
+ "\n",
+ "# Maak een raster van 6 rijen en 5 kolommen\n",
+ "grid = widgets.GridspecLayout(6, 5, width='1050px') # Breder raster\n",
+ "\n",
+ "# Veldnamen (eerste rij)\n",
+ "grid[0, 0] = widgets.Label(\"Parameters\", layout=widgets.Layout(width='300px')) # Brede eerste kolom\n",
+ "grid[0, 1] = widgets.Label(\"Snijpunt y-as\")\n",
+ "grid[0, 2] = widgets.Label(\"Schuifsterkteratio S\")\n",
+ "grid[0, 3] = widgets.Label(\"Sterkte toename exponent m\")\n",
+ "grid[0, 4] = widgets.Label(\"POP\")\n",
+ "\n",
+ "# Functie voor berekening van rij 6 kolom 5\n",
+ "def bereken_resultaat(change):\n",
+ " try:\n",
+ " kolom_2 = float(input_row_6_kol_2.value)\n",
+ " kolom_3 = float(input_row_6_kol_3.value)\n",
+ " kolom_4 = float(input_row_6_kol_4.value)\n",
+ " result.value = str(kolom_2 + kolom_3 + kolom_4)\n",
+ " except ValueError:\n",
+ " result.value = \"Invalid\"\n",
+ "\n",
+ "# Vul de tabel rijen 2 t/m 5 met berekende waarden\n",
+ "for i, (desc, y_as, S, m, pop) in enumerate(zip(\n",
+ " descriptions,\n",
+ " [SNIJPUNT_GEM_BENADERING_R2, SNIJPUNT_GEM_BENADERING_R3, SNIJPUNT_GEM_BENADERING_R4, SNIJPUNT_GEM_BENADERING_R5],\n",
+ " [S_GEM_BENADERING_UIT_S_en_POP_R2, S_GEM_BENADERING_UIT_S_en_m_R3, S_GEM_BENADERING_R4, S_GEM_BENADERING_UIT_S_OCR1_R5],\n",
+ " [m__GEM_BENADERING_R2, m_GEM_BENADERING_uit_S_en_m_R3, m_GEM_BENADERING_R4, m_GEM_BENADERING_R5],\n",
+ " [POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R1, POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R2, POP_GEM_BENADERING_UIT_TXT_R3, POP_GEM_BENADERING_R4]\n",
+ "), start=1):\n",
+ " grid[i, 0] = widgets.Label(desc, layout=widgets.Layout(width='350px')) # Beschrijving (eerste kolom)\n",
+ " grid[i, 1] = widgets.Label(str(y_as) if y_as is not None else \"-\") # Snijpunt y-as\n",
+ " grid[i, 2] = widgets.Label(str(S) if S is not None else \"-\") # Schuifsterkteratio S\n",
+ " grid[i, 3] = widgets.Label(str(m) if m is not None else \"-\") # Sterkte toename exponent m\n",
+ " grid[i, 4] = widgets.Label(str(pop) if pop is not None else \"-\") # POP\n",
+ "\n",
+ "# Toevoegen van invoervelden in rij 6\n",
+ "grid[5, 0] = widgets.Label(\"Handmatige invoer\", layout=widgets.Layout(width='350px'))\n",
+ "\n",
+ "PV_A1_SNIJPUNT_YAS_GEM_handmatig = widgets.FloatText(value=PV_A1_SNIJPUNT_YAS_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "PV_A2_S_GEM_handmatig = widgets.FloatText(value=PV_A2_S_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "PV_m_GEM_handmatig = widgets.FloatText(value=PV_m_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "result_label = widgets.Label(value='', layout=widgets.Layout(width='180px'))\n",
+ "\n",
+ "# PV_A1_SNIJPUNT_YAS_GEM_handmatig = widgets.FloatText(value=PV_A1_SNIJPUNT_YAS_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# PV_A2_S_GEM_handmatig = widgets.FloatText(value=PV_A2_S_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# PV_m_GEM_handmatig = widgets.FloatText(value=PV_m_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# result_label = widgets.Label(value='', layout=widgets.Layout(width='180px'))\n",
+ "\n",
+ "# Functie om POP live te berekenen op basis van handmatig opgegeven waarden.\n",
+ "def bereken_POP_gem(change=None):\n",
+ " try:\n",
+ " v1 = PV_A1_SNIJPUNT_YAS_GEM_handmatig.value\n",
+ " v2 = PV_A2_S_GEM_handmatig.value\n",
+ " v3 = PV_m_GEM_handmatig.value\n",
+ " if v2 == 0 or v3 == 0:\n",
+ " result_label.value = \"Niet gedefinieerd\"\n",
+ " else:\n",
+ " result_label.value = f\"{v1 / v2 / v3:.3f}\"\n",
+ " except Exception:\n",
+ " result_label.value = \"Fout\"\n",
+ "\n",
+ "bereken_POP_gem()\n",
+ "\n",
+ "# Koppel de functie aan elke wijziging\n",
+ "PV_A1_SNIJPUNT_YAS_GEM_handmatig.observe(bereken_POP_gem, names=\"value\")\n",
+ "PV_A2_S_GEM_handmatig.observe(bereken_POP_gem, names=\"value\")\n",
+ "PV_m_GEM_handmatig.observe(bereken_POP_gem, names=\"value\")\n",
+ "\n",
+ "# Koppel invoervelden aan de tabel\n",
+ "grid[5, 1] = PV_A1_SNIJPUNT_YAS_GEM_handmatig\n",
+ "grid[5, 2] = PV_A2_S_GEM_handmatig\n",
+ "grid[5, 3] = PV_m_GEM_handmatig\n",
+ "grid[5, 4] = result_label\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 159,
+ "id": "b8ca6495-2258-4e21-8a15-4cbaf841f5b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Opgeven gemiddelde waarden (fit):**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "18c25647c7554a99bffbc0909e611ddf",
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+ "version_minor": 0
+ },
+ "text/plain": [
+ "GridspecLayout(children=(Label(value='Parameters', layout=Layout(grid_area='widget001', width='300px')), Label…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Toon de tabelwidget voor de Shansep parameters\n",
+ "\n",
+ "display(Markdown(\"**Opgeven gemiddelde waarden (fit):**\"))\n",
+ "display(grid)"
+ ]
+ },
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+ {
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+ "output_type": "stream",
+ "text": [
+ " S [-] m [-] POP [kPa]\n",
+ "Verwachtingswaarde 0.300000 0.7 38.095238\n",
+ "Karakteristieke waarde 0.250000 0.7 28.571429\n",
+ "Standaarddeviatie 0.032295 0.0 6.386059\n"
+ ]
+ },
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+ "#7201a8"
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+ [
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+ [
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+ "#fdca26"
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+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "Bepaling S en POP uit TXT_S_POP proef"
+ },
+ "width": 1280,
+ "xaxis": {
+ "title": {
+ "text": "$\\sigma'_{v} [kPa]$"
+ }
+ },
+ "yaxis": {
+ "title": {
+ "text": "$s_{u} [kPa]$"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#voer Shansep analyse uit met invoer en toon grafieken\n",
+ "\n",
+ "analyse.set_parameters_handmatig(snijpunt_gem = PV_A1_SNIJPUNT_YAS_GEM_handmatig.value, s_gem = PV_A2_S_GEM_handmatig.value, m_gem= PV_m_GEM_handmatig.value, snijpunt_kar=5, s_kar=0.25, m_kar=0.7)\n",
+ "extra_verzamelingen_tonen = list(multi_select_verzameling.value)\n",
+ "\n",
+ "# analyse.apply_settings(material_factor_cohesion = PV_PARTCOH[0], material_factor_tan_phi = PV_PARTPHI[0], alpha = PV_TYPEVERZAMELING[0])\n",
+ "\n",
+ "output_df = analyse.get_short_results()\n",
+ "print(output_df)\n",
+ "\n",
+ "analyse.show_figure_sv_su(plot_extra_dataset=extra_verzamelingen_tonen) #deze werkt niet\n",
+ "analyse.show_figure_ln_ocr_ln_s(plot_extra_dataset=extra_verzamelingen_tonen)\n",
+ "analyse.show_figure_sv_su_nc(plot_extra_dataset=extra_verzamelingen_tonen) #hier moet het NC verloop worden ingetekend met POP =0\n",
+ "\n",
+ "\n",
+ "# pdf_path = analyse.save_to_pdf(path=str(export_path))\n",
+ "# analyse.save_total_to_excel(path=str(export_path))\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "b12fe78e-5333-4770-8a7f-828deecef7be",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "'SHANSEP' object has no attribute 'add_results_to_template'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[17]\u001b[39m\u001b[32m, line 13\u001b[39m\n\u001b[32m 6\u001b[39m os.makedirs(output_path1, exist_ok=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m 8\u001b[39m \u001b[38;5;66;03m# Sla resultaten op als PDF\u001b[39;00m\n\u001b[32m 9\u001b[39m \u001b[38;5;66;03m#analyse.save_to_pdf(path=output_path1) \u001b[39;00m\n\u001b[32m 10\u001b[39m \n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# schrijf invoer en resultaten naar PV_template, tabblad [Resultaten SHANSEP]\u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m#analyse.add_results_to_dbase(path=dir2, file_name=filename_export) # wordt naar de verkeerde regel weggeschreven\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m \u001b[43manalyse\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_results_to_template\u001b[49m(path=dir2, export_name=filename_export)\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m#Plotly bestand maken \u001b[39;00m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# fig_s_t = analyse.figure # of analyse.fig als die bestaat\u001b[39;00m\n\u001b[32m 17\u001b[39m \u001b[38;5;66;03m# bestandsnaam = dropdown_verzameling.value.replace(' ', '_') + '.html'\u001b[39;00m\n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# output_path = os.path.join(output_path1, bestandsnaam)\u001b[39;00m\n\u001b[32m 19\u001b[39m \u001b[38;5;66;03m# fig_s_t.write_html(output_path)\u001b[39;00m\n\u001b[32m 20\u001b[39m \u001b[38;5;66;03m# print(f\"Figuur opgeslagen als {output_path}\")\u001b[39;00m\n",
+ "\u001b[31mAttributeError\u001b[39m: 'SHANSEP' object has no attribute 'add_results_to_template'"
+ ]
+ }
+ ],
+ "source": [
+ "# Resultaten Shansep analyse opslaan naar: ##_PVtool5_0.xlsx, pdf en exporteer tussenstappen naar excel\n",
+ "import os\n",
+ "\n",
+ "#directory om resultaten weg te schrijven\n",
+ "output_path1 = os.path.join(os.getcwd(), \"resultaten\")\n",
+ "os.makedirs(output_path1, exist_ok=True)\n",
+ "\n",
+ "# Sla resultaten op als PDF\n",
+ "#analyse.save_to_pdf(path=output_path1) \n",
+ "\n",
+ "# schrijf invoer en resultaten naar PV_template, tabblad [Resultaten SHANSEP]\n",
+ "#analyse.add_results_to_dbase(path=dir2, file_name=filename_export) # wordt naar de verkeerde regel weggeschreven\n",
+ "analyse.add_results_to_template(path=dir2, export_name=filename_export)\n",
+ "\n",
+ "#Plotly bestand maken \n",
+ "# fig_s_t = analyse.figure # of analyse.fig als die bestaat\n",
+ "# bestandsnaam = dropdown_verzameling.value.replace(' ', '_') + '.html'\n",
+ "# output_path = os.path.join(output_path1, bestandsnaam)\n",
+ "# fig_s_t.write_html(output_path)\n",
+ "# print(f\"Figuur opgeslagen als {output_path}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "7eee2e80-33cc-4b58-84d9-54cf20746100",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "C:\\python_cursus\\PVtool2025_github\\import_files\n"
+ ]
+ }
+ ],
+ "source": [
+ "print (dir2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d7e1e162-7b9b-4030-8b38-151e67f84337",
+ "metadata": {},
+ "source": [
+ "## Stap 4b: Bepalen ongedraineerde parameters triaxiaalproeven en DSS-proeven op Su tabel methode\n",
+ "\n",
+ "In deze stap worden op basis van de data vastgelegd in het template PV-tool 5.0. de gedraineerde parameters bepaald volgens de methode vastgelegd in de schematiseringshandleiding macrostabiliteit. Maak een keuze uit de verzameling waarop de statistische analyse moet plaatsvinden. Als er nog geen aparte verzamelingen onderscheiden zijn in het veld (PV_NAAM) worden alle triaxaalproeven in één verzameling opgenomen. \n",
+ "\n",
+ "Als gebruiker kunnen handmatig verzamelingen worden opgegeven in de Excel template in het veld PV_NAAM of er kan gebruik worden gemaakt van de functie:\n",
+ "Definiëren en aanpassen van verzamelingen (stap #). Met behulp van de functie ## kunnen in de grafiek met behulp van de selectietool (should have) ook één of meer proefresultaten worden geselecteerd en worden toegekend aan een andere groep.\n",
+ "\n",
+ "Geef het te hanteren rekpercentage op waarbij de Su is bepaald: 2%, 5%, 15%, eindrek of bij pieksterkte. \n",
+ "Indien er eerder parameters zijn vastgesteld worden de gemaakte keuzes voor het % en de raaklijnen ingeladen.\n",
+ "\n",
+ "Ook kunnen meerdere verzamelingen getoond worden. NB. Deze extra verzamelingen worden niet meegenomen in de statistische analyse."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "7d1bbaa2-5100-46f8-a28a-576590fcc29c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies type proef:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "748745df279a4fc1ad4295f72865151e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('TXT_S_SU', 'DSS_S_SU'), value='TXT_S_SU')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies verzameling voor statistische analyse:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "738138c53ef446b78e0751496285a6b4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('geen', 'TXT_testset_klei', 'TXT', 'TXT_klei_17.5_21_ondiep', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies rekpercentage Su:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "676b8ba129ef4d18b2307a3a53bf9038",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a195438064a24350b1338f7842d3dcfd",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "80c2ee85497d46e29486ffba9c6fe609",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "SelectMultiple(index=(0,), layout=Layout(height='150px', width='400px'), options=('geen', 'TXT_testset_klei', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown, clear_output\n",
+ "\n",
+ "# Dataframe met proefdata\n",
+ "dbase_df = dbase.dbase_df\n",
+ "\n",
+ "# Lijsten maken met proevenverzamelingen in database onder PVNAAM\n",
+ "PV_txt_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__TRIAXIAAL'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "PV_dss_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__DSS'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "gekozen_rekpercentage = 'eindsterkte'\n",
+ "\n",
+ "# Dropdown widgets\n",
+ "dropdown_type_proef_su = widgets.Dropdown(options=['TXT_su_tabel', 'DSS_su_tabel'], value='TXT_su_tabel', layout=widgets.Layout(width='400px'))\n",
+ "\n",
+ "\n",
+ "dropdown_rekpercentage_txt = widgets.Dropdown(options=['2% rek', '5% rek', '15% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_rekpercentage_dss = widgets.Dropdown(options=['2% rek', '5% rek', '10% rek', '15% rek', '20% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_verzameling = widgets.Dropdown(options=PV_txt_lijst, value=PV_txt_lijst[0] if PV_txt_lijst else None, layout=widgets.Layout(width='400px'))\n",
+ "multi_select_verzameling = widgets.SelectMultiple(options=PV_txt_lijst, value=[PV_txt_lijst[0]] if PV_txt_lijst else [], layout=widgets.Layout(width='400px', height='150px'))\n",
+ "# Outputs\n",
+ "container_rekpercentage = widgets.Output()\n",
+ "output_rekpercentage = widgets.Output()\n",
+ "\n",
+ "# Lijsten rekpercentage en verzamelingen aanpassen bij wijziging van proeftype txt/dss\n",
+ "def update_verzamelings_dropdowns(change): \n",
+ " with container_rekpercentage: \n",
+ " clear_output(); \n",
+ " (dropdown_verzameling.options, dropdown_verzameling.value, multi_select_verzameling.options, multi_select_verzameling.value, _d) = \\\n",
+ " (PV_txt_lijst, PV_txt_lijst[0], PV_txt_lijst, [PV_txt_lijst[0]], display(dropdown_rekpercentage_txt)) if dropdown_type_proef.value == 'TXT_su_tabel' \\\n",
+ " else (PV_dss_lijst, PV_dss_lijst[0], PV_dss_lijst, [PV_dss_lijst[0]], display(dropdown_rekpercentage_dss))\n",
+ " update_rekpercentage_output(None)\n",
+ "\n",
+ "def update_rekpercentage_output(change):\n",
+ " global gekozen_rekpercentage\n",
+ " with output_rekpercentage:\n",
+ " clear_output()\n",
+ " if dropdown_type_proef_su.value == 'TXT_su_tabel':\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_txt.value\n",
+ " else:\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "dropdown_type_proef_su.observe(update_verzamelings_dropdowns, names='value')\n",
+ "dropdown_rekpercentage_txt.observe(update_rekpercentage_output, names='value')\n",
+ "dropdown_rekpercentage_dss.observe(update_rekpercentage_output, names='value')\n",
+ "\n",
+ "# Initialiseren container met juiste rekpercentage-dropdown\n",
+ "with container_rekpercentage:\n",
+ " clear_output()\n",
+ " display(dropdown_rekpercentage_txt)\n",
+ "with output_rekpercentage:\n",
+ " clear_output()\n",
+ "\n",
+ "# Display widgets\n",
+ "display(Markdown(\"**Kies type proef:**\"))\n",
+ "display(dropdown_type_proef_su)\n",
+ "display(Markdown(\"**Kies verzameling voor statistische analyse:**\"))\n",
+ "display(dropdown_verzameling)\n",
+ "display(Markdown(\"**Kies rekpercentage Su:**\"))\n",
+ "display(container_rekpercentage)\n",
+ "display(output_rekpercentage)\n",
+ "display(Markdown(\"**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**\"))\n",
+ "display(multi_select_verzameling)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "605dde11-09ff-4f2a-98c6-99113b2e9dba",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.13.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/PV_tool0.5.ipynb b/PV_tool0.5.ipynb
new file mode 100644
index 0000000..941390d
--- /dev/null
+++ b/PV_tool0.5.ipynb
@@ -0,0 +1,15933 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5b519e9d-47fb-4aaf-88e6-ea99037b66ce",
+ "metadata": {},
+ "source": [
+ "STOWA Proevenverzameling tool v5.0
\n",
+ "\n",
+ "Deze Jupyter notebook bevat functies opgenomen in de python package PV-tooling ### voor het opstellen van lokale of regionale proevenverzamelingen voor het bepalen van geotechnische parameters. De methode is ontwikkeld voor het uitvoeren van analyses in relatie tot de geotechnische stabiliteit van dijken, maar kan ook breder worden toegepast. De notebook dient tevens als handleiding om de gebruiker stapsgewijs te ondersteunen bij het opstellen van een proevenverzameling.\n",
+ "\n",
+ "Met de beschikbare functies kunnen zowel gedraineerde als ongedraineerde sterkteparameters worden berekend alsmede enkele samendrukkingsparameters. Van deze parameters worden verwachtingswaarde, karakteristieke waarde en rekenwaarde bepaald. \n",
+ "\n",
+ "De functies zijn opgesteld conform de werkwijze beschreven in [Statistische methoden t.b.v. proevenverzamelingen, DIV, v1.0], zie tevens: https://publicwiki.deltares.nl/spaces/HWBPMacro/pages/217120830/Sterkte+van+grond#Sterktevangrond-150.\n",
+ "De tool bevat tevens hulpmiddelen voor het onderscheiden of samenvoegen van groepen in een verzameling op basis van verschillende kenmerken. \n",
+ "\n",
+ "De onderliggende data om een proevenverzameling samen te stellen is beschreven in een vaste structuur. Deze structuur is vastgelegd in een uitwisselformat. Het uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx. De geotechnische laboratoria kennen deze database en kunnen deze database vullen met resultaten van grond- en laboratoriumonderzoek. Op deze wijze ontstaat er uniformering op het gebied van data-uitwisseling en –opslag van proefresultaten.\n",
+ "\n",
+ "NB. De verantwoordelijkheid voor het gebruik van deze tools ligt bij de gebruiker."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "424ad52e-54df-4a4d-8546-36eb585eb7a8",
+ "metadata": {},
+ "source": [
+ "## Benodigde installaties"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e3612aae-c928-4b18-b531-328810c8acc1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Importeren van benodigde package\n",
+ "# Na het eerte keer installeren van de benodigde packages de kernel handmatig opnieuw optarten via het menu Kernel → Restart\n",
+ "\n",
+ "path_to_wheel = r\"C:\\Users\\deenekat7271\\Documents\\GitHub\\pv-tool\\dist\\pv_tool-0.2.7-py3-none-any.whl\"\n",
+ "!pip install \"{path_to_wheel}\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6ddea397-c025-4f29-93ff-936bc26ff4b7",
+ "metadata": {},
+ "source": [
+ "## Stap 1: Inladen van benodigde data\n",
+ "\n",
+ "De benodigde data voor het bepalen van geotechnische parameters kan op 3 manieren worden ingeladen in deze notebook.\n",
+ "- Op basis van de data in de excel versie van de proevenverzamelingtool (vanaf versie 4.2n of hoger) waarin zowel reeds proevenverzamelingen zijn onderscheiden als geotechnische parameters zijn vastgesteld. NB. de onderscheiden verzamelingen worden als uitgangspunt worden ingeladen, de sterkteparameters worden opnieuw berekend. Onderliggende keuzes zoals het gewenste rekpercentage en de gekozen raaklijnen worden niet ingelezen uit de Excelversie. \n",
+ "- Op basis van het Excel Uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx waarin nog geen verzamelingen onderscheiden zijn.\n",
+ "\n",
+ "Zie tevens: https://github.com/kkpdata/Proevenverzamelingentool/\n",
+ "\n",
+ "De proevenverzamelingtool of het uitwisselformat worden omgezet naar het template proevenverzamelingtool 5.0 opgenomen in pv_tool\\templates.\n",
+ "\n",
+ "- Op basis van het Excel template voor de Proevenverzameling 5.0. Deze bestaat uit de data conform het uitwisselformat 4.2 aangevuld met de benodigde invoerdata van voorliggende tooling. De uitvoer wordt in het vervolg weggeschreven in 3 aparte tabbladen: [Resultaten c-phi], [Resultaten SHANSEP] en [Resultaten SU-tabel-m]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6763641d-41d8-4046-a14f-745ff77b26d3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# all imports\n",
+ "import importlib.util\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "from ipyfilechooser import FileChooser\n",
+ "from pathlib import Path\n",
+ "import os\n",
+ "\n",
+ "from pv_tool.imports.import_data import Dbase\n",
+ "from pv_tool.cphi_analysis.c_phi_analysis import CPhiAnalyse\n",
+ "from pv_tool.cphi_analysis.variables import *\n",
+ "\n",
+ "# widgetfuncties nog verder aanvullen!\n",
+ "from widgetfunctie_cphi import *"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "27ee1016-292f-43f6-8f49-f5000db7f162",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**openpyxl is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**ipyfilechooser is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**pandas_schema is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**xlsxwriter is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**ipywidgets is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**reportlab is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**reportlab is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**kaleido is al geïnstalleerd.**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Controleert en installeer indien nodig\n",
+ "packages = ['openpyxl', 'ipyfilechooser', 'pandas_schema', 'xlsxwriter', 'ipywidgets', 'reportlab', 'reportlab', 'kaleido']\n",
+ "for package in packages:\n",
+ "\n",
+ " check_package_install(package)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "05713100-659e-43ce-b3bc-5231e7d28045",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "229ba24b-906d-49ac-b5a8-213bd644d22b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Stap 1: Kies Excel template voor uploaden data:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1e7a8331062f4e71951fa15b06900a32",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(description='Template:', index=2, layout=Layout(width='400px'), options=('Proevenverzamelingtool 4.2n…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "76aabff6260a4f808f60468e701d18aa",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "FileChooser(path='C:\\', filename='', title='Selecteer een bestand om te uploaden', show_hidden=False, select_d…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Laden van widgets voor uploaden bestanden\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "from ipyfilechooser import FileChooser\n",
+ "\n",
+ "# Dropdown widget voor keuze template\n",
+ "dropdown_template = widgets.Dropdown(\n",
+ " options=[\n",
+ " 'Proevenverzamelingtool 4.2n of hoger', \n",
+ " 'STOWA uitwisselingsformat 4.2x', \n",
+ " 'Proevenverzamelingtool 5.0'\n",
+ " ], # De opties in het menu\n",
+ " value='Proevenverzamelingtool 5.0', # Standaard geselecteerde waarde\n",
+ " description='Template:',\n",
+ " disabled=False, \n",
+ " layout=widgets.Layout(width='400px') \n",
+ ")\n",
+ "\n",
+ "# Maak een FileChooser-widget\n",
+ "file_chooser_import = FileChooser(\n",
+ " '/',\n",
+ " title=\"Selecteer een bestand om te uploaden\",\n",
+ " show_hidden=False # Verberg verborgen bestanden\n",
+ ")\n",
+ "\n",
+ "#Titel\n",
+ "display(Markdown(\"**Stap 1: Kies Excel template voor uploaden data:**\"))\n",
+ "\n",
+ "# Toon de dropdown widget keuze template\n",
+ "display(dropdown_template)\n",
+ "\n",
+ "# Toon widget voor keuze uploaden bestand\n",
+ "display(file_chooser_import)\n",
+ "\n",
+ "print()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27b344b2-e7af-4404-80f4-6ae20e8cc688",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "49c409f6-3e78-478e-8d55-e1d1a47cb7eb",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true
+ },
+ "source": [
+ "## Stap 2a: Importeren inclusief toevoegen/herberekenen analyse kolommen\n",
+ "De importfunctie laadt de data in. Daarnaast worden analyse kolommen (ANA) toegevoegd die benodigd zijn voor de functies in \"shansep_analysis\" en \"sutabel_analysis\". \n",
+ "\n",
+ "Hiervoor worden verschillende kolommen aangemaakt: \n",
+ "ANA_TERREINSPANNING, ANA_TXT_MAX_VERTICALE_CONSOLIDATIE_SPANNING, ANA_DSS_MAX_CONSOLIDATIE_SPANNING, ANA_TXT_CONSOLIDATIE_TYPE_VOORSTEL, ANA_TXT_CONSOLIDATIE_TYPE_HANDMATIG, ANA_TXT_CONSOLIDATIE_TYPE_REKEN, ANA_DSS_CONSOLIDATIE_TYPE_VOORSTEL, ANA_DSS_CONSOLIDATIE_TYPE_HANDMATIG\n",
+ "ANA_DSS_CONSOLIDATIE_TYPE_REKEN, ANA_GRENSSPANNING_PROEF, ANA_POP_VELD, ANA_POP_VELD_GEMIDDELD, ANA_GRENSSPANNING_VOORSTEL, ANA_GRENSSPANNING_HANDMATIG\n",
+ "ANA_GRENSSPANNING_REKEN, OCR_TXT, OCR_DSS.\n",
+ "\n",
+ "Er moet worden vastgesteld of een proef is uitgevoerd op een normaal geconsolideerd monster (NC) ruim boven de grensspanning of overconsolideerd (OC) bij de geschatte dagelijkse effectieve terreinspanning (OCR>1). De tooling doet hier een voorstel voor door de consolidatiespanning (sigma'vc) te vergelijken met de terreinspanning (sigma'vi). Indien deze niet meer dan +30% afwijkt wordt aangenomen dat het een OC-proef betreft en bij grotere afwijkingen een NC-proef. Het is noodzakelijk om dit voorstel te controleren. In de regel is een OC-proef vrijwel altijd dicht bij de terreinspanning gekozen en ligt een NC-proef er ruim boven, maar hier zijn altijd uitzonderingen op mogelijk.\n",
+ "\n",
+ "Daarnaast is een schatting van de OCR benodigd. Om deze te berekenen is een schatting van de grensspanning benodigd. Indien er op dezelfde regel een resultaat beschikbaar is uit een samendrukkingsproef of CRS-proef wordt deze gebruikt. Indien deze ontbreekt wordt een voorstel gedaan op basis van de gemiddelde POP (POP = grensspanning - effectieve terreinspanning o.b.v. de monsters waar wel een proef is uitgevoerd ) per boring. Indien er bij de betreffende boring geen grensspanning beschikbaar is wordt geen waarde ingevuld. Het is dan niet mogelijk om geautomatiseerd een OCR te schatten. \n",
+ "De data wordt weggeschreven in de velden (ANA_GRENSSPANNING_VOORSTEL). Het is noodzakelijk om de voorgestelde waarden te controleren. Als gebruiker kan je het voorstel overrulen in de kolom (ANA_GRENSSPANNING_HANDMATIG).\n",
+ "\n",
+ "NB. Bij gebruik van de bestaande Excel versie van de proevenverzamelingtool zijn de cellen ANA_GRENSSPANNING_HANDMATIG veelal reeds ingevuld. In dat geval worden deze waarden overgenomen.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae6f0418-c80a-4bef-b7d6-e30530e4ac3d",
+ "metadata": {},
+ "source": [
+ "## Stap 2a: Valideren data \n",
+ "De validatie bestaat uit de volgende controles: voor de verschillende typen proeven – classificatie, CRS-proef, samendrukkingsproef, DSS-proef en triaxiaalproef – wordt per regel gecontroleerd of er een proef is uitgevoerd (data ingevuld) en zo ja, of de velden die in de PV-tool benodigd zijn volledig en correct zijn ingevuld.\n",
+ "\n",
+ "Het resultaat van de validatie wordt opgeslagen in een twee Excel-bestanden Validation_log_##_critical_errors.xlsx en Validation_log_##_warnings.xlsx, waarin per gevalideerde kolom en regel het resultaat wordt weergegeven. Ir is per kolom aangegeven hoeveel fouten zijn aangetroffen. Er is onderscheid gemaakt tussen 'critical errors' en 'warnings'. De critical errors leiden tot fouten wanneer deze data wordt gebruikt bij het bepalen van de parameters. Deze dienen te worden gecorrigeerd of aangevuld. De warnings betreffen aanbevelingen. De resultaten worden weggeschreven in dezelfde directory als de importmap. \n",
+ "\n",
+ "Indien de validatie goed is doorlopen worden proevenverzamelingtool (vanaf versie 4.2n of hoger) of Uitwisselformat-database-proevenverzameling_versie_4_2x.xlsx weggeschreven naar het Template_PVtool5_0.xlsx. Indien reeds gebruik is gemaakt van het nieuwe template worden de analyse kolommen opnieuw berekend. Dit is noodzakelijk indien er data gewijzigd is. \n",
+ "\n",
+ "Indien er reeds een gevalideerde dataset beschikbaar is conform het Template_PVtool5_0 laadt de data dan in via stap 2b. Dit kost aanzienlijk minder rekentijd bij een grote dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "0afe3ee1-a4bd-4935-8c80-de311f3f0d47",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Je hebt gekozen: Proevenverzamelingtool 4.2n of hoger\n",
+ "Code in script: PV-tool\n",
+ "Geselecteerd bestand: PV-tool.xlsm\n",
+ "Geselecteerde directory: C:\\python_cursus\\PVtool2025_github\\PV-tool\\test_files\n",
+ "Volledig pad: C:\\python_cursus\\PVtool2025_github\\PV-tool\\test_files\\PV-tool.xlsm\n",
+ "number of critical_errors in validate_kenmerken_boring = 0\n",
+ "number of critical_errors in validate_clas = 0\n",
+ "number of critical_errors in validate_crs = 4\n",
+ "number of critical_errors in validate_samendrukking = 0\n",
+ "number of critical_errors in validate_monster = 0\n",
+ "number of critical_errors in validate_triaxiaal = 28\n",
+ "number of critical_errors in validate_dss = 17\n",
+ "number of warnings in validate_alg = 0\n",
+ "number of warnings in validate_kenmerken_boring = 353\n",
+ "number of warnings in validate_clas = 1843\n",
+ "number of warnings in validate_crs = 76\n",
+ "number of warnings in validate_samendrukking = 246\n",
+ "number of warnings in validate_monster = 0\n",
+ "number of warnings in validate_triaxiaal = 96\n",
+ "number of warnings in validate_dss = 33\n",
+ "DataFrame naar template geëxporteerd in C:\\python_cursus\\PVtool2025_github\\PV-tool\\test_files\\Template_PVtool5_0.xlsx\n"
+ ]
+ }
+ ],
+ "source": [
+ "from pv_tool.imports.import_data import Dbase\n",
+ "from pathlib import Path\n",
+ "dbase = Dbase()\n",
+ "\n",
+ "# Controleer dropdown template en geef de gekozen waarde terug\n",
+ "if dropdown_template.value:\n",
+ " print(f'Je hebt gekozen: {dropdown_template.value}')\n",
+ "else:\n",
+ " print(\"Er is geen template geselecteerd.\")\n",
+ "\n",
+ "# bepaal source voor template\n",
+ "template_mapping = {\n",
+ " \"Proevenverzamelingtool 4.2n of hoger\" : \"PV-tool\",\n",
+ " \"STOWA uitwisselingsformat 4.2x\" : \"Stowa\",\n",
+ " \"Proevenverzamelingtool 5.0\" : \"Dbase\"\n",
+ "}\n",
+ "template_code = template_mapping.get(dropdown_template.value, \"Onbekend template\")\n",
+ "print(f\"Code in script: {template_code}\")\n",
+ "\n",
+ "# Controleer of een bestand is geselecteerd\n",
+ "if file_chooser_import.selected:\n",
+ " # Verkrijg het volledige pad van het geselecteerde bestand\n",
+ " full_path = file_chooser_import.selected\n",
+ " # Verkrijg de directory en bestandsnaam\n",
+ " selected_directory = file_chooser_import.selected_path\n",
+ " filename = file_chooser_import.selected_filename\n",
+ "\n",
+ " # Print de resultaten\n",
+ " print(f\"Geselecteerd bestand: {filename}\")\n",
+ " print(f\"Geselecteerde directory: {selected_directory}\")\n",
+ " print(f\"Volledig pad: {full_path}\")\n",
+ "else:\n",
+ " print(\"Er is geen bestand geselecteerd. Selecteer een bestand voordat je verder gaat.\")\n",
+ "\n",
+ "#import dir en bestandsnaam en export directory\n",
+ "dir_in = (file_chooser_import.selected)\n",
+ "dir2 = Path(file_chooser_import.selected_path)\n",
+ "\n",
+ "#starten importeren \n",
+ "dbase.import_data(source = template_code, source_dir=dir_in)\n",
+ "\n",
+ "#starten validatie \n",
+ "dbase.validate_data(export_path=dir2)\n",
+ "\n",
+ "# Bepaal bestandsnaam voor de export van de data, bij PV4.2 of 4.2 uitwisselformat standaard export naar Template_PVtool5_0.xlsx\n",
+ "if dropdown_template.value in ['Proevenverzamelingtool 4.2n of hoger', 'STOWA uitwisselingsformat 4.2x']:\n",
+ " filename_export = 'Template_PVtool5_0.xlsx'\n",
+ "elif dropdown_template.value == 'Proevenverzamelingtool 5.0':\n",
+ " filename_export = file_chooser_import.selected_filename\n",
+ "\n",
+ "#Export naar Template_PVtool5_0.xlsx \n",
+ "dbase.export_dbase_to_template(export_dir=dir2, export_name=filename_export)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b72b6fa4-5e92-4b90-a335-099a8058cfdd",
+ "metadata": {},
+ "source": [
+ "## Stap 2b: Importeren data zonder validatie of herberekening analysekolommen\n",
+ "\n",
+ "De importfunctie laadt de data conform het Template_PVtool5_0. Deze functie mag alleen gebruikt worden indien er reeds een gevalideerde dataset beschikbaar is conform het Template_PVtool5_0. Na het aanbrengen van wijzigingen in de data altijd stap 2a toepassen voor het importeren. Dit is noodzakelijk omdat de analsyekolommen benodigd voor onderstaande functies opnieuw berekend moeten worden bij aanpassingen aan de data. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "cfab8ba1-c3c7-4711-85f3-11f26d3db623",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Importeren: C:\\python_cursus\\PVtool2025_github\\PV-tool\\import_files\\WSRL 2025 PVtool5_0_gevalideerd.xlsx\n"
+ ]
+ },
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+ "#fdca26"
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+ "#f0f921"
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+ ],
+ "sequentialminus": [
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+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
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+ "#7201a8"
+ ],
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+ "#9c179e"
+ ],
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+ "#bd3786"
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+ "#d8576b"
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+ },
+ "colorway": [
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+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "TXT_CPhi analyse met eindsterkte op TXT_klei_14_16"
+ },
+ "width": 1280,
+ "xaxis": {
+ "title": {
+ "text": "s' [kPa]"
+ }
+ },
+ "yaxis": {
+ "title": {
+ "text": "t [kPa]"
+ }
+ }
+ }
+ },
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#voer C-phi analyse uit met invoer en toon grafieken\n",
+ "if dropdown_type_proef.value.startswith('TXT'):\n",
+ " rekpercentage = dropdown_rekpercentage_txt.value\n",
+ "else:\n",
+ " rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "analyse = CPhiAnalyse(dbase=dbase, investigation_groups=[dropdown_verzameling.value], effective_stress= rekpercentage, analysis_type=dropdown_type_proef.value)\n",
+ "analyse.apply_parameters(cohesie_gem = PV_A1_COH_GEM_handmatig[0], phi_kar = PV_A2_PHI_KAR_handmatig[0], cohesie_kar = PV_A1_COH_KAR_handmatig[0])\n",
+ "analyse.apply_settings(material_factor_cohesion = PV_PARTCOH[0], material_factor_tan_phi = PV_PARTPHI[0], alpha = PV_TYPEVERZAMELING[0])\n",
+ "\n",
+ "#Toon resultaten figuur en tabel met cohesie en hoek van inwendige wrijving\n",
+ "output_df = analyse.get_short_results()\n",
+ "print(output_df)\n",
+ "extra_verzamelingen_tonen = list(multi_select_verzameling.value)\n",
+ "analyse.show_figure(plot_extra_dataset= extra_verzamelingen_tonen )\n",
+ "analyse.show_figure(plot_spanningspaden=True)\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e9b57eef-764e-4a4f-84d3-c185663e395f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PDF succesvol opgeslagen op: C:\\python_cursus\\PVtool2025_github\\resultaten/c_phi_pdf_export_TXT_klei_14_16_TXT_CPhi_eindsterkte.pdf\n",
+ "Resultaat toegevoegd aan template in tabblad 'Resultaten c-phi'.\n",
+ "Figuur opgeslagen als C:\\python_cursus\\PVtool2025_github\\resultaten\\TXT_klei_14_16.html\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Resultaten opslaan naar: ##_PVtool5_0.xlsx, pdf en exporteer tussenstappen naar excel\n",
+ "import os\n",
+ "\n",
+ "#directory om resultaten weg te schrijven\n",
+ "output_path1 = os.path.join(os.getcwd(), \"resultaten\")\n",
+ "os.makedirs(output_path1, exist_ok=True)\n",
+ "\n",
+ "# Sla resultaten op als PDF\n",
+ "analyse.save_to_pdf(path=output_path1) \n",
+ "\n",
+ "# schrijf invoer en resultaten naar PV_template, tabblad resultaten c-phi.\n",
+ "analyse.add_results_to_template(path=dir2, export_name=filename_export)\n",
+ "\n",
+ "# Opslaan tussenresultaten naar Excel\n",
+ "bestand_naam = os.path.join(output_path1, \"c_phi_data_geforceerd.xlsx\")\n",
+ "analyse.cphi_analyses_data_df.to_excel(bestand_naam, index=False)\n",
+ "\n",
+ "#Plotly bestand maken \n",
+ "fig_s_t = analyse.figure # of analyse.fig als die bestaat\n",
+ "bestandsnaam = dropdown_verzameling.value.replace(' ', '_') + '.html'\n",
+ "output_path = os.path.join(output_path1, bestandsnaam)\n",
+ "fig_s_t.write_html(output_path)\n",
+ "print(f\"Figuur opgeslagen als {output_path}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "32cf9cd6-1186-4e75-ae7a-b58096a45f75",
+ "metadata": {},
+ "source": [
+ "## Stap 3b: Bepalen gedraineerde parameters triaxiaalproeven en DSS-proeven op basis van methode schematiseringshandleiding (alleen Phi)\n",
+ "\n",
+ "In deze stap worden op basis van de data vastgelegd in het template PV-tool 5.0. de gedraineerde parameters bepaald volgens de methode vastgelegd in de schematiseringshandleiding macrostabiliteit. Maak een keuze uit de verzameling waarop de statistische analyse moet plaatsvinden. Als er nog geen aparte verzamelingen onderscheiden zijn in het veld (PV_NAAM) worden alle triaxaalproeven in één verzameling opgenomen. \n",
+ "\n",
+ "Als gebruiker kunnen handmatig verzamelingen worden opgegeven in de Excel template in het veld PV_NAAM of er kan gebruik worden gemaakt van de functie:\n",
+ "Definiëren en aanpassen van verzamelingen (stap #). Met behulp van de functie ## kunnen in de grafiek met behulp van de selectietool (should have) ook één of meer proefresultaten worden geselecteerd en worden toegekend aan een andere groep.\n",
+ "\n",
+ "Geef het te hanteren rekpercentage op waarbij de s' en t zijn bepaald: 2%, 5%, 15%, eindrek of bij pieksterkte. \n",
+ "Indien er eerder parameters zijn vastgesteld worden de gemaakte keuzes voor het % en de raaklijnen ingeladen.\n",
+ "\n",
+ "Ook kunnen meerdere verzamelingen getoond worden. NB. Deze extra verzamelingen worden niet meegenomen in de statistische analyse."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "57d6cea9-32c3-4a02-b720-b9c4d4717fa6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies type proef:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "baf578a525fd435cb16adf68dd8620e8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('TXT_SH', 'DSS_SH'), value='TXT_SH')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies verzameling voor statistische analyse:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c88aae60428647c29b09f1ae1499bb4d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('geen', 'TXT_testset_klei', 'TXT', 'TXT_klei_17.5_21_ondiep', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies rekpercentage s'en t:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "134a8c1cce224cdd8e5688350ad3c477",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a430f47726ef4a5b9c58a6245e5971d4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9b589733599b4c42aed5edadee252cb5",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "SelectMultiple(index=(0,), layout=Layout(height='150px', width='400px'), options=('geen', 'TXT_testset_klei', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown, clear_output\n",
+ "\n",
+ "# Dataframe met proefdata\n",
+ "dbase_df = dbase.dbase_df\n",
+ "\n",
+ "# Lijsten maken met proevenverzamelingen in database onder PVNAAM\n",
+ "PV_txt_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__TRIAXIAAL'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "PV_dss_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__DSS'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "gekozen_rekpercentage = 'eindsterkte'\n",
+ "\n",
+ "# Dropdown widgets\n",
+ "dropdown_type_proef_sh = widgets.Dropdown(options=['TXT_SH', 'DSS_SH'], value='TXT_SH', layout=widgets.Layout(width='400px'))\n",
+ "\n",
+ "\n",
+ "dropdown_rekpercentage_txt = widgets.Dropdown(options=['2% rek', '5% rek', '15% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_rekpercentage_dss = widgets.Dropdown(options=['2% rek', '5% rek', '10% rek', '15% rek', '20% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_verzameling = widgets.Dropdown(options=PV_txt_lijst, value=PV_txt_lijst[0] if PV_txt_lijst else None, layout=widgets.Layout(width='400px'))\n",
+ "multi_select_verzameling = widgets.SelectMultiple(options=PV_txt_lijst, value=[PV_txt_lijst[0]] if PV_txt_lijst else [], layout=widgets.Layout(width='400px', height='150px'))\n",
+ "\n",
+ "# Outputs\n",
+ "container_rekpercentage = widgets.Output()\n",
+ "output_rekpercentage = widgets.Output()\n",
+ "\n",
+ "# Lijsten rekpercentage en verzamelingen aanpassen bij wijziging van proeftype txt/dss\n",
+ "def update_verzamelings_dropdowns(change): \n",
+ " with container_rekpercentage: \n",
+ " clear_output(); \n",
+ " (dropdown_verzameling.options, dropdown_verzameling.value, multi_select_verzameling.options, multi_select_verzameling.value, _d) = \\\n",
+ " (PV_txt_lijst, PV_txt_lijst[0], PV_txt_lijst, [PV_txt_lijst[0]], display(dropdown_rekpercentage_txt)) if dropdown_type_proef.value == 'TXT_SH' \\\n",
+ " else (PV_dss_lijst, PV_dss_lijst[0], PV_dss_lijst, [PV_dss_lijst[0]], display(dropdown_rekpercentage_dss))\n",
+ " update_rekpercentage_output(None)\n",
+ "\n",
+ "def update_rekpercentage_output(change):\n",
+ " global gekozen_rekpercentage\n",
+ " with output_rekpercentage:\n",
+ " clear_output()\n",
+ " if dropdown_type_proef_sh.value == 'TXT_SH':\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_txt.value\n",
+ " else:\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "dropdown_type_proef_sh.observe(update_verzamelings_dropdowns, names='value')\n",
+ "dropdown_rekpercentage_txt.observe(update_rekpercentage_output, names='value')\n",
+ "dropdown_rekpercentage_dss.observe(update_rekpercentage_output, names='value')\n",
+ "\n",
+ "# Initialiseren container met juiste rekpercentage-dropdown\n",
+ "with container_rekpercentage:\n",
+ " clear_output()\n",
+ " display(dropdown_rekpercentage_txt)\n",
+ "with output_rekpercentage:\n",
+ " clear_output()\n",
+ "\n",
+ "# Display widgets\n",
+ "display(Markdown(\"**Kies type proef:**\"))\n",
+ "display(dropdown_type_proef_sh)\n",
+ "display(Markdown(\"**Kies verzameling voor statistische analyse:**\"))\n",
+ "display(dropdown_verzameling)\n",
+ "display(Markdown(\"**Kies rekpercentage s'en t:**\"))\n",
+ "display(container_rekpercentage)\n",
+ "display(output_rekpercentage)\n",
+ "display(Markdown(\"**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**\"))\n",
+ "display(multi_select_verzameling)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aefecf81-4b57-40f9-b26b-a736db27cae2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
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+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Resultaten TXT C-phi analyse (schematiseringshandleiding):\n",
+ " tan phi [-] phi [graden]\n",
+ "Verwachtingswaarde 0.779053 37.920473\n",
+ "Karakteristieke waarde 0.650774 33.055051\n",
+ "Rekenwaarde 0.650774 33.055051\n",
+ "Standaarddeviatie D-stability [-] 3.094335\n"
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+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ],
+ "sequentialminus": [
+ [
+ 0,
+ "#0d0887"
+ ],
+ [
+ 0.1111111111111111,
+ "#46039f"
+ ],
+ [
+ 0.2222222222222222,
+ "#7201a8"
+ ],
+ [
+ 0.3333333333333333,
+ "#9c179e"
+ ],
+ [
+ 0.4444444444444444,
+ "#bd3786"
+ ],
+ [
+ 0.5555555555555556,
+ "#d8576b"
+ ],
+ [
+ 0.6666666666666666,
+ "#ed7953"
+ ],
+ [
+ 0.7777777777777778,
+ "#fb9f3a"
+ ],
+ [
+ 0.8888888888888888,
+ "#fdca26"
+ ],
+ [
+ 1,
+ "#f0f921"
+ ]
+ ]
+ },
+ "colorway": [
+ "#636efa",
+ "#EF553B",
+ "#00cc96",
+ "#ab63fa",
+ "#FFA15A",
+ "#19d3f3",
+ "#FF6692",
+ "#B6E880",
+ "#FF97FF",
+ "#FECB52"
+ ],
+ "font": {
+ "color": "#2a3f5f"
+ },
+ "geo": {
+ "bgcolor": "white",
+ "lakecolor": "white",
+ "landcolor": "#E5ECF6",
+ "showlakes": true,
+ "showland": true,
+ "subunitcolor": "white"
+ },
+ "hoverlabel": {
+ "align": "left"
+ },
+ "hovermode": "closest",
+ "mapbox": {
+ "style": "light"
+ },
+ "paper_bgcolor": "white",
+ "plot_bgcolor": "#E5ECF6",
+ "polar": {
+ "angularaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "scene": {
+ "xaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "yaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ },
+ "zaxis": {
+ "backgroundcolor": "#E5ECF6",
+ "gridcolor": "white",
+ "gridwidth": 2,
+ "linecolor": "white",
+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
+ }
+ },
+ "shapedefaults": {
+ "line": {
+ "color": "#2a3f5f"
+ }
+ },
+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "TXT_SH analyse met 5% rek op TXT_klei_14_16"
+ },
+ "width": 1280,
+ "xaxis": {
+ "title": {
+ "text": "s' [kPa]"
+ }
+ },
+ "yaxis": {
+ "title": {
+ "text": "t [kPa]"
+ }
+ }
+ }
+ },
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PDF succesvol opgeslagen op: C:\\python_cursus\\PVtool2025_github\\PV-tool\\resultaten/c_phi_pdf_export_TXT_klei_14_16_TXT_SH_5procent_rek.pdf\n",
+ "Resultaat toegevoegd aan template in tabblad 'Resultaten c-phi'.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Analyse hoek van inwendige wrijving uitvoeren methode schematiseringshandleiding\n",
+ "import os\n",
+ "\n",
+ "#directory om resultaten weg te schrijven\n",
+ "output_path1 = os.path.join(os.getcwd(), \"resultaten\")\n",
+ "os.makedirs(output_path1, exist_ok=True)\n",
+ "\n",
+ "#Run analyse bepaling hoek van inwendige wrijving \n",
+ "analyse = CPhiAnalyse(\n",
+ " dbase=dbase,\n",
+ " investigation_groups=[dropdown_verzameling.value],\n",
+ " effective_stress=rekpercentage,\n",
+ " analysis_type=dropdown_type_proef_sh.value\n",
+ ")\n",
+ "\n",
+ "# Pas instellingen toe\n",
+ "analyse.apply_settings(alpha=0.75)\n",
+ "\n",
+ "# Print en exporteer resultaten\n",
+ "print('\\nResultaten TXT C-phi analyse (schematiseringshandleiding):')\n",
+ "print(analyse.get_short_results())\n",
+ "\n",
+ "# Visualisatie\n",
+ "extra_verzamelingen_tonen = list(multi_select_verzameling.value)\n",
+ "analyse.show_figure(plot_extra_dataset = extra_verzamelingen_tonen, plot_spanningspaden= True)\n",
+ "analyse.save_to_pdf(path=str(output_path1))\n",
+ "\n",
+ "# Exporteer de data naar database results\n",
+ "analyse.add_results_to_template(path=dir2, export_name=filename_export)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9776bef6-6ce0-4a70-81a1-ddfb61597b18",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7901fc55-c958-4c9e-9b48-8f5dab183a2e",
+ "metadata": {},
+ "source": [
+ "## Stap 4a: Bepalen ongedraineerde parameters triaxiaalproeven en DSS-proeven op basis van methode Shansep (S, m en POP)\n",
+ "\n",
+ "In deze stap worden op basis van de data vastgelegd in het template PV-tool 5.0. de gedraineerde parameters bepaald volgens de methode vastgelegd in de schematiseringshandleiding macrostabiliteit. Maak een keuze uit de verzameling waarop de statistische analyse moet plaatsvinden. Als er nog geen aparte verzamelingen onderscheiden zijn in het veld (PV_NAAM) worden alle triaxaalproeven in één verzameling opgenomen. \n",
+ "\n",
+ "Als gebruiker kunnen handmatig verzamelingen worden opgegeven in de Excel template in het veld PV_NAAM of er kan gebruik worden gemaakt van de functie:\n",
+ "Definiëren en aanpassen van verzamelingen (stap #). Met behulp van de functie ## kunnen in de grafiek met behulp van de selectietool (should have) ook één of meer proefresultaten worden geselecteerd en worden toegekend aan een andere groep.\n",
+ "\n",
+ "Geef het te hanteren rekpercentage op waarbij de Su is bepaald: 2%, 5%, 15%, eindrek of bij pieksterkte. \n",
+ "Indien er eerder parameters zijn vastgesteld worden de gemaakte keuzes voor het % en de raaklijnen ingeladen.\n",
+ "\n",
+ "Ook kunnen meerdere verzamelingen getoond worden. NB. Deze extra verzamelingen worden niet meegenomen in de statistische analyse."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "7b154111-f216-4a91-8956-575a22011b06",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies type proef:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5c32d0049c724739a5ab9fc55710a2d8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('TXT_S_POP', 'DSS_S_POP'), value='TXT_S_POP')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies verzameling voor statistische analyse:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ffef45a705454b6db46b005a6e373ef8",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('geen', 'TXT_testset_klei', 'TXT_WAM_outlier', 'TXT_WAM_klei_z…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies rekpercentage Su:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "63a044b5330f49f59543c4b38aeeba58",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a206101f7bd447bdb52dc9e2f747e85d",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "20105c5255624614b7e2e6364c8911a3",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "SelectMultiple(index=(0,), layout=Layout(height='150px', width='400px'), options=('geen', 'TXT_testset_klei', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown, clear_output\n",
+ "\n",
+ "# Dataframe met proefdata\n",
+ "dbase_df = dbase.dbase_df\n",
+ "\n",
+ "# Lijsten maken met proevenverzamelingen in database onder PVNAAM\n",
+ "PV_txt_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__TRIAXIAAL'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "PV_dss_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__DSS'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "gekozen_rekpercentage = 'eindsterkte'\n",
+ "\n",
+ "# Dropdown widgets\n",
+ "dropdown_type_proef_shansep = widgets.Dropdown(options=['TXT_S_POP', 'DSS_S_POP'], value='TXT_S_POP', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_rekpercentage_txt = widgets.Dropdown(options=['2% rek', '5% rek', '15% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_rekpercentage_dss = widgets.Dropdown(options=['2% rek', '5% rek', '10% rek', '15% rek', '20% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_verzameling = widgets.Dropdown(options=PV_txt_lijst, value=PV_txt_lijst[0] if PV_txt_lijst else None, layout=widgets.Layout(width='400px'))\n",
+ "multi_select_verzameling = widgets.SelectMultiple(options=PV_txt_lijst, value=[PV_txt_lijst[0]] if PV_txt_lijst else [], layout=widgets.Layout(width='400px', height='150px'))\n",
+ "\n",
+ "# Outputs\n",
+ "container_rekpercentage = widgets.Output()\n",
+ "output_rekpercentage = widgets.Output()\n",
+ "\n",
+ "# Lijsten rekpercentage en verzamelingen aanpassen bij wijziging van proeftype txt/dss\n",
+ "def update_verzamelings_dropdowns(change): \n",
+ " with container_rekpercentage: \n",
+ " clear_output(); \n",
+ " (dropdown_verzameling.options, dropdown_verzameling.value, multi_select_verzameling.options, multi_select_verzameling.value, _d) = \\\n",
+ " (PV_txt_lijst, PV_txt_lijst[0], PV_txt_lijst, [PV_txt_lijst[0]], display(dropdown_rekpercentage_txt)) if dropdown_type_proef.value == 'TXT_S_POP' \\\n",
+ " else (PV_dss_lijst, PV_dss_lijst[0], PV_dss_lijst, [PV_dss_lijst[0]], display(dropdown_rekpercentage_dss))\n",
+ " update_rekpercentage_output(None)\n",
+ "\n",
+ "def update_rekpercentage_output(change):\n",
+ " global gekozen_rekpercentage\n",
+ " with output_rekpercentage:\n",
+ " clear_output()\n",
+ " if dropdown_type_proef_shansep.value == 'TXT_S_POP':\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_txt.value\n",
+ " else:\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "dropdown_type_proef_shansep.observe(update_verzamelings_dropdowns, names='value')\n",
+ "dropdown_rekpercentage_txt.observe(update_rekpercentage_output, names='value')\n",
+ "dropdown_rekpercentage_dss.observe(update_rekpercentage_output, names='value')\n",
+ "\n",
+ "# Initialiseren container met juiste rekpercentage-dropdown\n",
+ "with container_rekpercentage:\n",
+ " clear_output()\n",
+ " display(dropdown_rekpercentage_txt)\n",
+ "with output_rekpercentage:\n",
+ " clear_output()\n",
+ "\n",
+ "# Display widgets\n",
+ "display(Markdown(\"**Kies type proef:**\"))\n",
+ "display(dropdown_type_proef_shansep)\n",
+ "display(Markdown(\"**Kies verzameling voor statistische analyse:**\"))\n",
+ "display(dropdown_verzameling)\n",
+ "display(Markdown(\"**Kies rekpercentage Su:**\"))\n",
+ "display(container_rekpercentage)\n",
+ "display(output_rekpercentage)\n",
+ "display(Markdown(\"**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**\"))\n",
+ "display(multi_select_verzameling)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "12800281-f317-42ed-85e0-5069bfa16c35",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Er zijn geen eerdere resultaten gevonden voor de opgegeven parameters.\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Input berekenen of uitlezen uit Resultaten SHANSEP voor Shansep Widget met gemiddelde en karakteristieke parameters\n",
+ "\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "from pv_tool.shansep_analysis.shansep_analysis import SHANSEP\n",
+ "from pv_tool.shansep_analysis.variables import *\n",
+ "\n",
+ "dbase_df = dbase.dbase_df\n",
+ "\n",
+ "# Ophalen geselecteerde waarden\n",
+ "verzameling = dropdown_verzameling.value\n",
+ "\n",
+ "if dropdown_type_proef_shansep.value.startswith('TXT'):\n",
+ " rekpercentage = dropdown_rekpercentage_txt.value\n",
+ "else:\n",
+ " rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "# Analyse Shansep parameters\n",
+ "analyse = SHANSEP(\n",
+ " dbase=dbase,\n",
+ " investigation_groups=[dropdown_verzameling.value],\n",
+ " effective_stress=rekpercentage,\n",
+ " analysis_type=dropdown_type_proef_shansep.value\n",
+ ")\n",
+ "\n",
+ "analyse.apply_settings(alpha=0.75)\n",
+ "\n",
+ "analyse.get_shansep_data()\n",
+ "df_gem, df_kar = analyse.get_result_values_shansep()\n",
+ "\n",
+ "# print(analyse.get_short_results())\n",
+ "# print(df_gem)\n",
+ "#print(df_kar)\n",
+ "\n",
+ "# Benaderingswaarden voor het snijpunt y-as, S, m en POP gemiddelde waarden\n",
+ "# Kolom 2: Snijpunt y-as\n",
+ "SNIJPUNT_GEM_BENADERING_R2 = round(analyse.e_a1_oc, 2)\n",
+ "SNIJPUNT_GEM_BENADERING_R3 = None\n",
+ "SNIJPUNT_GEM_BENADERING_R4 = None\n",
+ "SNIJPUNT_GEM_BENADERING_R5 = None\n",
+ "\n",
+ "# Kolom 3: Schuifsterkteratio S\n",
+ "S_GEM_BENADERING_UIT_S_en_POP_R2 = round(analyse.e_a2_oc, 2)\n",
+ "S_GEM_BENADERING_UIT_S_en_m_R3 = round(analyse.exp_e_a1_nc_oc, 2)\n",
+ "S_GEM_BENADERING_R4 = None\n",
+ "S_GEM_BENADERING_UIT_S_OCR1_R5 = round(analyse.exp_gem_ln_su_svc_nc, 2)\n",
+ "\n",
+ "# Kolom 4: Sterktetoename exponent m\n",
+ "m__GEM_BENADERING_R2 = None\n",
+ "m_GEM_BENADERING_uit_S_en_m_R3 = round(analyse.e_a2_nc_oc, 2)\n",
+ "m_GEM_BENADERING_R4 = None\n",
+ "m_GEM_BENADERING_R5 = None\n",
+ "\n",
+ "# Kolom 5: POP\n",
+ "POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R1 = round(analyse.e_a1_oc/analyse.e_a2_oc/analyse.e_a2_nc_oc, 2)\n",
+ "POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R2 = POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R1\n",
+ "POP_GEM_BENADERING_UIT_TXT_R3 = round(analyse.pop_gem_oc, 2)\n",
+ "POP_GEM_BENADERING_R4 = None\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Benaderingswaarden voor het snijpunt y-as, S, m en POP karakteristieke waarden\n",
+ "# Kolom 2: Snijpunt y-as\n",
+ "SNIJPUNT_KAR_BENADERING_R2 = round(analyse.a1_kar_oc, 2)\n",
+ "SNIJPUNT_KAR_BENADERING_R3 = None\n",
+ "SNIJPUNT_KAR_BENADERING_R4 = None\n",
+ "SNIJPUNT_KAR_BENADERING_R5 = None\n",
+ "\n",
+ "# Kolom 3: Schuifsterkteratio S\n",
+ "S_KAR_BENADERING_UIT_S_en_POP_R2 = round(analyse.a2_kar_oc, 2)\n",
+ "S_KAR_BENADERING_UIT_S_en_m_R3 = round(analyse.exp_a1_kar_nc_oc, 2)\n",
+ "S_KAR_BENADERING_R4 = None\n",
+ "S_KAR_BENADERING_UIT_S_OCR1_R5 = round(analyse.exp_kar_ln_su_svc_nc, 2)\n",
+ "\n",
+ "# Kolom 4: Sterktetoename exponent m\n",
+ "m__KAR_BENADERING_R2 = None\n",
+ "m_KAR_BENADERING_uit_S_en_m_R3 = round(analyse.a2_kar_nc_oc, 2)\n",
+ "m_KAR_BENADERING_R4 = None\n",
+ "m_KAR_BENADERING_R5 = None\n",
+ "\n",
+ "# Kolom 5: POP\n",
+ "POP_KAR_BENADERING_UIT_snijpunt_S_en_m_R1 = round(analyse.a1_kar_oc/analyse.a2_kar_oc/analyse.a2_kar_nc_oc, 2)\n",
+ "POP_KAR_BENADERING_UIT_snijpunt_S_en_m_R2 = POP_KAR_BENADERING_UIT_snijpunt_S_en_m_R1\n",
+ "POP_KAR_BENADERING_UIT_TXT_R3 = round(analyse.pop_kar_oc, 2)\n",
+ "POP_KAR_BENADERING_R4 = None\n",
+ "\n",
+ "\n",
+ "# Ophalen ingevulde handmatige waardes uit Dbase of gelijk aan benadering/default waarden indien deze ontbreekt in dbase\n",
+ "laatste_resultaten = analyse.get_previous_results(path=str(dir2), file_name=str(filename_export))\n",
+ "\n",
+ "if (laatste_resultaten is not None and \n",
+ " not laatste_resultaten.empty and \n",
+ " 'PV_A1_SNIJPUNT_YAS_GEM [-]' in laatste_resultaten):\n",
+ " PV_A1_SNIJPUNT_YAS_GEM_handmatig = [laatste_resultaten['PV_A1_SNIJPUNT_YAS_GEM [-]']]\n",
+ " PV_A2_S_GEM_handmatig = [laatste_resultaten['PV_A2_S_GEM [-]']]\n",
+ " PV_m_GEM_handmatig = [laatste_resultaten['PV_m_GEM [-]']]\n",
+ " PV_A1_SNIJPUNT_YAS_KAR_handmatig = [laatste_resultaten['PV_A1_SNIJPUNT_YAS_KAR [-]']]\n",
+ " PV_A2_S_KAR_handmatig = [laatste_resultaten['PV_A2_S_KAR [-]']]\n",
+ " PV_m_KAR_handmatig = [laatste_resultaten['PV_m_KAR [-]']]\n",
+ "\n",
+ "else:\n",
+ " PV_A1_SNIJPUNT_YAS_GEM_handmatig = [SNIJPUNT_GEM_BENADERING_R2]\n",
+ " PV_A2_S_GEM_handmatig = [S_GEM_BENADERING_UIT_S_en_POP_R2]\n",
+ " PV_m_GEM_handmatig = [m_GEM_BENADERING_uit_S_en_m_R3]\n",
+ " PV_A1_SNIJPUNT_YAS_KAR_handmatig = [SNIJPUNT_KAR_BENADERING_R2]\n",
+ " PV_A2_S_KAR_handmatig = [S_KAR_BENADERING_UIT_S_en_POP_R2]\n",
+ " PV_m_KAR_handmatig = [m_KAR_BENADERING_uit_S_en_m_R3]\n",
+ "\n",
+ "\n",
+ "# PV_A1_SNIJPUNT_YAS_KAR [-]\t\n",
+ "# PV_A2_S_KAR [-]\t\n",
+ "# PV_m_KAR [-]\t\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "e889f125-5031-420b-a4f9-571957f76242",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[np.float64(9.75)]\n",
+ "[np.float64(0.32)]\n",
+ "[np.float64(0.96)]\n",
+ "[np.float64(5.91)]\n",
+ "[np.float64(0.32)]\n",
+ "[np.float64(0.93)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(PV_A1_SNIJPUNT_YAS_GEM_handmatig)\n",
+ "print(PV_A2_S_GEM_handmatig)\n",
+ "print(PV_m_GEM_handmatig)\n",
+ "print(PV_A1_SNIJPUNT_YAS_KAR_handmatig)\n",
+ "print(PV_A2_S_KAR_handmatig)\n",
+ "print(PV_m_KAR_handmatig)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "6c17d7b6-68c4-4c44-bc9b-a4b54b962c33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Grid voor Widget gemiddelde shansep waarden.\n",
+ "\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "\n",
+ "## Widget voor gemiddelde parameters benadering en handmatig opgeven waarden\n",
+ "\n",
+ "# Beschrijvingen (eerste kolom)\n",
+ "descriptions = [\n",
+ " \"bepaling S en POP uit triaxiaal- of DSS proeven\",\n",
+ " \"bepaling S en m uit triaxiaal- of DSS proeven\",\n",
+ " \"o.b.v. opgegeven POP bij triaxiaal- of DSS proeven\",\n",
+ " \"bepaling S uit triaxiaal- of DSS proeven OCR=1\"\n",
+ "]\n",
+ "\n",
+ "# Maak een raster van 6 rijen en 5 kolommen\n",
+ "grid = widgets.GridspecLayout(6, 5, width='1050px') # Breder raster\n",
+ "\n",
+ "# Veldnamen (eerste rij)\n",
+ "grid[0, 0] = widgets.Label(\"Parameters\", layout=widgets.Layout(width='300px')) # Brede eerste kolom\n",
+ "grid[0, 1] = widgets.Label(\"Snijpunt y-as\")\n",
+ "grid[0, 2] = widgets.Label(\"Schuifsterkteratio S\")\n",
+ "grid[0, 3] = widgets.Label(\"Sterkte toename exponent m\")\n",
+ "grid[0, 4] = widgets.Label(\"POP\")\n",
+ "\n",
+ "# Functie voor berekening van rij 6 kolom 5\n",
+ "def bereken_resultaat(change):\n",
+ " try:\n",
+ " kolom_2 = float(input_row_6_kol_2.value)\n",
+ " kolom_3 = float(input_row_6_kol_3.value)\n",
+ " kolom_4 = float(input_row_6_kol_4.value)\n",
+ " result.value = str(kolom_2 + kolom_3 + kolom_4)\n",
+ " except ValueError:\n",
+ " result.value = \"Invalid\"\n",
+ "\n",
+ "# Vul de tabel rijen 2 t/m 5 met berekende waarden\n",
+ "for i, (desc, y_as, S, m, pop) in enumerate(zip(\n",
+ " descriptions,\n",
+ " [SNIJPUNT_GEM_BENADERING_R2, SNIJPUNT_GEM_BENADERING_R3, SNIJPUNT_GEM_BENADERING_R4, SNIJPUNT_GEM_BENADERING_R5],\n",
+ " [S_GEM_BENADERING_UIT_S_en_POP_R2, S_GEM_BENADERING_UIT_S_en_m_R3, S_GEM_BENADERING_R4, S_GEM_BENADERING_UIT_S_OCR1_R5],\n",
+ " [m__GEM_BENADERING_R2, m_GEM_BENADERING_uit_S_en_m_R3, m_GEM_BENADERING_R4, m_GEM_BENADERING_R5],\n",
+ " [POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R1, POP_GEM_BENADERING_UIT_snijpunt_S_en_m_R2, POP_GEM_BENADERING_UIT_TXT_R3, POP_GEM_BENADERING_R4]\n",
+ "), start=1):\n",
+ " grid[i, 0] = widgets.Label(desc, layout=widgets.Layout(width='350px')) # Beschrijving (eerste kolom)\n",
+ " grid[i, 1] = widgets.Label(str(y_as) if y_as is not None else \"-\") # Snijpunt y-as\n",
+ " grid[i, 2] = widgets.Label(str(S) if S is not None else \"-\") # Schuifsterkteratio S\n",
+ " grid[i, 3] = widgets.Label(str(m) if m is not None else \"-\") # Sterkte toename exponent m\n",
+ " grid[i, 4] = widgets.Label(str(pop) if pop is not None else \"-\") # POP\n",
+ "\n",
+ "# Toevoegen van invoervelden in rij 6\n",
+ "grid[5, 0] = widgets.Label(\"Handmatige invoer\", layout=widgets.Layout(width='350px'))\n",
+ "\n",
+ "PV_A1_SNIJPUNT_YAS_GEM_handmatig = widgets.FloatText(value=PV_A1_SNIJPUNT_YAS_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "PV_A2_S_GEM_handmatig = widgets.FloatText(value=PV_A2_S_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "PV_m_GEM_handmatig = widgets.FloatText(value=PV_m_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "result_label = widgets.Label(value='', layout=widgets.Layout(width='180px'))\n",
+ "\n",
+ "# PV_A1_SNIJPUNT_YAS_GEM_handmatig = widgets.FloatText(value=PV_A1_SNIJPUNT_YAS_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# PV_A2_S_GEM_handmatig = widgets.FloatText(value=PV_A2_S_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# PV_m_GEM_handmatig = widgets.FloatText(value=PV_m_GEM_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# result_label = widgets.Label(value='', layout=widgets.Layout(width='180px'))\n",
+ "\n",
+ "# Functie om POP live te berekenen op basis van handmatig opgegeven waarden.\n",
+ "def bereken_POP_gem(change=None):\n",
+ " try:\n",
+ " v1 = PV_A1_SNIJPUNT_YAS_GEM_handmatig.value\n",
+ " v2 = PV_A2_S_GEM_handmatig.value\n",
+ " v3 = PV_m_GEM_handmatig.value\n",
+ " if v2 == 0 or v3 == 0:\n",
+ " result_label.value = \"Niet gedefinieerd\"\n",
+ " else:\n",
+ " result_label.value = f\"{v1 / v2 / v3:.3f}\"\n",
+ " except Exception:\n",
+ " result_label.value = \"Fout\"\n",
+ "\n",
+ "bereken_POP_gem()\n",
+ "\n",
+ "# Koppel de functie aan elke wijziging\n",
+ "PV_A1_SNIJPUNT_YAS_GEM_handmatig.observe(bereken_POP_gem, names=\"value\")\n",
+ "PV_A2_S_GEM_handmatig.observe(bereken_POP_gem, names=\"value\")\n",
+ "PV_m_GEM_handmatig.observe(bereken_POP_gem, names=\"value\")\n",
+ "\n",
+ "# Koppel invoervelden aan de tabel\n",
+ "grid[5, 1] = PV_A1_SNIJPUNT_YAS_GEM_handmatig\n",
+ "grid[5, 2] = PV_A2_S_GEM_handmatig\n",
+ "grid[5, 3] = PV_m_GEM_handmatig\n",
+ "grid[5, 4] = result_label\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "id": "9908544e-f93a-443b-b496-0889e2e6199b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Grid voor Widget karakteristieke shansep waarden.\n",
+ "\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown\n",
+ "\n",
+ "## Widget voor karakteristieke parameters benadering en handmatig opgeven waarden\n",
+ "\n",
+ "# Beschrijvingen (eerste kolom)\n",
+ "descriptions = [\n",
+ " \"bepaling S en POP uit triaxiaal- of DSS proeven\",\n",
+ " \"bepaling S en m uit triaxiaal- of DSS proeven\",\n",
+ " \"o.b.v. opgegeven POP bij triaxiaal- of DSS proeven\",\n",
+ " \"bepaling S uit triaxiaal- of DSS proeven OCR=1\"\n",
+ "]\n",
+ "\n",
+ "# Maak een raster van 6 rijen en 5 kolommen\n",
+ "grid_kar = widgets.GridspecLayout(6, 5, width='1050px') # Breder raster\n",
+ "\n",
+ "# Veldnamen (eerste rij)\n",
+ "grid_kar[0, 0] = widgets.Label(\"Parameters\", layout=widgets.Layout(width='300px')) # Brede eerste kolom\n",
+ "grid_kar[0, 1] = widgets.Label(\"Snijpunt y-as\")\n",
+ "grid_kar[0, 2] = widgets.Label(\"Schuifsterkteratio S\")\n",
+ "grid_kar[0, 3] = widgets.Label(\"Sterkte toename exponent m\")\n",
+ "grid_kar[0, 4] = widgets.Label(\"POP\")\n",
+ "\n",
+ "# Functie voor berekening van rij 6 kolom 5\n",
+ "def bereken_resultaat(change):\n",
+ " try:\n",
+ " kolom_2 = float(input_row_6_kol_2.value)\n",
+ " kolom_3 = float(input_row_6_kol_3.value)\n",
+ " kolom_4 = float(input_row_6_kol_4.value)\n",
+ " result.value = str(kolom_2 + kolom_3 + kolom_4)\n",
+ " except ValueError:\n",
+ " result.value = \"Invalid\"\n",
+ "\n",
+ "# Vul de tabel rijen 2 t/m 5 met berekende waarden\n",
+ "for i, (desc, y_as, S, m, pop) in enumerate(zip(\n",
+ " descriptions,\n",
+ " [SNIJPUNT_KAR_BENADERING_R2, SNIJPUNT_KAR_BENADERING_R3, SNIJPUNT_KAR_BENADERING_R4, SNIJPUNT_KAR_BENADERING_R5],\n",
+ " [S_KAR_BENADERING_UIT_S_en_POP_R2, S_KAR_BENADERING_UIT_S_en_m_R3, S_KAR_BENADERING_R4, S_KAR_BENADERING_UIT_S_OCR1_R5],\n",
+ " [m__KAR_BENADERING_R2, m_KAR_BENADERING_uit_S_en_m_R3, m_KAR_BENADERING_R4, m_KAR_BENADERING_R5],\n",
+ " [POP_KAR_BENADERING_UIT_snijpunt_S_en_m_R1, POP_KAR_BENADERING_UIT_snijpunt_S_en_m_R2, POP_KAR_BENADERING_UIT_TXT_R3, POP_KAR_BENADERING_R4]\n",
+ "), start=1):\n",
+ " grid_kar[i, 0] = widgets.Label(desc, layout=widgets.Layout(width='350px')) # Beschrijving (eerste kolom)\n",
+ " grid_kar[i, 1] = widgets.Label(str(y_as) if y_as is not None else \"-\") # Snijpunt y-as\n",
+ " grid_kar[i, 2] = widgets.Label(str(S) if S is not None else \"-\") # Schuifsterkteratio S\n",
+ " grid_kar[i, 3] = widgets.Label(str(m) if m is not None else \"-\") # Sterkte toename exponent m\n",
+ " grid_kar[i, 4] = widgets.Label(str(pop) if pop is not None else \"-\") # POP\n",
+ "\n",
+ "# Toevoegen van invoervelden in rij 6\n",
+ "grid_kar[5, 0] = widgets.Label(\"Handmatige invoer\", layout=widgets.Layout(width='350px'))\n",
+ "\n",
+ "PV_A1_SNIJPUNT_YAS_KAR_handmatig = widgets.FloatText(value=PV_A1_SNIJPUNT_YAS_KAR_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "PV_A2_S_KAR_handmatig = widgets.FloatText(value=PV_A2_S_KAR_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "PV_m_KAR_handmatig = widgets.FloatText(value=PV_m_KAR_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "result_label = widgets.Label(value='', layout=widgets.Layout(width='180px'))\n",
+ "\n",
+ "# PV_A1_SNIJPUNT_YAS_KAR_handmatig = widgets.FloatText(value=PV_A1_SNIJPUNT_YAS_KAR_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# PV_A2_S_KAR_handmatig = widgets.FloatText(value=PV_A2_S_KAR_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# PV_m_KAR_handmatig = widgets.FloatText(value=PV_m_KAR_handmatig[0], layout=widgets.Layout(width='180px'))\n",
+ "# result_label = widgets.Label(value='', layout=widgets.Layout(width='180px'))\n",
+ "\n",
+ "# Functie om POP live te berekenen op basis van handmatig opgegeven waarden.\n",
+ "def bereken_POP_KAR(change=None):\n",
+ " try:\n",
+ " v1 = PV_A1_SNIJPUNT_YAS_KAR_handmatig.value\n",
+ " v2 = PV_A2_S_KAR_handmatig.value\n",
+ " v3 = PV_m_KAR_handmatig.value\n",
+ " if v2 == 0 or v3 == 0:\n",
+ " result_label.value = \"Niet gedefinieerd\"\n",
+ " else:\n",
+ " result_label.value = f\"{v1 / v2 / v3:.3f}\"\n",
+ " except Exception:\n",
+ " result_label.value = \"Fout\"\n",
+ "\n",
+ "bereken_POP_KAR()\n",
+ "\n",
+ "# Koppel de functie aan elke wijziging\n",
+ "PV_A1_SNIJPUNT_YAS_KAR_handmatig.observe(bereken_POP_KAR, names=\"value\")\n",
+ "PV_A2_S_KAR_handmatig.observe(bereken_POP_KAR, names=\"value\")\n",
+ "PV_m_KAR_handmatig.observe(bereken_POP_KAR, names=\"value\")\n",
+ "\n",
+ "# Koppel invoervelden aan de tabel\n",
+ "grid_kar[5, 1] = PV_A1_SNIJPUNT_YAS_KAR_handmatig\n",
+ "grid_kar[5, 2] = PV_A2_S_KAR_handmatig\n",
+ "grid_kar[5, 3] = PV_m_KAR_handmatig\n",
+ "grid_kar[5, 4] = result_label"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "b8ca6495-2258-4e21-8a15-4cbaf841f5b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "\n",
+ "**Stel de raaklijnen vast voor de bepaling van de gemiddelde en karakteristieke waarden van:**\n",
+ "- S en POP door het opgeven van het snijpunt met de y-as (a1) en de schuifsterkteratio (a2)\n",
+ "- S en m door het opgeven van het schuifsterkteratio (a1) en de macht (a2)\n",
+ "\n",
+ "Op basis van regressie wordt een eerste benadering gegeven voor de gemiddelde en karakteristieke waarden van al deze parameters, tevens wordt de POP op basis van de CRS of samendrukkingsproeven gegeven en de S op basis van de normaal geconsolideerde proeven.\n",
+ "\n",
+ "Toets in de volgende stap bij het genereren van de grafieken of de raaklijnen juist zijn gekozen en pas deze aan naar eigen inzicht.\n",
+ "De invoer wordt automatisch opgehaald uit het template_PVtool5_0.xlsx [resultaten Shansep] indien er eerder resultaten zijn opgeslagen voor de betreffende verzameling.\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Opgeven gemiddelde waarden (fit):**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
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+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "GridspecLayout(children=(Label(value='Parameters', layout=Layout(grid_area='widget001', width='300px')), Label…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Opgeven karakteristieke waarden (fit):**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "03833a8036ed4b69af57f68bcbcb53e0",
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+ "GridspecLayout(children=(Label(value='Parameters', layout=Layout(grid_area='widget001', width='300px')), Label…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Toon de tabelwidget voor de Shansep parameters\n",
+ "\n",
+ "## toelichting\n",
+ "from IPython.display import display, Markdown\n",
+ "tekst = \"\"\"\n",
+ "**Stel de raaklijnen vast voor de bepaling van de gemiddelde en karakteristieke waarden van:**\n",
+ "- S en POP door het opgeven van het snijpunt met de y-as (a1) en de schuifsterkteratio (a2)\n",
+ "- S en m door het opgeven van het schuifsterkteratio (a1) en de macht (a2)\n",
+ "\n",
+ "Op basis van regressie wordt een eerste benadering gegeven voor de gemiddelde en karakteristieke waarden van al deze parameters, tevens wordt de POP op basis van de CRS of samendrukkingsproeven gegeven en de S op basis van de normaal geconsolideerde proeven.\n",
+ "\n",
+ "Toets in de volgende stap bij het genereren van de grafieken of de raaklijnen juist zijn gekozen en pas deze aan naar eigen inzicht.\n",
+ "De invoer wordt automatisch opgehaald uit het template_PVtool5_0.xlsx [resultaten Shansep] indien er eerder resultaten zijn opgeslagen voor de betreffende verzameling.\n",
+ "\"\"\"\n",
+ "display(Markdown(tekst))\n",
+ "\n",
+ "display(Markdown(\"**Opgeven gemiddelde waarden (fit):**\"))\n",
+ "display(grid)\n",
+ "\n",
+ "display(Markdown(\"**Opgeven karakteristieke waarden (fit):**\"))\n",
+ "display(grid_kar)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "b270d822-cd07-480f-a21c-0eb91ab783be",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " S [-] m [-] POP [kPa]\n",
+ "Verwachtingswaarde 0.32 0.960000 31.738281\n",
+ "Karakteristieke waarde 0.32 0.930000 19.858871\n",
+ "Standaarddeviatie 0.00 0.018424 8.523248\n"
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+ "bgcolor": "#E5ECF6",
+ "radialaxis": {
+ "gridcolor": "white",
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+ "scene": {
+ "xaxis": {
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+ },
+ "yaxis": {
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+ "gridwidth": 2,
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+ },
+ "zaxis": {
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+ "gridwidth": 2,
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+ "showbackground": true,
+ "ticks": "",
+ "zerolinecolor": "white"
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+ },
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+ "line": {
+ "color": "#2a3f5f"
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+ "ternary": {
+ "aaxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "baxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ },
+ "bgcolor": "#E5ECF6",
+ "caxis": {
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": ""
+ }
+ },
+ "title": {
+ "x": 0.05
+ },
+ "xaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ },
+ "yaxis": {
+ "automargin": true,
+ "gridcolor": "white",
+ "linecolor": "white",
+ "ticks": "",
+ "title": {
+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "Bepaling S en POP uit TXT_S_POP proef"
+ },
+ "width": 1280,
+ "xaxis": {
+ "title": {
+ "text": "$\\sigma'_{v} [kPa]$"
+ }
+ },
+ "yaxis": {
+ "title": {
+ "text": "$s_{u} [kPa]$"
+ }
+ }
+ }
+ }
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#voer Shansep analyse uit met invoer en toon grafieken\n",
+ "\n",
+ "analyse.set_parameters_handmatig(snijpunt_gem = PV_A1_SNIJPUNT_YAS_GEM_handmatig.value, s_gem = PV_A2_S_GEM_handmatig.value, m_gem= PV_m_GEM_handmatig.value, snijpunt_kar= PV_A1_SNIJPUNT_YAS_KAR_handmatig.value, s_kar= PV_A2_S_KAR_handmatig.value, m_kar= PV_m_KAR_handmatig.value)\n",
+ "extra_verzamelingen_tonen = list(multi_select_verzameling.value)\n",
+ "\n",
+ "output_df = analyse.get_short_results()\n",
+ "print(output_df)\n",
+ "\n",
+ "analyse.show_figure_sv_su(plot_extra_dataset=extra_verzamelingen_tonen) #deze werkt niet\n",
+ "analyse.show_figure_ln_ocr_ln_s(plot_extra_dataset=extra_verzamelingen_tonen)\n",
+ "analyse.show_figure_sv_su_nc(plot_extra_dataset=extra_verzamelingen_tonen) #hier moet het NC verloop worden ingetekend met POP =0\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "b12fe78e-5333-4770-8a7f-828deecef7be",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SHANSEP PDF export voltooid: C:\\python_cursus\\PVtool2025_github\\PV-tool\\resultaten/shansep_pdf_export_TXT_SAFE_klei_licht_16_175_TXT_S_POP_15procent_rek.pdf\n"
+ ]
+ },
+ {
+ "ename": "AttributeError",
+ "evalue": "'SHANSEP' object has no attribute 'add_results_to_template'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+ "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
+ "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[60]\u001b[39m\u001b[32m, line 13\u001b[39m\n\u001b[32m 9\u001b[39m analyse.save_to_pdf(path=output_path1) \n\u001b[32m 11\u001b[39m \u001b[38;5;66;03m# schrijf invoer en resultaten naar PV_template, tabblad [Resultaten SHANSEP]\u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m#analyse.add_results_to_dbase(path=dir2, file_name=filename_export) # wordt naar de verkeerde regel weggeschreven\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m \u001b[43manalyse\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_results_to_template\u001b[49m(path=dir2, export_name=filename_export)\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m#Plotly bestand maken \u001b[39;00m\n\u001b[32m 16\u001b[39m \u001b[38;5;66;03m# fig_s_t = analyse.figure # of analyse.fig als die bestaat\u001b[39;00m\n\u001b[32m 17\u001b[39m \u001b[38;5;66;03m# bestandsnaam = dropdown_verzameling.value.replace(' ', '_') + '.html'\u001b[39;00m\n\u001b[32m 18\u001b[39m \u001b[38;5;66;03m# output_path = os.path.join(output_path1, bestandsnaam)\u001b[39;00m\n\u001b[32m 19\u001b[39m \u001b[38;5;66;03m# fig_s_t.write_html(output_path)\u001b[39;00m\n\u001b[32m 20\u001b[39m \u001b[38;5;66;03m# print(f\"Figuur opgeslagen als {output_path}\")\u001b[39;00m\n",
+ "\u001b[31mAttributeError\u001b[39m: 'SHANSEP' object has no attribute 'add_results_to_template'"
+ ]
+ }
+ ],
+ "source": [
+ "# Resultaten Shansep analyse opslaan naar: ##_PVtool5_0.xlsx, pdf en exporteer tussenstappen naar excel\n",
+ "import os\n",
+ "\n",
+ "#directory om resultaten weg te schrijven\n",
+ "output_path1 = os.path.join(os.getcwd(), \"resultaten\")\n",
+ "os.makedirs(output_path1, exist_ok=True)\n",
+ "\n",
+ "# Sla resultaten op als PDF\n",
+ "analyse.save_to_pdf(path=output_path1) \n",
+ "\n",
+ "# schrijf invoer en resultaten naar PV_template, tabblad [Resultaten SHANSEP]\n",
+ "#analyse.add_results_to_dbase(path=dir2, file_name=filename_export) # wordt naar de verkeerde regel weggeschreven\n",
+ "analyse.add_results_to_template(path=dir2, export_name=filename_export)\n",
+ "\n",
+ "#Plotly bestand maken \n",
+ "# fig_s_t = analyse.figure # of analyse.fig als die bestaat\n",
+ "# bestandsnaam = dropdown_verzameling.value.replace(' ', '_') + '.html'\n",
+ "# output_path = os.path.join(output_path1, bestandsnaam)\n",
+ "# fig_s_t.write_html(output_path)\n",
+ "# print(f\"Figuur opgeslagen als {output_path}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "7eee2e80-33cc-4b58-84d9-54cf20746100",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "C:\\python_cursus\\PVtool2025_github\\import_files\n"
+ ]
+ }
+ ],
+ "source": [
+ "print (dir2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d7e1e162-7b9b-4030-8b38-151e67f84337",
+ "metadata": {},
+ "source": [
+ "## Stap 4b: Bepalen ongedraineerde parameters triaxiaalproeven en DSS-proeven op Su tabel methode\n",
+ "\n",
+ "In deze stap worden op basis van de data vastgelegd in het template PV-tool 5.0. de gedraineerde parameters bepaald volgens de methode vastgelegd in de schematiseringshandleiding macrostabiliteit. Maak een keuze uit de verzameling waarop de statistische analyse moet plaatsvinden. Als er nog geen aparte verzamelingen onderscheiden zijn in het veld (PV_NAAM) worden alle triaxaalproeven in één verzameling opgenomen. \n",
+ "\n",
+ "Als gebruiker kunnen handmatig verzamelingen worden opgegeven in de Excel template in het veld PV_NAAM of er kan gebruik worden gemaakt van de functie:\n",
+ "Definiëren en aanpassen van verzamelingen (stap #). Met behulp van de functie ## kunnen in de grafiek met behulp van de selectietool (should have) ook één of meer proefresultaten worden geselecteerd en worden toegekend aan een andere groep.\n",
+ "\n",
+ "Geef het te hanteren rekpercentage op waarbij de Su is bepaald: 2%, 5%, 15%, eindrek of bij pieksterkte. \n",
+ "Indien er eerder parameters zijn vastgesteld worden de gemaakte keuzes voor het % en de raaklijnen ingeladen.\n",
+ "\n",
+ "Ook kunnen meerdere verzamelingen getoond worden. NB. Deze extra verzamelingen worden niet meegenomen in de statistische analyse."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "7d1bbaa2-5100-46f8-a28a-576590fcc29c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies type proef:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "748745df279a4fc1ad4295f72865151e",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('TXT_S_SU', 'DSS_S_SU'), value='TXT_S_SU')"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies verzameling voor statistische analyse:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "738138c53ef446b78e0751496285a6b4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Dropdown(layout=Layout(width='400px'), options=('geen', 'TXT_testset_klei', 'TXT', 'TXT_klei_17.5_21_ondiep', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies rekpercentage Su:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "676b8ba129ef4d18b2307a3a53bf9038",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "a195438064a24350b1338f7842d3dcfd",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Output()"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "80c2ee85497d46e29486ffba9c6fe609",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "SelectMultiple(index=(0,), layout=Layout(height='150px', width='400px'), options=('geen', 'TXT_testset_klei', …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display, Markdown, clear_output\n",
+ "\n",
+ "# Dataframe met proefdata\n",
+ "dbase_df = dbase.dbase_df\n",
+ "\n",
+ "# Lijsten maken met proevenverzamelingen in database onder PVNAAM\n",
+ "PV_txt_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__TRIAXIAAL'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "PV_dss_lijst = ['geen'] + dbase_df.loc[dbase_df['ALG__DSS'], 'PV_NAAM'].dropna().unique().tolist()\n",
+ "gekozen_rekpercentage = 'eindsterkte'\n",
+ "\n",
+ "# Dropdown widgets\n",
+ "dropdown_type_proef_su = widgets.Dropdown(options=['TXT_su_tabel', 'DSS_su_tabel'], value='TXT_su_tabel', layout=widgets.Layout(width='400px'))\n",
+ "\n",
+ "\n",
+ "dropdown_rekpercentage_txt = widgets.Dropdown(options=['2% rek', '5% rek', '15% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_rekpercentage_dss = widgets.Dropdown(options=['2% rek', '5% rek', '10% rek', '15% rek', '20% rek', 'eindsterkte', 'pieksterkte'], value='eindsterkte', layout=widgets.Layout(width='400px'))\n",
+ "dropdown_verzameling = widgets.Dropdown(options=PV_txt_lijst, value=PV_txt_lijst[0] if PV_txt_lijst else None, layout=widgets.Layout(width='400px'))\n",
+ "multi_select_verzameling = widgets.SelectMultiple(options=PV_txt_lijst, value=[PV_txt_lijst[0]] if PV_txt_lijst else [], layout=widgets.Layout(width='400px', height='150px'))\n",
+ "# Outputs\n",
+ "container_rekpercentage = widgets.Output()\n",
+ "output_rekpercentage = widgets.Output()\n",
+ "\n",
+ "# Lijsten rekpercentage en verzamelingen aanpassen bij wijziging van proeftype txt/dss\n",
+ "def update_verzamelings_dropdowns(change): \n",
+ " with container_rekpercentage: \n",
+ " clear_output(); \n",
+ " (dropdown_verzameling.options, dropdown_verzameling.value, multi_select_verzameling.options, multi_select_verzameling.value, _d) = \\\n",
+ " (PV_txt_lijst, PV_txt_lijst[0], PV_txt_lijst, [PV_txt_lijst[0]], display(dropdown_rekpercentage_txt)) if dropdown_type_proef.value == 'TXT_su_tabel' \\\n",
+ " else (PV_dss_lijst, PV_dss_lijst[0], PV_dss_lijst, [PV_dss_lijst[0]], display(dropdown_rekpercentage_dss))\n",
+ " update_rekpercentage_output(None)\n",
+ "\n",
+ "def update_rekpercentage_output(change):\n",
+ " global gekozen_rekpercentage\n",
+ " with output_rekpercentage:\n",
+ " clear_output()\n",
+ " if dropdown_type_proef_su.value == 'TXT_su_tabel':\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_txt.value\n",
+ " else:\n",
+ " gekozen_rekpercentage = dropdown_rekpercentage_dss.value\n",
+ "\n",
+ "dropdown_type_proef_su.observe(update_verzamelings_dropdowns, names='value')\n",
+ "dropdown_rekpercentage_txt.observe(update_rekpercentage_output, names='value')\n",
+ "dropdown_rekpercentage_dss.observe(update_rekpercentage_output, names='value')\n",
+ "\n",
+ "# Initialiseren container met juiste rekpercentage-dropdown\n",
+ "with container_rekpercentage:\n",
+ " clear_output()\n",
+ " display(dropdown_rekpercentage_txt)\n",
+ "with output_rekpercentage:\n",
+ " clear_output()\n",
+ "\n",
+ "# Display widgets\n",
+ "display(Markdown(\"**Kies type proef:**\"))\n",
+ "display(dropdown_type_proef_su)\n",
+ "display(Markdown(\"**Kies verzameling voor statistische analyse:**\"))\n",
+ "display(dropdown_verzameling)\n",
+ "display(Markdown(\"**Kies rekpercentage Su:**\"))\n",
+ "display(container_rekpercentage)\n",
+ "display(output_rekpercentage)\n",
+ "display(Markdown(\"**Kies één of meerdere verzamelingen om naast de gekozen verzameling voor de statistische analyse te tonen:**\"))\n",
+ "display(multi_select_verzameling)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "605dde11-09ff-4f2a-98c6-99113b2e9dba",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.13.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/import_files/vervallen/Template_PVtool5_0.xlsx b/import_files/vervallen/Template_PVtool5_0.xlsx
new file mode 100644
index 0000000..d4e1062
Binary files /dev/null and b/import_files/vervallen/Template_PVtool5_0.xlsx differ
diff --git a/pv_tool/cphi_analysis/c_phi_analysis.py b/pv_tool/cphi_analysis/c_phi_analysis.py
index c16eea1..8c13564 100644
--- a/pv_tool/cphi_analysis/c_phi_analysis.py
+++ b/pv_tool/cphi_analysis/c_phi_analysis.py
@@ -14,44 +14,39 @@
from pv_tool.cphi_analysis.expand_analysis_df import (calculate_tan_a, calculate_ln_tan_a, calculate_s_tt,
calculate_s_ty, calculate_kappa_2, calculate_s,
calculate_5pr_ondergrens, calculate_5pr_bovengrens,
- calculate_s_tt_ondergrens, calculate_s_ty_ondergrens,
- calculate_kappa_2_ondergrens, calculate_correctie_t,
- kappa_2_2pr_cor,
- calculate_5pr_ondergrens_correctie_c,
- calculate_5pr_bovengrens_correctie_c,
- calculate_s_ty_ondergrens_correctie_c,
- calculate_kappa_2_ondergrens_correctie_c)
+ calculate_s_tt_ondergrens, calculate_s_ty_ondergrens,
+ calculate_kappa_2_ondergrens, calculate_correctie_t, kappa_2_2pr_cor,
+ calculate_5pr_ondergrens_correctie_c,
+ calculate_5pr_bovengrens_correctie_c,
+ calculate_s_ty_ondergrens_correctie_c,
+ calculate_kappa_2_ondergrens_correctie_c)
from pv_tool.cphi_analysis.visualization import (add_proefresultaten, add_extra_proefresultaten, add_5pr_bovengrens,
- add_5pr_ondergrens, add_fysische_realiseerbare_ondergrens,
- add_gemiddelde,
- set_layout, add_gemiddelde_sh, add_raaklijn_kar_boven,
+ add_5pr_ondergrens, add_fysische_realiseerbare_ondergrens, add_gemiddelde,
+ set_layout, add_gemiddelde_sh, add_raaklijn_kar_boven,
add_raaklijn_kar_onder)
from pv_tool.cphi_analysis.calc_parameters import (calc_watergehalte_gem, calc_watergehalte_sd, calc_vgwnat_gem,
- calc_vgwnat_sd, calc_a2_phi_gem, calc_a2_kar, calc_phi_d,
+ calc_vgwnat_sd, calc_a2_phi_gem, calc_a2_kar, calc_phi_d,
helling_gecorrigeerd, calc_a1_c_gem, calc_tan_phi_gem, calc_phi_kar,
calc_a1_kar, calc_phi_gem, calc_c_gem, calc_tan_phi_kar,
- calc_c_kar, calc_tan_phi_d, calc_c_d, calc_st_dev_phi, calc_st_dev_c,
- calc_a2_phi_gem_sh, calc_a2_phi_kar_boven_sh,
- calc_a2_phi_kar_onder_sh,
+ calc_c_kar, calc_tan_phi_d, calc_c_d, calc_st_dev_phi, calc_st_dev_c,
+ calc_a2_phi_gem_sh, calc_a2_phi_kar_boven_sh, calc_a2_phi_kar_onder_sh,
calc_tan_phi_kar_sh)
from openpyxl import load_workbook
from pv_tool.imports.excel_utils import format_excel_sheet
-
class CPhiAnalyse:
"""
- Class voor het uitvoeren van c-phi analyses op grondmonsters.
+ Klasse voor het uitvoeren van c-phi analyses op grondmonsters.
Ondersteunt zowel triaxiaal (TXT) als direct simple shear (DSS) testen,
en kan zowel reguliere c-phi als schematiseringshandleiding (SH) analyses uitvoeren.
"""
- def __init__(
- self, dbase: Dbase,
- analysis_type: Literal['TXT_CPhi', 'TXT_SH', 'DSS_CPhi', 'DSS_SH'],
- investigation_groups: List,
- effective_stress: Literal[
- '2% rek', '5% rek', '10% rek', '15% rek', '20% rek', 'pieksterkte', 'eindsterkte']):
+ def __init__(self, dbase: Dbase,
+ analysis_type: Literal['TXT_CPhi', 'TXT_SH', 'DSS_CPhi', 'DSS_SH'],
+ investigation_groups: List,
+ effective_stress: Literal['2% rek', '5% rek', '10% rek', '15% rek', '20% rek',
+ 'pieksterkte', 'eindsterkte']):
"""
Initialiseert een nieuwe c-phi analyse.
@@ -144,7 +139,7 @@ def get_cphi_data(self):
if self.analysis_type in ['TXT_CPhi', 'TXT_SH']:
self.cphi_analyses_data_df = self.dbase_df[self.dbase_df['ALG__TRIAXIAAL']]
self.cphi_analyses_data_df = self.cphi_analyses_data_df[self.cphi_analyses_data_df['PV_NAAM'].isin(
- self.investigation_groups)]
+ self.investigation_groups)]
self.calc_watergehalte_gem = calc_watergehalte_gem(self)
self.calc_watergehalte_sd = calc_watergehalte_sd(self)
self.calc_vgwnat_gem = calc_vgwnat_gem(self)
@@ -154,16 +149,15 @@ def get_cphi_data(self):
# Valideer of er data overblijft na filtering
if self.cphi_analyses_data_df.empty:
- raise ValueError(f"Geen data gevonden na filtering op investigation_groups {self.investigation_groups}"
- f"en effective_stress '{self.effective_stress}' "
- f"voor analyse type '{self.analysis_type}'")
+ raise ValueError(f"Geen data gevonden na filtering op investigation_groups {self.investigation_groups} "
+ f"en effective_stress '{self.effective_stress}' voor analyse type '{self.analysis_type}'")
- # print(f"Data na filtering: {len(self.cphi_analyses_data_df)} rijen gevonden")
+ print(f"Data na filtering: {len(self.cphi_analyses_data_df)} rijen gevonden")
elif self.analysis_type in ['DSS_CPhi', 'DSS_SH']:
self.cphi_analyses_data_df = self.dbase_df[self.dbase_df['ALG__DSS']]
self.cphi_analyses_data_df = self.cphi_analyses_data_df[self.cphi_analyses_data_df['PV_NAAM'].isin(
- self.investigation_groups)]
+ self.investigation_groups)]
self.calc_watergehalte_gem = calc_watergehalte_gem(self)
self.calc_watergehalte_sd = calc_watergehalte_sd(self)
self.calc_vgwnat_gem = calc_vgwnat_gem(self)
@@ -174,10 +168,9 @@ def get_cphi_data(self):
# Valideer of er data overblijft na filtering
if self.cphi_analyses_data_df.empty:
raise ValueError(f"Geen data gevonden na filtering op investigation_groups {self.investigation_groups} "
- f"en effective_stress '{self.effective_stress}' "
- f"voor analyse type '{self.analysis_type}'")
+ f"en effective_stress '{self.effective_stress}' voor analyse type '{self.analysis_type}'")
- # print(f"Data na filtering: {len(self.cphi_analyses_data_df)} rijen gevonden")
+ print(f"Data na filtering: {len(self.cphi_analyses_data_df)} rijen gevonden")
self.cphi_analyses_data_df.columns = NEW_COLUMN_NAMES
@@ -238,8 +231,7 @@ def plot_spanningspaden(self):
tijdens de proeven. Voor elk monster (uit de index van de dataframes)
wordt een apart spanningspad gemaakt met alle beschikbare spanningsstappen.
- Wordt aangeroepen binnen set_figure(). Het is aan de gebruiker om de spanningspaden wel of niet toe te voegen
- aan de figuur
+ Wordt aangeroepen binnen set_figure(). Het is aan de gebruiker om de spanningspaden wel of niet toe te voegen aan de figuur
"""
if self.analysis_type in ['TXT_CPhi', 'TXT_SH']:
relevant_df = self.dbase_df[self.dbase_df['ALG__TRIAXIAAL']]
@@ -248,21 +240,18 @@ def plot_spanningspaden(self):
elif self.analysis_type in ['DSS_CPhi', 'DSS_SH']:
relevant_df = self.dbase_df[self.dbase_df['ALG__DSS']]
relevant_df = relevant_df[relevant_df['PV_NAAM'].isin(self.investigation_groups)]
- effective_stress_options = ['consolidatie', '2% rek', '5% rek', '10% rek', '15% rek', '20% rek',
- 'pieksterkte', 'eindsterkte']
- relevant_df['DSS_T_CONSOLIDATIE'] = [0] * len(relevant_df)
+ effective_stress_options = ['consolidatie', '2% rek', '5% rek', '10% rek', '15% rek', '20% rek', 'pieksterkte', 'eindsterkte']
+ relevant_df['DSS_T_CONSOLIDATIE'] = [0]*len(relevant_df)
else:
raise ValueError("Ongeldig analysetype. Gebruik 'TXT_CPhi', 'TXT_SH', 'DSS_CPhi' of 'DSS_SH'.")
# Eerst verzamelen we alle data per spanningsstap
all_data = {}
for stress in effective_stress_options:
- columns = ALL_TEXTUAL_NAMES.get(stress, []) if self.analysis_type in ['TXT_CPhi',
- 'TXT_SH'] \
- else ALL_TEXTUAL_NAMES_DSS.get(stress, [])
+ columns = ALL_TEXTUAL_NAMES.get(stress, []) if self.analysis_type in ['TXT_CPhi', 'TXT_SH'] else ALL_TEXTUAL_NAMES_DSS.get(stress, [])
if len(columns) > 0:
data = relevant_df[columns].copy()
- if not data.empty and not data.isna().all().all(): # TODO: data.isna geeft warning. oplossen.
+ if not data.empty and not data.isna().all().all():
data.columns = NEW_COLUMN_NAMES
all_data[stress] = data
@@ -297,17 +286,15 @@ def plot_spanningspaden(self):
stress_df = stress_df[stress_df['stress_state'] != 'pieksterkte']
if rek_bij_t_piek < 2:
insert_index = 1
- elif rek_bij_t_piek < 5:
+ elif rek_bij_t_piek <5:
insert_index = 2
- elif rek_bij_t_piek < 15:
+ elif rek_bij_t_piek <15:
insert_index = 3
elif not isna(rek_bij_t_eind) and rek_bij_t_eind > rek_bij_t_piek:
insert_index = 4
else:
insert_index = len(stress_df)
- stress_df = concat(
- [stress_df.iloc[:insert_index], piek_row, stress_df.iloc[insert_index:]]).reset_index(
- drop=True)
+ stress_df = concat([stress_df.iloc[:insert_index], piek_row, stress_df.iloc[insert_index:]]).reset_index(drop=True)
elif self.analysis_type in ['DSS_CPhi', 'DSS_SH']:
rek_bij_t_max = self.total_cphi_analyses_data_df.loc[sample_name, 'DSS_REK_BIJ_T_MAX']
rek_bij_t_eind = self.total_cphi_analyses_data_df.loc[sample_name, 'DSS_REK_BIJ_T_EIND']
@@ -317,24 +304,24 @@ def plot_spanningspaden(self):
stress_df = stress_df[stress_df['stress_state'] != 'pieksterkte']
if rek_bij_t_max < 2:
insert_index = 1
- elif rek_bij_t_max < 5:
+ elif rek_bij_t_max <5:
insert_index = 2
- elif rek_bij_t_max < 10:
+ elif rek_bij_t_max <10:
insert_index = 3
- elif rek_bij_t_max < 15:
+ elif rek_bij_t_max <15:
insert_index = 4
- elif rek_bij_t_max < 20:
+ elif rek_bij_t_max <20:
insert_index = 5
elif not isna(rek_bij_t_eind) and rek_bij_t_eind > rek_bij_t_max:
insert_index = 6
else:
insert_index = len(stress_df)
- stress_df = concat(
- [stress_df.iloc[:insert_index], piek_row, stress_df.iloc[insert_index:]]).reset_index(
- drop=True)
+ stress_df = concat([stress_df.iloc[:insert_index], piek_row, stress_df.iloc[insert_index:]]).reset_index(drop=True)
sample_stress_paths[sample_name] = stress_df
+
+
# Plot de spanningspaden
if sample_stress_paths:
from pv_tool.cphi_analysis.visualization import add_stress_paths
@@ -359,8 +346,7 @@ def get_previous_results(self, path: str, file_name: str):
DataFrame met eerdere resultaten als deze gevonden zijn, anders None
"""
- file_path = Path(path) / file_name
- print(file_path)
+ file_path = f"{path}/{file_name}"
try:
with open(file_path, 'r'):
@@ -369,7 +355,7 @@ def get_previous_results(self, path: str, file_name: str):
raise FileNotFoundError("Er is geen dbase aanwezig onder de naam Template_PVtool5_0.xlsx")
try:
- results_df = read_excel(file_path, sheet_name='Resultaten c-phi', header=6)
+ results_df = read_excel(file_path, sheet_name='Resultaten c-phi')
except ValueError:
print("Er is geen tabblad 'Resultaten c-phi' aanwezig in het Excel-bestand.")
return None
@@ -378,7 +364,7 @@ def get_previous_results(self, path: str, file_name: str):
(results_df['PV_RESULTAAT_ID'].str.contains(self.investigation_groups[0])) &
(results_df['PV_RESULTAAT_ID'].str.contains(self.effective_stress)) &
(results_df['PV_RESULTAAT_ID'].str.contains(self.analysis_type))
- ]
+ ]
if filtered_df.empty:
print("Er zijn geen eerdere resultaten gevonden voor de opgegeven parameters.")
@@ -604,7 +590,7 @@ def get_short_results(self):
index = ['Verwachtingswaarde', 'Karakteristieke waarde', 'Rekenwaarde', 'Standaarddeviatie D-stability']
columns = ['tan phi [-]', 'phi [graden]']
analyse_output_df = DataFrame(index=index, columns=columns)
- analyse_output_df['tan phi [-]'] = [self.tan_phi_gem, self.tan_phi_kar, self.tan_phi_d, '[-]']
+ analyse_output_df['tan phi [-]'] = [self.tan_phi_gem, self.tan_phi_kar , self.tan_phi_d, '[-]']
analyse_output_df['phi [graden]'] = [self.phi_gem, self.phi_kar, self.phi_d, self.st_dev_phi]
else:
self._run()
@@ -618,93 +604,111 @@ def get_short_results(self):
# ========== Export Methodes ==========
- def add_results_to_template(self, path, export_name=None):
+ def add_results_to_dbase(self, path, file_name: str = 'Template_PVtool5_0.xlsx'):
"""
- Voegt een nieuwe resultatenrij toe aan tabblad 'Resultaten c-phi' in het Excel-template.
- Als het tabblad niet bestaat, wordt het aangemaakt en worden de kolomnamen weggeschreven.
+ Voegt analyseresultaten toe aan de database export.
+
+ Voegt de resultaten toe aan een tabblad 'Resultaten c-phi' in de Template_PVtool5_0.xlsx.
+ Als het tabblad al bestaat wordt het aangevuld, anders wordt het aangemaakt.
+
+ Parameters
+ ----------
+ path : str
+ Map locatie waar het Excel-bestand staat of moet komen
+ file_name : str, optioneel
+ Naam van het Excel-bestand (standaard 'Template_PVtool5_0.xlsx')
+
+ Returns
+ -------
+ DataFrame
+ DataFrame met alle resultaten in het tabblad
"""
- if export_name is None:
- export_name = "Template_PVtool5_0.xlsx"
- file_path = Path(path) / export_name
- sheet_name = 'Resultaten c-phi'
+ file_path = f"{path}/{file_name}"
+
+ if self.analysis_type in ['TXT_SH', 'DSS_SH']:
+ self._run_sh()
+ else:
+ self._run()
+
+ try:
+ with open(file_path, 'r'):
+ pass
+ except FileNotFoundError:
+ raise FileNotFoundError(f"Er is geen dbase aanwezig onder de naam {file_name}")
expected_columns = [
- 'PV_RESULTAAT_ID', 'PV_NAAM', 'PV_REK', 'PV_TYPE_PROEF', 'PV_ANALYSE',
- 'PV_A1_COH_GEM', 'PV_A2_TAN_PHI_GEM', 'PV_A1_COH_KAR', 'PV_A2_TAN_PHI_KAR',
- 'PV_PARTPHI', 'PV_PARTCOH', 'PV_TYPEVERZAMELING', 'PV_COH_GEM', 'PV_PHI_GEM',
- 'PV_COH_KAR', 'PV_PHI_KAR', 'PV_COH_SD_DSTAB', 'PV_PHI_SD_DSTAB',
- 'PV_VGWNAT_GEM', 'PV_VGWNAT_SD', 'PV_WATERGEHALTE_GEM', 'PV_WATERGEHALTE_SD', 'Timestamp'
+ 'PV_RESULTAAT_ID', 'PVNAAM', 'PV_REK', 'PV_TYPE_PROEF', 'PV_ANALYSE',
+ 'PV_A1_COH_GEM [kPa]', 'PV_A2_TAN_PHI_GEM [-]', 'PV_A1_COH_KAR [kPa]', 'PV_A2_TAN_PHI_KAR [-]',
+ 'PV_PARTPHI [-]', 'PV_PARTCOH [-]', 'PV_TYPEVERZAMELING', 'PV_COH_GEM [kPa]', 'PV_PHI_GEM [graden]',
+ 'PV_COH_KAR [kPa]', 'PV_PHI_KAR [graden]', 'PV_COH_SD_DSTAB [-]', 'PV_PHI_SD_DSTAB [-]',
+ 'PV_VGWNAT_GEM [kN/m3]', 'PV_VGWNAT_SD [kN/m3]', 'PV_WATERGEHALTE_GEM', 'PV_WATERGEHALTE_SD', 'Timestamp'
]
- # Maak de resultaat-rij aan
new_row = {
- 'PV_NAAM': self.investigation_groups[0],
+ 'PVNAAM': self.investigation_groups[0],
'PV_REK': self.effective_stress,
'PV_TYPE_PROEF': self.analysis_type.split('_')[0],
'PV_ANALYSE': self.analysis_type.split('_')[1],
- 'PV_RESULTAAT_ID': f"{self.investigation_groups[0]}_{self.effective_stress}_"
- f"{self.analysis_type.split('_')[0]}_{self.analysis_type.split('_')[1]}",
+ 'PV_RESULTAAT_ID': f"{self.investigation_groups[0]}_{self.effective_stress}_{self.analysis_type.split('_')[0]}_{self.analysis_type.split('_')[1]}",
'PV_TYPEVERZAMELING': self.alpha,
- 'PV_A1_COH_GEM': round(self.gem_a1, 3) if self.gem_a1 is not None else None,
- 'PV_A2_TAN_PHI_GEM': round(self.gem_a2, 3) if self.gem_a2 is not None else None,
- 'PV_A1_COH_KAR': round(self.kar_a1, 3) if self.kar_a1 is not None else None,
- 'PV_A2_TAN_PHI_KAR': round(self.kar_a2, 3) if self.kar_a2 is not None else None,
-
- 'PV_COH_GEM': (
- round(self.c_gem, 3) if self.c_gem is not None and self.c_gem >= 0
- else "[-]" if self.c_gem is None
- else f"{round(self.c_gem, 3)} (kan niet - aanpassen!)"
- ),
- 'PV_PHI_GEM': round(self.phi_gem, 3) if self.phi_gem is not None else None,
-
- 'PV_COH_KAR': (
- round(self.c_kar, 3) if self.c_kar is not None and self.c_kar >= 0
- else "[-]" if self.c_kar is None
- else f"{round(self.c_kar, 3)} (kan niet - aanpassen!)"
- ),
- 'PV_PHI_KAR': round(self.phi_kar, 3) if self.phi_kar is not None else None,
-
- 'PV_COH_SD_DSTAB': (
- round(self.st_dev_c, 3) if (
- self.c_gem is not None and self.c_kar is not None and self.c_gem >= 0 and self.c_kar >= 0
- ) else "[-]" if self.c_gem is None or self.c_kar is None
- else "[-] (c < 0)"
- ),
- 'PV_PHI_SD_DSTAB': round(self.st_dev_phi, 3) if self.st_dev_phi is not None else None,
-
- 'PV_PARTPHI': self.material_tan_phi,
- 'PV_PARTCOH': self.material_cohesie,
- 'PV_VGWNAT_GEM': round(self.calc_vgwnat_gem, 3) if self.calc_vgwnat_gem is not None else None,
- 'PV_VGWNAT_SD': round(self.calc_vgwnat_sd, 3) if self.calc_vgwnat_sd is not None else None,
- 'PV_WATERGEHALTE_GEM': round(self.calc_watergehalte_gem,
- 3) if self.calc_watergehalte_gem is not None else None,
- 'PV_WATERGEHALTE_SD': round(self.calc_watergehalte_sd,
- 3) if self.calc_watergehalte_sd is not None else None,
+ 'PV_A1_COH_GEM [kPa]': round(self.gem_a1, 3) if self.gem_a1 is not None else None,
+ 'PV_A2_TAN_PHI_GEM [-]': round(self.gem_a2, 3) if self.gem_a2 is not None else None,
+ 'PV_A1_COH_KAR [kPa]': round(self.kar_a1, 3) if self.kar_a1 is not None else None,
+ 'PV_A2_TAN_PHI_KAR [-]': round(self.kar_a2, 3) if self.kar_a2 is not None else None,
+ 'PV_COH_GEM [kPa]': (round(self.c_gem, 3) if self.c_gem is not None and self.c_gem >= 0
+ else "[-]" if self.c_gem is None
+ else f"{round(self.c_gem, 3)} (kan niet - aanpassen!)"
+ ),
+ 'PV_PHI_GEM [graden]': round(self.phi_gem, 3) if self.phi_gem is not None else None,
+ 'PV_COH_KAR [kPa]': (round(self.c_kar, 3) if self.c_kar is not None and self.c_kar >= 0
+ else "[-]" if self.c_kar is None
+ else f"{round(self.c_kar, 3)} (kan niet - aanpassen!)"
+ ),
+ 'PV_PHI_KAR [graden]': round(self.phi_kar, 3) if self.phi_kar is not None else None,
+ 'PV_COH_SD_DSTAB [-]': (round(self.st_dev_c, 3) if self.c_gem is not None and self.c_kar is not None
+ and self.c_gem >= 0 and self.c_kar >= 0
+ else "[-]" if self.c_gem is None or self.c_kar is None
+ else "[-] (c < 0)"),
+ 'PV_PHI_SD_DSTAB [-]': round(self.st_dev_phi, 3) if self.st_dev_phi is not None else None,
+ 'PV_PARTPHI [-]': self.material_tan_phi,
+ 'PV_PARTCOH [-]': self.material_cohesie,
+ 'PV_VGWNAT_GEM [kN/m3]': round(self.calc_vgwnat_gem, 3) if self.calc_vgwnat_gem is not None else None,
+ 'PV_VGWNAT_SD [kN/m3]': round(self.calc_vgwnat_sd, 3) if self.calc_vgwnat_sd is not None else None,
+ 'PV_WATERGEHALTE_GEM': round(self.calc_watergehalte_gem, 3) if self.calc_watergehalte_gem is not None else None,
+ 'PV_WATERGEHALTE_SD': round(self.calc_watergehalte_sd, 3) if self.calc_watergehalte_sd is not None else None,
'Timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
}
- if file_path.exists():
- wb = load_workbook(file_path)
- else:
- template_path = Path(get_repo_root()) / "pv_tool" / "templates" / "Template_PVtool5_0.xlsx"
- wb = load_workbook(template_path)
+ workbook = load_workbook(file_path)
- if sheet_name in wb.sheetnames:
- ws = wb[sheet_name]
- first_empty_row = ws.max_row + 1 if any(
- ws.iter_rows(min_row=ws.max_row, max_row=ws.max_row, values_only=True)) else ws.max_row
+ if 'Resultaten c-phi' in workbook.sheetnames:
+ print('Tabblad Resultaten c-phi in dbase excel bestaat al en wordt aangevuld')
+ df_existing = read_excel(file_path, sheet_name='Resultaten c-phi')
+ # Filter out empty rows and ensure consistent types before concatenation
+ df_existing = df_existing.dropna(how='all')
+ new_row_df = DataFrame([new_row], columns=df_existing.columns)
+ df_updated = concat([df_existing, new_row_df], ignore_index=True)
else:
- ws = wb.create_sheet(sheet_name)
- for col_idx, col_name in enumerate(expected_columns, start=1):
- ws.cell(row=1, column=col_idx, value=col_name)
- first_empty_row = 2
-
- for col_idx, col_name in enumerate(expected_columns, start=1):
- ws.cell(row=first_empty_row, column=col_idx, value=new_row.get(col_name, ""))
-
- wb.save(file_path)
- print(f"Resultaat toegevoegd aan template in tabblad '{sheet_name}'.")
-
+ print('Tabblad Resultaten c-phi in dbase excel bestaat nog niet en wordt aangemaakt')
+ df_updated = DataFrame([new_row], columns=expected_columns)
+
+ # Write data to Excel
+ with ExcelWriter(file_path, engine='openpyxl', mode='a', if_sheet_exists='replace') as writer:
+ df_updated.to_excel(writer, sheet_name='Resultaten c-phi', index=False)
+
+ # Formatting
+ num_columns = df_updated.shape[1]
+ num_rows = df_updated.shape[0]
+ format_excel_sheet(
+ file_path=file_path,
+ sheet_name='Resultaten c-phi',
+ num_columns=num_columns,
+ num_rows=num_rows,
+ table_name='Resultaten_CPHI_Table',
+ index=False # Changed from True to False to match the to_excel call
+ )
+
+ return df_updated
@property
def save_total_to_excel(self):
diff --git a/resultaten/TXT_klei_14_16.html b/resultaten/TXT_klei_14_16.html
index 88638ba..d51df44 100644
--- a/resultaten/TXT_klei_14_16.html
+++ b/resultaten/TXT_klei_14_16.html
@@ -3880,6 +3880,6 @@
window.Plotly = Plotly;
return Plotly;
-}));
+}));