{ "cells": [ { "cell_type": "markdown", "id": "5556432f", "metadata": {}, "source": [ "# Tutorial 72: WORK IN PROGRESS: Longitudinal Sections" ] }, { "cell_type": "markdown", "id": "8899e69a", "metadata": {}, "source": [ "This example demonstrates how to ..." ] }, { "cell_type": "markdown", "id": "e2d40c36", "metadata": {}, "source": [ "# SIR 3S Installation" ] }, { "cell_type": "code", "execution_count": 1, "id": "7fb5a07b", "metadata": {}, "outputs": [], "source": [ "SIR3S_SIRGRAF_DIR = r\"C:\\3S\\SIR 3S\\SirGraf-90-15-00-20x64_Quebec-Upd1\" #change to local path" ] }, { "cell_type": "markdown", "id": "d6773e12", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "code", "execution_count": 2, "id": "2a62aead", "metadata": {}, "outputs": [], "source": [ "from sir3stoolkit.core import wrapper" ] }, { "cell_type": "code", "execution_count": 3, "id": "687f2fa2-afee-4376-bd8f-5a53f288b772", "metadata": {}, "outputs": [], "source": [ "from sir3stoolkit.mantle import mantle" ] }, { "cell_type": "markdown", "id": "5cdb23ce-d1bb-4f8c-a800-fb8c026e9266", "metadata": {}, "source": [ "The wrapper package has to be initialized with reference to a SIR 3S (SirGraf) installation." ] }, { "cell_type": "code", "execution_count": 4, "id": "ee4bc044", "metadata": {}, "outputs": [], "source": [ "wrapper.Initialize_Toolkit(SIR3S_SIRGRAF_DIR)" ] }, { "cell_type": "markdown", "id": "2007993a", "metadata": {}, "source": [ "## Additional imports" ] }, { "cell_type": "code", "execution_count": 5, "id": "60659f01", "metadata": {}, "outputs": [], "source": [ "import os" ] }, { "cell_type": "markdown", "id": "c3ab6600-34d9-4bfc-a816-96385dff31b0", "metadata": {}, "source": [ "..." ] }, { "cell_type": "markdown", "id": "0352dd43", "metadata": {}, "source": [ "# Initialization" ] }, { "cell_type": "code", "execution_count": 6, "id": "7e40a5af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialization complete\n" ] } ], "source": [ "s3s = mantle.SIR3S_Model_Mantle()" ] }, { "cell_type": "markdown", "id": "2d0f6dc9", "metadata": {}, "source": [ "# Open Model" ] }, { "cell_type": "code", "execution_count": 7, "id": "c2f6ab2c", "metadata": {}, "outputs": [], "source": [ "dbFilePath=r\"C:\\Users\\aUsername\\3S\\PT3S\\PT3S\\Examples\\Example3.db3\"" ] }, { "cell_type": "code", "execution_count": 8, "id": "efbe536c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model is open for further operation\n" ] } ], "source": [ "s3s.OpenModel(dbName=dbFilePath,\n", " providerType=s3s.ProviderTypes.SQLite,\n", " Mid=\"M-1-0-1\",\n", " saveCurrentlyOpenModel=False,\n", " namedInstance=\"\",\n", " userID=\"\",\n", " password=\"\")" ] }, { "cell_type": "markdown", "id": "749e3c64", "metadata": {}, "source": [ "# Get Data" ] }, { "cell_type": "code", "execution_count": 9, "id": "8ac84393", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Could not start Model Calculation: SirCalc Path 'C:\\3S Consult\\SIR3S-90-15-00-19-Quebec\\SirCalc-90-15-02-20_Quebec\\SirCalc.exe' not found.\n" ] } ], "source": [ "s3s.ExecCalculation(True)" ] }, { "cell_type": "code", "execution_count": 10, "id": "8c39c9a4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2026-01-18 22:35:34,457] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.AGSN_HydraulicProfile\n", "[2026-01-18 22:35:34,469] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 3 element(s) of element type ObjectTypes.AGSN_HydraulicProfile.\n", "[2026-01-18 22:35:34,495] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] No properties given → using ALL model_data properties for ObjectTypes.AGSN_HydraulicProfile.\n", "[2026-01-18 22:35:34,496] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 9 model_data properties.\n", "[2026-01-18 22:35:34,497] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieving model_data properties ['Name', 'Lfdnr', 'Aktiv', 'AllNodesAndLinks', 'ObjsString', 'MainWay', 'Tk', 'Pk', 'InVariant']...\n", "[2026-01-18 22:35:34,576] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (3, 10)\n", "[2026-01-18 22:35:34,583] INFO in sir3stoolkit.mantle.dataframes: Retrieving Hydraulic Profile with Lfdnr: 1.\n", "[2026-01-18 22:35:34,610] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.Pipe ...\n", "[2026-01-18 22:35:34,612] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.Pipe ...\n", "[2026-01-18 22:35:34,613] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.Pipe\n", "[2026-01-18 22:35:34,619] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.\n", "[2026-01-18 22:35:34,622] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] 44 tks remain after filtering for given tks.\n", "[2026-01-18 22:35:34,625] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] No properties given → using ALL model_data properties for ObjectTypes.Pipe.\n", "[2026-01-18 22:35:34,629] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 46 model_data properties.\n", "[2026-01-18 22:35:34,631] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieving model_data properties ['Name', 'FkdtroRowd', 'Fkltgr', 'Fkstrasse', 'L', 'Lzu', 'Rau', 'Jlambs', 'Lambda0', 'Zein', 'Zaus', 'Zuml', 'Asoll', 'Indschall', 'Baujahr', 'Hal', 'Fkcont', 'Fk2lrohr', 'Beschreibung', 'Idreferenz', 'Iplanung', 'Kvr', 'LineWidthMM', 'DottedLine', 'DN', 'Di', 'KvrKlartext', 'HasClosedNSCHs', 'Tk', 'Pk', 'InVariant', 'Xkor', 'Ykor', 'GeometriesDiffer', 'bz.Fk', 'bz.Qsvb', 'bz.Irtrenn', 'bz.Leckstatus', 'bz.Leckstart', 'bz.Leckend', 'bz.Leckort', 'bz.Leckmenge', 'bz.Imptnz', 'bz.Zvlimptnz', 'bz.Kantenzv', 'bz.ITrennWithNSCH'], geometry, end nodes...\n", "[2026-01-18 22:35:35,698] INFO in sir3stoolkit.mantle.dataframes: [model_data] 2 non-empty end node columns were created.\n", "[2026-01-18 22:35:35,900] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832\n", "[2026-01-18 22:35:35,900] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (44, 50)\n", "[2026-01-18 22:35:35,905] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.Pipe ...\n", "[2026-01-18 22:35:35,912] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe\n", "[2026-01-18 22:35:35,928] WARNING in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] Timestamp is not valid (SIR3S_Model.GetTimeStamps()). It will be excluded.\n", "[2026-01-18 22:35:35,930] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 0 valid timestamp(s) will be used.\n", "[2026-01-18 22:35:35,930] WARNING in sir3stoolkit.mantle.dataframes: [results] No valid timestamps. Returning empty dataframe.\n", "[2026-01-18 22:35:35,934] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Merging df_model_data with df_results for element type: ObjectTypes.Pipe ...\n", "[2026-01-18 22:35:35,935] ERROR in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Error Generating df for element type: ObjectTypes.Pipe: Too many levels: Index has only 1 level, not 2\n", "ERROR:root:Error retrieving Hydraulic Profile with Lfdnr: 1.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Error : \n", "Error : \n" ] } ], "source": [ "dfs = s3s.generate_longitudinal_section_dataframes()" ] }, { "cell_type": "code", "execution_count": 11, "id": "afa6825e", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[11], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m=\u001b[39mdfs[\u001b[38;5;241m0\u001b[39m]\n", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "df=dfs[0]" ] }, { "cell_type": "code", "execution_count": null, "id": "71282142", "metadata": {}, "outputs": [], "source": [ "df_vec=s3s.add_interior_points_as_multiindex(df)" ] }, { "cell_type": "code", "execution_count": null, "id": "1b752006", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 15.351701\n", "1 27.860648\n", "2 34.126153\n", "3 48.533695\n", "4 62.049538\n", "5 79.324254\n", "6 113.666420\n", "7 149.752335\n", "8 213.782486\n", "9 265.851250\n", "10 315.314808\n", "11 325.177948\n", "12 339.208532\n", "13 371.733842\n", "14 378.362803\n", "15 448.136789\n", "16 463.003848\n", "17 562.262027\n", "18 620.887233\n", "19 708.601420\n", "20 735.670985\n", "21 740.545722\n", "22 743.968120\n", "23 751.727923\n", "24 758.287896\n", "25 777.158410\n", "26 795.942267\n", "27 828.020167\n", "28 841.297316\n", "29 911.066336\n", "30 947.495684\n", "31 1004.713492\n", "32 1045.708308\n", "33 1052.541387\n", "34 1060.034488\n", "35 1068.355962\n", "36 1151.294797\n", "37 1155.836302\n", "38 1161.025165\n", "39 1164.982003\n", "40 1186.143680\n", "41 1212.402452\n", "42 1240.748040\n", "43 1281.552541\n", "Name: (l_sum, -1), dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_vec[(\"l_sum\", -1)]" ] }, { "cell_type": "markdown", "id": "396b22d2", "metadata": {}, "source": [ "# Plot" ] }, { "cell_type": "code", "execution_count": null, "id": "f81df861", "metadata": {}, "outputs": [], "source": [ "\n", "from __future__ import annotations\n", "\n", "from collections import OrderedDict\n", "from typing import Any\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.ticker as mticker\n", "\n", "\n", "def plot_time_curves(\n", " df: pd.DataFrame,\n", " start=None,\n", " end=None,\n", " properties=None,\n", " axis_labels=None,\n", " ylims=None,\n", " tks_per_property=None, # kept for signature compatibility (unused in new logic)\n", " y_label_ticks=None,\n", " y_grid_ticks=None,\n", " x_label_ticks=None,\n", " x_grid_ticks=None,\n", " figsize=(20, 14),\n", " axis_offset=60,\n", " linestyles=None,\n", " legend=True,\n", " legend_fontsize=8,\n", " legend_loc=\"upper left\",\n", " legend_coords=(0.67, 0.33),\n", " legend_in_figure=True,\n", " missing=\"skip\",\n", " normalize=str.upper,\n", " aliases=None,\n", " title_prefix=\"\",\n", " show_title=True,\n", " rotate_xticks=0,\n", " grid=True,\n", " grid_style=\"--\",\n", " grid_alpha=0.3,\n", " logger=None,\n", "):\n", " \"\"\"\n", " Plot multiple properties along cumulative length.\n", "\n", " Expected columns:\n", " - (\"l_sum\", -1): cumulative segment end length\n", " - (\"VAV\", -1): direction; negative reverses sample order within the segment\n", " - For each property P in `properties`: columns (P, k) with k=0..N (level1 numeric),\n", " and optionally (P, -1) metadata (ignored).\n", "\n", " Segment definition:\n", " row i spans x in [l_sum[i-1], l_sum[i]] with l_sum[-1] := 0.\n", "\n", " For each row + property:\n", " - take samples from k=0 up to last non-NaN (trailing NaNs)\n", " - map to equidistant x over the segment\n", " - reverse samples if VAV < 0\n", " \"\"\"\n", "\n", " # -------------------------------------------------------------------------\n", " # Logging helpers\n", " # -------------------------------------------------------------------------\n", " def _log_info(msg: str) -> None:\n", " if logger is not None:\n", " logger.info(f\"[time curves] {msg}\")\n", "\n", " def _log_warn(msg: str) -> None:\n", " if logger is not None and hasattr(logger, \"warning\"):\n", " logger.warning(f\"[time curves] {msg}\")\n", " elif logger is not None:\n", " logger.info(f\"[time curves] {msg}\")\n", "\n", " # -------------------------------------------------------------------------\n", " # Input validation\n", " # -------------------------------------------------------------------------\n", " if not isinstance(df.columns, pd.MultiIndex) or df.columns.nlevels != 2:\n", " raise ValueError(\"DataFrame must have 2-level MultiIndex columns (level0=property, level1=sample index).\")\n", "\n", " df = df.copy()\n", "\n", " lvl0_name = df.columns.names[0] or \"property\"\n", " lvl1_name = df.columns.names[1] or \"sample\"\n", "\n", " def norm_prop(s: Any) -> str:\n", " s2 = normalize(str(s)).strip()\n", " if aliases:\n", " s2 = aliases.get(s2, s2)\n", " return s2\n", "\n", " lvl0_raw = df.columns.get_level_values(0).astype(str)\n", " lvl0_norm = lvl0_raw.map(norm_prop)\n", " lvl1_vals = df.columns.get_level_values(1)\n", "\n", " def _is_minus_one(x: Any) -> bool:\n", " return (x == -1) or (str(x).strip() == \"-1\")\n", "\n", " # Find special columns (\"l_sum\",-1), (\"VAV\",-1)\n", " lsum_key_norm = norm_prop(\"l_sum\")\n", " vav_key_norm = norm_prop(\"VAV\")\n", "\n", " lsum_mask = (lvl0_norm == lsum_key_norm) & pd.Index(lvl1_vals).map(_is_minus_one)\n", " vav_mask = (lvl0_norm == vav_key_norm) & pd.Index(lvl1_vals).map(_is_minus_one)\n", "\n", "\n", " \n", "\n", " lvl1_idx = pd.Index(lvl1_vals)\n", "\n", " lsum_mask = ((lvl0_norm == lsum_key_norm) & lvl1_idx.map(_is_minus_one)).to_numpy()\n", " vav_mask = ((lvl0_norm == vav_key_norm) & lvl1_idx.map(_is_minus_one)).to_numpy()\n", "\n", " if np.count_nonzero(lsum_mask) != 1:\n", " raise ValueError(f\"Expected exactly one ('l_sum', -1) column, found {np.count_nonzero(lsum_mask)}.\")\n", " if np.count_nonzero(vav_mask) != 1:\n", " raise ValueError(f\"Expected exactly one ('VAV', -1) column, found {np.count_nonzero(vav_mask)}.\")\n", "\n", "\n", " lsum_col = df.columns[lsum_mask][0]\n", " vav_col = df.columns[vav_mask][0]\n", "\n", " # Ensure l_sum numeric and sort by it\n", " lsum_series = pd.to_numeric(df[lsum_col], errors=\"coerce\")\n", " if lsum_series.isna().any():\n", " bad = df.index[lsum_series.isna()].tolist()[:10]\n", " raise ValueError(f\"Some l_sum values are NaN/non-numeric. Example rows: {bad}\")\n", "\n", " df = df.loc[lsum_series.sort_values().index]\n", " lsum = lsum_series.loc[df.index].to_numpy(dtype=float)\n", "\n", " # Segment starts/ends\n", " seg_end = lsum\n", " seg_start = np.concatenate(([0.0], seg_end[:-1]))\n", "\n", " # Interpret start/end as length bounds\n", " start_l = float(start) if start is not None else 0.0\n", " end_l = float(end) if end is not None else float(seg_end.max())\n", " if start_l > end_l:\n", " raise ValueError(\"start must be <= end (interpreted as length range).\")\n", "\n", " overlaps = (seg_end >= start_l) & (seg_start <= end_l)\n", " if not overlaps.any():\n", " raise ValueError(\"No segments overlap the requested start/end length window.\")\n", "\n", " # -------------------------------------------------------------------------\n", " # Determine properties to plot\n", " # -------------------------------------------------------------------------\n", " special_norm = {lsum_key_norm, vav_key_norm}\n", " available_props = sorted(set(p for p in lvl0_norm.unique().tolist() if p not in special_norm))\n", "\n", " if properties is None:\n", " used_properties = available_props\n", " else:\n", " used_properties = [norm_prop(p) for p in properties]\n", "\n", " n_props = len(used_properties)\n", " if n_props == 0:\n", " raise ValueError(\"No properties to plot.\")\n", "\n", " # -------------------------------------------------------------------------\n", " # Axis labels and limits\n", " # -------------------------------------------------------------------------\n", " if axis_labels is None:\n", " used_axis_labels = [f\"{lvl0_name}={p}\" for p in used_properties]\n", " else:\n", " if len(axis_labels) != n_props:\n", " raise ValueError(\"axis_labels must have same length as properties.\")\n", " used_axis_labels = list(axis_labels)\n", "\n", " if ylims is not None:\n", " if len(ylims) != n_props:\n", " raise ValueError(\"ylims must have same length as properties (or be None).\")\n", " for lim in ylims:\n", " if lim is not None and (not isinstance(lim, (tuple, list)) or len(lim) != 2):\n", " raise ValueError(\"Each ylims entry must be (ymin, ymax) or None.\")\n", "\n", " # -------------------------------------------------------------------------\n", " # Tick validation\n", " # -------------------------------------------------------------------------\n", " if y_label_ticks is not None:\n", " if len(y_label_ticks) != n_props:\n", " raise ValueError(\"y_label_ticks must have same length as properties (or be None).\")\n", " if any((not isinstance(n, int) or n < 2) for n in y_label_ticks):\n", " raise ValueError(\"Each y_label_ticks entry must be an int >= 2.\")\n", "\n", " if y_grid_ticks is not None and (not isinstance(y_grid_ticks, int) or y_grid_ticks < 2):\n", " raise ValueError(\"y_grid_ticks must be an int >= 2 (or None).\")\n", "\n", " if x_label_ticks is not None and (not isinstance(x_label_ticks, int) or x_label_ticks < 2):\n", " raise ValueError(\"x_label_ticks must be an int >= 2 (or None).\")\n", "\n", " if x_grid_ticks is not None and (not isinstance(x_grid_ticks, int) or x_grid_ticks < 2):\n", " raise ValueError(\"x_grid_ticks must be an int >= 2 (or None).\")\n", "\n", " # -------------------------------------------------------------------------\n", " # Linestyles and colors\n", " # -------------------------------------------------------------------------\n", " if linestyles is None:\n", " linestyles = [\"-\", \"--\", \":\", \"-.\"]\n", " prop_linestyle = {p: linestyles[i % len(linestyles)] for i, p in enumerate(used_properties)}\n", "\n", " colors = plt.rcParams[\"axes.prop_cycle\"].by_key().get(\"color\", [])\n", " if not colors:\n", " colors = [\"C0\", \"C1\", \"C2\", \"C3\", \"C4\", \"C5\", \"C6\", \"C7\", \"C8\", \"C9\"]\n", " prop_color = {p: colors[i % len(colors)] for i, p in enumerate(used_properties)}\n", "\n", " # -------------------------------------------------------------------------\n", " # Create figure and stacked left y-axes (unchanged styling)\n", " # -------------------------------------------------------------------------\n", " fig, ax0 = plt.subplots(figsize=figsize)\n", " axes = [ax0]\n", " for i in range(1, n_props):\n", " ax = ax0.twinx()\n", " ax.yaxis.set_label_position(\"left\")\n", " ax.yaxis.tick_left()\n", " ax.spines[\"left\"].set_position((\"outward\", axis_offset * i))\n", " ax.spines[\"right\"].set_visible(False)\n", " axes.append(ax)\n", "\n", " for ax in axes:\n", " ax.set_xlim(start_l, end_l)\n", " ax.margins(x=0)\n", " ax.autoscale(enable=False, axis=\"x\")\n", "\n", " # X ticks\n", " if x_label_ticks is not None:\n", " major_xticks = np.linspace(start_l, end_l, x_label_ticks)\n", " ax0.xaxis.set_major_locator(mticker.FixedLocator(major_xticks))\n", " else:\n", " ax0.xaxis.set_major_locator(mticker.AutoLocator())\n", "\n", " if x_grid_ticks is not None:\n", " minor_xticks = np.linspace(start_l, end_l, x_grid_ticks)\n", " ax0.xaxis.set_minor_locator(mticker.FixedLocator(minor_xticks))\n", "\n", " if rotate_xticks:\n", " for lbl in ax0.get_xticklabels():\n", " lbl.set_rotation(rotate_xticks)\n", " lbl.set_ha(\"right\")\n", "\n", " # -------------------------------------------------------------------------\n", " # Build property -> sorted sample columns (numeric level1 only)\n", " # -------------------------------------------------------------------------\n", " def _sample_index(x: Any) -> int:\n", " # level1 expected to be int; allow numeric strings\n", " try:\n", " return int(x)\n", " except Exception as e:\n", " raise ValueError(f\"Non-numeric sample index in level1: {x!r}. Expected 0..N.\") from e\n", "\n", " prop_to_sample_cols: dict[str, list[tuple[Any, Any]]] = {}\n", " for p in used_properties:\n", " mask_p = (lvl0_norm == p) & ~pd.Index(lvl1_vals).map(_is_minus_one)\n", " cols = df.columns[mask_p].tolist()\n", " if cols:\n", " cols_sorted = sorted(cols, key=lambda c: _sample_index(c[1]))\n", " prop_to_sample_cols[p] = cols_sorted\n", " else:\n", " prop_to_sample_cols[p] = []\n", "\n", " # Direction series\n", " vav = pd.to_numeric(df[vav_col], errors=\"coerce\").to_numpy(dtype=float)\n", "\n", " # -------------------------------------------------------------------------\n", " # Plot segments\n", " # -------------------------------------------------------------------------\n", " all_lines = []\n", " plotted_any_for_property = {p: False for p in used_properties}\n", " labeled_property = {p: False for p in used_properties}\n", "\n", " idx = df.index.to_numpy()\n", " for i, row_idx in enumerate(idx):\n", " if not overlaps[i]:\n", " continue\n", "\n", " x0 = float(seg_start[i])\n", " x1 = float(seg_end[i])\n", " if x1 <= x0:\n", " continue\n", "\n", " direction_negative = (vav[i] < 0)\n", "\n", " for prop_idx, prop in enumerate(used_properties):\n", " ax = axes[prop_idx]\n", " cols = prop_to_sample_cols.get(prop, [])\n", " if not cols:\n", " continue\n", "\n", " vals = df.loc[row_idx, cols]\n", "\n", " notna = vals.notna().to_numpy()\n", " if not notna.any():\n", " continue\n", "\n", " last = int(np.where(notna)[0].max())\n", " vals_upto = vals.iloc[: last + 1].dropna().to_numpy(dtype=float)\n", "\n", " m = len(vals_upto)\n", " if m < 2:\n", " # You said this won't happen; keep safe anyway.\n", " continue\n", "\n", " if direction_negative:\n", " vals_upto = vals_upto[::-1]\n", "\n", " x_seg = np.linspace(x0, x1, m)\n", "\n", " label = prop if not labeled_property[prop] else \"_nolegend_\"\n", " (ln,) = ax.plot(\n", " x_seg,\n", " vals_upto,\n", " linestyle=prop_linestyle[prop],\n", " color=prop_color[prop],\n", " label=label,\n", " )\n", "\n", " labeled_property[prop] = True\n", " all_lines.append(ln)\n", " plotted_any_for_property[prop] = True\n", "\n", " ax.set_ylabel(used_axis_labels[prop_idx])\n", " if ylims is not None and ylims[prop_idx] is not None:\n", " ax.set_ylim(*ylims[prop_idx])\n", "\n", " # Missing property handling\n", " missing_props = [p for p, ok in plotted_any_for_property.items() if not ok]\n", " if missing_props:\n", " msg = f\"Requested properties produced no plotted lines (missing or all-NaN): {missing_props}\"\n", " if missing == \"error\":\n", " raise ValueError(msg)\n", " elif missing == \"warn\":\n", " _log_warn(msg)\n", "\n", " # Y ticks and y-grid ticks\n", " for prop_idx, ax in enumerate(axes):\n", " if y_label_ticks is not None:\n", " y0, y1 = ax.get_ylim()\n", " ax.set_yticks(np.linspace(y0, y1, y_label_ticks[prop_idx]))\n", "\n", " if y_grid_ticks is not None:\n", " y0, y1 = ax0.get_ylim()\n", " ax0.yaxis.set_minor_locator(mticker.FixedLocator(np.linspace(y0, y1, y_grid_ticks)))\n", "\n", " # Labels, grid, title, legend\n", " ax0.set_xlabel(\"l_sum\")\n", " ax0.set_axisbelow(True)\n", "\n", " if grid:\n", " ax0.grid(True, which=\"major\", axis=\"both\", linestyle=grid_style, alpha=grid_alpha)\n", " if x_grid_ticks is not None:\n", " ax0.grid(True, which=\"minor\", axis=\"x\", linestyle=grid_style, alpha=grid_alpha)\n", " if y_grid_ticks is not None:\n", " ax0.grid(True, which=\"minor\", axis=\"y\", linestyle=grid_style, alpha=grid_alpha)\n", "\n", " if show_title:\n", " ax0.set_title(f\"{title_prefix}l_sum: {start_l:g} - {end_l:g}\")\n", "\n", " if legend:\n", " by_label = OrderedDict()\n", " for ln in all_lines:\n", " lab = ln.get_label()\n", " if lab and lab != \"_nolegend_\" and lab not in by_label:\n", " by_label[lab] = ln\n", "\n", " handles = list(by_label.values())\n", " labels = list(by_label.keys())\n", "\n", " if handles:\n", " if legend_in_figure:\n", " fig.legend(handles, labels, loc=legend_loc, bbox_to_anchor=legend_coords, fontsize=legend_fontsize)\n", " else:\n", " ax0.legend(handles, labels, loc=legend_loc, bbox_to_anchor=legend_coords, fontsize=legend_fontsize)\n", "\n", " fig.tight_layout()\n", " _log_info(f\"Plotted {len(all_lines)} segment-lines for {n_props} properties.\")\n", " return fig, axes, used_properties\n" ] }, { "cell_type": "code", "execution_count": null, "id": "76d6442a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['A',\n", " 'ACALC',\n", " 'CPI',\n", " 'CPK',\n", " 'DH',\n", " 'DP',\n", " 'DRAGRED',\n", " 'DRAKONZ',\n", " 'DSI',\n", " 'DSK',\n", " 'DTTR',\n", " 'DWVERL',\n", " 'DWVERLABS',\n", " 'ETAAV',\n", " 'FS',\n", " 'HR',\n", " 'HVEC',\n", " 'IAKTIV',\n", " 'IRTRENN',\n", " 'JV',\n", " 'JV2',\n", " 'LAMBDA',\n", " 'LECKEINAUS',\n", " 'LECKMENGE',\n", " 'LECKORT',\n", " 'LINEPACK',\n", " 'LINEPACKGEOM',\n", " 'LINEPACKRATE',\n", " 'MAINELEMENT',\n", " 'MAV',\n", " 'MI',\n", " 'MK',\n", " 'MKOND',\n", " 'MMAX_INST',\n", " 'MMIN_INST',\n", " 'MVEC',\n", " 'MVECMAX_INST',\n", " 'MVECMIN_INST',\n", " 'PAV',\n", " 'PDAMPF',\n", " 'PHR',\n", " 'PHVEC',\n", " 'PMAX',\n", " 'PMIN',\n", " 'PR',\n", " 'PVEC',\n", " 'PVECMAX_INST',\n", " 'PVECMIN_INST',\n", " 'QI2',\n", " 'QK2',\n", " 'QMAV',\n", " 'QMI',\n", " 'QMK',\n", " 'QMMAX_INST',\n", " 'QMMIN_INST',\n", " 'QMVEC',\n", " 'QSVB',\n", " 'RHOAV',\n", " 'RHOI',\n", " 'RHOK',\n", " 'RHOVEC',\n", " 'SVEC',\n", " 'TAV',\n", " 'TI',\n", " 'TK',\n", " 'TTRVEC',\n", " 'TVEC',\n", " 'TVECMAX_INST',\n", " 'TVECMIN_INST',\n", " 'VAV',\n", " 'VI',\n", " 'VK',\n", " 'VMAX_INST',\n", " 'VMIN_INST',\n", " 'VOLDA',\n", " 'WALTERI',\n", " 'WALTERK',\n", " 'WVL',\n", " 'ZAUS',\n", " 'ZEIN',\n", " 'ZHKNR',\n", " 'ZVEC']" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s3s.GetResultProperties_from_elementType(s3s.ObjectTypes.Pipe,False)" ] }, { "cell_type": "code", "execution_count": null, "id": "aa299df1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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tkNameFkdtroRowdFkltgrFkstrasseLLzuRauJlambsLambda0ZeinZausZumlAsollIndschallBaujahrHalFkcontFk2lrohrBeschreibungIdreferenzIplanungKvrLineWidthMMDottedLineDNDiKvrKlartextHasClosedNSCHsTkPkInVariantXkorYkorGeometriesDifferbz.Fkbz.Qsvbbz.Irtrennbz.Leckstatusbz.Leckstartbz.Leckendbz.Leckortbz.Leckmengebz.Imptnzbz.Zvlimptnzbz.Kantenzvbz.ITrennWithNSCHgeometryfkKIfkKKAACALCCPICPKDHDPDRAGREDDRAKONZDSIDSKDTTRDWVERLDWVERLABSETAAVFSHRIAKTIVIRTRENNJVJV2LAMBDALECKEINAUSLECKMENGELECKORTLINEPACKLINEPACKGEOMLINEPACKRATEMAINELEMENTMAVMIMKMKONDMMAX_INSTMMIN_INSTMVECPAVPDAMPFPHRPMAXPMINPRPVECPVECMAX_INSTPVECMIN_INSTQI2QK2QMAVQMIQMKQMMAX_INSTQMMIN_INSTQSVBRHOAVRHOIRHOKRHOVECSVECTAVTITKTTRVECTVECVAVVIVKVMAX_INSTVMIN_INSTVOLDAWALTERIWALTERKWVLZAUSZEINZHKNRZVECl_sumAGSN_LfdnrAGSN_Name
interior points-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1012345678910-1-1-1-1-1-1012345678910012345678910012345678910-1-1-1-1-1-1-1-1-1-1-1012345678910012345678910-1-1-1012345678910012345678910-1-1-1-1-1-1-1-1-1-1-1-1012345678910-1-1-1
05691533564979419761Rohr V-E0 V-K1683S48165111674553109734779752876656844188520482933228154734615.35170000.051000010000050291288749724631185025945677694931826OSM: Knoten 476971238 -> Knoten 299394923; Län...39785520010.0050350345.6Vorlauf56915335649794197615691533564979419761False713619.9213835.578219e+06False569153356497941976100000000000LINESTRING (713619.921 5578218.954, 713614.649...539810069428410477948253915804674840320.099999.099999.099999.099999.099999.099999.099999.099999.099999.00.00484443.120160.66196899999.099999.099999.00.00.00.15272799999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.079.7476479.7476479.74764NaNNaNNaNNaNNaNNaNNaNNaN99999.00.7010740.00234599999.05.80067899999.05.8787855.8397305.800680NaNNaNNaNNaNNaNNaNNaNNaN5.8787855.8397305.800680NaNNaNNaNNaNNaNNaNNaNNaN5.8787855.8397305.800680NaNNaNNaNNaNNaNNaNNaNNaN99999.099999.0287.0915287.0915287.091599999.099999.099999.099999.0965.7965.7012965.7000965.7006965.7012NaNNaNNaNNaNNaNNaNNaNNaN07.67585015.35170NaNNaNNaNNaNNaNNaNNaNNaN99999.089.9999989.998020.0000000.0024220.004844NaNNaNNaNNaNNaNNaNNaNNaN90.0000089.9990289.99802NaNNaNNaNNaNNaNNaNNaNNaN0.8803150.8803150.88031499999.099999.00.099999.099999.010082.8899999.099999.099999.0541.49541.89542.29NaNNaNNaNNaNNaNNaNNaNNaN15.3517011Längsschnitt
15048873293262650113Rohr V-K1683S V-K1693S48165111674553109734779752876656844188520482933228154734612.50895000.051000010000050291288749724631185216742060270992761OSM: Knoten 299394923 -> Knoten 4105649557; Lä...39785520010.0050350345.6Vorlauf50488732932626501135048873293262650113False713614.6487125.578233e+06False504887329326265011300000000000LINESTRING (713614.649 5578233.372, 713614.465...482539158046748403251806177803628615930.099999.099999.099999.099999.099999.099999.099999.099999.099999.00.00394743.58080.5451599999.099999.099999.00.00.00.15272799999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.079.7476479.7476479.74764NaNNaNNaNNaNNaNNaNNaNNaN99999.00.7010270.0019199999.05.73247599999.05.8006805.7665755.732475NaNNaNNaNNaNNaNNaNNaNNaN5.8006805.7665755.732475NaNNaNNaNNaNNaNNaNNaNNaN5.8006805.7665755.732475NaNNaNNaNNaNNaNNaNNaNNaN99999.099999.0287.0915287.0915287.091599999.099999.099999.099999.0965.7012965.7021965.7012965.7017965.7021NaNNaNNaNNaNNaNNaNNaNNaN06.25447412.50895NaNNaNNaNNaNNaNNaNNaNNaN99999.089.9980289.99640.0048440.0068180.008791NaNNaNNaNNaNNaNNaNNaNNaN89.9980289.9972289.99640NaNNaNNaNNaNNaNNaNNaNNaN0.8803140.8803140.88031399999.099999.00.099999.099999.010081.8199999.099999.099999.0542.29542.64542.99NaNNaNNaNNaNNaNNaNNaNNaN27.8606481Längsschnitt
25715081934973525403Rohr V-K2163S V-K1693S4816511167455310973477975287665684418852048293322815473466.26550500.051000010000050291288749724631184919359344392474301OSM: Knoten 299394922 -> Knoten 4105649557; Lä...595926249010.0050350345.6Vorlauf57150819349735254035715081934973525403False713614.3690435.578252e+06False571508193497352540300000000000LINESTRING (713614.369 5578252.145, 713614.465...496492048704118354551806177803628615930.099999.099999.099999.099999.099999.099999.099999.099999.099999.00.00197743.504070.27257599999.099999.099999.00.00.00.15272799999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.099999.0-79.74764-79.74764NaNNaNNaNNaNNaNNaNNaNNaNNaN99999.00.7009950.00095799999.05.69931799999.05.6993155.732475NaNNaNNaNNaNNaNNaNNaNNaNNaN5.6993155.732475NaNNaNNaNNaNNaNNaNNaNNaNNaN5.6993155.732475NaNNaNNaNNaNNaNNaNNaNNaNNaN99999.099999.0-287.0915-287.0915-287.091599999.099999.099999.099999.0965.7026965.7021965.7026965.7021NaNNaNNaNNaNNaNNaNNaNNaNNaN06.265505NaNNaNNaNNaNNaNNaNNaNNaNNaN99999.089.995689.99640.0107680.008791NaNNaNNaNNaNNaNNaNNaNNaNNaN89.9956189.99640NaNNaNNaNNaNNaNNaNNaNNaNNaN-0.880313-0.880313-0.88031399999.099999.00.099999.099999.0-10080.9299999.099999.099999.0543.33542.99NaNNaNNaNNaNNaNNaNNaNNaNNaN34.1261531Längsschnitt
\n", "
" ], "text/plain": [ " tk Name \\\n", "interior points -1 -1 \n", "0 5691533564979419761 Rohr V-E0 V-K1683S \n", "1 5048873293262650113 Rohr V-K1683S V-K1693S \n", "2 5715081934973525403 Rohr V-K2163S V-K1693S \n", "\n", " FkdtroRowd Fkltgr \\\n", "interior points -1 -1 \n", "0 4816511167455310973 4779752876656844188 \n", "1 4816511167455310973 4779752876656844188 \n", "2 4816511167455310973 4779752876656844188 \n", "\n", " Fkstrasse L Lzu Rau Jlambs Lambda0 Zein \\\n", "interior points -1 -1 -1 -1 -1 -1 -1 \n", "0 5204829332281547346 15.351700 0 0.05 1 0 0 \n", "1 5204829332281547346 12.508950 0 0.05 1 0 0 \n", "2 5204829332281547346 6.265505 0 0.05 1 0 0 \n", "\n", " Zaus Zuml Asoll Indschall Baujahr Hal Fkcont \\\n", "interior points -1 -1 -1 -1 -1 -1 -1 \n", "0 0 0 1000 0 0 5029128874972463118 \n", "1 0 0 1000 0 0 5029128874972463118 \n", "2 0 0 1000 0 0 5029128874972463118 \n", "\n", " Fk2lrohr \\\n", "interior points -1 \n", "0 5025945677694931826 \n", "1 5216742060270992761 \n", "2 4919359344392474301 \n", "\n", " Beschreibung Idreferenz \\\n", "interior points -1 -1 \n", "0 OSM: Knoten 476971238 -> Knoten 299394923; Län... 39785520 \n", "1 OSM: Knoten 299394923 -> Knoten 4105649557; Lä... 39785520 \n", "2 OSM: Knoten 299394922 -> Knoten 4105649557; Lä... 595926249 \n", "\n", " Iplanung Kvr LineWidthMM DottedLine DN Di KvrKlartext \\\n", "interior points -1 -1 -1 -1 -1 -1 -1 \n", "0 0 1 0.005 0 350 345.6 Vorlauf \n", "1 0 1 0.005 0 350 345.6 Vorlauf \n", "2 0 1 0.005 0 350 345.6 Vorlauf \n", "\n", " HasClosedNSCHs Tk Pk \\\n", "interior points -1 -1 -1 \n", "0 5691533564979419761 5691533564979419761 \n", "1 5048873293262650113 5048873293262650113 \n", "2 5715081934973525403 5715081934973525403 \n", "\n", " InVariant Xkor Ykor GeometriesDiffer \\\n", "interior points -1 -1 -1 -1 \n", "0 False 713619.921383 5.578219e+06 False \n", "1 False 713614.648712 5.578233e+06 False \n", "2 False 713614.369043 5.578252e+06 False \n", "\n", " bz.Fk bz.Qsvb bz.Irtrenn bz.Leckstatus \\\n", "interior points -1 -1 -1 -1 \n", "0 5691533564979419761 0 0 0 \n", "1 5048873293262650113 0 0 0 \n", "2 5715081934973525403 0 0 0 \n", "\n", " bz.Leckstart bz.Leckend bz.Leckort bz.Leckmenge bz.Imptnz \\\n", "interior points -1 -1 -1 -1 -1 \n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "\n", " bz.Zvlimptnz bz.Kantenzv bz.ITrennWithNSCH \\\n", "interior points -1 -1 -1 \n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "\n", " geometry \\\n", "interior points -1 \n", "0 LINESTRING (713619.921 5578218.954, 713614.649... \n", "1 LINESTRING (713614.649 5578233.372, 713614.465... \n", "2 LINESTRING (713614.369 5578252.145, 713614.465... \n", "\n", " fkKI fkKK A ACALC \\\n", "interior points -1 -1 -1 -1 \n", "0 5398100694284104779 4825391580467484032 0.0 99999.0 \n", "1 4825391580467484032 5180617780362861593 0.0 99999.0 \n", "2 4964920487041183545 5180617780362861593 0.0 99999.0 \n", "\n", " CPI CPK DH DP DRAGRED DRAKONZ \\\n", "interior points -1 -1 -1 -1 -1 -1 \n", "0 99999.0 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "1 99999.0 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "2 99999.0 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "\n", " DSI DSK DTTR DWVERL DWVERLABS ETAAV \\\n", "interior points -1 -1 -1 -1 -1 -1 \n", "0 99999.0 99999.0 0.004844 43.12016 0.661968 99999.0 \n", "1 99999.0 99999.0 0.003947 43.5808 0.54515 99999.0 \n", "2 99999.0 99999.0 0.001977 43.50407 0.272575 99999.0 \n", "\n", " FS HR IAKTIV IRTRENN JV JV2 LAMBDA \\\n", "interior points -1 -1 -1 -1 -1 -1 -1 \n", "0 99999.0 99999.0 0.0 0.0 0.152727 99999.0 99999.0 \n", "1 99999.0 99999.0 0.0 0.0 0.152727 99999.0 99999.0 \n", "2 99999.0 99999.0 0.0 0.0 0.152727 99999.0 99999.0 \n", "\n", " LECKEINAUS LECKMENGE LECKORT LINEPACK LINEPACKGEOM \\\n", "interior points -1 -1 -1 -1 -1 \n", "0 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "1 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "2 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "\n", " LINEPACKRATE MAINELEMENT MAV MI MK MKOND \\\n", "interior points -1 -1 -1 -1 -1 -1 \n", "0 99999.0 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "1 99999.0 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "2 99999.0 99999.0 99999.0 99999.0 99999.0 99999.0 \n", "\n", " MMAX_INST MMIN_INST MVEC \\\n", "interior points -1 -1 0 1 2 3 4 5 \n", "0 99999.0 99999.0 79.74764 79.74764 79.74764 NaN NaN NaN \n", "1 99999.0 99999.0 79.74764 79.74764 79.74764 NaN NaN NaN \n", "2 99999.0 99999.0 -79.74764 -79.74764 NaN NaN NaN NaN \n", "\n", " PAV PDAMPF PHR PMAX \\\n", "interior points 6 7 8 9 10 -1 -1 -1 -1 \n", "0 NaN NaN NaN NaN NaN 99999.0 0.701074 0.002345 99999.0 \n", "1 NaN NaN NaN NaN NaN 99999.0 0.701027 0.00191 99999.0 \n", "2 NaN NaN NaN NaN NaN 99999.0 0.700995 0.000957 99999.0 \n", "\n", " PMIN PR PVEC \\\n", "interior points -1 -1 0 1 2 3 4 5 \n", "0 5.800678 99999.0 5.878785 5.839730 5.800680 NaN NaN NaN \n", "1 5.732475 99999.0 5.800680 5.766575 5.732475 NaN NaN NaN \n", "2 5.699317 99999.0 5.699315 5.732475 NaN NaN NaN NaN \n", "\n", " PVECMAX_INST \\\n", "interior points 6 7 8 9 10 0 1 2 3 4 \n", "0 NaN NaN NaN NaN NaN 5.878785 5.839730 5.800680 NaN NaN \n", "1 NaN NaN NaN NaN NaN 5.800680 5.766575 5.732475 NaN NaN \n", "2 NaN NaN NaN NaN NaN 5.699315 5.732475 NaN NaN NaN \n", "\n", " PVECMIN_INST \\\n", "interior points 5 6 7 8 9 10 0 1 2 3 \n", "0 NaN NaN NaN NaN NaN NaN 5.878785 5.839730 5.800680 NaN \n", "1 NaN NaN NaN NaN NaN NaN 5.800680 5.766575 5.732475 NaN \n", "2 NaN NaN NaN NaN NaN NaN 5.699315 5.732475 NaN NaN \n", "\n", " QI2 QK2 QMAV \\\n", "interior points 4 5 6 7 8 9 10 -1 -1 -1 \n", "0 NaN NaN NaN NaN NaN NaN NaN 99999.0 99999.0 287.0915 \n", "1 NaN NaN NaN NaN NaN NaN NaN 99999.0 99999.0 287.0915 \n", "2 NaN NaN NaN NaN NaN NaN NaN 99999.0 99999.0 -287.0915 \n", "\n", " QMI QMK QMMAX_INST QMMIN_INST QSVB RHOAV \\\n", "interior points -1 -1 -1 -1 -1 -1 \n", "0 287.0915 287.0915 99999.0 99999.0 99999.0 99999.0 \n", "1 287.0915 287.0915 99999.0 99999.0 99999.0 99999.0 \n", "2 -287.0915 -287.0915 99999.0 99999.0 99999.0 99999.0 \n", "\n", " RHOI RHOK RHOVEC \\\n", "interior points -1 -1 0 1 2 3 4 5 \n", "0 965.7 965.7012 965.7000 965.7006 965.7012 NaN NaN NaN \n", "1 965.7012 965.7021 965.7012 965.7017 965.7021 NaN NaN NaN \n", "2 965.7026 965.7021 965.7026 965.7021 NaN NaN NaN NaN \n", "\n", " SVEC \\\n", "interior points 6 7 8 9 10 0 1 2 3 4 5 6 \n", "0 NaN NaN NaN NaN NaN 0 7.675850 15.35170 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN 0 6.254474 12.50895 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN 0 6.265505 NaN NaN NaN NaN NaN \n", "\n", " TAV TI TK TTRVEC \\\n", "interior points 7 8 9 10 -1 -1 -1 0 \n", "0 NaN NaN NaN NaN 99999.0 89.99999 89.99802 0.000000 \n", "1 NaN NaN NaN NaN 99999.0 89.99802 89.9964 0.004844 \n", "2 NaN NaN NaN NaN 99999.0 89.9956 89.9964 0.010768 \n", "\n", " TVEC \\\n", "interior points 1 2 3 4 5 6 7 8 9 10 0 \n", "0 0.002422 0.004844 NaN NaN NaN NaN NaN NaN NaN NaN 90.00000 \n", "1 0.006818 0.008791 NaN NaN NaN NaN NaN NaN NaN NaN 89.99802 \n", "2 0.008791 NaN NaN NaN NaN NaN NaN NaN NaN NaN 89.99561 \n", "\n", " VAV \\\n", "interior points 1 2 3 4 5 6 7 8 9 10 -1 \n", "0 89.99902 89.99802 NaN NaN NaN NaN NaN NaN NaN NaN 0.880315 \n", "1 89.99722 89.99640 NaN NaN NaN NaN NaN NaN NaN NaN 0.880314 \n", "2 89.99640 NaN NaN NaN NaN NaN NaN NaN NaN NaN -0.880313 \n", "\n", " VI VK VMAX_INST VMIN_INST VOLDA WALTERI \\\n", "interior points -1 -1 -1 -1 -1 -1 \n", "0 0.880315 0.880314 99999.0 99999.0 0.0 99999.0 \n", "1 0.880314 0.880313 99999.0 99999.0 0.0 99999.0 \n", "2 -0.880313 -0.880313 99999.0 99999.0 0.0 99999.0 \n", "\n", " WALTERK WVL ZAUS ZEIN ZHKNR ZVEC \\\n", "interior points -1 -1 -1 -1 -1 0 1 \n", "0 99999.0 10082.88 99999.0 99999.0 99999.0 541.49 541.89 \n", "1 99999.0 10081.81 99999.0 99999.0 99999.0 542.29 542.64 \n", "2 99999.0 -10080.92 99999.0 99999.0 99999.0 543.33 542.99 \n", "\n", " l_sum AGSN_Lfdnr \\\n", "interior points 2 3 4 5 6 7 8 9 10 -1 -1 \n", "0 542.29 NaN NaN NaN NaN NaN NaN NaN NaN 15.351701 1 \n", "1 542.99 NaN NaN NaN NaN NaN NaN NaN NaN 27.860648 1 \n", "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN 34.126153 1 \n", "\n", " AGSN_Name \n", "interior points -1 \n", "0 Längsschnitt \n", "1 Längsschnitt \n", "2 Längsschnitt " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_vec.head(3)" ] }, { "cell_type": "code", "execution_count": null, "id": "52352582", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes, used = plot_time_curves(\n", " df=df_vec,\n", "\n", " # NEW: interpreted as length window on l_sum (floats), not timestamps\n", " start=0.0,\n", " end=1300, # <-- set this to the l_sum range you want\n", "\n", " properties=[\"PVEC\", \"TVEC\"],\n", " axis_labels=[\"PH bar\", \"H Druck in barBzg\"],\n", " ylims=[(2, 6), (80, 100)],\n", "\n", " # kept for compatibility but not used by the new logic (can be omitted)\n", " # tks_per_property=...,\n", "\n", " y_label_ticks=[5, 6],\n", " y_grid_ticks=9,\n", " x_label_ticks=27,\n", " x_grid_ticks=14,\n", "\n", " legend=True,\n", " legend_in_figure=True,\n", " legend_loc=\"upper left\",\n", " legend_coords=(0.67, 0.33),\n", " legend_fontsize=15,\n", "\n", " title_prefix=\"Testplot: \",\n", " show_title=True,\n", "\n", " axis_offset=70,\n", " linestyles=[\"-\", \"--\", \":\"],\n", " rotate_xticks=0,\n", "\n", " grid=True,\n", " grid_style=\"--\",\n", " grid_alpha=0.8,\n", "\n", " missing=\"warn\",\n", " logger=None,\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "fe232772", "metadata": {}, "outputs": [ { "ename": "ZeroDivisionError", "evalue": "division by zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[18], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m0\u001b[39m\n", "\u001b[1;31mZeroDivisionError\u001b[0m: division by zero" ] } ], "source": [ "1/0" ] }, { "cell_type": "code", "execution_count": null, "id": "abfd0471", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "id": "995f4030", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "id": "e54b6533", "metadata": {}, "outputs": [], "source": [ "def plot(dfAGSN=pd.DataFrame()\n", " ,dfAGSNRL=pd.DataFrame()\n", " ,PHCol='(\"PVEC\", \"0\")'\n", " ,mlcCol='mlc_n'\n", " ,zKoorCol='ZKOR_n'\n", " ,barBzgCol='H_n'\n", " ,QMCol='QM'\n", " ,TCol='T_n'\n", " ,xCol='LSum'\n", " \n", "):\n", " \n", " fig, ax0 = plt.subplots(figsize=(20,11.9))\n", " \n", " ax0.set_yticks(np.linspace(0, 10, 21))\n", " ax0.yaxis.set_ticklabels([])\n", " ax0.grid()\n", "\n", " #PH\n", " ax1 = ax0.twinx() \n", " fyPH(ax1) \n", " PH_SL=ax1.plot(dfAGSN[xCol], dfAGSN[PHCol], color='red', label='PH SL',ls='dotted')\n", " PH_RL=ax1.plot(dfAGSNRL[xCol], dfAGSNRL[PHCol], color='blue', label='PH RL',ls='dotted')\n", " \n", " #mlc\n", " ax11 = ax0.twinx() \n", " fymlc(ax11) \n", " mlc_SL=ax11.plot(dfAGSN[xCol], dfAGSN[mlcCol], color='red', label='mlc SL')\n", " mlc_RL=ax11.plot(dfAGSNRL[xCol], dfAGSNRL[mlcCol], color='blue', label='mlc RL')\n", " \n", " z=ax11.plot(dfAGSN[xCol], dfAGSN[zKoorCol], color='black', label='z',ls='dashed',alpha=.5)\n", " \n", " #barBZG\n", " ax12 = ax0.twinx() \n", " fybarBzg(ax12) \n", " barB_SL=ax12.plot(dfAGSN[xCol], dfAGSN[barBzgCol], color='red', label='H SL',ls='dashdot')\n", " barB_RL=ax12.plot(dfAGSNRL[xCol], dfAGSNRL[barBzgCol], color='blue', label='H RL',ls='dashdot')\n", " \"\"\"\n", " #M\n", " ax2 = ax0.twinx() \n", " fyM(ax2)\n", " QM_SL=ax2.step(dfAGSN[xCol], dfAGSN[QMCol]*dfAGSN['direction'], color='orange', label='M SL')\n", " QM_RL=ax2.step(dfAGSNRL[xCol], dfAGSNRL[QMCol]*dfAGSNRL['direction'], color='cyan', label='M RL',ls='--')\n", " \n", " #T\n", " ax3 = ax0.twinx() \n", " fyT(ax3)\n", " T_SL=ax3.plot(dfAGSN[xCol], dfAGSN[TCol], color='pink', label='T SL')\n", " T_RL=ax3.plot(dfAGSNRL[xCol], dfAGSNRL[TCol], color='lavender', label='T RL')\n", " \"\"\"\n", " ax0.set_title('Longitudinal Section for ')#+dbFilename)\n", "\n", " # added these three lines\n", " lns = PH_SL+ PH_RL + mlc_SL+ mlc_RL + barB_SL+ barB_RL#+ QM_SL+ QM_RL + T_SL+ T_RL + z\n", " labs = [l.get_label() for l in lns]\n", " ax0.legend(lns, labs)#, loc=0)\n", " \n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "9fb9087f", "metadata": {}, "outputs": [], "source": [ "def plot(\n", " dfAGSN=df_vec,\n", " xCol=\"l_sum\",\n", " PHCol=(\"PVEC\", 0),\n", " barBzgCol=(\"HVEC\", 0)\n", "):\n", " fig, ax0 = plt.subplots(figsize=(20,11.9))\n", " \n", " ax0.set_yticks(np.linspace(0, 10, 21))\n", " ax0.yaxis.set_ticklabels([])\n", " ax0.grid()\n", "\n", " #PH\n", " ax1 = ax0.twinx() \n", " fyPH(ax1) \n", " PH_SL=ax1.plot(dfAGSN[xCol], dfAGSN[PHCol], color='red', label='PH SL',ls='dotted')\n", " #PH_RL=ax1.plot(dfAGSNRL[xCol], dfAGSNRL[PHCol], color='blue', label='PH RL',ls='dotted')\n", "\n", " #barBZG\n", " ax12 = ax0.twinx() \n", " fybarBzg(ax12) \n", " barB_SL=ax12.plot(dfAGSN[xCol], dfAGSN[barBzgCol], color='red', label='H SL',ls='dashdot')\n", " \n", " ax0.set_title('Longitudinal Section for ')#+dbFilename)\n", "\n", " # added these three lines\n", " lns = PH_SL + barB_SL#+ PH_RL + mlc_SL+ mlc_RL + barB_SL+ barB_RL#+ QM_SL+ QM_RL + T_SL+ T_RL + z\n", " labs = [l.get_label() for l in lns]\n", " ax0.legend(lns, labs)#, loc=0)\n", " \n", " plt.show()\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "a43ed47b", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'np' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[23], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m plot(df_vec)\n", "Cell \u001b[1;32mIn[22], line 9\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(dfAGSN, xCol, PHCol, barBzgCol)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[0;32m 2\u001b[0m dfAGSN\u001b[38;5;241m=\u001b[39mdf_vec,\n\u001b[0;32m 3\u001b[0m xCol\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ml_sum\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 4\u001b[0m PHCol\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPVEC\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m),\n\u001b[0;32m 5\u001b[0m barBzgCol\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHVEC\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 6\u001b[0m ):\n\u001b[0;32m 7\u001b[0m fig, ax0 \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m20\u001b[39m,\u001b[38;5;241m11.9\u001b[39m))\n\u001b[1;32m----> 9\u001b[0m ax0\u001b[38;5;241m.\u001b[39mset_yticks(np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m21\u001b[39m))\n\u001b[0;32m 10\u001b[0m ax0\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mset_ticklabels([])\n\u001b[0;32m 11\u001b[0m ax0\u001b[38;5;241m.\u001b[39mgrid()\n", "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined" ] }, { "data": { "image/png": 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