From 9534ed0cfa797ac67678dc7cda1fe2addfba6fa8 Mon Sep 17 00:00:00 2001
From: rch <rostislav.chudoba@rwth-aachen.de>
Date: Thu, 21 Apr 2022 13:09:23 +0200
Subject: [PATCH] removed the traitsui

---
 extras/sparse_solver/dense_mtx.py             | 32 --------
 tour2_constant_bond/fragmentation.ipynb       | 62 +++++++++-------
 tour5_damage_bond/5_2_PO_cfrp_damage.ipynb    |  8 +-
 ...thin a finite element implementation.ipynb | 73 +++++++++++++++----
 4 files changed, 98 insertions(+), 77 deletions(-)

diff --git a/extras/sparse_solver/dense_mtx.py b/extras/sparse_solver/dense_mtx.py
index 564b433..08270db 100755
--- a/extras/sparse_solver/dense_mtx.py
+++ b/extras/sparse_solver/dense_mtx.py
@@ -4,33 +4,6 @@ from numpy import allclose, arange, eye, linalg, ones, ix_, array, zeros, \
     ones
 from traits.api import HasTraits, Array, Property, cached_property, Instance, \
     Delegate, Any
-from traitsui.api \
-    import View, Item, TabularEditor
-from traitsui.tabular_adapter \
-    import TabularAdapter
-
-
-#from sys_mtx_assembly import SysMtxAssembly
-class ArrayAdapter(TabularAdapter):
-
-    columns = Property
-
-    def _get_columns(self):
-        n_columns = getattr(self.object, self.name).shape[1]
-        cols = [(str(i), i) for i in range(n_columns)]
-        return [('i', 'index')] + cols
-
-    font = 'Courier 10'
-    alignment = 'right'
-    format = '%6.2f'
-    index_text = Property
-
-    def _get_index_text(self):
-        return str(self.row)
-
-tabular_editor = TabularEditor(
-    adapter=ArrayAdapter())
-
 
 class DenseMtx(HasTraits):
 
@@ -65,8 +38,3 @@ class DenseMtx(HasTraits):
         '''String representation - delegate to matrix'''
         return str(self.mtx)
 
-    view = View(Item('mtx', editor=tabular_editor, show_label=False),
-                resizable=True,
-                scrollable=True,
-                buttons=['OK', 'Cancel'],
-                width=1.0, height=0.5)
diff --git a/tour2_constant_bond/fragmentation.ipynb b/tour2_constant_bond/fragmentation.ipynb
index 5f8d0fa..25d28e1 100644
--- a/tour2_constant_bond/fragmentation.ipynb
+++ b/tour2_constant_bond/fragmentation.ipynb
@@ -29,7 +29,7 @@
    "outputs": [
     {
      "data": {
-      "image/jpeg": 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\n",
       "text/html": [
        "\n",
        "        <iframe\n",
@@ -43,7 +43,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fddcc338c40>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f1e60280a60>"
       ]
      },
      "execution_count": 1,
@@ -157,14 +157,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "labeled-regression",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff944969a7074a2097257aa64f975bc0",
+       "model_id": "831340191a0e46bbb0924728c5d9838d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -431,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "regulation-bronze",
    "metadata": {},
    "outputs": [],
@@ -454,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "stock-interval",
    "metadata": {},
    "outputs": [],
@@ -469,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "junior-patrol",
    "metadata": {},
    "outputs": [
@@ -479,7 +479,7 @@
        "26570.0"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "e77defe7-ce4a-42f2-9ac2-8cbdc5302d5c",
    "metadata": {},
    "outputs": [
@@ -500,7 +500,7 @@
        "0.01"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -519,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "fundamental-bulletin",
    "metadata": {},
    "outputs": [],
@@ -535,7 +535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "bb9b6f0f-ad91-4b18-a65f-b19873afd2b4",
    "metadata": {},
    "outputs": [
@@ -545,7 +545,7 @@
        "(3.1884, 13.8)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -564,14 +564,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "nuclear-firmware",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "264d36e796a045ad8cf0f730588d0a07",
+       "model_id": "72740e1b8ea54068b96b532efee1b626",
        "version_major": 2,
        "version_minor": 0
       },
@@ -625,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "engaged-breast",
    "metadata": {},
    "outputs": [
@@ -635,7 +635,7 @@
        "97.79047929936306"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -660,7 +660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "molecular-french",
    "metadata": {},
    "outputs": [
@@ -670,7 +670,7 @@
        "0.020096"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -679,6 +679,16 @@
     "A_f / A_c"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "35550041-f70f-4054-8a59-3bf5897fba0c",
+   "metadata": {},
+   "source": [
+    "## **Question:** How to evaluate the corresponding crack opening?\n",
+    "\n",
+    "In the design of steel reinforced concrete or carbon concrete, it is necessary to limit the maximum crack opening to a prescribed value, i.e. $w_\\max < 0.1$ mm. As an exercise propose a formula for $w_\\max$."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "transsexual-overall",
@@ -692,14 +702,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "martial-sense",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f78782968464654a365983515afa681",
+       "model_id": "e82b054e323d4678b4e03d3ade37467a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -760,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "hungry-momentum",
    "metadata": {},
    "outputs": [],
@@ -776,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "stone-harmony",
    "metadata": {},
    "outputs": [
@@ -786,7 +796,7 @@
        "32771.0843373494"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -803,14 +813,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "needed-suspension",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c302b69931ed4a9c89584ad33457108f",
+       "model_id": "66c01c5df4884d799401e54730fcefdf",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/tour5_damage_bond/5_2_PO_cfrp_damage.ipynb b/tour5_damage_bond/5_2_PO_cfrp_damage.ipynb
index d87909f..3ff1973 100644
--- a/tour5_damage_bond/5_2_PO_cfrp_damage.ipynb
+++ b/tour5_damage_bond/5_2_PO_cfrp_damage.ipynb
@@ -7,7 +7,7 @@
     "<a id=\"top\"></a>\n",
     "# **5.2 Pullout behavior governed by damage**\n",
     "\n",
-    "!!! This notebook is still under editing and and video production\n",
+    "!!! This notebook is still under editing and video production\n",
     "\n",
     " * Define a bond-slip law governed by damage and loading history using unloading.\n",
     " * What is different in comparison to elastic-plastic models?\n",
@@ -46,7 +46,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c83b50c2460e423a812818347ec740e4",
+       "model_id": "7f07880961e544ae98891b9af0cda542",
        "version_major": 2,
        "version_minor": 0
       },
@@ -77,7 +77,7 @@
        "$\\displaystyle 1 - \\begin{cases} 1 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\e^{\\frac{\\left(\\kappa - \\kappa_{0}\\right) \\left(\\sqrt{E_{b}} \\sqrt{- E_{b} \\kappa_{0}^{2} + 4 G_{f}} + E_{b} \\kappa_{0}\\right)}{E_{b} \\kappa_{0}^{2} - 2 G_{f}}} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.GfDamageFn at 0x7f438e188e50>"
+       "<ibvpy.tmodel.mats_damage_fn.GfDamageFn at 0x7fcd36df0950>"
       ]
      },
      "execution_count": 3,
@@ -101,7 +101,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "db84454c52e94a6594f8b4b78b8b10de",
+       "model_id": "4a77c43514b04359bbb783df4e296076",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/tour6_energy/8_3_Damage function within a finite element implementation.ipynb b/tour6_energy/8_3_Damage function within a finite element implementation.ipynb
index 22613ad..ddb907f 100644
--- a/tour6_energy/8_3_Damage function within a finite element implementation.ipynb	
+++ b/tour6_energy/8_3_Damage function within a finite element implementation.ipynb	
@@ -2,7 +2,11 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "# 8.3 Damage function within a fininite element solver\n",
     "@todo - this notebook must be updated - currently not running"
@@ -13,7 +17,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": [
@@ -29,7 +36,10 @@
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
    },
    "source": [
     "## Define the damage functions\n",
@@ -44,7 +54,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": [
@@ -60,7 +73,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": [
@@ -74,7 +90,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": [
@@ -86,7 +105,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": [
@@ -102,7 +124,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": [
@@ -115,7 +140,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": []
@@ -124,7 +152,10 @@
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
    },
    "source": [
     "## Nonlinear solver"
@@ -135,7 +166,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": [
@@ -196,7 +230,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": [
@@ -208,7 +245,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": []
@@ -218,7 +258,10 @@
    "execution_count": null,
    "metadata": {
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "pycharm": {
+     "name": "#%%\n"
+    }
    },
    "outputs": [],
    "source": []
@@ -245,4 +288,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
-- 
GitLab