changed image to latest (jupyterlab 3)

cfd5ee31 · Rostislav Chudoba · cd6cd820 · cfd5ee31 · cfd5ee31 · cfd5ee31
Commit cfd5ee31 authored 3 years ago by Rostislav Chudoba
--- a/Dockerfile
+++ b/Dockerfile
 # Specify parent image. Please select a fixed tag here.
-ARG BASE_IMAGE=registry.git.rwth-aachen.de/jupyter/profiles/rwth-courses:2020-ss.1
+# ARG BASE_IMAGE=registry.git.rwth-aachen.de/jupyter/profiles/rwth-courses:2020-ss.1
+ARG BASE_IMAGE=registry.git.rwth-aachen.de/jupyter/profiles/rwth-minimal-2021-ss:latest
 FROM ${BASE_IMAGE}
 # .. Or update conda base environment to match specifications in environment.yml

--- a/mkappa/mkappa.ipynb
+++ b/mkappa/mkappa.ipynb
@@ -3,7 +3,7 @@
  {
   "cell_type": "code",
   "execution_count": 1,
-   "id": "funky-illustration",
+   "id": "million-hotel",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -13,7 +13,7 @@
  {
   "cell_type": "code",
   "execution_count": 2,
-   "id": "fresh-century",
+   "id": "purple-japan",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -28,7 +28,7 @@
  {
   "cell_type": "code",
   "execution_count": 3,
-   "id": "bigger-extent",
+   "id": "secondary-kruger",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -38,7 +38,7 @@
  {
   "cell_type": "code",
   "execution_count": 4,
-   "id": "cutting-finnish",
+   "id": "talented-catalog",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -55,7 +55,7 @@
  {
   "cell_type": "code",
   "execution_count": 5,
-   "id": "champion-badge",
+   "id": "informational-patrick",
   "metadata": {},
   "outputs": [
    {
@@ -122,7 +122,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "difficult-retail",
+   "id": "unauthorized-metallic",
   "metadata": {},
   "outputs": [],
   "source": []

-%% Cell type:code id:funky-illustration tags:
+%% Cell type:code id:million-hotel tags:
 ``` python
 %matplotlib widget
 ```
-%% Cell type:code id:fresh-century tags:
+%% Cell type:code id:purple-japan tags:
 ``` python
 from bmcs_cross_section.mkappa.mkappa import MKappa
 from bmcs_cross_section.cs_design import TShape, BarLayer, CrossSectionDesign
 from bmcs_cross_section.matmod import PWLConcreteMatMod, EC2ConcreteMatMod
 import numpy as np
 import sympy as sp
 sp.init_printing()
 ```
-%% Cell type:code id:bigger-extent tags:
+%% Cell type:code id:secondary-kruger tags:
 ``` python
 mc = MKappa(name='moment-curvature', low_kappa=-0.000014, high_kappa=0.00003)
 ```
-%% Cell type:code id:cutting-finnish tags:
+%% Cell type:code id:talented-catalog tags:
 ``` python
 mc.matrix = 'piecewise linear'
 css = mc.cross_section_shape = 'T-shape'
 csl = mc.cross_section_layout
 bl1 = BarLayer(name='steel 1', z=10, ds=10, count=2 )
 bl2 = BarLayer(name='steel 2', z=30, ds=10, count=2 )
 csl.add_layer(bl1)
 csl.add_layer(bl2)
 #csl.update_observers()
 ```
-%% Cell type:code id:champion-badge tags:
+%% Cell type:code id:informational-patrick tags:
 ``` python
 mc.interact()
 ```
 %% Output
    eta 0.3204545454545455
    M - k recalculated
-%% Cell type:code id:difficult-retail tags:
+%% Cell type:code id:unauthorized-metallic tags:
 ``` python
 ```

--- a/tension/fragmentation.ipynb
+++ b/tension/fragmentation.ipynb
@@ -3,7 +3,7 @@
  {
   "cell_type": "code",
   "execution_count": 1,
-   "id": "industrial-defendant",
+   "id": "thick-postcard",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -14,7 +14,7 @@
  {
   "cell_type": "code",
   "execution_count": 2,
-   "id": "typical-peripheral",
+   "id": "affected-mother",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -24,13 +24,13 @@
  {
   "cell_type": "code",
   "execution_count": 3,
-   "id": "existing-omega",
+   "id": "colored-clause",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "54e9687bb36b4050abf6e800a1ee0339",
+       "model_id": "24abea4c54174a62a893fbdd1127c3d9",
       "version_major": 2,
       "version_minor": 0
      },
@@ -49,7 +49,7 @@
  {
   "cell_type": "code",
   "execution_count": 4,
-   "id": "mighty-recipe",
+   "id": "european-colombia",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -59,7 +59,7 @@
  {
   "cell_type": "code",
   "execution_count": 5,
-   "id": "eligible-bahamas",
+   "id": "scientific-silly",
   "metadata": {},
   "outputs": [
    {
@@ -84,7 +84,7 @@
  {
   "cell_type": "code",
   "execution_count": 9,
-   "id": "amber-subdivision",
+   "id": "lonely-dairy",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -94,7 +94,7 @@
  {
   "cell_type": "code",
   "execution_count": 10,
-   "id": "material-hotel",
+   "id": "accessory-citizenship",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -104,7 +104,7 @@
  {
   "cell_type": "code",
   "execution_count": 12,
-   "id": "blocked-honduras",
+   "id": "subsequent-watershed",
   "metadata": {},
   "outputs": [],
   "source": [
@@ -114,7 +114,7 @@
  {
   "cell_type": "code",
   "execution_count": 13,
-   "id": "executive-throat",
+   "id": "several-holiday",
   "metadata": {},
   "outputs": [
    {
@@ -139,7 +139,7 @@
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "rental-applicant",
+   "id": "neural-lesson",
   "metadata": {},
   "outputs": [],
   "source": []

-%% Cell type:code id:industrial-defendant tags:
+%% Cell type:code id:thick-postcard tags:
 ``` python
 %matplotlib widget
 from pmcm.api import PMCM
 ```
-%% Cell type:code id:typical-peripheral tags:
+%% Cell type:code id:affected-mother tags:
 ``` python
 pmcm = PMCM()
 ```
-%% Cell type:code id:existing-omega tags:
+%% Cell type:code id:colored-clause tags:
 ``` python
 pmcm.interact()
 ```
 %% Output
-%% Cell type:code id:mighty-recipe tags:
+%% Cell type:code id:european-colombia tags:
 ``` python
 from ipytree import Tree
 ```
-%% Cell type:code id:eligible-bahamas tags:
+%% Cell type:code id:scientific-silly tags:
 ``` python
 Tree()
 ```
 %% Output
-%% Cell type:code id:amber-subdivision tags:
+%% Cell type:code id:lonely-dairy tags:
 ``` python
 import ipyregulartable as rt
 ```
-%% Cell type:code id:material-hotel tags:
+%% Cell type:code id:accessory-citizenship tags:
 ``` python
 import numpy as np
 ```
-%% Cell type:code id:blocked-honduras tags:
+%% Cell type:code id:subsequent-watershed tags:
 ``` python
 w = rt.RegularTableWidget(rt.NumpyDataModel(np.random.random((10,10))) )
 ```
-%% Cell type:code id:executive-throat tags:
+%% Cell type:code id:several-holiday tags:
 ``` python
 w
 ```
 %% Output
-%% Cell type:code id:rental-applicant tags:
+%% Cell type:code id:neural-lesson tags:
 ``` python
 ```