modified yml parts and referencing issue

Data/1_Writing/0_Deco/2_0_Task_Def.qmd

@@ -1,3 +1,2 @@
 # Task defintion {.unnumbered}
-<iframe src="./4_Mast.pdf" width="100%" height="97%"></iframe>
-<!-- <iframe src="./5_Mast.pdf" width="100%" height="100%"></iframe> -->
+<iframe src="./4_Mast.pdf" width="100%" height="97%"></iframe>

Data/1_Writing/1_Task/1_Introduction copy.qmd

@@ -1,18 +0,0 @@
-# Introduction {#sec-chap_1_Intro}
-In this work, a tool called \glsfirst{cnmc} is further developed.
-The overall goal, in very brief terms, is to generate a model, which is able to 
-predict the trajectories of general dynamical systems. The model
-shall be capable of predicting the trajectories when a model parameter
-value is changed. 
-Some basics about dynamical systems are covered in 
-subsection [-@sec-subsec_1_1_1_Principles] and in-depth explanations about \gls{cnmc} are given in
-chapter [-@sec-chap_2_Methodology]. \newline 
-
-However, for a short and broad introduction to \gls{cnmc} the workflow depicted in figure @fig-fig_1_CNMC_Workflow shall be highlighted. 
-The input it receives is data of a dynamical system or space state vectors for a range of model parameter values. The two main important outcomes are some accuracy measurements and the predicted trajectory for each desired model parameter value.
-Any inexperienced user may only have a look at the predicted trajectories to 
-quickly decide visually whether the prediction matches the trained data. Since \gls{cnmc} is written in a modular manner, meaning it can be regarded as 
-a black-box function, it can easily be integrated into other existing codes or 
-workflows. \newline
-
-![Broad overview: Workflow of \gls{cnmc}](../../3_Figs_Pyth/1_Task/1_CNMc_1.svg){#fig-fig_1_CNMC_Workflow}

Data/1_Writing/1_Task/1_Introduction.qmd

@@ -1,9 +1,18 @@
-# Chap Test_4 {#sec-chap_1_Abc}
-Here is some more text
-
 # Introduction {#sec-chap_1_Intro}
-Here is some more text
-
+In this work, a tool called \glsfirst{cnmc} is further developed.
+The overall goal, in very brief terms, is to generate a model, which is able to 
+predict the trajectories of general dynamical systems. The model
+shall be capable of predicting the trajectories when a model parameter
+value is changed. 
+Some basics about dynamical systems are covered in 
+subsection [-@sec-subsec_1_1_1_Principles] and in-depth explanations about \gls{cnmc} are given in
+chapter [-@sec-chap_2_Methodology]. \newline 
 
+However, for a short and broad introduction to \gls{cnmc} the workflow depicted in figure @fig-fig_1_CNMC_Workflow shall be highlighted. 
+The input it receives is data of a dynamical system or space state vectors for a range of model parameter values. The two main important outcomes are some accuracy measurements and the predicted trajectory for each desired model parameter value.
+Any inexperienced user may only have a look at the predicted trajectories to 
+quickly decide visually whether the prediction matches the trained data. Since \gls{cnmc} is written in a modular manner, meaning it can be regarded as 
+a black-box function, it can easily be integrated into other existing codes or 
+workflows. \newline
 
-It does work when first the ids are defined like first: chap_1_Abc and then chap_1_Intro. In that case the chap_1_Abc is not able to be referenced
+![Broad overview: Workflow of \gls{cnmc}](../../3_Figs_Pyth/1_Task/1_CNMc_1.svg){#fig-fig_1_CNMC_Workflow}

Data/1_Writing/3_Task/5_Pred.qmd

@@ -25,5 +25,5 @@ The motion of the Lorenz system @eq-eq_6_Lorenz is not as complex as of the, e.g
 Nevertheless, the Lorenz system already contains quasi-random elements, i.e., the switching from one ear to the other cannot be captured exactly with a surrogate mode. However, the characteristic of the Lorenz system and other chaotic dynamical systems as well can be replicated.
 In order to prove the latter, more than one method to measure the prediction quality is required.
 
-{{<include 6_SLS.qmd>}}
-{{<include 7_Models.qmd>}}
+{{ <include 6_SLS.qmd >}}
+{{ <include 7_Models.qmd >}}

_quarto.yml

@@ -17,7 +17,6 @@ filters:
   # - first-line-indent
 
   # see: https://github.com/quarto-ext/lightbox :MIT 
-  # destroys the figure cross referecning, because it removes the fig id
   - lightbox
 
   # https://github.com/schochastics/quarto-social-share :MIT
@@ -105,7 +104,7 @@ cluster-based network modeling"
 
   downloads: [pdf, epub]
   sharing: [twitter, facebook]
-  # cover-image: 
+  # cover-image: Data/6_Html_Data/1_Logo_Img/0_Tornado.svg
   
 
   # -------------------------------------------------------------------------- #
@@ -116,9 +115,12 @@ cluster-based network modeling"
     - index.qmd 
     
     
-    - part: "Good manners"
+    - part: "Good Manners"
       chapters: 
-        - trials.qmd
+
+        # testing purposes
+        # - trials.qmd
+
         - Data/1_Writing/0_Deco/2_Thanks.qmd
         - Data/1_Writing/0_Deco/1_Erkl.qmd
 
@@ -131,10 +133,11 @@ cluster-based network modeling"
 
         - Data/1_Writing/0_Deco/3_Used_Abbrev.qmd
     # -------------------------------- 1 task -------------------------------- #
+    - part: "Introduction"
     # - part: "Introduction"
-    - part: Data/1_Writing/1_Task/1_Introduction.qmd
 
       chapters:
+        - Data/1_Writing/1_Task/1_Introduction.qmd
         - Data/1_Writing/1_Task/2_0_Motivation.qmd
         - Data/1_Writing/1_Task/2_State_Of_Art.qmd
         - Data/1_Writing/1_Task/3_CNM.qmd
@@ -142,23 +145,26 @@ cluster-based network modeling"
 
 
     # -------------------------------- 2 task -------------------------------- #
-    # - part: "Methodology"
-    - part: Data/1_Writing/2_Task/0_Methodlogy.qmd
+    - part: "Methodology"
 
       chapters:
+        - Data/1_Writing/2_Task/0_Methodlogy.qmd
         - Data/1_Writing/2_Task/1_0_CNMC_Data.qmd
         - Data/1_Writing/2_Task/1_Data_Gen.qmd
         - Data/1_Writing/2_Task/2_Clustering.qmd
         - Data/1_Writing/2_Task/3_Tracking.qmd    # master
+
         # - Data/1_Writing/2_Task/4_Track_Workflow.qmd  # subsec
         # - Data/1_Writing/2_Task/5_Track_Validity.qmd  # subsec
+
         - Data/1_Writing/2_Task/6_Modeling.qmd  # master
         # - Data/1_Writing/2_Task/7_QT.qmd  # subsec
 
     # -------------------------------- 3 task -------------------------------- #
-    - part: Data/1_Writing/3_Task/0_Results.qmd
+    - part: "Results"
 
       chapters:
+        - Data/1_Writing/3_Task/0_Results.qmd
         - Data/1_Writing/3_Task/1_Track_Results.qmd
         - Data/1_Writing/3_Task/2_Mod_CPE.qmd
         - Data/1_Writing/3_Task/3_SVD_NMF.qmd
@@ -169,8 +175,9 @@ cluster-based network modeling"
         # - Data/1_Writing/3_Task/7_Models.qmd  # subsec
 
     # -------------------------------- 4 task -------------------------------- #
-    - part: Data/1_Writing/4_Task/1_Concl.qmd
+    - part: "Conclusion & Outlook"
       chapters:
+        - Data/1_Writing/4_Task/1_Concl.qmd
         - Data/1_Writing/4_Task/2_Zusammen_Deutsch.qmd
 
     # ------------------------------ references ------------------------------ #
@@ -178,9 +185,11 @@ cluster-based network modeling"
 
 
     # ---------------------- additional to master thesis --------------------- #
-    - part: Data/8_Add_2_Master/0_Add_2_Mast.qmd
+    - part: "Additionals 2 Thesis"
+
       chapters:
     # -------------------------------- license ------------------------------- #
+        - Data/8_Add_2_Master/0_Add_2_Mast.qmd
         - Data/10_Law/0_Lic_Main.qmd
 
 

index.qmd

@@ -1,4 +1,4 @@
-# Meet The Tornado {.unnumbered}
+# Cover {.unnumbered}
 
 <!-- load dot lotti js code -->
 <script src="https://unpkg.com/@dotlottie/[email protected]/dist/dotlottie-player.js"></script>