Minor syntax fixes

README.md

@@ -15,13 +15,9 @@ It is highly recommended to follow the following steps and install ``PeakPerform
 1. Install the package manager [Mamba](https://github.com/conda-forge/miniforge/releases).
 Choose the latest installer at the top of the page, click on "show all assets", and download an installer denominated by "Mambaforge-version number-name of your OS.exe", so e.g. "Mambaforge-23.3.1-1-Windows-x86_64.exe" for a Windows 64 bit operating system. Then, execute the installer to install mamba and activate the option "Add Mambaforge to my PATH environment variable".
 
-```{caution}
-If you have already installed Miniconda, you can install Mamba on top of it but there are compatibility issues with Anaconda.
-```
+⚠ If you have already installed Miniconda, you can install Mamba on top of it but there are compatibility issues with Anaconda.
 
-```{note}
-The newest conda version should also work, just replace `mamba` with `conda` in step 2.
-```
+ℹ The newest conda version should also work, just replace `mamba` with `conda` in step 2.
 
 2. Create a new Python environment in the command line using the provided [`environment.yml`](https://github.com/JuBiotech/peak-performance/blob/main/environment.yml) file from the repo.
    Download `environment.yml` first, then navigate to its location on the command line interface and run the following command:

docs/source/markdown/Recreate_data_from_scratch.md

@@ -7,12 +7,13 @@ Navigate to `docs/source/notebooks` and run the `Create_results_in_figure_2.ipyn
 It is separated into two sections which work and are structured in an analogous manner.
 The first creates the results figure for the single peak and the second for the double peak.
 Both sections walk through the following sequential steps:
-  1. open and plot example raw data
-  2. define a model
-  3. perform both sampling and posterior predictive sampling
-  4. display the summary DataFrame containing the results of the peak fitting
-  5. display cumulative plot of the posterior predictive check
-  6. display the posterior predictive check and the peak fit against the raw data points.
+
+1. open and plot example raw data
+2. define a model
+3. perform both sampling and posterior predictive sampling
+4. display the summary DataFrame containing the results of the peak fitting
+5. display cumulative plot of the posterior predictive check
+6. display the posterior predictive check and the peak fit against the raw data points.
 
 ## Recreate the validation plot from the documentation