Change readme.md and readme.txt in reproducibility map

README.md

@@ -109,8 +109,12 @@ Download the code with:
 
     git clone https://github.com/davidnabergoj/bootplot.git
 
+Step into the repository:
+
+    cd bootplot
+
 To reproduce Fig. 6 in the paper, which shows 95% confidence intervals for mean penguin bill length, run (on macOS 11 or newer):
 
-    bash bootplot/Replicability_Stamp/install_dependencies.sh
+    bash Replicability_Stamp/install_dependencies.sh
 
 The script automatically installs all dependencies into a local folder inside the repository and generates the figure as `mean_estimate_95.png`, opening it in Preview. It takes a few minutes, most of which is downloading the dependencies into the local environment; the figure itself is generated in approximately 1 second. For more details, see [Replicability_Stamp/README.txt](Replicability_Stamp/README.txt).

Replicability_Stamp/README.txt

@@ -1,18 +1,26 @@
-Title: A General Approach to Visualizing Uncertainty in Statistical Graphics
+Title: A General Approach to Visualizing Uncertainty in Statistical
+       Graphics
 Authors: Bernarda Petek, David Nabergoj, Erik Strumbelj
 Venue: IEEE Transactions on Visualization and Computer Graphics
 Operating system: macOS 11 or newer
-Code repository: https://github.com/davidnabergoj/bootplot (this submission is the Replicability_Stamp directory inside it)
-Reproduced result: Figure 6, left-hand image, produced by our approach that shows 95% confidence intervals for mean bill length per penguin species, created with bootplot, n = 39 samples.
-
+Code repository: https://github.com/davidnabergoj/bootplot
+  (this submission is the Replicability_Stamp directory inside it)
+Reproduced result: Figure 6, left-hand image, produced by our approach
+  that shows 95% confidence intervals for mean bill length per penguin
+  species, created with bootplot, n = 39 samples.
 ========================================================================
-This is a submission for the Graphics Replicability Stamp Initiative (GRSI). Please refer to https://www.replicabilitystamp.org for more information.
+This is a submission for the Graphics Replicability Stamp Initiative
+(GRSI). Please refer to https://www.replicabilitystamp.org for more
+information.
 
 WHAT THIS DIRECTORY CONTAINS
 ------------------------------------------------------------------------
-1. install_dependencies.sh -- installs all dependencies (including bootplot) and runs the Python script below.
-2. reproduce_figure6.py -- Python script that runs bootplot to produce the figure.
-3. penguins.csv -- the dataset used by our method to produce the figure (Palmer penguins, CC0 license).
+1. install_dependencies.sh -- installs all dependencies (including
+   bootplot) and runs the Python script below.
+2. reproduce_figure6.py -- Python script that runs bootplot to produce
+   the figure.
+3. penguins.csv -- the dataset used by our method to produce the figure
+   (Palmer penguins, CC0 license).
 4. README.txt -- this file: description and instructions.
 5. LIABILITY_FORM.txt -- the liability form.
 
@@ -23,16 +31,42 @@ Terminal.app). Download the code with:
 
     git clone https://github.com/davidnabergoj/bootplot.git
 
-Then run the replication script by giving bash its path:
+Step into the repository:
+
+    cd bootplot
+
+Then run the replication script:
 
-    bash bootplot/Replicability_Stamp/install_dependencies.sh
+    bash Replicability_Stamp/install_dependencies.sh
 
-(The two commands can be run as-is, one after the other: the clone creates a folder named bootplot in your current location, and the second command points into it. If you cloned or downloaded the repository elsewhere, adjust the path accordingly.) That is the only command needed. After a few minutes -- most of which is downloading the dependencies into the local environment -- the reproduced figure opens in Preview and is saved next to the script as mean_estimate_95.png. No administrator password is needed, and nothing is asked during the run, but macOS may ask for permission to access the folder (depending on the folder); click OK.
+Those are the only commands needed. After a few minutes -- most of
+which is downloading the dependencies into the local environment -- the
+reproduced figure opens in Preview and is saved next to the script as
+mean_estimate_95.png. No administrator password is needed, and nothing
+is asked during the run, but macOS may ask for permission to access the
+folder; click OK.
 
 WHAT THE SCRIPT DOES, STEP BY STEP
 ------------------------------------------------------------------------
-1. Downloads Miniconda from Anaconda's official server (repo.anaconda.com), automatically choosing the Apple Silicon or Intel build.
-2. Installs Miniconda into the subfolder named miniconda3, inside this directory. Hence, nothing is written into system folders or the home directory, and no administrator password is ever needed.
-3. Creates a Python environment in the subfolder named env containing Python 3.12, pip, and pycairo from the conda-forge channel. Figures are rendered with matplotlib's cairo backend, the backend used for the figures in the paper (it allows disabling anti-aliasing, as the paper recommends). conda-forge provides pycairo prebuilt with the cairo graphics library bundled in, so nothing is compiled on your machine.
-4. Installs the bootplot library (version 0.0.18) from PyPI into that environment. Installing bootplot automatically brings its own dependencies (numpy, pandas, matplotlib, and others).
-5. Runs reproduce_figure6.py with no parameters. The script reads the bundled penguins.csv, applies bootplot with n = 39 resamples, which yields 95% coverage (Table I in the paper), saves the aggregate image to mean_estimate_95.png, and opens it in Preview. On success, the final output message is: "Done. The figure was saved as mean_estimate_95.png in this directory."
+1. Downloads Miniconda from Anaconda's official server
+   (repo.anaconda.com), automatically choosing the Apple Silicon or
+   Intel build.
+2. Installs Miniconda into the subfolder named miniconda3, inside this
+   directory. Hence, nothing is written into system folders or the home
+   directory, and no administrator password is ever needed.
+3. Creates a Python environment in the subfolder named env containing
+   Python 3.12, pip, and pycairo from the conda-forge channel. Figures
+   are rendered with matplotlib's cairo backend, the backend used for
+   the figures in the paper (it allows disabling anti-aliasing, as the
+   paper recommends). conda-forge provides pycairo prebuilt with the
+   cairo graphics library bundled in, so nothing is compiled on your
+   machine.
+4. Installs the bootplot library (version 0.0.18) from PyPI into that
+   environment. Installing bootplot automatically brings its own
+   dependencies (numpy, pandas, matplotlib, and others).
+5. Runs reproduce_figure6.py with no parameters. The script reads the
+   bundled penguins.csv, applies bootplot with n = 39 resamples, which
+   yields 95% coverage (Table I in the paper), saves the aggregate
+   image to mean_estimate_95.png, and opens it in Preview. On success,
+   the final output message is: "Done. The figure was saved as
+   mean_estimate_95.png in this directory."