Slight changes to README for explicit information

README.md

@@ -8,51 +8,51 @@ See the [change log](https://github.com/YaleDHLab/pix-plot/wiki/Change-Log) for
 
 ## Dependencies
 
-To install the Python dependencies, we recommend you [install Anaconda](https://www.anaconda.com/products/individual#Downloads) and then create a conda environment with a Python 3.7 runtime:
+To install the Python dependencies, we recommend you [install Anaconda](https://www.anaconda.com/products/individual#Downloads) and then create a conda environment with a Python 3.7 runtime. The following is example on the terminal:
 
 ```bash
-conda create --name=3.7 python=3.7
-source activate 3.7
+$ conda create --name=3.7 python=3.7
+$ source activate 3.7
 ```
 
 Then you can install the dependencies by running:
 
 ```bash
-pip uninstall pixplot
-pip install https://github.com/yaledhlab/pix-plot/archive/master.zip
+$ pip uninstall pixplot
+$ pip install https://github.com/yaledhlab/pix-plot/archive/master.zip
 ```
 
-Please note that you will need to use Python 3.6 or Python 3.7 to install and use this package. The HTML viewer also requires a WebGL-enabled browser.
+Please note that you will need to use Python 3.6 or Python 3.7 to install and use this package. The HTML viewer also _**requires a WebGL-enabled browser**_ (most modern browsers updated to the latest version has this enabled to default but no guarantees).
 
 ## Quickstart
 
 If you have a WebGL-enabled browser and a directory full of images to process, you can prepare the data for the viewer by installing the dependencies above then running:
 
 ```bash
-pixplot --images "path/to/images/*.jpg"
+$ pixplot --images "path/to/images/*.jpg"
 ```
 
 To see the results of this process, you can start a web server by running:
 
 ```bash
 # for python 3.x
-python -m http.server 5000
+$ python -m http.server 5000
 
 # for python 2.x
-python -m SimpleHTTPServer 5000
+$ python -m SimpleHTTPServer 5000
 ```
 
-The visualization will then be available at `http://localhost:5000/output`.
+The visualization will then be available at http://localhost:5000/output.
 
 ## Sample Data
 
-To acquire some sample data with which to build a plot, feel free to use some data prepared by Yale's DHLab:
+To acquire some sample data with which to build a plot, feel free to use some data prepared by Yale's DHLab. Run the following on your terminal:
 
 ```bash
-pip install image_datasets
+$ pip install image_datasets
 ```
 
-Then in a Python script:
+Then in Python, run:
 
 ```python
 import image_datasets
@@ -62,15 +62,15 @@ image_datasets.oslomini.download()
 The `.download()` command will make a directory named `datasets` in your current working directory. That `datasets` directory will contain a subdirectory named 'oslomini', which contains a directory of images and another directory with a CSV file of image metadata. Using that data, we can next build a plot:
 
 ```bash
-pixplot --images "datasets/oslomini/images/*" --metadata "datasets/oslomini/metadata/metadata.csv"
+$ pixplot --images "datasets/oslomini/images/*" --metadata "datasets/oslomini/metadata/metadata.csv"
 ```
 
 ## Creating Massive Plots
 
 If you need to plot more than 100,000 images but don't have an expensive graphics card with which to visualize huge WebGL displays, you might want to specify a smaller "cell_size" parameter when building your plot. The "cell_size" argument controls how large each image is in the atlas files; smaller values require fewer textures to be rendered, which decreases the GPU RAM required to view a plot:
 
 ```bash
-pixplot --images "path/to/images/*.jpg" --cell_size 10
+$ pixplot --images "path/to/images/*.jpg" --cell_size 10
 ```
 
 ## Controlling UMAP Layout
@@ -86,7 +86,7 @@ The [UMAP algorithm](https://github.com/lmcinnes/umap) is particularly sensitive
 UMAP's creator, Leland McInnes, has written up a [helpful overview of these hyperparameters](https://umap-learn.readthedocs.io/en/latest/parameters.html). To specify the value for one or more of these hyperparameters when building a plot, one may use the flags above, e.g.:
 
 ```bash
-pixplot --images "path/to/images/*.jpg" --n_neighbors 2
+$ pixplot --images "path/to/images/*.jpg" --n_neighbors 2
 ```
 
 ## Curating Automatic Hotspots
@@ -100,7 +100,7 @@ If you have metadata associated with each of your images, you can pass in that m
 To specify the metadata for your image collection, you can add ` --metadata=path/to/metadata.csv` to the command you use to call the processing script. For example, you might specify:
 
 ```bash
-pixplot --images "path/to/images/*.jpg" --metadata "path/to/metadata.csv"
+$ pixplot --images "path/to/images/*.jpg" --metadata "path/to/metadata.csv"
 ```
 
 Metadata should be in a comma-separated value file, should contain one row for each input image, and should contain headers specifying the column order. Here is a sample metadata file:
@@ -126,17 +126,21 @@ The following column labels are accepted:
 
 ## IIIF Images
 
-If you would like to process images that are hosted on a IIIF server, you can specify a newline-delimited list of IIIF image manifests as the `--images` argument. For example, the following could be saved as `manifest.txt`:
+If you would like to process images that are hosted on a IIIF server, you can specify a newline-delimited list of IIIF image manifests as the `--images` argument. For example, the following create a `manifest.txt` that contains links to the images, for example:
 
 ```bash
+$ cat manifest.txt # print out contents of manifest.txt
 https://manifests.britishart.yale.edu/manifest/40005
 https://manifests.britishart.yale.edu/manifest/40006
 https://manifests.britishart.yale.edu/manifest/40007
 https://manifests.britishart.yale.edu/manifest/40008
 https://manifests.britishart.yale.edu/manifest/40009
 ```
 
-One could then specify these images as input by running `pixplot --images manifest.txt --n_clusters 2`
+One could then specify these images as input by running on the terminal:
+```bash
+$ pixplot --images manifest.txt --n_clusters 2
+```
 
 
 ## Demonstrations (Developed with PixPlot 2.0 codebase)