Merge pull request #202 from datacarpentry/166-figshare

Add FigShare link to setup

Author: tobyhodges

fig/jupyter_overview.png

@@ -23,3 +23,4 @@ This lesson shows how to use Python and skimage to do basic image processing.
 > to be familiar with.
 {: .prereq}
 
+Before following the lesson, please [make sure you have the software and data required]({{ page.root }}/{% link setup.md %}.

index.md

@@ -4,7 +4,22 @@ title: Setup
 permalink: /setup/
 ---
 
-## Setup instructions for the Image Processing workshop
+Before joining the workshop or following the lesson, please complete the data and software setup described in this page.
+
+
+## Data
+
+The example images used in this lesson are available on [FigShare](https://figshare.com/).
+To download the data, please visit [the dataset page for this workshop][figshare-data]
+and click the "Download all" button.
+Unzip the downloaded file, and save the contents as a folder  called `data` somewhere you will easily find it again,
+e.g. your Desktop or a folder you have created for using in this workshop.
+(The name `data` is optional but recommended, as this is the name we will use to refer to the folder throughout the lesson.)
+
+[figshare-data]: https://figshare.com/articles/dataset/Data_Carpentry_Image_Processing_Data_beta_/19260677
+
+
+## Software
 
 1. Download and install the latest [Anaconda
    distribution](https://www.anaconda.com/distribution/) for your
@@ -41,15 +56,32 @@ permalink: /setup/
    > package.
    {: .callout}
 
-   To test your environment, open a Jupyter notebook and copy the following lines into a cell:
+3. Open a Jupyter notebook:
+
+   > ## Instructions for Linux & Mac
+   >
+   > Open a terminal and type `jupyter lab`.
+   {: .solution }
+
+   > ## Instructions for Windows
+   >
+   > Launch the Anaconda Prompt program and type `jupyter lab`.
+   > (Running this command on the standard Command Prompt will return an error:
+   > `'conda' is not recognized as an internal or external command, operable program or batch file.`
+   {: .solution }
+
+   After Jupyter Lab has launched, click the "Python 3" button under "Notebook" in the launcher window,
+   or use the "File" menu, to open a new Python 3 notebook.
+
+4. To test your environment, run the following lines in a cell of the notebook:
    ~~~
    import skimage.io
    import matplotlib.pyplot as plt
    %matplotlib widget
-   
+
    # load an image
-   image = skimage.io.imread(fname='fig/00-colonies01.jpg')
-   
+   image = skimage.io.imread(fname='data/colonies-01.tif')
+
    # display the image
    fig, ax = plt.subplots()
    plt.imshow(image, cmap='gray')
@@ -58,8 +90,15 @@ permalink: /setup/
    {: .language-python}
    Upon execution of the cell, an image should be displayed in an interactive widget. When hovering over the image with the mouse pointer, the pixel coordinates and color values are displayed below the image.
 
-3. The example image files are available through Figshare. Learners
-   can download the images from [FIXME
-   figshare](https://figshare.com/). We recommend to create a
-   directory for the Jupyter notebooks/code created during the
-   lesson. The images should be located in a subfolder named `images/`.
+   > ## Running Cells in a Notebook
+   >
+   >
+   > ![Overview of the Jupyter Notebook graphical user interface]({{ page.source}}{% link fig/jupyter_overview.png %})
+   > To run Python code in a Jupyter notebook cell, click on a cell in the notebook
+   > (or add a new one by clicking the `+` button in the toolbar),
+   > make sure that the cell type is set to "Code" (check the dropdown in the toolbar),
+   > and add the Python code in that cell.
+   > After you have added the code,
+   > you can run the cell by selecting "Run" -> "Run selected cell" in the top menu,
+   > or pressing <kbd>Shift</kbd>+<kbd>Enter</kbd>.
+   {: .solution }