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Source  : 235e2ff
Branch  : main
Author  : ShebMichel <[email protected]>
Time    : 2025-12-09 03:48:37 +0000
Message : Merge pull request #251 from datacarpentry/typos

assorted typos

Author: actions-user

01-query.md

@@ -64,7 +64,7 @@ Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook).
 ## Using Jupyter
 
 If you have not worked with Jupyter notebooks before, you might start
-with [the tutorial on from Jupyter.org called "Try Classic
+with [the tutorial from Jupyter.org called "Try Classic
 Notebook"](https://jupyter.org/try), or [this tutorial from
 DataQuest](https://www.dataquest.io/blog/jupyter-notebook-tutorial/).
 

02-coords.md

@@ -469,7 +469,7 @@ phi2_max = 4 * u.degree
 Throughout this lesson we are going to be defining a rectangle often.
 Rather than copy and paste multiple lines of code, we will write a function to build the rectangle for us.
 By having the code contained in a single location, we can easily fix bugs or update our implementation as needed.
-By choosing an explicit function name our code is also self documenting, meaning its easy for us to
+By choosing an explicit function name our code is also self documenting, meaning it's easy for us to
 understand that we are building a rectangle when we call this function.
 
 To create a rectangle, we will use the following function, which takes the lower and upper bounds as parameters and returns a list of x and y coordinates of the corners of a rectangle starting with the lower left corner and working clockwise.
@@ -490,7 +490,7 @@ phi1_rect, phi2_rect = make_rectangle(
     phi1_min, phi1_max, phi2_min, phi2_max)
 ```
 
-`phi1_rect` and `phi2_rect` contains the coordinates of the corners of
+`phi1_rect` and `phi2_rect` contain the coordinates of the corners of
 a rectangle in the GD-1 frame.
 
 While it is easier to visualize the regions we want to define in the GD-1 frame, the coordinates in the Gaia catalog are in the ICRS frame.

03-transform.md

@@ -578,7 +578,7 @@ two data types. We will choose to use Pandas `DataFrame`, for two reasons:
 
 However, compared to an Astropy `Table`, Pandas has one big drawback:
 it does not keep the metadata associated with the table, including the
-units for the columns.  Nevertheless, we think its a useful data type
+units for the columns.  Nevertheless, we think it's a useful data type
 to be familiar with.
 
 

04-motion.md

@@ -13,7 +13,7 @@ exercises: 15
 
 :::::::::::::::::::::::::::::::::::::::: questions
 
-- How do efficiently explore our data and identify appropriate filters to produce a clean sample (in this case of GD-1 stars)?
+- How to efficiently explore our data and identify appropriate filters to produce a clean sample (in this case of GD-1 stars)?
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
@@ -503,7 +503,7 @@ To clean up the plot a little bit we can add two new Matplotlib commands:
   sets the `fontsize` to be `medium`, a little smaller than the default `large`.
 
 In an example like this, where `x` and `y` represent coordinates in
-space, equal axes ensures that the distance between points is
+space, equal axes ensure that the distance between points is
 represented accurately. Since we are now constraining the relative proportions
 of our axes, the data may not fill the entire figure.
 

05-select.md

@@ -199,7 +199,7 @@ In the following exercise, we will identify a rectangle that encompasses the maj
 
 Looking at the proper motion of the stars we identified along the centerline of GD-1, in the ICRS reference frame
 define a rectangle (`pmra_min`, `pmra_max`, `pmdec_min`, and `pmdec_max`)
-that encompass the proper motion of the majority of the stars near the centerline of GD-1 without including to much contamination from other stars.
+that encompass the proper motion of the majority of the stars near the centerline of GD-1 without including too much contamination from other stars.
 
 :::::::::::::::  solution
 
@@ -518,7 +518,7 @@ plt.ylabel('dec (degree ICRS)');
 ![](fig/05-select_files/05-select_66_0.png){alt='Scatter plot of right ascension and declination of selected stars in ICRS frame.'}
 
 This plot shows why it was useful to transform these coordinates to the GD-1 frame.  In
-ICRS, it is more difficult to identity the stars near the centerline
+ICRS, it is more difficult to identify the stars near the centerline
 of GD-1.
 
 We can use our `make_dataframe` function from episode 3 to transform the results back

06-join.md

@@ -893,7 +893,7 @@ that for each candidate star we have identified exactly one source in
 Pan-STARRS that is likely to be the same star.
 
 To check whether there are any values other than `1`, we can convert
-this column to a Pandas `Series` and use `describe`, which we saw in
+this column to a Pandas `Series` and use `describe`, which we saw
 in episode 3.
 
 ```python

07-photo.md

@@ -492,7 +492,7 @@ this polygon.  But first we need to do some data cleaning.
 
 ## Save the polygon
 
-[Reproducibile
+[Reproducible
 research](https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research)
 is "the idea that ... the full computational environment used to
 produce the results in the paper such as the code, data, etc. can be

08-plot.md

@@ -719,7 +719,7 @@ ax.set_ylim(-10, 10)
 ![](fig/08-plot_files/08-poly_example.png){alt='An orange rectangle at the coordinates used to select stars based on proper motion.'}
 
 We can now call our plot\_proper\_motion function to plot the
-proper motion for each star, and the add a shaded `Polygon` to show the
+proper motion for each star, and then add a shaded `Polygon` to show the
 region we selected.
 
 ```python

md5sum.txt

@@ -3,14 +3,14 @@
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