@eageo this is in reference to PR #29

There are numerous warnings in your Jupyter notebook:

<ipython-input-8-d0ab17fd4069>:60: RuntimeWarning: Mean of empty slice
  mean_ndvi = np.nanmean(ndvi_clean)

Perhaps related to using np to take the mean of an xr DataArray?

@cnorristeller this is related to PR #46

The Jupyter notebook is referencing DateFormatter in cell 14 which throws NameError: name 'DateFormatter' is not defined

Hi Jacquie! First off, I'm very glad I got to review your code because it's definitely a few levels more advanced than mine! I love how you used multiple functions, even for some of the simple steps, because it made the end result so much more precise and modular. Super impressed, and gives me something to think about for how I design my stuff in the future!

My comments for #23

Pull Request Template
Review Checklist
CI Checks

The notebook runs from start to finish on all operating systems:

• Mac
• Windows
• Linux

I think this will be the case, but I didn't think about the use of the "/" operator within certain filepaths variables and how that would run on other OS' until you mentioned it. Good thing to keep in mind!

Reproducibility

• Are the data downloaded in the code
• Are paths created to ensure they work on all operating systems (using os.path.join)
• Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)
• Does the notebook run from start to finish?

PEP 8 standards & Code Readability
Functions

• Do functions follow PEP 8 format conventions?
• Are function docstrings clear (all inputs and outputs clearly described and defined)
• Are function names expressive (the name describes what the function does)?
• Are functions easy to understand and read?
• How many tasks does each function do? (ideally a function does one thing well).

Seeing how you used functions was awesome! Yours were so simple and targeted. Really speaks to the last comment above about designing your functions to just do one thing well.

Package imports

• Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?
• Are variable names throughout the code, expressive?
• Suggest changes if not, highlight what was done well
• Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?

Mentioned this before, but use of simple functions for even basic steps really paid off with a clean output of processing the data.

DRY Code

• Are segments of code repeated in the file or is the code DRY?
• Are loops used to optimize DRY code?
• Are functions used to optimize DRY code?
• Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.

Tradeoff could be that by combining some of the simpler functions, you could be more efficient in number of functions used. But overall I liked this approach much better than mine!

Novel Approaches to Problem solving

• Highlight any novel approaches to completing the assignment.

@nkorinek please make these changes to PR #18

• There's an extra library imported at the top
• [ ]

#20

@S-Kur

Really nice job on the code, I think I picked up a few tiny things but it was really clean and concise.

Review Checklist

CI Checks
The notebook runs from start to finish on all operating systems:
[x] Mac
[x] Windows
[x] Linux

Reproducibility
[x] Are the data downloaded in the code
[x] Are paths created to ensure they work on all operating systems (using os.path.join)
[x] Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)
[x] Does the notebook run from start to finish?

PEP 8 standards & Code Readability
Functions
[x] Do functions follow PEP 8 format conventions?
[x] Are function docstrings clear (all inputs and outputs clearly described and defined)
[x] Are function names expressive (the name describes what the function does)?
[x] Are functions easy to understand and read?
[x] How many tasks does each function do? (ideally a function does one thing well).

Package imports
[x] Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?
[x] Are variable names throughout the code, expressive?
**Your code was very easy to read and follow because of your well-named variables!
[x] Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?

DRY Code
[x] Are segments of code repeated in the file or is the code DRY?
[x] Are loops used to optimize DRY code?
[x] Are functions used to optimize DRY code?
[x] Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.

Novel Approaches to Problem solving
[x] Highlight any novel approaches to completing the assignment.
**I hadn't seen the code you used for plotting before, it works really well

@Tsrockross thank you for such a clear and easy to follow code submited with PR #19 ! I suggested few improvements, but most of them relate to adding more explanations along the way

Review Checklist

CI Checks
The notebook runs from start to finish on all operating systems:
[x] Mac
[x] Windows
[x ] Linux
UPD: after new commit went through, ndvi workflow run successfuly on all three OS.

Reproducibility
[x] Are the data downloaded in the code
[x] Are paths created to ensure they work on all operating systems (using os.path.join)
[x] Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone?
[x] Does the notebook run from start to finish?

PEP 8 standards & Code Readability
Functions
[x] Do functions follow PEP 8 format conventions?
[x] Are function docstrings clear (all inputs and outputs clearly described and defined)
I would correct one the docstrings (for the first function).
[x] Are function names expressive (the name describes what the function does)?
[x] Are functions easy to understand and read?
I would add few more comments to the second function to clarify main steps.
[x] How many tasks does each function do?
Some functions were doing few things, but I think the combination of tasks made sense.

Package imports
[x] Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?
[x] Are variable names throughout the code, expressive?
[x] Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?
I would add couple more comments for a part where landsat directories are looped through and final df is created

DRY Code
[x] Are segments of code repeated in the file or is the code DRY?
[x] Are loops used to optimize DRY code?
[x] Are functions used to optimize DRY code?
[x] Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.

Novel Approaches to Problem solving
[x] Highlight any novel approaches to completing the assignment.
The third function combining the first two was a hit :) In fact, I liked it so much that I used this solution as well.

@ericnutt5 I've completed my review for PR #26. Let me know if you have any questions. Overall I found very little to comment on - nice job!

Review Checklist
CI Checks
The notebook runs from start to finish on all operating systems: Yes to all
Mac
Windows
Linux

Reproducibility
Are the data downloaded in the code? Yes.
Are paths created to ensure they work on all operating systems (using os.path.join)? Yes.
Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)? Yes.
Does the notebook run from start to finish? Yes.

PEP 8 standards & Code Readability
Functions
Do functions follow PEP 8 format conventions? Yes.
Are function docstrings clear (all inputs and outputs clearly described and defined)? See comments in review.
Are function names expressive (the name describes what the function does)? Yes.
Are functions easy to understand and read? Yes.
How many tasks does each function do? (ideally a function does one thing well). See comments in review.

Package imports
Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group? Yes.
Are variable names throughout the code, expressive? Yes.
Suggest changes if not, highlight what was done well
Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner? Code is very easy to follow and understand.

DRY Code
Are segments of code repeated in the file or is the code DRY? See comments.
Are loops used to optimize DRY code? Yes.
Are functions used to optimize DRY code? Yes.
Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.

Your code is very efficient, and I didn't see any opportunities for further optimizing. Nice job!

Novel Approaches to Problem solving
Highlight any novel approaches to completing the assignment.
I really liked how you were able to use primarily one function to process all the relevant bands for both sites at one time.

@csandberg303

Please see the comments on the code in your PR #28

Review Checklist
CI Checks

The notebook runs from start to finish on all operating systems:

• Mac
• Windows
• Linux

Reproducibility

• Are the data downloaded in the code
• Are paths created to ensure they work on all operating systems (using os.path.join) (#28 (comment))
• Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)
• Does the notebook run from start to finish?

PEP 8 standards & Code Readability
Functions

• Do functions follow PEP 8 format conventions?
• Are function docstrings clear (all inputs and outputs clearly described and defined)
• Are function names expressive (the name describes what the function does)?
• Are functions easy to understand and read?
• How many tasks does each function do? (ideally a function does one thing well). One of the functions: process_single_scene is quite multimodal

Package imports

• Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group? _One or two modules included but not used_#28 (comment)
• Are variable names throughout the code, expressive?
  Suggest changes if not, highlight what was done well
• Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?
  DRY Code
• Are segments of code repeated in the file or is the code DRY?
• Are loops used to optimize DRY code?
• Are functions used to optimize DRY code?
• Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient. #28 (comment)
  Novel Approaches to Problem solving
• Highlight any novel approaches to completing the assignment.
  I'm somewhat torn because I think your function that was multimodal (and therefore frowned upon) was also really powerful!
  #28 (comment)

Great work, reviewing your code was enjoyable and easy, and was even moreso because Mitchell had already been through it before me!

@kristentortorelli the y axis in your figure 1 plot is defined as "ndvi" and should ideally be "mean ndvi".

Hi @rami8797 - Great work on your assignment- PR #22 ! I'll work through the checklist and give comments below. Thanks!

Review Checklist

CI Checks

• The notebook runs from start to finish on all operating systems:
• [x ] Mac
• Windows
• [x ] Linux
  **Your notebook passed all the checks!

Reproducibility

• [x ] Are the data downloaded in the code
• [ x] Are paths created to ensure they work on all operating systems (using os.path.join)
• [ x] Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)
• [ x] Does the notebook run from start to finish?
  **You have really great expressive names for your variables and functions! Because you have these great names, you may not need as many comments. You could take out a lot of comments and just leave the ones that highlight sections of code or steps that might not be as clear.

PEP 8 standards & Code Readability
Functions

• [ x] Do functions follow PEP 8 format conventions?
• [X ] Are function docstrings clear (all inputs and outputs clearly described and defined)
• [ x] Are function names expressive (the name describes what the function does)?
• [x ] Are functions easy to understand and read?
• [ x] How many tasks does each function do? (ideally a function does one thing well).
  **Function line lengths are shorter. I commented in one of your functions where your line was a little too long.
  **I was confused by the description of the sitename_location and datetime_location parameters in the docstring. I tagged one of these in your code and suggested a way to make it clearer.
  **You have really great expressive names!

Package imports

• [x ] Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?
• [ x] Are variable names throughout the code, expressive?
• [ x] Suggest changes if not, highlight what was done well
• [ x] Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?
  **In problem 1, I was confused by your code and I tagged the comment that made me understand what problem that you had solved. I think I solved a different one- in that I calculated the NDVI for all the scenes. If you want to do that, I'm happy to tag edits in your code. Just let me know.
  **I think your code was easy to read because of our expressive names and your comments. I think you could probably have less comments or even simplify them. In some places, the longer comments make the code harder to read.

DRY Code

• [x ] Are segments of code repeated in the file or is the code DRY?
• [ x] Are loops used to optimize DRY code?
• [ x] Are functions used to optimize DRY code?
• [ x] Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.
  ** You used loops and functions to optimize your code. I think within each problem, you do not repeat any code. If you think of the assignment as a whole, you could maybe write a few more functions so that you don't repeat code within the notebook. For example, maybe you could write a function to open and crop your QA raster or to mask for clouds.

Novel Approaches to Problem solving

• [ x] Highlight any novel approaches to completing the assignment.
  **I liked your function to extract the date and sitename from the path. I also have never used the date time.strptime() function before, so it was interesting to see how to use it.

@esgeo here are some notes and my review checklist for your pull request #29 !

Great work! Clean code and it all runs well for me. One thing I would look at is reducing some language in your docstrings. I think specifically the first description line should only be one line long, however I can't remember specifically. Especially for the function "mask_crop_ndvi".

Review Checklist
CI Checks

• [ X] The notebook runs from start to finish on all operating systems:

Mac
Windows
Linux
Reproducibility

• [ X] Are the data downloaded in the code
• [X ] Are paths created to ensure they work on all operating systems (using os.path.join)
• [ X] Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)
• [ X] Does the notebook run from start to finish?
  PEP 8 standards & Code Readability
  Functions
• Do functions follow PEP 8 format conventions? ** I think here we need to review the first line & initial description length.
• [ X] Are function docstrings clear (all inputs and outputs clearly described and defined)
• [X ] Are function names expressive (the name describes what the function does)?
• [X ] Are functions easy to understand and read?
• How many tasks does each function do? (ideally a function does one thing well).
  Package imports
• [ X] Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?
• [ X] Are variable names throughout the code, expressive?
  Suggest changes if not, highlight what was done well
• [ X] Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner? **some of the lines extended the PEP 8 line length, and I think the descriptions were good, however I wonder if you could combine some code lines and have the commented section describe a few things instead of every line.
  DRY Code.
• [X ] Are segments of code repeated in the file or is the code DRY?
• [ X] Are loops used to optimize DRY code?
• [ X] Are functions used to optimize DRY code?
• Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.
  Novel Approaches to Problem solving
  Highlight any novel approaches to completing the assignment.
  ** great work making the graph your own! I really like how you changed the date format. I really like how descriptive your variables are, and how you included a lot of description in the comments. I think the thing that might need the most review is the first line in your doc-strings. I might need to review this, but I feel like I remember that needs to be one-line long and pretty brief. Also there were a few lines that seemed to be too long for PEP 8. Otherwise, great work!

In this issue, I will keep track of who is reviewing each others work!

Group 1

Brian
Christian

Group 2

Lauren
Maeve

Group 3

Emi
Korrina

Group 4

Group 5

Group 6

Group 7

@cnorristeller this is in reference to PR #46

Recommend to remove extraneous notebook files

Review Checklist - PR #24 @mthomp89

CI Checks
The notebook runs from start to finish on all operating systems:
[x] Mac
[ ] Windows
[ ] Linux
I found the file wouldn't initially run on my Windows laptop, and it appears it also failed the Linux check. I tracked this issue to an
unsorted glob list in creating the initial single-scene HARV dataframe (and left a few specific comments in your code about this).

Reproducibility
[x] Are the data downloaded in the code
[x] Are paths created to ensure they work on all operating systems (using os.path.join)
[x] Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone?
[ ] Does the notebook run from start to finish?
I tested the code after making the suggested corrections mentioned above, and was able to see the code complete to finish - NICE JOB!! I liked your use of Seaborn to create an attractive plot.

PEP 8 standards & Code Readability
Functions
[ ] Do functions follow PEP 8 format conventions?
I did notice some of the red comments within the function definition exceeded the PEP 8 line length
[x] Are function docstrings clear (all inputs and outputs clearly described and defined)
I thought your docstrings were succinctly clear, with the input and output parameters very well defined. I'd suggest possibly expanding some details after the one-line summary (such as how the 'mask_crop_ndvi' function will also mask and crop the ndvi output, in addition to simply calculating it.
[x] Are function names expressive (the name describes what the function does)?
[x] Are functions easy to understand and read?
[x] How many tasks does each function do?

Package imports
[x] Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?
[x] Are variable names throughout the code, expressive?
[x] Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?
I missed seeing some extra Markdown cells to give additional clarity about the workflow, and this seems a bit nit-picky but I don't think the vector and landsat-crop paths need to be expressly defined, as the directory names could simply be typed into the glob function after 'site-path'.

DRY Code
[x] Are segments of code repeated in the file or is the code DRY?
[x] Are loops used to optimize DRY code?
[x] Are functions used to optimize DRY code?
[x] Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.
Please note my comments about sorting the glob list in your 'sites_path' variable, but other than that I thought your workflow was pretty splendid!

Novel Approaches to Problem solving
[ ] Highlight any novel approaches to completing the assignment.
I thought your use of the np.nanmean function was a great solution to removing the nan values from the final dataframe 🥇 I noticed you got full points in the sanity check, for the dataframe having the correct number of masked values, WELL DONE!

@AreteY Awesome use of functions in your assignment (PR #21)! Your code was really efficient! The checklist that I worked through is below along with a few comments.

Review Checklist

CI Checks
The notebook runs from start to finish on all operating systems:
[x] Mac
[x] Windows
[x] Linux

Reproducibility
[x] Are the data downloaded in the code
[x] Are paths created to ensure they work on all operating systems (using os.path.join)
[x] Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)
[x] Does the notebook run from start to finish?

PEP 8 standards & Code Readability
Functions
[x] Do functions follow PEP 8 format conventions?
[x] Are function docstrings clear (all inputs and outputs clearly described and defined)
[x] Are function names expressive (the name describes what the function does)?
[x] Are functions easy to understand and read?
[x] How many tasks does each function do? (ideally a function does one thing well).
**Each function does indeed do one thing well.

Package imports
[x] Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?
[x] Are variable names throughout the code, expressive?
**Your code was very easy to read and follow because of your well-named variables!
[x] Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?
**The code is easy to understand and read.

DRY Code
[x] Are segments of code repeated in the file or is the code DRY?
**The code is DRY!
[x] Are loops used to optimize DRY code?
[x] Are functions used to optimize DRY code?
[x] Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.
**Your use of functions and loops inside functions is great and makes the final line of code where you calculate the mean NDVI for all sites extremely efficient!

Novel Approaches to Problem solving
[x] Highlight any novel approaches to completing the assignment.
**The use of register_matplotlib_converters() was interesting. I haven't seen it before, so I enjoyed getting the chance to read the available documentation. I also liked your approach to dropping NaN values.

**Review Checklist

CI Checks

The notebook runs from start to finish on all operating systems:**

• Mac
• Windows
• Linux

👍 Great use of the os module throughout. 👍

Reproducibility

• Are the data downloaded in the code
• Are paths created to ensure they work on all operating systems (using os.path.join)
• Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)
• Does the notebook run from start to finish?

PEP 8 standards & Code Readability

Functions

• Do functions follow PEP 8 format conventions?
• Are function docstrings clear (all inputs and outputs clearly described and defined
• Are function names expressive (the name describes what the function does)?
• Are functions easy to understand and read?
• How many tasks does each function do? (ideally a function does one thing well).

Package imports

• Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?
• Are variable names throughout the code, expressive?
• Suggest changes if not, highlight what was done well
• Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?

DRY Code

• Are segments of code repeated in the file or is the code DRY?
• Are loops used to optimize DRY code?
• Are functions used to optimize DRY code?
• Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.

Novel Approaches to Problem solving

• Highlight any novel approaches to completing the assignment.

See [inline comments on the pull request](emily cassidy's .ipynb and.py for review of ndvi automation code. #25).

@jensenwid

Hi Jensen
Let me preface all comments below by saying this was lovely to read. I work with other people’s code which are all cryptic and a nightmare so thank you for that.

I have included a few comments in the checklist below, but I also will also add in-line comments in your pull request. I hope you get them all.

Review Checklist

CI Checks
The notebook runs from start to finish on all operating systems:
X Mac
Windows
X Linux

In Cell 6 and Cell 10
I know I have this problem in my own assignment, but using all_sites = glob(path + "//") is not reproducible for Microsoft OS because they use ‘\’ . I think in this case you may want to use all_sites = glob(os.path.join(path, “”)) . The lesson I’ve learned here is to avoid including ‘/’ and ‘\’ in path searches.

Reproducibility

X Are the data downloaded in the code
Are paths created to ensure they work on all operating systems (using os.path.join)

• See my comment above - both are tied.

X Are comments used to clarify the contents of the code that can’t be clarified using expressive variable and function names alone? (not too many comments - just enough)
X Does the notebook run from start to finish?

PEP 8 standards & Code Readability

Functions

X Do functions follow PEP 8 format conventions?
X Are function docstrings clear (all inputs and outputs clearly described and defined)
X Are function names expressive (the name describes what the function does)?
X Are functions easy to understand and read?
X How many tasks does each function do? (ideally a function does one thing well).

Package imports

Are standard modules (those included with the base python install) vs. third party (related but externally developed tools) import groups correct with appropriate spacing in between each group?

• The xarray, plotting_extent, and re modules are imported but not used in your code.

X Are variable names throughout the code, expressive?
X Suggest changes if not, highlight what was done well
X Is the code overall easy to understand and read? Are there things that would make it more clear / cleaner?

DRY Code
X Are segments of code repeated in the file or is the code DRY?
X Are loops used to optimize DRY code?
X Are functions used to optimize DRY code?
X Are there any areas that could be potentially improved (you can suggest improvements OR you can just highlight parts of the code where you suspect it could be cleaner / more efficient.

• I’ll put all my suggestions in the in-line comments of your pull request.

Novel Approaches to Problem solving
X Highlight any novel approaches to completing the assignment.

• I like how you coded the filtering of the dataArray. It made sense and is elegant.
• I liked your use of pd.to_datetime(). I always have such issues with date-time conversion. I'm definitely going to use this!