Development.md

@@ -7,31 +7,31 @@
 ## How to contribute to NES
 
 1. Create an issue and complete the description
-   - Complete the issue description as much as possible with (estimation time, milestone, assigned one, etc.)
-2. Create a branch (from master) directly in the issue (name should start with `develop-` following the number and title of the issue)
-3. Clone and checkout to the new branch without modifications in Nord3v2.
+   - Complete the issue description as much as possible with (estimated time, corresponding milestone, assigned person, etc.)
+2. Create a branch (from master) directly in the issue (name should start with `develop-` following the number and title of the issue that appears by default)
+3. Clone and checkout to the new branch, without any modification, in Nord3v2.
    - It is recommended to run some tests to ensure the current behavior.
 4. Modify the code.
 5. Run the simulation with the new branch:
    - To achieve it is important to prepend the cloned path in the `PYTHONPATH`
    - e.g., `export PYTHONPATH=/gpfs/scratch/bsc32/bsc32538/NES:${PYTHONPATH}`
-6. Create and run a specific test for your case.
-7. Update the `CHANGELOG.md` with the new option-
+6. Create and run a specific test for your case in the folder `tests`.
+7. Update the `CHANGELOG.md` and include information on the new development or bug fix.
 8. Update the wiki with the new specifications.
 9. Merge `master` into your development branch.
-   - To ensure that if there has been news on the master, they are included.
-10. Run all tests.
-11. Create a merge request assigning it to Alba or Carles
+   - To ensure that if there has been any changes in the master branch, these are included.
+10. Run all tests in `tests`.
+11. Create a merge request and assign it to Alba (@avilanov) or Carles (@ctena), who will review it.
 
 ## Vertical interpolation
 
 - [Numpy linear interpolation](https://numpy.org/doc/stable/reference/generated/numpy.interp.html) is faster than [scipy.interpolate.interp1d](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html)
-- Do the operation using float32 sometimes produces negative values on very small results
-  - Force to float64 fixes the problem
+- Doing the operation using float32 sometimes produces negative values on very small results
+- Forcing it to float64 fixes the problem
 
 ```python
 # We force the interpolation with float64 to avoid negative values
-# We don't know why the negatives appear with float34
+# We don't know why the negatives appear with float32
 if kind == 'linear':
     dst_data[t, :, j, i] = np.array(
         np.interp(new_levels,