Update ctd-pycnv-starter.ipynb

Added comment to top of file to 'not edit'.

Update ctd-pycnv-starter.ipynb
56447d21 · Eleanor Frajka-Williams · 3a636601 · 56447d21
Commit 56447d21 authored Nov 29, 2023 by Eleanor Frajka-Williams
--- a/Messmethoden_U6/ctd-pycnv-starter.ipynb
+++ b/Messmethoden_U6/ctd-pycnv-starter.ipynb
@@ -6,7 +6,11 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "# Importing the required module for plotting data\n",
+    "#\n",
+    "# Don't edit this file.  Make a copy and rename it (e.g. with your name/group names)\n",
+    "# \n",
+    "\n",
+    "# # Importing the required module for plotting data\n",
    "import gsw  # See https://teos-10.github.io/GSW-Python/\n",
    "import pycnv # See https://pypi.org/project/pycnv/\n",
    "import pylab as pl\n",

 %% Cell type:code id: tags:

 ``` python
-# Importing the required module for plotting data
+#
+# Don't edit this file.  Make a copy and rename it (e.g. with your name/group names)
+#
+
+# # Importing the required module for plotting data
 import gsw  # See https://teos-10.github.io/GSW-Python/
 import pycnv # See https://pypi.org/project/pycnv/
 import pylab as pl

 # Defining the file path where the CNV file is located
 # Choose either Seepraktikum 2023 data: https://bit.ly/3ut6NtV
 file_path = 'Seepraktikum_2023/SBE19plus_01907321_2023_05_12_Cast36_loop_window.cnv'
 # Or MSM121 data: https://bit.ly/3Ri19E2
 file_path = 'msm121_profiles/MSM121_015_1db.cnv'
 # Or MSM121 to-yo profiles: https://bit.ly/3GlQh1D
 # file_path = 'MSM121_060/MSM121_060_001_05db.cnv'

 # Load the data from the given file_path (may need to update to match your file location)
 cnv = pycnv.pycnv(file_path)

 # Print some info to the screen
 print('Test if we are in the Baltic Sea (usage of different equation of state): ' + str(cnv.baltic))
 print('Position of cast is: Longitude:', cnv.lon,'Latitude:',cnv.lat)
 print('Time of cast was:', cnv.date)
 print('Number of sensor entries (len(cnv.data.keys())):',len(cnv.data.keys()))
 print('Names of sensor entries (cnv.data.keys()):',cnv.data.keys())
 ```

 %% Cell type:code id: tags:

 ``` python
 # Get data of entry
 key0 = list(cnv.data.keys())[0]
 data0 = cnv.data[key0]

 # Get derived data:
 keyd0 = list(cnv.cdata.keys())[0]
 datad0 = cnv.cdata[keyd0]
 # Get unit of derived data
 datad0_unit = cnv.cunits[keyd0]

 # Standard names are mapped to
 # cnv.p,cnv.CT,cnv.T,cnv.SP,cnv.oxy
 # units are _unit, e.g. cnv.p_unit
 ```

 %% Cell type:code id: tags:

 ``` python
 # Plot standard parameters
 pl.figure(1)
 pl.clf()
 pl.subplot(1,2,1)
 pl.plot(cnv.SA,cnv.p) # Note that pycnv has done the TEOS-10 conversion to SA for you using gsw
 pl.xlabel('Absolute salinity [' + cnv.SA_unit + ']')
 pl.ylabel('Pressure [' + cnv.p_unit + ']')
 pl.gca().invert_yaxis()  # Question: What happens if you comment out this line?


 # Step 1a: Add grid lines

 # Step 1b: Limit the top of the plot to the surface (p=0)

 # Step 1c: Add a second plot for temperature, to the right of the salinity plot

 # Step 1d: Add a title to your figure, perhaps the station number, latitude and longitude

 # Step 1e: Print the figure to a *png file
 ```

 %% Cell type:code id: tags:

 ``` python
 # Plotting profile data on the same axes
 pl.subplot(1,2,2)
 pl.plot(cnv.oxy,cnv.p)
 pl.plot(cnv.cdata['oxy0'],cnv.p)
 pl.plot(cnv.cdata['oxy1'],cnv.p)
 pl.xlabel('Oxygen [' + cnv.oxy_unit + ']')
 pl.ylabel('Pressure [' + cnv.p_unit + ']')
 pl.gca().invert_yaxis()

 # Step 2a-e: Clean up the figure as above

 # Step 2f (optional): Compute the apparent oxygen utilisation
 # See: https://en.wikipedia.org/wiki/Apparent_oxygen_utilisation
 # you will need to calculate oxygen solubility (see the gsw documentation for O2_sol)
 ```

 %% Cell type:code id: tags:

 ``` python
 # Make a T-S diagram
 pl.figure(1)
 pl.clf()
 pl.subplot(1,2,1)
 pl.plot(cnv.SA,cnv.CT)
 pl.xlabel('Absolute salinity [' + cnv.SA_unit + ']')
 pl.ylabel('Conservative temperature [' + cnv.CT_unit + ']')

 # Step 3a: Add contours of potential density
 #    - Hint, you will need to create a vector of temperature and salinity spanning at
 #      least the ranges in the profile
 #    - You will then need to use these gridded vectors to calculate potential density using the gsw toolbox


 # Step 3b: Add contour labels for the density

 # Step 3c: Add a title

 # Step 3d: Print the figure as a *png
 ```

 %% Cell type:code id: tags:

 ``` python
 # Challenge 4: Load multiple cnv files into python.

 # Step 4a: Plot a map of the stations

 # Step 4b (optional): Add bathymetry
 #    - see e.g. GEBCO https://www.gebco.net/data_and_products/gridded_bathymetry_data/
 #    - or ETOPO1.  Note these files are big when full resolution and global.
 #    You will probably want to slice/prepare the data in a separate python notebook/file

 # Step 4c: Load multiple stations into python
 #    - Option one: Keep them all as separate variables (cnv1, cnv2, cnv3, etc)
 #    - Option two: Combine them into a multidimensaionl np.ndarray
 #      In order to combine them, they will need to be interpolated onto the same
 #      pressure grid (suggested 1 dbar grid).

 # Step 4d: Plot multiple temperature profiles on a single set of axes (see Step 1)

 # Step 4e: Repeat for salinity

 # Step 4f: Repeat for T-S diagrams (see Step 3)

 # Noting the data type that cnv is using may help:
 type(cnv.SA)
 ```

 %% Cell type:code id: tags:

 ``` python
 # Challenge 5: Only works with option two in challenge 4.

 # Step 5a: Choose stations that are in a line (plot a map)

 # Step 5b: Calculate buoyancy frequency N^2 = db/dz.  See gsw.Nsquared
 #    - Plot this.  Note where it is large / small.

 # Step 5b: Calculate distance between CTD profiles

 # Step 5c: Calculate buoyancy b = -g\rho/\rho_0.
 #    - See e.g. Clement et al. 2023: https://doi.org/10.1175/JPO-D-22-0178.1

 # Step 5d: Calculate the density gradient db/dx where x is the distance between stations

 ```