Merge pull request #141 from MOchiara/chiara-patch-39

[FIX] Removed cline computation and plots

glidertest/plots.py

@@ -79,8 +79,7 @@ def plot_basic_vars(ds: xr.Dataset, v_res=1, start_prof=0, end_prof=-1):
     
     Returns
     -------
-    Line plots for the averages of the different variables. 
-    Thermo, halo and pycnocline are computed and plotted. A sentence stating the depth of the clines is printed too
+    Line plots for the averages of the different variables.
     
     Original author
     ----------------
@@ -100,11 +99,6 @@ def plot_basic_vars(ds: xr.Dataset, v_res=1, start_prof=0, end_prof=-1):
     denG = denG[start_prof:end_prof, :]
     depthG = depthG[start_prof:end_prof, :]
 
-    halo = utilities.compute_cline(salG, depthG)
-    thermo = utilities.compute_cline(tempG, depthG)
-    pycno = utilities.compute_cline(denG, depthG)
-    print(
-        f'The thermocline, halocline and pycnocline are located at respectively {thermo}, {halo} and {pycno}m as shown in the plots as well')
     with plt.style.context(glidertest_style_file):
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", category=RuntimeWarning)
@@ -118,9 +112,6 @@ def plot_basic_vars(ds: xr.Dataset, v_res=1, start_prof=0, end_prof=-1):
             ax[0].plot(np.nanmean(tempG, axis=0), depthG[0, :], c='blue')
             ax1.plot(np.nanmean(salG, axis=0), depthG[0, :], c='red')
             ax2.plot(np.nanmean(denG, axis=0), depthG[0, :], c='black')
-            ax[0].axhline(thermo, linestyle='dashed', c='blue')
-            ax1.axhline(halo, linestyle='dashed', c='red')
-            ax2.axhline(pycno, linestyle='dashed', c='black')
 
             ax[0].set(xlabel=f'Temperature [C]', ylabel='Depth (m)')
             ax[0].tick_params(axis='x', colors='blue')

glidertest/utilities.py

@@ -113,28 +113,6 @@ def construct_2dgrid(x, y, v, xi=1, yi=1):
     YI, XI = np.meshgrid(yi, xi)
     return grid, XI, YI
 
-def compute_cline(var, depth_array):
-    """
-    Find the depth of the maximum vertical difference for a specified variables
-    
-    Parameters
-    ----------
-    var: 2D array containing data from a selected variable gridded over time/profile/distance etc. and depth (y-axis))
-    depth_array: 2D array containing pressure/depth data gridded over time/profile/distance etc. and depth (y-axis))
-    
-    Returns
-    -------
-    1D array containing the depth of the cline at each timestamp/profile/distance etc.
-    Original author
-    ----------------
-    Chiara Monforte
-    """
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=RuntimeWarning)
-        clin = np.where(np.abs(np.diff(np.nanmean(var, axis=0))) == np.nanmax(np.abs(np.diff(np.nanmean(var, axis=0)))))
-    return np.round(depth_array[0, clin[0]], 1)
-
-
 def compute_sunset_sunrise(time, lat, lon):
     """
     Calculates the local sunrise/sunset of the glider location from GliderTools.

tests/test_utilities.py

@@ -12,8 +12,6 @@ def test_utilitiesmix():
     tempG, profG, depthG = utilities.construct_2dgrid(ds.PROFILE_NUMBER, ds.DEPTH, ds.TEMP, p, z)
     denG, profG, depthG = utilities.construct_2dgrid(ds.PROFILE_NUMBER, ds.DEPTH, ds.DENSITY, p, z)
 
-    halo = utilities.compute_cline(denG, depthG)
-
 
 def test_sunset_sunrise():
     ds = fetchers.load_sample_dataset()