Removed deprecated TGLF instructions

Author: pabloprf

src/mitim_tools/gacode_tools/TGLFtools.py

@@ -38,90 +38,6 @@ def __init__(
         avTime=0.0,  # 		   Averaging window to extract CDF file
         alreadyRun=None,  # Option2: Do more stuff with a class that has already been created and store
     ):
-        """
-        TGLF class that manages the run and the results.
-
-        The philosophy of this is that a single 'tglf' class will handle the tglf simulation and results
-        at one time slice but with possibility of several radii at once.
-
-        It can also handle different TGLF settings, running them one by one, storing results in folders and then
-        grabbing them.
-
-        Scans can also be run. At several radii at once if wanted.
-
-        *Note*
-        The 'run' command does not require label. When performing a 'read', the results extracted from
-        the specified folder will be saved with the label indicated in 'read', in the "results" or "scans"
-        dictionaries. Plotting then can happen with more than one label of the same category.
-
-        *Note*
-        The 'run' command uses input.tglf from the specified folder, but one can change the Settings presets,
-        extraOptions and multipliers. The modified inputs is not rewritten in the actual folder, it is only written
-        in the tmp folder on which the simulation takes place.
-
-        *Note*
-        After a 'prep' command, the class can be detached from the file system, as it stores the input tglf file
-        to run later with different options. It also stores the Normalizations, since the runs are expected
-        to only change dimensionless parameteres.
-
-        **************************************
-        ***** Example use for standalone *****
-        **************************************
-
-            # Initialize class, by specifying where the inputs to TGLF come from (TRANSP cdf)
-            tglf = TGLF(cdf='~/testTGLF/12345B12.CDF',time=1.45,avTime=0.1,rhos=[0.4,0.6])
-
-            # Prepare TGLF (this will create input.tglf in the specified folder)
-            cdf = tglf.prep_using_tgyro('~/testTGLF/')
-
-            # Run standalone TGLF (this will find the input.tglf in the previous folder,
-            # and then copy to this specify TGLF run, and run it there)
-            tglf.run(subfolder='tglf1/',Settings=1,extraOptions={'NS':3})
-
-            # Read results
-            tglf.read(label='run1',folder='~/testTGLF/tglf1/')
-
-            # Plot
-            plt.ion();	tglf.plot(labels=['run1'])
-
-        *********************************
-        ***** Example use for scans *****
-        *********************************
-
-            # Initialize class, by specifying where the inputs to TGLF come from (TRANSP cdf)
-            tglf = TGLF(cdf='~/testTGLF/12345B12.CDF',time=1.45,avTime=0.1,rhos=[0.4,0.6])
-
-            # Prepare TGLF (this will create input.tglf in the specified folder)
-            cdf = tglf.prep_using_tgyro('~/testTGLF/')
-
-            # Run
-            tglf.run_scan('scan1/',Settings=1,varUpDown=np.linspace(0.5,2.0,20),variable='RLTS_2')
-
-            # Read scan
-            tglf.read_scan(label='scan1',variable='RLTS_2')
-
-            # Plot
-            plt.ion(); tglf.plot_scan(labels=['scan1'],variableLabel='RLTS_2')
-
-        ****************************
-        ***** Special analysis *****
-        ****************************
-
-            Following the prep phase, we can run "runAnalysis()" and select among the different options:
-                - Chi_inc
-                - D and V for trace impurity
-            Then, plotAnalysis() with the right option for different labels too
-
-        ****************************
-        ***** Do more stuff with a class that has already been created and store
-        ****************************
-
-            tglf = TGLF(alreadyRun=previousClass)
-            tglf.FolderGACODE = '~/testTGLF/'
-
-            ** Modify the class as wish, and do run,read, etc **
-            ** Because normalizations are stored in the prep phase, that's all ready **
-        """
 
         super().__init__(rhos=rhos)