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c_profiler -> gives times of functions
line_profiler -> gives  times of line by lie

#@profile -> needed for linebyline
def func():
	aklw;ejlkajselksjlkjaslkejawlkjwel;kaj
    
terminal: python -m cProfile -s cumulative profileme.py
terminal: kernprof -l -v profileme.py #todo cpu profiling line-by-line -> needs @profile for the func you need lbyl for
terminal: python -m memory_profiler profileme.py #todo memory profiling -> need @profile for func you want memory details for

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find /users/username/onedrivepath/ -name ".DS_Store" -depth -exec rm {} \;

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**General File/Directory Commands**
rm _fileName_  ->  remove file
rm -rf _dirName_ -> remove folder 

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# include standard modules
import argparse

# initiate the parser
parser = argparse.ArgumentParser()  
parser.add_argument("-V", "--version", help="show program version", action="store_true") #true of false b/c action
parser.add_argument("-W", "--width", help="store width") #no set action = store value 

# read arguments from the command line
args = parser.parse_args()

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# WRITE CODE BLOCK IN DEBUG CONSOLE (cmd shift D in anki main window)
# RUN CODE w Cmd Return

#GET ALL TAGS
from aqt import mw
out=mw.col.tags.all()
for t in out:
    print(t)
    
    

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git init
git add .
git commit -m "message"
git remote add origin __URL__
git push -u origin master

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v host file 
/usr/local/etc/httpd/extra/httpd-vhosts.conf

start apache
sudo apachectl start

start wsgi
mod_wsgi-express start-server app.wsgi

start wsgi nohup

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def get_data_lists(selected_col=[], indvGroupOnly=False):

  	# Data input
    xls = pd.ExcelFile("final_data_NMR_DILC_comb.xlsx")
    day1_org = pd.read_excel(xls,"Day1")
    day3_org = pd.read_excel(xls,"Day3")
    day10_org = pd.read_excel(xls,"Day10-11")

#     print(selected_col)
    if len(selected_col)>0:

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TOP_FEATURES = len(feature_list)

forest = RandomForestClassifier(n_estimators=10,max_depth=6)#, random_state=3)
forest.fit(l_mat[1],l_label[1])

importances = forest.feature_importances_
std = np.std(
    [tree.feature_importances_ for tree in forest.estimators_],
    axis=0
)

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feat_true, feat_top_rank = boruta_MP(l_mat,l_label,l_title,l_features,nest='auto',top_rank=5)

# TRUE Feat 

l_cv = 3,5,6,7
times = 5

for i,feat in enumerate(true_feat_T10_R5_tres100):
    l_mat_true_T10_R5_tres100, l_label_true_T10_R5_tres100, l_title_true_T10_R5_tres100, _ = get_data_lists(feat)
#     print(feat)