Merge branch 'master' of qo.physics.wm.edu:evmik/GradeBookHEADmaster

1 files changed, 40 insertions, 34 deletions

diff --git a/regenTheExpertTA.py b/regenTheExpertTA.py
index d0277bc..34c7f8e 100755
--- a/regenTheExpertTA.py
+++ b/regenTheExpertTA.py
@@ -9,25 +9,25 @@ import os
 infile = sys.argv[1]
 
 f = open(infile)
-l= f.readlines()
-h= l[0]
-hsub=l[1] # TheExpertTA keep headers in 2 lines (mixed with max possible points)
-maxPossible=l[1]
+l = f.readlines()
+h = l[0]
+hsub = l[1]  # TheExpertTA keep headers in 2 lines (mixed with max possible points)
+maxPossible = l[1]
 f.close()
 
 # clean up of headers
-h=h.strip()
-h = h.replace('"', '')
+h = h.strip()
+h = h.replace('"', "")
 hsub = hsub.strip()
-hsub = hsub.replace('"', '')
-hsub = hsub.split(',')
-headers = h.split(',')
+hsub = hsub.replace('"', "")
+hsub = hsub.split(",")
+headers = h.split(",")
 
 # we should fail hard if this column names are not present
-headers[hsub.index('Last')]='LastName'
-headers[hsub.index('First')]='FirstName'
-headers[hsub.index('Email')]='UserName'
-headers[hsub.index('Student No')]='SID'
+headers[hsub.index("Last")] = "LastName"
+headers[hsub.index("First")] = "FirstName"
+headers[hsub.index("Email")] = "UserName"
+headers[hsub.index("Student No")] = "SID"
 
 # headers[0]='FullName'
 # headers[1]='UserName'
@@ -38,16 +38,19 @@ headers[hsub.index('Student No')]='SID'
 d = pd.read_csv(infile, skiprows=[0], header=None, names=headers)
 
 # First row contains max points information, so let's reassign it
-d.loc[0, 'UserName']='_Max_Points_'
-d.loc[0, 'LastName']='MaxScore'
-d.loc[0, 'FirstName']='MaxScore'
-d.loc[0, 'SID']=pd.NA
+d.loc[0, "UserName"] = "_Max_Points_"
+d.loc[0, "LastName"] = "MaxScore"
+d.loc[0, "FirstName"] = "MaxScore"
+d.loc[0, "SID"] = pd.NA
 
-specialUsers=[]
-specialUsers.append('_Max_Points_')
+specialUsers = []
+specialUsers.append("_Max_Points_")
 # lets add row which will be in charge of the column type
-d=pd.concat([d, pd.DataFrame({'UserName': ['_Col_Category_']}, columns=d.columns)], ignore_index=True)
-specialUsers.append('_Col_Category_')
+d = pd.concat(
+    [d, pd.DataFrame({"UserName": ["_Col_Category_"]}, columns=d.columns)],
+    ignore_index=True,
+)
+specialUsers.append("_Col_Category_")
 
 # cleanup
 # c = d.columns
@@ -58,22 +61,24 @@ specialUsers.append('_Col_Category_')
 # d[index] = np.nan
 
 # TheExperTA last column contains 'Averages' per student which we do not need
-d.drop(columns=['Averages'], inplace=True)
+d.drop(columns=["Averages"], inplace=True)
 # TheExperTA last row contains Averages per assignment which we do not need
-row =  d[(d['LastName']=='Averages') & (d['FirstName'].isna()) & (d['UserName'].isna())]
+row = d[
+    (d["LastName"] == "Averages") & (d["FirstName"].isna()) & (d["UserName"].isna())
+]
 d.drop(row.index, inplace=True)
 
 # hand tuned fixes
-d['UserName'].replace('@email.wm.edu$', '@wm.edu', regex=True, inplace=True)
+d["UserName"].replace("@email.wm.edu$", "@wm.edu", regex=True, inplace=True)
 # d['UserName'].replace('phanng@hotmail.com@tj.va$', 'kphan@wm.edu', regex=True, inplace=True)
 
 # Now let's convert percentage which TheExperTA reports to points as GradeTable expects
 for c in d.columns:
-    if c in ['LastName', 'FirstName', 'UserName', 'SID']:
+    if c in ["LastName", "FirstName", "UserName", "SID"]:
         continue
-    maxP = d.loc[(d['UserName'] == '_Max_Points_')][c].values[0]
-    index = ~d['UserName'].isin( specialUsers )
-    d.loc[index,c] *= maxP/100  # convert percentage to points
+    maxP = d.loc[(d["UserName"] == "_Max_Points_")][c].values[0]
+    index = ~d["UserName"].isin(specialUsers)
+    d.loc[index, c] *= maxP / 100  # convert percentage to points
 
     # TheExpertTA due to their percentage system generates +/- 0.0000001 scores
     # which is useless and make very long numbers in tables.
@@ -81,15 +86,16 @@ for c in d.columns:
     d.loc[index, c] = d.loc[index, c].round(4)
 
     # now we are trying to guess column category
-    if 'hw' in c.lower():
-        d.loc[(d['UserName'] == '_Col_Category_'), c] = 'HomeWork'
+    if "hw" in c.lower():
+        d.loc[(d["UserName"] == "_Col_Category_"), c] = "HomeWork"
 
 
-d.to_csv('TheExpertTA.csv', index=False)
+d.to_csv("TheExpertTA.csv", index=False)
 
 # now import to sqlite3
 
-os.popen('rm -f TheExpertTA.db')
-p = os.popen('printf ".mode csv\n.import \"TheExpertTA.csv\" export_table\n.q" | sqlite3 TheExpertTA.db')
+os.popen("rm -f TheExpertTA.db")
+p = os.popen(
+    'printf ".mode csv\n.import "TheExpertTA.csv" export_table\n.q" | sqlite3 TheExpertTA.db'
+)
 p.close()
-