course merge, sem setup
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Coding with Peter
parent
28fd54a8b7
commit
f16288322e
14
courses.py
14
courses.py
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@ -74,17 +74,17 @@ def get_plague_passers():
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oo = { 'passed': passed_by_deptdidnt': didnt_by_dept
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for cr in crossref:
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student_int = course_enrollment(cr)
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student_by_dept{ str(k): v for k,v in student_int.items() }
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oo[cr] = student_by_dept
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student_by_dict{ str(k): v for k,v in student_int.items() }
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oo[cr] = student_by_dict
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output_by_course[cr] = { 'passed':{}, 'didnt':{}, 'missing':{} }
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course_s[cr] = set( [ str(k) for k in student_by_deptys() ])
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course_s[cr] = set( [ str(k) for k in student_by_dict.keys() ])
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for k,v in student_by_deptems():
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for k,v in student_by_dict.items():
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key_s = str(k)
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if key_s in passed_by_dept output_by_course[cr]['passed'][key_s] = passed_by_depty_s]
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elif key_s in didnt_by_dept output_by_course[cr]['didnt'][key_s] = didnt_by_depty_s]
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if key_s in passed_by_dict output_by_course[cr]['passed'][key_s] = passed_by_dicty_s]
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elif key_s in didnt_by_dict output_by_course[cr]['didnt'][key_s] = didnt_by_dicty_s]
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else:
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output_by_course[cr]['missing'][key_s] = v['user']
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@ -1742,7 +1742,7 @@ def fetch_rubric_scores(course_id=16528, assignment_id=1):
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print(json.dumps(rubric,indent=2))
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for r in rubric:
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for rat in r.get('ratings',[]):
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ratings_by_dept[rat['id']] = { 'rub_by_deptription': r['description'], 'rat_by_deptription': rat['description'], 'points': rat['points']}
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ratings_by_dept[rat['id']] = { 'description': r['description'], 'long_description': rat['description'], 'points': rat['points']}
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# Print the assignments dictionary
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91
stats.py
91
stats.py
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@ -1194,6 +1194,96 @@ def lstm_model_sections():
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df
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def visualize_course_modes_multi_semester():
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import plotly.express as px
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from plotly.subplots import make_subplots
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seasons = {'sp':'30','su':'50','fa':'70'}
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semcodes = "sp18 su18 fa18 sp19 su19 fa19 sp20 su20 fa20 sp21 su21 fa21 sp22 su22 fa22 sp23 su23 fa23 sp24".split(" ")
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# sems = {'sp23':'202330','su23':'202350','fa23':'202370'}
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sems = { x:'20' + x[2:] + seasons[x[:2]] for x in semcodes }
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sem_dfs = []
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sem_dfs_depts = []
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for s in sems.keys():
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sched = requests.get(f"http://gavilan.cc/schedule/{s}_sched_expanded.json").json()
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for crs in sched:
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if 'extra' in crs: del crs['extra']
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crs['dept'] = crs['code'].split(' ')[0]
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df = pd.DataFrame(sched)
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df_depts = df.copy()
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df_depts = df_depts.drop(columns=['crn','sec','code','cmp','name','days','time','rem','wl_cap','wl_act','wl_rem','teacher','date','loc','ztc','time_start','time_end','start','end','doy'])
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df = df.drop(columns=['crn','sec','code','cmp','name','days','time','rem','wl_cap','wl_act','wl_rem','teacher','date','loc','ztc','time_start','time_end','start','end','doy'])
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dept_counts = grouped_by_dept = df_depts.groupby(['dept','type']).size().reset_index(name='count')
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grouped_by_mode = df['type'].value_counts().reset_index()
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grouped_by_dept["semester"] = sems[s]
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grouped_by_mode["semester"] = sems[s]
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#print(dept_counts)
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sem_dfs.append(grouped_by_mode)
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sem_dfs_depts.append(grouped_by_dept)
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#grouped_json = grouped_by_dept.to_json(orient='records')
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#j = json.loads(grouped_json)
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#print(json.dumps(j,indent=2))
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#grouped_by_dept.columns = ['Department', 'Count'] # rename the column names appropriately
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#fig = px.bar(grouped_by_dept, x='Department', y='Count', title='Section Counts by Department')
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#fig.write_html(f"cache/output_{s}.html")
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combined_data = pd.concat(sem_dfs, axis=0)
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combined_data = combined_data.rename(columns={'type':'count','index':'type'})
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combined_data.reset_index(drop=True,inplace=True)
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pivoted_data = combined_data.pivot(index='semester', columns='type', values='count')
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pivoted_data.reset_index(inplace=True)
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fig = px.bar(pivoted_data, x='semester',y=['hybrid', 'in-person', 'online', 'online live'], barmode='stack',
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title='Course Delivery by Semester',
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color_discrete_sequence=["#000066","#660000","#333366","#9400D3"])
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fig.write_html(f"cache/sections_by_deliverymode.html")
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combined_data_depts = pd.concat(sem_dfs_depts, axis=0)
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combined_data_depts.reset_index(drop=True,inplace=True)
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#print(combined_data_depts)
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combined_data_depts.to_csv('cache/section_delivery_by_dept.csv')
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'''pivoted_data_depts = combined_data_depts.pivot(index='semester', columns='type', values='count')
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pivoted_data_depts.reset_index(inplace=True)
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fig = px.bar(pivoted_data_depts, x='semester',y=['hybrid', 'in-person', 'online', 'online live'], barmode='stack',
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title='Course Delivery by Semester',
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color_discrete_sequence=["#000066","#660000","#333366","#9400D3"])
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fig.write_html(f"cache/sections_depts_by_deliverymode.html")'''
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unique_depts = combined_data_depts['dept'].unique()
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fig = make_subplots(rows=len(unique_depts), cols=1,
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subplot_titles=unique_depts,
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)
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for i, dept in enumerate(unique_depts, start=1):
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#if i>1: break
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# Filter the dataframe for the current department
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dept_data = combined_data_depts[combined_data_depts['dept'] == dept]
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# Pivot the data frame
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pivoted_dept_data = dept_data.pivot(index='semester', columns='type', values='count').reset_index()
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pivoted_dept_data.fillna(0,inplace=True)
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print(pivoted_dept_data)
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# Plot the data
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columns_to_plot = ['hybrid', 'in-person', 'online', 'online live']
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valid_columns = [col for col in columns_to_plot if col in pivoted_dept_data.columns]
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fig_sub = px.bar(pivoted_dept_data, x='semester', y=valid_columns, barmode='stack',
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#title=f'Course Delivery by Semester for {dept}',
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color_discrete_sequence=["#000066","#660000","#333366","#9400D3"])
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fig.add_traces(fig_sub['data'], rows=[i]*len(fig_sub['data']), cols=[1]*len(fig_sub['data']))
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fig.update_layout(height=70*len(fig['data']), width=1100, showlegend=False)
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fig.write_html(f"cache/sections_depts_by_deliverymode.html")
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if __name__ == "__main__":
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options = { 1: ['get all historical grades from ilearn',get_all] ,
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2: ['process grades csv file',process_grades] ,
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@ -1212,6 +1302,7 @@ if __name__ == "__main__":
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15: ['student courses by semester', student_by_semester],
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16: ['LSTM model sections', lstm_model_sections],
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17: ['rearrange section data to yearly form', sections_grouped_by_year_mode],
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30: ['visualize course modes multi semester', visualize_course_modes_multi_semester],
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}
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print ('')
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