Merge pull request #233 from Sunbird-cQube/dev

merge dev to staging

adapter/NVSK/DIKSHA_content_coverage_on_qr.py

@@ -0,0 +1,17 @@
+from main import CollectData
+
+obj=CollectData()
+program=obj.program
+df_data=obj.get_file()
+
+def qr_coverage():
+    df_snap = df_data[['state_code', 'qr_coverage']]
+    df_snap.columns = ['state_id', 'content_coverage_on_qr']
+    obj.upload_file(df_snap, 'contentcoverage-event.data.csv')
+
+
+if df_data is not None:
+    qr_coverage()
+
+
+

adapter/NVSK/DIKSHA_etb_coverage_status.py

@@ -4,14 +4,25 @@
 program=obj.program
 df_data=obj.get_file()
 
-def linked_qr_count():
-    df_snap = df_data[['textbook_id','grade','subject','medium','linked_qr_count']]
-    obj.upload_file(df_snap,'linkedqrcount-event.data.csv')
 
-def resource_count():
-    df_snap = df_data[['textbook_id','grade','subject','medium','resource_count']]
-    obj.upload_file(df_snap, 'resourcecount-event.data.csv')
+
+def total_energised_tb():
+    df_snap = df_data[['state_code','total_energised']]
+    df_snap.columns = ['state_id','total_energized_textbooks']
+    obj.upload_file(df_snap, 'totalenergizedtb-event.data.csv')
+
+def total_curriculum_textbooks():
+    df_snap = df_data[['state_code','total_physical_textbooks_excluding_adopted']]
+    df_snap.columns = ['state_id','total_curriculum_textbooks']
+    obj.upload_file(df_snap, 'totalcurriculumtb-event.data.csv')
+
+def perc_etb_coverage():
+    df_snap = df_data[['state_code','etb_coverage_%']]
+    df_snap.columns = ['state_id','perc_energized_textbooks']
+    obj.upload_file(df_snap, 'percenergizedtb-event.data.csv')
+
 
 if df_data is not None:
-    linked_qr_count()
-    resource_count()
+    total_energised_tb()
+    total_curriculum_textbooks()
+    perc_etb_coverage()

adapter/NVSK/DIKSHA_etb_program_started.py → adapter/NVSK/DIKSHA_implementation_status.py

@@ -6,8 +6,8 @@
 
 def energised_textbooks():
     df_snap = df_data[['state_code','energised_textbooks']]
-    df_snap.columns = ['state_id','energised_textbooks']
-    obj.upload_file(df_snap, 'energisedtextbooks-event.data.csv')
+    df_snap.columns = ['state_id','energized_textbooks']
+    obj.upload_file(df_snap, 'energizedtextbooks-event.data.csv')
 
 if df_data is not None:
     energised_textbooks()

adapter/NVSK/DIKSHA_etb_plays_per_capita.py → adapter/NVSK/DIKSHA_learning_session_potential_user.py

@@ -6,8 +6,8 @@
 
 def plays_per_capita():
     df_snap = df_data[['state_code','plays_per_capita']]
-    df_snap.columns = ['state_id','plays_per_capita']
-    obj.upload_file(df_snap, 'playspercapita-event.data.csv')
+    df_snap.columns = ['state_id','learning_session_on_potential_user']
+    obj.upload_file(df_snap, 'potentialuser-event.data.csv')
 
 if df_data is not None:
     plays_per_capita()

adapter/NVSK/MICRO_Improvement_status.py

@@ -5,13 +5,13 @@
 df_data=obj.get_file()
 
 def category_event_data():
-    df_melt = df_data.melt(id_vars=['district_code'],
+    df_melt = df_data.melt(id_vars=['state_code'],
                            value_vars=["total_micro_improvement_projects","total_micro_improvement_started",
                                        "total_micro_improvement_inprogress","total_micro_improvement_submitted",
                                        "total_micro_improvement_submitted_with_evidence"],
                            var_name="category_name", value_name="category_value")
-    df_snap = df_melt[['district_code','category_name', 'category_value']]
-    df_snap.columns = ['district_id','category_name', 'category_value']
+    df_snap = df_melt[['state_code','category_name', 'category_value']]
+    df_snap.columns = ['state_id','category_name', 'category_value']
     df_snap.update(df_snap[['category_name']].applymap("'{}'".format))
     return df_snap
 

adapter/NVSK/NISHTHA_course_wise_status.py

@@ -9,10 +9,6 @@ def totalenrolment_event_data():
     df_snap.columns = ['state_id','program_name','course_name','total_enrolment']
     obj.upload_file(df_snap, 'courseenrolment-event.data.csv')
 
-def totalcompletion_event_data():
-    df_snap = df_data[['state_code','program','course_name','completion']]
-    df_snap.columns = [ 'state_id','program_name','course_name','total_completion']
-    obj.upload_file(df_snap, 'coursecompletion-event.data.csv')
 
 def totalcertification_event_data():
     df_snap = df_data[['state_code','program','course_name', 'certification']]
@@ -30,6 +26,5 @@ def course_dimension():
 
 if df_data is not None:
     totalenrolment_event_data()
-    totalcompletion_event_data()
     totalcertification_event_data()
     course_dimension()

adapter/NVSK/NISHTHA_courses_medium_status.py

@@ -11,8 +11,8 @@ def totalmedium_event_data():
 
 def totalcourses_event_data():
     df_snap = df_data[['state_code','program_name','total_courses']]
-    df_snap.columns = ['state_id','program_name','total_courses']
-    obj.upload_file(df_snap, 'mediumtotalcourses-event.data.csv')
+    df_snap.columns = ['state_id','program_name','total_courses_launched']
+    obj.upload_file(df_snap, 'totalcourseslaunched-event.data.csv')
 
 if df_data is not None:
     totalmedium_event_data()

adapter/NVSK/NISHTHA_district_wise_status.py

@@ -6,23 +6,13 @@
 
 def totalenrolment_event_data():
     df_snap = df_data[['state_code','district_code','program', 'total_enrollments']]
-    df_snap.columns = ['state_id','district_id','program_name', 'total_enrolment']
-    obj.upload_file(df_snap, 'consumptionenrolment-event.data.csv')
-
-def totalcompletion_event_data():
-    df_snap = df_data[['state_code','district_code','program', 'total_completion']]
-    df_snap.columns = ['state_id','district_id','program_name', 'total_completion']
-    obj.upload_file(df_snap, 'consumptioncompletion-event.data.csv')
+    df_snap.columns = ['state_id','district_id','program_name', 'total_enrolments']
+    obj.upload_file(df_snap, 'districtwiseenrolments-event.data.csv')
 
 def totalcertification_event_data():
     df_snap = df_data[['state_code', 'district_code', 'program', 'total_certifications']]
-    df_snap.columns = ['state_id', 'district_id', 'program_name',  'total_certification']
-    obj.upload_file(df_snap, 'consumptioncertification-event.data.csv')
-
-def perccertification_event_data():
-    df_snap = df_data[['state_code', 'district_code', 'program', 'certification%']]
-    df_snap.columns = ['state_id', 'district_id', 'program_name', 'perc_certification']
-    obj.upload_file(df_snap, 'consumptionperccertification-event.data.csv')
+    df_snap.columns = ['state_id', 'district_id', 'program_name',  'total_certifications']
+    obj.upload_file(df_snap, 'districtwisecertifications-event.data.csv')
 
 def program_dimension_data():
     df_snap = df_data[['program']].drop_duplicates()
@@ -35,7 +25,5 @@ def program_dimension_data():
 
 if df_data is not None:
     totalenrolment_event_data()
-    totalcompletion_event_data()
     totalcertification_event_data()
-    perccertification_event_data()
     program_dimension_data()

adapter/NVSK/NISHTHA_percentage_against_potential_base.py

@@ -4,78 +4,43 @@
 program=obj.program
 df_data=obj.get_file()
 
-def total_enrolment():
+def actual_enrolment():
     df_snap = df_data[['state_code','program','total_enrolments']]
-    df_snap.columns = ['state_id','program_name','total_enrolment']
-    obj.upload_file(df_snap, 'totalenrolment-event.data.csv')
+    df_snap.columns = ['state_id','program_name','actual_enrolment']
+    obj.upload_file(df_snap, 'actualenrolment-event.data.csv')
 
-def total_completion():
-    df_snap = df_data[['state_code','program','total_completions']]
-    df_snap.columns = ['state_id','program_name','total_completion']
-    obj.upload_file(df_snap, 'totalcompletion-event.data.csv')
-
-def total_certificates_issued():
+def actual_certification():
     df_snap = df_data[['state_code', 'program','total_certificates_issued']]
-    df_snap.columns = ['state_id','program_name','total_certificates_issued']
-    obj.upload_file(df_snap, 'totalcertificatesissued-event.data.csv')
-
-def total_courses():
-    df_snap = df_data[['state_code','program', 'total_courses']]
-    df_snap.columns = ['state_id','program_name',  'total_courses']
-    obj.upload_file(df_snap, 'totalcourses-event.data.csv')
-
-def doe_event_data():
-    df_snap = df_data[['state_code','program', 'doe']]
-    df_snap.columns = ['state_id','program_name','doe']
-    obj.upload_file(df_snap, 'doe-event.data.csv')
+    df_snap.columns = ['state_id','program_name','actual_certification']
+    obj.upload_file(df_snap, 'actualcertification-event.data.csv')
 
-def localbody_event_data():
-    df_snap = df_data[['state_code', 'program','local_body']]
-    df_snap.columns = ['state_id','program_name',  'local_body']
-    obj.upload_file(df_snap, 'localbody-event.data.csv')
 
 def target_achieved_enrolment():
     df_snap = df_data[['state_code','program','%_target_achieved_enrolment']]
     df_snap.columns = ['state_id','program_name','perc_target_achieved_enrolment']
-    obj.upload_file(df_snap, 'achievedenrolment-event.data.csv')
+    obj.upload_file(df_snap, 'targetachievedenrolment-event.data.csv')
 
 def target_achieved_certificates():
     df_snap = df_data[['state_code', 'program','%_target_achieved_certificates']]
     df_snap.columns = ['state_id','program_name', 'perc_target_achieved_certificates']
-    obj.upload_file(df_snap, 'achievedcertificates-event.data.csv')
-
-def target_remaining_enrolment():
-    df_snap = df_data[['state_code', 'program','%_target_remaining_enrolment']]
-    df_snap.columns = ['state_id', 'program_name','perc_target_remaining_enrolment']
-    obj.upload_file(df_snap, 'targetremainingenrolment-event.data.csv')
-
-def target_remaining_certificates():
-    df_snap = df_data[['state_code','program','%_target_remaining_certificates']]
-    df_snap.columns = [ 'state_id','program_name','perc_target_remaining_certificates']
-    obj.upload_file(df_snap, 'targetremainingcertificates-event.data.csv')
+    obj.upload_file(df_snap, 'targetachievedcertificates-event.data.csv')
 
-def total_expected_enrolment():
+def expected_enrolment():
     df_snap = df_data[['state_code','program','total_expected_enrolment']]
-    df_snap.columns = ['state_id','program_name','total_expected_enrolment']
+    df_snap.columns = ['state_id','program_name','expected_enrolment']
     obj.upload_file(df_snap, 'expectedenrolment-event.data.csv')
 
-def total_expected_certification():
+def expected_certification():
     df_snap = df_data[['state_code', 'program','total_expected_certification']]
-    df_snap.columns = ['state_id','program_name','total_expected_certification']
-    obj.upload_file(df_snap, 'expectedcertification-event.data.csv')
+    df_snap.columns = ['state_id','program_name','total_expected_certificates']
+    obj.upload_file(df_snap, 'expectedcertificates-event.data.csv')
 
 if df_data is not None:
-    total_enrolment()
-    total_completion()
-    total_certificates_issued()
-    total_courses()
-    doe_event_data()
-    localbody_event_data()
+    actual_certification()
+    actual_enrolment()
     target_achieved_enrolment()
     target_achieved_certificates()
-    target_remaining_enrolment()
-    target_remaining_certificates()
-    total_expected_enrolment()
-    total_expected_certification()
+    expected_enrolment()
+    expected_certification()
 
 

adapter/NVSK/NVSK_data_transformation.sh

@@ -1,11 +1,8 @@
-python3 DIKSHA_etb_coverage_state_wise.py diksha diksha_etb_etb-coverage.csv
-python3 DIKSHA_etb_coverage_status.py diksha diksha_etb_coverage-status.csv
-python3 DIKSHA_etb_plays_per_capita.py diksha diksha_etb_plays-per-capita.csv
+python3 DIKSHA_content_coverage_on_qr.py diksha diksha_etb_qr-coverage.csv
+python3 DIKSHA_etb_coverage_status.py diksha diksha_etb_etb-coverage.csv
 python3 DIKSHA_etb_learning_session.py diksha diksha_etb_learning-session.csv
-python3 DIKSHA_etb_program_started.py diksha diksha_etb_program-started.csv
-python3 DIKSHA_etb_qr_coverage.py diksha vsk_diksha_etb_qr-coverage.csv
-python3 DIKSHA_etb_qr_coverage_state.py diksha diksha_etb_qr-coverage.csv
-
+python3 DIKSHA_implementation_status.py diksha diksha_etb_program-started.csv
+python3 DIKSHA_learning_session_potential_user.py diksha diksha_etb_plays-per-capita.csv
 
 python3 MICRO_Implementation_status.py micro-improvements micro-improvement_all-dashboard.csv
 python3 MICRO_Improvement_status.py micro-improvements micro-improvement_district.csv

adapter/NVSK/PM_Poshan_progress_status.py

@@ -5,16 +5,16 @@
 df_data = obj.get_file()
 
 def total_meal_served():
-    df_snap = df_data[['district_code', 'meal_served']]
-    df_snap.columns = ['district_id', 'total_meals_served']
+    df_snap = df_data[['state_code','district_code', 'meal_served']]
+    df_snap.columns = ['state_id','district_id', 'total_meals_served']
     obj.upload_file(df_snap, 'mealserved-event.data.csv')
 
 def category_event_data():
-    df_melt=df_data.melt(id_vars=['district_code'],
+    df_melt=df_data.melt(id_vars=['state_code','district_code'],
                      value_vars=['enrolled','total_schools'],
                      var_name="category_name",value_name="category_value")
-    df_snap=df_melt[['district_code','category_name','category_value']]
-    df_snap.columns=['district_id','category_name','category_value']
+    df_snap=df_melt[['state_code','district_code','category_name','category_value']]
+    df_snap.columns=['state_id','district_id','category_name','category_value']
     df_snap.update(df_snap[['category_name']].applymap("'{}'".format))
     return df_snap