Comments added, index page trend updated for classrooms, Presentation added

config.py

+default_state_code = 27 # for Maharashtra
+
+fig_size_ds_w, fig_size_ds_h = 10, 6 # width and height of distribution graphs 
+fig_size_tr_w, fig_size_tr_h = 8, 6 # width and height of trend graphs
+
 form_value_to_sub_dimension_mapping = {
     'govt':'Government',
     'private':'Private',

utils.py

@@ -8,9 +8,10 @@ import matplotlib.pyplot as plt
 from babel.numbers import format_number
 # dict_summary={"top3":[],"bottom3":[]}
 
-fig_size_ds_w, fig_size_ds_h = 10, 6
-fig_size_tr_w, fig_size_tr_h = 8, 6
+fig_size_ds_w, fig_size_ds_h = 10, 6 # width and height of distribution graphs 
+fig_size_tr_w, fig_size_tr_h = 8, 6 # width and height of trend graphs
 
+# to read all raw data csv files
 def read_all_csv(path='./rawdata_csv/', extension='csv'):
     result = glob.glob(path+'SRC_R*.{}'.format(extension))
     df=None
@@ -30,6 +31,7 @@ def read_all_csv(path='./rawdata_csv/', extension='csv'):
 
     return final_df
 
+# to plot statewise distribution graphs
 def statewise_distribution(output_filename, input_df, year, ylabel, main_dimension, sub_dimension=''):
     dict_summary={"top3":[],"bottom3":[]}
     if sub_dimension:
@@ -65,6 +67,7 @@ def statewise_distribution(output_filename, input_df, year, ylabel, main_dimensi
     
     return dict_summary
 
+# to plot trend graph for enrolments
 def total_enrolment_by_category(output_filename, input_df, main_dimension, state_code, ylabel):
     fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
     df_fil = input_df.loc[input_df['Main_Dimension'] == main_dimension]
@@ -97,6 +100,7 @@ def total_enrolment_by_category(output_filename, input_df, main_dimension, state
     plt.savefig(output_filename)
     plt.close(fig)
 
+# to plot trend graph for teachers
 def total_teachers_by_main_dimension(output_filename, input_df, main_dimension, state_code):
     fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
     df_fil = input_df.loc[input_df['Main_Dimension'] == main_dimension]
@@ -155,6 +159,7 @@ def total_teachers_by_main_dimension(output_filename, input_df, main_dimension,
     plt.savefig(output_filename)
     plt.close(fig)
 
+# to plot trend graph for classrooms
 def total_classrooms_by_trend(output_filename, input_df, state_code):
     fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
     df_fil=input_df.loc[input_df['Main_Dimension'] == "Total Classrooms"]
@@ -186,6 +191,7 @@ def total_classrooms_by_trend(output_filename, input_df, state_code):
     plt.savefig(output_filename)
     plt.close(fig)
 
+# to plot trend graph for facilities
 def total_schools_for_facilities(output_filename, input_df, state_code):
     fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
     df_fil=input_df.loc[input_df['Main_Dimension'] == "School Facilities"]
@@ -229,6 +235,7 @@ def total_schools_for_facilities(output_filename, input_df, state_code):
     plt.savefig(output_filename)
     plt.close(fig)
 
+# to plot trend graph by main_dimension for index page
 def main_dimension_overall(output_filename, input_df, main_dimension, ylabel):
     fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
     df_fil=input_df.loc[input_df['Main_Dimension'] == main_dimension]
@@ -255,13 +262,13 @@ def main_dimension_overall(output_filename, input_df, main_dimension, ylabel):
         df_major_repair = df_sum.loc[(df_sum['Sub_Dimension'] == "Need Major Repair")]
         plt.plot(df_good_cond['Year'],df_good_cond['total'],marker='o',color='r', label='Good Condition')
         for x, y in zip(df_good_cond['Year'],df_good_cond['total']):
-            plt.text(x = x, y = y, s = '{:.0f}'.format(y))
+            plt.text(x = x, y = y, s = '{:.0f}'.format(y), color='r', rotation=15, fontsize=7, weight='bold')
         plt.plot(df_minor_repair['Year'],df_minor_repair['total'],marker='+',color='g', label='Need Minor Repair')
         for x, y in zip(df_minor_repair['Year'],df_minor_repair['total']):
-            plt.text(x = x, y = y, s = '{:.0f}'.format(y))
+            plt.text(x = x, y = y, s = '{:.0f}'.format(y), color='g', rotation=15, fontsize=7, weight='bold')
         plt.plot(df_major_repair['Year'],df_major_repair['total'],marker='*',color='b', label='Need Major Repair')
         for x, y in zip(df_major_repair['Year'],df_major_repair['total']):
-            plt.text(x = x, y = y, s = '{:.0f}'.format(y))
+            plt.text(x = x, y = y, s = '{:.0f}'.format(y), color='b', rotation=15, fontsize=7, weight='bold')
     
     plt.legend()
     plt.title(label='KPI: ' + ylabel, fontweight='bold', fontsize=10, fontstyle='italic')
@@ -271,6 +278,7 @@ def main_dimension_overall(output_filename, input_df, main_dimension, ylabel):
     plt.savefig(output_filename)
     plt.close(fig)
 
+# to plot trend graph by school facilities for index page
 def total_schools_for_facilities_overall(output_filename, input_df):
     fig = plt.figure(figsize=(fig_size_tr_w,fig_size_tr_h))
     df_fil=input_df.loc[input_df['Main_Dimension'] == "School Facilities"]
@@ -312,6 +320,7 @@ def total_schools_for_facilities_overall(output_filename, input_df):
     plt.savefig(output_filename)
     plt.close(fig)
 
+# to get overall card values for index page
 def get_index_summary(input_df):
     latest_year = max(input_df['Year'])
     students = sum(input_df[(input_df['Main_Dimension']=='Elementary Enrolment by School Category') & (input_df['Year']==latest_year)]['total'])
@@ -325,6 +334,7 @@ def get_index_summary(input_df):
         'classrooms': format_number(int(classrooms), locale='en_IN')
     }
 
+# to get dimension wise card values for details page
 def get_dimension_wise_summary(input_df, state_code, main_dimension):
     latest_year = max(input_df['Year'])
     df_fil = input_df[(input_df['State_Code']==state_code) & (input_df['Year']==latest_year)]