diff --git a/requirements.txt b/requirements.txt
index 498d21bfe757438419a973100c5bbb0b26d359da..f08277cbe4e5c839bfbccc8260f12fc0c2ef8e59 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
-django==1.8
+django==1.11
django-widget-tweaks
pandas==0.18.1
django-extension
psycopg2
+xlrd
diff --git a/script/analysis/degree_analysis.py b/script/analysis/degree_analysis.py
index 5f01427780f96a35cf253bb80ec908f402058f6e..4855ba4aeee2128776e316dc0a33057946d87806 100644
--- a/script/analysis/degree_analysis.py
+++ b/script/analysis/degree_analysis.py
@@ -1,5 +1,4 @@
import pandas as pd
-import numpy as np
import math
from utils.situations import Situation, EvasionForm
diff --git a/script/analysis/student_analysis.py b/script/analysis/student_analysis.py
index ac2304251da7005785dd8dcc77853dbd0e8dd3ce..ba017dc3c0d7f2247ac6cf8a7f20f022daa13d46 100644
--- a/script/analysis/student_analysis.py
+++ b/script/analysis/student_analysis.py
@@ -1,161 +1,155 @@
-import pandas as pd
-from utils.situations import *
+from utils.situations import *
ANO_ATUAL = 2017
SEMESTRE_ATUAL = 2
+
def listagem_evasao(df):
- #~ print(df["FORMA_EVASAO"].drop_duplicates())
- #~ print(df)
- #~ print(Situation.SITUATION_AFFECT_IRA)
- #~ print(df)
- aux = df[df.FORMA_EVASAO != 1]
- print(aux)
- #~ print(aux.where(aux.SITUACAO != 1)["SITUACAO"])
- #~ print(df[df.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)])
- #~ print(df.where(df["SITUACAO"] in Situation.SITUATION_AFFECT_IRA))
- #~ aux = df.drop_duplicates(['MATR_ALUNO'], keep='last')
- #~ print(aux["FORMA_EVASAO"].drop_duplicates())
+ # ~ print(df["FORMA_EVASAO"].drop_duplicates())
+ # ~ print(df)
+ # ~ print(Situation.SITUATION_AFFECT_IRA)
+ # ~ print(df)
+ aux = df[df.FORMA_EVASAO != 1]
+ print(aux)
+
+
+# ~ print(aux.where(aux.SITUACAO != 1)["SITUACAO"])
+# ~ print(df[df.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)])
+# ~ print(df.where(df["SITUACAO"] in Situation.SITUATION_AFFECT_IRA))
+# ~ aux = df.drop_duplicates(['MATR_ALUNO'], keep='last')
+# ~ print(aux["FORMA_EVASAO"].drop_duplicates())
def average_ira(d):
temp = d.dropna(subset=['MEDIA_FINAL'])
temp = temp[temp['MEDIA_FINAL'] <= 100]
if not temp.empty:
- #print(temp[['MEDIA_FINAL', 'CH_TOTAL']])
- aux = np.sum(temp['MEDIA_FINAL']*temp['CH_TOTAL'])
+ # print(temp[['MEDIA_FINAL', 'CH_TOTAL']])
+ aux = np.sum(temp['MEDIA_FINAL'] * temp['CH_TOTAL'])
ch_total = np.sum(temp['CH_TOTAL']) * 100
- return(aux/ch_total)
+ return (aux / ch_total)
+
def posicao_turmaIngresso_semestral(df):
- iras = ira_semestra(df)
- iraMax = {}
- for matr in iras:
- for semestreAno in iras[matr]:
- if not(semestreAno in iraMax):
- iraMax[semestreAno] = iras[matr][semestreAno]
- else:
- if(iras[matr][semestreAno] > iraMax[semestreAno]):
- iraMax[semestreAno] = iras[matr][semestreAno]
- for matr in iras:
- for semestreAno in iras[matr]:
- iras[matr][semestreAno]/=iraMax[semestreAno]
-
- return iras
+ iras = ira_semestra(df)
+ iraMax = {}
+ for matr in iras:
+ for semestreAno in iras[matr]:
+ if not (semestreAno in iraMax):
+ iraMax[semestreAno] = iras[matr][semestreAno]
+ else:
+ if (iras[matr][semestreAno] > iraMax[semestreAno]):
+ iraMax[semestreAno] = iras[matr][semestreAno]
+ for matr in iras:
+ for semestreAno in iras[matr]:
+ iras[matr][semestreAno] /= iraMax[semestreAno]
+
+ return iras
+
def periodo_real(df):
- aux = df.groupby(["MATR_ALUNO"])
- students = {}
- for x in aux:
- students[x[0]] = None
- return students
+ aux = df.groupby(["MATR_ALUNO"])
+ students = {}
+ for x in aux:
+ students[x[0]] = None
+ return students
+
def periodo_pretendido(df):
- aux = df.groupby(["MATR_ALUNO","ANO_INGRESSO","SEMESTRE_INGRESSO"])
- students = {}
- for x in aux:
- print(x[0][0] + " : "+x[0][1]+" "+x[0][2])
- students[x[0][0]] = (ANO_ATUAL - int(x[0][1]))*2 + SEMESTRE_ATUAL - int(x[0][2]) + 1
- return students
+ aux = df.groupby(["MATR_ALUNO", "ANO_INGRESSO", "SEMESTRE_INGRESSO"])
+ students = {}
+ for x in aux:
+ print(x[0][0] + " : " + x[0][1] + " " + x[0][2])
+ students[x[0][0]] = (ANO_ATUAL - int(x[0][1])) * 2 + SEMESTRE_ATUAL - int(x[0][2]) + 1
+ return students
+
def ira_semestra(df):
- aux = ira_por_quantidade_disciplinas(df)
- for matr in aux:
- for periodo in aux[matr]:
- aux[matr][periodo] = aux[matr][periodo][0]
- return aux
+ aux = ira_por_quantidade_disciplinas(df)
+ for matr in aux:
+ for periodo in aux[matr]:
+ aux[matr][periodo] = aux[matr][periodo][0]
+ return aux
+
def ira_por_quantidade_disciplinas(df):
- students = {}
- df = df.dropna(subset=["MEDIA_FINAL"])
- #~ print(df["MATR_ALUNO"][178])
- #~ print(df["NOME_ATIV_CURRIC"][178])
- #~ print(df["PERIODO"][178])
- #~ print(df["ANO"][178])
- #~ print(df["SITUACAO"][178])
-
- total_students = len(df["MATR_ALUNO"])
- for i in range(total_students):
- matr = (df["MATR_ALUNO"][i])
- if(not (matr in students)):
- students[matr] = {}
-
-
-
- ano = str(int(df["ANO"][i]))
- semestre = str(df["PERIODO"][i])
- situacao = int(df["SITUACAO"][i])
- nota = float(df["MEDIA_FINAL"][i])
- media_credito = int(df["MEDIA_CREDITO"][i])
-
-
- if(situacao in Situation.SITUATION_AFFECT_IRA and media_credito != 0):
-
-
-
- if not(ano+"/"+semestre in students[matr]):
- students[matr][ano+"/"+semestre] = [0,0]
- students[matr][ano+"/"+semestre][0]+=nota
- students[matr][ano+"/"+semestre][1]+=1
-
-
- for matr in students:
- for periodo in students[matr]:
- if(students[matr][periodo][1] != 0):
- students[matr][periodo][0]/=students[matr][periodo][1]*100
- return(students)
+ students = {}
+ df = df.dropna(subset=["MEDIA_FINAL"])
+ # ~ print(df["MATR_ALUNO"][178])
+ # ~ print(df["NOME_ATIV_CURRIC"][178])
+ # ~ print(df["PERIODO"][178])
+ # ~ print(df["ANO"][178])
+ # ~ print(df["SITUACAO"][178])
+
+ total_students = len(df["MATR_ALUNO"])
+ for i in range(total_students):
+ matr = (df["MATR_ALUNO"][i])
+ if (not (matr in students)):
+ students[matr] = {}
+
+ ano = str(int(df["ANO"][i]))
+ semestre = str(df["PERIODO"][i])
+ situacao = int(df["SITUACAO"][i])
+ nota = float(df["MEDIA_FINAL"][i])
+ media_credito = int(df["MEDIA_CREDITO"][i])
+
+ if (situacao in Situation.SITUATION_AFFECT_IRA and media_credito != 0):
+
+ if not (ano + "/" + semestre in students[matr]):
+ students[matr][ano + "/" + semestre] = [0, 0]
+ students[matr][ano + "/" + semestre][0] += nota
+ students[matr][ano + "/" + semestre][1] += 1
+
+ for matr in students:
+ for periodo in students[matr]:
+ if (students[matr][periodo][1] != 0):
+ students[matr][periodo][0] /= students[matr][periodo][1] * 100
+ return (students)
+
def indice_aprovacao_semestral(df):
- students = {}
- df = df.dropna(subset=['MEDIA_FINAL'])
- total_students = len(df["MATR_ALUNO"])
- for i in range(total_students):
- matr = (df["MATR_ALUNO"][i])
- if(not (matr in students)):
- students[matr] = {}
-
-
- ano = str(int(df["ANO"][i]))
- semestre = str(df["PERIODO"][i])
- situacao = int(df["SITUACAO"][i])
-
-
- if not(ano+"/"+semestre in students[matr]):
- students[matr][ano+"/"+semestre] = [0,0]
-
- if(situacao in Situation.SITUATION_PASS):
- students[matr][ano+"/"+semestre][0]+=1
- students[matr][ano+"/"+semestre][1]+=1
- if(situacao in Situation.SITUATION_FAIL):
- students[matr][ano+"/"+semestre][1]+=1
- return(students)
-
+ students = {}
+ df = df.dropna(subset=['MEDIA_FINAL'])
+ total_students = len(df["MATR_ALUNO"])
+ for i in range(total_students):
+ matr = (df["MATR_ALUNO"][i])
+ if (not (matr in students)):
+ students[matr] = {}
+
+ ano = str(int(df["ANO"][i]))
+ semestre = str(df["PERIODO"][i])
+ situacao = int(df["SITUACAO"][i])
+
+ if not (ano + "/" + semestre in students[matr]):
+ students[matr][ano + "/" + semestre] = [0, 0]
+
+ if situacao in Situation.SITUATION_PASS:
+ students[matr][ano + "/" + semestre][0] += 1
+ students[matr][ano + "/" + semestre][1] += 1
+ if situacao in Situation.SITUATION_FAIL:
+ students[matr][ano + "/" + semestre][1] += 1
+ return (students)
+
def aluno_turmas(df):
- students = {}
- df = df.dropna(subset=['MEDIA_FINAL'])
- total_students = len(df["MATR_ALUNO"])
- for i in range(total_students):
- matr = (df["MATR_ALUNO"][i])
- if(not (matr in students)):
- students[matr] = []
-
- for s in Situation.SITUATIONS:
- if(s[0] == df["SITUACAO"][i]):
- situacao = s[1]
- break
- ano = str(int(df["ANO"][i]))
- codigo = (df["COD_ATIV_CURRIC"][i])
- nome = (df["NOME_ATIV_CURRIC"][i])
- nota = (df["MEDIA_FINAL"][i])
- semestre = (df["PERIODO"][i])
-
- students[matr].append({
- "ano": ano,
- "codigo": codigo,
- "nome": nome,
- "nota": nota,
- "semestre": semestre,
- "situacao": situacao
- })
- return(students)
+ students = {}
+ df = df.dropna(subset=['MEDIA_FINAL'])
+
+ situations = dict(Situation.SITUATIONS)
+
+ for matr, hist in df.groupby('MATR_ALUNO'):
+ students[matr] = []
+
+ for _, row in hist.iterrows():
+ data = {
+ 'ano': str(int(row["ANO"])),
+ 'codigo': row["COD_ATIV_CURRIC"],
+ 'nome': row["NOME_ATIV_CURRIC"],
+ 'nota': row["MEDIA_FINAL"],
+ 'semestre': row["PERIODO"],
+ 'situacao': situations.get(row["SITUACAO"], Situation.SIT_OUTROS)
+ }
+
+ students[matr].append(data)
+
+ return students
diff --git a/script/build_cache.py b/script/build_cache.py
index 553b94cf9134907781ce481b1e456e5265de6ac0..809139090786fe111cbf34b4ee1c300c9c12ee0e 100644
--- a/script/build_cache.py
+++ b/script/build_cache.py
@@ -1,14 +1,12 @@
-import sys
-import os
-import time
-import math
-
-from datetime import timedelta
-from pathlib import Path
-from utils.utils import build_path
+
+
+from utils.utils import *
+from utils.situations import *
from analysis.degree_analysis import *
from analysis.student_analysis import *
+
+
try:
to_unicode = unicode
except NameError:
@@ -17,37 +15,95 @@ except NameError:
def build_cache(dataframe):
# os.chdir("../src")
- path = "cache"
- build_path(path)
- path += "/curso"
- build_path(path)
-
-# generate_degree_data(path, dataframe)
- generate_student_data(path,dataframe)
-# generate_student_list(path)
-# generate_admission_data(path)
-# generate_admission_list(path)
-# generate_course_data(path)
-# generate_course_general_data(path)
+ path = 'cache/curso/'
+
+ ensure_path_exists(path)
+
+ for cod, df in dataframe.groupby('COD_CURSO'):
+ generate_degree_data(path+'/'+cod+'/', df)
+
+ #generate_degree_data(path, dataframe)
+ #generate_student_data(path, dataframe)
+ #generate_student_list(path)
+ #generate_admission_data(path)
+ #generate_admission_list(path)
+ #generate_course_data(path)
+ #generate_course_general_data(path)
def generate_degree_data(path, dataframe):
- average_graduation(dataframe)
- general_failure(dataframe)
- general_ira(dataframe)
- pass
+ ensure_path_exists(path)
+ ensure_path_exists(path+'students')
+
+ students = dataframe[['MATR_ALUNO', 'FORMA_EVASAO']].drop_duplicates()
+
+ data = {
+ 'average_graduation': average_graduation(dataframe),
+ 'general_failure': general_failure(dataframe),
+ 'general_ira': general_ira(dataframe),
+ 'active_students': students[students.FORMA_EVASAO == EvasionForm.EF_ATIVO].shape[0],
+ 'graduated_students': students[students.FORMA_EVASAO == EvasionForm.EF_FORMATURA].shape[0],
+ }
+
+ save_json(path+'/degree.json', data)
+
+ for ind, hist in dataframe.groupby('MATR_ALUNO'):
+ generate_student_data(path+'students/{}.json'.format(ind), hist)
+
+
+
+def historico(dataframe):
+ res = []
+
+ for _, row in dataframe.iterrows():
+ res.append(dict(row[['ANO', 'MEDIA_FINAL', 'PERIODO', 'SITUACAO', 'COD_ATIV_CURRIC', 'NOME_ATIV_CURRIC',
+ 'CREDITOS', 'CH_TOTAL', 'DESCR_ESTRUTURA', 'FREQUENCIA']]))
+
+ return res
+
+
+def process_semestre(per, df):
+ ira = df[df.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)].MEDIA_FINAL.mean()
+ completas = df[df.SITUACAO.isin(Situation.SITUATION_PASS)].shape[0]
+ tentativas = df[df.SITUACAO.isin(Situation.SITUATION_COURSED)].shape[0]
+
+ return {
+ 'semestre': per,
+ 'ira': ira,
+ 'completas': completas,
+ 'tentativas': tentativas,
+ 'aprovacao': completas/tentativas if tentativas else 0,
+ 'ira_por_quantidade_disciplinas': ira/tentativas if tentativas else 0
+ }
+
+def generate_student_data(path, dataframe):
+ ensure_path_exists(os.path.dirname(path))
+
+ data = dict(dataframe.iloc[0][['MATR_ALUNO', 'NOME_ALUNO', 'SEXO', 'FORMA_INGRESSO', 'FORMA_EVASAO', 'ANO_INGRESSO',
+ 'SEMESTRE_INGRESSO', 'ANO_EVASAO', 'SEMESTRE_EVASAO']])
+
+ data.update({
+ 'ira': dataframe[dataframe.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)].MEDIA_FINAL.mean(),
+ 'completas': dataframe[dataframe.SITUACAO.isin(Situation.SITUATION_PASS)].shape[0],
+ 'tentativas': dataframe[dataframe.SITUACAO.isin(Situation.SITUATION_COURSED)].shape[0],
+ 'semestres': [process_semestre(per, dataframe[dataframe.PERIODO == per]) for per in sorted(dataframe.PERIODO.unique())],
+ 'historico': historico(dataframe)
+ })
+
+ save_json(path, data)
+
-def generate_student_data(path,dataframe):
- #~ print(aluno_turmas(dataframe))
- #~ print(indice_aprovacao_semestral(dataframe))
- #~ print("2007/1" in ira_por_quantidade_disciplinas(dataframe)["GRR20066955"])
- #~ print(ira_semestra(dataframe)["GRR20079775"])
- #~ aluno_turmas(dataframe)
- #~ indice_aprovacao_semestral(dataframe)
- #~ ira_por_quantidade_disciplinas(dataframe)
- #~ ira_semestra(dataframe)
- #~ periodo_pretendido(dataframe)
- #~ print(periodo_real(dataframe))
- #~ print(posicao_turmaIngresso_semestral(dataframe))
+def generate_student_data_old(path, dataframe):
+ print(aluno_turmas(dataframe))
+ print(indice_aprovacao_semestral(dataframe))
+ print("2007/1" in ira_por_quantidade_disciplinas(dataframe)["GRR20066955"])
+ print(ira_semestra(dataframe)["GRR20079775"])
+ aluno_turmas(dataframe)
+ indice_aprovacao_semestral(dataframe)
+ ira_por_quantidade_disciplinas(dataframe)
+ ira_semestra(dataframe)
+ periodo_pretendido(dataframe)
+ print(periodo_real(dataframe))
+ print(posicao_turmaIngresso_semestral(dataframe))
print(listagem_evasao(dataframe))
pass
diff --git a/script/main.py b/script/main.py
index 8e3f306ac70d8eacb870f61e43898d09700f435e..9cec7b09a78b458494d823b843b30664a92b2c40 100644
--- a/script/main.py
+++ b/script/main.py
@@ -5,6 +5,8 @@ from build_cache import build_cache
from datetime import timedelta
from analysis.degree_analysis import *
+
+
def main():
start_time = time.clock()
start_time_exec = time.time()
diff --git a/script/utils/situations.py b/script/utils/situations.py
index 80f2b175949b2a61c20ed04ceb44d4101a912c12..e62c509f8884a379b25826ec9f7a5423bfb6b838 100644
--- a/script/utils/situations.py
+++ b/script/utils/situations.py
@@ -137,3 +137,22 @@ class Situation:
SIT_REPROVADO_FREQ,
SIT_CONHECIMENTO_REPROVADO
)
+
+ """
+ isso deve ser pra filtrar fora coisas que não são disciplinas cumpridas
+
+ como "trancamento administrativo" e "horas"
+
+ importante pra saber quantas matérias um aluno REALMENTE fez em um semestre
+ """
+ SITUATION_COURSED = (
+ SIT_APROVADO,
+ SIT_REPROVADO,
+ SIT_REPROVADO_FREQ,
+ SIT_DISPENSA_COM_NOTA,
+ SIT_CONHECIMENTO_APROVADO,
+ SIT_CONHECIMENTO_REPROVADO,
+ SIT_REPROVADO_SEM_NOTA,
+ SIT_INCOMPLETO,
+ SIT_CANCELADO,
+ )
diff --git a/script/utils/utils.py b/script/utils/utils.py
index 871e935c35c6ddcec2d8c6facd6ffad779d6e1cc..f69d762eecf5bc3791b4b0f693cb7f7b2b8f0331 100644
--- a/script/utils/utils.py
+++ b/script/utils/utils.py
@@ -1,5 +1,35 @@
import os
+import json
+
+try:
+ from django.conf import settings
+
+ DEBUG = settings.DEBUG
+except:
+ DEBUG = True
+
+
def build_path(path):
if not os.path.exists(path):
os.mkdir(path)
+
+
+def ensure_path_exists(complete_path):
+ parts = complete_path.split('/')
+
+ for i in range(len(parts)):
+ if not os.path.exists('/'.join(parts[:i+1])):
+ os.mkdir('/'.join(parts[:i+1]))
+
+
+def save_json(path, data):
+
+ ensure_path_exists(os.path.dirname(path))
+
+ params = {} if not DEBUG else {'indent': 4}
+
+ with open(path, 'w') as f:
+ json.dump(data, f, **params)
+
+