Merge branch '175-revisar-analises' into 'development'

Update "Revisar análises"

See merge request !85

src/adega/static/adega/js/adega-chart.js

@@ -18,6 +18,7 @@ class AdegaChart{
         this.xaxis_title = config.xaxis_title || "";
         
         this.mode = config.mode;
+        this.text = config.text;
         
         this.marker = config.marker;
 
@@ -120,11 +121,12 @@ class AdegaChart{
                 }
             }
             if(this.mode && this.mode[i]){
-                data[i].mode = this.mode[i];
+                data[i].text = this.text[i];
             }
             if(this.marker != undefined){
                 data[i].marker = this.marker;
             }
+            data[i].connectgaps = true;
         }
 
         var layout = {

src/adega/templatetags/adega_templatetag.py

@@ -4,12 +4,18 @@ register = template.Library()
 
 @register.filter
 def to_percent(value):
-    return "{:.2f}".format(float(value)*100) + "%"
-
+    if type(value) == int or type(value) == float:
+        return "{:.2f}".format(float(value)*100) + "%"
+    
+    return ""
 @register.filter
 def fix_2digit(value):
-    return "{:.2f}".format(float(value))
+    if type(value) == int or type(value) == float:
+        return "{:.2f}".format(float(value))
+    return ""
 
 @register.filter
 def remove_spaces(value):
-    return value.replace(' ', '')
\ No newline at end of file
+    if type(value) == str:
+        return value.replace(' ', '')
+    return value
\ No newline at end of file

src/course/templates/course/compare.html

@@ -64,12 +64,13 @@
 
     <div class="row">
         <div class="col-12">
-            <div id="compara_aprov"></div>
+            <div id="approvation_rate"></div>
         </div>
     </div>
+
     <div class="row">
         <div class="col-12">
-            <div id="approvation_rate"></div>
+            <div id="compara_aprov"></div>
         </div>
     </div>
     
@@ -154,6 +155,15 @@
         var approvation_rate_y2 = approvation_rate_2[1].map(function(x){
             return x[0];
         });
+        
+        
+        var approvation_rate_text1 = approvation_rate_1[1].map(function(x){
+            // Quantity of student that register on course on each semester
+            return "<br>" + x[1] + " Matrículas";
+        });
+        var approvation_rate_text2 = approvation_rate_2[1].map(function(x){
+            return "<br>" + x[1] + " Matrículas";
+        });
 
         var chart2 = new AdegaChart({
             data_x: approvation_rate_x,
@@ -162,6 +172,7 @@
             title: "Taxa de aprovação por semestre",
             fill: "none",
             type: ["scatter", "scatter"],
+            text: [approvation_rate_text1, approvation_rate_text2],
             mode: ["lines+markers", "lines+markers"],
             legend: [codeCourse1,codeCourse2],
             xaxis_title: "Período",
@@ -169,7 +180,7 @@
             marker: {
                 opacity:0.6,
                 size: 13
-            }
+            },
         });
     }
 

src/course/views.py

@@ -74,7 +74,10 @@ def compare(request, submission_id):
     
     chart_approvation_rate = {}
     for course_name in courses_list:
-        course_detail = get_course_detail(request.session, degree, course_name, submission_id)
+        course_detail = get_course_detail(request.session,
+                                          degree, course_name,
+                                          submission_id)
+        
         chart_approvation_rate[course_name] = course_detail["aprovacao_semestral"]
     
     charts["approvation_rate"] = chart_approvation_rate

src/degree/templates/degree/index.html

@@ -165,9 +165,9 @@
       title: "Distribuição de alunos por faixas de IRA",
       fill: "none",
       legend: [
-          "Quantidade total de alunos",
-          "Quantidade total de alunos sem evasão",
-          "Quantidade total de alunos formados"
+          "Total de alunos",
+          "Alunos ativos",
+          "Alunos formados"
       ],
       xaxis_title: "Faixa de IRA",
       yaxis_title: "Quantidade de alunos",

src/student/templates/student/detail.html

@@ -104,8 +104,9 @@
                             <span class="data">{{ analysis_result.indice_aprovacao|to_percent }}</span>
                         </td>
                     </tr>
-        
-                    <tr>
+                    
+                    <!-- This analysis was not implemented yet -->
+                    <!-- <tr>
                         <td>Período real</td>
                         <td>
                             {% if analysis_result.periodo_real %}
@@ -116,7 +117,7 @@
                                 <span class="data">Desconhecido</span>
                             {% endif %}
                         </td>
-                    </tr>
+                    </tr> -->
         
                     <tr>
                         <td>Período pretendido</td>

src/submission/analysis/analysis/admission_analysis.py

@@ -8,8 +8,6 @@ from collections import defaultdict
 
 import numpy as np
 
-ANO_ATUAL = 2017
-SEMESTRE_ATUAL = 2
 
 
 class Admission(object):
@@ -110,10 +108,12 @@ class Admission(object):
         Sao dois .apply, um dentro do outro, sendo que um intera sobre a turma
         ingresso e ai faz o agrupamento de alunos e o outro intera sobre os
         alunos da turma ingresso para calcular o ira. """
+        
         ira_medio = submission_groupby.apply(lambda x:\
             x.groupby(["MATR_ALUNO"]).apply(lambda y:\
             (y.MEDIA_FINAL * y.TOTAL_CARGA_HORARIA).sum() /\
             (y.TOTAL_CARGA_HORARIA.sum()*100)).mean())
+
         self.analysis["ira_medio"] = ira_medio
 
     def admission_list(self):
@@ -136,6 +136,13 @@ class Admission(object):
             # This will create an directory when build_cache create the json
             # By instance: The files and directories admission/2010/1.json will
             # be created
+            
+            # The ira_medio can be undefined for some admissions
+            # Then, we need to verify if it was computed 
+            ira_medio = 0
+            if i in self.analysis["ira_medio"].keys():
+                ira_medio = self.analysis["ira_medio"][i]
+
             admission_dict["ano"] = i[0]
             admission_dict["semestre"] = i[1]
             admission_dict["abandono"] = int(self.analysis["qtd_abandono"][i])
@@ -145,7 +152,7 @@ class Admission(object):
             admission_dict["outras_formas_evasao"] = int(self.analysis["outras_formas_evasao"][i])
             admission_dict["formatura_media"] = float(formatura_medio[i]) if i in formatura_medio.index else -1
             admission_dict["quantidade_alunos"] =  int(self.analysis["qtd_alunos_ingresso"][i])
-            admission_dict["ira_medio"] = float(self.analysis["ira_medio"][i])
+            admission_dict["ira_medio"] = float(ira_medio)
             admission_dict["taxa_evasao"] = float(self.analysis["taxa_evasao"][i])
             admission_dict["taxa_reprovacao"] = float(self.analysis["taxa_reprovacao"][i])
             admissions.append(admission_dict)

src/submission/analysis/analysis/course_analysis.py

@@ -50,7 +50,7 @@ class Course(Analysis):
             "SITUACAO",
             [sit.SIT_CONHECIMENTO_APROVADO],
             list(sit.SITUATION_KNOWLDGE),
-            1
+            2
         ),
         rate(
             "taxa_reprovacao_frequencia",
@@ -69,7 +69,8 @@ class Course(Analysis):
     __semestral_rate = [__rates[1]]
     last_rate = [__rates[0], __rates[4]]
 
-    def __init__(self, df):
+    def __init__(self, current_year, df):
+        self.current_year = current_year
         df_filted = df[df['SITUACAO'].isin(sit.SITUATION_COURSED)]
         dict_df = {
             "normal_dataframe": df,
@@ -138,7 +139,7 @@ class Course(Analysis):
         last_rates = self.last_rate
 
         def f(x, rate):
-            x1 = x.loc[x.ANO == x.ANO.max()]
+            x1 = x.loc[x.ANO == x.ANO.max() - 1]
             x_num = x1[x1[rate.collumn_name].isin(rate.fields_x)].shape[0]
             x_deno = x1[x1[rate.collumn_name].isin(rate.fields_X)].shape[0]
             return x_num / x_deno if x_deno > 0 else 0
@@ -199,9 +200,9 @@ class Course(Analysis):
         serie_mean = group.apply(lambda x: x["MEDIA_FINAL"].mean())
         serie_std = group.apply(lambda x: x["MEDIA_FINAL"].std())
         last_year_mean = group.apply(
-            lambda x: x.loc[x.ANO == x.ANO.max()].MEDIA_FINAL.mean())
+            lambda x: x.loc[x.ANO == x.ANO.max() - 1].MEDIA_FINAL.mean())
         last_year_std = group.apply(
-            lambda x: x.loc[x.ANO == x.ANO.max()].MEDIA_FINAL.std())
+            lambda x: x.loc[x.ANO == x.ANO.max() - 1].MEDIA_FINAL.std())
         # caso tenha algum nan, troque por 0.0
         serie_mean[np.isnan(serie_mean)] = 0.0
         serie_std[np.isnan(serie_std)] = 0.0

src/submission/analysis/analysis/degree_analysis.py

 import math
 import json
 import pandas as pd
+import numpy as np
 from submission.analysis.utils.situations import Situation, EvasionForm
+from submission.analysis.utils.utils import IntervalCount, save_json
+from submission.analysis.analysis.student_analysis import *
 
 
-def average_graduation(df):
-    total_student = df['MATR_ALUNO'].drop_duplicates().shape[0]
-    total_graduate = df[df.FORMA_EVASAO == EvasionForm.EF_FORMATURA].shape[0]
 
+def average_graduation(df):
+    """
+    Calculates the ratio of students who have already graduated
+    to number of students on the original dataframe.
+
+    Returns
+    -------
+    float
+
+    Examples
+    --------
+    13.395865237366003
+    """
+    students = df['MATR_ALUNO'].drop_duplicates()
+    total_student = students.shape[0]
+    total_graduate = students.loc[df.FORMA_EVASAO == EvasionForm.EF_FORMATURA].shape[0]
     return total_graduate / total_student
 
 
 def general_failure(df):
+    """
+
+    Returns
+    -------
+
+    Examples
+    --------
+    """
     affect_ira = df[df.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)]
     failures = affect_ira[affect_ira.SITUACAO.isin(Situation.SITUATION_FAIL)]
 
+    # average = reprovados/
     average = failures.shape[0] / affect_ira.shape[0]
 
     student_courses = affect_ira.groupby(['MATR_ALUNO'], as_index=False)\
@@ -49,23 +74,23 @@ def current_students_failure(df):
     standard_deviation = math.sqrt(variance)
     return (average, standard_deviation)
 
-def general_ira(df):
-    fixed = df[df.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)]
-    fixed = fixed[fixed.MEDIA_FINAL <= 100]
-    return (fixed.MEDIA_FINAL.mean(), fixed.MEDIA_FINAL.std())
+def general_ira(student_analysis):
+    iras = np.array(list(student_analysis.ira_alunos().values()))
+
+    return (iras.mean(), iras.std())
 
-def current_ira(df):
+def current_ira(df, student_analysis):
     ano_grade = int(df.loc[df['NUM_VERSAO'].idxmax()]['NUM_VERSAO'])
     fixed = df.loc[(df['NUM_VERSAO'] == ano_grade)]
-    fixed = fixed[fixed.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)]
-    fixed = fixed[fixed.MEDIA_FINAL <= 100]
-    return (fixed.MEDIA_FINAL.mean(), fixed.MEDIA_FINAL.std())
+    iras = np.array(list(student_analysis.ira_alunos(df = fixed).values()))
+ 
+    return (iras.mean(), iras.std())
 
-def current_students_ira(df):
+def current_students_ira(df, student_analysis):
     fixed = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_ATIVO)]
-    fixed = fixed[fixed.SITUACAO.isin(Situation.SITUATION_AFFECT_IRA)]
-    fixed = fixed[fixed.MEDIA_FINAL <= 100]
-    return (fixed.MEDIA_FINAL.mean(), fixed.MEDIA_FINAL.std())
+    iras = np.array(list(student_analysis.ira_alunos(df = fixed).values()))
+
+    return (iras.mean(), iras.std())
 
 def general_evasion_rate(df):
     students = df['MATR_ALUNO'].drop_duplicates()
@@ -84,23 +109,35 @@ def current_evasion_rate(df):
     return total_evasion / total_student
 
 def average_graduation_time(df):
-    graduates = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_FORMATURA)]
+    """
+   
+   
+    Returns
+    -------
+    float
+
+    Examples
+    --------
+    5.3741640468705345 (years?)
+    """
+    students = df.drop_duplicates('MATR_ALUNO')
+    graduates = students.loc[(df.FORMA_EVASAO == EvasionForm.EF_FORMATURA)]
     total_graduate = graduates.shape[0]
     average_time = 0
     year_end = int(df['ANO'].max())
     for index, row in graduates.iterrows():
         if pd.notnull(row['ANO_EVASAO']):
             year_end = int(row['ANO_EVASAO'])
-            try: 
-                semester_end = int(row['SEMESTRE_EVASAO'])
-            except ValueError:
+            try:
+                evasion_dt = int(row["DT_EVASAO"].split("/")[1])
+                if(evasion_dt > 7):
+                    semester_end = 2
+                else:
+                    semester_end = 1
+            except (ValueError, AttributeError):
                 try:
-                    evasion_dt = int(row["DT_EVASAO"].split("/")[1])
-                    if(evasion_dt > 7):
-                        semester_end = 2
-                    else:
-                        semester_end = 1
-                except ValueError:
+                    semester_end = int(row['SEMESTRE_EVASAO'])
+                except (ValueError, AttributeError):
                     # TODO: Some students will be not considered
                     # The interface must inform the user this information
                     # and how many students wasnt considered
@@ -112,7 +149,6 @@ def average_graduation_time(df):
         average_time += difference
     average_time /= total_graduate
     average_time /= 2
-
     return average_time
 
 def total_students(df):
@@ -125,37 +161,35 @@ def taxa_abandono(df):
     students = df['MATR_ALUNO'].drop_duplicates()
     total_student = students.shape[0]
     total_abandono = students.loc[(df.FORMA_EVASAO == EvasionForm.EF_ABANDONO)].shape[0]
-
     return total_abandono / total_student
 
-def average_ira_graph(df):
-    alunos = df.drop_duplicates('MATR_ALUNO')
 
-    dic = build_dict_ira_medio(alunos)
 
+#The following 3 functions are auxiliar to make the 3 dicts the function merge_dicts receives 
+def average_ira_graph(student_analysis):
+    dic = build_dict_ira_medio(student_analysis.ira_alunos())
     return dic
 
-def current_students_average_ira_graph(df):
-    alunos_se = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_ATIVO)]
-    alunos_se = alunos_se.drop_duplicates('MATR_ALUNO')
-
-    dic_se = build_dict_ira_medio(alunos_se)
-
+def current_students_average_ira_graph(df, student_analysis):
+    alunos_se = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_ATIVO)]    
+    dic_se = build_dict_ira_medio(student_analysis.ira_alunos(df = alunos_se))
     return dic_se
 
-def graduates_average_ira_graph(df):
+def graduates_average_ira_graph(df, student_analysis):
     alunos_for = df.loc[(df.FORMA_EVASAO == EvasionForm.EF_FORMATURA)]
-    alunos_for = alunos_for.drop_duplicates('MATR_ALUNO')
 
-    dic_for = build_dict_ira_medio(alunos_for)
+    dic_for = build_dict_ira_medio(student_analysis.ira_alunos(df = alunos_for))
 
     return dic_for
 
+
+
 def period_evasion_graph(df):
+ 
     di_qtd = {}
     dic = {}
     evasions_total = 0
-    
+
     # Discover the minimum and maximum values for year
     year_start = int(df['ANO'].min())
     year_end = int(df['ANO'].max()) + 1
@@ -166,7 +200,7 @@ def period_evasion_graph(df):
     # Iterate between all semester/year possible
     for year in range(year_start, year_end):
         for semester in range(1, 3):
-
+            
             # Filter the rows and mantain only the registers
             # that match with year and semester of this iteration
             evasions = students.loc[
@@ -179,18 +213,18 @@ def period_evasion_graph(df):
             # and keeping the first row founded
             # Than, get the number of rows computed
             evasions = evasions.drop_duplicates(
-                subset="MATR_ALUNO",
+                subset= "MATR_ALUNO",
                 keep='first'
             ).shape[0]
 
             # Name of string on dictionary generated
             date = str(year) + ' {}º Período'.format(semester)
             di_qtd[date] = evasions
-            
+
             # Count the total of evasions identified, that will be
             # used to compute the rate
             evasions_total += evasions
-    
+
     # If at least one evasion was computed
     if evasions_total:
         # Compute the ratio of evasion per
@@ -198,55 +232,93 @@ def period_evasion_graph(df):
         for di in di_qtd:
             qtd = di_qtd[di]
             dic[di] = {'qtd': qtd, 'taxa': (float(qtd)/evasions_total)*100}
-    
-    return dic
-
-def build_dict_ira_medio(alunos):
-    dic = {"00-4.9":0, "05-9.9":0, "10-14.9":0, "15-19.9":0, "20-24.9":0, "25-29.9":0, "30-34.9":0,
-           "35-39.9":0, "40-44.9":0, "45-49.9":0, "50-54.9":0, "55-59.9":0, "60-64.9":0, "65-69.9":0,
-           "70-74.9":0, "75-79.9":0, "80-84.9":0, "85-89.9":0, "90-94.9": 0,"95-100":0}
-
-    iras = []
-    for index, row in alunos.iterrows():
-        if(row['MEDIA_FINAL'] is not None):
-            iras.append(row['MEDIA_FINAL'])
-
-    for d in dic:
-        aux = d.split('-')
-        v1 = float(aux[0])
-        if (v1 == 0.0):
-            v1 += 0.01
-        v2 = float(aux[1])
-        dic[d] = sum((float(num) >= v1) and (float(num) < v2) for num in iras)
 
     return dic
 
-def build_degree_json(path,df):
-    
-    def merge_dicts(dict1, dict2, dict3):
-        dict_out = {}
-        for key, value in dict1.items():
-            v2 = dict2[key] if key in dict2 else None
-            v3 = dict3[key] if key in dict3 else None
-            dict_out[key] = {
-                'ira_medio': value,
-                'sem_evasao': v2,
-                'formatura': v3
-            }
-        
-        return dict_out
 
+def build_dict_ira_medio(iras):
+    """
+    Uses numpy.histogram to create the intervals of iras (dict's keys)
+    and counts how many iras on each interval (dict's values)     
+
+    Parameters
+    -------
+    iras = {grr: ira, 
+            grr: ira,
+            ...}
+
+    Returns
+    -------
+    dict = {'0.50-0.55': 91, 
+            '0.55-0.60': 98,   
+            '0.05-0.10': 38, 
+            '0.65-0.70': 90, 
+            ... }
+    """
+    iras_values = list(iras.values())
+
+    # keys = ira intervals borders
+    # values = quantity of students in the interval 
+    values, keys = np.histogram(iras_values, bins=20, range=(0,1))
+    dict = {}
+    for i, count in enumerate(values):
+        inf = keys[i]
+        sup = keys[i+1]
+        convert_key = "{:.2f}".format(inf) + "-" + "{:.2f}".format(sup)
+        dict[convert_key] = int(count)
+    return dict
+
+
+def merge_dicts(dict1, dict2, dict3):
+    """
+    Makes a single dict for the STUDENTS per IRA graph.
+
+    Takes 3 dictionaries whose keys are IRA intervals and merge them.
+    Each IRA interval got as value another dictionary with 3 itens:
+    number of all students with that IRA range;
+    number of active students with that IRA range;
+    number of graduated students with that IRA range;
+
+    Parameters
+    ----------
+    3 x dicts = {'0.50-0.55': 91, 
+                 '0.55-0.60': 98,
+                 ...}
+
+    Returns
+    -------
+        {'05-9.9': {'sem_evasao': 9,
+                    'formatura': 3,
+                    'ira_medio': 43},
+        '10-14.9': {'sem_evasao': 12,
+                    'formatura': 7,
+                    'ira_medio': 37},
+        ...}
+    """
+    dict_out = {}
+    for key, value in dict1.items():
+        v2 = dict2[key] if key in dict2 else None
+        v3 = dict3[key] if key in dict3 else None
+        dict_out[key] = {
+            'ira_medio': value,
+            'sem_evasao': v2,
+            'formatura': v3
+        }
+    return dict_out
+
+
+def build_degree_json(path,df,student_analysis):
     dic = merge_dicts(
-        average_ira_graph(df),
-        current_students_average_ira_graph(df),
-        graduates_average_ira_graph(df)
+        average_ira_graph(student_analysis),
+        current_students_average_ira_graph(df, student_analysis),
+        graduates_average_ira_graph(df, student_analysis)
     )
-
+      
     degree_json = {
-        "ira_medio_grafico": json.dumps(sorted(dic.items())),
+        "ira_medio_grafico": sorted(dic.items()),
         "evasao_grafico": json.dumps(sorted(period_evasion_graph(df).items())),
-        "ira_atual": current_students_ira(df),
-        "ira_medio": general_ira(df),
+        "ira_atual": current_students_ira(df, student_analysis),
+        "ira_medio": general_ira(student_analysis),
         "qtd_alunos": total_students(df),
         "qtd_alunos_atuais": current_total_students(df),
         "taxa_evasao": general_evasion_rate(df),
@@ -255,5 +327,8 @@ def build_degree_json(path,df):
         "taxa_reprovacao_atual": current_students_failure(df),
         "tempo_formatura": average_graduation_time(df),
     }
-    with open(path+"/degree.json",'w') as f:
-        f.write(json.dumps(degree_json,indent=4))
+
+    save_json(path+"/degree.json", degree_json)
+
+    # with open(path+"/degree.json",'w') as f:
+    #     f.write(json.dumps(degree_json,indent=4))

src/submission/analysis/analysis/student_analysis.py

 import numpy as np