the big format

app/lib/irt/item_information_function.py

@@ -3,22 +3,28 @@ import logging
 from lib.irt.models.three_parameter_logistic import ThreeParameterLogistic
 from lib.errors.item_generation_error import ItemGenerationError
 
-class ItemInformationFunction():
-  def __init__(self, irt_model):
-    self.model_data = irt_model
 
-  # determines the amount of information for a given question at a given theta (ability level)
-  # further detailed on page 161, equation 4 here:
-  #  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978482/pdf/10.1177_0146621615613308.pdf
-  def calculate(self, **kwargs):
-    try:
-      if self.model_data.model == '3PL':
-        p = ThreeParameterLogistic(self.model_data, kwargs).result()
-        q = 1 - p
-        return (self.model_data.a_param * q * (p - self.model_data.c_param)**2) / (p * ((1 - self.model_data.c_param)**2))
-      else:
-        # potentially error out
-        raise ItemGenerationError("irt model not supported or provided")
-    except ZeroDivisionError as error:
-      logging.error(error)
-      raise ItemGenerationError("params not well formatted", error.args[0])
+class ItemInformationFunction():
+
+    def __init__(self, irt_model):
+        self.model_data = irt_model
+
+    # determines the amount of information for a given question at a given theta (ability level)
+    # further detailed on page 161, equation 4 here:
+    #  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978482/pdf/10.1177_0146621615613308.pdf
+    def calculate(self, **kwargs):
+        try:
+            if self.model_data.model == '3PL':
+                p = ThreeParameterLogistic(self.model_data, kwargs).result()
+                q = 1 - p
+                return (self.model_data.a_param * q *
+                        (p - self.model_data.c_param)**2) / (p * (
+                            (1 - self.model_data.c_param)**2))
+            else:
+                # potentially error out
+                raise ItemGenerationError(
+                    "irt model not supported or provided")
+        except ZeroDivisionError as error:
+            logging.error(error)
+            raise ItemGenerationError("params not well formatted",
+                                      error.args[0])

app/lib/irt/item_response_function.py

@@ -1,12 +1,14 @@
 from lib.irt.models.three_parameter_logistic import ThreeParameterLogistic
 from lib.errors.item_generation_error import ItemGenerationError
 
-class ItemResponseFunction():
-  def __init__(self, irt_model):
-    self.model_data = irt_model
 
-  def calculate(self, **kwargs):
-    if self.model_data.model == '3PL':
-      return ThreeParameterLogistic(self.model_data, kwargs).result()
-    else:
-      raise ItemGenerationError("irt model not supported or provided")
+class ItemResponseFunction():
+
+    def __init__(self, irt_model):
+        self.model_data = irt_model
+
+    def calculate(self, **kwargs):
+        if self.model_data.model == '3PL':
+            return ThreeParameterLogistic(self.model_data, kwargs).result()
+        else:
+            raise ItemGenerationError("irt model not supported or provided")

app/lib/irt/models/three_parameter_logistic.py

@@ -1,16 +1,18 @@
 class ThreeParameterLogistic:
-  def __init__(self, model_params, kwargs):
-    self.model_params = model_params
-    # check if exists, if not error out
-    self.b_param = kwargs['b_param']
-    self.e = 2.71828
-    self.theta = kwargs['theta']
 
-  # contains the primary 3pl function, determining the probably of an inidividual
-  # that an individual at a certain theta would get a particular question correct
-  #  detailed further on page 161, equation 1 here:
-  # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978482/pdf/10.1177_0146621615613308.pdf
-  def result(self):
-    a = self.model_params.a_param
-    c = self.model_params.c_param
-    return c + (1 - c) * (1 / (1 + self.e**(-a * (self.theta - self.b_param))))
+    def __init__(self, model_params, kwargs):
+        self.model_params = model_params
+        # check if exists, if not error out
+        self.b_param = kwargs['b_param']
+        self.e = 2.71828
+        self.theta = kwargs['theta']
+
+    # contains the primary 3pl function, determining the probably of an inidividual
+    # that an individual at a certain theta would get a particular question correct
+    #  detailed further on page 161, equation 1 here:
+    # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978482/pdf/10.1177_0146621615613308.pdf
+    def result(self):
+        a = self.model_params.a_param
+        c = self.model_params.c_param
+        return c + (1 - c) * (1 / (1 + self.e**(-a *
+                                                (self.theta - self.b_param))))

app/lib/irt/test_information_function.py

@@ -1,19 +1,22 @@
 from lib.irt.item_information_function import ItemInformationFunction
 
+
 class TestInformationFunction():
-  def __init__(self, irt_model):
-    self.irt_model = irt_model
-    self.iif = ItemInformationFunction(irt_model)
 
-  # determins the amount of information
-  # at a certain theta (ability level) of the sum of a question set correct
-  # detailed further on page 166, equation 4 here:
-  # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978482/pdf/10.1177_0146621615613308.pdf
-  def calculate(self, items, **kwargs):
-    sum = 0
+    def __init__(self, irt_model):
+        self.irt_model = irt_model
+        self.iif = ItemInformationFunction(irt_model)
 
-    for item in items:
-      result = self.iif.calculate(b_param=item.b_param, theta=kwargs['theta'])
-      sum += result
+    # determins the amount of information
+    # at a certain theta (ability level) of the sum of a question set correct
+    # detailed further on page 166, equation 4 here:
+    # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978482/pdf/10.1177_0146621615613308.pdf
+    def calculate(self, items, **kwargs):
+        sum = 0
 
-    return sum
+        for item in items:
+            result = self.iif.calculate(b_param=item.b_param,
+                                        theta=kwargs['theta'])
+            sum += result
+
+        return sum

app/lib/irt/test_response_function.py

@@ -1,20 +1,23 @@
 from lib.irt.item_response_function import ItemResponseFunction
 
+
 # otherwise known as the Test Characteristic Curve (TCC)
 class TestResponseFunction():
-  def __init__(self, irt_model):
-    self.irt_model = irt_model
-    self.irf = ItemResponseFunction(irt_model)
 
-  # determins the probably of an inidividual
-  # at a certain theta (ability level) would get a sum of questions correct
-  # detailed further on page 166, equation 3 here:
-  # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978482/pdf/10.1177_0146621615613308.pdf
-  def calculate(self, items, **kwargs):
-    sum = 0
+    def __init__(self, irt_model):
+        self.irt_model = irt_model
+        self.irf = ItemResponseFunction(irt_model)
 
-    for item in items:
-      result = self.irf.calculate(b_param=item.b_param, theta=kwargs['theta'])
-      sum += result
+    # determins the probably of an inidividual
+    # at a certain theta (ability level) would get a sum of questions correct
+    # detailed further on page 166, equation 3 here:
+    # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5978482/pdf/10.1177_0146621615613308.pdf
+    def calculate(self, items, **kwargs):
+        sum = 0
 
-    return sum
+        for item in items:
+            result = self.irf.calculate(b_param=item.b_param,
+                                        theta=kwargs['theta'])
+            sum += result
+
+        return sum