def gamma(*args):
if len(args) != 3:
raise my_exceptions.ArgumentoInvalido()
k = args[0]
nu = args[1]
o_instance = args[2]
if isinstance(k, str):
if k in dir(o_instance):
k = getattr(o_instance, k)
else:
raise my_exceptions.ReferenciaInvalida()
if isinstance(nu, str):
if nu in dir(o_instance):
nu = getattr(o_instance, nu)
else:
raise my_exceptions.ReferenciaInvalida()
if False in [is_number(arg) for arg in args[0:-1]]:
raise my_exceptions.ErrorDeTipo()
def gamma_x(x, k=k, nu=nu):
if x < 0:
raise my_exceptions.ErrorMatematico()
else:
if k < 1.0:
raise my_exceptions.ErrorMatematico()
if type(k) != int:
raise my_exceptions.ImposibleProcesar()
f_x = nu**k * x**(k - 1) * e**(-nu * x) / factorial(k - 1)
return f_x
return gamma_x
def normal(*args):
if len(args) != 3:
raise my_exceptions.ArgumentoInvalido()
mu = args[0]
sigma = args[1]
o_instance = args[2]
if isinstance(mu, str):
if mu in dir(o_instance):
mu = getattr(o_instance, mu)
else:
raise my_exceptions.ReferenciaInvalida()
if isinstance(sigma, str):
if sigma in dir(o_instance):
sigma = getattr(o_instance, sigma)
else:
raise my_exceptions.ReferenciaInvalida()
if False in [is_number(arg) for arg in args[0:-1]]:
raise my_exceptions.ErrorDeTipo()
def normal_x(x, mu=mu, sigma=sigma):
if not is_number(x):
raise my_exceptions.ErrorDeTipo()
if sigma == 0:
raise my_exceptions.ErrorMatematico()
f_x = (1 / sqrt(2 * pi * sigma**2)) * e**((-1 / 2) *
((x - mu) / sigma)**2)
return f_x
return normal_x
def normal_x(x, mu=mu, sigma=sigma):
if not is_number(x):
raise my_exceptions.ErrorDeTipo()
if sigma == 0:
raise my_exceptions.ErrorMatematico()
f_x = (1 / sqrt(2 * pi * sigma**2)) * e**((-1 / 2) *
((x - mu) / sigma)**2)
return f_x
def LEN(column, o_instance):
if isinstance(column, str):
if column in dir(o_instance):
column = getattr(o_instance, column)
else:
raise my_exceptions.ReferenciaInvalida()
if not is_iterable(column):
raise my_exceptions.ErrorDeTipo()
return len(list(column))
def evaluar(function, start, end, step, o_instance):
if isinstance(function, str):
if function in dir(o_instance):
function = getattr(o_instance, function)
else:
raise my_exceptions.ReferenciaInvalida()
if type(function).__name__ != 'function':
raise my_exceptions.ErrorDeTipo()
for i in (function(x) for x in float_range(start, end, step)):
yield i
def comparar(num_1, symbol, num_2, o_instance):
if isinstance(num_1, str):
if num_1 in dir(o_instance):
num_1 = getattr(o_instance, num_1)
else:
raise my_exceptions.ReferenciaInvalida()
if isinstance(num_2, str):
if num_2 in dir(o_instance):
num_2 = getattr(o_instance, num_2)
else:
raise my_exceptions.ReferenciaInvalida()
if not is_number(num_1):
raise my_exceptions.ErrorDeTipo()
if not is_number(num_2):
raise my_exceptions.ErrorDeTipo()
if not isinstance(symbol, str):
raise my_exceptions.ErrorDeTipo()
return to_filter(symbol, num_2)(num_1)
def operar(column, symbol, value, o_instance):
if isinstance(column, str):
if column in dir(o_instance):
column = getattr(o_instance, column)
else:
raise my_exceptions.ReferenciaInvalida()
if isinstance(value, str):
if value in dir(o_instance):
value = getattr(o_instance, value)
else:
raise my_exceptions.ReferenciaInvalida()
if not is_iterable(column):
raise my_exceptions.ImposibleProcesar()
if not isinstance(symbol, str):
raise my_exceptions.ErrorDeTipo()
if not str(value).isnumeric():
raise my_exceptions.ErrorDeTipo()
for i in map(to_operation(symbol, value), column):
yield i
def generate_distribution(model_name, *args):
available_models = {
'NORMAL': normal,
'GAMMA': gamma,
'EXPONENTIAL': exponential
}
if model_name in available_models:
function = available_models[model_name](*args)
return function
else:
raise my_exceptions.ErrorDeTipo()
def DESV(column, o_instance):
if isinstance(column, str):
if column in dir(o_instance):
column = getattr(o_instance, column)
else:
raise my_exceptions.ReferenciaInvalida()
if not is_iterable(column):
raise my_exceptions.ErrorDeTipo()
l_col = list(column)
avg = PROM(l_col, o_instance)
sigma = sqrt(sum((i - avg)**2 for i in l_col) / len(l_col))
return sigma
def PROM(column, o_instance):
if isinstance(column, str):
if column in dir(o_instance):
column = getattr(o_instance, column)
else:
raise my_exceptions.ReferenciaInvalida()
if not is_iterable(column):
raise my_exceptions.ErrorDeTipo()
enum = [(i, v) for i, v in enumerate(column)]
n = len(enum)
if n == 0:
raise my_exceptions.ErrorMatematico()
Sum = sum(j for i, j in enum)
return Sum / n
def graficar(column, option, o_instance):
if isinstance(column, str):
if column in dir(o_instance):
column = getattr(o_instance, column)
else:
raise my_exceptions.ReferenciaInvalida()
if not is_iterable(column):
raise my_exceptions.ImposibleProcesar()
if not isinstance(option, str):
raise my_exceptions.ImposibleProcesar()
available_options = ['numerico', 'normalizado']
y = np.array(list(column))
if len(y) == 0:
raise my_exceptions.ErrorDeTipo()
if option in available_options:
if option == 'numerico':
x = np.array([i for i in range(0, len(y))])
elif option == 'normalizado':
suma = sum(y)
x = np.array([i for i in range(0, len(y))]) / suma
elif 'rango' in option:
my_range = to_range(option)
x = np.array([i for i in my_range])
if len(x) != len(y):
raise my_exceptions.ImposibleProcesar()
elif isinstance(option, str):
if option in dir(o_instance):
option = getattr(o_instance, option)
if is_iterable(option):
x = np.array(list(option))
if len(x) != len(y):
raise my_exceptions.ImposibleProcesar()
else:
raise my_exceptions.ImposibleProcesar()
elif is_iterable(option):
x = np.array(list(option))
if len(x) != len(y):
raise my_exceptions.ImposibleProcesar()
else:
raise my_exceptions.ImposibleProcesar()
plt.plot(x, y)
plt.xlabel('X')
plt.ylabel('Y')
plt.show()
return 'Graficando'
def get_column(file_name, column_name, o_instance):
if not isinstance(file_name, str):
raise my_exceptions.ErrorDeTipo()
if not isinstance(column_name, str):
raise my_exceptions.ErrorDeTipo()
if not isfile(file_name + '.csv'):
raise my_exceptions.ReferenciaInvalida()
with open(file_name + '.csv') as f:
reader = my_csv.reader(f, delimiter=';')
header = next(reader)
header_wo_types = [i.split(':')[0] for i in header]
df = {header_wo_types[i]: i for i in range(len(header))}
if column_name not in df:
raise my_exceptions.ReferenciaInvalida()
for row in reader:
v = row[df[column_name]]
if not is_number(v):
if v in dir(o_instance):
v = getattr(o_instance, v)
else:
raise my_exceptions.ReferenciaInvalida()
yield float(v)
def to_operation(symbol, value):
value = float(value)
available_symbols = ['*', '/', '+', '-', '>=<']
if symbol not in available_symbols:
raise my_exceptions.ErrorDeTipo()
else:
if symbol == '*':
return lambda x: x * value
elif symbol == '/':
return lambda x: x / value
elif symbol == '+':
return lambda x: x + value
elif symbol == '-':
return lambda x: x - value
elif symbol == '>=<':
return lambda x: round(x, value)
def to_filter(symbol, value):
value = float(value)
available_symbols = ['==', '>', '<', '>=', '<=', '!=']
if symbol not in available_symbols:
raise my_exceptions.ErrorDeTipo()
else:
if symbol == '==':
return lambda x: x == value
elif symbol == '>':
return lambda x: x > value
elif symbol == '<':
return lambda x: x < value
elif symbol == '>=':
return lambda x: x >= value
elif symbol == '<=':
return lambda x: x <= value
elif symbol == '!=':
return lambda x: x != value
def MEDIAN(column, o_instance):
if isinstance(column, str):
if column in dir(o_instance):
column = getattr(o_instance, column)
else:
raise my_exceptions.ReferenciaInvalida()
if not is_iterable(column):
raise my_exceptions.ErrorDeTipo()
l_col = list(column)
n = len(l_col)
if n == 0:
raise my_exceptions.ImposibleProcesar()
if n % 2 == 0:
m_1 = l_col[int(n / 2)]
m_2 = l_col[int(n / 2 - 1)]
return (m_1 + m_2) / 2
else:
m = l_col[int(n / 2)]
return m
def exponential(*args):
if len(args) != 2:
raise my_exceptions.ArgumentoInvalido()
nu = args[0]
o_instance = args[1]
if isinstance(nu, str):
if nu in dir(o_instance):
nu = getattr(o_instance, nu)
else:
raise my_exceptions.ReferenciaInvalida()
if False in [is_number(arg) for arg in args[0:-1]]:
raise my_exceptions.ErrorDeTipo()
def exponential_x(x, nu=nu):
if x < 0:
raise my_exceptions.ErrorMatematico()
else:
f_x = nu * e**(-nu * x)
return f_x
return exponential_x
