feat: derivates

src/main.py

@@ -1,4 +1,8 @@
+from sympy import diff, limit, oo, symbols
+
 from modules.math import (
+    t_build_partial_derivate,
+	t_build_derivate,
 	t_calculate_e,
 	t_calculate_e_limit,
 	t_compound_interest,
@@ -23,3 +27,14 @@ if __name__=="__main__":
 	print(t_limit())
 	print(t_calculate_e_limit())
 	print(t_calculate_e_limit().evalf())
+	x = symbols("x")
+	derivative = t_build_derivate(x ** 2)
+	value_on_two = derivative.subs(x,2)
+	print(derivative, value_on_two)
+	# Partial derivates are applied to one variable at a time
+	y = symbols("y")
+	z = symbols("z")
+	partial_derivative_y = t_build_partial_derivate((3 * y ** 3) + 2 * z ** 2, y)
+	partial_derivative_z = t_build_partial_derivate((3 * y ** 3) + 2 * z ** 2, z)
+	print(partial_derivative_y, partial_derivative_z)
+

src/modules/math.py

@@ -2,7 +2,7 @@ from cmath import log as complex_log  # used for complex numbers
 from math import e, exp, log
 
 # Sympy is powerful since it does not make aproximations, it keeps every primitive as it is, this means that it's slower but more precisse
-from sympy import limit, oo, symbols
+from sympy import diff, limit, oo, symbols
 
 
 def t_summ():
@@ -50,3 +50,13 @@ def t_calculate_e_limit():
 	x = symbols("x")
 	f = (1 + 1 / x) ** x
 	return limit(f, x, oo)
+
+# Calculate the slope by taking a close enough point
+def t_calculate_derivative(f, x, step_size):
+	return (f(x + step_size) - f(x)) / (x +step_size - x)
+
+def t_build_derivate(f):
+	return diff(f)
+
+def t_build_partial_derivate(f,symbol):
+	return diff(f, symbol)