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mflibbymflibby
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added economics, furthered probistics
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12 files changed

+1779
-260
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‎.ipynb_checkpoints/Distributions-checkpoint.py‎

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class Distribution:
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def __init__(self):
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self.e = 2.718281828459045235360287471352
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class Discrete_Distribution(Distribution):
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pass
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class Continuous_Distribution(Distribution):
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pass
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##############################################
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class Weibull_Distribution(Continuous_Distribution):
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def __init__(self, alpha, beta):
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self.alpha = alpha
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self.beta = beta
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self.pdf = lambda x : (alpha/(beta**alpha))*(x**(alpha-1))*self.e**(-(x/beta)**alpha)
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self.cdf = lambda x : 1-self.e**(-(x/beta)**alpha)
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def p(self,X, left = True):
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if (type(X) is not list):
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return self.cdf(X) if left else 1-self.cdf(X)
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return self.cdf(X[1])-self.cdf(X[0])
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def point_perc(self,percent):
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return self.beta*(-np.log(1-percent))**(1/self.alpha)
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class Gamma_Distribution(Continuous_Distribution):
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def __init__(self,alpha,beta=1):
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self.alpha = alpha
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self.beta = beta
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self.e = 2.718281828459045235360287471352
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self.pdf = lambda x : (1/( (beta**alpha) * gamma(alpha) ))*x**(alpha-1)*self.e**(-x/beta)
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self.cdf = lambda x, n : integrate(self.pdf,[0,n])
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#TODO:
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class ChiSquared_Distribution(Continuous_Distribution):
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pass
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class Lognormal_Distribution(Continuous_Distribution):
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pass
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class Beta_Distribution(Continuous_Distribution):
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pass
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class Exponential_Distribution(Continuous_Distribution):
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pass
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class Normal_Distribution(Continuous_Distribution):
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pass
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class Uniform_Distribution(Continuous_Distribution):
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pass
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###################################
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class Poisson_Distribution(Discrete_Distribution):
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pass
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class Binomial_Distribution(Discrete_Distribution):
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pass

‎.ipynb_checkpoints/Probistics-checkpoint.ipynb‎

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‎.ipynb_checkpoints/Signalysis-checkpoint.ipynb‎

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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎.ipynb_checkpoints/Superica-checkpoint.ipynb‎

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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎Economics/functions.py‎

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def simple_interest ( P , i , n ) -> float:
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"""
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Calculates the simple interest over n periods of applied interest i, given a starting principle P.
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INPUT:
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P : numerical - the starting principle
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i : numerical - the interest rate
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n : int - the number of periods the interest compounds
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RETURN:
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F : numerical - P(1+i*n), the future value of P given i and n.
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"""
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return P * ( 1 + i * n )
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def compound_worth ( O , i , n , forward = True ) -> float:
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"""
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Calculates the worth of O at some other time, either a future worth (when forward == True, which gives O == P);
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else present worth - i.e. O == F.
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INPUT:
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O : numerical - the worth at some time, P if forward is True, else F
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i : numerical - the interest rate
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n : int - the number of periods the interest compounds
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forward : bool - the direction of worth to be calculated, defaults to True
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RETURN:
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Z : numerical - P(1+i)^n, the future value of P given i and n, when forward == True,
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else F(1+i)^-n, giving the present worth, i.e. the principle P needed to obtain F given i and n.
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"""
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return O * ( ( 1 + i ) ** n ) if forward else O * ( ( 1 + i ) ** ( -n ) )
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def series_F ( O , i , n , forward = True ) -> float:
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"""
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Calculates the worth of n 'payments' of O at some other time, either a future worth (when forward == True, which gives O == A, Z == F);
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else worth at time of 'payment' - i.e. O == F, Z == A.
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INPUT:
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O : numerical - the known variable, either A - the regular payments if forward == True, else F - the future worth of those payments
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i : numerical - the interest rate
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n : int - the number of periods the interest compounds
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forward : bool - the direction of worth to be calculated, defaults to True
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RETURN:
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Z : numerical - A(((1+i)^n-1)/i), the future value of n payments of A given i and n, when forward == True,
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else F(i/( (1+i)^n-1 )), the value of each A (assuming constant A, effectively an average) at time of payment.
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"""
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return O * ( ( ( 1 + i ) ** n - 1 ) / i ) if forward else O * ( i / ( ( 1 + i ) ** n - 1 ) )
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def series_P ( O , i , n , forward = True ) -> float:
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"""
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Calculates the worth of n 'payments' of O at some other time, either worth at time of 'payment' (when forward == True, which gives O == P, Z == A);
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else a present worth - i.e. O == A, Z == P.
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INPUT:
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O : numerical - the known variable, either P - the present worth of the payments A if forward == True, else A - the worth of each payment at the time of payment
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i : numerical - the interest rate
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n : int - the number of periods the interest compounds
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forward : bool - the direction of worth to be calculated, defaults to True
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RETURN:
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Z : numerical - P( ( i(1+i)^n ) / ( (1+i)^n - 1 ) ), the neccessary value of A - for instance the needed payments to recoup a loss P
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else A( ( (1+i)^n - 1 ) / ( i(1+i)^n ) ), the present worth of payments A, for instance the loss that n payments of A could cover.
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"""
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return O * ( ( i * ( 1 + i ) ** n ) / ( ( 1 + i ) ** n - 1 ) ) if forward else O * ( ( ( 1 + i ) ** n - 1 ) / ( i * ( 1 + i ) ** n ) )
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def arith_grad_series ( O , i , n , forward = True ) -> float:
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"""
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Calculates the worth of n 'payments' of O at some other time, either worth at time of 'payment' (when forward == True, which gives O == P, Z == A);
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else a present worth - i.e. O == A, Z == P.
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INPUT:
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O : numerical - the known variable, either G - the gradient factor if forward == True, else A - the worth of each payment at the time of payment
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i : numerical - the interest rate
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n : int - the number of periods the interest compounds
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forward : bool - the direction of worth to be calculated, defaults to True
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RETURN:
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Z : numerical - G( ( 1 / i ) - n / ( ( 1 + i )^n - 1 ) ), the neccessary value of A
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else A/( ( 1 / i ) - n / ( ( 1 + i )^n - 1 ) ), the gradient value.
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"""
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return O * ( ( 1 / i ) - n / ( ( 1 + i ) ** n - 1 ) ) if forward else O / ( ( 1 / i ) - n / ( ( 1 + i ) ** n - 1 ) )
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def arith_grad_worth ( O , i , n , forward = True ) -> float:
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"""
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Calculates the worth of n 'payments' of O at some other time, either worth at time of 'payment' (when forward == True, which gives O == P, Z == A);
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else a present worth - i.e. O == A, Z == P.
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INPUT:
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O : numerical - the known variable, either G - the gradient factor if forward == True, else A - the worth of each payment at the time of payment
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i : numerical - the interest rate
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n : int - the number of periods the interest compounds
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forward : bool - the direction of worth to be calculated, defaults to True
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RETURN:
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Z : numerical - G( ( 1 / i ) - n / ( ( 1 + i )^n - 1 ) ), the neccessary value of A
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else A/( ( 1 / i ) - n / ( ( 1 + i )^n - 1 ) ), the gradient value.
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"""
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return O * ( ( 1 / i ) - n / ( ( 1 + i ) ** n - 1 ) ) if forward else O / ( ( 1 / i ) - n / ( ( 1 + i ) ** n - 1 ) )

‎Economics/objects.py‎

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#Objects related to economics
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class Currency:
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pass

‎Probistics.ipynb‎

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‎Signalysis.ipynb‎

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‎Superica.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import re"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"exp1 = \"x = 2\"\n",
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"exp2 = \"$cos(x)$\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"func = dict(cos = np.cos)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"-1.0"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"func['cos'](np.pi)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"class express:\n",
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" \n",
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" def __init__(self,strinput : str):\n",
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" self.expression : str = strinput\n",
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" self.vars,self.funcs = self._decompose()\n",
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" \n",
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" \n",
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" def _decompose(self):\n",
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" return 0,0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<__main__.express at 0x2168e554148>"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"express(exp1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"spec = re.compile(\"\\((.+)\\)\")\n",
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"encase = re.compile(\"\\$.+\\$\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<re.Match object; span=(4, 7), match='(x)'>\n",
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"<re.Match object; span=(0, 8), match='$cos(x)$'>\n"
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]
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}
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],
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"source": [
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"print(spec.search(exp2))\n",
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"print(encase.search(exp2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"index.html <-> home\n",
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"base.html <-> base\n"
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]
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}
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],
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"source": [
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"w = \"TEMPLATES = ( ('index.html', 'home'), ('base.html', 'base'))\"\n",
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"\n",
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"# find outer parens\n",
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"outer = re.compile(\"\\((.+)\\)\")\n",
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"m = outer.search(w)\n",
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"inner_str = m.group(1)\n",
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"\n",
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"# find inner pairs\n",
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"innerre = re.compile(\"\\('([^']+)', '([^']+)'\\)\")\n",
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"\n",
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"results = innerre.findall(inner_str)\n",
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"for x,y in results:\n",
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" print(\"%s <-> %s\" % (x,y))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'cos(x)'"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"encase.search(exp2).group(0).lstrip('$').rstrip('$')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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