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x = 1
y = 2
x + y

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# this code block should gistify as it is >5 lines
x = np.linspace(0, 1, 101)
y = x**2

fig, ax = plt.subplots(figsize=(20,10))
plt.plot(x, y)
ax.set_title('Graph showing y=x**2')
ax.set_xlabel('x')
ax.set_ylabel('y');

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import numpy as np

x = 1+1
y = 2+2
print(x)

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# import numba
from numba import jit
# define our function to profile our compiled code
def find_instr(func, keyword, sig=0, limit=5):
    count = 0
    # iterate over each of the assembly instructions and check if they match our chosen string
    for l in func.inspect_asm(func.signatures[sig]).split('\n'):
        if keyword in l or keyword == '':
            count += 1
            print(l)

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import numpy as np
# python list of 100m ints
L_py = [x for x in range(0, 100_000_000)]
# nd array of 100m ints
L_np = np.arange(0, 100_000_000)
# time each
%timeit sum(L_py)
%timeit np.sum(L_np)

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# plot them
fig, ax = plt.subplots(figsize=(25,10))

for e, op_px in op_pxs.items():
    if e != 0:
        ax.plot(ks, [x/y for x,y in zip(op_px, op_pxs[0])], label="e = {}".format(e))

ax.set_title("Percentage Chg Time Value for Various Option Strikes", fontsize=24)
ax.set_xlabel("Option Strike, K", fontsize=20)
ax.set_ylabel("% Change Time Value", fontsize=20)

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# set stock params
spot = 100
sigma = 0.20
n = 10000
# option strikes
ks = np.linspace(65, 165, 21)
eps = [0, 0.3, 0.6, 0.85]

# compute closing prices
pxs = {}

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# define our gaussian look-a-like distribution
class cust_dist(stats.rv_continuous):
    
    # define init with sigma deviation param e
    def __init__(self, e, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # init our variance divergence
        self.e = e
        # init our cdf and ppf functions
        self.cdf_func, self.ppf_func = self.create_cdf_ppf()

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# define normal pdf
def norm_pdf(x, sigma=1):
    return (1 / np.sqrt(2*np.pi)) * (1 / sigma) * np.exp(-0.5 * (x/sigma)**2)

# define x's we want to compute pdf for
xs = np.linspace(-4, 4, 8*200 + 1)
# define epsilsons
eps = [0, 0.1, 0.2, 0.5]
# create pdfs
perturbs = []

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# usual suspects
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import warnings

from scipy.integrate import simps
from scipy.interpolate import interp1d