el-hult

{- A module implementing a simple (and quite dumb) set -}
module MySet1 (Set, empty, toList, insert, contains) where
import Data.List(nub)

{- Set
A set, backed by a regular list.
If the list has duplicates, it is nothing the user should notice
-}
data Set a = SetC [a]

el-hult

/* 
This code reads the BNO055 fast using the RPi linux kernel support for I2C.

for running this file as a program, compile and run!
gcc -o faster faster.c -O3 && ./faster

You can also compile as a shared lib, and then call it from python.
gcc -Wall -shared -o faster.so faster.c && python faster.py

Page number and table number references are to the BNO055 data sheet.

el-hult

import numpy as np
import scipy.stats as sps
import matplotlib.pyplot as plt
from scipy.special import digamma

#
# Config
#
np.random.seed(999)
plt.rcParams['font.family'] = "monospace"

el-hult

# nice library that gives us access to the microphone
import pyaudio

# python standard libraries that come built-in
import threading
import queue
import wave

# optional libraries
import numpy as np

el-hult

{-# LANGUAGE DataKinds #-}
{-# LANGUAGE KindSignatures #-}
{-# LANGUAGE ScopedTypeVariables #-}

module Pointers where

import Data.Proxy (Proxy (..))
import Foreign.Marshal.Array (newArray, peekArray)
import Foreign.Ptr (Ptr)
import GHC.TypeNats (KnownNat, Nat, natVal)

el-hult

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Rectangle
from matplotlib.transforms import Affine2D
from scipy.stats import t, f
from scipy.linalg import sqrtm
import statsmodels.api as sm

el-hult

"""
Comparison of empirical bernstein measures

See https://el-hult.github.io/2022/03/18/empirical-bernstein-bounds.html

"""
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as sps
import pandas as pd

el-hult

import System.IO.Unsafe ( unsafeInterleaveIO )
import System.Random ( randomRIO )
import Text.Printf ( printf )

-- | An infinite list of random die rolls, lazily generated
--
-- This is dangerous since calling this function steps the global random generator
-- not when running the action, but when accessing its result.
diceRolls :: IO [Int]
diceRolls = do

el-hult

# %% set up the plot
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.widgets import Slider

LOC_MAX = 2


# The parametrized function to be plotted
def f1(t, amplitude, location):

el-hult

-- first monoid over Floats
data MaxNum = MaxNum Float deriving (Show)
instance Semigroup MaxNum where
  (MaxNum a) <> (MaxNum b) = MaxNum $ max a b
instance Monoid MaxNum where
  mempty = MaxNum 0 -- N.B. this does not fulfil the monoid laws fully. consider negative values!
aOne = MaxNum 4
aTwo = MaxNum 1
aThree = aOne <> aTwo