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"""
This is the pipeline `do_stuff`
generated using kedro <blah>
"""

from .pipeline_do_stuff import create_pipeline

__all__ = ["create_pipeline"]

__version__ = "0.1"

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import numpy as np
import scipy.stats as st

class GaussianMixture(object):
    def __init__(self, mu, sigma, w):
        self.mu = np.asarray(mu)
        self.sigma = np.asarray(sigma)
        
        self.w = np.asarray(w)
        self.w /= self.w.sum()

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# Based off the implementation that is already in becquerel.parsers.SpeFile,
# but adapted to read *some* of the info from a Spectrum instance. 
# I'm a little fuzzy on the details of the SPE format (e.g. what is the difference
# between $ENER_CAL and $MCA_CAL) but this produces files that load without
# issue in Sandia's InterSpec tool. 

# This is a rough implementation as a prototype, it needs a lot of work.

def write_spe(
    spec,

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### Time ###

All of this needs to be done as root.

This assumes the GPS module is connected to the 4th channel of the
FT4232H. If it isn't, you need to edit the DEVICES variable below to
point to the correct one. The connections you need to make are:

    GPS Pin   <-->    FT4232 Pin
    ----------------------------

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# See https://blog.3d-logic.com/2015/01/10/using-a-tm1638-based-board-with-arduino/
import atexit

from pyftdi.spi import SpiController
from time import sleep

SPI_FREQ = 10000
SPI_MODE = 0

# Initialize the controller

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import pyftdi as F
from pyftdi import i2c

from ctypes import c_short

from time import sleep

import atexit

### Some utility functions

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##################################################################################
# Copyright (c) 2018, Jason M. Hite
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
#    list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright notice,

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jasonmhite

import numpy as np
from scipy.optimize import minimize

def least_squares(A, b, x0): # Least-squares solution for Ax=b, initial guess of x0
    return minimize(
        lambda x: np.linalg.norm(A.dot(x) - b),
        x0,
    )

def least_squares_tikhonov(A, b, alpha, x0): # Least squares for Ax=b with simple Tikhonov regularization

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