# Fileset

[JSA_PW論文コード.zip](https://mdr.nims.go.jp/filesets/cf2f3bcb-871e-45b7-ab61-64ea766a5cc8/download)

## Creator

[Shinjiro Yagyu](https://orcid.org/0000-0002-9825-5719), [Michiko Yoshitake](https://orcid.org/0000-0002-0973-5666), [Takahiro Nagata](https://orcid.org/0000-0002-8591-2943)

## Rights

[In Copyright](http://rightsstatements.org/vocab/InC/1.0/)

## Other metadata

[Preprocessing Method for Spectra Interpreted by the Power Law Using Segment Regression](https://mdr.nims.go.jp/datasets/423c8216-a978-4798-8f62-05a165d5c35c)

## Fulltext

JSA_PW論文コード/.gitignore# Byte-compiled / optimized / DLL files__pycache__/*.py[cod]*$py.class# C extensions*.so# Distribution / packaging.Pythonbuild/develop-eggs/dist/downloads/eggs/.eggs/lib/lib64/parts/sdist/var/wheels/share/python-wheels/*.egg-info/.installed.cfg*.eggMANIFEST# PyInstaller#  Usually these files are written by a python script from a template#  before PyInstaller builds the exe, so as to inject date/other infos into it.*.manifest*.spec# Installer logspip-log.txtpip-delete-this-directory.txt# Unit test / coverage reportshtmlcov/.tox/.nox/.coverage.coverage.*.cachenosetests.xmlcoverage.xml*.cover*.py,cover.hypothesis/.pytest_cache/cover/# Translations*.mo*.pot# Django stuff:*.loglocal_settings.pydb.sqlite3db.sqlite3-journal# Flask stuff:instance/.webassets-cache# Scrapy stuff:.scrapy# Sphinx documentationdocs/_build/# PyBuilder.pybuilder/target/# Jupyter Notebook.ipynb_checkpoints# IPythonprofile_default/ipython_config.py# pyenv#   For a library or package, you might want to ignore these files since the code is#   intended to run in multiple environments; otherwise, check them in:# .python-version# pipenv#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.#   However, in case of collaboration, if having platform-specific dependencies or dependencies#   having no cross-platform support, pipenv may install dependencies that don't work, or not#   install all needed dependencies.#Pipfile.lock# poetry#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.#   This is especially recommended for binary packages to ensure reproducibility, and is more#   commonly ignored for libraries.#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control#poetry.lock# pdm#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.#pdm.lock#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it#   in version control.#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control.pdm.toml.pdm-python.pdm-build/# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm__pypackages__/# Celery stuffcelerybeat-schedulecelerybeat.pid# SageMath parsed files*.sage.py# Environments.env.venvenv/venv/ENV/env.bak/venv.bak/# Spyder project settings.spyderproject.spyproject# Rope project settings.ropeproject# mkdocs documentation/site# mypy.mypy_cache/.dmypy.jsondmypy.json# Pyre type checker.pyre/# pytype static type analyzer.pytype/# Cython debug symbolscython_debug/# PyCharm#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore#  and can be added to the global gitignore or merged into this file.  For a more nuclear#  option (not recommended) you can uncomment the following to ignore the entire idea folder.#.idea/JSA_PW論文コード/comlib/comlib.py"""共通で使う機能をまとめたライブラリ"""from contextlib import redirect_stdoutimport datetimefrom pathlib import Pathimport osfrom pprint import pprintfrom pprint import pformat as pfimport numpy as npimport matplotlib.pyplot as pltimport pandas as pdfrom scipy import integrate# import warnings# warnings.simplefilter('ignore')# warnings.resetwarnings()# warnings.simplefilter('ignore', FutureWarning)# %load_ext autoreload# %autoreload 2def get_nearest_value(lst: list, num: float):    """    Returns the element from the input list that is closest to the given number.        Args:        lst: A list of numbers.        num: A float number.        Returns:        The index of the element in the list that is closest to the given number.    """    # Calculate the difference between each element in the list and the target number,    # and get the index of the minimum difference    idx = np.abs(np.asarray(lst) - num).argmin()        return idxdef now_datetime(date_type: int = 1):    """    Returns the current date and time as a string in a specified format.        Args:        date_type: An integer representing the desired format of the date and time.            1: "%Y-%m-%d %H:%M:%S"            2: "%Y%m%d%H%M%S"            3: "%Y%m%d_%H%M%S"            4: "%Y%m%d%H%M"            5: "%m%d_%H:%M:%S"            6: "%Y%m%d"            (Default: 1)        Returns:        A string representing the current date and time in the specified format.    """        now = datetime.datetime.now()    if date_type == 1:        now_string = now.strftime("%Y-%m-%d %H:%M:%S")    elif date_type == 2:        now_string = now.strftime("%Y%m%d%H%M%S")    elif date_type == 3:        now_string = now.strftime("%Y%m%d_%H%M%S")    elif date_type == 4:        now_string = now.strftime("%Y%m%d%H%M")    elif date_type == 5:        now_string = now.strftime("%m%d_%H:%M:%S")    elif date_type == 6:        now_string = now.strftime("%Y%m%d")    else:        now_string = now    return now_stringdef getEncode(filepath):    """Character code automatic judgment function     If the file name contains Japanese, the encoding method may be Shift-jis.     Function that returns the encoding method.    Args:        filepath (str): fil path and name    Returns:        encode type(str): encode type    """    encs = "iso-2022-jp euc-jp shift_jis utf-8".split()    for enc in encs:        with open(filepath, encoding=enc) as fr:            try:                fr = fr.read()            except UnicodeDecodeError:                continue        return enc    # Python Tips: 出力を一時的に無効化したい# # https://www.lifewithpython.com/2019/03/python-disable-stdout-temporarily.html# import os# from contextlib import redirect_stdout# def func_with_stdout():#     print('通常だとこれは標準出力に出力されます。')# # `通常だとこれは標準出力に出力されます。` が出力されない# with redirect_stdout(open(os.devnull, 'w')):#     func_with_stdout()def no_print_out(function,*args, **kwargs):    with redirect_stdout(open(os.devnull, 'w')):        function(*args, **kwargs)    # print(*args, **kwargs)# nanの処理def nan_inf_rm(xdata,ydata,zero_replace=False,info=False):    """remove nan and inf values        Logをとった場合        マイナスの値:定義されていないためにNan        0:マイナス無限大になる        これらの値が含まれている場合、機能しない関数が出てくるためにその値(Index)を取り除く    Args:        xdata (ndarray or list): xdata        ydata (ndarrayor list): ydata        zero_replace(bool): True: Nan -> 0 replace  False:Nan remove. default False        info (bool): show infomation            Returns:        trimmed data (ndarray): re_xdata, re_ydata    """    # inf -> nan    # nan_inf_rm(np.log(xf),np.log(yf))    xarray = np.array(xdata)    yarray = np.array(ydata)    tr_inf_x = np.where(np.isinf(xarray),np.nan,xarray)    tr_inf_y = np.where(np.isinf(yarray),np.nan,yarray)        re_xdata = tr_inf_x.copy()    re_ydata = tr_inf_y.copy()        if zero_replace:        re_xdata = np.nan_to_num(re_xdata, copy=False)        re_ydata = np.nan_to_num(re_ydata, copy=False)            else:        nan_ind_x = np.where(~np.isnan(tr_inf_x))        re_xdata = re_xdata[nan_ind_x[0]]        re_ydata = re_ydata[nan_ind_x[0]]                nan_ind_y = np.where(~np.isnan(re_ydata))        re_xdata = re_xdata[nan_ind_y[0]]        re_ydata = re_ydata[nan_ind_y[0]]        if info:        print(f'number of Nan of (x, y) : ({np.count_nonzero(np.isnan(tr_inf_x))}, {np.count_nonzero(np.isnan(tr_inf_y))})')        print(f'Original shape x, y: {xarray.shape}, {yarray.shape}')        print(f'output shape x, y: {re_xdata.shape}, {re_ydata.shape}')            return re_xdata, re_ydata# データの並びを調べる　x軸を昇順にする。ｙの値もそれに合わせるdef array_order_check(xdata, ydata):    """    Checks if the order of xdata and ydata are reversed.    Args:        xdata (numpy.ndarray): Input x data        ydata (numpy.ndarray): Input y data    Returns:        cxdata (numpy.ndarray): Output x data        cydata (numpy.ndarray): Output y data    Raises:        ValueError: If the lengths of xdata and ydata do not match    """    if len(xdata) != len(ydata):        raise ValueError("The lengths of xdata and ydata do not match")    cxdata = xdata.copy()    cydata = ydata.copy()    if xdata[0] > xdata[-1]:        print("replacement")        cxdata = xdata[::-1]        cydata = ydata[::-1]    else:        pass    return cxdata, cydatadef save_csv_nd(xdata, ydata, filename='out.csv', header='x,y'):    """ndarry to save csv without using Pandas    Args:        xdata (ndarray): ndarray        ydata (ndarray):ndarray        filename (str): filename        header (str, optional): header name. Defaults to 'x,y'.     Examples:        #### 例としてsinカーブを作る        x = np.linspace(-5,5,100)        y = np.sin(x)        data = np.c_[x,y]                #### csvファイルとして保存        np.savetxt('out.csv', data, header='x,y', comments='',delimiter=',')    """           data = np.c_[xdata,ydata]    #csvファイルとして保存    np.savetxt(filename, data, header=header, comments='',delimiter=',')    JSA_PW論文コード/comlib/mathlib.pyfrom contextlib import redirect_stdoutimport datetimefrom pathlib import Pathimport osfrom pprint import pprintfrom pprint import pformat as pfimport numpy as npimport matplotlib.pyplot as pltfrom scipy import integratedef line_fit(xdata, ydata):    slope, slice = np.polyfit(xdata, ydata, 1)    xslice = -(slice/slope)        # plt.plot(xdata, a*xdata + b,'b-',label='Liner-fit')    return slope, slice, xslice# diffarantiate and integratedef shift_diff(xdata,ydata,window=1):# 移動平均    # 移動平均の範囲 window =     # length len(xdata)-1    # if plot-> plt.plot(xdata[1:],dy)    w = np.ones(window)/window    y_smooth = np.convolve(ydata, w, mode='same')    # 差分    dy = np.gradient(y_smooth)          dx = np.gradient(xdata)      return dy/dx, dx, dy    def moving_ave(ndata,window=1):    # 移動平均の範囲 window     #data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])    # moving_ave(data,window=2)    # >>> array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])    # ndata2=ndata.copy()    w = np.ones(window)/window    y_smooth = np.convolve(ndata, w, mode='same')        return y_smoothdef c_dif(xdata,ydata,flag='g'):    # data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])    # c_dif(data,data,'d')    # array([1., 1., 1., 1., 1., 1., 1., 1.]))    if flag == 'g': #lenght: len(xdata)        dy = np.gradient(ydata)              dx = np.gradient(xdata)       elif flag == 'd' :  # lenght: len(xdata)-1        dy = np.diff(ydata)              dx = np.diff(xdata)           return   dy/dx, dx, dydef intg(xdata,ydata):    # simpsonによる積分    intg_ =  integrate.simps(xdata,ydata)    return intg_def r2(xx, yy, a, b):    """    線形回帰の決定係数を計算する関数    Args:        xx: 説明変数        yy: 目的変数        a: 回帰係数        b: 切片    Returns:        決定係数                # 回帰直線の係数を計算        a, b = np.polyfit(xx, yy, 1)        print(r2(xx, yy, a, b))        # # 回帰直線を表示        # print("回帰直線の式: y = {}x + {}".format(a, b))        # # 回帰直線を描画        # plt.plot(x, y, "o")        # plt.plot(x, a * x + b, "r")        # plt.show()    """    # 回帰平方和    y_pred = a * xx + b    rss = np.sum((yy - y_pred)**2)    # 全平方和    y_mean = np.mean(yy)    tss = np.sum((yy - y_mean)**2)    # 決定係数    r2 = 1 - rss / tss    return r2JSA_PW論文コード/Datas/C60 20210607140335_5430.dat  PE  0.005550  10  0.50  2960.00  0.05  AC-3  45.00  4.00  7.00  0  0.00  2021/06/07 14:03:35  C60  1.06  1.00  1nW_210607.ldat  1.00  1.00  4.00  0.11  0  0  0.18  4.05  1.57  0  0  0.19  4.10  0.00  0  0  0.20  4.15  2.48  0  0  0.21  4.20  0.56  0  0  0.23  4.25  0.22  0  0  0.24  4.30  0.11  0  0  0.26  4.35  0.67  0  0  0.26  4.40  0.22  0  0  0.26  4.45  0.45  0  0  0.25  4.50  0.67  0  0  0.25  4.55  0.67  0  0  0.24  4.60  0.00  0  0  0.24  4.65  0.22  0  0  0.23  4.70  0.22  0  0  0.23  4.75  0.00  0  0  0.23  4.80  0.00  0  0  0.24  4.85  0.78  0  0  0.25  4.90  0.11  0  0  0.26  4.95  0.56  0  0  0.27  5.00  0.00  -1  0  0.30  5.05  0.22  -1  0  0.32  5.10  0.00  -1  0  0.36  5.15  0.11  -1  0  0.40  5.20  0.00  -1  0  0.45  5.25  0.11  -1  0  0.50  5.30  0.00  -1  0  0.55  5.35  0.11  -1  0  0.60  5.40  0.22  -1  0  0.64  5.45  0.67  -1  0  0.67  5.50  0.33  -1  0  0.69  5.55  0.33  -1  0  0.72  5.60  0.78  -1  0  0.78  5.65  0.33  0  0  0.84  5.70  0.33  0  0  0.90  5.75  0.22  0  0  0.95  5.80  0.22  0  0  0.99  5.85  0.89  0  0  1.04  5.90  0.22  0  0  1.07  5.95  0.56  0  0  1.10  6.00  0.56  0  0  1.09  6.05  1.12  0  0  1.10  6.10  0.89  0  0  1.12  6.15  1.46  0  0  1.13  6.20  1.57  0  0  1.14  6.25  2.82  0  0  1.14  6.30  8.86  0  -1  1.14  6.35  11.21  0  -1  1.13  6.40  24.48  0  -1  1.13  6.45  35.99  0  -1  1.11  6.50  45.86  0  -1  1.11  6.55  69.84  0  -1  1.10  6.60  95.04  0  0  1.08  6.65  122.41  0  0  1.05  6.70  148.63  0  0  1.00  6.75  166.16  0  0  0.94  6.80  186.60  0  0  0.86  6.85  204.46  0  0  0.72  6.90  183.40  0  0  0.57  6.95  124.30  0  0  0.40  7.00  80.55  0  0  0.26JSA_PW論文コード/Datas/Fc3 20210607134453_5429.dat  PE  0.005550  10  0.50  2960.00  0.05  AC-3  45.00  4.00  7.00  0  0.00  2021/06/07 13:44:53  Fc  1.06  1.00  1nW_210607.ldat  1.00  1.00  4.00  0.56  0  0  0.18  4.05  0.00  0  0  0.19  4.10  1.01  0  0  0.20  4.15  1.01  0  0  0.21  4.20  0.22  0  0  0.23  4.25  0.11  0  0  0.24  4.30  0.78  0  0  0.26  4.35  0.78  0  0  0.26  4.40  0.22  0  0  0.26  4.45  1.80  0  0  0.25  4.50  0.56  0  0  0.25  4.55  0.22  0  0  0.24  4.60  0.56  0  0  0.24  4.65  0.78  0  0  0.23  4.70  1.91  0  0  0.23  4.75  0.78  0  0  0.23  4.80  0.45  0  0  0.24  4.85  0.89  -1  0  0.25  4.90  1.46  -1  0  0.26  4.95  0.89  -1  0  0.27  5.00  1.57  -1  0  0.30  5.05  2.14  -1  0  0.32  5.10  1.34  -1  0  0.36  5.15  1.68  -1  0  0.40  5.20  3.86  -1  0  0.45  5.25  2.14  -1  0  0.50  5.30  2.48  -1  0  0.55  5.35  3.05  0  0  0.60  5.40  2.25  0  0  0.64  5.45  4.09  0  0  0.67  5.50  5.38  0  0  0.69  5.55  4.44  0  0  0.72  5.60  11.59  0  0  0.78  5.65  18.10  0  0  0.84  5.70  31.45  0  -1  0.90  5.75  56.86  0  -1  0.95  5.80  91.21  0  -1  0.99  5.85  170.32  0  -1  1.04  5.90  232.73  0  -1  1.07  5.95  312.86  0  0  1.10  6.00  376.76  0  0  1.09  6.05  423.98  0  0  1.10  6.10  469.66  0  0  1.12  6.15  540.16  0  0  1.13  6.20  540.16  0  0  1.14  6.25  579.49  0  0  1.14  6.30  565.91  0  0  1.14  6.35  583.46  0  0  1.13  6.40  589.49  0  0  1.13  6.45  567.82  0  0  1.11  6.50  545.52  0  0  1.11  6.55  529.66  0  0  1.10  6.60  503.10  0  0  1.08  6.65  507.92  0  0  1.05  6.70  599.76  0  0  1.00  6.75  832.16  0  0  0.94  6.80  1132.29  0  0  0.86  6.85  1419.76  0  0  0.72  6.90  1437.48  0  0  0.57  6.95  1200.51  0  0  0.40  7.00  907.54  0  0  0.26JSA_PW論文コード/Datas/P-Low_230403122620_5_r.dat  PE  0.004750  5  0.50  2600.00  0.05  AC-5  64.00  3.40  6.20  0  0.00  2023/04/03 12:26:24  P-Low  10.03  10.00  10nW 230403093802.ldat  1.00  1.00  3.4  1.0  0  0  2.06  3.45  0.0  0  0  2.21  3.5  0.0  -1  0  2.32  3.55  0.0  -1  0  2.42  3.6  0.0  -1  0  2.6  3.65  0.0  -1  0  2.81  3.7  0.0  -1  0  3.02  3.75  0.0  -1  0  3.24  3.8  0.0  -1  0  3.45  3.85  0.0  -1  0  3.66  3.9  0.0  -1  0  3.87  3.95  0.0  -1  0  4.06  4.0  0.0  -1  0  4.3  4.05  0.0  -1  0  4.53  4.1  0.0  -1  0  4.75  4.15  0.0  -1  0  4.96  4.2  0.0  -1  0  5.18  4.25  0.0  -1  0  5.34  4.3  0.0  -1  0  5.49  4.35  0.0  -1  0  5.65  4.4  0.0  -1  0  5.83  4.45  0.0  0  0  6.03  4.5  0.0  0  0  6.27  4.55  0.0  0  0  6.56  4.6  0.0  0  0  6.9  4.65  0.5  0  0  7.22  4.7  1.0  0  0  7.55  4.75  1.0  0  0  7.87  4.8  2.25  0  -1  8.23  4.85  4.5  0  -1  8.61  4.9  6.5  0  -1  9.02  4.95  8.75  0  -1  9.33  5.0  14.5  0  -1  9.65  5.05  15.5  0  -1  9.93  5.1  22.5  0  -1  10.36  5.15  32.5  0  -1  10.57  5.2  34.5  0  -1  10.72  5.25  45.75  0  -1  10.91  5.3  48.0  0  -1  11.01  5.35  52.75  0  0  11.08  5.4  57.5  0  0  11.03  5.45  67.0  0  0  10.98  5.5  68.5  0  0  10.94  5.55  79.0  0  0  10.96  5.6  85.25  0  0  10.9  5.65  92.75  0  0  10.89  5.7  99.25  0  0  10.77  5.75  105.75  0  0  10.65  5.8  106.5  0  0  10.51  5.85  113.75  0  0  10.31  5.9  115.0  0  0  10.03  5.95  120.75  0  0  9.64  6.0  126.5  0  0  9.31  6.05  128.25  0  0  8.97  6.1  128.0  0  0  8.59  6.15  132.0  0  0  8.18  6.2  131.0  0  0  7.71JSA_PW論文コード/JSA_figs_240418.ipynb{ "cells": [  {   "attachments": {},   "cell_type": "markdown",   "metadata": {},   "source": [    "### JSAの論文用Fig\n",    "\n",    "\n",    "\n",    "20240418  修正用に図面作成\n",    "\n",    "\n",    "\n",    "\n",    "Figs:\n",    "\n",    "- セグメント回帰の説明図\n",    "\n",    "- バックグラウンド及びピークのパターン\n",    "- (case1) 定数バックグラウンド + べき乗（BP1）\n",    "- (case2) 減少バックグラウンド + 定数バックグラウンド + べき乗（BP2）\n",    "- (case3) 定数バックグラウンド + ガウス分布（BP3）\n",    "※BPはBreakpoint\n",    "\n",    "AC data Ferrocene powder \n",    "ROI -> 1/n analysis\n",    "\n"   ]  },  {   "cell_type": "code",   "execution_count": 1,   "metadata": {},   "outputs": [],   "source": [    "from contextlib import redirect_stdout\n",    "import datetime\n",    "from pathlib import Path\n",    "import os\n",    "from pprint import pprint\n",    "from pprint import pformat as pf\n",    "\n",    "import numpy as np\n",    "import matplotlib.pyplot as plt\n",    "import pandas as pd\n",    "import sklearn.metrics as metrics\n",    "\n",    "# import statistics\n",    "import scipy as sp\n",    "import scipy.optimize as optimize\n",    "from scipy.optimize import curve_fit  \n",    "import scipy.stats \n",    "from scipy import integrate\n",    "\n",    "from sklearn.metrics import r2_score\n",    "from sklearn.cluster import KMeans\n",    "\n",    "import piecewise_regression\n",    "\n",    "import comlib as cl\n",    "import mathlib as mtl\n",    "import model_functions as mf\n",    "import pw_mini_lib as pw\n",    "\n",    "import warnings\n",    "warnings.simplefilter('ignore')\n",    "# warnings.resetwarnings()\n",    "# warnings.simplefilter('ignore', FutureWarning)\n",    "\n",    "%load_ext autoreload\n",    "%autoreload 2"   ]  },  {   "cell_type": "code",   "execution_count": 43,   "metadata": {},   "outputs": [    {     "name": "stdout",     "output_type": "stream",     "text": [      "['sans-serif']\n"     ]    }   ],   "source": [    "\n",    "print(plt.rcParams[\"font.family\"]) # => ['sans-serif']"   ]  },  {   "cell_type": "code",   "execution_count": 4,   "metadata": {},   "outputs": [],   "source": [    "#　図の作成、PWの計算\n",    "def ax_plots(xdata, ydata, n_breakpoints, axs, process_plot=False, **kwargs ):\n",    "    \n",    "    res_model = pw.pw_model(xdata, ydata, bps_max=n_breakpoints, plot=process_plot, info=process_plot)\n",    "    \n",    "    pw_fit = piecewise_regression.Fit(xdata, ydata, res_model[\"bps_list_asc\"])\n",    "    pw_results = pw_fit.get_results()\n",    "    # --> dict_keys(['estimates', 'bic', 'rss', 'converged'])\n",    "\n",    "    pw_estimates = pw_results[\"estimates\"]\n",    "    # --> dict_keys(['const', 'beta1', 'breakpoint1', 'alpha1',...])\n",    "    \n",    "    # pw_estimate['const'].keys()\n",    "    # dict_keys(['estimate', 'se', 'confidence_interval', 't_stat', 'p_t'])\n",    "    \n",    "    key_breakpoints = [s for s in pw_estimates .keys() if 'breakpoint' in s]\n",    "    val_breakpoints = [pw_estimates [v]['estimate'] for v in key_breakpoints]\n",    "    re_val_breakpoints = sorted(val_breakpoints)\n",    "\n",    "    pw_fit = piecewise_regression.Fit(xdata, ydata, re_val_breakpoints)\n",    "    pw_results = pw_fit.get_results()\n",    "    pw_estimates = pw_results[\"estimates\"]\n",    "    \n",    "    def bp_alpha(pw_estimates):\n",    "        key_breakpoints = [s for s in pw_estimates.keys() if 'breakpoint' in s]\n",    "        val_breakpoints = [pw_estimates[v]['estimate'] for v in key_breakpoints]\n",    "\n",    "        key_alphas = [s for s in pw_estimates.keys() if 'alpha' in s]\n",    "        val_alphas = [pw_estimates[v]['estimate'] for v in key_alphas]\n",    "        \n",    "        key_betas = [s for s in pw_estimates.keys() if 'beta' in s]\n",    "        val_betas = [pw_estimates[v]['estimate'] for v in key_betas]\n",    "        \n",    "        return  val_breakpoints, val_alphas, val_betas\n",    "    \n",    "\n",    "    def draw_pw_ax(xdata, ydata, pw_estimates, axs, **kwargs):\n",    "        \n",    "        val_breakpoints, val_alphas, val_betas = bp_alpha(pw_estimates)\n",    "       \n",    "        num = len(val_breakpoints)+1\n",    "        \n",    "        yy_predicted = pw_estimates['const']['estimate'] + pw_estimates['alpha1']['estimate'] *xdata\n",    "        for bp_count in range(len(val_breakpoints)):\n",    "            alpha_n = f'alpha{bp_count+2}'\n",    "            beta_n = f'beta{bp_count+1}'\n",    "            breakpoint_n = f'breakpoint{bp_count+1}'\n",    "            yy_predicted += pw_estimates[beta_n]['estimate'] * \\\n",    "                np.maximum(xdata - pw_estimates[breakpoint_n]['estimate'], 0)\n",    "                \n",    "            axs.axvline(pw_estimates[breakpoint_n]['estimate'])\n",    "            axs.text(pw_estimates[breakpoint_n]['estimate'],\n",    "                    # np.max(ydata)*(1/num)*1, \n",    "                    np.max(ydata)*(1.5/num)*(np.mod(bp_count+1,2)+1), \n",    "                    # np.max(ydata)*(0.9-0.1*np.mod(bp_count+1,2)), \n",    "                    #  np.max(ydata)-np.max(ydata)*(1/num)*(bp_count+1), \n",    "                    # f'BP{bp_count+1}:{pw_estimates[breakpoint_n][\"estimate\"]:.1f}\\n$\\\\alpha${bp_count+2}:{pw_estimates[alpha_n][\"estimate\"]:.2f}')\n",    "                    f'BP{bp_count+1}')\n",    "            \n",    "        axs.axvline(np.min(xdata))\n",    "        axs.text(np.min(xdata),\n",    "                # np.max(ydata)*0.9, \n",    "                np.max(ydata)*(1.5/num)*1, \n",    "                #  np.max(ydata)-np.max(ydata)*(1/num)*1, \n",    "                f\"BP0\")\n",    "                # f\"BP0:{np.min(xdata):.1f}\\n$\\\\alpha$1:{pw_estimates['alpha1']['estimate']:.2f}\")\n",    "        \n",    "        axs.axvline(np.max(xdata))\n",    "        axs.text(np.max(xdata),\n",    "                 np.max(ydata)*(1.5/num)*(np.mod(bp_count+2,2)+1), \n",    "                 f'BP{len(val_breakpoints)+1}',ha='right')\n",    "        # axs.text(np.max(xdata), np.max(ydata)*(1.5/num)*1, \n",    "        #          f'BP{len(val_breakpoints)+1}',ha='right')\n",    "                \n",    "                \n",    "        axs.plot(xdata,yy_predicted,**kwargs)\n",    "        # num = len(val_breakpoints)\n",    "        \n",    "        # for i, bp in enumerate(val_breakpoints):\n",    "        #     axs.axvline(bp)\n",    "        #     axs.text(bp,np.max(ydata)*(1/num)*(i+1), f'BP{i+1}')\n",    "        \n",    "        return axs , {'bp':val_breakpoints, 'alpha':val_alphas}\n",    "    \n",    "    axs, p = draw_pw_ax(xdata,ydata, pw_estimates, axs)\n",    "    return axs, p\n",    "    "   ]  },  {   "cell_type": "code",   "execution_count": 3,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiEAAAFzCAYAAADoudnmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABvQElEQVR4nO3dd3gU1frA8e9kd7MpJKGlktCbSFMpIr0FUBFEUCIqIOq9ClcwP7uCoihWRBTBCnoVQbkBFRUIHaSDNFEEDCQhhZ6QhCSb3fn9scmSJT3Z3cmG9/M8POzOnJk558zMzpszZ84oqqqqCCGEEEK4mIfWGRBCCCHEtUmCECGEEEJoQoIQIYQQQmhCghAhhBBCaEKCECGEEEJoQoIQIYQQQmhCghAhhBBCaEKCECGEEEJoQq91Bqoji8VCUlISfn5+KIqidXaEEEIIt6GqKpcuXSIsLAwPj9LbOiQIKUZSUhIRERFaZ0MIIYRwWwkJCYSHh5eaRoKQYvj5+QHWCvT396/y+rJy8+jy2loAfnu6FwG+3lVepwCTycTq1auJjIzEYDBonR23V7g+TapiO2Z3vtAfH0/3/qkofA66sjzOOEa1KoszVLQsrjzntapnV27XWdem9PR0IiIibNfS0rjv0etEBbdg/P39HRKE6HPz8DD6XFmnBCEOYTKZ8PHxwd/fX4IQByhcnyZVsTtm3flCB0XPQVcGIY4+RrUqizNUtCyuPOe1qmdXbtfZ16bydGeQjqlCCCGE0IQEIUIIIYTQhAQhQgghhNCE+95M1JiqquTl5WE2m8tMm5ObRwM/HQC5OTlk6+SxX0cwmUzo9Xqys7PL3A8GgwGdTueinAkhhCgPCUIqITc3l+TkZLKyssqV3qKqvNw3CIDTyUmc9ZAgxBFUVSUkJISEhIQyO0ApikJ4eDi1atVyUe6EEEKURdMgZObMmcTExPDXX3/h7e3NLbfcwptvvkmrVq1safr06cPGjRvtlvvXv/7F/PnzS1yvqqq89NJLfPrpp1y8eJHu3bszb948WrRoUeU8WywW4uLi0Ol0hIWF4enpWeYF0GxRyTt9CYBGgbXQ6+QumCNYLBYyMjKoVatWqQPiqKrKmTNnSExMpEWLFtIiIoQQ1YSmQcjGjRuZOHEinTt3Ji8vj+eff57IyEgOHz6Mr6+vLd3DDz/MK6+8Yvvu4+NT6nrfeust5syZw5dffkmTJk2YOnUqgwYN4vDhw3h5eVUpz7m5uVgsFiIiIsrMRwGzRUXR5wDg5eUlQYiDWCwWcnNz8fLyKnNUvsDAQE6cOIHJZJIgRAjhXGYzbN4MyckQGgo9e4L87hRL0yBk5cqVdt8XLlxIUFAQe/bsoVevXrbpPj4+hISElGudqqoye/ZsXnzxRYYNGwbAV199RXBwMMuXL2f06NEOyXtZFz1Rvcjw+0IIl4iJgcmTITHxyrTwcHj/fRgxQrt8VVPV6kqalpYGQN26de2mf/PNN9SvX5+2bdvy3HPPldoXIy4ujpSUFAYMGGCbFhAQQNeuXdm2bZtzMi6EEOLaYDbDhg3w7bfW/wt3io+JgZEj7QMQgFOnrNNjYlyZU7dQbTqmWiwWpkyZQvfu3Wnbtq1t+r333kujRo0ICwvjwIEDPPPMMxw5coSYEnZmSkoKAMHBwXbTg4ODbfOulpOTQ05Oju17eno6YH36wmQy2aU1mUyoqorFYsFisZSrbKpa+LNa7uVE6dT8ii1PnVosFlRVldsxpSg41k0mEyZVsZtuUtSSFnMLJlNeoc+uK0/hOnXcOrUpizNUtCzOqM8izGaULVvIO5UM1LXLm7JsGbroaJRTp2zJ1QYNMM+ahXrHHegffxxUlSLtrqqKqigweTJ5t95a6q0ZV+5f+23lOaxeK7KeahOETJw4kUOHDrFlyxa76Y888ojtc7t27QgNDaV///4cP36cZs2aOWTbM2fOZPr06UWmr169uki/D71eT0hICBkZGeTm5pZr/ZZCx9ClS5fwUACzGf22bSgpKaghIeR166bJPcN//etftGzZkv/7v/8rM+1ff/3FXXfdxc6dO+367GjhjTfe4Oeff2bz5s1cunSpzPS5ublcvnyZTZs2kZeXV2b6a1lsbCw5Zij4eVi1ajVGN4/btC5PbGysw9aldVkcqbJlcWR9Fha6bRvtPvsM73Pn0BuMEP0/APa/+ipeWOj85ptFFzp1Ct099/BXVBTXFQpOrqaoKiQmsuOddzjXpg31Dh/G68IFsuvU4VybNrbff1fu38LbWrduncO2Vd4nR69sXWOTJk1ixYoVbNq0qcw37nXt2hWAY8eOFRuEFPQdSU1NJTQ01DY9NTWVjh07FrvO5557jujoaNv3gpfvREZGFnl3THZ2NgkJCdSqVavcnVwtKpBpbV3x8/NDt3wZyhNPoBRqslPDw1Hfe8+l9wz379/PmjVr+PTTT8v16GqXLl24+eab+fzzz3nxxRdLTLdw4UImTJgAWPtihIWFMWDAAN544w2CgoIcknej0Whr0fDz8yuzz0d2djbe3t706tWryp2TayqTyURsbCwDBw7EpCo8vXMdAIMGRbr1+0nA+qIuLcpTuE4d+QK7mrJvKloWZ9RnAWXZMnRvvWXfdJ3vplmz8PHzJdPgxeqW3Vh2fV9O1AkjNP0M4elnCEs/TYMTmZxt3JEGaacJSz+Dl7n41oBup0/jMX9+8a0pd97p0v1beFv9+vUjwNcxv40FdxPKQ9OjV1VV/vOf/7Bs2TI2bNhAkyZNylxm3759AHYBRmFNmjQhJCSEtWvX2oKO9PR0duzYwaOPPlrsMkajEaPRWGS6wWAocqCbzWYURcHDw6PcnVPVQk0hHsuX4XH33UUOdOXUKZS774alS10WiMydO5dRo0ZV6CV9Dz74IA8//DDPP/88en3xh4+Hhwf+/v4cOXIEi8XC/v37GT9+PMnJyaxatcoheS8cdBTsj9J4eHigKEqx+1TYMxgMUOh2jLXO3PdCB2DQuDyOPO60LosjVbYsDj+PzWb4v/8rNgAB2B7Rjp9b92Rlq1vI8rzykreE2iHsLGGV9TMu0CD9DA3ST1v/peX/v3gFDdIvElAorZKUhH70aFi6FMPtd9imG7Ztx3AmxWlP2NjXv95xx2gF1qPp0Ttx4kQWLVrEDz/8gJ+fn63PRkBAAN7e3hw/fpxFixZx6623Uq9ePQ4cOMATTzxBr169aN++vW09rVu3ZubMmdx5550oisKUKVOYMWMGLVq0sD2iGxYWxvDhwzUqaT6zGY8nnij+QFdVUBSYMgWGDXP6rRmz2czSpUv55ptv7Kbn5OQwbdo0Fi1axOnTp4mIiOC5556ztWwMHDiQ8+fPs3HjRvr371/i+hVFsbVKhYWF8fjjjzN16lQuX76Mt7c3n332Ge+++y5xcXE0btyYxx9/nMcee8y2/DPPPMOyZctITEwkJCSEMWPGMG3atBIP7uPHjzNw4EBuvfVWPvjgA3kaRghRfps3F+lMerxuA9vnCaNetn1ufD6JEX+so0vCIVJr1SXJP5BT/kGcCgjiVJ1QTtWqS6anD2dr1eFsrTrsD2tZ7Cb9cjK57nQcQw9v5Pa/tlAnJ8P6+z/ktiuJbh0Cpvz+ijX0CRtNg5B58+YB1gHJCluwYAHjxo3D09OTNWvWMHv2bDIzM4mIiOCuu+4qcivgyJEjtidrAJ5++mkyMzN55JFHuHjxIj169GDlypWaN8P77txmdwumCFWFhATrCXFVnTjagQMHSEtLo1OnTnbTH3jgAbZt28acOXPo0KEDcXFxnD171jbf09OTjh07snnz5lKDkKt5e3tjsVjIy8vjm2++Ydq0aXz44YfccMMN/P777zz88MP4+voyduxYwHqLZeHChYSFhXHw4EEefvhh/Pz8ePrpp4sty5AhQ5gwYQIzZsyoZI0IIa5Zycm2j2d9Anhs+HPsjLjygIR/dgZD/9zEiEPruDHpr6IdTwtMn4768qOkedUi0S/QGpgUBCj+gSQGBHPKP5ALPgFcMvqyM6ItOyPa8sqAR+j9zx5G/LGebm++A3Qsuu6CJ2xc2FruCprfjilNREREkdFSy7MeRVF45ZVX7AY4qw4MqcU/nVNEoRPCWU6ePIlOp7Pro/H333/z3XffERsba3vEuWnTpkWWDQsL4+TJk+Xe1tGjR5k/fz6dOnXCz8+Pl156iXfffZcR+SdSkyZNOHz4MB9//LEtCCkcaDZu3Jgnn3ySxYsXFwlCduzYQVRUFC+88EK5OtcKIUQRhW7vf3DLaHZGtEVvziNPZ71Ebvz4IepkZ5S8vKJYWypeeAGlbVtqT55M7cR/aHv6H+v8iAi46y6YPQWALIORxIBgNjW5kWVt+vBHSHPWtLiZNS1uxi8tC/J7B1gKhzsubi13lWo1TkhNZwou34BrlNDfxZEuX76M0Wi0u22xb98+dDodvXv3LnVZb2/vMns/p6WlUatWLXx8fGjVqhXBwcF88803ZGZmcvz4cSZMmECtWrVs/2bMmMHx48dtyy9ZsoTu3bsTEhJCrVq1ePHFF4mPj7fbRnx8PCNGjGDq1KkSgAghKq9nTwgP54xvHRa3jwRg/rLXbLONljyoV88aBFx9q7fg++zZ1sBgxAg4cQLWr4dFi6z/x8VZA4d8PqYcWp6N56Fdy/n5yyms/vwxHt32PWHpp7lkvPJEZuSEj9gbduU1Jnat5TWE+/ZockOZXbqhhodbe0UX1wpUEE337On0vNSvX5+srCxyc3Px9PQErMFFeZw/f77Mx6P9/PzYu3cvHh4ehIaG2tadmpoKwKeffmp70qlAwdMu27ZtY8yYMUyfPp1BgwYREBDA4sWLeffdd+3SBwYGEhQUxOLFi5kwYUKFOtgKIYSNTgfvv89nH/xEjsFIx6S/6BZ/wD7NJ59Y/y9uNNTZs+1vkeh0RW+p5wc6FPP73/JsPM9s/oqnDq1gU0Ajxt1tbcVPCgjimSGPs/KL/6BTC42F5ILWcleRlhBX0umwvPee9XNZ0bSTFTw5dPjwYdu0du3aYbFYyrwFdujQIW644YZS03h4eNC8eXOaNm1qF9wEBwcTFhbGP//8Q/Pmze3+FTwdtXXrVho1asQLL7xAp06daNGiRbG3f7y9vVm8eDFeXl4MGjSoXOOFCCFEcS4Ovp2vu90FwKSt39n3+/j6G2uQUVIrR3n6aOQHOkCJv/8ekx+nS+Iftsn+2Zc4Wr8Ry9v0sU/vgtZyV5EgxMXUO0dYOxY1aGA/IzzcpR2OAgMDufHGG+0Gh2vcuDFjx47lwQcfZPny5cTFxbFhwwa+++47W5oTJ05w6tQpu2HxK2r69OnMnDmTOXPm8Pfff3Pw4EEWLFjArFmzAGjRogXx8fEsXryY48ePM2fOHJYtW1bsunx9ffnpp5/Q6/UMGTKEjIxS7tsKIUQJFm49QaZFoXWIH/0/fQO+WHBl5rArj83aWjmioqz/V+SPxhFl/P6/8AKEXZn30K7lALzX415yPfTWYCUiwiWt5a4iQYgWqhJNO9BDDz1U5BHdefPmMXLkSB577DFat27Nww8/TGZmpm3+t99+S2RkJI0aNarSdj/77DMWLFhAu3bt6N27NwsXLrS1hNxxxx088cQTTJo0iY4dO7J161amTp1a4vpq1arFr7/+iqqq3HbbbXb5FUKIsmTk5LHgtxMATOzbHKVvX7h7lHM2Vtrvv04Hb79tSzrm918IzDhPYu0QFnccZJ3ootZyV5E+IVop7p6hi40bN46ZM2eybds2unXrBoCXlxezZs2ytUoUlpuby/z581m0aFGZ6x03blypae69917uvffeEue/9dZbvPXWW3bTpkyZYvv88ssvM23aNNvIfLVq1eK3334rdZtCCFGcb7afJO2yiab1fbm1nQtudZT2+z/sDthjHdTROy+Xx7cuZmrkY8zpcS8jn30Qnxr0eC5IS8g1zdvbm6+++spuHJDSxMfH8/zzz9O9e3cn50wIIVwj22Tm081xAPy7TzN0HtVooMNffuWe/7uPCG+Fs94BLAxsX/YybkZaQq5xVw8UV5qCDqRCCFFTLNmVwNmMHBrU9ubOGxqUvYAr9eqJp6eeJ/YmEv3dfuZvOM6YLo0I8Kk5r56QlhAhhBDXpNw8Cx9vtI5P9O/eTTHoquclcVjHBrQMrkV6dh6fbD5e9gJupHrWuBBCCOFky38/RVJaNoF+RkZ1itA6OyXSeSj8X6R10LIvtpzg9KVsjXPkOBKEVFJZQ86L6kX2lxCiMLNFZV5+K8jDPZvgZajeT5xEtgmmQ0RtLpvMfLS+5rSGSBBSQQVvcS1r2HJRveTm5gJXRmUVQlzbfj6YTNzZTGr7GBjTtfJDDriKoig8PcjaGvLNjpMknK8Z1yDpmFpBOp2O2rVrc/r0aQB8fHzKfG282aKi5lkvgtnZ2eir6X1Hd2OxWMjNzSU7OxsPj5Lr1GKxcObMGXx8fNDr5ZAX4lpnsah8tP4YAONvaYKv0T1+F7o3r88tzeqx9fg53l97lHdGddA6S1XmHjVfzYSEWF9EVxCIlMWiqpy+aL2Hp8/0wqM6PQLmxlRV5fLly3h7e5cZCHp4eNCwYcMy0wkhar61f53mr5RL1DLqGXdLY62zUyFPDWrFnR9tJWZvIv/q1ZQWwX5aZ6lKJAipBEVRCA0NJSgoCJPJVGb6y7l5PLLMOjz68kdvxs/Hy9lZvCaYTCY2bdpEr169bLfJSuLp6Vlqa4kQ4hpgNqNu2sSHmy8BOu7rGuF2j7ve0LAOA9sEE3s4lVmLfmNeyAXru2R69nTLkVQlCKkCnU5Xrj4GFo88Tl0yA+BpNOLlJUGII+h0OvLy8vDy8iozCBFCXONiYmDyZH7T1WP/6NcwmnKYMHkkZM9w+SszqupJjwTWqHp+TYUDb02jfcox6/tn3n/f7coifxoKIYSo2WJiYORISEzkw273ABC1fxWBx/+yTo+J0TiDFRATQ6sH7mL4HxsAeLvXA9bpp065X1mQIEQIIURNZjbD5MmgquxucB3bG7XHYDbxyM4YKHh0f8oUa7rqrlBZntjyDXpzHpub3Mi2iHbuV5Z8EoQIIYSouTZvhsREAFsryF2H1hF2Kf+dWaoKCQnWdNVdobI0TEtl9H7ri+7e7v0AKrhXWfJJECKEEKLmSk4G4FBwMzY064SHxcy/ty8tMV21dlUeH9+6GC9TNnsbXMe6Zp1LTFedSRAihBCi5goNBWBut7sBGPrnZhpfLOYinZ+uWrsqj0GZFxi7ZwVg7RtiQSk2XXUmQYgQQoiaq2dPjrXpxMqW3QB4bPv39vMVBSIirI+4Vnc9e1qfgik03tGjO5bil5PJX0FN+KlNL/cpSz4JQoQQQtRcOh0fjZuKqngQ+fc2Wp09eWVewcV89mz3GGNDp7M+hgu2vNfOzuCRHdYnYt7rMQbTrNnuUZZ8EoQIIYSoseLPZfHDBeuQWJOOr7efGR4OS5e619gaI0ZY89yggW3Sg7t/oF72JU7UCeP7iE4aZq7iNA1CZs6cSefOnfHz8yMoKIjhw4dz5MgR2/zz58/zn//8h1atWuHt7U3Dhg15/PHHSUtLK3W948aNQ1EUu3+DBw92dnGEEEJUM/M3HcdsUenVMpD2ezfB+vWwaJH1/7g49wpACowYASdO2Mriu/pXJt5l7Zg6Z+1Rsk3u84iupiOmbty4kYkTJ9K5c2fy8vJ4/vnniYyM5PDhw/j6+pKUlERSUhLvvPMObdq04eTJk/z73/8mKSmJpUuL6d1cyODBg1mwYIHtu9FodHZxhBBCVCMpadks3W19pHVS3+bW2xR9+mibKUe5qixj8sx8tuUESWnZfL39JA/1bKpd3ipA0yBk5cqVdt8XLlxIUFAQe/bsoVevXrRt25b//e9/tvnNmjXjtdde47777iMvL6/UN6IajUbbi+aEEEJcez7d/A+5ZgtdGtelS5O6WmfHqYx6HVMGtOTp/x1g7vpj3HNjA/x2bbM+rluN3y1Trd4dU3CbpW7dkg+WtLQ0/P39y3wl+4YNGwgKCqJOnTr069ePGTNmUK9evWLT5uTkkJOTY/uenp4OWF+QVp4X1JXFZMqz++yIdQps9Sj16RiF69OkKnbTTYqqVbYcwv4cdF15nHGMalUWZ6hoWSpSn+cyc/lmh7UT6r97Na7wPtDumKn8doe2C2L+Rh/+OZvF5yMnM2Xlx7Z5aoMGmGfNQr3zzhK25bhrU0XWo6iqWi2OYIvFwh133MHFixfZsmVLsWnOnj3LTTfdxH333cdrr71W4roWL16Mj48PTZo04fjx4zz//PPUqlWLbdu2FfvCuZdffpnp06cXmb5o0SJ8fHwqX6h8OWZ4eqc1aHqrSx7G6heMCmGnph2zNak8UpbyWRHvQewpDyJ8Vf6vnbnwU62a582Z2z2x7SjvcR21crLY9PFD1L1s/aO64EK/65lnSO7WzSHbKklWVhb33nuvrdGgNNUmCHn00Uf59ddf2bJlC+Hh4UXmp6enM3DgQOrWrcuPP/5Yobem/vPPPzRr1ow1a9bQv3//IvOLawmJiIjg7NmzZVZgeWTl5tHh1XUA7H62FwG+8hZdRzCZTMTGxjJw4EB5i64DFK5Pk6rYjtn9U/vh41mtGk0rrPA56MryOOMY1aoszlDRspRZn2YzypYtpCem0OtoHTLMMDeqA5Ftgp2eN0ep0nbNZjyat2DYgCc5FNKch3Yu48X1n9tmq4oCDRqQd/Qo6HROuzalp6dTv379cgUh1eLonTRpEitWrGDTpk3FBiCXLl1i8ODB+Pn5sWzZsgqfzE2bNqV+/focO3as2CDEaDQW23HVYDA45IfDUKhp22DQywXTwRy1n4SVwWAAu2PWgMFQLX4qKs2gcXkceYxqXRZHqmxZiq3PmBjry90SE1nU7W4yej1Ay4unGHIsB48OFX8CRqt6rtJ2f/sNTiXy5KavGHf3K3x1421M2L2c0EvnAFBUFRITMWzfDn36OO3aVJH1aPqIrqqqTJo0iWXLlrFu3TqaNGlSJE16ejqRkZF4enry448/4uVV8UgtMTGRc+fOEepGQ9kKIYQop5gY62vsExPJMhj5vNMwACZu/haPUe73evtKy39nTO+4vXRJOESu3pM5t4wuMV11oGkQMnHiRL7++msWLVqEn58fKSkppKSkcPnyZeBKAJKZmcnnn39Oenq6LY250KuKW7duzbJlywDIyMjgqaeeYvv27Zw4cYK1a9cybNgwmjdvzqBBgzQppxBCCCcp9Hp7gEUdBnPBJ4BGF5K47c9N1jRu9nr7Ssv/Q1sBntr4FQDftY/kRO3QYtNVB5oGIfPmzSMtLY0+ffoQGhpq+7dkyRIA9u7dy44dOzh48CDNmze3S5OQkGBbz5EjR2xP1uh0Og4cOMAdd9xBy5YtmTBhAjfddBObN2+WsUKEEKKmKfR6+xydnk+7WG+9PLp9KXrV4pavt6+0Qu+W6XzqMH2P78LsoWNWzzHW+dXwPTma3kwsq09snz59ykxz9Xq8vb1ZtWpVlfMmhBDCDRS6tbC07QBS/eoRmn6GEYfWlZiuxip4t8zIkaAoPLnpK9Y368yPbfrw7x3/o82ZE9XuPTny7hghhBDuK//WgslDx7ybRwLwyM4YPC15xaar8Qq9W+b603Hcnn9L6t3Ih6vle3IkCBFCCOG+8m9B/NimN4m1Q6iXeZHR+1dfmV8Nb0E4XaF3y0TfeSM6BdY2aM+em/ponbMiJAgRQgjhvnQ6LLPf56ObRwHw0K5leOflj/tUMEJZNbsF4RL575ZpOu4eRnWKAOCtlUfK1cXBlSQIEUII4dZWtuzG8XoR+Odkct/vv1yZER5eLW9BuNrj/VvgqfdgR9x5Nh89q3V27EgQIoQQwm2pqsrc9ccAGDe4PX6rfoFFi6yvuY+Lu+YDEICw2t7cf3MjAN5eVb1aQ9x3qD0hhBDXvA1HzvBHUjo+njrG92gKvq21zlK19FifZizeGc/BU2msPJTCkHbVo6OutIQIIYRwS6qq8sG6owDcd3Mj6vh6apyj6qteLSMTejYF4J3VRzBbqkdriAQhQggh3NK2f86xN/4innoPHupR9LUfwt5DPZtQ28fA8TOZxOxN1Do7gAQhQggh3FRBX5B7OkUQ5C9vJy+Lv5eBx/o0A2D2mqPk5lk0zpEEIUIIIdzQ7/EX+O3YOfQeCv/q3VTr7LiNB7o1JtjfyKmLl/lud0LZCziZBCFCCCHcTkEryPAbGhBex0fj3LgPL4OO//RrAcDHG//RODcShAghhHAHZjPKxo002LSJIyvWs+bP0ygKPJp/e0GU3z2dI2hY14dzmbm2acqWLZq8aViCECGEENVbTAw0box+4EA6zZrF/IVrAbi1jplmgbU0zpz7Meg8iK6dZjdNP3QoNG5srWsXkiBECCFE9RUTY30rbKL1aY7jdRvwc+seAEx8d4rLL5o1QkwMd/z7LlqeOWk//dQpa127sE4lCBFCCFE9mc0weTIUGuFz3s0jURUP+h/baX01/ZQpmtxGcFv5deqhWnj8t0W2yWd8al+pZxfWqQQhQgghqqfNm20tIACJ/oEsb9MXgInbllgvmgkJ1nSifArVad9/dtsm17mcbv3g4jqVIEQIIUT1lJxs9/XjrneRp9PT/cQ+bkw6UmI6UYpCdaUUmqxXLSWmcyYJQoQQQlRPoVfeb3Latw5L2kcC+a0gJaQTZShvXbmoTiUIEUIIUT317Anh4aAofNZ5OLl6T2489Sfd4g9a5ysKRERY04nyKVSnxXJxnUoQIoQQonrS6eD997ng5cfXN9wKwKStS6y3EQouorNnW9OJ8smvU6BoIKJBnUoQIoQQovoaMYIFb3xFlqc3bVKPX+lMGR4OS5fCiBHa5s8djRhhrbvQMPvpGtSpBCFCCCGqrUvZJhaet76c7t8DWrEnOpq82FiIi5MApCpGjIA/D9u+5v30kyZ1qnfp1oQQQogK+O/2k6Rn59E00JfIkbewqlY2HXr3llswjlCoDtUePTSpU01bQmbOnEnnzp3x8/MjKCiI4cOHc+TIEbs02dnZTJw4kXr16lGrVi3uuusuUlNTS12vqqpMmzaN0NBQvL29GTBgAEePHnVmUYQQQjjY5Vwzn2+OA+CxPs3ReZTQmVK4LU2DkI0bNzJx4kS2b99ObGwsJpOJyMhIMjMzbWmeeOIJfvrpJ77//ns2btxIUlISI8poLnrrrbeYM2cO8+fPZ8eOHfj6+jJo0CCys7OdXSQhhBAOsnhXPOcycwmv482wjmFlLyDcjqa3Y1auXGn3feHChQQFBbFnzx569epFWloan3/+OYsWLaJfv34ALFiwgOuuu47t27dz8803F1mnqqrMnj2bF198kWHDhgHw1VdfERwczPLlyxk9erTzCyaEEKJKcvMsfLLJ+qr5f/duhkHngckiw7PXNNWqT0hamvWtfnXr1gVgz549mEwmBgwYYEvTunVrGjZsyLZt24oNQuLi4khJSbFbJiAggK5du7Jt27Zig5CcnBxycnJs39PTrcPXmkwmTCZTlctlMuXZfXbEOgW2epT6dIzC9WlSFbvpJkUtaTG3YH8Ouq48zjhGtSqLM5RWlu93J5Kclk2Qn5Hh7YPtfo9dcc5rd8y4brvOujZVZD3VJgixWCxMmTKF7t2707ZtWwBSUlLw9PSkdu3admmDg4NJSUkpdj0F04ODg8u9zMyZM5k+fXqR6atXr8bHx6eiRSkixwwFVb1u3TqM0p/KoWJjY7XOQo0SGxtrd8yuWrXa7Y9ZrcvjyGNU67I4UkllMavw3u86QOGWulmsjV1lt5wrznmt6tmV23XWtSkrK6vcaatNEDJx4kQOHTrEli1bXL7t5557jujoaNv39PR0IiIiiIyMxN/fv8rrz8rN4+md6wDo168fAb5eVV6nsEbbsbGxDBw4EIPBoHV23F7h+jSpiu2YHTQoEh/PavNTUSmFz0FXlscZx6hWZXGGksry4/5kzm4/SB0fA9Mf6Geb7spzXqt6duV2nXVtKribUB7V4uidNGkSK1asYNOmTYSHh9umh4SEkJuby8WLF+1aQ1JTUwkJCSl2XQXTU1NTCS009n1qaiodO3Ysdhmj0YjRaCwy3WAwOORANxRq2jYY9HLBdDBH7SdhZTAYwO6YNWAwVIufikozaFweRx6jWpfFkYori8Wi8nH+EzEPdm9CgK930eVccM5rVc+u3K6zrk0VWY+mT8eoqsqkSZNYtmwZ69ato0mTJnbzb7rpJgwGA2vXrrVNO3LkCPHx8XTr1q3YdTZp0oSQkBC7ZdLT09mxY0eJywghhNCY2QwbNhD7yVL+Ts3Az6jngVsaa50r4WSaBiETJ07k66+/ZtGiRfj5+ZGSkkJKSgqXL18GrB1KJ0yYQHR0NOvXr2fPnj2MHz+ebt262XVKbd26NcuWLQNAURSmTJnCjBkz+PHHHzl48CAPPPAAYWFhDB8+XItiCiGEKMt1bVD79mXutkQA7t/zEwG//qRxpoSzadqON2/ePAD69OljN33BggWMGzcOgPfeew8PDw/uuusucnJyGDRoEB999JFd+iNHjtierAF4+umnyczM5JFHHuHixYv06NGDlStX4uUlfTGEEKJaSjrF5sY3cCC0JV6mbCas/xp++UjeD1PDaRqEqGrZjx55eXkxd+5c5s6dW+71KIrCK6+8wiuvvFLlPAohhHASs/24Hx/ecg8AUftXUS8rzfpW1ylTYNgwGaa9hpIX2AkhhNDGb1ttH/eHtGBnRFsMZhOP7IyxTlRVSEiAzZs1yqBwNglChBBCaKPQ2E2LOwwCYOifmwm9dM4+XXKyK3MlXEiCECGEENooNNTCr626A3D/3hVF0xUabkHULBKECCGE0Eb3W2wfc/WetEs+Ssfkv6/MVxSIiICePTXInHAFCUKEEEJo46rOpvf//gu24bOU/E+zZ0un1BpMghAhhBCa88/JZOifm65MCA+Xx3OvAe473q8QQogaY0Sf6/AettLaCTU01HoLRlpAajwJQoQQQmgi/vyVt63e06UhhLbTMDdCC3I7RgghhCaW7EqwfW5Uz1fDnAitSBAihBDC5bJNZmL2ntI6G0JjEoQIIYRwuZ/2J5F22aR1NoTGJAgRQgjhcv/dflLrLIhqQIIQIYQQLrUv4SIHEtMw6JSyE4saTYIQIYQQLvXfbdZWkCFtZTj2a50EIUIIIVzmfGYuPx1IAiCqS4TGuRFakyBECCGEy3y/O4HcPAttG/jTPjxA6+wIjUkQIoQQwiXMFpWvd1hvxTxwc2MURfqEXOskCBFCCOESG/8+TcL5ywR4GxjaIUzr7IhqQIZtF0II4VxmM2zezH9/uwgYGHVjA7w9dWTl5mmdM6ExaQkRQgjhPDEx0Lgx8cNHsyHd+kK6+56+3zpdXPMkCBFCCOEcMTEwciQkJvJNxyGoige9/tlD4yP7rNN/+FHrHAqNSRAihBDC8cxmmDwZVJVcDz1L2w0A4L7ffwFVtaZ5+mkNMyiqAwlChBBCON7mzZCYCMCaFl0551uboEvn6Hd8l3W+qsKpRA0zKKoDCUKEEEI4XnKy7eO3HQYBMOrgGvSqRasciWpI0yBk06ZNDB06lLCwMBRFYfny5XbzFUUp9t/bb79d4jpffvnlIulbt27t5JIIIYSwE2odkj3BP4gtjTsCcM+B1RpmSFRHmgYhmZmZdOjQgblz5xY7Pzk52e7fF198gaIo3HXXXaWu9/rrr7dbbsuWLc7IvhBCiJL07Anh4XzfIRJV8aD7iX00TEu9Ml9RoEG4dvkT1YKm44QMGTKEIUOGlDg/JCTE7vsPP/xA3759adq0aanr1ev1RZYVQgjhQjodebPf57t1lwAYvX/VlXkFI6W+9Rbs0SBvotpwm8HKUlNT+fnnn/nyyy/LTHv06FHCwsLw8vKiW7duzJw5k4YNG5aYPicnh5ycHNv39PR0AEwmEyaTqcp5N5ny7D47Yp0CWz1KfTpG4fo0qYrddJOiapUth7A/B11XHmcco1qVpTLWtbqZlF2/Uyc7g8ij22zT1QYNML/7LqZbh8CedUD5yuLKc167Y8Z123XWtaki63GbIOTLL7/Ez8+PESNGlJqua9euLFy4kFatWpGcnMz06dPp2bMnhw4dws/Pr9hlZs6cyfTp04tMX716NT4+PlXOe44ZCqp63bp1GHVVXqUoJDY2Vuss1CixsbF2x+yqVavd/pjVujyOPEa1LktFfPaXB+BBh8Ze7Hr5JbwuXCC7Th3OtWkDOh05q1ZTmbK44pzXqp5duV1nXZuysrLKnVZRVbVahNGKorBs2TKGDx9e7PzWrVszcOBAPvjggwqt9+LFizRq1IhZs2YxYcKEYtMU1xISERHB2bNn8ff3r9D2ipOVm0eHV63R/u5nexHg61XldQprtB0bG8vAgQMxGAxaZ8ftFa5Pk6rYjtn9U/vh4+k2f68Uq/A56MryOOMY1aosFXX6Ug693tmE2aLy639uoXlQrSJpKloWV57zWtWzK7frrGtTeno69evXJy0trcxraPU8eq+yefNmjhw5wpIlSyq8bO3atWnZsiXHjh0rMY3RaMRoNBaZbjAYHHKgGwo1bRsMerlgOpij9pOwMhgMYHfMGjAY3OKnokQGjcvjyGNU67KU1/L9JzFbVDo1qsN1DeoUm6ayZXHFOa9VPbtyu866NlVkPW4xTsjnn3/OTTfdRIcOHSq8bEZGBsePHyc0/3ExIYQQzmWxqCzeFQ/A6C4l98cTQtMgJCMjg3379rFv3z4A4uLi2LdvH/Hx8bY06enpfP/99zz00EPFrqN///58+OGHtu9PPvkkGzdu5MSJE2zdupU777wTnU5HVFSUU8sihBDCauvxcyScv4yfUc+t7eRJRVEyTdvxdu/eTd++fW3fo6OjARg7diwLFy4EYPHixaiqWmIQcfz4cc6ePWv7npiYSFRUFOfOnSMwMJAePXqwfft2AgMDnVcQIYQQNgWtIMNuCKu2fVZE9aDp0dGnTx/K6hf7yCOP8Mgjj5Q4/8SJE3bfFy9e7IisCSGEqITzmbms/sM6KNnoznIrRpTOLfqECCGEcA8xexPJNVto1yCAtg0CtM6OqOaknUwIIUTVmM2weTNqUjLfHrcGHvd0jtA4U8IdSEuIEEKIyouJgcaNoW9fdj89g+OZKt55OQxLkPHYRdkkCBFCCFE5MTEwciQkJgKwuEMkALcf3oTfPSOt84UohQQhQgghKs5shsmTIf/hgjSjLz+37gHA6AP5L6ubMsWaTogSSBAihBCi4jZvtrWAAPzYpjfZBi9anD3Jjaf+sgYnCQnWdEKUQIIQIYQQFZecbPuoAt92GATA6P2rUEpIJ8TVJAgRQghRcYVehXEouBmHg5vhmWdixKH1JaYT4moShAghhKi4nj0hPBwUhW87DAZg0N9bqZN9yTpfUSAiwppOiBJIECKEEKLidDp4/30yDV782KY3AFH78zukKvk3ZGbPtqYTogQShAghhKicESP4ec4iMow+NLqQxM3xB63Tw8Nh6VIYMULb/IlqT0ZMFUIIUWmLzYHARe7p2giPPt9Y+4D07CktIKJcJAgRQghRKX+nXmJv/EV0HgojR/YCfy+tsyTcjNyOEUIIUSnf7owHoH/rIIIkABGVIEGIEEKICss2mVn2+ykAoro01Dg3wl1JECKEEKLCVv2RwsUsE6EBXvRqGah1doSbkiBECCFEhS3emQDAqE4R6DyUMlILUTwJQoQQQlTIibOZbPvnHIoCd3cK1zo7wo1JECKEEKJCluy2toL0ahFIeB0fjXMj3JkEIUIIIcrNZLbw/W7r23OjukRonBvh7iQIEUIIUW5r/zzN2Ywc6tfypP91wVpnR7g5CUKEEEKU2+Jd1rFB7ropHINOLiGiamTEVCGEEKUzm2HzZpJOprDxiB8AozvL2CCi6jQNYzdt2sTQoUMJCwtDURSWL19uN3/cuHEoimL3b/DgwWWud+7cuTRu3BgvLy+6du3Kzp07nVQCIYSo4WJioHFj6NuX7z79ERW4OeUITTat0jpnogbQNAjJzMykQ4cOzJ07t8Q0gwcPJjk52fbv22+/LXWdS5YsITo6mpdeeom9e/fSoUMHBg0axOnTpx2dfSGEqNliYmDkSEhMxKx48F37gQBE7frJOj0mRuMMCnen6e2YIUOGMGTIkFLTGI1GQkJCyr3OWbNm8fDDDzN+/HgA5s+fz88//8wXX3zBs88+W6X8CiHENcNshsmTQVUB2Ny4I0n+QQRcvsSgI79Z00yZAsOGyRtzRaVV+z4hGzZsICgoiDp16tCvXz9mzJhBvXr1ik2bm5vLnj17eO6552zTPDw8GDBgANu2bStxGzk5OeTk5Ni+p6enA2AymTCZTFUug8mUZ/fZEesU2OpR6tMxCtenSVXsppsUVatsOYT9Oei68jjjGHVVWZSNG9EnJtq+L+4wCIA7/1iPlzm/PAkJ5K1fj9q7d6W2UdGyuPKc1+6Ycd12nXVtqsh6qnUQMnjwYEaMGEGTJk04fvw4zz//PEOGDGHbtm3oiom8z549i9lsJjjY/rGx4OBg/vrrrxK3M3PmTKZPn15k+urVq/HxqfpAPDlmKKjqdevWYZQ/GhwqNjZW6yzUKLGxsXbH7KpVq93+mNW6PI48Rl1VlgabNtEp//MZn9qsad4VgKj99n1B9v36K6cyMyu1jcqWxRXnvFbHjCu366xrU1ZWVrnTVjgIGTt2LBMmTKBXr14VXbTCRo8ebfvcrl072rdvT7NmzdiwYQP9+/d32Haee+45oqOjbd/T09OJiIggMjISf3//Kq8/KzePp3euA6Bfv34E+Morrx3BZDIRGxvLwIEDMRgMWmfH7RWuT5Oq2I7ZQYMi8fGs1n+vlKnwOejK8jjjGHVVWRRfX5g1C4Cl7fqTp9Nzw6m/aHX2pF26jkOG0KGSLSEVLYsrz3mtjhlXbtdZ16aCuwnlUeHSpaWlMWDAABo1asT48eMZO3YsDRo0qOhqKqVp06bUr1+fY8eOFRuE1K9fH51OR2pqqt301NTUUvuVGI1GjEZjkekGg8EhB7qhUNO2waCXC6aDOWo/CSuDwQB2x6wBg8G9gxCDxuVx5DHqsrL07Qvh4ainTrGkvfVWzOgDhVpBFAXCw9H37VvpPiGVLYsrznmtjhlXbtdZ16aKrKfCT8csX76cU6dO8eijj7JkyRIaN27MkCFDWLp0qdPv0yUmJnLu3DlCQ0OLne/p6clNN93E2rVrbdMsFgtr166lW7duTs2bEELUKDodvP8+2yPacqJuGL45Wdz+52brPCX/4jV7tnRKFVVSqUd0AwMDiY6OZv/+/ezYsYPmzZtz//33ExYWxhNPPMHRo0fLtZ6MjAz27dvHvn37AIiLi2Pfvn3Ex8eTkZHBU089xfbt2zlx4gRr165l2LBhNG/enEGDBtnW0b9/fz788EPb9+joaD799FO+/PJL/vzzTx599FEyMzNtT8sIIYQopxEjWDxpBgB3/LkRX1O2dXp4OCxdCiNGaJg5URNUqZ0nOTmZ2NhYYmNj0el03HrrrRw8eJA2bdrw1ltv8cQTT5S6/O7du+nbt6/te0G/jLFjxzJv3jwOHDjAl19+ycWLFwkLCyMyMpJXX33V7tbJ8ePHOXv2rO37Pffcw5kzZ5g2bRopKSl07NiRlStXFumsKoQQonQXs3L5Nc0AWIh69E4Y2x1CQ6FnT2kBEQ5R4SDEZDLx448/smDBAlavXk379u2ZMmUK9957r60T57Jly3jwwQfLDEL69OmDqpb8+NGqVWWPyHfixIki0yZNmsSkSZPKXFYIIUTJlv1+itw8C9eF+tPujh5XbsMI4SAVDkJCQ0OxWCxERUWxc+dOOnbsWCRN3759qV27tgOyJ4QQQguqqrJ4ZwIAUV0iUCQAEU5Q4SDkvffeY9SoUXh5lfwoT+3atYmLi6tSxoQQQmjn94SLHEm9hJfBg2EdXfMEpLj2VDgIuf/++52RDyGEENXIkvxWkFvbhRLgLY/AC+fQ9AV2Qgghqp+MnDx+OpAEwOjODTXOjajJJAgRQghh58d9SWTlmmkW6EvnxnW0zo6owSQIEUIIYWfJrnjA2goiHVKFM0kQIoQQwuZwUjr7E9Mw6BRG3CgdUoVzSRAihBDCZnF+K0hkmxDq1Sr6Ti0hHEmCECGEEABkm8ws+/0UAKO7RGicG3EtkCBECCEEAL8cTOZSdh7hdbzp3qy+1tkR1wAJQoQQQgDYRki9p1MEHh7SIVU4nwQhQgghOHY6g50nzuOhwKhOcitGuEaV3qIrhBDCzZnNsHkz3+05Dxjp1yqQkICSX8shhCNJS4gQQlyrYmKgcWNy+w/kfycuAzB6/svW6UK4gAQhQghxLYqJgZEjITGR2BZdOedbm+BL5+iza7V1ugQiwgUkCBFCiGuN2QyTJ4OqArC4wyAARh2MRW8xW9NMmWJNJ4QTSRAihBDXms2bITERgAT/ILY07gjAPQdirfNVFRISrOmEcCIJQoQQ4lqTnGz7+F2HSFTFgx4nficiLbXEdEI4gwQhQghxrQkNBSBP8eC7dgMBGL1/VYnphHAWeURXCCGuNT17Qng4G73CSPWrR92sNAYe3X5lvqJAeLg1nRBOJC0hQghxrdHp4P33+bZ9JAB3HVqL0Zxnnafkj5Q6e7Y1nRBOJEGIEEJcg1IH3Mr6Fl0BuOfA6iszwsNh6VIYMUKjnIlridyOEUKIa9D3uxMwo9C5UR2aL/2vtRNqaKj1Foy0gAgX0bQlZNOmTQwdOpSwsDAURWH58uW2eSaTiWeeeYZ27drh6+tLWFgYDzzwAElJSaWu8+WXX0ZRFLt/rVu3dnJJhBDCfVgsKkt2W19WN7pLQ+jTB6KirP9LACJcSNMgJDMzkw4dOjB37twi87Kysti7dy9Tp05l7969xMTEcOTIEe64444y13v99deTnJxs+7dlyxZnZF8IIdzS1uPnSDh/GT8vPbe2kydghHY0vR0zZMgQhgwZUuy8gIAAYmNj7aZ9+OGHdOnShfj4eBo2bFjievV6PSEhIQ7NqxBC1BTf7ooHYHjHBnh7SsuH0I5bdUxNS0tDURRq165darqjR48SFhZG06ZNGTNmDPHx8a7JoBBCVHPnMnJY/UcKAKO7RGicG3Gtc5uOqdnZ2TzzzDNERUXh7+9fYrquXbuycOFCWrVqRXJyMtOnT6dnz54cOnQIPz+/YpfJyckhJyfH9j09PR2w9ksxmUxVzrvJlGf32RHrFNjqUerTMQrXp0lV7KabFFWrbDmE/TnouvI44xitalm+3x2PyazSroE/LQN9ND1/KloWV57z2h0zrtuus65NFVmPWwQhJpOJu+++G1VVmTdvXqlpC9/ead++PV27dqVRo0Z89913TJgwodhlZs6cyfTp04tMX716NT4+PlXLPJBjhoKqXrduHUZp/XSoq2/biaqJjY21O2ZXrVrt9ses1uVx5DFalbKoKizYrwMUrjNe4JdffnFYviqjsmVxxTmv1THjyu0669qUlZVV7rTVPggpCEBOnjzJunXrSm0FKU7t2rVp2bIlx44dKzHNc889R3R0tO17eno6ERERREZGVnh7xcnKzePpnesA6NevHwG+XlVep7AeG7GxsQwcOBCDwaB1dtxe4fo0qYrtmB00KBIfz2r/U1GqwuegK8vjjGO0KmXZffICqdt34W3w4Nmofvh5abtfK1oWV57zWh0zrtyus65NBXcTyqNa/7IUBCBHjx5l/fr11KtXr8LryMjI4Pjx49x///0lpjEajRiNxiLTDQaDQw50Q6GmbYNBLxdMB3PUfhJWBoMB7I5ZAwZDtf6pKJNB4/I48hitSlm+32sd4mBohzDq+nk7JD9VUdmyuOKc1+qYceV2nXVtqsh6NO2YmpGRwb59+9i3bx8AcXFx7Nu3j/j4eEwmEyNHjmT37t188803mM1mUlJSSElJITc317aO/v378+GHH9q+P/nkk2zcuJETJ06wdetW7rzzTnQ6HVFRUa4unhBCVBtpl038ctD6VtzRXUp+ulAIV9L0z5vdu3fTt29f2/eCWyJjx47l5Zdf5scffwSgY8eOdsutX7+ePn36AHD8+HHOnj1rm5eYmEhUVBTnzp0jMDCQHj16sH37dgIDA51bGCGEqMZ+2HeKbJOFlsG1uCGittbZEQLQOAjp06cPqlpyz9/S5hU4ceKE3ffFixdXNVtCCFFzmM2omzbx7cZLgI7RncJRFKXMxYRwBbcaJ0QIIUQFxMRA48YcvPcR/szW4ZmXy4hxt1qnC1ENSBAihBA1UUwMjBwJiYl822EQAIP/3krtuKPW6RKIiGpAghAhhKhpzGaYPBlUlUyDFz9e1xuA0ftXWQcLAZgyxZpOCA1JECKEEDXN5s2QmAjAz617kmn0ofH5JLrFH7TOV1VISLCmE0JDEoQIIURNk5xs+1hwK+buA6sp0h21UDohtCBBiBBC1DShoQAcqd+I3xu0Rm/OY+ShNSWmE0Ir7j0MohBCiKJ69oTwcL5tZW0F6X9sJ0GZF6/MVxQID7emE0JDEoQIIURNo9OR/d77LNtsHV169IFVV+YVjBEyezbo3PzNhMLtye0YIYSogVY170qatx9hmefpFff7lRnh4bB0KYwYoV3mhMgnQYgQolSPPPQgiqLY/tWrV4/Bgwdz4MABW5rC8wMCAujevTvr1q2zzd+0aRNDhw4lLCwMRVFYvny5BiWpeUrbN9/ujAdg24cPoFctKECAry/dw8NZV7u2bR3z5s2jffv2+Pv74+/vT7du3fj111+1KVA15Yhz4OWXX7ZLoygKrVu3LnPb48aNq/K2GzduXGTbiqLwxOP/cVANVZ4EIUKIMg0ePJjk5GSSk5NZu3Yter2e22+/3S7NggULSE5O5rfffqN+/frcfvvt/PPPPwBkZmbSoUMH5s6dq0X2a7Ti9s3gW29j+z/nbXdebPtm+3bqBwba7Zvw8HDeeOMN9uzZw+7du+nXrx/Dhg3jjz/+0LBU1U9VzwGA66+/3raO5ORktmzZ4pJt79q1y267sbGxANx518iqVIlDSBAihCiT0WgkJCSEkJAQOnbsyLPPPktCQgJnzpyxpalduzYhISG0bduWefPmcfnyZduP3ZAhQ5gxYwZ33nmnVkWosYrbN8mnEjFnpdG7pfXFnaXtm6FDh3LrrbfSokULWrZsyWuvvUatWrXYvn27lsWqdqp6DgDo9XrbOkJCQqhfv75Lth0YGGi33RUrVtCsWTN69urloNqpPAlChBAVkpGRwddff03z5s2pV69esWm8vb0ByM3NdWXWrnkZGRl89d//Yqwbhoe3H6M7NyySprR9YzabWbx4MZmZmXTr1s3p+XVXlT0Hjh49SlhYGE2bNmXMmDHEx8e7bNsFcnNz+frrr3nwwQerxYsM5ekYIUSZVqxYQa1atQDrrZXQ0FBWrFiBh0fRv2OysrJ48cUX0el09O7d29VZveZcvW/qBgZTZ+hzBPp50/+6ILu0Je2bgwcP0q1bN7Kzs6lVqxbLli2jTZs2Li1HdVfVc6Br164sXLiQVq1akZyczPTp0+nZsyeHDh3Cz8/PqdsubPny5Vy8eJFx48ZVtAqcQoIQIUSZ+vbty7x58wC4cOECH330EUOGDGHnzp00atQIgKioKHQ6HZcvXyYwMJDPP/+c9u3ba5nta8LV+ybqienEff8yD8xdikFnvUiVtW9atWrFvn37SEtLY+nSpYwdO5aNGzdKIFJIVc+BIUOG2NbVvn17unbtSqNGjfjuu++YMGGCU7dd2Oeff86QIUMICwsjKzev6hVTRRKECCHK5OvrS/PmzW3fP/vsMwICAvj000+ZMWMGAO+99x4DBgwgICCAwMBArbJ6zSm8b05dvExe90dQd64l88AqoD9Q9r7x9PS0reOmm25i165dvP/++3z88ccuK0d15+hzoHbt2rRs2ZJjx465bNsnT55kzZo1xFSjNyhLECKEqDBFUfDw8ODy5cu2aSEhIXY/lML1vtuVgIqCTueBUbnyhtyK7huLxUJOTo4zslhjVPUcyMjI4Pjx49x///0u2/aCBQsICgritttuq/A2nUWCECFEmXJyckhJSQGszcEffvghGRkZDB06tFzLZ2Rk2P3FFxcXx759+6hbty4NGxbtPCnKr2DfmC0qX/6yhfMbYzDnXC73vnnuuecYMmQIDRs25NKlSyxatIgNGzawatWqshe+hlT1HHjyyScZOnQojRo1IikpiZdeegmdTkdUVJTTtw3WwHLBggWMHTsWvb76XPqrT06EENXWypUrCc1/2Zmfnx+tW7fm+++/p0+fPuVafvfu3fTt29f2PTo6GoCxY8eycOFCR2f3mlJ43yie3ngFRvDNt0vKvW9Onz7NAw88QHJyMgEBAbRv355Vq1YxcOBAJ+ba/VT1HEhMTCQqKopz584RGBhIjx492L59e7luXVZ12wBr1qwhPj6eBx98sNzLuIIEIUKIUn3y2Rd8/dVXpaZRVbXU+X369Ckzjai4wvvmX//dzao/UhnfvTFRQ6+3pSmr3j///HOn5rEmcMQ5sHjx4kpte+HChWUG6uU5tyIjI6vlOSjjhAghhJs7fSmbtX+eBiCqi9zeEu5DWkKEEMJdmc2wYQtL950jz+LFjQ1r0zK49DEnhKhOpCVECCHc1XVtUPv2Zcmf5wEYvWQOVKPHL4UoiwQhQgjhrpJOsa1hO07WCaNWTha3b/8RRo6UQES4DU2DkLJe762qKtOmTSM0NBRvb28GDBjA0aNHy1zv3Llzady4MV5eXnTt2pWdO3c6qQRCCOFiZrPd18UdBgFwx+GN+ORmWydOmVIknRDVkaZBSFmv937rrbeYM2cO8+fPZ8eOHfj6+jJo0CCys7NLXOeSJUuIjo7mpZdeYu/evXTo0IFBgwZx+vRpZxVDCCFc57etto+Hg5qwonVPAO7dv9I6UVUhIQE2b9Yid0JUiKZBSGmv91ZVldmzZ/Piiy8ybNgw2rdvz1dffUVSUlKRFpPCZs2axcMPP8z48eNp06YN8+fPx8fHhy+++MKJJRFCCBfJH7QK4JX+j2Dx0HHH4Q20TT1uny452cUZE6Liqu3TMXFxcaSkpDBgwADbtICAALp27cq2bdsYPXp0kWVyc3PZs2cPzz33nG2ah4cHAwYMYNu2bSVuKycnx26I4vT0dABMJhMmk6nKZTGZ8uw+O2KdAls9Sn06RuH6NKmK3XSTUv3GF6gI+3PQdeVxxjGaF1gfsL6i/UBoS2rlZPHC+qJ/ZOUFBqJW83OjovvFlee8dseM67brrGtTRdZTbYOQgiFqg4OD7aYHBwfb5l3t7NmzmM3mYpf566+/StzWzJkzmT59epHpq1evxsfHp6JZLyLHDAVVvW7dOoy6Kq9SFBIbG6t1FmqU2NhYu2N21arVbn/Mal0eRx6jOblmwGj7PmXLNwRnnLd9V4HL9esTm54Ov/zisO06Q2X3iyvOea2OGVdu11nXpqysrHKnrbZBiCs999xztmGkwdoSEhERQWRkJP7+/lVef1ZuHk/vXAdAv379CPD1qvI6hTXajo2NZeDAgRgMBq2z4/YK16dJVWzH7KBBkfh4uvdPReFz0JXlccYxmpWbx9N7rGVpeeYk4/b8ZJunKtYWLM+5c7m1Au8V0UpF94srz3mtjhlXbtdZ16aCuwnlUW1/WUJCQgBITU21jZlf8L1jx47FLlO/fn10Oh2pqal201NTU23rK47RaMRoNBaZbjAYHHKgGwo1bRsMerlgOpij9pOwMhgMYHfMGjAYqu1PRbkYNC6PI4/Rw/Fpts8vHliGXrXYvivh4TB7NvoRIxyyLWer7H5xxTmv1THjyu0669pUkfVU23FCmjRpQkhICGvXrrVNS09PZ8eOHXTr1q3YZTw9PbnpppvslrFYLKxdu7bEZYQQwl2YLSqvrvjT9r3TphWwfj0sWmT9Py4O3CQAEQI0bgkp6/XeU6ZMYcaMGbRo0YImTZowdepUwsLCGD58uG2Z/v37c+eddzJp0iTA+nbOsWPH0qlTJ7p06cLs2bPJzMxk/Pjxri6eEEI41KIdJzmcXKipW6eDCrxJVYjqRtMgpKzXez/99NNkZmbyyCOPcPHiRXr06MHKlSvx8rpy3+r48eOcPXvW9v2ee+7hzJkzTJs2jZSUFDp27MjKlSuLdFYVQohqz2y2jveRnMzZuiG8vS1X6xwJ4VCaBiFlvd5bURReeeUVXnnllRLTnDhxosi0SZMm2VpGhBDCLcXEwOTJkJgIwBu3Tia93UCu87bw5+VqeyddiAqRI1kIIaqbmBjrO2DyA5DdDa5jabuBAEz74gUtcyaEQ0kQIoQQ1YnZbG0ByW8lzlM8eDHyMQDu2b+KDillvz9LCHchQYgQQlQnmzfbWkAAvrrxdv4KakLA5Us8s/FLW3AiRE0gQYgQQlQnhd75kugfyHs9xwDw9MYvqXu5/INACeEOJAgRQojqJH9wxgT/IEZHzeSS0ZcOSX8z+sBqjTMmhOO59zCIQghR0/TsSUKrDozu+zinAoJpdCGJectfR1cwMqqilL68EG5EghAhhKhGEtJyGH33DE7lKjS+kMS33z5H6KVz1pkSgIgaRm7HCCFENRF/LovRn2znVK5CE6OFxevnXAlAAMLD4etvtMugEA4mLSFCCKGFQqOhEhrKyTY3EfX5TpLSsmla35dvH7mZ4Gm32qWhZ08wq7Bnlda5F8IhJAgRQghXu2o01BO1Q4m6702SfevSNNCXxQ/fTJB//usprn43jDnPtXkVwokkCBFCCFcqGA01f7yPE7VDGX3vTFJ869LsXALfdgy6EoAIUcNJnxAhhHCVq0ZDjasTxj33vkGKX31anD3J4m+fJ+jpKdZ0QlwDJAgRQghXKTQa6j91wrgnaiapfvVoeeYki759gcDMC5CQYE0nxDVAbscIIYSr5I+GerxuA6JGv87pggBk8fPUz0orkk6Imk6CECGEcJXQUI7VDScq6nXO1KpL69NxfLP4BepdPRx7/qipQtR0EoQIIYSLHGt1A6Pve5Oz3gHFByCKYh0LpGdP7TIphAtJnxAhhHCBo6mXGP35Ls56B3Bd6j8sWvJi0QAEYPZs0Ok0yaMQriZBiBBCONnR1AyiPt3O2Ywc2oT6s2hwA+rW9bNPFB4OS5fCiBHaZFIIDcjtGCGEcKKkLJi+YBfnM01cH+bP1xO6UsfXE0YOKzoaqrSAiGuMBCFCCOEMZjN//7yeefsspCsm2ob58/VDXant42mdr9MVHQ1ViGuM3I4RQghHi4nhz47duX/dWdIVT9olH+Xr9x+i9soVWudMiGpFghAhhHCkmBgOP/ok9/Z9nPM+AbRP/puvl7xI7bij1uHaY2K0zqEQ1YYEIUII4ShmM3+8/DZj7pnBBZ8AOiT9zX+XTCUgJ9M2VDtTpsiw7ELkq/ZBSOPGjVEUpci/iRMnFpt+4cKFRdJ6ecnLoIQQznfo5w2M6ft4fgByhP8uedEagBRQVRmWXYhCqn3H1F27dmEu9FfDoUOHGDhwIKNGjSpxGX9/f44cOWL7rhQ8fy+EEI5gNhd5suVQSgZjdlwmzdufjkl/8dWSafjnZhW/vAzLLgTgBkFIYGCg3fc33niDZs2a0bt37xKXURSFkJAQZ2dNCHEtiomxvgk3/0V0AAfb38J9tz9HmlnhxlN/8uV30/DLvVzyOmRYdiEANwhCCsvNzeXrr78mOjq61NaNjIwMGjVqhMVi4cYbb+T111/n+uuvLzF9Tk4OOTk5tu/p6dZRDE0mEyaTqcr5Npny7D47Yp0CWz1KfTpG4fo0qYrddJOiapUth7A/BytfHmXZMnSjR4OqUlBDB0Kac1/fx0k3K9zoa2bhlvnUMmUXu7yqKNCgAXk33wyVPG4dVZbqoKJlceU5r1U9u3K7zro2VWQ9bhWELF++nIsXLzJu3LgS07Rq1YovvviC9u3bk5aWxjvvvMMtt9zCH3/8QXh4eLHLzJw5k+nTpxeZvnr1anx8fKqc7xwzFFT1unXrMMp4RA4VGxurdRZqlNjYWLtjdtWq1W5/zDqkPGYzkY89hq5QALI/pAX33fMql7xq0SnxD+at+4C/7oui89tvowKF/1RSAVSVXWPGkLxqlbZlqSYqWxZXnPNa1bMrt+usa1NWVgm3IYuhqKrqNmH0oEGD8PT05Keffir3MiaTieuuu46oqCheffXVYtMU1xISERHB2bNn8ff3r3K+s3Lz6PDqOgB2P9uLAF/pKOsIJpOJ2NhYBg4ciMFg0Do7bq9wfZpUxXbM7p/aDx9Pt/p7pYjC52Bly6Ns3Ih+4EDb932hLbn/7le45FWLzgl/sGDpy9TKvUxebCycP48uOhrl1ClbejU8HPO776LeeafmZakuKloWV57zWtWzK7frrGtTeno69evXJy0trcxrqNscvSdPnmTNmjXEVPAZe4PBwA033MCxY8dKTGM0GjEajcUu64gD3VCoadtg0MsF08EctZ+ElcFgALtj1oDB4DY/FcUyOKI8Z87YPv4e2pIH7nmVS0ZfuiQcYsH3L+ObfwtGf+YMREXBXXeRt349+379lY5DhqDv2xe9A4Zld0hZqonKlsUV57xW9ezK7Trr2lSR9VT7R3QLLFiwgKCgIG677bYKLWc2mzl48CCh0hFMCFEV+b8he8Jac/89M7hk9KVr/EG7AKRwOnQ61N69OdWrF2rv3vJeGCGK4RYhtMViYcGCBYwdOxa93j7LDzzwAA0aNGDmzJkAvPLKK9x88800b96cixcv8vbbb3Py5EkeeughLbIuhHBHxTyCS8+e7OnYi7F9JpFh9OHmkwf44n/T8THl38pVFOubcHv21DbvQrgRtwhC1qxZQ3x8PA8++GCRefHx8Xh4XGnQuXDhAg8//DApKSnUqVOHm266ia1bt9KmTRtXZlkI4a6KeQSX8HB2z5jD2CFPkWlR6HZyP5//7xX7AARg9mxp8RCiAtwiCImMjKSk/rMbNmyw+/7ee+/x3nvvuSBXQogaJybG+n6Xq35vdioBjNtvJsvTk1v8LXy+7TO8TVc6sxMebg1ARoxwbX6FcHNuEYQIIYTTmc3WFpCrApAd4dczfuTLZHl60yP5Tz596XG8nzla9HaNtIAIUWEShAghBFiDisK3YIDtEW15cORLZHl60zNuL5/GzMBrfCfo08f6TwhRJRKECCEEFHmfy7aIdjw48iUue3pdCUDycuW9L0I4kAQhQggBdu9z2dqwPQ+OnEa2wYte/+zhk5gZeJlNRdIJIapGghAhhABrv47wcH7T1WPCXVPJNnjR+5/dfBzzmjUAkUdwhXA4CUKEEAJAp2PLK3OYcEglx2Ck7/FdzFv2+pUABOQRXCEczG1GTBVCCGfafPQME457kWMw0u/UAeYve+3KLZjwcFi6VB7BFcLBpCVECHHN2/j3GR7+aje5eRb6tw7io+lPYpzQTR7BFcLJJAgRQlxbzGbYsMUWYGwIbs0j3/xObp6FAdcFM3fMDRj1OnkEVwgXkCBECHFtua4NnPwHgPVNb+JfI14kV2dgYJtg5t57I556uUsthKvI2SaEuLYknQJgfdNO/OtOawAS+fc25vrESwAihItJS4gQovoo7u21juiLYTbbPubq9HzXbiCv951Art7A4CO/8cFPb2PYHQp3DpO+H0K4kAQhQojqoYS31/L++xV7KqW4QOa3rbbZt477gKSAIACGHPmNOT++hcFihoQE63LSF0QIl5G2Rw2MGzcORVFs/+rVq8fgwYM5cOCALU3h+QEBAXTv3p1169bZrWfu3Lk0btwYLy8vunbtys6dO11dFCEco+DttVe9u4VTp6zTY2LKv57GjaFvX7j3XujbF0vjJvzy65VzIykgiIzlb3DyzduZv3wmnhYz9YDBwIEdO2zpyjoHZ86cSefOnfHz8yMoKIjhw4dz5MiRyteBKNdv4/Dhw/H09Cz1t7HAG2+8gaIoTJkyxUUlcDxHXS9OnTrFfffdR7169fD29qZdu3bs3bPb1cUpQoIQjQwePJjk5GSSk5NZu3Yter2e22+/3S7NggULSE5O5rfffqN+/frcfvvt/POPtUPdkiVLiI6O5qWXXmLv3r106NCBQYMGcfr0aS2KI0TllfD2WuDKtClT7G6pFOuqQEYFYpt34daBT/Okoa0t2VMbFzLk760MBpLz/63F2ix8+6xZdqss7RzcuHEjEydOZPv27cTGxmIymYiMjCQzM7MSlSAKlOe38bPPPitxvxTYtWsXH3/8Me3bt3dl9p2iqteLCxcu0L17dwwGA7/++iuHDx/m3XffpXbtOloUx44EIRoxGo2EhIQQEhJCx44defbZZ0lISODMmTO2NLVr1yYkJIS2bdsyb948Ll++TGxsLACzZs3i4YcfZvz48bRp04b58+fj4+PDF198oVWRhKicYt5ea0dVr9wqKUmhQCbN6MvKlt0Yfv+7PHzXNP4KakKtnCuBwfg9P6FTLRiBkPx/HRWFZ4OCSDh9utzn4MqVKxk3bhzXX389HTp0YOHChcTHx7Nnz56q1cc1rjy/jQEBASXuF4CMjAzGjBnDp59+Sp062l9oq6qq14s333yTiIgIFixYQJcuXWjSpAmRkZE0bdZMqyLZSBBSDWRkZPD111/TvHlz6tWrV2wab29vAHJzc8nNzWXPnj0MGDDANt/Dw4MBAwawbds2l+RZCIcp71tpS0h3Oj2bFd+tZ1rr2xk8/gM6Tv6Wf9/5AvvDWuGdm81j275j9eePXVmgYAj2Qt8zVJWvO3Ys9zlYnLS0NADq1q1bvvKIMlX0t7HAxIkTue222+x+I2uKytTJjz/+SKdOnRg1ahRBQUHccMMNfPrppy7Lc2mkY6pGVqxYQa1atQDIzMwkNDSUFStW4OFRNC7MysrixRdfRKfT0bt3b86ePYvZbCY4ONguXXBgIH/t3AnffiujPAr3Ud630oaGoqoq8eez2Bl3np1x59l14jwnzmVZ5990pXm66bkEBhzbycM7lxGYdZEsg7HQesLg5D+sAGrlT8oEQg8eLPc5eDWLxcKUKVPo3r07bdu2LTJflF9pv43mq27JFbdfFi9ezN69e9m1a5fL8+4sVbleAPzzzz/MmzeP6Ohonn/+eXbt2sXjjz+O6qEDgousw5UkCHGFQieOsmULqCp9+/Zl3rx5gPV+3UcffcSQIUPYuXMnjRo1AiAqKgqdTsfly5cJDAzk888/p3379iQlJRXdRkwMfPwxZGRYO+RB0ScLnPX4o6i8mrRPKluW/LfXcupUkX4hFhT+DmzErnbd2XHKj10z15KanmOXRlGgjZ8Hndctp2vCITolHiYw62LJ2/vzMNw5jL6nTjHv2WehUycupKdX6By82sSJEzl06BBbtmwpu7yiVKX9NoaFhQFw//33M27cuCL7JSEhgcmTJxMbG4uXl5eWxXCoqlwvwBokd+rUiddffx2AG264gUOHDvH5p59An6naFCqfBCHOFhMD0U/B6DkA6IcOBYMO3+uvp3nz5rZkn332GQEBAXz66afMmDEDgPfee48BAwYQEBBAYGCgLW39+vXR6XSkpqZe2cbIkaSqKiGFt13wZMHSpdbvjnj80V24w8XdUY+kukppdVqVsuh01nQjR2LS6TkU1JRdEdezM7wtu8LbkObtZ013MAUAg06hvZ9CF8/LdInw56Zbe+DvZYB5jxQbyAD2t2B0OggJwdfLi+ZjxtgmV+QcLGzSpEmsWLGCTZs2ER4eXnpZRZl8fX1L/G186aWXAHjnnXcYNGhQkf2yZ88eTp8+zY033mibZjab2bRpEx9++CE5OTnoqtvvQDmUViflOVZDQ0Np06aN3bTrrruOpf/7H57Oz36pJAhxpoLe+vqrdnNWFuzaZZ2f/wOtKAoeHh5cvnzZliwkJOTKgVfoAuAZGspNN97I2rVrGT50KEyejEVVWQtMKrwdVbX++D7yCJw/X/THuXCQUt6LnlzcHaPg2HDEPnGF0uoUKl2Wy7lmfk+4wC7/duyc8QN7z5u4rDfapfHxULmxaSBdmtSlS8IhOr78f3idjCuaj/xABkWxz8vVfUBKUOY5eBVVVfnPf/7DsmXL2LBhA02aNCnXdkTFFLdfgoODi90v/fv35+DBg3bTxo8fT+vWrXnmmWfcMgApTkWP1e7duxd5fPzvv/+mYcOGpDg1p2WTIMRZSnvsEMgBUv7zH+jalQvp6Xz44YdkZGQwdOjQoomLuQBE163L2H376OTrS5fERGZjva89/uplVRXOnSs+jwVBypQpMKwcI0XWtIu7IwKqyqyjrEdSK7JPHFWO0vzwI9xTQp3edRfUq1fusqRlZLP31y3siE9np8mbg5dUTOaCZT1AbyTAAJ29TXQJ9aVL3xu5PqIOBp2Hdd8+WMa+Xbq0+GP03dlw1UMrOTk5pKRYf4IvXLhQ+jlYjIkTJ7Jo0SJ++OEH/Pz8bOsKCAiwdQwUFVeV/eLn51ekT46vry/16tVz6746VT1Wn3jiCW655RZef/117r77bnbu3Mknn3zCBx/N45U/nZnzskkQ4ixlPHa4EghNSoLwcPz8/GjdqhXfv/QSfZKTYcOGKwlLuKjec+ECZ1SVaR99RArQMX+dFe5iVPjxx9JGinTlX+7luaiazSgbN9Jg0yYUX1/r4FRQ/ov7Dz+UHVCVlY/yBGXFraMij6SWNXqnKwLDp54qfQyPkoJc4Ix3ALt8GrLz4zXsPJvLn5mgKh6AEbAAEGxQ6dKmAV0a16FLk3q0CKqFh8dVrRflDdzi4qz79+o6N6uwZ5XdYitXriQ0v1Osn58frVu35vvvv6dPOUdMLbhHf3X6BQsWMG7cuHKtQxRV2n4xmUwa504bVT1WO3fuzLJly3juued45ZVXaNKkCbNnz2Z01L28Mm1V2StwIglCnKWExwkXdRhMpCmbWy1mPM156Cc9ht5gwPDZJ+i//JGt5jz0FjN7OvTEMy2Pv955G32dBhgseejNZuv/FjN6cx4TVAv/8vZFf+kS5Wtwrnh+gYr95Q7lCiCq1LcgP40+MZFOALNmWdM8/HD5Lu6vvQYvv1x6QAWl56M8QVlJ6xg5suQ8FlawT0qqL1cFhvkvfCuLCiT6B1n7c0S0ZWf49fxTL7+PRHwe4AEKNLqQRJeEP+iScIiuiX8QkZaKsnQpdLuh5JVXNHC7+sfZnGf3deHChSxcuLD08pTQilne+aLiyrNfli9fzq233lrudW4o/EedG3LEsQpw++23FxngLCs3r4TUrlOtg5CXX36Z6dOn201r1aoVf/31V4nLfP/990ydOpUTJ07QokUL3nzzzQodsA5TwmOHM/o/bD/hZP7/fSYWTfwncNu0cm3OYDahN5vRW/Iw5AcpBov1u95ixpAf3OgteRgK0pmvpNWn1sGw+Hf0Og8MOgW9hwd6nYKnzgN9YgL6Rr0wROSVvo3pczGsXon+dOqV9davi+GJJ9AP6G9d79o16F+ZjuFUojXPFjOGkGD077yNQQFlVDku7CVdePM7rZXp/fdLD6jK6kOzZAlER1d+HbNnly+foaElB2WzZpWdh8K3dJxwy0YFjtWLYGe4NejYFdGGJP8guzSKaqHVmZN0PXuczkd30yXhD4IyL9ivqDy3n6o4logQonqq1kEIwPXXX8+aNWts3/X6krO8detWoqKimDlzJrfffjuLFi1i+PDh7N271/X3Aws/dlhI5N9bUVHI0+nJ8/HFpOgw5ZnJ89Bj0unI89CT56HDpLvyv+mqaRaPoj/UJp0Bk85Q+fymmCGlmEd/C/S4t+x15FJ8MHUYOFww2qUnDH+taJr8Vxjo/2+ZXZCjt5ivtALFnrdOf+A9u2CqcJBVkFZvybO2NJUWhBXaRpG0dUpYr2rG8MJrGDIt6P2DbEHY1ev3KK0fDlgvthZLyU9yhIfD2bNw993FBzJ33136vijcMnD+vENu2Vz0qkVcnTD2NLiOnRFt2R3ehvM+AXZp9OY82qUco0vCIbok/kGnU38S4OcNhUZ2LDWvJTUvV2AsESGE+6j2QYheryckJKTshMD777/P4MGDeeqppwB49dVXiY2N5cMPP2T+/PnOzGZRhR47LNw7f/aKd/HJyx/Z7+WXy//XeyEWFEw6HSadwRqcLP+RvAsXyHv5FUynz5Cn02Py0JEX2oC8J6IxqWCa/ip5Oh0mRXdlvk5vDX4efZQ8C+QtW47pUoYtIDLVrkte/wGYjF7kLfvhynIeeuu6PPRXgqUi064Kpgye5HnoyMMDU/704oKpPJ2ePJ2e7MrWezXhYTHbBzHmPNutNNvnq4OswvO7dkb//V70QyYXH5AVui3nWcy6bEHWT1sx/LAMvb4++og6eFryW8xMZgz/ikZ/WcEwZBB6nQIWM+dzYEfceeLOXel1P2rceyT41CXdq1aRcnqZcrjx9DG6xO2jS+JhOib9hY8pfxyPguP+3w+Vr/WntFaMUsYSsW0rPNyaTgjhNqp9EHL06FHCwsLw8vKiW7duzJw5k4YNGxabdtu2bURHR9tNGzRoEMuXLy91Gzk5OeTkXBkAKT09HQCTyVS1jlBDh6IsXoz61LN2k9UGDTC/+y7k5JRrB6hg1+fDAxWjOQ9PixkaNCCvdzdr0DP8VutgaPlN7mqPHrbmbcXHhC46GqVQy4waHm7NB7noRo8GVbXbjqoo8PNczIsWoTv8IyQloRRzAbg6f+VVEEzZByyFA6RCwU0xLUJ5hYKgwsvl2gVB9ulMXW7GvPd3u+nFr7f0VqmCgMykM2AuJpiyeOjI9dCRe/Xj2eV1AWjSpXLLXm1kh5LnHQQOri80QQ977d+s+UfdK+db0KVztEs9TueEP+iS+AfXpx5Ht+hroDG66E0opivnke04r1sXfTmCkLzAQNRSzjfl3Xetx6mi2B2Han6wY37nHVSLxdrCdBWTKa/QZxMmxTX9OQp+PxzZoVKrsjhDRcvijPoseVtaHTOu2679tvIcVq8VWU+1DkK6du3KwoULadWqFcnJyUyfPp2ePXty6NAh/Pz8iqRPSUkpOpR5cLDt0aaSzJw5s0jfE4DVq1fj4+NTtUIYjeS8847t8cCtU6eReX1r0Omo9/ff9Cjnaq6+0KsAqsquMWNIXnVV72Z/f8jMhMLTjUaYM4d6hw/jdeEC2XXqcC5/8JrIRx5Bd1UAAqCoKiqQ+5//cGj8eDq//Xbx+aikgmDKaHZs56g/R4+mcWws3oVuiWTVr8+hCRNI7lSbyE++xevcuWIDp/IGVDn+/nimp6NgDaasgUl+S5CHDnOhIOXqlqKCIGbfuPFcjGiET0ICHhlZXPatRVpYA8yKB35HjxG6YZNdq1Ph4McWQOkKBWAeerv5uZ6emFXlSgBVTMBm8tCR6+VNnuKBWVXQKSp1jVDXqHIkzTok9NgWZtod3Ue/BfOpe/pKa0VW/frsfzKaZGP+uB7FHV86HaSnE1mvXql1frl+fWLT0+GXX0qudKOR0Kefpt1nn9nt28v16ln3rdFY4vI5Zij4uVu1ajVGFw8XUfjlalWldVkcqbJlcWR9lkSrenbldgtva926dQ7bVlZWVrnTKqobdfG+ePEijRo1YtasWUyYMKHIfE9PT7788kuioqJs0z766COmT59+ZXTRYhTXEhIREcHZs2fx9/evcr6zcvPo8Oo6AHY/24sA3/zhhM1m9M2bl9zCoCjQoAHmt99G9+STxbZiqHfeWaW8KRs3oh84sMx0ebGxcP58sa0plgcfRPfKK1XKh219FB8EqIoC+UM2l1VfeUePApTcKrRsmfUvaij6F7WqQt26cOFC2fskf3j8yq4j7+jREjtilne/qFBiy4DlP/9BN2dOmevI++or1FGjyFu/nkNr1tBu4EBybulJh9c3ALB/aj98PPXWx6JLqNOylFrngHnx4vIfy5XIR+Fz0FYeFzCZTMTGxjJw4EAMhir02SpEq7I4Q0XL4oz6dFTe3HG7JV6bqig9PZ369euTlpZW5jXUrY7e2rVr07JlS44dO1bs/JCQkCLBRmpqapl9SoxGI8aCv+YKMRgMDjnQDeqVy6rBoL+yToMB5swpcZRHBeD999GPGAGjRtk93aD07IneEQNSldZhsBD9mTMQFWUdnOqqfOgAvvii9Pv1DRpYP5eWpm5dlPPnrd+Lq4uCC2oZ9WUoeGdESW/QvPtu0OuLdNZUwsOv9F0ozz7x9KzSOgylvduib99y9YFQZs2CJ54oNg+6unWv1Fkp9HFx0KIFhsREOgPMmoWlUVPbqwas54HeerxW9q2kZdS5viKPElciH/bnYH55XMhRvyWgfVkcqbJlcWR9lrgNjerZldst8dpU1fVWYD1udfRmZGRw/Phx7r///mLnd+vWjbVr1zJlyhTbtNjYWLp16+aiHFbCiBElj/I4e/aVpxd0urIHrqqMij51UFI+yhoyu/Dw3iWl+eQT6/9l1UV56qssI0YUP6hVQWBXnm04Yh0lubpjc3H1VbCeO+8sPg9mc9mBTN26xY+ZklzKk1KVVVZ9CSGuOdU6CHnyyScZOnQojRo1IikpiZdeegmdTme73fLAAw/QoEEDZs6cCcDkyZPp3bs37777LrfddhuLFy9m9+7dfFJwcauutPxxdtRTB+UNpsqTpqy6yK+vvPXr2ffrr3QcMgR9374Vr6/SArvy7hNHrKMkVQ1QywpkCr6XNhoq5L8F2kE/Fc4KpoUQbqlaByGJiYlERUVx7tw5AgMD6dGjB9u3b7e9ITA+Ph4PDw9b+ltuuYVFixbx4osv8vzzz9OiRQuWL1/uHu8M0OrHubx/cZfnwlmei2550pSnLnQ61N69OZWZSYfevZ0TsDlin1R1Hc4MZB56qHyPiP+2FQb0rVz+hRCiFNU6CFm8eHGp84sbjnfUqFGMGjXKSTmqocr7F3d5lDOAkL+GK8BZgcx335Vv+TKeLhNCiMqq1kGIcCG5X1+zFRfIlLc/UDkHCxRCiIqSIERcIS0U15ay+gMV6H6L6/IkhLimeJSdRAhRIxX0BwK7VwsU+S6tYUIIJ5EgRIhrWUF/oIJxXAqENSg+vRBCOJAEIUJc60aMgBMnyIuNZXd0tHV03MN/aJ0rIcQ1QPqECCGKPvKsKmUvI4QQVSQtIUIIIYTQhAQhQgghhNCEBCFCCCGE0IQEIUIIIYTQhAQhQgghhNCEBCFCCCGE0IQ8olsMNX8I6/T0dIesLys3D0tOlm2ditnkkPVe60wmE1lZWaSnp2MwGLTOjtsrXJ8mVbE7ZvM83fun4upz0FXlccYxqlVZnKGiZXHlOa9VPbtyu866NhVcO9XSXgeRT1HLk+oak5iYSEREhNbZEEIIIdxWQkIC4eHhpaaRIKQYFouFpKQk/Pz8UK5+p0YlpaenExERQUJCAv7+/g5Z57VO6tSxpD4dT+rUsaQ+Hc8ZdaqqKpcuXSIsLAwPj9J7fbhvO54TeXh4lBm9VZa/v7+cPA4mdepYUp+OJ3XqWFKfjufoOg0ICChXOumYKoQQQghNSBAihBBCCE1IEOIiRqORl156CaPRqHVWagypU8eS+nQ8qVPHkvp0PK3rVDqmCiGEEEIT0hIihBBCCE1IECKEEEIITUgQIoQQQghNSBAihBBCCE1IEOIic+fOpXHjxnh5edG1a1d27typdZbc0syZM+ncuTN+fn4EBQUxfPhwjhw5onW2apQ33ngDRVGYMmWK1llxW6dOneK+++6jXr16eHt7065dO3bv3q11ttyW2Wxm6tSpNGnSBG9vb5o1a8arr75arneTCKtNmzYxdOhQwsLCUBSF5cuX281XVZVp06YRGhqKt7c3AwYM4OjRo07PlwQhLrBkyRKio6N56aWX2Lt3Lx06dGDQoEGcPn1a66y5nY0bNzJx4kS2b99ObGwsJpOJyMhIMjMztc5ajbBr1y4+/vhj2rdvr3VW3NaFCxfo3r07BoOBX3/9lcOHD/Puu+9Sp04drbPmtt58803mzZvHhx9+yJ9//smbb77JW2+9xQcffKB11txGZmYmHTp0YO7cucXOf+utt5gzZw7z589nx44d+Pr6MmjQILKzs52bMVU4XZcuXdSJEyfavpvNZjUsLEydOXOmhrmqGU6fPq0C6saNG7XOitu7dOmS2qJFCzU2Nlbt3bu3OnnyZK2z5JaeeeYZtUePHlpno0a57bbb1AcffNBu2ogRI9QxY8ZolCP3BqjLli2zfbdYLGpISIj69ttv26ZdvHhRNRqN6rfffuvUvEhLiJPl5uayZ88eBgwYYJvm4eHBgAED2LZtm4Y5qxnS0tIAqFu3rsY5cX8TJ07ktttusztWRcX9+OOPdOrUiVGjRhEUFMQNN9zAp59+qnW23Nott9zC2rVr+fvvvwHYv38/W7ZsYciQIRrnrGaIi4sjJSXF7twPCAiga9euTr9OyQvsnOzs2bOYzWaCg4PtpgcHB/PXX39plKuawWKxMGXKFLp3707btm21zo5bW7x4MXv37mXXrl1aZ8Xt/fPPP8ybN4/o6Gief/55du3axeOPP46npydjx47VOntu6dlnnyU9PZ3WrVuj0+kwm8289tprjBkzRuus1QgpKSkAxV6nCuY5iwQhwm1NnDiRQ4cOsWXLFq2z4tYSEhKYPHkysbGxeHl5aZ0dt2exWOjUqROvv/46ADfccAOHDh1i/vz5EoRU0nfffcc333zDokWLuP7669m3bx9TpkwhLCxM6tTNye0YJ6tfvz46nY7U1FS76ampqYSEhGiUK/c3adIkVqxYwfr16wkPD9c6O25tz549nD59mhtvvBG9Xo9er2fjxo3MmTMHvV6P2WzWOotuJTQ0lDZt2thNu+6664iPj9coR+7vqaee4tlnn2X06NG0a9eO+++/nyeeeIKZM2dqnbUaoeBapMV1SoIQJ/P09OSmm25i7dq1tmkWi4W1a9fSrVs3DXPmnlRVZdKkSSxbtox169bRpEkTrbPk9vr378/BgwfZt2+f7V+nTp0YM2YM+/btQ6fTaZ1Ft9K9e/cij43//fffNGrUSKMcub+srCw8POwvVzqdDovFolGOapYmTZoQEhJid51KT09nx44dTr9Oye0YF4iOjmbs2LF06tSJLl26MHv2bDIzMxk/frzWWXM7EydOZNGiRfzwww/4+fnZ7lcGBATg7e2tce7ck5+fX5E+Nb6+vtSrV0/62lTCE088wS233MLrr7/O3Xffzc6dO/nkk0/45JNPtM6a2xo6dCivvfYaDRs25Prrr+f3339n1qxZPPjgg1pnzW1kZGRw7Ngx2/e4uDj27dtH3bp1adiwIVOmTGHGjBm0aNGCJk2aMHXqVMLCwhg+fLhzM+bUZ2+EzQcffKA2bNhQ9fT0VLt06aJu375d6yy5JaDYfwsWLNA6azWKPKJbNT/99JPatm1b1Wg0qq1bt1Y/+eQTrbPk1tLT09XJkyerDRs2VL28vNSmTZuqL7zwgpqTk6N11tzG+vXri/3tHDt2rKqq1sd0p06dqgYHB6tGo1Ht37+/euTIEafnS1FVGXJOCCGEEK4nfUKEEEIIoQkJQoQQQgihCQlChBBCCKEJCUKEEEIIoQkJQoQQQgihCQlChBBCCKEJCUKEEEIIoQkJQoQQQgihCQlChBBCCKEJCUKEEEIIoQkJQoQQbuHMmTOEhITw+uuv26Zt3boVT09Pu7d/CiHch7w7RgjhNn755ReGDx/O1q1badWqFR07dmTYsGHMmjVL66wJISpBghAhhFuZOHEia9asoVOnThw8eJBdu3ZhNBq1zpYQohIkCBFCuJXLly/Ttm1bEhIS2LNnD+3atdM6S0KISpI+IUIIt3L8+HGSkpKwWCycOHFC6+wIIapAWkKEEG4jNzeXLl260LFjR1q1asXs2bM5ePAgQUFBWmdNCFEJEoQIIdzGU089xdKlS9m/fz+1atWid+/eBAQEsGLFCq2zJoSoBLkdI4RwCxs2bGD27Nn897//xd/fHw8PD/773/+yefNm5s2bp3X2hBCVIC0hQgghhNCEtIQIIYQQQhMShAghhBBCExKECCGEEEITEoQIIYQQQhMShAghhBBCExKECCGEEEITEoQIIYQQQhMShAghhBBCExKECCGEEEITEoQIIYQQQhMShAghhBBCExKECCGEEEIT/w+aQH32po5LAQAAAABJRU5ErkJggg==",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "# データを作成する 4Fiel\n",    "xf = np.linspace(0,10,51)\n",    "# yf = np.linspace(0,10,21)\n",    "np.random.seed(0)\n",    "y_gn = np.random.normal(loc=0,scale=0.2, size=len(xf))\n",    "\n",    "para1 = (5,5,5,5,0,0,0,2,2) #(1) case\n",    "para2 = (3,6,6,5,0,2,2,2,2) #(2) case\n",    "para3 = (2,6,6,5,-2,2,2,2,2) #(3) case\n",    "para4 = (2,5,7,5,-2,2,2,2,2)  #(4) case\n",    "y1 = mf.three_break_func(xf,*para1)\n",    "y2 = mf.three_break_func(xf,*para2)\n",    "y3 = mf.three_break_func(xf,*para3)\n",    "y4 = mf.three_break_func(xf,*para4)\n",    "\n",    "para5=(4,9,5,0,0,20,1.5) #(5) case\n",    "y5 = mf.two_break_gauss(xf,*para5)\n",    "\n",    "# (b1,b2,b3,const,ap1,ap2,ap3,pa,ps)\n",    "para6=(4,7,9,5,0,1,0,20,1) #(6) case\n",    "y6=mf.three_break_gauss(xf,*para6)\n",    "\n",    "y_list = [y1+y_gn,y3+y_gn,y5+y_gn]\n",    "y_explain=['(a) Normal','(b) Attenuated Bg(AG)',' (c) Peak']\n",    "for i ,si in zip(y_list,y_explain):\n",    "    fig = plt.figure(figsize=(6, 4))\n",    "    ax = fig.add_subplot(111)\n",    "    ax.plot(xf, i,'ro', label=f'{si}')\n",    "    ax.grid(True)\n",    "    ax.set_xlabel('x')\n",    "    ax.set_ylabel('y')\n",    "    ax.legend(loc='upper left')\n",    "    plt.show()\n",    "    \n",    "fit_params=[]\n",    "for i ,si in zip(y_list,y_explain):\n",    "    fig = plt.figure(figsize=(6, 4))\n",    "    ax = fig.add_subplot(111)\n",    "    ax.plot(xf,i,'ro',label=f'{si}')\n",    "    ax , p = ax_plots(xf, i, n_breakpoints=7, axs=ax )  \n",    "    fit_params.append(p)\n",    "    # print(p)\n",    "    ax.grid(True)\n",    "    ax.set_xlabel('x')\n",    "    ax.set_ylabel('y')\n",    "    ax.legend(loc='upper left')\n",    "    plt.show()"   ]  },  {   "cell_type": "code",   "execution_count": 32,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "name": "stdout",     "output_type": "stream",     "text": [      "Running fit with n_breakpoint = 0 . . \n",      "Running fit with n_breakpoint = 1 . . \n",      "Running fit with n_breakpoint = 2 . . \n",      "Running fit with n_breakpoint = 3 . . \n",      "Running fit with n_breakpoint = 4 . . \n",      "Running fit with n_breakpoint = 5 . . \n",      "Running fit with n_breakpoint = 6 . . \n",      "Running fit with n_breakpoint = 7 . . \n",      "\n",      "                 Breakpoint Model Comparision Results                 \n",      "====================================================================================================\n",      "n_breakpoints            BIC    converged          RSS \n",      "----------------------------------------------------------------------------------------------------\n",      "0                     223.42         True       3492.7 \n",      "1                      39.31         True       80.983 \n",      "2                    -48.403         True       12.431 \n",      "3                    -97.583         True        4.062 \n",      "4                    -127.88         True       1.9221 \n",      "5                     -128.2         True       1.6371 \n",      "6                    -122.63         True       1.5651 \n",      "7                    -114.94         True       1.5597 \n",      "\n",      "Min BIC (Bayesian Information Criterion) suggests best model\n",      "Minimum BIC:-128.20, index:5\n",      "Plotting fit for model with 1 breakpoint(s) . . . \n"     ]    },    {     "data": {      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGzCAYAAACPa3XZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABSV0lEQVR4nO3deVhTZ/o38G8SIEEgUVAIqCgurSJ1QaVStYuiYjuoo11sa2ur03YctC6/men4Tlukm60zXbQi3RzbjmM7tetgWyzuWrVQqW2R1hWrVZYiGjaBkJz3j5iQSBISSE4Wvp/r4tKcc3LOzZNAbu5nORJBEAQQERERiUTq6QCIiIioc2HyQURERKJi8kFERESiYvJBREREomLyQURERKJi8kFERESiYvJBREREomLyQURERKJi8kFERESiYvJBXuX06dOQSCR4++23vfa6xmP/+c9/uj8wK1asWAGJRILKykqPXN8aT7bJAw88gL59+4p+XSJqPyYfJKq3334bEonE6tff/vY3q8/54osvsGLFCnEDdfN1n332WUybNg1RUVGQSCQe+f5IvPfWAw88YPFeDwgIQO/evTF79mwUFxdbHLtr1y5IJBJ8+OGHrc5z8uRJPPLII+jXrx8UCgWUSiXGjh2L1atX4/Lly3ZjuHz5MubPn4+EhASoVCqEhoZi2LBhWL16NbRabavj8/LyMG7cOHTp0gXdunXD7bffjtOnTzv0/b755pu46aabEBUVBblcjri4ODz44IMOP5/8X4CnA6DO6amnnkJcXJzFtoSEBPTp0weXL19GYGCgafsXX3yBrKwst35IiH3dxx9/HGq1GiNGjMDWrVtdfv7O5M0334Rer2/Xc8V4bxnJ5XK89dZbAIDm5macPHkSr732GnJzc1FcXIyYmBi7z//8889xxx13QC6X4/7770dCQgKampqwb98+/OUvf8GRI0fwxhtv2Hz+5cuXceTIEdx6663o27cvpFIp9u/fj6VLl+Kbb77Bpk2bTMdu2bIF06dPR2JiIp5//nlUV1dj9erVGDduHL777jv06NHDbqzfffcd4uLiMG3aNHTr1g0lJSV48803sWXLFnz//fdtfq/k/5h8kEdMnToVo0aNsrpPoVCIHA0gkUhEvW5JSQn69u2LysrKNn+Ru5Jer0dTU5NH2thdzBNGbxYQEIA5c+ZYbBszZgx+97vf4fPPP8dDDz1k87klJSWYPXs2+vTpgx07diA6Otq0Lz09HSdOnMDnn39u9/rh4eE4ePCgxbY//vGPUKlUWLt2LV566SWo1WoAwGOPPYZ+/frh66+/RlBQEAAgLS3NlIy8+OKLdq+1bt26VttmzJiBUaNG4d1337VZ5aTOg90u5FWuHnvxwAMPICsrCwAsyta2LFu2DBERETC/WfOiRYsgkUiwZs0a07by8nJIJBJkZ2d36LpvvPEG+vfvD7lcjtGjR6OgoMCh79MVYxQqKytx5513QqlUIiIiAosXL0ZDQ4PFMRKJBAsXLsR//vMfDBkyBHK5HLm5uQCAc+fOYd68eabS+JAhQ/Cvf/3L4vlNTU148sknMXLkSKhUKoSEhGD8+PHYuXNnm/EJgoCHH34YQUFB+PjjjwG0dLvt2bMHjzzyCCIiIqBUKnH//ffj4sWLrc6xbt06U9wxMTFIT0/HpUuXLI65esyH+fgTe69PW6/x+++/j5EjRyIsLAxKpRLXXXcdVq9e3eb37Qzjh31AgP2/A1etWoXa2lqsX7/eIvEwGjBgABYvXmx6XFlZiZ9//hn19fVtxmBsO2O7VlVVobi4GL///e9NiQcADBs2DIMHD8b777/f5jkduQ51bqx8kEdoNJpWAya7d+/e6rhHHnkE58+fR15eHv7973+3ed7x48fj5ZdfxpEjR5CQkAAA2Lt3L6RSKfbu3YtHH33UtA0AbrzxRqvnceS6mzZtQk1NDR555BFIJBKsWrUKM2fOxKlTp0T5a/zOO+9E3759sXLlShw8eBBr1qzBxYsX8e6771oct2PHDnzwwQdYuHAhunfvjr59+6K8vBxjxowxJSc9evTAl19+ifnz56O6uhpLliwBAFRXV+Ott97C3XffjYceegg1NTVYv349pkyZgvz8fAwfPtxqbDqdDvPmzcN///tffPLJJ7jtttss9i9cuBBdu3bFihUrcPToUWRnZ+OXX34xjXcADANrMzMzkZKSggULFpiOKygowNdff91mG7f1+th7jfPy8nD33Xdj4sSJeOGFFwAAP/30E77++muLD3lnGd/zOp0Op06dwmOPPYaIiAj87ne/s/u8nJwc9OvXDzfccIND11m7di0yMzOxc+dO3HzzzRb7mpqaUF1djcuXL+Pbb7/FP//5T/Tp0wcDBgwAADQ2NgIAgoODW523S5cuOHLkCMrKykyJkz0XLlyATqfDmTNn8NRTTwEAJk6c6ND3QH5OIBLRhg0bBABWvwRBEEpKSgQAwoYNG0zPSU9PFxx9q1ZUVAgAhHXr1gmCIAiXLl0SpFKpcMcddwhRUVGm4x599FEhPDxc0Ov1Tl/XeGxERIRQVVVl2v7ZZ58JAIScnByH2+O3334TAAgZGRkOPycjI0MAIEybNs1i+5/+9CcBgPD999+btgEQpFKpcOTIEYtj58+fL0RHRwuVlZUW22fPni2oVCqhvr5eEARBaG5uFhobGy2OuXjxohAVFSXMmzfPtM3YJv/4xz8ErVYr3HXXXUJwcLCwdetWi+caX/+RI0cKTU1Npu2rVq0SAAifffaZIAiG1zEoKEiYPHmyoNPpTMetXbtWACD861//Mm2bO3eu0KdPn1axOPL62HqNFy9eLCiVSqG5ubnVvvaYO3eu1fd8z549hUOHDlkcu3PnTgGAsHnzZkEQBEGj0QgAhOnTpzt8PeN7ZOfOna32vffeexYxjBo1Svjhhx9M+3U6ndC1a1dh4sSJFs+rrKwUQkJCBADCt99+61AccrncdJ2IiAhhzZo1Dn8P5N/Y7UIekZWVhby8PIsvV+jRowcGDRqEPXv2AAC+/vpryGQy/OUvf0F5eTmOHz8OwFD5GDdunN0unLbcdddd6Natm+nx+PHjAQCnTp3qwHfguPT0dIvHixYtAmAYRGnupptuQnx8vOmxIAj46KOPkJaWBkEQUFlZafqaMmUKNBoNCgsLAQAymcxUetfr9aiqqkJzczNGjRplOsZcU1MT7rjjDmzZsgVffPEFJk+ebDX2hx9+2KJysWDBAgQEBJhi37ZtG5qamrBkyRJIpS2/ph566CEolco2xzcAHXt9unbtirq6Ope9LwHDWCbje33r1q14/fXXERoailtvvRXHjh2z+bzq6moAQFhYmMPXWrFiBQRBaFX1AIBbbrkFeXl52Lx5M/74xz8iMDAQdXV1pv1SqRSPPPIItm/fjuXLl+P48eM4dOgQ7rzzTjQ1NQFAmzNrjL788kt88cUXePHFFxEbG2txHerc2O1CHpGUlGRzwGlHjR8/3vQhtnfvXowaNQqjRo1CeHg49u7di6ioKHz//fe45557OnSd2NhYi8fGDzprYxfcYeDAgRaP+/fvD6lU2mo649Wzin777TdcunQJb7zxhs3ZERUVFab/v/POO3jxxRfx888/W0zJvPq8ALBy5UrU1tbiyy+/tPrBZyv20NBQREdHm2L/5ZdfAADXXnutxXFBQUHo16+fab89HXl9/vSnP+GDDz7A1KlT0bNnT0yePBl33nknUlNT23yuLTKZDCkpKRbbbr31VgwcOBDLly/HRx99ZPV5SqUSAFBTU9Pua5uLiopCVFQUAOD222/Hc889h0mTJuH48eOmrpSnnnoKlZWVWLVqFZ5//nkAwOTJkzF//ny89tprCA0Ndehat9xyCwDDAPPp06cjISEBoaGhWLhwoUu+F/JdrHyQ3xk3bhzOnTuHU6dOYe/evRg/fjwkEgnGjRuHvXv3Yv/+/dDr9aa/hNtLJpNZ3S6YDXYVk60qztV998ZpqXPmzGlVfTJ+jR07FgCwceNGPPDAA+jfvz/Wr1+P3Nxc5OXlYcKECVant06ZMgUhISFYtWpVq8GvYuvI6xMZGYnDhw/jf//7H6ZNm4adO3di6tSpmDt3rktj7NWrF6699lpTpc4apVKJmJgYFBUVufTaRrfffjtqa2vx2WefmbYFBQXhrbfewvnz57Fnzx4cPXoUW7duhUajgVQqNY0PcUb//v0xYsQI/Oc//3Fl+OSjmHyQ13O2a8SYVOTl5aGgoMD0+MYbb8TevXuxd+9ehISEYOTIkS69rtiMXUhGJ06cgF6vb3MmTY8ePRAWFgadToeUlBSrX5GRkQCADz/8EP369cPHH3+M++67D1OmTEFKSorNxGLMmDH49NNPsX//ftxxxx1obm52KPba2lqUlpaaYu/Tpw8A4OjRoxbHNTU1oaSkxLS/o+y9xkFBQUhLS8O6detMi3u9++67OHHihEuubdTc3Iza2lq7x/zud7/DyZMnceDAAZdeG2jpQtFoNK32RUVFYfz48bjmmmug0+mwa9cuXH/99Q5XPqxdy9p1qPNh8kFeLyQkBIDjU/Ti4uLQs2dPvPzyy9Bqtaa/4sePH4+TJ0/iww8/xJgxY9qc3ujsdcVmnCZq9OqrrwIwlLjtkclkmDVrFj766COrf03/9ttvFscCltWCb775xu6HYEpKCt5//33k5ubivvvus1oheeONNyy6cLKzs9Hc3GyKPSUlBUFBQVizZo3FtdevXw+NRtNq9kx72XqNL1y4YPFYKpVi6NChAFpmg7jCsWPHcPToUQwbNszucX/9618REhKCP/zhDygvL2+1/+TJkxbTgK1Nta2srLRa9TEufNZWN+g///lPlJaW4v/+7/9aXfvkyZOmx83NzVa7tvLz8/Hjjz+6rbuVfAvHfJDXM1YoHn30UUyZMgUymQyzZ8+2+5zx48fj/fffx3XXXWfq609MTERISAiOHTvm0HiP9lzXUf/+97/xyy+/mD4c9uzZg2eeeQYAcN999zn0l31JSQmmTZuG1NRUHDhwABs3bsQ999zT5gcZADz//PPYuXMnrr/+ejz00EOIj49HVVUVCgsLsW3bNlRVVQEw/MX98ccf4/e//z1uu+02lJSU4LXXXkN8fLzdv9ZnzJiBDRs24P7774dSqcTrr79usb+pqQkTJ07EnXfeiaNHj2LdunUYN24cpk2bBsBQnVm+fDkyMzORmpqKadOmmY4bPXp0q8W62svWa/yHP/wBVVVVmDBhAnr16oVffvkFr776KoYPH47Bgwebnm+s1DiybHhzczM2btwIwND1dfr0abz22mvQ6/XIyMiw+9z+/ftj06ZNuOuuuzB48GCLFU7379+PzZs344EHHjAdb22q7caNG/Haa69hxowZ6NevH2pqarB161bk5eUhLS0NEyZMMD1/48aN+Oijj3DjjTciNDQU27ZtwwcffIA//OEPmDVrlkVsxqmzxjaora1F7969cdddd2HIkCEICQnBjz/+iA0bNkClUuGJJ55os62oE/DcRBvqjIxTLQsKCqzutzbltbm5WVi0aJHQo0cPQSKRODTtNisrSwAgLFiwwGJ7SkqKAEDYvn17u69rPq30anBw2uxNN91kc8qxtemR5ozTKIuLi4Xbb79dCAsLE7p16yYsXLhQuHz5cqt40tPTrZ6nvLxcSE9PF3r37i0EBgYKarVamDhxovDGG2+YjtHr9cJzzz0n9OnTR5DL5cKIESOELVu22JzeenWbrFu3TgAg/PnPfxYEoeX13717t/Dwww8L3bp1E0JDQ4V7771XuHDhQqsY165dKwwaNEgIDAwUoqKihAULFggXL160OMbRWIztYf762HqNP/zwQ2Hy5MlCZGSkEBQUJMTGxgqPPPKIUFpaanG+7t27C2PGjLHavlfHePXrrFQqhYkTJwrbtm2zOPbqqbbmjh07Jjz00ENC3759haCgICEsLEwYO3as8OqrrwoNDQ2m46xNtS0oKBDuuOMOITY2VpDL5UJISIiQmJgovPTSS4JWq7W4zjfffCPceOONQrdu3QSFQiEMGzZMeO2110xT08316dPHov0bGxuFxYsXC0OHDhWUSqUQGBgo9OnTR5g/f75QUlLSZltR5yARBA+NjiOiTuftt9/Ggw8+iIKCAp8vvxcXF2PIkCHYsmWLy7qBiDoLjvkgImqHnTt3Ijk5mYkHUTsw+SAiaof09HTs37/f02EQ+SQmH0RERCQqjvkgIiIiUbHyQURERKJi8kFERESi8rpFxvR6Pc6fP4+wsDCvX96aiIiIDARBQE1NDWJiYizuRm2N1yUf58+fR+/evT0dBhEREbXD2bNn0atXL7vHeF3yERYWBsAQvPFW0q6i1Wrx1VdfYfLkyQgMDHTpuakF21kcbGdxiNnOFdUtN+y7rNXhtjX7AACfPzoOwYEtd+mNVCrcGoen8D0tDne1c3V1NXr37m36HLfH65IPY1eLUql0S/LRpUsXKJVKvrHdiO0sDrazOMRs58sIMv0/oEkHqbwLACAsTIngoJbkQ+nHyQff0+7n7nZ2ZMgEB5wSERGRqJh8EBERkaiYfBAREZGomHwQERGRqJh8EBERkaiYfBAREZGomHwQERGRqJh8EBERkaiYfBAREXUSOr2A/JIqAEB+SRV0esEjcTD5ICIi6gRyi0ox7oUdmPdOAQBg3jsFGPfCDuQWlYoeC5MPIiIiP5dbVIoFGwtRqmmw2F6macCCjYWiJyBMPoiIiPyYTi8gM6cY1jpYjNsyc4pF7YJh8kFEROTH8kuqWlU8zAkASjUNprEgYmDyQURE5McqamwnHu05zhWYfBAREfmxyDCFS49zBSYfREREfiwpLhzRKgUkNvZLAESrFEiKCxctJiYfREREfkwmlSAjLR4AWiUgxscZafGQSW2lJ67H5IOIiMjPpSZEI3tOItQqy64VtUqB7DmJSE2IFjWeAFGvRkRERB6RmhCNSfFqHDxRgcqfDuJfc0djzIBIUSseRqx8EBERdRIyqcQ0tiMpLtwjiQfA5IOIiIhExuSDiIiIRMXkg4iIiETF5IOIiIhExeSDiIiIRMXkg4iIiETF5IOIiIhExeSDiIiIRMXkg4iIiETF5IOIiIhExeSDiIiIRMXkg4iIiETF5IOIiIhEFeDpAIiIiEgcgiBg9fYTUF72bBxMPoiIiPyATi8gv6QKFTUNiAxTICkuHDKpxOKYPccrsXbXKcilMtzxu2Z0Cwz0SKxMPoiIiHxcblEpMnOKUappMG2LVimQkRaP1IRoAIaqxyvbjgEAkqMEhMo9lwJwzAcREZEPyy0qxYKNhRaJBwCUaRqwYGMhcotKAQB7j1fiuzOXIA+QYmKM3hOhmjD5ICIi8lE6vYDMnGIIVvYZt2XmFKNZpzdVPe5J6g1lkGghWsXkg4iIyEfll1S1qniYEwCUahqwfl8JCq9UPR4a11e0+Gxh8kFEROSjKmpsJx7m3ss/AwCYM6YPeoTJ3RmSQ5h8EBER+ajIMIVDx52+UA95gBSP3NTPzRE5hskHERGRj0qKC0e0SgGJnWMCZYa9917fx+Fkxd2cSj5WrFgBiURi8TVo0CDT/oaGBqSnpyMiIgKhoaGYNWsWysvLXR40ERERATKpBBlp8QDQKgExPtbqBMgDpPijl1Q9gHZUPoYMGYLS0lLT1759+0z7li5dipycHGzevBm7d+/G+fPnMXPmTJcGTERERC1SE6KRPScRapVlVSNKKUe/HiEArlQ9lN5R9QDaschYQEAA1Gp1q+0ajQbr16/Hpk2bMGHCBADAhg0bMHjwYBw8eBBjxozpeLRERETUSmpCNCbFqy1WOG1q1mHuhgKvq3oA7Ug+jh8/jpiYGCgUCiQnJ2PlypWIjY3FoUOHoNVqkZKSYjp20KBBiI2NxYEDB2wmH42NjWhsbDQ9rq6uBgBotVpotVpnw7PLeD5Xn5cssZ3FwXYWh5jt3NzcbPZ/ncX2ZmnLSg7++przPd1xo2KVAJQQBAGz3yoAANw9uhe6Bctata+7PmMd4VTycf311+Ptt9/Gtddei9LSUmRmZmL8+PEoKipCWVkZgoKC0LVrV4vnREVFoayszOY5V65ciczMzFbbv/rqK3Tp0sWZ8ByWl5fnlvOSJbazONjO4hC7nRt1gPFXdOH+XZDLRL28R/E93XE/X5Kg8IwMgRIB/ZpO4YsvTrU6xtXtXF9f7/CxTiUfU6dONf1/6NChuP7669GnTx988MEHCA4OduZUJsuXL8eyZctMj6urq9G7d29MnjwZSqWyXee0RavVIi8vD5MmTUKgh26m0xmwncXBdhaHmO1cUdNSBb7cpAPyDWPqEm+4GcFBLdlHpBes0+AOfE+7hiAIePetAgCXcM+YPrj71kEW+93VzsaeC0d06K4yXbt2xTXXXIMTJ05g0qRJaGpqwqVLlyyqH+Xl5VbHiBjJ5XLI5a1/kAIDA9325nPnuakF21kcbGdxiNHOAQEtXS0BeonZ9gAEBLQkH/7+evM93TH7jlfi0JXVTNNvGWizLV3dzs6cq0PrfNTW1uLkyZOIjo7GyJEjERgYiO3bt5v2Hz16FGfOnEFycnJHLkNEREQOML9z7T3Xx3rVDBdzTlU+/vznPyMtLQ19+vTB+fPnkZGRAZlMhrvvvhsqlQrz58/HsmXLEB4eDqVSiUWLFiE5OZkzXYiIiETw9YkL+PaXi5AHSLHgpv6eDscmp5KPX3/9FXfffTcuXLiAHj16YNy4cTh48CB69OgBAHj55ZchlUoxa9YsNDY2YsqUKVi3bp1bAiciIqIWvlL1AJxMPt5//327+xUKBbKyspCVldWhoIiIiMg5vlL1AHhvFyIiIp8nCAJWbzdUPe5O8u6qB8Dkg4iIyOftP3kBBacvIihAigU3e3fVA2DyQURE5NMsxnokxSLKy6seAJMPIiIin+ZrVQ+AyQcREZHP8sWqB8Dkg4iIyGf5YtUDYPJBRETkkwRBwOptxwH4VtUDYPJBRETkkw6cvID801UICpDij16+rsfVOnRjOSIiInI/nV5AfkkVKmoaEBmmwOi+3fCKWdVDrfKdqgfA5IOIiMir5RaVIjOnGKWaBtO28JBAVNVpfbLqATD5ICIi8lq5RaVYsLEQwlXbq+q0AICx/SN8ruoBcMwHERGRV9LpBWTmFLdKPMwdOV8Nnd7eEd6JyQcREZEXyi+psuhqsaaiphH5JVUiReQ6TD6IiIi8UEWN/cTD2eO8CZMPIiIiLxQZ5thYDkeP8yZMPoiIiLxQUlw4olUKSGzslwCIVimQFBcuZlguweSDiIjIC8mkEmSkxVvdZ0xIMtLiIZPaSk+8F5MPIiIiL5WaEI3sOYkIDwm02K5WKZA9JxGpCdEeiqxjuM4HERGRF0tNiMaGr0/jm5IqjB/YHX+6eQCS4sJ9suJhxOSDiIjIix04eQHflFQhSCbFqtuHIloV7OmQOozdLkRERF7slW3HAACzk3r7ReIBMPkgIiLyWuZVjwU3+949XGxh8kFEROSl/LHqATD5ICIi8kr+WvUAmHwQERF5pdXbDVWPu0b7V9UDYPJBRETkdQ6cvICDp/yz6gEw+SAiIvI65lWPmK7+VfUAmHwQERF5FX+vegBcZIyIiMijdHoB+SVVqKhpQGSYwjTDxV+rHgCTDyIiIo/JLSpFZk4xSjUNFtsDpBK/rXoA7HYhIiLyiNyiUizYWNgq8QCAZr2AH369JH5QImHyQUREJDKdXkBmTjEEO8dk5hRDp7d3hO9i8kFERCSy/JIqqxUPc6WaBuSXVIkUkbiYfBAREYmsosZ+4uHscb6GyQcREZHIIsMULj3O1zD5ICIiEllSXDiiVQpIbOyXAIhWKZAUFy5mWKJh8kFERCQymVSCjLR4q/uMCUlGWjxkUlvpiW9j8kFEROQBqQnRWDZpYKvtapUC2XMSkZoQ7YGoxMFFxoiIiDzkwCnDbJaUwZFIGxaDyDBDV4u/VjyMmHwQERF5wDenLmD/yQsIlEmQOT0BPf10KXVr2O1CRETkAau3HwcA3Dmqd6dKPAAmH0RERKIzr3r86ZYBng5HdEw+iIiIRGasetzRCaseAJMPIiIiUeWXVLVUPfz4zrX2MPkgIiIS0ertxwAYqh69unXxcDSeweSDiIhIJAWnq/D1ic5d9QCYfBAREYnmlW2segBMPoiIiESRX8KqhxGTDyIiIhFwrEcLJh9ERERuxrEelph8EBERudnqbYZ1PW4fyaoHwOSDiIjIrQpOV2HfiUoESCVIv4VVD4DJBxERkVsZqx4c69GCyQcREZGbsOphHZMPIiIiNzGu65EUF46zVZeh0wsejsg7dCj5eP755yGRSLBkyRLTtoaGBqSnpyMiIgKhoaGYNWsWysvLOxonERGRT1m74zi+PnEBALD/5AXc/eZBjHthB3KLSj0cmee1O/koKCjA66+/jqFDh1psX7p0KXJycrB582bs3r0b58+fx8yZMzscKBERka/ILSrFP7861mp7maYBCzYWdvoEpF3JR21tLe699168+eab6Natm2m7RqPB+vXr8dJLL2HChAkYOXIkNmzYgP379+PgwYMuC5qIiMhb6fQC/v5JkdV9xk6XzJziTt0FE9CeJ6Wnp+O2225DSkoKnnnmGdP2Q4cOQavVIiUlxbRt0KBBiI2NxYEDBzBmzJhW52psbERjY6PpcXV1NQBAq9VCq9W2JzybjOdz9XnJEttZHGxncYjZzs3NzWb/11lsb5a2fFD562vuL+/p/JIqVNU1ApBAKhEQaOXP/Krayzh4ogJJceGix+eudnbmfE4nH++//z4KCwtRUFDQal9ZWRmCgoLQtWtXi+1RUVEoKyuzer6VK1ciMzOz1favvvoKXbq4Z0pSXl6eW85LltjO4mA7i0Psdm7UAcZf0YX7d0EuE/XyHuXr7+lT1YCAAEglAh4frkOEwvpxlT8dxBc/iRubOVe3c319vcPHOpV8nD17FosXL0ZeXh4UChut6aTly5dj2bJlpsfV1dXo3bs3Jk+eDKVS6ZJrGGm1WuTl5WHSpEkIDAx06bmpBdtZHGxncYjZzhU1LVXgy006IH8fACDxhpsRHNSSfUSGyd0ah6f4y3v699kHARiq+C/8YDtr/Nfc0R6rfLijnY09F45wKvk4dOgQKioqkJiYaNqm0+mwZ88erF27Flu3bkVTUxMuXbpkUf0oLy+HWq22ek65XA65vPUPUmBgoNvefO48N7VgO4uD7SwOMdo5IKClqyVALzHbHoCAgJYPMX9/vX35PX3olyoUnTd8COsFyZUKliUJALVKgTEDIiGTSlofIBJXt7Mz53JqwOnEiRPx448/4vDhw6avUaNG4d577zX9PzAwENu3bzc95+jRozhz5gySk5OduRQREZHPeeXKaqZjB0RAAkOiYc74OCMt3qOJh6c5VfkICwtDQkKCxbaQkBBERESYts+fPx/Lli1DeHg4lEolFi1ahOTkZKuDTYmIiPzFoV8uYu9xw2qmz88ciiPnNcjMKUappsF0jFqlQEZaPFIToj0Yqee1a7aLPS+//DKkUilmzZqFxsZGTJkyBevWrXP1ZYiIiLyKcTXTWYm90Du8C3qHd8GkeDXyS6pQUdOAyDAFkuLCO3XFw6jDyceuXbssHisUCmRlZSErK6ujpyYiIvIJh36pMlU9Fk4YYNouk0qQ3D/Cg5F5J97bhYiIqIOMYz1uH2moepB9TD6IiIg6wHysR/otA9p+AjH5ICIi6ojV2w1VD+NYD2obkw8iIqJ2OvTLRew59hurHk5i8kFERNRO5lWP2AhWPRzF5IOIiKgdWPVoPyYfRERE7cCqR/sx+SAiInJS4RlD1UPGqke7MPkgIiJy0uptxqpHT1Y92oHJBxERkRMKz1zE7itVjxv6d8dnh8/hwMkL0OkFT4fmM1x+bxciIiJ/Zqx6BMmkWPLfw6bt0bxpnMNY+SAiInKQseoBAJe1Oot9ZZoGLNhYiNyiUk+E5lOYfBARETnIeA8Xa4ydLpk5xeyCaQOTDyIiIgd8d2WGiz0CgFJNA/JLqsQJykcx+SAiInKAcV0PR1TUNLgxEt/H5IOIiKgN3525iF1Hf4NU4tjxkWEK9wbk45h8EBERtcFY9fj9iJ6IVilgKweRwDDrJSkuXLTYfBGTDyIiIjuMVQ+ZVIJHJw5ERlo8ALRKQIyPM9LiIXO0RNJJMfkgIiKyw1j1mDmiJ/pEhCA1IRrZcxKhVll2rahVCmTPSeQ6Hw7gImNEREQ2HD57yVT1WDih5R4uqQnRmBSvRn5JFSpqGhAZZuhqYcXDMUw+iIiIbFi97RgAw1iPPhEhFvtkUgmS+0d4Iiyfx24XIiIiKw6fvYSdxqoH71zrUkw+iIiIrDCvevTtHtLG0eQMJh9ERERXYdXDvZh8EBERXYVVD/di8kFERGTme1Y93I7JBxERkRnjuh4zhrPq4S5MPoiIiK74/uwl7Pi5AjKpBIsmsOrhLkw+iIiIrmDVQxxMPoiIiMCqh5i4wikREREsqx69w7vgwMkLXDrdTZh8EBFRp/fDr4aqh1QCDO2lxLgXdqBU02DaH61SICMtnjeNcxF2uxARUae3epuh6pEUF44V/yu2SDwAoEzTgAUbC5FbVOqJ8PwOkw8iIurUfvj1ErZfqXqcrKiFYOUY47bMnGLo9NaOIGcw+SAiok7NWPUY2787fqttsnmcAKBU04D8kiqRIvNfTD6IiKjTMq963HhtD4eeU1HT0PZBZBeTDyIi6rSMVY8ZI3oiIUbl0HMiwxTuDKlT4GwXIiLqlH78VWOqeiyaMBCx4V0QrVKgTNNgddyHBIBaZZh2Sx3DygcREXVKq7cb7lw7Y3hPxHUPgUwqQUZaPABDomHO+DgjLZ7rfbgAkw8iIup0fvxVg20/GaoeC81WM01NiEb2nESoVZZdK2qVAtlzErnOh4uw24WIiDod86pHvx6hFvtSE6IxKV6N/JIqrnDqJkw+iIioU7FV9TAnk0qQ3D9C5Mg6D3a7EBFRp2Kv6kHiYPJBRESdRtG5tqse5H5MPoiIqNN45cq6HtNZ9fAoJh9ERNQpGKoe5ax6eAEOOCUiok7BWPWYNiwGFdWNKDqn4UwWD2HyQUREfs9Y9ZBIgK9PVOLTw+dN+6JVCmSkxXMNDxGx24WIiPyeseohCGh159oyTQMWbCxEblGpJ0LrlJh8EBGRXzNWPWwx3sclM6cYOr21u7qQqzH5ICIiv7Z6+/E2jxEAlGoakF9S5f6AiMkHERH5r6JzGuQVG8Z6OKKipsG9AREAJh9EROTHjFWPG/o5tlR6ZJii7YOow5h8EBGRXzJWPaQSICNtCKJVCtgqgEhgmPWSFBcuZoidFpMPIiLyS8aqx7RhMbhGHYaMtHgAaJWAGB9npMVzvQ+RMPkgIiK/Yz7WY+GEgQCA1IRoZM9JhFpl2bWiVimQPSeR63yIyKnkIzs7G0OHDoVSqYRSqURycjK+/PJL0/6Ghgakp6cjIiICoaGhmDVrFsrLbU9vIiIicoc1ZlWPAZEt93BJTYjGvscm4L2HxmD17OF476Ex2PfYBCYeInMq+ejVqxeef/55HDp0CN9++y0mTJiA6dOn48iRIwCApUuXIicnB5s3b8bu3btx/vx5zJw50y2BExERWVN0ToOvrlQ9Fl2pepiTSSVI7h+B6cN7Irl/BLtaPMCp5dXT0tIsHj/77LPIzs7GwYMH0atXL6xfvx6bNm3ChAkTAAAbNmzA4MGDcfDgQYwZM8Z1URMREdlgq+pB3qPd93bR6XTYvHkz6urqkJycjEOHDkGr1SIlJcV0zKBBgxAbG4sDBw7YTD4aGxvR2NhoelxdXQ0A0Gq10Gq17Q3PKuP5XH1essR2FgfbWRxitnNzc7PZ/3UW25ulLStv+utr7oq2PnK+2lT1WHBjnN+2VUe46z3tzPmcTj5+/PFHJCcno6GhAaGhofjkk08QHx+Pw4cPIygoCF27drU4PioqCmVlZTbPt3LlSmRmZrba/tVXX6FLly7OhueQvLw8t5yXLLGdxcF2FofY7dyoA4y/ogv374JcJurlPaojbf3Wz1IAUiRG6HG0YDeOui4sv+Pq93R9fb3DxzqdfFx77bU4fPgwNBoNPvzwQ8ydOxe7d+929jQmy5cvx7Jly0yPq6ur0bt3b0yePBlKpbLd57VGq9UiLy8PkyZNQmBgoEvPTS3YzuJgO4tDzHauqGmpAl9u0gH5+wAAiTfcjOCgluwjMkzu1jg8paNtXVxajR8PHIREAjx7z3j07xHihih9n7ve08aeC0c4nXwEBQVhwIABAICRI0eioKAAq1evxl133YWmpiZcunTJovpRXl4OtVpt83xyuRxyeesfpMDAQLf9oLvz3NSC7SwOtrM4xGjngICWrpYAvcRsewACAlqSD39/vdvb1ut2lwAA0obGYFBMVxdH5X9c/Z525lwdXudDr9ejsbERI0eORGBgILZv327ad/ToUZw5cwbJyckdvQwREZFNR85rsPWIYazHoxMHeDocaoNTlY/ly5dj6tSpiI2NRU1NDTZt2oRdu3Zh69atUKlUmD9/PpYtW4bw8HAolUosWrQIycnJnOlCRERuZZzhkjY0BgMiwzwcDbXFqeSjoqIC999/P0pLS6FSqTB06FBs3boVkyZNAgC8/PLLkEqlmDVrFhobGzFlyhSsW7fOLYETEREBrHr4IqeSj/Xr19vdr1AokJWVhaysrA4FRURE5ChWPXwP7+1CREQ+q/h8NasePojJBxER+Sxj1eN3rHr4FCYfRETkk4rPVyP3SJmh6jGBVQ9f0u7l1YmIiDzJvOoxMMpQ9dDpBeSXVKGipgGRYQokxYXzxnFeiMkHERH5HGtVj9yiUmTmFKNU02A6LlqlQEZaPFIToj0VKlnBbhciIvI5V1c9cotKsWBjoUXiAQBlmgYs2FiI3KJST4RJNjD5ICIin/JTqWXVQ6cXkJlTDMHKscZtmTnF0OmtHUGewOSDiIh8irHqcdt10RgYFYb8kqpWFQ9zAoBSTQPyS6pEipDawuSDiIh8xk+l1fiy6ErVY+JAAEBFje3Ew5yjx5H7MfkgIiKfYV71uObKDJfIMIVDz3X0OHI/Jh9EROQTrFU9ACApLhzRKgVsTaiVwDDrJSkuXJQ4qW1MPoiIyCdYq3oAgEwqQUZaPAC0SkCMjzPS4rnehxdh8kFERF7v5zLrVQ+j1IRoZM9JhFpl2bWiVimQPSeR63x4GS4yRkREXs9Y9bj1qqqHudSEaEyKV3OFUx/A5IOIiLzaz2XV+OJH47oerase5mRSCZL7R4gUGbUXu12IiMirmVc9rlXzzrX+gMkHERF5LWeqHuQ7mHwQEZHXYtXDPzH5ICIir8Sqh/9i8kFERF7p1e0nAAC3JrDq4W8424WIiLzO0bIafP5jKYCWdT10eoHTaP0Ekw8iIvI65quZXqsOQ25RKTJzii3uXhutUiAjLZ4LiPkgdrsQEZFXOVbeUvVYNHEAcotKsWBjoUXiAQBlmgYs2FiI3KJST4RJHcDkg4iIvMranacAGKoeAyPDkJlTDMHKccZtmTnF0OmtHUHeiskHERF5jfP1wJdHygEYxnrkl1S1qniYEwCUahqQX1IlUoTkCkw+iIjIa3z1q+Fj6dbr1LhWHYaKGtuJhzlHjyPvwOSDiIi8wrHyGhy+YJi9YpzhEhmmsPcUE0ePI+/A5IOIiLxC1q5TECBB6pAoDFIrAQBJceGIVilga0KtBIZZL0lx4aLFSR3H5IOIiDzuaFmNaazHwpv7mbbLpBJkpMUDQKsExPg4Iy2e6334GCYfRETkcWt2HIcgAMPD9a1WM01NiEb2nESoVZZdK2qVAtlzErnOhw/iImNERORRx8pr8MWVdT2m9NJbPSY1IRqT4tVc4dRPMPkgIiKPWrPdUPWYEh+JmJDzNo+TSSVI7h8hYmTkLux2ISIijzFfzXThLf09HA2JhckHERF5jLHqMTVBjUG8c22nweSDiIg8wrzqYVzXgzoHJh9EROQR5lWPwdFKT4dDImLyQUREojt+VdVDpxdM92fJL6nijeL8HJMPIiIS3ZodJyAIQOoQNX65UIdxL+zAvHcKAADz3inAuBd2ILeo1MNRkrsw+SAiIlEdL6/Blh8MU2pH9e2KBRsLW925tkzTgAUbC5mA+CkmH0REJCpj1WPKkCis33ca1jpYjNsyc4rZBeOHmHwQEZFozKset1wb2ariYU4AUKppMI0FIf/B5IOIiERjPtYjOEjm0HMqamwnKOSbuLw6ERGJwrzq8ejEgdBc1jr0vMgwRdsHkU9h8kFERKIwH+sRH6OETi8gWqVAmabB6rgPCQx3rk2KCxc7VHIzdrsQEZHbXV31AAw3istIiwdgSDTMGR9npMXzzrV+iMkHERG53atmVY8hMSrT9tSEaGTPSYRaZdm1olYpkD0nEakJ0WKHSiJgtwsREbnViYoa5FxV9TCXmhCNSfFqHDxRgcqfDuJfc0djzIBIVjz8GCsfRETkVmu2W696mJNJJaaxHUlx4Uw8/ByTDyIicpu2qh7UOTH5ICIitzFWPSbH2656UOfD5IOIiNyCVQ+yhckHERG5hbHqMSk+CjUNzfjs8DkcOHmB92ohznYhIiLXO1FRa6p6fHfmIu4uLjfti1YpkJEWz2m0nRgrH0RE5HKv7jgO4UqBo7K2yWJfmaYBCzYWIreo1AORkTdg8kFERC51oqIW//v+vM39xk6XzJxidsF0Ukw+iIjIpcyrHrYIAEo1DcgvqRIlJvIuTiUfK1euxOjRoxEWFobIyEjMmDEDR48etTimoaEB6enpiIiIQGhoKGbNmoXy8nIbZyQiIn/SVtXjahU1DW6MhryVU8nH7t27kZ6ejoMHDyIvLw9arRaTJ09GXV2d6ZilS5ciJycHmzdvxu7du3H+/HnMnDnT5YETEZH3MVY9RvXp5tDxkWGKtg8iv+PUbJfc3FyLx2+//TYiIyNx6NAh3HjjjdBoNFi/fj02bdqECRMmAAA2bNiAwYMH4+DBgxgzZozrIiciIq9yoqIWOVeqHk+mxeORfx9CmaYB1npgJDDcPM64pDp1Lh2aaqvRaAAA4eGGN8+hQ4eg1WqRkpJiOmbQoEGIjY3FgQMHrCYfjY2NaGxsND2urq4GAGi1Wmi12o6E14rxfK4+L1liO4uD7SwOMdu5ubnZ7P86i+3N0paPcG99zddsOwa9AKQM6oHBUSF48rZrsfS/hwHAIgEx3rXlyduuhV7XDP2Vb5XvaXG4q52dOZ9EENoaFmSdXq/HtGnTcOnSJezbtw8AsGnTJjz44IMWyQQAJCUl4ZZbbsELL7zQ6jwrVqxAZmZmq+2bNm1Cly5d2hMaEZHPa9QBf803/H24KqkZcpmHA2pD+WVg5WEZBEjwl6HN6BXi6YhIbPX19bjnnnug0WigVCrtHtvuykd6ejqKiopMiUd7LV++HMuWLTM9rq6uRu/evTF58uQ2g3eWVqtFXl4eJk2ahMDAQJeem1qwncXBdhaHmO1cUdPyh9vlJh2Qb/j9mnjDzQgOask+IsPkbo2jPf5v848QUIqJg3rg4TtGWOzT6QUc+uUiKmsb0T1UjpF9ulm9ay3f0+JwVzsbey4c0a7kY+HChdiyZQv27NmDXr16mbar1Wo0NTXh0qVL6Nq1q2l7eXk51Gq11XPJ5XLI5a1/kAIDA9325nPnuakF21kcbGdxiNHOAQEtXS0BeonZ9gAEBLQkH972ep+oqMWWHw0Lhi2ddG2r+AIBjL0myuHz8T0tDle3szPncmq2iyAIWLhwIT755BPs2LEDcXFxFvtHjhyJwMBAbN++3bTt6NGjOHPmDJKTk525FBER+Yi1O44bxnoMjkJCT965ltrmVOUjPT0dmzZtwmeffYawsDCUlZUBAFQqFYKDg6FSqTB//nwsW7YM4eHhUCqVWLRoEZKTkznThYjID538rWVdjyUpvHMtOcap5CM7OxsAcPPNN1ts37BhAx544AEAwMsvvwypVIpZs2ahsbERU6ZMwbp161wSLBEReZdXtxuqHgk9lahpaIZOL1gdz0Fkzqnkw5GJMQqFAllZWcjKymp3UERE5P3e/roEnx42VD2KzlXj7jcP8o615BDe24WIiJyWW1SKFTnFrbbzjrXkCCYfRETkFJ1ewBOfFlndxzvWkiOYfBARkVPyS6rwW22Tzf28Yy21hckHERE5pejcJYeO4x1ryRYmH0RE5JQ9xyodOo53rCVbOnRjOSIi6lxO/VaLr0/aTz54x1pqCysfRETksLU7TkAvAEN7qiBByx1qjYyPM9Liud4H2cTkg4iIHHLqt1p8evgcAODZ31+H7DmJUKssu1bUKgWy5yRynQ+yi90uRETkEGPVY+KgSFzXS4XreqkwKV6N/JIqVNQ0IDLM0NXCige1hckHERFZpdMLpsRCpxdMVY/FZvdwkUklSO4f4akQyUcx+SAiolZyi0qRmVOMUo3ldNnreioxtFdXzwRFfoNjPoiIyEJuUSkWbCxslXgAwI/nqrl0OnUYkw8iIjLR6QVk5hTD1sLoEnDpdOo4Jh9ERGSSX1JlteJhxKXTyRWYfBARkYmjS6Jz6XTqCCYfRERk4uiS6Fw6nTqCyQcREZkkxYUjWqVotXKpkQRANJdOpw5i8kFERCYyqQQZafFWB5xy6XRyFSYfRERkITUhGmP6ta5scOl0chUuMkZERBZOV9ah4PRFAMDT0xOgDA7g0unkUkw+iIjIwqs7TkCnF3DLtT1wX3IfT4dDfojdLkREZHK6ss7sHi7XeDga8lesfBARdULmN40z71JZu7Ol6jG8d1dPh0l+iskHEVEnY+2mcdEqBRbc3A+ffMeqB7kfkw8iok7EeNO4q6fSlmka8ORnxQDAqge5Hcd8EBF1EvZuGme+bdGEgWKFRJ0Ukw8iok6irZvGGTU260WIhjozJh9ERJ0EbxpH3oLJBxFRJ8GbxpG3YPJBRNRJtHXTOIA3jSNxMPkgIuokjDeNA2A1AZGAN40jcTD5ICLqRFITopE9JxFqlWXXSkRIEG8aR6Jh8kFE1MmkJkRj32MTcNM1PQAAw3qpkP/3FCYeJBomH0REndCvF+ux70QlACBj2hB2tZCouMIpEZGfsXXfFnNZV+7hctM1PZAY281DkVJnxeSDiMiP2LpvS0ZavKlb5cyFenxUaLyHC1czJfGx24WIyE8Y79ty9SqmZZoGLNhYiNyiUgDA2p3HWfUgj2LyQUTkBxy5b0tmTjFKKutY9SCPY/JBROQH2rpviwCgVNOAzP8dgU4v4EZWPciDmHwQEfkBR+/Hsuf4bwCAxRNZ9SDPYfJBROQHHL0fi14AbrymB0b2YdWDPIfJBxGRH3Dkvi1GrHqQpzH5ICLyA/bu22L+mFUP8gZMPoiI/ISt+7Z0DwuCcY0xVj3IG3CRMSIiP5KaEI1J8WqLFU4/+e5XfPDtrxg/sDurHuQVmHwQEfkQnV7AgZMX7C6dLpNKkNw/AgBwtqoeH19Z12MJ1/UgL8Hkg4jIR+z8uRwv5R1HRU2jadvVS6dfLWvnCTTrhStVj3CxQiWyi2M+iIh8wM6fy/G3j4ssEg+g9dLp5s5W1ePDQ78CYNWDvAuTDyIiL6fTC3gp77jVfeZLp+v0lours+pB3orJBxGRl/v+10utKh7mjEun55dUmbax6kHejMkHEZGXq6prcug48yXWWfUgb8bkg4jIy4WHBDl0nHGJdfOqB9f1IG/E5IOIyMsN69UVkWFym/slMMx6SYozVDjW7Wqpeozqy6oHeR8mH0REXk4mlWDZJOsVDOMKHxlp8ZBJJThbVY/N37LqQd6NyQcRkQ+4ZVAUnp+Z0KoColYpkD0n0bTOB6se5Au4yBgRkY+4ZVAUbrwmEr9cqLe6wimrHuQrnK587NmzB2lpaYiJiYFEIsGnn35qsV8QBDz55JOIjo5GcHAwUlJScPy49fnpRERkoNML+Pb0RWw9UoZvT19stWaHkXHp9OnDeyK5f4TF0urGqse4Aax6kHdzuvJRV1eHYcOGYd68eZg5c2ar/atWrcKaNWvwzjvvIC4uDk888QSmTJmC4uJiKBQKK2ckIurccotKkZlTjFJNy1TZHqGOzXAxsqh6cF0P8nJOJx9Tp07F1KlTre4TBAGvvPIKHn/8cUyfPh0A8O677yIqKgqffvopZs+e3eo5jY2NaGxsWTynuroaAKDVaqHVap0Nzy7j+Vx9XrLEdhYH21kc7m7nbT+VY+l/D0MAIJe1bNfUN8I4nLS5uRnN0pZKiLVY1u44jma9gBv6h2N4zzCffF/wPS0Od7WzM+eTCIJgvbbnyJMlEnzyySeYMWMGAODUqVPo378/vvvuOwwfPtx03E033YThw4dj9erVrc6xYsUKZGZmttq+adMmdOnSpb2hERH5tEYd8Nd8w9+Hq5KaLRKTq1U1Ak9/J4NekODRIc3orxQpSCIz9fX1uOeee6DRaKBU2n8TunTAaVlZGQAgKirKYntUVJRp39WWL1+OZcuWmR5XV1ejd+/emDx5cpvBO0ur1SIvLw+TJk1CYGCgS89NLdjO4mA7i8Od7ZxfUoV57xRY3Wf+Z6EsdjhG9YswPb56xsvjnxVDL/yKG/qHY9HsUS6NUUx8T4vDXe1s7LlwhMdnu8jlcsjlrRfPCQwMdNubz53nphZsZ3GwncXhjnaurG9Go07S5nEXL+sQENDy69o8jl8v1uOjwnMAgKWTrvWL9wLf0+JwdTs7cy6XrvOhVqsBAOXl5Rbby8vLTfuIiMjAuBx6W+wtr5618ySa9QLGDojAaM5wIR/h0uQjLi4OarUa27dvN22rrq7GN998g+TkZFdeiojIZ+j0Ag6cvIDPDp/DgZMXTNNok+LCEa1SoK3ax7BeXa1u//ViPTZ/exYAsHjiNS6MmMi9nO52qa2txYkTJ0yPS0pKcPjwYYSHhyM2NhZLlizBM888g4EDB5qm2sbExJgGpRIRdSbWptFGqxTISItHakI0MtLisWBjISQAbI3+N1/Lw9y6XSevzHCJMN3XhcgXOF35+PbbbzFixAiMGDECALBs2TKMGDECTz75JADgr3/9KxYtWoSHH34Yo0ePRm1tLXJzc7nGBxF1OrlFpViwsdAi8QCAMk0DFmwsRG5RKVITopE9JxFqleXvyLbW+TCveixJYdWDfIvTlY+bb74Z9mbnSiQSPPXUU3jqqac6FBgRkS/T6QVk5hRbrWYIMKzgkZlTjEnxaqQmRGNSvBq5RWW4UNeIiBA5BqnDMPGl3TbPv27XSWh1hrEerHqQr/H4bBciIn+UX1LVquJhTgBQqmlAfkmVaZn0UX27mfZfbtLZfC7HepCvY/JBRNQBOr2A/JKqVjd6q6ixnXiYc/Q4c8aqB8d6kK9i8kFE1E72BpM6Oo3W0eOMyjQNZlUP3sOFfJNLp9oSEXUWbQ0mvVjXaHcarQSGRMXZysU7+0+bqh7Xm616SuRLmHwQETmprcGkAPD05z/hidviAaBVAmJ8nJEWb3MarTVlmgb87/vzAFj1IN/G5IOIyEmODibtFhJkdRqtWqVA9pxEpCZEO3Xdd/afRrNeQHI/Vj3It3HMBxGRk5wZTDp9eE9MildbHZTqjLNV9S1VjxRWPci3MfkgIrLBOJMFMFQ7xgyIhEwqcXowqUwqQXL/jlUqsnaeQLNewJh+4RjDqgf5OCYfRERWGGeyVNVexqokYN47BQgPDUZGWjwmxasRrVKgTNNgddyHBIauFVdNg/3h10vYefQ3SCXAoxNY9SDfxzEfRERXaWsmS15xGTLSXDuY1J7sXScBAGnDYtA/MtQl5yTyJCYfRNQp2brTrCMzWYzLortyMKk9P5XVoEuQDI/c2M9l5yTyJHa7EFGnY29xMFVwkMPLohvvydLRwaSOuG9MH0SEyl1+XiJPYPJBRJ2KsUvl6sqGsUtl3ti+Dp3HOOPFFYNJ29I9NAj3XB/r1msQiYnJBxF1Go7cafaTw+ccOpezy6LbIggCGrR61DY249eL9ahv0qGusRkX6ppMxzw0vh8UgTKXXI/IGzD5ICK/Y+tmb44sDlZVp0V4SBAu1jVZTVIAIEopR+/wYJyoqEVdYzPqmppR16hDfVMzahubUd+oM/zb1IzaK9vrGq/sazLsq7tyXF1TM/S2LnTFpPiodrcFkTdi8kFEfsXaeI6oMDkWTRyAmoZmh86hVASg6krloUkHPF0oQ6PZHe7Lqxsx7oWdLo0bALoEyRAcKEMXuQyKABmOV9QCAKQS148hIfIkJh9E5DUEQcBlrQ51jTqLSkHL/w2VhJZqg3mVQYdzl+pRUlnf6rzlNY14/NMjDsdx+kLLOQRIUNnY+hiZVIKQIBlC5AEtX8bHV/4NlQegS1AAQuQyw//lAQiVywzbzLaHyAMQHCjDb7UtF7rcpMPN/9zlVPsR+QomH0TUbs06PeqaWroV6kyJQUvCYP7Y1A1xVWJh7Laoa2qG0EYXREdIJYAEEujsXCRMHoBlk6+5kjjIcOZCLRrP/YSoAUORPCASYQpDsiAPkELCigRRu3Sa5MPWMsnuvJa9qXeOHEPkSoIgoLFZf9UHfuuEofV2y4Shrqml2tDYrHdLrBIJEBJk+PA3VgaM/7eoHphVGUo1DcjaecLuefUCsDRlIF7ZdszQJubXvPLvP+4YarFGh1arxRdf/IRbR/ZCYGCgi79Tos6pUyQf9pZJdnYhoLaSBnvrBxiv5cgx/swXEy9PxCwIgiFRaDJLBMyqBNX1jSg4L8GpnSfR0CxYHmeWMJh3XzS3NbLRRQJlEvTvEYrY8C5XEgbZlW6GgCvdEYbkwVhduLrbIjhQBqmN9rX1Wnzm4CyVvt27IHtOYqufQXUn+hkk8jS/Tz7M5/TLzWaqGef0m69E2NHEoq31A7LnJAJAm8c488tPzA9FV1zL0cTLVd+XK6pQjsZs7IKou6qrwTxhqGloxvHyGlTVN0ECCULkstZdD8aZEFqdA10QMuCXk063iyJQahqPIIFh/IIyOBAxKgVCFQEtiYFxrEKQebXBOK4hAN+cuoA/f/hDq/M36wQcLavBkpSBLn0/23stnLnZW3L/CNEWByOi1vw6+XBkTr9xmeS84rIOJRZZ94zA05//ZPdaK/53BIDEoXgc+SXoyg9yV30A2+veciQ5M7a1K74ve+eZMkSNxmY9cr4/h1Vbj+K3mpY1Fbp2CcS0YTHo1z0Eh89ewqeHz7dq+1JNA/64sRDdQ4OgF4A6N3dBhAZdqR7IWwYqdgmU4VJlOQbGxSJMEXilemA45kRFLT797hwu1mtN54kMk2P51EGYNrwnZFKJ1fYpr3b8r3+dXsCLeces7nPH+9mRn0FnbvYmxuJgRGSdXycfjszpL9U0YO2OE3hl27EOJRaPf1aEqjqtlSNajiurtjJk3ko8+SVVSO4fYffD1ZUf5K6o6Jhfy9ZdQB1JBPV6IH1T62sZP+yfn5mApLgI5BWX4/U9p0zTIQEgTBGAm6/pgeiuwfi5tBp7jle2upbxPFIJbK6tcKlei3cP/GJ951Uqa5ts7pNJgCilAlEqBS436fBzWY3NY/8wPg5jB3RHSJChy+Hkb7WoadSiV9cuGD+wOwJkrW/DZBiL8AVuvTXeYixCblEpHv/6dKs2/K2mEcs++B7BQYYSoKMVuI6smWH+frbHFcn905//hCdui0f6pkJIYH08hytv9kZE7efXyYdx+eO2bPi6pMOJhb39zqqoabCbEHT0g9yZbiBHfulffS25DNDpAUFo+bCfPbqXQx9Ui//7nc2FnQDgbx8X2dxX09CMnB9K7Ty7hSNDH4JkEjTp2j7wvjF98O+DrZMV/ZXv//HbBuPpz3+y+XwJgM9/KMXyqYPbrMC1xZFqnzMVOHvxOFrpMf4c2kpiHInZkZ/BUk0DuoUEcTwHkQ/w6+TD0T7gS5fFSywccbqy3m4lZknKQIc+yB//rKhDH0IA8NePfkCt+epKNq616L2WpKFRByz7xvKt9X7BrzbPYU7rwIf91X/VXk0RIEWDC7pAHEk8ACDn+9bdMoDzH5xtVeCsVSMAy+4tR6oRjlbg2opnScpAu+cxigxTdPhGbo7+DFbUNGD68J4cz0Hk5fw6+UiKC2+zD1gVHGg3+XCGvSWZJTAsyQxIUF5tO54opRzv5Z+xmzT8a1+JQ/GYd0lYO1dbH0IA7CYe5izXTXDzLJA29rsi8XCGq5LXtipwV1cjrHVvuXLcSVvxvJd/Bmqlwu77Wa1S4GJdk90KnKM3cnOE8Q8Ojucg8m6tO5L9iEwqQUZaPIDWH4fGxw86+IsvPCTI5keqBIa/4p6ZnmD1WoDhF/aiCQPw0I1xNj88BQDRqmCUVdv/K1Dj4BLRnhIkFbBydDOCpJbfadfgQLttGB7ifWsotPW6dw12XcxtJTHGasSCjYWtKgXGD/LTlXWixVNW3Yi7kwx3WrX18/XEbYPx9Oe2u1QAx2/k5sjPoHEwKRF5N79OPgAgNSEa2XMSoVZZdsGoVQpkz0nEwgkDEa1S2P1bPSIkCHeP7m03aVArFXiv4Az6dg+xWd79+6dH8PQW233/APDd2Ut29zsqSOa66kNbv/SvThokEqBLgOFfcw+OjTM95+pzAMAz0xPsvhbOJChtxaxWyqFW2r+WvYTS1cmro0mMvWoE0FKN6Oj37mg8xjUzbP18dQuRO3wjt46+FhxMSuQ7/D75AAwJyMcLbsCEQWr896QUiX0iEB+txNv7T+P3676GXhDslvIv1DUha5f9tRS+O3sJe49XoqSyzuZCTkEBUoSHBKFXt2Bcqw7DwMhQDFKHISkuHDMTe2Juch9MHx7j0Pe06JYBAKz/IpYAeOWu4W1+kLvqA9iRpCFapcDCCQPsflDdOjSmzUqVo9dqK+YV04ZgxTT718pIi8etQzuWvLo6iXFFNcKR793ReCLDFEhNiMa+xybgvYfGYPXs4XjvoTHY99gEpCZEOzzoe8aV931HXgsOJiXyHX495sNcvVaHL4+UA5ACFVUOPUcCQBkciPCQIMO6CkGGFRgbtHoIENCtSxD69whFmKJlyWfzVRxbbjJlWKMh0Mp0yasZBxK2tVbBkknXYEhPpd1R/VKpBAs22p52uGLaEACwe4zxXNlS+zMIzK91dbzG88ikEqQmRNsdDGisVHXk+3I0ZgAOzYxoK+aMtPgOxzMpXo33C866ZHySoyt42jvGkXgcWTPD0UHfk+LVSIoL7/BrQUS+QSII7ryNk/Oqq6uhUqmg0WigVCpddl5NvRav7jiGc6dPYsR18VAGB7VKGBSBMhwrr0FNgxa9unXBmH4RHvmlZlzzALD+YebKVVkdPcaZaxkGQurw13yZR5exd+Q8jh7TFlfE09brviRlIF7edty0XS4TTO3cqGuJ972HxrS5Towr4nGk2qDTCxj3wo42k5h9j00wTbv1tsSiZT2VW91+b5dys/Fe5ne13fXnm03rswCG9WP8kZht3Zm5q52d+fzuNMkH4FtvbFfe/0XMG93p9AIOnqhA5U8H0X3wGI/fwE9M7k5iJsWrLT7Ir04+rv4gdwVXvA9dkcR4EpMP8fjS72hf5g3JR6fpdvE1riwvOzLt0FVTE2VSCZLiwvHFT3B7MuBt0yldEY8zXTzm3DXo0hXvQ0e60oioc2Hy4cW87cOVxGHvdTf/IK+qvWza7s4PcjGSKiLqXJh8EPkY4we5sXvrX3NHu7V7y1WYTBORUaeYakvkb4zdW4D7u7eIiFyNyQcRERGJiskHERERiYrJBxEREYmKyQcRERGJiskHERERiYrJBxEREYmKyQcRERGJiskHERERiYrJBxEREYnK65ZXN95kt7q62uXn1mq1qK+vR3V1Ne+Y6EZsZ3GwncUhZjvXmN/VVquDvrHesL2mGs2BLXe1DUaTW+PwFL6nxeGudjZ+bhs/x+2RCI4cJaJff/0VvXv39nQYRERE1A5nz55Fr1697B7jdcmHXq/H+fPnERYWBonEtferqK6uRu/evXH27FkolUqXnptasJ3FwXYWB9tZPGxrcbirnQVBQE1NDWJiYiCV2h/V4XXdLlKptM2MqaOUSiXf2CJgO4uD7SwOtrN42NbicEc7q1Qqh47jgFMiIiISFZMPIiIiElWnSj7kcjkyMjIgl8s9HYpfYzuLg+0sDrazeNjW4vCGdva6AadERETk3zpV5YOIiIg8j8kHERERiYrJBxEREYmKyQcRERGJiskHERERiarTJB9ZWVno27cvFAoFrr/+euTn53s6JL+zcuVKjB49GmFhYYiMjMSMGTNw9OhRT4fl955//nlIJBIsWbLE06H4nXPnzmHOnDmIiIhAcHAwrrvuOnz77beeDsuv6HQ6PPHEE4iLi0NwcDD69++Pp59+2qGbk5F9e/bsQVpaGmJiYiCRSPDpp59a7BcEAU8++SSio6MRHByMlJQUHD9+XJTYOkXy8d///hfLli1DRkYGCgsLMWzYMEyZMgUVFRWeDs2v7N69G+np6Th48CDy8vKg1WoxefJk1NXVeTo0v1VQUIDXX38dQ4cO9XQofufixYsYO3YsAgMD8eWXX6K4uBgvvvgiunXr5unQ/MoLL7yA7OxsrF27Fj/99BNeeOEFrFq1Cq+++qqnQ/N5dXV1GDZsGLKysqzuX7VqFdasWYPXXnsN33zzDUJCQjBlyhQ0NDRYPd6lhE4gKSlJSE9PNz3W6XRCTEyMsHLlSg9G5f8qKioEAMLu3bs9HYpfqqmpEQYOHCjk5eUJN910k7B48WJPh+RXHnvsMWHcuHGeDsPv3XbbbcK8efMsts2cOVO49957PRSRfwIgfPLJJ6bHer1eUKvVwj/+8Q/TtkuXLglyuVx477333B6P31c+mpqacOjQIaSkpJi2SaVSpKSk4MCBAx6MzP9pNBoAQHh4uIcj8U/p6em47bbbLN7b5Dr/+9//MGrUKNxxxx2IjIzEiBEj8Oabb3o6LL9zww03YPv27Th27BgA4Pvvv8e+ffswdepUD0fm30pKSlBWVmbx+0OlUuH6668X5bPR6+5q62qVlZXQ6XSIioqy2B4VFYWff/7ZQ1H5P71ejyVLlmDs2LFISEjwdDh+5/3330dhYSEKCgo8HYrfOnXqFLKzs7Fs2TL8v//3/1BQUIBHH30UQUFBmDt3rqfD8xt/+9vfUF1djUGDBkEmk0Gn0+HZZ5/Fvffe6+nQ/FpZWRkAWP1sNO5zJ79PPsgz0tPTUVRUhH379nk6FL9z9uxZLF68GHl5eVAoFJ4Ox2/p9XqMGjUKzz33HABgxIgRKCoqwmuvvcbkw4U++OAD/Oc//8GmTZswZMgQHD58GEuWLEFMTAzb2Y/5fbdL9+7dIZPJUF5ebrG9vLwcarXaQ1H5t4ULF2LLli3YuXMnevXq5elw/M6hQ4dQUVGBxMREBAQEICAgALt378aaNWsQEBAAnU7n6RD9QnR0NOLj4y22DR48GGfOnPFQRP7pL3/5C/72t79h9uzZuO6663Dfffdh6dKlWLlypadD82vGzz9PfTb6ffIRFBSEkSNHYvv27aZter0e27dvR3Jysgcj8z+CIGDhwoX45JNPsGPHDsTFxXk6JL80ceJE/Pjjjzh8+LDpa9SoUbj33ntx+PBhyGQyT4foF8aOHdtqqvixY8fQp08fD0Xkn+rr6yGVWn4UyWQy6PV6D0XUOcTFxUGtVlt8NlZXV+Obb74R5bOxU3S7LFu2DHPnzsWoUaOQlJSEV155BXV1dXjwwQc9HZpfSU9Px6ZNm/DZZ58hLCzM1G+oUqkQHBzs4ej8R1hYWKtxNCEhIYiIiOD4GhdaunQpbrjhBjz33HO48847kZ+fjzfeeANvvPGGp0PzK2lpaXj22WcRGxuLIUOG4LvvvsNLL72EefPmeTo0n1dbW4sTJ06YHpeUlODw4cMIDw9HbGwslixZgmeeeQYDBw5EXFwcnnjiCcTExGDGjBnuD87t82m8xKuvvirExsYKQUFBQlJSknDw4EFPh+R3AFj92rBhg6dD83ucauseOTk5QkJCgiCXy4VBgwYJb7zxhqdD8jvV1dXC4sWLhdjYWEGhUAj9+vUT/v73vwuNjY2eDs3n7dy50+rv5Llz5wqCYJhu+8QTTwhRUVGCXC4XJk6cKBw9elSU2CSCwGXkiIiISDx+P+aDiIiIvAuTDyIiIhIVkw8iIiISFZMPIiIiEhWTDyIiIhIVkw8iIiISFZMPIiIiEhWTDyIiIhIVkw8iIiISFZMPIiIiEhWTDyIiIhLV/wcvCtx5cvNsIgAAAABJRU5ErkJggg==",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "name": "stdout",     "output_type": "stream",     "text": [      "Plotting fit for model with 2 breakpoint(s) . . . \n"     ]    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "name": "stdout",     "output_type": "stream",     "text": [      "Plotting fit for model with 3 breakpoint(s) . . . \n"     ]    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "name": "stdout",     "output_type": "stream",     "text": [      "Plotting fit for model with 4 breakpoint(s) . . . \n"     ]    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "name": "stdout",     "output_type": "stream",     "text": [      "Plotting fit for model with 5 breakpoint(s) . . . \n"     ]    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "name": "stdout",     "output_type": "stream",     "text": [      "Plotting fit for model with 6 breakpoint(s) . . . \n"     ]    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "name": "stdout",     "output_type": "stream",     "text": [      "Plotting fit for model with 7 breakpoint(s) . . . \n"     ]    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "y_list = [y1+y_gn,]\n",    "y_explain=['(a) Normal',]\n",    "for i ,si in zip(y_list,y_explain):\n",    "    fig = plt.figure(figsize=(6, 4))\n",    "    ax = fig.add_subplot(111)\n",    "    ax.plot(xf, i,'ro', label=f'{si}')\n",    "    ax.grid(True)\n",    "    ax.set_xlabel('x')\n",    "    ax.set_ylabel('y')\n",    "    ax.legend(loc='upper left')\n",    "    plt.show()\n",    "    \n",    "fit_params=[]\n",    "for i ,si in zip(y_list,y_explain):\n",    "    # fig = plt.figure(figsize=(6, 4))\n",    "    ax = fig.add_subplot(111)\n",    "    ax.plot(xf,i,'ro',label=f'{si}')\n",    "    ax , p = ax_plots(xf, i, n_breakpoints=7, axs=ax, process_plot=True )  \n",    "    fit_params.append(p)\n",    "    # print(p)\n",    "    # ax.grid(True)\n",    "    # ax.set_xlabel('x')\n",    "    # ax.set_ylabel('y')\n",    "    # ax.legend(loc='upper left')\n",    "    # plt.show()"   ]  },  {   "cell_type": "code",   "execution_count": 6,   "metadata": {},   "outputs": [    {     "name": "stdout",     "output_type": "stream",     "text": [      "[WindowsPath('Datas/C60 20210607140335_5430.dat'),\n",      " WindowsPath('Datas/Fc3 20210607134453_5429.dat'),\n",      " WindowsPath('Datas/P-Low_230403122620_5_r.dat')]\n",      "files:3\n"     ]    }   ],   "source": [    "# PYS data読み込み\n",    "# PYS\n",    "pys_path = Path(r\"Datas\")\n",    "p_lists = list(pys_path.glob('*.dat'))\n",    "pprint(p_lists)\n",    "print(f'files:{len(p_lists)}')"   ]  },  {   "cell_type": "code",   "execution_count": 7,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x500 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "from reader import datconv as dv\n",    "\n",    "pys_inst=[]\n",    "for inum, i in  enumerate(p_lists[1:2]):\n",    "    acdata = dv.AcConv(str(i))\n",    "    acdata.convert()\n",    "    pys_inst.append(acdata)        \n",    "    fig =plt.figure(figsize=(6,5)) \n",    "    ax = fig.add_subplot(111)\n",    "    # ax.set_title(f'{acdata.metadata[\"sampleName\"]}')\n",    "    ax.plot(acdata.df[\"uvEnergy\"],acdata.df[\"npyield\"],'ro-',label='Data')\n",    "    ax.plot(acdata.df[\"uvEnergy\"],acdata.df[\"guideline\"],'b-',label=f'Estimated line\\n {acdata.metadata[\"thresholdEnergy\"]:.2f} eV')\n",    "    ax.legend()\n",    "    ax.grid()\n",    "    ax.axvline(acdata.metadata[\"thresholdEnergy\"] )\n",    "    ax.text(acdata.metadata[\"thresholdEnergy\"] ,np.max(acdata.df[\"npyield\"])*0.8, f'{acdata.metadata[\"thresholdEnergy\"]:.2f}')\n",    "    ax.set_xlabel('Energy [eV]')\n",    "    ax.set_ylabel(f'Intensity$^{{{acdata.metadata[\"powerNumber\"]:.2f}}}$')\n",    "    ax.set_ylabel('Intensity$^{1/2}$')"   ]  },  {   "cell_type": "code",   "execution_count": 19,   "metadata": {},   "outputs": [],   "source": [    "xp = acdata.df[\"uvEnergy\"].values\n",    "yp = acdata.df[\"pyield\"].values"   ]  },  {   "cell_type": "code",   "execution_count": 20,   "metadata": {},   "outputs": [    {     "data": {      "text/plain": [       "[<matplotlib.lines.Line2D at 0x1bae42a6350>]"      ]     },     "execution_count": 20,     "metadata": {},     "output_type": "execute_result"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "plt.plot(xp,yp)"   ]  },  {   "cell_type": "code",   "execution_count": 21,   "metadata": {},   "outputs": [],   "source": [    "dydx,_,_= mtl.c_dif(xp,yp)"   ]  },  {   "cell_type": "code",   "execution_count": 31,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x800 with 2 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "fig, ax = plt.subplots(nrows=2, figsize=(6,8))\n",    "ax[0].plot(xp, yp, 'ro-', label='Ferrocene')\n",    "ax[0].set_ylabel('PYS')\n",    "ax[0].set_xlabel(\"Energy[eV]\")\n",    "ax[0].grid()\n",    "ax[0].legend()\n",    "ax[1].plot(xp,dydx, 'bo-', label='Ferrocene')\n",    "ax[1].grid()\n",    "ax[1].set_ylabel('d(PYS)/dE')\n",    "ax[1].set_xlabel(\"Energy[eV]\")\n",    "ax[1].legend()\n",    "plt.show()\n"   ]  },  {   "cell_type": "code",   "execution_count": 34,   "metadata": {},   "outputs": [],   "source": [    "# Piecewise説明図\n",    "alpha_1 = -4\n",    "alpha_2 = -2\n",    "constant = 100\n",    "breakpoint_1 = 7\n",    "n_points = 200\n",    "np.random.seed(0)\n",    "xx = np.linspace(0, 20, n_points)\n",    "yy = constant + alpha_1*xx + (alpha_2-alpha_1) * np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points)"   ]  },  {   "cell_type": "code",   "execution_count": 35,   "metadata": {},   "outputs": [],   "source": [    "# Given some data, fit the model\n",    "pw_fit = piecewise_regression.Fit(xx, yy, start_values=[5], n_breakpoints=1)\n",    "# Print a summary of the fit\n",    "# pw_fit.summary()\n",    "a, b = np.polyfit(xx, yy, 1)"   ]  },  {   "cell_type": "code",   "execution_count": 19,   "metadata": {},   "outputs": [    {     "data": {      "text/plain": [       "6.487059545116942"      ]     },     "execution_count": 19,     "metadata": {},     "output_type": "execute_result"    }   ],   "source": [    "pw_fit.get_results()['estimates']['breakpoint1']['estimate']"   ]  },  {   "cell_type": "code",   "execution_count": 39,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x800 with 2 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "plt.subplot(211)\n",    "# Plot the data, fit, breakpoints and confidence intervals\n",    "pw_fit.plot_data(color=\"Black\", marker= '.', label='Data')\n",    "# Pass in standard matplotlib keywords to control any of the plots\n",    "plt.plot(xx,a*xx+b,color=\"blue\", marker= '^',alpha=0.3,label='1-line\\n$R^2$:0.96')\n",    "pw_fit.plot_fit(color=\"red\", marker= 'v', alpha=0.8,label=f'2-lines\\n$R^2$:0.99')\n",    "pw_fit.plot_breakpoints( color=\"green\")\n",    "# pw_fit.plot_breakpoint_confidence_intervals()\n",    "\n",    "plt.xlim(np.min(xx),np.max(xx))\n",    "plt.xlabel(\"x\")\n",    "plt.ylabel(\"y\")\n",    "\n",    "plt.grid()\n",    "plt.legend()\n",    "\n",    "plt.subplot(212)\n",    "# 2つのグラフを作成\n",    "dy1,_,_=mtl.c_dif(xx,yy)\n",    "\n",    "yy2 = mtl.moving_ave(yy,window=10)\n",    "dy2,_,_=mtl.c_dif(xx,yy2)\n",    "# 移動平均\n",    "\n",    "aw=10\n",    "plt.plot(xx[aw:-aw],dy1[aw:-aw],'ro-',label='Differentiation')\n",    "plt.plot(xx[aw:-aw],dy2[aw:-aw],'b^-',alpha=0.5,label=f'Smoothing({aw}) \\n+ Differentiation')\n",    "plt.xlim(np.min(xx),np.max(xx))\n",    "# plt.plot(xx[3:-2],dy[2:-2],'ro-',label='Differentiation')\n",    "plt.xlabel(\"x\")\n",    "plt.ylabel(\"dy\")\n",    "plt.grid()\n",    "\n",    "plt.axvline(pw_fit.get_results()['estimates']['breakpoint1']['estimate'], color=\"green\")\n",    "plt.legend(loc='upper right')\n",    "\n",    "# グラフのサイズを変更\n",    "plt.gcf().set_size_inches(6, 8)\n",    "# # タイトルを設定\n",    "# plt.suptitle(\"2つのグラフ\")\n",    "plt.show()\n",    "plt.close()\n"   ]  },  {   "cell_type": "code",   "execution_count": 27,   "metadata": {},   "outputs": [],   "source": [    "#　図の作成、PWの計算\n",    "def ax_plots(xdata, ydata, n_breakpoints, axs,  **kwargs ):\n",    "    \n",    "    res_model = pw.pw_model(xdata, ydata, bps_max=n_breakpoints, plot=False, info=False)\n",    "    \n",    "    pw_fit = piecewise_regression.Fit(xdata, ydata, res_model[\"bps_list_asc\"])\n",    "    pw_results = pw_fit.get_results()\n",    "    # --> dict_keys(['estimates', 'bic', 'rss', 'converged'])\n",    "\n",    "    pw_estimates = pw_results[\"estimates\"]\n",    "    # --> dict_keys(['const', 'beta1', 'breakpoint1', 'alpha1',...])\n",    "    \n",    "    # pw_estimate['const'].keys()\n",    "    # dict_keys(['estimate', 'se', 'confidence_interval', 't_stat', 'p_t'])\n",    "    \n",    "    key_breakpoints = [s for s in pw_estimates .keys() if 'breakpoint' in s]\n",    "    val_breakpoints = [pw_estimates [v]['estimate'] for v in key_breakpoints]\n",    "    re_val_breakpoints = sorted(val_breakpoints)\n",    "\n",    "    pw_fit = piecewise_regression.Fit(xdata, ydata, re_val_breakpoints)\n",    "    pw_results = pw_fit.get_results()\n",    "    pw_estimates = pw_results[\"estimates\"]\n",    "    \n",    "    def bp_alpha(pw_estimates):\n",    "        key_breakpoints = [s for s in pw_estimates.keys() if 'breakpoint' in s]\n",    "        val_breakpoints = [pw_estimates[v]['estimate'] for v in key_breakpoints]\n",    "\n",    "        key_alphas = [s for s in pw_estimates.keys() if 'alpha' in s]\n",    "        val_alphas = [pw_estimates[v]['estimate'] for v in key_alphas]\n",    "        \n",    "        key_betas = [s for s in pw_estimates.keys() if 'beta' in s]\n",    "        val_betas = [pw_estimates[v]['estimate'] for v in key_betas]\n",    "        \n",    "        return  val_breakpoints, val_alphas, val_betas\n",    "    \n",    "\n",    "    def draw_pw_ax(xdata, ydata, pw_estimates, axs, **kwargs):\n",    "        \n",    "        val_breakpoints, val_alphas, val_betas = bp_alpha(pw_estimates)\n",    "       \n",    "        num = len(val_breakpoints)+1\n",    "        \n",    "        yy_predicted = pw_estimates['const']['estimate'] + pw_estimates['alpha1']['estimate'] *xdata\n",    "        for bp_count in range(len(val_breakpoints)):\n",    "            alpha_n = f'alpha{bp_count+2}'\n",    "            beta_n = f'beta{bp_count+1}'\n",    "            breakpoint_n = f'breakpoint{bp_count+1}'\n",    "            yy_predicted += pw_estimates[beta_n]['estimate'] * \\\n",    "                np.maximum(xdata - pw_estimates[breakpoint_n]['estimate'], 0)\n",    "                \n",    "            axs.axvline(pw_estimates[breakpoint_n]['estimate'])\n",    "            axs.text(pw_estimates[breakpoint_n]['estimate'],\n",    "                    # np.max(ydata)*(1/num)*1, \n",    "                    np.max(ydata)*(1.5/num)*(np.mod(bp_count+1,2)+1), \n",    "                    # np.max(ydata)*(0.9-0.1*np.mod(bp_count+1,2)), \n",    "                    #  np.max(ydata)-np.max(ydata)*(1/num)*(bp_count+1), \n",    "                    # f'BP{bp_count+1}:{pw_estimates[breakpoint_n][\"estimate\"]:.1f}\\n$\\\\alpha${bp_count+2}:{pw_estimates[alpha_n][\"estimate\"]:.2f}')\n",    "                    f'BP{bp_count+1}')\n",    "            \n",    "        axs.axvline(np.min(xdata))\n",    "        axs.text(np.min(xdata),\n",    "                # np.max(ydata)*0.9, \n",    "                np.max(ydata)*(1.5/num)*1, \n",    "                #  np.max(ydata)-np.max(ydata)*(1/num)*1, \n",    "                f\"BP0\")\n",    "                # f\"BP0:{np.min(xdata):.1f}\\n$\\\\alpha$1:{pw_estimates['alpha1']['estimate']:.2f}\")\n",    "        \n",    "        axs.axvline(np.max(xdata))\n",    "        axs.text(np.max(xdata),\n",    "                 np.max(ydata)*(1.5/num)*(np.mod(bp_count+2,2)+1), \n",    "                 f'BP{len(val_breakpoints)+1}',ha='right')\n",    "        # axs.text(np.max(xdata), np.max(ydata)*(1.5/num)*1, \n",    "        #          f'BP{len(val_breakpoints)+1}',ha='right')\n",    "                \n",    "                \n",    "        axs.plot(xdata,yy_predicted,**kwargs)\n",    "        # num = len(val_breakpoints)\n",    "        \n",    "        # for i, bp in enumerate(val_breakpoints):\n",    "        #     axs.axvline(bp)\n",    "        #     axs.text(bp,np.max(ydata)*(1/num)*(i+1), f'BP{i+1}')\n",    "        \n",    "        return axs , {'bp':val_breakpoints, 'alpha':val_alphas}\n",    "    \n",    "    axs, p = draw_pw_ax(xdata,ydata, pw_estimates, axs)\n",    "    return axs, p\n",    "    "   ]  },  {   "cell_type": "code",   "execution_count": 28,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhMAAAFzCAYAAACEv4vjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAzQUlEQVR4nO3de3RU9b3+8WcyCQm3QEEggSSCFUFFRKFARORigIUeGhqwVtBDkdXTtYj8CKm22nrDIyJWIWgjXg6ix9XIEQy2ekRJKQSsoBgPPaAeREUJEILaknCRZJjs3x/jxAyZZHYye2bP5f1aKyvMnp093/lkMvOwL5+vwzAMQwAAAO2UYPcAAABAdCNMAACAoBAmAABAUAgTAAAgKIQJAAAQFMIEAAAICmECAAAEhTABAACCkmj3AEKtoaFBR44cUdeuXeVwOOweDgAAUcMwDJ04cUJ9+/ZVQkLL+x9iPkwcOXJEmZmZdg8DAICoVVlZqYyMjBbvj/kw0bVrV0meQqSmplqyTZfLpU2bNmny5MlKSkqyZJvxjppai3paj5pai3paLxQ1ra2tVWZmZuNnaUtiPkx4D22kpqZaGiY6deqk1NRU/ggsQk2tRT2tR02tRT2tF8qaBjpNgBMwAQBAUAgTAAAgKIQJAAAQlJg/Z8IMwzB09uxZud1uU+u7XC4lJibqzJkzpn8GrQt1TZ1OpxITE7k8GABCIO7DRH19vaqqqnT69GnTP2MYhtLS0lRZWcmHk0XCUdNOnTopPT1dHTp0CMn2ASBexXWYaGho0IEDB+R0OtW3b1916NDB1AdZQ0ODTp48qS5durTaxAPmhbKmhmGovr5eX331lQ4cOKCBAwfyewMAC8V1mKivr1dDQ4MyMzPVqVMn0z/X0NCg+vp6paSk8KFkkVDXtGPHjkpKStKXX37Z+DgAEDPcbjnKy9Vv2zY5OneWJkyQnM6wPTyfhBKBIE7wewYQk0pLpf79lThpkkYsX67ESZOk/v09y8OEd1cAAKJVaak0c6Z06JDv8sOHPcvDFCgIEwAARCO3W1q4UDKM5vd5lxUUeNYLMcKEFdxuaetW6aWXPN/D8Iv75ptv1Lt3b33xxRemf+bOO+/UggULQjcomz3//PPq3r273cMAgPDYvr35HommDEOqrPSsF2KEiWB9d6xKEyZIs2Z5vofhWNWSJUuUm5ur/v37m/6Z22+/XS+88II+//zzVtcbP368HA6H1q5d67O8qKioTY8HAAihqipr1wsCYSIYNh2rOn36tFavXq158+a16efOO+88TZkyRatWrQq4bkpKiu6++265XK72DtMvq7cHAHErPd3a9YJAmGgvt1uORYtsOVb1xhtvKDk5WaNHj24yHLfmzZunAQMGqGPHjho0aJBWrlzZ7GenTZvWbI+DPzfddJOOHz+uZ599ttX1Vq1apR/+8Ifq0KGDBg0apBdffNHnfofDoVWrVunHP/6xOnfurCVLluj+++/XsGHD9NxzzykrK0tdunRRfn6+3G63fv/73ystLU29e/fWkiVLfLa1fPlyXXbZZercubMyMzM1f/58nTx5MuBzAYCYNHaslJEhtdQfyeGQMjM964UYYaKdEnfskMOmY1Xbt2/X8OHDfZY1NDQoIyND69at00cffaR7771Xv/3tb/Xyyy/7rDdy5EgdOnQo4LkWqamp+t3vfqcHHnhAp06d8rvOhg0btHDhQv3qV7/S3r179ctf/lJz587Vli1bfNa7//779ZOf/ER79uzRrbfeKkn67LPPtHHjRr355pt66aWX9Nxzz+nGG2/UoUOHVF5ermXLlunuu+/Wu+++27idhIQEPf744/rwww/1wgsv6K9//at+/etfmy0bAMQWp1Py/qfx3EDhvV1UFJZ+E4SJdnIcPWpuxRAcq/ryyy/Vt29fn2VJSUlavHixRowYoQEDBmj27NmaO3duszDh/bkvv/wy4OPMnz9fKSkpWr58ud/7H330Uf385z/X/PnzddFFF6mwsFB5eXl69NFHfdabNWuW5s6dqwsuuEBZWVmSPOHnueee0yWXXKJp06Zp/Pjx2r9/v1asWKFBgwZp7ty5GjRokE8wKSgo0IQJE9S/f39NnDhRDz74YLPnBwBxJS9PWr9e6tfPd3lGhmd5Xl5YhkGYaCcjLc3ciiE4VvXtt9/67eBYXFys4cOHq1evXurSpYueeeYZHTx40Gedjh07SpKpuUiSk5P1wAMP6NFHH9XXX3/d7P6PP/5YY8aM8Vk2ZswYffzxxz7LRowY0exn+/fvr65duzbe7tOnjwYNGuTTWKpPnz46duxY4+2//OUvuvbaa9WvXz917dpVt9xyi7755ps2zasCADEnL0/64gudLSvT+4WFOltWJh04ELYgIREm2u1sdrYMm45VnXfeefrnP//ps2zt2rW6/fbbNW/ePG3atEm7d+/W3LlzVV9f77PeP/7xD0lSr169TD3WzTffrPPPP18PPvhgu8fbuXPnZsuSkpJ8bjscDr/LGhoaJElffPGF/uVf/kVDhw7VK6+8ooqKChUXF0tSs+cIAHHH6ZQxbpwOX3ONjHHjwtpKWyJMtJ/TKWPFCs+/w3ys6oorrtBHH33ks+xvf/ubrrrqKs2fP19XXHGFLrzwQn322WfNfnbv3r1KSkrSpZdeauqxEhIStHTpUq1atarZeRYXX3yx/va3vzUbxyWXXNK2J2RCRUWFGhoa9Nhjj2n06NG66KKLdOTIEcsfBwDQdoSJYNh0rGrKlCn68MMPffZODBw4UO+//77eeustffLJJ7rnnnu0a9euZj+7fft2jR07tvFwhxnXX3+9Ro0apaefftpn+R133KHnn39eq1at0v79+7V8+XKVlpbq9ttvb/+Ta8GFF14ol8ulJ554Qp9//rlefPFFPfXUU5Y/DgCg7QgTwfruWJW2bJFKSjzfQ3ys6rLLLtOVV17pc/LhL3/5S+Xl5enGG2/UqFGj9M0332j+/PnNfnbt2rX6xS9+0ebHXLZsmc6cOeOzbPr06Vq5cqUeffRRXXrppXr66ae1Zs0ajR8/vs3bD+Tyyy/X8uXLtWzZMg0ZMkR//OMftXTpUssfBwDQdg7D8NcoIXbU1taqW7duqqmpUWpqqs99Z86c0YEDBzRgwIA2TUnd0NCg2tpapaam2jYT5X//93/rjjvu0N69e02PYePGjfrVr36l//3f/1ViYmTNPh+Omrb39x2NXC6X3njjDV133XXNzkVB+1BTa1FP64Wipq19hjYVWZ8oMO3666/X/v37dfjwYWVmZpr6mVOnTmnNmjURFyQAANGNT5UoVlBQ0Kb1Z86cGZqBAADiGudMAACAoBAmAABAUAgTkmL8HFR8h98zAIRGXJ8z4T3b9fTp023qu4Do5G27zZnjAKKK2+2ZNLKqyjNFw9ixYe9wGUhchwmn06nu3bs3zv/QqVMnOVpqj91EQ0OD6uvrdebMGdsuDY01oaypYRg6ffq0jh07pu7du8sZYX+EANCi0lJp4UKp6SzVGRme2ULDOPdGIHEdJiQp7bsJu5pOKBWIYRj69ttv1bFjR1PhA4GFo6bdu3dv/H0DQMQrLZVmzpTOPUR7+LBneRhnBQ0k7sOEw+FQenq6evfuLZfLZepnXC6Xtm3bpmuuuYZd5hYJdU2TkpLYIwEgerjdnj0S/s71MgzPHFAFBVJubkQc8oj7MOHldDpNf9g4nU6dPXtWKSkphAmLUFMAaGL7dt9DG+cyDKmy0rNeCKYwaCsO+AMAEGmqqqxdL8RsDRP333+/HA6Hz9fgwYMb7z9z5ozy8/PVs2dPdenSRTNmzFB1dbWNIwYAIAzS061dL8Rs3zNx6aWXqqqqqvHr7bffbrxv0aJFeu2117Ru3TqVl5fryJEjyouQk00AAAiZsWM9V220dEK6wyFlZnrWiwC2nzORmJjo9wz7mpoarV69WiUlJZo4caIkac2aNbr44ou1c+dOjR49OtxDBQAgPJxOz+WfM2d6gkPTEzG9AaOoKCJOvpQiYM/E/v371bdvX11wwQWaPXu2Dh48KEmqqKiQy+VSTk5O47qDBw9WVlaWduzYYddwAQAIj7w8z+Wf/fr5Ls/IiKjLQiWb90yMGjVKzz//vAYNGqSqqiotXrxYY8eO1d69e3X06FF16NBB3bt39/mZPn366OjRoy1us66uTnV1dY23a2trJXkuPTR76Wcg3u1YtT1QU6tRT+tRU2tRT5OmTZOuu06Ot99u7IBpXH21Z4/EObULRU3NbsthRNCEBcePH9f555+v5cuXq2PHjpo7d65PMJCkkSNHasKECVq2bJnfbdx///1avHhxs+UlJSXq1KlTSMYNAEAsOn36tGbNmqWamhqlpqa2uJ7t50w01b17d1100UX69NNPNWnSJNXX1+v48eM+eyeqq6tb7WJ41113qbCwsPF2bW2tMjMzNXny5FYL0RYul0tlZWWaNGkSPREsQk2tRT2tR02tRT2tF4qaevfuBxJRYeLkyZP67LPPdMstt2j48OFKSkrS5s2bNWPGDEnSvn37dPDgQWVnZ7e4jeTkZCUnJzdbnpSUZPkLNhTbjHfU1FrU03rU1FrU03pW1tTsdmwNE7fffrumTZum888/X0eOHNF9990np9Opm266Sd26ddO8efNUWFioHj16KDU1VQsWLFB2djZXcgAAEEFsDROHDh3STTfdpG+++Ua9evXS1VdfrZ07d6pXr16SpBUrVighIUEzZsxQXV2dpkyZoieffNLOIQMAgHPYGibWrl3b6v0pKSkqLi5WcXFxmEYEAADayvY+EwAAILoRJgAAQFAIEwAAICiECQAAEBTCBAAACEpENa0CACDuuN3S9u2Nc29o7NiImQ3ULMIEAAB2KS2VFi6UDh36fllGhmf68QiaFTQQDnMAAGCH0lJp5kzfICFJhw97lpeW2jOudiBMAAAQbm63Z4+Ev4m7vcsKCjzrRQHCBAAA4bZ9e/M9Ek0ZhlRZ6VkvChAmAAAIt6oqa9ezGWECAIBwS0+3dj2bESYAAAi3sWM9V204HP7vdzikzEzPelGAMAEAQLg5nZ7LP6XmgcJ7u6goavpNECYAALBDXp60fr3Ur5/v8owMz/Io6jNB0yoAAOySlyfl5tIBEwAABMHplMaPt3sUQeEwBwAACAphAgAABIUwAQAAgkKYAAAAQSFMAACAoBAmAABAULg0FACAUHG7o76HhBmECQAAQqG0VFq40Heq8YwMTxvtKOpuaQaHOQAAsFppqTRzpm+QkKTDhz3LS0vtGVeIECYAALCS2+3ZI2EYze/zLiso8KwXIwgTAABYafv25nskmjIMqbLSs16MIEwAAGClqipr14sChAkAAKyUnm7telGAMAEAgJXGjvVcteFw+L/f4ZAyMz3rxQjCBAAAVnI6PZd/Ss0Dhfd2UVFM9ZsgTAAAYLW8PGn9eqlfP9/lGRme5THWZ4KmVQAAhEJenpSbSwdMAAAQBKdTGj/e7lGEHIc5AABAUAgTAAAgKIQJAAAQFMIEAAAICmECAAAEhTABAACCQpgAAABBIUwAAICgECYAAEBQ6IAJAEB7ud1x0S47kIjZM/Hwww/L4XCooKCgcdmZM2eUn5+vnj17qkuXLpoxY4aqq6vtGyQAAF6lpVL//tKECdKsWZ7v/ft7lseZiAgTu3bt0tNPP62hQ4f6LF+0aJFee+01rVu3TuXl5Tpy5IjyYmymNQBAFCotlWbOlA4d8l1++LBneZwFCtvDxMmTJzV79mw9++yz+sEPftC4vKamRqtXr9by5cs1ceJEDR8+XGvWrNE777yjnTt32jhiAEBcc7ulhQslw2h+n3dZQYFnvThh+zkT+fn5uv7665WTk6MHH3ywcXlFRYVcLpdycnIalw0ePFhZWVnasWOHRo8e7Xd7dXV1qqura7xdW1srSXK5XHK5XJaM2bsdq7YHamo16mk9amqtaK6no7xciefukWjKMKTKSp3dskXGuHFhG1coamp2W7aGibVr1+qDDz7Qrl27mt139OhRdejQQd27d/dZ3qdPHx09erTFbS5dulSLFy9utnzTpk3q1KlT0GNuqqyszNLtgZpajXpaj5paKxrr2W/bNo0wsd7ujRt1+NSpkI/nXFbW9PTp06bWsy1MVFZWauHChSorK1NKSopl273rrrtUWFjYeLu2tlaZmZmaPHmyUlNTLXkMl8ulsrIyTZo0SUlJSZZsM95RU2tRT+tRU2tFcz0dnTtLy5cHXG/Y1Km6PMx7JqyuqXfvfiC2hYmKigodO3ZMV155ZeMyt9utbdu26Q9/+IPeeust1dfX6/jx4z57J6qrq5WWltbidpOTk5WcnNxseVJSkuUv2FBsM95RU2tRT+tRU2tFZT0nTJAyMjwnW/o7b8LhkDIylDhhgi2XiVpZU7Pbse0EzGuvvVZ79uzR7t27G79GjBih2bNnN/47KSlJmzdvbvyZffv26eDBg8rOzrZr2ACAeOd0SitXev7tcPje571dVBRX/SZs2zPRtWtXDRkyxGdZ586d1bNnz8bl8+bNU2FhoXr06KHU1FQtWLBA2dnZLZ58CQBAWOTlSevXe67qaHoyZkaGJ0jEWRsD26/maM2KFSuUkJCgGTNmqK6uTlOmTNGTTz5p97AAAPAEhtxcOmAqwsLE1q1bfW6npKSouLhYxcXF9gwIAIDWOJ3S+PF2j8J2tjetAgAA0Y0wAQAAgkKYAAAAQSFMAACAoBAmAABAUCLqag4AACKG281lnyYRJgAAOFdpqf+GVCtXxl1DKjM4zAEAQFOlpdLMmb5BQvLMxTFzpud++CBMAADg5XZ79kj4m8DLu6ygwLMeGhEmAADw2r69+R6JpgxDqqz0rIdGhAkAALyqqqxdL04QJgAA8EpPt3a9OEGYAADAa+xYz1UbDof/+x0OKTPTsx4aESYAAPByOj2Xf0rNA4X3dlER/SbOQZgAAKCpvDxp/XqpXz/f5RkZnuX0mWiGplUAAJwrL0/KzaUDpkmECQAA/HE6pfHj7R5FVOAwBwAACAphAgAABIUwAQAAgkKYAAAAQSFMAACAoBAmAABAULg0FAAQn9xu+khYhDABAIg/paXSwoW+041nZHhaadPhss04zAEAiC+lpdLMmb5BQpIOH/YsLy21Z1xRjDABAIgfbrdnj4RhNL/Pu6ygwLMeTCNMAADix/btzfdINGUYUmWlZz2YRpgAAMSPqipr14MkwgQAIJ6kp1u7HiQRJgAA8WTsWM9VGw6H//sdDikz07MeTCNMAADih9PpufxTah4ovLeLiug30UaECQBAfMnLk9avl/r1812ekeFZTp+JNqNpFQAg/uTlSbm5dMC0CGECABCfnE5p/Hi7RxETOMwBAACCQpgAAABB4TAHACA2MSto2BAmAACxh1lBw4rDHACA2MKsoGFHmAAAxA5mBbUFYQIAEDuYFdQWhAkAQOxgVlBbECYAALGDWUFtQZgAAMQOZgW1ha1hYtWqVRo6dKhSU1OVmpqq7Oxsbdy4sfH+M2fOKD8/Xz179lSXLl00Y8YMVVdX2zhiAEBEY1ZQW9gaJjIyMvTwww+roqJC77//viZOnKjc3Fx9+OGHkqRFixbptdde07p161ReXq4jR44oj+uDAQCtYVbQsLO1adW0adN8bi9ZskSrVq3Szp07lZGRodWrV6ukpEQTJ06UJK1Zs0YXX3yxdu7cqdGjR9sxZABANGBW0LBqc5iYM2eO5s2bp2uuucbSgbjdbq1bt06nTp1Sdna2Kioq5HK5lJOT07jO4MGDlZWVpR07drQYJurq6lRXV9d4u7a2VpLkcrnkcrksGat3O1ZtD9TUatTTetTUWmGr55gx3/+7ocHzFaNCUVOz22pzmKipqVFOTo7OP/98zZ07V3PmzFG/c3cltcGePXuUnZ2tM2fOqEuXLtqwYYMuueQS7d69Wx06dFD37t191u/Tp4+OHj3a4vaWLl2qxYsXN1u+adMmderUqd3j9KesrMzS7YGaWo16Wo+aWot6Ws/Kmp4+fdrUeg7D8NcmrHVfffWVXnzxRb3wwgv66KOPlJOTo3nz5ik3N1dJSUlt2lZ9fb0OHjyompoarV+/Xv/xH/+h8vJy7d69W3PnzvXZyyBJI0eO1IQJE7Rs2TK/2/O3ZyIzM1Nff/21UlNT2/pU/XK5XCorK9OkSZPa/HzhHzW1FvW0HjW1FvW0XihqWltbq/POO081NTWtfoa265yJXr16qbCwUIWFhfrggw+0Zs0a3XLLLerSpYtuvvlmzZ8/XwMHDjS1rQ4dOujCCy+UJA0fPly7du3SypUrdeONN6q+vl7Hjx/32TtRXV2ttLS0FreXnJys5OTkZsuTkpIsf8GGYpvxjppai3paj5pai3paz8qamt1OUFdzVFVVqaysTGVlZXI6nbruuuu0Z88eXXLJJVqxYkW7ttnQ0KC6ujoNHz5cSUlJ2rx5c+N9+/bt08GDB5WdnR3MsAEAgIXavGfC5XLpz3/+s9asWaNNmzZp6NChKigo0KxZsxp3gWzYsEG33nqrFi1a1Oq27rrrLk2dOlVZWVk6ceKESkpKtHXrVr311lvq1q2b5s2bp8LCQvXo0UOpqalasGCBsrOzuZIDAIAI0uYwkZ6eroaGBt1000167733NGzYsGbrTJgwodmJk/4cO3ZM//qv/6qqqip169ZNQ4cO1VtvvaVJkyZJklasWKGEhATNmDFDdXV1mjJlip588sm2DhkAAIRQm8PEihUrdMMNNyglJaXFdbp3764DBw4E3Nbq1atbvT8lJUXFxcUqLi5u6zABAECYtDlM3HLLLaEYBwAA5rndNKSKILZ2wAQAoM1KS6WFC6VDh75flpHhmZODVtm2YNZQAED0KC2VZs70DRKSdPiwZ3lpqT3jinOECQBAdHC7PXsk/PVa9C4rKPCsh7AiTAAAosP27c33SDRlGFJlpWc9hBVhAgAQHaqqrF0PliFMAACiQ3q6tevBMoQJAEB0GDvWc9WGw+H/fodDysz0rIewIkwAAKKD0+m5/FNqHii8t4uK6DdhA8IEACB65OVJ69dL/fr5Ls/I8Cynz4QtaFoFAIgueXlSbi4dMCMIYQIAEH2cTmn8eLtHge9wmAMAAASFMAEAAIJCmAAAAEHhnAkAQORhivGoQpgAAEQWphiPOhzmAABEDqYYj0qECQBAZGCK8ahFmAAARAamGI9ahAkAQGRgivGoRZgAAEQGphiPWoQJAEBkYIrxqEWYAABEBqYYj1qECQBA5GCK8ahE0yoAQGRhivGoQ5gAAEQephiPKhzmAAAAQSFMAADCy+2Wo7xc/bZtk6O8nI6WMYAwAQAIn9JSqX9/JU6apBHLlytx0iSpf3/m3IhyhAkAQHgwiVfMIkwAAEKPSbxiGmECABB6TOIV0wgTAIDQYxKvmEaYAACEHpN4xTTCBAAg9JjEK6YRJgAAocckXjGNMAEACA8m8YpZzM0BAAif7ybxOrtli3Zv3KhhU6cqccIE9khEOcIEACC8nE4Z48bp8KlTunzcOIJEDCBMAACs5XYzfXicIUwAAKxTWurpdNm0QVVGhufkS86JiFmcgAkAsAZzb8QtwgQAIHjMvRHXbA0TS5cu1Y9+9CN17dpVvXv31vTp07Vv3z6fdc6cOaP8/Hz17NlTXbp00YwZM1RdXW3TiAEAfjH3RlyzNUyUl5crPz9fO3fuVFlZmVwulyZPnqxTp041rrNo0SK99tprWrduncrLy3XkyBHlcdwNACILc2/ENVtPwHzzzTd9bj///PPq3bu3KioqdM0116impkarV69WSUmJJk6cKElas2aNLr74Yu3cuVOjR4+2Y9gAgHMx90Zci6irOWpqaiRJPXr0kCRVVFTI5XIpJyencZ3BgwcrKytLO3bs8Bsm6urqVFdX13i7trZWkuRyueRyuSwZp3c7Vm0P1NRq1NN61DSA0aOV2K+fdOSIHH7OmzAcDqlfP50dPVpq8n5MPa0Tipqa3ZbDMPydLRN+DQ0N+vGPf6zjx4/r7bffliSVlJRo7ty5PuFAkkaOHKkJEyZo2bJlzbZz//33a/Hixc2Wl5SUqFOnTqEZPABA6Tt26EffvS83nX3D+yGz6ze/UVV2dtjHhfY7ffq0Zs2apZqaGqWmpra4XsTsmcjPz9fevXsbg0R73XXXXSosLGy8XVtbq8zMTE2ePLnVQrSFy+VSWVmZJk2apKSkJEu2Ge+oqbWop/WoqQnXXSf3lVfKWVjouRzUKyND7sce0xU/+Ymu+G4R9bReKGrq3bsfSESEidtuu02vv/66tm3bpoyMjMblaWlpqq+v1/Hjx9W9e/fG5dXV1UpLS/O7reTkZCUnJzdbnpSUZPkLNhTbjHfU1FrU03rUNICf/lSaMcOnA6Zj7FglttABk3paz8qamt2OrWHCMAwtWLBAGzZs0NatWzVgwACf+4cPH66kpCRt3rxZM2bMkCTt27dPBw8eVDa7ygAgMjmd0vjxdo8CYWRrmMjPz1dJSYn+9Kc/qWvXrjp69KgkqVu3burYsaO6deumefPmqbCwUD169FBqaqoWLFig7OxsruQAADsw7wb8sDVMrFq1SpI0/pwEu2bNGv385z+XJK1YsUIJCQmaMWOG6urqNGXKFD355JNhHikAgHk30BLbD3MEkpKSouLiYhUXF4dhRAAAv7zzbpz7vu2dd2P9egJFHGNuDgBA65h3AwEQJgAArWPeDQRAmAAAtI55NxAAYQIA0Drm3UAAhAkAQOvGjvVcteFw+L/f4ZAyMz3rIS4RJgAArXM6PZd/Ss0Dhfd2URH9JuIYYQIA8D23W9q6VXrpJc937xUaeXmeyz/79fNdPyODy0IRGXNzAAAiQKCmVHl5Um4uHTDRDGECAGC+KRXzbsAPDnMAQLyjKRWCRJgAgHhHUyoEiTABAPGOplQIEmECAOIdTakQJMIEAMQ7mlIhSIQJAIh3NKVCkAgTAACaUiEo9JkAAHjQlArtRJgAgHjhdgcOCjSlQjsQJgAgHgRqlQ0EgXMmACDWeVtln9uYytsqu7TUnnEhZhAmACCW0SobYUCYAIBYRqtshAFhAgBiGa2yEQaECQCIZbTKRhgQJgAgltEqG2FAmACAWEarbIQBYQIAYh2tshFiNK0CgFjRWodLWmUjhAgTABALzHS4pFU2QoTDHAAQ7ehwCZsRJgAgmtHhEhGAMAEA0YwOl4gAhAkAiGZ0uEQEIEwAQDSjwyUiAGECAKIZHS4RAQgTABDN6HCJCECYAIBo4HZLW7dKL73k+d706gw6XMJmNK0CgEhnpiEVHS5hI8IEAEQyb0Oqc/tIeBtSNd3zQIdL2ITDHAAQqWhIhShBmACASEVDKkQJwgQARCoaUiFKECYAIFLRkApRgjABAJGKhlSIEoQJAIhUNKRClLA1TGzbtk3Tpk1T37595XA49Oqrr/rcbxiG7r33XqWnp6tjx47KycnR/v377RksANiBhlSIAraGiVOnTunyyy9XcXGx3/sfeeQRPf7443rqqaf07rvvqnPnzpoyZYrOnDkT5pECQIgF6nD5xRfSli1SSYnn+4EDBAlEDFubVk2dOlVTp071e59hGCoqKtLdd9+t3NxcSdJ//ud/qk+fPnr11Vf1s5/9LJxDBYDQMdPhkoZUiGAR2wHzwIEDOnr0qHJychqXdevWTaNGjdKOHTtaDBN1dXWqq6trvF1bWytJcrlccrlclozNux2rtgdqajXqab1Q1dSxYYOcP/uZZBhqelaE8V2HS/fatTJ+8hNLHzMS8Bq1XihqanZbERsmjh49Kknq06ePz/I+ffo03ufP0qVLtXjx4mbLN23apE6dOlk6xrKyMku3B2pqNeppPUtr6nZr8vz5cp4TJCTJYRgyJNXn56ssMTFmT7LkNWo9K2t6+vRpU+tFbJhor7vuukuFhYWNt2tra5WZmanJkycrNTXVksdwuVwqKyvTpEmTlJSUZMk24x01tRb1tF4oauooL1fiN9+0fL+kTl9/retTU2WMG2fJY0YKXqPWC0VNvXv3A4nYMJGWliZJqq6uVnqThizV1dUaNmxYiz+XnJys5OTkZsuTkpIsf8GGYpvxjppai3par901dbubz+j51VemfjTxq6+kGP098hq1npU1NbudiO0zMWDAAKWlpWnz5s2Ny2pra/Xuu+8qOzvbxpEBQBuVlkr9+0sTJkizZnm+9+8vmb3UnQ6XiHC27pk4efKkPv3008bbBw4c0O7du9WjRw9lZWWpoKBADz74oAYOHKgBAwbonnvuUd++fTV9+nT7Bg0AbdHaFOL33Sf17Cn94x/+ZwZ1ODxXddDhEhHO1jDx/vvva8KECY23vec6zJkzR88//7x+/etf69SpU/q3f/s3HT9+XFdffbXefPNNpaSk2DVkADAv0BTiTbtaOhy+69HhElHE1jAxfvx4Gf7+yL7jcDj0wAMP6IEHHgjjqACgjfydD+F0mptC/JtvpMWLpWefbd5noqiIxlSIChF7AiYARIXWGk416XnTqoEDPR0u/QUSIAoQJgCgvVo7H2LmTOn++81tJz2dDpeIahF7NQcARLRA50NInkMXTCGOOECYAID2MHM+xKFD0i9+4bnNFOKIYYQJAGiPqipz6w0cyBTiiHmcMwEAgbjdcpSXq9+2bXJ07uxpOmW2kVR6uudciNxcTrBEzCJMAEBrvrtaI/HQIY2QpOXLPXsVvN8PHzbXcIoTLBHDOMwBIL653dLWrdJLL3m+u93f3+e9WuPccyMOH5ZuvFG66SbPbc6HQJwjTACIXy3NmVFaau5qjbVrpZdf5nwIxD0OcwCIT2Z6RAS6WqOyUjrvPBpOIe4RJgDENn+trqXAc2asXGlu+1VVnA+BuEeYABC7Wmp1/YtfBN7r8I9/mHsMpgcHCBMAYlSgqb/N6NFD+uc/mR4cCIATMCNRa2eXA9HCztexmZMnzVi40POdqzWAVrFnoq38Na+x8s2ktRkIm54Z3tKUx7EoWp5rtIwzHMy+joPV3qm/A/Hudfjd76QhQ/w/F6YHBxoRJtqipeY1Td8gzXygtLROoLPLvZeaWfVGHY4Pv2Afw8xzteJ5hGOckSTQ8w2mHmZfx8GO04qpvyVPcGg61nP3OuTlSbm5Ortli3Zv3KhhU6cq0er/RADRzohxNTU1hiSjpqYmuA298ophOByG4Xnb+f7L4fB8vfKK5ysjw/f+jAzP8qbb8bfOyy83X37u42RmGsa6dYHHYfb5BBrr2bOGsWWLYZSUeL6fPdt8O62tY+YxvtuGq6zM2FVYaLjKyr7fhlU1t3CcfrdhZpxma2qm5ibU19cbr776qlFfX9/8zkDP12w9/Dl71tzr2Mzzam0cgWq+eHHLY2j6tXhx88fIzPT7XFutKdqMelovFDU1+xlKmDDDzBtkz57mPvhaWsfMG59kGL16mXujDvThGcpgZObNPtAHl5lwZabmkTBO7+/Eqg9xE4GjxTeVQM/3jjvM1aOlMWzZYu517P2Z9rxGJc/vvrWaZ2R4vlr62zL7t2KmpmgX6mk9wkQIWRImzL5BmnlzC2Y7Zr/8/W/L+6EUrmAU6M0+0F6WYGtg9jHCNc7Fi635EDcZOPy+qQT63UuG4XSaq0dLYygpMVePgoL2v0bbWvNz69rWvXit1RTtRj2tR5gIIUvChNk3yEj+auvu39a2Y1Uwam0vi1VfVjyGFdvo0aP1+81+iJvca+D3sFGwoTjUry+rXqPer5IS/+GrhcMYgfDhZy3qaT07wwQnYJoRCU1pHA5P296vvmrfzxtG27r6tbadYM6Sb6q9zyXcj2HFNgI1QGrtsknD8LRtnj/f829/9zscUkGB1NAgLVrk/yThtpyU2FbeMTz7bOszaUqeExf9PV+rXqNeTP0NhA19JswYO9bzBnnuteah0tI17cXFwY3DMMx39YM1HA5P4yMrtBZqvIHjhhv8z3A5c6a0f78142htDIcOebpLSi2/jgMFp2Bfow6HlJnZfOrvm27yfCdIAJYjTJjhdH7/v6WW3iB79mz5Q957zXprQcD7BrhuXcszEN5wQ+BxmNGjR/iCkT8Oh9SrV9vW93c7UM3b8hhWbKOlcXobH9nFu4fAu9egtd+90xn8a2PgQM/r1d/ruKDA3DZae406HN//7mkmBUQEwoRZeXktv0G+8or0zDOe2y29ua1cGTgIFBV5/gf5xRfSli1SSYnn+4ED31+X39o4Fi8291wCdfWzIhgFerMPtJclULgyU3MzjxHqca5f72l8FMyHuBXByMxeA4dDKixs+X6z0tM9r1N/r+PcXHPbCPQafeaZlv8OmPobCD/LztKIUJb1mfBq6eQ2wzB3spdVJ4T5u5zNexa8mcvhWhuH90qN1s6CN7tOa8/VzDZaeq5m6xlJ42xtG96rOVq633sJarBXupg5KbGl+wONwUwPCateo639HYQIJwxai3paj6s5QsjyMGEE+IUF2+gpWGY/+AKNw6pgFOi5WhGurHiMcIyzvR/iZkKN2TCxZYu55xuoQVcwl1ta9RoNMz78rEU9rUeYCKGwh4lIEMq9H+1Zx8TjtLi3xyoWjTPk22hvqLFir4FZ4QhWESji/+6jDPW0HpeGwlrfzSUQ9OVw3rPgg13HxOMY48bp8KlTunzcuNCcOGfROEO+jUD3t/a7dTo959wEmmsiWFa8vqx6jQKICISJWGXFBx8iU0u/W+/JueGY4TJSwhmAiECYAGIJM1wCsAFhAog14ThsBABN0GcCAAAEhTABAACCQpgAAABBIUwAAICgECYAAEBQCBMAACAoMX9pqPFdJ8Da2lrLtulyuXT69GnV1tYqKSnJsu3GM2pqLeppPWpqLeppvVDU1PvZaTTtqutHzIeJEydOSJIyMzNtHgkAANHpxIkT6tatW4v3O4xAcSPKNTQ06MiRI+ratasc3jkKglRbW6vMzExVVlYqNTXVkm3GO2pqLeppPWpqLeppvVDU1DAMnThxQn379lVCQstnRsT8nomEhARlZGSEZNupqan8EViMmlqLelqPmlqLelrP6pq2tkfCixMwAQBAUAgTAAAgKISJdkhOTtZ9992n5ORku4cSM6iptain9aiptain9eysacyfgAkAAEKLPRMAACAohAkAABAUwgQAAAgKYQIAAASFMNEOxcXF6t+/v1JSUjRq1Ci99957dg8pKi1dulQ/+tGP1LVrV/Xu3VvTp0/Xvn377B5WzHj44YflcDhUUFBg91Ci2uHDh3XzzTerZ8+e6tixoy677DK9//77dg8rarndbt1zzz0aMGCAOnbsqB/+8If693//94BzP+B727Zt07Rp09S3b185HA69+uqrPvcbhqF7771X6enp6tixo3JycrR///6Qjokw0Ub/9V//pcLCQt1333364IMPdPnll2vKlCk6duyY3UOLOuXl5crPz9fOnTtVVlYml8ulyZMn69SpU3YPLert2rVLTz/9tIYOHWr3UKLaP//5T40ZM0ZJSUnauHGjPvroIz322GP6wQ9+YPfQotayZcu0atUq/eEPf9DHH3+sZcuW6ZFHHtETTzxh99CixqlTp3T55ZeruLjY7/2PPPKIHn/8cT311FN699131blzZ02ZMkVnzpwJ3aAMtMnIkSON/Pz8xttut9vo27evsXTpUhtHFRuOHTtmSDLKy8vtHkpUO3HihDFw4ECjrKzMGDdunLFw4UK7hxS1fvOb3xhXX3213cOIKddff71x6623+izLy8szZs+ebdOIopskY8OGDY23GxoajLS0NOP3v/9947Ljx48bycnJxksvvRSycbBnog3q6+tVUVGhnJycxmUJCQnKycnRjh07bBxZbKipqZEk9ejRw+aRRLf8/Hxdf/31Pq9TtM+f//xnjRgxQjfccIN69+6tK664Qs8++6zdw4pqV111lTZv3qxPPvlEkvT3v/9db7/9tqZOnWrzyGLDgQMHdPToUZ+//27dumnUqFEh/ZyK+Ym+rPT111/L7XarT58+Psv79Omj//u//7NpVLGhoaFBBQUFGjNmjIYMGWL3cKLW2rVr9cEHH2jXrl12DyUmfP7551q1apUKCwv129/+Vrt27dL/+3//Tx06dNCcOXPsHl5UuvPOO1VbW6vBgwfL6XTK7XZryZIlmj17tt1DiwlHjx6VJL+fU977QoEwgYiQn5+vvXv36u2337Z7KFGrsrJSCxcuVFlZmVJSUuweTkxoaGjQiBEj9NBDD0mSrrjiCu3du1dPPfUUYaKdXn75Zf3xj39USUmJLr30Uu3evVsFBQXq27cvNY1iHOZog/POO09Op1PV1dU+y6urq5WWlmbTqKLfbbfdptdff11btmwJ2XTx8aCiokLHjh3TlVdeqcTERCUmJqq8vFyPP/64EhMT5Xa77R5i1ElPT9cll1zis+ziiy/WwYMHbRpR9Lvjjjt055136mc/+5kuu+wy3XLLLVq0aJGWLl1q99BigvezKNyfU4SJNujQoYOGDx+uzZs3Ny5raGjQ5s2blZ2dbePIopNhGLrtttu0YcMG/fWvf9WAAQPsHlJUu/baa7Vnzx7t3r278WvEiBGaPXu2du/eLafTafcQo86YMWOaXa78ySef6Pzzz7dpRNHv9OnTSkjw/ehxOp1qaGiwaUSxZcCAAUpLS/P5nKqtrdW7774b0s8pDnO0UWFhoebMmaMRI0Zo5MiRKioq0qlTpzR37ly7hxZ18vPzVVJSoj/96U/q2rVr4/G8bt26qWPHjjaPLvp07dq12fkmnTt3Vs+ePTkPpZ0WLVqkq666Sg899JB++tOf6r333tMzzzyjZ555xu6hRa1p06ZpyZIlysrK0qWXXqr/+Z//0fLly3XrrbfaPbSocfLkSX366aeNtw8cOKDdu3erR48eysrKUkFBgR588EENHDhQAwYM0D333KO+fftq+vTpoRtUyK4TiWFPPPGEkZWVZXTo0MEYOXKksXPnTruHFJUk+f1as2aN3UOLGVwaGrzXXnvNGDJkiJGcnGwMHjzYeOaZZ+weUlSrra01Fi5caGRlZRkpKSnGBRdcYPzud78z6urq7B5a1NiyZYvf9845c+YYhuG5PPSee+4x+vTpYyQnJxvXXnutsW/fvpCOiSnIAQBAUDhnAgAABIUwAQAAgkKYAAAAQSFMAACAoBAmAABAUAgTAAAgKIQJAAAQFMIEAAAICmECAAAEhTABAACCQpgAEHZfffWV0tLS9NBDDzUue+edd9ShQwef2Q4BRAfm5gBgizfeeEPTp0/XO++8o0GDBmnYsGHKzc3V8uXL7R4agDYiTACwTX5+vv7yl79oxIgR2rNnj3bt2qXk5GS7hwWgjQgTAGzz7bffasiQIaqsrFRFRYUuu+wyu4cEoB04ZwKAbT777DMdOXJEDQ0N+uKLL+weDoB2Ys8EAFvU19dr5MiRGjZsmAYNGqSioiLt2bNHvXv3tntoANqIMAHAFnfccYfWr1+vv//97+rSpYvGjRunbt266fXXX7d7aADaiMMcAMJu69atKioq0osvvqjU1FQlJCToxRdf1Pbt27Vq1Sq7hwegjdgzAQAAgsKeCQAAEBTCBAAACAphAgAABIUwAQAAgkKYAAAAQSFMAACAoBAmAABAUAgTAAAgKIQJAAAQFMIEAAAICmECAAAEhTABAACC8v8B+HCPylPZNtcAAAAASUVORK5CYII=",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhMAAAFzCAYAAACEv4vjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABGGklEQVR4nO3de3xT9f0/8NdpmqYtvQCF0pakFBS5IwhDqnKpUC46vsXCFHEK08nUoi116lBRcCLoJhQngmwIc6PigALqb1xKgRbGHangDQFhllKuQgstbdPk8/sjS2hI0qTNSU8ur+fjkUfJJyefvPtOmvPmnPP5fCQhhAARERFREwUpHQARERH5NhYTRERE5BYWE0REROQWFhNERETkFhYTRERE5BYWE0REROQWFhNERETkFhYTRERE5JZgpQPwNKPRiDNnziAyMhKSJCkdDhERkc8QQuDq1atISEhAUJDj4w9+X0ycOXMGOp1O6TCIiIh8VklJCbRarcPH/b6YiIyMBGBKRFRUlCx96vV6bN68GSNGjIBarZalz0DHnMqL+ZQfcyov5lN+nshpRUUFdDqdZV/qiN8XE+ZTG1FRUbIWE+Hh4YiKiuIfgUyYU3kxn/JjTuXFfMrPkzl1dpkAL8AkIiIit7CYICIiIrewmCAiIiK3+P01E64QQqCurg4Gg8Gl7fV6PYKDg1FdXe3yc6hhzKm8PJVPtVoNlUolW39E5B8Cvpiora1FWVkZqqqqXH6OEAJxcXEoKSnh3BUyYU7l5al8SpIErVaLiIgI2fokIt8X0MWE0WjEyZMnoVKpkJCQgJCQEJe+eI1GI65du4aIiIgGJ/Eg1zGn8vJEPoUQuHDhAk6fPo3OnTvzCAURWQR0MVFbWwuj0QidTofw8HCXn2c0GlFbW4vQ0FDu+GTCnMrLU/ls27YtTp06Bb1ez2KCyJsYDJAKC9G+qAhSixZASgrQjH+j/NYGuPMichFPQRF5obw8ICkJwamp6D9vHoJTU4GkJFN7M+FelIiIyFfl5QHjxwOnT1u3l5aa2pupoGAxQURE5IsMBiAzExDC9jFzW1aWaTsPYzEhB4MB2L4d+OQT089meOMuXbqE2NhYnDp1CgCwfft2SJKEK1euOHzOxo0b0adPHxiNRo/HFwiGDh2KrKysZnu9goICdOvWzWNDZxcvXowxY8Z4pG8i8oAdO2yPSNQnBFBSYtrOw1hMuOt/56qQkgJMnGj62QznqmbPno20tDQkJSW5/JxRo0ZBrVZjxYoVLm1/+vRphISEoGfPnjaPLV++HC1btrRpT0pKQk5OjssxNbfmLACWL18OSZIst4iICPTr1w95TfxsvPjii3j11VdtLny8fv06WrdujTZt2qCmpsbuc9esWYN7770XrVq1QlhYGLp06YLHH38chw4dsmzz+OOP48svv8SOZvjiISIZlJXJu50bvKaYmDt3LiRJsvqir66uRkZGBmJiYhAREYFx48bh3LlzygV5M4XOVVVVVWHp0qV44oknGv3cyZMn47333nNp2+XLl+PBBx9ERUUF9u7d2+jXItMCc2VlZSgrK8OhQ4cwcuRIPPjggzh69Gij+tm5cydOnDiBcePG2Ty2Zs0a9OjRA127dsW6detsHn/ppZfw0EMPoU+fPvjss89w9OhR5ObmolOnTpg+fbplu5CQEEycONHlzwcRKSw+Xt7t3CG8wL59+0RSUpLo3bu3yMzMtLQ/9dRTQqfTiYKCAnHgwAExcOBAcddddzWq7/LycgFAlJeX2zx2/fp18e2334rr1683qk+DwSAuX7wojFqtEKYDSbY3SRJCpxOirq5Rfbti1apVom3btlZt27ZtEwDEF198IXr16iU0Go248847xZEjR6y2++9//ysAiOPHjzf4GkajUXTq1Els3LhRvPTSS+LJJ5+0ea36t9dff10MGTLEpt1sx44d4p577hGhoaFCq9WKZ599Vly7ds3yeIcOHcSrr74qJk+eLCIiIoROpxMffvihzWtevnzZ0nbo0CEBQJw8eVIIIcTFixfFhAkTREJCgggLCxM9e/YUubm5lu0nTZpkE5/5uUeOHBGjRo0SLVq0ELGxseLXv/61uHDhguW5165dE48++qho0aKFiIuLE3/+85/FkCFDrD6vN1u2bJmIjo62ajMYDEKtVot//etflrYzZ86I++67T4SGhoqkpCSxYsUK0aFDBzF//nzLNhkZGWL8+PF2X2fo0KFi8eLFYtGiRSI1NdXyOpcvXxb/+c9/BACxYMECu881Go1W9wsLC0VISIioqqqyu31T/2b8QW1trVi3bp2ora1VOhS/wHzKoK5OCK3WtL/x0H6ooX1ofYoXE1evXhWdO3cW+fn5Vl/OV65cEWq1Wqxatcqy7XfffScAiN27d7vcv6eKiauff+64kKh/27atUX274rnnnhOjRo2yajPvbLt16yY2b94sDh8+LH75y1+KpKQkmz/Wdu3aiWXLljX4GgUFBSIuLk7U1dWJI0eOiMjISMvOv6amRuTk5IioqChRVlYmysrKxNWrV8WlS5eEVqsVb7zxhqVdCCGOHz8uWrRoIebPny9++OEH8Z///Ef07dtXTJ482fJ6HTp0EK1atRLvv/++OHbsmJgzZ44ICgoS33//vdXv11Axcfr0afGnP/1JHDp0SJw4cUK89957QqVSib179wohTJ+p5ORk8eSTT1riq6urE5cvXxZt27YV06dPF99995348ssvRWpqqkhJSbG81tNPPy0SExPFli1bLLmNjIxsVDFRV1cnPvroI6FWq62KueHDh4s+ffqIPXv2iIMHD4ohQ4aIsLAwq2Kid+/eYu7cuTavcfz4caHRaMTPP/8sLl26JEJDQ8WpU6csxcSzzz4rIiIihF6vb/D9NqusrBRBQUFim4PPLYsJ7vzkwnzKZM0aU9Fwc0Fhbluzxq3uXS0mFJ+0KiMjA/fffz+GDx+ON99809J+8OBB6PV6DB8+3NLWtWtXJCYmYvfu3Rg4cKDd/mpqaqzOG1dUVAAwrVWg1+utttXr9RBCwGg0NuqiRCEEpLNnXdrWWFoKyHzB46lTpxAfH28Vs/nfM2bMwLBhwwAAy5YtQ2JiItasWYMHH3zQsm1CQgJOnTrV4O/8t7/9DQ899BAkSUL37t3RqVMnfPrpp5g8eTKCg4MRGRkJSZIQGxtreU54eDhUKhUiIiIs7UajEW+99RYmTpyI5557DgBwyy23ICcnBykpKVi4cCFCQ0MBAKmpqXjqqacgSRJeeOEFzJ8/HwUFBejcubMl1vrv1c1t8fHxyM7OtsSTkZGBjRs34tNPP0X//v0RGRmJkJAQhIWFWcX9l7/8BX369LH6/P3tb39Dhw4d8P333yMhIQFLly7Fxx9/jJSUFKvcmj8/9hiNRpSXl1umnr5+/TrUajUWL16Mjh07wmg04vvvv8eWLVuwd+9e9O/fHwCwZMkSdOnSxarv//73v4iLi7N5raVLl2LUqFGIjo4GAIwYMQIfffQRXnvtNQDADz/8gE6dOiEoKMjy3Pnz5+P111+39FFSUmJ5fmhoKKKjo3Hy5EkMHjzY7u8khAjISavM3x83f49Q0zCfMhkzBtLKlVBlZ0MqLbU0i/btYXj3XYgxYwA3cuzq+6NoMbFy5Up8+eWX2L9/v81jZ8+eRUhIiM1Ffu3atcPZBnbkc+bMwaxZs2zaN2/ebDPLZXBwMOLi4nDt2jXU1tY2KvbguDiXtquKjkbd/woauVy7dg1t27a1FEoALGuL9OzZ09IeHByMW2+9FV999RVGjRpl2VatVuPy5ctWz6+vvLwca9euxYYNGyzbjBs3Dn/961+Rnp4OwHQ9ixDCpg+j0Yjq6mqr9kOHDuGbb75Bbm6upc28ozxy5Ai6dOkCo9GIHj164OrVq5Zt2rZti9OnT6OiosLy+129etUyyVhlZaUlHxUVFTAYDJg3bx7Wrl2LsrIy6PV61NTUICQkxBJPXV0damtrreI7ePAgtm/fjqioKJtcHDlyBBcuXEBtbS26d+9uk9ub+6qvuroakZGR2L59OwDTe1RYWIhnnnkGYWFhGD16NIqLiy19mfuJjY1Fy5YtrfJ4/fp1m3wbDAb8/e9/x5w5cyzt6enpmDFjBjIzMxEUFGRZwK7+88aPH4+UlBQcOHAAv/vd71BRUWE1GVVoaCh+/vlnu79XbW0trl+/jqKiItTV1dn9vf1dfn6+0iH4FeZTBhoN8N57iPn2W4RevozqVq1wqXt30wyY//63W127um6VYsVESUkJMjMzkZ+fb/mfqRymT59u9b/TiooK6HQ6jBgxwmZnUV1djZKSEkRERDQqBiEEriYnQ2i1QGkpJDtjfIUkAVotwkeOlH1K03bt2qGystLq9zEXSpGRkVbtKpUKGo3Gqq2iogJardbuzhMAVqxYgerqaqujQuad/9mzZ3HbbbchNDQUkiTZ9BEUFITQ0FCr9uvXr2PKlCl49tlnbV4rMTERISEhCAoKglqtthzxAEw7bLVajaioKMv/7iMiIix9h4SEWLW9/fbb+PDDDzFv3jz06tULLVq0wLRp02A0Gi3PCQ4ORkhIiFV81dXV+OUvf4m5c+faxBcfH4/jx487zO3NfdVnnsq6T58+lra77roLRUVFWLhwIR566CGEhYUBMF2oWX8mVkmSrPLYpk0bVFdXW73Wv//9b5w5cwaPP/641esaDAbs27cPAwcORLdu3bB3716EhYVBrVZbXkun01mGEd/8e12+fBk6nc7u71VdXY2wsDAMHjxY1r9bX6DX65Gfn4/U1FRLLqnpmE/56UeNkj2njv6zdDPFiomDBw/i/PnzuOOOOyxtBoMBRUVFeP/997Fp0ybU1tbiypUrVkcnzp07h7gGjgpoNBpoNBqbdrVabZNcg8EASZIQFBTUqCm1jUYjoFJBzJ+PoAcfBCTJetIQSYIEADk5kDzwR3LHHXfgn//8p1XM5n/v27fPMlz08uXL+OGHH9C9e3fL49XV1Thx4gTuuOMOh7/zRx99hOeffx6TJ0+2an/mmWewfPlyzJ07F6GhoTAYDDZ9hISEwGg0WrXfcccd+O6773Dbbbc5/d3M78fN99u1awfA9P7HxMQAAA4fPmz53YOCgrBr1y6kpaXhscceA2B6n44dO2b1+9uLr1+/flizZg06deqE4GDbP4nOnTtDrVZj//79NrkdMmSIwzya229+PDg4GNevX0dQUBC6deuGuro6fPXVV+jXrx8A4Pjx47h8+bJVLvr27Yvvv//eqq9ly5ZhwoQJeOWVV6z6nz17NpYtW4aBAwdiwoQJeP/997F48WJkZmY6jM/87xMnTqC6uhr9+vWz+3sFBQVBkiS7f0+BIpB/d09gPuUnZ05d7UexoaHDhg3DkSNHUFxcbLn1798fjzzyiOXfarUaBQUFluccPXoUP/30E5KTk5UK21p6OrB6NdC+vXW7Vmtq/98pAbmNHDkS33zzDS5fvmzz2BtvvIGCggJ8/fXXmDx5Mtq0aYOxY8daHt+zZw80Go3DHBYXF+PLL7/Eb3/7W/Ts2dPq9vDDD+Pvf/876urqkJSUhGvXrqGgoAAXL160HApLSkpCUVERSktLcfHiRQCmoYm7du3C1KlTUVxcjGPHjmH9+vWYOnWqy7/zrbfeCp1Oh5kzZ+LYsWP4f//v/+Hdd9+12qZz587Iz8/Hrl278N133+F3v/udzVDipKQk7N27F6dOncLFixdhNBqRkZGBn3/+GQ8//DD279+PEydOYNOmTfjNb34Dg8GAiIgIPPHEE3jhhRewdetWS25dKUCFEDh79izOnj2LkydPYsmSJdi0aRPS0tIAmK4DGj58OKZMmYJ9+/bh0KFDmDJlCsLCwqxOPYwcORI7d+603L9w4QI+//xzTJo0yeZ9euyxx7B+/XpcvnwZycnJeP755/H8888jOzsbO3fuxH//+1/s2bMHS5cutSneduzYgU6dOuGWW25x+b0hIlJ8NEd9Nw+1e+qpp0RiYqLYunWrOHDggEhOThbJycmN6tNjQ0MvXxYGg8HUUFdnGrWRm2v66YHhoDcbMGCAWLx4seW+ebTD559/Lnr06CFCQkLEgAEDxFdffWX1vClTpojf/e53DvudOnWq6N69u93HysrKRFBQkFi/fr0QwvT+xMTEWIaGCiHE7t27Re/evYVGo7EaGrpv3z6RmpoqIiIiRIsWLUTv3r3F7NmzLY936NBBvPXWWzdyKoS4/fbbLf0KIcTOnTtFr169RGhoqBg0aJBYtWqV1WiOS5cuibS0NBERESFiY2PFq6++Kh577DGRlpZm6ePo0aNi4MCBIiwszOq5P/zwg3jggQdEy5YtRVhYmOjatavIysqyDJ28evWq+PWvfy3Cw8NFu3btxDvvvOPS0FDUG4aq0WjEbbfdJmbPni3q6n1Gzpw5I0aPHi00Go3o0KGDyM3NFbGxsVbvr3mkhnl0y5///GfRsmVLu1fC19TUiJYtW4q5c+da8vnpp5+KoUOHiujoaKFWq4VWqxUTJ04Ue/bssXruiBEjxJw5cxz+ThzNwdEHcmE+5eeJnPrM0ND6bv5yvn79unjmmWdEq1atRHh4uHjggQcsww1d1SzFhAK++OIL0a1bt0bFcOHCBdG6dWvx448/ejCypvGGnHqLkpISAUBs2bLFqv33v/+9mDJlikt9NCWfX3/9tYiNjRVXrlxxuA2LCe785MJ8yk/JYkLxoaH1ma96NwsNDcXChQuxcOFCZQLyYvfffz+OHTuG0tJS6HQ6l55z6tQpfPDBB+jYsaOHo6PG2Lp1K65du4ZevXqhrKwML774IpKSkmyGZr7yyiv44IMPbK75kEtZWRk+/vhjyzBRIiJXeVUxQY3T2DUm+vfvb5nLgLyHXq/Hyy+/jB9//BGRkZG46667sGLFCpsLn1q2bImXX37ZY3HUH71DRNQYLCaIFDZy5EiMHDlS6TCIiJrMaxb6IiIiIt/EYgKm4XtE5Bz/VojInoAuJsznpF2dLpQo0JmnnQ+0dTmIqGEBfc2ESqVCy5Ytcf78eQCmKanrTxTkiNFoRG1tLaqrqz1yVX0gYk7l5Yl8Go1GXLhwAeHh4XZnCiWiwBXw3wjmqbnNBYUrhBC4fv26zSyF1HTMqbw8lc+goCAkJibyPSJqTgYDsGMHUFYGxMcDgwbJvuaTuwK+mJAkCfHx8YiNjXV5qVW9Xo+ioiIMHjyYc8rLhDmVl6fyaV6UjYiaSV4ekJkJnD59o02rBRYs8NiSDU0R8MWEmUqlcvk8sEqlQl1dHUJDQ7njkwlzKi/mk8gP5OUB48dbLyQJAKWlpnYPrgHVWPwvBhERkbcxGExHJOyNoDK3ZWWZtvMCLCaIiIi8zY4d1qc2biYEUFJi2s4LsJggIiLyNmVl8m7nYSwmiIiIvE18vLzbeRiLCSIiIm8zaJBp1IajYdiSBOh0pu28AIsJIiIib6NSmYZ/ArYFhfl+To7XzDfBYoKIiMgbpaebhn+2b2/drtV61bBQgPNMEBERea/0dCAtjTNgEhERkRtUKmDoUKWjaBBPcxAREZFbWEwQERGRW1hMEBERkVtYTBAREZFbWEwQERGRW1hMEBERkVsULSYWLVqE3r17IyoqClFRUUhOTsaGDRssjw8dOhSSJFndnnrqKQUjJiIiopspOs+EVqvF3Llz0blzZwgh8Pe//x1paWk4dOgQevToAQB48skn8cYbb1ieEx4erlS4REREZIeixcSYMWOs7s+ePRuLFi3Cnj17LMVEeHg44uLilAiPiIiIXOA1M2AaDAasWrUKlZWVSE5OtrSvWLEC//znPxEXF4cxY8ZgxowZDR6dqKmpQU1NjeV+RUUFAECv10Ov18sSq7kfufoj5lRuzKf8mFN5MZ/y80ROXe1LEkII2V61CY4cOYLk5GRUV1cjIiICubm5uO+++wAAS5YsQYcOHZCQkIDDhw/jpZdewoABA5CXl+ewv5kzZ2LWrFk27bm5uTxFQkRE1AhVVVWYOHEiysvLERUV5XA7xYuJ2tpa/PTTTygvL8fq1avxt7/9DYWFhejevbvNtlu3bsWwYcNw/Phx3HLLLXb7s3dkQqfT4eLFiw0mojH0ej3y8/ORmpoKtVotS5+BjjmVF/MpP+ZUXsyn/DyR04qKCrRp08ZpMaH4aY6QkBDceuutAIB+/fph//79WLBgAT788EObbe+8804AaLCY0Gg00Gg0Nu1qtVr2D6wn+gx0zKm8mE/5MafyYj7lJ2dOXe1H8WLiZkaj0erIQn3FxcUAgPj4+GaMiIiIyIMMBq9fYtwZRYuJ6dOnY/To0UhMTMTVq1eRm5uL7du3Y9OmTThx4oTl+omYmBgcPnwY06ZNw+DBg9G7d28lwyYiIpJHXh6QmQmcPn2jTasFFiwA0tOVi6uRFC0mzp8/j8ceewxlZWWIjo5G7969sWnTJqSmpqKkpARbtmxBTk4OKisrodPpMG7cOLz66qtKhkxERCSPvDxg/Hjg5ksXS0tN7atX+0xBoWgxsXTpUoeP6XQ6FBYWNmM0REREzcRgMB2RsDcGQghAkoCsLCAtzSdOeXBtDiIioua2Y4f1qY2bCQGUlJi28wEsJoiIiJpbWZm82ymMxQQREVFzc3VUoo+MXmQxQURE1NwGDTKN2pAk+49LEqDTmbbzASwmiIiImptKZRr+CdgWFOb7OTk+cfElwGKCiIhIGenppuGf7dtbt2u1PjUsFPDCGTCJiIgCRnq6afgnZ8AkIiKiJlOpgKFDlY7CLTzNQURERG5hMUFERERuYTFBREREbmExQURERG5hMUFERERuYTFBREREbmExQURERG7hPBNERESeYjD4/IRUrmAxQURE5Al5eUBmJnD69I02rda0JocPTZXtCp7mICIiklteHjB+vHUhAQClpab2vDxl4vIQFhNERERyMhhMRySEsH3M3JaVZdrOT7CYICIiktOOHbZHJOoTAigpMW3nJ1hMEBERyamsTN7tfACLCSIiIjnFx8u7nQ9gMUFERCSnQYNMozYkyf7jkgTodKbt/ASLCSIiIjmpVKbhn4BtQWG+n5PjV/NNKFpMLFq0CL1790ZUVBSioqKQnJyMDRs2WB6vrq5GRkYGYmJiEBERgXHjxuHcuXMKRkxEROSC9HRg9WqgfXvrdq3W1M55JuSj1Woxd+5cHDx4EAcOHMC9996LtLQ0fPPNNwCAadOm4fPPP8eqVatQWFiIM2fOIN3P3gAiIvJT6enAqVPAtm1Abq7p58mTfldIAArPgDlmzBir+7Nnz8aiRYuwZ88eaLVaLF26FLm5ubj33nsBAMuWLUO3bt2wZ88eDBw4UImQiYiIXKdSAUOHKh2Fx3nNdNoGgwGrVq1CZWUlkpOTcfDgQej1egwfPtyyTdeuXZGYmIjdu3c7LCZqampQU1NjuV9RUQEA0Ov10Ov1ssRq7keu/og5lRvzKT/mVF7Mp/w8kVNX+1K8mDhy5AiSk5NRXV2NiIgIrF27Ft27d0dxcTFCQkLQsmVLq+3btWuHs2fPOuxvzpw5mDVrlk375s2bER4eLmvs+fn5svZHzKncmE/5MafyYj7lJ2dOq6qqXNpO8WKiS5cuKC4uRnl5OVavXo1JkyahsLCwyf1Nnz4d2dnZlvsVFRXQ6XQYMWIEoqKi5AgZer0e+fn5SE1NhVqtlqXPQMecyov5lB9zKi/mU36eyKn56L4zihcTISEhuPXWWwEA/fr1w/79+7FgwQI89NBDqK2txZUrV6yOTpw7dw5xcXEO+9NoNNBoNDbtarVa9g+sJ/oMdMypvJhP+TGn8mI+5SdnTl3tx+vmmTAajaipqUG/fv2gVqtRUFBgeezo0aP46aefkJycrGCEREREVJ+iRyamT5+O0aNHIzExEVevXkVubi62b9+OTZs2ITo6Gk888QSys7PRunVrREVF4dlnn0VycjJHchAREXkRRYuJ8+fP47HHHkNZWRmio6PRu3dvbNq0CampqQCA+fPnIygoCOPGjUNNTQ1GjhyJDz74QMmQiYiI6CaKFhNLly5t8PHQ0FAsXLgQCxcubKaIiIiIqLG87poJIiIi8i0sJoiIiMgtLCaIiIjILSwmiIiIyC2KT1pFRETkswwGYMcOoKwMiI8HBg0yLe4VYFhMEBERNUVeHpCZCZw+faNNqwUWLPDLZcYbwtMcREREjZWXB4wfb11IAEBpqak9L0+ZuBTCYoKIiKgxDAbTEQkhbB8zt2VlmbYLECwmiIiIGmPHDtsjEvUJAZSUmLYLECwmiIiIGqOsTN7t/ACLCSIiosaIj5d3Oz/AYoKIiKgxBg0yjdqQJPuPSxKg05m2CxAsJoiIiBpDpTIN/wRsCwrz/ZycgJpvgsUEERFRY6WnA6tXA+3bW7drtab2AJtngpNWERERNUV6OpCWxhkwwWKCiIio6VQqYOhQpaNQHE9zEBERkVtYTBAREZFbWEwQERGRW1hMEBERkVtYTBAREZFbWEwQERGRW1hMEBERkVsULSbmzJmDX/ziF4iMjERsbCzGjh2Lo0ePWm0zdOhQSJJkdXvqqacUipiIiAKGwQBs3w588onpp8GgdEReS9FiorCwEBkZGdizZw/y8/Oh1+sxYsQIVFZWWm335JNPoqyszHJ75513FIqYiIgCQl4ekJQEpKQAEyeafiYlmdrJhqIzYG7cuNHq/vLlyxEbG4uDBw9i8ODBlvbw8HDExcU1d3hERBSI8vKA8eMBIazbS0tN7QG49oYzXnXNRHl5OQCgdevWVu0rVqxAmzZt0LNnT0yfPh1VVVVKhEdERP7OYAAyM20LCeBGW1YWT3ncxGvW5jAajcjKysLdd9+Nnj17WtonTpyIDh06ICEhAYcPH8ZLL72Eo0ePIs/BoaaamhrU1NRY7ldUVAAA9Ho99Hq9LLGa+5GrP2JO5cZ8yo85lZe35lMqLETw6dOONxACKClB3bZtEEOGNF9gLvBETl3tSxLCXvnV/J5++mls2LABO3fuhFardbjd1q1bMWzYMBw/fhy33HKLzeMzZ87ErFmzbNpzc3MRHh4ua8xERORf2hcVof+8eU63O5CdjdJ6p+P9VVVVFSZOnIjy8nJERUU53M4riompU6di/fr1KCoqQseOHRvctrKyEhEREdi4cSNGjhxp87i9IxM6nQ4XL15sMBGNodfrkZ+fj9TUVKjValn6DHTMqbyYT/kxp/Ly1nxKhYUITk11ul1dfr5XHpmQO6cVFRVo06aN02JC0dMcQgg8++yzWLt2LbZv3+60kACA4uJiAEB8fLzdxzUaDTQajU27Wq2W/QPriT4DHXMqL+ZTfsypvLwunykpgFZrutjS3v+1JQnQahGckmJaftwLyZlTV/tRtJjIyMhAbm4u1q9fj8jISJw9exYAEB0djbCwMJw4cQK5ubm47777EBMTg8OHD2PatGkYPHgwevfurWToRETkj1QqYMEC06gNSbIuKCTJ9DMnx2sLCaUoOppj0aJFKC8vx9ChQxEfH2+5ffrppwCAkJAQbNmyBSNGjEDXrl3x/PPPY9y4cfj888+VDJuIiPxZerpp+Gf79tbtWi2HhTqg+GmOhuh0OhQWFjZTNERERP+Tng6kpQE7dgBlZUB8PDBoEI9IOOA1Q0OJiIi8ikoFDB2qdBQ+wasmrSIiIiLfw2KCiIiI3MJigoiIiNzCYoKIiIjcwmKCiIiI3MJigoiIiNzCYoKIiIjcwnkmiIgoMBkMnJRKJiwmiIgo8OTlAZmZwOnTN9q0WtO6HJwuu9F4moOIiAJLXp5pIa/6hQRgWil0/HjT49QoLCaIiChwGAymIxL21oYyt2VlmbYjl7GYICKiwLFjh+0RifqEAEpKTNuRy1hMEBFR4Cgrk3c7AsBigoiIAkl8vLzbEQAWE0REFEgGDTKN2pAk+49LEqDTmbYjl7GYICKiwKFSmYZ/ArYFhfl+Tg7nm2gkFhNERBRY0tOB1auB9u2t27VaUzvnmWg0TlpFRESBJz0dSEvjDJgyYTFBRESBSaUChg5VOgq/wNMcRERE5JZGFxOTJk1CUVGRJ2IhIiIiH9ToYqK8vBzDhw9H586d8dZbb6G0tNQTcREREZGPaHQxsW7dOpSWluLpp5/Gp59+iqSkJIwePRqrV6+GXq/3RIxERESNZzAA27cDn3xi+sn1NjymSddMtG3bFtnZ2fjqq6+wd+9e3HrrrXj00UeRkJCAadOm4dixYy71M2fOHPziF79AZGQkYmNjMXbsWBw9etRqm+rqamRkZCAmJgYREREYN24czp0715SwiYgoUOTlAUlJQEoKMHGi6WdSElcE9RC3LsAsKytDfn4+8vPzoVKpcN999+HIkSPo3r075s+f7/T5hYWFyMjIwJ49e5Cfnw+9Xo8RI0agsrLSss20adPw+eefY9WqVSgsLMSZM2eQzjHARETkCJcYb3aNHhqq1+vx2WefYdmyZdi8eTN69+6NrKwsTJw4EVFRUQCAtWvX4vHHH8e0adMa7Gvjxo1W95cvX47Y2FgcPHgQgwcPRnl5OZYuXYrc3Fzce++9AIBly5ahW7du2LNnDwYOHNjY8ImIyJ85W2JckkxLjKelcU4JGTW6mIiPj4fRaMTDDz+Mffv2oU+fPjbbpKSkoGXLlo0Opry8HADQunVrAMDBgweh1+sxfPhwyzZdu3ZFYmIidu/ebbeYqKmpQU1NjeV+RUUFAFMRJNc1HeZ+eI2IfJhTeTGf8mNO5eWpfEqFhQh2YYnxum3bIIYMkfW1leaJnLraV6OLifnz5+NXv/oVQkNDHW7TsmVLnDx5slH9Go1GZGVl4e6770bPnj0BAGfPnkVISIhNYdKuXTucPXvWbj9z5szBrFmzbNo3b96M8PDwRsXkTH5+vqz9EXMqN+ZTfsypvOTOZ/uiIvR3YbviDRtQWu+Uuj+RM6dVVVUubdfoYuLRRx9tdDCuyMjIwNdff42dO3e61c/06dORnZ1tuV9RUQGdTocRI0ZYTsO4S6/XIz8/H6mpqVCr1bL0GeiYU3kxn/JjTuXlqXxKLVoA8+Y53a7P6NG43Q+PTMidU/PRfWe8YjrtqVOn4osvvkBRURG0Wq2lPS4uDrW1tbhy5YrV0Ylz584hLi7Obl8ajQYajcamXa1Wy/4F4Ik+Ax1zKi/mU37Mqbxkz2dKimnBrtJS+9dNSBKg1SI4JcVvr5mQM6eu9qPodNpCCEydOhVr167F1q1b0bFjR6vH+/XrB7VajYKCAkvb0aNH8dNPPyE5Obm5wyUiIm/HJcYVoeiRiYyMDOTm5mL9+vWIjIy0XAcRHR2NsLAwREdH44knnkB2djZat26NqKgoPPvss0hOTuZIDiIiss+8xHhmpvXwUK3WVEhwegHZKVpMLFq0CAAw9KZV25YtW4bJkycDMF3wGRQUhHHjxqGmpgYjR47EBx980MyREhGRT+ES481K0WJC2DufdZPQ0FAsXLgQCxcubIaIiIjIb3CJ8WbDJciJiIjILSwmiIiIyC0sJoiIiMgtLCaIiIjILSwmiIiIyC0sJoiIiMgtLCaIiIjILSwmiIiIyC0sJoiIiMgtXrFqKBERUaMYDJwq24uwmCAiIt+Sl2d/Ea8FC7iIl0J4moOIiHxHXh4wfrx1IQEApaWm9rw8ZeIKcCwmiIjINxgMpiMS9haJNLdlZZm2o2bFYoKIiHzDjh22RyTqEwIoKTFtR82KxQQREfmGsjJ5tyPZsJggIiLfEB8v73YkGxYTRETkGwYNMo3akCT7j0sSoNOZtqNmxWKCiIh8g0plGv4J2BYU5vs5OZxvQgEsJoiIyHekpwOrVwPt21u3a7Wmds4zoQhOWkVERL4lPR1IS+MMmF6ExQQREfkelQoYOlTpKOh/eJqDiIiI3MJigoiIiNzCYoKIiIjcomgxUVRUhDFjxiAhIQGSJGHdunVWj0+ePBmSJFndRo0apUywRETUfAwGYPt24JNPTD+53oZXU7SYqKysxO23346FCxc63GbUqFEoKyuz3D755JNmjJCIiJpdXh6QlASkpAATJ5p+JiVxRVAvpuhojtGjR2P06NENbqPRaBAXF9dMERERkaLMS4zfvDKoeYlxziXhlbx+aOj27dsRGxuLVq1a4d5778Wbb76JmJgYh9vX1NSgpqbGcr+iogIAoNfrodfrZYnJ3I9c/RFzKjfmU37Mqbzs5tNgQPBzzwFCwGbCbCEgJAnIzETdffdxTgk7PPEZdbUvSQh7C8M3P0mSsHbtWowdO9bStnLlSoSHh6Njx444ceIEXn75ZURERGD37t1QOfggzZw5E7NmzbJpz83NRXh4uKfCJyIiN8UcOYJ7Zsxwut3OP/4Rl3r1aoaIqKqqChMnTkR5eTmioqIcbufVxcTNfvzxR9xyyy3YsmULhg0bZncbe0cmdDodLl682GAiGkOv1yM/Px+pqalQq9Wy9BnomFN5MZ/yY07lZS+f0sqVCH7sMafPrfv4Y4gJEzwdos/xxGe0oqICbdq0cVpMeP1pjvo6deqENm3a4Pjx4w6LCY1GA41GY9OuVqtl/wLwRJ+BjjmVF/MpP+ZUXlb51Olcek6wTgfwPXBIzs+oq/341DwTp0+fxqVLlxDPteqJiPwPlxj3WYoWE9euXUNxcTGKi4sBACdPnkRxcTF++uknXLt2DS+88AL27NmDU6dOoaCgAGlpabj11lsxcuRIJcMmIiJP4BLjPkvRYuLAgQPo27cv+vbtCwDIzs5G37598dprr0GlUuHw4cP4v//7P9x222144okn0K9fP+zYscPuaQwiIvIDXGLcJyl6zcTQoUPR0PWfmzZtasZoiIjIK3CJcZ/jUxdgEhFRgOAS4z7Fpy7AJCIiIu/DYoKIiIjcwmKCiIial8EAqbAQ7YuKIBUWckVQP8BigoiIms//VgQNTk1F/3nzEJyayhVB/QCLCSIiah7mFUFPn7ZuN68IyoLCZ7GYICIizzMYgMxM26XFgRttWVk85eGjWEwQEZHn7dhhe0SiPiGAkhLTduRzWEwQEZHnlZXJux15FRYTRETkea4u0MiFHH0SiwkiIvI8rgjq11hMEBGR53FFUL/GYoKIiJoHVwT1W1zoi4iIms//VgSt27YNxRs2oM/o0QhOSeERCR/HYoKIiJqXSgUxZAhKKytx+5AhLCT8AE9zEBERkVt4ZIKIiORlMJgmnyorMw31HDSIRx/8HIsJIiKST16eadrs+rNdarWmkRy8wNJv8TQHERHJgwt5BSwWE0RE5D4u5BXQWEwQEZH7uJBXQGMxQURE7uNCXgGNxQQREbmPC3kFNEWLiaKiIowZMwYJCQmQJAnr1q2zelwIgddeew3x8fEICwvD8OHDcezYMWWCJSIix7iQV0BTtJiorKzE7bffjoULF9p9/J133sF7772HxYsXY+/evWjRogVGjhyJ6urqZo6UiIgaxIW8ApqixcTo0aPx5ptv4oEHHrB5TAiBnJwcvPrqq0hLS0Pv3r3x8ccf48yZMzZHMIiIyAtwIa+A5bXXTJw8eRJnz57F8OHDLW3R0dG48847sXv3bgUjIyIih9LTgVOngG3bgNxc08+TJ1lI+DmvnQHz7NmzAIB27dpZtbdr187ymD01NTWoqamx3K+oqAAA6PV66PV6WWIz9yNXf8Scyo35lB9z2kh3333j30aj6VYP8yk/T+TU1b68tphoqjlz5mDWrFk27Zs3b0Z4eLisr5Wfny9rf8Scyo35lF/A59RgQMy33yL08mVUt2qFS927u3UdRMDn0wPkzGlVVZVL23ltMREXFwcAOHfuHOLrDSU6d+4c+vTp4/B506dPR3Z2tuV+RUUFdDodRowYgaioKFli0+v1yM/PR2pqKtRqtSx9BjrmVF7Mp/yYU0Bauxaq7GxIpaWWNtG+PQzz5kHYufatIcyn/DyRU/PRfWe8tpjo2LEj4uLiUFBQYCkeKioqsHfvXjz99NMOn6fRaKDRaGza1Wq17B9YT/QZ6JhTeTGf8gvYnOblARMm2EyXLZ05g+AJE5p8gWXA5tOD5Mypq/0oWkxcu3YNx48ft9w/efIkiouL0bp1ayQmJiIrKwtvvvkmOnfujI4dO2LGjBlISEjA2LFjlQuaiCjQOFt3Q5JM626kpXHoZ4BStJg4cOAAUlJSLPfNpycmTZqE5cuX48UXX0RlZSWmTJmCK1eu4J577sHGjRsRGhqqVMhERIGnMetuDB3abGGR91C0mBg6dCiEvUr3fyRJwhtvvIE33nijGaMiIiIrXHeDnPDaeSaIiMhLcN0NcoLFBBERNYzrbpATLCaIiKhhXHeDnGAxQURENxgMwPbtwCefmH4aDKZ2rrtBDfDaeSaIiKiZ5eWZhoDWH7mh1ZqOSqSnm25paaZRG2VlpmskBg3iEQliMUFERDAVEuPH284lUVpqajcffVCpOPyTbPA0BxFRoHM2KRVgmpTKfMqD6CYsJoiIAl1jJqUisoPFBBFRoOOkVOQmFhNERIGOk1KRm1hMEBEFOk5KRW5iMUFEFOg4KRW5icUEERFxUipyC+eZICIKFAZDwxNOcVIqaiIWE0REgcDZ7JZmnJSKmoCnOYiI/J15dsub55Iwz26Zl6dMXOQ3WEwQEfkzzm5JzYDFBBGRP+PsltQMWEwQEfkzzm5JzYDFBBGRP+PsltQMWEwQEfkzzm5JzYDFRGMZDJAKC9G+qAhSYSEvWiIi78bZLakZsJhojLw8ICkJwamp6D9vHoJTU4GkJA6rIiLvxtktycM4aZWrzOO0bx5eZR6nzT9IIlJaQzNccnZL8iCvPjIxc+ZMSJJkdevatWvzB8Jx2kTk7f535BQpKcDEiaafNx85Nc9u+fDDpp8sJEgmXl1MAECPHj1QVlZmue3cubP5g+A4bSLyZpzhkhTm9ac5goODERcXp2wQjRmn7WwhHSIiOTk7cipJpiOnaWn8LiKP8fpi4tixY0hISEBoaCiSk5MxZ84cJCYmOty+pqYGNTU1lvsVFRUAAL1eD71e36QYpLZtXUqU4fvvEdShA6TSUkubaN8ehnnzIB54oEmvHSjM701T3yOyxnzKz1tzKhUWItiFI6d127ZBDBnSfIE54a359GWeyKmrfUlC2CtnvcOGDRtw7do1dOnSBWVlZZg1axZKS0vx9ddfIzIy0u5zZs6ciVmzZtm05+bmIjw8vGmBGAwYMWUKQi9dgr2R2gJAbWQkQq5eBQCrbczJ3f/SSyhLTrb0F/Pttwi9fBnVrVrhUvfu/B8DETVJ+6Ii9J83z+l2B7KzUTp4cDNERP6kqqoKEydORHl5OaKiohxu59XFxM2uXLmCDh06YN68eXjiiSfsbmPvyIROp8PFixcbTIQz0tq1UE2YYPp3vZQJSTJV/q1bAz//bL/YkCSgfXvUHTsG6bPPoMrObvjohcEAaedOy6kScc89fl9s6PV65OfnIzU1FWq1WulwfB7zKT9vzalUWGgapu5EXX6+1x2Z8MZ8+jJP5LSiogJt2rRxWkx4/WmO+lq2bInbbrsNx48fd7iNRqOBRqOxaVer1e4l98EHgeBg07nJeocUJa0W+O1vgddfd/hUSQjg9Gmo33kHmDnT5tymdOYMgidMMA0vBWxeA1qtadKZABh66vb7RFaYT/l5XU5TUkzfEaWl9q+bkCRAq0VwSopX/qfE6/LpB+TMqav9eP1ojvquXbuGEydOIF6pOeTT04FTp1CXn48D2dmoy88HTp4EOnd27fkLFjQ8vHTKFF6RTUSNwxkuyQt4dTHx+9//HoWFhTh16hR27dqFBx54ACqVCg8//LByQalUEEOGoHTwYNMhQ5XK9QVyfv7Z8WNCAJcuuT6XhcEAbN8OfPKJ6SfnuCDybw39zXOGS1KYV5/mOH36NB5++GFcunQJbdu2xT333IM9e/agbdu2SodmzbyQTkOHGVu1ariYcKb+XBY//xzQp0KIAk5envO/ec5wSQry6mJi5cqVSofgGvNhxvHjTYVD/YLCfJgxM7PB6ypctn69/dMlnNabyD81Zip/8wyXRM3Mq09z+BRnhxlfeaXhZYBdtWIFp/UmChScyp98BIsJOf3vAk1s2wbk5pp+njxpanflIqmYGMfFhiQBbdsCFy44fn1O603kXziVP/kIFhNya2ghnYaOXqxZAyxZYrrvqNh45BHXYnB1+m8i8m6NmcqfSEFefc2EX3J2kdTq1fYvtMrJMU2MlZPj/DWUGjpLRPJy9W+Zf/OkMBYTSmjoIqmGig2DwaXJaTBokGsLjnFRMiLv5spIMfPfPJGCeJrDGzk6VeLq5DTr1wNJSaaZ8SZONP1MSrKe9Covz/k2RKQsTkhFPoLFhK9xNmoEcD6LpnmoGWfaJPIejial4oRU5AN4msMXOToVApiOLjgaRiZJpusxzPcdbZOVZeqf/9shah7OJqXihFTk5VhM+Cp7111s3+58GFlDj5u3MQ81GzSIX15EnubqpFSckIq8GE9z+BM5h4e5ct0FEbmHk1KRn2Ax4U/kHB6Wk8NrKojkZO+aCE5KRX6Cpzn8iSvDyMwXcTnaBrgxDPVm9q6p4PBSIuccXRMxfrxrz+ekVOTlWEz4E1cWHDMPM3O0jRANH1Ll6qVEthoqqhu6JsKVSegATkpFXo+nOfyNK8PIGtomK8u111m/3rXhpY6Gu5H/C5T3vqE5W1y5JkKlanhNHp2Ok1KR1+ORCX/kyjAyR9vs2OHa/5YaWr3UfCrEaASmTXP/yIWvnErxlTibg7Ohjs3J2fvizvvmbCTGzJnOR1CZiyxHRxM5KRX5AuHnysvLBQBRXl4uW5+1tbVi3bp1ora2VrY+vUZdnRBarRCSJITpq836JklCtG1r/zFXbpJkuq1ZY/WyDnO6Zo0pnvp9aLXWz6+rE2LbNiFyc00/6+o8niYbrsTZjJx+Rp3lzJ2crllj//Pj4L33KGfvSyPeN5ucmv9WGvqst27t2t9FVpZtXzqdYp+f5uDX36MK8UROXd2HsphoAr//IzDvDG7eIZjbsrKaXkyY+9Hpbuyg6uqEPj9f7M/OFvr8/BvtruyUXN0ZyLHzdLRNY3aentyJ19PgZ1TGHawNV3aw9d97V/prar6cvS8vvNCo983mM7ptm3t/B/Vv5tiVLoqbkd9/jyqAxYQHsZhoIns7FPP/lOT6Et22zfGO61//cr5TiolxbWcgx87TnTjNO0+5duIu7HQaPNIjxw7WUQyufja2bWvaZ9DVfDkragAhVCr33jdXi+rWrRs+0teY4sqPBMT3aDNjMeFBLCbc4GiH4exUiKu3rCzHOy53CxXzl/SqVe7vPBvaAbsaz6xZ8uzEXSw47H5G5drBrlrlOIbcXNfykZvr2aMKs2a5/xly9r41tg9HR/r8+FRGQwLme7QZsZjwIBYTHtLQqRBXv2TdufZCrtdwtvPUap3vgF25OTt37upO3MXD8nY/o3IelncUg6s78VmzPHtUwdVrFeR431w56tDQkb4Axe9R+SlZTHBoKDVNQ8NL//Uv08+Ghru1bQtcuOD5OJ29hrM5NU6fdn41vit+/tm9OEpKgGeeMf3b3uPAjWmXDQZIhYVoX1QEqbDwRt+enPjIHMNf/+r8vY+JsT/KwTwCYvZs10dAOIrFWb5d5cr7JoTz5cHT04FTp4Bt24DcXNPPkyc5Hwv5DRYT1HSOviB/9asbk2M5+pJ95JFmDVUxkgS0bi1PXw0VRuaCY/ZsICkJwamp6D9vHoJTU2/MeeDpiY/MxdeTT5ru23vvzUVHQ0WR+bPjrtatHRc1gPP5HVx937KyXFse3LxQ18MPm35yuCf5ERYT5B5HX5DOJs9KS3P9NRwVJDExzo9+NCdHcZqXfW8Or7/u+H/8Fy40fNQAaHgH66rOnR2/97NmAZcuOX6unEcVzHm3975IEpCd7fjx+s93Ji2NRx0o4LGYIM9p6NCueR0RZzP/rVplf6e0Zg2wZMmNbW9+LgAsXOjezlOSTM93J87Vq4FXXnE/DncKI/P/+J9/Hpg//0afN7+GKztYV8THO37vO3d2rQ93jyrodKa8N1TQvvNOw487e9/qz07Jow4U6GS7SsOD3n//fdGhQweh0WjEgAEDxN69e11+Li/A9GLO5rNwNgzR3EdDF7Y5ew3zqICGYpArTnfiMA9BdXeki6PhuDfnzN7jzmJwZZijqxeBOhsB4cr7ZibHXBUciSE7fo/Kj6M5GrBy5UoREhIiPvroI/HNN9+IJ598UrRs2VKcO3fOpeezmPByclzl7mxn0dSdp7N5Jhobp7txyDGCJjfXtZw5m6CrqTtXV2ZYdXUERHONkOBIDI/g96j8WEw0YMCAASIjI8Ny32AwiISEBDFnzhyXns9iwgc4mgFT5tfw2AyYzRmHox2bq0MyXZksyhl3d66NKUiaacZQp5rjMxpg+D0qPyWLCa9e6Ku2thYHDx7E9OnTLW1BQUEYPnw4du/ebfc5NTU1qKmpsdyvqKgAAOj1euj1elniMvcjV38E6O+6C6WVleh+110QRqNpkTC53X33jX/bew1nj7u6jSfjGDMGuO8+SDt3WhamEvfcAwAIXrIEOHMGkhA2LykkCWjfHnUDBwLufm4dxaBSudb3mDGQVq6EKjsbUmnpjRjbt4fh3Xchxoyx7keO900GzfIZDSD8HpWfJ3Lqal+SEHa+ebzEmTNn0L59e+zatQvJycmW9hdffBGFhYXYu3evzXNmzpyJWbNm2bTn5uYiPDzco/ESKSl+92784u23AQD1Lxk0/4Hvf+kllNX7O1KcwYCYb79F6OXLqG7VCpe6d+eFi0RepqqqChMnTkR5eTmioqIcbufVRyaaYvr06cg2X5EO05EJnU6HESNGNJiIxtDr9cjPz0dqairUarUsfQY65lQG990Hwx13QJWdbRoOaqbVwvDuu+j7wAPoq1x09o0Zo3QELuNnVF7Mp/w8kVPz0X1nvLqYaNOmDVQqFc6dO2fVfu7cOcTFxdl9jkajgUajsWlXq9Wyf2A90WegY07d9OCDwLhxqNu2DcUbNqDP6NEITklBMP/HLxt+RuXFfMpPzpy62o9XzzMREhKCfv36oaCgwNJmNBpRUFBgddqDiOpRqSCGDEHp4MEQQ4bw1AEReZxXH5kAgOzsbEyaNAn9+/fHgAEDkJOTg8rKSvzmN79ROjQiIiKCDxQTDz30EC5cuIDXXnsNZ8+eRZ8+fbBx40a0a9dO6dCIiIgIPlBMAMDUqVMxdepUpcMgIiIiO7z6mgkiIiLyfiwmiIiIyC0sJoiIiMgtPnHNhDvME3y6OvGGK/R6PaqqqlBRUcHx0TJhTuXFfMqPOZUX8yk/T+TUvO90Nlm23xcTV69eBQDodDqFIyEiIvJNV69eRXR0tMPHvXptDjkYjUacOXMGkZGRkCTJ+RNcYJ6iu6SkRLYpugMdcyov5lN+zKm8mE/5eSKnQghcvXoVCQkJCApyfGWE3x+ZCAoKglar9UjfUVFR/COQGXMqL+ZTfsypvJhP+cmd04aOSJjxAkwiIiJyC4sJIiIicguLiSbQaDR4/fXX7a5OSk3DnMqL+ZQfcyov5lN+SubU7y/AJCIiIs/ikQkiIiJyC4sJIiIicguLCSIiInILiwkiIiJyC4uJJli4cCGSkpIQGhqKO++8E/v27VM6JJ80Z84c/OIXv0BkZCRiY2MxduxYHD16VOmw/MbcuXMhSRKysrKUDsWnlZaW4te//jViYmIQFhaGXr164cCBA0qH5bMMBgNmzJiBjh07IiwsDLfccgv++Mc/Ol37gW4oKirCmDFjkJCQAEmSsG7dOqvHhRB47bXXEB8fj7CwMAwfPhzHjh3zaEwsJhrp008/RXZ2Nl5//XV8+eWXuP322zFy5EicP39e6dB8TmFhITIyMrBnzx7k5+dDr9djxIgRqKysVDo0n7d//358+OGH6N27t9Kh+LTLly/j7rvvhlqtxoYNG/Dtt9/i3XffRatWrZQOzWe9/fbbWLRoEd5//3189913ePvtt/HOO+/gL3/5i9Kh+YzKykrcfvvtWLhwod3H33nnHbz33ntYvHgx9u7dixYtWmDkyJGorq72XFCCGmXAgAEiIyPDct9gMIiEhAQxZ84cBaPyD+fPnxcARGFhodKh+LSrV6+Kzp07i/z8fDFkyBCRmZmpdEg+66WXXhL33HOP0mH4lfvvv188/vjjVm3p6enikUceUSgi3wZArF271nLfaDSKuLg48ac//cnSduXKFaHRaMQnn3zisTh4ZKIRamtrcfDgQQwfPtzSFhQUhOHDh2P37t0KRuYfysvLAQCtW7dWOBLflpGRgfvvv9/qc0pN89lnn6F///741a9+hdjYWPTt2xd//etflQ7Lp911110oKCjADz/8AAD46quvsHPnTowePVrhyPzDyZMncfbsWau//+joaNx5550e3U/5/UJfcrp48SIMBgPatWtn1d6uXTt8//33CkXlH4xGI7KysnD33XejZ8+eSofjs1auXIkvv/wS+/fvVzoUv/Djjz9i0aJFyM7Oxssvv4z9+/fjueeeQ0hICCZNmqR0eD7pD3/4AyoqKtC1a1eoVCoYDAbMnj0bjzzyiNKh+YWzZ88CgN39lPkxT2AxQV4hIyMDX3/9NXbu3Kl0KD6rpKQEmZmZyM/PR2hoqNLh+AWj0Yj+/fvjrbfeAgD07dsXX3/9NRYvXsxioon+9a9/YcWKFcjNzUWPHj1QXFyMrKwsJCQkMKc+jKc5GqFNmzZQqVQ4d+6cVfu5c+cQFxenUFS+b+rUqfjiiy+wbds2jy0XHwgOHjyI8+fP44477kBwcDCCg4NRWFiI9957D8HBwTAYDEqH6HPi4+PRvXt3q7Zu3brhp59+Uigi3/fCCy/gD3/4AyZMmIBevXrh0UcfxbRp0zBnzhylQ/ML5n1Rc++nWEw0QkhICPr164eCggJLm9FoREFBAZKTkxWMzDcJITB16lSsXbsWW7duRceOHZUOyacNGzYMR44cQXFxseXWv39/PPLIIyguLoZKpVI6RJ9z99132wxX/uGHH9ChQweFIvJ9VVVVCAqy3vWoVCoYjUaFIvIvHTt2RFxcnNV+qqKiAnv37vXofoqnORopOzsbkyZNQv/+/TFgwADk5OSgsrISv/nNb5QOzedkZGQgNzcX69evR2RkpOV8XnR0NMLCwhSOzvdERkbaXG/SokULxMTE8DqUJpo2bRruuusuvPXWW3jwwQexb98+LFmyBEuWLFE6NJ81ZswYzJ49G4mJiejRowcOHTqEefPm4fHHH1c6NJ9x7do1HD9+3HL/5MmTKC4uRuvWrZGYmIisrCy8+eab6Ny5Mzp27IgZM2YgISEBY8eO9VxQHhsn4sf+8pe/iMTERBESEiIGDBgg9uzZo3RIPgmA3duyZcuUDs1vcGio+z7//HPRs2dPodFoRNeuXcWSJUuUDsmnVVRUiMzMTJGYmChCQ0NFp06dxCuvvCJqamqUDs1nbNu2ze5356RJk4QQpuGhM2bMEO3atRMajUYMGzZMHD161KMxcQlyIiIicguvmSAiIiK3sJggIiIit7CYICIiIrewmCAiIiK3sJggIiIit7CYICIiIrewmCAiIiK3sJggIiIit7CYICIiIrewmCAiIiK3sJggomZ34cIFxMXF4a233rK07dq1CyEhIVarHRKRb+DaHESkiH//+98YO3Ysdu3ahS5duqBPnz5IS0vDvHnzlA6NiBqJxQQRKSYjIwNbtmxB//79ceTIEezfvx8ajUbpsIiokVhMEJFirl+/jp49e6KkpAQHDx5Er169lA6JiJqA10wQkWJOnDiBM2fOwGg04tSpU0qHQ0RNxCMTRKSI2tpaDBgwAH369EGXLl2Qk5ODI0eOIDY2VunQiKiRWEwQkSJeeOEFrF69Gl999RUiIiIwZMgQREdH44svvlA6NCJqJJ7mIKJmt337duTk5OAf//gHoqKiEBQUhH/84x/YsWMHFi1apHR4RNRIPDJBREREbuGRCSIiInILiwkiIiJyC4sJIiIicguLCSIiInILiwkiIiJyC4sJIiIicguLCSIiInILiwkiIiJyC4sJIiIicguLCSIiInILiwkiIiJyC4sJIiIicsv/B7ZtYtV2lLkzAAAAAElFTkSuQmCC",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x400 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "# データを作成する 4Fiel\n",    "xf = np.linspace(0,10,51)\n",    "# yf = np.linspace(0,10,21)\n",    "np.random.seed(0)\n",    "y_gn = np.random.normal(loc=0,scale=0.2, size=len(xf))\n",    "\n",    "para1 = (5,5,5,5,0,0,0,2,2) #(1) case\n",    "para2 = (3,6,6,5,0,2,2,2,2) #(2) case\n",    "para3 = (2,6,6,5,-2,2,2,2,2) #(3) case\n",    "para4 = (2,5,7,5,-2,2,2,2,2)  #(4) case\n",    "y1 = mf.three_break_func(xf,*para1)\n",    "y2 = mf.three_break_func(xf,*para2)\n",    "y3 = mf.three_break_func(xf,*para3)\n",    "y4 = mf.three_break_func(xf,*para4)\n",    "\n",    "para5=(4,9,5,0,0,20,1.5) #(5) case\n",    "y5 = mf.two_break_gauss(xf,*para5)\n",    "\n",    "# (b1,b2,b3,const,ap1,ap2,ap3,pa,ps)\n",    "para6=(4,7,9,5,0,1,0,20,1) #(6) case\n",    "y6=mf.three_break_gauss(xf,*para6)\n",    "\n",    "y_list = [y1+y_gn,y3+y_gn,y5+y_gn]\n",    "y_explain=['(a) Normal','(b) Attenuated Bg(AG)',' (c) Peak']\n",    "for i ,si in zip(y_list,y_explain):\n",    "    fig = plt.figure(figsize=(6, 4))\n",    "    ax = fig.add_subplot(111)\n",    "    ax.plot(xf, i,'ro', label=f'{si}')\n",    "    ax.grid(True)\n",    "    ax.set_xlabel('x')\n",    "    ax.set_ylabel('y')\n",    "    ax.legend(loc='upper left')\n",    "    plt.show()\n",    "    \n",    "fit_params=[]\n",    "for i ,si in zip(y_list,y_explain):\n",    "    fig = plt.figure(figsize=(6, 4))\n",    "    ax = fig.add_subplot(111)\n",    "    ax.plot(xf,i,'ro',label=f'{si}')\n",    "    ax , p = ax_plots(xf, i, n_breakpoints=7, axs=ax )  \n",    "    fit_params.append(p)\n",    "    # print(p)\n",    "    ax.grid(True)\n",    "    ax.set_xlabel('x')\n",    "    ax.set_ylabel('y')\n",    "    ax.legend(loc='upper left')\n",    "    plt.show()"   ]  },  {   "cell_type": "code",   "execution_count": 7,   "metadata": {},   "outputs": [],   "source": [    "def auto_process(xdata,ydata,th_method='a'):\n",    "    aptmp = pw.PWRXY(xdata,ydata)\n",    "    # aptmp.BPs_Max=6\n",    "    aptmp.peak_search()\n",    "    aptmp.auto_peak_transform()\n",    "    # aptmp.bg_search()\n",    "    # aptmp.auto_slope_transform(level=th_method,info=True)\n",    "    \n",    "    return aptmp"   ]  },  {   "cell_type": "code",   "execution_count": 8,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "# [y1+y_gn,y3+y_gn,y5+y_gn]\n",    "insty3 = auto_process(xf,y3+y_gn)"   ]  },  {   "cell_type": "code",   "execution_count": 9,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "# [y1+y_gn,y3+y_gn,y5+y_gn]\n",    "insty5 = auto_process(xf,y5+y_gn)"   ]  },  {   "cell_type": "code",   "execution_count": 10,   "metadata": {},   "outputs": [    {     "name": "stdout",     "output_type": "stream",     "text": [      "[WindowsPath('Datas/C60 20210607140335_5430.dat'),\n",      " WindowsPath('Datas/Fc3 20210607134453_5429.dat'),\n",      " WindowsPath('Datas/P-Low_230403122620_5_r.dat')]\n",      "files:3\n"     ]    }   ],   "source": [    "# PYS data読み込み\n",    "# PYS\n",    "pys_path = Path(r\"Datas\")\n",    "p_lists = list(pys_path.glob('*.dat'))\n",    "pprint(p_lists)\n",    "print(f'files:{len(p_lists)}')"   ]  },  {   "cell_type": "markdown",   "metadata": {},   "source": [    "#### Ferrocene powder (FC3)\n",    "\n",    "- https://doi.org/10.1039/D1TC05545C\n",    "- donor\t\n",    "- Fe(C5H5)2\t\n",    "- Aldrich社製 (F408) 98%, \n",    "- https://www.sigmaaldrich.com/JP/ja/substance/ferrocene18603102545\n"   ]  },  {   "cell_type": "code",   "execution_count": 11,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x500 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "from reader import datconv as dv\n",    "\n",    "pys_inst=[]\n",    "for inum, i in  enumerate(p_lists[1:2]):\n",    "    acdata = dv.AcConv(str(i))\n",    "    acdata.convert()\n",    "    pys_inst.append(acdata)        \n",    "    fig =plt.figure(figsize=(6,5)) \n",    "    ax = fig.add_subplot(111)\n",    "    # ax.set_title(f'{acdata.metadata[\"sampleName\"]}')\n",    "    ax.plot(acdata.df[\"uvEnergy\"],acdata.df[\"npyield\"],'ro-',label='Data')\n",    "    ax.plot(acdata.df[\"uvEnergy\"],acdata.df[\"guideline\"],'b-',label=f'Estimated line\\n {acdata.metadata[\"thresholdEnergy\"]:.2f} eV')\n",    "    ax.legend()\n",    "    ax.grid()\n",    "    ax.axvline(acdata.metadata[\"thresholdEnergy\"] )\n",    "    ax.text(acdata.metadata[\"thresholdEnergy\"] ,np.max(acdata.df[\"npyield\"])*0.8, f'{acdata.metadata[\"thresholdEnergy\"]:.2f}')\n",    "    ax.set_xlabel('Energy [eV]')\n",    "    ax.set_ylabel(f'Intensity$^{{{acdata.metadata[\"powerNumber\"]:.2f}}}$')\n",    "    ax.set_ylabel('Intensity$^{1/2}$')"   ]  },  {   "cell_type": "code",   "execution_count": 12,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 600x500 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "# pys_inst[0]\n",    "fig =plt.figure(figsize=(6,5)) \n",    "ax = fig.add_subplot(111)\n",    "# ax.set_title(f'{pys_inst[0].metadata[\"sampleName\"]}')\n",    "ax.plot(pys_inst[0].df[\"uvEnergy\"],pys_inst[0].df[\"pyield\"],'ro-',label='Data')\n",    "ax.grid()"   ]  },  {   "cell_type": "code",   "execution_count": 13,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "pys_fc3 = auto_process(pys_inst[0].df[\"uvEnergy\"],pys_inst[0].df[\"pyield\"])"   ]  },  {   "cell_type": "code",   "execution_count": 19,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 640x480 with 1 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    }   ],   "source": [    "plt.plot(pys_fc3.select_x,pys_fc3.select_y,'ro-')\n",    "plt.grid()"   ]  },  {   "cell_type": "code",   "execution_count": 18,   "metadata": {},   "outputs": [    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 1200x500 with 2 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 1200x500 with 2 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 1200x500 with 2 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "data": {      "image/png": "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",      "text/plain": [       "<Figure size 1200x500 with 2 Axes>"      ]     },     "metadata": {},     "output_type": "display_data"    },    {     "name": "stdout",     "output_type": "stream",     "text": [      "shift: 5.57, power: 2.00, r2:0.99\n"     ]    }   ],   "source": [    "# 1/n fit\n",    "from pfit import power_fit as powfit\n",    "\n",    "search_n_list = [2,3] # n=2,3\n",    "inv_n = powfit.PfAnalysis(pys_fc3.select_x,pys_fc3.select_y)\n",    "\n",    "inv_n.shift_estimate_by_power_scan(search_range=search_n_list,\n",    "                                        zero_replace=True,\n",    "                                        info=False, \n",    "                                        min_error='mae',\n",    "                                        likely_evaluation='r2',\n",    "                                        plot=True)\n",    "    \n",    "# print(inv_n.shebypw[\"popt\"][2]**inv_n.shebypw[\"power\"])\n",    "# print(inv_n.shebypw[\"fit\"])\n",    "print(f'shift: {inv_n.shebypw[\"shift\"]:.2f}, power: {inv_n.shebypw[\"power\"]:.2f}, r2:{inv_n.shebypw[\"r2\"]:.2f}')\n"   ]  },  {   "cell_type": "code",   "execution_count": null,   "metadata": {},   "outputs": [],   "source": [    "#　図の作成、PWの計算\n",    "\n",    "def draw_pw_ax(xdata, ydata, fit_ext_dict, axs, **kwargs):\n",    "    \n",    "    bps_list = fit_ext_dict['bps']\n",    "    alphas_list = fit_ext_dict['alphas']\n",    "    betas_list = fit_ext_dict['betas']\n",    "    const_list = fit_ext_dict['const']\n",    "    ext_bps_list = fit_ext_dict['ext_bps']\n",    "    num = len(bps_list)+1\n",    "    \n",    "    yy_predicted = const_list[0] + alphas_list[0] *xdata\n",    "    for i in range(len(bps_list)):\n",    "        yy_predicted += betas_list[i] * \\\n",    "            np.maximum(xdata - bps_list[i], 0)\n",    "    \n",    "        axs.axvline(bps_list[i])\n",    "        axs.text(bps_list[i],\n",    "                np.max(ydata)*(1/num)*1, \n",    "                # np.max(ydata)*(1.5/num)*(np.mod(i+1,2)+1), \n",    "                # np.max(ydata)*(0.9-0.1*np.mod(bp_count+1,2)), \n",    "                #  np.max(ydata)-np.max(ydata)*(1/num)*(bp_count+1), \n",    "                f'BP{i+1}:{bps_list[i]:.1f}\\n$\\\\alpha${i+2}:{alphas_list[i+1]:.2f}')\n",    "        \n",    "    axs.axvline(ext_bps_list[0])\n",    "    axs.text(ext_bps_list[0],\n",    "             np.max(ydata)*(1/num)*1,\n",    "            # np.max(ydata)*0.9, \n",    "            # np.max(ydata)*(1.5/num)*1, \n",    "            #  np.max(ydata)-np.max(ydata)*(1/num)*1, \n",    "            f\"BP0:{ext_bps_list[0]:.1f}\\n$\\\\alpha$1:{alphas_list[0]:.2f}\")\n",    "    \n",    "    axs.axvline(ext_bps_list[-1])\n",    "    axs.text(np.max(xdata), \n",    "             np.max(ydata)*(1/num)*1,\n",    "            #  np.max(ydata)*(1.5/num)*1, \n",    "             f'BP{len(bps_list)+1}',ha='right')\n",    "            \n",    "            \n",    "    axs.plot(xdata,yy_predicted,**kwargs)\n",    "    # num = len(val_breakpoints)\n",    "    \n",    "    # for i, bp in enumerate(val_breakpoints):\n",    "    #     axs.axvline(bp)\n",    "    #     axs.text(bp,np.max(ydata)*(1/num)*(i+1), f'BP{i+1}')\n",    "    \n",    "    return axs , {'bp':ext_bps_list, 'alpha':alphas_list}\n",    "\n",    "def ax_plots(xdata, ydata, n_breakpoints ):\n",    "    \n",    "    res_model = pw.pw_model(xdata, ydata, bps_max=n_breakpoints, plot=False, info=False)\n",    "    \n",    "    res_alpha = pw.pw_alpha(xdata, ydata, res_model[\"bps_list_asc\"], plot=False)\n",    "    \n",    "    return res_alpha\n",    "    \n",    "    # axs = draw_pw_ax(xdata, ydata, res_alpha['fit_ext_dict'], axs, **kwargs)\n",    "    "   ]  },  {   "cell_type": "code",   "execution_count": null,   "metadata": {},   "outputs": [],   "source": [    "res_fit =ax_plots(xdata=xx, ydata=yy, n_breakpoints=3 ) \n",    "fig = plt.figure(figsize=(6, 4))\n",    "ax = fig.add_subplot(111)\n",    "ax.plot(xx,yy,'ro')\n",    "ax = draw_pw_ax(xx, yy, res_fit['fit_ext_dict'], ax)"   ]  } ], "metadata": {  "kernelspec": {   "display_name": "Python 3.7.1 ('base')",   "language": "python",   "name": "python3"  },  "language_info": {   "codemirror_mode": {    "name": "ipython",    "version": 3   },   "file_extension": ".py",   "mimetype": "text/x-python",   "name": "python",   "nbconvert_exporter": "python",   "pygments_lexer": "ipython3",   "version": "3.10.11"  },  "orig_nbformat": 4,  "vscode": {   "interpreter": {    "hash": "b66da06cecefeedcb0375b3971526220e456f8615e08c7fc3d59ab7f27c37be5"   }  } }, "nbformat": 4, "nbformat_minor": 2}JSA_PW論文コード/LICENSEBSD 3-Clause LicenseCopyright (c) 2024, s-yagyuRedistribution and use in source and binary forms, with or withoutmodification, 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,   this list of conditions and the following disclaimer in the documentation   and/or other materials provided with the distribution.3. Neither the name of the copyright holder nor the names of its   contributors may be used to endorse or promote products derived from   this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THEIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE AREDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLEFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIALDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS ORSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVERCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USEOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.JSA_PW論文コード/mathlib.pyfrom contextlib import redirect_stdoutimport datetimefrom pathlib import Pathimport osfrom pprint import pprintfrom pprint import pformat as pfimport numpy as npimport matplotlib.pyplot as pltfrom scipy import integrate# diffarantiate and integratedef shift_diff(xdata,ydata,window=1):# 移動平均    # 移動平均の範囲 window =     # length len(xdata)-1    # if plot-> plt.plot(xdata[1:],dy)    w = np.ones(window)/window    y_smooth = np.convolve(ydata, w, mode='same')    # 差分    dy = np.gradient(y_smooth)          dx = np.gradient(xdata)      return dy/dx, dx, dy    def moving_ave(ndata,window=1):    # 移動平均の範囲 window     #data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])    # moving_ave(data,window=2)    # >>> array([0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])    # ndata2=ndata.copy()    w = np.ones(window)/window    y_smooth = np.convolve(ndata, w, mode='same')        return y_smoothdef c_dif(xdata,ydata,flag='g'):    # data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0])    # c_dif(data,data,'d')    # array([1., 1., 1., 1., 1., 1., 1., 1.]))    if flag == 'g': #lenght: len(xdata)        dy = np.gradient(ydata)              dx = np.gradient(xdata)       elif flag == 'd' :  # lenght: len(xdata)-1        dy = np.diff(ydata)              dx = np.diff(xdata)           return   dy/dx, dx, dydef intg(xdata,ydata):    # simpsonによる積分    intg_ =  integrate.simps(xdata,ydata)    return intg_def r2(xx, yy, a, b):    """    線形回帰の決定係数を計算する関数    Args:        xx: 説明変数        yy: 目的変数        a: 回帰係数        b: 切片    Returns:        決定係数                # 回帰直線の係数を計算        a, b = np.polyfit(xx, yy, 1)        print(r2(xx, yy, a, b))        # # 回帰直線を表示        # print("回帰直線の式: y = {}x + {}".format(a, b))        # # 回帰直線を描画        # plt.plot(x, y, "o")        # plt.plot(x, a * x + b, "r")        # plt.show()    """    # 回帰平方和    y_pred = a * xx + b    rss = np.sum((yy - y_pred)**2)    # 全平方和    y_mean = np.mean(yy)    tss = np.sum((yy - y_mean)**2)    # 決定係数    r2 = 1 - rss / tss    return r2JSA_PW論文コード/model_functions.py# test dataimport numpy as np# バックグラウンドやピークが乗ったべき乗スペクトルの関数def gauss(x,a,mu,sigma):    return a*np.exp(-(x-mu)**2 / (2*sigma**2))def three_break_func(x,b1,b2,b3,const,ap1,ap2,ap3,pa,pc):    alpha1 = ap1    alpha2 = ap2    alpha3 = ap3    constant = const        breakpoint1 = b1    breakpoint2 = b2    breakpoint3 = b3    power_a = pa    power_c = pc    # n_points = 200    # np.random.seed(0)    # xx = np.linspace(0, 10, n_points)    # yy = constant + alpha_1*xx + (alpha_2-alpha_1) * np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points)    y_ = constant + alpha1*x + \            alpha2* np.maximum(x - breakpoint1, 0) + \            alpha3* np.maximum(x - breakpoint2, 0) + \            power_a*np.maximum(x - breakpoint3, 0)**power_c        return y_def two_break_func(x,b1,b2,const,ap1,ap2,pa,pc):    alpha1 = ap1    alpha2 = ap2    constant = const    breakpoint1 = b1    breakpoint2 = b2    power_a = pa    power_c = pc    # n_points = 200    # np.random.seed(0)    # xx = np.linspace(0, 10, n_points)    # yy = constant + alpha_1*xx + (alpha_2-alpha_1) * np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points)    y_ = constant + alpha1*x + \        alpha2* np.maximum(x - breakpoint1, 0) + \        power_a*np.maximum(x - breakpoint2, 0)**power_c        return y_def two_break_gauss(x,b1,b2,const,ap1,ap2,pa,ps,):    alpha1 = ap1    alpha2 = ap2    constant = const    breakpoint1 = b1    breakpoint2 = b2     # n_points = 200    # np.random.seed(0)    # xx = np.linspace(0, 10, n_points)    # yy = constant + alpha_1*xx + (alpha_2-alpha_1) * np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points)    y_ = constant + alpha1*x + \        alpha2* np.maximum(x - breakpoint1, 0) + \        gauss(x=x,a=pa,mu=breakpoint2,sigma=ps)    return y_def three_break_gauss(x,b1,b2,b3,const,ap1,ap2,ap3,pa,ps):    alpha1 = ap1    alpha2 = ap2    alpha3 = ap3    constant = const        breakpoint1 = b1    breakpoint2 = b2    breakpoint3 = b3    # n_points = 200    # np.random.seed(0)    # xx = np.linspace(0, 10, n_points)    # yy = constant + alpha_1*xx + (alpha_2-alpha_1) * np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points)    y_ = constant + alpha1*x + \            alpha2* np.maximum(x - breakpoint1, 0) + \            alpha3* np.maximum(x - breakpoint2, 0) + \            gauss(x=x,a=pa,mu=breakpoint3,sigma=ps)        return y_if __name__ == "__main__":    pass    # # データを作成する 4 files    # xf = np.linspace(0,10,51)    # # yf = np.linspace(0,10,21)    # para1 = (5,5,5,5,0,0,0,2,2) #(1) case    # para2 = (3,6,6,5,0,2,2,2,2) #(2) case    # para3 = (2,6,6,5,-2,2,2,2,2) #(3) case    # para4 = (2,5,7,5,-2,2,2,2,2)  #(4) case    # y1 = three_break_func(xf,*para1)    # y2 = three_break_func(xf,*para2)    # y3 = three_break_func(xf,*para3)    # y4 = three_break_func(xf,*para4)    # para5=(4,9,5,0,0,20,1.5) #(5) case    # y5 = two_break_gauss(xf,*para5)+ np.random.normal(size=len(xf))    # # (b1,b2,b3,const,ap1,ap2,ap3,pa,ps)    # para6=(4,7,9,5,0,1,0,20,1) #(6) case    # y6=three_break_gauss(xf,*para6)JSA_PW論文コード/pfit/power_fit.py"""Inv-Power-Plot, Shift-log-log-plotanalysis module"""    import mathimport timefrom datetime import datetimeimport refrom pathlib import Pathimport scipy as spimport scipy.optimize as optimizefrom scipy.optimize import curve_fit  from sklearn.metrics import r2_score# from sklearn.metrics import mean_squared_errorfrom sklearn.metrics import mean_absolute_errorimport matplotlib.pyplot as pltimport numpy as npimport warningswarnings.simplefilter('ignore')# warnings.resetwarnings()# warnings.simplefilter('ignore', FutureWarning)from pfit import relu_abs_fit as raf   class PfAnalysis():    # べき乗の解析Class        def __init__(self,xdata, ydata):        self.xdata = xdata        self.ydata = ydata        def estimate(self,power_num=2,info=False,ini_para=None, retry_r2=0.9, min_error='mae',zero_replace=False):        # 1/n Power        self.res_pof = const_inv_power_fit(self.xdata,                                        self.ydata,                                        power_num,                                        ini_params=ini_para,                                         retry_r2=retry_r2,                                        min_error= min_error,                                        zero_replace=zero_replace,                                        plot=info,                                         info=info)        # self.res_prof(dict)のkeys        # 'rex':re_xdata,        # 'rey':re_ydata,         # 'fit':re_fit,         # 'popt':res_params,tuple -> Nor, Ip, bg        # 'r2':r2,        # 'adj_r2':自由度調整済みR2        # 'r2_bp':Bp以降のR2,        # 'er':error_range,        # 'cv':変動係数            def shift_estimate_by_power_scan(self, search_range=None,                                      zero_replace=False,                                     min_error='mae',                                      likely_evaluation='r2_bp',                                     info=False, plot=True,                                     plot_fig_step=1):        # 1/n Power        self.shebypw = xpower_search(self.xdata, self.ydata,                                         search_range=search_range,                                        r2_plot=plot, process_plot=plot,                                        min_error=min_error,                                        zero_replace=zero_replace,                                        likely_evaluation =likely_evaluation,                                        info=info,                                         every_plot=False,                                         plot_save=False,                                        plot_fig_step=plot_fig_step)                # shebypw:keys        # ['r2','shift', 'power', 'rex', 'rey', 'fit','popt']    def power_estimate_by_shift_scan(self, search_range,                                        fit_type='weight',                                        bg_num=3,                                       lim_val=0.5,                                        min_error='mae',                                       info=True, plot=True,                                       plot_fig_step=1):        # shift-log-log        self.pwebysh = xshift_search(self.xdata, self.ydata,                                         search_range,                                        bg_num=bg_num,                                         lim_val=lim_val,                                         fit_type=fit_type,                                        r2_plot=plot,                                         process_plot=plot,                                        min_error=min_error,                                        info=info,                                        every_plot=False,                                         plot_save=False,                                        plot_fig_step=plot_fig_step)                # pwebysh:keys        # ['r2','shift', 'power', 'rex','rey','fit']         # ------def m_line(x, a, b):    """liner function    Args:        x (array_like): x data        a (float): slope        b (float): y-slice    Returns:        array_like: f    """    f = a * x  + b    return fdef mae_line(param,x,y):    # resouidual of Line function    fit = m_line(x,*param)    mae = mean_absolute_error(y,fit)    return  maedef y_slice(Ip, Nor, Bg):    # for ReLu    b = Bg - Nor*Ip    return bdef get_nearest_value(lst: list, num: float):    """    Returns the element from the input list that is closest to the given number.        Args:        lst: A list of numbers.        num: A float number.        Returns:        The index of the element in the list that is closest to the given number.    """    # Calculate the difference between each element in the list and the target number,    # and get the index of the minimum difference    idx = np.abs(np.asarray(lst) - num).argmin()        return idxdef nan_inf_rm(xdata,ydata,zero_replace=False,info=False):    """remove nan and inf values        If you take Log            Negative value: Nan for not defined            0: Negative infinity because it is not defined            If these values are included, remove them (Index) to avoid an error    Args:        xdata (ndarray or list): xdata        ydata (ndarrayor list): ydata        zero_replace(bool): True: Nan -> 0 replace  False:Nan remove. default False        info (bool): show infomation            Returns:        trimmed data (ndarray): re_xdata, re_ydata    """    # inf -> nan    # nan_inf_rm(np.log(xf),np.log(yf))    xarray = np.array(xdata)    yarray = np.array(ydata)    tr_inf_x = np.where(np.isinf(xarray),np.nan,xarray)    tr_inf_y = np.where(np.isinf(yarray),np.nan,yarray)        re_xdata = tr_inf_x.copy()    re_ydata = tr_inf_y.copy()        if zero_replace:        re_xdata = np.nan_to_num(re_xdata, copy=False)        re_ydata = np.nan_to_num(re_ydata, copy=False)            else:        nan_ind_x = np.where(~np.isnan(tr_inf_x))        re_xdata = re_xdata[nan_ind_x[0]]        re_ydata = re_ydata[nan_ind_x[0]]                nan_ind_y = np.where(~np.isnan(re_ydata))        re_xdata = re_xdata[nan_ind_y[0]]        re_ydata = re_ydata[nan_ind_y[0]]        if info:        print(f'number of Nan of (x, y) : ({np.count_nonzero(np.isnan(tr_inf_x))}, {np.count_nonzero(np.isnan(tr_inf_y))})')        print(f'Original shape x, y: {xarray.shape}, {yarray.shape}')        print(f'output shape x, y: {re_xdata.shape}, {re_ydata.shape}')            return re_xdata, re_ydatadef adj_r2_score(y_true, y_pred):    # 決定係数と自由度調整済み決定係数、誤差範囲を返す    # 誤差範囲 = √(1-R^2)*標準偏差    # 決定係数と誤差範囲はどちらも回帰分析の評価指標（異なる側面からモデルの性能を評価）。    # 決定係数はモデルの当てはまりを、誤差範囲はモデルの精度を評価。        r2 = r2_score(y_true,y_pred)        # 自由度調整済み決定係数    # https://qiita.com/bianca26neve/items/4ddcf5ca12652b652f04    adj_r2 = 1-(1-r2)*((len(y_true)-1)/(len(y_true)-2-1))         # 誤差範囲    # 目的変数の予測値の平方和を求める    y_hat_square = y_pred ** 2    # 目的変数の予測値の数を求める    n = len(y_true)    # 目的変数の予測値の標準偏差を求める    std_y_hat = np.sqrt(np.sum(y_hat_square) / n)    # # 決定係数を求める    # R2 = np.sum(y_pred ** 2) / np.sum((y_true ** 2)        # 誤差範囲を求める 誤差範囲が小さいほど、目的変数の真の値を正確に推定    error_range = np.sqrt(1 - r2) * std_y_hat            return r2, adj_r2, error_rangedef static_inf(sdata,info=True):        medi_ = np.median(sdata)    mean_ = np.mean(sdata)    std_= np.std(sdata)    cv_ = std_/mean_    skew_ = sp.stats.skew(sdata)    kurtosis_ =  sp.stats.kurtosis(sdata, bias=False)    asymetric_ = medi_/ mean_    # 変動係数(CV): 標準偏差を平均値で割ったもの。     # median =< mean-std とする　(mean-std)/mean = 1 - cv >= median/mean    # median/mean は変動係数を用いて表すこともできる       info_dict = {'median':medi_, 'mean':mean_, 'std':std_, 'cv':cv_, 'sk':skew_,'kt':kurtosis_,'asymetric':asymetric_}        if info:        for k, v in info_dict.items():            print(k, v)            return info_dict#---- plot# 黄金比 1：1.62 wdth:height = 4.86:3# 白銀比 1：1.43  wdth:height = 4.3:3 # wdth:height= 6:5# memo figsize=(width,height) wdth:height= 4:3 # (row,col)->figsize(col*4,row*3): (3,4)->figsize(16,9)# width_u = 5.2# height_u = 4# https://qiita.com/skotaro/items/5c9893d186ccd31f459d# cmap = plt.get_cmap("tab10") # point# for i in range(4):#     ax.plot(t,i*(t+1),   color=cmap(i), linestyle = '-') #tuple# cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] # point# for i in range(4):#     ax.plot(t,i*(t+1), color=cycle[i], linestyle = '-')#     ax.plot(t,i*(t+1)+.3,color=cycle[i], linestyle = ':')# w_in, h_in = plt.rcParams[cParams["figure.figsize"]# dpi =plt.rcParams["figure.dpi"]# print("figsize (inch)", (w_in, h_in))# figsize (inch) (6.0, 4.0)# print("dpi (px/inch)", dpi)# dpi (px/inch) 72.0# print("save size (px)", (w_in * dpi, h_in * dpi))# save size (px) (432.0, 288.0)cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']def re_replace(text):    """Remove special symbols with regular expressions     Args:        text (str): text    Returns:        str: Text with special symbols removed    Examples:        text = '4 inch $\phi$=0'        re_replace(text)        >>> '4_inch_phi0    Ref:        https://qiita.com/ganyariya/items/42fc0ed3dcebecb6b117     """    # code_regex = re.compile('[!"#$%&\'\\\\()*+,-./:;<=>?@[\\]^_`{|}~「」〔〕“”〈〉『』【】＆＊・（）＄＃＠。、？！｀＋￥％]')    code_regex = re.compile('[!"#$%&\'\\\\()*+,-./:;<=>?@[\\]^_`{|}~]')    cleaned_text = code_regex.sub('', text).replace(' ', '_')    # print(cleaned_text)    return cleaned_textdef search_r2_plot(shifts,powers,r2,msg,save=False,ylabel=None):    """shift vs r2, power vs r2 plot    Args:        shifts (array like): shift data        powers (array like): power data        r2 (array like): r2 data        msg (str): graph title        save (bool, optional): figure save. Defaults to False.    """        # fig = plt.figure(figsize=(12,5), tight_layout=True)    # ax1 = fig.add_subplot(1, 3, 1)    # ax2 = fig.add_subplot(1, 3, 2)    # ax3 = fig.add_subplot(1, 3, 3)        if ylabel is None:        ylabel = '$R^2$'            fig = plt.figure(figsize=(12,5), tight_layout=True)    ax1 = fig.add_subplot(1, 2, 1)    ax2 = fig.add_subplot(1, 2, 2)        ax1.plot(shifts,r2,'ro-')    ax1.set_xlabel('shift')    ax1.set_ylabel(ylabel)    ax1.grid(which='both')        ax2.plot(powers,r2,'b^-')    ax2.set_xlabel('n')    ax2.set_ylabel(ylabel)    ax2.grid(which='both')        # ax3.plot(r2,shifts,'ro-',label="shift")    # ax3.plot(r2,powers,'b^-',label="power")    # ax3.set_xlabel('$r^2$')    # ax3.set_ylabel('')    # ax3.legend()    # ax3.grid(True)        fig.suptitle(msg)        if save:        filename = re_replace(msg)        fig.savefig(f'{filename}.png', dpi=300)            plt.show()    def inv_power_plot(xdata, ydata, shift, power):    # power 2-> 1/2 plot    #---    inv_power = 1/power    fig = plt.figure(figsize=(12,5), tight_layout=True)    ax1 = fig.add_subplot(1, 2, 1)    ax2 = fig.add_subplot(1, 2, 2)        ax1.plot(xdata,ydata,'ro-',label='Data')    ax1.set_xlabel('x')    ax1.set_ylabel('y')    ax1.legend()    ax1.grid(which='both')        ax2.plot(xdata,np.power(ydata,1/power),'b^-',label='Data')    ax2.set_xlabel('x')    ax2.set_ylabel('$y^{{{1/power}}}$')    ax2.legend(title=f'Shift: {shift:.2f}\nn: {power:.2f}')    ax2.grid(which='both')            plt.show()def plot_ax(xdata, ydata, m_xdata, m_ydata, breakpoints=None, lgtitle=None, title='', axi=None):        if axi is None:        fig_ = plt.figure()        ax_ = fig_.add_subplot(111)    else:        ax_ = axi                      ax_.set_title(f'{title}')        ax_.plot(xdata, ydata,'ro',label='Data')    ax_.plot(m_xdata, m_ydata,'bo-',label='Processed')        if breakpoints is not None:        for i, bp in enumerate(breakpoints):            ax_.axvline(bp)            ax_.text(bp,np.max(ydata)*0.8, f'{bp:.2f}')            ax_.grid(which='both')        if lgtitle is None:        ax_.legend()             else:           ax_.legend(title=lgtitle)        if axi is None:        plt.show()        return fig_        return ax_   def plot3_pw_ax(xdata, ydata, m_xdata=None, m_ydata=None, n_xdata=None, n_ydata=None, breakpoints=None, title='', axi=None):        if axi is None:        fig_ = plt.figure()        ax_ = fig_.add_subplot(111)    else:        ax_ = axi                      ax_.set_title(title)        ax_.plot(xdata, ydata, color=cycle[0], marker="o",label='Data')        if m_xdata is not None:        ax_.plot(m_xdata, m_ydata, color=cycle[1], linestyle = '-', label='Fit')        if n_xdata is not None:        ax_.plot(n_xdata, n_ydata, color=cycle[2], linestyle = '-', label='User')                if breakpoints is not None:        for i, bp in enumerate(breakpoints):            ax_.axvline(bp,color=cycle[i+1] )            ax_.text(bp,np.max(ydata)*0.1*(i+5), f'{bp:.2f}')            ax_.grid()    ax_.legend()             if axi is None:        plt.show()        return fig_        return ax_      def multi_plots(search_lists, res_lists, title='Shift evaluation',                 nrows=None, ncols=3,                 save=False, para_inv=False,                 plot_fig_step=1):    """Multiplot    para_inv =True -> search_list= Power number        Args:        search_lists (array like): shift data -> para_inv=False                                    power data -> para_inv=True        res_lists (array like): if shift data, power data. if power data, shift data        title (str, optional): graph title. Defaults to 'Shift evaluation'.        nrows (int, optional): rows. Defaults to None.        ncols (int, optional): cols. Defaults to 4.        save (bool, optional): save. Defaults to False.        para_inv (bool, optional): para_inv =True -> search_list= Power number. Defaults to False.    """    all_data = len(search_lists)    total = len(search_lists[::plot_fig_step])        if nrows == None:        nrows = (len(search_lists) + ncols -1)//ncols    # fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*4.8,nrows*3.6), squeeze=False, tight_layout=True)    fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*6,nrows*5), squeeze=False, tight_layout=True)        ax_list=[]        for i in range(nrows):        for j in range(ncols):            ax_list.append(ax[i,j])    for  idx, (i_sea, i_res) in enumerate(zip(search_lists[::plot_fig_step], res_lists[::plot_fig_step])):                 p_para = i_res['popt']        ax_list[idx].plot(i_res['rex'],i_res['rey'],'ro',label='data')        ax_list[idx].plot(i_res['rex'],i_res['fit'],'b-',label='fit')        ax_list[idx].grid(which='both')                if para_inv:            comment = f'No.{idx*plot_fig_step+1}/{all_data}\nshift: {i_res["popt"][1]:.2f}\nn: {i_sea:.2f}\n$R^2$: {i_res["r2"]:.3f}\n$R^{2}_{{bp}}$: {i_res["r2_bp"]:.3f}'            ax_list[idx].set_xlabel('x')            ax_list[idx].set_ylabel(f'y${{1/{i_sea:.1f}}}$')        else:                comment = f'No.{idx*plot_fig_step+1}/{all_data}\nshift: {i_sea:.2f}\nn: {i_res["popt"][0]:.2f}\nslice: {i_res["popt"][1]:.2f}\n$R^2$: {i_res["r2"]:.3f}'            ax_list[idx].set_xlabel('$ln{x}$')            ax_list[idx].set_ylabel('$ln{y}$')        ax_list[idx].legend(title=comment)            if len(ax_list) != total:        for ij in range(len(ax_list)-total):            newi= ij + total            ax_list[newi].axis("off")    fig.suptitle(title)    # plt.tight_layout()        if save:        filename=re_replace(title)        plt.savefig(f'{filename}.png', dpi=300)            plt.show()    # ----def const_inv_power_fit(xdata, ydata, power_num=2, ini_params=None,                        min_error='mae',                        retry_r2=0.9,                         negative=False, zero_replace=False,                        plot=False, info=False,):    """ReLU & MAE fitting  data^(1/power_num)    Args:        xdata (ndarray): xdata        ydata (ndarray): ydata        power_num (float, optional): number of power. Defaults to 2.        ini_params (list, optional): None -> (Nor,Ip,Bg)=[1, 4.5, 0.0]        min_error:(str,option): 'mae':absolute error, 'mse':squares error, defalut is 'mae'        retry_r2(float, optional):retry r2 lower limit. Defaults to 0.9.                Handling of negative values when set to a power.        negative(bool, option):             After taking the logarithm of the negative value, it becomes Nan.            After taking the absolute value of a negative value and the logarithm,             add a negative sign to the value.            Ensure that a power of a negative value does not result in Nan            Defaults to False.        zero_replace(bool, option): if True, Replace nan to zero. Defaults to False.                plot (bool, optional): plot. Defaults to False.        info (bool, optional): info. Defaults to False.    Returns:        dict: {'rex':re_xdata, 'rey':re_ydata, 'fit':re_fit,                 'popt':res_params,                'r2':r2,'adj_r2':adj_r2,'r2_bp':r2_bp,'er':error_range,                'cv':cv}            """    if ini_params is None:        bg = np.nanmean(ydata[:5]) # 　バックグラウンドの推定        # nor = np.max(ydata)/10 # 　ノーマライズのオーダーを推定        nor = 1        # ip = np.percentile(xdata,50)        ipy = np.percentile(ydata,5)        ip_ind = get_nearest_value(ydata, ipy)        # ip = xdata[ip_ind]        ip = 5        ini_params = (nor, ip, bg)     # xxdata = np.power(xdata, power_num)             # 負の値のべき乗をとってもNanにならないようにする。    # 正負を調べる、絶対値でべき乗を取り、その後に正負をかけて戻す。    def none_negative_power(ydata,pw):        ysing = np.sign(ydata)        y_power = ysing*np.power(np.abs(ydata),pw)            return y_power        if negative:        yydata = none_negative_power(ydata, pw=1/power_num)    else:        yydata = np.power(ydata, 1/power_num)                         re_xdata, re_ydata = nan_inf_rm(xdata,yydata,                                    zero_replace=zero_replace,info=info)    re_fit = re_ydata.copy()    st_info = static_inf(re_ydata,info=False)    res_params = raf.fit_m_relu(re_xdata, re_ydata, ini_params,  min_error)    re_fit = raf.m_relu(re_xdata, *res_params)    # print(re_fit,re_ydata)        # Breakpoint index    idx = get_nearest_value(re_xdata, res_params[1])        try:         r2, adj_r2, error_range =  adj_r2_score(re_ydata, re_fit)         r2_bp = r2_score(re_ydata[idx:], re_fit[idx:])    except:        r2, adj_r2, error_range, r2_bp = -1, -1, -1, -1    # 実データでこの項目が必要かどうかは検討（Fittingがうまくいかなかたっら初期値のパラメータを変えてFitting）    if r2 < retry_r2: # この値は引数に含めている        bg = np.mean(re_ydata[:5])        nor = np.max(re_ydata)/2        for p in [45,55,40,60,35,65]:        # for p in [45,55]:            ip = np.percentile(re_xdata,p)            res_params = raf.fit_m_relu(re_xdata, re_ydata, (nor,ip,bg), min_error)            re_fit = raf.m_relu(re_xdata, *res_params)            idx = get_nearest_value(re_xdata, res_params[1])            try:                r2, adj_r2, error_range =  adj_r2_score(re_ydata, re_fit)                 r2_bp = r2_score(re_ydata[idx:], re_fit[idx:])            except:                r2, adj_r2, error_range, r2_bp = -1, -1, -1, -1            if r2 > retry_r2 + 0.02:                break        if info:        print(f'power(n): {power_num:.2f}, 1/power(1/n):{1/power_num:.2f}')        print(f'$R^2$: {r2:.2f}')        print(f'Error range: {error_range:.2f}')        print(f'$R^2$ > threshold {r2_bp:.2f}')        print(f'params(Nor,Ip,bg): {res_params}')                     if plot:        fig = plt.figure(figsize=(12,5), tight_layout=True)        ax1 = fig.add_subplot(1,2,1)        ax2 = fig.add_subplot(1,2,2)        ax1.plot(xdata,ydata,'ro-',label='Data')        ax1.set_xlabel('x')        ax1.set_ylabel('y')        ax1.legend()        ax1.grid(which='both')        ax2= plot_ax(re_xdata, re_ydata, re_xdata, re_fit,                     breakpoints=[res_params[1]],                    lgtitle=f'n: {power_num:.2f}\n1/n: {1/power_num:.2f}',                #    title=f'power(n): {power_num:.2f}, 1/n: {1/power_num:.2f}',                     title='',                    axi=ax2)        ax2.set_xlabel('x')        ax2.set_ylabel(f'y$^{{1/{power_num:.1f}}}$')        plt.show()             return {'rex':re_xdata, 'rey':re_ydata, 'fit':re_fit,             'popt':res_params,'r2':r2,'adj_r2':adj_r2, 'r2_bp':r2_bp,            'er':error_range,            'cv':st_info['cv']}# --- log-logdef val_rm(xdata,ydata,lim_val,info=True):    """Remove values smaller than the specified value along the x-axis            Args:        xdata (ndarray): x data        ydata (ndarray): y data        lim_val (float): specified value. ex 0.1, 0.3, 0.5        info (bool, optional): show infomation. Defaults to True.    Returns:        ndarray: xdata, ydata            Note:        x軸方向で、指定された値より小さい値を削除する        閾値付近は、データのばらつきが大きい。Shift移動させた場所からどのくらい離れた値から評価するか        lim_val:x軸のLogを取る前の値(0.1,0.5など）               -inf < ln(x) < 0  at 0 < x < 1 ->xが1以下の時Logを取るとマイナスになる。                ln(lim_val)= val        ln(1) = 0        ln(0.1) = -2.3        ln(0.5) = -0.69        ln(0) = -inf        lim_val                np.e**(val)= lim_val        np.e**(0) = 1        np.e**(-0.69) = 0.5        np.e**(-1) = 0.36        np.e**(-2.3) = 0.1        np.e**(-3) = 0.05       Example:        val_rm(np.log(xf),np.log(yf),0.3)    """    val = np.log(lim_val)    tr_val_x = np.where(xdata > val)        re_xdata = xdata.copy()    re_ydata = ydata.copy()    re_xdata = xdata[tr_val_x[0]]    re_ydata = ydata[tr_val_x[0]]        if info:        print(f'number of remove under {val} on xdata: {np.count_nonzero(tr_val_x)}')        print(f'shape x, y: {re_xdata.shape}, {re_ydata.shape}')    return re_xdata, re_ydatadef log_log_fit(xdata, ydata, bg_num=None,                 lim_val=0.36, fit_type='mae',                 comment='',                 info=True, plot=True):    """estimate power and shift    Args:        xdata (ndarray): xdata        ydata (ndarray): ydata        num (int, optional): Background average point number. Defaults to 3.        lim_val (float, optional): Remove values smaller than the specified value along the x-axis.                        Defaults to 0.36.  np.e**(0.36) = -1         fit_type (str,optinal): fitting type:         'mae'= mean absolute error,'weight'=rmse and weight,'mse'= root mean squared error        comment(str,optional)        info (bool, optional): information. Defaults to True.        plot(bool, optional):  Plots. Defaults to True.    Returns:        dict: {'popt':r_popt,'r2':r2,'adj_r2':adj_r2, 'rex':re_xdata, 'rey':re_ydata, 'fit':fity}                                popt=[slope, slice]    """    bg_rm_ydata = ydata        if bg_num is not None:        # バックグランドの値を差し引く        # Xの最初から数個の値の平均値をとりその値を引く            ybg = np.nanmean(ydata[:bg_num])        bg = ybg        bgn = ybg        bg_rm_ydata = ydata - bg            re_xdata, re_ydata = nan_inf_rm(np.log(xdata), np.log(bg_rm_ydata),zero_replace=False, info=info)    re_xdata, re_ydata = val_rm(re_xdata, re_ydata, lim_val, info=info)    e_re_xdata = np.exp(re_xdata)        r_popt = [0,0]    if len(re_xdata) and len(re_ydata) >= 5: # Fittigする点が5つ以上        try:            if fit_type == 'mae':            #rmse            # r_popt, _ = curve_fit(m_line, re_xdata, re_ydata, p0=(1,1))                            # mae                r_popt_ = optimize.minimize(mae_line, (1,1), args=(re_xdata, re_ydata))                r_popt = r_popt_.x                        # print(r_popt)            elif fit_type == 'weight':                # 重み付                r_popt, _ = curve_fit(m_line, re_xdata, re_ydata, p0=(1,1),sigma=1/re_xdata)                            else:                r_popt, _ = curve_fit(m_line, re_xdata, re_ydata, p0=(1,1))                            fity = m_line(re_xdata,*r_popt)            r2, adj_r2, error_range =  adj_r2_score(re_ydata, fity)                     except:            r_popt[0], r_popt[1] = np.nan, np.nan            r2, adj_r2, error_range = np.nan, np.nan, np.nan            fity = np.nan        # print(f'slope, slice: {r_popt}, r2: {r2:.3f}')                if info:            # print(f'number of NaN: {np.count_nonzero(np.isnan(re_ydata))}')            print(comment)            # print(f'estimate background: {bgn}')            print(f'slope: {r_popt[0]:.3f}, slice: {r_popt[1]:.3f}, $R^2$: {r2:.3f}')                if plot:                plt.plot(re_xdata, re_ydata,'ro',label='data')            # plt.plot(np.log(xdata),m_line(np.log(xdata),*r_popt),'b-',label='fit')            plt.plot(np.log(e_re_xdata),m_line(np.log(e_re_xdata),*r_popt),'b-',label='fit')            # plt.xlim(val_rang,np.max(re_xdata))            plt.grid(True)            plt.legend(title=f'slope: {r_popt[0]:.3f}\nslice: {r_popt[1]:.3f}\n$R^2$: {r2:.3f}\nadj $R^2$: {adj_r2:.3f}')            plt.show()    else:        r_popt[0],r_popt[1] = np.nan, np.nan        r2, adj_r2, error_range = np.nan, np.nan, np.nan        re_xdata, re_ydata,fity = np.nan, np.nan, np.nan            return {'popt':r_popt,'r2':r2,'adj_r2':adj_r2,'er':error_range, 'rex':re_xdata, 'rey':re_ydata, 'fit':fity}def create_serach_range(center, range, step):    """create search range    Args:        center (float): estimated shift position        range (flaot): search range        step (float): step    Returns:        ndarray : search range    s_range = create_serach_range(center=5, range=1, step=0.2)    >>>[4.  4.2 4.4 4.6 4.8 5.  5.2 5.4 5.6 5.8 6. ]        """    s_range = np.arange(center-range, center+range+step, step)    return s_rangedef xshift_search(xdata, ydata, search_range,                  bg_num=None, lim_val=0.36, fit_type='mae',                  r2_plot=True,                  process_plot=True,                   min_error='mae',                  info=False, every_plot=False, plot_save=False,                  plot_fig_step=1):    """shift, power estimation using shift list        閾値を変えながらべき乗数を見積もる            Args:        xdata (ndarray): xdata        ydata (ndarray): ydata        search_range (list or tuple): shift list        r2_plot (bool, optional): View result(slope,shift,r2) information and plots. Defaults to True.        bg_num (int, optional): Background average point number. Defaults to None.        lim_val (float, optional): Remove values smaller than the specified value along the x-axis.                Defaults to 0.36.  np.e**(0.36) = -1         fit_type (str,optinal): fitting type:         'mae'= mean absolute error,'weight'=rmse and weight,'mse'= root mean squared error        r2_plot (bool, optional): shift vs r2, power vs r2  plots. Defaults to True.        process_plot (bool, optional): fitting process plots. Defaults to True.        plot_save(bool, optional): save. Defaults to False.    Returns:        dict: {'r2':likeli_r2, 'shift':likeli_shift, 'power':likeli_power,            'rex':likeli_rex,'rey':likeli_rey, 'fit':likeli_fit}    Examples:    res_data = xshift_search(xdata, ydata, search_range=[3,3.5,4,4.5,5],                  bg_num=None, lim_val=0.36, fit_type='mae',                  r2_plot=True, process_plot=True, plot_save=False):    """    power = []    r2 = []    res_list = []    adj_r2 = []    error_range =[]    applied_serach_range = []        for i in search_range:        # print(f'shift: {i:.2f}'        res_para = log_log_fit(xdata-i,ydata,bg_num=bg_num,                               lim_val=lim_val,                               fit_type=fit_type,                                comment=f'shift: {i:.2f}',                               info=info,                               plot=every_plot)                if math.isnan(res_para['r2']):            pass                else:            applied_serach_range.append(i)            power.append(res_para['popt'][0])            r2.append(res_para['r2'])            adj_r2.append(res_para['adj_r2'])            res_list.append(res_para)            error_range.append(res_para['er'])                if process_plot:            multi_plots(applied_serach_range, res_list, title='Shift-Log-Log',save=plot_save,                    plot_fig_step=plot_fig_step)            # 決定係数が1に最も近いものを選ぶ    max_r2_idx = get_nearest_value(r2, 1)    # max_r2_idx = idx_of_the_nearest(data=r2, value=1)    likeli_power = power[max_r2_idx]    likeli_shift = applied_serach_range[max_r2_idx]    likeli_r2 = r2[max_r2_idx]    likeli_er = error_range[max_r2_idx]    likeli_rex = res_list[max_r2_idx]['rex']    likeli_rey = res_list[max_r2_idx]['rey']    likeli_fit = res_list[max_r2_idx]['fit']           msg = f'likelihood shift: {likeli_shift:.3f}, power: {likeli_power:.3f}'        if info:        print(msg)        if r2_plot:        search_r2_plot(shifts=applied_serach_range,                       powers=power,                       r2=r2,                       msg=msg,                       save=plot_save)                search_r2_plot(shifts=applied_serach_range,                        powers=power,                        r2=error_range,                        msg=msg,                        save=plot_save,                        ylabel='error range')                with_bg_fit = const_inv_power_fit(xdata, ydata,                                 power_num=likeli_power,                                ini_params=(1,likeli_shift,0),                                 min_error=min_error,                                retry_r2=0.9,                                 zero_replace=True,                                plot=True, info=False)                # # Backgroundを差し引いて解析を行う            # bg = with_bg_fit['popt'][2]**likeli_power        # if bg <= 0:        #     bg=0        # const_inv_power_fit(xdata, ydata-bg,         #                     power_num=likeli_power,        #                     ini_params=(1,likeli_shift,0),         #                     min_error=min_error,        #                     retry_r2=0.9,         #                     zero_replace=True,        #                     plot=True, info=False)                        # inv_power_plot(xdata, ydata,         #                shift=likeli_shift,         #                power=likeli_power)              return {'r2':likeli_r2, 'er':likeli_er,             'shift':likeli_shift, 'power':likeli_power,            'rex':likeli_rex, 'rey':likeli_rey, 'fit':likeli_fit}def xpower_search(xdata, ydata, search_range=None,                  r2_plot=True, process_plot=True,                  min_error='mae',                  zero_replace=False,                  likely_evaluation ='r2',                  info=True, every_plot=False, plot_save=False,                  plot_fig_step=1):    """べき乗数を変えながら閾値を見積もる 1/n-power-plot    Args:        xdata (ndarray): xdata        ydata (ndarray): ydata        search_range (list or tuple): Power range. Defaults to None. If None,[2,2.5,3,3.5,4]        r2_plot (bool, optional): View result(slope,shift,r2) information and plots. Defaults to True.        process_plot (bool, optional): View plots of each trial. Defaults to True.        likely_evaluation (str, optional):Evaluation method. 'r2 or 'r2_bp' Defaults to 'r2'        'r2' :all region, 'r2_bp' : after bp region        info (bool, optional): info. Defaults to True.        every_plot (bool, optional): each plot show. Defaults to False.        plot_save (bool, optional): save. Defaults to False.    Returns:        dict :  {'r2':likeli_r2, 'shift':likeli_shift, 'power':likeli_power,                 'rex':likeli_rex,'rey':likeli_rey, 'fit':likeli_fit}            Examples:        """    shift = []    r2 = []    r2_bp = []    res_list = []    adj_r2 = []    error_range = []    applied_serach_range = []        if search_range is None:        search_range = [1.5,2,2.5,3,3.5,4,4.5,5]        if len(search_range) == 3:        ncols = 3    elif len(search_range) == 2:        ncols = 2     else:        ncols = 4             for i in search_range:        # print(f'shift: {i:.2f}'        res_para = const_inv_power_fit(xdata, ydata, power_num=i,                                        ini_params=None,                                        min_error=min_error,                                       zero_replace=zero_replace,                                       plot=every_plot, info=info)        # {'rex':re_xdata, 'rey':re_ydata, 'fit':re_fit,             # 'popt':res_params,'r2':r2,'adj_r2':adj_r2, 'r2_bp':r2_bp,            # 'er':error_range,            # 'cv':st_info['cv']}        if math.isnan(res_para['r2']):            pass                else:            applied_serach_range.append(i)            shift.append(res_para['popt'][1])            r2.append(res_para['r2'])            adj_r2.append(res_para['adj_r2'])            r2_bp.append(res_para['r2_bp'])            error_range.append(res_para['er'])            res_list.append(res_para)                if process_plot:            multi_plots(applied_serach_range, res_list, title='1/n-Power', ncols=ncols,                     para_inv=True,save=plot_save,                    plot_fig_step=plot_fig_step)            # max_r2_idx = idx_of_the_nearest(data=r2, value=1)    # max_r2_idx = get_nearest_value(r2_bp, 1)    if likely_evaluation == 'r2_bp':        eva_r2 = r2_bp        decision_eva = r'$R^{2}_{bp}$'     else:        eva_r2 = r2        decision_eva = r'$R^{2}$'    max_r2_idx = get_nearest_value(eva_r2, 1)     likeli_shift = shift[max_r2_idx]    likeli_power = applied_serach_range[max_r2_idx]    likeli_r2 = eva_r2[max_r2_idx]    likeli_er = error_range[max_r2_idx]    likeli_rex = res_list[max_r2_idx]['rex']    likeli_rey = res_list[max_r2_idx]['rey']    likeli_fit = res_list[max_r2_idx]['fit']    likeli_popt = res_list[max_r2_idx]['popt']            msg = f'{decision_eva}, likelihood shift: {likeli_shift:.3f}, power: {likeli_power:.1f}, 1/power: {1/likeli_power:.3f}'        if info:        print(msg)        if r2_plot:        search_r2_plot(shifts=shift,                        powers=applied_serach_range,                        r2=eva_r2,                        msg=msg,                        save=plot_save)                with_bg_fit = const_inv_power_fit(xdata, ydata,                         power_num=likeli_power,                        ini_params=(1,likeli_shift,0),                         zero_replace=zero_replace,                        retry_r2=0.9, plot=True, info=False)                  # Backgroundを差し引いて解析を行う        const_inv_power_fit(xdata, ydata-with_bg_fit['popt'][2]**likeli_power,                             power_num=likeli_power,                            ini_params=(1,likeli_shift,0),                             zero_replace=zero_replace,                            retry_r2=0.9,                             plot=True, info=False)                # inv_power_plot(xdata, ydata,         #                shift=likeli_shift,         #                power=likeli_power)             return {'r2':likeli_r2, 'er':likeli_er,             'shift':likeli_shift, 'power':likeli_power,            'rex':likeli_rex,'rey':likeli_rey, 'fit':likeli_fit,            'popt':likeli_popt}JSA_PW論文コード/pfit/relu_abs_fit.py# ReLU absolute fitimport numpy as npfrom scipy.optimize import minimizeimport scipy.optimize as optimize# m_relu関数の定義@np.vectorizedef m_relu(x, Nor, Ip, Bg):    u = (x - Ip)    return Nor * u * (u > 0.0) + Bg# # 平均絶対誤差の定義# def absolute_error(params, x, y):#     y_pred = m_relu(x, *params)#     return np.sum(np.abs(y - y_pred))/len(x)# 絶対誤差の定義def absolute_error(params, x, y):    y_pred = m_relu(x, *params)    return np.sum(np.abs(y - y_pred))# # 最小平均二乗誤差の定義# def least_squares_error(params, x, y):#     y_pred = m_relu(x, *params)#     return np.sum((y - y_pred) ** 2)/len(x)# 最小二乗誤差の定義def least_squares_error(params, x, y):    y_pred = m_relu(x, *params)    return np.sum((y - y_pred) ** 2)def fit_m_relu(x, y, params_init=None, min_error='mae'):    """Absolute fitting    Args:        x (ndarray): _description_        y (ndarray): _description_        params_init (list or ndarray, optional): _description_. Defaults to None.        min_error:(str,option): 'mae':absolute error, 'mse':squares error    Returns:        float: Nor_opt, Ip_opt, Bg_opt            examples:    fit_param = fit_m_relu(xdata,ydata, params_init=[1, 4.5, 0.0])        """        if params_init is None:        params_init = np.array([1, 4.5, 0.0])    if min_error == 'mse':        result = minimize(least_squares_error, params_init, args=(x, y))            else:        result = minimize(absolute_error, params_init, args=(x, y))    Nor_opt, Ip_opt, Bg_opt = result.x    return Nor_opt, Ip_opt, Bg_optdef de_fit_m_relu(x,y,params_init=None):    #differential_evolution(func, bounds[, args, …]) 多変数関数の大域最小値を求める。    # para=(Nor,ip,bg)    bounds = [(0, np.max(y)), (np.min(x), np.max(x)), (0,np.max(y))]    # print(bounds)    result  = optimize.differential_evolution(absolute_error, bounds, args=(x, y))        Nor_opt, Ip_opt, Bg_opt = result.x    return Nor_opt, Ip_opt, Bg_optdef bh_fit_m_relu(x,y,params_init=None):    # basinhopping(func, x0[, niter, T, stepsize, …]) basin-hoppingアルゴリズムを使って関数の大域最小値を求める。    if params_init is None:        nor_m = np.max(y)/10        params_init = np.array([nor_m, 4.5, 0.0])    result  = optimize.basinhopping(absolute_error, params_init, minimizer_kwargs={"method": "L-BFGS-B","args":(x, y)})    Nor_opt, Ip_opt, Bg_opt = result.x    return Nor_opt, Ip_opt, Bg_opt# if __name__ == "__main__":#     pass    # import matplotlib.pyplot as plt    # xx = np.arange(4,6.1,0.1)    # yy=m_relu(xx,2,4.5,2)    # f1=fit_m_relu(xx, yy, params_init=None)    # f2=de_fit_m_relu(xx,yy,params_init=None)    # # f3= bh_fit_m_relu(xx,yy,params_init=None)    # print(f1)    # print(f2)    # # print(f3)    # plt.plot(xx,yy)    # plt.plot(xx,m_relu(xx,*f1))    # plt.plot(xx,m_relu(xx,*f2))    # # plt.plot(xx,m_relu(xx,*f3))JSA_PW論文コード/pfit/sim_func.pyimport numpy as npimport matplotlib.pyplot as pltdef power_plus(x,a,b,c,d):    # f = a*np.power((x-b),c) + d    u = (x-b)    g = a*(np.power(u,c))*(u>0)+ d     return g# pys function@np.vectorizedef pys(x, Nor, Ip, T, Bg):    '''    pys calculation    :param x: list of energy  or value    :param Ip:    :param T:    :param Nor:    :param Bg:    :return: pys calculation    example:    -----    >>>p=pys(np.array([4,4.2,4.3,4.5]),4.3,300,1,1)    >>>print(p,type(p))    [ 1.00000912  1.02078619  1.81083333 32.5716379 ] <class 'numpy.ndarray'>    >>>p=pys(4.2,4.3,300,1,1)    >>>print(p,type(p))    1.020786189180001 <class 'numpy.ndarray'>    Note:    -----    numpyのVectorizeを利用すると、配列を受け付けるようになる    '''    u = (x - Ip) / (T * 8.6171e-5)    if u <= 0:        f = Nor * (np.exp(u) - (np.exp(2 * u) / (2 * 2)) + (np.exp(3 * u) / (3 * 3))                   - (np.exp(4 * u) / (4 * 4)) + (np.exp(5 * u) / (5 * 5)) - (np.exp(6 * u) / (6 * 6))) + Bg    else:        f = Nor * (np.pi * np.pi / 6 + (1 / 2) * u * u - (np.exp(-u)) + (np.exp(-2 * u) / (2 * 2))                   - (np.exp(-3 * u) / (3 * 3)) + (np.exp(-4 * u) / (4 * 4)) - (np.exp(-5 * u) / (5 * 5))                   + (np.exp(-6 * u) / (6 * 6))) + Bg    return f# 論文用のシミュレーション作成コードdef lnln(x,a,b,c,d):    # f = a*np.power((x-b),c) + d    u = (x-b)    f = a*(u**c)*(u>0.0) + d    return {'pw':f,'ln':np.log(f),'lnx':np.log(x)}def pw_ln_plot(xdata,para1,para2,para3,exlim=1000,leg_title=None,axi=None,figsize=(12,5)):    """    Args:        xdata (_type_): _description_        para1 (_type_): _description_        para2 (_type_): _description_        para3 (_type_): _description_        exlim (int, optional): _description_. Defaults to 1000.        leg_title (_type_, optional): _description_. Defaults to None.        axi (_type_, optional): _description_. Defaults to None.        figsize (tuple, optional): _description_. Defaults to (12,5).    Returns:        _type_: _description_            Examples:        xx2 = np.linspace(0,10,100)        para1b=(2,0,3,0)        para2b=(2,-1,3,0)        para3b=(2,1,3,0)        pw_ln_plot(xx2,para1b,para2b,para3b,                exlim=1000,                leg_title='$y=a \cdot (x-b)^c + d$',                axi=None,figsize=(12,5))                para1bc=(2,0,3,0)        para2bc=(2,0,3,1)        para3bc=(2,0,3,5)        pw_ln_plot(xx2,para1bc,para2bc,para3bc,                exlim=1000,                leg_title='$y=a \cdot (x-b)^c + d$',                axi=None,figsize=(12,5))        """    if axi is None:        fig_ = plt.figure(figsize=figsize, tight_layout=True)        ax0 = fig_.add_subplot(121)        ax1 = fig_.add_subplot(122)            else:        ax0 = axi[0]        ax1 = axi[1]                ax0.plot(xdata,lnln(xdata,*para1)['pw'],'ro-',label=f'{para1}')    ax0.plot(xdata,lnln(xdata,*para2)['pw'],'gs-',label=f'{para2}')    ax0.plot(xdata,lnln(xdata,*para3)['pw'],'b^-',label=f'{para3}')            ax0.set_xlabel('$\it{x}$')    ax0.set_ylabel('$\it{y}$')    ax0.set_ylim(0,exlim)    ax0.grid(True)            ax1.plot(lnln(xdata,*para1)['lnx'],lnln(xdata,*para1)['ln'],'ro-',label=f'{para1}')    ax1.plot(lnln(xdata,*para2)['lnx'],lnln(xdata,*para2)['ln'],'gs-',label=f'{para2}')    ax1.plot(lnln(xdata,*para3)['lnx'],lnln(xdata,*para3)['ln'],'b^-',label=f'{para3}')            ax1.set_xlabel('$ln{x}$')    ax1.set_ylabel('$ln{y}$')        def x2ln(x):            return np.log(x)    def ln2x(x):        return np.exp(x)    secax = ax1.secondary_xaxis('top', functions=(ln2x, x2ln))    secax.set_xlabel('$\it{x}$')    # secax.grid(True)    # ax1.set_ylim(0,1000)    # ax1.set_xlim(min(lnln(xdata,*para3)['lnx']),max(lnln(xdata,*para3)['lnx']))    ax1.grid(True)    if leg_title is None:        ax0.legend(loc='upper left')        ax1.legend(loc='upper left')    else:        # ax0.legend(title=f'{leg_title}\n(a,b,c,d)',loc='upper left')        # ax1.legend(title=f'{leg_title}\n(a,b,c,d)',loc='upper left')        ax0.legend(title=f'{leg_title}\n            (a,b,c,d)')        ax1.legend(title=f'{leg_title}\n            (a,b,c,d)')            if axi == None:        plt.show()        return  None    else:        return ax0,ax1    def pw_ln_plot_v2(xdata,para1,para2,para3,                  exlim=1000,                  leg_title=None,                  axi=None,                  l_ind=1,                  l_txt='b=',                  fig_title=None,                  figsize=(12,5),                  fig_label1='(a)',fig_label2='(b)'):    """    論文用に修正を加えた    Args:        xdata (_type_): _description_        para1 (_type_): _description_        para2 (_type_): _description_        para3 (_type_): _description_        exlim (int, optional): _description_. Defaults to 1000.        leg_title (_type_, optional): _description_. Defaults to None.        axi (_type_, optional): _description_. Defaults to None.        figsize (tuple, optional): _description_. Defaults to (12,5).    Returns:        _type_: _description_            Examples:        xx2 = np.linspace(0,10,100)        para1b=(2,0,3,0)        para2b=(2,-1,3,0)        para3b=(2,1,3,0)        pw_ln_plot(xx2,para1b,para2b,para3b,                exlim=1000,                leg_title='$y=a \cdot (x-b)^c + d$',                axi=None,figsize=(12,5))                para1bc=(2,0,3,0)        para2bc=(2,0,3,1)        para3bc=(2,0,3,5)        pw_ln_plot(xx2,para1bc,para2bc,para3bc,                exlim=1000,                leg_title='$y=a \cdot (x-b)^c + d$',                axi=None,figsize=(12,5))        """    if axi is None:        fig_ = plt.figure(figsize=figsize, tight_layout=True)        ax0 = fig_.add_subplot(121)        ax1 = fig_.add_subplot(122)            else:        ax0 = axi[0]        ax1 = axi[1]                ax0.plot(xdata,lnln(xdata,*para1)['pw'],'ro-',label=f'{l_txt}{para1[l_ind]}')    ax0.plot(xdata,lnln(xdata,*para2)['pw'],'gs-',label=f'{l_txt}{para2[l_ind]}')    ax0.plot(xdata,lnln(xdata,*para3)['pw'],'b^-',label=f'{l_txt}{para3[l_ind]}')            ax0.set_xlabel('$\it{x}$')    ax0.set_ylabel('$\it{y}$')    ax0.set_ylim(0,exlim)    ax0.text(0.95, 0.05, f'{fig_label1}',              horizontalalignment='right', transform=ax0.transAxes,fontsize=18)    ax0.grid(True)            ax1.plot(lnln(xdata,*para1)['lnx'],lnln(xdata,*para1)['ln'],'ro-',label=f'{l_txt}{para1[l_ind]}')    ax1.plot(lnln(xdata,*para2)['lnx'],lnln(xdata,*para2)['ln'],'gs-',label=f'{l_txt}{para2[l_ind]}')    ax1.plot(lnln(xdata,*para3)['lnx'],lnln(xdata,*para3)['ln'],'b^-',label=f'{l_txt}{para3[l_ind]}')            ax1.set_xlabel('$ln{x}$')    ax1.set_ylabel('$ln{y}$')    ax1.text(0.95, 0.05, f'{fig_label2}',              horizontalalignment='right', transform=ax1.transAxes,fontsize=18)        # def x2ln(x):    #         return np.log(x)    # def ln2x(x):    #     return np.exp(x)    # secax = ax1.secondary_xaxis('top', functions=(ln2x, x2ln))    # secax.set_xlabel('$\it{x}$')    # # secax.grid(True)    # # ax1.set_ylim(0,1000)    # # ax1.set_xlim(min(lnln(xdata,*para3)['lnx']),max(lnln(xdata,*para3)['lnx']))    ax1.grid(True)    # fig_.suptitle(fig_title)    if leg_title is None:        ax0.legend(loc='upper left')        ax1.legend(loc='upper left')    else:        # ax0.legend(title=f'{leg_title}\n(a,b,c,d)',loc='upper left')        # ax1.legend(title=f'{leg_title}\n(a,b,c,d)',loc='upper left')        ax0.legend(title=f'{leg_title}\n            (a,b,c,d)')        ax1.legend(title=f'{leg_title}\n            (a,b,c,d)')            if axi == None:        plt.show()        return  None    else:        return ax0,ax1JSA_PW論文コード/pfit/__init__.pyJSA_PW論文コード/pw_mini_lib.pyimport collectionsfrom contextlib import redirect_stdoutfrom pathlib import Pathimport osfrom pprint import pprintimport numpy as npimport matplotlib.pyplot as pltfrom scipy import integratefrom sklearn.cluster import KMeansimport piecewise_regressionimport comlib as cl# import warnings# warnings.simplefilter('ignore')## warnings.resetwarnings()## warnings.simplefilter('ignore', FutureWarning)class PWRXY():     BPs_Max = 10 # Max break poits        def __init__(self, xdata, ydata):        """        Args:            xdata (ndarray): x data            ydata (ndarray): y data                Example:            pw1 = PWRXY(xf,y6)             pw1.fit()            pw1.transform(0,5)                    attributes list:            attributes = dir(incetance name)            # print(attributes)            for attribute in attributes:                print(attribute)                    """        # データにNanやinfなどが含まれている場合、エラーが起こるので、予めそのデータを取り除く        rxdata, rydata = nan_inf_rm(xdata,ydata)        # データの並び（昇順）をCheckして並び替ええる。xの始まりが大きい値から始まる場合は反転させる        rxdata, rydata = array_order_check(rxdata, rydata)                self.xdata = rxdata        self.ydata = rydata            def _auto_pw(self, xdata, ydata, plot=False, info=False,):        # Auto bp estimation        self.auto_res_dict = pw_model(xdata,                                      ydata,                                      bps_max=self.BPs_Max,                                     plot=plot,                                     info=info)        self.likely_res_dict = pw_alpha(xdata,                                        ydata,                                        bps_list=self.auto_res_dict['bps_list_asc'],                                         plot=False)        # likely_res_dict.keys()        #       'res_dict':　_est_fit_dividで分解する前の値        #       'bps': bps_list,         #       'alphas': alphas_list,        #       'betas': betas_list,        #       'const': const_list,        #       'ext_bps': ext_bps_list,        #       'fit_ext_dict'-> keys: 'bps','alphas','betas','const','ext_bps'        #       'fit_summary'            def _plot(self,m_xdata=None, m_ydata=None):        plot_pw_ax(self.xdata, self.ydata,                     m_xdata=m_xdata, m_ydata=m_ydata,                     bps_list=self.likely_res_dict['bps'],                     title='',                     axi=None)    def _region_estimate(self, info=False):        self.peak_pos = evaluation_alpha_peak(bps_list=self.likely_res_dict['bps'],                                              alphas_list=self.likely_res_dict['alphas'],                                               info=info)                     def _select_region(self, start_ext_bp_num=0, end_ext_bp_num=None,plot=False):        # 指定したBPの間のスペクトルだけ切り出す        self.select_x, self.select_y = remaining_region(self.xdata,                                                         self.ydata,                                                         bps_list=self.likely_res_dict['bps'],                                                         start_ext_bp_num=start_ext_bp_num,                                                        end_ext_bp_num=end_ext_bp_num,                                                        plot=plot)        def _bg_estimate(self,info=False,plot=False):        self.slope_pos = evaluation_alpha_slope(self.select_x,                                                 self.select_y,                                                 self.likely_res_dict['fit_ext_dict'],                                                 info=info, plot=plot)                def _remove_bg(self,fit_ext_dict,                   start_ext_bp_num=0, end_ext_bp_num=None,plot=False):        self.bg_remove_x, self.bg_remove_y, _ = remove_bg(self.select_x,                                                          self.select_y,                                                           fit_ext_dict,                                                          start_ext_bp_num,                                                           end_ext_bp_num,                                                          plot)                def peak_search(self,plot=True):        self._auto_pw(self.xdata,self.ydata)        if plot:            self._plot()    def auto_peak_transform(self,plot=True):        self._region_estimate()        self._select_region(start_ext_bp_num = self.peak_pos['ext_bp_name'][0],                             end_ext_bp_num = self.peak_pos['ext_bp_name'][1],                            plot=plot)            def bg_search(self,plot=True):        self._auto_pw(self.select_x, self.select_y)        if plot:            self._plot(m_xdata=self.select_x, m_ydata=self.select_y)        def auto_slope_transform(self, level='r', info=False, plot=True):        self._bg_estimate(info=info, plot=info)                if level == 'g':            end_level = self.slope_pos['ex_bp_d_gini']        elif level == 'bg':            end_level = self.slope_pos['ex_bp_bp']         elif level == 'r':            end_level = self.slope_pos['ex_bp_MAR']        elif level == 'nr':            end_level = self.slope_pos['ex_bp_nor_MAR']         # 多数決を取って決める           else:            # 全部値が異なる場合は、Listの一番先頭の値が優先される            pos_list = [self.slope_pos['ex_bp_bp'],                        self.slope_pos['ex_bp_MAR'],                        self.slope_pos['ex_bp_nor_MAR'],                        self.slope_pos['ex_bp_d_gini']                        ]            c = collections.Counter(pos_list)            end_level = int(c.most_common()[0][0])                    if info:            print(f'alphas{self.likely_res_dict["alphas"]}')            print(f'level: {level}, end BP number: {end_level}')                    self._remove_bg(self.likely_res_dict['fit_ext_dict'],                        start_ext_bp_num=0,                         end_ext_bp_num=end_level,                        # end_ext_bp_num=self.slope_pos['ex_bp_MAR'],                        # end_ext_bp_num=self.slope_pos['ex_bp_d_gini'],                        # end_ext_bp_num=self.slope_pos['ex_bp_bp'],                        plot=plot)                def transform(self, start_ext_bp_num=0, end_ext_bp_num=None):        self._select_region(start_ext_bp_num=start_ext_bp_num, end_ext_bp_num=end_ext_bp_num, plot=False)        self._plot(m_xdata=self.select_x, m_ydata=self.select_y)# -- for Flet     def _plot_fig(self,m_xdata=None, m_ydata=None,axi=None):        axi = plot_pw_ax(self.xdata, self.ydata,                     m_xdata=m_xdata, m_ydata=m_ydata,                     bps_list=self.likely_res_dict['bps'],                     title='',                     axi=axi)        return axi        def fit_fig(self,axi=None):          self._auto_pw(self.xdata,self.ydata)        axi = self._plot_fig(axi=axi)        return axi            def transform_fig(self, start_ext_bp_num=0, end_ext_bp_num=None,axi=None):        self._select_region(start_ext_bp_num=start_ext_bp_num, end_ext_bp_num=end_ext_bp_num, plot=False)        axi.cla()        axi = self._plot_fig(m_xdata=self.select_x, m_ydata=self.select_y,axi=axi)        return axi  # --# ----------------- # X,Ydata -> pw_model : resonable bps -> pw_alpha : likely fitting results      def pw_model(xdata, ydata, bps_max, plot=True, info=True):    """    While increasing the number of breakpoints,     find the breakpoint with the smallest BIC and its fitting.    Args:        xdata (ndarray): xdata        ydata (ndarray): ydata        max_n_breakpoints (int): break point number        plot (bool, optional): _description_. Defaults to True.        info (bool, optional): _description_. Defaults to True.    Returns:        dict:            'bps_list_asc': Breakpoints list (昇順),             'res_dict': parametare resluts at min BIC                        {'asc_bps': re_val_breakpoints, 'res_dict': estimate_min}             Note:        """        def get_model_selection(xdata,ydata,bps_max,info=True):        # Python Tips: 出力を一時的に無効化        # https://www.lifewithpython.com/2019/03/python-disable-stdout-temporarily.html        if info:            ms = piecewise_regression.ModelSelection(xdata, ydata, max_breakpoints=bps_max)                    else:               with redirect_stdout(open(os.devnull, 'w')):                ms = piecewise_regression.ModelSelection(xdata, ydata, max_breakpoints=bps_max)                     return ms    # ms.models                def get_min_bic_and_params(ms,bps_max,info=True):        # Search BIC min        bics = np.array([ms.model_summaries[i]["bic"] for i in range(bps_max+1)],dtype=float)        # print(f'BIC list:{bics}')            bic_min = np.nanmin(bics)        bic_min_ind = np.nanargmin(bics)        # bpが0の時 次のプログラムでエラーとなるので、その場合bpを1にする        if bic_min_ind == 0:            bic_min_ind = 1            bic_min = bics[1]                    estimate_min = ms.model_summaries[bic_min_ind]["estimates"]        # print(f'{estimate_min.keys()}')        if info:            print(f'Minimum BIC:{bic_min:.2f}, index:{bic_min_ind}')        return bics, bic_min, bic_min_ind, estimate_min    def get_sorted_breakpoints(estimate_min):        # Breakpointが昇順に並んでいない。        # Breakpointの値を取り出す。        key_breakpoints = [s for s in estimate_min.keys() if 'breakpoint' in s]        val_breakpoints = [estimate_min[v]['estimate'] for v in key_breakpoints]        # Breakpointを昇順に並べる        re_val_breakpoints = sorted(val_breakpoints)        return re_val_breakpoints        def plot_model_fits(ms):        for model in ms.models:            if ms.model_summaries[model.n_breakpoints]["converged"]:                print("Plotting fit for model with {} breakpoint(s) . . . ".format(model.n_breakpoints))                model.plot()                plt.title(f'Fit with {model.n_breakpoints} breakpoints, BIC:{ms.model_summaries[model.n_breakpoints]["bic"]:.1f}' )                # plt.legend(title=f'BIC:{ms.model_summaries[model.n_breakpoints]["bic"]:.3f}')                plt.grid()                plt.show()                       rxdata, rydata = nan_inf_rm(xdata,ydata)    ms = get_model_selection(rxdata, rydata,bps_max,info=info)    bics, bic_min, bic_min_ind, estimate_min = get_min_bic_and_params(ms,bps_max,info=info)       re_val_breakpoints = get_sorted_breakpoints(estimate_min)    # print(re_val_breakpoints)    # alpha1 = estimate_min['alpha1']['estimate']    # print(f"alpha1:{estimate_min['alpha1']['estimate']}")    # for ikey in estimate_min.keys():    #     print(f"{ikey}, {estimate_min[ikey]['estimate']}")        if plot:        plot_model_fits(ms)    return {'bps_list_asc': re_val_breakpoints, 'res_dict': estimate_min, 'bics': bics} def _est_fit_divid(para_dict):    """Breakpoints, alphas, const, betaの値を取り出す。    Args:        para_dict (dict): dict_keys(['const': value, 'brakepoint1':value,....])            # pw_results = pw_fit.get_results()            # print(pw_results.keys())            # dict_keys(['davies', 'estimates', 'bic', 'rss', 'converged'])            # pw_estimates = pw_results["estimates"]            # para_dict = {k: pw_estimates[k]["estimate"] for k in pw_estimates.keys()}    Returns:        list[float]: bps_list, alphas_list, betas_list, const_list        example:        bps_list, alphas_list, betas_list, const_list = est_para_divid(para_dict)    """    # Fitting結果の辞書からbrekepointsとalphaの値を取り出す。    # Extract the values of breakpoints and alpha from the dictionary of fitting results.    # len(bp) = n -> alpha=n+1, beta=n, const=1        key_breakpoints = [s for s in para_dict.keys() if 'breakpoint' in s]    bps_list = [para_dict[v] for v in key_breakpoints]    key_alphas = [s for s in para_dict.keys() if 'alpha' in s]    alphas_list = [para_dict[v] for v in key_alphas]        key_betas = [s for s in para_dict.keys() if 'beta' in s]    betas_list = [para_dict[v] for v in key_betas]        key_const = [s for s in para_dict.keys() if 'const' in s]    const_list = [para_dict[v] for v in key_const]        return bps_list, alphas_list, betas_list, const_listdef _ext_bps(xdata,bps_list):    ext_bps_list = bps_list.copy()    ext_bps_list.insert(0, np.min(xdata))    ext_bps_list.append(np.max(xdata))    return ext_bps_listdef pw_alpha(xdata, ydata, bps_list, plot=True):    """    Since the breakpoints obtained from model selection may not be in ascending order,     recalculate the list of breakpoints in ascending order.     Args:        xdata (ndarray): xdata        ydata (ndarray): ydata        brakepoints_list (list, optional): breakpoints.                         Defaults to None. ex: [3,5,8]    Returns            {'res_dict': fit_est_dict, #para_dictと同じ            'bps': bps_list,             'alphas': alphas_list,            'betas': betas_list,            'const': const_list,            'ext_bps': ext_bps_list,            'fit_ext_dict': fit_ext_dict,            'fit_summary': summary_rep}                """    # sorted breakpoints in ascending order.    ordered_breakpoints = sorted(bps_list)    n_breakpoints = len(ordered_breakpoints)        rxdata, rydata = nan_inf_rm(xdata,ydata)        pw_fit = piecewise_regression.Fit(rxdata, rydata,                                       start_values=ordered_breakpoints)       # Note:    # pw_fit.keys()--> dict_keys(['estimates', 'bic', 'rss', 'converged'])    # pw_fit['estimates'].keys() --> dict_keys(['const', 'beta1', 'breakpoint1', 'alpha1', ...])    # pw_fit['estimates']['const].keys() --> dict_keys(['estimate', 'se', 'confidence_interval', 't_stat', 'p_t'])        if plot:            pw_fit.plot_data(color="grey", s=20)        # Pass in standard matplotlib keywords to control any of the plots        pw_fit.plot_fit(color="red", linewidth=4)        pw_fit.plot_breakpoints()        pw_fit.plot_breakpoint_confidence_intervals()        plt.title("Fit with {} breakpoints".format(n_breakpoints))        # plt.xlabel("x")        # plt.ylabel("y")        plt.grid()        plt.show()        # plt.close()        # Print a summary of the fit        adj_r2_ = pw_fit.best_muggeo.best_fit.adjusted_r_squared        rss_ = pw_fit.best_muggeo.best_fit.residual_sum_squares        bic_ = pw_fit.best_muggeo.best_fit.bic                print(f'residual sum squares:{rss_}')        print(f'Adj_R2:{adj_r2_:.3f}, BIC:{bic_:.2f}')                _ = pw_fit.summary()        with redirect_stdout(open(os.devnull, 'w')):        summary_rep = pw_fit.summary()                pw_results = pw_fit.get_results()    # print(pw_results.keys())    # dict_keys(['davies', 'estimates', 'bic', 'rss', 'converged'])        pw_estimates = pw_results["estimates"]    # pw_estimates.keys() -->    #           dict_keys(['const', 'beta1', 'breakpoint1', 'alpha1', ...])        # Fit resluts paramater    fit_est_dict ={k: pw_estimates[k]["estimate"] for k in pw_estimates.keys()}    # print(fit_est_dict)    # ['const', 'beta1', 'breakpoint1', 'alpha1', ...] の値だけになる    # dict_keys(['const': value, 'brakepoint1':value,....])    bps_list, alphas_list, betas_list, const_list =  _est_fit_divid(fit_est_dict)    ext_bps_list =  _ext_bps(rxdata,bps_list)           fit_ext_dict ={'bps': bps_list,                    'alphas': alphas_list,                    'betas': betas_list,                   'const': const_list,                   'ext_bps': ext_bps_list}    # Note:    # ex bp=6 -> alha=7　(bp+1), beta=6    # bp_list=[bp1,bp2,bp3,bp4,bp5,bp6]    #     # bp_listに最初と最後にbpを追加する->ext_bp_list    # ext_bp_list=[bp0,bp1,bp2,bp3,bp4,bp5,bp6,bp7]    # bp0=min(xdata), bp7=max(xdata)    #     # alpha=[a1,a2,a3,a4,a5,a6,a7]    # a1-> between bp0 and bp1, a7-> bp6-bp7    # beta=[b1,b2,b3,b4,b5,b6]    # b1=a1-a2,...b6=a7-a6    # ext_beta=[eb1,...eb7]    # eb1=a1,     # alphaからbetaを算出　配列の０番目はalpha[0]の値    # ex_beta =[alphas[0]]    # ex_betas[1:] = [alphas[j+1] - alphas[j]  for j in range(len(alphas)-1) ]           return {'res_dict': fit_est_dict, #para_dictと同じ            'bps': bps_list,             'alphas': alphas_list,            'betas': betas_list,            'const': const_list,            'ext_bps': ext_bps_list,            'fit_ext_dict': fit_ext_dict,            'fit_summary': summary_rep}def remaining_region(xdata, ydata, bps_list,                      start_ext_bp_num=0, end_ext_bp_num=None,                     plot=True):        """Extracts the data between two breakpoints from the input data.    Args:        xdata: A list of x-axis data.        ydata: A list of y-axis data.        bp_list: A list of breakpoint values.        start_ext_bp_num: The index of the start breakpoint in the `extended bp_list`.         end_ext_bp_num: The index of the end breakpoint in the `extended bp_list`. If not specified,            the data will be extracted up to the last breakpoint in the `ext_bp_list`.                    ex) bps_list = [2,3,5,6] Bp =['bp1','bp2','bp3','bp4']            ext_bps_list =[0,2,3,5,6,10] 0 = np.min(xdata), 10 = np.max(xdata)             ext_Bp ==['bp0','bp1','bp2','bp3','bp4','bp5']           Returns:        A tuple of two lists, containing the extracted x-axis data and y-axis data, respectively.    """    rxdata, rydata = nan_inf_rm(xdata,ydata)        bp_len = len(bps_list)    if end_ext_bp_num == None or end_ext_bp_num >= bp_len + 1 :        end_ext_bp_num = bp_len + 1 # 一番最後まで        if start_ext_bp_num < 0:        start_ext_bp_num = 0                if start_ext_bp_num > end_ext_bp_num:        start_ext_bp_num = 0        end_ext_bp_num = bp_len + 1        start_ext_bp_num = int(start_ext_bp_num)    end_ext_bp_num = int(end_ext_bp_num)        # bp_listを拡張bp0とbpの最後を付け足す。len(bp)より2つ増える    ext_bps_list = _ext_bps(rxdata, bps_list)        xs_ind = cl.get_nearest_value(rxdata, ext_bps_list[start_ext_bp_num])    xe_ind = cl.get_nearest_value(rxdata, ext_bps_list[end_ext_bp_num])    # print(ext_bp_list[end_bp_num])    # print(xe_ind)    # Breakpoint 未満の値を抽出する    rx_data = rxdata[xs_ind:xe_ind+1]    ry_data = rydata[xs_ind:xe_ind+1]        if plot:        _ = plot_pw_ax(xdata, ydata,                       m_xdata=rx_data, m_ydata=ry_data,                        bps_list=bps_list)                      return rx_data, ry_datadef remove_bg(xdata, ydata, fit_ext_dict,              start_ext_bp_num=0, end_ext_bp_num=1,              plot=True):        bps_list = fit_ext_dict['bps']    alphas_list = fit_ext_dict['alphas']    betas_list = fit_ext_dict['betas']    const_list = fit_ext_dict['const']    ext_bps_list = fit_ext_dict['ext_bps']        def _make_list_with_nane(index,target_lists):         """        Args:            index (int): 指定したIndexまで採用            target_lists (List(float)): 対象となるリスト        Returns:            list(float): Nanを含むリストを返す                    Examples:        target_lists = [0,1,2,3,4]        index = 2        _make_list_with_nane(index,target_lists)        >>> [0,1,2,nan,nan]         """        list1 = target_lists[:index + 1]        list2 = [np.nan] * len(target_lists[index +1:])        list_select = list1 + list2                return list_select    # -----    # もしbp1までならば、alpha[0]までend_bp_num-1    slope_select = _make_list_with_nane(end_ext_bp_num-1,alphas_list)    # bg_select = bps_list[:end_ext_bp_num]            y_bg = const_list[0] + slope_select[0] *xdata    for i, v in enumerate(slope_select[1:]):        if v is not np.nan:            y_bg += betas_list[i] * \                np.maximum(xdata - bps_list[i], 0)        else:            break                y_rm = ydata - y_bg        if plot:          _ = plot_pw_ax(xdata, ydata,                       m_xdata=xdata, m_ydata=y_rm, m_label='Bg deletion',                        bps_list=bps_list)                return xdata, y_rm, y_bg#########################　Preproessing functions######################### nanの処理def nan_inf_rm(xdata,ydata,zero_replace=False,info=False):    """remove nan and inf values        Logをとった場合        マイナスの値:定義されていないためにNan        0:マイナス無限大になる        これらの値が含まれている場合、機能しない関数が出てくるためにその値(Index)を取り除く    Args:        xdata (ndarray or list): xdata        ydata (ndarrayor list): ydata        zero_replace(bool): True: Nan -> 0 replace  False:Nan remove. default False        info (bool): show infomation            Returns:        trimmed data (ndarray): re_xdata, re_ydata    """    # inf -> nan    # nan_inf_rm(np.log(xf),np.log(yf))    xarray = np.array(xdata)    yarray = np.array(ydata)        # Replace inf with nan     tr_inf_x = np.where(np.isinf(xarray),np.nan,xarray)    tr_inf_y = np.where(np.isinf(yarray),np.nan,yarray)        re_xdata = tr_inf_x.copy()    re_ydata = tr_inf_y.copy()        # Remove nan    if zero_replace:        re_xdata = np.nan_to_num(re_xdata, copy=False)        re_ydata = np.nan_to_num(re_ydata, copy=False)            else:        nan_ind_x = np.where(~np.isnan(tr_inf_x))        re_xdata = re_xdata[nan_ind_x[0]]        re_ydata = re_ydata[nan_ind_x[0]]                nan_ind_y = np.where(~np.isnan(re_ydata))        re_xdata = re_xdata[nan_ind_y[0]]        re_ydata = re_ydata[nan_ind_y[0]]                # これにまとめることができる？        # re_xdata = tr_inf_x[~np.isnan(tr_inf_x)]        # re_ydata = tr_inf_y[~np.isnan(tr_inf_y)]        if info:        print(f'number of Nan of (x, y) : ({np.count_nonzero(np.isnan(tr_inf_x))}, {np.count_nonzero(np.isnan(tr_inf_y))})')        print(f'Original shape x, y: {xarray.shape}, {yarray.shape}')        print(f'output shape x, y: {re_xdata.shape}, {re_ydata.shape}')            return re_xdata, re_ydata# データの並びを調べる　x軸を昇順にする。ｙの値もそれに合わせるdef array_order_check(xdata, ydata):    """    Checks if the order of xdata and ydata are reversed.    Args:        xdata (numpy.ndarray): Input x data        ydata (numpy.ndarray): Input y data    Returns:        cxdata (numpy.ndarray): Output x data        cydata (numpy.ndarray): Output y data    Raises:        ValueError: If the lengths of xdata and ydata do not match    """    if len(xdata) != len(ydata):        raise ValueError("The lengths of xdata and ydata do not match")    cxdata = xdata.copy()    cydata = ydata.copy()    if xdata[0] > xdata[-1]:        print("replacement")        cxdata = xdata[::-1]        cydata = ydata[::-1]    else:        pass    return cxdata, cydata#########################　plot######################### 'Corrected'def plot_pw_ax(xdata, ydata,               m_xdata=None, m_ydata=None, m_label='Revision',                bps_list=None, title=None, axi=None):        if axi is None:        fig_ = plt.figure()        ax_ = fig_.add_subplot(111)    else:        ax_ = axi        if title is not None:                      ax_.set_title(f'{title}')                ax_.plot(xdata, ydata,'ro',label='Data')        y_pos = plot_txt_pos(ydata, factor=0.2)    y_pos_fin = plot_txt_pos(ydata, factor=0.4)        if  m_ydata is not None:        ax_.plot(m_xdata, m_ydata,'b^-', label=m_label)        # if bps_list is not None:    #     for i, bp in enumerate(bps_list):    #         ax_.axvline(bp)    #         ax_.text(bp, y_pos, f'BP{i+1}\n{bp:.1f}')            # ax_.text(bp, y_pos*(np.mod(i,2)+1), f'BP{i+1}\n{bp:.1f}')            # ax_.text(bp, y_pos*(i+1), f'BP{i+1}')                if bps_list is not None:        if bps_list[0] != np.min(xdata):            ext_bps_lists =  _ext_bps(xdata,bps_list)            else:            ext_bps_lists = bps_list                    for i, bp in enumerate(ext_bps_lists):            if bp == np.max(xdata):                ax_.axvline(bp)                # ax_.text(bp, y_pos_fin, f'BP{i}\n{bp:.1f}',ha='right')                # ax_.text(bp, y_pos_fin, f'BP{i}:{bp:.1f}',ha='right')                ax_.text(bp, y_pos_fin, f'BP{i}',ha='right')            else:                   ax_.axvline(bp)                # ax_.text(bp, y_pos, f'BP{i}\n{bp:.1f}',ha='left')                ax_.text(bp, y_pos, f'BP{i}',ha='left')            # ax_.text(bp, y_pos*(np.mod(i,2)+1), f'BP{i+1}\n{bp:.1f}')            # ax_.text(bp, y_pos*(i+1), f'BP{i+1}')            ax_.grid()    ax_.legend(loc='upper left')             if axi is None:        plt.show()        return         return ax_    def plot_txt_pos(ydata, factor=0.1):    """    Calculates the y-position for text placement in a plot.    Args:        ydata (numpy.ndarray): The y-data array.        factor (float, optional): The factor that determines the offset from            the minimum y-value. Defaults to 0.1.    Returns:        float: The y-position for text placement.    """    ymax = np.max(ydata)    ymin = np.min(ydata)    yrange = np.abs(ymax - ymin)    y_pos = ymin + yrange * factor    return y_posdef draw_pw(xdata, para_dict, plot=True):        bps_list , _ , _ , _ =  _est_fit_divid(para_dict)    yy_predicted = para_dict['const'] + para_dict['alpha1'] *xdata    for bp_count in range(len(bps_list)):        beta_n = f'beta{bp_count+1}'        breakpoint_n = f'breakpoint{bp_count+1}'        yy_predicted += para_dict[beta_n] * \            np.maximum(xdata - para_dict[breakpoint_n], 0)        if plot:        _ = plot_pw_ax(xdata, yy_predicted,                       bps_list=bps_list,                        title='PW plot')            return yy_predicteddef draw_pw_fit_list(xdata, ydata, fit_ext_dict, plot=True):        bps_list = fit_ext_dict['bps']    alphas_list = fit_ext_dict['alphas']    betas_list = fit_ext_dict['betas']    const_list = fit_ext_dict['const']    ext_bps_list = fit_ext_dict['ext_bps']        yy_predicted = const_list[0] + alphas_list[0] *xdata    for i in range(len(bps_list)):            yy_predicted += betas_list[i] * \                np.maximum(xdata - bps_list[i], 0)       if plot:        _ = plot_pw_ax(xdata, yy_predicted,                       bps_list=bps_list,                        title='PW plot')                return yy_predicted    #########################　alphaの判定 Peak######################### ----- # alpha negative and peak estimationdef find_peak(alphas_list, info=False):    """peakがあるかないかを見つける    前後の傾きを評価して後ろの傾きが小さければ、前までの傾きまでとする。        Args:        alphas (list): alpha list    Returns:         indx list        if no index: list is empty             Examples:        data = [-1,0,1,5,1,3,5,2,3,3,1]        find_peak(data)            Index 4 has a smaller value than index 3            Index 7 has a smaller value than index 6            Index 10 has a smaller value than index 9        >>> [3,6,9]    """    peak_max = 0    peak_max_ind = []    for i in range(len(alphas_list)-1):        if alphas_list[i+1] < alphas_list[i]:            if info:                print(f"Index {i+1} has a smaller value than index {i}")            peak_max = i            peak_max_ind.append(peak_max)                    peak_max = i+1            # indは0から        return peak_max_inddef find_negative(alphas_list, neg_level=-0.1, info=False):    """マイナスの傾きがあるかないかを見つける        Args:        alphas (list): alpha list        neg_level (float): Threshold of negative value    urns:         indx list        if no index: list is empty             Examples:        data2 = [-1,0,1,2,-3,4,5,6]        print(find_negative(data2,True))            Index 0 has a negative value            Index 4 has a negative value            [0, 4]    """    negative_ind_flag = 0    negative_ind_list =[]    for i in range(len(alphas_list)-1):         # Todo ノイズが乗るとBGレベルでわずかに傾きがマイナスになることがある         # グラフのスケール（最大値）の3桁ほど小さい値        if alphas_list[i] < neg_level:             if info:                print(f"Index {i} has a negative value")            negative_ind_flag = i            negative_ind_list.append( negative_ind_flag)                   negative_ind_flag = i+1            # indは0から        return negative_ind_list# Peakと負の値の判定　alphaのインデックスを返すdef evaluation_alpha_peak(bps_list, alphas_list, info=True):    """    傾きが負の値、傾きが鈍化するIndexを求める        Args:                info (bool, optional): print infomation. Defaults to True.        plot (bool, optional): plot. Defaults to False.    Returns:        dict:{'rx':rx_data, 'ry':ry_data, 'peak_ind':peak_ind, 'x_ind':x_ind}    """        max_len = len(alphas_list)    peak_ind_list = find_peak(alphas_list)    if peak_ind_list == []:        end_ind = max_len-1    else:        end_ind = peak_ind_list[0]                   negative_ind_list = find_negative(alphas_list)    if negative_ind_list == []:        start_ind = 0    else:        start_ind = negative_ind_list[0] + 1        # Note: BP=4(Bp1,Bp2,Bp3,Bp4)      #       alphas=5(a1,a2...a5),    #       extended BP=6 (Bp0...Bp5)    # if negative index = 0 -> start index = 1     #   peak index = 3 -> end index = 3    # index --> alphas_list        #20240126追記    if start_ind > end_ind:        start_ind = 0        if info:        inds= {'alpha_ind':[start_ind, end_ind], 'ext_bp_name':[start_ind,end_ind+1]}        print(f'bps:{bps_list}')        print(f'alphas:{alphas_list}')        pprint(inds,sort_dicts=False)        return {'alpha_ind':[start_ind, end_ind], 'ext_bp_name':[start_ind,end_ind+1]}    #########################　alphaの判定 傾きのバックグラウンドを削除する指標と評価   ########################    # background estimation limit of alpha    def normalize_array(arr):    """    配列を最大値と最小値で規格化します。    Args:        arr (numpy.ndarray): 規格化する配列。    Returns:        numpy.ndarray: 規格化された配列。            Examples:        arr = np.array([1, 2, 3, 4, 5])        print(normalize_array(arr))        >>> [0.   0.25 0.5  0.75 1.  ]    """    # 最大値と最小値の取得    min_val = np.min(arr)    max_val = np.max(arr)    range_val = max_val - min_val    # 規格化    normalized_arr = (arr - min_val) / range_val    normalized_factor = normalized_arr*range_val + min_val    return {'nor':normalized_arr,'nor_range':range_val, 'nor2origin':normalized_factor}def minmax_slope(xarr,yarr):    norm_slope = (np.max(yarr)-np.min(yarr))/(np.max(xarr)-np.min(xarr))    slice = yarr[0]-norm_slope*xarr[0]    ya = xarr*norm_slope + slice        mean_ = np.mean(yarr)    median_= np.median(yarr)    ratio_ = median_/mean_    return {'slope':norm_slope, 'slice':slice, 'line_func':ya, 'ratio':ratio_}def gini_MAR(xdata, ydata, info=False, plot=False):    xx = xdata.copy()    yy = ydata.copy()        slope_para = minmax_slope(xx,yy)            nor_x = normalize_array(xx)    nor_y = normalize_array(yy)        #  gini    compS = integrate.simps(nor_x['nor'],nor_x['nor'])    lorentzS =  integrate.simps(nor_y['nor'],nor_x['nor'])    h_gini = compS - lorentzS    gini_ = 2*h_gini        # median/Average ratio maratio    mean_ = np.mean(nor_y['nor'])    median_= np.median(nor_y['nor'])    ratio_ = median_/mean_        evaluation_1gini = (1-gini_)*slope_para["slope"]    evaluation_nor_ratio = (ratio_)*slope_para["slope"]    evaluation_ratio = slope_para["ratio"]*slope_para["slope"]    if info:        print('------')        print(f'Triangle:{compS:.2f}, Lorentz:{lorentzS:.2f}, 1/2_gini:{h_gini:.2f}')        print(f'mean:{mean_:.2f}, median:{median_:.2f}, nor MAR:{ratio_:.3f}')        print(f'gini:{gini_:.2f}, 1-gini:{1-gini_:.3f}')        print(f'MAR:{slope_para["ratio"]:.3f}')        print(f'slope:{slope_para["slope"]:.3f}')        print(f'evaluation MAR:{evaluation_ratio:.3f} ')        print(f'evaluation nor MAR:{evaluation_nor_ratio:.3f} ')        print(f'evaluation 1-gini:{evaluation_1gini:.3f}')        print('---end---')            if plot:        fig = plt.figure(figsize=(10,4))        ax1 = fig.add_subplot(121)           ax2 = fig.add_subplot(122)           ax1.set_title(f'Gini-ratio Normlized')            ax1.plot(nor_x['nor'], nor_x['nor'],'ro-',label='Equal line')        ax1.plot(nor_x['nor'], nor_y['nor'],'bo-',label='Data')        # ax.axhline(mean_,color='r',label='mean')        # ax.axhline(median_,color='b',label='medain')        ax1.axhline(ratio_,color='g',label=f'Norm ratio:{ratio_:.2f}')        ax1.axhline(1-gini_,color='c',label=f'1-gini:{1-gini_:.2f}')        ax1.axhline(slope_para["ratio"],color='b',label=f'ratio:{slope_para["ratio"]:.2f}')        # ax1.axvline(np.mean(nor_x['nor']))        ax1.grid()        ax1.legend()                  ax2.set_title(f'Gini-ratio')            ax2.plot(xx, slope_para['line_func'],'ro-',label=f'slope:{slope_para["slope"]:.3f}')        ax2.plot(xx, yy,'bo-',label='Data')        ax2.grid()        ax2.legend()                  plt.show()                           return {'gini':gini_, '1-gini':1-gini_,             'nor_ratio':ratio_, 'nor_mean':mean_, 'nor_median':median_,             'ratio':slope_para["ratio"],            'slope':slope_para['slope'],            'ev_1gini':evaluation_1gini,            'ev_ratio':evaluation_ratio,            'ev_nor_ratio':evaluation_nor_ratio            }    def find_bg_slope(alphas_list, slope_point, level='a',info=True):        """        傾きの評価        基準Slopeの値と比較        最小のIndexを返す        Args:            alphas (_type_): _description_            slope_point (_type_): _description_            region_ind (_type_, optional): _description_. Defaults to None.            level (str, optional): Slope評価をalphaで行うかBetaで行うか。Alpha=a, Beta=b Defaults to 'a'.            Betaを選択した場合、強めにBackgroundを削除することになる。            Betaは前の変化率のとの差分となるので差分が非対称変化率より大きいところが判断基準となる        Returns:            int : slope_ind        """        region_ind = len(alphas_list)        # alphaからbetaを算出　配列の０番目はalpha[0]の値        betas =[alphas_list[0]]        betas[1:] = [alphas_list[j+1] - alphas_list[j]  for j in range(len(alphas_list)-1) ]                slope_ind = 0        if level == 'b':            for i in range(region_ind):                if  betas[i] >= slope_point :                    slope_ind = i-1                    break                slope_ind = i            if slope_ind == -1:                     slope_ind = 0        else:            for i in range(region_ind):                if  alphas_list[i] >= slope_point :                    slope_ind = i-1                    break                slope_ind = i            if slope_ind == -1:                     slope_ind = 0                         if info:            print(f'threshold: {slope_point:.3f}')            # print(f'alphas: {alphas_list}')            print(f'alphas:{list(map(round, alphas_list, [2]*len(alphas_list)))}')            print(f'slope index: {slope_ind}')                            return slope_inddef bp_evaluation(ext_bps_list, info=True):    # Bpの間隔から判断する方法        ext_bps_array = np.array(ext_bps_list)    temp = np.diff(ext_bps_array)/(ext_bps_array[-1]-ext_bps_array[0])    ext_bps_len = len(temp)    data = temp.reshape(-1, 1)    kmeans = KMeans(n_clusters=2, random_state=0).fit(data)            # for i in range(ext_bps_len):    #     if i+1 >= ext_bps_len:    #         break    #     if kmeans.labels_[i]!=kmeans.labels_[i+1]:    #         border_bp_ind = i    #         # breakを入れると一番最初に変化した場所、いれないと一番最後の場所    #         # 今回は一番最後の変化の場所        # 231206 要検討    k_label_ext = kmeans.labels_.copy()    k_label_ext = np.append(k_label_ext,[kmeans.labels_[-1],kmeans.labels_[-1]])        # Labelsが変化している先の2つまでを見る    # [0, 1, 1, 0, 1]--->変化している境界0    # [0, 1, 0, 0, 1]--->変化している境界1    # [0, 1, 0 ,1, 1]--->変化している境界2        for i in range(ext_bps_len):        if i+1 >= ext_bps_len:            break        if k_label_ext[i] != k_label_ext[i+1] and k_label_ext[i+1] == k_label_ext[i+2]:            border_bp_ind = i            break                    if info:        # print(f'Bp list:{list(map(round, ext_bps_list, [2]*len(ext_bps_list)))}')        print(f'Bp list:{np.round(ext_bps_array,2).tolist()}')        print(f'Bp diff list:{np.round(temp,2).tolist()}')        print(f'kmeans label:{kmeans.labels_}')        print(f'border index:{border_bp_ind}')                   return border_bp_inddef evaluation_alpha_slope(xdata, ydata, fit_ext_dict,                            info=True, plot=False):            bps_list = fit_ext_dict['bps']    alphas_list = fit_ext_dict['alphas']    betas_list = fit_ext_dict['betas']    const_list = fit_ext_dict['const']    ext_bps_list = fit_ext_dict['ext_bps']    d_gini_mar = gini_MAR(xdata, ydata, info=info, plot=plot)    # {'gini':gini_, '1-gini':1-gini_,     # 'ratio':ratio_, 'mean':mean_, 'median':median_,     # 'slope':slope_para['slope'],    # 'ev_1gini':evaluation_1gini,'ev_ratio':evaluation_ratio}           slope_d_gini_ind = find_bg_slope(alphas_list, slope_point=d_gini_mar['ev_1gini'], level='a',info=info)    slope_MAR_ind = find_bg_slope(alphas_list, slope_point=d_gini_mar['ev_ratio'], level='a',info=info)    slope_nor_MAR_ind = find_bg_slope(alphas_list, slope_point=d_gini_mar['ev_nor_ratio'], level='a',info=info)    slope_bp_ind = bp_evaluation(ext_bps_list, info=info)    slope_ensemble_list = [slope_MAR_ind,slope_d_gini_ind,slope_bp_ind]    slope_ensemble_ind = alpha_ensemble(slope_ensemble_list,info=info)        # ext_bp_nameにするためには、index+1をする必要がある-->ex. MAR=0 -> ext_bp_MAR=0+1    sum_dict = {'d_gini':slope_d_gini_ind, 'MAR':slope_MAR_ind,             'nor_MAR':slope_nor_MAR_ind, 'bp':slope_bp_ind,            'ensemble':slope_ensemble_ind,            'ex_bp_d_gini':slope_d_gini_ind + 1,            'ex_bp_MAR':slope_MAR_ind + 1,            'ex_bp_nor_MAR':slope_nor_MAR_ind + 1,            'ex_bp_bp':slope_bp_ind + 1,            'ex_bp_ens':slope_ensemble_ind + 1}        if info:        pprint(sum_dict,sort_dicts=False)    # ext_bp_nameにするためには、index+1をする必要がある-->ex. MAR=0 -> ext_bp_MAR=0+1    return sum_dict    def alpha_ensemble(ind_list,info=True):        c = collections.Counter(ind_list)    if  int(c.most_common()[0][1]) == 1:        end_level = int(np.median(np.array(ind_list)))    else:            end_level = int(c.most_common()[0][0])    if info:        print(c)        print(f'ensamble select : {end_level}')        return end_level                      JSA_PW論文コード/reader/datconv.py"""AC Series Data Converter"""import csvimport jsonfrom pathlib import Pathimport numpy as npimport pandas as pddef getEncode(filepath):    """Character code automatic judgment function     If the file name contains Japanese, the encoding method may be Shift-jis.     Function that returns the encoding method.    Args:        filepath (str): fil path and name    Returns:        encode type(str): encode type    """    encs = "iso-2022-jp euc-jp shift_jis utf-8".split()    for enc in encs:        with open(filepath, encoding=enc) as fr:            try:                fr = fr.read()            except UnicodeDecodeError:                continue        return encclass AcConv():    """    Extraction of measurement metadata and measurement data from the AC.dat file.        args:        file_name (str): .dat filename    Example :         file_name = r'./datafile.dat'        acdata = AcConv(file_name)        acdata.convert                print('metadata')        print(acdata.metadata)                print('metadata json-type')        print(acdata.json)           print('pys data by dataframe type')        ##### "uvEnergy", "countingCorrection", "photonCorrection", "yield", "nyield",         #####  "guideline", "countingRate","flGrandLevel","flRegLevel","uvIntensity"        print(acdata.df)                print("user analysis values")        print(acdata.estimate_value)                print('export dataframe data to csv')        acdata.export_df2csv()            """    def __init__(self,file_name):        self.file_name = Path(file_name)            def convert(self,json_export=False):        """        Attribution                - original metadata keys                "fileType","deadTime","countingTime","powerNumber","anodeVoltage",        "step","model","yAxisMaximum","startEnergy",        "finishEnergy","flagDifDataGroundLevel","bgCountingRate",        "measureDate","sampleName",        "uvIntensity59","targetUv","nameLightCorrection","sensitivity1","sensitivity2"        - original data coloum keys                "uvEnergy", "countingRate", "flGrandLevel", "flRegLevel","uvIntensity"         - file optional keys                "countingCorrection", "photonCorrection", "pyield", "npyield","nayield", "guideline"                "estimate_value" include ('thresholdEnergy', 'slope', 'yslice', 'bg')                        """        _ = self._read_para()        self.countingCorrection = self._count_calibration()        self.photonCorrection = self._photon_calibration()        self.ydata, self.npyield = self._pyield_intensity()        _ = self.user_estimation()                meta_keys = ["fileType","deadTime","countingTime","powerNumber",                      "anodeVoltage","step","model","yAxisMaximum","startEnergy",                      "finishEnergy","flagDifDataGroundLevel","bgCountingRate","measureDate","sampleName",                      "uvIntensity59","targetUv","nameLightCorrection","sensitivity1","sensitivity2"]                meta_values = [self.fileType, self.deadTime, self.countingTime, self.powerNumber,                      self.anodeVoltage, self.step,self.model, self.yAxisMaximum, self.startEnergy,                      self.finishEnergy,self.flagDifDataGroundLevel, self.bgCountingRate,self.measureDate,self.sampleName,                      self.uvIntensity59,self.targetUv, self.nameLightCorrection, self.sensitivity1,self.sensitivity2]                calc_data_keys = ["uvEnergy", "countingCorrection", "photonCorrection", "pyield", "npyield","nayield", "guideline",                         "countingRate","flGrandLevel","flRegLevel","uvIntensity"]                calc_data_values = [self.uvEnergy, self.countingCorrection, self.photonCorrection,self.ydata, self.npyield, self.nayield, self.guideline,                            self.countingRate,self.flGrandLevel,self.flRegLevel,self.uvIntensity]        calc_data_values_list = [d.tolist() for d in calc_data_values]                file_meta ={'file_name':self.file_name.name}                self.metadata = dict(zip(meta_keys + calc_data_keys, meta_values + calc_data_values_list))         self.metadata.update(self.estimate_value)        self.metadata.update(file_meta)                self.calcdata = dict(zip(calc_data_keys,calc_data_values))                # self.metadata_wo_calc means that the calculated data, which is array data, is not included.        self.metadata_wo_calc = dict(zip(meta_keys,meta_values))         self.metadata_wo_calc.update(self.estimate_value)        self.metadata_wo_calc.update(file_meta)                        self.json = self._json_out(self.metadata)        self.df = self._df_out(self.calcdata)            def _read_para(self):        # read parameters up to the third line        enc = getEncode(str(self.file_name))        with open(str(self.file_name),encoding=enc) as f:            reader = csv.reader(f)            meta = [row for row in reader]        # Match the parameter list to the full parameter list.        # this case is AC-5 old dat type        if len(meta[0]) == 10:                meta[0].extend(['0','0.0'])            meta[2].extend(['1','1'])                    else:            pass                self.fileType = meta[0][0]        self.deadTime = float(meta[0][1])        self.countingTime = float(meta[0][2])        self.powerNumber = float(meta[0][3])        self.anodeVoltage = float(meta[0][4])        self.step = float(meta[0][5])        self.model = meta[0][6]        self.yAxisMaximum = float(meta[0][7])        self.startEnergy = float(meta[0][8])        self.finishEnergy = float(meta[0][9])        self.flagDifDataGroundLevel = int(meta[0][10])        self.bgCountingRate = float(meta[0][11])        self.measureDate = meta[1][0]        self.sampleName = meta[1][1]        self.uvIntensity59 = float(meta[2][0])        self.targetUv = float(meta[2][1])        self.nameLightCorrection = meta[2][2]        self.sensitivity1 = float(meta[2][3])        self.sensitivity2 = float(meta[2][4])                # Transpose row to col        raw_data = [list(x) for x in zip(*meta[3:])]        self.uvEnergy = np.array([float(v) for v in raw_data[0]])        self.countingRate = np.array([float(v) for v in raw_data[1]])        self.flGrandLevel = np.array([int(v) for v in raw_data[2]])        self.flRegLevel = np.array([int(v) for v in raw_data[3]])        self.uvIntensity = np.array([float(v) for v in raw_data[4]])                  def _count_calibration(self):        """        count_calibration        "AC-3" and "AC-2" data do not require count calibration.        """        # The "AC-3" and "AC-2" counts do not need to be calibrated.        if self.model == 'AC-3' or self.model == 'AC-2':            # print('AC-2,AC-3')            self.countingCorrection = self.countingRate                    else:            part1 = (self.countingRate)/(1-self.deadTime*(self.countingRate))            part2 = np.exp(0.13571/(1-0.0028*(self.countingRate)))*self.sensitivity1            part3 = (self.bgCountingRate)/(1-self.deadTime*(self.bgCountingRate))            part4 = np.exp(0.13571/(1-0.0028*(self.bgCountingRate)))*self.sensitivity1            self.countingCorrection = part1*part2-part3*part4                  return self.countingCorrection    def _photon_calibration(self):                # number of Photons        self.nPhoton = 0.625*(self.uvIntensity/self.uvEnergy)        # number of photons per unit         self.unitPhoton = (self.uvIntensity59*0.625)/5.9        self.photonCorrection = self.nPhoton/self.unitPhoton        return self.photonCorrection    def _pyield_intensity(self):        self.ydata = self.countingCorrection/self.photonCorrection        self.npyield = np.power(self.ydata, self.powerNumber)                # replace Nan to 0        self.ydata = np.where(self.ydata < 0, 0, self.ydata)        # replace Nan to 0        self.npyield[np.isnan(self.npyield)] = 0        return self.ydata, self.npyield            @staticmethod    @np.vectorize    def relu(xdata, a, b, bg):        ip = (bg - b)/a        u = (xdata - ip)        return a * u * (u > 0.0) + bg        @staticmethod    def user_fit(bg_ydata, reg_xdata, reg_ydata, printf=False):                bg = np.nanmean(bg_ydata)        popt = np.polyfit(reg_xdata, reg_ydata, 1)        a = popt[0]        b = popt[1]                cross_point =  (bg - b) / a        if printf:            print(f"bg:{bg}, a(slope):{a}, b(yslice):{b}")            print(f"thresholdEnergy -> {cross_point}" )                    return {'thresholdEnergy': cross_point, 'slope': a,'yslice': b, 'bg':bg}            def user_estimation(self):        """ user analized  threshold value        Returns:            dict[float]: 'thresholdEnergy', 'slope','yslice','bg'        """        # self.df_data =  self.convert_df()        self.bg_flag_ind = np.where(self.flGrandLevel == -1)[0].tolist()        self.reg_flag_ind = np.where(self.flRegLevel == -1)[0].tolist()        # print( bg_flag_ind, reg_flag_ind)                if  self.bg_flag_ind != [] and self.reg_flag_ind != []:            # back ground difference caribration            if self.flagDifDataGroundLevel == -1:                # average                bg_ave = np.nanmean(self.ydata[self.bg_flag_ind])                c_pys = self.ydata - bg_ave                self.cc_pys = np.where(c_pys < 0, 0, c_pys)                self.cc_npys = np.power(self.cc_pys, self.powerNumber)                                # bg_xdata = self.uvEnergy[bg_flag_ind]                bg_ydata = np.array([0.0]*len(self.bg_flag_ind))                reg_xdata = self.uvEnergy[self.reg_flag_ind]                reg_ydata = self.cc_npys[self.reg_flag_ind]                # print(cc_pys)                        #  elif self.flagDifDataGroundLevel == 0:              else:                   # bg_xdata = self.uvEnergy[bg_flag_ind]                                self.cc_pys = self.ydata                self.cc_npys = np.power(self.cc_pys, self.powerNumber)                reg_xdata = self.uvEnergy[self.reg_flag_ind]                reg_ydata = self.cc_npys[self.reg_flag_ind]                bg_ydata = self.cc_npys[self.bg_flag_ind]                            self.estimate_value = AcConv.user_fit(bg_ydata, reg_xdata, reg_ydata, printf=False)             self.nayield = self.cc_npys            self.guideline = AcConv.relu(xdata=self.uvEnergy, a=self.estimate_value['slope'],                                         b=self.estimate_value['yslice'],                                         bg=self.estimate_value['bg'])                else:            self.estimate_value =  {'thresholdEnergy': np.nan, 'slope': np.nan,'yslice':np. nan,'bg':np.nan}            self.nayield = self.npyield            self.guideline = np.array([np.nan]*len(self.uvEnergy.tolist()))                    def _json_out(self, dict_metadata,export=False):                if export:            if jsonfile_name is None:                jsonfile_name =self.file_name.with_suffix('.json')            with open(jsonfile_name, 'w') as f:                json.dump(dict_metadata, f, indent=4)                json_meta = json.dumps(dict_metadata)                return json_meta            def _df_out(self,dict_data):                df_temp = pd.DataFrame(dict_data)            return df_temp          def export_df2csv(self,df_out_file_name=None):        """export Dataframe data to csv        Args:            df_out_file_name (str, optional):output filename. Defaults to None.        """        if df_out_file_name is None:            df_out_file_name =self.file_name.with_suffix('.csv')                self.df.to_csv(df_out_file_name, index=False)        def export_json(self,json_out_file_name=None):        """export json        Args:            json_out_file_name (str, optional):output filename. Defaults to None.        """        if json_out_file_name is None:            json_out_file_name =self.file_name.with_suffix('.json')        dict_json = json.loads(self.json)        with open(json_out_file_name, 'w') as f:            json.dump(dict_json, f, indent=4)class AdvAcConv(AcConv):        def _xy_convert(self):        # Overflowのデータは0になっている。        def find_first_positive_index(arr):            """後ろから探索して最初の正の数のインデックスを取得            Args:                arr (ndarray): ndarray            Returns:                int: index                            # テスト用の配列            array = np.array([-3, -2, 0, -1, 4, 5, -6])            print(len(array))            index = find_first_positive_index(array)            if index != -1:                print("最初の正の数はインデックス", index, "にあります。")            else:                print("正の数は見つかりませんでした。")                        >>>最初の正の数はインデックス 5 にあります。                        """            for i in range(len(arr)-1, -1, -1):                if arr[i] > 0:                    return i            return -1  # 正の数が見つからなかった場合                index = find_first_positive_index(self.ydata)        # print(index)        if index != len(self.ydata)-1:            self.xdata = self.xdata[:index]            self.ydata = self.ydata[:index]        # もう一度繰り返す        index = find_first_positive_index(self.ydata)        # print(index)        if index != len(self.ydata)-1:            self.xdata = self.xdata[:index]            self.ydata = self.ydata[:index]        def flag_index(self):        self.bg_flag_ind = np.where(self.flGrandLevel == -1)[0].tolist()        self.reg_flag_ind = np.where(self.flRegLevel == -1)[0].tolist()        # print( bg_flag_ind, reg_flag_ind)                # if  bg_flag_ind != [] and reg_flag_ind != []:        return self.bg_flag_ind, self.reg_flag_ind        def user_estimation_23(self, powerN):        """ user analized  threshold value                args:            powerN (float): 1/n  example: 1/2        Returns:            dict[float]: 'thresholdEnergy', 'slope','yslice','bg'        """        # self.df_data =  self.convert_df()        bg_flag_ind = np.where(self.flGrandLevel == -1)[0].tolist()        reg_flag_ind = np.where(self.flRegLevel == -1)[0].tolist()        # print( bg_flag_ind, reg_flag_ind)                if  bg_flag_ind != [] and reg_flag_ind != []:            # back ground difference caribration            if self.flagDifDataGroundLevel == -1:                # average                bg_ave = np.nanmean(self.ydata[bg_flag_ind])                c_pys = self.ydata - bg_ave                self.cc_pys = np.where(c_pys < 0, 0, c_pys)                self.cc_npys = np.power(self.cc_pys, powerN)                                # bg_xdata = self.uvEnergy[bg_flag_ind]                bg_ydata = np.array([0.0]*len(bg_flag_ind))                reg_xdata = self.uvEnergy[reg_flag_ind]                reg_ydata = self.cc_npys[reg_flag_ind]                # print(cc_pys)                        #  elif self.flagDifDataGroundLevel == 0:              else:                   # bg_xdata = self.uvEnergy[bg_flag_ind]                                self.cc_pys = self.ydata                self.cc_npys = np.power(self.cc_pys, powerN)                reg_xdata = self.uvEnergy[reg_flag_ind]                reg_ydata = self.cc_npys[reg_flag_ind]                bg_ydata = self.cc_npys[bg_flag_ind]                            estimate_value = AcConv.user_fit(bg_ydata, reg_xdata, reg_ydata, printf=False)             nayield = self.cc_npys            guideline = AcConv.relu(xdata=self.uvEnergy, a=estimate_value['slope'],                                         b=estimate_value['yslice'],                                         bg=estimate_value['bg'])                else:            estimate_value =  {'thresholdEnergy': np.nan, 'slope': np.nan,'yslice':np. nan,'bg':np.nan}            nayield = self.npyield            guideline = np.array([np.nan]*len(self.uvEnergy.tolist()))              return {'est_val':estimate_value, 'rex':self.uvEnergy, 'rey':nayield, 'fit':guideline}                       if __name__ =='__main__':    pass    # fp=r'////.dat'    # fpdata = AcConv(fp)     # fpdata.convert()    # print(fpdata.df)    # print(fpdata.json)    # print(fpdata.metadata)   JSA_PW論文コード/reader/SuperConlib2.py"""Refactering the program created by Dr. MatsunamiSuperConB -> read and analysis of Dr. Banno's measurement filesSuperConM -> read and analysis of Dr. Matsumoto's measurement files (inherited from SuperConB)"""import datetimefrom pathlib import Pathimport reimport pandas as pd# import scipy as sp# from scipy.optimize import leastsqimport numpy as npfrom natsort import natsortedimport matplotlib.pyplot as pltimport warningswarnings.simplefilter('ignore')# warnings.resetwarnings()# warnings.simplefilter('ignore', FutureWarning)# %load_ext autoreload# %autoreload 2def now_datetime(type=1):    """    日時を文字列で返す    type1:通常表示 "%Y-%m-%d %H:%M:%S"    type2:"%Y%m%d%H%M%S"    type3:"%Y%m%d_%H%M%S"    type4:Base_file_nameで使用する形式 "%Y%m%d%H%M"    elae:日付のみ "%Y%m%d"    :return:    """    now = datetime.datetime.now()    if type == 1:        now_string = now.strftime("%Y-%m-%d %H:%M:%S")    elif type == 2:        now_string = now.strftime("%Y%m%d%H%M%S")    elif type == 3:        now_string = now.strftime("%Y%m%d_%H%M%S")    elif type == 4:        now_string = now.strftime("%Y%m%d%H%M")    elif type == 5:        now_string = now.strftime("%m%d_%H:%M:%S")    elif type == 6:        now_string = now.strftime("%Y%m%d")        else:        now_string = now    return  now_stringdef getNearestValue(list, num):    """    概要: リストからある値に最も近い値を返却する関数    @param list: データ配列    @param num: 対象値    @return 対象値に最も近い値    """    # リスト要素と対象値の差分を計算し最小値のインデックスを取得    idx = np.abs(np.asarray(list) - num).argmin()        return idxplt.rcParams["font.size"] = 14       class SuperConB():    """    Example:        tmp_ins = scl.SuperConB(fi)        tmp_ins.read_file()         tmp_ins.estimate_n_j0()        tmp_ins.make_plots()        n = tmp_ins.meta["slope"]        j0 = tmp_ins.meta["j0"]    """        def __init__(self,file):        self.file = Path(file)            def read_file(self):                try:            file_name = self.file.stem            #ファイル名から磁場強度の読み出し            pattern = "\d+T"                        field = re.search(pattern,file_name).group(0)            self.magnetic_field = int(field.split('T')[0])                    except:            self.magnetic_field = 0                #端子間距離は1cmと固定        VV_length = 1        ave = 0                #数値部の取り出し        self.df = pd.read_csv(self.file, sep='\t',names =['current','voltage'], header=1)                #データ削除（熱起電力カット）        self.df = self.df[:-5]        self.df = self.df.query('current > 0').reset_index(inplace=False, drop=True)        self.df = self.df.query('index > 20')                #ベースライン・オフセット補正        ave = self.df.query('20< index < 30').mean()           self.df['voltage'] = self.df['voltage']-ave['voltage']                #電界強度の追加            self.df['Electric_field_strength'] = self.df['voltage']/float(VV_length)        # return self.df           def estimate_n_j0(self):        #Estimate N        # 電圧領域として1～10V/cm範囲での電流電圧特性からn値を算出        index_min = getNearestValue(self.df['voltage'], 1)        index_max = getNearestValue(self.df['voltage'], 10)        Ic = np.array(self.df['current'][index_min:index_max]).reshape(-1)        Vc = np.array(self.df['voltage'][index_min:index_max]).reshape(-1)                        # print(Ic,Vc)        x = np.log(Ic)        y = np.log(Vc)                # もしマイナスの値があった時にはnanが入ってしまうのでそれを避ける必要がある。        #　np.isfinite-> Test element-wise for finiteness (not infinity and not Not a Number).        idx = np.isfinite(x) & np.isfinite(y)        try:            # 直線Fitting            # fit_y = a * x + b            a, b = np.polyfit(x[idx], y[idx], 1)        except:            print('Fitting error')            print(f'Ic:{Ic}\nVc:{Vc}')             print(f'slope= None, slice = None')            a, b = np.nan, np.nan                # Estimate J0        # 臨界電流値の取得        index = getNearestValue(self.df['Electric_field_strength'], 1)        j0 = self.df['current'][index]               jc = (1-(1/a))*j0                self.analysis_region ={'Ic':Ic,'Vc':Vc}        self.meta = {'slope':a,'slice':b,'j0':j0,'jc':jc,'magnetic_filed':self.magnetic_field}               def make_plots(self,title='$J_{0}$, n plot', save_path=None, save=False):               nrows, ncols = 1, 2        fig, ax = plt.subplots(nrows=nrows, ncols=ncols,                                figsize=(ncols*6,nrows*5),                                squeeze=False, tight_layout=True)        n = self.meta['slope']        j0 = self.meta['j0']        jc = j0*(1-(1/n))                X = self.df['current'].values        Y = self.df['voltage'].values        select_X = self.analysis_region['Ic']        select_Y = self.analysis_region['Vc']         ax[0,0].plot(X,Y,'ro',label='Data')        ax[0,0].plot(select_X,select_Y,'b-',label='Fitting Region')               ax[0,0].set_xlabel('Current [A]')        ax[0,0].set_ylabel('Voltage [uV]')        ax[0,0].tick_params(direction = "inout")        ax[0,0].set_title("$J_{0}$")        ax[0,0].grid(which = "major", axis = "both",                     color = "black", alpha = 0.8,linestyle = "--", linewidth = 0.3)        ax[0,0].axhline(y=1,color='g')        ax[0,0].text(np.min(X),1+np.max(Y)*0.1, "$J_{0}$")        # ax[0,0].axhline(y=np.min(select_Y), xmin=np.median(X), xmax=np.max(X))        # ax[0,0].axhline(y=np.max(select_Y), xmin=np.median(X), xmax=np.max(X),color='g')        # ax[0,0].text(np.median(X),np.max(select_Y)*0.9, 'n estimate range')        ax[0,0].legend(title=f"$J_0$ = {self.meta['j0']:.2f}\n$J_c=(1-1/n) \cdot J_0$ ={jc:.2f}\nFiled = {self.meta['magnetic_filed']}T")                ax[0,1].plot(np.log(X),np.log(Y),'ro',label='Data')        # ax[0,1].plot(np.log(select_X),np.log(select_Y),'ro',label='Data')                if self.meta['slope'] == np.nan:            fit_line = 0        else:               fit_line = self.meta['slope']*np.log(select_X) + self.meta['slice']                ax[0,1].plot(np.log(select_X), fit_line,'b-',label='Fitting Region')        ax[0,1].legend(title=f"n = {self.meta['slope']:.2f}\nFiled = {self.meta['magnetic_filed']}T")        ax[0,1].set_xlabel('ln(Current) [A]')        ax[0,1].set_ylabel('ln(Voltage) [uV]')        ax[0,1].tick_params(direction = "inout", which = "both")        ax[0,1].set_title(f"n")        ax[0,1].grid(which = "major", axis = "x", color = "black", alpha = 0.8,linestyle = "--", linewidth = 0.3)        ax[0,1].grid(which = "major", axis = "y", color = "black", alpha = 0.8,linestyle = "--", linewidth = 0.3)        # ax[0,1].set_xlim(np.log(X[1]),np.log(X[-1]))        # ax[0,1].set_xlim(np.log(Y[1]),np.log(Y[-1]))                  fig.suptitle(f'{self.file.stem}')        # fig.suptitle(f'{self.file.stem}\n{title}')        plt.tight_layout()                if save:            if save_path is None:                s_name = f'{self.file.stem}.png'            else:                s_path =Path(save_path)                s_name = str(s_path/f'{self.file.stem}.png')                # print(s_name)            plt.savefig(s_name)            plt.close()                    else:            plt.show()            class SuperConM(SuperConB):        def read_file(self):                # ファイルの読み込みとヘッダー部の読み出し        df_header = pd.read_csv(self.file, header=None,nrows=5, delimiter=';')                 #　端子間距離の取得        #sample = df_header[1][0]        VV_length = float(df_header[1][1])        self.magnetic_field = round(float(df_header[1][2]))        #temperature = float(df_header[1][3])        #angle = df_header[1][4]        #　数値部の取り出し        self.df = pd.read_csv(self.file,                    header= 6,                     delim_whitespace=True,                     names=('time', 'current', 'voltage', 'Temp.1', 'Temp.2', 'Temp.3'))                #　掃引速度の計算        self.sweep_time = self.df.iloc[-1][1]/self.df.iloc[-1][0]                #　電界強度の追加            self.df['Electric_field_strength'] = self.df['voltage']/float(VV_length)                # Currentがマイナスの領域のIndexを見つける。マイナス領域削除        rem_max_ind = np.where(self.df['current']< 0)[0]        # self.df.drop(self.df.index[rem_max_ind],inplace=True)        self.df = self.df.drop(self.df.index[rem_max_ind])                # 231205 indexを振り直す処理を追加        self.df.reset_index(inplace=True, drop=True)                # リーク電流の削除　（V値の差分で10以上）        for i in range(len(self.df)):            try:                j = self.df['voltage'][i+1]-self.df['voltage'][i]                        if j > 10:                    print (i,j)                    self.df = self.df.drop(index= i+1)            except:                break    #         def estimate_n_j0(xi,yv):    # VI測定からISOに従ったn値と臨界電流値を求める    #Estimate N    # 電圧領域として1～10V/cm範囲での電流電圧特性からn値を算出    index_min = getNearestValue(yv, 1)    index_max = getNearestValue(yv, 10)    Ic = np.array(xi[index_min:index_max]).reshape(-1)    Vc = np.array(yv[index_min:index_max]).reshape(-1)            # print(Ic,Vc)    x = np.log(Ic)    y = np.log(Vc)        # もしマイナスの値があった時にはnanが入ってしまうのでそれを避ける必要がある。    #　np.isfinite-> Test element-wise for finiteness (not infinity and not Not a Number).    idx = np.isfinite(x) & np.isfinite(y)    try:        # 直線Fitting        # fit_y = a * x + b        a, b = np.polyfit(x[idx], y[idx], 1)    except:        print('Fitting error')        print(f'Ic:{Ic}\nVc:{Vc}')         print(f'slope= None, slice = None')        a, b = np.nan, np.nan        # Estimate J0    # 臨界電流値の取得    index = getNearestValue(yv, 1)    j0 = xi[index]        analysis_region ={'Ic':Ic,'Vc':Vc}    meta = {'slope':a,'slice':b,'j0':j0}         return meta, analysis_regiondef make_plots(xdata, ydata, title='$J_{0}$, n plot', save_path=None, save=False):        meta, analysis_region = estimate_n_j0(xdata,ydata)           nrows, ncols = 1, 2    fig, ax = plt.subplots(nrows=nrows, ncols=ncols,                             figsize=(ncols*6,nrows*5),                             squeeze=False, tight_layout=True)    n = meta['slope']    j0 = meta['j0']    jc = j0*(1-(1/n))        X = xdata    Y = ydata    select_X = analysis_region['Ic']    select_Y = analysis_region['Vc']     ax[0,0].plot(X,Y,'ro',label='Data')    ax[0,0].plot(select_X,select_Y,'b-',label='Fitting Region')        ax[0,0].set_xlabel('Current [A]')    ax[0,0].set_ylabel('Voltage [uV]')    ax[0,0].tick_params(direction = "inout")    ax[0,0].set_title("$J_{0}$")    ax[0,0].grid(which = "major", axis = "both",                    color = "black", alpha = 0.8,linestyle = "--", linewidth = 0.3)    ax[0,0].axhline(y=1,color='g')    ax[0,0].text(np.min(X),1+np.max(Y)*0.1, "$J_{0}$")    # ax[0,0].axhline(y=np.min(select_Y), xmin=np.median(X), xmax=np.max(X))    # ax[0,0].axhline(y=np.max(select_Y), xmin=np.median(X), xmax=np.max(X),color='g')    # ax[0,0].text(np.median(X),np.max(select_Y)*0.9, 'n estimate range')    ax[0,0].legend(title=f"$J_0$ = {meta['j0']:.2f}\n$J_c=(1-1/n) \cdot J_0$ ={jc:.2f}")        ax[0,1].plot(np.log(X),np.log(Y),'ro',label='Data')    # ax[0,1].plot(np.log(select_X),np.log(select_Y),'ro',label='Data')        if meta['slope'] == np.nan:        fit_line = 0    else:           fit_line = meta['slope']*np.log(select_X) + meta['slice']        ax[0,1].plot(np.log(select_X), fit_line,'b-',label='Fitting Region')    ax[0,1].legend(title=f"n = {meta['slope']:.2f}")    ax[0,1].set_xlabel('ln(Current) [A]')    ax[0,1].set_ylabel('ln(Voltage) [uV]')    ax[0,1].tick_params(direction = "inout", which = "both")    ax[0,1].set_title(f"n")    ax[0,1].grid(which = "major", axis = "x", color = "black", alpha = 0.8,linestyle = "--", linewidth = 0.3)    ax[0,1].grid(which = "major", axis = "y", color = "black", alpha = 0.8,linestyle = "--", linewidth = 0.3)    # ax[0,1].set_xlim(np.log(X[1]),np.log(X[-1]))    # ax[0,1].set_xlim(np.log(Y[1]),np.log(Y[-1]))            # fig.suptitle(f'{file.stem}')    # fig.suptitle(f'{self.file.stem}\n{title}')    plt.tight_layout()    plt.show()        # if save:    #     if save_path is None:    #         s_name = f'{self.file.stem}.png'    #     else:    #         s_path =Path(save_path)    #         s_name = str(s_path/f'{self.file.stem}.png')    #         # print(s_name)    #     plt.savefig(s_name)    #     plt.close()            # else:    #     plt.show()        if __name__ == " __main__":        pass        # from reader import SuperConlib2 as scl2        # spctest = scl2.SuperConM(r'Datas\s3_2022_12m26d13h02m07_patent.txt')         # spctest.read_file()        # spctest.estimate_n_j0()        # spctest.make_plots()JSA_PW論文コード/reader/__init__.pyJSA_PW論文コード/README.md# JSA_PWJSA PW paper source code### セグメント回帰を用いたべき乗則で解釈されるスペクトルの前処理法JSA 2024セグメント回帰を用いて，べき乗則で解釈されるスペクトルの解析領域を抽出するための自動前処理法について報告する．スペクトルに対してセグメント回帰を適用することで，スペクトルの変化点やその前後の傾きの特徴を得ることができる．これらの特徴を評価・判定することで，解析領域を自動的に抽出することができる．べき乗則で解釈される光電子収量分光スペクトルについて本法を適用した解析例を示す．