import pandas as pd

import numpy as np

import sklearn.preprocessing

import matplotlib.pyplot as plt

from scipy import signal

from scipy.interpolate import spline

import DisplayData

import LoadData

from libs.detect_peaks import detect_peaks

import peakutils.peak

# normtype={"min-max","Z-score"}

def NormData(data, norm_type, ddof=0):

return df_norm

# change one column to enum type

# pvname:col name

# cur_ranges:divide range,[1,5,7]

# right:default Ture,Indicates whether the bins include the rightmost edge or not. If right == True (the default), then the bins [1,2,3,4] indicate (1,2], (2,3], (3,4].

# enumnames:array or boolean, default None. Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, return only integer indicators of the bins.

def EnumData(data, pvname, cut_ranges, right=True, enumnames=None):

return data

# use of boolean vectors to filter the data. The operators are: | for or, & for and, and ~ for not. These must be grouped by using parentheses.

# data[(data['pvname']>1000)&data['pvname']<20]

# fillna_type:{'backfill', 'bfill', 'pad', 'ffill', None}, default pad

def fillempty(data, method='pad'):

data.fillna(method=method)

return data

#detect burr(peak) in data

def detect_burr(data,pv,left=None,right=None,method=0,minimum_peak_distance=100):

print("Wrong PV name, not in ",titleList)