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def interpolate_scandata_2d(x,y,z, fast_axis=0, min_signal_size=500, points=100, filter_positive=False, center_to_peak=False, normalize=False):

    if fast_axis == 0:
        vector = x
    else:
        vector = y

    # consider only the positive direction of the x values
    if filter_positive:

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from scipy import interpolate
import numpy as np
# x_resolution = 0.38
# y_resolution = 0.38

# z = matrix_data
# xsize,ysize = z.shape

# x = np.arange(0,xsize+extra,1)*x_resolution
# y = np.arange(0,ysize+extra,1)*y_resolution

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def clip_remainder(x, clip):
    if isinstance(clip, tuple):
        print("is tuple")
        assert len(clip) == 2, "Clip should be (min, max)"
        clipmin,clipmax = clip
    else:
        clipmin = -clip
        clipmax = clip
    clipptp = np.ptp([clipmin, clipmax])

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def remap(x, in_min, in_max, out_min, out_max):
    """ Remaps X in the range [in_min, in_max] to the range [out_min, out_max]
    Equal to the arduino map function."""
    return (x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min

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# from https://stackoverflow.com/a/15860757/7996766




import time, sys
from IPython.display import clear_output
def update_progress(progress):
    bar_length = 20

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import struct
import zlib
import os
import math

def readarc(f, verbose=False):

    
    

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from collections import deque

class CircularBuffer (deque):
    """ Creates a circular buffer class which inherits the deque collection 
    and implements extract functions"""
    
    def extract(self,n):
        """ Extracts n items from the right """
        return list(reversed([self.pop() for i in range(n)]))
    

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from collections import deque

class CircularBuffer (deque):
    """ Creates a circular buffer class which inherits the deque collection 
    and implements extract functions"""
    
    def extract(self,n):
        """ Extracts n items from the right """
        return list([self.pop() for i in range(n)])
    

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import asyncio
from bleak import BleakScanner


import matplotlib
import PySimpleGUI as sg

from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import numpy as np
import matplotlib.pyplot as plt

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def parse_profilometer_xy_data(filename):
    
    

    df_header = pd.read_csv(filename, sep='\s+',header=None, nrows=7 )
    df_header = df_header.T
    df_header.columns = df_header.iloc[0]
    df_header = df_header.reindex(df_header.index.drop(0))
    name = df_header.Data
    x_resolution = df_header['X-Resolution'].astype('float').values