from numpy import pi, sin, cos, mgrid

import numpy as np

from mayavi import mlab

import pandas as pd

from scipy.signal import argrelextrema

from scipy import interpolate

from scipy.interpolate import griddata

from sklearn.utils import resample

from scipy import signal

def scale_function(lcms_data):

return x_scaling, y_scaling, z_scaling

# process the data to arrays

data = pd.read_csv("ExampleData.csv")

data = np.array(data)

x = data[:, 0]

y = data[:, 1]

z = data[:, 2]

# Retrieve scaling scalars from scale function

x_scale, y_scale, z_scale = scale_function(data)

# full downsampling of data

xnew = []

ynew = []

znew = []

data_BucketX = []

data_BucketY = []

data_BucketZ = []

index = 0

for item in x:

if index == 0:

index += 1

continue

if item == x[index - 1]:

data_BucketX.append(item)

data_BucketY.append(y[index])

data_BucketZ.append(z[index])

else:

if len(data_BucketX) < 100:

index += 1

continue

xn = []

yn = []

zn = []

sample = int(len(data_BucketX) / 1.2)

xn.extend(signal.resample(data_BucketX, sample))

yn.extend(signal.resample(data_BucketY, sample))

zn.extend(signal.resample(data_BucketZ, sample))

i = 0

for foo in zn:

xnew.append(xn[i] * x_scale)

ynew.append(yn[i] / y_scale)

znew.append(zn[i] / z_scale)

i += 1

data_BucketX.clear()

data_BucketY.clear()

data_BucketZ.clear()

index += 1

foo = []

# set a max z-axis threshold

z_threshold = max(znew) * 0.01

for i in range(len(znew)):

# append to a list the indices of z-axis values that are less than the threshold

if znew[i] < z_threshold:

foo.append(i)

# removes all corresponding elements from each array

xnew = np.delete(xnew, foo)

ynew = np.delete(ynew, foo)

znew = np.delete(znew, foo)

# Visualize the points

# pts = mlab.points3d(x_3d, y_3d, z_3d, z_3d, mode='point')

pts = mlab.points3d(xnew, ynew, znew, znew, mode='point')

# pts = mlab.points3d(xnew, ynew, znew, znew, mode='point', scale_factor=3.0)

# Create and visualize the mesh

mesh = mlab.pipeline.delaunay2d(pts)

surf = mlab.pipeline.surface(mesh)

pts.remove()

# Simple plot axis info rendering

mlab.xlabel("Retention")

mlab.ylabel("m/z")

mlab.zlabel("Intensity")

mlab.show()