dipy/dipy

import numpy as np
from dipy.tracking.streamline import transform_streamlines
from dipy.stats.analysis import assignment_map
from scipy.ndimage.interpolation import map_coordinates
from dipy.io.image import load_nifti,

bundle #load your bundle
FA, affine = load_nifti(metric_file_name)

n = 100 #this is for total number of segments and won't change the number of points per streamline
indx = assignment_map(bundle, bundle, n)
indx = np.array(indx)

affine_r = np.linalg.inv(affine)
transformed_bundle = transform_streamlines(bundle, affine_r)

values = map_coordinates(FA,  transformed_bundle._data.T, order=1)

# here  indx has segment number/label per point of all streamlines
# values has FA value per point of all streamlines

#you can take an average of FA values based on which segment its corresponding point belongs to.

fa_mean = [0]*n

for i in range(n):

    fa_mean[i] = np.mean(values[indx==i])

#plot the mean FA profile

import matplotlib.pyplot as plt
plt.plot(list(range(n)), fa_mean) 
plt.title("FA mean profile")
plt.xlabel("Segment number")
plt.ylim([0,1])
plt.ylabel("FA")

# you can also visualize your bundle with n segments 

colors = [np.random.rand(3) for si in range(n)]

disks_color = []
for i in range(len(indx)):
    disks_color.append(tuple(colors[indx[i]]))

from dipy.viz import window, actor    
scene = window.Scene()
scene.SetBackground(1, 1, 1)
scene.add(actor.line(bundle, fake_tube=True, colors=disks_color,
                     linewidth=6))

window.show(scene)