polarization correction fitting

pol.py

import numpy as np
from pyFAI.geometry import Geometry
from scipy.optimize import minimize_scalar
import scipy.stats as sts
import tifffile
import matplotlib.pyplot as plt
from xpdan.tools import *
from skimage.morphology import erosion, binary_erosion
import os
import pyFAI


plt.style.use(
    os.path.join('/media/christopher/DATA/papers/mask_paper/figures/',
                 'thesis.mplstyle'))

img = tifffile.imread(
    '/media/christopher/DATA/WrightJun16/Quartz/d25_glass-00000-00004.tif'
    # '/media/christopher/DATA/WrightJun16/Quartz/calib/d25_CeO2-00000.tif'
)
geo = Geometry(
    detector='Perkin', pixel1=.0002, pixel2=.0002,
    dist=.23,
    poni1=.209, poni2=.207,
    # rot1=.0128, rot2=-.015, rot3=-5.2e-8,
    wavelength=1.43e-11
)
geo = pyFAI.load('/media/christopher/DATA/WrightJun16/Quartz/calib/d25_CeO2-00000.poni')

npt = 1041
int_rings = range(npt)
q = geo.qArray(img.shape)
q_range = np.linspace(np.min(q), np.max(q), npt)
int_q = np.digitize(q, q_range)
chi = geo.chiArray(img.shape)

for i in range(2):
    pol = None
    img3 = img.copy()
    if pol:
        img3 /= geo.polarization(img.shape, .95)
    # mask = mask_img(img3, geo, alpha=3, bs_width=None)
    mask = None

    def opt(polarization_factor):
        cimg = img.copy()
        cimg /= geo.polarization(img.shape, polarization_factor)
        if mask is None:
            rq = q.ravel()
            cimg = cimg.ravel()
        else:
            rq = q[mask]
            cimg = cimg[mask]
        std = sts.binned_statistic(rq, cimg, bins=q_range, statistic='std')[0]
        std = np.nan_to_num(std)
        return np.sum(std)

    res =minimize_scalar(opt)
    print(res.x)
    pol = res.x