Code cleanup

conda-recipe/meta.yaml

@@ -23,10 +23,8 @@ requirements:
     - scipy
     - h5py
     - bokeh =2.3
-    - matplotlib
     - numba
     - lmfit
-    - uncertainties
 
 
 about:

pyzebra/xtal.py

@@ -1,15 +1,5 @@
-import math
-
 import numpy as np
 from numba import njit
-from scipy.optimize import curve_fit
-
-import pyzebra
-
-try:
-    from matplotlib import pyplot as plt
-except ImportError:
-    print("matplotlib is not available")
 
 pi_r = 180 / np.pi
 
@@ -393,84 +383,3 @@ def gauss(x, *p):
     """
     A, mu, sigma = p
     return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
-
-
-def box_int(file, box):
-    """Calculates center of the peak in the NB-geometry angles and Intensity of the peak
-
-    Args:
-        file name, box size [x0:xN, y0:yN, fr0:frN]
-
-    Returns:
-        gamma, omPeak, nu polar angles, Int and data for 3 fit plots
-    """
-
-    dat = pyzebra.read_detector_data(file)
-
-    sttC = dat["gamma"][0]
-    om = dat["omega"]
-    nuC = dat["nu"][0]
-    ddist = dat["ddist"]
-
-    # defining indices
-    x0, xN, y0, yN, fr0, frN = box
-
-    # omega fit
-    om = dat["omega"][fr0:frN]
-    cnts = np.sum(dat["data"][fr0:frN, y0:yN, x0:xN], axis=(1, 2))
-
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(cnts)), cnts, p0=p0)
-
-    frC = fr0 + coeff[1]
-    omF = dat["omega"][math.floor(frC)]
-    omC = dat["omega"][math.ceil(frC)]
-    frStep = frC - math.floor(frC)
-    omStep = omC - omF
-    omP = omF + omStep * frStep
-    Int = coeff[1] * abs(coeff[2] * omStep) * math.sqrt(2) * math.sqrt(np.pi)
-    # omega plot
-    x_fit = np.linspace(0, len(cnts), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.figure()
-    plt.subplot(131)
-    plt.plot(range(len(cnts)), cnts)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("Frame N of the box")
-    label = "om"
-    # gamma fit
-    sliceXY = dat["data"][fr0:frN, y0:yN, x0:xN]
-    sliceXZ = np.sum(sliceXY, axis=1)
-    sliceYZ = np.sum(sliceXY, axis=2)
-
-    projX = np.sum(sliceXZ, axis=0)
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(projX)), projX, p0=p0)
-    x = x0 + coeff[1]
-    # gamma plot
-    x_fit = np.linspace(0, len(projX), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.subplot(132)
-    plt.plot(range(len(projX)), projX)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("X-pixel of the box")
-
-    # nu fit
-    projY = np.sum(sliceYZ, axis=0)
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(projY)), projY, p0=p0)
-    y = y0 + coeff[1]
-    # nu plot
-    x_fit = np.linspace(0, len(projY), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.subplot(133)
-    plt.plot(range(len(projY)), projY)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("Y-pixel of the box")
-
-    ga, nu = pyzebra.det2pol(ddist, sttC, nuC, x, y)
-
-    return ga[0], omP, nu[0], Int