Merge pull request #4 from till-schertenleib/package

first working app

diffpy/labpdfproc/functions.py

@@ -4,13 +4,32 @@
 def compute_cve(diffraction_data, mud, wavelength):
     # for a given mu and d and lambda, we will compute cve on a tth grid
     # something arbitrary for the moment
-    cve = Diffraction_object()
+
+    # calculate corresponding cve for the given mud
+    radius_mm = 1
+    n_points_on_diameter = 249
+    mu = mud/2
+    tth_grid = np.arange(1, 141, 1)
+    abs_correction = Gridded_circle(radius_mm, n_points_on_diameter, mu=mu)
+    distances, muls = [], []
+
+    for angle in tth_grid:
+        abs_correction.set_distances_at_angle(angle)
+        abs_correction.set_muls_at_angle(angle)
+        distances.append(sum(abs_correction.distances))
+        muls.append(sum(abs_correction.muls))
+    distances = np.array(distances) / abs_correction.total_points_in_grid
+    muls = np.array(muls) / abs_correction.total_points_in_grid
+
+    # interpolate
+    cve = Diffraction_object(wavelength=wavelength)
     cve_x = diffraction_data.on_tth[0]
-    cve_y = cve_x * mud * wavelength
-    cve.insert_scattering_quantity(cve_x, cve_y, "tth", metadata={ })
+    cve_y = np.interp(cve_x, tth_grid, muls)
+    cve.insert_scattering_quantity(cve_x, cve_y, "tth", metadata={})
     return cve
 
+
 def apply_corr(i_m, cve):
     # we apply the absorption correction by doing: I(tth) * c_ve
-    i_c = i_m * cve
+    i_c = i_m / cve.on_tth[1]
     return i_c

diffpy/labpdfproc/labpdfprocapp.py

@@ -1,38 +1,16 @@
 import sys
 from argparse import ArgumentParser
 
+import numpy as np
 from diffpy.labpdfproc.functions import compute_cve, apply_corr, wavelengths
 from diffpy.utils.parsers.loaddata import loadData
 from diffpy.utils.scattering_objects.diffraction_objects import Diffraction_object
 
 
 known_sources = ["Ag", "Mo"]
-# def load_data(input_file):
-#     # we want to load .xy, xye, file types. These are the most common. For anyting else (.snc, .txt, .csv, .dat) we return an error message. Why: some of them have different delimineters.
-#     # we want to load the tth column in a numpy array 
-#     # we want to load the intensitie Im into a numpy array
-#     # the input files should not contain any header. Typically, .xy or .xye files don't contain headers.
-#     tth = np.loadtxt(input_file, usecols=0)
-#     i_m = np.loadtxt(input_file, usecols=1)
-#     # this should return an error if the first row contains anything except a float, and if the columns are not separated by a space. 
-#     # I think the latter is also dealt with if we check if the first elemnt in one tth is a flaot.
-#     if np.issubdtype(tth[0], np.floating) and np.issubdtype(i_m[0], np.floating):
-#         return tth, i_m
-#     else:
-#         raise ValueError('Error: your .xy contains headers. Delete the header rows in your .xy or .xye file')
 
-# def tth_to_q(tth, wl):
-#     tth_rad = np.deg2rad(tth)
-#     q = (4 * np.pi / wl) * np.sin(tth_rad / 2)
-#     return q
 
 
-
-# def write_files(base_name):
-    # we save the corrected intensities in a two-column file on a q-grid and a tth-grid.
-    # we need to know the x-ray wavelenth so that we can convert tth to q
-    # we make a new two-column file .chi where column 1 contains the q grid and columnt 2 contains the corrected intensities.
-
 def get_args():
     p = ArgumentParser()
     p.add_argument("data_file", help="the filename of the datafile to load")
@@ -51,20 +29,15 @@ def main():
 
     args = get_args()
     wavelength = wavelengths[args.anode_type]
-    input_pattern = Diffraction_object()
+    input_pattern = Diffraction_object(wavelength=wavelength)
     xarray, yarray = loadData(args.data_file, unpack=True)
     input_pattern.insert_scattering_quantity(xarray, yarray, "tth", metadata={ })
 
     cve = compute_cve(input_pattern, args.mud, wavelength)
-    i_c = input_pattern * cve
+    i_c = apply_corr(yarray, cve)
 
-    # get the basename from the input_file and save the corrected patter as a .tth and a .chi file.
-    # base_name = input_file.split('.')[0]
-    # output_chi = f"{base_name}.chi"
-    # output_tth = f"{base_name}.tth"
-    # np.savetxt(output_tth, np.column_stack((tth, i_c)), header='tth I(tth)')
-    # np.savetxt(output_chi, np.column_stack((q, i_c)), header='tth I(tth)')
-    input_pattern.dump("filename", type="chi")
+    data_to_save = np.column_stack((xarray, i_c))
+    np.savetxt(f'{base_name}_proc.chi', data_to_save)
 
 if __name__ == '__main__':
     main()