diff --git a/Calibration/calculation_for_dspacing_resolution_by_pixel_peak.py b/Calibration/calculation_for_dspacing_resolution_by_pixel_peak.py
new file mode 100644
index 00000000..49d04f1c
--- /dev/null
+++ b/Calibration/calculation_for_dspacing_resolution_by_pixel_peak.py
@@ -0,0 +1,82 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mantid.simpleapi import *
+
+chi2_threshold = 1e12
+
+# Input dvalues (here are the diamond ones)
+dvalues = (0.3117,0.3257,0.3499,0.4205,0.4645,0.4768,0.4996,0.5150,0.5441,0.5642,0.5947,
+           0.6307,.6866,.7283,.8185,.8920,1.0758,1.2615,2.0599)
+
+def get_fit_params_from_instrument_preselected_file(instrument):
+    # Load files
+    filename="%s_pdcalibration_diagnostics.nxs" % instrument
+
+    cal_wksp = LoadNexusProcessed(Filename=filename)
+
+    # Get the fitting parameters workspace
+    if instrument == "NOM":
+        wksp_name = "NOM_122825_diag_fitparam"
+    if instrument == "PG3":
+        wksp_name = "PG3_39169_diag_fitparam"
+    return mtd[wksp_name]
+
+def get_fit_params_by_fitting(wksp):
+    pass
+
+def get_column_data(wksp, val_col, chi2_col, threshold):
+    values = list()
+    for (val, chi2) in zip(wksp.column(val_col), wksp.column(chi2_col)):
+        if chi2 < chi2_threshold:
+            values.append(val)
+    return values
+
+def deltaD(tof_center, tof_width, dspace_center):
+    tof_fwhm = 2.* np.sqrt(2.*np.log(2.)) * tof_width
+    dspace_fwhm = (tof_fwhm / tof_center) * dspace_center
+    return dspace_fwhm
+    
+if __name__=="__main__":
+    wksp = get_fit_params_from_instrument_preselected_file("NOM")
+
+    # Extract the column IDs of interest
+    wksp_col = wksp.getColumnNames().index('wsindex')
+    center_col = wksp.getColumnNames().index('centre')
+    width_col = wksp.getColumnNames().index('width')
+    peak_col = wksp.getColumnNames().index('peakindex')
+    chi2_col = wksp.getColumnNames().index('chi2')
+
+    # Extract data
+    pixel_idx_vals = get_column_data(wksp, wksp_col, chi2_col, chi2_threshold)
+    center_vals = get_column_data(wksp, center_col, chi2_col, chi2_threshold)
+    width_vals = get_column_data(wksp, width_col, chi2_col, chi2_threshold)
+    dspace_idx_vals = get_column_data(wksp, peak_col, chi2_col, chi2_threshold)
+
+    # Make array where rows are pixels and cols are delta-d values (FWHM in dspace)
+    pixels = np.unique(np.array(pixel_idx_vals))
+    idx2pixel = { i:pid for i, pid in enumerate(pixels) }
+    pixel2idx = { pid:i for i, pid in enumerate(pixels)}
+
+    '''
+     TODO: vectorize the for-loop to operate on arrays for : tof_center, tof_widths, dvalues
+       step 1 - get vector for dvalues, maybe use np.vstack, to create a len(pixels) * len(dvalues) vector for step 3
+       step 2 - vector operate on tof_widths to get tof_fwhms
+       step 3 - vector operate on tof_fwhms / tof_centers * dvalues to get dspace_fwhms
+     TODO: then, use histogramming (probably with a set number of bins) to reduce the number of points plotted.
+           could do an array of len(n_histograms)*len(dvalues) that can be plotted quickly and easily and easier for linear regression
+           would also be necessary before interactivity could be introduced for say: displaying number of pixels per dspacing,
+                                                                                     changing sigma for inclusion around linear regression line, etc.
+    '''
+    pixel_deltaD = np.zeros((len(pixels), len(dvalues)))
+    fig, ax = plt.subplots(1)
+    for pidx, tof_center, tof_width, dspace_idx in zip(pixel_idx_vals, center_vals, width_vals, dspace_idx_vals):
+        dspace_width = deltaD(tof_center, tof_width, dvalues[dspace_idx])
+        pixel_deltaD[pixel2idx[pidx],dspace_idx] = dspace_width
+
+    for idx, row in enumerate(pixel_deltaD):
+        ax.plot(dvalues, row, 'o', label="Pixel: %d" % idx2pixel[idx])
+
+    ax.legend()
+    plt.show()
+        
+