diff --git a/GUniDec.spec b/GUniDec.spec
index c89a287a..bf2e8e60 100644
--- a/GUniDec.spec
+++ b/GUniDec.spec
@@ -10,6 +10,7 @@ from multiprocessing import freeze_support
 from os import listdir
 from PyInstaller import compat
 import matplotlib
+import hashing
 
 freeze_support()
 
@@ -148,5 +149,7 @@ for root, dirs, files in os.walk(outputdir):
 zipf.close()
 print("Zipped to", zipdirectory, "from", outputdir)
 
+hashing.hashfile(zipdirectory)
+
 tend = time.perf_counter()
 print("Build Time: %.2gm" % ((tend - tstart) / 60.0))
diff --git a/PublicScripts/MutantCycleAnalysis/Step1_ListExtractor.py b/PublicScripts/MutantCycleAnalysis/Step1_ListExtractor.py
new file mode 100644
index 00000000..211aa3cb
--- /dev/null
+++ b/PublicScripts/MutantCycleAnalysis/Step1_ListExtractor.py
@@ -0,0 +1,67 @@
+# Script to extract the peak areas from each deconvolved native mass spectrum
+# Note, you must first run UniDec on all the raw data to generate the deconvolved spectra
+# This can be done either individually or in batch
+
+import numpy as np
+import os
+import glob
+from unidec.modules.Extract2D import *
+
+R = 8.31446261815324 / 1000.
+units = "kJ/mol"
+
+#SpecifyingDirectory
+globaldir = "Z:\\Group Share\\Hiruni Jayasekera\\HSJ_MAE AqpZ CL\\Final2\\"
+
+os.chdir(globaldir)
+
+#ListExtractorProcessingParameters
+edges = [("WT", "W14A")]
+temps = [15, 20, 25, 30, 35, 40]
+width = 95
+method = 6
+params1 = [98590, 1400, 350, 0, 7, 0, 1, width, method]
+params2 = [98260, 1400, 680, 0, 7, 0, 1, width, method]
+params3 = [98260, 1400, 320, 0, 7, 0, 1, width, method]
+params4 = [98415, 1400, 500, 0, 7, 0, 1, width, method]
+params5 = [98706, 1400, 208, 0, 7, 0, 1, width, method]
+params = [params4]
+
+#SpecifyingPath
+app = wx.App(False)
+for ed in range(0, 1):
+    e1 = edges[ed][0]
+    e2 = edges[ed][1]
+    #if e2 is "DM":
+        #e2 = "R224-75A"
+    dirname = e1 + "_" + e2 + " Titrations All"
+    pathtop = os.path.join(globaldir, dirname)
+    print(os.path.isdir(pathtop), pathtop)
+    out = True
+
+    files = []
+    paths = []
+    datalist = []
+    # Loop through temperatures
+    for t in temps:
+        newpath = os.path.join(pathtop, e1 + "_" + e2 + "_" + str(t) + "C")
+        os.chdir(newpath)
+        temp = t
+        for path in glob.glob(f'{newpath}/*/**/', recursive=True):
+            mass_file = [f for f in os.listdir(path) if "_mass.txt" in f]
+            if len(mass_file) > 0:
+                mass_file = os.path.join(path, mass_file[0])
+                files.append(mass_file)
+                paths.append(path)
+                data = np.loadtxt(mass_file)
+                datalist.append(data)
+        print(files)
+        if len(files) < 0:
+            print("Failed at t=", t)
+            break
+    print(len(datalist))
+    # Load all data into the Extraction GUI, it will automatically export the required text files for Step 2 while
+    # Allowing users to check with the window that everything looks good. Just click next through all.
+    frame = Extract2DPlot(None, datalist, params=params[ed], directory=paths)
+    frame.on_all_next()
+    app.MainLoop()
diff --git a/PublicScripts/MutantCycleAnalysis/Step2_DeltaGToolWithErrorBars.py b/PublicScripts/MutantCycleAnalysis/Step2_DeltaGToolWithErrorBars.py
new file mode 100644
index 00000000..11643ab3
--- /dev/null
+++ b/PublicScripts/MutantCycleAnalysis/Step2_DeltaGToolWithErrorBars.py
@@ -0,0 +1,131 @@
+# Script to calculate DDG values from each quartet of extracted peak areas
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+from matplotlib import rcParams
+import glob
+
+# rcParams['ps.useafm']=True
+rcParams['ps.fonttype'] = 42
+rcParams['pdf.fonttype'] = 42
+rcParams['lines.linewidth'] = 2
+rcParams['axes.linewidth'] = 1
+
+
+# rcParams['font.size']=8
+
+#Definition of Kd and delta(deltaG)
+def get_kd(mutant, wt, n=0, l=1, T=37):
+    a = wt[n]
+    al = wt[n + l]
+    b = mutant[n]
+    bl = mutant[n + l]
+    kd = a * bl / (al * b)
+    TK = T + 273.15
+    deltag = -R * TK * np.log(kd)
+    return kd, deltag, n
+
+
+R = 8.31446261815324 / 1000.
+units = "kJ/mol"
+# R = 1.98720425864083 / 1000.
+# units = "kcal/mol"
+
+# Data info
+temps = [15, 20, 25, 30, 35, 40]
+globaldir = "Z:\\Group Share\\Hiruni Jayasekera\\HSJ_MAE AqpZ CL\\Final2\\WT_R224A Titrations All\\WT_R224A_"
+fname = "Extract_grid_2D_Extract6.txt" # Sets the name of the file to be read in
+
+#Selecting txt files
+for t in temps:
+    temp = t
+    topdir = globaldir+str(t)+"C"
+    print(topdir)
+    files = []
+    for path in glob.glob(f'{topdir}/*/**/', recursive=True):
+        testpath = os.path.join(path,fname)
+        if os.path.isfile(testpath):
+            files.append(testpath)
+    print(files)
+    if len(files)<2:
+        print("Failed at t=", t)
+        break
+    kds = []
+    wts = []
+    mts = []
+    maxl = 7
+
+#Loading txt file data and normalization
+    fig, ax = plt.subplots()
+    for f in files:
+        data = np.loadtxt(f)
+
+        bm = data[:, 1] == 0
+        mutant = data[bm][:, 2]
+        wt = data[np.logical_not(bm)][:, 2]
+
+        wt = wt[:maxl]
+        mutant = mutant[:maxl]
+        wt = wt / np.sum(wt)
+        mutant = mutant / np.sum(mutant)
+        wts.append(wt)
+        mts.append(mutant)
+
+        ax.plot(np.arange(len(wt)) + 0.5, wt, color="g")
+        ax.plot(np.arange(len(mutant)), mutant, color="b")
+
+        # Calculate the DDG for each
+        kdlist = []
+        for n in range(0, maxl - 1):
+            for m in range(1, 2):# maxl - n):
+                value = get_kd(mutant, wt, n, m, T=temp)
+                kdlist.append(value)
+        kds.append(kdlist)
+
+    kds = np.array(kds)
+    wts = np.array(wts)
+    mts = np.array(mts)
+
+    # Get Mean and Std. Dev
+    wavg = np.mean(wts, axis=0)
+    mavg = np.mean(mts, axis=0)
+
+    wstd = np.std(wts, axis=0)
+    mstd = np.std(mts, axis=0)
+
+    #Printing Kd, ddG, ddG, n>n+1
+    print(kds)
+
+    # Make Plots
+    maxn = maxl
+    labels = ["0", "1", "2", "3", "4", "5", "6"]
+    ax.bar(np.arange(maxn), mavg[:maxn], 0.45, color="b", yerr=mstd[:maxn], capsize=3, label="Mutant")
+    ax.bar(np.arange(maxn) + 0.45, wavg[:maxn], 0.45, color="g", yerr=wstd[:maxn], capsize=3, label="WT")
+    ax.set_xticks(np.arange(maxn) + 0.225, labels=labels[:maxn])
+    ax.set_yticks([0, 0.5, 1.0], labels=["0", "%", "100"])
+    ax.spines['right'].set_visible(False)
+    ax.spines['top'].set_visible(False)
+    ax.set_ylim(0, 1.0)
+    ax.set_xlabel("Number of Bound Lipids")
+    ax.legend()
+    ax.set_title(str(t))
+
+    dgs = []
+
+    for n in range(0, maxn - 1):
+        avgdg = np.mean(kds[:, n, 1])
+        sddg = np.std(kds[:, n, 1])
+
+        dgvals = list(kds[:, n, 1])
+        dgvals.append(avgdg)
+        dgvals.append(sddg)
+
+        dgs.append(dgvals)
+        #ddG labels
+        text = "\u0394\u0394G = " + str(round(avgdg, 2)) + " \u00B1 " + str(round(sddg, 2)) + " " + units
+        ax.text(n, wavg[0] + 0.1 + 0.15 * n, text)
+    # Save results
+    np.savetxt(os.path.join(topdir, "DG_resultsN2_Fixed.txt"), dgs)
+    plt.savefig(os.path.join(topdir, "PlotN2_Fixed.pdf"))
+plt.show()
diff --git a/PublicScripts/MutantCycleAnalysis/Step3_VantHoffAnalysis.py b/PublicScripts/MutantCycleAnalysis/Step3_VantHoffAnalysis.py
new file mode 100644
index 00000000..3d5b09fd
--- /dev/null
+++ b/PublicScripts/MutantCycleAnalysis/Step3_VantHoffAnalysis.py
@@ -0,0 +1,241 @@
+# Script to perform the Van't Hoff analysis on the extracted peak areas
+# DDG values are calculated with confidence intervals based on the fit.
+
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+import matplotlib.cm as cm
+import scipy.stats as stats
+from matplotlib import rcParams
+import statsmodels.api as sm
+import scipy.optimize as opt
+import scipy.stats as stats
+
+rcParams['ps.useafm'] = True
+rcParams['ps.fonttype'] = 42
+rcParams['pdf.fonttype'] = 42
+rcParams['lines.linewidth'] = 0.75
+rcParams['errorbar.capsize'] = 3
+rcParams['patch.force_edgecolor'] = True
+rcParams['patch.facecolor'] = 'b'
+rcParams['lines.markersize'] = 7
+rcParams['font.size'] = 16
+rcParams['font.sans-serif'] = "Arial"
+
+# Some parameters and equations
+R = 8.31446261815324 / 1000.
+units = "kJ/mol"
+
+
+def Kfunc(t, h, s):
+    return np.exp((-(h / (R * t)) + s / R))
+
+#Selecting the file
+topdir = "Z:\\Group Share\\Hiruni Jayasekera\\HSJ_MAE AqpZ CL\\Final2\\"
+
+os.chdir(topdir)
+# Edges to consider
+edges = [("WT", "W14A")]
+for ed in range(0, 1):
+    e1 = edges[ed][0]
+    e2 = edges[ed][1]
+    # if e2 is "DM":
+    # e2 = "R224-75A"
+    dirname = e1 + "_" + e2 + " Titrations All"
+    path = os.path.join(topdir, dirname)
+    print(os.path.isdir(path), path)
+    out = True
+
+    #Setting the Temperature
+    temps = [15, 20, 25, 30, 35, 40]
+    maxn = 6
+    temp_val = 25
+
+    alldata = []
+    avgstd = []
+    # Read in the data
+    for t in temps:
+        newpath = os.path.join(path, e1 + "_" + e2 + "_" + str(t) + "C")
+        os.chdir(newpath)
+        data = np.loadtxt("DG_resultsN2_Fixed.txt")
+
+        for i, d in enumerate(data):
+            avgstd.append([i, t, d[-2], d[-1], 0, 0, 0, 0, 0, 0, 0, 0])
+            for j, x in enumerate(d[:-2]):
+                alldata.append([i, t, x, j])
+    os.chdir(path)
+    alldata = np.array(alldata)
+    avgstd = np.array(avgstd)
+
+    # Perform the fits
+    fig = plt.figure()
+    fig.suptitle(dirname)
+    for l in range(maxn):
+        plt.subplot(round(maxn / 2.) + 1, 2, l + 1)
+        lipidn = l
+        b1 = alldata[:, 0] == lipidn
+        data = alldata[b1]
+        b1 = avgstd[:, 0] == l
+        meanstd = avgstd[b1]
+
+        # Put data in the right form
+        x = 1 / (data[:, 1] + 273.15)
+        y = -data[:, 2] * x / R
+        df = len(x) - 2
+        tval = abs(stats.t.ppf(0.05 / 2, df))
+
+        # Actual fit here
+        lr = stats.linregress(x, y)
+        fit = [lr.slope, lr.intercept]
+        errors = np.array([lr.stderr, lr.intercept_stderr])
+        print(fit, errors, errors * tval)
+
+        # Make plots
+        plt.scatter(x, y)
+        plt.plot(x, x * fit[0] + fit[1])
+        x2 = 1 / (meanstd[:, 1] + 273.15)
+        y2 = -meanstd[:, 2] * x2 / R
+        e2 = -meanstd[:, 3] * x2 / R
+        plt.errorbar(x2, y2, np.abs(e2))
+        plt.xlabel("1/T (K)")
+        plt.ylabel("ln K")
+        # plt.ylim(-0.5, 0.5)
+        plt.title(str(l))
+
+        # Extract the DDH and DDS values
+        mH = -fit[0] * R
+        mS = fit[1] * R
+        sH = errors[0] * R * tval
+        sS = errors[1] * R * tval
+
+        # Get confidence intervals
+        model = sm.OLS(y, sm.add_constant(x))
+        results = model.fit()
+        # print(results.params[::-1]* R, results.bse[::-1]* R*tval)
+        #print(results.rsquared_adj)
+        #print(results.conf_int(0.05)* R)
+        #print(results.pvalues)
+        #print(results.t_test([0, 1]))
+        #print(results.t_test([1, 0]))
+
+        # Find the closest temperature to the desired temperature
+        tk = temp_val + 273.15
+        xk = 1 / tk
+        closeindex = np.argmin(np.abs(x - xk))
+        t_number = closeindex
+
+        preds = results.get_prediction().summary_frame(0.05)
+        diffs = (preds["mean_ci_upper"].to_numpy() - preds["mean_ci_lower"].to_numpy())[t_number]
+        dGAvg = (preds["mean"][t_number] * -R) / x[t_number]
+        dGStd = (diffs / 2. * -R) / x[t_number]
+        # print(dGAvg, dGStd)
+        # plt.scatter(x, y)
+        # plt.plot(x, preds["mean"])
+        # plt.plot(x, preds["mean_ci_upper"])
+        # plt.plot(x, preds["mean_ci_lower"])
+        # plt.show()
+        # exit()
+
+        mTS = -tk * mS
+        sTS = -tk * sS
+        # dGAvg = np.mean(data[:,2])
+        # dGStd = np.std(data[:,2])
+        print('L=', l, "H=", mH, "+/-", sH, "S=", mS, "+/-", sS, "TS=", mTS, "+/-", sTS)
+
+        b1 = avgstd[:, 0] == lipidn
+        avgstd[b1, 4] = mH
+        avgstd[b1, 5] = mS
+        avgstd[b1, 6] = mTS
+        avgstd[b1, 7] = sH
+        avgstd[b1, 8] = sS
+        avgstd[b1, 9] = sTS
+        avgstd[b1, 10] = dGAvg
+        avgstd[b1, 11] = dGStd
+
+    # Save Results
+    if out:
+        np.savetxt("AnalysisResults2.csv", avgstd, delimiter=",", header="Lnum,Temp,G,GE,H,S,TS,HE,SE,TSE,GA,GAE")
+    # plt.tight_layout()
+    # plt.show()
+
+    # Make more plots
+    plt.figure()
+
+    for l in range(maxn):
+        index = 0
+        plt.subplot(round(maxn / 2.) + 1, 2, l + 1)
+        b1 = avgstd[:, 0] == l
+        meanstd = avgstd[b1]
+
+        ts = meanstd[:, 1]
+        for i, t in enumerate(ts):
+            c = cm.spring((t - np.amin(ts)) / np.amax(ts))
+            plt.bar(index, meanstd[i, 2], yerr=meanstd[i, 3], color=c, tick_label=str(t))
+            index += 1
+        plt.bar(index, meanstd[i, 4], yerr=meanstd[i, 7], color="g", tick_label="dH")
+        index += 1
+        plt.bar(index, meanstd[i, 6], yerr=np.abs(meanstd[i, 9]), color="b", tick_label="dTS")
+        index += 1
+
+        plt.bar(index, meanstd[i, 10], yerr=np.abs(meanstd[i, 11]), color="y", tick_label="avgdG")
+        index += 1
+        print("Avg dG=", meanstd[i, 10], "+/-", meanstd[i, 11])
+        plt.title("Lipid " + str(l + 1))
+
+        plt.gca().set_ylim(-6, 6)
+
+    if out:
+        np.savetxt("AnalysisResults.csv", meanstd, delimiter=",")
+        plt.savefig(os.path.join(topdir, "VH_Plot1.pdf"))
+    # plt.show()
+
+    plt.figure()
+    x = []
+    y = []
+    e = []
+    for l in range(maxn):
+        b1 = avgstd[:, 0] == l
+        b2 = avgstd[:, 1] == temp_val
+        b3 = np.logical_and(b1, b2)
+        meanstd = avgstd[b3][0]
+        x.append(l + 1)
+        y.append(meanstd[10])  # 10 # 2 means avg ddG from data and 10 means avg ddG from fit
+        e.append(meanstd[11])  # 11 # or 3 for std ddG from data and 11 for std ddG from fit
+
+    plt.bar(x, y, yerr=np.abs(e))  # , tick_labels=x+1)
+    plt.gca().set_ylim(-0.5, 1.0)
+    plt.yticks([-0.5, 0, 0.5, 1.0])
+    plt.xlabel("Number of Lipids")
+    plt.ylabel("Delta G")
+    if out:
+        plt.savefig(os.path.join(topdir, "VH_Plot2.pdf"))
+    # plt.show()
+
+    x3 = []
+    y3 = []
+    plt.figure()
+    for i, t in enumerate(temps):
+        # plt.subplot(2, round(len(temps) / 2.), i + 1)
+
+        b1 = avgstd[:, 1] == t
+        meanstd = avgstd[b1]
+
+        x2 = meanstd[:, 0]
+        y2 = meanstd[:, 2]
+        e2 = meanstd[:, 3]
+        fit = np.polyfit(x2, y2, 1)
+        # plt.plot(x2, x2 * fit[0] + fit[1])
+        plt.plot(x2, x2 * 0)
+
+        x3.append(t)
+        y3.append(fit[0])
+        plt.errorbar(x2, y2, e2, label=str(t))
+        plt.xlabel("Number of Lipids")
+        plt.ylabel("Delta G")
+        # plt.ylim(-1.25, 0.5)
+        # plt.title(str(t))
+    # plt.tight_layout()
+    plt.legend()
+    if out:
+        plt.savefig(os.path.join(topdir, "VH_Plot3.pdf"))
+plt.show()
diff --git a/PublicScripts/MutantCycleAnalysis/Step4_DoubleMutantCyclePlot.py b/PublicScripts/MutantCycleAnalysis/Step4_DoubleMutantCyclePlot.py
new file mode 100644
index 00000000..ebb7e504
--- /dev/null
+++ b/PublicScripts/MutantCycleAnalysis/Step4_DoubleMutantCyclePlot.py
@@ -0,0 +1,108 @@
+# Script for plotting the final results of the double mutant cycle analysis
+
+import pandas as pd
+import os
+import networkx as nx
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib import rcParams
+
+matplotlib.use('WxAgg')
+
+rcParams['ps.useafm'] = True
+rcParams['ps.fonttype'] = 42
+rcParams['pdf.fonttype'] = 42
+rcParams['lines.linewidth'] = 0.75
+rcParams['errorbar.capsize'] = 3
+rcParams['patch.force_edgecolor'] = True
+rcParams['patch.facecolor'] = 'b'
+rcParams['lines.markersize'] = 7
+rcParams['font.size'] = 14
+rcParams['font.sans-serif'] = "Arial"
+
+
+def make_graph(df, m_name="G", s_name=None):
+    if s_name is None:
+        s_name=m_name+"E"
+    if m_name == "S":
+        r = 4
+    else:
+        r=2
+    print(df)
+    els = {}
+    edges = [("WT", "R224A"), ("WT", "R75A"), ("WT", "DM"), ("R224A", "DM"), ("R75A", "DM")]
+    G = nx.Graph()
+    weights = []
+    for i in range(0, 5):
+        e1 = edges[i][0]
+        e2 = edges[i][1]
+        mutant = e1 + "_" + e2
+        row = df[df["Mutant"] == mutant]
+        m = float(row[m_name])
+        s = np.abs(float(row[s_name]))
+
+        G.add_edge(e1, e2, weight=m)
+        label = str(round(m, r)) + "\n± " + str(round(s, r))
+        els.update({edges[i]: label})
+        sm = np.sqrt(3)
+        t = 3.182
+        cil = m - s #* t/sm
+        clh = m + s #* t/sm
+        if cil < 0 < clh:
+            weights.append(0)
+        else:
+            weights.append(m)
+    return G, els, weights
+
+
+path = "Z:\\Group Share\\Hiruni Jayasekera\\HSJ_MAE AqpZ CL\\Final2\\"
+os.chdir(path)
+df = pd.read_excel("CombinedResults2.xlsx")
+names=[ "H", "TS", "GA"]#["G", "GA", "S", "H", "TS"]
+for name in names:
+    #name = "G"
+    outstring = "Square_"+name+"_"
+    if name == "S":
+        minmax = [-0.01, 0.01]
+    elif name == "H" or name == "TS":
+        minmax = [-3, 3]
+    else:
+        minmax=[-0.75, 0.75]
+    if name in ["H", "S", "GA", "TS"]:
+        temps = [35]
+    else:
+        temps = [15, 20, 25, 30, 35, 40]
+    for j in range(0, len(temps)):
+        fig = plt.figure(figsize=(6, 8))
+        t = temps[j]
+        for i in range(0, 6):
+            df2 = df[df["# Lnum"] == i]
+            df2 = df2[df2["Temp"] == t]
+
+            G, els, weights = make_graph(df2, m_name=name)
+
+            p = {"WT": [0, 1], "DM": [1, 0], "R224A": [1, 1], "R75A": [0, 0]}
+            ax1 = plt.subplot(3, 2, i + 1, aspect="equal")
+            ax1.get_xaxis().set_visible(False)
+            ax1.get_yaxis().set_visible(False)
+            nx.draw(G, p)
+            ns = 1800
+            fs = 12
+            bbox = {"boxstyle": "round, pad=0.1", "ec": "None", "fc": "None", "alpha": 0.5}
+            # weights = list(nx.get_edge_attributes(G, 'weight').values())
+            nx.draw_networkx_nodes(G, p, margins=0.15, node_color="w", node_shape="o", node_size=ns, edgecolors="k")
+            nx.draw_networkx_edges(G, p, width=32, edge_color="k")
+            nx.draw_networkx_edges(G, p, width=30, edge_color=weights, edge_cmap=plt.cm.bwr,
+                                   node_size=ns, edge_vmin=minmax[0], edge_vmax=minmax[1])
+            nx.draw_networkx_labels(G, p, font_size=fs)
+            nx.draw_networkx_edge_labels(G, p, edge_labels=els, font_size=fs, bbox=bbox, font_color="k")
+
+            plt.title("Lipid " + str(i) + "→" + str(i + 1))
+        plt.tight_layout()
+        figfile = outstring + str(t)
+        fig.suptitle('ΔΔ' + name + " "+ str(t) + ' °C', fontsize=16)
+        plt.savefig(figfile + ".png")
+        plt.savefig(figfile + ".pdf")
+
+plt.show()
diff --git a/unidec/DataCollector.py b/unidec/DataCollector.py
index a5bed5f8..64553e57 100644
--- a/unidec/DataCollector.py
+++ b/unidec/DataCollector.py
@@ -1149,8 +1149,8 @@ def on_kd_fit(self, e):
             maxsites = int(self.maxsites)
         except (ValueError, TypeError):
             maxsites = 0
-        model = UniFit.KDmodel(np.transpose(self.extract), self.yvals[:, 2].astype(np.float64),
-                               self.yvals[:, 1].astype(np.float64), nodelist, os.path.join(self.directory, "fits"),
+        model = UniFit.KDmodel(np.transpose(self.extract), self.yvals[:, 2].astype(float),
+                               self.yvals[:, 1].astype(float), nodelist, os.path.join(self.directory, "fits"),
                                numtotprot=self.numprot, numtotlig=self.numlig, removeoutliers=outlierflag,
                                plot1=self.plot2.subplot1,
                                plot2=self.plot3.axes, plot3=self.plot3h, bootnum=self.bootstrap, maxsites=maxsites,
@@ -1332,8 +1332,8 @@ def on_msms_norm(self, e):
             dlg.Destroy()
             print("Openening: ", path)
             msdat = np.loadtxt(path)
-            mids = np.array([y[1] for y in self.yvals]).astype(np.float)
-            wins = np.array([y[2] for y in self.yvals]).astype(np.float)
+            mids = np.array([y[1] for y in self.yvals]).astype(float)
+            wins = np.array([y[2] for y in self.yvals]).astype(float)
 
             vals = []
             for i, m in enumerate(mids):
diff --git a/unidec/GUniDec.py b/unidec/GUniDec.py
index ec7e55e4..138ccab3 100644
--- a/unidec/GUniDec.py
+++ b/unidec/GUniDec.py
@@ -550,16 +550,16 @@ def make_im_plots(self):
             self.makeplot5(1)
             self.makeplot3(1)
             self.view.plot2ccs.plotrefreshtop(self.eng.data.ccsdata[:, 0], self.eng.data.ccsdata[:, 1],
-                                              title="CCS Distribution", xlabel="CCS (${\AA}$$^2$)", ylabel="Intensity",
+                                              title="CCS Distribution", xlabel="CCS (${\\AA}$$^2$)", ylabel="Intensity",
                                               label="CCS Summation", config=self.eng.config, nopaint=False)
             self.view.plot5mccs.contourplot(xvals=self.eng.data.massdat[:, 0], yvals=self.eng.data.ccsdata[:, 0],
                                             zgrid=np.ravel(self.eng.data.massccs), config=self.eng.config,
-                                            ylab="CCS (${\AA}$$^2$)", title="Mass vs. CCS", test_kda=True)
+                                            ylab="CCS (${\\AA}$$^2$)", title="Mass vs. CCS", test_kda=True)
 
             ccsgrid2, zgrid2 = np.meshgrid(self.eng.data.ztab, self.eng.data.ccsdata[:, 0], sparse=False, indexing='ij')
             self.view.plot5ccsz.contourplot(
                 np.transpose([np.ravel(ccsgrid2), np.ravel(zgrid2), np.ravel(self.eng.data.ccsz)]), self.eng.config,
-                xlab="Charge", ylab="CCS (${\AA}$$^2$)", title="CCS vs. Charge")
+                xlab="Charge", ylab="CCS (${\\AA}$$^2$)", title="CCS vs. Charge")
             print("Made IM Plots")
             try:
                 self.view.plot3color.make_color_plot(self.eng.data.mztgrid, np.unique(self.eng.data.data3[:, 0]),
@@ -626,7 +626,7 @@ def make_cube_plot(self, event=None):
                                       self.eng.data.massccs, self.eng.data.massgrid.reshape(
                     (len(self.eng.data.massdat), len(self.eng.data.ztab))), self.eng.data.ccsz.transpose(),
                                       xlab="Mass (Da)",
-                                      ylab="CCS (${\AA}$$^2$)", zlab="Charge", cmap=self.eng.config.cmap)
+                                      ylab="CCS (${\\AA}$$^2$)", zlab="Charge", cmap=self.eng.config.cmap)
             endtime = time.perf_counter()
             print("Finished Final Cube in: ", (endtime - starttime), " s")
         except Exception as ex:
@@ -1099,7 +1099,7 @@ def on_calibrate(self, e=None):
             result = dialog.value
             if result != "None":
                 coeff = np.array(result.split())
-                coeff = coeff.astype(np.float)
+                coeff = coeff.astype(float)
             else:
                 coeff = None
             self.eng.data.rawdata = ud.cal_data(self.eng.data.rawdata, coeff)
diff --git a/unidec/bin/Example Data/BSA_unidecfiles/BSA_conf.dat b/unidec/bin/Example Data/BSA_unidecfiles/BSA_conf.dat
index 143166c6..08894ea9 100644
--- a/unidec/bin/Example Data/BSA_unidecfiles/BSA_conf.dat	
+++ b/unidec/bin/Example Data/BSA_unidecfiles/BSA_conf.dat	
@@ -1,4 +1,4 @@
-version 6.0.4
+version 6.0.5
 imflag 0
 cdmsflag 0
 input C:\Python\UniDec3\unidec\bin\Example Data\BSA_unidecfiles\BSA_input.dat
diff --git a/unidec/bin/UniDec.exe b/unidec/bin/UniDec.exe
index 62f38e8d..711cfd4c 100644
Binary files a/unidec/bin/UniDec.exe and b/unidec/bin/UniDec.exe differ
diff --git a/unidec/engine.py b/unidec/engine.py
index d508f4a6..b502093c 100644
--- a/unidec/engine.py
+++ b/unidec/engine.py
@@ -1285,8 +1285,8 @@ def dscore(self, xfwhm=2, power=2):
             ints = []
             for p in self.pks.peaks:
                 scores = np.array([p.mscore, p.uscore, p.fscore, p.cs_score])  # p.rsquared,
-                p.dscore = np.product(scores)
-                p.lscore = np.product(scores[:-1])
+                p.dscore = np.prod(scores)
+                p.lscore = np.prod(scores[:-1])
                 dscores.append(p.dscore)
                 ints.append(p.height)
                 '''
diff --git a/unidec/metaunidec/gui_elements/ud_menu_meta.py b/unidec/metaunidec/gui_elements/ud_menu_meta.py
index b2f09d84..e1765804 100644
--- a/unidec/metaunidec/gui_elements/ud_menu_meta.py
+++ b/unidec/metaunidec/gui_elements/ud_menu_meta.py
@@ -489,7 +489,7 @@ def on_custom_defaults(self, e):
         # print("Clicked", e)
         try:
             nid = e.GetId()
-            ids = self.masterd[:, 0].astype(np.float)
+            ids = self.masterd[:, 0].astype(float)
             pos = np.argmin(np.abs(ids - nid))
             path = self.masterd[pos, 1]
             print("Opening Config:", path)
@@ -502,7 +502,7 @@ def on_example_data(self, e):
         # print("Clicked", e)
         try:
             nid = e.GetId()
-            ids = self.masterd2[:, 0].astype(np.float)
+            ids = self.masterd2[:, 0].astype(float)
             pos = np.argmin(np.abs(ids - nid))
             path = self.masterd2[pos, 1]
             print("Opening Path:", path)
diff --git a/unidec/modules/CDEng.py b/unidec/modules/CDEng.py
index cfcc3deb..6ffd1329 100644
--- a/unidec/modules/CDEng.py
+++ b/unidec/modules/CDEng.py
@@ -222,8 +222,8 @@ def open_file(self, path, refresh=False):
                 intkey = "Charge"
                 mzcol = np.argmax(data[0] == mzkey)
                 intcol = np.argmax(data[0] == intkey)
-                mz = data[1:, mzcol].astype(np.float)
-                intensity = data[1:, intcol].astype(np.float)
+                mz = data[1:, mzcol].astype(float)
+                intensity = data[1:, intcol].astype(float)
                 scans = np.arange(len(mz))
             # Don't do post-processing for thermo data
             self.thermodata = False
@@ -866,9 +866,9 @@ def setup_msmooth(self):
         indexoffsets = np.round(mzoffsets / self.config.mzbins)
         lmz = len(self.mz)
         indexes = np.arange(0, lmz)
-        upperindexes = np.array([indexes + i for i in indexoffsets]).astype(np.int)
-        lowerindexes = np.array([indexes - i for i in indexoffsets]).astype(np.int)
-        normindexes = np.array([indexes for i in indexoffsets]).astype(np.int)
+        upperindexes = np.array([indexes + i for i in indexoffsets]).astype(int)
+        lowerindexes = np.array([indexes - i for i in indexoffsets]).astype(int)
+        normindexes = np.array([indexes for i in indexoffsets]).astype(int)
 
         upperindexes[upperindexes < 0] = normindexes[upperindexes < 0]
         lowerindexes[lowerindexes < 0] = normindexes[lowerindexes < 0]
diff --git a/unidec/modules/ChromEng.py b/unidec/modules/ChromEng.py
index f0ec4302..62d36f7f 100644
--- a/unidec/modules/ChromEng.py
+++ b/unidec/modules/ChromEng.py
@@ -20,6 +20,7 @@ def __init__(self):
         self.outpath = None
         self.attrs = None
         self.scans = None
+        self.fullscans = None
         self.filename = None
         self.dirname = None
         self.path = None
@@ -91,7 +92,7 @@ def load_mzml(self, path, load_hdf5=True, refresh=False, *args, **kwargs):
         self.auto_polarity(path, self.chromdat)
         self.tic = self.chromdat.get_tic()
         self.ticdat = np.array(self.tic)
-
+        self.fullscans = self.chromdat.scans
         return hdf5
 
     def get_data_from_scans(self, scan_range=None):
@@ -119,6 +120,9 @@ def get_data_from_times(self, minval, maxval):
         self.get_data_from_scans([minscan, maxscan])
         return self.mzdata
 
+    def get_minmax_times(self):
+        return np.amin(self.ticdat[:, 0]), np.amax(self.ticdat[:, 0])
+
     def get_times(self):
         starts = []
         ends = []
diff --git a/unidec/modules/DoubleDec.py b/unidec/modules/DoubleDec.py
index 1c507095..454ad571 100644
--- a/unidec/modules/DoubleDec.py
+++ b/unidec/modules/DoubleDec.py
@@ -152,7 +152,7 @@ def PlotPeaks(self):
         for i, p in enumerate(peaks):
             sticks = self.dec2[:, 0] == p[0]
             sticks = np.array(sticks)
-            sticks = sticks.astype(np.float) * p[1]
+            sticks = sticks.astype(float) * p[1]
             sticks = cconv2(sticks, self.kernel)
 
             simdat[:, 1] += sticks
diff --git a/unidec/modules/Extract2D.py b/unidec/modules/Extract2D.py
index 101aaebf..5bd26c47 100644
--- a/unidec/modules/Extract2D.py
+++ b/unidec/modules/Extract2D.py
@@ -567,7 +567,7 @@ def on_fit4(self, e):
     def run_multip(self, string):
         try:
             array = string.split(",")
-            array = np.array(array).astype(np.int)
+            array = np.array(array).astype(int)
             fits, fitdat, i1, i2 = ud.complex_poisson(self.data1d, array, background=True)
             print("Fits:", fits)
             self.plot1.plotadd(self.data1d[:, 0], fitdat, "green", nopaint=False)
diff --git a/unidec/modules/UniFit.py b/unidec/modules/UniFit.py
index e63677c7..751cd10e 100644
--- a/unidec/modules/UniFit.py
+++ b/unidec/modules/UniFit.py
@@ -190,7 +190,7 @@ def MakeGrid(pfree, lfree, ureact, prottab, ligtab, paths, kds, nfactors):
     for i in range(0, len(ureact)):
         nump = ureact[i, 2, 0]
         numl = ureact[i, 2, 1]
-        denom = np.product([kds[j] for j in paths[i]])
+        denom = np.prod([kds[j] for j in paths[i]])
         if denom != 0:
             intgrid[nump, numl] += (pfree ** nump) * (lfree ** (numl * h)) / denom
     if nfactors is not None:
@@ -481,7 +481,7 @@ def __init__(self, data, pconc, lconc, nodelist=None, header=None, numtotprot=0,
 
         if nodelist is None:
             nprot = np.ones(numtotlig + 1)
-            nodelist = np.transpose([nprot, self.nlig]).astype(np.int)
+            nodelist = np.transpose([nprot, self.nlig]).astype(int)
         self.kdargs.nodelist = np.array(nodelist)
 
         # Getting in the experimenatl data
diff --git a/unidec/modules/fitting.py b/unidec/modules/fitting.py
index dba93ea3..c3873942 100644
--- a/unidec/modules/fitting.py
+++ b/unidec/modules/fitting.py
@@ -397,8 +397,8 @@ def poisson_mono_di(datatop):
 def multipoisson(array, datatop, oarray, background=False):
     l = len(oarray)
     fitdat = np.zeros_like(datatop[:, 1])
-    integrals = np.empty_like(oarray).astype(np.float)
-    integrals2 = np.empty_like(oarray).astype(np.float)
+    integrals = np.empty_like(oarray).astype(float)
+    integrals2 = np.empty_like(oarray).astype(float)
     for i in range(0, l):
         oligomer = oarray[i]
         mu = array[i * 2]
@@ -450,8 +450,8 @@ def complex_poisson(datatop, oarray=None, background=False):
 
 
 if __name__ == "__main__":
-    path = "C:\\UniDecPastedSpectra\PastedSpectrum_2017_Dec_11_09_02_49_unidecfiles\Extract_total_2D_Extract.txt"
-    path = "C:\\UniDecPastedSpectra\PastedSpectrum_2017_Dec_11_11_30_45_unidecfiles\Extract_total_2D_Extract.txt"
+    path = "C:\\UniDecPastedSpectra\\PastedSpectrum_2017_Dec_11_09_02_49_unidecfiles\\Extract_total_2D_Extract.txt"
+    path = "C:\\UniDecPastedSpectra\\PastedSpectrum_2017_Dec_11_11_30_45_unidecfiles\\Extract_total_2D_Extract.txt"
     data = np.loadtxt(path)[1:18]
     data[:, 1] -= np.amin(data[:, 1])
 
diff --git a/unidec/modules/gui_elements/CDMenu.py b/unidec/modules/gui_elements/CDMenu.py
index 9cb19a8a..46fd06af 100644
--- a/unidec/modules/gui_elements/CDMenu.py
+++ b/unidec/modules/gui_elements/CDMenu.py
@@ -446,7 +446,7 @@ def on_custom_defaults(self, e):
         # print("Clicked", e)
         try:
             nid = e.GetId()
-            ids = self.masterd[:, 0].astype(np.float)
+            ids = self.masterd[:, 0].astype(float)
             pos = np.argmin(np.abs(ids - nid))
             path = self.masterd[pos, 1]
             print("Opening Config:", path)
@@ -459,7 +459,7 @@ def on_example_data(self, e):
         # print("Clicked", e)
         try:
             nid = e.GetId()
-            ids = self.masterd2[:, 0].astype(np.float)
+            ids = self.masterd2[:, 0].astype(float)
             pos = np.argmin(np.abs(ids - nid))
             self.load_example_data(pos)
         except Exception as e:
diff --git a/unidec/modules/gui_elements/ud_menu.py b/unidec/modules/gui_elements/ud_menu.py
index 10a1f4e1..dabdf2ac 100644
--- a/unidec/modules/gui_elements/ud_menu.py
+++ b/unidec/modules/gui_elements/ud_menu.py
@@ -536,7 +536,7 @@ def on_custom_defaults(self, e):
         # print("Clicked", e)
         try:
             nid = e.GetId()
-            ids = self.masterd[:, 0].astype(np.float)
+            ids = self.masterd[:, 0].astype(float)
             pos = np.argmin(np.abs(ids - nid))
             path = self.masterd[pos, 1]
             print("Opening Config:", path)
@@ -549,7 +549,7 @@ def on_example_data(self, e):
         # print("Clicked", e)
         try:
             nid = e.GetId()
-            ids = self.masterd2[:, 0].astype(np.float)
+            ids = self.masterd2[:, 0].astype(float)
             pos = np.argmin(np.abs(ids - nid))
             self.load_example_data(pos)
         except Exception as e:
diff --git a/unidec/modules/hramtools.py b/unidec/modules/hramtools.py
index 69037ea3..17e5ac26 100644
--- a/unidec/modules/hramtools.py
+++ b/unidec/modules/hramtools.py
@@ -88,7 +88,7 @@ def filter_indexes(self):
 
     def generate_masses(self):
         lens = self.ends - self.starts + 1
-        print("Generating combinations:", lens, np.product(lens))
+        print("Generating combinations:", lens, np.prod(lens))
         self.indexes = np.array(list(np.ndindex(tuple(lens))))
         self.massvals = np.array([np.sum(self.isomasses * (i + self.starts)) for i in self.indexes])
 
diff --git a/unidec/modules/isolated_packages/mql_tool.py b/unidec/modules/isolated_packages/mql_tool.py
index e5036947..151e7b67 100644
--- a/unidec/modules/isolated_packages/mql_tool.py
+++ b/unidec/modules/isolated_packages/mql_tool.py
@@ -29,7 +29,7 @@ def __init__(self, file):
     def query(self, input_query, pks):
         self.input_query = input_query
         self.results_df = msql_engine.process_query(self.input_query, self.file, ms1_df=self.ms1_df, ms2_df=self.ms2_df)
-        mz = self.results_df["comment"].to_numpy(dtype=np.float)
+        mz = self.results_df["comment"].to_numpy(dtype=float)
         for p in pks.peaks:
             if np.any(p.mass == mz):
                 p.ignore = False
diff --git a/unidec/modules/isolated_packages/preset_manager.py b/unidec/modules/isolated_packages/preset_manager.py
index 39d536ac..7f34c3f0 100644
--- a/unidec/modules/isolated_packages/preset_manager.py
+++ b/unidec/modules/isolated_packages/preset_manager.py
@@ -100,7 +100,7 @@ def __init__(self, parent=None):
     def on_defaults(self, e):
         try:
             nid = e.GetId()
-            ids = self.masterd[:, 0].astype(np.float)
+            ids = self.masterd[:, 0].astype(float)
             pos = np.argmin(np.abs(ids - nid))
             path = self.masterd[pos, 1]
             print("Opening Path:", path)
diff --git a/unidec/modules/matchtools.py b/unidec/modules/matchtools.py
index ac16df44..43033aae 100644
--- a/unidec/modules/matchtools.py
+++ b/unidec/modules/matchtools.py
@@ -86,7 +86,7 @@ def get_sitematch_list(gdf, sites=None, probs_cutoff=0,
         inds = indexes[:, i]
         probs[i] = pvals[i][inds]
         masses[i] = mvals[i][inds]
-    probs = np.product(probs, axis=0)
+    probs = np.prod(probs, axis=0)
     masses = np.sum(masses, axis=0)
 
     if sort is not None:
diff --git a/unidec/modules/mzMLimporter.py b/unidec/modules/mzMLimporter.py
index 5ebc3dfa..c9f705cc 100644
--- a/unidec/modules/mzMLimporter.py
+++ b/unidec/modules/mzMLimporter.py
@@ -598,7 +598,7 @@ def get_polarity(self, scan=1):
 
 
 if __name__ == "__main__":
-    test = u"C:\Python\\UniDec3\TestSpectra\JAW.mzML"
+    test = u"C:\\Python\\UniDec3\\TestSpectra\\JAW.mzML"
     # test = "C:\Data\CytC_Intact_MMarty_Share\\221114_STD_Pro_CytC_2ug_r1.mzML.gz"
     # test = "C:\Data\CytC_Intact_MMarty_Share\\221114_STD_Pro_CytC_2ug_r1_2.mzML"
     #test = "C:\Data\IMS Example Data\imstest2.mzML"
diff --git a/unidec/modules/mzXML_importer.py b/unidec/modules/mzXML_importer.py
index 802670c2..4805c9cf 100644
--- a/unidec/modules/mzXML_importer.py
+++ b/unidec/modules/mzXML_importer.py
@@ -203,7 +203,7 @@ def get_polarity(self):
 
 
 if __name__ == "__main__":
-    test = u"C:\Data\Wilson_Genentech\Raw Files for Michael Marty\B4.mzXML"
+    test = u"C:\\Data\\Wilson_Genentech\\Raw Files for Michael Marty\\B4.mzXML"
     import time
 
     tstart = time.perf_counter()
diff --git a/unidec/modules/plot1d.py b/unidec/modules/plot1d.py
index f73cf2e4..41b6a165 100644
--- a/unidec/modules/plot1d.py
+++ b/unidec/modules/plot1d.py
@@ -432,7 +432,7 @@ def colorplotMD(self, xvals, yvals, cvals, title="", xlabel="", ylabel="", clabe
         self.cbar = colorbar.ColorbarBase(cax, cmap=cmap2, orientation="vertical", ticks=ticks)
         ticks = ticks * max
         if max > 10:
-            ticks = np.round(ticks).astype(np.int)
+            ticks = np.round(ticks).astype(int)
         else:
             ticks = np.round(ticks, 1)
         ticks = ticks.astype(str)
diff --git a/unidec/modules/thermo_reader/RawFileReader.py b/unidec/modules/thermo_reader/RawFileReader.py
index 7a1af735..720b188f 100644
--- a/unidec/modules/thermo_reader/RawFileReader.py
+++ b/unidec/modules/thermo_reader/RawFileReader.py
@@ -193,8 +193,10 @@ def __init__(self, filename, **kwargs):
         self.timerange = [self.StartTime, self.EndTime]
 
         self.get_scan_header()
+        self.get_status_log()
+        #self.get_tune_data()
 
-    def get_scan_header(self, scannumber=2):
+    def get_scan_header(self, scannumber=1):
         try:
             self.header = {}
             extra_header_info = self.source.GetTrailerExtraHeaderInformation()
@@ -218,6 +220,42 @@ def get_scan_header(self, scannumber=2):
             print("Error getting header")
         return None, None, None, None
 
+    def get_status_log(self, scannumber=1):
+        try:
+            self.statuslog = {}
+
+            status_log_info = self.source.GetStatusLogHeaderInformation()
+            status_log_values = self.source.GetStatusLogValues(int(scannumber), True).Values
+            status_log_values = DotNetArrayToNPArray(status_log_values, dtype=str)
+
+            for i in range(len(status_log_info)):
+                item = status_log_info[i]
+                self.statuslog[item.Label] = status_log_values[i]
+
+            try:
+                self.uhv_pressure = float(self.statuslog['UHV Sensor (mbar)'])
+            except:
+                self.uhv_pressure = None
+
+        except:
+            self.statuslog = None
+            print("Error getting status log")
+
+    def get_tune_data(self, scannumber=1):
+        try:
+            self.tunedata = {}
+
+            tune_data_info = self.source.GetTuneDataHeaderInformation()
+            tune_data_values = self.source.GetTuneDataValues(int(scannumber))
+            tune_data_values = DotNetArrayToNPArray(tune_data_values, dtype=str)
+
+            for i in range(len(tune_data_info)):
+                item = tune_data_info[i]
+                self.tunedata[item.Label] = tune_data_values[i]
+        except:
+            self.tunedata = None
+            print("Error getting tune data")
+
     def Get_Header_Item(self, item):
         return self.header[item]
 
diff --git a/unidec/modules/thermo_reader/ThermoImporter.py b/unidec/modules/thermo_reader/ThermoImporter.py
index 51ace83e..09d6ed91 100644
--- a/unidec/modules/thermo_reader/ThermoImporter.py
+++ b/unidec/modules/thermo_reader/ThermoImporter.py
@@ -26,7 +26,7 @@ def __init__(self, path, silent=False, *args, **kwargs):
         self.msrun = rr(path)
         self.scanrange = self.msrun.scan_range()
         # print(self.scanrange)
-        self.scans = np.arange(self.scanrange[0], self.scanrange[1])
+        self.scans = np.arange(self.scanrange[0], self.scanrange[1]+1)
         self.times = []
         self.data = None
         for s in self.scans:
@@ -64,7 +64,7 @@ def get_data(self, scan_range=None, time_range=None):
         :return: merged data
         """
         if scan_range is not None:
-            scan_range = np.array(scan_range, dtype=np.int)
+            scan_range = np.array(scan_range, dtype=int)
             scan_range = scan_range + 1
         if time_range is not None:
             scan_range = self.get_scans_from_times(time_range)
@@ -163,6 +163,23 @@ def get_analog_voltage2(self):
             vs.append(an2)
         return np.array(vs)
 
+    def get_sid_voltage(self, scan=1):
+        try:
+            scan_mode = self.msrun.source.GetScanEventStringForScanNumber(scan)
+            #Find where sid= is in the string
+            sid_index = scan_mode.find("sid=")
+            #Find the next space after sid=
+            space_index = scan_mode.find(" ", sid_index)
+            #Get the sid value
+            sid_value = scan_mode[sid_index+4:space_index]
+            #Convert to float
+            sid_value = float(sid_value)
+        except:
+            sid_value = 0
+
+        return sid_value
+
+
     def get_polarity(self, scan=1):
         #print(dir(self.msrun.source))
         """
@@ -187,6 +204,7 @@ def get_polarity(self, scan=1):
             print("Polarity: Negative")
             return "Negative"
         print("Polarity: Unknown")
+
         return None
 
 if __name__ == "__main__":
diff --git a/unidec/modules/tims_import_wizard/data_importer.py b/unidec/modules/tims_import_wizard/data_importer.py
index 6f30bdcb..4ff44311 100644
--- a/unidec/modules/tims_import_wizard/data_importer.py
+++ b/unidec/modules/tims_import_wizard/data_importer.py
@@ -490,7 +490,7 @@ def GetLines(InputFileName):
 def GetStartEndMass(RawFile):
     ce, s, e = None, None, None
     if platform.uname()[0] == 'Windows':
-        ParamFile = RawFile + "\_extern.inf"
+        ParamFile = RawFile + "\\_extern.inf"
     else:
         ParamFile = RawFile + "/_extern.inf"
 
diff --git a/unidec/modules/unidec_enginebase.py b/unidec/modules/unidec_enginebase.py
index 7425cb15..4ba8dd04 100644
--- a/unidec/modules/unidec_enginebase.py
+++ b/unidec/modules/unidec_enginebase.py
@@ -6,7 +6,7 @@
 import webbrowser
 from unidec.modules.html_writer import *
 
-version = "6.0.4"
+version = "6.0.5"
 
 
 def copy_config(config):
diff --git a/unidec/src/UniDec.c b/unidec/src/UniDec.c
index f400bab4..a1d2888f 100644
--- a/unidec/src/UniDec.c
+++ b/unidec/src/UniDec.c
@@ -50,8 +50,6 @@ Config ImportConfig(int argc, char * argv[], Config config)
 
 int main(int argc, char *argv[])
 {
-	
-
 
 	int result = 0;
 
diff --git a/unidec/src/UniDec_Main.h b/unidec/src/UniDec_Main.h
index 265cd5a0..212da2b3 100644
--- a/unidec/src/UniDec_Main.h
+++ b/unidec/src/UniDec_Main.h
@@ -34,6 +34,10 @@ Decon ExitToBlank(const Config config, Decon decon)
 
 Decon MainDeconvolution(const Config config, const Input inp, const int silent, const int verbose)
 {
+	//Insert time
+	time_t starttime;
+	starttime = clock();
+
 	Decon decon = SetupDecon();
 	char* barr = NULL;
 
@@ -574,6 +578,8 @@ Decon MainDeconvolution(const Config config, const Input inp, const int silent,
 	decon.uniscore = score(config, &decon, inp, scorethreshold, silent);
 
 	}
+	// Print out time
+	printf("Deconvolution Time: %f\n", (float)(clock() - starttime) / CLOCKS_PER_SEC);
 
 	//Free Memory
 	free(closeval);
diff --git a/unidec/tools.py b/unidec/tools.py
index 525020dc..918e48fc 100644
--- a/unidec/tools.py
+++ b/unidec/tools.py
@@ -2166,7 +2166,7 @@ def nonlinstickconv(xvals, mztab, fwhm, psfun):
         window = 15 * fwhm
     xlen = len(xvals)
     stick = np.zeros(xlen)
-    stick[np.array(mztab[:, 2]).astype(np.int)] = mztab[:, 1]
+    stick[np.array(mztab[:, 2]).astype(int)] = mztab[:, 1]
     bool1 = [np.abs(xvals - xvals[i]) < window for i in range(0, xlen)]
     kernels = np.array([make_peak_shape(-xvals[bool1[i]], psfun, fwhm, -xvals[i]) for i in range(0, xlen)],
                        dtype=object)
@@ -2183,7 +2183,7 @@ def stickconv(mztab, kernel):
     """
     xlen = len(kernel)
     temp = np.zeros(xlen)
-    temp[np.array(mztab[:, 2]).astype(np.int)] = mztab[:, 1]
+    temp[np.array(mztab[:, 2]).astype(int)] = mztab[:, 1]
     return cconv(temp, kernel)
 
 
@@ -2196,7 +2196,7 @@ def sticks_only(mztab, kernel):
     """
     xlen = len(kernel)
     temp = np.zeros(xlen)
-    temp[np.array(mztab[:, 2]).astype(np.int)] = mztab[:, 1]
+    temp[np.array(mztab[:, 2]).astype(int)] = mztab[:, 1]
     return temp
 
 
@@ -2255,11 +2255,11 @@ def combine(array2):
     lens = lengths(array2)
     tup = tuple(lens)
     startindex = array2[:, 2]
-    startindex = startindex.astype(np.int)
+    startindex = startindex.astype(int)
     basemass = array2[:, 0]
-    basemass = basemass.astype(np.float)
+    basemass = basemass.astype(float)
     omass = array2[:, 1]
-    omass = omass.astype(np.float)
+    omass = omass.astype(float)
     finlist = []
     for index in np.ndindex(tup):
         total = np.sum((index + startindex) * omass + basemass)
@@ -2284,13 +2284,13 @@ def index_to_oname(index, startindex, names):
 def combine_all(array2):
     lens = lengths(array2)
     tup = tuple(lens)
-    print("Starting combining all: ", np.product(lens))
+    print("Starting combining all: ", np.prod(lens))
     startindex = array2[:, 2]
-    startindex = startindex.astype(np.int)
+    startindex = startindex.astype(int)
     basemass = array2[:, 0]
-    basemass = basemass.astype(np.float)
+    basemass = basemass.astype(float)
     omass = array2[:, 1]
-    omass = omass.astype(np.float)
+    omass = omass.astype(float)
     # names = array2[:, 4]
 
     # namelist = np.array([index for index in np.ndindex(tup)])
@@ -2403,7 +2403,7 @@ def get_glyco_indexes(oligomerlist, printoutput=False):
 
 def pair_glyco_matches(oligomasslist, oligonames, oligomerlist):
     oligomerlist = np.array(oligomerlist)
-    startindex = oligomerlist[:, 2].astype(np.int)
+    startindex = oligomerlist[:, 2].astype(int)
 
     sindex, hindex, gindex, findex = get_glyco_indexes(oligomerlist)
 
@@ -2439,7 +2439,7 @@ def match(pks, oligomasslist, oligonames, oligomerlist, tolerance=None, return_n
     numbers = []
 
     oligomerlist = np.array(oligomerlist)
-    startindex = oligomerlist[:, 2].astype(np.int)
+    startindex = oligomerlist[:, 2].astype(int)
     onames = oligomerlist[:, 4]
 
     for i in range(0, pks.plen):
@@ -2493,7 +2493,7 @@ def cconv(a, b):
 
 
 def FD_gauss_wavelet(length, width):
-    xvals = np.arange(0, length).astype(np.float)
+    xvals = np.arange(0, length).astype(float)
     mu = np.round(length / 2)
     sigma = width
     return [stats.norm.pdf(x, mu, sigma) * (mu - x) / sigma ** 2 for x in xvals]