diff --git a/Synthetic_Test/ETAS_complete.txt b/Synthetic_Test/ETAS_complete.txt
new file mode 100644
index 0000000..27194db
--- /dev/null
+++ b/Synthetic_Test/ETAS_complete.txt
@@ -0,0 +1,15741 @@
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diff --git a/Synthetic_Test/ETAS_incomplete.txt b/Synthetic_Test/ETAS_incomplete.txt
new file mode 100644
index 0000000..ac6f8e4
--- /dev/null
+++ b/Synthetic_Test/ETAS_incomplete.txt
@@ -0,0 +1,7744 @@
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diff --git a/Synthetic_Test/RESTORE.py b/Synthetic_Test/RESTORE.py
new file mode 100644
index 0000000..8b83d42
--- /dev/null
+++ b/Synthetic_Test/RESTORE.py
@@ -0,0 +1,934 @@
+"""
+
+RESTORE: REal catalogs STOchastic REplenishment
+Written by Angela Stallone with help from Giuseppe Falcone
+For any comment, question or suggestion write to:
+angela.stallone@ingv.it
+
+This project has been founded by the Seismic Hazard Center
+(Centro di Pericolosità Sismica, CPS, at the Istituto Nazionale di Geofisica e Vulcanologia, INGV)
+
+|---------------------------|
+
+Latest revision: March, 2021
+
+|--------------------------------------------------------------------------|
+
+ABOUT
+
+RESTORE simulates time, location and magnitude of those earthquakes
+that have not been detected by the seismic network due to the overlap of earthquake signals in seismic records
+after the occurrence of a large earthquake (short term aftershock incompleteness - STAI).
+The algorithm assesses the temporal variability of the magnitude of completeness Mc
+by means of a sliding overlapping windows approach, which collects estimates of
+Mc at the end of each window. Since the window has a fixed number of events k and its
+shift dk is constant, estimates of Mc are elapsed by dk events.
+A statistic-based approach is implemented to pinpoint those time intervals where a threshold value for the
+magnitude of completeness is significantly exceeded ("STAI gaps").
+The number and magnitude of missing events are estimated by calculating the difference between the
+observed counts and the counts predicted by the Gutenberg-Richter relationship.
+Occurrence time and location of the simulated events are reconstructed implementing Monte
+Carlo sampling techniques.
+
+"""
+
+# Column numbers in the saved catalog
+
+LonColumn = 0
+LatColumn = 1
+YearColumn = 2
+MonthColumn = 3
+DayColumn = 4
+MagnColumn = 5
+DepthColumn = 6
+HourColumn = 7
+MinuteColumn = 8
+SecondColumn = 9
+
+
+def read_catalog_zmap(name_catalog, delimiter):
+    import csv
+    import numpy as np
+    lon = []
+    lat = []
+    year = []
+    month = []
+    day = []
+    mag = []
+    depth = []
+    hour = []
+    minute = []
+    sec = []
+    with open(name_catalog) as csvfile:
+        read_csv = csv.reader(csvfile, skipinitialspace=False, delimiter=delimiter)
+        for row in read_csv:
+            lon.append(float(row[0]))
+            lat.append(float(row[1]))
+            year.append(int(float(row[2])))
+            month.append(int(float(row[3])))
+            day.append(int(float(row[4])))
+            mag.append(round(float(row[5]), 1))
+            depth.append(float(row[6]))
+            hour.append(int(float(row[7])))
+            minute.append(int(float(row[8])))
+            sec.append(round(float(row[9]), 2))
+    nrows2 = len(lon)
+    ncols = 10
+    catalog = np.ndarray((nrows2, ncols), dtype=object)
+    catalog[:, LonColumn] = lon[:]
+    catalog[:, LatColumn] = lat[:]
+    catalog[:, YearColumn] = year[:]
+    catalog[:, MonthColumn] = month[:]
+    catalog[:, DayColumn] = day[:]
+    catalog[:, MagnColumn] = mag[:]
+    catalog[:, DepthColumn] = depth[:]
+    catalog[:, HourColumn] = hour[:]
+    catalog[:, MinuteColumn] = minute[:]
+    catalog[:, SecondColumn] = sec[:]
+    return catalog
+
+
+def acquisition_data(depthm, mmin, xmin, xmax, ymin, ymax, time_start, time_end):
+    from urllib.request import urlopen
+    import json
+    import dateparser
+    import csv
+    import numpy as np
+    from numpy import flipud
+    lon = []
+    lat = []
+    year = []
+    month = []
+    day = []
+    mag = []
+    depth = []
+    hour = []
+    minute = []
+    sec = []
+    type_mag = []
+    url = "http://webservices.rm.ingv.it/fdsnws/event/1/query?starttime=" + str(time_start) + "&endtime=" + str(
+        time_end) + "&minlat=" + str(ymin) + "&maxlat=" + str(ymax) + "&minlon=" + str(xmin) + "&maxlon=" + str(
+        xmax) + "&maxdepth=" + str(depthm * 1000) + "&minmag=" + str(mmin) + "&format=geojson"
+    print(url)
+    html = urlopen(url).read()
+    data = json.loads(html)
+    field_list = data['features']
+    i = 0
+    for field in field_list:
+        mytime = data['features'][i]['properties']['time']
+        time = dateparser.parse(mytime)
+        year.append(int(time.year))
+        month.append(int(time.month))
+        day.append(int(time.day))
+        hour.append(int(time.hour))
+        minute.append(int(time.minute))
+        second = float(time.second) + (float(time.microsecond) / 1000000)
+        sec.append(round(second, 2))
+        lon.append(float(data['features'][i]['geometry']['coordinates'][0]))
+        lat.append(float(data['features'][i]['geometry']['coordinates'][1]))
+        depth.append(float(data['features'][i]['geometry']['coordinates'][2]))
+        mag.append(data['features'][i]['properties']['mag'])
+        type_mag.append(data['features'][i]['properties']['magType'])
+        i = i + 1
+    nrows2 = len(lon)
+    ncols = 11
+    catalog = np.ndarray((nrows2, ncols), dtype=object)
+    catalog[:, LonColumn] = flipud(lon[:])
+    catalog[:, LatColumn] = flipud(lat[:])
+    catalog[:, YearColumn] = flipud(year[:])
+    catalog[:, MonthColumn] = flipud(month[:])
+    catalog[:, DayColumn] = flipud(day[:])
+    catalog[:, MagnColumn] = flipud(mag[:])
+    catalog[:, DepthColumn] = flipud(depth[:])
+    catalog[:, HourColumn] = flipud(hour[:])
+    catalog[:, MinuteColumn] = flipud(minute[:])
+    catalog[:, SecondColumn] = flipud(sec[:])
+    rows = zip(flipud(lon), flipud(lat), flipud(year), flipud(month), flipud(day), flipud(mag), flipud(depth),
+               flipud(hour), flipud(minute), flipud(sec))
+    with open('Zmap_catalog', 'w', newline='') as f:
+        writer = csv.writer(f, delimiter=' ', skipinitialspace=True, quoting=csv.QUOTE_NONE)
+        for x in rows:
+            writer.writerow(x)
+    return catalog
+
+
+def acquisition_xml(depth_m, mmin, xmin, xmax, ymin, ymax, time_start, time_end):
+    import xmltodict
+    from urllib.request import urlopen
+    import dateparser
+    import csv
+    import numpy as np
+    from numpy import flipud
+    lon = []
+    lat = []
+    year = []
+    month = []
+    day = []
+    mag = []
+    depth = []
+    hour = []
+    minute = []
+    sec = []
+    type_mag = []
+    url = "http://webservices.rm.ingv.it/fdsnws/event/1/query?starttime=" + str(time_start) + "&endtime=" + str(
+        time_end) + "&minlat=" + str(ymin) + "&maxlat=" + str(ymax) + "&minlon=" + str(xmin) + "&maxlon=" + str(
+        xmax) + "&maxdepth=" + str(depth_m * 1000) + "&minmag=" + str(mmin) + "&includeallmagnitudes"
+    print(url)
+    html = urlopen(url).read()
+    doc = xmltodict.parse(html)
+    root = doc["q:quakeml"]
+    eventparameters = root["eventParameters"]
+    i = 0
+    for data in eventparameters["event"]:
+        mytime = data['origin']['time']['value']
+        time = dateparser.parse(mytime)
+        year.append(int(time.year))
+        month.append(int(time.month))
+        day.append(int(time.day))
+        hour.append(int(time.hour))
+        minute.append(int(time.minute))
+        second = float(time.second) + (float(time.microsecond) / 1000000)
+        sec.append(round(second, 2))
+        lon.append(float(data['origin']['longitude']['value']))
+        lat.append(float(data['origin']['latitude']['value']))
+        depth.append(float(data['origin']['depth']['value']) / 1000)
+        mag.append(data['magnitude']['mag']['value'])
+        type_mag.append(data['magnitude']['type'])
+        i = i + 1
+    nrows2 = len(lon)
+    ncols = 11
+    catalog = np.ndarray((nrows2, ncols), dtype=object)
+    catalog[:, LonColumn] = flipud(lon[:])
+    catalog[:, LatColumn] = flipud(lat[:])
+    catalog[:, YearColumn] = flipud(year[:])
+    catalog[:, MonthColumn] = flipud(month[:])
+    catalog[:, DayColumn] = flipud(day[:])
+    catalog[:, MagnColumn] = flipud(mag[:])
+    catalog[:, DepthColumn] = flipud(depth[:])
+    catalog[:, HourColumn] = flipud(hour[:])
+    catalog[:, MinuteColumn] = flipud(minute[:])
+    catalog[:, SecondColumn] = flipud(sec[:])
+    rows = zip(flipud(lon), flipud(lat), flipud(year), flipud(month), flipud(day), flipud(mag), flipud(depth),
+               flipud(hour), flipud(minute), flipud(sec))
+    with open('Zmap_catalog', 'w', newline='') as f:
+        writer = csv.writer(f, delimiter=' ', skipinitialspace=True, quoting=csv.QUOTE_NONE)
+        for x in rows:
+            writer.writerow(x)
+    return catalog
+
+
+def serial_time(input_):
+    # Converts the input date (string format) or date vectors (year, month, day, hour, minute, second) into timestamps
+    # Origin time: Year 0, Month 0, Day 0 (same as Matlab)
+
+    import datetime
+    import numpy as np
+
+    secs_per_day = 24.0 * 60.0 * 60.0
+
+    # input: catalog
+    if isinstance(input_, np.ndarray):
+        catalog_sel = input_
+        out1 = []
+        for i in range(len(catalog_sel[:, YearColumn])):
+            microseconds = round((catalog_sel[i, SecondColumn] - int(catalog_sel[i, SecondColumn])) * 1000000.0)
+            dates = datetime.datetime(int(catalog_sel[i, YearColumn]), int(catalog_sel[i, MonthColumn]),
+                                      int(catalog_sel[i, DayColumn]), int(catalog_sel[i, HourColumn]),
+                                      int(catalog_sel[i, MinuteColumn]), int(catalog_sel[i, SecondColumn]),
+                                      microseconds)
+            mdn = dates + datetime.timedelta(days=366)
+            frac_seconds = (dates - datetime.datetime(dates.year, dates.month, dates.day, 0, 0, 0)).seconds / (
+                secs_per_day)
+            frac_microseconds = dates.microsecond / (secs_per_day * 1000000.0)
+            out1.append(mdn.toordinal() + frac_seconds + frac_microseconds)
+        return out1
+
+    # input: date (as string)
+    elif isinstance(input_, str):
+        input_date = input_
+        time = datetime.datetime.strptime(input_date, '%Y,%m,%d,%H,%M,%S.%f')
+
+        mdn = time + datetime.timedelta(days=366)
+        frac_seconds = (time - datetime.datetime(time.year, time.month, time.day, 0, 0, 0)).seconds / (secs_per_day)
+        frac_microseconds = time.microsecond / (secs_per_day * 1000000)
+        out2 = mdn.toordinal() + frac_seconds + frac_microseconds
+        return out2
+
+
+def timestamp_to_datetime(timestamp):
+    # Converts custom timestamps to datetime objects
+
+    import datetime
+
+    days = timestamp % 1
+    hours = days % 1 * 24
+    minutes = hours % 1 * 60
+    seconds = minutes % 1 * 60
+
+    date_out = datetime.datetime.fromordinal(int(timestamp)) + datetime.timedelta(days=int(days)) + \
+               datetime.timedelta(hours=int(hours)) + datetime.timedelta(minutes=int(minutes)) + \
+               datetime.timedelta(seconds=seconds) - datetime.timedelta(days=366)
+
+    return date_out
+
+
+def lilliefors(magcat, alpha):
+    # Implements the Python routine "mc_lilliefors" [Marcus Herrmann, & Warner Marzocchi. (2020, November 22).
+    # Mc-Lilliefors: a completeness magnitude that complies with the exponential-like Gutenberg–Richter relation
+    # (Version 0.1). Zenodo. http://doi.org/10.5281/zenodo.4162497]
+
+    import pandas as pd
+    import sys
+
+    try:
+        import mc_lilliefors
+    except ImportError:
+        print("Ops! Module mc_lilliefors is missing! Download it here: http://doi.org/10.5281/zenodo.4162496")
+        sys.exit(1)
+
+    mag_series = pd.Series(magcat, dtype='float64')
+
+    lill = mc_lilliefors.McLilliefors(
+        mag_series,
+        alpha
+    )
+
+    lill.calc_testdistr_mcutoff(
+        n_repeats=50  # number of iterations for the random noise
+       )
+
+    mc = lill.estimate_Mc_expon_test()
+
+    return mc
+
+
+def b_value_zmap(magcat):
+    # Calculate the b_value, tha a_value and the completeness magnitude using the ZMAP approach
+
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from math import sqrt, log, log10
+
+    bin_m = 0.1
+    magmin = 0.0
+    magmax = max(magcat)
+    x = np.arange(magmin, magmax + bin_m, bin_m)
+    nevents = len(magcat)
+    y = np.arange(0, nevents)
+    rad_mag_cutoff = np.zeros(shape=(len(x), nevents))
+    ntotmag = np.zeros(shape=(len(x)))
+    difference = np.zeros(shape=(len(x)))
+    sumup = np.zeros(shape=(len(x)))
+    rad_mag_cutoff.fill(np.nan)
+    idx = -1
+    for i in x:
+        idx = idx + 1
+        for j in y:
+            if magcat[j] >= i:
+                ntotmag[idx] = ntotmag[idx] + 1
+                rad_mag_cutoff[idx][j] = magcat[j]
+    mean_mag_cutoff = np.nanmean(rad_mag_cutoff, axis=1)
+    idx = -1
+    for i in x:
+        idx = idx + 1
+        for j in y:
+            rad_mag_cutoff[idx][j] = (rad_mag_cutoff[idx][j] - mean_mag_cutoff[idx]) ** 2
+    sum_mag_cutoff = np.nansum(rad_mag_cutoff, axis=1)
+    bin_centers = 0.5 * (x[1:] + x[:-1])
+    no_cumulative, av, b = plt.hist(magcat, bin_centers)
+    avm = np.delete(av, [len(av) - 1])
+    # B_value from Marzocchi and Sandri (2003)
+    b_cutoff = 1 / (log(10) * ((mean_mag_cutoff) - (x - bin_m / 2)))
+    deltab_value = 2.3 * (b_cutoff ** 2) * np.sqrt(sum_mag_cutoff / (ntotmag * (ntotmag - 1)))
+    d_m1 = 0.5
+    dm = 0.1
+    b_ave = [(a + b + c + d + e) * dm / d_m1 for a, b, c, d, e in
+             zip(b_cutoff[0::1], b_cutoff[1::1], b_cutoff[2::1], b_cutoff[3::1], b_cutoff[4::1])]
+    dbi_old = [(a - b) for a, b in zip(b_cutoff[0::1], b_cutoff[1::1])]
+    idx = -1
+    q = np.arange(0, len(b_ave))
+    flag = 0
+    for i in q:
+        idx = idx + 1
+        difference[idx] = abs(b_ave[idx] - b_cutoff[idx])
+        sumup[idx] = b_ave[idx] + b_cutoff[idx]
+        if difference[idx] <= deltab_value[idx] and abs(dbi_old[idx]) <= 0.03:
+            sigmacomp = b_cutoff[idx] / sqrt(ntotmag[idx])
+            avaluecomp = log10(ntotmag[idx]) + b_cutoff[idx] * x[idx]
+            if flag == 0:
+                print('The completeness magnitude is=', x[idx], 'b-value=', b_cutoff[idx], 'error b-value=', sigmacomp,
+                      'a-value=', avaluecomp)
+                x = np.linspace(x[idx], magmax, 1000)
+                plt.semilogy(x, pow(10, avaluecomp - b_cutoff[idx] * x), linestyle='-', label='b-value')
+            flag = 1
+    # compare to normed hist output"
+    plt.semilogy(avm, no_cumulative, '^r')
+    fig21 = plt.gcf()
+    fig21.set_size_inches(6.5, 4.0)
+    cumulativerev = list(reversed(no_cumulative))
+    d2 = list(reversed(np.cumsum(cumulativerev)))
+    plt.semilogy(avm, d2, 'ok', linestyle='--')
+    plt.xlabel('$Magnitude$')
+    plt.ylabel('$Cumulative Number$')
+    plt.show()
+    return b_cutoff, deltab_value
+
+
+def b_value(magcat, mc):
+    from math import sqrt, log, log10
+    import numpy as np
+    bin_m = 0.1
+    magcat_new = ([mgev for mgev in magcat if mgev >= mc])
+    n = len(magcat_new)
+    diffmagcat = np.nansum(magcat_new - np.nanmean(magcat_new)) ** 2
+    b = 1.0 / (log(10) * (np.nanmean(magcat_new) - (mc - bin_m / 2)))
+    deltab = 2.3 * (b ** 2) * sqrt(diffmagcat / (n * (n - 1)))
+    sigma = b / sqrt(n)
+    avalue = log10(n) + b * mc
+    return b, deltab, sigma, avalue, magcat_new
+
+
+def bootstrap_mc(mag_data, alpha):
+    # Estimates uncertainty on magnitude of completeness mc by bootstrap method
+
+    import numpy as np
+
+    iterations = 200
+    mc_bootstrap = []
+    for _ in range(iterations):
+        boot = np.random.choice(mag_data, size=len(mag_data), replace=True)
+        boot_mc = lilliefors(boot, alpha)
+        mc_bootstrap.append(boot_mc)
+    mc_sigma_bootstrap = np.std(mc_bootstrap)  # mc standard deviation
+
+    return mc_sigma_bootstrap
+
+
+def mc_vs_time(magcat, magcat_presequence, mc_ok, alpha, serial_times, size, step):
+    # Analyses how the magnitude of completeness mc varies with time and identifies
+    # critical regions ("STAI gaps"), where mc >= mc_ok + 2 * sigma
+    # mc_ok is the reference value for mc estimated for the pre-sequence period
+
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import matplotlib.dates as mdates
+    from pandas.plotting import register_matplotlib_converters
+    register_matplotlib_converters()
+
+    mc_ok_sigma = bootstrap_mc(magcat_presequence, alpha)
+    upper_lim = mc_ok + 2 * mc_ok_sigma
+
+    i = np.arange(0, len(magcat) - size, step)  # starting indexes of the windows
+
+    # Moving window approach to estimate mc vs time
+    # mc is estimated every "step" ( = 250 by default) events, at t_window (the end time of the moving window)
+    mc_time = []
+    t_window = []  # time of the moving window (represented by the time of the last event within each window)
+    for j in i:
+        window = magcat[j: j + size]
+        mc_new = lilliefors(window, alpha)
+        mc_time.append(mc_new)
+        t_window.append(serial_times[j + size])
+
+    # Find the temporal bounds of the STAI gaps (where mc >= mc_ok + 2 * sigma)
+    tmp_hole_lower_lim = []
+    tmp_hole_upper_lim = []
+    for i in range(len(t_window) - 1):
+
+        # If these conditions are met, mc is exceeding the upper limit (mc >= mc_ok + 2 * sigma)
+        # --> STAI gap starts
+        if i == 0:
+            if mc_time[i] >= upper_lim:
+                # Set the STAI gap starting time to the time of the first event
+                tmp_hole_lower_lim.append(serial_times[0])
+
+        else:
+            if (mc_time[i] >= upper_lim and mc_time[i - 1] < upper_lim):
+                # Set the STAI gap starting time to the time of the largest shock in the step,
+                # which has caused the raise of the magnitude of completeness
+                idx_critical = [idx for idx, val in enumerate(serial_times) if t_window[i - 1] <= val <= t_window[i]]
+                val = magcat[idx_critical]
+                iidx_main = [j for j, v in enumerate(val) if v == max(val)]
+                idx_main = idx_critical[iidx_main[0]]
+                tmp_hole_lower_lim.append(serial_times[idx_main])
+
+        # If these conditions are met STAI gap ends
+        if i == len(t_window) - 2:
+            if mc_time[i] >= upper_lim and mc_time[i + 1] >= upper_lim:
+                tmp_hole_upper_lim.append(t_window[i + 1])
+        else:
+            if mc_time[i] >= upper_lim and mc_time[i + 1] < upper_lim:
+                tmp_hole_upper_lim.append(t_window[i])
+
+    # Exclude too small gaps, which could simply arise from statistical fluctuations  of the magnitude of completeness
+    hole_lower_lim , hole_upper_lim = [], []
+    for j in range(len(tmp_hole_lower_lim)):
+        index = [idx for idx, val in enumerate(serial_times) if
+                 tmp_hole_lower_lim[j] <= val < tmp_hole_upper_lim[j]]
+        if len(index) >= 2 * step:
+            hole_lower_lim.append(tmp_hole_lower_lim[j])
+            hole_upper_lim.append(tmp_hole_upper_lim[j])
+
+    print('Found:', len(hole_lower_lim), 'STAI gaps')
+
+    # Extract mc and relative times within each STAI gap
+    mc_times_hole, mc_hole = [], []
+    for j in range(len(hole_lower_lim)):
+        index = [idx for idx, val in enumerate(t_window) if hole_lower_lim[j] <= val < hole_upper_lim[j]]
+        tmp_mc_times_hole = [t_window[i] for i in index]
+        tmp_mc_hole = [mc_time[i] for i in index]
+        mc_times_hole.append(tmp_mc_times_hole)
+        mc_hole.append(tmp_mc_hole)
+
+    # plot mc vs time
+    t_window_plot = [val for idx, val in enumerate(t_window) if val >= hole_lower_lim[0]]
+    idx_t_window_plot = [idx for idx, val in enumerate(t_window) if val >= hole_lower_lim[0]]
+    dates1 = [timestamp_to_datetime(t_window_plot[i]) for i in range(len(t_window_plot))]
+    datenums1 = mdates.date2num(dates1)
+    mc_time_plot = [mc_time[i] for i in idx_t_window_plot]
+
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.xaxis_date()
+    fmt = mdates.DateFormatter('%b %Y')
+    ax.xaxis.set_major_formatter(fmt)
+    ax.plot(datenums1, mc_time_plot, label='Mc(t)', ls='-')
+    ax.fill_between(datenums1, upper_lim, mc_time_plot,
+                    where=upper_lim <= mc_time_plot, color='C1', alpha=0.7, label='$ M_{c} \geq M^{*}_{c} + 2 \sigma$')
+    ax.set_xlabel("Time")
+    ax.set_ylabel("Mc")
+    ax.legend(loc='upper right')
+    plt.savefig('fig/Mc_Time.pdf', format='pdf')
+
+    return hole_lower_lim, hole_upper_lim, mc_times_hole, mc_hole
+
+
+def map_plot(lon1, lat1, lon2, lat2, llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat):
+    from mpl_toolkits.basemap import Basemap
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    m = Basemap(projection='merc', resolution='i', llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat,
+                urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat)
+
+    try:
+        m.drawcoastlines(linewidth=0.5)
+    except:
+        pass
+    m.drawcountries()
+    m.drawmapboundary()
+    parallels = np.arange(-90, 90, 0.5)
+    meridians = np.arange(-180, 180, 0.5)
+    m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10, dashes=[1, 5])
+    m.drawmeridians(meridians, labels=[0, 0, 1, 0], fontsize=10, dashes=[1, 5], rotation=45)
+
+    x1, y1 = m(lon1, lat1)
+    x2, y2 = m(lon2, lat2)
+
+    plt.plot(x1, y1, 'o', color='0.3', alpha=.7, markersize=5, markeredgecolor='w')
+    plt.plot(x2, y2, 'o', color='steelblue', alpha=.7, markersize=5, markeredgecolor='w')
+    plt.xlabel('Longitude', fontsize=12, labelpad=15)
+    plt.ylabel('Latitude', fontsize=12, labelpad=40)
+
+    return m
+
+
+def spatial_smoothing(subcatalog, xmin, xmax, ymin, ymax):
+    # Smooth seismicity with Gaussian kernel
+
+    import numpy as np
+    pi = np.pi
+    sbin = 0.01
+    sbinx = sbin
+    sbiny = sbin
+    Sigma = 1
+
+    rows_cat, columns_cat = np.shape(subcatalog)
+    X = np.arange(xmin, xmax + sbinx, sbinx)
+    Y = np.arange(ymin, ymax + sbiny, sbiny)
+    xi, yi = np.meshgrid(X, Y)
+    Smooth = np.zeros((len(X) * len(Y), 5))
+    Smooth[:, 0] = np.reshape(xi, (len(X) * len(Y)))
+    Smooth[:, 1] = np.reshape(yi, (len(X) * len(Y)))
+    onetoten = range(0, rows_cat)
+    for i in onetoten:
+        A = (np.sin(Smooth[:, 1] * pi / 180) * np.sin(float(subcatalog[i, 1]) * pi / 180) + np.cos(
+            Smooth[:, 1] * pi / 180) * np.cos(float(subcatalog[i, 1]) * pi / 180) * np.cos(
+            ((float(subcatalog[i, 0])) - Smooth[:, 0]) * pi / 180))
+        R = np.arccos(A) * 6371
+        W = (1 / (2 * pi * pow(Sigma, 2))) * (np.exp((-pow(R, 2)) / (2 * pow(Sigma, 2))))
+        Smooth[:, 3] = Smooth[:, 3] + W
+
+    TotalRate = sum(Smooth[:, 3])
+    Smooth[:, 4] = Smooth[:, 3] / TotalRate
+
+    lon_cell_grid = Smooth[:, 0]
+    lan_cell_grid = Smooth[:, 1]
+    smoothed_rate = Smooth[:, 4]
+
+    return lon_cell_grid, lan_cell_grid, smoothed_rate, sbin
+
+
+def trainv(lat1, long1, distx, disty):
+    import math
+
+    r0 = 6367.
+    alfa = 57.29578
+    fi1 = lat1 + alfa * disty / r0
+    fr1 = fi1 / alfa
+    lat2 = fi1 - alfa * distx * distx / (2. * r0 * r0) * math.tan(lat1 / alfa)
+    lon2 = long1 + alfa * distx / (r0 * math.cos(fr1)) - alfa * distx ** 3 / (3. * r0 ** 3) * (
+        math.tan(fr1)) ** 2 / math.cos(fr1)
+
+    return lat2, lon2
+
+
+def magnitude_distribution(magcat):
+    import numpy as np
+    import math
+
+    min_m = min(magcat)
+    max_m = max(magcat)
+    bin_m = 0.1
+
+    bin_edges = np.arange(min_m, max_m + bin_m, bin_m)
+    bin_counts = np.zeros(len(bin_edges), dtype=int)
+    for mag in magcat:
+        bin = int(round(mag / bin_m - min_m*10))
+        bin_counts[bin] += 1
+
+    idx_zeros = np.where(bin_counts == 0)[0]
+    bin_counts = np.delete(bin_counts, idx_zeros)
+    bin_edges = np.delete(bin_edges, idx_zeros)
+
+    log_bin_counts = [math.log10(i) for i in bin_counts]  # non-cumulative counts
+
+    cum_counts = np.cumsum(bin_counts[::-1])[::-1]
+    log_cum_counts = [math.log10(i) for i in cum_counts]  # cumulative counts
+
+    return bin_edges, log_bin_counts, log_cum_counts
+
+
+def ghost_element(catalog_sel, mc_ok, magcat, magcat_presequence, b, size, step, llcrnrlon, llcrnrlat,
+                  urcrnrlon, urcrnrlat, alpha, serial_times):
+    # Simulates magnitude, time, lat and lon of ghost events
+
+    import matplotlib.pyplot as plt
+    import matplotlib.dates as mdates
+    import math
+    import numpy as np
+    import decimal
+    decimal.getcontext().prec = 2
+
+    bin_m = 0.1
+
+    # "hole" --> STAI gap
+    hole_lower_lim, hole_upper_lim, mc_times_hole, mc_hole = mc_vs_time(magcat, magcat_presequence, mc_ok,
+                                                                        alpha, serial_times, size, step)
+    m_ghost, t_ghost, n_ghost_hole = [], [], []
+    for i in range(len(hole_lower_lim)):  # iterate over all the STAI gaps
+
+        n_ghost_step_list = []
+        for j in range(len(mc_hole[i])):  # iterate over all the steps in the STAI gap
+
+            mc_step = mc_hole[i][j]  # mc value for a given step
+
+            if j == 0:
+
+                index = [idx for idx, val in enumerate(serial_times) if hole_lower_lim[i] < val <= mc_times_hole[i][j]]
+
+            else:
+
+                index = [idx for idx, val in enumerate(serial_times) if
+                         mc_times_hole[i][j - 1] < val <= mc_times_hole[i][j]]
+
+            nbin = int(mc_step * 10 - mc_ok * 10)  # total number of bins in the step
+
+            # Compare expected and observed number of events for each bin in the step
+            count = 0
+            m2 = mc_step  # current upper bound of magnitude bin
+            m1 = float(decimal.Decimal(mc_step) - decimal.Decimal(bin_m))  # current lower bound of magnitude bin
+
+            # Expected number of magnitudes m >= mc_step (since magnitudes in the step are complete for m >= mc_step,
+            # the EXPECTED number of magnitudes with m >= mc_step equals the OBSERVED number of magnitudes with m >= mc_step)
+            n_m2 = len([magcat[i] for i in index if magcat[i] >= m2])
+
+            n_ghost_bin_list = []
+            while count < nbin:  # iterate over all the bins in the step
+
+                # Expected number of magnitude m >= m1
+                n_m1 = int(n_m2 * 10 ** (b * bin_m))
+                # Expected number of magnitudes in the bin (i.e. with m == m1)
+                n_exp_bin = n_m1 - n_m2
+
+                # Observed number of magnitudes in the bin (i.e. with m == m1)
+                n_obs_bin = len([magcat[i] for i in index if magcat[i] == m1])
+
+                # Estimate the number of ghost magnitudes in the bin
+                n_ghost_bin = n_exp_bin - n_obs_bin
+
+                if n_ghost_bin <= 0:  # due to the inherent variability, bins can have more magnitude values than those expected!
+                    # Update variables
+                    m2 = m1
+                    m1 = float(decimal.Decimal(m2) - decimal.Decimal(bin_m))
+                    n_m2 = n_m1
+                    count += 1
+                    continue
+
+                else:
+                    # List of missing magnitudes
+                    m_ghost_bin = [m1] * n_ghost_bin
+
+                    # Update variables
+                    m2 = m1
+                    m1 = float(decimal.Decimal(m2) - decimal.Decimal(bin_m))
+                    n_m2 = n_m1
+                    count += 1
+
+                    m_ghost.extend(m_ghost_bin)  # append all magnitudes to one list
+                    n_ghost_bin_list.append(n_ghost_bin)  # from bin to step scale
+
+            n_ghost_step = sum(n_ghost_bin_list)  # number of ghost events in the current step
+
+            # Times random sampling
+            for _ in range(n_ghost_step):
+
+                u = np.random.random()
+
+                # times are sampled from an uniform distribution whose support are the limits of the interval
+                if j == 1:
+                    random_t = hole_lower_lim[i] + u * (mc_times_hole[i][1] - hole_lower_lim[i])
+                    # Add the events occurred after the large event to the first interval, i.e.
+                    # the events occurred in the range [hole_lower_lim[i], mc_times_hole[i][0]]
+                    # to those occurred in the first interval ( [mc_times_hole[i][0], mc_times_hole[i][1]] )
+                else:
+                    random_t = mc_times_hole[i][j - 1] + u * (mc_times_hole[i][j] - mc_times_hole[i][j - 1])
+                    # Eqs occurred in the range [mc_times_hole[i][j-1], mc_times_hole[i][j]]
+
+                t_ghost.append(random_t)  # append all occurrence times to one list
+
+            n_ghost_step_list.append(n_ghost_step)  # from step to STAI gap scale
+        print('Filled', i + 1, 'over a total of', len(hole_lower_lim), 'STAI gaps with', sum(n_ghost_step_list),
+                      'events')
+
+        n_ghost_hole.append(sum(n_ghost_step_list))  # append the number of ghost events in the current STAI gap
+
+    # Simulate lat e lon of ghost events
+    lon_ghost, lat_ghost = [], []
+    for i in range(len(hole_lower_lim)):  # iterate over all the STAI gaps
+        lon_ghost_hole = []
+        lat_ghost_hole = []
+        index = [idx for idx, val in enumerate(serial_times) if hole_lower_lim[i] <= val <= hole_upper_lim[i]]
+        subcatalog_tmp = catalog_sel[index]
+        mag_big_shock = max(subcatalog_tmp[:, MagnColumn])
+        lat_big_shock = subcatalog_tmp[(subcatalog_tmp[:, MagnColumn] == mag_big_shock), LatColumn][0]
+        lon_big_shock = subcatalog_tmp[(subcatalog_tmp[:, MagnColumn] == mag_big_shock), LonColumn][0]
+
+        moment_big_shock = 10 ** (3 / 2 * (mag_big_shock + 10.7)) * 10 ** (-7)
+        l_big_shock = 10 ** (-5.20 + 0.35 * math.log10(moment_big_shock))  # rupture length (from Mai and Beroza (2000))
+
+        ymax, xmax = trainv(lat_big_shock, lon_big_shock, l_big_shock, l_big_shock)
+        ymin, xmin = trainv(lat_big_shock, lon_big_shock, -l_big_shock, -l_big_shock)
+
+        subcatalog = subcatalog_tmp[
+                     (subcatalog_tmp[:, LatColumn] >= ymin) & (subcatalog_tmp[:, LatColumn] <= ymax) &
+                     (subcatalog_tmp[:, LonColumn] >= xmin) & (subcatalog_tmp[:, LonColumn] <= xmax), :]
+
+        lon_cell_grid, lat_cell_grid, smoothed_rate, sbin = spatial_smoothing(subcatalog, xmin, xmax, ymin, ymax)
+
+        # Simulate lat and lon of ghost eqs
+        idx = np.argsort(smoothed_rate)
+        cumulative_sum = np.cumsum(smoothed_rate[idx])
+
+        # Generate n=n_ghost_hole[i] random numbers btw 0 and 1
+        u = np.random.random(n_ghost_hole[i])
+
+        for j in range(len(u)):
+            if u[j] < cumulative_sum[0]:
+                rand_lon = lon_cell_grid[idx[0]] - np.random.random() * sbin
+                rand_lat = lat_cell_grid[idx[0]] - np.random.random() * sbin
+            else:
+                for k in range(1, len(smoothed_rate)):
+                    if (cumulative_sum[k - 1] <= u[j]) and (u[j] < cumulative_sum[k]):
+                        rand_lon = lon_cell_grid[idx[k - 1]] + np.random.random() * sbin
+                        rand_lat = lat_cell_grid[idx[k - 1]] + np.random.random() * sbin
+
+            lon_ghost_hole.append(round(rand_lon, 2))
+            lat_ghost_hole.append(round(rand_lat, 2))
+
+        lon_ghost.extend(lon_ghost_hole)
+        lat_ghost.extend(lat_ghost_hole)
+
+    idx_magcatok = [idx for idx, val in enumerate(magcat) if val >= mc_ok]
+    magcat_ok = [magcat[i] for i in idx_magcatok]
+    serial_times_ok = [serial_times[i] for i in idx_magcatok]
+
+    # plots
+    # Plot original (m >= mc) + simulated eqs
+
+    dates1 = [timestamp_to_datetime(serial_times_ok[i]) for i in range(len(serial_times_ok))]
+    dates2 = [timestamp_to_datetime(t_ghost[i]) for i in range(len(t_ghost))]
+    datenums1 = mdates.date2num(dates1)
+    datenums2 = mdates.date2num(dates2)
+
+    fig, ax = plt.subplots(2, figsize=(10, 6))
+    fmt = mdates.DateFormatter('%b %Y')
+    ax[0].plot(datenums1, magcat_ok, 'o', markersize=3, markerfacecolor='0.3', markeredgecolor='None',
+               label='Original data')
+    ax[0].plot(datenums2, m_ghost, 'o', markersize=3, markerfacecolor='steelblue', markeredgecolor='None',
+               label='Replenished data')
+    ax[0].xaxis_date()
+    ax[0].xaxis.set_major_formatter(fmt)
+    ax[0].set_xlabel("Time")
+    ax[0].set_ylabel("Magnitude")
+    ax[0].legend(loc='upper left')
+    ax[1].plot(datenums1, magcat_ok, 'o', markersize=3, markerfacecolor='0.3', markeredgecolor='None',
+               label='Original data')
+    ax[1].xaxis_date()
+    ax[1].xaxis.set_major_formatter(fmt)
+    ax[1].set_xlabel("Time")
+    ax[1].set_ylabel("Magnitude")
+    ax[1].legend(loc='upper left')
+    plt.subplots_adjust(hspace=0.5)
+    plt.savefig('fig/Magnitude_Time.pdf', format='pdf')
+
+    # Map
+    # Plot original (m >= mc) + simulated eqs
+
+    lat = catalog_sel[:, LatColumn]
+    lon = catalog_sel[:, LonColumn]
+    lat_compl = [lat[i] for i in idx_magcatok]
+    lon_compl = [lon[i] for i in idx_magcatok]
+
+    fig, ax = plt.subplots()
+    m = map_plot(lon_compl, lat_compl, lon_ghost, lat_ghost, llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat)
+    plt.savefig('fig/Spatial_map.pdf', format='pdf')
+
+    return m_ghost, t_ghost, lon_ghost, lat_ghost
+
+
+def replenished_catalog(catalog_sel, magcat, magcat_presequence, mc_ok, b, size, step,
+                       llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat, alpha, serial_times):
+    # Returns the replenished catalog (original + ghost events)
+
+    import numpy as np
+    import csv
+    import matplotlib.pyplot as plt
+    import os
+
+    path = "fig"
+    if not os.path.exists(path):
+        os.mkdir(path)
+
+    m_ghost, t_ghost, lon_ghost, lat_ghost = ghost_element(catalog_sel, mc_ok, magcat, magcat_presequence, b,
+                                                           size, step, llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat,
+                                                           alpha, serial_times)
+
+    magcat_compl_idx = [idx for idx, val in enumerate(magcat) if val >= mc_ok]
+
+    magcat_compl = [magcat[i] for i in magcat_compl_idx]
+    serial_times_compl = [serial_times[i] for i in magcat_compl_idx]
+
+    new_times = np.array(serial_times_compl + t_ghost)
+    sorted_new_times = new_times[np.argsort(new_times)]
+
+    new_mag = np.array(magcat_compl + m_ghost)
+    sorted_new_mag = new_mag[np.argsort(new_times)]
+
+    lat = catalog_sel[:, LatColumn]
+    lon = catalog_sel[:, LonColumn]
+    lat_compl = [lat[i] for i in magcat_compl_idx]
+    lon_compl = [lon[i] for i in magcat_compl_idx]
+
+    new_lat = np.array(lat_compl + lat_ghost)
+    sorted_new_lat = new_lat[np.argsort(new_times)]
+
+    new_lon = np.array(lon_compl + lon_ghost)
+    sorted_new_lon = new_lon[np.argsort(new_times)]
+
+    # flag: 0 for original events, 1 for simulated events
+    flag = np.array([0] * len(new_times))
+    for i in range(len(new_times)):
+        if i <= len(serial_times_compl):
+            flag[i] = 0
+        else:
+            flag[i] = 1
+
+    sorted_flag = flag[np.argsort(new_times)]
+
+    # Magnitude-Time plots
+
+    x1 = np.arange(1, len(magcat_compl) + 1)
+    x2 = np.arange(1, len(sorted_new_mag) + 1)
+
+    fig, ax = plt.subplots()
+    ax.set_xlabel("Sequential number")
+    ax.set_ylabel("Magnitude")
+    ax.set_title('Original catalog')
+    plt.plot(x1, magcat_compl, 'o', markersize=3, markerfacecolor='0.3', markeredgecolor='None')
+    plt.savefig('fig/Magnitude_SeqNumbers_Original.pdf', format='pdf')
+
+    fig, ax = plt.subplots()
+    ax.set_xlabel("Sequential number")
+    ax.set_ylabel("Magnitude")
+    ax.set_title('Replenished catalog')
+    plt.plot(x2, sorted_new_mag, 'o', markersize=3, markerfacecolor='0.3', markeredgecolor='None')
+    plt.savefig('fig/Magnitude_SeqNumbers_Replenished.pdf', format='pdf')
+
+    depth = np.zeros(len(new_times))
+
+    sorted_new_year, sorted_new_month, sorted_new_day, sorted_new_hour, sorted_new_min, sorted_new_sec = \
+        [], [], [], [], [], []
+
+    for i in range(len(new_times)):
+        date_out = timestamp_to_datetime(sorted_new_times[i])
+        sorted_new_year.append(int(date_out.year))
+        sorted_new_month.append(int(date_out.month))
+        sorted_new_day.append(int(date_out.day))
+        sorted_new_hour.append(int(date_out.hour))
+        sorted_new_min.append(int(date_out.minute))
+        sec = float(date_out.second) + (float(date_out.microsecond) / 1000000)
+        sorted_new_sec.append(int(sec*100)/100.)
+
+    nrows = len(new_times)
+    ncols = 11
+    replenished_catalog = np.ndarray((nrows, ncols), dtype=object)
+    replenished_catalog[:, LonColumn] = sorted_new_lon[:]
+    replenished_catalog[:, LatColumn] = sorted_new_lat[:]
+    replenished_catalog[:, YearColumn] = sorted_new_year[:]
+    replenished_catalog[:, MonthColumn] = sorted_new_month[:]
+    replenished_catalog[:, DayColumn] = sorted_new_month[:]
+    replenished_catalog[:, MagnColumn] = sorted_new_mag[:]
+    replenished_catalog[:, DepthColumn] = np.zeros(len(new_times))
+    replenished_catalog[:, HourColumn] = sorted_new_hour[:]
+    replenished_catalog[:, MinuteColumn] = sorted_new_min[:]
+    replenished_catalog[:, SecondColumn] = sorted_new_sec[:]
+    replenished_catalog[:, ncols - 1] = sorted_flag[:]
+
+    rows = zip(sorted_new_lon, sorted_new_lat, sorted_new_year, sorted_new_month, sorted_new_day,
+               sorted_new_mag, depth, sorted_new_hour, sorted_new_min, sorted_new_sec, sorted_flag)
+    with open('Replenished_catalog.txt', 'w') as f:
+        writer = csv.writer(f, delimiter=' ', skipinitialspace=False, quoting=csv.QUOTE_NONE, lineterminator='\n')
+        for x in rows:
+            writer.writerow(x)
+
+    bin_edges_original, log_bin_counts_original, log_cum_counts_original = \
+        magnitude_distribution(catalog_sel[:, MagnColumn])
+    bin_edges_replenished, log_bin_counts_replenished, log_cum_counts_replenished = \
+        magnitude_distribution(replenished_catalog[:, MagnColumn])
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 6))
+    ax1.plot(bin_edges_original, log_bin_counts_original, 'o', color='0.5', markersize=3, label='Non-cumulative counts')
+    ax1.plot(bin_edges_original, log_cum_counts_original, 'ok', markersize=3, label='Cumulative counts')
+    ax1.legend(loc='upper right')
+    ax1.set_xlabel("Magnitude")
+    ax1.set_ylabel("Frequency")
+    ax1.set_title("Original Catalog")
+    ax2.plot(bin_edges_replenished, log_bin_counts_replenished, 'o', color='0.5', markersize=3, label='Non-cumulative counts')
+    ax2.plot(bin_edges_replenished, log_cum_counts_replenished, 'ok', markersize=3, label='Cumulative counts')
+    ax2.legend(loc='upper right')
+    ax2.set_xlabel("Magnitude")
+    ax2.set_ylabel("Frequency")
+    ax2.set_title("Replenished Catalog")
+    plt.savefig('fig/Magnitude_Frequency_Distribution.pdf', format='pdf')
+
+    return replenished_catalog
diff --git a/Synthetic_Test/Run_Synthetic_Test.py b/Synthetic_Test/Run_Synthetic_Test.py
new file mode 100644
index 0000000..6e9a13a
--- /dev/null
+++ b/Synthetic_Test/Run_Synthetic_Test.py
@@ -0,0 +1,85 @@
+if __name__ == '__main__':
+    
+    # |---------------------------|
+    # |--- SET INPUT PARAMETERS --|
+    # |---------------------------|
+    
+    # ZMAP catalog name and delimiter
+    name_catalog = "ETAS_incomplete.txt"
+    delimiter = ","
+
+    # Moving window parameters
+    size = 1000
+    step = 250
+
+    # Spatial map domain limits: lower-left longitude, lower-left latitude, upper-right longitude, upper-right latitude
+    llcrnrlon = -123.5
+    llcrnrlat = 30.
+    urcrnrlon = -112.5
+    urcrnrlat = 37.
+
+    # b-value: comment this row if you want RESTORE to estimate the b-value for you! - DEFAULT: user's input
+    b = 1
+
+    # mc_ok: uncomment this row if you want to provide your reference value for the magnitude of completeness!
+    # - DEFAULT: estimated with RESTORE.lilliefors(magcat_presequence, alpha)
+    mc_ok = 2.0
+
+    # Significance level for the Lilliefors test
+    alpha = 0.05
+
+
+    # ------------------------------------------------------------------------------------------------------------- #
+    # ------------------------------------------------- RESTORE --------------------------------------------------- #
+    # ------------------------------------------------------------------------------------------------------------- #
+
+    import RESTORE
+
+    # |---------------------------|
+    # |--- LOAD (ZMAP) CATALOG ---|
+    # |---------------------------|
+  
+    catalog_sel = RESTORE.read_catalog_zmap(name_catalog, delimiter)
+
+    # |-----------------------------------------------------------------|
+    # |--- REFERENCE VALUE FOR THE MAGNITUDE OF COMPLETENESS (mc_ok) ---|
+    # |-----------------------------------------------------------------|
+
+    MagnColumn = 5
+    magcat = catalog_sel[:, MagnColumn]
+    
+    serial_times = RESTORE.serial_time(catalog_sel)  # time stamps
+    iidx_main = [j for j, v in enumerate(catalog_sel[:, MagnColumn]) if v == max(catalog_sel[:, MagnColumn])]
+    starting_time_sequence = serial_times[iidx_main[0]]
+    
+    idx_pre_sequence = [idx for idx, val in enumerate(serial_times) if val < starting_time_sequence]
+
+    magcat_presequence = magcat[idx_pre_sequence]
+
+    if 'mc_ok' not in globals():
+        mc_ok = RESTORE.lilliefors(magcat_presequence, alpha)
+
+    # |-------------------------------------------------|
+    # |--- COMPLETE CATALOG (m >= mc_ok) AND B-VALUE ---|
+    # |-------------------------------------------------|
+
+    magcat_compl_idx = [idx for idx, val in enumerate(magcat) if val >= mc_ok]
+    catalog_sel_compl = [catalog_sel[i, :] for i in magcat_compl_idx]
+    
+    if 'b' not in globals():
+        b, _, _, _, _ = RESTORE.b_value(magcat, mc_ok)
+    
+    # |-----------------------|
+    # |--- RUN RESTORE.py ----|
+    # |-----------------------|
+
+    replenished_catalog = RESTORE.replenished_catalog(catalog_sel, magcat, magcat_presequence, mc_ok, b, size, step,
+                                                       llcrnrlon, llcrnrlat, urcrnrlon, urcrnrlat, alpha, serial_times)
+                                                       
+    # |---------------------------|
+    # |--- LILLIEFORS CHECK.py ---|
+    # |---------------------------|
+    
+    mc_original_cat = RESTORE.lilliefors(catalog_sel[:, MagnColumn], alpha)
+    mc_replenished_cat = RESTORE.lilliefors(replenished_catalog[:, MagnColumn], alpha)
+