diff --git a/new_scripts.py b/new_scripts.py
index 38dd212ce..20e7fc265 100644
--- a/new_scripts.py
+++ b/new_scripts.py
@@ -186,5 +186,286 @@ c = int(sys.argv[3])
 # run the code
 generate_zama_curves64(sd_range= (b,c), target_security_levels=[a], name="{}".format(a))
 
+from estimator_new import *
+from sage.all import oo, save, load
+from math import log2
+import multiprocessing
 
 
+def old_models(security_level, sd, logq=32):
+    """
+    Use the old model as a starting point for the data gathering step
+    :param security_level: the security level under consideration
+    :param sd            : the standard deviation of the LWE error distribution Xe
+    :param logq          : the (base 2 log) value of the LWE modulus q
+    """
+
+    def evaluate_model(a, b, stddev=sd):
+        return (stddev - b)/a
+
+    models = dict()
+
+    models["80"] = (-0.04049295502947623, 1.1288318226557081 + logq)
+    models["96"] = (-0.03416314056943681, 1.4704806061716345 + logq)
+    models["112"] = (-0.02970984362676178, 1.7848907787798667 + logq)
+    models["128"] = (-0.026361288425133814, 2.0014671315214696 + logq)
+    models["144"] = (-0.023744534465622812, 2.1710601038230712 + logq)
+    models["160"] = (-0.021667220727651954, 2.3565507936475476 + logq)
+    models["176"] = (-0.019947662046189942, 2.5109588704235803 + logq)
+    models["192"] = (-0.018552804646747204, 2.7168913723130816 + logq)
+    models["208"] = (-0.017291091126923574, 2.7956961446214326 + logq)
+    models["224"] = (-0.016257546811508806, 2.9582401000615226 + logq)
+    models["240"] = (-0.015329741032015766, 3.0744579055889782 + logq)
+    models["256"] = (-0.014530554319171845, 3.2094375376751745 + logq)
+
+    (a, b) = models["{}".format(security_level)]
+    n_est = evaluate_model(a, b, sd)
+
+    return round(n_est)
+
+
+def estimate(params, red_cost_model=RC.BDGL16, skip=("arora-gb", "bkw")):
+    """
+    Retrieve an estimate using the Lattice Estimator, for a given set of input parameters
+    :param params: the input LWE parameters
+    :param red_cost_model: the lattice reduction cost model
+    :param skip: attacks to skip
+    """
+
+    est = LWE.estimate(params, red_cost_model=red_cost_model, deny_list=skip)
+
+    return est
+
+
+def get_security_level(est, dp=2):
+    """
+    Get the security level lambda from a Lattice Estimator output
+    :param est: the Lattice Estimator output
+    :param dp: the number of decimal places to consider
+    """
+    attack_costs = []
+    # note: key does not need to be specified est vs est.keys()
+    for key in est:
+        attack_costs.append(est[key]["rop"])
+    # get the security level correct to 'dp' decimal places
+    security_level = round(log2(min(attack_costs)), dp)
+
+    return security_level
+
+
+def inequality(x, y):
+    """ A utility function which compresses the conditions x < y and x > y into a single condition via a multiplier
+    :param x: the LHS of the inequality
+    :param y: the RHS of the inequality
+    """
+    if x <= y:
+        return 1
+
+    if x > y:
+        return -1
+
+
+def automated_param_select_n(params, target_security=128):
+    """ A function used to generate the smallest value of n which allows for
+    target_security bits of security, for the input values of (params.Xe.stddev,params.q)
+    :param params: the standard deviation of the error
+    :param target_security: the target number of bits of security, 128 is default
+
+    EXAMPLE:
+    sage: X = automated_param_select_n(Kyber512, target_security = 128)
+    sage: X
+    456
+    """
+
+    # get an estimate based on the prev. model
+    print("n = {}".format(params.n))
+    n_start = old_models(target_security, log2(params.Xe.stddev), log2(params.q))
+    # n_start = max(n_start, 450)
+    # TODO: think about throwing an error if the required n < 450
+
+    params = params.updated(n=n_start)
+    costs2 = estimate(params)
+    security_level = get_security_level(costs2, 2)
+    z = inequality(security_level, target_security)
+
+    # we keep n > 2 * target_security as a rough baseline for mitm security (on binary key guessing)
+    while z * security_level < z * target_security:
+        # TODO: fill in this case! For n > 1024 we only need to consider every 256 (optimization)
+        params = params.updated(n = params.n + z * 8)
+        costs = estimate(params)
+        security_level = get_security_level(costs, 2)
+
+        if -1 * params.Xe.stddev > 0:
+            print("target security level is unattainable")
+            break
+
+    # final estimate (we went too far in the above loop)
+    if security_level < target_security:
+        # we make n larger
+        print("we make n larger")
+        params = params.updated(n=params.n + 8)
+        costs = estimate(params)
+        security_level = get_security_level(costs, 2)
+
+    print("the finalised parameters are n = {}, log2(sd) = {}, log2(q) = {}, with a security level of {}-bits".format(params.n,
+                                                                                                                      log2(params.Xe.stddev),
+                                                                                                                      log2(params.q),
+                                                                                                                      security_level))
+
+    if security_level < target_security:
+        params.updated(n=None)
+
+    return params, security_level
+
+
+def generate_parameter_matrix(params_in, sd_range, target_security_levels=[128], name="default_name"):
+    """
+    :param params_in: a initial set of LWE parameters
+    :param sd_range: a tuple (sd_min, sd_max) giving the values of sd for which to generate parameters
+    :param target_security_levels: a list of the target number of bits of security, 128 is default
+    :param name: a name to save the file
+    """
+
+    (sd_min, sd_max) = sd_range
+    for lam in target_security_levels:
+        for sd in range(sd_min, sd_max + 1):
+            print("run for {}".format(lam, sd))
+            Xe_new = nd.NoiseDistribution.DiscreteGaussian(2**sd)
+            (params_out, sec) = automated_param_select_n(params_in.updated(Xe=Xe_new), target_security=lam)
+
+            try:
+                results = load("{}.sobj".format(name))
+            except:
+                results = dict()
+                results["{}".format(lam)] = []
+
+            results["{}".format(lam)].append((params_out.n, log2(params_out.q), log2(params_out.Xe.stddev), sec))
+            save(results, "{}.sobj".format(name))
+
+    return results
+
+
+def generate_zama_curves64(sd_range=[2, 58], target_security_levels=[128], name="default_name"):
+    """
+    The top level function which we use to run the experiment
+
+    :param sd_range: a tuple (sd_min, sd_max) giving the values of sd for which to generate parameters
+    :param target_security_levels: a list of the target number of bits of security, 128 is default
+    :param name: a name to save the file
+    """
+    if __name__ == '__main__':
+
+        D = ND.DiscreteGaussian
+        vals = range(sd_range[0], sd_range[1])
+        pool = multiprocessing.Pool(2)
+        init_params = LWE.Parameters(n=1024, q=2 ** 64, Xs=D(0.50, -0.50), Xe=D(2 ** 55), m=oo, tag='params')
+        inputs = [(init_params, (val, val), target_security_levels, name) for val in vals]
+        res = pool.starmap(generate_parameter_matrix, inputs)
+
+    return "done"
+
+
+# The script runs the following commands
+import sys
+# grab values of the command-line input arguments
+a = int(sys.argv[1])
+b = int(sys.argv[2])
+c = int(sys.argv[3])
+# run the code
+generate_zama_curves64(sd_range= (b,c), target_security_levels=[a], name="{}".format(a))
+
+import numpy as np
+from sage.all import save, load
+
+def sort_data(security_level):
+    from operator import itemgetter
+
+    # step 1. load the data
+    X = load("{}.sobj".format(security_level))
+
+    # step 2. sort by SD
+    x = sorted(X["{}".format(security_level)], key = itemgetter(2))
+
+    # step3. replace the sorted value
+    X["{}".format(security_level)] = x
+
+    return X
+
+def generate_curve(security_level):
+
+    # step 1. get the data
+    X = sort_data(security_level)
+
+    # step 2. group the n and sigma data into lists
+    N = []
+    SD = []
+    for x in X["{}".format(security_level)]:
+        N.append(x[0])
+        SD.append(x[2] + 0.5)
+    
+    # step 3. perform interpolation and return coefficients
+    (a,b) = np.polyfit(N, SD, 1)
+
+    return a, b
+    
+
+def verify_curve(security_level, a = None, b = None):
+
+    # step 1. get the table and max values of n, sd
+    X = sort_data(security_level)
+    n_max = X["{}".format(security_level)][0][0]
+    sd_max = X["{}".format(security_level)][-1][2]
+
+    # step 2. a function to get model values
+    def f_model(a, b, n):
+        return ceil(a * n + b)
+
+    # step 3. a function to get table values
+    def f_table(table, n):
+        for i in range(len(table)):
+            n_val = table[i][0]
+            if n < n_val:
+                pass
+            else:
+                j = i 
+                break
+        
+        # now j is the correct index, we return the corresponding sd
+        return table[j][2]
+
+    # step 3. for each n, check whether we satisfy the table
+    n_min = max(2 * security_level, 450, X["{}".format(security_level)][-1][0])
+    print(n_min)
+    print(n_max)
+
+    for n in range(n_max, n_min, - 1):
+        model_sd = f_model(a, b, n)
+        table_sd = f_table(X["{}".format(security_level)], n)
+        print(n , table_sd, model_sd, model_sd >= table_sd)
+
+        if table_sd > model_sd:
+            print("MODEL FAILS at n = {}".format(n))
+            return "FAIL"
+
+    return "PASS", n_min
+
+
+def generate_and_verify(security_levels, log_q, name = "verified_curves"):
+
+    data = []
+
+    for sec in security_levels:
+        print("WE GO FOR {}".format(sec))
+        # generate the model for security level sec
+        (a_sec, b_sec) = generate_curve(sec)
+        # verify the model for security level sec
+        res = verify_curve(sec, a_sec, b_sec)
+        # append the information into a list
+        data.append((a_sec, b_sec - log_q, sec, res[0], res[1]))
+        save(data, "{}.sobj".format(name))
+    
+    return data
+
+# To verify the curves we use
+generate_and_verify([80, 96, 112, 128, 144, 160, 176, 192, 256], log_q = 64)
+