Commit 2be6ad72 authored by Ding, Lijie's avatar Ding, Lijie
Browse files

make saving config and Iq2d_af flexible

parent 9824c2af

analyze/ML_analyze.py

+9 −8
Original line number Diff line number Diff line
@@ -38,7 +38,7 @@ def get_feature_Iq_gq_data(folder, parameters, random=True): 
        data = np.genfromtxt(filename, delimiter=",", skip_header=1)

        bnum_phi = len(data[0]) - 7

        print(f"bnum_phi: {bnum_phi}")

        bnum_r = int((len(data) - 2) / 3)

        bnum_r = int((len(data) - 2) / 2)

        print(f"bnum_r: {bnum_r}")



        Rmu, n, sigma, sqrtD, gxy = data[0, 1], data[0, 2], data[0, 3], data[0, 4], data[0, 5]
@@ -46,16 +46,17 @@ def get_feature_Iq_gq_data(folder, parameters, random=True): 
        qphi = data[1, 7:]

        qr = data[2 : 2 + bnum_r, 6]

        Iq2D = data[2 : 2 + bnum_r, 7:]

        Iq2D_af = data[2 + bnum_r : 2 + 2 * bnum_r, 7:]

        IqIq_af = data[2 + 2 * bnum_r : 2 + 3 * bnum_r, 7:]

        #Iq2D_af = data[2 + bnum_r : 2 + 2 * bnum_r, 7:]

        IqIq_af = data[2 + bnum_r : 2 + 2 * bnum_r, 7:]



        gq = IqIq_af / (Iq2D * Iq2D)

        QPHI, QR = np.meshgrid(qphi, qr)

        gq_phi = np.mean(gq*QR, axis=0)/np.mean(QR, axis=0)

        #gq_phi = np.mean(gq*QR, axis=0)/np.mean(QR, axis=0)

        gq_phi = np.mean(gq, axis=0)

        gq_r = np.mean(gq, axis=1)





        all_Iq2D.append(Iq2D)

        #all_Iq2D.append(Iq2D)

        all_IqIq_af.append(IqIq_af)

        all_gq.append(gq)

        all_gq_phi.append(gq_phi)
@@ -267,9 +268,9 @@ def GaussianProcess_optimization(folder, parameters_train): 
    grid_size = 30



    theta_per_feature = {

        "sigma": (np.logspace(-1, 0, grid_size), np.logspace(-3, -1, grid_size)),

        "sqrtD": (np.logspace(-1, 0, grid_size), np.logspace(-3, -2, grid_size)),

        "gxy": (np.logspace(-1, 1, grid_size), np.logspace(-4, -2, grid_size)),

        "sigma": (np.logspace(-2, 0, grid_size), np.logspace(-3, -1, grid_size)),

        "sqrtD": (np.logspace(-2, 0, grid_size), np.logspace(-3, -1, grid_size)),

        "gxy": (np.logspace(-2, 1, grid_size), np.logspace(-3, 0, grid_size)),

    }



    # feature normalization

analyze/main_ML_analyze.py

+4 −4
Original line number Diff line number Diff line
@@ -10,9 +10,9 @@ import time 
def main():



    print("analyzing data using ML model")

    folder = "../data/20241126_rand"

    folder = "../data/20241201_rand"

    rand_num = 4000

    rand_max = 4000

    rand_max = 3000

    parameters = []

    for i in range(rand_num):

        filename = f"{folder}/obs_random_run{i}.csv"
@@ -23,9 +23,9 @@ def main(): 
    print("parameters", parameters)

    print("total number of parameters", len(parameters))



    calc_svd(folder, parameters)

    #calc_svd(folder, parameters)



    return 0

    #return 0

    random.shuffle(parameters)

    parameters_train = parameters[: int(0.7 * len(parameters))]

    parameters_test = parameters[int(0.7 * len(parameters)) :]

code/main.cpp

+6 −2
Original line number Diff line number Diff line
@@ -49,11 +49,14 @@ int main(int argc, char const *argv[]) 
        std::string finfo = "n" + std::string(argv[1]) + "_Rmu" + std::string(argv[2]) + "_sigma" + std::string(argv[3]) + "_sqrtD" + std::string(argv[4]) + "_gxy" + std::string(argv[5]);





        int number_of_config = 10000; // 2000 for local test

        int number_of_config = 20000; // 2000 for local test

        int bnum_r = 100;

        int bnum_phi = 101;



        gas_2d.run_simulation(number_of_config, bnum_r, bnum_phi, folder, finfo);

        //gas_2d.save_gas_config_to_file(folder + "/config_" + finfo + ".csv");

        //gas_2d.save_avg_observable_to_file(folder + "/obs_" + finfo + ".csv", true);

        gas_2d.save_avg_observable_to_file(folder + "/obs_" + finfo + ".csv");

    }



    // random run
@@ -67,10 +70,11 @@ int main(int argc, char const *argv[]) 
        suspension gas_2d(R0, 1.0*R0, 100, 0.0, 1.0, 0.0, true);

        std::string finfo = "random_run" + std::to_string(run_num);



        int number_of_config = 10000; // 2000 for local test

        int number_of_config = 20000; // 2000 for local test

        int bnum_r = 100;

        int bnum_phi = 101;

        gas_2d.run_simulation(number_of_config, bnum_r, bnum_phi, folder, finfo);

        gas_2d.save_avg_observable_to_file(folder + "/obs_" + finfo + ".csv");

    }



    std::clock_t c_end = std::clock();

code/suspension

+448 B (94.9 KiB)

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code/suspension.cpp

+30 −30
Original line number Diff line number Diff line
@@ -33,11 +33,11 @@ suspension::suspension(double R0_, double Rmu_, int n_, double sigma_, double sq 
        // gxx*gyy-gxy*gyx=1

        // Rmu = (0.9+0.2*rand_uni(gen))*R0;

        Rmu = 1.0 * R0;

        n = 100 + 50 * rand_uni(gen);

        n = 100 + 100 * rand_uni(gen);

        // n = 100;

        sigma = 0.0 + 0.5 * rand_uni(gen);

        sqrtD = (0.0 + 5 * rand_uni(gen)) * R0;

        gxy = (50 * rand_uni(gen)) * R0;

        sigma = 0.0 + 0.4 * rand_uni(gen);

        sqrtD = (1 + 4 * rand_uni(gen)) * R0;

        gxy = (10 + 40 * rand_uni(gen)) * R0;

    }

    else

    {
@@ -412,7 +412,7 @@ void suspension::save_observable_to_file(std::string filename, std::vector<obser 
    }

}



void suspension::save_avg_observable_to_file(std::string filename)

void suspension::save_avg_observable_to_file(std::string filename, bool save_Iqaf)

{

    // save the observable to file

    std::ofstream f(filename);
@@ -443,28 +443,30 @@ void suspension::save_avg_observable_to_file(std::string filename) 
            }

        }



        // it's the same as Iq_2D on average

        /*

        for (int kr = 0; kr < avg_obs.Iq2D_af.size(); kr++)

        for (int kr = 0; kr < avg_obs.IqIq_af.size(); kr++)

        {

            f << "\nIq2D_af,NA,NA,NA,NA,NA";

            f << "\nIqIq_af,NA,NA,NA,NA,NA";

            f << "," << avg_obs.qr[kr];

            for (int kphi = 0; kphi < avg_obs.Iq2D[kr].size(); kphi++)

            {

                f << "," << avg_obs.Iq2D_af[kr][kphi];

                f << "," << avg_obs.IqIq_af[kr][kphi];

            }

        }

        */



        for (int kr = 0; kr < avg_obs.IqIq_af.size(); kr++)

        // it's the same as Iq_2D on average

        if (save_Iqaf)

        {

            f << "\nIqIq_af,NA,NA,NA,NA,NA";

            for (int kr = 0; kr < avg_obs.Iq2D_af.size(); kr++)

            {

                f << "\nIq2D_af,NA,NA,NA,NA,NA";

                f << "," << avg_obs.qr[kr];

                for (int kphi = 0; kphi < avg_obs.Iq2D[kr].size(); kphi++)

                {

                f << "," << avg_obs.IqIq_af[kr][kphi];

                    f << "," << avg_obs.Iq2D_af[kr][kphi];

                }

            }

        }



        f.close();

    }

}
@@ -511,7 +513,5 @@ void suspension::run_simulation(int N_config, int bnum_r, int bnum_phi, std::str 
        }

    }



    //save_gas_config_to_file(folder + "/config_" + finfo + ".csv");

    // save_observable_to_file(folder + "/obs_" + finfo + ".csv", obs_ensemble);

    save_avg_observable_to_file(folder + "/obs_" + finfo + ".csv");

}
 
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