def exp_test_indoorloc(db_path, filename):
th = -80
pct = 0.1
k = 11
fp_train, loc_train = myf.get_all_data_month(db_path, "01", 15, 0)
v_index_train = myf.get_index_ap_with_data(fp_train, th, pct)
my_file = open(filename, 'wt')
my_file.write("month, size_l_both, pct_50, pct_75\n")
print("month, size_l_both, pct_50, pct_75")
for i in range(2, 16):
month = "{0:02d}".format(i)
fp_test, loc_test = myf.get_all_data_month(db_path, month, 0, 5)
v_index_test = myf.get_index_ap_with_data(fp_test, th, pct)
l_new, l_gone, l_both = myf.get_new_gone_both_aps(
v_index_train, v_index_test)
mean_acc, p75_acc = myf.go_common_approach(fp_train, fp_test, l_both,
loc_train, loc_test, k)
s = month + "," + str(len(l_both)) + "," + "{:0.2f}".format(
mean_acc) + "," + "{:0.2f}".format(p75_acc)
print(s)
my_file.write(s + "\n")
my_file.close()
def Experiment10():
fingerprints_month01, locations_month01 = get_all_data_month(db_path, "01", 15, 0)
fingerprints_month15, locations_month15 = get_all_data_month(db_path, "15", 0, 5)
l_both_org =[16,50,51,70]
acc = go_common_approach(fingerprints_month01, fingerprints_month15, l_both_org, locations_month01, locations_month15)
print(str(l_both_org) + "->" + str(acc))
l_both_org =[5,8, 16,50,51,70]
acc = go_common_approach(fingerprints_month01, fingerprints_month15, l_both_org, locations_month01, locations_month15)
print(str(l_both_org) + "->" + str(acc))
l_both_org =[6,8,16,50,51,70]
acc = go_common_approach(fingerprints_month01, fingerprints_month15, l_both_org, locations_month01, locations_month15)
print(str(l_both_org) + "->" + str(acc))
l_both_org =[7,8,16,50,51,70]
acc = go_common_approach(fingerprints_month01, fingerprints_month15, l_both_org, locations_month01, locations_month15)
print(str(l_both_org) + "->" + str(acc))
l_both_org =[11,8,16,50,51,70]
acc = go_common_approach(fingerprints_month01, fingerprints_month15, l_both_org, locations_month01, locations_month15)
print(str(l_both_org) + "->" + str(acc))
l_both_org =[21,8,16,50,51,70]
acc = go_common_approach(fingerprints_month01, fingerprints_month15, l_both_org, locations_month01, locations_month15)
print(str(l_both_org) + "->" + str(acc))
def Experiment7(db_path):
th = -80
pct = 0.1
fingerprints_month01, locations_month01 = get_all_data_month(db_path, "01", 15, 0)
v_index_db01 = get_index_ap_with_data(fingerprints_month01, th, pct)
fingerprints_month02, locations_month02 = get_all_data_month(db_path, "02", 0, 5)
v_index_db02 = get_index_ap_with_data(fingerprints_month02, th, pct)
fingerprints_month15, locations_month15 = get_all_data_month(db_path, "15", 0, 5)
v_index_db15 = get_index_ap_with_data(fingerprints_month15, th, pct)
l_new, l_gone, l_both02 = get_new_gone_both_aps(v_index_db01, v_index_db02)
print(str(len(l_new)) + " " + str(len(l_gone)) + " " + str(len(l_both02)))
print(l_both02)
l_new, l_gone, l_both15 = get_new_gone_both_aps(v_index_db01, v_index_db15)
print(str(len(l_new)) + " " + str(len(l_gone)) + " " + str(len(l_both15)))
print(l_both15)
acc = go_common_approach(fingerprints_month01, fingerprints_month02, l_both02, locations_month01, locations_month02, "02",l_both02)
print("01 vs 02 -> " + str(acc))
acc = go_common_approach(fingerprints_month01, fingerprints_month15, l_both15, locations_month01, locations_month15, "15",l_both15)
print("01 vs 15 -> " + str(acc))
def exp_paper2(db_path, k):
fp_month01, loc_train = myf.get_all_data_month(db_path, "01", 15, 0)
fp_month15, loc_test = myf.get_all_data_month(db_path, "15", 0, 5)
fp_month01_norm, fp_month15_norm = myf.normalize_data(
fp_month01, fp_month15)
l_both = [16, 50, 51, 70]
l_gone = [5, 6, 7, 8, 11, 21]
l_all = list(l_both)
l_all.extend(l_gone)
common_train_data = fp_month01_norm[:, l_both]
common_test_data = fp_month15_norm[:, l_both]
ideal_train_data = fp_month01_norm[:, l_all]
ideal_test_data = fp_month15_norm[:, l_all]
acc_common, p75_common = myf.ips(common_train_data, common_test_data,
loc_train, loc_test, k)
acc_ideal, p75_ideal = myf.ips(ideal_train_data, ideal_test_data,
loc_train, loc_test, k)
new_test_data_reg = np.zeros((fp_month15_norm.shape[0], len(l_gone)))
new_test_data_mean = np.zeros((fp_month15_norm.shape[0], len(l_gone)))
i = 0
for wap in l_gone:
# reg
x_train = common_train_data
y_train = fp_month01_norm[:, wap]
x_test = fp_month15_norm[:, l_both]
new_test_data_reg[:,
i] = myf.impute_regression(x_train, y_train, x_test)
#mean
mean_wap = np.mean(y_train)
new_test_data_mean[:, i] = mean_wap
i = i + 1
reg_train_data = ideal_train_data
reg_test_data = np.concatenate(
(fp_month15_norm[:, l_both], new_test_data_reg), axis=1)
acc_reg, p75_reg = myf.ips(reg_train_data, reg_test_data, loc_train,
loc_test, k)
mean_train_data = ideal_train_data
mean_test_data = np.concatenate(
(fp_month15_norm[:, l_both], new_test_data_mean), axis=1)
acc_mean, p75_mean = myf.ips(mean_train_data, mean_test_data, loc_train,
loc_test, k)
s1 = "{:0.2f}".format(acc_ideal) + "," + "{:0.2f}".format(
acc_common) + "," + "{:0.2f}".format(acc_reg) + "," + "{:0.2f}".format(
acc_mean)
s2 = "{:0.2f}".format(p75_ideal) + "," + "{:0.2f}".format(
p75_common) + "," + "{:0.2f}".format(p75_reg) + "," + "{:0.2f}".format(
p75_mean)
s = s1 + "," + s2
print(s)
def Experiment4(db_path):
fingerprints_month01, locations_month01 = get_all_data_month(db_path, "01", 15, 5)
fingerprints_month02, locations_month02 = get_all_data_month(db_path, "02", 1, 5)
fingerprints_month15, locations_month15 = get_all_data_month(db_path, "15", 1, 5)
th = -80
pct = 0.1
v_index_db01 = get_index_ap_with_data(fingerprints_month01, th, pct)
v_index_db02 = get_index_ap_with_data(fingerprints_month02, th, pct)
v_index_db15 = get_index_ap_with_data(fingerprints_month15, th, pct)
l_new, l_gone, l_both = get_new_gone_both_aps(v_index_db01, v_index_db02)
print(str(len(l_new)) + " " + str(len(l_gone)) + " " + str(len(l_both)))
l_new, l_gone, l_both = get_new_gone_both_aps(v_index_db01, v_index_db15)
print(str(len(l_new)) + " " + str(len(l_gone)) + " " + str(len(l_both)))
def Experiment3(db_path):
fingerprints_month1, locations_month1 = get_all_data_month(db_path, "01", 15, 5)
v_index_db1 = get_index_ap_with_data(fingerprints_month1)
data = np.zeros([14,1])
for i in range(2, 16):
month_str = "{0:02d}".format(i)
print("Month: " + month_str)
fingerprints_monthi, locations_monthi = get_all_data_month(db_path, month_str, 1, 5)
v_index_dbi = get_index_ap_with_data(fingerprints_monthi)
l_new, l_gone, l_both = get_new_gone_both_aps(v_index_db1, v_index_dbi)
data[i-2,0] = go_common_approach(fingerprints_month1, fingerprints_monthi, l_both, locations_month1, locations_monthi)
print(data[i-2,0])
print(data)
plt.plot(data[:,0])
plt.show()
def Experiment2(db_path):
fingerprints_month1, locations_month1 = get_all_data_month(db_path, "01", 15, 5)
v_index_db1 = get_index_ap_with_data(fingerprints_month1)
data = np.zeros([14,3])
for i in range(2,16):
month_str = "{0:02d}".format(i)
fingerprints_monthi, locations_monthi = get_all_data_month(db_path, month_str, 1, 5)
v_index_dbi = get_index_ap_with_data(fingerprints_monthi)
l_new, l_gone, l_both = get_new_gone_both_aps(v_index_db1, v_index_dbi)
data[i-2,] = [len(l_both), len(l_new), len(l_gone)]
print(data)
plt.plot(data[:, 0])
plt.plot(data[:, 1])
plt.plot(data[:, 2])
plt.legend(['Both', 'New', 'Gone'], loc='upper left')
plt.show()
def Experiment1(db_path):
fingerprints_month1, locations_month1 = get_all_data_month(db_path, "01", 15, 5)
stats_aps = get_stats_aps(fingerprints_month1,-80)
df = pd.DataFrame(stats_aps)
df.to_csv("ap01.csv")
fingerprints_month1, locations_month1 = get_all_data_month(db_path, "02", 1, 5)
stats_aps = get_stats_aps(fingerprints_month1,-80)
df = pd.DataFrame(stats_aps)
df.to_csv("ap02.csv")
fingerprints_month1, locations_month1 = get_all_data_month(db_path, "15", 1, 5)
stats_aps = get_stats_aps(fingerprints_month1,-80)
df = pd.DataFrame(stats_aps)
df.to_csv("ap15.csv")
def exp_paper4(db_path, k, pct):
fp_month01, loc_train = myf.get_all_data_month(db_path, "01", 15, 0)
fp_month15, loc_test = myf.get_all_data_month(db_path, "15", 0, 5)
fp_month01_norm, fp_month15_norm = myf.normalize_data(
fp_month01, fp_month15)
n_test_samples_for_modeling = math.floor(fp_month15_norm.shape[0] * pct)
fp_test_pre = fp_month15_norm[0:n_test_samples_for_modeling, :]
fp_test_post = fp_month15_norm[
n_test_samples_for_modeling:fp_month15_norm.shape[0], :]
l_both = [16, 50, 51, 70]
l_gone = [5, 6, 7, 8, 11, 21]
l_all = list(l_both)
l_all.extend(l_gone)
common_train_data = fp_month01_norm[:, l_both]
common_test_data = fp_test_post[:, l_both]
ideal_train_data = fp_month01_norm[:, l_all]
ideal_test_data = fp_test_post[:, l_all]
acc_common, p75_common = myf.ips(common_train_data, common_test_data,
loc_train, loc_test, k)
acc_ideal, p75_ideal = myf.ips(ideal_train_data, ideal_test_data,
loc_train, loc_test, k)
acc_reg = 0
p75_reg = 0
acc_mean = 0
p75_mean = 0
s1 = "{:0.2f}".format(acc_ideal) + "," + "{:0.2f}".format(
acc_common) + "," + "{:0.2f}".format(acc_reg) + "," + "{:0.2f}".format(
acc_mean)
s2 = "{:0.2f}".format(p75_ideal) + "," + "{:0.2f}".format(
p75_common) + "," + "{:0.2f}".format(p75_reg) + "," + "{:0.2f}".format(
p75_mean)
s = s1 + "," + s2
print(s)
def Experiment9():
fingerprints_month15, locations_month15 = get_all_data_month(db_path, "15", 1, 5)
distances = distance.cdist(locations_month15[:,1:2], locations_month15[:,1:2], "euclidean")
print(np.max(distances))
#quintando uno de la lista
#l_both_org =[16,21,50,51,70]
#l_both = list(l_both_org)
#for l in l_both_org:
# l_both.remove(l)
# acc = go_common_approach(fingerprints_month01, fingerprints_month15, l_both, locations_month01, locations_month15)
# print (str(l_both) + "->" + str(acc))
# l_both = list(l_both_org)
"""
fingerprints_month1, locations_month1 = get_all_data_month(db_path, "01", 15, 5)
fingerprints_month2, locations_month2 = get_all_data_month(db_path, "15", 1, 5)
v_index_db1 = get_index_ap_with_data(fingerprints_month1)
v_index_db2 = get_index_ap_with_data(fingerprints_month2)
l_new, l_gone, l_both = get_new_gone_both_aps(v_index_db1, v_index_db2)
print("DB 1 has " + str(v_index_db1.sum()))
print("DB 2 has " + str(v_index_db2.sum()))
print("APs in both [" + str(len(l_both)) + "] ->")
print(l_both)
print("New APs in DB2 [" + str(len(l_new)) + "] ->")
print(l_new)
print("APs in DB1 but not in BD2 [" + str(len(l_gone)) + "] ->")
print(l_gone)
def exp_paper1(db_path):
fp_month01, loc_month01 = myf.get_all_data_month(db_path, "01", 15, 0)
fp_month15, loc_month15 = myf.get_all_data_month(db_path, "15", 0, 5)
method = "SVR"
k = 7
l_all = [5, 6, 7, 8, 11, 16, 21, 50, 51, 70]
acc_ideal, p75_ideal = myf.go_common_approach(fp_month01, fp_month15,
l_all, loc_month01,
loc_month15, k)
s = "{:0.2f}".format(acc_ideal) + "," + "{:0.2f}".format(p75_ideal)
print(s)
#print("gone,common_50,reg_50,nei_50,mean_50,max_50,zero_50,common_75,reg_75,nei_75,mean_75,max_75,zero_75")
print("gone,common_50,reg_50,mean_50,common_75,reg_75,mean_75")
acc_common = np.zeros((10, 1))
acc_reg = np.zeros((10, 1))
acc_nei = np.zeros((10, 1))
acc_mean = np.zeros((10, 1))
acc_max = np.zeros((10, 1))
acc_zero = np.zeros((10, 1))
p75_common = np.zeros((10, 1))
p75_reg = np.zeros((10, 1))
p75_nei = np.zeros((10, 1))
p75_mean = np.zeros((10, 1))
p75_max = np.zeros((10, 1))
p75_zero = np.zeros((10, 1))
for i in range(len(l_all)):
l_both = []
l_gone = [l_all[i]]
for j in range(len(l_all)):
if i != j:
l_both.append(l_all[j])
train_fps_norm, test_fps_norm = myf.get_normalized_data(
fp_month01, fp_month15, l_both, l_gone)
size_l_gone = len(l_gone)
size_l_both = len(l_both)
acc_common[i, 0], p75_common[i, 0] = myf.go_common_approach(
fp_month01, fp_month15, l_both, loc_month01, loc_month15, k)
acc_reg[i, 0], p75_reg[i, 0] = myf.go_regression_approach(
train_fps_norm, test_fps_norm, loc_month01, loc_month15,
size_l_gone, size_l_both, method, k)
#acc_nei[i, 0], p75_nei[i, 0] = myf.go_neighbours_approach(train_fps_norm, test_fps_norm, loc_month01, loc_month15, size_l_gone, size_l_both, k)
acc_mean[i, 0], p75_mean[i, 0] = myf.go_basic_approach(
train_fps_norm, test_fps_norm, loc_month01, loc_month15,
size_l_gone, size_l_both, "mean", k)
#acc_max[i, 0], p75_max[i, 0] = myf.go_basic_approach(train_fps_norm, test_fps_norm, loc_month01, loc_month15, size_l_gone, size_l_both, "max", k)
#acc_zero[i, 0], p75_zero[i, 0] = myf.go_basic_approach(train_fps_norm, test_fps_norm, loc_month01, loc_month15, size_l_gone, size_l_both, "zero", k)
#s1 = str(l_gone) + ","
#s2 = "{:0.2f}".format(acc_common[i, 0]) + "," + "{:0.2f}".format(acc_reg[i, 0]) + "," + "{:0.2f}".format(acc_nei[i, 0]) + ","
#s3 = "{:0.2f}".format(acc_mean[i, 0]) + "," + "{:0.2f}".format(acc_max[i, 0]) + "," + "{:0.2f}".format(acc_zero[i, 0]) + ","
#s4 = "{:0.2f}".format(p75_common[i, 0]) + "," + "{:0.2f}".format(p75_reg[i, 0]) + "," + "{:0.2f}".format(p75_nei[i, 0]) + ","
#s5 = "{:0.2f}".format(p75_mean[i, 0]) + "," + "{:0.2f}".format(p75_max[i, 0]) + "," + "{:0.2f}".format(p75_zero[i, 0])
#print(s1 + s2 + s3 + s4 + s5)
s1 = str(l_gone) + ","
s2 = "{:0.2f}".format(acc_common[i, 0]) + "," + "{:0.2f}".format(
acc_reg[i, 0]) + "," + "{:0.2f}".format(acc_mean[i, 0]) + ","
s3 = "{:0.2f}".format(p75_common[i, 0]) + "," + "{:0.2f}".format(
p75_reg[i, 0]) + "," + "{:0.2f}".format(p75_mean[i, 0])
print(s1 + s2 + s3)
#s2 = "{:0.2f}".format(np.mean(acc_common)) + "," + "{:0.2f}".format(np.mean(acc_reg)) + "," + "{:0.2f}".format(np.mean(acc_nei)) + ","
#s3 = "{:0.2f}".format(np.mean(acc_mean)) + "," + "{:0.2f}".format(np.mean(acc_max)) + "," + "{:0.2f}".format(np.mean(acc_zero)) + ","
#s4 = "{:0.2f}".format(np.mean(p75_common)) + "," + "{:0.2f}".format(np.mean(p75_reg)) + "," + "{:0.2f}".format(np.mean(p75_nei)) + ","
#s5 = "{:0.2f}".format(np.mean(p75_mean)) + "," + "{:0.2f}".format(np.mean(p75_max)) + "," + "{:0.2f}".format(np.mean(p75_zero))
#print(s2 + s3 + s4 + s5)
s1 = "{:0.2f}".format(np.mean(acc_common)) + "," + "{:0.2f}".format(
np.mean(acc_reg)) + "," + "{:0.2f}".format(np.mean(acc_mean)) + ","
s2 = "{:0.2f}".format(np.mean(p75_common)) + "," + "{:0.2f}".format(
np.mean(p75_reg)) + "," + "{:0.2f}".format(np.mean(p75_mean))
print(s1 + s2)
def exp_best_imputation_models_train(db_path, filename):
method = "SVR"
n_folds = 5
k = 11
l_exps = [[16], [50], [51], [70], [16, 50], [16, 51], [16, 70], [50, 51],
[50, 70], [51, 70], [16, 50, 51], [16, 50, 70], [16, 51, 70],
[50, 51, 70], [16, 50, 51, 70]]
l_new = [[5], [6], [7], [8], [11]]
fp_all_train, loc_all_train = myf.get_all_data_month(db_path, "01", 15, 0)
results_reg_50 = np.zeros((len(l_exps), len(l_new)))
results_reg_75 = np.zeros((len(l_exps), len(l_new)))
results_nei_50 = np.zeros((len(l_exps), len(l_new)))
results_nei_75 = np.zeros((len(l_exps), len(l_new)))
fold = 1
kf = KFold(n_splits=n_folds)
for train_index, test_index in kf.split(fp_all_train):
fp_train, fp_test = fp_all_train[train_index], fp_all_train[test_index]
loc_train, loc_test = loc_all_train[train_index], loc_all_train[
test_index]
j = 0
for l_gone in l_new:
i = 0
for l_comb in l_exps:
acc_reg, p75_reg = myf.go_regression_approach(
fp_train, fp_test, loc_train, loc_test, l_gone, l_comb,
method, k)
acc_nei, p75_nei = myf.go_neighbours_approach(
fp_train, fp_test, loc_train, loc_test, l_gone, l_comb, k)
results_reg_50[i, j] += acc_reg
results_reg_75[i, j] += p75_reg
results_nei_50[i, j] += acc_nei
results_nei_75[i, j] += p75_nei
s = str(fold) + "," + str(l_comb) + "," + str(
l_gone) + "," + "{:0.2f}".format(
acc_reg) + "," + "{:0.2f}".format(
acc_nei) + "," + "{:0.2f}".format(
p75_reg) + "," + "{:0.2f}".format(p75_nei)
print(s)
i = i + 1
j = j + 1
fold = fold + 1
results_reg_50 = results_reg_50 / n_folds
results_reg_75 = results_reg_75 / n_folds
results_nei_50 = results_nei_50 / n_folds
results_nei_75 = results_nei_75 / n_folds
print("------")
my_file = open(filename, 'wt')
print("X Y reg_50 nei_50 reg_75 nei_75")
my_file.write("X Y reg_50 nei_50 reg_75 nei_75\n")
for j in range(len(l_new)):
for i in range(len(l_exps)):
s = str(l_exps[i]) + "," + str(l_new[j]) + "," + "{:0.2f}".format(
results_reg_50[i, j]) + "," + "{:0.2f}".format(
results_nei_50[i, j]) + "," + "{:0.2f}".format(
results_reg_75[i, j]) + "," + "{:0.2f}".format(
results_nei_75[i, j])
print(s)
my_file.write(s)
my_file.close()
def exp_ap_gone(db_path, filename):
fp_month01, loc_month01 = myf.get_all_data_month(db_path, "01", 15, 0)
fp_month15, loc_month15 = myf.get_all_data_month(db_path, "15", 0, 5)
method = "SVR"
k = 11
l_both = [16, 50, 51, 70]
l_exps = [[5], [6], [7], [8], [11], [5, 6], [5, 7], [5, 8], [5, 11],
[6, 7], [6, 8], [6, 11], [7, 8], [7, 11], [8, 11], [5, 6, 7],
[5, 6, 8], [5, 6, 11], [5, 7, 8], [5, 7, 11], [5, 8, 11],
[5, 6, 7, 8], [5, 6, 7, 11], [5, 6, 7, 8, 11]]
my_file = open(filename, 'wt')
print(
"gone,all_50,reg_50,nei_50,cmb_50,mean_50,max_50,zero_50,all_75,reg_75,nei_75,cmb_75,mean_75,max_75,zero_75"
)
my_file.write(
"gone,all_50,reg_50,nei_50,cmb_50,mean_50,max_50,zero_50,all_75,reg_75,nei_75,cmb_75,mean_75,max_75,zero_75\n"
)
acc_common, p75_common = myf.go_common_approach(fp_month01, fp_month15,
l_both, loc_month01,
loc_month15, k)
s = "Using common approach (only in both sets " + str(
l_both) + ") -> " + "{:0.2f}".format(
acc_common) + " [" + "{:0.2f}".format(p75_common) + "] "
print(s)
my_file.write(s + "\n")
for l_gone in l_exps:
l_all = list(l_both)
l_all.extend(l_gone)
train_fps_norm, test_fps_norm = myf.get_normalized_data(
fp_month01, fp_month15, l_both, l_gone)
size_l_gone = len(l_gone)
size_l_both = len(l_both)
acc_all, p75_all = myf.go_common_approach(fp_month01, fp_month15,
l_all, loc_month01,
loc_month15, k)
acc_mean, p75_mean = myf.go_basic_approach(train_fps_norm,
test_fps_norm, loc_month01,
loc_month15, size_l_gone,
size_l_both, "mean", k)
acc_max, p75_max = myf.go_basic_approach(train_fps_norm, test_fps_norm,
loc_month01, loc_month15,
size_l_gone, size_l_both,
"max", k)
acc_zero, p75_zero = myf.go_basic_approach(train_fps_norm,
test_fps_norm, loc_month01,
loc_month15, size_l_gone,
size_l_both, "zero", k)
acc_reg, p75_reg = myf.go_regression_approach(train_fps_norm,
test_fps_norm,
loc_month01, loc_month15,
size_l_gone, size_l_both,
method, k)
acc_nei, p75_nei = myf.go_neighbours_approach(train_fps_norm,
test_fps_norm,
loc_month01, loc_month15,
size_l_gone, size_l_both,
k)
acc_cmb, p75_cmb = myf.go_combination_approach(
train_fps_norm, test_fps_norm, loc_month01, loc_month15,
size_l_gone, size_l_both, k)
s1 = str(l_gone) + ","
s2 = "{:0.2f}".format(acc_all) + "," + "{:0.2f}".format(
acc_reg) + "," + "{:0.2f}".format(
acc_nei) + "," + "{:0.2f}".format(acc_cmb) + ","
s3 = "{:0.2f}".format(acc_mean) + "," + "{:0.2f}".format(
acc_max) + "," + "{:0.2f}".format(acc_zero) + ","
s4 = "{:0.2f}".format(p75_all) + "," + "{:0.2f}".format(
p75_reg) + "," + "{:0.2f}".format(
p75_nei) + "," + "{:0.2f}".format(p75_cmb) + ","
s5 = "{:0.2f}".format(p75_mean) + "," + "{:0.2f}".format(
p75_max) + "," + "{:0.2f}".format(p75_zero)
print(s1 + s2 + s3 + s4 + s5)
my_file.write(s1 + s2 + s3 + s4 + s5 + "\n")
my_file.close()
