def run_ba_cov_neutral_sims(shape=1,
scale=1,
G=50,
N=50,
iter1=1000,
iter2=1000):
df_out = open(pt.get_path() + '/data/simulations/ba_cov_neutral_sims.txt',
'w')
df_out.write('\t'.join([
'N', 'G', 'lamba_mean', 'lambda_neutral', 'Cov', 'Iteration',
'dist_percent'
]) + '\n')
covs = [0.2]
mean_gamma = shape * scale
neutral_range = np.logspace(-2, 1, num=20, endpoint=True, base=10.0)
neutral_range = neutral_range[::-1]
for neutral_ in neutral_range:
for cov in covs:
for i in range(iter1):
C = pt.get_ba_cov_matrix(G, cov)
lambda_genes = np.random.gamma(shape=shape,
scale=scale,
size=G)
lambda_genes_null = np.asarray([neutral_] * G)
test_cov_adapt = np.stack(
[pt.get_count_pop(lambda_genes, C=C) for x in range(N)],
axis=0)
# matrix with diaganol values equal to one
test_cov_neutral = np.stack([
pt.get_count_pop(lambda_genes_null, C=np.identity(G))
for x in range(N)
],
axis=0)
test_cov = test_cov_adapt + test_cov_neutral
X = pt.hellinger_transform(test_cov)
pca = PCA()
pca_fit = pca.fit_transform(X)
euc_dist = pt.get_mean_pairwise_euc_distance(pca_fit)
euc_dists = []
for j in range(iter2):
#X_j = pt.hellinger_transform(pt.random_matrix(test_cov))
X_j = pt.hellinger_transform(
pt.get_random_matrix(test_cov))
pca_fit_j = pca.fit_transform(X_j)
euc_dists.append(
pt.get_mean_pairwise_euc_distance(pca_fit_j))
euc_percent = len([k for k in euc_dists if k < euc_dist
]) / len(euc_dists)
print(neutral_, cov, i, euc_percent)
df_out.write('\t'.join([
str(N),
str(G),
str(mean_gamma),
str(neutral_),
str(cov),
str(i),
str(euc_percent)
]) + '\n')
df_out.close()
def run_ba_cor_sub_sims(shape=1, scale=1, G=50, N=50):
cov = 0.1
lambda_genes = np.random.gamma(shape=shape, scale=scale, size=G)
lambda_genes.sort()
C, edge_count = pt.get_ba_cov_matrix(G, cov, get_node_edge_sum=True)
zipped = list(zip(list(range(G)), edge_count.tolist()[0]))
zipped.sort(key=lambda t: t[1])
# figure out how to sort covariance matrix
total_inversions = 30
inversion_count = 0
while inversion_count < total_inversions:
pair = np.random.choice(list(range(G)), size=2, replace=False)
pair.sort()
if lambda_genes[pair[0]] < lambda_genes[pair[1]]:
lambda_0 = lambda_genes[pair[0]].copy()
lambda_1 = lambda_genes[pair[1]].copy()
lambda_genes[pair[0]] = lambda_1
lambda_genes[pair[1]] = lambda_0
inversion_count += 1
unzipped = list(zip(*zipped))
rezipped = list(zip(unzipped[0], unzipped[1], lambda_genes))
rezipped.sort(key=lambda t: t[0])
unrezipped = list(zip(*rezipped))
lambda_genes_sorted = list(unrezipped[2])
print(lambda_genes_sorted)
def run_cov_neutral_sims(out_name,
covs=[0.1, 0.15, 0.2],
shape=1,
scale=1,
G=50,
N=50,
iter1=1000,
iter2=1000):
df_out = open(out_name, 'w')
df_out.write('\t'.join([
'N', 'G', 'lamba_mean', 'lambda_neutral', 'Cov', 'Iteration',
'dist_percent', 'z_score'
]) + '\n')
mean_gamma = shape * scale
neutral_range = np.logspace(-2, 1, num=20, endpoint=True, base=10.0)
neutral_range = neutral_range[::-1]
for neutral_ in neutral_range:
for cov in covs:
for i in range(iter1):
C = pt.get_ba_cov_matrix(G, cov)
while True:
lambda_genes = np.random.gamma(shape=shape,
scale=scale,
size=G)
lambda_genes_null = np.asarray([neutral_] * G)
test_cov_adapt = np.stack([
pt.get_count_pop(lambda_genes, C=C) for x in range(N)
],
axis=0)
# matrix with diaganol values equal to one
test_cov_neutral = np.stack([
pt.get_count_pop(lambda_genes_null, C=np.identity(G))
for x in range(N)
],
axis=0)
test_cov = test_cov_adapt + test_cov_neutral
if (np.any(test_cov.sum(axis=1) == 0)) == False:
break
# check and remove empty columns
test_cov = test_cov[:, ~np.all(test_cov == 0, axis=0)]
euc_percent, z_score = pt.matrix_vs_null_one_treat(
test_cov, iter2)
df_out.write('\t'.join([
str(N),
str(G),
str(mean_gamma),
str(neutral_),
str(cov),
str(i),
str(euc_percent),
str(z_score)
]) + '\n')
print(neutral_, cov)
df_out.close()
def run_ba_cov_sims(gene_list, pop_list, out_name, iter1=1000, iter2=1000):
df_out = open(pt.get_path() + '/data/simulations/' + out_name + '.txt',
'w')
df_out.write('\t'.join(['N', 'G', 'Cov', 'Iteration', 'dist_percent']) +
'\n')
covs = [0.1, 0.15, 0.2]
for G in gene_list:
for N in pop_list:
for cov in covs:
for i in range(iter1):
C = pt.get_ba_cov_matrix(G, cov)
while True:
lambda_genes = np.random.gamma(shape=1,
scale=1,
size=G)
test_cov = np.stack([
pt.get_count_pop(lambda_genes, cov=C)
for x in range(N)
],
axis=0)
#test_cov_row_sum = test_cov.sum(axis=1)
if (np.any(test_cov.sum(axis=1) == 0)) == False:
break
#if np.count_nonzero(test_cov_row_sum) == len(test_cov_row_sum):
# break
X = pt.hellinger_transform(test_cov)
pca = PCA()
pca_fit = pca.fit_transform(X)
euc_dist = pt.get_mean_pairwise_euc_distance(pca_fit)
euc_dists = []
for j in range(iter2):
X_j = pt.hellinger_transform(
pt.get_random_matrix(test_cov))
#X_j = pt.hellinger_transform(pt.random_matrix(test_cov))
pca_fit_j = pca.fit_transform(X_j)
euc_dists.append(
pt.get_mean_pairwise_euc_distance(pca_fit_j))
euc_percent = len([k for k in euc_dists if k < euc_dist
]) / len(euc_dists)
print(N, G, cov, i, euc_percent)
df_out.write('\t'.join(
[str(N),
str(G),
str(cov),
str(i),
str(euc_percent)]) + '\n')
df_out.close()
def run_cov_rho_sims(out_name,
covs=[0.1, 0.15, 0.2],
rhos=[-0.2, 0, 0.2],
shape=1,
scale=1,
G=50,
N=50,
iter1=10,
iter2=1000):
df_out = open(out_name, 'w')
df_out.write('\t'.join([
'N', 'G', 'Cov', 'Rho_goal', 'Rho_estimated', 'Iteration',
'dist_percent', 'z_score'
]) + '\n')
for cov in covs:
for rho in rhos:
for i in range(iter1):
C, rho_estimated = pt.get_ba_cov_matrix(n_genes=G,
cov=cov,
rho=rho)
while True:
lambda_genes = np.random.gamma(shape=1, scale=1, size=G)
test_cov = np.stack([
pt.get_count_pop(lambda_genes, C=C) for x in range(N)
],
axis=0)
if (np.any(test_cov.sum(axis=1) == 0)) == False:
break
# check and remove empty columns
test_cov = test_cov[:, ~np.all(test_cov == 0, axis=0)]
euc_percent, z_score = pt.matrix_vs_null_one_treat(
test_cov, iter2)
df_out.write('\t'.join([
str(N),
str(G),
str(cov),
str(rho),
str(rho_estimated),
str(i),
str(euc_percent),
str(z_score)
]) + '\n')
print(N, G, cov, rho, rho_estimated, i)
df_out.close()
def run_cov_dist_sims_unequal(out_name,
to_reshuffle=[5],
N1=20,
N2=20,
covs_12=[0.05],
G=100,
shape=1,
scale=1,
iter1=10,
iter2=1000):
df_out = open(out_name, 'w')
df_out.write('\t'.join([
'N1', 'N2', 'G', 'Reshuf', 'Cov', 'Iteration', 'Euc_dist',
'F_2_percent', 'F_2_z_score', 'V_1_percent', 'V_1_z_score',
'V_2_percent', 'V_2_z_score'
]) + '\n')
# re write code for covariance matrix to get
for reshuf in to_reshuffle:
for cov in covs:
reshuf_list = []
for i in range(iter1):
C = pt.get_ba_cov_matrix(G, cov)
while True:
rates = np.random.gamma(shape, scale=scale, size=G)
rates1 = rates.copy()
rates2 = rates.copy()
# fix this so you're not resampling the same pairs
for j in range(reshuf)[0::2]:
rates2[j], rates2[j + 1] = rates2[j + 1], rates2[j]
#shuffle(rates)#[:reshuf])
counts1 = np.stack(
[pt.get_count_pop(rates1, C=C) for x in range(N1)],
axis=0)
counts2 = np.stack(
[pt.get_count_pop(rates2, C=C) for x in range(N2)],
axis=0)
if (np.any(counts1.sum(axis=1) == 0) == False) or (np.any(
counts2.sum(axis=1) == 0) == False):
break
euc_dist = np.linalg.norm(rates1 - rates2)
count_matrix = np.concatenate((counts1, counts2), axis=0)
# check and remove empty columns
count_matrix = count_matrix[:,
~np.all(count_matrix == 0, axis=0)]
F_2_percent, F_2_z_score, \
V_1_percent, V_1_z_score, \
V_2_percent, V_2_z_score = \
pt.matrix_vs_null_two_treats(count_matrix, N1, N2, iter=iter2)
reshuf_list.append(euc_dist)
print(reshuf, cov, i, F_2_percent, F_2_z_score, euc_dist,
V_1_percent, V_2_percent)
df_out.write('\t'.join([
str(N1),
str(N2),
str(G),
str(reshuf),
str(cov),
str(i),
str(euc_dist),
str(F_2_percent),
str(F_2_z_score),
str(V_1_percent),
str(V_1_z_score),
str(V_2_percent),
str(V_2_z_score)
]) + '\n')
print(cov, np.mean(reshuf_list))
df_out.close()
def run_ba_ntwk_cluster_sims(iter1=1000, iter2=1000, cov=0.2):
df_out = open(mydir + '/data/simulations/cov_ba_ntwrk_cluster_methods.txt', 'w')
df_out.write('\t'.join(['Prob', 'CC_mean', 'CC_025', 'CC_975', 'Method', 'Power', 'Power_025', 'Power_975', 'Z_mean', 'Z_025', 'Z_975']) + '\n')
n_pops=100
n_genes=50
#covs = [0.05, 0.1, 0.15, 0.2]
ps = [0, 0.2, 0.4, 0.6, 0.8, 1]
for p in ps:
eig_p_list = []
mcd_k1_p_list = []
mcd_k3_p_list = []
mpd_k1_p_list = []
mpd_k3_p_list = []
eig_z_list = []
mcd_k1_z_list = []
mcd_k3_z_list = []
mpd_k1_z_list = []
mpd_k3_z_list = []
cc_list = []
for i in range(iter1):
if i %100 ==0:
print(ps, i)
lambda_genes = np.random.gamma(shape=3, scale=1, size=n_genes)
C, cc = pt.get_ba_cov_matrix(n_genes, cov=cov, p=p)
test_cov = np.stack( [pt.get_count_pop(lambda_genes, cov= C) for x in range(n_pops)] , axis=0 )
X = test_cov/test_cov.sum(axis=1)[:,None]
X -= np.mean(X, axis = 0)
pca = PCA()
pca_fit = pca.fit_transform(X)
mpd_k1 = pt.get_mean_pairwise_euc_distance(pca_fit,k=1)
mpd_k3 = pt.get_mean_pairwise_euc_distance(pca_fit,k=3)
eig = pt.get_x_stat(pca.explained_variance_[:-1], n_features=n_genes)
mcd_k1 = pt.get_mean_centroid_distance(pca_fit, k = 1)
mcd_k3 = pt.get_mean_centroid_distance(pca_fit, k = 3)
eig_null_list = []
mcd_k1_null_list = []
mcd_k3_null_list = []
mpd_k1_null_list = []
mpd_k3_null_list = []
for j in range(iter2):
test_cov_rndm = pt.get_random_matrix(test_cov)
X_j = test_cov_rndm/test_cov_rndm.sum(axis=1)[:,None]
X_j -= np.mean(X_j, axis = 0)
pca_j = PCA()
pca_fit_j = pca_j.fit_transform(X_j)
#pca_fit_j = pca.fit_transform(X_j)
mpd_k1_null_list.append( pt.get_mean_pairwise_euc_distance(pca_fit_j, k = 1 ) )
mpd_k3_null_list.append( pt.get_mean_pairwise_euc_distance(pca_fit_j, k = 3 ) )
mcd_k1_null_list.append(pt.get_mean_centroid_distance(pca_fit_j, k = 1))
mcd_k3_null_list.append(pt.get_mean_centroid_distance(pca_fit_j, k = 3))
eig_null_list.append( pt.get_x_stat(pca_j.explained_variance_[:-1], n_features=n_genes) )
#print(len( [k for k in eig_null_list if k > eig] ) / iter1)
eig_p_list.append(len( [k for k in eig_null_list if k > eig] ) / iter1)
mcd_k1_p_list.append( len( [k for k in mcd_k1_null_list if k > mcd_k1] ) / iter1 )
mcd_k3_p_list.append( len( [k for k in mcd_k3_null_list if k > mcd_k3] ) / iter1 )
mpd_k1_p_list.append( len( [k for k in mpd_k1_null_list if k > mpd_k1] ) / iter1 )
mpd_k3_p_list.append( len( [k for k in mpd_k3_null_list if k > mpd_k3] ) / iter1 )
cc_list.append(cc)
eig_z_list.append( (eig - np.mean(eig_null_list)) / np.std(eig_null_list) )
mcd_k1_z_list.append( (mcd_k1 - np.mean(mcd_k1_null_list)) / np.std(mcd_k1_null_list) )
mcd_k3_z_list.append( (mcd_k3 - np.mean(mcd_k3_null_list)) / np.std(mcd_k3_null_list) )
mpd_k1_z_list.append( (mpd_k1 - np.mean(mpd_k1_null_list)) / np.std(mpd_k1_null_list) )
mpd_k3_z_list.append( (mpd_k3 - np.mean(mpd_k3_null_list)) / np.std(mpd_k3_null_list) )
# calculate
cc_mean = np.mean(cc_list)
cc_bs_mean_list = []
for iter_i in range(10000):
cc_bs_mean_list.append( np.mean( np.random.choice(cc_list, size=50, replace=True ) ))
cc_bs_mean_list.sort()
cc_975 = cc_bs_mean_list[ int(0.975 * 10000) ]
cc_025 = cc_bs_mean_list[ int(0.025 * 10000) ]
eig_power = len([n for n in eig_p_list if n < 0.05]) / iter1
eig_power_025, eig_power_975 = get_bootstrap_power_ci(eig_p_list)
mcd_k1_power = len([n for n in mcd_k1_p_list if n < 0.05]) / iter1
mcd_k1_power_025, mcd_k1_power_975 = get_bootstrap_power_ci(mcd_k1_p_list)
mcd_k3_power = len([n for n in mcd_k3_p_list if n < 0.05]) / iter1
mcd_k3_power_025, mcd_k3_power_975 = get_bootstrap_power_ci(mcd_k3_p_list)
mpd_k1_power = len([n for n in mpd_k1_p_list if n < 0.05]) / iter1
mpd_k1_power_025, mpd_k1_power_975 = get_bootstrap_power_ci(mpd_k1_p_list)
mpd_k3_power = len([n for n in mpd_k3_p_list if n < 0.05]) / iter1
mpd_k3_power_025, mpd_k3_power_975 = get_bootstrap_power_ci(mpd_k3_p_list)
eig_z_025, eig_z_975 = get_bootstrap_ci(eig_z_list)
mcd_k1_z_025, mcd_k1_z_975 = get_bootstrap_ci(mcd_k1_z_list)
mcd_k3_z_025, mcd_k3_z_975 = get_bootstrap_ci(mcd_k3_z_list)
mpd_k1_z_025, mpd_k1_z_975 = get_bootstrap_ci(mpd_k1_z_list)
mpd_k3_z_025, mpd_k3_z_975 = get_bootstrap_ci(mpd_k3_z_list)
df_out.write('\t'.join([str(p), str(cc_mean), str(cc_025), str(cc_975), 'Eig', str(eig_power), str(eig_power_025), str(eig_power_975), str(np.mean(eig_z_list)), str(eig_z_025), str(eig_z_975)]) + '\n')
df_out.write('\t'.join([str(p), str(cc_mean), str(cc_025), str(cc_975), 'MCD_k1', str(mcd_k1_power), str(mcd_k1_power_025), str(mcd_k1_power_975), str(np.mean(mcd_k1_z_list)), str(mcd_k1_z_025), str(mcd_k1_z_975)]) + '\n')
df_out.write('\t'.join([str(p), str(cc_mean), str(cc_025), str(cc_975), 'MCD_k3', str(mcd_k3_power), str(mcd_k3_power_025), str(mcd_k3_power_975), str(np.mean(mcd_k3_z_list)), str(mcd_k3_z_025), str(mcd_k3_z_975)]) + '\n')
df_out.write('\t'.join([str(p), str(cc_mean), str(cc_025), str(cc_975), 'MPD_k1', str(mpd_k1_power), str(mpd_k1_power_025), str(mpd_k1_power_975), str(np.mean(mpd_k1_z_list)), str(mpd_k1_z_025), str(mpd_k1_z_975)]) + '\n')
df_out.write('\t'.join([str(p), str(cc_mean), str(cc_025), str(cc_975), 'MPD_k3', str(mpd_k3_power), str(mpd_k3_power_025), str(mpd_k3_power_975), str(np.mean(mpd_k3_z_list)), str(mpd_k3_z_025), str(mpd_k3_z_975)]) + '\n')
df_out.close()
def run_ba_ntwk_cov_sims(iter1=1000, iter2=1000, n_pops=100, n_genes=50):
df_out = open(mydir + '/data/simulations/cov_ba_ntwrk_methods.txt', 'w')
df_out.write('\t'.join(['Cov', 'Method', 'Power', 'Power_025', 'Power_975', 'Z_mean', 'Z_025', 'Z_975']) + '\n')
covs = [0.05, 0.1, 0.15, 0.2]
#covs = [0.2]
for cov in covs:
eig_p_list = []
mcd_k1_p_list = []
mcd_k3_p_list = []
mpd_k1_p_list = []
mpd_k3_p_list = []
eig_z_list = []
mcd_k1_z_list = []
mcd_k3_z_list = []
mpd_k1_z_list = []
mpd_k3_z_list = []
for i in range(iter1):
if i %100 ==0:
print(cov, i)
lambda_genes = np.random.gamma(shape=3, scale=1, size=n_genes)
C = pt.get_ba_cov_matrix(n_genes, cov=cov)
test_cov = np.stack( [pt.get_count_pop(lambda_genes, cov= C) for x in range(n_pops)] , axis=0 )
X = test_cov/test_cov.sum(axis=1)[:,None]
X -= np.mean(X, axis = 0)
pca = PCA()
pca_fit = pca.fit_transform(X)
mpd_k1 = pt.get_mean_pairwise_euc_distance(pca_fit,k=1)
mpd_k3 = pt.get_mean_pairwise_euc_distance(pca_fit,k=3)
eig = pt.get_x_stat(pca.explained_variance_[:-1], n_features=n_genes)
mcd_k1 = pt.get_mean_centroid_distance(pca_fit, k = 1)
mcd_k3 = pt.get_mean_centroid_distance(pca_fit, k = 3)
#print(pca.explained_variance_[:-1])
#print(pt.get_x_stat(pca.explained_variance_[:-1]))
eig_null_list = []
mcd_k1_null_list = []
mcd_k3_null_list = []
mpd_k1_null_list = []
mpd_k3_null_list = []
for j in range(iter2):
test_cov_rndm = pt.get_random_matrix(test_cov)
X_j = test_cov_rndm/test_cov_rndm.sum(axis=1)[:,None]
X_j -= np.mean(X_j, axis = 0)
pca_j = PCA()
pca_fit_j = pca_j.fit_transform(X_j)
#pca_fit_j = pca.fit_transform(X_j)
mpd_k1_null_list.append( pt.get_mean_pairwise_euc_distance(pca_fit_j, k = 1 ) )
mpd_k3_null_list.append( pt.get_mean_pairwise_euc_distance(pca_fit_j, k = 3 ) )
mcd_k1_null_list.append(pt.get_mean_centroid_distance(pca_fit_j, k = 1))
mcd_k3_null_list.append(pt.get_mean_centroid_distance(pca_fit_j, k = 3))
eig_null_list.append( pt.get_x_stat(pca_j.explained_variance_[:-1], n_features=n_genes) )
eig_p_list.append(len( [k for k in eig_null_list if k > eig] ) / iter1)
mcd_k1_p_list.append( len( [k for k in mcd_k1_null_list if k > mcd_k1] ) / iter1 )
mcd_k3_p_list.append( len( [k for k in mcd_k3_null_list if k > mcd_k3] ) / iter1 )
mpd_k1_p_list.append( len( [k for k in mpd_k1_null_list if k > mpd_k1] ) / iter1 )
mpd_k3_p_list.append( len( [k for k in mpd_k3_null_list if k > mpd_k3] ) / iter1 )
eig_z_list.append( (eig - np.mean(eig_null_list)) / np.std(eig_null_list) )
mcd_k1_z_list.append( (mcd_k1 - np.mean(mcd_k1_null_list)) / np.std(mcd_k1_null_list) )
mcd_k3_z_list.append( (mcd_k3 - np.mean(mcd_k3_null_list)) / np.std(mcd_k3_null_list) )
mpd_k1_z_list.append( (mpd_k1 - np.mean(mpd_k1_null_list)) / np.std(mpd_k1_null_list) )
mpd_k3_z_list.append( (mpd_k3 - np.mean(mpd_k3_null_list)) / np.std(mpd_k3_null_list) )
# calculate power
eig_power = len([n for n in eig_p_list if n < 0.05]) / iter1
eig_power_025, eig_power_975 = get_bootstrap_power_ci(eig_p_list)
mcd_k1_power = len([n for n in mcd_k1_p_list if n < 0.05]) / iter1
mcd_k1_power_025, mcd_k1_power_975 = get_bootstrap_power_ci(mcd_k1_p_list)
mcd_k3_power = len([n for n in mcd_k3_p_list if n < 0.05]) / iter1
mcd_k3_power_025, mcd_k3_power_975 = get_bootstrap_power_ci(mcd_k3_p_list)
mpd_k1_power = len([n for n in mpd_k1_p_list if n < 0.05]) / iter1
mpd_k1_power_025, mpd_k1_power_975 = get_bootstrap_power_ci(mpd_k1_p_list)
mpd_k3_power = len([n for n in mpd_k3_p_list if n < 0.05]) / iter1
mpd_k3_power_025, mpd_k3_power_975 = get_bootstrap_power_ci(mpd_k3_p_list)
eig_z_025, eig_z_975 = get_bootstrap_ci(eig_z_list)
mcd_k1_z_025, mcd_k1_z_975 = get_bootstrap_ci(mcd_k1_z_list)
mcd_k3_z_025, mcd_k3_z_975 = get_bootstrap_ci(mcd_k3_z_list)
mpd_k1_z_025, mpd_k1_z_975 = get_bootstrap_ci(mpd_k1_z_list)
mpd_k3_z_025, mpd_k3_z_975 = get_bootstrap_ci(mpd_k3_z_list)
df_out.write('\t'.join([str(cov), 'Eig', str(eig_power), str(eig_power_025), str(eig_power_975), str(np.mean(eig_z_list)), str(eig_z_025), str(eig_z_975)]) + '\n')
df_out.write('\t'.join([str(cov), 'MCD_k1', str(mcd_k1_power), str(mcd_k1_power_025), str(mcd_k1_power_975), str(np.mean(mcd_k1_z_list)), str(mcd_k1_z_025), str(mcd_k1_z_975)]) + '\n')
df_out.write('\t'.join([str(cov), 'MCD_k3', str(mcd_k3_power), str(mcd_k3_power_025), str(mcd_k3_power_975), str(np.mean(mcd_k3_z_list)), str(mcd_k3_z_025), str(mcd_k3_z_975)]) + '\n')
df_out.write('\t'.join([str(cov), 'MPD_k1', str(mpd_k1_power), str(mpd_k1_power_025), str(mpd_k1_power_975), str(np.mean(mpd_k1_z_list)), str(mpd_k1_z_025), str(mpd_k1_z_975)]) + '\n')
df_out.write('\t'.join([str(cov), 'MPD_k3', str(mpd_k3_power), str(mpd_k3_power_025), str(mpd_k3_power_975), str(np.mean(mpd_k3_z_list)), str(mpd_k3_z_025), str(mpd_k3_z_975)]) + '\n')
df_out.close()