def distribution_for_half(self):
if self._distribution_half != None:
return self._distribution_half
bycond = {}
for o in self.halfoffer_set.all():
if o.active:
cond = map_half_cond_to_internal(o.condition)
if not bycond.has_key(cond):
bycond[cond] = []
bycond[cond].append(float(o.price))
from stats import quantile
dist = {}
for k in bycond.keys():
if len(bycond[k]) > 1:
min = quantile(bycond[k],0)
p25 = quantile(bycond[k],0.25)
med = quantile(bycond[k],0.5)
p75 = quantile(bycond[k],0.75)
max = quantile(bycond[k],1)
#print k,"N=%d, %03.2f<< %03.2f - %03.2f - %03.2f >>%03.2f" % (len(bycond[k]),min,p25,med,p75,max)
dist[k] = (len(bycond[k]),Decimal("%3.2f" % min), Decimal("%3.2f" % p25), Decimal("%3.2f" % med), Decimal("%3.2f" % p75), Decimal("%3.2f" % max))
else:
#print k,"%03.2f" % bycond[k][0]
dist[k] = (1,"","",Decimal("%3.2f" % bycond[k][0]),"","")
self._distribution_half = dist
return dist
def get_seq_feature(seq, seq_name, user_id):
# total 11 features
if not seq:
print('seq is empty!')
return
df = pd.DataFrame()
df[seq_name + '_mean'] = [np.mean(seq)]
df[seq_name + '_median'] = [np.median(seq)]
df[seq_name + '_max'] = [np.max(seq)]
df[seq_name + '_min'] = [np.min(seq)]
df[seq_name + '_var'] = [np.var(seq)]
df[seq_name + '_std'] = [np.std(seq)]
if len(seq) == 1:
df[seq_name + '_upquantile'] = seq[0]
df[seq_name + '_downquantile'] = 0
else:
df[seq_name + '_upquantile'] = [sts.quantile(seq, p=0.75)]
df[seq_name + '_downquantile'] = [sts.quantile(seq, p=0.25)]
if np.mean(seq) != 0: df[seq_name + '_discrete'] = [np.std(seq) / np.mean(seq)]
else: df[seq_name + '_discrete'] = [np.NaN]
try: df[seq_name + 'skew'] = [sts.skewness(seq)]
except: df[seq_name + 'skew'] = [np.NaN]
try: df[seq_name + 'kurt'] = [sts.kurtosis(seq)]
except: df[seq_name + 'kurt'] = [np.NaN]
df['user_id'] = [user_id]
return df
def data_description(index, start, end):
returns = download_data.get_returns(index, start, end)
print('个数：', len(returns))
print('平均值:', np.mean(returns))
print('中位数:', np.median(returns))
print('上四分位数', sts.quantile(returns, p=0.25))
print('下四分位数', sts.quantile(returns, p=0.75))
#离散趋势的度量
print('最大值:', np.max(returns))
print('最小值:', np.min(returns))
print('极差:', np.max(returns) - np.min(returns))
print('四分位差',
sts.quantile(returns, p=0.75) - sts.quantile(returns, p=0.25))
print('标准差:', np.std(returns))
print('方差:', np.var(returns))
print('离散系数:', np.std(returns) / np.mean(returns))
#偏度与峰度的度量
print('偏度:', sts.skewness(returns))
print('峰度:', sts.kurtosis(returns))
print(st.kstest(returns, 'norm'))
length = len(returns)
sns.distplot(returns, bins=100, label='Empirical')
sns.plt.legend()
sns.plt.title('Empirical')
sns.plt.show()
def extend_feature(scores):
"""
特征构造
Args:
scores: 原始滑动窗口获得的特征
Returns:
返回基于滑动窗口特征增加的统计特征
"""
features = scores
features.append(np.sum(scores)) #总数
features.append(np.mean(scores)) #平均数
features.append(np.median(scores)) #中位数
# features.append(sts.mode(scores)) #众数
features.append(sts.quantile(scores, p=0.25)) #上四分位
features.append(sts.quantile(scores, p=0.75)) #上七分位
features.append(np.max(scores)) #最大值
features.append(np.min(scores)) #最小值
features.append(np.max(scores) - np.min(scores)) #极差
features.append(
sts.quantile(scores, p=0.75) - sts.quantile(scores, p=0.25)) #四分位差
features.append(np.var(scores)) #方差
features.append(np.std(scores) / np.mean(scores)) #离散系数
features.append(sts.skewness(scores)) #偏度
features.append(sts.kurtosis(scores)) #峰度
return features
def test_quantile(self):
self.assertEqual(stats.quantile([1, 2, 3, 4, 5], 0.2), 2)
def test_quantile_wrong_type(self):
with self.assertRaises(TypeError) as raised_exception:
stats.quantile([2, 3, 4], 3)
self.assertEqual(raised_exception.exception.args[0], "p must be float")
def test_quantile_wrong_range_p(self):
with self.assertRaises(ValueError) as raised_exception:
stats.quantile([2, 3, 4], 3.0)
self.assertEqual(raised_exception.exception.args[0],
"p must be in range (0,1)")
#!/usr/bin/python26
# encoding=utf-8
"""
异常值检测
使用算法：Tukey's Test
"""
import numpy as np
import stats as sts
list = [1, 4, 8, 90, 98, 44, 35, 56, 2, 41, 11, 24, 23, 45, 500, 150]
print(list)
# 求四分位数
print('下四分位数：', sts.quantile(list, p=0.25))
print('上四分位数：', sts.quantile(list, p=0.75))
q1 = sts.quantile(list, p=0.25)
q3 = sts.quantile(list, p=0.75)
# k=1.5 中度异常
k1 = 1.5
g_min_m = q1 - k1 * (q3 - q1)
g_max_m = q3 + k1 * (q3 - q1)
# k=3 重度异常
k2 = 3
g_min_b = q1 - k2 * (q3 - q1)
g_max_b = q3 + k2 * (q3 - q1)
# g_min_b, g_min_m, g_max_m, g_max_b
import numpy as np
import stats as sts
data = [1, 2, 2, 3]
#集中趋势的度量
print('求和：', np.sum(data))
print('个数：', len(data))
print('平均值:', np.mean(data))
print('中位数:', np.median(data))
print('众数:', sts.mode(data))
print('上四分位数', sts.quantile(data, p=0.25))
print('下四分位数', sts.quantile(data, p=0.75))
#离散趋势的度量
print('最大值:', np.max(data))
print('最小值:', np.min(data))
print('极差:', np.max(data) - np.min(data))
print('四分位差', sts.quantile(data, p=0.75) - sts.quantile(data, p=0.25))
print('标准差:', np.std(data))
print('方差:', np.var(data))
print('变异系数:', np.std(data) / np.mean(data))
#偏度与峰度的度量
print('偏度:', sts.skewness(data))
print('峰度:', sts.kurtosis(data))
# 随机生成两个样本
x = np.random.randint(0, 9, 1000)
y = np.random.randint(0, 9, 1000)
# 计算平均值
mx = x.mean()
my = y.mean()
def main():
usage = 'usage: %prog [options] <roc_dir>'
parser = OptionParser(usage)
parser.add_option('-t', dest='targets_file')
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error('Must provide ROC points file')
else:
roc_dir = args[0]
# read target labels
if options.targets_file:
target_labels = [
line.split()[0] for line in open(options.targets_file)
]
else:
target_labels = [
'Target %d' % (ti + 1)
for ti in range(len(glob.glob('%s/roc*.txt' % roc_dir)))
]
#######################################################
# make all ROC plots
#######################################################
target_fpr = []
target_tpr = []
for roc_file in glob.glob('%s/roc*.txt' % roc_dir):
ti = int(roc_file[roc_file.find('roc') + 3:-4]) - 1
target_fpr.append([])
target_tpr.append([])
for line in open(roc_file):
a = line.split()
target_fpr[-1].append(float(a[0]))
target_tpr[-1].append(float(a[1]))
plt.figure(figsize=(6, 6))
plt.scatter(target_fpr[-1],
target_tpr[-1],
s=8,
linewidths=0,
c=sns_colors[0])
plt.title(target_labels[ti])
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.xlim((0, 1))
plt.ylim((0, 1))
plt.grid(True)
plt.tight_layout()
out_pdf = '%s.pdf' % os.path.splitext(roc_file)[0]
plt.savefig(out_pdf)
plt.close()
#######################################################
# multiple ROC curve plot
#######################################################
# read AUCs
target_aucs = [
float(line.split()[1]) for line in open('%s/aucs.txt' % roc_dir)
]
# choose cells
auc_targets = [(target_aucs[ti], ti) for ti in range(len(target_aucs))]
auc_targets.sort()
fig_quants = [0.05, .33, 0.5, .67, .95]
auc_target_quants = quantile(auc_targets, fig_quants)
# plot
sns.set(style='white', font_scale=1.2)
plt.figure(figsize=(6, 6))
si = 0
for auc, ti in auc_target_quants:
target_label = '%-9s AUC: %.3f' % (target_labels[ti], target_aucs[ti])
plt.plot(target_fpr[ti],
target_tpr[ti],
c=sns_colors[si],
label=target_label,
linewidth=2.5,
alpha=0.8)
si += 1
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.xlim((0, 1))
plt.ylim((0, 1))
ax = plt.gca()
ax.xaxis.label.set_fontsize(17)
ax.yaxis.label.set_fontsize(17)
map(lambda xl: xl.set_fontsize(13), ax.get_xticklabels())
map(lambda yl: yl.set_fontsize(13), ax.get_yticklabels())
ax.grid(True, linestyle=':')
plt.tight_layout()
matplotlib.rcParams.update({'font.family': 'monospace'})
plt.legend(loc=4, fontsize=12)
plt.savefig('%s/range.pdf' % roc_dir)
plt.close()
data[:, :, 65] = walking_12[0:480, 1:7]
data[:, :, 66] = walking_13[0:480, 1:7]
data[:, :, 67] = walking_14[0:480, 1:7]
data[:, :, 68] = walking_15[0:480, 1:7]
y = [[0] for row in range(480)]
dataset = np.zeros((69, 7, 6))
for a in range(6):
for i in range(69):
dataset[i, 0, a] = data[:, a, i].min()
dataset[i, 1, a] = data[:, a, i].max()
dataset[i, 2, a] = data[:, a, i].mean()
dataset[i, 3, a] = np.median(data[:, a, i])
dataset[i, 4, a] = data[:, a, i].std()
y = data[:, a, i].tolist()
dataset[i, 5, a] = sts.quantile(y, p=0.25)
dataset[i, 6, a] = sts.quantile(y, p=0.75)
scatter_matrix = np.zeros((69, 3, 3))
scatter_matrix[:, :, 0] = dataset[:, 0:3, 0]
scatter_matrix[:, :, 1] = dataset[:, 0:3, 1]
scatter_matrix[:, :, 2] = dataset[:, 0:3, 5]
fig = plt.figure()
plt.title('Scatter Plots')
#
ax1 = fig.add_subplot(991)
ax1.scatter(scatter_matrix[0:9, 0, 0].tolist(),
scatter_matrix[0:9, 0, 0].tolist(),
c='b',
marker='.')
def main():
usage = 'usage: %prog [options] <gtf> <diff>'
parser = OptionParser(usage)
parser.add_option('-o', dest='out_dir', default='te_diff_regress', help='Output directory to print regression summaries [Default: %default]')
parser.add_option('-c', dest='scale', default=1, type='float', help='Plot scale [Default: %default]')
parser.add_option('-t', dest='te_gff', default='%s/hg19.fa.out.tp.gff'%os.environ['MASK'])
parser.add_option('-r', dest='orientation', default=False, action='store_true', help='Split TEs by orientation [Default: %default]')
parser.add_option('-m', dest='max_stat', default=None, type='float', help='Maximum stat for plotting [Default: %default]')
parser.add_option('-s', dest='spread_factor', default=None, type='float', help='Allow multiplicative factor between the shortest and longest transcripts, used to filter [Default: %default]')
parser.add_option('-l', dest='spread_lower', default=None, type='float', help='Allow multiplicative factor between median and shortest transcripts [Defafult: %default]')
parser.add_option('-u', dest='spread_upper', default=None, type='float', help='Allow multiplicative factor between median and longest transcripts [Defafult: %default]')
(options,args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide .gtf and .diff files')
else:
ref_gtf = args[0]
diff_file = args[1]
# make output directory
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
if options.spread_factor or options.spread_lower or options.spread_upper:
# filter for similar length
if options.spread_factor:
options.spread_lower = math.sqrt(options.spread_factor)
options.spread_upper = math.sqrt(options.spread_factor)
spread_gtf = '%s/spread_factor.gtf' % options.out_dir
gff.length_filter(ref_gtf, spread_gtf, options.spread_lower, options.spread_lower, verbose=True)
ref_gtf = spread_gtf
# hash genes -> TEs -> occurence num
gene_te_num = te.hash_genes_repeats_num(ref_gtf, options.te_gff, gene_key='transcript_id', add_star=True, stranded=options.orientation)
# hash diffs stats
gene_diffs = cuffdiff.hash_diff(diff_file, stat='fold', max_stat=options.max_stat, sample_first='input')
table_lines = []
pvals = []
for spair in gene_diffs:
sample1, sample2 = spair
gene_list = list(set(gene_te_num.keys()) & set(gene_diffs[spair].keys()))
for fam in count_tes:
if options.orientation:
orients = ['+','-']
else:
orients = ['+']
for orient in orients:
# hash diff values by TE count
count_diff = []
for gene_id in gene_diffs[spair]:
if options.orientation:
count = gene_te_num.get(gene_id,{}).get(('*',fam,orient), 0)
else:
count = gene_te_num.get(gene_id,{}).get(('*',fam), 0)
while count >= len(count_diff):
count_diff.append([])
count_diff[count].append(gene_diffs[spair][gene_id])
df = {'TEs':[], 'stat_low':[], 'stat_mid':[], 'stat_hi':[]}
for c in range(len(count_diff)):
if len(count_diff[c]) > 12:
stat_low, stat_mid, stat_hi = stats.quantile(count_diff[c], [.25, .5, .75])
df['TEs'].append(c)
df['stat_low'].append(stat_low)
df['stat_mid'].append(stat_mid)
df['stat_hi'].append(stat_hi)
else:
break
if len(df['TEs']) > 1:
fam_plot = fam[fam.find('/')+1:]
if options.orientation:
out_pdf = '%s/%s-%s_%s_%s.pdf' % (options.out_dir, sample1, sample2, fam_plot, orient)
out_df = '%s/%s-%s_%s_%s.df' % (options.out_dir, sample1, sample2, fam_plot, orient)
else:
out_pdf = '%s/%s-%s_%s.pdf' % (options.out_dir, sample1, sample2, fam_plot)
out_df = '%s/%s-%s_%s.df' % (options.out_dir, sample1, sample2, fam_plot)
ggplot.plot('%s/te_diff_count.r' % os.environ['RDIR'], df, [out_pdf, options.scale], df_file=out_df)
if options.spread_factor or options.spread_lower or options.spread_upper:
os.remove(spread_gtf)
import numpy as np
import stats as sts
a = [31, 24, 23, 25, 14, 25, 13, 12, 14, 23,
32, 34, 43, 41, 21, 23, 26, 26, 34, 42,
43, 25, 24, 23, 24, 44, 23, 14, 52,32,
42, 44, 35, 28, 17, 21, 32, 42, 12, 34]
scores=np.array(a)
print('總合為：',np.sum(scores))
print('筆數為：',len(scores))
print('平均值為:',np.mean(scores))
print('中位數為:',np.median(scores))
print('眾數為:',sts.mode(scores))
print('上四分位數為',sts.quantile(scores,p=0.25))
print('下四分位數為',sts.quantile(scores,p=0.75))
print('最大值:',np.max(scores))
print('最小值:',np.min(scores))
print('全距:',np.ptp(scores))
print('標準差:',np.std(scores))
print('變異數:',np.var(scores))
print('離散係數:',np.std(scores)/np.mean(scores))
print('偏態係數:',sts.skewness(scores))
print('峰態係數:',sts.kurtosis(scores))
def main():
usage = 'usage: %prog [options] <model_file>'
parser = OptionParser(usage)
parser.add_option('-c', dest='center_dist', default=10, type='int', help='Distance between the motifs and sequence center [Default: %default]')
parser.add_option('-d', dest='model_hdf5_file', default=None, help='Pre-computed model output as HDF5 [Default: %default]')
parser.add_option('-g', dest='cuda', default=False, action='store_true', help='Run on the GPGPU [Default: %default]')
parser.add_option('-l', dest='seq_length', default=600, type='int', help='Sequence length [Default: %default]')
parser.add_option('-o', dest='out_dir', default='heat', help='Output directory [Default: %default]')
parser.add_option('-t', dest='targets', default='0', help='Comma-separated list of target indexes to plot (or -1 for all) [Default: %default]')
(options,args) = parser.parse_args()
if len(args) != 1:
parser.error('Must provide Basset model file')
else:
model_file = args[0]
out_targets = [int(ti) for ti in options.targets.split(',')]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
random.seed(1)
# torch options
cuda_str = ''
if options.cuda:
cuda_str = '-cuda'
#################################################################
# place filter consensus motifs
#################################################################
# determine filter consensus motifs
filter_consensus = get_filter_consensus(model_file, options.out_dir, cuda_str)
seqs_1hot = []
num_filters = len(filter_consensus)
# num_filters = 40
filter_len = filter_consensus[0].shape[1]
# position the motifs
left_i = options.seq_length/2 - options.center_dist - filter_len
right_i = options.seq_length/2 + options.center_dist
ns_1hot = np.zeros((4,options.seq_length)) + 0.25
# ns_1hot = np.zeros((4,options.seq_length))
# for i in range(options.seq_length):
# nt_i = random.randint(0,3)
# ns_1hot[nt_i,i] = 1
for i in range(num_filters):
for j in range(num_filters):
# copy the sequence of N's
motifs_seq = np.copy(ns_1hot)
# write them into the one hot coding
motifs_seq[:,left_i:left_i+filter_len] = filter_consensus[i]
motifs_seq[:,right_i:right_i+filter_len] = filter_consensus[j]
# save
seqs_1hot.append(motifs_seq)
# make a full array
seqs_1hot = np.array(seqs_1hot)
# reshape for spatial
seqs_1hot = seqs_1hot.reshape((seqs_1hot.shape[0],4,1,options.seq_length))
#################################################################
# place filter consensus motifs
#################################################################
# save to HDF5
seqs_file = '%s/motif_seqs.h5' % options.out_dir
h5f = h5py.File(seqs_file, 'w')
h5f.create_dataset('test_in', data=seqs_1hot)
h5f.close()
# predict scores
scores_file = '%s/motif_seqs_scores.h5' % options.out_dir
torch_cmd = 'th basset_place2_predict.lua %s %s %s %s' % (cuda_str, model_file, seqs_file, scores_file)
subprocess.call(torch_cmd, shell=True)
# load in scores
hdf5_in = h5py.File(scores_file, 'r')
motif_seq_scores = np.array(hdf5_in['scores'])
hdf5_in.close()
#################################################################
# analyze
#################################################################
for ti in out_targets:
#################################################################
# compute pairwise expectations
#################################################################
# X = np.zeros((motif_seq_scores.shape[0],num_filters))
# xi = 0
# for i in range(num_filters):
# for j in range(num_filters):
# X[xi,i] += 1
# X[xi,j] += 1
# xi += 1
X = np.zeros((motif_seq_scores.shape[0],2*num_filters))
xi = 0
for i in range(num_filters):
for j in range(num_filters):
X[xi,i] += 1
X[xi,num_filters+j] += 1
xi += 1
# fit model
model = BayesianRidge()
model.fit(X, motif_seq_scores[:,ti])
# predict pairwise expectations
motif_seq_preds = model.predict(X)
print model.score(X, motif_seq_scores[:,ti])
# print filter coefficients
coef_out = open('%s/coefs_t%d.txt' % (options.out_dir,ti), 'w')
for i in range(num_filters):
print >> coef_out, '%3d %6.2f' % (i,model.coef_[i])
coef_out.close()
#################################################################
# normalize pairwise predictions
#################################################################
filter_interaction = np.zeros((num_filters,num_filters))
table_out = open('%s/table_t%d.txt' % (options.out_dir,ti), 'w')
si = 0
for i in range(num_filters):
for j in range(num_filters):
filter_interaction[i,j] = motif_seq_scores[si,ti] - motif_seq_preds[si]
cols = (i, j, motif_seq_scores[si,ti], motif_seq_preds[si], filter_interaction[i,j])
print >> table_out, '%3d %3d %6.3f %6.3f %6.3f' % cols
si += 1
table_out.close()
scores_abs = abs(filter_interaction.flatten())
max_score = stats.quantile(scores_abs, .999)
print 'Limiting scores to +-%f' % max_score
filter_interaction_max = np.zeros((num_filters, num_filters))
for i in range(num_filters):
for j in range(num_filters):
filter_interaction_max[i,j] = np.min([filter_interaction[i,j], max_score])
filter_interaction_max[i,j] = np.max([filter_interaction_max[i,j], -max_score])
# plot heat map
plt.figure()
sns.heatmap(filter_interaction_max, xticklabels=False, yticklabels=False)
plt.savefig('%s/heat_t%d.pdf' % (options.out_dir,ti))
def main():
usage = 'usage: %prog [options] <gtf> <diff>'
parser = OptionParser(usage)
parser.add_option(
'-o',
dest='out_dir',
default='te_diff_regress',
help=
'Output directory to print regression summaries [Default: %default]')
parser.add_option('-c',
dest='scale',
default=1,
type='float',
help='Plot scale [Default: %default]')
parser.add_option('-t',
dest='te_gff',
default='%s/hg19.fa.out.tp.gff' % os.environ['MASK'])
parser.add_option('-r',
dest='orientation',
default=False,
action='store_true',
help='Split TEs by orientation [Default: %default]')
parser.add_option('-m',
dest='max_stat',
default=None,
type='float',
help='Maximum stat for plotting [Default: %default]')
parser.add_option(
'-s',
dest='spread_factor',
default=None,
type='float',
help=
'Allow multiplicative factor between the shortest and longest transcripts, used to filter [Default: %default]'
)
parser.add_option(
'-l',
dest='spread_lower',
default=None,
type='float',
help=
'Allow multiplicative factor between median and shortest transcripts [Defafult: %default]'
)
parser.add_option(
'-u',
dest='spread_upper',
default=None,
type='float',
help=
'Allow multiplicative factor between median and longest transcripts [Defafult: %default]'
)
(options, args) = parser.parse_args()
if len(args) != 2:
parser.error('Must provide .gtf and .diff files')
else:
ref_gtf = args[0]
diff_file = args[1]
# make output directory
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
if options.spread_factor or options.spread_lower or options.spread_upper:
# filter for similar length
if options.spread_factor:
options.spread_lower = math.sqrt(options.spread_factor)
options.spread_upper = math.sqrt(options.spread_factor)
spread_gtf = '%s/spread_factor.gtf' % options.out_dir
gff.length_filter(ref_gtf,
spread_gtf,
options.spread_lower,
options.spread_lower,
verbose=True)
ref_gtf = spread_gtf
# hash genes -> TEs -> occurence num
gene_te_num = te.hash_genes_repeats_num(ref_gtf,
options.te_gff,
gene_key='transcript_id',
add_star=True,
stranded=options.orientation)
# hash diffs stats
gene_diffs = cuffdiff.hash_diff(diff_file,
stat='fold',
max_stat=options.max_stat,
sample_first='input')
table_lines = []
pvals = []
for spair in gene_diffs:
sample1, sample2 = spair
gene_list = list(
set(gene_te_num.keys()) & set(gene_diffs[spair].keys()))
for fam in count_tes:
if options.orientation:
orients = ['+', '-']
else:
orients = ['+']
for orient in orients:
# hash diff values by TE count
count_diff = []
for gene_id in gene_diffs[spair]:
if options.orientation:
count = gene_te_num.get(gene_id, {}).get(
('*', fam, orient), 0)
else:
count = gene_te_num.get(gene_id, {}).get(('*', fam), 0)
while count >= len(count_diff):
count_diff.append([])
count_diff[count].append(gene_diffs[spair][gene_id])
df = {'TEs': [], 'stat_low': [], 'stat_mid': [], 'stat_hi': []}
for c in range(len(count_diff)):
if len(count_diff[c]) > 12:
stat_low, stat_mid, stat_hi = stats.quantile(
count_diff[c], [.25, .5, .75])
df['TEs'].append(c)
df['stat_low'].append(stat_low)
df['stat_mid'].append(stat_mid)
df['stat_hi'].append(stat_hi)
else:
break
if len(df['TEs']) > 1:
fam_plot = fam[fam.find('/') + 1:]
if options.orientation:
out_pdf = '%s/%s-%s_%s_%s.pdf' % (options.out_dir,
sample1, sample2,
fam_plot, orient)
out_df = '%s/%s-%s_%s_%s.df' % (options.out_dir,
sample1, sample2,
fam_plot, orient)
else:
out_pdf = '%s/%s-%s_%s.pdf' % (
options.out_dir, sample1, sample2, fam_plot)
out_df = '%s/%s-%s_%s.df' % (options.out_dir, sample1,
sample2, fam_plot)
ggplot.plot('%s/te_diff_count.r' % os.environ['RDIR'],
df, [out_pdf, options.scale],
df_file=out_df)
if options.spread_factor or options.spread_lower or options.spread_upper:
os.remove(spread_gtf)
def main():
usage = "usage: %prog [options] <roc_dir>"
parser = OptionParser(usage)
parser.add_option("-t", dest="targets_file")
(options, args) = parser.parse_args()
if len(args) != 1:
parser.error("Must provide ROC points file")
else:
roc_dir = args[0]
# read target labels
if options.targets_file:
target_labels = [line.split()[0] for line in open(options.targets_file)]
else:
target_labels = ["Target %d" % (ti + 1) for ti in range(len(glob.glob("%s/roc*.txt" % roc_dir)))]
#######################################################
# make all ROC plots
#######################################################
target_fpr = []
target_tpr = []
for roc_file in glob.glob("%s/roc*.txt" % roc_dir):
ti = int(roc_file[roc_file.find("roc") + 3 : -4]) - 1
target_fpr.append([])
target_tpr.append([])
for line in open(roc_file):
a = line.split()
target_fpr[-1].append(float(a[0]))
target_tpr[-1].append(float(a[1]))
plt.figure(figsize=(6, 6))
plt.scatter(target_fpr[-1], target_tpr[-1], s=8, linewidths=0, c=sns_colors[0])
plt.title(target_labels[ti])
plt.xlabel("False positive rate")
plt.ylabel("True positive rate")
plt.xlim((0, 1))
plt.ylim((0, 1))
plt.grid(True)
plt.tight_layout()
out_pdf = "%s.pdf" % os.path.splitext(roc_file)[0]
plt.savefig(out_pdf)
plt.close()
#######################################################
# multiple ROC curve plot
#######################################################
# read AUCs
target_aucs = [float(line.split()[1]) for line in open("%s/aucs.txt" % roc_dir)]
# choose cells
auc_targets = [(target_aucs[ti], ti) for ti in range(len(target_aucs))]
auc_targets.sort()
fig_quants = [0.05, 0.33, 0.5, 0.67, 0.95]
auc_target_quants = quantile(auc_targets, fig_quants)
# plot
sns.set(style="white", font_scale=1.2)
plt.figure(figsize=(6, 6))
si = 0
for auc, ti in auc_target_quants:
target_label = "%-9s AUC: %.3f" % (target_labels[ti], target_aucs[ti])
plt.plot(target_fpr[ti], target_tpr[ti], c=sns_colors[si], label=target_label, linewidth=2.5, alpha=0.8)
si += 1
plt.xlabel("False positive rate")
plt.ylabel("True positive rate")
plt.xlim((0, 1))
plt.ylim((0, 1))
ax = plt.gca()
ax.xaxis.label.set_fontsize(17)
ax.yaxis.label.set_fontsize(17)
map(lambda xl: xl.set_fontsize(13), ax.get_xticklabels())
map(lambda yl: yl.set_fontsize(13), ax.get_yticklabels())
ax.grid(True, linestyle=":")
plt.tight_layout()
matplotlib.rcParams.update({"font.family": "monospace"})
plt.legend(loc=4, fontsize=12)
plt.savefig("%s/range.pdf" % roc_dir)
plt.close()
import numpy as np
import stats as sts
scares = [
31, 24, 23, 25, 14, 25, 13, 12, 14, 23, 32, 34, 43, 41, 21, 23, 26, 26, 34,
42, 43, 25, 24, 23, 24, 44, 23, 14, 52, 32, 42, 44, 35, 28, 17, 21, 32, 42,
12, 34
]
print('求和：', np.sum(scares))
print('個數：', len(scares))
print('平均值：', np.mean(scares))
print('中位數：', np.median(scares))
print('眾數：', sts.mode(scares))
print('上四分位數：', sts.quantile(scares, p=0.25))
print('下四分位數：', sts.quantile(scares, p=0.75))
print('最大值：', np.max(scares))
print('最小值：', np.min(scares))
print('極差：', np.std(scares))
print('四分位數：', sts.quantile(scares, p=0.75), sts.quantile(scares, p=0.25))
print('標準差：', np.std(scares))
print('方差', np.var(scares))
print('離散係數', np.std(scares) / np.mean(scares))
print('遍度：', sts.skewness(scares))
print('峰度：', sts.kurtosis(scares))
def quantile2(self, data):
print('下四分位数:', sts.quantile(data, p=0.75))
print("\n\n")
print("*** Test Module <stats> ***")
A = [1, 3, 5, 7, 9, 2, 3, 4, 4, 4, 6, 8, 10, 13, 15, 17]
print("vector A = ", A)
print("sorted A = ", sorted(A))
mean = st.mean(A)
print("A's mean = ", mean)
median = st.median(A)
print("A's median = ", median)
quantile = st.quantile(A, 0.2)
print("A's 20% quantile = ", quantile)
quantile = st.quantile(A, 0.9)
print("A's 90% quantile = ", quantile)
mode = st.mode(A)
print("A's mode = ", mode)
data_range = st.data_range(A)
print("A's range = ", data_range)
variance = st.variance(A)
print("A's variance = ", variance)
standard_deviation = st.standard_deviation(A)
def interquartile_range(self, data):
print('四分位差:', sts.quantile(data, p=0.75) - sts.quantile(data, p=0.25))
print('方差', df['身高'].var())
print('标准差', df['身高'].std())
print('极差', df['身高'].max() - df['身高'].min())
print('偏度', df['身高'].skew())
print('峰度', df['身高'].kurt())
import numpy as np
import stats as sts
scores = [1, 2, 2, 2, 5]
#集中趋势的度量
print('求和：', np.sum(scores))
print('个数：', len(scores))
print('平均值:', np.mean(scores))
print('中位数:', np.median(scores))
print('众数:', sts.mode(scores))
print('上四分位数', sts.quantile(scores, p=0.25))
print('下四分位数', sts.quantile(scores, p=0.75))
#离散趋势的度量
print('最大值:', np.max(scores))
print('最小值:', np.min(scores))
print('极差:', np.max(scores) - np.min(scores))
print('四分位差', sts.quantile(scores, p=0.75) - sts.quantile(scores, p=0.25))
print('标准差:', np.std(scores))
print('方差:', np.var(scores))
print('离散系数:', np.std(scores) / np.mean(scores))
#偏度与峰度的度量
print('偏度:', sts.skewness(scores))
print('峰度:', sts.kurtosis(scores))
def quantile1(self, data):
print('上四分位数:', sts.quantile(data, p=0.25))
