def user_defined_exons(tmp_sg, line):
chr, strand = utils.get_chr(
line[utils.TARGET]), line[utils.TARGET][0] # get chr and strand
upstream_exon = utils.get_pos(
line[utils.UPSTREAM_EXON]) # get user-defined flanking exons
downstream_exon = utils.get_pos(line[utils.DOWNSTREAM_EXON])
first_primer, second_primer = utils.get_primer_coordinates(
line[utils.PRIMER_COORD])
# get possible exons for primer amplification
tmp = sorted(tmp_sg.get_graph().nodes(), key=lambda x: (x[0], x[1]))
first_ex = utils.find_first_exon(first_primer, tmp)
last_ex = utils.find_last_exon(second_primer, tmp)
my_exons = tmp[first_ex:last_ex + 1]
# if tmp_sg.strand == '+':
# my_exons = tmp[tmp.index(upstream_exon):tmp.index(downstream_exon) + 1]
# else:
# my_exons = tmp[tmp.index(downstream_exon):tmp.index(upstream_exon) + 1]
# Use correct tx's and estimate counts/psi
all_paths = algs.AllPaths(
tmp_sg,
my_exons,
utils.get_pos(line[utils.TARGET]), # tuple (start, end)
chr=chr,
strand=strand)
# all_paths.trim_tx_paths()
fexon = upstream_exon if strand == "+" else downstream_exon
lexon = downstream_exon if strand == "+" else upstream_exon
all_paths.trim_tx_paths_using_primers(first_primer, second_primer, fexon,
lexon)
all_paths.set_all_path_coordinates()
paths, counts = all_paths.estimate_counts() # run EM algorithm
return paths, counts
def main():
t0 = get_times()
#Capture the data
if args.iterations is None:
cap = cpd(nsamples=100000, divisor=args.divisor, volt_range=args.volt_range, nblocks=args.nblocks, dual_mode=True)
print(cap.shape)
else:
cap=[]
for i in range(args.iterations):
cap.append(cpd(divisor=args.divisor, volt_range=args.volt_range, nblocks=args.nblocks, dual_mode=True))
print('completed {} captures'.format(i+1))
tf = get_times()
cap = np.array(cap).reshape(args.iterations, 2, -1, 100000)
cap = cap.transpose([1,0,2,3]).copy()
cap.shape = (2, -1, 100000)
#Organize real and complex components
#cap = organize_caps(cap, args)
#test with a histogram
#test_hist(cap)
# Compute coordintes before and after data capture, store in dict
coords_0 = get_pos(args, t0)
coords_f = get_pos(args, tf)
np.savez(args.path + '\captures' + args.file_name , real=cap[0], image=cap[1], t0=t0, tf=tf, coords_0=coords_0, coords_f=coords_f)
def accept(self, shape_in):
if (shape_in[1] + 2 * self.pad[0] - self.hk + 1) % self.stride[0] != 0:
return False
if (shape_in[2] + 2 * self.pad[1] - self.wk + 1) % self.stride[1] != 0:
return False
self.shape_in = shape_in
self.dim_in = np.prod(self.shape_in, dtype=int)
self.din, self.hin, self.win = self.shape_in
self.dk = self.din
self.hpos = utils.get_pos(self.hin, self.hk, self.pad[0],
self.stride[0])
self.wpos = utils.get_pos(self.win, self.wk, self.pad[1],
self.stride[1])
self.hout = self.hpos.size
self.wout = self.wpos.size
self.shape = [self.dout, self.hout, self.wout]
self.dim_out = np.prod(self.shape, dtype=int)
self.indice = utils.flatten_index(self.shape_in, self.shape_k,
self.pad, self.stride)
self.dim_k = self.dk * self.hk * self.wk
# params
self.w = np.random.normal(0, 1.0 / np.sqrt(self.dim_k),
[self.dim_k, self.dout])
self.b = np.random.normal(0, 1.0 / np.sqrt(self.dim_k), [1, self.dout])
# cache
self.fx = None
self.dw = np.zeros([self.dim_k, self.dout])
self.db = np.zeros([1, self.dout])
self.dw_cache = None
self.db_cache = None
return True
def user_defined_exons(tmp_sg, line):
chr, strand = utils.get_chr(line[utils.TARGET]), line[utils.TARGET][0] # get chr and strand
upstream_exon = utils.get_pos(line[utils.UPSTREAM_EXON]) # get user-defined flanking exons
downstream_exon = utils.get_pos(line[utils.DOWNSTREAM_EXON])
first_primer, second_primer = utils.get_primer_coordinates(line[utils.PRIMER_COORD])
# get possible exons for primer amplification
tmp = sorted(tmp_sg.get_graph().nodes(), key=lambda x: (x[0], x[1]))
first_ex = utils.find_first_exon(first_primer, tmp)
last_ex = utils.find_last_exon(second_primer, tmp)
my_exons = tmp[first_ex:last_ex + 1]
# if tmp_sg.strand == '+':
# my_exons = tmp[tmp.index(upstream_exon):tmp.index(downstream_exon) + 1]
# else:
# my_exons = tmp[tmp.index(downstream_exon):tmp.index(upstream_exon) + 1]
# Use correct tx's and estimate counts/psi
all_paths = algs.AllPaths(tmp_sg,
my_exons,
utils.get_pos(line[utils.TARGET]), # tuple (start, end)
chr=chr,
strand=strand)
# all_paths.trim_tx_paths()
fexon = upstream_exon if strand == "+" else downstream_exon
lexon = downstream_exon if strand == "+" else upstream_exon
all_paths.trim_tx_paths_using_primers(first_primer, second_primer, fexon, lexon)
all_paths.set_all_path_coordinates()
paths, counts = all_paths.estimate_counts() # run EM algorithm
return paths, counts
def test_get_pos(self):
val = utils.get_pos(3, 2, 0, 1)
self.assertTrue(np.array_equal(val, [0, 1]))
val = utils.get_pos(7, 3, 0, 1)
self.assertTrue(np.array_equal(val, [0, 1, 2, 3, 4]))
val = utils.get_pos(5, 3, 1, 2)
self.assertTrue(np.array_equal(val, [0, 2, 4]))
def add_figures_to_image(self, img, left_fit, right_fit):
"""
Draws information about the center offset and the current lane curvature onto the given image.
"""
# Calculate curvature
y_eval = 500
left_curverad = np.absolute(((1 + (2 * left_fit[0] * y_eval + left_fit[1])**2) ** 1.5) \
/(2 * right_fit[0]))
right_curverad = np.absolute(((1 + (2 * right_fit[0] * y_eval + right_fit[1]) ** 2) ** 1.5) \
/(2 * right_fit[0]))
curvature = (left_curverad + right_curverad) / 2
min_curvature = min(left_curverad, right_curverad)
vehicle_position = get_pos(719, left_fit, right_fit)
# Convert from pixels to meters
vehicle_position = vehicle_position / 12800 * 3.7
curvature = curvature / 128 * 3.7
min_curvature = min_curvature / 128 * 3.7
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, 'Radius of Curvature = %d(m)' % curvature, (50, 50),
font, 1, (255, 255, 255), 2)
left_or_right = "left" if vehicle_position < 0 else "right"
cv2.putText(
img, 'Vehicle is %.2fm %s of center' %
(np.abs(vehicle_position), left_or_right), (50, 100), font, 1,
(255, 255, 255), 2)
cv2.putText(img, 'Min Radius of Curvature = %d(m)' % min_curvature,
(50, 150), font, 1, (255, 255, 255), 2)
def get_motifs(model, data_x, data_y, protein_name):
model.trainable = False
pos_x = get_pos(data_x, data_y)
data_x = pos_x.reshape(pos_x.shape[0], pos_x.shape[1], pos_x.shape[2], 1)
features = get_feature(model=model, data=data_x)
features = features.reshape(features.shape[0], features.shape[1], features.shape[3])
if os.path.exists('./' + protein_name + '.fa'):
os.remove('./' + protein_name + '.fa')
fp = open('./' + protein_name + '.fa', 'w')
count = 0
for i in range(features.shape[0]):
seq = get_seq(data_x[i])
for j in range(features.shape[1]):
count_1 = 0
for k in range(features.shape[2]):
if features[i][j][k] > 0:
count_1 += 1
if count_1 < 33 + int(len(data_y) / 4000):
continue
else:
for k in range(features.shape[2]):
if features[i][j][k] > 0.4:
fp.write('>' + 'seq_' + str(i) + '_' + 'filter' + str(j) + '_' + str(k) + '\n')
fp.write(seq[j:j + 7] + '\n')
count += 1
fp.close()
print('count：', count)
print('{}start get {} logo{}'.format('*' * 10, protein_name, '*' * 10))
get_logo(protein_name)
print('{}draw {} logo done{}'.format('*' * 10, protein_name, '*' * 10))
def run_js_pos(dataset_name,
data_dir,
save_dir,
models,
feature_types,
k_list,
second=False):
train_tokens, dev_tokens, train_dev_tokens, test_tokens, \
train_labels, dev_labels, train_dev_labels, test_labels = utils.load_data(dataset_name)
train_pos, dev_pos, train_dev_pos, test_pos = utils.get_pos(
dataset_name, data_dir)
token_pos_d = als.get_token_pos_d(test_tokens, test_pos)
# compare with background
y_data, min_vals, max_vals, y_min_val, y_max_val = [], [], [], 0, 0
for model_name in models:
dicts, d_keys = als.create_model_d(save_dir,
model_name,
test_labels=test_labels)
tmp_y_data = get_jensen_shannon(test_tokens, dicts, d_keys, k_list, 'background', \
combinations=d_keys, token_pos_d=token_pos_d)
tmp_min_val, tmp_max_val = als.get_min_max(tmp_y_data)
min_vals.append(tmp_min_val)
max_vals.append(tmp_max_val)
y_data.append(tmp_y_data)
y_min_val = np.min(min_vals) - 0.05
y_max_val = np.max(max_vals) + 0.05
simi.show_simi_plot(k_list, y_data, 'Number of important features (k)', 'Jensen-Shannon Score', '', \
(13, 12), '', y_min=y_min_val, y_max=y_max_val, if_model=True, second=second, \
if_combi=False, if_background=True, if_builtin_posthoc=True)
def test(model, test_x, test_y, draw_motifs=None, protein_name='ALKBH5'):
print('{}begin testing{}'.format('*' * 10, '*' * 10))
scores = []
pos_support = get_pos(train_x, train_y)
pos_x = pos_support.reshape(pos_support.shape[0], pos_support.shape[1], pos_support.shape[2], 1)
for i, test_X in enumerate(test_x):
repeat_test = test_X.reshape(1, test_X.shape[0], test_X.shape[1], 1).repeat(len(pos_x), axis=0)
preds = model.predict([repeat_test, pos_x])
scores.append(np.mean(preds))
AUCs = roc_auc_score(test_y, scores)
print("Mean AUC: {}, protein name: {}".format(np.mean(AUCs), protein_name))
if draw_motifs:
get_motifs(model, test_x, test_y, protein_name)
def save_isforms_and_counts(line, options):
# get information about each row
ID, target_coordinate = line[:2]
strand = target_coordinate[0]
chr = utils.get_chr(target_coordinate[1:])
tmp_start, tmp_end = utils.get_pos(target_coordinate)
logging.debug('Saving isoform and count information for event %s . . .' %
ID)
# get information from GTF annotation
gene_dict, gene_name = retrieve_gene_information(options, strand, chr,
tmp_start, tmp_end)
# get edge weights
edge_weights_list = [
sam_obj.extractSamRegion(chr, gene_dict['start'], gene_dict['end'])
for sam_obj in options['rnaseq']
]
# construct splice graph for each BAM file
bam_splice_graphs = sg.construct_splice_graph(edge_weights_list,
gene_dict,
chr,
strand,
options['read_threshold'],
options['min_jct_count'],
output_type='list',
both=options['both_flag'])
for bam_ix, my_splice_graph in enumerate(bam_splice_graphs):
# this case is meant for user-defined flanking exons
if line[utils.PSI_UP] == '-1' and line[utils.PSI_DOWN] == '-1':
# find path and count information
paths, counts = user_defined_exons(my_splice_graph, line)
# filter out single exon paths
# my_tmp = [(path, count) for path, count in zip(paths, counts) if len(path) > 1]
# paths, counts = zip(*my_tmp)
# this case is meant for automatic choice of flanking exons
else:
paths, counts = primerseq_defined_exons(my_splice_graph, line,
options['psi'])
utils.save_path_info('%s.%d' % (ID, bam_ix),
paths,
counts,
save_dir='tmp/indiv_isoforms/')
logging.debug(
'Finished saving isoform and count information for event %s.' % ID)
def primerseq_defined_exons(tmp_sg, line, psi_option):
"""
Get information about counts and paths if using PrimerSeq to define the flanking exons.
"""
# not the best use of the ExonSeek object, initially intended to find appropriate flanking exons
# but in this case ExonSeek is used to get the transcripts and associate counts
ID = line[utils.ID]
tgt_pos = utils.get_pos(line[utils.TARGET])
exon_seek_obj = ExonSeek(tgt_pos,
tmp_sg,
ID,
psi_option,
None, # no defined upstream exon
None) # no defined downstream exon
all_paths, upstream, downstream, component, psi_target, psi_upstream, psi_downstream = exon_seek_obj.get_info()
return exon_seek_obj.paths, exon_seek_obj.counts
def __init__(self):
pygame.sprite.Sprite.__init__(self)
self.cor = get_color()
self.width,self.height = (40,40)
self.x,self.y,self.dir = get_pos()
self.imageMaster = pygame.Surface((self.width,self.height))
self.imageMaster.fill(self.cor)
self.imageMaster.set_colorkey((255,255,255))
self.image = self.imageMaster
pygame.draw.rect(self.image, self.cor, (self.x,self.y,self.width,self.height))
self.rect = self.image.get_rect()
self.rect.x = self.x
self.rect.y = self.y
self.speed = random.randrange(1,10)
self.dx = 0
self.dy = 0
self.colide = False
def primerseq_defined_exons(tmp_sg, line, psi_option):
"""
Get information about counts and paths if using PrimerSeq to define the flanking exons.
"""
# not the best use of the ExonSeek object, initially intended to find appropriate flanking exons
# but in this case ExonSeek is used to get the transcripts and associate counts
ID = line[utils.ID]
tgt_pos = utils.get_pos(line[utils.TARGET])
exon_seek_obj = ExonSeek(
tgt_pos,
tmp_sg,
ID,
psi_option,
None, # no defined upstream exon
None) # no defined downstream exon
all_paths, upstream, downstream, component, psi_target, psi_upstream, psi_downstream = exon_seek_obj.get_info(
)
return exon_seek_obj.paths, exon_seek_obj.counts
def save_isforms_and_counts(line, options):
# get information about each row
ID, target_coordinate = line[:2]
strand = target_coordinate[0]
chr = utils.get_chr(target_coordinate[1:])
tmp_start, tmp_end = utils.get_pos(target_coordinate)
logging.debug('Saving isoform and count information for event %s . . .' % ID)
# get information from GTF annotation
gene_dict, gene_name = retrieve_gene_information(options,
strand, chr, tmp_start, tmp_end)
# get edge weights
edge_weights_list = [sam_obj.extractSamRegion(chr, gene_dict['start'], gene_dict['end'])
for sam_obj in options['rnaseq']]
# construct splice graph for each BAM file
bam_splice_graphs = sg.construct_splice_graph(edge_weights_list,
gene_dict,
chr,
strand,
options['read_threshold'],
options['min_jct_count'],
output_type='list',
both=options['both_flag'])
for bam_ix, my_splice_graph in enumerate(bam_splice_graphs):
# this case is meant for user-defined flanking exons
if line[utils.PSI_UP] == '-1' and line[utils.PSI_DOWN] == '-1':
# find path and count information
paths, counts = user_defined_exons(my_splice_graph, line)
# filter out single exon paths
# my_tmp = [(path, count) for path, count in zip(paths, counts) if len(path) > 1]
# paths, counts = zip(*my_tmp)
# this case is meant for automatic choice of flanking exons
else:
paths, counts = primerseq_defined_exons(my_splice_graph, line, options['psi'])
utils.save_path_info('%s.%d' % (ID, bam_ix),
paths, counts,
save_dir='tmp/indiv_isoforms/')
logging.debug('Finished saving isoform and count information for event %s.' % ID)
def run_pos_percent(dataset_name, data_dir, save_dir, models, feature_types,
k):
train_tokens, dev_tokens, train_dev_tokens, test_tokens, \
train_labels, dev_labels, train_dev_labels, test_labels = utils.load_data(dataset_name)
train_pos, dev_pos, train_dev_pos, test_pos = utils.get_pos(
dataset_name, data_dir)
pos_types = ['NOUN', 'VERB', 'ADJ', 'ADV', 'PRON', 'DET']
token_pos_d = als.get_token_pos_d(test_tokens, test_pos)
vocab_size = len(test_tokens) * k
min_vals, max_vals, y_min_val, y_max_val = [], [], 0, 0
for idx, feature_type in enumerate(feature_types):
dicts, d_keys = als.create_explainer_d(save_dir,
feature_type,
len(test_labels),
test_labels=test_labels)
tmp_y_data = als.get_combi_pos(d_keys, dicts, test_tokens, k,
token_pos_d, vocab_size)
tmp_min_val, tmp_max_val = als.get_min_max(tmp_y_data)
y_min_val = max(tmp_min_val - 1, 0)
y_max_val = min(tmp_max_val + 1, 100)
y_data = als.format_pos_data(tmp_y_data, pos_types)
assert len(y_data) == len(pos_types)
display_model_names = als.get_explainer_combinations(combi=False)
display_feature_names = als.get_model_combinations(combi=False)
x_data = []
x_data.append('Background')
for model_name in display_model_names:
label = '{}'.format(model_name)
x_data.append(label)
show_bar_plot(x_data, y_data, '', 'Percentage', \
'', (15, 14), '', y_min=y_min_val, \
y_max=y_max_val, labels=pos_types)
def choose_offense(self, formation: of.OffenseFormation):
# how are we ordering this. Kind of want to do the optional stuff first. Still just assuming it works
# which is bad. Soooo lets go with FB, SLOT2, TE2, SLOT1, TE1, RB1, WR1, WR2, OT1, OT2, OG1, OG2, C, QB
# Then order like OT, OG, C, OG, OT, WR1, WR2, WR3, WR4, RB1, QB
# Maybe go with the REC1 REC2 things etc.
# TODO: Kicking
# Need to assure no duplicates
# While True is emulating a do loop
# This is all overly long and weird, need a better way of doing this
cur_list = []
k = get_pos(cur_list,
self._pos_rota[Position.K]) if formation.no_k > 0 else None
cur_list.append(k)
gnr2 = get_pos(
cur_list,
self._pos_rota[Position.GNR]) if formation.no_gnr > 1 else None
cur_list.append(gnr2)
gnr1 = get_pos(
cur_list,
self._pos_rota[Position.GNR]) if formation.no_gnr > 0 else None
cur_list.append(gnr1)
fb = get_pos(
cur_list,
self._pos_rota[Position.FB]) if formation.no_rbs > 1 else None
cur_list.append(fb)
wr4 = get_pos(
cur_list,
self._pos_rota[Position.SLOT]) if formation.no_wrs > 3 else None
cur_list.append(wr4)
te2 = get_pos(
cur_list,
self._pos_rota[Position.TE]) if formation.no_tes > 1 else None
cur_list.append(te2)
wr3 = get_pos(
cur_list,
self._pos_rota[Position.SLOT]) if formation.no_wrs > 2 else None
cur_list.append(wr3)
te = get_pos(
cur_list,
self._pos_rota[Position.TE]) if formation.no_tes > 0 else None
cur_list.append(te)
rb = get_pos(
cur_list,
self._pos_rota[Position.RB]) if formation.no_rbs > 0 else None
cur_list.append(rb)
wr2 = get_pos(
cur_list,
self._pos_rota[Position.WR]) if formation.no_wrs > 1 else None
cur_list.append(wr2)
wr1 = get_pos(
cur_list,
self._pos_rota[Position.WR]) if formation.no_wrs > 0 else None
cur_list.append(wr1)
ot2 = get_pos(cur_list, self._pos_rota[Position.OT])
cur_list.append(ot2)
ot1 = get_pos(cur_list, self._pos_rota[Position.OT])
cur_list.append(ot1)
og2 = get_pos(cur_list, self._pos_rota[Position.OG])
cur_list.append(og2)
og1 = get_pos(cur_list, self._pos_rota[Position.OG])
cur_list.append(og1)
c = get_pos(cur_list, self._pos_rota[Position.C])
cur_list.append(c)
qb = get_pos(
cur_list,
self._pos_rota[Position.QB]) if formation.no_k < 1 else None
# Should eventually be a class really instead of relying on numbers.
players = [
ot2, og2, c, og1, ot1, wr1, wr2, wr3, wr4, rb, fb, te, te2, gnr1,
gnr2, qb, k
]
plyrs = [x for x in players if x is not None]
return {
GenOff.OT_L: plyrs[0],
GenOff.OG_L: plyrs[1],
GenOff.C: plyrs[2],
GenOff.OG_R: plyrs[3],
GenOff.OT_R: plyrs[4],
GenOff.REC1: plyrs[5],
GenOff.REC2: plyrs[6],
GenOff.REC3: plyrs[7],
GenOff.REC4: plyrs[8],
GenOff.REC5: plyrs[9],
GenOff.QB: plyrs[10]
}
def main(options, args_output='tmp/debug.json'):
"""
The gtf main function is the function designed to be called from other
scripts. It iterates through each target exons and returns the necessary
information for primer design.
"""
genome, args_gtf, args_target = options['fasta'], options['gtf'], options[
'target']
# the sam object interfaces with the user specified BAM/SAM file!!!
sam_obj_list = options['rnaseq']
# iterate through each target exon
output = [] # output from program
for line in args_target: # was line in handle
name, line = line # bad style of reassignment
tgt = line[0]
strand = tgt[0]
tmp_start, tmp_end = get_pos(tgt)
chr = get_chr(tgt[1:]) # [1:] since strand is first character
USER_DEFINED_FLANKING_EXONS = True if len(line) == 3 else False
if USER_DEFINED_FLANKING_EXONS:
up_exon = utils.get_pos(line[1]) # user's upstream exon
down_exon = utils.get_pos(line[2]) # user's downstream exon
else:
up_exon = None # user did not provide upstream exon
down_exon = None # user did not provide downstream exon
# This try block is to catch assertions made about the graph. If a
# PrimerSeqError is raised it only impacts a single target for primer
# design so complete exiting of the program is not warranted.
try:
# if the gtf doesn't have a valid gene_id attribute then use
# the first method otherwise use the second method.
if options['no_gene_id']:
gene_dict, gene_name = get_weakly_connected_tx(
args_gtf, strand, chr, tmp_start,
tmp_end) # hopefully filter out junk
else:
gene_dict, gene_name = get_from_gtf_using_gene_name(
args_gtf, strand, chr, tmp_start, tmp_end)
# extract all edge weights only once
edge_weights_list = [
sam_obj.extractSamRegion(chr, gene_dict['start'],
gene_dict['end'])
for sam_obj in sam_obj_list
]
# The following options['both_flag'] determines how the splice graph is constructed.
# The splice graph can be either constructed from annotation junctions
# where options['both_flag']==False or RNA-Seq + annotation junctions when
# options['both_flag']==True.
# single pooled count data splice graph
splice_graph = construct_splice_graph(edge_weights_list,
gene_dict,
chr,
strand,
options['read_threshold'],
options['min_jct_count'],
output_type='single',
both=options['both_flag'])
# Second, get a splice graph for each BAM file
single_bam_splice_graphs = construct_splice_graph(
edge_weights_list,
gene_dict,
chr,
strand,
options['read_threshold'],
options['min_jct_count'],
output_type='list',
both=options['both_flag'])
### Logic for choosing methodology of primer design ###
# user-defined flanking exon case
if up_exon and down_exon:
if gene_dict['target'] not in gene_dict['exons']:
raise utils.PrimerSeqError(
'Error: target exon was not found in gtf annotation')
elif up_exon not in gene_dict['exons']:
raise utils.PrimerSeqError(
'Error: upstream exon not in gtf annotation')
elif down_exon not in gene_dict['exons']:
raise utils.PrimerSeqError(
'Error: downstream exon not in gtf annotation')
tmp = predefined_exons_case(
name, # ID for exon (need to save as json)
gene_dict['target'], # target exon tuple (start, end)
splice_graph, # SpliceGraph object
genome, # pygr genome variable
up_exon, # upstream flanking exon
down_exon) # downstream flanking exon
# always included case
elif options['psi'] > .9999:
# note this function ignores edge weights
tmp = get_flanking_biconnected_exons(tgt, gene_dict['target'],
splice_graph, genome)
# user specified a sufficient psi value to call constitutive exons
else:
tmp = get_sufficient_psi_exons(
tgt, gene_dict['target'], splice_graph, genome, name,
options['psi'], up_exon,
down_exon) # note, this function utilizes edge wieghts
### End methodology specific primer design ###
# Error msgs are of length one, so only do psi calculations for
# non-error msgs
if len(tmp) > 1:
# edit target psi value
tmp_all_paths = tmp[
-4] # CAREFUL the index for the AllPaths object may change
tmp[2] = calculate_target_psi(gene_dict['target'],
single_bam_splice_graphs,
tmp_all_paths.component,
up_exon=None,
down_exon=None)
# up_exon=up_exon,
# down_exon=down_exon) # CAREFUL index for psi_target may change
tmp.append(gene_name)
# append result to output list
output.append(tmp)
except (utils.PrimerSeqError, ):
t, v, trace = sys.exc_info()
output.append([str(v)]) # just append assertion msg
return output
def choose_defense(self, formation: df.DefenseFormation) -> list:
cur_list = []
dime = get_pos(
cur_list,
self._pos_rota[Position.DIME]) if formation.no_cb > 3 else None
cur_list.append(dime)
nick = get_pos(
cur_list,
self._pos_rota[Position.NICKEL]) if formation.no_cb > 2 else None
cur_list.append(nick)
mlb2 = get_pos(
cur_list,
self._pos_rota[Position.MLB]) if formation.no_lb > 3 else None
cur_list.append(mlb2)
olb1 = get_pos(
cur_list,
self._pos_rota[Position.OLB]) if formation.no_lb > 1 else None
cur_list.append(olb1)
olb2 = get_pos(
cur_list,
self._pos_rota[Position.OLB]) if formation.no_lb > 2 else None
cur_list.append(olb2)
dt2 = get_pos(
cur_list,
self._pos_rota[Position.DT]) if formation.no_dl > 3 else None
cur_list.append(dt2)
de2 = get_pos(cur_list, self._pos_rota[Position.DE])
cur_list.append(de2)
de1 = get_pos(cur_list, self._pos_rota[Position.DE])
cur_list.append(de1)
dt1 = get_pos(cur_list, self._pos_rota[Position.DT])
cur_list.append(dt1)
mlb1 = get_pos(cur_list, self._pos_rota[Position.MLB])
cur_list.append(mlb1)
cb1 = get_pos(cur_list, self._pos_rota[Position.CB])
cur_list.append(cb1)
cb2 = get_pos(cur_list, self._pos_rota[Position.CB])
cur_list.append(cb2)
sf1 = get_pos(cur_list, self._pos_rota[Position.SF])
cur_list.append(sf1)
sf2 = get_pos(cur_list, self._pos_rota[Position.SF])
cur_list.append(sf2)
players = [
de1, dt1, dt2, de2, mlb1, mlb2, olb1, olb2, nick, dime, cb1, cb2,
sf1, sf2
]
return [x for x in players if x is not None]
def primer3(options, primer3_options):
"""
The primer.py main function uses the gtf module to find information about constitutive flanking exons for the target exons of interest.
It then designs primers by calling primer3. Next it parses the primer3 output and outputs the final results to a file. The output file
is then emailed to the designated address in the command line parameters.
"""
# tmp directory
mkdir_tmp() # make any necessary tmp directories
# find flanking exons
logging.debug('Calling splice_graph.main to find flanking exons')
flanking_info = splice_graph.main(options)
logging.debug('Finished splice_graph.main')
# iterate over all target sequences
STRAND, EXON_TARGET, PSI_TARGET, UPSTREAM_TARGET, PSI_UPSTREAM, DOWNSTREAM_TARGET, PSI_DOWNSTREAM, ALL_PATHS, UPSTREAM_Seq, TARGET_SEQ, DOWNSTREAM_SEQ, GENE_NAME = range(12)
output_list = []
for z in range(len(flanking_info)):
jobs_ID = str(z+1) # base file name for primer3 output
# no flanking exon information case
if len(flanking_info[z]) == 1:
logging.debug(flanking_info[z][0])
output_list.append(flanking_info[z]) # write problem msg
# has flanking exon information case
else:
genome_chr = options['fasta'][flanking_info[z][ALL_PATHS].chr]
tar = options['target'][z][1][0] # flanking_info[z][1] # target interval (used for print statements)
tar_id = options['target'][z][0]
####################### Primer3 Parameter Configuration###########
P3_FILE_FLAG = '1'
PRIMER_EXPLAIN_FLAG = '1'
PRIMER_THERMODYNAMIC_PARAMETERS_PATH = os.path.join(config_options['primer3'],
'src/primer3_config/')
SEQUENCE_ID = tar # use the 'chr:start-stop' format for the sequence ID in primer3
#SEQUENCE_TEMPLATE = flanking_info[z][UPSTREAM_Seq] + flanking_info[z][TARGET_SEQ].lower() + flanking_info[z][DOWNSTREAM_SEQ]
#SEQUENCE_TARGET = str(len(flanking_info[z][UPSTREAM_Seq]) + 1) + ',' + str(len(flanking_info[z][TARGET_SEQ]))
# SEQUENCE_PRIMER_PAIR_OK_REGION_LIST = '0,' + str(len(flanking_info[z][UPSTREAM_Seq])) + ',' + str(len(flanking_info[z][UPSTREAM_Seq]) + len(flanking_info[z][TARGET_SEQ])) + ',' + str(len(flanking_info[z][DOWNSTREAM_SEQ]))
if options['short_isoform']:
# this option uses the shortest available isoform for designing primers
shortest_isoform = flanking_info[z][ALL_PATHS].get_shortest_path()
middle_sequence = utils.get_seq_from_list(genome_chr,
flanking_info[z][STRAND],
shortest_isoform[1:-1])
SEQUENCE_TEMPLATE = '%s%s%s' % (str(flanking_info[z][UPSTREAM_Seq]).upper(),
middle_sequence,
str(flanking_info[z][DOWNSTREAM_SEQ]).upper())
SEQUENCE_PRIMER_PAIR_OK_REGION_LIST = '0,%d,%d,%d' % (len(flanking_info[z][UPSTREAM_Seq]),
len(flanking_info[z][UPSTREAM_Seq]) + len(middle_sequence),
len(flanking_info[z][DOWNSTREAM_SEQ]))
middle_pos = (0, len(middle_sequence))
else:
# this uses upstream flanking exon, target exon, and downstream flanking exon to design primers
SEQUENCE_TEMPLATE = '%s%s%s' % (str(flanking_info[z][UPSTREAM_Seq]).upper(),
str(flanking_info[z][TARGET_SEQ]).lower(),
str(flanking_info[z][DOWNSTREAM_SEQ]).upper())
SEQUENCE_PRIMER_PAIR_OK_REGION_LIST = '0,%d,%d,%d' % (len(flanking_info[z][UPSTREAM_Seq]),
len(flanking_info[z][UPSTREAM_Seq]) + len(flanking_info[z][TARGET_SEQ]),
len(flanking_info[z][DOWNSTREAM_SEQ]))
middle_pos = utils.get_pos(flanking_info[z][EXON_TARGET])
#############################################################
####################### Write jobs_ID.conf##################
with open(os.path.join(config_options['tmp'], jobs_ID + '.conf'), 'w') as outfile:
# hard coded options
outfile.write('SEQUENCE_ID=' + SEQUENCE_ID + '\n')
outfile.write('SEQUENCE_TEMPLATE=' + SEQUENCE_TEMPLATE + '\n')
#outfile.write('SEQUENCE_TARGET=' + SEQUENCE_TARGET + '\n')
outfile.write('SEQUENCE_PRIMER_PAIR_OK_REGION_LIST=' + SEQUENCE_PRIMER_PAIR_OK_REGION_LIST + '\n')
outfile.write('P3_FILE_FLAG=' + P3_FILE_FLAG + '\n')
outfile.write('PRIMER_EXPLAIN_FLAG=' + PRIMER_EXPLAIN_FLAG + '\n')
outfile.write('PRIMER_THERMODYNAMIC_PARAMETERS_PATH=' + PRIMER_THERMODYNAMIC_PARAMETERS_PATH + '\n') # make sure primer3 finds the config files
# options from primer3.cfg
for o in primer3_options:
outfile.write(o)
outfile.write('=' + '\n') # primer3 likes a '=' at the end of sequence params
logging.debug('Wrote the input file (%s) for primer3' % (config_options['tmp'] + '/' + jobs_ID + '.conf'))
###################### Primer3 #####################################
if os.path.exists(config_options['tmp'] + jobs_ID + '.Primer3'):
os.remove(config_options['tmp'] + jobs_ID + '.Primer3') # delete old files
call_primer3(tar, jobs_ID) # command line call to Primer3!
shutil.copy(os.path.join(config_options['tmp'], jobs_ID + '.Primer3'),
config_options['primer3_log']) # copy primer3 results
shutil.copy(os.path.join(config_options['tmp'], jobs_ID + '.conf'),
config_options['primer3_log']) # copy config file
#################### Parse '.Primer3' ################################
primer3_dict = read_primer3(config_options['tmp'] + '/' + jobs_ID + '.Primer3')
# checks if no output
if(primer3_dict.keys().count('PRIMER_LEFT_0_SEQUENCE') == 0):
str_params = (tar, os.path.abspath(os.path.join(config_options['primer3_log'], str(jobs_ID) + '.Primer3')))
primer3_problem = 'No Primer3 results for %s. Check %s for more details.' % str_params
logging.debug(primer3_problem)
output_list.append([primer3_problem])
continue
# there is output case
else:
logging.debug('There are primer3 results for %s' % SEQUENCE_ID)
# get info about product sizes
target_exon_len = len(flanking_info[z][TARGET_SEQ])
Primer3_PRIMER_PRODUCT_SIZE = int(primer3_dict['PRIMER_PAIR_0_PRODUCT_SIZE']) - target_exon_len
primer3_coords = primer_coordinates(primer3_dict, flanking_info[z][STRAND], flanking_info[z][ALL_PATHS].chr,
# utils.get_pos(flanking_info[z][EXON_TARGET]),
# (0, len(middle_sequence)),
middle_pos, # either the target exon or a dummy pos for using shortest isoform
utils.get_pos(flanking_info[z][UPSTREAM_TARGET]),
utils.get_pos(flanking_info[z][DOWNSTREAM_TARGET]),
use_target=True)
flanking_info[z][ALL_PATHS].set_all_path_lengths(map(utils.get_pos, primer3_coords.split(';')))
skipping_size_list = flanking_info[z][ALL_PATHS].skip_lengths
inclusion_size_list = flanking_info[z][ALL_PATHS].inc_lengths
skipping_size = ';'.join(map(str, filter(lambda x: x>0, skipping_size_list)))
inclusion_size = ';'.join(map(str, filter(lambda x: x>0, inclusion_size_list)))
# left_seq = Sequence(primer3_dict['PRIMER_LEFT_0_SEQUENCE'], 'left')
# right_seq = Sequence(primer3_dict['PRIMER_RIGHT_0_SEQUENCE'], 'right')
# forward_seq, reverse_seq = (-right_seq, -left_seq) if str(flanking_info[z][STRAND]) == '-' else (left_seq, right_seq) # reverse complement sequence
my_strand = flanking_info[z][STRAND]
forward_pos, reverse_pos = map(utils.get_pos, primer3_coords.split(';')) if flanking_info[z][STRAND] == '+' else map(utils.get_pos, reversed(primer3_coords.split(';')))
forward_seq = genome_chr[forward_pos[0]:forward_pos[1]] if my_strand == '+' else -genome_chr[forward_pos[0]:forward_pos[1]]
reverse_seq = -genome_chr[reverse_pos[0]:reverse_pos[1]] if my_strand == '+' else genome_chr[reverse_pos[0]:reverse_pos[1]]
asm_region = '%s:%d-%d' % (flanking_info[z][ALL_PATHS].chr,
flanking_info[z][ALL_PATHS].asm_component[0][0],
flanking_info[z][ALL_PATHS].asm_component[-1][1])
# append results to output_list
tmp = [tar_id, tar, primer3_coords, flanking_info[z][PSI_TARGET], str(forward_seq).upper(), str(reverse_seq).upper(),
str((float(primer3_dict['PRIMER_LEFT_0_TM']) + float(primer3_dict['PRIMER_RIGHT_0_TM'])) / 2), skipping_size, inclusion_size,
flanking_info[z][UPSTREAM_TARGET], flanking_info[z][PSI_UPSTREAM], flanking_info[z][DOWNSTREAM_TARGET],
flanking_info[z][PSI_DOWNSTREAM], asm_region, flanking_info[z][GENE_NAME]]
output_list.append(tmp)
# write output information
with open(options['user_output'], 'wb') as outputfile_tab, open(options['output'], 'wb') as tmp_output:
# define csv header
header = ['ID', 'target coordinate', 'primer coordinates', 'PSI target', 'forward primer', 'reverse primer', 'average TM',
'skipping product size', 'inclusion product size', 'upstream exon coordinate', 'PSI upstream',
'downstream exon coordinate', 'PSI downstream', 'ASM Region', 'Gene']
output_list = [header] + output_list # pre-pend header to output file
csv.writer(outputfile_tab, dialect='excel', delimiter='\t').writerows(output_list) # output primer design to a tab delimited file
csv.writer(tmp_output, dialect='excel', delimiter='\t').writerows(output_list) # output primer design to a tmp file location
do_draw = True
do_perf = False
"""Training images"""
pos_path = r'/home/skynet/Datasets/PeopleDataset/Positive'
neg_path = r'/home/skynet/Datasets/PeopleDataset/Negative'
"""Test videos"""
frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_008'
# frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_007'
# frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_006'
# frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_005'
# frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_001'
"""Get positive and negative examples"""
pos_list, pos_lab = utils.get_pos(pos_path)
# neg_list, neg_lab = utils.get_neg_fix(neg_path)
neg_list, neg_lab = utils.get_neg_rnd(neg_path)
del pos_path, neg_path
"""Initialize HOG"""
hog_obj = utils.load_hog('hog.xml')
hog_list = utils.get_hog(pos_list, pos_lab, neg_list, neg_lab, hog_obj)
del pos_list, pos_lab, neg_list, neg_lab
# hog_par = {'winStride': (8, 8), 'padding': (0, 0), 'scale': 1.2}
# hog_par = {'hitThreshold': 1.2, 'winStride': (8, 8), 'padding': (0, 0), 'scale': 1.2, 'finalThreshold': 4}
hog_par = {'hitThreshold': 1.4, 'winStride': (8, 8), 'padding': (0, 0), 'scale': 1.2, 'finalThreshold': 2}
"""Initialize and train the SVM"""
x, y = utils.get_data(hog_list)
del hog_list
toponym_to_find = " ".join(sys.argv[1:])
geocoder_api_server = "http://geocode-maps.yandex.ru/1.x/"
geocoder_params = {
"apikey": "40d1649f-0493-4b70-98ba-98533de7710b",
"geocode": toponym_to_find,
"format": "json"}
response = requests.get(geocoder_api_server, params=geocoder_params)
if not response:
exit(-1)
pass
json_response = response.json()
pos = get_pos(json_response)
map_params = {
"ll": ",".join(pos),
"spn": ",".join(get_size(json_response)),
"l": "map",
"pt": f"{pos[0]},{pos[1]},flag"
}
map_api_server = "http://static-maps.yandex.ru/1.x/"
response = requests.get(map_api_server, params=map_params)
Image.open(BytesIO(response.content)).show()
def main(options, args_output='tmp/debug.json'):
"""
The gtf main function is the function designed to be called from other
scripts. It iterates through each target exons and returns the necessary
information for primer design.
"""
genome, args_gtf, args_target = options['fasta'], options['gtf'], options['target']
# the sam object interfaces with the user specified BAM/SAM file!!!
sam_obj_list = options['rnaseq']
# iterate through each target exon
output = [] # output from program
for line in args_target: # was line in handle
name, line = line # bad style of reassignment
tgt = line[0]
strand = tgt[0]
tmp_start, tmp_end = get_pos(tgt)
chr = get_chr(tgt[1:]) # [1:] since strand is first character
USER_DEFINED_FLANKING_EXONS = True if len(line) == 3 else False
if USER_DEFINED_FLANKING_EXONS:
up_exon = utils.get_pos(line[1]) # user's upstream exon
down_exon = utils.get_pos(line[2]) # user's downstream exon
else:
up_exon = None # user did not provide upstream exon
down_exon = None # user did not provide downstream exon
# This try block is to catch assertions made about the graph. If a
# PrimerSeqError is raised it only impacts a single target for primer
# design so complete exiting of the program is not warranted.
try:
# if the gtf doesn't have a valid gene_id attribute then use
# the first method otherwise use the second method.
if options['no_gene_id']:
gene_dict, gene_name = get_weakly_connected_tx(args_gtf, strand, chr, tmp_start, tmp_end) # hopefully filter out junk
else:
gene_dict, gene_name = get_from_gtf_using_gene_name(args_gtf, strand, chr, tmp_start, tmp_end)
# extract all edge weights only once
edge_weights_list = [sam_obj.extractSamRegion(chr, gene_dict['start'], gene_dict['end'])
for sam_obj in sam_obj_list]
# The following options['both_flag'] determines how the splice graph is constructed.
# The splice graph can be either constructed from annotation junctions
# where options['both_flag']==False or RNA-Seq + annotation junctions when
# options['both_flag']==True.
# single pooled count data splice graph
splice_graph = construct_splice_graph(edge_weights_list,
gene_dict,
chr,
strand,
options['read_threshold'],
options['min_jct_count'],
output_type='single',
both=options['both_flag'])
# Second, get a splice graph for each BAM file
single_bam_splice_graphs = construct_splice_graph(edge_weights_list,
gene_dict,
chr,
strand,
options['read_threshold'],
options['min_jct_count'],
output_type='list',
both=options['both_flag'])
### Logic for choosing methodology of primer design ###
# user-defined flanking exon case
if up_exon and down_exon:
if gene_dict['target'] not in gene_dict['exons']:
raise utils.PrimerSeqError('Error: target exon was not found in gtf annotation')
elif up_exon not in gene_dict['exons']:
raise utils.PrimerSeqError('Error: upstream exon not in gtf annotation')
elif down_exon not in gene_dict['exons']:
raise utils.PrimerSeqError('Error: downstream exon not in gtf annotation')
tmp = predefined_exons_case(name, # ID for exon (need to save as json)
gene_dict['target'], # target exon tuple (start, end)
splice_graph, # SpliceGraph object
genome, # pygr genome variable
up_exon, # upstream flanking exon
down_exon) # downstream flanking exon
# always included case
elif options['psi'] > .9999:
# note this function ignores edge weights
tmp = get_flanking_biconnected_exons(tgt, gene_dict['target'],
splice_graph,
genome)
# user specified a sufficient psi value to call constitutive exons
else:
tmp = get_sufficient_psi_exons(tgt, gene_dict['target'],
splice_graph,
genome,
name,
options['psi'],
up_exon,
down_exon) # note, this function utilizes edge wieghts
### End methodology specific primer design ###
# Error msgs are of length one, so only do psi calculations for
# non-error msgs
if len(tmp) > 1:
# edit target psi value
tmp_all_paths = tmp[-4] # CAREFUL the index for the AllPaths object may change
tmp[2] = calculate_target_psi(gene_dict['target'],
single_bam_splice_graphs,
tmp_all_paths.component,
up_exon=None,
down_exon=None)
# up_exon=up_exon,
# down_exon=down_exon) # CAREFUL index for psi_target may change
tmp.append(gene_name)
# append result to output list
output.append(tmp)
except (utils.PrimerSeqError,):
t, v, trace = sys.exc_info()
output.append([str(v)]) # just append assertion msg
return output
def read_depth_plot(my_bigwigs, output, options):
if type(options['position']) == type(list()):
chr = utils.get_chr(options['position'][0])
start, stop = zip(*map(lambda x: utils.get_pos(x), options['position']))
else:
chr = utils.get_chr(options['position'])
start, stop = utils.get_pos(options['position'])
bigwigs = my_bigwigs.split(',')
num_subplots = len(bigwigs) # num of bam files equals number of subplots
fig, axes = plt.subplots(num_subplots, 1, sharex=True, sharey=True, figsize=(6, options['size'] * num_subplots))
gray = (0.9, 0.9, 0.9)
# iterate over subplots (bigwig files)
max_count_holder = 0
if num_subplots == 1:
# axes.set_title('Read Depth Plot on %s' % chr)
iterable = [axes]
else:
# axes.flat[0].set_title('Read Depth Plot on %s' % chr)
iterable = axes.flat
for i, ax in enumerate(iterable):
#ax.locator_params(nbins=2)
ax.yaxis.set_label_text('')
# set bg
ax.patch.set_facecolor(gray)
ax.patch.set_edgecolor(gray)
ax.grid()
# plot/label
max_count, real_start, real_stop = generate_plot(ax, bigwigs[i], chr, start, stop, options) # does the actual work
draw_text(ax, '%s -- ' % options['gene'] + os.path.splitext(os.path.basename(bigwigs[i]))[0])
# format options
ax.xaxis.grid(color='white', linestyle='--', linewidth=1.5)
ax.yaxis.grid(color='white', linestyle='--', linewidth=1.5)
ax.xaxis.set_major_formatter(DropFormatter())
ax.yaxis.set_major_formatter(DropFormatter())
ax.set_axisbelow(True)
# hide some ugly lines
for line in ax.xaxis.get_ticklines() + ax.yaxis.get_ticklines():
line.set_color(gray)
# set y-axis
if max_count > max_count_holder:
ax.set_ylim(0, 1.5 * max_count)
ax.set_yticks([0, int( .375 * max_count ), int( .75 * max_count ), int( 1.125 * max_count ), int(1.5 * max_count)])
max_count_holder = max_count
# set x-axis options
ax.set_xlim(real_start, real_stop) # set x limits
ax.set_xticks([real_start, real_stop]) # explicitly set ticks
ax.xaxis.set_ticklabels(map(addCommas, [real_start, real_stop])) # make nice looking text for labels
ax.get_xticklabels()[0].set_horizontalalignment('left')
ax.get_xticklabels()[1].set_horizontalalignment('right')
# make text box to display chromosome information
if i == num_subplots - 1:
offset_text(ax, '%s:' % chr, 3, (-.15, -.17))
# adjust spacing between subplots
fig.subplots_adjust(wspace=0.05, hspace=0.05, bottom=.12)
# save figure
plt.savefig(output)
def read_depth_plot(my_bigwigs, output, options):
if type(options['position']) == type(list()):
chr = utils.get_chr(options['position'][0])
start, stop = zip(
*map(lambda x: utils.get_pos(x), options['position']))
else:
chr = utils.get_chr(options['position'])
start, stop = utils.get_pos(options['position'])
bigwigs = my_bigwigs.split(',')
num_subplots = len(bigwigs) # num of bam files equals number of subplots
fig, axes = plt.subplots(num_subplots,
1,
sharex=True,
sharey=True,
figsize=(6, options['size'] * num_subplots))
gray = (0.9, 0.9, 0.9)
# iterate over subplots (bigwig files)
max_count_holder = 0
if num_subplots == 1:
# axes.set_title('Read Depth Plot on %s' % chr)
iterable = [axes]
else:
# axes.flat[0].set_title('Read Depth Plot on %s' % chr)
iterable = axes.flat
for i, ax in enumerate(iterable):
#ax.locator_params(nbins=2)
ax.yaxis.set_label_text('')
# set bg
ax.patch.set_facecolor(gray)
ax.patch.set_edgecolor(gray)
ax.grid()
# plot/label
max_count, real_start, real_stop = generate_plot(
ax, bigwigs[i], chr, start, stop, options) # does the actual work
draw_text(
ax, '%s -- ' % options['gene'] +
os.path.splitext(os.path.basename(bigwigs[i]))[0])
# format options
ax.xaxis.grid(color='white', linestyle='--', linewidth=1.5)
ax.yaxis.grid(color='white', linestyle='--', linewidth=1.5)
ax.xaxis.set_major_formatter(DropFormatter())
ax.yaxis.set_major_formatter(DropFormatter())
ax.set_axisbelow(True)
# hide some ugly lines
for line in ax.xaxis.get_ticklines() + ax.yaxis.get_ticklines():
line.set_color(gray)
# set y-axis
if max_count > max_count_holder:
ax.set_ylim(0, 1.5 * max_count)
ax.set_yticks([
0,
int(.375 * max_count),
int(.75 * max_count),
int(1.125 * max_count),
int(1.5 * max_count)
])
max_count_holder = max_count
# set x-axis options
ax.set_xlim(real_start, real_stop) # set x limits
ax.set_xticks([real_start, real_stop]) # explicitly set ticks
ax.xaxis.set_ticklabels(
map(addCommas,
[real_start, real_stop])) # make nice looking text for labels
ax.get_xticklabels()[0].set_horizontalalignment('left')
ax.get_xticklabels()[1].set_horizontalalignment('right')
# make text box to display chromosome information
if i == num_subplots - 1:
offset_text(ax, '%s:' % chr, 3, (-.15, -.17))
# adjust spacing between subplots
fig.subplots_adjust(wspace=0.05, hspace=0.05, bottom=.12)
# save figure
plt.savefig(output)
import numpy as np
import utils
"""Enable drawing and performances"""
do_draw = True
do_perf = False
"""Training images"""
pos_path = r'/home/skynet/Datasets/PeopleDataset/Positive'
neg_path = r'/home/skynet/Datasets/PeopleDataset/Negative'
"""Test videos"""
frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_008'
# frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_007'
# frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_006'
# frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_005'
# frm_path = r'/home/skynet/Datasets/Crowd_PETS09/S2/L1/Time_12-34/View_001'
"""Get positive and negative examples"""
pos_list, pos_lab = utils.get_pos(pos_path)
# neg_list, neg_lab = utils.get_neg_fix(neg_path)
neg_list, neg_lab = utils.get_neg_rnd(neg_path)
del pos_path, neg_path
"""Initialize HOG"""
hog_obj = utils.load_hog('hog.xml')
hog_list = utils.get_hog(pos_list, pos_lab, neg_list, neg_lab, hog_obj)
del pos_list, pos_lab, neg_list, neg_lab
# hog_par = {'winStride': (8, 8), 'padding': (0, 0), 'scale': 1.2}
# hog_par = {'hitThreshold': 1.2, 'winStride': (8, 8), 'padding': (0, 0), 'scale': 1.2, 'finalThreshold': 4}
hog_par = {
'hitThreshold': 1.4,
'winStride': (8, 8),
'padding': (0, 0),
'scale': 1.2,
'finalThreshold': 2