def main():
desc = 'Convert between [integer, hexadecimal, IPv4 address] <-> proquint representations. '\
'See https://arxiv.org/html/0901.4016'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-n', '--uint', action='store_true',
help='convert from proquint to 32-bit integer', required=False)
parser.add_argument('-x', '--hex', action='store_true',
help='convert from proquint to hexadecimal', required=False)
parser.add_argument('-i', '--ip', action='store_true',
help='convert from proquint to IPv4', required=False)
parser.add_argument('val', nargs='?', type=str, default=None,
help='value to convert (if not specified, ' \
'IP address of the current host is printed)')
args = parser.parse_args()
target = None
if args.uint:
target = 'uint'
elif args.hex:
target = 'hex'
elif args.ip:
target = 'ip'
try:
if args.val is None:
res = convert(get_my_ip())
else:
res = convert(args.val, target)
except ValueError as e:
print('{}'.format(str(e)))
else:
print('{}'.format(res))
def db_statistics_sync():
result = []
data = convert(redisapi.hgetall(name='sitestatus'))
if not data:
logger.warning('no site status data in redis cache')
return {'failed': 'no site status data in redis cache'}
try:
state = Statistics(
system_capacity=data['system_capacity'],
managed_nodes=data['managed_nodes'],
system_utilization=convert(redisapi.hgetall(
name='sitestatus')).get('system_utilization', ''),
user_count=data['user_count'],
registered_master=data['registered_master'],
total_task=data['total_task'],
service_level=convert(redisapi.hgetall(
name='sitestatus')).get('service_level', ''),
uptime=data['uptime'],
page_visit_count=data.get('page_visit_count',0),
api_visit_count=data.get('api_visit_count',0)
)
db.session.add(state)
db.session.commit()
result.append(state)
except Exception as e:
logger.warning('error in creating data in statistics :', e)
logger.exception(e)
return {'failed': e}
logger.info('Completed in writing data to statistics' + str(result))
return {'successed': 'db_statistics_sync'}
def statistics_sync():
result = []
data = convert(redisapi.hgetall(name='sitestatus'))
if not data:
logger.warning('no site status data in redis cache')
return {'failed': 'no site status data in redis cache'}
try:
state = Statistics(
system_capacity = data['system_capacity'],
managed_nodes = data['managed_nodes'],
system_utilization = convert(redisapi.hgetall(name='sitestatus')).get('system_utilization',''),
user_count = data['user_count'],
registered_master = data['registered_master'],
total_task = data['total_task'],
service_level = convert(redisapi.hgetall(name='sitestatus')).get('service_level',''),
uptime = data['uptime'],
page_visit_count = data['page_visit_count'],
api_visit_count = data['api_visit_count']
)
session.add(state)
session.commit()
result.append(state)
except Exception as e:
logger.warning('error in creating data in statistics')
return {'failed': e}
logger.info('Completed in writing data to statistics' + str(result))
return {'successed': result}
def exp6():
'''
stream.sh generates streams for set of episoed
For each ep in eval03
1. rescore wrt interpolated LM
2. convert 1bh (.mlf) to .dat file -- DIY
3. Get perplexities for all 5 LM{1..5} -- to use for interpolated_LM (EM) to get new interpolation weights
4. RESCORE LATTICE -- to get 1 best hypothesis
'''
#dev03
#get_plex('lm_int_dev03','stream_lm_intdev03', 'lib/texts/dev03.dat')
episode_list = 'eval03'
episodes = open('lib/testlists/'+ episode_list+'.lst', 'r')
episodes = episodes.readlines()
episodes = [e.strip() for e in episodes]
save_dir = 'plp/plp-ilm'
for e in episodes:
#step 1
htk.lm_rescore(e, 'lm_int_dev03', save_dir)
#step 2
dat_file = 'dat_files/sentences.dat'
mlf_file = save_dir + '/'+ e + '/rescore/rescore.mlf' #plp-ilm/eval03_DEV011-20010206-XX1830/rescore
utl.convert(mlf_file, dat_file)
#step 3
lm_dir = 'interpolated_lms/lm_' + episode_list
utl.LM_interpolation(dat_file, lm_dir)
#step 4
output = 'lm_rescore/rescore_' + episode_list
htk.lm_rescore(e, lm_dir, output)
#get WER
htk.score(output, episode_list, 'rescore')
def converter(amount, input_currency, output_currency):
if output_currency is not None:
results = convert(amount, input_currency, output_currency)
else:
results = convert(amount, input_currency)
click.echo(json.dumps(results, indent=3))
def convert(self, file, ext="mp3"):
file_ext = file.split('.')[-1]
saveas = (file.replace(file_ext, ext)).replace(self.remote, self.local)
local_name = self.to_local(file)
file_name = file.replace(self.remote, '')
status = False
start_time = timestamp()
# if exists check for size
if os.path.exists(file_name):
orig_size, comp_size, status = is_incomplete(file_name, file, 8)
if not status: # not incomplete
pass
else:
print(
f'AC/CS {orig_size//1024**2}MB/{comp_size//1024**2}MB -> ',
self.local)
os.unlink(local_file)
if self.count:
self.counter()
# print('converting', self.shorten_name(file))
convert(file_name=file, saveas=saveas, to=ext)
end_time = timestamp()
# increase compresed files value
with self.value.get_lock():
self.value.value += 1
print(self._time_taken(start_time, end_time), 'Converted\t',
file_name.replace(self.remote, ''))
def converter(args):
amount = args["amount"]
input_currency = args["input_currency"]
if args.get("output_currency"):
output_currency = args["output_currency"]
results = convert(amount, input_currency, output_currency)
else:
results = convert(amount, input_currency)
return jsonify(results)
def salttoken():
try:
if redisapi.hexists(name='salt', key='token'):
if (time.time() - float(bytes.decode(redisapi.hget(name='salt', key='expire')))) < 0.0:
ret = convert(redisapi.hget(name='salt', key='token'))
return convert(ret)
else:
return saltlogin(saltapi.login(user, pawd, 'pam'))
else:
return saltlogin(saltapi.login(user, pawd, 'pam'))
except Exception as e:
return {'falid': e}
def _process(self, url):
log.info(f'Processing {url}')
episode = Episode.cached(url)
if not episode:
return []
tmp_audio_path = utils.convert(file_path=episode.audio_path,
extension='.flac')
subtitles = Subtitles.from_srt(episode.captions_path)
results = []
start = None
accum_text = []
for line, next_line in pairwise(subtitles.lines):
# log.info(f'Processing {line}')
if start is None:
start = line.start
accum_text.append(line.text)
if line.end - start > 10 * 1000:
self._save_part(start, line.end, tmp_audio_path,
' '.join(accum_text), results)
accum_text = []
start = next_line.start
log.info(f'Processed {url}')
return results
def generate_kpts(video_name):
kpt_results = []
cap = cv2.VideoCapture(video_name)
length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
opWrapper = load_model()
for i in tqdm(range(length)):
try:
datum = op.Datum()
_, imageToProcess = cap.read()
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
results = datum.poseKeypoints
#25 to 17
assert len(
results
) == 1, 'videopose3D only support one pserson restruction'
kpts = convert(results[0])
kpt_results.append(kpts)
except Exception as e:
print(e)
# pose processes
result = np.array(kpt_results)
# save
name = '/home/xyliu/experiments/VideoPose3D/data/tmp.npz'
kpts = result.astype(np.float32)
print('kpts npz save in ', name)
np.savez_compressed(name, kpts=kpts)
return kpts
return result
def run_test(args):
it_network = ImageTransformNet(
input_shape=hparams['test_size'],
residual_layers=hparams['residual_layers'],
residual_filters=hparams['residual_filters'])
ckpt_dir = os.path.join(args.name, 'pretrained')
ckpt = tf.train.Checkpoint(network=it_network, step=tf.Variable(0))
ckpt.restore(tf.train.latest_checkpoint(ckpt_dir)).expect_partial()
print('\n###################################################')
print('Perceptual Losses for Real-Time Style Transfer Test')
print('###################################################\n')
print('Restored {} step: {}\n'.format(args.name, str(ckpt.step.numpy())))
dir_size = 'step_{}_{}x{}'.format(str(ckpt.step.numpy()),
str(hparams['test_size'][0]),
str(hparams['test_size'][1]))
dir_model = 'output_img_{}'.format(args.name)
out_dir = os.path.join(args.output_path, dir_model, dir_size)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
content_img_list = os.listdir(args.test_content_img)
for c_file in content_img_list:
content = convert(os.path.join(args.test_content_img, c_file),
hparams['test_size'][:2])[tf.newaxis, :]
output = it_network(content, training=False)
tensor = tensor_to_image(output)
c_name = '{}_{}'.format(args.name, os.path.splitext(c_file)[0])
save_path = os.path.join(out_dir, c_name)
tensor.save(save_path + '.jpeg')
print('Image: {}.jpeg saved'.format(save_path))
def results():
"""
This route is used to save the file, convert the audio to 16000hz monochannel,
and predict the emotion using the saved binary model
"""
if not os.path.isdir("./audio"):
os.mkdir("audio")
if request.method == 'POST':
try:
f = request.files['file']
filename = secure_filename(f.filename)
f.save(os.path.join(app.config["UPLOAD_FOLDER"], filename))
except:
return render_template('main.html', value="")
wav_file_pre = os.listdir("./audio")[0]
wav_file_pre = f"{os.getcwd()}/audio/{wav_file_pre}"
wav_file = convert(wav_file_pre)
os.remove(wav_file_pre)
model = pickle.load(open(f"{os.getcwd()}/model.model", "rb"))
x_test =extract_feature(wav_file)
y_pred=model.predict(np.array([x_test]))
os.remove(wav_file)
print(y_pred)
return render_template('main.html', value=y_pred[0])
def cb_fridge_pose(msg):
tf_handle_to_map = convert_posemsg_to_transform(msg)
tf_map_to_base = listener.lookupTransform("/base_link", "/map", rospy.Time(0))
tf_handle_to_base = convert(tf_handle_to_map, tf_map_to_base)
trans, quat = tf_handle_to_base
rpy = tf.transformations.euler_from_quaternion(quat)
co = Coordinate(trans, rpy)
co.trans_local([-0.02, 0, 0])
group = ('larm')
av_pre = robot.get_angle_vector(group)
av_post, success = robot.solve_ik(co.trans, rpy, group_names=group, overwrite=True)
"""
av_diff = av_post - av_pre
av_com = av_pre + av_diff * 2
robot.set_angle_vector(av_com, group)
av_com_full = robot.get_angle_vector()
"""
#av_post = robot.get_angle_vector()
print("ik success: {0}".format(success))
#ri.angle_vector(av_post, time=2.0)
ri.angle_vector(robot.get_angle_vector(), time=0.2)
def generate_kpts(video_name):
kpt_results = []
cap = cv2.VideoCapture(video_name)
length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
opWrapper = load_model()
for i in tqdm(range(length)):
try:
datum = op.Datum()
_, imageToProcess = cap.read()
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
results = datum.poseKeypoints
#25 to 17
assert len(
results
) == 1, 'videopose3D only support one pserson restruction'
kpts = convert(results[0])
kpt_results.append(kpts)
except Exception as e:
print(e)
# pose processes
result = np.array(kpt_results)
return result
def test(alg_num, data_sets):
if alg_num == 1:
uf = unionfind1.UnionFind()
elif alg_num == 2:
uf = unionfind2.UnionFind()
elif alg_num == 3:
uf = unionfind3.UnionFind()
elif alg_num == 4:
uf = unionfind4.UnionFind()
elif alg_num == 5:
uf = unionfind5.UnionFind()
elif alg_num == 6:
uf = unionfind5.UnionFind()
time_set = []
data_sets = data_sets * 100
for date_set in data_sets:
starttime = time.time()
for item in date_set:
result = convert(item)
if result[0] == "find":
uf.find(result[1], True)
elif result[0] == "union":
uf.union(result[1][0], result[1][1])
endtime = time.time()
time_set.append(endtime - starttime)
del uf
return time_set
def redirect_url(code):
# Takes variable from url and attempts to decode and check DB for a match. If a match found or the original is a
# none coded url it will attempt redirect. If not it will error.
if utils.check_url(utils.convert(code)):
return redirect(utils.convert(code), code=302)
else:
try:
result = dal.lookup_url(code)
if result:
return redirect(utils.convert(result), code=302)
else:
return 'Malformed URL: {}{}'.format(
HOST, code), status.HTTP_400_BAD_REQUEST
except:
return 'Malformed URL: {}{}'.format(
HOST, code), status.HTTP_400_BAD_REQUEST
def main():
dbFile = '/home/opit/Desktop/PhD/CALIFA/data/CALIFA.sqlite'
iso25D = 40 / 0.396
listFile = '../data/SDSS_photo_match.csv'
fitsDir = '../data/SDSS/'
dataDir = '../data'
outputFile = '../data/growthCurvePhotometry.csv'
imgDir = 'img/'
simpleFile = '../data/CALIFA_mother_simple.csv'
maskFile = '../data/maskFilenames.csv'
noOfGalaxies = 939
i = 0
ra = convert(db.dbUtils.getFromDB('ra', dbFile, 'mothersample', ' where CALIFA_id = 1'))
dec = convert(db.dbUtils.getFromDB('dec', dbFile, 'mothersample', ' where CALIFA_id = 1'))
print ra, dec
#exit()
x0 = Astrometry.getPixelCoords(listFile, 0, dataDir)[1]
y0 = Astrometry.getPixelCoords(listFile, 0, dataDir)[0]
print 'pixel coords', y0, x0
sdssPA = convert(db.dbUtils.getFromDB('isoPhi_r', dbFile, 'mothersample', ' where CALIFA_id = 1'))
sdss_ba = convert(db.dbUtils.getFromDB('isoB_r', dbFile, 'mothersample', ' where CALIFA_id = 1'))/convert(db.dbUtils.getFromDB('isoA_r', dbFile, 'mothersample', ' where CALIFA_id = 1'))
sdss_isoA = convert(db.dbUtils.getFromDB('isoA_r', dbFile, 'mothersample', ' where CALIFA_id = 1'))
nadinePA = convert(db.dbUtils.getFromDB('PA', dbFile, 'nadine', ' where CALIFA_id = 1'))
print nadinePA, 'pa'
nadine_ba = convert(db.dbUtils.getFromDB('ba', dbFile, 'nadine', ' where CALIFA_id = 1'))
nadineR_90 = convert(db.dbUtils.getFromDB('R90', dbFile, 'nadine', ' where CALIFA_id = 1'))/0.396
print nadine_ba
nPoints = 3000000
#Photometry.createDistanceArray(listFile, i, dataDir, center)
#Photometry.ellipseContours(x0, y0, angle, isoA, axisRatio, nPoints)
inputFileName = '../data/filled/fpC-004152-r6-0064.fits'
inputFile = pyfits.open(inputFileName)
inputImage = inputFile[0].data
inputImage[:] = 1000
#inputImage = np.zeros((50, 50))
print y0, x0
ellipseCoords = np.round(Photometry.ellipseContours(y0, x0, nadinePA, nadineR_90, nadine_ba), 0).astype('int16')
print ellipseCoords[0], ellipseCoords[1], 'ellipseCoords'
inputImage[ellipseCoords[0], ellipseCoords[1]] = 100000
#distances = Astrometry.makeDistanceArray(inputImage, (y0, x0))
#inputImage[np.where(distances < iso25D)] = 10000000
#inputImage[np.where(distances == iso25D+1)] = 10000000
#inputImage[np.where(distances == iso25D-1)] = 10000000
#inputImage[np.where(distances == iso25D-2)] = 10000000
head = inputFile[0].header
hdu = pyfits.PrimaryHDU(inputImage, header = head)
hdu.writeto('ellipse.fits')
def create_moodle_zip(blackboard_zip_fname, out_name):
try:
shutil.rmtree('elixer_tmp')
shutil.rmtree('course_files')
except OSError:
pass
course = Course(blackboard_zip_fname)
moodle_zip = zipfile.ZipFile(out_name, 'w')
moodle_xml_str = utils.convert(course).encode('utf-8')
moodle_zip.writestr('moodle.xml', moodle_xml_str)
err_fh = open(os.path.devnull, 'w')
command = ('unzip %s -d elixer_tmp' % blackboard_zip_fname).split(' ')
subprocess.Popen(command, stdout=err_fh, stderr=err_fh).communicate()
skip_parent = False
for root, dirs, files in os.walk('elixer_tmp'):
if not skip_parent:
skip_parent = True
continue
for bb_fname in files:
moodle_fname = bb_fname
if moodle_fname.endswith('.xml'):
continue
if bb_fname.startswith('!'):
if '.' in bb_fname:
ext, fname = [
s[::-1] for s in bb_fname[1:][::-1].split('.', 1)
]
moodle_fname = (base64.b16decode(fname.upper()) + '.' +
ext)
else:
ext, fname = '', bb_fname[1:]
moodle_fname = (base64.b16decode(fname.upper()))
moodle_fname = urllib2.unquote(moodle_fname)
res_num = root.split(os.sep,
1)[1].split(os.sep)[0].replace('res', '')
fixed_filename = utils.fix_filename(moodle_fname, res_num)
bb_fname = os.path.join(root, bb_fname)
moodle_fname = os.path.join('course_files', fixed_filename)
moodle_zip.write(bb_fname, moodle_fname)
shutil.rmtree('elixer_tmp')
moodle_zip.close()
def db_salt_nodes_sync():
result = []
count = 0
data = redisapi.hgetall(name='status')
if not data:
return {'failed': 'no status data in redis cache '}
try:
for (k, v) in convert(data).items():
if v == 'down':
salt_mark_status(k, v)
continue
target_node = db.session.query(
Nodedb).filter_by(node_name=k).first()
node_data = salt_minion(k)
db_data = node_data['return'][0][k]
master = ret_master()
# TODO
try:
if target_node:
target_node.minion_data = db_data
target_node.node_ip = db_data.get('ipv4', '1.1.1.1'),
target_node.os = db_data.get('lsb_distrib_description') or (
db_data.get('lsb_distrib_id') + db_data.get('lsb_distrib_release')) or (db_data.get('osfullname') + db_data('osrelease'))
target_node.cpu = str(db_data[
'num_cpus']) + ' * ' + str(db_data['cpu_model'])
target_node.kenel = db_data['kernelrelease']
target_node.core = int(db_data['num_cpus']),
target_node.mem = db_data['mem_total']
target_node.host = db_data['host']
target_node.status = v
target_node.master = master
else:
target_node = Nodedb(
id=uuid.uuid4(),
node_name=db_data['id'],
node_ip=db_data.get('ipv4', '1.1.1.1'),
minion_data=db_data,
os=db_data.get('lsb_distrib_description') or (
db_data.get('lsb_distrib_id') + db_data.get('lsb_distrib_release')) or (db_data.get('osfullname') + db_data('osrelease')),
cpu=str(db_data['num_cpus']) + ' * ' +
str(db_data['cpu_model']),
kenel=db_data['kernelrelease'],
core=int(db_data['num_cpus']),
mem=db_data['mem_total'],
host=db_data['host'],
master=master,
status=v
)
except KeyError as e:
logger.warning('updating ' + k + ' with error:' + str(e.args))
continue
result.append(target_node)
db.session.add(target_node)
except Exception as e:
logger.warning('Error while updaing ' + str(((k, v))) + str(e.args))
logger.exception(e)
db.session.commit()
return {'ok': 'db_salt_nodes_sync' + ' updated with redis return: ' + str(count)}
def foo():
x1, y1, x2, y2 = utils.convert(portableLoc.getLoc())
mouse.move(x1 // 4,
y1 // 4,
min((x2 - x1) // 4, (y2 - y1) // 4),
teleportRandom=True)
pyautogui.click(button="right")
sleep(1)
loc = pyautogui.locateOnScreen(optionPath)
x1, y1, x2, y1 = utils.convert(loc)
mouse.move(x1 // 4,
y1 // 4,
min((x2 - x1) // 4, (y2 - y1) // 4),
teleportRandom=False)
sleep(0.2)
pyautogui.click()
def emit_salt_task_list(room=None):
try:
data = {}
data['el'] = convert(redisapi.hgetall('salt_exec_list'))
data['tl'] = convert(redisapi.hgetall('salt_task_list'))
except Exception as e:
logger.warning('error in loading salt_task_list ' + str(data), e)
logger.exception(e)
return {'failed': e}
meta = json.dumps(data)
if room:
socket_emit(meta=meta, event='salt_task_list', room=room)
logger.info({'ok': 'emit_salt_task_list ' + str(room)})
else:
socket_emit(meta=meta, event='salt_task_list')
logger.info({'ok': 'emit_salt_task_list to all'})
return {'ok': 'emit_salt_task_list'}
def main():
result = scrape(
'https://scholar.google.com/scholar?hl=en&as_sdt=0,6&q=%22responsible+ai%22&scisbd=1')
sleep(randint(2,10))
result += scrape(
'https://scholar.google.com/scholar?hl=en&as_sdt=0,6&q=harmful+ai&scisbd=1'
)
sleep(randint(2, 10))
result += scrape(
'https://scholar.google.com/scholar?hl=en&as_sdt=0,6&q=ethics+ai&scisbd=1'
)
sleep(randint(2, 10))
result += scrape(
'https://scholar.google.com/scholar?hl=en&as_sdt=0,6&q=%22ai+ethics%22&scisbd=1'
)
convert(result)
upload()
def salt_nodes_sync():
result = []
count = 0
data = redisapi.hgetall(name='status')
if not data:
return {'failed': 'no status data in redis cache '}
try:
for (k, v) in convert(data).items():
if v == 'down':
salt_mark_status(k, v)
continue
target_node = db.session.query(
Nodedb).filter_by(node_name=k).first()
node_data = salt_minion(k)
db_data = node_data['return'][0][k]
master = ret_master()
#TODO
try:
if target_node:
target_node.minion_data = db_data
target_node.node_ip=db_data.get('ipv4','1.1.1.1'),
target_node.os = db_data.get('lsb_distrib_description') or (
db_data.get('lsb_distrib_id') + db_data.get('lsb_distrib_release')) or (db_data.get('osfullname') + db_data('osrelease'))
target_node.cpu = str(db_data[
'num_cpus']) + ' * ' + str(db_data['cpu_model'])
target_node.kenel = db_data['kernelrelease']
target_node.core = int(db_data['num_cpus']),
target_node.mem = db_data['mem_total']
target_node.host = db_data['host']
target_node.status = v
target_node.master = master
else:
target_node = Nodedb(
id=uuid.uuid4(),
node_name=db_data['id'],
node_ip=db_data.get('ipv4','1.1.1.1'),
minion_data=db_data,
os=db_data.get('lsb_distrib_description') or (
db_data.get('lsb_distrib_id') + db_data.get('lsb_distrib_release')) or (db_data.get('osfullname') + db_data('osrelease')),
cpu=str(db_data['num_cpus']) + ' * ' +
str(db_data['cpu_model']),
kenel=db_data['kernelrelease'],
core=int(db_data['num_cpus']),
mem=db_data['mem_total'],
host=db_data['host'],
master=master,
status=v
)
except KeyError as e:
logger.warning('updating ' + k + ' with error:' + str(e.args))
continue
result.append(target_node)
db.session.add(target_node)
except Exception as e:
logger.warning('Error while updaing ' + str(((k, v))) + str(e.args))
db.session.commit()
return {'ok': str(result) + ' updated with redis return: ' + str(count)}
def draw_text(context, request):
text = request.GET['text']
size = int(request.GET['size'] or 12)
color = convert('#' + request.GET.get('color', '000000'))
out = cStringIO.StringIO()
#file_font = context.file.retrieve()
context.create_image(text, out, size, color, 'png')
out.seek(0)
return Response(out.read(), content_type="image/png")
def create_test_batch(args):
# Tensorboard defalut test images
test_content_img = ['chameleon.jpg', 'islas.jpeg', 'face.jpg']
test_content_batch = tf.concat([
convert(os.path.join(args.test_img, img))[tf.newaxis, :]
for img in test_content_img
],
axis=0)
return test_content_batch
def main():
iso25D = 40 / 0.396
#dataDir = '/media/46F4A27FF4A2713B_/work2/data/'
dataDir = '../data'
band = setBand()
#outputFile = dataDir+'/growthCurvePhotometry.csv'
listFile = dataDir+'/SDSS_photo_match.csv'
fitsDir = dataDir+'/SDSS/'
#filledDir = 'filled_'+band+'/'
filledDir = 'filled2/'
imgDir = 'img/'+band
simpleFile = dataDir+'/CALIFA_mother_simple.csv'
maskFile = dataDir+'/maskFilenames.csv'
dataFile = 'list.txt'
for ID in range(1, 940):
print 'id,', int(ID)
ra = GalaxyParameters.SDSS(listFile, int(ID)).ra
dec = GalaxyParameters.SDSS(listFile, int(ID)).dec
run = GalaxyParameters.SDSS(listFile, int(ID)).run
rerun = GalaxyParameters.SDSS(listFile, int(ID)).rerun
camcol = GalaxyParameters.SDSS(listFile, int(ID)).camcol
field = GalaxyParameters.SDSS(listFile, int(ID)).field
runstr = GalaxyParameters.SDSS(listFile, int(ID)).runstr
field_str = sdss.field2string(field)
print 'getting header info...'
rFile = fitsDir+'r/fpC-'+runstr+'-r'+camcol+'-'+field_str+'.fit.gz'
WCS=astWCS.WCS(rFile)
pa = utils.convert(db.dbUtils.getFromDB('pa', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+str(ID)))[0][0]
pa_align = utils.convert(db.dbUtils.getFromDB('pa_align', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id = '+str(ID)))[0][0]
ang = WCS.getRotationDeg()
print ang
pa_align2 = (360 - ang) - 90 + pa
#if pa_align2 >= 180:
# pa_align2 = pa_align2 - 180
# if pa_align2 <= 0:
# pa_align2 = pa_align2 + 180
out = int(ID), pa, pa_align2
print out
utils.writeOut(out, "align_pa.csv")
def ocr_text(text, fontname, angle=0, output_filename=None, peek=False):
"""
Wrapper over ocr which takes plain text.
"""
img = utils.convert(text, fontname, output_filename, angle)
matched_text, taken = ocr(img.copy(), fontname, peek=peek)
accuracy = utils.measure_correctness_lcs(matched_text, text)
return img, taken, matched_text, accuracy
def update_nodes():
global NODE_LIST
if is_online():
try:
r = requests.get(URL, timeout=30)
r_json = convert(r.json())
NODE_LIST = r_json['Nodes']
logging.info("Updated Node list. Found " + str(len(NODE_LIST)) + " nodes")
except (requests.Timeout, requests.ConnectionError):
logging.error("Failed to retrieve Node list from Master Node")
def dream_seq(self):
"""Dreams a sequence with optical flow"""
for i, path in enumerate(self.img_list[self.config["start_position"] :]):
if i == 0:
img1 = Image.open(path)
d_img = self.deep_dream(
self.transform(img1), self.model, i, seq="first"
)
self.save_img(d_img, "", i)
d_img = convert(d_img)
flow_iter = 0
# the iterations needs to be reduced
self.config["num_iterations"] -= 5
if i > 0:
img2 = Image.open(path)
feature_img, background_masked = self.get_opflow_image(
img1, d_img, img2
)
feature_img = np.clip(feature_img, 0, 255)
background_masked[background_masked > 0] = 1 - (flow_iter * 0.1) # 0.5
background_masked[background_masked == 0] = flow_iter * 0.1
d_img = self.deep_dream(
self.transform(np.uint8(feature_img)),
self.model,
i,
seq="first",
mask=background_masked,
)
# change position
img1 = img2
self.save_img(d_img, "", i)
d_img = convert(d_img)
flow_iter += 1
flow_iter = 0 if flow_iter > 5 else flow_iter
def shorten_url():
# Takes url encoded in json from the body and returns a shorted url if submitted is valid returns error otherwise
if request.is_json:
url = request.get_json()["url"]
if utils.check_url(utils.convert(url)):
return jsonify({
"shortened_url":
'{}{}'.format(HOST, dal.add_url(url))
}), status.HTTP_201_CREATED
return 'Malformed URL: {}'.format(url), status.HTTP_400_BAD_REQUEST
def main():
desc = 'Convert between [integer, hexadecimal, IPv4 address] <-> proquint representations. '\
'See https://arxiv.org/html/0901.4016'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('-n',
'--uint',
action='store_true',
help='convert from proquint to 32-bit integer',
required=False)
parser.add_argument('-x',
'--hex',
action='store_true',
help='convert from proquint to hexadecimal',
required=False)
parser.add_argument('-i',
'--ip',
action='store_true',
help='convert from proquint to IPv4',
required=False)
parser.add_argument('val', nargs='?', type=str, default=None,
help='value to convert (if not specified, ' \
'IP address of the current host is printed)')
args = parser.parse_args()
target = None
if args.uint:
target = 'uint'
elif args.hex:
target = 'hex'
elif args.ip:
target = 'ip'
try:
if args.val is None:
res = convert(get_my_ip())
else:
res = convert(args.val, target)
except ValueError as e:
print('{}'.format(str(e)))
else:
print('{}'.format(res))
def ret_socket_sitestatus():
d = convert(redisapi.hgetall('sitestatus'))
d['service_level'] = str(100.0 - float(mean_status(d['service_level'])))
d['system_utilization'] = str(mean_status(d['system_utilization']))
a = db.session.query(Statistics.managed_nodes).order_by(
desc(Statistics.update_at)).limit(8).all()
d['n'] = [r for r, in a]
b = db.session.query(Statistics.registered_master).order_by(
desc(Statistics.update_at)).limit(8).all()
d['m'] = [r for r, in b]
return d
def extendPortable(portableKeyCoordinates, extendInterfaceLocation, tries = 0):
(x, y, xT, yT) = portableKeyCoordinates
(x, y, xT, yT) = (5 + x//2, 5 + y//2, -5 + xT//2, -5 + yT//2)
if tries == 10:
print("Failed to extend portable")
return False
def moveToPortableKey():
mouse.moveRect(x, y, xT, yT)
sleepDuration = -1
while sleepDuration < 0:
sleepDuration = np.random.normal(0.2, 0.01)
sleep(sleepDuration)
pyautogui.click(button='right')
portableClickLocation = utils.Location()
succ = utils.performAction(moveToPortableKey,
utils.makeSuccess(portableClickLocation, portableClickInterfacePath, False))
if not succ:
print("Failed to right click portable key, retrying")
extendPortable(portableKeyCoordinates, extendInterfaceLocation, tries = tries + 1)
sleep(3)
(x, y, xT, yT) = utils.convert(portableClickLocation.getLoc())
(x, y, xT, yT) = (x//2, y//2, xT//2, yT//2)
mouse.moveRect(x, y, xT, yT, teleportRandom=False)
pyautogui.click()
sleep(3)
if not utils.checkLocation(extendInterfaceLocation, extendInterfacePath):
print("Failed to open extend interface, retrying")
extendPortable(portableKeyCoordinates, extendInterfaceLocation, tries = tries + 1)
sleepDuration = -1
while sleepDuration < 0:
sleepDuration = np.random.normal(0.2, 0.2)
utils.spamPress('1')
sleep(sleepDuration)
utils.spamPress('enter')
sleep(3)
if utils.checkLocation(extendInterfaceLocation, extendInterfacePath):
print("Failed to extend portable, retrying")
extendPortable(portableKeyCoordinates, extendInterfaceLocation, tries = tries + 1)
return True
def get_next_global_id():
gid = None
if master.is_online():
try:
r = requests.get(master.URL + "next/", timeout=30)
r_json = convert(r.json())
gid = int(r_json['nextID'])
logging.info('Global id received: ' + str(gid))
except (requests.Timeout, requests.ConnectionError):
logging.error('Global ID request timeout. Using default value')
return gid
def getShadow(self, frame, width=256, height=256):
outim = self.model.test(convert(frame))
im_out = outim[0].cpu().float().numpy()
im_out = np.transpose(im_out, (1, 2, 0))
im_out = (im_out + 1) / 2 * 255
im_out = im_out.astype('uint8')
gray = Image.fromarray(np.squeeze(im_out, axis=2)).resize(
(int(width), int(height)))
shadowFrame = np.array(gray)
return shadowFrame
def read_contract(filename, stem=True):
file = open(filename, "rb")
read_file = PyPDF2.PdfFileReader(file)
num_pages = read_file.getNumPages()
final_string = ""
for i in range(num_pages):
text = convert(filename, pages=[i])
cur_string = preprocess_text(text, stem=stem)
final_string += cur_string + " "
# final = " ".join(final_string)
return final_string
def raster(context, request):
text = request.GET['text']
filename = uuid.uuid4()
color = convert('#' + request.GET.get('color', '000000'))
pathname_bmp = './makeart/tmp/%s.bmp' % filename
pathname_svg = './makeart/tmp/%s.svg' % filename
out = cStringIO.StringIO()
context.create_image(text, out, 100, color, 'png')
#context.create_image(text, pathname_bmp, 100, (0, 0, 0), 'bmp')
out.seek(0)
return Response(out.read(), content_type="image/png")
def generate_frame_kpt(frame, opWrapper):
'''
提供frame and model
'''
datum = op.Datum()
datum.cvInputData = frame
opWrapper.emplaceAndPop([datum])
re = datum.poseKeypoints
assert len(re) == 1, 'videopose3D only support one pserson restruction'
kpt = convert(re[0])
return kpt
def salttoken():
try:
if redisapi.hexists(name='salt', key='token'):
if (time.time() - float(bytes.decode(redisapi.hget(name='salt', key='expire')))) < 0.0:
ret = redisapi.hget(name='salt', key='token')
return convert(ret)
else:
return saltlogin(saltapi.login(user, pawd, 'pam'))
else:
return saltlogin(saltapi.login(user, pawd, 'pam'))
except Exception as e:
return {'falid': e}
def test(model):
model = model.to(device)
number = 0
running_loss = 0.0
acc = 0.0
H = 0
S = 0
common = 0
containLink = 0
linkNumber = 0
model.eval()
for i in range(len(test_list)):
pred_span=[]
for j in range(len(test_list[i])):
model.hidden = model.init_hidden()
sentence_in = v.prepare_sequence(test_list[i][j]).to(device)
labels = tag.prepare_sequence(test_labels[i][j],tag_to_indx).to(device)
n = len(test_list[i][j])
number += n
output = model(sentence_in)
loss = nn.functional.nll_loss(output,labels)
_,pred = torch.max(output,dim=1)
# print(pred.data)
for indexs in convert(pred.data):
pred_span.append([test_span_list[i][j][indexs[0]][0],test_span_list[i][j][indexs[1]][1]])
acc += torch.sum(torch.eq(pred,labels).float()).data
running_loss += n*loss.data
S += len(pred_span)
H += len(test_labels_span[i])
common += metrics(pred_span,test_labels_span[i])
tmpContainLink,tmpLinkNumber = linkMetrics(pred_span,test_linkSpans[i])
containLink += tmpContainLink
linkNumber +=tmpLinkNumber
print(S,H,common)
if(S != 0):
precision = common/S
else:
precision = 0.0
recall = common/H
if(common==0):
F1 = 0.0
else:
F1 = 2*recall*precision/float(recall+precision)
print(containLink,linkNumber)
print('loss: %.4f , acc: %.4f , precision: %.4f, recall: %.4f,F1: %.4f,LinkRecall: %.4f Testing'
%(running_loss/number,acc/number,precision,recall,F1,containLink/linkNumber))
return running_loss/number,acc/number,precision,recall,F1,containLink/linkNumber
def main():
# obtain first two results
result = scrape(
'https://search.techcrunch.com/search;_ylt=Awr9ImItSrtfWq0AA7ynBWVH;_ylc=X1MDMTE5NzgwMjkxOQRfcgMyBGZyA3RlY2hjcnVuY2gEZ3ByaWQDV1JYVG5TV3JRWHVWXy5tSkNvNzNVQQRuX3JzbHQDMARuX3N1Z2cDMQRvcmlnaW4Dc2VhcmNoLnRlY2hjcnVuY2guY29tBHBvcwMwBHBxc3RyAwRwcXN0cmwDMARxc3RybAM5BHF1ZXJ5A2V0aGljcyUyMGFpBHRfc3RtcAMxNjA2MTA5NzU5?p=ethics+ai&fr2=sb-top&fr=techcrunch'
)
sleep(randint(2, 10))
result += scrape(
'https://search.techcrunch.com/search;_ylt=Awr9JnE_SrtfNYYAUfynBWVH;_ylu=Y29sbwNncTEEcG9zAzEEdnRpZAMEc2VjA3BhZ2luYXRpb24-?p=ethics+ai&fr=techcrunch&fr2=sb-top&b=11&pz=10&bct=0&xargs=0'
)
# scrape others
current = 21
base = 'https://search.techcrunch.com/search;_ylt=Awr9CKpUTrtfRGMAlx2nBWVH;_ylu=Y29sbwNncTEEcG9zAzEEdnRpZAMEc2VjA3BhZ2luYXRpb24-?p=ethics+ai&pz=10&fr=techcrunch&fr2=sb-top&bct=0&b='
end = '&pz=10&bct=0&xargs=0'
while current <= 121:
sleep(randint(2, 10))
result += scrape(base + str(current) + end)
current += 10
convert(result)
upload()
def emit_salt_ping(room, tgt, func,info):
try:
if convert(redisapi.hget('salt_task_lock', room + tgt)) == func:
meta = json.dumps({'msg': 'Task execulting.Waitting for result.',
'type': 'warning', 'tgt': tgt, 'func': func, 'jid':'Job Waiting','info':info})
socket_emit(meta=meta, event='salt_task_warn', room=room)
return 1
else:
redisapi.hset('salt_task_lock', room + tgt, func)
logger.info({'task': 'emit_salt_ping'.'room': room,'tgt': tgt})
salt_exec_func(tgt=tgt, func='test.ping', room=room,info=info)
redisapi.hdel('salt_task_lock',room+tgt)
return 0
def statistics_api_visit():
try:
data = convert(redisapi.hgetall(name='sitestatus'))
if not data:
logger.warning('no site status data in redis cache')
return {'failed': 'no site status data in redis cache'}
if data.get('api_visit_count' , None):
page_visit_count = int(data['api_visit_count'])
else:
page_visit_count = 0
redisapi.hset('sitestatus', 'api_visit_count', page_visit_count + 1)
except Exception as e:
return {'failed': e}
return {'successed': 'page visit updated'}
def test(alg_num, data_set):
if alg_num == 1:
uf = unionfind1.UnionFind()
elif alg_num == 2:
uf = unionfind2.UnionFind()
elif alg_num == 3:
uf = unionfind3.UnionFind()
starttime = time.time()
for item in data_set:
result = convert(item)
if result[0] == 'find':
uf.find(result[1], True)
elif result[0] == 'union':
uf.union(result[1][0], result[1][1])
endtime = time.time()
uf.internalNameOfSet(1)
del uf
return (endtime - starttime)
num_des=numerical.describe()
# comment start
# f1!!!!!!
#comment end
acc_f1=False
if(acc_f1):
f1=[]
shape=[]
positive=[]
for col in category:
col_label=np.append(col,labels)
df_col=df_all[col_label]
df_col=df_col.dropna(axis='rows')
df_col[col]=u.convert(df_col[col])
shape.append( df_col.shape[0])
f1.append(u.selectF(df_col,'churn'))
positive.append(df_col[df_col[label]==1].shape[0])
np.savetxt('f1_obj.txt',f1)
np.savetxt('shape_obj.txt',shape)
np.savetxt('positive_obj.txt',positive)
acc_f1=False
if(acc_f1):
f1=[]
shape=[]
positive=[]
for col in numerical_category:
col_label=np.append(col,labels)
df_col=df_all[col_label]
def ret_socket_sitestatus():
d = convert(redisapi.hgetall('sitestatus'))
d['service_level'] = str(100.0 - float(mean_status(d['service_level'])))
d['system_utilization'] = str(mean_status(d['system_utilization']))
return d
metavar="2:00")
parser.add_option("--cluster-mem", dest="clusterMemory", help="memory for submitted job (cluster only)",
metavar="16000")
parser.add_option("--valgrind", dest="valgrind", default=False, action="store_true", help="profile with valgrind",
metavar="TTS")
return parser
if __name__ == "__main__":
# import required stuff which wasn't possible at the beginning
utils.lazy_imports()
# some initial checking
check_setup()
m_parser = create_parser()
(m_options, args) = m_parser.parse_args()
if m_options.unitTests is not None:
unit.run_bit_parallel_unit_test(m_options.unitTests)
elif m_options.convert == "tpx-to-bed":
utils.convert(m_options)
elif m_options.dataAnalysis is not None:
# unit.unit_data_analysis()
da.main(m_options)
else:
if m_options.type == "local":
m_options = check_input_local(m_options, m_parser)
run_locally(m_options)
elif m_options.type == "cluster":
run_cluster(m_options)
def getAllDealCities():
params = []
url = "http://api.dianping.com/v1/metadata/get_cities_with_deals"
return _api(url,params)
if __name__ == '__main__':
# print getCategories()
# gpons = getGrouponByCity('桂林')
# print repr(gpons).decode('utf-8')
# all_cities = getAllDealCities()
# for city in all_cities['cities']:
# print city.encode('utf8')
gpons = getGrouponByCity('北京')
gpons = utils.convert(gpons)
for deal in gpons['deals']:
print 'title:',deal['title']
print 'description:',deal['description']
print 'image_url:',deal['image_url']
print 'list_price:',deal['list_price']
print 'current_price:',deal['current_price']
print 'purchase_count:',deal['purchase_count']
print 'deal_url:',deal['deal_url']
print 'city:',deal['city']
print 'categories:',','.join(deal['categories'])
print
def main():
filenames = ["data/j8cw52080_drz.fits", "data/j8cw52041_drz.fits", "data/j8cw51011_drz.fits"]
inputRedshift = 0.022
for filename in filenames:
frameData = FrameData()
data = read_frame(filename)
frame_params = read_header(filename)
print frame_params
print data.shape
#HST filters and their UBVRI (Cousin) counterparts:
#U: F336W
#B: F439W
#V: F555W
#R: F675W
#I: F814W
frame_filter = getFrameFilter(frame_params['photmode'])
#due to sky subtraction, I pad non-positive data points w/ 0.001
data[np.where(data <= 0)] = 0.001
data = data[2000:2010, 2000:2010]
frameData.data = data
magnitudes_array = countsToMag(data, frame_params)
magnitudes_array = magnitudes_array.flatten()
print magnitudes_array.shape
magnitudes = np.hstack(magnitudes_array)
print magnitudes.shape
uncertainties = np.ones((magnitudes.shape))*0.02
redshift = np.ones((magnitudes.shape))*inputRedshift
kc = ACSKCorrect(redshift, magnitudes, uncertainties, 0.02, cosmo=(Wm, Wl, H0/100))
# redshift, stmag, stmag_sigma, extinction,
lamb, templates = load_vmatrix()
lamb = lambda_to_centers(lamb)
print lamb.shape, templates.shape
useful_wavel = np.where((np.round(lamb, 0) > 3000) & (np.round(lamb,0) < 9000))[0]
print lamb.shape, 'lambda'
lamb = lamb[useful_wavel]
print lamb.shape, 'lambda'
kcorr = kc.kcorrect()
coeffs = kc.coeffs#[:, 1:4]
print coeffs.shape
coeffs = coeffs
spec = np.dot(coeffs, templates)
spec = spec[:, useful_wavel]
#spec: y pixels, x (10 k) lambda datapoints
#lamb = lamb[useful_wavel]
lamb = lamb/10 #convert to Angstroms
spec = spec*10**17
print spec.shape, lamb.shape
#xyz = np.empty((spec.shape[0], 1)) #test
r = np.empty((spec.shape[0], 1))
g = r.copy()
b = r.copy()
for i in range(0, spec.shape[0]):
#rgb_vals = ciexyz.xyz_from_spectrum(np.transpose(np.array((lamb, spec[0, :]))))
print np.array((lamb, spec[i, :])).shape, 'hstack'
rgb_vals = ciexyz.xyz_from_spectrum(np.transpose(np.array((lamb, spec[i, :]))))
print rgb_vals, rgb_vals.shape
r[i], g[i], b[i] = colormodels.irgb_from_xyz(rgb_vals)
#g = colormodels.irgb_from_xyz(rgb_vals)[1]
#b = colormodels.irgb_from_xyz(rgb_vals)[2]
rgb = np.dstack([r, g, b])
print rgb.shape, 'after dstack'
rgb = np.reshape(rgb, (10, 10, 3))
fig = plt.figure()
plt.imshow(rgb)
plt.savefig('rgb')
exit()
fig = plt.figure()
plt.plot(lamb[2000:4000], (10**17) * spec[2000:4000])
plt.savefig('spec.png')
exit()
exit()
data = np.empty((939, 16))
califa_id = db.dbUtils.getFromDB('califa_id', dbDir+'CALIFA.sqlite', 'gc')
u = db.dbUtils.getFromDB('petroMag_u', dbDir+'CALIFA.sqlite', 'mothersample')
g = db.dbUtils.getFromDB('petroMag_g', dbDir+'CALIFA.sqlite', 'mothersample')
r = db.dbUtils.getFromDB('petroMag_r', dbDir+'CALIFA.sqlite', 'mothersample')
i = db.dbUtils.getFromDB('petroMag_i', dbDir+'CALIFA.sqlite', 'mothersample')
z = db.dbUtils.getFromDB('petroMag_z', dbDir+'CALIFA.sqlite', 'mothersample')
ext_u = db.dbUtils.getFromDB('extinction_u', dbDir+'CALIFA.sqlite', 'extinction')
ext_g = db.dbUtils.getFromDB('extinction_g', dbDir+'CALIFA.sqlite', 'extinction')
ext_r = db.dbUtils.getFromDB('extinction_r', dbDir+'CALIFA.sqlite', 'extinction')
ext_i = db.dbUtils.getFromDB('extinction_i', dbDir+'CALIFA.sqlite', 'extinction')
ext_z = db.dbUtils.getFromDB('extinction_z', dbDir+'CALIFA.sqlite', 'extinction')
err_u = db.dbUtils.getFromDB('petroMagErr_u', dbDir+'CALIFA.sqlite', 'extinction')
err_g = db.dbUtils.getFromDB('petroMagErr_g', dbDir+'CALIFA.sqlite', 'extinction')
err_r = db.dbUtils.getFromDB('petroMagErr_r', dbDir+'CALIFA.sqlite', 'extinction')
err_i = db.dbUtils.getFromDB('petroMagErr_i', dbDir+'CALIFA.sqlite', 'extinction')
err_z = db.dbUtils.getFromDB('petroMagErr_z', dbDir+'CALIFA.sqlite', 'extinction')
redshift = db.dbUtils.getFromDB('z', dbDir+'CALIFA.sqlite', 'ned_z')
data[:, 0] = u[:]
data[:, 1] = g[:]
data[:, 2] = r[:]
data[:, 3] = i[:]
data[:, 4] = z[:]
data[:, 5] = ext_u[:]
data[:, 6] = ext_g[:]
data[:, 7] = ext_r[:]
data[:, 8] = ext_i[:]
data[:, 9] = ext_z[:]
data[:, 10] = err_u[:]
data[:, 11] = err_g[:]
data[:, 12] = err_r[:]
data[:, 13] = err_i[:]
data[:, 14] = err_z[:]
data[:, 15] = redshift[:]
maggies = data[:, 0:5]
extinction = data[:, 5:10]
maggies_err = data[:, 10:15]
print maggies.shape, extinction.shape, maggies_err.shape
lamb, templates = load_vmatrix()
lamb = lambda_to_centers(lamb)
print lamb.shape, templates.shape
#outputArray = np.empty((939, 9))
kc = SDSSKCorrect(redshift, maggies, maggies_err, extinction, cosmo=(Wm, Wl, H0/100))
kcorr = kc.kcorrect()
#absmag = getAbsMag(redshift, maggies[:, 2], extinction[:, 2])#kc.absmag()
#outputArray[:,0] = califa_id[:]
#print kcorr[:, 2][:].shape
#outputArray[:, 1:6] = kc.absmag()
coeffs = kc.coeffs#[:, 1:4]
#print coeffs.shape
#tmremain = np.array([[0.601525, 0.941511, 0.607033, 0.523732, 0.763937]])
#ones = np.ones((1, len(redshift)))
#prod = np.dot(tmremain.T, ones).T
print coeffs.shape
coeffs = coeffs[-1]
print coeffs
spec = np.dot(coeffs, templates)
np.savetxt('spec.txt', spec)
np.savetxt('lambda.txt', lamb)
print spec.shape, lamb.shape
fig = plt.figure()
plt.plot(lamb[2000:4000], (10**17) * spec[2000:4000])
plt.savefig('spec.png')
exit()
modelMasses = coeffs*prod
#print modelMasses.shape
mass = np.sum(modelMasses, axis=1)
for i in range (0, (len(data))):
distmod = KC.utils.cosmology.ztodm(redshift[i])
exp = 10 ** (0.4 * distmod)
outputArray[i, 6] = mass[i] * exp
#outputArray[i, 7] = getAbsMag(redshift[i], maggies[i, 2], extinction[i, 2])
outputArray[i, 8] = distmod
outputArray[:, 7] = kcorr[:, 2]
np.savetxt("kcorrect_sdss.csv", outputArray, fmt = '%i, %10.3f, %10.3f, %10.3f, %10.3e, %10.3f, %10.3e, %10.3e, %10.3e')
exit()
data = np.empty((939, 16))
califa_id = utils.convert(db.dbUtils.getFromDB('califa_id', dbDir+'CALIFA.sqlite', 'gc'))
u = utils.convert(db.dbUtils.getFromDB('el_mag', dbDir+'CALIFA.sqlite', 'u_tot'))
g = utils.convert(db.dbUtils.getFromDB('el_mag', dbDir+'CALIFA.sqlite', 'g_tot'))
r = utils.convert(db.dbUtils.getFromDB('el_mag', dbDir+'CALIFA.sqlite', 'r_tot'))
i = utils.convert(db.dbUtils.getFromDB('el_mag', dbDir+'CALIFA.sqlite', 'i_tot'))
z = utils.convert(db.dbUtils.getFromDB('el_mag', dbDir+'CALIFA.sqlite', 'z_tot'))
ext_u = utils.convert(db.dbUtils.getFromDB('extinction_u', dbDir+'CALIFA.sqlite', 'extinction'))
ext_g = utils.convert(db.dbUtils.getFromDB('extinction_g', dbDir+'CALIFA.sqlite', 'extinction'))
ext_r = utils.convert(db.dbUtils.getFromDB('extinction_r', dbDir+'CALIFA.sqlite', 'extinction'))
ext_i = utils.convert(db.dbUtils.getFromDB('extinction_i', dbDir+'CALIFA.sqlite', 'extinction'))
ext_z = utils.convert(db.dbUtils.getFromDB('extinction_z', dbDir+'CALIFA.sqlite', 'extinction'))
err_u = utils.convert(db.dbUtils.getFromDB('petroMagErr_u', dbDir+'CALIFA.sqlite', 'extinction'))
err_g = utils.convert(db.dbUtils.getFromDB('petroMagErr_g', dbDir+'CALIFA.sqlite', 'extinction'))
err_r = utils.convert(db.dbUtils.getFromDB('petroMagErr_r', dbDir+'CALIFA.sqlite', 'extinction'))
err_i = utils.convert(db.dbUtils.getFromDB('petroMagErr_i', dbDir+'CALIFA.sqlite', 'extinction'))
err_z = utils.convert(db.dbUtils.getFromDB('petroMagErr_z', dbDir+'CALIFA.sqlite', 'extinction'))
redshift = utils.convert(db.dbUtils.getFromDB('z', dbDir+'CALIFA.sqlite', 'ned_z'))
data[:, 0] = u[:, 0]
data[:, 1] = g[:, 0]
data[:, 2] = r[:, 0]
data[:, 3] = i[:, 0]
data[:, 4] = z[:, 0]
data[:, 5] = ext_u[:, 0]
data[:, 6] = ext_g[:, 0]
data[:, 7] = ext_r[:, 0]
data[:, 8] = ext_i[:, 0]
data[:, 9] = ext_z[:, 0]
data[:, 10] = err_u[:, 0]
data[:, 11] = err_g[:, 0]
data[:, 12] = err_r[:, 0]
data[:, 13] = err_i[:, 0]
data[:, 14] = err_z[:, 0]
data[:, 15] = redshift[:, 0]
maggies = data[:, 0:5]
extinction = data[:, 5:10]
maggies_err = data[:, 10:15]
print maggies.shape, extinction.shape, maggies_err.shape
outputArray = np.empty((939, 9))
kc = SDSSKCorrect(redshift, maggies, maggies_err, extinction, cosmo=(Wm, Wl, H0/100))
kcorr = kc.kcorrect()
#absmag = getAbsMag(redshift, maggies[:, 2], extinction[:, 2])#kc.absmag()
outputArray[:,0] = califa_id[:, 0]
#print kcorr[:, 2][:].shape
outputArray[:, 1:6] = kc.absmag()
coeffs = kc.coeffs#[:, 1:4]
tmremain = np.array([[0.601525, 0.941511, 0.607033, 0.523732, 0.763937]])
ones = np.ones((1, len(redshift)))
prod = np.dot(tmremain.T, ones).T
modelMasses = coeffs*prod
#print modelMasses.shape
mass = np.sum(modelMasses, axis=1)
for i in range (0, (len(data))):
distmod = KC.utils.cosmology.ztodm(redshift[i])
exp = 10 ** (0.4 * distmod)
outputArray[i, 6] = mass[i] * exp
#outputArray[i, 7] = getAbsMag(redshift[i], maggies[i, 2], extinction[i, 2])
outputArray[i, 8] = distmod
outputArray[:, 7] = kcorr[:, 2]
np.savetxt("absmag.csv", outputArray, fmt = '%i, %10.3f, %10.3f, %10.3f, %10.3e, %10.3f, %10.3e, %10.3e, %10.3e')
# -*- coding: utf-8 -*-
import numpy as np
import db
import utils
dbDir = "../db/"
data = np.empty((939, 14), dtype=float)
califa_id = utils.convert(db.dbUtils.getFromDB("CALIFA_ID", dbDir + "CALIFA.sqlite", "gc_r"))
ra = utils.convert(db.dbUtils.getFromDB("ra", dbDir + "CALIFA.sqlite", "mothersample"))
dec = utils.convert(db.dbUtils.getFromDB("dec", dbDir + "CALIFA.sqlite", "mothersample"))
el_u = utils.convert(db.dbUtils.getFromDB("el_mag", dbDir + "CALIFA.sqlite", "gc_u")) - 0.04 # AB correction
el_g = utils.convert(db.dbUtils.getFromDB("el_mag", dbDir + "CALIFA.sqlite", "gc_g"))
el_r = utils.convert(db.dbUtils.getFromDB("el_mag", dbDir + "CALIFA.sqlite", "gc_r"))
el_i = utils.convert(db.dbUtils.getFromDB("el_mag", dbDir + "CALIFA.sqlite", "gc_i"))
el_z = utils.convert(db.dbUtils.getFromDB("el_mag", dbDir + "CALIFA.sqlite", "gc_z"))
el_hlr = 0.396 * utils.convert(db.dbUtils.getFromDB("el_hlma", dbDir + "CALIFA.sqlite", "gc_r")) # SDSS pixel scale
print el_hlr
# circ_mag = utils.convert(db.dbUtils.getFromDB('circ_mag', dbDir+'CALIFA.sqlite', 'gc_r'))
# circ_hlr = 0.396*utils.convert(db.dbUtils.getFromDB('circ_hlr', dbDir+'CALIFA.sqlite', 'gc_r'))
r_sky = utils.convert(db.dbUtils.getFromDB("gc_sky", dbDir + "CALIFA.sqlite", "gc_r"))
pa = utils.convert(db.dbUtils.getFromDB("PA", dbDir + "CALIFA.sqlite", "angles"))
pa_align = utils.convert(db.dbUtils.getFromDB("PA_align", dbDir + "CALIFA.sqlite", "angles"))
ba = utils.convert(db.dbUtils.getFromDB("ba", dbDir + "CALIFA.sqlite", "nadine"))
for comment in jira.issue(issue.key).fields.comment.comments:
jira_issue.comments.append(
JiraComment(
comment.author.name,
comment.body,
comment.created))
# if len(issue.fields.components) == 0:
# if len(issue.fields.components) > 0 and issue.fields.components[0].name == 'Backend':
# print issue.fields.components
print jira_issue
github_labels = []
label = convert(jira_issue.issue_type, JiraAttr.TYPE, GithubAttr.LABEL)
if label is not None:
github_labels.append(create_label(label, github_repo))
label = convert(jira_issue.status, JiraAttr.STATUS, GithubAttr.LABEL)
if label is not None:
github_labels.append(create_label(label, github_repo))
github_issue = github_repo.create_issue(
jira_issue.summary,
body=gen_body(jira_issue),
assignee=convert(jira_issue.assignee, JiraAttr.USER,
GithubAttr.LOGIN),
labels=github_labels)
github_issue.edit(state=convert(jira_issue.status, JiraAttr.STATUS, GithubAttr.STATE))
def main():
data = np.empty((939, 16))
califa_id = utils.convert(db.dbUtils.getFromDB('califa_id', dbDir+'CALIFA.sqlite', 'gc'))
u = utils.convert(db.dbUtils.getFromDB('u_mag', dbDir+'CALIFA.sqlite', 'gc'))
g = utils.convert(db.dbUtils.getFromDB('g_mag', dbDir+'CALIFA.sqlite', 'gc'))
r = utils.convert(db.dbUtils.getFromDB('r_mag', dbDir+'CALIFA.sqlite', 'gc'))
i = utils.convert(db.dbUtils.getFromDB('i_mag', dbDir+'CALIFA.sqlite', 'gc'))
z = utils.convert(db.dbUtils.getFromDB('z_mag', dbDir+'CALIFA.sqlite', 'gc'))
ext_u = utils.convert(db.dbUtils.getFromDB('extinction_u', dbDir+'CALIFA.sqlite', 'extinction'))
ext_g = utils.convert(db.dbUtils.getFromDB('extinction_g', dbDir+'CALIFA.sqlite', 'extinction'))
ext_r = utils.convert(db.dbUtils.getFromDB('extinction_r', dbDir+'CALIFA.sqlite', 'extinction'))
ext_i = utils.convert(db.dbUtils.getFromDB('extinction_i', dbDir+'CALIFA.sqlite', 'extinction'))
ext_z = utils.convert(db.dbUtils.getFromDB('extinction_z', dbDir+'CALIFA.sqlite', 'extinction'))
err_u = utils.convert(db.dbUtils.getFromDB('petroMagErr_u', dbDir+'CALIFA.sqlite', 'extinction'))
err_g = utils.convert(db.dbUtils.getFromDB('petroMagErr_g', dbDir+'CALIFA.sqlite', 'extinction'))
err_r = utils.convert(db.dbUtils.getFromDB('petroMagErr_r', dbDir+'CALIFA.sqlite', 'extinction'))
err_i = utils.convert(db.dbUtils.getFromDB('petroMagErr_i', dbDir+'CALIFA.sqlite', 'extinction'))
err_z = utils.convert(db.dbUtils.getFromDB('petroMagErr_z', dbDir+'CALIFA.sqlite', 'extinction'))
redshift = utils.convert(db.dbUtils.getFromDB('z', dbDir+'CALIFA.sqlite', 'ned_z'))
data[:, 0] = u[:, 0]
data[:, 1] = g[:, 0]
data[:, 2] = r[:, 0]
data[:, 3] = i[:, 0]
data[:, 4] = z[:, 0]
data[:, 5] = ext_u[:, 0]
data[:, 6] = ext_g[:, 0]
data[:, 7] = ext_r[:, 0]
data[:, 8] = ext_i[:, 0]
data[:, 9] = ext_z[:, 0]
data[:, 10] = err_u[:, 0]
data[:, 11] = err_g[:, 0]
data[:, 12] = err_r[:, 0]
data[:, 13] = err_i[:, 0]
data[:, 14] = err_z[:, 0]
data[:, 15] = redshift[:, 0]
maggies = data[:, 0:5]
extinction = data[:, 5:10]
maggies_err = data[:, 10:15]
print maggies.shape, extinction.shape, maggies_err.shape
outputArray = np.empty((939, 9))
kc = SDSSKCorrect(redshift, maggies, maggies_err, extinction, cosmo=(Wm, Wl, H0/100))
kcorr = kc.kcorrect()
#absmag = getAbsMag(redshift, maggies[:, 2], extinction[:, 2])#kc.absmag()
outputArray[:,0] = califa_id[:, 0]
#print kcorr[:, 2][:].shape
outputArray[:, 1:6] = kc.absmag()
coeffs = kc.coeffs#[:, 1:4]
tmremain = np.array([[0.601525, 0.941511, 0.607033, 0.523732, 0.763937]])
ones = np.ones((1, len(redshift)))
prod = np.dot(tmremain.T, ones).T
modelMasses = coeffs*prod
#print modelMasses.shape
mass = np.sum(modelMasses, axis=1)
for i in range (0, (len(data))):
distmod = KC.utils.cosmology.ztodm(redshift[i])
exp = 10 ** (0.4 * distmod)
outputArray[i, 6] = mass[i] * exp
#outputArray[i, 7] = getAbsMag(redshift[i], maggies[i, 2], extinction[i, 2])
outputArray[i, 8] = distmod
outputArray[:, 7] = kcorr[:, 2]
np.savetxt("absmag.csv", outputArray, fmt = '%i, %10.3f, %10.3f, %10.3f, %10.3e, %10.3f, %10.3e, %10.3e, %10.3e')
def _decodeType3Results(self, res):
"""
Decoder for cross identification type results file. Example of the format used:
80374 0000000017133729a 80359 0000000016842970b 2.1088616847991943 FALSE META_VAL1
148407 0000260007968376b 89823 0000000008087650a 0.33669018745422363 FALSE META_VAL1
179408 03ea7cce-a192626a 80372 0000000016749939b 1.26323664188385 FALSE META_VAL2
80344 0000000016888750a 80344 0000000015560933b 4.423274517059326 TRUE META_VAL2
etc.
:param res: list of strings (text lines) of raw data resulting from a series of trials.
Type 3 data contains 7 fields:
field 1: string: label identifying a subject (training data)
field 2: string: name of data file containing biometric features or raw data originating
from the subject denoted by field 1 used for training the test model
field 3: string: label identifying a subject (test data)
field 4: string: name of data file containing biometric features or raw data originating
from the subject denoted by field 3 used for training the reference model
field 5: string: float value: score of trial
field 6: boolean: ground truth
field 7: string: meta data value for the trial
Field 7 can be used to contrast experiments in the zoo plot.
So if you have 2 experiments where you change one variable, when doing a cross
identification test, the meta value can be used to group the experiment's scores.
"""
totCnt = 0
resCnt = 0
# For type 3 scores we assume that the scores are (Log) Likelyhood Ratios ranging between 0 and +infinity.
onlyOnce = set()
revRepeatCnt = 0
selfCnt = 0
valuesCnt = collections.Counter()
# Set max and min function for this type.
self.getMaximum4ThisType = self.config.getMaximum4Type3
self.getMinimum4ThisType = self.config.getMinimum4Type3
# Scores are scalar float values.
self._miAll = self.getMaximum4ThisType()
self._maAll = self.getMinimum4ThisType()
for line in res:
if ',' in line:
splitChar = ','
else:
splitChar = None
try:
l1, f1, l2, f2, score, truth, metaValue = line.split(splitChar)
if splitChar:
l1, f1, l2, f2, score, truth, metaValue = self._sanitize(l1, f1, l2, f2, score, truth, metaValue)
except Exception as e:
print('Error in', line)
print('Use either comma or space as separator.')
print(e)
else:
# We want to sort the data when choosing colors.
# Therefore we convert to numbers if possible
# otherwise we assume string values.
metaValue = convert(metaValue)
# Keep track of distinct meta data values.
valuesCnt[metaValue] += 1
if not metaValue in self._minimumScore:
self._minimumScore[metaValue] = self.getMaximum4ThisType()
self._maximumScore[metaValue] = self.getMinimum4ThisType()
l1_0 = l1 + '---' + f1
l2_0 = l2 + '---' + f2
# If the score is not numerical, then we skip everything.
try:
score = float(score)
except Exception as e:
print('Error in', line)
print(e)
else:
self._miAll = min(self._miAll, score)
self._maAll = max(self._maAll, score)
self._minimumScore[metaValue] = min(self._minimumScore[metaValue], score)
self._maximumScore[metaValue] = max(self._maximumScore[metaValue], score)
if l1_0 == l2_0:
selfCnt += 1
# Selfies are not interesting and therefore skipped
# continue
if not (l1_0, l2_0) in onlyOnce:
onlyOnce.add((l1_0, l2_0))
if not self._allowDups:
# We do not want to include an experiment twice,
# assuming that the scores are symmetric.
# This may not be the case!
if (l2_0, l1_0) in onlyOnce:
revRepeatCnt += 1
continue
resCnt += 1
# Keep track of labels associated with meta data values.
metaValue = str(metaValue)
pattern = l1 + self.LABEL_SEPARATOR + metaValue
# Keep track of results for ranking purposes.
# print 'adding element to results[', l1 + self.LABEL_SEPARATOR + metaValue, ']'
self._results[pattern].append((l2, score))
self._results4Subject[metaValue, l1].append((l2, score)) # code is just for debugging
self._metaDataValues[metaValue].add(l1)
self._metaDataValues[metaValue].add(l2)
totCnt += 1
if truth.lower() == 'true':
self._targetScores[pattern].append(score)
self._targetScores4Label[l1].append(score)
self._targetScores4MetaValue[metaValue].append(score)
self._targetCnt[metaValue] += 1
self._targetLabels.add(l1)
else:
self._nonTargetScores[pattern].append(score)
self._nonTargetScores4Label[l1].append(score)
self._nonTargetScores4MetaValue[metaValue].append(score)
self._nonTargetCnt[metaValue] += 1
self._nonTargetLabels.add(l1)
if self.debug:
print('Number of results in file:', resCnt)
print('Number of subjects:', len(self._results4Subject))
print('Number of scores:', totCnt)
if totCnt == 0:
print('No scores were found. Maybe the dataType is not set correctly.')
print("DataType is '%s'" % self._dataType)
print('Is this correct?')
sys.exit(1)
print("Number of target and non target scores for: ")
maxLen = 0
for metaValue in self._nonTargetCnt:
maxLen = max(maxLen, len(metaValue))
template = "{:<%d}" % maxLen
scoreLen = len(str(self.compLen(self._nonTargetScores)))
template += " {:>%d} {:>%d}" % (scoreLen + 1, scoreLen + 1)
for metaValue in self._targetCnt:
#print("{:<10} {:>7} {:>7}".format(metaValue, self._targetCnt[metaValue], self._nonTargetCnt[metaValue]))
print(template.format(metaValue, self._targetCnt[metaValue], self._nonTargetCnt[metaValue]))
# print("Number of non target scores for: ")
print(template.format("Total", self.compLen(self._targetScores), self.compLen(self._nonTargetScores)))
# print('Total number of target scores:', self.compLen(self._targetScores))
# print('Total number of non target scores:', self.compLen(self._nonTargetScores))
print('Number of repeats (multiple instances of same data in input):', revRepeatCnt)
print('Number of selfies (A vs A):', selfCnt)
self._nrDistinctMetaDataValues = len(self._metaDataValues)
print('Nr of distinct meta data values:', self._nrDistinctMetaDataValues)
# -*- coding: utf-8 -*-
import numpy as np
import db
import utils
dbDir = '../db/'
data = np.empty((939, 3), dtype = object)
#califa_id = utils.convert(db.dbUtils.getFromDB('CALIFA_ID', dbDir+'CALIFA.sqlite', 'gc_r'))
ra = utils.convert(db.dbUtils.getFromDB('ra', dbDir+'CALIFA.sqlite', 'mothersample'))
dec = utils.convert(db.dbUtils.getFromDB('dec', dbDir+'CALIFA.sqlite', 'mothersample'))
print ra.shape
for i in range(0, 939):
data[i, 0] = str(ra[i, 0])+'d'
data[i, 1] = str(dec[i, 0])+'d'
data[i, 2] = 0.05
print i, data[i]
utils.writeOut(data[i, 0:3], "ned_query_coords.csv", ";")
#np.savetxt("ned_query_coords.csv", data, delimiter = ';')
def get(self):
data = redisapi.hgetall('sitestatus')
if data:
return json.dumps(convert(data))
def main():
#selecting by ids when some of them are missing
fit_sky_ids = utils.convert(db.dbUtils.getFromDB('califa_id', dbDir+'CALIFA.sqlite', 'u_tot'))[:, 0]
ids = ''
id_length = 0
for i in fit_sky_ids:
ids = ids+","+str(int(i))
id_length+=1
ids = ids[1:]
tot_mag = utils.convert(db.dbUtils.getFromDB('u_mag', dbDir+'CALIFA.sqlite', 'u_tot', ' where califa_id in('+ids+')')) #parsing tuples
gc_mag = utils.convert(db.dbUtils.getFromDB('u_mag', dbDir+'CALIFA.sqlite', 'gc', ' where califa_id in('+ids+')')) #parsing tuples
#circ_mag = utils.convert(db.dbUtils.getFromDB('circ_r_mag', dbDir+'CALIFA.sqlite', 'gc')) #parsing tuples
nadines_mag = utils.convert(db.dbUtils.getFromDB('r_mag', dbDir+'CALIFA.sqlite', 'nadine', ' where califa_id in('+ids+')')) #parsing tuples
sdss_mag = utils.convert(db.dbUtils.getFromDB('petroMag_u', dbDir+'CALIFA.sqlite', 'mothersample', ' where califa_id in('+ids+')')) #parsing tuples
atlas_mag = utils.convert(db.dbUtils.getFromDB('r_mag', dbDir+'CALIFA.sqlite', 'atlas', ' where califa_id in('+ids+')'))
#gc_hlr = utils.convert(db.dbUtils.getFromDB('circ_hlr', dbDir+'CALIFA.sqlite', 'gc'))
#nadine_hlr = utils.convert(db.dbUtils.getFromDB('re', dbDir+'CALIFA.sqlite', 'nadine'))
#sdss_hlr = utils.convert(db.dbUtils.getFromDB('petroR50_r', dbDir+'CALIFA.sqlite', 'mothersample')) #parsing tuples
#lucie_hlr = utils.convert(db.dbUtils.getFromDB('hlr', dbDir+'CALIFA.sqlite', 'lucie')) #parsing tuples
#el_hlr = utils.convert(db.dbUtils.getFromDB('el_hlma', dbDir+'CALIFA.sqlite', 'gc'))
#lucie_sky = utils.convert(db.dbUtils.getFromDB('sky', dbDir+'CALIFA.sqlite', 'lucie', ' where califa_id in('+ids+')')) - 1000 #parsing tuples
tot_sky = utils.convert(db.dbUtils.getFromDB('gc_sky', dbDir+'CALIFA.sqlite', 'u_tot', ' where califa_id in('+ids+')')) #parsing tuples
gc_sky = utils.convert(db.dbUtils.getFromDB('gc_sky', dbDir+'CALIFA.sqlite', 'gc', ' where califa_id in('+ids+')'))
sdss_sky = utils.convert(db.dbUtils.getFromDB('sky', dbDir+'CALIFA.sqlite', 'sdss_sky', ' where califa_id in('+ids+')'))
#plot relations between various magnitude results
graph = plot.Plots()
#gc_magData = plot.GraphData(((nadines_mag, tot_mag)), 'k', 'best')
#graph.plotScatter([gc_magData], "/analysis/new_gc_mag_vs_nadine", plot.PlotTitles("Comparison between my and Nadine's photometry values", "Nadine's gc magnitude, mag", "Updated gc r magnitude, mag"), (11, 16, 11, 16))
gc_magData = plot.GraphData(((sdss_mag, tot_mag)), 'k', 'best')
graph.plotScatter([gc_magData], "/analysis/new_gc_mag_vs_sdss_u", plot.PlotTitles("Comparison between my and SDSS photometry values", "SDSS Petrosian u magnitude, mag", "Updated gc r magnitude, mag"),(12, 18, 12, 18))
#gc_magData = plot.GraphData(((atlas_mag, tot_mag)), 'k', 'best')
#graph.plotScatter([gc_magData], "/analysis/new_gc_mag_vs_nsatlas", plot.PlotTitles("Comparison between my and NASA Sloan Atlas photometry values", "Updated gc r magnitude, mag", "NSAtlas magnitude, mag"),(11, 16, 11, 16))
gc_magData = plot.GraphData(((gc_mag, tot_mag)), 'k', 'best')
graph.plotScatter([gc_magData], "/analysis/gc_vs_gc_new_u", plot.PlotTitles("Comparison between GC and sky-fit GC photometry values", "GC r magnitude, mag", "Updated GC magnitude, mag"),(12, 17, 12, 17))
#gc_magData = plot.GraphData(((circ_mag, gc_mag)), 'k', 'best')
#graph.plotScatter([gc_magData], "/analysis/el_mag_vs_circ_apert", plot.PlotTitles("Comparison between elliptical and circular annuli", "r magnitude, mag", "r magnitude, mag"),(11, 16, 11, 16))
#compare sky values
graph = plot.Plots()
#plotData = plot.GraphData(((np.arange(1, 938), gc_sky - lucie_sky)), 'k', 'best')
#graph.plotScatter([plotData], "/analysis/sky_comparison", plot.PlotTitles("Comparison between my and Lucie's sky values", "counts", "counts"), (70, 170, -2, 1))
#print sdss_sky
#print np.reshape((tot_sky - sdss_sky), (id_length, 1)).shape, np.reshape(np.arange(1, id_length+1), (id_length, 1)).shape
plotData = plot.GraphData(((np.arange(1, id_length+1), tot_sky - sdss_sky)), 'k', 'best')
graph.plotScatter([plotData], "/analysis/sdss_sky_comparison_u", plot.PlotTitles("Comparison between my and SDSS sky values", "No.", "counts"), (0, id_length, -2, 0.5))
plotData = plot.GraphData(((np.arange(1, id_length+1), gc_sky - tot_sky)), 'k', 'best')
graph.plotScatter([plotData], "/analysis/gc_sky_comparison_u", plot.PlotTitles("GC sky - updated GC sky", "No.", "counts"))
exit()
#plot various HLR values
graph = plot.Plots()
plotData = plot.GraphData(((lucie_hlr, gc_hlr)), 'k', 'best')
graph.plotScatter([plotData], "/analysis/hlr_vs_lucie_noscale", plot.PlotTitles("Comparison between my and Lucie's HLR values", "Lucie's $r_e$, arcsec (?)", "gc hlr, arcsec"), (0, 50, 0, 50))
plotData = plot.GraphData(((nadine_hlr, el_hlr)), 'k', 'best')
graph.plotScatter([plotData], "/analysis/el_hlr_vs_nadine", plot.PlotTitles("Comparison between my and Nadine's HLR values", "Nadine's $r_e$, arcsec", "gc hlr, arcsec"), (0, 70, 0, 50))
plotData = plot.GraphData(((sdss_hlr, gc_hlr)), 'k', 'best')
graph.plotScatter([plotData], "/analysis/circ_hlr_vs_sdss", plot.PlotTitles("Comparison between my and SDSS HLR values", "SDSS Petrosian $r_50$, arcsec", "gc hlr, arcsec"), (0, 50, 0, 50))
#stellar mass, absmag comparison with Jakob's values:
'''
absmag_kc = utils.convert(db.dbUtils.getFromDB('r', dbDir+'CALIFA.sqlite', 'jakobs'))[:937, :]
absmag_j = utils.convert(db.dbUtils.getFromDB('r', dbDir+'CALIFA.sqlite', 'kcorrect_ned'))[:937, :]
graph = plot.Plots()
gc_magData = plot.GraphData(((absmag_kc, absmag_j)), 'k', 'best')
graph.plotScatter([gc_magData], "/analysis/absolute_magnitudes", plot.PlotTitles("Comparison between my and Jakob's absolute magnitudes", "gc M_r, mag", "JW M_r, mag"))
stmass_kc = utils.convert(db.dbUtils.getFromDB('st_mass', dbDir+'CALIFA.sqlite', 'kcorrect_ned'))[:937, :]
stmass_kc_sdss = utils.convert(db.dbUtils.getFromDB('st_mass', dbDir+'CALIFA.sqlite', 'kcorrect_sdss_phot'))[:937, :]
stmass_kc_no_z = utils.convert(db.dbUtils.getFromDB('st_mass', dbDir+'CALIFA.sqlite', 'kcorrect_no_uz'))[:937, :]
stmass_j = utils.convert(db.dbUtils.getFromDB('mstar', dbDir+'CALIFA.sqlite', 'jakobs'))[:937, :]
stmass_kc = np.log10(stmass_kc)
stmass_kc_no_z = np.log10(stmass_kc_no_z)
stmass_kc_sdss = np.log10(stmass_kc_sdss)
stmass_j = np.log10(stmass_j)
graph = plot.Plots()
gc_magData = plot.GraphData(((stmass_kc_sdss, stmass_j)), 'k', 'best')
graph.plotScatter([gc_magData], "/analysis/stellar masses", plot.PlotTitles("Comparison between kcorrect's and Jakob's stellar masses", "M_{kc}", "M_{JW}"), (7.5, 13, 7.5, 13))
gc_magData = plot.GraphData(((stmass_kc_sdss, stmass_j)), 'k', 'best')
graph.plotScatter([gc_magData], "/analysis/stellar_sdss_kc_masses", plot.PlotTitles("Comparison between kcorrect's and Jakob's stellar masses", "M_{kc}", "M_{JW}"), (7.5, 13, 7.5, 13))
gc_magData = plot.GraphData(((stmass_kc_no_z, stmass_j)), 'k', 'best')
graph.plotScatter([gc_magData], "/analysis/stMass_jw_gc_no_uz_bands", plot.PlotTitles("Comparison between kcorrect's gri and Jakob's stellar masses", "M_{kc}", "M_{JW}"), (7.5, 13, 7.5, 13))
'''
'''
def create_moodle_zip(blackboard_zip_fname, out_name, course_id, parameters):
try:
shutil.rmtree('elixer_tmp')
shutil.rmtree('course_files')
except OSError:
pass
course = Course(blackboard_zip_fname, course_id, parameters)
moodle_zip = zipfile.ZipFile(out_name, 'w')
moodle_xml_str = utils.convert(course).encode('utf-8')
moodle_zip.writestr('moodle.xml', moodle_xml_str)
err_fh = open(os.path.devnull, 'w')
#nikoloup
#fix unzip encoding issues
#command = ('unzip %s -d elixer_tmp' % blackboard_zip_fname).split(' ')
#subprocess.Popen(command, stdout=err_fh, stderr=err_fh).communicate()
bbzip = zipfile.ZipFile(blackboard_zip_fname, 'r')
bbzip.extractall('elixer_tmp')
#end
skip_parent = False
for root, dirs, files in os.walk('elixer_tmp'):
if not skip_parent:
skip_parent = True
continue
for bb_fname in files:
moodle_fname = bb_fname
#nikoloup
#exclude xml files
if moodle_fname.find('.xml')!=-1:
continue
#nikoloup
#filename cleanup
parts1 = moodle_fname.split('__')
parts2 = moodle_fname.split('.')
moodle_fname = parts1[0] + '.' + parts2[len(parts2)-1]
#end
if bb_fname.startswith('!'):
if '.' in bb_fname:
ext, fname = [s[::-1] for s in bb_fname[1:][::-1].split('.', 1)]
moodle_fname = (base64.b16decode(fname.upper()) + '.' + ext)
else:
ext, fname = '', bb_fname[1:]
moodle_fname = (base64.b16decode(fname.upper()))
moodle_fname = urllib2.unquote(moodle_fname)
#nikoloup
#filename cleanup
#res_num = root.split(os.sep, 1)[1].split(os.sep)[0].replace('res', '')
res_num = ''
#end
fixed_filename = utils.fix_filename(moodle_fname, res_num)
bb_fname = os.path.join(root, bb_fname)
moodle_fname = os.path.join('course_files', fixed_filename)
moodle_zip.write(bb_fname, moodle_fname)
shutil.rmtree('elixer_tmp')
moodle_zip.close()
logger = logging.getLogger('task')
celery, session = create_celery_app()
celery.config_from_object('celery_config')
indbapi = Indb(config['INDB_HOST'] + ':' + config['INDB_PORT'])
sensuapi = SensuAPI(config['SENSU_HOST'] + ':' + config['SENSU_PORT'])
master = session.query(Masterdb).first()
try:
saltapi = Pepper(master.ret_api())
user = master.username
pawd = convert(base64.b64decode(master.password))
except:
saltapi = Pepper(config['SALTAPI_HOST'])
user = config['SALTAPI_USER']
pawd = config['SALTAPI_PASS']
redisapi = redis.StrictRedis(host=config['REDIS_HOST'], port=config['REDIS_PORT'], db=config['REDIS_DB'])
'''
### DOC ###
Celery function description
*self test*
import sys
import textwrap
from utils import convert
if __name__ == "__main__":
info = "Usage: converter.py input.txt"
try:
source = sys.argv[1]
except (TypeError, ValueError, IndexError):
sys.exit(info)
if len(sys.argv) < 2:
sys.exit(info)
with open(source) as file:
out_line = "".join(line for line in file)
out_line = out_line.replace("-\n", "").replace("\n", " ")
with open("converted.txt", "w") as out:
source_str = convert(out_line)
out.write(textwrap.fill(source_str, 60))
