def merging_cc(params, nb_cpu, nb_gpu, use_gpu):
def remove(result, distances, cc_merge):
do_merge = True
to_merge = numpy.zeros((0, 2), dtype=numpy.int32)
g_idx = range(len(distances))
while do_merge:
dmax = distances.max()
idx = numpy.where(distances == dmax)
one_merge = [idx[0][0], idx[1][0]]
do_merge = dmax >= cc_merge
if do_merge:
elec_ic1 = result['electrodes'][one_merge[0]]
elec_ic2 = result['electrodes'][one_merge[1]]
nic1 = one_merge[0] - numpy.where(
result['electrodes'] == elec_ic1)[0][0]
nic2 = one_merge[1] - numpy.where(
result['electrodes'] == elec_ic2)[0][0]
mask1 = result['clusters_' + str(elec_ic1)] > -1
mask2 = result['clusters_' + str(elec_ic2)] > -1
tmp1 = numpy.unique(result['clusters_' + str(elec_ic1)][mask1])
tmp2 = numpy.unique(result['clusters_' + str(elec_ic2)][mask2])
elements1 = numpy.where(result['clusters_' +
str(elec_ic1)] == tmp1[nic1])[0]
elements2 = numpy.where(result['clusters_' +
str(elec_ic2)] == tmp2[nic2])[0]
if len(elements1) > len(elements2):
to_remove = one_merge[1]
to_keep = one_merge[0]
elec = elec_ic2
elements = elements2
else:
to_remove = one_merge[0]
to_keep = one_merge[1]
elec = elec_ic1
elements = elements1
result['data_' + str(elec)] = numpy.delete(result['data_' +
str(elec)],
elements,
axis=0)
result['clusters_' + str(elec)] = numpy.delete(
result['clusters_' + str(elec)], elements)
result['times_' + str(elec)] = numpy.delete(
result['times_' + str(elec)], elements)
result['peaks_' + str(elec)] = numpy.delete(
result['peaks_' + str(elec)], elements)
result['electrodes'] = numpy.delete(result['electrodes'],
to_remove)
distances = numpy.delete(distances, to_remove, axis=0)
distances = numpy.delete(distances, to_remove, axis=1)
to_merge = numpy.vstack(
(to_merge, numpy.array([g_idx[to_keep],
g_idx[to_remove]])))
g_idx.pop(to_remove)
return to_merge, result
data_file = params.data_file
N_e = params.getint('data', 'N_e')
N_total = params.nb_channels
N_t = params.getint('detection', 'N_t')
template_shift = params.getint('detection', 'template_shift')
blosc_compress = params.getboolean('data', 'blosc_compress')
N_tm = load_data(params, 'nb_templates')
nb_temp = int(N_tm // 2)
to_merge = []
cc_merge = params.getfloat('clustering', 'cc_merge')
norm = N_e * N_t
result = []
overlap = get_overlaps(params,
extension='-merging',
erase=True,
normalize=True,
maxoverlap=False,
verbose=False,
half=True,
use_gpu=use_gpu,
nb_cpu=nb_cpu,
nb_gpu=nb_gpu)
overlap.close()
filename = params.get('data', 'file_out_suff') + '.overlap-merging.hdf5'
SHARED_MEMORY = get_shared_memory_flag(params)
if not SHARED_MEMORY:
over_x, over_y, over_data, over_shape = load_data(params,
'overlaps-raw',
extension='-merging')
else:
over_x, over_y, over_data, over_shape = load_data_memshared(
params,
'overlaps-raw',
extension='-merging',
use_gpu=use_gpu,
nb_cpu=nb_cpu,
nb_gpu=nb_gpu)
#sub_comm, is_local = get_local_ring(True)
#if is_local:
distances = numpy.zeros((nb_temp, nb_temp), dtype=numpy.float32)
to_explore = numpy.arange(nb_temp - 1)[comm.rank::comm.size]
for i in to_explore:
idx = numpy.where((over_x >= i * nb_temp + i + 1)
& (over_x < ((i + 1) * nb_temp)))[0]
local_x = over_x[idx] - (i * nb_temp + i + 1)
data = numpy.zeros((nb_temp - (i + 1), over_shape[1]),
dtype=numpy.float32)
data[local_x, over_y[idx]] = over_data[idx]
distances[i, i + 1:] = numpy.max(data, 1) / norm
distances[i + 1:, i] = distances[i, i + 1:]
#Now we need to sync everything across nodes
distances = gather_array(distances,
comm,
0,
1,
'float32',
compress=blosc_compress)
if comm.rank == 0:
distances = distances.reshape(comm.size, nb_temp, nb_temp)
distances = numpy.sum(distances, 0)
#sub_comm.Barrier()
#sub_comm.Free()
if comm.rank == 0:
result = load_data(params, 'clusters')
to_merge, result = remove(result, distances, cc_merge)
to_merge = numpy.array(to_merge)
to_merge = comm.bcast(to_merge, root=0)
if len(to_merge) > 0:
slice_templates(params, to_merge=to_merge)
slice_clusters(params, result)
comm.Barrier()
del result, over_x, over_y, over_data
if comm.rank == 0:
os.remove(filename)
return [nb_temp, len(to_merge)]
def delete_mixtures(params, nb_cpu, nb_gpu, use_gpu):
data_file = params.data_file
N_e = params.getint('data', 'N_e')
N_total = params.nb_channels
N_t = params.getint('detection', 'N_t')
template_shift = params.getint('detection', 'template_shift')
cc_merge = params.getfloat('clustering', 'cc_mixtures')
mixtures = []
to_remove = []
filename = params.get('data', 'file_out_suff') + '.overlap-mixtures.hdf5'
norm_templates = load_data(params, 'norm-templates')
best_elec = load_data(params, 'electrodes')
limits = load_data(params, 'limits')
nodes, edges = get_nodes_and_edges(params)
inv_nodes = numpy.zeros(N_total, dtype=numpy.int32)
inv_nodes[nodes] = numpy.argsort(nodes)
overlap = get_overlaps(params,
extension='-mixtures',
erase=True,
normalize=False,
maxoverlap=False,
verbose=False,
half=True,
use_gpu=use_gpu,
nb_cpu=nb_cpu,
nb_gpu=nb_gpu)
overlap.close()
SHARED_MEMORY = get_shared_memory_flag(params)
if SHARED_MEMORY:
c_overs = load_data_memshared(params,
'overlaps',
extension='-mixtures',
use_gpu=use_gpu,
nb_cpu=nb_cpu,
nb_gpu=nb_gpu)
else:
c_overs = load_data(params, 'overlaps', extension='-mixtures')
if SHARED_MEMORY:
templates = load_data_memshared(params, 'templates', normalize=False)
else:
templates = load_data(params, 'templates')
x, N_tm = templates.shape
nb_temp = int(N_tm // 2)
merged = [nb_temp, 0]
overlap_0 = numpy.zeros(nb_temp, dtype=numpy.float32)
distances = numpy.zeros((nb_temp, nb_temp), dtype=numpy.int32)
for i in xrange(nb_temp - 1):
data = c_overs[i].toarray()
distances[i, i + 1:] = numpy.argmax(data[i + 1:, :], 1)
distances[i + 1:, i] = distances[i, i + 1:]
overlap_0[i] = data[i, N_t]
all_temp = numpy.arange(comm.rank, nb_temp, comm.size)
sorted_temp = numpy.argsort(
norm_templates[:nb_temp])[::-1][comm.rank::comm.size]
M = numpy.zeros((2, 2), dtype=numpy.float32)
V = numpy.zeros((2, 1), dtype=numpy.float32)
to_explore = xrange(comm.rank, len(sorted_temp), comm.size)
if comm.rank == 0:
to_explore = get_tqdm_progressbar(to_explore)
for count, k in enumerate(to_explore):
k = sorted_temp[k]
electrodes = numpy.take(inv_nodes, edges[nodes[best_elec[k]]])
overlap_k = c_overs[k]
is_in_area = numpy.in1d(best_elec, electrodes)
all_idx = numpy.arange(len(best_elec))[is_in_area]
been_found = False
t_k = None
for i in all_idx:
t_i = None
if not been_found:
overlap_i = c_overs[i]
M[0, 0] = overlap_0[i]
V[0, 0] = overlap_k[i, distances[k, i]]
for j in all_idx[i + 1:]:
t_j = None
M[1, 1] = overlap_0[j]
M[1, 0] = overlap_i[j, distances[k, i] - distances[k, j]]
M[0, 1] = M[1, 0]
V[1, 0] = overlap_k[j, distances[k, j]]
try:
[a1, a2] = numpy.dot(scipy.linalg.inv(M), V)
except Exception:
[a1, a2] = [0, 0]
a1_lim = limits[i]
a2_lim = limits[j]
is_a1 = (a1_lim[0] <= a1) and (a1 <= a1_lim[1])
is_a2 = (a2_lim[0] <= a2) and (a2 <= a2_lim[1])
if is_a1 and is_a2:
if t_k is None:
t_k = templates[:, k].toarray().ravel()
if t_i is None:
t_i = templates[:, i].toarray().ravel()
if t_j is None:
t_j = templates[:, j].toarray().ravel()
new_template = (a1 * t_i + a2 * t_j)
similarity = numpy.corrcoef(t_k, new_template)[0, 1]
local_overlap = numpy.corrcoef(t_i, t_j)[0, 1]
if similarity > cc_merge and local_overlap < cc_merge:
if k not in mixtures:
mixtures += [k]
been_found = True
#print "Template", k, 'is sum of (%d, %g) and (%d,%g)' %(i, a1, j, a2)
break
sys.stderr.flush()
#print mixtures
to_remove = numpy.unique(numpy.array(mixtures, dtype=numpy.int32))
to_remove = all_gather_array(to_remove, comm, 0, dtype='int32')
if len(to_remove) > 0 and comm.rank == 0:
result = load_data(params, 'clusters')
slice_templates(params, to_remove)
slice_clusters(params, result, to_remove=to_remove)
comm.Barrier()
del c_overs
if comm.rank == 0:
os.remove(filename)
return [nb_temp, len(to_remove)]
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
parallel_hdf5 = h5py.get_config().mpi
user_path = pjoin(os.path.expanduser('~'), 'spyking-circus')
tasks_list = None
if not os.path.exists(user_path):
os.makedirs(user_path)
try:
import cudamat as cmt
cmt.init()
HAVE_CUDA = True
except Exception:
HAVE_CUDA = False
all_steps = [
'whitening', 'clustering', 'fitting', 'gathering', 'extracting',
'filtering', 'converting', 'deconverting', 'benchmarking',
'merging', 'validating', 'thresholding'
]
config_file = os.path.abspath(pkg_resources.resource_filename('circus', 'config.params'))
header = get_colored_header()
header += Fore.GREEN + 'Local CPUs : ' + Fore.CYAN + str(psutil.cpu_count()) + '\n'
# header += Fore.GREEN + 'GPU detected : ' + Fore.CYAN + str(HAVE_CUDA) + '\n'
header += Fore.GREEN + 'Parallel HDF5 : ' + Fore.CYAN + str(parallel_hdf5) + '\n'
do_upgrade = ''
if not SHARED_MEMORY:
do_upgrade = Fore.WHITE + ' [please consider upgrading MPI]'
header += Fore.GREEN + 'Shared memory : ' + Fore.CYAN + str(SHARED_MEMORY) + do_upgrade + '\n'
header += '\n'
header += Fore.GREEN + "##################################################################"
header += Fore.RESET
method_help = '''by default, all steps are performed,
but a subset x,y can be done. Steps are:
- filtering
- whitening
- clustering
- fitting
- merging [with or without a GUI for meta merging]
- (extra) converting [export results to phy format]
- (extra) thresholding [to get MUA activity only]
- (extra) deconverting [import results from phy format]
- (extra) gathering [force collection of results]
- (extra) extracting [get templates from spike times]
- (extra) benchmarking [with -o and -t]
- (extra) validating [to compare performance with GT neurons]'''
parser = argparse.ArgumentParser(description=header,
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('datafile', help='data file (or a list of commands if batch mode)')
parser.add_argument('-i', '--info', help='list the file formats supported by SpyKING CIRCUS', action='store_true')
parser.add_argument('-m', '--method',
default='filtering,whitening,clustering,fitting,merging',
help=method_help)
parser.add_argument('-c', '--cpu', type=int, default=max(1, int(psutil.cpu_count()/2)), help='number of CPU')
# parser.add_argument('-g', '--gpu', type=int, default=0, help='number of GPU')
parser.add_argument('-H', '--hostfile', help='hostfile for MPI',
default=pjoin(user_path, 'circus.hosts'))
parser.add_argument('-b', '--batch', help='datafile is a list of commands to launch, in a batch mode',
action='store_true')
parser.add_argument('-p', '--preview', help='GUI to display the first second filtered with thresholds',
action='store_true')
parser.add_argument('-r', '--result', help='GUI to display the results on top of raw data',
action='store_true')
parser.add_argument('-s', '--second', type=int, default=0, help='If preview mode, begining of the preview [in s]')
parser.add_argument('-e', '--extension', help='extension to consider for merging, converting and deconverting',
default='None')
parser.add_argument('-o', '--output', help='output file [for generation of synthetic benchmarks]')
parser.add_argument('-t', '--type', help='benchmark type',
choices=['fitting', 'clustering', 'synchrony'])
if len(argv) == 0:
parser.print_help()
sys.exit(0)
args = parser.parse_args(argv)
steps = args.method.split(',')
for step in steps:
if step not in all_steps:
print_error(['The method "%s" is not recognized' % step])
sys.exit(0)
# To save some typing later
nb_gpu = 0
(nb_cpu, hostfile, batch, preview, result, extension, output, benchmark, info, second) = \
(args.cpu, args.hostfile, args.batch, args.preview, args.result, args.extension, args.output, args.type, args.info, args.second)
filename = os.path.abspath(args.datafile)
real_file = filename
f_next, extens = os.path.splitext(filename)
if info:
if args.datafile.lower() in __supported_data_files__:
filename = 'tmp'
if len(__supported_data_files__[args.datafile.lower()].extension) > 0:
filename += __supported_data_files__[args.datafile.lower()].extension[0]
__supported_data_files__[args.datafile.lower()](filename, {}, is_empty=True)._display_requirements_()
else:
print_and_log([
'',
'To get info on any particular file format, do:',
'>> spyking-circus file_format -i',
''
], 'default')
print_and_log(list_all_file_format())
sys.exit(0)
if extens == '.params':
print_error(['You should launch the code on the data file!'])
sys.exit(0)
file_params = f_next + '.params'
if not os.path.exists(file_params) and not batch:
print(Fore.RED + 'The parameter file %s is not present!' % file_params)
create_params = query_yes_no(Fore.WHITE + "Do you want SpyKING CIRCUS to create a parameter file?")
if create_params:
print(Fore.WHITE + "Creating %s" % file_params)
print(Fore.WHITE + "Fill it properly before launching the code! (see documentation)")
print_info(['Keep in mind that filtering is performed on site, so please',
'be sure to keep a copy of your data elsewhere'])
shutil.copyfile(config_file, file_params)
sys.exit(0)
elif batch:
tasks_list = filename
if not batch:
file_params = f_next + '.params'
if not os.path.exists(file_params):
print_and_log(["%s does not exist" % file_params], 'error')
sys.exit(0)
import ConfigParser as configparser
parser = configparser.ConfigParser()
myfile = open(file_params, 'r')
lines = myfile.readlines()
myfile.close()
myfile = open(file_params, 'w')
for l in lines:
myfile.write(l.replace('\t', ''))
myfile.close()
parser.read(file_params)
for section in CircusParser.__all_sections__:
if parser.has_section(section):
for (key, value) in parser.items(section):
parser.set(section, key, value.split('#')[0].rstrip())
else:
parser.add_section(section)
try:
use_output_dir = parser.get('data', 'output_dir') != ''
except Exception:
use_output_dir = False
if use_output_dir:
path = os.path.abspath(os.path.expanduser(parser.get('data', 'output_dir')))
file_out = os.path.join(path, os.path.basename(f_next))
if not os.path.exists(file_out):
os.makedirs(file_out)
else:
file_out = f_next
logfile = file_out + '.log'
if os.path.exists(logfile):
os.remove(logfile)
logger = init_logging(logfile)
params = CircusParser(filename)
data_file = params.get_data_file(source=True, has_been_created=False)
overwrite = params.getboolean('data', 'overwrite')
file_format = params.get('data', 'file_format')
if overwrite:
support_parallel_write = data_file.parallel_write
is_writable = data_file.is_writable
else:
support_parallel_write = __supported_data_files__['raw_binary'].parallel_write
is_writable = __supported_data_files__['raw_binary'].is_writable
if preview:
print_and_log(['Preview mode, showing only seconds [%d-%d] of the recording' % (second, second+1)], 'info', logger)
tmp_path_loc = os.path.join(os.path.abspath(params.get('data', 'file_out')), 'tmp')
if not os.path.exists(tmp_path_loc):
os.makedirs(tmp_path_loc)
filename = os.path.join(tmp_path_loc, 'preview.dat')
f_next, extens = os.path.splitext(filename)
preview_params = f_next + '.params'
shutil.copyfile(file_params, preview_params)
steps = ['filtering', 'whitening']
chunk_size = int(params.rate)
data_file.open()
nb_chunks, _ = data_file.analyze(chunk_size)
if nb_chunks <= (second + 1):
print_and_log(['Recording is too short to display seconds [%d-%d]' % (second, second+1)])
sys.exit(0)
local_chunk = data_file.get_snippet(int(second*params.rate), int(1.2*chunk_size))
description = data_file.get_description()
data_file.close()
new_params = CircusParser(filename, create_folders=False)
new_params.write('data', 'chunk_size', '1')
new_params.write('data', 'file_format', 'raw_binary')
new_params.write('data', 'data_dtype', 'float32')
new_params.write('data', 'data_offset', '0')
new_params.write('data', 'dtype_offset', '0')
new_params.write('data', 'stream_mode', 'None')
new_params.write('data', 'overwrite', 'True')
new_params.write('triggers', 'ignore_times', 'False')
new_params.write('data', 'sampling_rate', str(params.rate))
new_params.write('whitening', 'safety_time', '0')
new_params.write('clustering', 'safety_time', '0')
new_params.write('whitening', 'chunk_size', '1')
new_params.write('data', 'preview_path', params.file_params)
new_params.write('data', 'output_dir', '')
description['data_dtype'] = 'float32'
description['dtype_offset'] = 0
description['data_offset'] = 0
description['gain'] = 1.
new_params = CircusParser(filename)
data_file_out = new_params.get_data_file(is_empty=True, params=description)
support_parallel_write = data_file_out.parallel_write
is_writable = data_file_out.is_writable
data_file_out.allocate(shape=local_chunk.shape, data_dtype=numpy.float32)
data_file_out.open('r+')
data_file_out.set_data(0, local_chunk)
data_file_out.close()
if tasks_list is not None:
with open(tasks_list, 'r') as f:
for line in f:
if len(line) > 0:
subprocess.check_call(['spyking-circus'] + line.replace('\n', '').split(" "))
else:
print_and_log(['Config file: %s' % (f_next + '.params')], 'debug', logger)
print_and_log(['Data file : %s' % filename], 'debug', logger)
print(get_colored_header())
print(Fore.GREEN + "File : " + Fore.CYAN + real_file)
if preview:
print(Fore.GREEN + "Steps : " + Fore.CYAN + "preview mode")
elif result:
print(Fore.GREEN + "Steps : " + Fore.CYAN + "result mode")
else:
print(Fore.GREEN + "Steps : " + Fore.CYAN + ", ".join(steps))
# print Fore.GREEN + "GPU detected : ", Fore.CYAN + str(HAVE_CUDA)
print(Fore.GREEN + "Number of CPU : " + Fore.CYAN + str(nb_cpu) + "/" + str(psutil.cpu_count()))
# if HAVE_CUDA:
# print Fore.GREEN + "Number of GPU : ", Fore.CYAN + str(nb_gpu)
print(Fore.GREEN + "Parallel HDF5 : " + Fore.CYAN + str(parallel_hdf5))
do_upgrade = ''
use_shared_memory = get_shared_memory_flag(params)
if not SHARED_MEMORY:
do_upgrade = Fore.WHITE + ' [please consider upgrading MPI]'
print(Fore.GREEN + "Shared memory : " + Fore.CYAN + str(use_shared_memory) + do_upgrade)
print(Fore.GREEN + "Hostfile : " + Fore.CYAN + hostfile)
print("")
print(Fore.GREEN + "##################################################################")
print("")
print(Fore.RESET)
# Launch the subtasks
subtasks = [('filtering', 'mpirun'),
('whitening', 'mpirun'),
('clustering', 'mpirun'),
('fitting', 'mpirun'),
('extracting', 'mpirun'),
('gathering', 'python'),
('converting', 'mpirun'),
('deconverting', 'mpirun'),
('benchmarking', 'mpirun'),
('merging', 'mpirun'),
('validating', 'mpirun'),
('thresholding', 'mpirun')]
# if HAVE_CUDA and nb_gpu > 0:
# use_gpu = 'True'
# else:
use_gpu = 'False'
time = data_stats(params) / 60.0
if preview:
params = new_params
if nb_cpu < psutil.cpu_count():
if use_gpu != 'True' and not result:
print_and_log(['Using only %d out of %d local CPUs available (-c to change)' % (nb_cpu, psutil.cpu_count())], 'info', logger)
if params.getboolean('detection', 'matched-filter') and not params.getboolean('clustering', 'smart_search'):
print_and_log(['Smart Search should be activated for matched filtering'], 'info', logger)
if time > 30 and not params.getboolean('clustering', 'smart_search'):
print_and_log(['Smart Search should be activated for long recordings'], 'info', logger)
n_edges = get_averaged_n_edges(params)
if n_edges > 100 and not params.getboolean('clustering', 'compress'):
print_and_log(['Template compression is highly recommended based on parameters'], 'info', logger)
if not result:
for subtask, command in subtasks:
if subtask in steps:
if command == 'python':
# Directly call the launcher
try:
circus.launch(subtask, filename, nb_cpu, nb_gpu, use_gpu)
except:
print_and_log(['Step "%s" failed!' % subtask], 'error', logger)
sys.exit(0)
elif command == 'mpirun':
# Use mpirun to make the call
mpi_args = gather_mpi_arguments(hostfile, params)
one_cpu = False
if subtask in ['filtering', 'benchmarking'] and not is_writable:
if not preview and overwrite:
print_and_log(['The file format %s is read only!' % file_format,
'You should set overwite to False, to create a copy of the data.',
'However, note that if you have streams, informations on times',
'will be discarded'], 'info', logger)
sys.exit(0)
if subtask in ['filtering'] and not support_parallel_write and (args.cpu > 1):
print_and_log(['No parallel writes for %s: only 1 node used for %s' %(file_format, subtask)], 'info', logger)
nb_tasks = str(1)
one_cpu = True
else:
if subtask != 'fitting':
nb_tasks = str(args.cpu)
else:
# if use_gpu == 'True':
# nb_tasks = str(args.gpu)
# else:
nb_tasks = str(args.cpu)
if subtask == 'benchmarking':
if (output is None) or (benchmark is None):
print_and_log(["To generate synthetic datasets, you must provide output and type"], 'error', logger)
sys.exit(0)
mpi_args += [
'-np', nb_tasks, 'spyking-circus-subtask',
subtask, filename, str(nb_cpu), str(nb_gpu),
use_gpu, output, benchmark
]
elif subtask in ['merging', 'converting']:
mpi_args += [
'-np', nb_tasks, 'spyking-circus-subtask',
subtask, filename, str(nb_cpu), str(nb_gpu),
use_gpu, extension
]
elif subtask in ['deconverting']:
nb_tasks = str(1)
nb_cpu = 1
mpi_args += [
'-np', nb_tasks, 'spyking-circus-subtask', subtask,
filename, str(nb_cpu), str(nb_gpu), use_gpu,
extension
]
else:
mpi_args += [
'-np', nb_tasks, 'spyking-circus-subtask',
subtask, filename, str(nb_cpu), str(nb_gpu),
use_gpu, str(one_cpu)
]
print_and_log(['Launching task %s' % subtask], 'debug', logger)
print_and_log(['Command: %s' % str(mpi_args)], 'debug', logger)
try:
subprocess.check_call(mpi_args)
except subprocess.CalledProcessError as e:
print_and_log(['Step "%s" failed for reason %s!' % (subtask, e)], 'error', logger)
sys.exit(0)
if preview or result:
from circus.shared import gui
import pylab
try:
from PyQt5.QtWidgets import QApplication
except ImportError:
from matplotlib.backends import qt_compat
use_pyside = qt_compat.QT_API == qt_compat.QT_API_PYSIDE
if use_pyside:
from PySide.QtGui import QApplication
else:
from PyQt4.QtGui import QApplication
app = QApplication([])
try:
pylab.style.use('ggplot')
except Exception:
pass
if preview:
print_and_log(['Launching the preview GUI...'], 'debug', logger)
mygui = gui.PreviewGUI(new_params)
shutil.rmtree(tmp_path_loc)
elif result:
data_file = params.get_data_file()
print_and_log(['Launching the result GUI...'], 'debug', logger)
mygui = gui.PreviewGUI(params, show_fit=True)
sys.exit(app.exec_())
