import matplotlib
matplotlib.use('Agg')
from grid_cell_model.submitting import flagparse
import noisefigs
from noisefigs.env import MplEnvironment
from noisefigs.plotters.base import SeparateMultipageSaver
import config
parser = flagparse.FlagParser()
parser.add_flag('--param_exploration')
parser.add_argument('--data_root', type=str, help='Data root directory',
default='simulation_data/network_test/150pA')
args = parser.parse_args()
env = MplEnvironment(config=config.get_config())
if args.param_exploration or args.all:
for file_name in os.listdir(args.data_root):
if 'rasters' in file_name:
print(file_name)
env.register_class(
noisefigs.plotters.PopulationActivityPlotter,
config={
'data_root' : args.data_root,
'data_file_name': file_name,
'PopulationActivityPlotter': {
'fig_size': (6, 5),
't_limits': (5e3, 6e3),
0.98,
'cbar_kw':
dict(ticks=ti.MultipleLocator(0.2), ),
},
})
env.plot()
if args.rasters or args.all:
shape = (31, 31)
output_dir_0 = join('simulation_data', 'ee_connections_ei_flat',
'standard_sweep_g_EE_3060_pEE_sigma_0_0833',
'gamma_bump', '0pA')
sp_0 = JobTrialSpace2D(shape, output_dir_0)
new_config = deepcopy(config.get_config())
env = MplEnvironment(config=new_config)
env.register_class(noisefigs.plotters.PopulationActivityPlotter,
config={
'data_root': output_dir_0,
'data_file_name': sp_0[1][22].file_name_base,
'output_dir': 'panels_standard_gEE_3060',
'PopulationActivityPlotter': {
'fname_prefix': '0pA_r1_c22_',
'raster_rect': (.075, 0.35, 0.93, 0.97),
'fig_saver':
SeparateMultipageSaver(None, 'pdf'),
'fig_size': (8, 6),
't_limits': (0, 5e3),
'snapshot_tstep': 4,
'e_snapshots_rect': (.075, .15, 0.93, 0.25),
'i_snapshots_rect': (.075, .02, 0.93, 0.12),
parser.add_argument('--shapeCols', type=int, required=True)
parser.add_argument("--output_dir", type=str, required=True)
parser.add_argument("--figure_dir", type=str, required=True)
parser.add_argument("--job_num", type=int) # unused
parser.add_argument("--type", type=str, choices=common.allowed_types,
required=True, nargs="+")
o = parser.parse_args()
###############################################################################
startT = time.time()
shape = (o.shapeRows, o.shapeCols)
sp = JobTrialSpace2D(shape, o.output_dir)
if common.pop_type in o.type:
env = MplEnvironment(config=config.get_config())
env.register_class(
noisefigs.plotters.PopulationActivityPlotter,
config={
'data_root' : o.output_dir,
'data_file_name': sp[o.row][o.col].file_name_base,
'output_dir' : o.figure_dir,
'PopulationActivityPlotter': {
'fname_prefix': 'r%03d_c%03d_' % (o.row, o.col),
'raster_rect': (.075, 0.35, 0.95, 0.97),
'fig_saver': SeparateMultipageSaver(None, 'pdf'),
'fig_size': (10, 6),
't_limits': (0, 5e3),
'snapshot_tstep': 4,
from grid_cell_model.submitting import flagparse
import noisefigs
from noisefigs.env import MplEnvironment
from noisefigs.plotters.base import SeparateMultipageSaver
import config
parser = flagparse.FlagParser()
parser.add_flag('--param_exploration')
parser.add_argument('--data_root',
type=str,
help='Data root directory',
default='simulation_data/network_test/150pA')
args = parser.parse_args()
env = MplEnvironment(config=config.get_config())
if args.param_exploration or args.all:
for file_name in os.listdir(args.data_root):
if 'job' in file_name:
print(file_name)
env.register_class(noisefigs.plotters.PopulationActivityPlotter,
config={
'data_root': args.data_root,
'data_file_name': file_name,
'PopulationActivityPlotter': {
'fname_prefix':
'test_%s_' % file_name,
'fig_size': (4, 6),
't_limits': (0, 2.5e3),
'fig_saver':
parser.add_argument("--job_num", type=int) # unused
parser.add_argument("--type",
type=str,
choices=common.allowed_types,
required=True,
nargs="+")
o = parser.parse_args()
###############################################################################
startT = time.time()
shape = (o.shapeRows, o.shapeCols)
sp = JobTrialSpace2D(shape, o.output_dir)
if common.pop_type in o.type:
env = MplEnvironment(config=config.get_config())
env.register_class(noisefigs.plotters.PopulationActivityPlotter,
config={
'data_root': o.output_dir,
'data_file_name': sp[o.row][o.col].file_name_base,
'output_dir': o.figure_dir,
'PopulationActivityPlotter': {
'fname_prefix': 'r%03d_c%03d_' % (o.row, o.col),
'raster_rect': (.075, 0.35, 0.95, 0.97),
'fig_saver':
SeparateMultipageSaver(None, 'pdf'),
'fig_size': (10, 6),
't_limits': (0, 5e3),
'snapshot_tstep': 4,
'e_snapshots_rect': (.075, .15, 0.95, 0.25),
'i_snapshots_rect': (.075, .02, 0.95, 0.12),
#!/usr/bin/env python
from __future__ import absolute_import, print_function
import matplotlib; matplotlib.use('Agg')
from grid_cell_model.submitting import flagparse
import noisefigs
from noisefigs.env import MplEnvironment
import config
parser = flagparse.FlagParser()
parser.add_flag('--rasters_and_bumps')
parser.add_flag('--rasters_and_bumps_test')
args = parser.parse_args()
env = MplEnvironment(config=config.get_config())
if args.rasters_and_bumps or args.all:
env.register_class(noisefigs.plotters.PopulationActivityPlotter)
if args.rasters_and_bumps_test or args.all:
env.register_class(
noisefigs.plotters.PopulationActivityPlotter,
config={
'data_root' : 'simulation_data_local/network_test/150pA',
'PopulationActivityPlotter': {
'fname_prefix': 'test_',
'fig_size': (4, 6),
't_limits': (0, 2.5e3),
},
from grid_cell_model.submitting import flagparse
import noisefigs
from noisefigs.env import MplEnvironment
from noisefigs.plotters.base import SeparateMultipageSaver
import config
parser = flagparse.FlagParser()
parser.add_flag('--param_exploration')
parser.add_argument('--data_root',
type=str,
help='Data root directory',
default='simulation_data/network_test/150pA')
args = parser.parse_args()
env = MplEnvironment(config=config.get_config())
if args.param_exploration or args.all:
for file_name in os.listdir(args.data_root):
if 'rasters' in file_name:
print(file_name)
env.register_class(noisefigs.plotters.PopulationActivityPlotter,
config={
'data_root': args.data_root,
'data_file_name': file_name,
'PopulationActivityPlotter': {
'fig_size': (6, 5),
't_limits': (5e3, 6e3),
'raster_rect': (.125, 0.35, 0.99, 0.97),
'snapshot_tstep':
1,
