c=dict(crop=(0.35, 0.45), center=0.4),
d=dict(crop=(0.45, 0.55), center=0.5),
e=dict(crop=(0.55, 0.65), center=0.6),
f=dict(crop=(0.65, 0.75), center=0.7),
)
COLORS_272 = np.load(os.path.join(os.path.dirname(__file__),
'..', 'tools', 'colors_272.npy'))
RE_COMPUTE_ENTROPY = False
NUM_BINS = 10
TMP_DIR = os.path.join('.', 'tmp_mutual')
if not os.path.exists(TMP_DIR):
os.mkdir(TMP_DIR)
DRAWER = Drawer()
SHOW = False
# %% Tools
def hist(x, y, num_bins=NUM_BINS):
_range = (min((min(x), min(y))), max((max(x), max(y))))
x_hist, edges = np.histogram(x, bins=num_bins, range=_range)
y_hist, _ = np.histogram(y, bins=edges)
xy_hist, _, _ = np.histogram2d(x, y, bins=(edges, edges), range=_range)
return (x_hist / num_bins,
y_hist / num_bins,
np.ravel(xy_hist) / num_bins)
# numpy
import numpy as np
# sklearn
from sklearn import metrics
# Plotting --------------------------------------
import matplotlib.pyplot as plt
# Local Settings --------------------------------------
# Tools
sys.path.append(os.path.join('..', 'tools')) # noqa
from figure_tools import Drawer
# Drawer
DRAWER = Drawer()
# Init results_folder
RESULTS_FOLDER = os.path.join('.', 'results_baseline')
RESULTS_NAME = os.path.join('.', 'results_baseline.json')
def fuck_report(report):
# I don't know why report has to be 2-layers dictionary,
# this method is to make it human useable.
fucked_report = dict()
for key1 in report:
if isinstance(report[key1], dict):
for key2 in report[key1]:
fucked_report[f'{key1}-{key2}'] = report[key1][key2]
else:
c=dict(crop=(0.35, 0.45), center=0.4),
d=dict(crop=(0.45, 0.55), center=0.5),
e=dict(crop=(0.55, 0.65), center=0.6),
f=dict(crop=(0.65, 0.75), center=0.7),
)
COLORS_272 = np.load(os.path.join(os.path.dirname(__file__),
'..', 'tools', 'colors_272.npy'))
RE_COMPUTE_ENTROPY = False
NUM_BINS = 10
TMP_DIR = os.path.join('.', 'tmp_mutual')
if not os.path.exists(TMP_DIR):
os.mkdir(TMP_DIR)
DRAWER = Drawer()
SHOW = False
# %% Tools
def hist(x, y, num_bins=NUM_BINS):
_range = (min((min(x), min(y))), max((max(x), max(y))))
x_hist, edges = np.histogram(x, bins=num_bins, range=_range)
y_hist, _ = np.histogram(y, bins=edges)
xy_hist, _, _ = np.histogram2d(x, y, bins=(edges, edges), range=_range)
return (x_hist / num_bins,
y_hist / num_bins,
np.ravel(xy_hist) / num_bins)
# Plotting
import tqdm
import matplotlib.pyplot as plt
# Local tools
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'tools')) # noqa
from MEG_worker import MEG_Worker
from figure_tools import Drawer
# Settings
BAND_NAME = 'U07'
COLORS_272 = np.load(
os.path.join(os.path.dirname(__file__), '..', 'tools', 'colors_272.npy'))
DRAWER = Drawer()
SHOW = False
# %%
idx = 3
# Setting -------------------------------------------
running_name = f'MEG_S{idx:02d}'
plt.style.use('ggplot')
# Worker pipeline -----------------------------------
worker = MEG_Worker(running_name=running_name)
worker.pipeline(band_name=BAND_NAME)
# %%
# numpy
import numpy as np
# sklearn
from sklearn import metrics
# Plotting --------------------------------------
import matplotlib.pyplot as plt
# Local Settings --------------------------------------
# Tools
sys.path.append(os.path.join('..', 'tools')) # noqa
from figure_tools import Drawer
# Drawer
DRAWER = Drawer()
# Init results_folder
RESULTS_FOLDER = os.path.join('.', 'results')
TMP_FOLDER = os.path.join('.', 'tmp')
RE_COMPUTE = False
def fuck_report(report):
# I don't know why report has to be 2-layers dictionary,
# this method is to make it human useable.
fucked_report = dict()
for key1 in report:
if isinstance(report[key1], dict):
def __init__(self, title_prefix='', show=False):
self.epochs = None
self.drawer = Drawer()
self.title_prefix = title_prefix
self.show = show
class Visualizer():
def __init__(self, title_prefix='', show=False):
self.epochs = None
self.drawer = Drawer()
self.title_prefix = title_prefix
self.show = show
def _title(self, title):
# Built-in title method
prefix = self.title_prefix
return f'{prefix}-{title}'
def load_epochs(self, epochs):
# Load epochs for ploting
self.epochs = epochs
print(f'New epochs are loaded: {epochs}')
def plot_lags(self, paired_lags_timelines, title=None):
# Methods for visualizing the lags and timelines of button effect
# Make sure we have a title
if title is None:
title = 'Lags vs. Timeline'
# Prepare times, _lags, _timelines,
# _lags: the lags of behavior button response to target picture,
# _timelines: the estimated time line of the button effect.
times = self.epochs.times
_lags = paired_lags_timelines['sorted_lags']
_timelines = paired_lags_timelines['sorted_timelines']
# Filter too long lags
_timelines = _timelines[_lags < 0.8]
_lags = _lags[_lags < 0.8]
# Compute number of samples
num_samples = _lags.shape[0]
# Compute corr between _lags and peak _timelines
def _corr(_lags, _timelines, num_samples):
def _smooth_max(a, b=np.ones(10)):
new_a = np.convolve(a, b, mode='same')
return np.where(new_a == max(new_a))[0][0]
_idx_peaks = np.array([_smooth_max(e) for e in _timelines])
return np.corrcoef(_lags, _idx_peaks)[0][1]
_corrcoef = _corr(_lags, _timelines, num_samples)
title = f'{title} - {_corrcoef}'
# Plot in two layers,
# Bottom is the _timelines matrix,
# Top is the _lags curve.
fig, ax = plt.subplots(1, 1)
ax.plot(_lags, c='red', alpha=0.7, linewidth=3)
ax.set_ylim([min(times), max(times)])
im = ax.imshow(_timelines.transpose(),
extent=(0, num_samples - 1, min(times), max(times)),
aspect=200,
origin='lower')
ax.set_title(title)
fig.colorbar(im, ax=ax)
# Save fig into drawer
self.drawer.fig = fig
def plot_joint(self, event_id, title=None, times='peaks'):
# Plot joint
# Make sure we have a title
if title is None:
title = event_id
# Compute evoked
evoked = self.epochs[event_id].average()
# Plot evoked in joint plot
self.drawer.fig = evoked.plot_joint(show=self.show,
times=times,
title=self._title(title))
def save_figs(self, path):
# Save plotted figures into [path]
self.drawer.save(path)
self.drawer.clear_figures()
# numpy
import numpy as np
# sklearn
from sklearn import metrics
# Plotting --------------------------------------
import matplotlib.pyplot as plt
# Local Settings --------------------------------------
# Tools
sys.path.append(os.path.join('..', 'tools')) # noqa
from figure_tools import Drawer
# Drawer
DRAWER = Drawer()
# Init results_folder
RESULTS_FOLDER = os.path.join('.', 'results')
def fuck_report(report):
# I don't know why report has to be 2-layers dictionary,
# this method is to make it human useable.
fucked_report = dict()
for key1 in report:
if isinstance(report[key1], dict):
for key2 in report[key1]:
fucked_report[f'{key1}-{key2}'] = report[key1][key2]
else:
fucked_report[key1] = report[key1]