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src/n/i/nitime-0.4/doc/examples/grasshopper.py   nitime(Download)
"""
 
stim_time_series = ts.TimeSeries(t0=0,
                                 data=stim,
                                 sampling_interval=50,
means = []
for stim, spike in zip([stim, stim2], [spike_times, spike_times2]):
    stim_time_series = ts.TimeSeries(t0=0, data=stim, sampling_interval=50,
                                     time_unit='us')
 
 
fig02 = viz.plot_tseries(
    ts.TimeSeries(data=np.vstack([et[0].data, et[1].data]),
                  sampling_rate=et[0].sampling_rate, time_unit='ms'))
 

src/n/i/nitime-HEAD/doc/examples/grasshopper.py   nitime(Download)
"""
 
stim_time_series = ts.TimeSeries(t0=0,
                                 data=stim,
                                 sampling_interval=50,
means = []
for stim, spike in zip([stim, stim2], [spike_times, spike_times2]):
    stim_time_series = ts.TimeSeries(t0=0, data=stim, sampling_interval=50,
                                     time_unit='us')
 
 
fig02 = viz.plot_tseries(
    ts.TimeSeries(data=np.vstack([et[0].data, et[1].data]),
                  sampling_rate=et[0].sampling_rate, time_unit='ms'))
 

src/n/i/nitime-0.4/doc/examples/ar_est_1var.py   nitime(Download)
from nitime import utils
from nitime import algorithms as alg
from nitime.timeseries import TimeSeries
from nitime.viz import plot_tseries
 
X, noise, _ = utils.ar_generator(npts, sigma, coefs, drop_transients)
 
ts_x = TimeSeries(X, sampling_rate=Fs, time_unit='s')
ts_noise = TimeSeries(noise, sampling_rate=1000, time_unit='s')
 

src/n/i/nitime-HEAD/doc/examples/ar_est_1var.py   nitime(Download)
from nitime import utils
from nitime import algorithms as alg
from nitime.timeseries import TimeSeries
from nitime.viz import plot_tseries
 
X, noise, _ = utils.ar_generator(npts, sigma, coefs, drop_transients)
 
ts_x = TimeSeries(X, sampling_rate=Fs, time_unit='s')
ts_noise = TimeSeries(noise, sampling_rate=1000, time_unit='s')
 

src/n/i/nitime-0.4/doc/examples/resting_state_fmri.py   nitime(Download)
import nitime
#Import the time-series objects:
from nitime.timeseries import TimeSeries
#Import the analysis objects:
from nitime.analysis import CorrelationAnalyzer, CoherenceAnalyzer
"""
 
T = TimeSeries(data, sampling_interval=TR)
T.metadata['roi'] = roi_names
 
 
#Initialize the correlation analyzer
C = CorrelationAnalyzer(T)
 
#Display the correlation matrix
"""
 
C = CoherenceAnalyzer(T)
 
"""

src/n/i/nitime-0.4/doc/examples/multi_taper_coh.py   nitime(Download)
 
import nitime
from nitime.timeseries import TimeSeries
from nitime import utils
import nitime.algorithms as alg
"""
 
T = TimeSeries(pdata, sampling_interval=TR)
T.metadata['roi'] = roi_names
 
"""
 
C2 = MTCoherenceAnalyzer(T)
 
"""
"""
 
C3 = CoherenceAnalyzer(T)
 
freq_idx = np.where((C3.frequencies > f_lb) * (C3.frequencies < f_ub))[0]

src/n/i/nitime-0.4/doc/examples/filtering_fmri.py   nitime(Download)
 
# Import the time-series objects:
from nitime.timeseries import TimeSeries
 
# Import the analysis objects:
 
# Initialize TimeSeries object:
T = TimeSeries(data, sampling_interval=TR)
T.metadata['roi'] = roi_names
 
"""
 
S_original = SpectralAnalyzer(T)
 
# Initialize a figure to put the results into:
"""
 
F = FilterAnalyzer(T, ub=0.15, lb=0.02)
 
# Initialize a figure to display the results:

src/n/i/nitime-HEAD/doc/examples/resting_state_fmri.py   nitime(Download)
import nitime
#Import the time-series objects:
from nitime.timeseries import TimeSeries
#Import the analysis objects:
from nitime.analysis import CorrelationAnalyzer, CoherenceAnalyzer
"""
 
T = TimeSeries(data, sampling_interval=TR)
T.metadata['roi'] = roi_names
 
 
#Initialize the correlation analyzer
C = CorrelationAnalyzer(T)
 
#Display the correlation matrix
"""
 
C = CoherenceAnalyzer(T)
 
"""

src/n/i/nitime-HEAD/doc/examples/multi_taper_coh.py   nitime(Download)
 
import nitime
from nitime.timeseries import TimeSeries
from nitime import utils
import nitime.algorithms as alg
"""
 
T = TimeSeries(pdata, sampling_interval=TR)
T.metadata['roi'] = roi_names
 
"""
 
C2 = MTCoherenceAnalyzer(T)
 
"""
"""
 
C3 = CoherenceAnalyzer(T)
 
freq_idx = np.where((C3.frequencies > f_lb) * (C3.frequencies < f_ub))[0]

src/n/i/nitime-HEAD/doc/examples/filtering_fmri.py   nitime(Download)
 
# Import the time-series objects:
from nitime.timeseries import TimeSeries
 
# Import the analysis objects:
 
# Initialize TimeSeries object:
T = TimeSeries(data, sampling_interval=TR)
T.metadata['roi'] = roi_names
 
"""
 
S_original = SpectralAnalyzer(T)
 
# Initialize a figure to put the results into:
"""
 
F = FilterAnalyzer(T, ub=0.15, lb=0.02)
 
# Initialize a figure to display the results:

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