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intersphinx link to neurodsp
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ryanhammonds committed Aug 17, 2020
1 parent 7a520c4 commit 887263f
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Showing 3 changed files with 12 additions and 10 deletions.
4 changes: 4 additions & 0 deletions doc/conf.py
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Expand Up @@ -138,3 +138,7 @@
'reference_url': {'fooof': None},
'remove_config_comments': True,
}

intersphinx_mapping = {
'neurodsp': ('https://neurodsp-tools.github.io/neurodsp/', None),
}
4 changes: 2 additions & 2 deletions motivations/concepts/plot_DoYouEvenOscillate.py
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Expand Up @@ -46,14 +46,14 @@

# Use NeuroDSP for time series simulations & analyses
from neurodsp import sim
from neurodsp.utils import create_times, set_random_seed
from neurodsp.utils import create_times
from neurodsp.spectral import compute_spectrum_welch
from neurodsp.plts import plot_time_series, plot_power_spectra

###################################################################################################

# Set random seed, for consistency generating simulated data
set_random_seed(21)
sim.set_random_seed(21)

# Simulation Settings
n_seconds = 2
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14 changes: 6 additions & 8 deletions motivations/concepts/plot_IfYouFilterTheyWillCome.py
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Expand Up @@ -33,10 +33,10 @@
from fooof.bands import Bands

# Imports from NeuroDSP to simulate & plot time series
from neurodsp.sim import sim_powerlaw
from neurodsp.sim import sim_powerlaw, set_random_seed
from neurodsp.filt import filter_signal
from neurodsp.plts import plot_time_series
from neurodsp.utils import create_times, set_random_seed
from neurodsp.utils import create_times

###################################################################################################

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band_sig = filter_signal(sig, s_rate, 'bandpass', f_range)

# Plot the time series of the current band, and adjust plot aesthetics
plot_time_series(times, band_sig, title=label + ' ' + str(f_range), ax=ax)
ax.set_xlim(0, n_seconds); ax.set_ylim(-1, 1); ax.set_xlabel('');
plot_time_series(times, band_sig, title=label + ' ' + str(f_range), ax=ax,
xlim=(0, n_seconds), ylim=(-1, 1), xlabel='')

###################################################################################################
#
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band_sig = filter_signal(sig_delta_ap, s_rate, 'bandpass', bands.beta)

# Plot the filtered time series
plot_time_series(times, band_sig)
plt.xlim(0, n_seconds); plt.ylim(-1, 1);
plot_time_series(times, band_sig, xlim=(0, n_seconds), ylim=(-1, 1))

###################################################################################################
#
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band_sig = filter_signal(sig_delta_ap, s_rate, 'bandpass', bands.high_gamma)

# Plot the filtered time series
plot_time_series(times, band_sig)
plt.xlim(0, n_seconds); plt.ylim(-1, 1);
plot_time_series(times, band_sig, xlim=(0, n_seconds), ylim=(-1, 1))

###################################################################################################
#
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