def get_correlations(config):
logdir = os.path.join(config.get('paths', 'log_dir_of_current_run'),
'log_vars', '0.npz')
logvars = np.load(logdir, allow_pickle=True)
spiketrains = logvars['spiketrains_n_b_l_t']
activations = logvars['activations_n_b_l']
spikerates = spiketrains_to_rates(spiketrains,
config.getint('simulation', 'duration'),
config.get('conversion', 'spike_code'))
max_rate = 1. / config.getfloat('simulation', 'dt')
co = get_pearson_coefficients(spikerates, activations, max_rate)
return np.mean(co, axis=1)
def output_graphs(plot_vars, config, path=None, idx=0, data_format=None):
"""Wrapper function to display / save a number of plots.
Parameters
----------
plot_vars: dict
Example items:
- spiketrains_n_b_l_t: list[tuple[np.array, str]]
Each entry in ``spiketrains_batch`` contains a tuple
``(spiketimes, label)`` for each layer of the network (for the
first batch only, and excluding ``Flatten`` layers).
``spiketimes`` is an array where the last index contains the spike
times of the specific neuron, and the first indices run over the
number of neurons in the layer:
(batch_size, n_chnls, n_rows, n_cols, duration)
``label`` is a string specifying both the layer type and the index,
e.g. ``'03Dense'``.
- activations_n_b_l: list[tuple[np.array, str]]
Activations of the ANN.
- spikecounts_n_b_l: list[tuple[np.array, str]]
Spikecounts of the SNN. Used to compute spikerates.
config: configparser.ConfigParser
Settings.
path: Optional[str]
If not ``None``, specifies where to save the resulting image. Else,
display plots without saving.
idx: int
The index of the sample to display. Defaults to 0.
data_format: Optional[str]
One of 'channels_first' or 'channels_last'.
"""
from snntoolbox.simulation.utils import spiketrains_to_rates
from snntoolbox.simulation.utils import get_sample_activity_from_batch
if plot_vars == {}:
return
if path is not None:
print("Saving plots of one sample to {}...\n".format(path))
plot_keys = eval(config.get('output', 'plot_vars'))
duration = config.getint('simulation', 'duration')
if 'activations_n_b_l' in plot_vars:
plot_vars['activations_n_l'] = get_sample_activity_from_batch(
plot_vars['activations_n_b_l'], idx)
if 'spiketrains_n_b_l_t' in plot_vars:
plot_vars['spiketrains_n_l_t'] = get_sample_activity_from_batch(
plot_vars['spiketrains_n_b_l_t'], idx)
if any({'spikerates', 'correlation', 'hist_spikerates_activations'}
& plot_keys):
if 'spikerates_n_b_l' not in plot_vars:
plot_vars['spikerates_n_b_l'] = spiketrains_to_rates(
plot_vars['spiketrains_n_b_l_t'], duration,
config.get('conversion', 'spike_code'))
plot_vars['spikerates_n_l'] = get_sample_activity_from_batch(
plot_vars['spikerates_n_b_l'], idx)
plot_layer_summaries(plot_vars, config, path, data_format)
print("Plotting batch run statistics...")
if 'spikecounts' in plot_keys:
plot_spikecount_vs_time(plot_vars['spiketrains_n_b_l_t'], duration,
config.getfloat('simulation', 'dt'), path)
if 'correlation' in plot_keys:
plot_pearson_coefficients(plot_vars['spikerates_n_b_l'],
plot_vars['activations_n_b_l'], config, path)
if 'hist_spikerates_activations' in plot_keys:
s = []
a = []
for i in range(len(plot_vars['spikerates_n_b_l'])):
s += list(plot_vars['spikerates_n_b_l'][i][0].flatten())
a += list(plot_vars['activations_n_b_l'][i][0].flatten())
plot_hist({'Spikerates': s, 'Activations': a}, path=path)
print("Done.\n")