import pylab as plt
import helper as hf
import plot_helper as phf
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-bs', '--bitwise_spikefile', type=str)
parser.add_argument('-os', '--original_spikefile', type=str)
parser.add_argument('-bmem', '--bitwise_mem_pop_file', type=str)
parser.add_argument('-fn', '--filename', type=str)
args = parser.parse_args()
original_spikefile = args.original_spikefile
original_times, original_senders = hf.read_spikefile(original_spikefile)
bitwise_spikefile = args.bitwise_spikefile
bitwise_times, bitwise_senders = hf.read_spikefile(bitwise_spikefile)
bitwise_mem_pop = np.loadtxt(args.bitwise_mem_pop_file)
phf.latexify(columns=2)
excolor = 'C0'
incolor = 'C1'
fig = plt.figure()
gs0 = gridspec.GridSpec(2, 2)
gs0.update(left=0.1, right=0.97, top=0.97, bottom=0.1, hspace=0.25)
gs1 = gridspec.GridSpecFromSubplotSpec(7, 1, subplot_spec=gs0[0, :])
sns.xkcd_rgb["denim blue"], sns.xkcd_rgb["medium green"],
sns.xkcd_rgb["pale red"]
]
plt.figure()
current_palette = sns.color_palette(flatui)
sns.palplot(current_palette)
sns.set_palette(current_palette)
plt.savefig('palette.png')
plt.close()
args = parser.parse_args()
excolor = 'C0'
incolor = 'C1'
statistical_spikefile = args.statistical_spikefile
statistical_times, statistical_senders = hf.read_spikefile(
statistical_spikefile)
bitwise_spikefile = args.bitwise_spikefile
bitwise_times, bitwise_senders = hf.read_spikefile(bitwise_spikefile)
statistical_weights = hf.read_weightfile(args.statistical_weightfile)
bitwise_weights = hf.read_weightfile(args.bitwise_weightfile)
fig = plt.figure(figsize=(9, 8))
gs0 = gridspec.GridSpec(2, 2)
gs0.update(left=0.1, right=0.97, top=0.97, bottom=0.06, hspace=0.15)
gs1 = gridspec.GridSpecFromSubplotSpec(7, 1, subplot_spec=gs0[0, :])
ax01 = plt.subplot(gs1[:5, 0])
ax02 = plt.subplot(gs1[5:, 0])
import argparse
import mpl_toolkits.axes_grid.inset_locator
parser = argparse.ArgumentParser()
parser.add_argument('-cs', '--comp_spikefile', type=str)
parser.add_argument('-bs', '--bitwise_spikefile', type=str)
parser.add_argument('-cw', '--comp_weightfile', type=str)
parser.add_argument('-bw', '--bitwise_weightfile', type=str)
parser.add_argument('-fn', '--filename', type=str)
args = parser.parse_args()
comp_spikefile = args.comp_spikefile
comp_times, comp_senders = hf.read_spikefile(comp_spikefile)
bitwise_spikefile = args.bitwise_spikefile
bitwise_times, bitwise_senders = hf.read_spikefile(bitwise_spikefile)
comp_weights = hf.read_weightfile(args.comp_weightfile)
bitwise_weights = hf.read_weightfile(args.bitwise_weightfile)
bin_ms = 5.
phf.latexify(columns=2)
excolor = 'C0'
incolor = 'C1'
fig = plt.figure()
gs0 = gridspec.GridSpec(2, 2)
gs0.update(left=0.1, right=0.97, top=0.97, bottom=0.1, hspace=0.25)
gamma_peak = []
N_grps = []
c_high = 'C5'
c_low = 'C6'
for rep, (spk_fl, grp_stat_fl, con_fl) in enumerate(
zip(args.spikelist, args.groupstatlist, args.connectivitylist)):
connectivity = pd.read_json(con_fl)
connecitivty_e = connectivity.loc[connectivity['pre'] < 800]
connecitivty_e_e = connectivity.loc[connectivity['post'] < 800]
connecitivty_e_e['bin_w'] = pd.cut(connecitivty_e_e['weight'],
np.arange(0, 10.5, 0.5))
times, senders = hf.read_spikefile(spk_fl)
exc_times, exc_sender, inh_times, inh_sender = hf.split_in_ex(
times, senders)
exc_rate, exc_bins = hf.bin_pop_rate(exc_times, exc_sender, bin_ms)
exc_Pxx, exc_freqs = mlab.psd(exc_rate - np.mean(exc_rate),
NFFT=NFFT,
Fs=1000. / (exc_bins[1] - exc_bins[0]),
noverlap=noverlap)
exc_Pxx_tab[:, rep] = exc_Pxx
idx = np.argmax(exc_Pxx[exc_freqs > 20])
cut_freqs = exc_freqs[exc_freqs > 20]
max_freq = cut_freqs[idx]
if max_freq < 50:
if table_low is None:
table_low = pd.pivot_table(connecitivty_e_e,