def saliency():
"""
Plots avg LPs simulation
"""
fig = plt.figure(figsize = (1.5,4))
dm = DataMatrix(np.load(".cache/dm_sim_select_driftcorr.npy"))
# PLOT SIMULATED SACCADES:
for stimType in dm.unique("stim_type"):
if stimType == "object":
col = green[1]
elif stimType == "non-object":
col = red[1]
lSacc = []
for sacc in [1,2]:
stimDm = dm.select("stim_type == '%s'" % stimType)
m = stimDm["xNorm%s" % sacc].mean()
lSacc.append(m)
plt.plot([1,2], lSacc, color = col, marker = 'o', markerfacecolor = 'white',
markeredgecolor = col, markeredgewidth = 1)
# HACK:
# PLOT REAL LPs:
for stimType in dm.unique("stim_type"):
if stimType == "object":
col = blue[1]
elif stimType == "non-object":
col = orange[1]
lSacc = []
for sacc in [1,2]:
stimDm = dm.select("stim_type == '%s'" % stimType)
m = stimDm["xNorm%s" % sacc].mean()
lSacc.append(m)
plt.plot([1,2], lSacc, color = col, marker = 'o', markerfacecolor = 'white',
markeredgecolor = col, markeredgewidth = 1, label = stimType)
plt.axhline(0, linestyle = "--", color = gray[3])
plt.ylim(-.3, .07)
plt.xlim(0.8, 2.2)
plt.legend(frameon = False)
plt.savefig("./plots/simulation.png")
plt.savefig("./plots/simulation.svg")
from exparser.DataMatrix import DataMatrix
import numpy as np
f = ".cache/004C_lat_driftcorr_False.npy"
dm = DataMatrix(np.load(f))
for pp in dm.unique("file"):
ppDm = dm.select("file == '%s'" % pp, verbose = False)
_ppDm = ppDm.select("xNorm1 == -1000", verbose = False)
print pp, float(len(_ppDm))/float(len(ppDm))
mDev = gapDm['xNorm%s' % sacc].mean()
d[stimType, sacc]= mDev
return d
if __name__ == "__main__":
dm = dmSim(cacheId = "dm_sim_raw")
dm = addCommonFactors.addCommonFactors(dm, \
cacheId = "dm_sim_common_factors")
dm = addCoord.addCoord(dm, cacheId = "dm_sim_coord")
#dm = addLat.addLat(dm, cacheId = "dm_sim_lat_driftcorr")
dm = selectDm.selectDm(dm, cacheId = "dm_sim_select_driftcorr")
f = ".cache/dm_sim_select_driftcorr.npy"
dm = DataMatrix(np.load(f))
fig = plt.figure()
nPlot = 0
for stimType in dm.unique("stim_type"):
stimDm = dm.select("stim_type == '%s'" % stimType)
for sacc in (1,2):
nPlot +=1
plt.subplot(2,2,nPlot)
dv = "xNorm%s" % sacc
plt.title("%s sacc %s" % (stimType, sacc))
plt.hist(stimDm[dv], bins = 5)
plt.savefig("Distribution_simulated_saccades.png")
