def _getstimtype(self):
sortedstim = asc.stimulisorter(self.exp)
for key, val in sortedstim.items():
if self.stimnr in val:
stimtype = key
self.stimtype = stimtype
from stripesurround import stripesurround
from checkerflickerplusanalyzer import checkerflickerplusanalyzer
from OMSpatchesanalyzer import OMSpatchesanalyzer
from OMBanalyzer import OMBanalyzer
from ombtexturestas import ombtexturesta
from saccadegratingsanalyzer import saccadegratingsanalyzer
# Attempt to read from stdin
try:
exp_name = str(sys.argv[1])
except IndexError:
exp_name = input('Enter the experiment name to be analyzed: ')
start_time = datetime.datetime.now().strftime('%A %X')
print(f'Analysis started on {start_time}')
sorted_stimuli = asc.stimulisorter(exp_name)
spontaneous = sorted_stimuli['spontaneous']
fullfieldflicker = sorted_stimuli['fff']
onoffsteps = sorted_stimuli['onoffsteps']
checkerflicker = sorted_stimuli['checkerflicker']
stripeflicker = sorted_stimuli['stripeflicker']
checkerflickerplus = sorted_stimuli['frozennoise'] + sorted_stimuli['checkerflickerplusmovie']
OMSpatches = sorted_stimuli['OMSpatches']
OMB = sorted_stimuli['OMB']
saccadegrating = sorted_stimuli['saccadegrating']
asc.saveframetimes(exp_name)
# %%
spontanalyzer(exp_name, spontaneous)
def loadstim(exp, stim_nr, maxframenr=10000):
"""
Recreate the stimulus based on the seed for a given stimulus type.
Each type of stimulus requires a different way of handling the
random numbers from the PRNG.
"""
sortedstim = asc.stimulisorter(exp)
clusters, metadata = asc.read_spikesheet(exp)
pars = asc.read_parameters(exp, stim_nr)
for key, val in sortedstim.items():
if stim_nr in val:
stimtype = key
if stimtype in ['fff', 'stripeflicker', 'checkerflicker', 'frozennoise']:
seed = pars.get('seed', -10000)
bw = pars.get('blackwhite', False)
filter_length, frametimings = asc.ft_nblinks(exp, stim_nr)
total_frames = frametimings.shape[0]
if stimtype == 'fff':
if bw:
randnrs, seed = randpy.ranb(seed, total_frames)
# Since ranb returns zeros and ones, we need to convert
# the zeros into -1s.
stimulus = np.array(randnrs) * 2 - 1
else:
randnrs, seed = randpy.gasdev(seed, total_frames)
stimulus = np.array(randnrs)
elif stimtype in ['checkerflicker', 'frozennoise']:
scr_width = metadata['screen_width']
scr_height = metadata['screen_height']
stx_h = pars['stixelheight']
stx_w = pars['stixelwidth']
# Check whether any parameters are given for margins, calculate
# screen dimensions.
marginkeys = ['tmargin', 'bmargin', 'rmargin', 'lmargin']
margins = []
for key in marginkeys:
margins.append(pars.get(key, 0))
# Subtract bottom and top from vertical dimension; left and right
# from horizontal dimension
scr_width = scr_width - sum(margins[2:])
scr_height = scr_height - sum(margins[:2])
sx, sy = scr_height / stx_h, scr_width / stx_w
# Make sure that the number of stimulus pixels are integers
# Rounding down is also possible but might require
# other considerations.
if sx % 1 == 0 and sy % 1 == 0:
sx, sy = int(sx), int(sy)
else:
raise ValueError('sx and sy must be integers')
# HINT: fixing stimulus length for now because of memory
# capacity
total_frames = maxframenr
randnrs, seed = randpy.ranb(seed, sx * sy * total_frames)
# Reshape and change 0's to -1's
stimulus = np.reshape(randnrs,
(sx, sy, total_frames), order='F') * 2 - 1
return stimulus
if stimtype == 'OMB':
stimframes = pars.get('stimFrames', 108000)
preframes = pars.get('preFrames', 200)
nblinks = pars.get('Nblinks', 2)
seed = pars.get('seed', -10000)
seed2 = pars.get('objseed', -1000)
stepsize = pars.get('stepsize', 2)
ntotal = int(stimframes / nblinks)
clusters, metadata = asc.read_spikesheet(exp)
refresh_rate = metadata['refresh_rate']
filter_length, frametimings = asc.ft_nblinks(exp, stim_nr, nblinks,
refresh_rate)
frame_duration = np.ediff1d(frametimings).mean()
frametimings = frametimings[:-1]
if ntotal != frametimings.shape[0]:
print(
f'For {exp}\nstimulus {iof.getstimname(exp, stim_nr)} :\n'
f'Number of frames specified in the parameters file ({ntotal}'
f' frames) and frametimings ({frametimings.shape[0]}) do not'
' agree!'
' The stimulus was possibly interrupted during recording.'
' ntotal is changed to match actual frametimings.')
ntotal = frametimings.shape[0]
randnrs, seed = randpy.gasdev(seed, ntotal * 2)
randnrs = np.array(randnrs) * stepsize
xsteps = randnrs[::2]
ysteps = randnrs[1::2]
return np.vstack((xsteps, ysteps))
return None
import numpy as np import matplotlib.pyplot as plt import datetime as dt import iofuncs as iof import analysis_scripts as asc import nonlinearity as nlt import genlinmod as glm from omb import OMB from stimulus import Stimulus #exp, stim_nr = '20180710', 8 #exp, stim_nr = 'Kuehn', 13 fff_stimnr = asc.stimulisorter(exp)['fff'][0] st = OMB(exp, stim_nr) fff = Stimulus(exp, fff_stimnr) # Get rid of list of numpy arrays fff_stas = np.array(fff.read_datafile()['stas']) glmlabel = 'GLM_contrast' savepath = os.path.join(st.stim_dir, glmlabel) os.makedirs(savepath, exist_ok=True) texture_data = st.read_texture_analysis() all_spikes = st.allspikes()
def mirrorexpand(x, times=1):
"""
Rxpands a matrix by mirroring at all sides, to mimic OpenGL
texture wrapping (GL_MIRORED_REPEAT) behaviour.
"""
for i in range(times):
x = np.vstack((np.flipud(x), x, np.flipud(x)))
x = np.hstack((np.fliplr(x), x, np.fliplr(x)))
return x
#%%
exp, stim_nr = ('20180710', 8)
sortedstim = asc.stimulisorter(exp)
clusters, metadata = asc.read_spikesheet(exp)
pars = asc.read_parameters(exp, stim_nr)
for key, val in sortedstim.items():
if stim_nr in val:
stimtype = key
if stimtype != 'OMB':
ValueError('The stimulus is not OMB.')
bgnoise = pars.get('bgnoise', 4)
if bgnoise != 1:
bgstixel = pars.get('bgstixel', 5)
else:
bgstixel = pars.get('bgstixel', 10)
sys.path.append(str(Path.home() / 'repos/pymer/cca/'))
sys.path.append(str(Path.home() / 'repos/pymer/modules/'))
sys.path.append(str(Path.home() / 'repos/pymer/classes/'))
sys.path.append(str(Path.home() / 'repos/pymer/external_libs/'))
import analysis_scripts as asc
import cca_withpyrcca
import importlib
importlib.reload(cca_withpyrcca)
cca_omb_components = cca_withpyrcca.cca_omb_components
exps = ['20180712*kilosorted', '20180719_kilosorted', '20180815*_kilosorted']
exp_stimnr_pairs = []
# Find all the OMB stimuli in the experiment
for exp in exps:
ombstimnrs = asc.stimulisorter(exp)['OMB']
for ombstimnr in ombstimnrs:
exp_stimnr_pairs.append((exp, ombstimnr))
# exp, stimnr = '20180815*_kilosorted', 10
#%%
for exp, stimnr in exp_stimnr_pairs:
temp_save_folder = None
maxframes = None
cca_omb_components(
exp,
stimnr,
n_components=6,
regularization=100,
maxframes=maxframes,
Created on Wed Oct 17 14:24:09 2018
@author: ycan
"""
import warnings
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import gaussfitter as gfit
import iofuncs as iof
import analysis_scripts as asc
import miscfuncs as msc
exp = '20180710'
sorted_stimuli = asc.stimulisorter(exp)
checker = sorted_stimuli['frozennoise'][0]
data = iof.load(exp, checker)
parameters = asc.read_parameters(exp, checker)
stas = data['stas']
max_inds = data['max_inds']
i = 0
sta = stas[i]
max_i = max_inds[i]
bound = 1.5
#%%
def fitgaussian(sta, f_size=10):
max_i = np.unravel_index(np.argmax(np.abs(sta)), sta.shape)