import matplotlib.pyplot as plt
import numpy as np
from loadsums import ca_loadsum
from pylab import close, xlabel, ylabel, show, legend, savefig
from mpl_toolkits.axes_grid1 import ImageGrid
from load_vdaq_conds import vdaq_conds
from get_data import unpack_block_file
stim_start = 15
stim_length = 5
en_masse = True # whether or not to show the result for a single block file or export the results of a list of block files
if en_masse == False:
from get_data import data_name_get
data_name = data_name_get()
lenheader, nframesperstim, framewidth, frameheight, _, _, _, _ = unpack_block_file(data_name)
nframesperstim = np.array(nframesperstim)
timepts = np.array(nframesperstim)
img_range_zero = np.arange(0, nframesperstim) # put reading head at the beginning of the baseline condition images (and set limit)
img_range_one = np.arange(nframesperstim, nframesperstim * 2) # move reading point to the trial condition images (and set limit)
data_type, bytes_per_pixel = vdaq_conds(data_name)
fimg_ca_one = ca_loadsum(data_name, img_range_one, True, data_type, framewidth, frameheight, lenheader, bytes_per_pixel)
fimg_ca_zero = ca_loadsum(data_name, img_range_zero, True, data_type, framewidth, frameheight, lenheader, bytes_per_pixel)
fimg_ca = fimg_ca_one / fimg_ca_zero # normalize trial condition to baseline condition
pca = mdp.nodes.PCANode()
pca_result = pca(fimg_ca)
pca_time = pca.get_projmatrix()
def vdaq_conds(data_name):
_, nframesperstim, _, _, nstimuli, nvideoframesperdataframe, filesubtype, datatype = unpack_block_file(data_name)
nframes = numpy.array(nframesperstim) * numpy.array(nstimuli)
r = numpy.arange(1, nframes+1)
r1 = range(1, 10)
time_bin = 1000 / numpy.array(nvideoframesperdataframe)
tim = numpy.arange(1, numpy.array(nframesperstim)+1) * numpy.array(time_bin)
sumframes = False
if numpy.array(filesubtype) != 11:
raise InputError('not a VDAQ file')
if numpy.array(datatype) == 13:
data_type = '<u4'
bytes_per_pixel = 4
elif numpy.array(datatype) == 14:
data_type = 2
bytes_per_pixel = 4
else:
raise InputError('Check the data type')
return data_type, bytes_per_pixel
