import cv2 as cv
import numpy as np
from hdf5manager import hdf5manager as h5
import matplotlib.cm as cm
pMatrix = h5("Outputs/P5_MatrixData_full_interp.hdf5").load()['pMatrix']
cv.namedWindow('image')
def callback(x):
cutoff = x / 1000
print("Cutoff:", cutoff)
img = np.zeros((pMatrix.shape[0], pMatrix.shape[1], 3), dtype="uint8")
mult = 2
# preMatrix = np.zeros_like(pMatrix, dtype=bool)
# preMatrix[pMatrix < cutoff] = True
# preMatrix[pMatrix > 1 - cutoff] = True
# fullImg = np.empty((preMatrix.shape[0] * 2, preMatrix.shape[1] * 3), dtype='uint8')
# btmX = 0
# btmY = 0
# topY = preMatrix.shape[0]
# for i in range(1, 4):
# topX = btmX + preMatrix.shape[1]
# fullImg[btmY:topY, btmX:topX] = preMatrix[:,:,i]
# btmX = topX
# btmY = topY
# topY = btmY + preMatrix.shape[0]
from hdf5manager import hdf5manager as h5
import numpy as np
import cv2 as cv
import time
import math
f = h5("mouse_vectors.hdf5").load()
contour_data = f["contour_data"].astype("int32")
n_contours = f["n_contours"]
mv = np.empty((contour_data.shape[0], 480, 640, 3), dtype='uint8')
prev_frame = contour_data[0, :n_contours[0]]
for i in range(contour_data.shape[0]):
n = n_contours[i]
for x, y in contour_data[i, :n]:
min_v = (0, 0)
min_dist = (1000**2) * 2
min_pos = (0, 0)
for x2, y2 in prev_frame:
dx = x2 - x
dy = y2 - y
dist = dx * dx + dy * dy
if (dist < min_dist):
min_dist = dist
min_v = (dx, dy)
min_pos = (x2, y2)
cv.line(mv[i], (x, y), (x + min_v[0], y + min_v[1]), (0, 255, 0))
cv.circle(mv[i], min_pos, 3, (0, 0, 255), -1)
import time
import numpy as np
from matplotlib import pyplot as plt
from hdf5manager import hdf5manager as h5
from derivativeEventDetection import detectSpikes
from eventCharacterization import eventCharacterization
f = h5("P2_timecourses.hdf5").load()
brain_data = f["brain"]
numRows = brain_data.shape[0]
batches = 10000
na = 140
nb = 115
all_start_spikes = []
all_end_spikes = []
total_start_events = 0
total_end_events = 0
for i in range(numRows):
print("Doing timecourse number " + str(i))
dataRow = brain_data[i]
start_spikes, end_spikes, vals = detectSpikes(dataRow, -0.3)
total_start_events += start_spikes.shape[0]
total_end_events += end_spikes.shape[0]
all_start_spikes.append(start_spikes)
all_end_spikes.append(end_spikes)
t = time.clock()
ap.add_argument(
'-f',
'--data_filename',
nargs=1,
required=True,
help=
'name of hdf5 input file with ICA-filtered timecourses and domain map')
ap.add_argument('-t',
'--trigger',
action="store_true",
required=False,
help='generate the lookup table')
args = vars(ap.parse_args())
f = h5(args['coincidence_filename'][0])
data = f.load()
eventMatrix = data['eventMatrix']
maxRates = np.ones(eventMatrix.shape[0])
minRates = np.zeros(eventMatrix.shape[0])
pMatrix = data['pMatrix']
f2 = h5(args['data_filename'][0])
data2 = f2.load()
domain_map = data2['domainmap']
if (args['trigger']):
trigger = True
from hdf5manager import hdf5manager as h5
import numpy as np
import cv2 as cv
import time
import math
f = h5("new_mouse_vectors.hdf5").load()
f_bl = f["bl"].astype("int64")
f_br = f["br"].astype("int64")
f_fl = f["fl"].astype("int64")
f_fr = f["fr"].astype("int64")
feet = [f_bl, f_br, f_fl, f_fr]
mv = np.empty((f_bl.shape[0], 480, 640, 3), dtype='uint8')
for f in feet:
for i in range(f.shape[0]):
x, y = f[i]
cv.circle(mv[i], (x, y), 3, (255, 0, 0), -1)
print("Playing Movie")
print("Blue means new, Red means old")
cv.namedWindow("Dots")
fps = 30
frame_dt = 1.0 / fps
mv_i = 0
pause = False
while True:
if mv_i >= mv.shape[0]:
mv_i = 0
from hdf5manager import hdf5manager as h5
import cv2 as cv2
import numpy as np
domain_map = h5("P2_timecourses.hdf5").load()["domainmap"]
def interpAxis(contour, interp_x):
contour = contour[:, 0]
if interp_x:
xs = contour[:, 0]
ys = contour[:, 1]
else:
xs = contour[:, 1]
ys = contour[:, 0]
xs_interp = xs.tolist()
ys_interp = ys.tolist()
sz = xs.shape[0]
i = 1
while i < sz:
dif = xs_interp[i] - xs_interp[i - 1]
new_x = 0
if dif < -1:
new_x = xs_interp[i - 1] - 1
elif dif > 1:
new_x = xs_interp[i - 1] + 1
else:
for path in pathlist:
print('\n\n\tWorking on ' + path)
#find the average movtion vectors
if path.endswith('videodata.hdf5'):
print('Input found:')
print('\t' + path)
assert os.path.exists(path), 'Videodata file was not found!'
print('Processing file:', path)
#
# assert path.endswith('videodata.hdf5'), "Path did not end in 'videodata.hdf5'"
print(
'\nLoading videodata to create classifier metrics\n------------------------------------------------'
)
f = h5(path)
print('\t loading dfof mean')
dfof = f.load('dfof_mean')
if path.endswith('OpticFlow.hdf5'):
assert os.path.exists(path), 'opticFlow file was not found!'
assert path.endswith(
'.hdf5'), 'Unknown data type. Please load .hdf5 only'
print('Input found:')
print('\t' + path)
print('Processing file:', path)
if path.endswith('.hdf5'):
# assert path.endswith('opticFlow.hdf5'), "Path did not end in 'opticFlow.hdf5'"
savepath = path.replace('.hdf5', '_metrics.csv')
base = os.path.basename(path)
import time, math, sys
try:
path_file = open("path.txt", "r")
sys.path.append(path_file.read())
path_file.close()
except:
print("Can't import, path.txt doesn't exist")
pass
import fileManager as fm
experimentName = "180807_01"
vid_name = experimentName + "_result"
f = h5("mouse_vectors_" + experimentName + ".hdf5").load()
f_write = h5("new_mouse_vectors_" + experimentName + ".hdf5")
contour_data = f["contour_data"].astype("int32")
n_contours = f["n_contours"]
names = ["bl", "br", "fl", "fr", "t"]
write_dict = {
"bl": np.empty((contour_data.shape[0], 2)),
"br": np.empty((contour_data.shape[0], 2)),
"fl": np.empty((contour_data.shape[0], 2)),
"fr": np.empty((contour_data.shape[0], 2)),
"t": np.empty((contour_data.shape[0], 2))
}
stds = f["stds"]
max_std = np.max(stds)