import SZ_macros as SZM
import SZ_video as SZV
import SZ_utilities as SZU
# Read Folder List
folderListFile = base_path + r'\Python_ED\Social _Behaviour_Setup\PreProcessing'
folderListFile = folderListFile + r'\SocialFolderList_PreProcessing_2017_08_25_subset.txt'
control = False
groups, ages, folderNames, fishStatus = SZU.read_folder_list(folderListFile)
# Bulk analysis of all folders
for idx,folder in enumerate(folderNames):
# Get Folder Names
NS_folder, S_folder, C_folder = SZU.get_folder_names(folder)
# ----------------------
# Process Video (NS)
SZV.pre_process_video_summary_images(NS_folder, False)
# Check if this is a control experiment
if control:
# Process Video (NS_2) - Control
SZV.pre_process_video_summary_images(C_folder, False)
else:
# Process Video (S)
SZV.pre_process_video_summary_images(S_folder, True)
# print (cv2.__version__)
def analyze_single_fish_experiment(dataFolder, analysisFolder, group, age, fishStatus, save):
# Get Folder Names
NS_folder, S_folder, D_folder = SZU.get_folder_names(dataFolder);
# Load SocialSide Defintion
filename = NS_folder + '\\SocialSide.txt'
NS_socialSide = np.genfromtxt(filename, delimiter=' ', skiprows=0)
filename = S_folder + '\\SocialSide.txt'
S_socialSide = np.genfromtxt(filename, delimiter=' ', skiprows=0)
# Compare SocialSide Defintions betwen NS and S
comp = NS_socialSide - S_socialSide
if (np.sum(np.abs(comp)) != 0):
print ("Social Side defintion mismatch!!")
print ('Folder:', dataFolder)
socialSide = S_socialSide
else:
socialSide = S_socialSide
# Specify Social Angle
socialAngle = [270,90,270,90,270,90]
# Analyze Each of the 6 Fish per experiment
for f in range(0,6):
# Make Plot
fig = plt.figure(figsize = (12,9), dpi = 150)
fig.subplots_adjust(hspace=0.5)
# Load Test Fish Tracking (Non_social control)
filename = NS_folder + '\\tracking' + str(f+1) + '.csv'
test_tracking_ns = SZU.load_tracking(filename) #Tracking: X, Y, Ort, Major, Minor, Area
trackingErrors_test_ns = SZU.measure_tracking_errors(test_tracking_ns)
X_test_ns = test_tracking_ns[:, 0]
Y_test_ns = test_tracking_ns[:, 1]
Ort_test_ns = test_tracking_ns[:, 2]
# Quantify Social Position Bias (during Non-social condition)
if socialSide[f] == 0:
socialY_ns = Y_test_ns > 225
non_socialY_ns = Y_test_ns <= 225
else:
socialY_ns = Y_test_ns < 225
non_socialY_ns = Y_test_ns >= 225
# Relative Angle w.r.t. social position
RelOrt_test_ns = Ort_test_ns-socialAngle[f]
for i in range(0,np.size(RelOrt_test_ns)):
if RelOrt_test_ns[i] < 0:
RelOrt_test_ns[i] = RelOrt_test_ns[i]+360
ort_hist_test_ns, edges_test_ns = np.histogram(RelOrt_test_ns[socialY_ns], 36, (0, 360))
# Load Test Fish Tracking (Social condition)
filename = S_folder + '\\tracking' + str(f+1) + '.csv'
test_tracking_s = SZU.load_tracking(filename) #Tracking: X, Y, Ort, Major, Minor, Area
trackingErrors_test_s = SZU.measure_tracking_errors(test_tracking_s)
X_test_s = test_tracking_s[:, 0]
Y_test_s = test_tracking_s[:, 1]
Ort_test_s = test_tracking_s[:, 2]
# Quantify Social Position Bias (during Social condition)
if socialSide[f] == 0:
socialY_s = Y_test_s > 225
non_socialY_s = Y_test_s <= 225
else:
socialY_s = Y_test_s < 225
non_socialY_s = Y_test_s >= 225
# Relative Angle w.r.t. social position
RelOrt_test_s = Ort_test_s-socialAngle[f]
for i in range(0,np.size(RelOrt_test_s)):
if RelOrt_test_s[i] < 0:
RelOrt_test_s[i] = RelOrt_test_s[i]+360
ort_hist_test_s, edges_test_s = np.histogram(RelOrt_test_s[socialY_s], 36, (0, 360))
# Load Stimulus Fish Tracking (Social condition)
filename = S_folder + '\\Social_Fish\\tracking' + str(f+1) + '.csv'
stim_tracking_s = SZU.load_tracking(filename) #Tracking: X, Y, Ort, Major, Minor, Area
trackingErrors_stim_s = SZU.measure_tracking_errors(stim_tracking_s)
X_stim_s = stim_tracking_s[:, 0]
Y_stim_s = stim_tracking_s[:, 1]
Ort_stim_s = stim_tracking_s[:, 2]
# Relative Angle w.r.t. social position
RelOrt_stim_s = Ort_stim_s-socialAngle[f]
for i in range(0,np.size(RelOrt_stim_s)):
if RelOrt_stim_s[i] < 0:
RelOrt_stim_s[i] = RelOrt_stim_s[i]+360
ort_hist_stim_s, edges_stim_s = np.histogram(RelOrt_stim_s[socialY_s], 36, (0, 360))
# Compute Social Preference Index / Bias (Non-Social Condition)
totalFrames = float(np.size(X_test_ns))
socialFrames = float(np.sum(socialY_ns))
nonSocialFrames = float(np.sum(non_socialY_ns))
SPI_ns = ((socialFrames-nonSocialFrames)/totalFrames)
# Compute Social Preference Index / Bias (Social Condition)
totalFrames = float(np.size(X_test_s))
socialFrames = float(np.sum(socialY_s))
nonSocialFrames = float(np.sum(non_socialY_s))
SPI_s = ((socialFrames-nonSocialFrames)/totalFrames)
## Compute Correlation (Social Condition, when on Social-Side)
corrLength = 500
motion_filter = np.array([0.25, 0.25, 0.25, 0.25])
# Determine Movement (Test Fish) - NS
Movement = SZU.motion_signal(X_test_ns, Y_test_ns, Ort_test_ns)
#movement_test_ns = signal.fftconvolve(motion_filter, Movement)
movement_test_ns = Movement
movement_test_ns = movement_test_ns[socialY_ns]
# Determine Movement (Test Fish) - S
Movement = SZU.motion_signal(X_test_s, Y_test_s, Ort_test_s)
#movement_test_s = signal.fftconvolve(motion_filter, Movement)
movement_test_s = Movement
movement_test_s = movement_test_s[socialY_s]
# Determine Movement (Stimulus Fish) - S
Movement = SZU.motion_signal(X_stim_s, Y_stim_s, Ort_stim_s)
#movement_stim_s = signal.fftconvolve(motion_filter, Movement)
movement_stim_s = Movement
movement_stim_s = movement_stim_s[socialY_s]
# Cross Correlate - Zero Pad the Test Array
z = np.zeros(corrLength)
zero_padded_test_ns = np.concatenate((z, movement_test_ns, z), axis = 0)
zero_padded_test_s = np.concatenate((z, movement_test_s, z), axis = 0)
zero_padded_stim_s = np.concatenate((z, movement_stim_s, z), axis = 0)
zero_padded_test_s_rev = zero_padded_test_s[::-1] # Scramble/Reverse Test
# Compute Reversed Correlation
crcor = np.correlate(zero_padded_test_s, movement_stim_s, mode="valid")
crcor_rev = np.correlate(zero_padded_test_s_rev, movement_stim_s, mode="valid")
# Compute Auto-Correlations
auto_corr_test_ns = np.correlate(zero_padded_test_ns, movement_test_ns, mode="valid")
auto_corr_test_s = np.correlate(zero_padded_test_s, movement_test_s, mode="valid")
auto_corr_stim_s = np.correlate(zero_padded_stim_s, movement_stim_s, mode="valid")
## Compute Burst-Triggered Average of Test and Stimulus Fish
FPS = 100
btaOffset = 100
z = np.zeros(corrLength)
# Extract the bouts data
bouts_test_ns, numBouts_test_ns = SZU.extract_bouts_from_motion(movement_test_ns, 2.0)
#boutFreq_test_s = FPS * (numBouts_test_s/totalFrames)
bouts_test_s, numBouts_test_s = SZU.extract_bouts_from_motion(movement_test_s, 2.0)
#boutFreq_test_s = FPS * (numBouts_test_s/totalFrames)
bouts_stim_s, numBouts_stim_s = SZU.extract_bouts_from_motion(movement_stim_s, 2.0)
#boutFreq_stim_s = FPS * (numBouts_stim_s/totalFrames)
peaks_test_ns = bouts_test_ns[:, 1]
peaks_test_s = bouts_test_s[:, 1]
peaks_stim_s = bouts_stim_s[:, 1]
zero_padded = np.concatenate((movement_test_s, z), axis = 0)
aligned = SZU.burst_triggered_alignment(peaks_stim_s, zero_padded, btaOffset, corrLength)
BTA_test_s = np.mean(aligned, axis = 0)
aligned = SZU.burst_triggered_alignment(peaks_test_s, zero_padded, btaOffset, corrLength)
BTA_self_test_s = np.mean(aligned, axis = 0)
zero_padded = np.concatenate((movement_stim_s, z), axis = 0)
aligned = SZU.burst_triggered_alignment(peaks_test_s, zero_padded, btaOffset, corrLength)
BTA_stim_s = np.mean(aligned, axis = 0)
aligned = SZU.burst_triggered_alignment(peaks_stim_s, zero_padded, btaOffset, corrLength)
BTA_self_stim_s = np.mean(aligned, axis = 0)
# Plot Tracking
# ---------------#---------------- #
# Plot Test Non-Social Tracking
plt.subplot(3, 3, 1)
plt.plot(X_test_ns[non_socialY_ns],Y_test_ns[non_socialY_ns], '.', markersize = 1, color = [0.0,0.0,0.0,0.05])
plt.plot(X_test_ns[socialY_ns],Y_test_ns[socialY_ns], '.', markersize = 1, color = [1.0,0.0,0.0,0.05])
plt.axis([0, 180, 0, 450])
plt.title('Test Fish: Non-Social Condition\nSPI= %.2f' % SPI_ns, fontsize=8)
ax = plt.gca()
ax.invert_yaxis()
plt.tick_params(labelsize = 6)
# Plot Test Social Tracking
plt.subplot(3, 3, 2)
plt.plot(X_test_s[non_socialY_s],Y_test_s[non_socialY_s], '.', markersize = 1, color = [0.0,0.0,0.0,0.05])
plt.plot(X_test_s[socialY_s],Y_test_s[socialY_s], '.', markersize = 1, color = [1.0,0.0,0.0,0.05])
plt.axis([0, 180, 0, 450])
plt.title('Test Fish: Social Condition\nSPI= %.2f' % SPI_s, fontsize=8)
ax = plt.gca()
ax.invert_yaxis()
plt.tick_params(labelsize = 6)
# Plot Stimulus Fish Social Tracking
plt.subplot(3, 3, 3)
plt.plot(X_stim_s,Y_stim_s, '.', markersize = 1, color = [0.0,0.0,0.0,0.05])
plt.axis([0, 180, 0, 180])
plt.title('Stimulus Fish: Social Condition\n', fontsize=8)
ax = plt.gca()
ax.invert_yaxis()
plt.tick_params(labelsize = 6)
# Plot Orientation Histogram
# ---------------#---------------- #
# Make Orientation Plot, correct for social fish position
plt.subplot(3, 3, 4, projection='polar')
SZU.polar_orientation(RelOrt_test_ns[socialY_ns])
plt.title('Orientation Histogram (social side)\n', fontsize=8)
plt.tick_params(labelsize = 6)
# Make Orientation Plot, correct for social fish position
plt.subplot(3, 3, 5, projection='polar')
SZU.polar_orientation(RelOrt_test_s[socialY_s])
plt.title('Orientation Histogram (social side)\n', fontsize=8)
plt.tick_params(labelsize = 6)
# Make Orientation Plot, correct for social fish position
plt.subplot(3, 3, 6, projection='polar')
SZU.polar_orientation(RelOrt_stim_s[socialY_s])
plt.title('Orientation Histogram (test fish on social side)\n', fontsize=8)
plt.tick_params(labelsize = 6)
# Plot Correlation and Burst-Triggered Averages
# ------------------------#---------------------------- #
# Plot correlation and reversed correlation
corrAxis = range(0, (corrLength*2)+1)
corrAxis = np.array(corrAxis)-corrLength
btaTaxis = range(0, corrLength)
btaTaxis = np.array(btaTaxis)-btaOffset
plt.subplot(3, 3, 8)
plt.plot(corrAxis, crcor_rev, 'y')
plt.plot(corrAxis, crcor, 'g')
plt.title('Cross Correlation (test fish on social side)\n', fontsize=8)
plt.tick_params(labelsize = 6)
# Plot BTA of test fish movement triggered on stimulus fish "burst peaks"
plt.subplot(3, 3, 7)
plt.plot(btaTaxis, BTA_test_s, 'k')
plt.title('Stimulus-Fish-Burst Triggered Average of Test Fish Motion\n', fontsize=8)
plt.tick_params(labelsize = 6)
# Plot BTA of stimulus fish movement triggered on test fish "burst peaks"
plt.subplot(3, 3, 9)
plt.plot(btaTaxis, BTA_stim_s, 'k')
plt.title('Test-Fish-Burst Triggered Average of Stimulus Fish Motion\n', fontsize=8)
plt.tick_params(labelsize = 6)
# Save Figure and Data
if save:
# Prepare Summary Data
orts = np.vstack((ort_hist_test_ns, ort_hist_test_s, ort_hist_stim_s))
correlations = np.vstack((crcor, crcor_rev, auto_corr_test_ns, auto_corr_test_s, auto_corr_stim_s))
peaks = [peaks_test_ns, peaks_test_s, peaks_stim_s]
btas = np.vstack((BTA_test_s, BTA_stim_s, BTA_self_test_s, BTA_self_stim_s))
filename = analysisFolder + '\\' + str(np.int(group)) + '_' + str(f) +'.png'
plt.savefig(filename, dpi=300)
plt.close('all')
# Save Correlation Data
filename = analysisFolder + '\\' + str(np.int(group)) + '_' + str(f) +'.npz'
summary = np.array([socialFrames, SPI_ns, SPI_s])
np.savez(filename, summary, orts, correlations, btas, peaks)
# End of Loop
return 0