def binary_vector_score_event_accuracy(target, pred, est_tn=True):
hit_rate = sum((pred == 1) & (target == 1)) / sum(target == 1)
miss_rate = sum((pred == 0) & (target == 1)) / sum(target == 1)
fa_rate = sum((pred == 1) & (target == 0)) / sum(target == 0)
print('Raw: %1.3f Hit, %1.3f Miss, %1.3f FA ' %
(hit_rate, miss_rate, fa_rate))
#Per target event, detect prediction hits:
hit = []
on, off = signals.thresh(target, 0.5)
for o, f in zip(on, off):
ind = pred[o:f]
if any(ind):
hit.append(1)
else:
hit.append(0)
mean_dur = np.mean(off - on)
# Detect false alarms
fa = []
on, off = signals.thresh(pred, 0.5)
for o, f in zip(on, off):
ind = target[o:f]
if not (any(ind)):
fa.append(1)
else:
fa.append(0)
if est_tn == True:
true_negative = round(np.sum(target == 0) / mean_dur)
else:
true_negative = len(
hit
) # This can be made to be true by adding confirmed true negative events (see below)
cr = true_negative - sum(fa)
miss = np.sum(np.array(hit) == 0)
return sum(hit), sum(fa), cr, miss
step=2,
poly_order=2)
mhh = signals.max_normalize_per_dist(x, mouse_height, step=2, poly_order=2)
plt.figure()
plt.plot(raw['time'], mh / 100)
plt.plot(raw['time'], mhh)
# %% Plot some questionable frames [update: they look good!]:
parts = [
'dlc_front_centroid',
'dlc_rear_centroid',
]
part_colors = ['.c', '.y']
framesets = []
on, off = signals.thresh(mh, 30, sign='Pos')
for o, f in zip(on, off):
if o > f:
print('o > f')
m = round((o + f) / 2)
framesets.append([o, m, f])
# framesets=[[8349,8359,8369],[6327, 6337, 6347],[6159,6169,6179],[3356, 3366, 3376]]
plot_points = plots.gen_sidecam_plot_points(dlc, parts, framesets)
plots.etho_check_sidecamera(dataloc.video(pns.parent),
framesets,
plot_points=plot_points,
part_colors=part_colors)
# %% Different rear approach scaling mouse_height to body len
x = raw['x'].values
import pickle
# %%
#Dial 740 Cal:
# fn = Path('/home/brian/Dropbox/Gittis Lab Data/OptoBehavior/GPe/Naive/A2A/Ai32/Bilateral/50x2/exclude/2021-08-23-laser_cal/gpe_buttonB6_0-255_11mW_max_cal_cleaned.csv')
fn = button_base.joinpath('gpe_buttonB6_0-255_11mW_max_cal_cleaned.csv')
df_cal = pd.read_csv(fn)
plt.figure()
y = df_cal.loc[:, ' Power(W)'] * 1000 # Put in mW
plt.plot([x for x in range(0, len(y))], y)
#locate drop offs:
d = np.diff(y)
on, off = signals.thresh(d, -2, 'Neg')
for o in off:
plt.plot([o, o], [0, 33], '--r')
onsets = [2] + off[0:-1]
offsets = on
durs = [f - o for o, f in zip(onsets, offsets)]
use_dur = 3726 #3662
print(durs)
newclips = []
for o, f in zip(onsets, offsets):
dd = f - o
if dd > 3500:
if dd > use_dur:
from pathlib import Path
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from gittislab import signals, plots, behavior, dataloc, ethovision_tools
# %%
# fn = Path('/home/brian/Dropbox/Gittis Lab Hardware/Laser Glow/laser_cal_sweeps/pwm_to_analog/power_control_sweep_0-255_arduino_multi_sweep_clean.csv')
fn = Path('/home/brian/Dropbox/Gittis Lab Hardware/Laser Glow/laser_cal_sweeps/pwm_to_TTL/power_control_sweep_0-255_arduino_TTL_multi_test_clean.csv')
df=pd.read_csv(fn)
plt.figure()
y=df.loc[:,'Power (W)'] *1000 # Put in mW
plt.plot(df.loc[:,'Samples '],y)
#locate drop offs:
d=np.diff(y)
on,off=signals.thresh(d,-15,'Neg')
for o in off:
plt.plot([o,o],[0,33],'--r')
onsets= [2] + off[0:-1]
offsets = on
newclips=[]
for o,f in zip(onsets,offsets):
dd= f-o
if dd > 62:
o=f-62
if dd < 62:
o= o-(62-dd)
t=y[o:f]