def get_AS_intensity_vector(self,
feat,
bin_type='12bins',
atype='abs'): #, TEST=True):
"""returns array with active state intensity [mg/sec] for
feeding, drinking and movement into 11 2hr bins;
starting at 15 after midnight: bin0 = 15.00-17.00 = 08-10 CT
NB. intervals that:
-START in CT6-8, END in CT8-10,
-START in CT28-30, END in CT30-32
have been removed. this is part of the job of
getting rid of first bin: 13-15 (1-3pm, CT6-8)
"""
act = feat[0]
AS = self.load('AS_timeSet')
tbins = get_CT_bins(bin_type, self.experiment.BIN_SHIFT,
self.experiment.binTimeShift)
if self.experiment.short_name == '2CFast': # has a fast day
if self.dayNumber in self.experiment.fastDayNumbers:
if act == 'F':
_, tend = self.experiment.get_food_removal_times()
# zero food after fast begins
idx0 = np.where(AS[:, 1] <= tend)[0]
AS = AS[idx0]
arr = np.zeros(len(tbins))
b = 0
for tbin in tbins:
# get AS times in bins (strict). use tbin_eff if onset-based definition
AS_times, _, _, _ = count_onsets_in_bin(I=AS, tbin=tbin)
if len(AS_times) > 0:
# get amounts for each AS, in bin
if act in ['F', 'W']:
amounts = self.get_amounts(act, I=AS, tbin=tbin) # mg
elif act == 'M':
_amounts = self.get_move_distances_in_AS_or_Bout(
tbin=tbin, move_type='AS')
# some AS have no move events, but F/W only. correct
amounts = self.correct_AS_without_move(_amounts,
bin_type,
bin_num=b)
# if self.mouseNumber == 7210 and self.dayNumber == 15 and b==1: stop
np.testing.assert_allclose(len(amounts), len(AS_times), rtol=1e-5)
arr[b] = get_intensity(amounts, AS_times,
atype) # avg intensity, mg/s
# if self.mouseNumber == 7250 and self.dayNumber == 12 and b == 0: stop
b += 1
return arr
def get_bout_vector(self, feat, bin_type='12bins'):
""" returns array with (features, num_bins):
counts, duration, size, intensity
"""
tbins = my_utils.get_CT_bins(bin_type, self.experiment.BIN_SHIFT, self.experiment.binTimeShift)
arr = np.zeros(len(tbins))
b = 0
for tbin in tbins:
arr[b] = self.get_bout_feature_value(feat, tbin, b)
# arr[b] = self.get_bout_feature_value(feat, bin_type, tbin=tbin)
b +=1
# elif bin_type == 'LC':
# arr = self.get_bout_feature_value(feat, bin_type, tbin=tbins)
return arr
def _check_bout_counts_in_bins(self, act):
tbins = my_utils.get_CT_bins(bin_type='12bins')
B = self.load('%sB_timeSet' %act)
cnt_diff = np.zeros(len(tbins))
b = 0
for tbin in tbins:
B_times, B_cnt_eff, B_times_eff, tbin_eff = my_utils.count_onsets_in_bin(I=B, tbin=tbin)
B_cnt = B_times.shape[0]
if B_cnt != B_cnt_eff:
cnt_diff[b] = B_cnt - B_cnt_eff
b +=1
return cnt_diff
def get_device_firing_durations_in_AS_or_Bouts(self, EV, I, tbin):
""" get all durations for each AS/Bouts(I), for specified tbin
"""
if tbin is None: # use 24h bin
tbin = my_utils.get_CT_bins(bin_type='24H')
_I = Intervals(I).intersect(Intervals(tbin))
durs = np.zeros(_I.intervals.shape[0])
c = 0
for x in _I:
durs[c] = Intervals(x).intersect(Intervals(EV)).measure()
# if act == 'W':
# size[c] = Intervals(x).intersect(EV).intervals.shape[0] * coeff # grams
c += 1
return durs
def get_AS_vector(self, feat, bin_type='12bins'):
"""returns ASP, ASN, ASD in 12-2hr bins resp. 24H, DC , LC values
starting at 13 after midnight:
bin0: 13.00-15.00 or CT06-08
"""
tbins = get_CT_bins(bin_type, self.experiment.BIN_SHIFT,
self.experiment.binTimeShift
) # tbins do not have anything outside CT [6, 30]
# arr = np.zeros(len(tbins))
# b = 0
vect = [self.get_AS_feature_value(feat, tbin) for tbin in tbins]
# for tbin in tbins:
# arr[b] = self.get_AS_feature_value(feat, tbin)
# b +=1
return vect
def get_AS_in_cycle(self, cycle, AS_def='onset'):
""" for within AS structure
"""
AS = self.load('AS_timeSet')
if cycle == '24H':
return AS
else:
tbins = get_CT_bins(bin_type=cycle)
AS_eff = list()
for tbin in tbins:
bin_start, bin_end = tbin
for start, end in AS:
if (start > bin_start and start < bin_end):
AS_eff.append([start, end])
return np.array(AS_eff)
def get_total_vector(self, feat, bin_type='12bins'):
"""returns FWM totals in 11 2hr bins vs. 24H, DC , LC values
starting at 15 after midnight:
bin0: 15.00-17.00 or 08-10 CT
h13-15 after midnight (1-3pm) or CT6-8 is excluded
"""
tbins = my_utils.get_CT_bins(bin_type, self.experiment.BIN_SHIFT, self.experiment.binTimeShift) # tbins does not have anything outside CT [6, 30]
act = feat[1]
arr = np.zeros(len(tbins))
b = 0
for tbin in tbins:
# get total in bin (strict)
if act in ['F', 'W']:
amounts = self.get_amounts(act=act, tbin=tbin)
arr[b] = amounts.sum() / 1000 # grams
elif act == 'M':
dist = self.get_move_distances(tbin=tbin, move_type='AS')
arr[b] = dist.sum() / 100. # meters
b +=1
return arr
def get_time_budgets(self, cycle, num_items):
var = [
'recording_start_stop_time', 'AS_timeSet', 'FB_timeSet', 'WB_timeSet',
'MB_timeSet', 'IS_timeSet'
]
rec, AS, F, W, M, IS = [self.load(x) for x in var]
C = get_CT_bins(bin_type=cycle) # ignoring maintenance time
time = np.diff(C).sum()
if cycle in self.experiment.cycles:
if cycle == '24H':
tas, tf, tw, tm, tis = [
np.diff(x).sum() for x in [AS, F, W, M, IS]
]
elif cycle in ['DC', 'LC']:
AS, F, W, M, IS = [
intersect_arrays(x, C) for x in [AS, F, W, M, IS]
]
tas, tf, tw, tm, tis = [
np.diff(x).sum() for x in [AS, F, W, M, IS]
]
elif cycle in ['AS24H', 'ASDC', 'ASLC']:
stop
FWM_union = Intervals(M).union(Intervals(F)).union(Intervals(W))
other = Intervals(AS).intersect(~FWM_union).measure()
arr = np.array([tm, tf, tw, other, tis])
assert arr.sum() - time < 300, '%s %s screwed' % (
MD, cycle) # allow 5 minutes differences
return arr
def get_position_bin_times(self, cycle=None, tbin_type=None, xbins=2, ybins=4):
for varName in ['recording_start_stop_time', 'CT', 'CX', 'CY']:
if not hasattr(self, varName):
self.load(varName)
# start_time, stop_time = getattr(self, 'recording_start_stop_time')
# pull corrected loco T, X, Y during this day
M = np.vstack([self.CT, self.CX, self.CY])
C = Cage()
xlims, ylims = (C.CMXLower, C.CMXUpper), (C.CMYLower, C.CMYUpper)
if tbin_type is not None:
arr = get_CT_bins(tbin_type=tbin_type.strip('AS'))
# initialize times for this mouseday and bins
bin_times = np.zeros((arr.shape[0], ybins, xbins))
if tbin_type == '12bins':
b = 0
for tstart, tend in arr:
pos_subset = pull_locom_tseries_subset(M, tstart, tend)
bin_times[b] = total_time_rectangle_bins(pos_subset, xlims=xlims, ylims=ylims, xbins=xbins, ybins=ybins)
b += 1
elif tbin_type == 'AS12bins':
AS = self.load('AS_timeSet')
b = 0
for row in arr:
AS_bin = Intervals(AS).intersect(Intervals(row)).intervals
for tstart, tend in AS_bin:
pos_subset = pull_locom_tseries_subset(M, tstart, tend)
bin_times[b] += total_time_rectangle_bins(pos_subset, xlims=xlims, ylims=ylims, xbins=xbins, ybins=ybins)
b += 1
else:
# initialize times for this mouseday and cycle
bin_times = np.zeros((ybins, xbins))
if cycle == '24H':
start_time, stop_time = get_CT_bins(tbin_type='3cycles')[0]
pos_subset = pull_locom_tseries_subset(M, start_time, stop_time) # does not change M
# assert((pos_subset==M).all()), 'pos_subset differs from M'
bin_times = total_time_rectangle_bins(pos_subset, xlims=xlims, ylims=ylims,
xbins=xbins, ybins=ybins)
elif cycle == 'IS':
IS = self.load('IS_timeSet')
for deltaT in IS:
pos_subset = pull_locom_tseries_subset(M, deltaT[0], deltaT[1])
bin_times += total_time_rectangle_bins(pos_subset, xlims=xlims, ylims=ylims,
xbins=xbins, ybins=ybins)
elif cycle == 'DC':
DC_start, DC_end = get_CT_bins(tbin_type='3cycles')[1]
pos_subset = pull_locom_tseries_subset(M, DC_start, DC_end)
bin_times = total_time_rectangle_bins(pos_subset, xlims=xlims, ylims=ylims,
xbins=xbins, ybins=ybins)
elif cycle == 'LC':
LC1, LC2 = get_CT_bins(tbin_type='3cycles')[2]
for LC_start, LC_end in [LC1, LC2]:
pos_subset = pull_locom_tseries_subset(M, LC_start, LC_end)
btimes = total_time_rectangle_bins(pos_subset, xlims=xlims, ylims=ylims,
xbins=xbins, ybins=ybins)
bin_times += btimes
else:
AS, IS = [self.load(x) for x in ['AS_timeSet', 'IS_timeSet']]
if cycle == 'AS24H':
for deltaT in AS:
pos_subset = pull_locom_tseries_subset(M, deltaT[0], deltaT[1])
bin_times += total_time_rectangle_bins(pos_subset, xlims=xlims, ylims=ylims,
xbins=xbins, ybins=ybins)
else:
light_phase = cycle.strip('AS') #DC, LC
idx = 1 if light_phase == 'DC' else 2
I = get_CT_bins(tbin_type='3cycles')[idx]
I_AS = Intervals(AS).intersect(Intervals(I)).intervals
for deltaT in I_AS:
pos_subset = pull_locom_tseries_subset(M, deltaT[0], deltaT[1])
bin_times += total_time_rectangle_bins(pos_subset, xlims=xlims, ylims=ylims,
xbins=xbins, ybins=ybins)
print self
print "Total bin times, %s, xbins=%d, ybins=%d:" %(cycle, xbins, ybins)
print bin_times
assert (bin_times.sum()>=0), "bins_times < 0 !!"
if bin_times.sum() == 0:
print "No Activity mouse %d day: %d cycle: %s" %(mouse.mouseNumber, MD.dayNumber, cycle)
if cycle == '24H':
setattr(self, 'bin_times_24H_xbins%d_ybins%d' %(xbins, ybins), bin_times)
return bin_times
def generate_position_density(self,
cycle='24H',
tbin_type=None,
level='mouseday',
xbins=12,
ybins=24,
days=None,
err_type='sd',
GENERATE=False,
VERBOSE=False):
""" returns position density data
"""
cstart = time.clock()
if days is None:
days_to_use = self.daysToUse
num_md = self.num_mousedays
else:
days_to_use = days
num_md, _ = self.count_mousedays(days=days_to_use)
if tbin_type is not None:
num_tbins = my_utils.get_CT_bins(
tbin_type=tbin_type.strip('AS')).shape[0]
md_data = np.zeros((num_md, num_tbins, ybins, xbins))
else:
md_data = np.zeros((num_md, ybins, xbins))
text = my_utils.get_text(cycle, tbin_type)
print "Exp: %s, %s, tbin_type: %s, level:%s, xbins=%d, ybins=%d, err_type: %s, %s days" % (
self.short_name, self.generate_position_density.__name__, text,
level, xbins, ybins, err_type, days_to_use)
md_labels = np.zeros((num_md, 3), dtype=int)
cnt = 0
for group in self.groups:
for mouse in group.individuals:
for MD in mouse.mouse_days:
if MD.dayNumber in days_to_use:
if MD.ignored:
md_data[cnt] = np.nan
else:
bin_times = MD.generate_position_bin_times(
cycle, tbin_type, xbins, ybins, GENERATE)
if tbin_type is not None:
b = 0
for btimes in bin_times:
if btimes.sum() >= 0:
md_data[cnt, b] = btimes / btimes.sum()
b += 1
else:
if bin_times.sum() >= 0:
md_data[cnt] = bin_times / bin_times.sum()
md_labels[
cnt] = MD.groupNumber, MD.mouseNumber, MD.dayNumber
# if GENERATE and not VERBOSE:
# my_utils.print_progress(cnt, self.num_md_ok)
cnt += 1
cstop = time.clock()
if GENERATE:
if not VERBOSE:
print
dirname = self.derived_dir + \
'position_data/xbins%d_ybins%d/%s/bin_times/' %(xbins, ybins, text) # copied from: MD.generate_position_bin_times
print "binary output saved to: %s" % dirname
print "..took %1.2f minutes" % ((cstop - cstart) / 60.)
E = self
data, labels = my_utils.get_2d_averages_errors(E, md_data, md_labels,
tbin_type, level,
err_type)
return data, labels
def draw_position_density_tbins(E,
data,
labels,
tbin_type,
level,
err_type,
xbins,
ybins,
fname,
rect_HB=None,
obs_rect=None,
plot_type='position_density',
ADD_SOURCE=False):
C = Cage()
# figure
figsize, nrows, ncols = get_subplot_specs(E,
num_subplots=data.shape[0],
plot_type=plot_type,
sub_type=tbin_type)
fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
cmap = plt.get_cmap("viridis")
vmin, vmax = .0001, 1.
if (xbins, ybins) == (2, 4):
vmin = 0.01
extent = [C.CMXLower, C.CMXUpper, C.CMYLower, C.CMYUpper]
percent_time_color = E.fcolors['other'][0]
title, subtitles = get_figure_titles(E,
labels=labels,
level=level,
tbin_type=tbin_type,
err_type=err_type,
plot_type=plot_type)
data_ = data.swapaxes(0, 1)
arr = my_utils.get_CT_bins(
bin_type=tbin_type) / 3600 - 7 # CT in hours. num_tbins=arr.shape[0]
row_labels = ['CT%d-%d' % (row[0], row[1]) for row in arr]
column_labels = subtitles
for r in xrange(nrows):
# row labels
top, bottom, left, right = [
getattr(fig.subplotpars, x)
for x in ['top', 'bottom', 'left', 'right']
]
yy = top - .03 - (top - bottom) * r / nrows
fig.text(left - .1, yy, row_labels[r], va='center', fontsize=8)
for c in xrange(ncols):
# column labels
if r < 1:
xx = left + .01 + (right - left) * c / ncols
fig.text(xx, top + .03, column_labels[c], fontsize=8)
bin_data = data_[r, c, :, :]
try:
ax = axes[r, c] if type(
axes) is np.ndarray else axes # when just one axis
Z3 = bin_data[:, :, 0] # plt average only for the moment
if np.isnan(Z3).all():
# ax.text(.5, .5, subtitles[r] + ' excluded',
# fontsize=12, color='.5', ha='center', va='center')
ax.axis('off')
else:
Z3_ = Z3.copy()
Z3_[Z3 <
1e-4] = 1e-4 # round small numbers to almost zero for plotting
im = ax.imshow(Z3_,
interpolation='nearest',
cmap=cmap,
norm=LogNorm(vmin=vmin,
vmax=vmax)) #, extent=extent)
# ax.set_title(subtitles[c])
set_position_density_layout(fig, ax, im)
if (xbins, ybins) == (2, 4):
draw_homebase_star(ax,
rect_HB[c],
color=percent_time_color)
if r * c < 1:
hh, ll = ax.get_legend_handles_labels()
except IndexError:
ax.axis('off')
continue
set_colorbar(fig, ax, im)
set_legend(fig, hh, ll, xbins, ybins, text_color=percent_time_color)
typad = .15 #if level == 'group' else 0
add_titles_notes(E,
fig,
title=title,
typad=typad,
titlesize=14,
TL_NOTE=False)
plt.subplots_adjust(hspace=.2, wspace=.05)
sypad = -.05 if level == 'group' else .05
save_plot(fname, fig, ADD_SOURCE=ADD_SOURCE, sypad=sypad)
def plot_position_density_panel2(experiment,
cycle='24H',
tbin_type=None,
xbins=12,
ybins=24,
err_type='sd',
plot_type='position_density',
ADD_SOURCE=True):
E = experiment
text = cycle if tbin_type is None else tbin_type
string = E.get_days_to_use_text()
print "%s, %s, xbins:%d, ybins:%d, %s" % (
plot_position_density_panel2.__name__, text, xbins, ybins, E.use_days)
dirname_out = E.figures_dir + '%s/panel2/xbins%d_ybins%d/%s/%s_days/' % (
plot_type, xbins, ybins, text, E.use_days)
if not os.path.isdir(dirname_out): os.makedirs(dirname_out)
# load data
data, labels = E.generate_position_density(cycle=cycle,
tbin_type=tbin_type,
level='mouseday',
xbins=xbins,
ybins=ybins,
err_type=err_type)
new_data = data.swapaxes(0, 1)
# new_data = data_.reshape(data_.shape[0], data_.shape[1], -1) # tbins, mousedays, xbins, ybins, (avg, err)
# tbins
arr = my_utils.get_CT_bins(
bin_type=tbin_type) / 3600 - 7 # CT in hours. num_tbins=arr.shape[0]
tbins = ['CT%d-%d' % (row[0], row[1]) for row in arr]
# nest
rects_HB = np.array([None] * len(data))
obs_rects = np.array([None] * len(data))
condition = (xbins, ybins) == (2, 4)
if condition:
rects_HB, obs_rects = E.generate_homebase_data()
for strain in range(E.num_strains):
idx = labels[:, 0] == strain
mouseNumbers = np.unique(labels[idx, 1])
for b in xrange(new_data.shape[0]):
bin_data = new_data[b].reshape(len(mouseNumbers), len(E.daysToUse),
ybins, xbins, 2)
fig_title = '%s Experiment\n%s, %s\n%s days: %s\ngroup%d: %s, bin %d: %s' % (
E.short_name, plot_type.replace('_', ' ').title(), text,
E.use_days.replace('_', ' ').upper(), string, strain,
E.strain_names[strain], b, tbins[b])
fname = dirname_out + '%s_%s_group%d_xbins%d_ybins%d_tbin%d' % (
plot_type, text, strain, xbins, ybins, b)
rect_HB, obs_rect = None, None
if condition:
idx_start, idx_end = my_utils.find_nonzero_runs(idx)[0]
rect_HB = np.array(rects_HB[idx_start:idx_end]).reshape(
len(mouseNumbers), len(E.daysToUse))
# obs_rect = np.array(obs_rects[idx_start:idx_end]).reshape(len(mouseNumbers), len(E.daysToUse))
if tbin_type is not None:
draw_position_density_tbins2(E,
bin_data,
mouseNumbers,
tbin_type,
err_type,
xbins,
ybins,
fig_title,
fname,
rect_HB=rect_HB,
obs_rect=obs_rect,
plot_type=plot_type,
ADD_SOURCE=ADD_SOURCE)
def write_to_csv(experiment,
cycle='24H',
tbin_type=None,
AS_FLAG=False,
level='group',
xbins=12,
ybins=24,
err_type='sd',
plot_type='position_density'):
E = experiment
use_days = E.use_days + '_days'
text = my_utils.get_text(cycle, tbin_type, AS_FLAG)
print "%s, %s, %s, xbins:%d, ybins:%d, %s" % (
write_to_csv.__name__, text, level, xbins, ybins, use_days)
suffix = '%s/csv_files/xbins%d_ybins%d/%s/%s/%s_days/' % (
plot_type, xbins, ybins, level, text, E.use_days)
dirname = os.path.join(E.figures_dir, suffix)
if not os.path.isdir(dirname): os.makedirs(dirname)
fname = dirname + '%s_%s_%s_xbins%d_ybins%d.csv' % (plot_type, text, level,
xbins, ybins)
# load data
data, labels = E.generate_position_density(cycle=cycle,
tbin_type=tbin_type,
AS_FLAG=AS_FLAG,
level=level,
xbins=xbins,
ybins=ybins,
err_type=err_type)
if tbin_type is not None:
arr = my_utils.get_CT_bins(bin_type=tbin_type) / 3600 - 7
tbins = [
'bin%d: CT%d-%d' % (b, row[0], row[1]) for b, row in enumerate(arr)
]
new_data = data[:, :, :, :,
0].reshape(data.shape[0], data.shape[1],
-1) # (num_mousedays, 4x2 raveled)
else:
new_data = data[:, :, :, 0].reshape(data.shape[0],
-1) # (num_mousedays, 4x2 raveled)
# headers
# string = ', BIN_SHIFTED: %dm' %(E.binTimeShift/60) if E.BIN_SHIFT else ''
headers = [['Experiment: %s' % E.short_name],
['%s, xbins=%d, ybins=%d' % (plot_type, xbins, ybins)],
['%s, %s' % (level, text)], ['%s days' % E.use_days],
['days: %s' % E.daysToUse]]
if level == 'group':
sub_header = ['use_days', 'group']
if tbin_type is not None:
if (xbins, ybins) == (2, 4):
sub_header += [
'tbins', 'Niche', 'TopLeft', '[]', '[]', '[]', '[]',
'Feeder', 'Lick'
]
else:
sub_header += ['tbins']
else:
sub_header += [
'Niche', 'TopLeft', '[]', '[]', '[]', '[]', 'Feeder', 'Lick'
]
with open(fname, 'wb') as csv_file:
writer = csv.writer(csv_file, delimiter=",")
for h in headers:
writer.writerow(h)
writer.writerow('\n')
writer.writerow(sub_header)
writer.writerow('\n')
if tbin_type is not None:
new_data_ = new_data.swapaxes(
0, 1) # tbin, days, (raveled xbins, ybins)
for b in xrange(len(new_data_)):
bin_data = new_data_[b]
c = 0
for row in bin_data:
row_text = [
use_days + ', %s' % text, labels[c], tbins[b]
]
writer.writerow(
np.hstack([row_text, ['%1.5f' % r for r in row]]))
c += 1
writer.writerow('\n')
else:
c = 0
for row in new_data:
row_text = [use_days + ', %s' % text, labels[c]]
writer.writerow(
np.hstack([row_text, ['%1.5f' % r for r in row]]))
c += 1
writer.writerow('\n')
elif level == 'mouse':
sub_header = ['use_days', 'group', 'mouse']
if tbin_type is not None:
if (xbins, ybins) == (2, 4):
sub_header += [
'tbins', 'Niche', 'TopRight', '[]', '[]', '[]', '[]',
'Feeder', 'Lick'
]
else:
sub_header += ['tbins']
else:
sub_header += [
'Niche', 'TopLeft', '[]', '[]', '[]', '[]', 'Feeder', 'Lick'
]
with open(fname, 'wb') as csv_file:
writer = csv.writer(csv_file, delimiter=",")
for h in headers:
writer.writerow(h)
writer.writerow('\n')
writer.writerow(sub_header)
writer.writerow('\n')
for group_num in range(E.num_strains):
idx_group = labels[:, 0] == group_num
group_data = new_data[idx_group]
group_labels = labels[idx_group]
if tbin_type is not None:
group_data_ = group_data.swapaxes(
0, 1) # tbin, days, (raveled xbins, ybins)
for b in xrange(len(group_data_)):
bin_data = group_data_[b]
c = 0
for row in bin_data:
row_text = [
use_days + ', %s' % text, group_labels[c, 0],
group_labels[c, 1], tbins[b]
]
writer.writerow(
np.hstack(
[row_text, ['%1.5f' % r for r in row]]))
c += 1
# stop
writer.writerow('\n')
writer.writerow(['', '', '', 'Mean:'] + [
'%1.5f' % r for r in np.nanmean(bin_data, axis=0)
])
writer.writerow(
['', '', '', 'sd:'] +
['%1.5f' % r for r in np.nanstd(bin_data, axis=0)])
sem = np.nanstd(
bin_data, axis=0) / np.sqrt(bin_data.shape[0] - 1)
writer.writerow(['', '', '', 'sem:'] +
['%1.5f' % r for r in sem])
writer.writerow('\n')
else:
c = 0
for row in group_data:
row_text = [
use_days + ', %s' % text, group_labels[c, 0],
group_labels[c, 1]
]
writer.writerow(
np.hstack([row_text, ['%1.5f' % r for r in row]]))
c += 1
writer.writerow('\n')
writer.writerow(
['', '', 'Mean:'] +
['%1.5f' % r for r in np.nanmean(group_data, axis=0)])
writer.writerow(
['', '', 'sd:'] +
['%1.5f' % r for r in np.nanstd(group_data, axis=0)])
sem = np.nanstd(group_data,
axis=0) / np.sqrt(group_data.shape[0] - 1)
writer.writerow(['', '', 'sem:'] +
['%1.5f' % r for r in sem])
writer.writerow('\n')
elif level == 'mouseday':
sub_header = ['', 'group', 'mouse', 'mouseday']
if tbin_type is not None:
if (xbins, ybins) == (2, 4):
sub_header += [
'tbins', 'Niche', 'TopLeft', '[]', '[]', '[]', '[]',
'Feeder', 'Lick'
]
else:
sub_header += ['tbins']
else:
sub_header += [
'Niche', 'TopLeft', '[]', '[]', '[]', '[]', 'Feeder', 'Lick'
]
with open(fname, 'wb') as csv_file:
writer = csv.writer(csv_file, delimiter=",")
for h in headers:
writer.writerow(h)
writer.writerow('\n')
writer.writerow(sub_header)
writer.writerow('\n')
for group_num in range(E.num_strains):
idx_group = labels[:, 0] == group_num
group_data = new_data[idx_group]
group_labels = labels[idx_group]
m_labels = np.unique(group_labels[:, 1])
for mouse in m_labels:
idx_mouse = group_labels[:, 1] == mouse
m_data = group_data[idx_mouse]
m_labels = group_labels[idx_mouse]
if tbin_type is not None:
m_data_ = m_data.swapaxes(
0, 1) # tbin, days, (raveled xbins, ybins)
for b in xrange(len(m_data_)):
bin_data = m_data_[b]
c = 0
for row in bin_data:
row_text = [
use_days + ', %s' % text, m_labels[c, 0],
m_labels[c, 1], m_labels[c, 2], tbins[b]
]
writer.writerow(
np.hstack(
[row_text,
['%1.5f' % r for r in row]]))
c += 1
# stop
writer.writerow('\n')
writer.writerow(['', '', '', '', 'Mean:'] + [
'%1.5f' % r
for r in np.nanmean(bin_data, axis=0)
])
writer.writerow(['', '', '', '', 'sd:'] + [
'%1.5f' % r
for r in np.nanstd(bin_data, axis=0)
])
sem = np.nanstd(
bin_data,
axis=0) / np.sqrt(bin_data.shape[0] - 1)
writer.writerow(['', '', '', '', 'sem:'] +
['%1.5f' % r for r in sem])
writer.writerow('\n')
else:
c = 0
for row in m_data:
row_text = [
use_days + ', %s' % text, m_labels[c, 0],
m_labels[c, 1], m_labels[c, 2]
]
writer.writerow(
np.hstack(
[row_text, ['%1.5f' % r for r in row]]))
c += 1
writer.writerow('\n')
writer.writerow(
['', '', '', 'Mean:'] +
['%1.5f' % r for r in np.nanmean(m_data, axis=0)])
writer.writerow(
['', '', '', 'sd:'] +
['%1.5f' % r for r in np.nanstd(m_data, axis=0)])
sem = np.nanstd(m_data,
axis=0) / np.sqrt(m_data.shape[0] - 1)
writer.writerow(['', '', '', 'sem:'] +
['%1.5f' % r for r in sem])
writer.writerow('\n')
print "csv files saved to:\n%s" % fname
def draw_position_density(E,
data,
labels,
cycle,
tbin_type,
level,
err_type,
xbins,
ybins,
fname,
rect_HB=None,
obs_rect=None,
plot_type='position_density',
ADD_SOURCE=False):
# figure
figsize, nrows, ncols, sharex, sharey = get_subplot_specs(
num_subplots=data.shape[0], tbin_type=tbin_type)
fig_title, subtitles = get_figure_titles(E,
labels=labels,
level=level,
cycle=cycle,
tbin_type=tbin_type,
err_type=err_type,
plot_type=plot_type)
percent_time_color = E.fcolors['other'][0]
if tbin_type is not None:
arr = my_utils.get_CT_bins(bin_type=tbin_type.strip('AS')) / 3600 - 7
for b in xrange(data.shape[1]):
fig, axes = plt.subplots(nrows=nrows,
ncols=ncols,
figsize=figsize,
sharex=sharex,
sharey=sharey)
for c in xrange(nrows * ncols):
try:
ax = axes.flatten()[c] if type(
axes) is np.ndarray else axes # when just one axis
Z3 = data[c, b, :, :, 0]
fig, ax = draw_it(E, fig, ax, Z3, c, level, subtitles,
xbins, ybins, rect_HB, obs_rect)
if c < 1:
h, l = ax.get_legend_handles_labels()
except IndexError:
ax.axis('off')
continue
title = fig_title + '\nbin%d: CT%d-%d' % (b, arr[b, 0], arr[b, 1])
fname1 = fname + '_tbin%d' % b
finish_plot(E, fig, level, xbins, ybins, h, l, nrows, title,
fname1, ADD_SOURCE)
else:
fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
for c in xrange(nrows * ncols):
try:
ax = axes.flatten()[c] if type(
axes) is np.ndarray else axes # when just one axis
Z3 = data[c, :, :, 0] # plt average only for the moment
fig, ax = draw_it(E, fig, ax, Z3, c, level, subtitles, xbins,
ybins, rect_HB, obs_rect)
if c < 1:
h, l = ax.get_legend_handles_labels()
except IndexError:
ax.axis('off')
continue
finish_plot(E, fig, level, xbins, ybins, h, l, nrows, fig_title, fname,
ADD_SOURCE)