def make_histograms(config, data, colors, savename='histogram.pdf'):
print data
fig = plt.figure(figsize=(5,4))
ax = fig.add_subplot(111)
bins = np.linspace(-.5,.5,150)
fpl.histogram(ax, data.values(), bins=bins, bin_width_ratio=1, colors=colors.values(), edgecolor='none', bar_alpha=1, curve_fill_alpha=0.2, curve_line_alpha=1, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, smoothing_bins_to_exclude=[74,75,76])
xticks = [-.5, 0, .5]
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
#ax.set_xticklabels(xticklabels)
ax.set_xlabel('altitudes')
ax.set_ylabel('occurences, normalized')
path = config.path
figure_path = os.path.join(config.path, config.figure_path)
save_figure_path=os.path.join(figure_path, 'odor_traces/')
figure_path = os.path.join(path, config.figure_path)
save_figure_path = os.path.join(figure_path, 'odor_traces/')
fig_name_with_path = os.path.join(save_figure_path, savename)
print 'SAVING TO: ', fig_name_with_path
fig.savefig(fig_name_with_path, format='pdf')
def plot_digits_distribution(n):
fig = plt.figure()
ax = fig.add_subplot(111)
bins = np.arange(-.5, 10.5, 1)
npages = [30, 60, 400, 1500]
colors = ['black', 'blue', 'green', 'red']
digits = []
for npage in npages:
d = get_digits(n, npage)
digits.append(np.array(d))
xticks = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
fpl.histogram(ax,
digits,
bins=bins,
colors=colors,
normed=True,
show_smoothed=False)
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
ax.set_ylim(0, 0.15)
ax.set_xlim(-1, 10)
ax.set_xlabel('digit')
ax.set_ylabel('probability')
def make_histograms(config, data, colors, savename='orientation_histogram.pdf'):
print data
fig = plt.figure(figsize=(5,4))
ax = fig.add_subplot(111)
bins = np.linspace(-np.pi,np.pi,50)
fpl.histogram(ax, data.values(), bins=bins, bin_width_ratio=1, colors=colors.values(), edgecolor='none', bar_alpha=1, curve_fill_alpha=0.2, curve_line_alpha=1, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, smoothing_bins_to_exclude=[])
xticks = [-np.pi, -np.pi/2., 0, np.pi/2., np.pi]
ax.set_xlim(xticks[0], xticks[-1])
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
ax.set_xlabel('heading')
xticklabels = ['-180', '-90', 'upwind', '90', '180']
ax.set_xticklabels(xticklabels)
ax.set_ylabel('occurences, normalized')
path = config.path
figure_path = os.path.join(config.path, config.figure_path)
save_figure_path=os.path.join(figure_path, 'odor_traces/')
figure_path = os.path.join(path, config.figure_path)
save_figure_path = os.path.join(figure_path, 'odor_traces/')
fig_name_with_path = os.path.join(save_figure_path, savename)
print 'SAVING TO: ', fig_name_with_path
fig.savefig(fig_name_with_path, format='pdf')
def plot_trajectory_lengths(dataset):
lengths = []
for key, trajec in dataset.trajecs.items():
lengths.append(trajec.length)
fig = plt.figure()
ax = fig.add_subplot(111)
fpl.histogram(ax, [np.array(lengths)], bins=50, show_smoothed=False)
def plot_residency_time_histogram(config, dataset, save_figure_path=''):
odor_stimulus = 'on'
threshold_distance_min = 0.05
# no odor
keys_odor_off = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, threshold_distance=-1, odor_stimulus='none', upwind_only=True, threshold_distance_min=0.1, odor=True, post_behavior='landing')
residency_time_odor_off = []
for key in keys_odor_off:
trajec = dataset.trajecs[key]
if trajec.residency_time is not None:
residency_time_odor_off.append(trajec.residency_time)
# odor on, odor experienced
keys_odor_on_true = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, threshold_distance=-1, odor_stimulus='on', upwind_only=True, threshold_distance_min=0.1, odor=True, post_behavior='landing')
residency_time_odor_on_true = []
for key in keys_odor_on_true:
trajec = dataset.trajecs[key]
if trajec.residency_time is not None:
residency_time_odor_on_true.append(trajec.residency_time)
# odor on, odor not experienced
keys_odor_on_false = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, threshold_distance=-1, odor_stimulus='on', upwind_only=False, threshold_distance_min=0.1, odor=False, post_behavior='landing')
residency_time_odor_on_false = []
for key in keys_odor_on_false:
trajec = dataset.trajecs[key]
if trajec.residency_time is not None:
residency_time_odor_on_false.append(trajec.residency_time)
data = [np.array(residency_time_odor_off), np.array(residency_time_odor_on_true), np.array(residency_time_odor_on_false)]
colors = ['black', 'red', 'blue']
nbins = 30 # note: if show_smoothed=True with default butter filter, nbins needs to be > ~15
fig = plt.figure()
ax = fig.add_subplot(111)
fpl.histogram(ax, data, bins=nbins, bin_width_ratio=0.8, colors=colors, edgecolor='none', bar_alpha=1, curve_fill_alpha=0.4, curve_line_alpha=0, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, bootstrap_std=False, exponential_histogram=False)
#xticks = [0,.02,.04,.06,.08,.15]
fpl.adjust_spines(ax, ['left', 'bottom'])
#ax.set_xlim(xticks[0], xticks[-1])
ax.set_xlabel('residency time, frames')
ax.set_ylabel('Occurences, normalized')
ax.set_title('Residency time')
if save_figure_path == '':
save_figure_path = os.path.join(config.path, config.figure_path, 'activity/')
fig.set_size_inches(8,8)
figname = save_figure_path + 'residency_time_on_post_histogram' + '.pdf'
fig.savefig(figname, format='pdf')
def plot_distance_histogram(config, dataset, save_figure_path=''):
# no odor
keys_no_odor = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, odor_stimulus='none', threshold_distance_min=0.1, odor=True)
data_no_odor = []
for key in keys_no_odor:
trajec = dataset.trajecs[key]
if np.max(trajec.distance_to_post) > 0.1:
for f, d in enumerate(trajec.distance_to_post):
if trajec.positions[f,0] > 0:
data_no_odor.append(d)
# odor on, odor experienced
keys_odor_on_true = opa.get_keys_with_odor_before_post(config, dataset, threshold_odor=10, odor_stimulus='on', threshold_distance_min=0.1, odor=True)
data_odor_on_true = []
for key in keys_odor_on_true:
trajec = dataset.trajecs[key]
if np.max(trajec.distance_to_post) > 0.1:
for f, d in enumerate(trajec.distance_to_post):
if trajec.positions[f,0] > 0:
data_odor_on_true.append(d)
print len(data_no_odor)
print len(data_odor_on_true)
data = [np.array(data_no_odor), np.array(data_odor_on_true)]
colors = ['black', 'red']
nbins = 30
bins = np.linspace(0,0.1,nbins)
fig = plt.figure()
ax = fig.add_subplot(111)
fpl.histogram(ax, data, bins=bins, bin_width_ratio=0.8, colors=colors, edgecolor='none', bar_alpha=1, curve_fill_alpha=0.4, curve_line_alpha=0, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, bootstrap_std=True, exponential_histogram=False, n_bootstrap_samples=10000, smoothing_range=[0.005,0.1])
xticks = [0,.02,.04,.06,.08,.1]
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
ax.set_xlim(xticks[0], xticks[-1])
ax.set_xlabel('Distance to post, m')
ax.set_ylabel('Occurences, normalized')
title_text = 'Distance to post. red (upwind, odor) N = ' + str(len(keys_odor_on_true)) + '; black (upwind, no odor) N = ' + str(len(keys_no_odor))
ax.set_title(title_text)
if save_figure_path == '':
save_figure_path = os.path.join(config.path, config.figure_path, 'activity/')
fig.set_size_inches(8,8)
figname = save_figure_path + 'distance_to_post_histogram' + '.pdf'
fig.savefig(figname, format='pdf')
def plot_activity_histogram(dataset, save_figure_path=''):
keys = get_keys(dataset)
print 'number of keys: ', len(keys)
if len(keys) < 1:
print 'No data'
return
odor_local_time = []
no_odor_local_time = []
for i, key in enumerate(keys):
trajec = dataset.trajecs[key]
if trajec.odor is not False:
odor_local_time.append( trajec.timestamp_local_float )
else:
no_odor_local_time.append( trajec.timestamp_local_float )
odor_local_time = np.array(odor_local_time)
no_odor_local_time = np.array(no_odor_local_time)
fig = plt.figure()
ax = fig.add_subplot(111)
nbins = 24 # note: if show_smoothed=True with default butter filter, nbins needs to be > ~15
bins = np.linspace(0,24,nbins)
data = []
colors = []
if len(odor_local_time) > 0:
data.append(odor_local_time)
colors.append('red')
if len(no_odor_local_time) > 0:
data.append(no_odor_local_time)
colors.append('blue')
fpl.histogram(ax, data, bins=bins, bin_width_ratio=0.8, colors=colors, edgecolor='none', bar_alpha=1, curve_fill_alpha=0.4, curve_line_alpha=0, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=False, normed_occurences=False, bootstrap_std=False, exponential_histogram=False)
xticks = np.linspace(bins[0], bins[-1], 5, endpoint=True)
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
ax.set_xlabel('Time of day, hours')
ax.set_ylabel('Occurences, normalized')
ax.set_title('Activity, as measured by number of trajectories: odor=red, no odor=blue')
fig.set_size_inches(8,8)
figname = save_figure_path + 'activity_histogram' + '.pdf'
fig.savefig(figname, format='pdf')
def plot_slipangles(dataset, config, visual_stimulus='none', odor_stimulus='on', odor=True):
orientations, airheadings, groundheadings, eccentricities, speeds, airspeeds = get_orientation_data(dataset, config, visual_stimulus='none', odor_stimulus=odor_stimulus, odor=odor, eccentricity_threshold=0.8)
airslip = airheadings - orientations
groundslip = groundheadings - orientations
data = {'airslip': airslip, 'groundslip': groundslip}
#data = {'orientation': orientations-airheadings, 'airheadings': orientations-groundheadings, 'groundheadings': groundheadings}
color = {'airslip': 'green', 'groundslip': 'red'}
savename = 'slipangle_histogram.pdf'
fig = plt.figure(figsize=(5,4))
ax = fig.add_subplot(111)
bins = np.linspace(-np.pi,np.pi,50)
fpl.histogram(ax, data.values(), bins=bins, bin_width_ratio=1, colors=color.values(), edgecolor='none', bar_alpha=1, curve_fill_alpha=0.2, curve_line_alpha=1, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, smoothing_bins_to_exclude=[])
xticks = [-np.pi, -np.pi/2., 0, np.pi/2., np.pi]
ax.set_xlim(xticks[0], xticks[-1])
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
ax.set_xlabel('slip angle')
xticklabels = ['-180', '-90', '0', '90', '180']
ax.set_xticklabels(xticklabels)
ax.set_ylabel('occurences, normalized')
path = config.path
figure_path = os.path.join(config.path, config.figure_path)
save_figure_path=os.path.join(figure_path, 'odor_traces/')
figure_path = os.path.join(path, config.figure_path)
save_figure_path = os.path.join(figure_path, 'odor_traces/')
fig_name_with_path = os.path.join(save_figure_path, savename)
print 'SAVING TO: ', fig_name_with_path
fig.savefig(fig_name_with_path, format='pdf')
def make_histograms(config, data, colors, axis, savename='histogram.pdf'):
for key, item in data.items():
print key, len(item)
fig = plt.figure(figsize=(5,4))
ax = fig.add_subplot(111)
if axis=='z':
bins = np.linspace(-0.5,0.5,50,endpoint=True)
bins_to_exclude = [24]
elif axis=='xy':
bins = np.linspace(0.05,1,50,endpoint=True)
bins_to_exclude = []
fpl.histogram(ax, data.values(), bins=bins, bin_width_ratio=1, colors=colors.values(), edgecolor='none', bar_alpha=1, curve_fill_alpha=0.2, curve_line_alpha=1, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, smoothing_bins_to_exclude=bins_to_exclude)
if axis=='z':
xticks = [-0.5, -.25, 0, .25, .5]
elif axis == 'xy':
xticks = [0,.2,.4,.6,.8,1.]
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
#ax.set_xticklabels(xticklabels)
ax.set_xlabel('speed in ' + axis)
ax.set_ylabel('occurences, normalized')
ax.set_xlim(xticks[0], xticks[-1])
path = config.path
figure_path = os.path.join(config.path, config.figure_path)
save_figure_path=os.path.join(figure_path, 'odor_traces/')
figure_path = os.path.join(path, config.figure_path)
save_figure_path = os.path.join(figure_path, 'odor_traces/')
fig_name_with_path = os.path.join(save_figure_path, savename)
print 'SAVING TO: ', fig_name_with_path
fig.savefig(fig_name_with_path, format='pdf')
def plot_digits_distribution(n):
fig = plt.figure()
ax = fig.add_subplot(111)
bins = np.arange(-.5,10.5,1)
npages = [30,60,400,1500]
colors = ['black', 'blue', 'green', 'red']
digits = []
for npage in npages:
d = get_digits(n, npage)
digits.append(np.array(d))
xticks = [0,1,2,3,4,5,6,7,8,9]
fpl.histogram(ax, digits, bins=bins, colors=colors, normed=True, show_smoothed=False)
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
ax.set_ylim(0,0.15)
ax.set_xlim(-1,10)
ax.set_xlabel('digit')
ax.set_ylabel('probability')
print 'control odor on: ', len(keys_ok)
time_to_saccade_odor_on_false = get_time_to_saccade(dataset, keys_ok, threshold_odor=0.001)
data = [np.array(time_to_saccade_odor_off), np.array(time_to_saccade_odor_on_true)]
print [len(d) for d in data]
colors = ['black', 'red']
nbins = 25 # note: if show_smoothed=True with default butter filter, nbins needs to be > ~15
bins = np.linspace(0,1,nbins)
fig = plt.figure()
ax = fig.add_subplot(111)
fpl.histogram(ax, data, bins=bins, bin_width_ratio=0.8, colors=colors, edgecolor='none', bar_alpha=1, curve_fill_alpha=0.4, curve_line_alpha=0, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False, bootstrap_std=True, exponential_histogram=False)
xticks = [0,0.2,0.4,0.5, 1.]
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
ax.set_xlim(xticks[0], xticks[-1])
ax.set_xlabel('Time to saccade after leaving odor, secs')
ax.set_ylabel('Occurences, normalized')
ax.set_title('Time to saccade')
if save_figure_path == '':
save_figure_path = os.path.join(config.path, config.figure_path, 'activity/')
fig.set_size_inches(8,8)
figname = save_figure_path + 'time_to_saccade_histogram' + '.pdf'
fig.savefig(figname, format='pdf')
for h in heading_smooth_real:
if h < 0:
heading_flipped_real.append(h+np.pi)
else:
heading_flipped_real.append(h-np.pi)
heading_flipped_real = np.array(heading_flipped_real)
data = [heading_flipped_real, heading_flipped_drift_downwind, heading_flipped_drift_upwind]
fig = plt.figure(figsize=(5,4))
ax = fig.add_subplot(111)
bins = 100
fpl.histogram(ax, data, bins=bins, bin_width_ratio=1, colors=['black', 'green', 'red'], edgecolor='none', bar_alpha=1, curve_fill_alpha=0.2, curve_line_alpha=1, curve_butter_filter=[3,0.3], return_vals=False, show_smoothed=True, normed=True, normed_occurences=False)
xticks = [-np.pi, -np.pi/2., 0, np.pi/2., np.pi]
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks)
xticklabels = ['-180', '-90', 'upwind', '90', '180']
ax.set_xticklabels(xticklabels)
ax.set_xlabel('heading')
ax.set_ylabel('occurences, normalized')
path = config.path
figure_path = os.path.join(config.path, config.figure_path)
save_figure_path=os.path.join(figure_path, 'odor_traces/')
figure_path = os.path.join(path, config.figure_path)
save_figure_path = os.path.join(figure_path, 'odor_traces/')
fig_name_with_path = os.path.join(save_figure_path, 'heading_histogram_drifting.pdf')
def calc_statistical_significance_through_resampling_nested(
data1,
data2,
iterations=1000,
analysis='median',
pval_for_power_calc=0.05,
levels=2):
'''
two tailed only
'''
assert levels == 2, "levels must be 2"
measured_difference = get_meandian_from_nested_data(
data1, levels, analysis) - get_meandian_from_nested_data(
data2, levels, analysis)
data_mixture = []
data_mixture.extend(data1)
data_mixture.extend(data2)
resampled_differences = []
for i in range(iterations):
sample_data1 = get_nested_resampling(data_mixture, len(data1))
sample_data2 = get_nested_resampling(data_mixture, len(data2))
d = get_meandian_from_nested_data(
sample_data1, levels, analysis) - get_meandian_from_nested_data(
sample_data2, levels, analysis)
resampled_differences.append(d)
resampled_differences.sort()
index = np.argmin(np.abs(resampled_differences - measured_difference))
raw_pval = index / float(len(resampled_differences))
pval = 1 - 2 * np.abs(raw_pval - .5)
resampled_data1_differences = []
for i in range(iterations):
sample_data1 = get_nested_resampling(data_mixture, len(data_mixture))
sample_data2 = get_nested_resampling(data1, len(data1))
d = get_meandian_from_nested_data(
sample_data1, levels, analysis) - get_meandian_from_nested_data(
sample_data2, levels, analysis)
resampled_data1_differences.append(d)
resampled_data1_differences.sort()
resampled_data2_differences = []
for i in range(iterations):
sample_data1 = get_nested_resampling(data_mixture, len(data_mixture))
sample_data2 = get_nested_resampling(data2, len(data2))
d = get_meandian_from_nested_data(
sample_data1, levels, analysis) - get_meandian_from_nested_data(
sample_data2, levels, analysis)
resampled_data2_differences.append(d)
resampled_data2_differences.sort()
i_p_lo = resampled_differences[int(pval_for_power_calc / 2. *
len(resampled_differences))]
i_p_hi = resampled_differences[1 - int(pval_for_power_calc / 2. *
len(resampled_differences))]
data1_indices_above_p_hi = len(
np.where(resampled_data1_differences > i_p_hi)[0])
data1_indices_below_p_lo = len(
np.where(resampled_data1_differences < i_p_lo)[0])
data1_power = (data1_indices_above_p_hi +
data1_indices_below_p_lo) / float(
len(resampled_data1_differences))
data2_indices_above_p_hi = len(
np.where(resampled_data2_differences > i_p_hi)[0])
data2_indices_below_p_lo = len(
np.where(resampled_data2_differences < i_p_lo)[0])
data2_power = (data2_indices_above_p_hi +
data2_indices_below_p_lo) / float(
len(resampled_data2_differences))
fig = plt.figure()
ax = fig.add_subplot(111)
fpl.histogram(ax, [
resampled_differences, resampled_data1_differences,
resampled_data2_differences
],
bins=20,
bin_width_ratio=0.6,
colors=['black', 'red', 'green'],
edgecolor='none',
bar_alpha=0.7,
curve_fill_alpha=0.4,
curve_line_alpha=0,
curve_butter_filter=[3, 0.3],
return_vals=False,
show_smoothed=False,
normed=True,
peak_trace_alpha=0,
show_peak_curve=True)
return pval, data1_power, data2_power
def plot_cast_surge(cast_speeds, surge_speeds):
fig = plt.figure()
ax = fig.add_subplot(111)
fpl.histogram(ax, [np.array(cast_speeds),np.array(surge_speeds)], bins=50, colors=['red','green'], normed=False)
def analyze_statistical_exploration():
ndatapoints = [5, 10, 20, 50, 100, 200, 500, 1000, 10000]
medians = []
stdevs = []
for n in ndatapoints:
stats = explore_statistical_measures_across_repetitions(iterations=20,
ndatapoints=n)
medians.append(np.mean(stats, axis=0))
stdevs.append(np.std(stats, axis=0))
medians = np.array(medians)
stdevs = np.array(stdevs)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.semilogx()
ax.plot(ndatapoints, medians[:, 0], color='black', linewidth=4)
ax.vlines(ndatapoints,
medians[:, 0] - stdevs[:, 0],
medians[:, 0] + stdevs[:, 0],
color='black',
linewidth=2)
ax.plot(np.array(ndatapoints) + 0.1 * np.array(ndatapoints),
medians[:, 1],
color='red',
linewidth=4)
ax.vlines(np.array(ndatapoints) + 0.1 * np.array(ndatapoints),
medians[:, 1] - stdevs[:, 1],
medians[:, 1] + stdevs[:, 1],
color='red',
linewidth=2)
ax.plot(np.array(ndatapoints) - 0.1 * np.array(ndatapoints),
medians[:, 2],
color='green',
linewidth=4)
ax.vlines(np.array(ndatapoints) - 0.1 * np.array(ndatapoints),
medians[:, 2] - stdevs[:, 2],
medians[:, 2] + stdevs[:, 2],
color='green',
linewidth=2)
ax.set_xlim(0, 1000)
fpl.adjust_spines(ax, ['left', 'bottom'])
ax.set_xlabel('number of data points')
ax.set_ylabel('statistical measure')
fig.savefig('stats.pdf', format='pdf')
fig = plt.figure(figsize=(10, 8))
bins = np.linspace(0, 30, 30)
for i, n in enumerate(ndatapoints):
ax = fig.add_subplot(1, len(ndatapoints), i + 1)
data1, data2 = get_two_datasets(n)
fpl.histogram(ax, [data1, data2],
bins=bins,
colors=['blue', 'orange'],
show_smoothed=False,
normed=True,
bin_width_ratio=0.9)
ax.set_xlim(bins[0], bins[-1])
fpl.adjust_spines(ax, ['bottom'], xticks=[0, 30])
fig.savefig('distributions.pdf', format='pdf')
def calc_statistical_significance_through_resampling(data1,
data2,
iterations=1000,
analysis='median',
pval_for_power_calc=0.05,
ax=None):
'''
two tailed only
'''
if type(data1) is not list:
data1 = data1.tolist()
if type(data2) is not list:
data2 = data2.tolist()
if analysis == 'median':
measured_difference = np.median(data1) - np.median(data2)
elif analysis == 'mean':
measured_difference = np.mean(data1) - np.mean(data2)
data_mixture = []
data_mixture.extend(data1)
data_mixture.extend(data2)
data_mixture = np.array(data_mixture)
resampled_differences = []
for i in range(iterations):
indices1 = np.random.randint(0, len(data_mixture), len(data1))
indices2 = np.random.randint(0, len(data_mixture), len(data2))
sample_data1 = data_mixture[indices1]
sample_data2 = data_mixture[indices2]
if analysis == 'median':
median1 = np.median(sample_data1)
median2 = np.median(sample_data2)
resampled_differences.append(median1 - median2)
elif analysis == 'mean':
mean1 = np.mean(sample_data1)
mean2 = np.mean(sample_data2)
resampled_differences.append(mean1 - mean2)
resampled_differences.sort()
index = np.argmin(np.abs(resampled_differences - measured_difference))
raw_pval = index / float(len(resampled_differences))
pval = 1 - 2 * np.abs(raw_pval - .5)
resampled_data1_differences = []
for i in range(iterations):
indices1 = np.random.randint(0, len(data_mixture), len(data_mixture))
indices2 = np.random.randint(0, len(data1), len(data1))
sample_data1 = data_mixture[indices1]
sample_data2 = np.array(data1)[indices2]
if analysis == 'median':
median1 = np.median(sample_data1)
median2 = np.median(sample_data2)
resampled_data1_differences.append(median1 - median2)
elif analysis == 'mean':
mean1 = np.mean(sample_data1)
mean2 = np.mean(sample_data2)
resampled_data1_differences.append(mean1 - mean2)
resampled_data2_differences = []
for i in range(iterations):
indices1 = np.random.randint(0, len(data_mixture), len(data_mixture))
indices2 = np.random.randint(0, len(data2), len(data2))
sample_data1 = data_mixture[indices1]
sample_data2 = np.array(data2)[indices2]
if analysis == 'median':
median1 = np.median(sample_data1)
median2 = np.median(sample_data2)
resampled_data2_differences.append(median1 - median2)
elif analysis == 'mean':
mean1 = np.mean(sample_data1)
mean2 = np.mean(sample_data2)
resampled_data2_differences.append(mean1 - mean2)
i_p_lo = resampled_differences[int(pval_for_power_calc / 2. *
len(resampled_differences))]
i_p_hi = resampled_differences[1 - int(pval_for_power_calc / 2. *
len(resampled_differences))]
data1_indices_above_p_hi = len(
np.where(resampled_data1_differences > i_p_hi)[0])
data1_indices_below_p_lo = len(
np.where(resampled_data1_differences < i_p_lo)[0])
data1_power = (data1_indices_above_p_hi +
data1_indices_below_p_lo) / float(
len(resampled_data1_differences))
data2_indices_above_p_hi = len(
np.where(resampled_data2_differences > i_p_hi)[0])
data2_indices_below_p_lo = len(
np.where(resampled_data2_differences < i_p_lo)[0])
data2_power = (data2_indices_above_p_hi +
data2_indices_below_p_lo) / float(
len(resampled_data2_differences))
if ax is not None:
fpl.histogram(ax, [
resampled_differences, resampled_data1_differences,
resampled_data2_differences
],
bins=20,
bin_width_ratio=0.6,
colors=['black', 'red', 'blue'],
edgecolor='none',
bar_alpha=0.7,
curve_fill_alpha=0.4,
curve_line_alpha=0,
curve_butter_filter=[3, 0.3],
return_vals=False,
show_smoothed=False,
normed=True,
peak_trace_alpha=0,
show_peak_curve=True)
return pval, data1_power, data2_power
'''
mean_accels_in_odor.extend((np.diff(trajec.speed[frames_in_odor])*trajec.fps).tolist())
mean_accels_not_in_odor.extend((np.diff(trajec.speed[frames_not_in_odor])*trajec.fps).tolist())
data_in_odor.append(np.array(mean_accels_in_odor))
data_not_in_odor.append(np.array(mean_accels_not_in_odor))
print odor_stimulus, ': ', np.mean(mean_accels), ' +/- ', np.std(mean_accels)
# in odor
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(111)
nbins = 75
bins = np.linspace(-.05,.05,nbins)*100
fpl.histogram(ax, data_in_odor, bins=bins, bin_width_ratio=0.8, colors=['black', 'red'], edgecolor='none', normed=True, show_smoothed=True, bar_alpha=1, curve_line_alpha=0)
ax.set_xlim(bins[0], bins[-1])
fpl.adjust_spines(ax, ['left', 'bottom'])
ax.set_xlabel('Acceleration, m/s2')
ax.set_ylabel('Occurences, normalized')
save_figure_path = os.path.join(config.path, config.figure_path, 'activity/')
figname = save_figure_path + 'accceleration_histogram_in_odor' + '.pdf'
fig.savefig(figname, format='pdf')
# not in odor
fig = plt.figure(figsize=(4,2))
ax = fig.add_subplot(111)
nbins = 75
bins = np.linspace(-.05,.05,nbins)*100
fpl.histogram(ax, data_not_in_odor, bins=bins, bin_width_ratio=0.8, colors=['black', 'blue'], edgecolor='none', normed=True, show_smoothed=True, bar_alpha=1, curve_line_alpha=0)
import pickle
import fly_plot_lib.plot as fpl
import fly_plot_lib.text as flytext
import matplotlib.pyplot as plt
import numpy as np
if __name__ == '__main__':
f = open('sim_results.pickle')
results = pickle.load(f)
f.close()
fig = plt.figure(figsize=(3.3,2))
ax = fig.add_axes([0.05,0.25,0.9,0.65])
print len(results)
colors = ['black', 'orange', 'purple']
fpl.histogram(ax, results, colors=colors[0:len(results)], bins=20, normed=True, show_smoothed=False, bar_alpha=1, bin_width_ratio=1)
xticks = np.linspace(0,20000,11)
xticklabels = [str(int(tick/1000.)) for tick in xticks]
fpl.adjust_spines(ax, ['bottom'], xticks=xticks)
ax.set_xticklabels(xticklabels)
ax.set_xlabel('Time to localization, sec')
flytext.set_fontsize(fig, 8)
fig.savefig('results_plot.pdf', format='pdf')
