def plot_colored_cartesian_spagetti(dataset, axis='xy', xlim=(-0.2, .2), ylim=(-0.75, .25), zlim=(0, 0.3), keys=None, keys_to_highlight=[], show_saccades=False, colormap='jet', color_attribute='speed', norm=(0,0.5), artists=None, save_figure_path=''):
keys = get_keys(dataset)
print 'plotting spagetti, axis: ', axis
print 'number of keys: ', len(keys)
if len(keys) < 1:
print 'No data'
return
fig = plt.figure()
ax = fig.add_subplot(111)
if axis=='xy': # xy plane
ax.set_ylim(ylim[0], ylim[1])
ax.set_xlim(xlim[0], xlim[1])
ax.set_autoscale_on(True)
ax.set_aspect('equal')
axes=[0,1]
fap.cartesian_spagetti(ax, dataset, keys=keys, nkeys=300, start_key=0, axes=axes, show_saccades=show_saccades, keys_to_highlight=[], colormap=colormap, color_attribute=color_attribute, norm=norm, show_start=False)
if axis=='yz': # yz plane
ax.set_ylim(zlim[0], zlim[1])
ax.set_xlim(ylim[0], ylim[1])
ax.set_autoscale_on(True)
ax.set_aspect('equal')
axes=[1,2]
fap.cartesian_spagetti(ax, dataset, keys=keys, nkeys=300, start_key=0, axes=axes, show_saccades=show_saccades, keys_to_highlight=[], colormap=colormap, color_attribute=color_attribute, norm=norm, show_start=False)
if artists is not None:
for artist in artists:
ax.add_artist(artist)
#prep_cartesian_spagetti_for_saving(ax)
xticks = np.linspace(xlim[0], xlim[1], 3, endpoint=True).tolist()
yticks = np.linspace(ylim[0], ylim[1], 5, endpoint=True).tolist()
zticks = np.linspace(zlim[0], zlim[1], 3, endpoint=True).tolist()
if axis=='xy':
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=xticks, yticks=yticks)
ax.set_xlabel('x axis, m')
ax.set_ylabel('y axis, m')
ax.set_title('xy plot, color=speed from 0-0.5 m/s')
if axis=='yz':
fpl.adjust_spines(ax, ['left', 'bottom'], xticks=yticks, yticks=zticks)
ax.set_xlabel('y axis, m')
ax.set_ylabel('z axis, m')
ax.set_title('yz plot, color=speed from 0-0.5 m/s')
fig.set_size_inches(8,8)
figname = save_figure_path + 'spagetti_' + axis + '.pdf'
fig.savefig(figname, format='pdf')
return ax
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
local_time = []
for i, key in enumerate(keys):
trajec = dataset.trajecs[key]
local_time.append(trajec.timestamp_local_float)
local_time = np.array(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)
fpl.histogram(
ax,
[local_time],
bins=bins,
bin_width_ratio=0.8,
colors=["green"],
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 = 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")
fig.set_size_inches(8, 8)
figname = save_figure_path + "activity_histogram" + ".pdf"
fig.savefig(figname, format="pdf")
