def _update_plot(self, axis, view):
sns.tsplot(view.data,
view.xdata,
ax=axis,
condition=view.label,
zorder=self.zorder,
**self.style)
def plot_timeseries(experiment, kinematic=None):
ensemble = experiment.observations.kinematics
# traj_numbers = [int(i) for i in ensemble.index.get_level_values('trajectory_num').unique()]
data_dict = {}
# for each kinematic, slice out each trajectory, append to data_dict
if kinematic is None:
col_list = list(ensemble.columns.values)
col_list.remove('tsi')
col_list.remove('trajectory_num')
else:
col_list = []
for dim in ['x', 'y', 'z']:
col_list.append(kinematic + '_' + dim)
for col in col_list:
data = []
col_data = ensemble[['tsi', 'trajectory_num', col]] # this all trajectories
# # store each trajectory to data
# for i in traj_numbers:
# df = col_data.xs(i, level='trajectory_num')
# data.append(df)
#
# data_dict[col] = data # every key linked to list of lists
data_dict[col] = col_data
#### file naming and directory selection
fileappend, path, agent = get_agent_info(experiment.agent)
titlebase = "{agent} {kinematic} timeseries".format(agent=agent, kinematic="{kinematic}")
numbers = " (n = {})".format(ensemble['trajectory_num'].max())
# k is kinematic v is a list of trajectories
for k, v in data_dict.iteritems():
# print k
# print v
print "plotting {}".format(k)
plt.figure()
# for i, trial in enumerate(v):
# plt.plot(trial.index, trial, alpha=0.3)
# print v
sns.tsplot(data=v, times='tsi', value=k, err_style=None) # "unit_traces") FIXME
format_title = titlebase.format(kinematic=k)
plt.suptitle(format_title + numbers, fontsize=14)
plt.xlabel("Timestep index")
plt.ylabel("Value")
plt.legend() # FIXME remove after debugging
plt.savefig(os.path.join(path, format_title + fileappend + FIG_FORMAT))
plt.show()
def _update_plot(self, axis, view):
kwargs = self.style[self.cyclic_index]
label = view.label if self.overlaid >= 1 else ''
if label:
kwargs['condition'] = label
sns.tsplot(view.data, view.xdata, ax=axis, zorder=self.zorder, **kwargs)
def _update_plot(self, axis, view):
sns.tsplot(view.data, view.xdata, ax=axis, condition=view.label,
zorder=self.zorder, **self.style)
def init_artists(self, ax, plot_data, plot_kwargs):
return {'axis': sns.tsplot(*plot_data, ax=ax, **plot_kwargs)}
def init_artists(self, ax, plot_data, plot_kwargs):
return {'axis': sns.tsplot(*plot_data, ax=ax, **plot_kwargs)}
def forced_swim_timecourse(
df,
bp_style=True,
colorset=QUALITATIVE_COLORSET,
datacolumn_label="Immobility Ratio",
legend_loc="best",
plotstyle="tsplot",
rename_treatments={},
time_label="interval [1 min]",
save_as=False,
):
"""Plot timecourse of forced swim measurements.
Parameters
----------
df : {pandas.Dataframe, string}
Pandas Dataframe containing the experimental data, or path pointing to a csv containing such data.
bp_style : bool, optional
Whether to apply the default behaviopy style.
datacolumn_label : string, optional
A column name from df, the values in which column give the data to plot.
legend_loc : string, optional
Where to place the legend on the figure.
plotstyle : {"pointplot", "tsplot"}
Dictionary with strings as keys and values used to map treatment names onto new stings.
rename_treatments : dict, optional
Dictionary with strings as keys and values used to map treatment names onto new stings.
time_label : dict, optional
A column name from df, the values in which column give the time pointd of the data.
"""
try:
if isinstance(df, basestring):
df = path.abspath(path.expanduser(df))
df = pd.read_csv(df)
except NameError:
if isinstance(df, str):
df = path.abspath(path.expanduser(df))
df = pd.read_csv(df)
for key in rename_treatments:
df.loc[df["Treatment"] == key, "Treatment"] = rename_treatments[key]
df = control_first_reordering(df, "Treatment")
if bp_style:
sns.set_style("white", {'legend.frameon': True})
plt.style.use(u'seaborn-darkgrid')
plt.style.use(u'ggplot')
if plotstyle == "tsplot":
myplot = sns.tsplot(time=time_label,
value=datacolumn_label,
condition="Treatment",
unit="Identifier",
data=df,
err_style="unit_traces",
color=sns.color_palette(colorset))
myplot.set_xticks(list(set(df[time_label])))
elif plotstyle == "pointplot":
sns.pointplot(x=time_label,
y=datacolumn_label,
hue="Treatment",
data=df,
palette=sns.color_palette(colorset),
legend_out=False,
dodge=0.1)
plt.legend(loc=legend_loc, frameon=True)
if save_as:
plt.savefig(path.abspath(path.expanduser(save_as)),
bbox_inches='tight')
