def multiple_densities(results,
outcomes_to_show=[],
points_in_time=[],
group_by = None,
grouping_specifiers = None,
density=KDE,
titles={},
ylabels={},
legend=True,
experiments_to_show=None,
plot_type = ENVELOPE,
**kwargs):
'''
Make an envelope plot with multiple density plots over the run time
:param results: return from :meth:`perform_experiments`.
:param outcomes_to_show: list of outcome of interest you want to plot. If
empty, all outcomes are plotted. **Note**: just
names.
:param points_in_time: a list of points in time for which you want to see
the density. At the moment up to 6 points in time
are supported.
:param group_by: name of the column in the cases array to group results by.
Alternatively, `index` can be used to use indexing arrays
as the basis for grouping.
:param grouping_specifiers: set of categories to be used as a basis for
grouping by. Grouping_specifiers is only
meaningful if group_by is provided as well. In
case of grouping by index, the grouping
specifiers should be in a dictonary where the
key denotes the name of the group.
:param density: field, either KDE or HIST
:param titles: a way for controlling whether each of the axes should have
a title. There are three possibilities. If set to None, no
title will be shown for any of the axes. If set to an empty
dict, the default, the title is identical to the name of the
outcome of interest. If you want to override these default
names, provide a dict with the outcome of interest as key
and the desired title as value. This dict need only contain
the outcomes for which you want to use a different title.
:param ylabels: a way for controlling the ylablels. Works identical to
titles.
:param legend: boolean, if true, and there is a column specified for
grouping, show a legend.
:param experiments_to_show: numpy array containing the indices of the
experiments to be visualized. Defaults to None,
implying that all experiments should be shown.
:plot_type: kwarg for controling the type of main plot. Can be one of
ENVELOPE, LINES, or ENV_LIN
:rtype: a `figure <http://matplotlib.sourceforge.net/api/figure_api.html>`_ instance
Additional key word arguments will be passed along to the density function,
======== ===================================
property description
======== ===================================
log log the resulting histogram or GKDE
======== ===================================
.. rubric:: an example of use
.. note:: the current implementation is limited to seven different
categories in case of column, categories, and/or discretesize.
This limit is due to the colors specified in COLOR_LIST.
.. note:: the connection patches are for some reason not drawn if log
scaling is used for the density plots. This appears to be an
issue in matplotlib itself.
'''
if not outcomes_to_show:
outcomes_to_show = results[1].keys()
outcomes_to_show.remove(TIME)
elif type(outcomes_to_show)==StringType:
outcomes_to_show=[outcomes_to_show]
axes_dicts = {}
for outcome_to_show in outcomes_to_show:
axes_dict = {}
axes_dicts[outcome_to_show] = axes_dict
if plot_type != ENV_LIN:
# standard way of pre processing data
if experiments_to_show != None:
experiments, outcomes = results
experiments = experiments[experiments_to_show]
new_outcomes = {}
for key, value in outcomes.items():
new_outcomes[key] = value[experiments_to_show]
results = experiments, new_outcomes
data = prepare_data(results, [outcome_to_show], group_by,
grouping_specifiers)
outcomes, outcomes_to_show, time, grouping_labels = data
del outcomes_to_show
#start of plotting
fig = plt.figure()
axes_dict["fig"] = fig
# making of grid
if not points_in_time:
raise EMAError("no points in time specified")
if len(points_in_time) == 1:
ax_env = plt.subplot2grid((2,3), (0,0), colspan=3)
ax1 = plt.subplot2grid((2,3), (1,1), )
kde_axes = [ax1]
elif len(points_in_time) == 2:
ax_env = plt.subplot2grid((2,2), (0,0), colspan=2)
ax1 = plt.subplot2grid((2,2), (1,0), )
ax2 = plt.subplot2grid((2,2), (1,1), sharex=ax1)
kde_axes = [ax1, ax2]
elif len(points_in_time) == 3:
ax_env = plt.subplot2grid((2,3), (0,0), colspan=3)
ax1 = plt.subplot2grid((2,3), (1,0), )
ax2 = plt.subplot2grid((2,3), (1,1), sharex=ax1)
ax3 = plt.subplot2grid((2,3), (1,2), sharex=ax1)
kde_axes = [ax1, ax2, ax3]
elif len(points_in_time) == 4:
ax_env = plt.subplot2grid((2,4), (0,1), colspan=2)
ax1 = plt.subplot2grid((2,4), (1,0), )
ax2 = plt.subplot2grid((2,4), (1,1), sharex=ax1)
ax3 = plt.subplot2grid((2,4), (1,2), sharex=ax1)
ax4 = plt.subplot2grid((2,4), (1,3), sharex=ax1)
kde_axes = [ax1, ax2, ax3, ax4]
elif len(points_in_time) == 5:
ax_env = plt.subplot2grid((2,5), (0,1), colspan=3)
ax1 = plt.subplot2grid((2,5), (1,0), )
ax2 = plt.subplot2grid((2,5), (1,1), sharex=ax1)
ax3 = plt.subplot2grid((2,5), (1,2), sharex=ax1)
ax4 = plt.subplot2grid((2,5), (1,3), sharex=ax1)
ax5 = plt.subplot2grid((2,5), (1,4), sharex=ax1)
kde_axes = [ax1, ax2, ax3, ax4, ax5]
elif len(points_in_time) == 6:
ax_env = plt.subplot2grid((2,6), (0,1), colspan=4)
ax1 = plt.subplot2grid((2,6), (1,0), )
ax2 = plt.subplot2grid((2,6), (1,1), sharex=ax1)
ax3 = plt.subplot2grid((2,6), (1,2), sharex=ax1)
ax4 = plt.subplot2grid((2,6), (1,3), sharex=ax1)
ax5 = plt.subplot2grid((2,6), (1,4), sharex=ax1)
ax6 = plt.subplot2grid((2,6), (1,5), sharex=ax1)
kde_axes = [ax1, ax2, ax3, ax4, ax5, ax6, ]
else:
raise EMAError("too many points in time provided")
axes_dict["main plot"] = ax_env
for n, entry in enumerate(kde_axes):
axes_dict["density_%s" % n] = entry
#turn of ticks for all but the first density
if n > 0:
for tl in entry.get_yticklabels():
tl.set_visible(False)
# bit of a trick to avoid duplicating code. If no subgroups are
# specified, nest the outcomes one step deeper in de dict so the
# iteration below can proceed normally.
if not grouping_labels:
grouping_labels=[""]
outcomes[""]=outcomes
max_x = 0
for j, key in enumerate(grouping_labels):
value = outcomes[key][outcome_to_show]
if plot_type == ENVELOPE:
plot_envelope(ax_env, j, time, value, fill=False)
elif plot_type == LINES:
ax_env.plot(time.T, value.T)
elif plot_type == ENV_LIN:
plot_envelope(ax_env, j, time, value, fill=True)
if experiments_to_show!=None:
ax_env.plot(time.T, value[experiments_to_show].T)
else:
ax_env.plot(time.T, value.T)
ax_env.set_xlim(time[0], time[-1])
ax_env.set_xlabel(TIME_LABEL)
do_ylabels(ax_env, ylabels, outcome_to_show)
do_titles(ax_env, titles, outcome_to_show)
# this might seem a bit strange but under some conditions can the
# autoscaling of the y_axis be # changed due to the plot command
# for the crossection line. This overrides the autoscaling
# updating.
min_y, max_y = ax_env.get_ylim()
ax_env.autoscale(enable=False, axis='y')
for i, ax in enumerate(kde_axes):
time_value = points_in_time[i]
if time_value:
index = np.where(time==points_in_time[i])[0][0]
if density==KDE:
kde_x, kde_y = determine_kde(value[:,index])
plot_kde(ax, kde_x, kde_y, j,**kwargs)
#update max_x
max_kde =np.max(kde_x)
if max_kde > max_x and max_kde < 10:
max_x = max_kde
ax_env.plot([points_in_time[i],points_in_time[i]],
[min_y,max_y], c='k', ls='--')
con = ConnectionPatch(xyA=(time_value, 0),
xyB=(min_y,max_y), coordsA="data",
coordsB="data", axesA=ax_env,
axesB=ax)
ax_env.add_artist(con)
if density == HIST:
for i, ax in enumerate(kde_axes):
time_value = points_in_time[i]
index = np.where(time==points_in_time[i])[0][0]
values = [outcomes[key][outcome_to_show][:,index] for key in\
grouping_labels]
n, bins, patches = plot_histogram(ax, values, **kwargs)
del bins, patches
if np.max(n) > max_x and np.max(n)<10:
max_x = np.max(n)
for ax in kde_axes:
ax.get_yaxis().set_view_interval(
ax_env.get_yaxis().get_view_interval()[0],
ax_env.get_yaxis().get_view_interval()[1])
# ax.set_xlim(xmin=0,xmax=math.ceil(max_x))
ax.set_xlim(xmin=0,xmax=max_x)
if legend and group_by:
make_legend(grouping_labels, fig)
return fig, axes_dicts
def kde_over_time(results,
outcomes_to_show = [],
group_by = None,
grouping_specifiers = None,
results_to_show=None,
colormap='jet',
# color_bar=False,
log=True):
'''
This is the 2d equivalent of 3d envelopes, where the density is visualized
through a heatmap, rather then in the third dimension.
:param results: return from :meth:`perform_experiments`.
:param outcomes_to_show: list of outcome of interest you want to plot. If
empty, all outcomes are plotted. **Note**: just
names.
:param group: name of the column in the cases array to group results by.
:param grouping_specifiers: set of categories to be used as a basis for
grouping by. Categories is only meaningful if
column is provided as well. **Note**: grouping
specifiers should be an iterable.
:param colormap:
:param log:
TODO:: a colorbar boolean should be added. This controls whether a
colorbar is shown for each axes.
'''
#determine the minima and maxima over all runs
minima = {}
maxima = {}
for key, value in results[1].items():
minima[key] = np.min(value)
maxima[key] = np.max(value)
prepared_data = prepare_data(results, outcomes_to_show, group_by,
grouping_specifiers)
outcomes, outcomes_to_show, time, grouping_specifiers = prepared_data
del time
if group_by:
figures = []
axes_dicts = {}
for key, value in outcomes.items():
fig, axes_dict = simple_kde(value, outcomes_to_show, colormap, log, minima,
maxima)
fig.suptitle(key)
figures.append(fig)
axes_dicts[key] = axes_dict
for outcome in outcomes_to_show:
vmax = -1
for entry in axes_dicts.values():
vmax = max(entry[outcome].images[0].norm.vmax, vmax)
for entry in axes_dicts.values():
ax = entry[outcome]
ax.images[0].set_clim(vmin=0, vmax=vmax)
del vmax
return figures, axes_dicts
else:
return simple_kde(outcomes, outcomes_to_show, colormap, log, minima,
maxima)
def plot_lines_with_envelopes(results,
outcomes_to_show = [],
group_by = None,
grouping_specifiers = None,
density='',
titles={},
ylabels={},
legend=True,
experiments_to_show=None,
**kwargs):
'''
Helper function for generating a plot which contains both an envelope and
lines.
:param results: return from :meth:`perform_experiments`.
:param outcomes_to_show: list of outcome of interest you want to plot. If
empty, all outcomes are plotted. **Note**: just
:param group_by: name of the column in the cases array to group results by.
Alternatively, `index` can be used to use indexing arrays
as the basis for grouping.
:param grouping_specifiers: set of categories to be used as a basis for
grouping by. Grouping_specifiers is only
meaningful if group_by is provided as well. In
case of grouping by index, the grouping
specifiers should be in a dictonary where the
key denotes the name of the group.
:param density: boolean, if true, the density of the endstates will be
plotted.
:param titles: a way for controlling whether each of the axes should have
a title. There are three possibilities. If set to None, no
title will be shown for any of the axes. If set to an empty
dict, the default, the title is identical to the name of the
outcome of interest. If you want to override these default
names, provide a dict with the outcome of interest as key
and the desired title as value. This dict need only contain
the outcomes for which you want to use a different title.
:param ylabels: a way for controlling the ylablels. Works identical to
titles.
:param legend: boolean, if true, and there is a column specified for
grouping, show a legend
:param experiments_to_show: numpy array containing the indices of the
experiments to be visualized. Defaults to None,
implying that all experiments should be shown.
:rtype: a `figure <http://matplotlib.sourceforge.net/api/figure_api.html>`_ instance
Additional key word arguments will be passed along to the density function.
======== ===============================
property description
======== ===============================
log log scale the histogram or GKDE
======== ===============================
'''
# make sure we have the data
full_results = copy.deepcopy(results)
experiments, outcomes = results
experiments = experiments[experiments_to_show]
new_outcomes={}
for key, value in outcomes.items():
new_outcomes[key] =value[experiments_to_show]
results = experiments, new_outcomes
data = prepare_data(results, outcomes_to_show, group_by,
grouping_specifiers)
outcomes, outcomes_to_show, time, grouping_labels = data
full_outcomes = prepare_data(full_results, outcomes_to_show, group_by,
grouping_specifiers)[0]
figure, grid = make_grid(outcomes_to_show, density)
axes_dict = {}
# do the plotting
for i, outcome_to_plot in enumerate(outcomes_to_show):
ax = figure.add_subplot(grid[i,0])
axes_dict[outcome_to_plot] = ax
ax_d= None
if density:
ax_d = figure.add_subplot(grid[i,1])
axes_dict[outcome_to_plot+"_density"] = ax_d
for tl in ax_d.get_yticklabels():
tl.set_visible(False)
if group_by:
# group_by_labels = sorted(outcomes.keys())
for j, key in enumerate(grouping_labels):
full_value = full_outcomes[key][outcome_to_plot]
plot_envelope(ax, j, time, full_value, fill=True)
for j, key in enumerate(grouping_labels):
value = outcomes[key][outcome_to_plot]
full_value = full_outcomes[key][outcome_to_plot]
ax.plot(time.T[:, np.newaxis], value.T, COLOR_LIST[j])
if density=='kde':
simple_density(density, full_value, ax_d, ax, **kwargs)
# kde_x, kde_y = determine_kde(full_value[:,-1])
# plot_kde(ax_d, kde_x, kde_y, j, **kwargs)
if density:
if density=='hist':
values = [full_outcomes[key][outcome_to_plot][:,-1]\
for key in grouping_labels]
plot_histogram(ax_d, values, **kwargs)
ax_d.get_yaxis().set_view_interval(
ax.get_yaxis().get_view_interval()[0],
ax.get_yaxis().get_view_interval()[1])
else:
value = full_outcomes[outcome_to_plot]
plot_envelope(ax, 0, time, value, fill=True)
if density:
simple_density(density, value, ax_d, ax, **kwargs)
value = outcomes[outcome_to_plot]
ax.plot(time.T, value.T)
ax.set_xlim(xmin=time[0] , xmax=time[-1])
ax.set_xlabel(TIME_LABEL)
do_ylabels(ax, ylabels, outcome_to_plot)
do_titles(ax, titles, outcome_to_plot)
if legend and group_by:
make_legend(grouping_labels, figure)
if plotting_util.TIGHT:
grid.tight_layout(figure)
return figure, axes_dict
def envelopes(results,
outcomes_to_show = [],
group_by = None,
grouping_specifiers = None,
density='',
fill=False,
legend=True,
titles={},
ylabels={},
**kwargs):
'''
Make envelop plots. An envelope shows over time the minimum and maximum
value for a set of runs over time. It is thus to be used in case of time
series data. The function will try to find a result labeled "TIME". If this
is present, these values will be used on the X-axis. In case of Vensim
models, TIME is present by default.
:param results: return from :meth:`perform_experiments`.
:param outcomes_to_show: list of outcome of interest you want to plot. If
empty, all outcomes are plotted. **Note**: just
names.
:param group_by: name of the column in the cases array to group results by.
Alternatively, `index` can be used to use indexing arrays
as the basis for grouping.
:param grouping_specifiers: set of categories to be used as a basis for
grouping by. Grouping_specifiers is only
meaningful if group_by is provided as well. In
case of grouping by index, the grouping
specifiers should be in a dictionary where the
key denotes the name of the group.
:param density: boolean, if true, the density of the endstates will be
plotted.
:param fill: boolean, if true, fill the envelope.
:param legend: boolean, if true, and there is a column specified for
grouping, show a legend.
:param titles: a way for controlling whether each of the axes should have
a title. There are three possibilities. If set to None, no
title will be shown for any of the axes. If set to an empty
dict, the default, the title is identical to the name of the
outcome of interest. If you want to override these default
names, provide a dict with the outcome of interest as key
and the desired title as value. This dict need only contain
the outcomes for which you want to use a different title.
:param ylabels: a way for controlling the ylabels. Works identical to
titles.
:rtype: a `figure <http://matplotlib.sourceforge.net/api/figure_api.html>`_ instance
and a dict with the individual axes.
Additional key word arguments will be passed along to the density function,
if density is `True`.
======== ===================================
property description
======== ===================================
log log the resulting histogram or GKDE
======== ===================================
.. rubric:: an example of use
>>> import expWorkbench.util as util
>>> data = util.load_results(r'1000 flu cases.cPickle')
>>> envelopes(data, column='policy')
will show an envelope for three three different policies, for all the
outcomes of interest.
.. plot:: ../docs/source/pyplots/basicEnvelope.py
while
>>> envelopes(data, column='policy', categories=['static policy', 'adaptive policy'])
will only show results for the two specified policies, ignoring any results
associated with \'no policy\'.
.. plot:: ../docs/source/pyplots/basicEnvelope2.py
.. note:: the current implementation is limited to seven different
categories in case of column, categories, and/or discretesize.
This limit is due to the colors specified in COLOR_LIST.
'''
debug("generating envelopes")
prepared_data = prepare_data(results, outcomes_to_show, group_by,
grouping_specifiers)
outcomes, outcomes_to_show, time, grouping_labels = prepared_data
figure, grid = make_grid(outcomes_to_show, density)
# do the plotting
axes_dict = {}
for i, outcome_to_plot in enumerate(outcomes_to_show):
ax = figure.add_subplot(grid[i,0])
axes_dict[outcome_to_plot] = ax
ax_d= None
if density:
ax_d = figure.add_subplot(grid[i,1])
axes_dict[outcome_to_plot+"_density"] = ax_d
if group_by:
# group_labels = sorted(outcomes.keys())
group_by_envelopes(outcomes,outcome_to_plot, time, density,
ax, ax_d, fill, grouping_labels, **kwargs)
else:
single_envelope(outcomes, outcome_to_plot, time, density,
ax, ax_d, fill, **kwargs)
if ax_d:
for tl in ax_d.get_yticklabels():
tl.set_visible(False)
ax.set_xlabel(TIME_LABEL)
do_ylabels(ax, ylabels, outcome_to_plot)
do_titles(ax, titles, outcome_to_plot)
if legend and group_by:
make_legend(grouping_labels, figure)
if plotting_util.TIGHT:
grid.tight_layout(figure)
return figure, axes_dict
def lines(results,
outcomes_to_show = [],
group_by = None,
grouping_specifiers = None,
density='',
titles={},
ylabels={},
legend=True,
experiments_to_show=None,
show_envelope=False,
**kwargs):
'''
This function takes the results from :meth:`perform_experiments` and
visualizes these as line plots. It is thus to be used in case of time
series data. The function will try to find a result labeled "TIME". If this
is present, these values will be used on the X-axis. In case of Vensim
models, TIME is present by default.
:param results: return from :meth:`perform_experiments`.
:param outcomes_to_show: list of outcome of interest you want to plot. If
empty, all outcomes are plotted. **Note**: just
names.
:param group_by: name of the column in the cases array to group results by.
Alternatively, `index` can be used to use indexing arrays
as the basis for grouping.
:param grouping_specifiers: set of categories to be used as a basis for
grouping by. Grouping_specifiers is only
meaningful if group_by is provided as well. In
case of grouping by index, the grouping
specifiers should be in a dictionary where the
key denotes the name of the group.
:param density: boolean, if true, the density of the endstates will be
plotted.
:param legend: boolean, if true, and there is a column specified for
grouping, show a legend.
:param titles: a way for controlling whether each of the axes should have
a title. There are three possibilities. If set to None, no
title will be shown for any of the axes. If set to an empty
dict, the default, the title is identical to the name of the
outcome of interest. If you want to override these default
names, provide a dict with the outcome of interest as key
and the desired title as value. This dict need only contain
the outcomes for which you want to use a different title.
:param ylabels: a way for controlling the ylabels. Works identical to
titles.
:param experiments_to_show: numpy array containing the indices of the
experiments to be visualized. Defaults to None,
implying that all experiments should be shown.
:param show_envelope: boolean, indicates whether envelopes should be
plotted in combination with lines. Default is False.
:rtype: a `figure <http://matplotlib.sourceforge.net/api/figure_api.html>`_ instance
and a dict with the individual axes.
.. note:: the current implementation is limited to seven different
categories in case of column, categories, and/or discretesize.
This limit is due to the colors specified in COLOR_LIST.
'''
debug("generating line graph")
if show_envelope:
return plot_lines_with_envelopes(results,
outcomes_to_show=outcomes_to_show,
group_by=group_by, legend=legend, density=density,
grouping_specifiers=grouping_specifiers,
experiments_to_show=experiments_to_show,
titles=titles, ylabels=ylabels, **kwargs)
if experiments_to_show != None:
experiments, outcomes = results
experiments = experiments[experiments_to_show]
new_outcomes = {}
for key, value in outcomes.items():
new_outcomes[key] = value[experiments_to_show]
results = experiments, new_outcomes
data = prepare_data(results, outcomes_to_show, group_by,
grouping_specifiers)
outcomes, outcomes_to_show, time, grouping_labels = data
figure, grid = make_grid(outcomes_to_show, density)
axes_dict = {}
# do the plotting
for i, outcome_to_plot in enumerate(outcomes_to_show):
ax = figure.add_subplot(grid[i,0])
axes_dict[outcome_to_plot] = ax
ax_d= None
if density:
ax_d = figure.add_subplot(grid[i,1])
axes_dict[outcome_to_plot+"_density"] = ax_d
for tl in ax_d.get_yticklabels():
tl.set_visible(False)
if group_by:
# group_by_labels = sorted(outcomes.keys())
group_by_lines(outcomes,outcome_to_plot, time, density,
ax, ax_d, grouping_labels, **kwargs)
else:
simple_lines(outcomes, outcome_to_plot, time, density,
ax, ax_d, **kwargs)
ax.set_xlabel(TIME_LABEL)
do_ylabels(ax, ylabels, outcome_to_plot)
do_titles(ax, titles, outcome_to_plot)
if legend and group_by:
make_legend(grouping_labels, figure)
if plotting_util.TIGHT:
grid.tight_layout(figure)
return figure, axes_dict
