def plot_pooled_correlation_dists_by_condition(cfg):
"""
Plot the pooled (ie. combined, pooled) correlation distributions
separately for each of the conditions (each list in fname_group_list)
corresponds to a condition.
Parameters
----------
cfg : dict
the user-specified config dictionary.
The following keys are required::
"group_1_mat_fnames"
"group_2_mat_fnames"
"group_1_color"
"group_2_color"
"group_1_label"
"group_2_label"
Returns
-------
fig : the matplotlib figure object
"""
# CFGCHANGE?
config.require(cfg, [
"group_1_mat_fnames", "group_2_mat_fnames", "group_1_color",
"group_2_color", "group_1_label", "group_2_label"
])
fname_group_list = [cfg["group_1_mat_fnames"], cfg["group_2_mat_fnames"]]
colors = [cfg["group_1_color"], cfg["group_2_color"]]
labels = [cfg["group_1_label"], cfg["group_2_label"]]
n_bins = 100
corr_bins, corr_bin_centers = aux.get_lin_bins(n_bins, -1, 1.)
bin_counts = [np.zeros(n_bins) for f in fname_group_list]
fig = plt.figure()
ax = fig.add_subplot(111)
for i, fname_group in enumerate(fname_group_list):
for fname in fname_group:
flat_corr_mat = \
dataio.get_blacklist_filtered_and_flattened_adj_mat(
fname, cfg["blacklist_fname"]
)
bin_counts[i] += aux.get_bin_counts(flat_corr_mat, corr_bins)
# normalize
bin_counts[i] = bin_counts[i] / \
(np.sum(bin_counts[i] * (corr_bins[1] - corr_bins[0])))
ax.plot(corr_bin_centers,
bin_counts[i],
color=colors[i],
label=labels[i])
ax.set_xlabel(settings.get_prop_tex_name(settings.correlation_tag))
ax.set_ylabel(r"Probability density P(c)")
ax.legend(loc=0)
fig.savefig(cfg['outdata_dir'] + "pooledCorrDists.pdf",
format="pdf",
bbox_inches='tight')
return fig
def plot_pooled_corr_t_val_dists(cfg):
"""
Plot the tvalue distributions for movie and rest
Parameters
----------
config : dict
The following keys are required::
"group_1_mat_fnames"
"group_2_mat_fnames"
"group_1_color"
"group_2_color"
"group_1_label"
"group_2_label"
"outdata_dir"
"paired"
Returns
-------
fig : the matplotlib figure object
"""
config.require(cfg, [
"group_1_mat_fnames", "group_2_mat_fnames", "group_1_color",
"group_2_color", "group_1_label", "group_2_label", "outdata_dir",
"paired"
])
# get tvals
flat_mats = dataio.get_blacklist_filtered_and_flattened_adj_mats(
cfg["all_fnames"], cfg["blacklist_fname"])
if cfg["paired"]:
t_vals = measures.paired_t_value(flat_mats,
len(cfg['group_1_mat_fnames']))
else:
t_vals = measures.unpaired_t_value(flat_mats,
len(cfg['group_1_mat_fnames']))
minVal = np.min(t_vals)
maxVal = np.max(t_vals)
n_bins = 100
bins, binCenters = aux.get_lin_bins(n_bins,
minVal - (np.abs(minVal) * 0.1),
maxVal + (np.abs(maxVal) * 0.1))
bin_counts = aux.get_bin_counts(t_vals, bins)
fig = plt.figure()
ax = fig.add_subplot(111)
# normalize
bin_counts = bin_counts * 1. / (np.sum(bin_counts) * (bins[1] - bins[0]))
labels = cfg['group_1_label'], cfg["group_2_label"]
ax.plot(binCenters, bin_counts, label=r"" + labels[0] + "-" + labels[1])
ax.set_xlabel(settings.get_prop_tex_name(settings.tval_tag))
ax.set_ylabel(r"Probability density")
ax.legend(loc=0)
plt.savefig(cfg["outdata_dir"] + "tvalDist.pdf",
format="pdf",
bbox_inches='tight')
return fig
def plot_individual_correlation_dists(cfg):
"""
Plotting the individual correlation profiles to see the variations.
Parameters
----------
cfg : dict
the user-specified config dictionary.
The following keys are required::
"group_1_mat_fnames"
"group_2_mat_fnames"
"group_1_color"
"group_2_color"
"group_1_label"
"group_2_label"
"outdata_dir"
Returns
-------
fig : the matplotlib figure object
"""
# CFGCHANGE?
config.require(cfg, )
fname_group_list = [cfg["group_1_mat_fnames"], cfg["group_2_mat_fnames"]]
colors = [cfg["group_1_color"], cfg["group_2_color"]]
labels = [cfg["group_1_label"], cfg["group_2_label"]]
fig = plt.figure()
ax = fig.add_subplot(111)
n_bins = 100
corr_bins, corr_bin_centers = aux.get_lin_bins(n_bins, -1, 1.)
for i, fname_group in enumerate(fname_group_list):
for fname in fname_group:
flat_corr_mat = \
dataio.get_blacklist_filtered_and_flattened_adj_mat(
fname, cfg['blacklist_fname']
)
bin_counts = aux.get_bin_counts(flat_corr_mat, corr_bins)
# normalize
bin_counts = bin_counts / \
(np.sum(bin_counts * (corr_bins[1] - corr_bins[0])))
ax.plot(corr_bin_centers,
bin_counts,
color=colors[i],
label=labels[i])
ax.set_xlabel(settings.get_prop_tex_name("corr"))
ax.set_ylabel(r"Probability density P(c)")
ax.legend(loc=0)
fig.savefig(cfg['outdata_dir'] + "individualCorrDists.pdf",
format="pdf",
bbox_inches='tight')
return fig
def plotSameLinksShareVsLouvainSimilarityMeasures(cfg,
filenamesGroup1,
filenamesGroup2,
density=None):
allFNames = filenamesGroup1 + filenamesGroup2
data = dataio.mergeAndLoadLouvainProperties(allFNames, density)
clusterings = data[settings.louvain_cluster_tag]
simMatricesDict = gencomps.computeClusterSimilarityMeasures(clusterings)
linkSimMatData = dataio.load_pickle(
fnc.get_fname(cfg, settings.common_links_tag))
index = 0
densities = linkSimMatData[settings.densities_tag]
if density is None:
pass
else:
for j in range(len(densities)):
if int(densities[index]) == int(density):
index = j
break
linkSimMatrix = linkSimMatData[settings.common_links_tag][index]
linkSimMatrix = linkSimMatrix / float(linkSimMatrix[0, 0]) # get to ratio
triu_indices = np.triu_indices_from(linkSimMatrix, 1)
for measure, simMatrix in simMatricesDict.iteritems():
simMeasures = simMatrix[triu_indices]
linkSims = linkSimMatrix[triu_indices]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(linkSims, simMeasures, "o")
ax.set_ylabel(settings.get_prop_tex_name(measure))
ax.set_xlabel(settings.get_prop_tex_name(settings.common_links_tag))
(r, p) = pearsonr(linkSims, simMeasures)
fig.suptitle(r"Pearson correlation: {:.3f}".format(r))
fig.savefig(cfg["outdata_dir"] + settings.common_links_tag + "_vs_" +
measure + ".pdf",
format="pdf",
bbox_inches='tight')
def plot_sim_mat(ax, m, measure, vmin=None, vmax=None):
"""
Plot a similarity matrix in to the given axis "ax". "m" contains the image
and "measure" is the name of the measure to be plotted (ie. a string)
"""
sortedvals = np.sort(np.unique(m.flatten()))
if measure in ['vi']:
cmap = cm.hot
if vmax is None:
vmax = np.max(m)
if vmin is None:
try:
vmin = sortedvals[1]
except:
vmin = sortedvals[0]
elif measure in ['nmi', 'adjusted_rand', settings.common_links_tag]:
cmap = cm.hot_r
sortedvals = np.sort(np.unique(m.flatten()))
if vmax is None:
vmax_start = sortedvals[-1]
for i in range(2, len(sortedvals)):
vmax = sortedvals[-i]
if (vmax_start - vmax) / vmax_start > 0.0001:
break
if vmin is None:
vmin = np.min(m)
else:
print "trying to plot unknown measure in function plot_sim_mat..." + \
"defaulting the colormap hot_r"
cmap = cm.hot_r
if vmin is None:
vmin = np.min(m)
if vmax is None:
vmax = np.max(m)
im = ax.imshow(m, interpolation='nearest', cmap=cmap, vmax=vmax, vmin=vmin)
cbar = plt.colorbar(im, ax=ax, orientation='horizontal')
cbar.set_label(settings.get_prop_tex_name(measure))
ticks = np.arange(0, len(m))
ax.yaxis.set_ticklabels(ticks + 1)
ax.yaxis.set_ticks(ticks)
ax.xaxis.set_ticks(ticks)
ax.xaxis.set_ticklabels(ticks + 1)
ax.xaxis.set_ticks_position('top')
ax.tick_params(length=0, width=0, colors='k')
ymed = np.average(ax.get_ylim())
xmed = np.average(ax.get_xlim())
ax.axhline(y=ymed, xmin=0, xmax=1, color='0.0', lw=1.0, ls="-")
ax.axvline(x=xmed, ymin=0, ymax=1, color="0.0", lw=1.0, ls="-")
def plotDensityVsAvgPairedShare(cfg):
"""
Plots the avg. fraction of the common links between the two settings
(paired setup)
"""
data = dataio.load_pickle(fnc.get_fname(cfg, settings.common_links_tag))
linkSimMatrices = data[settings.common_links_tag]
n = len(linkSimMatrices[0]) / 2
densities = data[settings.densities_tag]
linkSimAvgs = []
linkSimStds = []
vals = []
for linkSimMatrix in linkSimMatrices:
linkSimMatrix = linkSimMatrix / \
float(linkSimMatrix[0, 0]) # get to ratio
linkSimMatrix[range(n)]
vals.append(linkSimMatrix[range(n), n + np.array(range(n))])
linkSimAvgs.append(np.average(vals[-1]))
linkSimStds.append(np.std(vals[-1]))
fig = plt.figure()
ax = fig.add_subplot(111)
densities = np.array(densities)
ax.set_xlabel(settings.get_prop_tex_name(settings.densities_tag))
ax.set_ylabel(r"Share of common links")
vals = np.array(vals).T
for i, val in enumerate(vals):
ax.plot(densities, val, "r", alpha=0.4, label=str(i + 1))
ax.errorbar(densities, linkSimAvgs, linkSimStds, color="b", label="avg")
ax.set_xscale('log')
l = ax.legend(loc=1)
l.get_frame().set_alpha(0.75)
if cfg['include_mst']:
plt.savefig(cfg["outdata_dir"] + settings.common_links_tag +
"_paired_vs_" + settings.densities_tag + "_with_mst.pdf",
format="pdf",
bbox_inches="tight")
else:
plt.savefig(cfg["outdata_dir"] + settings.common_links_tag +
"_paired_vs_" + settings.densities_tag + ".pdf",
format="pdf",
bbox_inches="tight")
def _plot_comparison_and_p_value(ax,
densities,
means0,
means1,
confInt0,
confInt1,
color0,
color1,
pVals=None,
measure_key="",
paired=False,
n=-1,
labels=None,
xscale="linear",
legendloc=0,
significanceTresholdLine=None):
ax.set_xlabel(settings.get_prop_tex_name(settings.densities_tag))
ax.set_ylabel(settings.get_prop_tex_name(measure_key))
# pax.set_ylabel("P-value")
p1 = ax.plot(densities, means0, label=labels[0], color=color0, lw=1.5)
p2 = ax.plot(densities, means1, label=labels[1], color=color1, lw=1.5)
alpha = 0.25
ax.fill_between(densities,
confInt0[0],
confInt0[1],
color=color0,
alpha=alpha)
ax.fill_between(densities,
confInt1[0],
confInt1[1],
color=color1,
alpha=alpha)
pax = ax.twinx()
if significanceTresholdLine is not None:
pax.axhline(significanceTresholdLine,
xmin=0,
xmax=1,
color="0.4",
lw=1.0,
ls="--",
zorder=-1000)
if paired:
pax.axhline(y=2**-(n - 1),
xmin=0,
xmax=1,
color='0.6',
lw=1.5,
ls="-",
zorder=-1000)
p3 = pax.semilogy(densities,
pVals,
"-o",
color="0.25",
markersize=2.5,
label=r"$p$-value",
zorder=-10)
# to take the left side log scale off (a bug in matplotlib with loglog)
pax.yaxis.tick_right()
pax.set_ylim((10**-4, 10**0))
pax.set_xscale(xscale)
ax.set_xscale(xscale)
if xscale == "log":
ax.set_xlim((np.min(densities), np.max(densities)))
pax.set_xlim((np.min(densities), np.max(densities)))
# make the legend
lns = p1 + p2 + p3
labs = [l.get_label() for l in lns]
l = pax.legend(lns, labs, loc=legendloc, numpoints=1, handlelength=1)
l.get_frame().set_alpha(0.5)
return ax, pax
def plot_link_dist_probs_by_condition(cfg):
"""
Plots the link distance PDFs pooled by condition.
Parameters
----------
cfg : dict
the user-specified config dictionary.
The following keys are required::
"group_1_mat_fnames"
"group_2_mat_fnames"
"group_1_color"
"group_2_color"
"group_1_label"
"group_2_label"
"outdata_dir"
Returns
-------
None
"""
# CFGCHANGE?
config.require(cfg, [
"group_1_mat_fnames", "group_2_mat_fnames", "group_1_color",
"group_2_color", "group_1_label", "group_2_label", "outdata_dir",
"density"
])
fname_group_list = [cfg["group_1_mat_fnames"], cfg["group_2_mat_fnames"]]
colors = [cfg["group_1_color"], cfg["group_2_color"]]
labels = [cfg["group_1_label"], cfg["group_2_label"]]
# ns = []
data_example = dataio.load_pickle(
fnc.get_ind_fname(fname_group_list[0][0], cfg,
settings.link_distance_tag))
densities = data_example[settings.config_tag][settings.densities_tag]
# for individual plots:
# figs = [plt.figure() for p in densities]
# axs = [fig.add_subplot(111) for fig in figs]
group_distances = []
for i, fname in enumerate(fname_group_list):
# ns.append(len(fname))
distances = [np.array([]) for p in densities]
for fname in fname:
print fname
data = dataio.load_pickle(
fnc.get_ind_fname(fname, cfg, settings.link_distance_tag))
p_f_dists = data[settings.link_distance_tag]
for j, p in enumerate(densities):
# print p
distances[j] = np.hstack((distances[j], p_f_dists[j]))
# for individual plots:
# for j in range(len(densities)):
# genplots.plot_inv_cdf(
# axs[j], distances[j], yscale='log',
# label=labels[i], color=colors[i])
group_distances.append(distances)
indices = range(len(densities)) # [6,7,10] #density indices
print densities
for k, j in enumerate(indices):
fig = plt.figure(figsize=(4, 3))
p = densities[j]
ax = fig.add_subplot(1, 1, 1)
ax.set_xlabel(settings.get_prop_tex_name(settings.link_distance_tag))
ax.set_ylabel(r"1-CDF(d)")
print p, j
ax.text(.5,
0.10,
r"$\rho$ = " + str(p * 100) + "\%",
ha='center',
va='center',
transform=ax.transAxes)
for i in range(len(fname_group_list)):
genplots.plot_inv_cdf(ax,
group_distances[i][j],
label=labels[i],
color=colors[i],
yscale='log')
plt.tight_layout()
fig.savefig(cfg['outdata_dir'] + "linkDistProbs_" + str(p) + ".pdf",
format="pdf",
bbox_inches="tight")
plt.close(fig)
return None