def get_clustered_repositories(input_path):
filenames = []
repositories = []
for root, dirpath, files in os.walk(input_path):
for filename in files:
if os.path.splitext(filename)[1] == ".csv" and \
os.path.splitext(filename)[0].endswith(CLUSTERED_FILENAME_POSFIX):
filenames.append(os.path.join(root, filename))
repositories.append(get_repo_name(filename))
return filenames, repositories
def plot_clustered(input_path, filenames, repositories):
fig, ax = set_plot_style(1, 1)
i = 0
max_x = 0
max_y = 0
repo_scatter = {}
cluster_scatter = {}
add_cluster_scatter = True
for filename in filenames:
add_repo_scatter = True
c_pubs, ck_pubs, c_tools, ck_tools = get_pub_tool_count(filename)
cluster_count = len(ck_pubs.keys())
j = 0
for k in ck_pubs:
max_x = max(max_x, ck_pubs[k])
max_y = max(max_y, ck_tools[k])
scatter = ax.scatter(ck_pubs[k], ck_tools[k], marker=get_marker(j), color=get_color(i), alpha=0.5, s=80)
if add_repo_scatter:
repo_scatter[get_repo_name(filename)] = scatter
add_repo_scatter = False
if add_cluster_scatter:
cluster_scatter[get_cluster_label(cluster_count, k)] = scatter
j += 1
add_cluster_scatter = False
i += 1
# The default range of plt when `s` is set in the `scatter`
# method does not keep all the points in the canvas; so their
# values are overridden.
ax.set_ylim(bottom=0.5, top=max_y + (max_y * 0.5))
ax.set_xlim(left=0.5, right=max_x + (max_x * 0.5))
ax.set_yscale('log')
ax.set_xscale('log')
ax.yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%d'))
ax.xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%d'))
ax.set_xlabel("\nPublications Count")
ax.set_ylabel("Tools Count\n")
# It is required to add legend through `add_artist` for it not be overridden by the second legend.
l1 = ax.legend(repo_scatter.values(), repo_scatter.keys(), scatterpoints=1, loc='lower right', ncol=2, title="Repositories")
ax.add_artist(l1)
l2 = ax.legend(cluster_scatter.values(), cluster_scatter.keys(), scatterpoints=1, loc='upper left', ncol=2, title="Clusters")
image_file = os.path.join(input_path, 'plot_pub_tool_clustered.png')
if os.path.isfile(image_file):
os.remove(image_file)
plt.savefig(image_file, bbox_inches='tight')
plt.close()
def run(input_path, plot_density):
files = []
for root, dirpath, filenames in os.walk(input_path):
for filename in filenames:
if os.path.splitext(filename)[1] == ".csv" and \
not os.path.splitext(filename)[0].endswith(CLUSTERED_FILENAME_POSFIX):
files.append(os.path.join(root, filename))
fig, axes = set_plot_style(1, len(files), 3, 16)
ylabel = "Count"
if plot_density:
ylabel = "Probability"
col_counter = 0
for file in files:
tools = pd.read_csv(file, header=0, sep='\t')
_, pre_citations_vectors, post_citations_vectors, _, _, _, delta = get_vectors(
tools)
pre_citations = []
for citation in pre_citations_vectors:
pre_citations.append(np.max(citation))
pre_citations = aggregate(pre_citations, 0, 500)
post_citations = []
for citation in post_citations_vectors:
post_citations.append(np.max(citation))
post_citations = aggregate(post_citations, 0, 500)
plot(axes[col_counter], pre_citations, post_citations, plot_density,
get_repo_name(file), ylabel if col_counter == 0 else None)
col_counter += 1
handles, labels = axes[-1].get_legend_handles_labels()
fig.legend(handles,
labels,
loc='center',
bbox_to_anchor=(0.410, 0.04),
ncol=2,
framealpha=0.0)
image_file = os.path.join(input_path, 'citations_distribution.png')
if os.path.isfile(image_file):
os.remove(image_file)
plt.savefig(image_file, bbox_inches='tight')
plt.close()
def get_pubs_count(input_path):
counts = {}
repos = []
cluster_count = 0
for root, dirpath, filenames in os.walk(input_path):
for filename in filenames:
if os.path.splitext(filename)[1] == ".csv" and \
os.path.splitext(filename)[0].endswith(CLUSTERED_FILENAME_POSFIX):
repos.append(get_repo_name(filename))
clusters = get_clusters(os.path.join(root, filename))
cluster_count = len(clusters.groups)
for k in clusters.groups:
if k not in counts:
counts[k] = {}
counts[k][filename] = len(clusters.groups[k])
return counts, repos, cluster_count
def plot(input_path, filenames, repositories):
fig, ax = set_plot_style(1, 1)
i = 0
max_x = 0
max_y = 0
repo_scatter = {}
cluster_scatter = {}
add_cluster_scatter = True
xs = []
ys = []
zs = []
for filename in filenames:
repo_color = get_color(i)
add_repo_scatter = True
c_pubs, _, c_tools, _ = get_pub_tool_count(filename)
max_x = max(max_x, c_pubs)
max_y = max(max_y, c_tools)
tools = pd.read_csv(filename, header=0, sep='\t')
pre_citations, post_citations = get_citations_count(tools)
xs.append(c_pubs)
ys.append(c_tools)
# it is multiplied by 2 so to make it a bit bigger on the plot so it can
# be seen more easily.
z = ((sum(pre_citations) + sum(post_citations)) / c_pubs) * 2
zs.append(z)
scatter = ax.scatter(c_pubs, c_tools, color=repo_color, alpha=0.5, s=z)
repo_name = get_repo_name(filename)
z_str = '{0:.1f}'.format(z / 2.0)
ax.annotate(\
f"{repo_name}\n({c_pubs}, {c_tools}, {z_str})", \
xy=(c_pubs, c_tools), \
color=repo_color,
textcoords="offset points", \
xytext=OFFSETS[repo_name], \
ha='center', \
#arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.95', color=repo_color)
)
repo_scatter[repo_name] = scatter
i += 1
print(repo_name)
print(f"\tpubs:\t{c_pubs}")
print(f"\ttools:\t{c_tools}")
print(f"\tcitations:\t{sum(pre_citations) + sum(post_citations)}")
#for x,y in zip(xs,ys):
# plt.annotate(f"({x}, {y})", # Label
# (x,y),
# textcoords="offset points", # how to position the text
# xytext=(0,10), # distance from text to points (x,y)
# ha='center') # horizontal alignment can be left, right or center
# The default range of plt when `s` is set in the `scatter`
# method does not keep all the points in the canvas; so their
# values are overridden.
ax.set_ylim(bottom=128, top=max_y + (max_y * 0.5))
ax.set_xlim(left=128, right=max_x + (max_x * 0.5))
ax.set_xscale('log', basex=2)
ax.set_yscale('log', basey=2)
ax.yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%d'))
ax.xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%d'))
ax.set_xlabel("\nPublications Count")
ax.set_ylabel("Tools Count\n")
# It is required to add legend through `add_artist` for it not be overridden by the second legend.
#ax.legend(repo_scatter.values(), repo_scatter.keys(), scatterpoints=1, loc='upper left', ncol=2)
#ax.add_artist(l1)
#l2 = ax.legend(cluster_scatter.values(), cluster_scatter.keys(), scatterpoints=1, loc='upper left', ncol=2, title="Clusters")
image_file = os.path.join(input_path, 'plot_pub_tool.png')
if os.path.isfile(image_file):
os.remove(image_file)
plt.savefig(image_file, bbox_inches='tight')
plt.close()
