def plot_year_wise_publications():
p = palette.Palette()
year = Counter()
labels = []
values = []
total = 0
for d in get_data():
year.update({d.year})
if d.year == 1980:
total += 1
for y in year.most_common():
labels.append(y[0])
values.append(y[1])
zipped = zip(labels, values)
zipped = sorted(zipped)
zipped.reverse()
labels, values = zip(*zipped)
labels = list(labels)
ind = range(len(labels))
fig = plt.figure()
ax = fig.add_subplot(111)
plt.bar(ind, values, color=p.cerulean())
plt.xticks(ind, labels, rotation=90)
plt.ylabel("Number of publications")
plt.xlabel("Year (till 11 March 2019)")
plt.tight_layout()
plt.savefig("year_wise.png", type="png", dpi=300)
plt.show()
def large_file_year_stats():
headers = ["Title", "URL", "Description", "Details", "ShortDetails", "Resource", "Type", "Identifiers", "Db",
"EntrezUID", "Properties"]
chunk_size = 100000
base_df = pd.DataFrame(columns=headers)
for chunk in pd.read_csv(FILE_PUBMED, chunksize=chunk_size, names=headers, low_memory=False):
chunk['year'] = chunk['ShortDetails'].str[-4:]
base_df = pd.concat([base_df, chunk], sort=False)
base_df = base_df.iloc[1:]
base_df = base_df[base_df['year'].apply(lambda x: check_if_number(x))]
base_df["year"] = base_df["year"].apply(pd.to_numeric)
base_df = base_df[base_df.year > 1979]
s = base_df["year"].value_counts()
labels = list(s.index)
values = s.values
zipped = zip(labels, values)
zipped = sorted(zipped)
zipped.reverse()
labels, values = zip(*zipped)
ind = range(len(labels))
plt.bar(ind, values, color=palette.Palette().magenta())
plt.xticks(ind, labels, rotation=90)
plt.ylabel("Number of publications")
plt.xlabel("Year (till 11 March 2019)")
plt.tight_layout()
plt.savefig("year_wise_other.png", type="png", dpi=300)
plt.show()
def author_per_year():
p = palette.Palette()
year = defaultdict(int)
articles = defaultdict(int)
labels = []
values = []
for d in get_data():
if d.year > 1979:
year[d.year] += len(d.authors)
articles[d.year] += 1
for y in year:
labels.append(y)
values.append(year[y] / articles[y])
zipped = zip(labels, values)
zipped = sorted(zipped)
zipped.reverse()
labels, values = zip(*zipped)
labels = list(labels)
ind = range(len(labels))
plt.bar(ind, values, color=p.green())
plt.xticks(ind, labels, rotation=90)
plt.ylabel("Number of authors per article")
plt.xlabel("Year (till 11 March 2019)")
plt.tight_layout()
plt.grid(axis='y', color="k", alpha=0.5, linestyle="--")
plt.savefig("author_wise.png", type="png", dpi=300)
plt.show()
def plot_expression_profile(gene: str, parent_structure: str = None):
p = palette.Palette()
entries_start = []
for d in get_expression_pattern_in(gene, parent_structure):
entries_start.append(float(d.start_stage.time))
plt.hist(entries_start, color=p.cerulean())
plt.ylabel("Frequency of Publications")
plt.xlabel("hpf")
plt.title("'{0}' Expression Star Stage".format(gene))
plt.show()
def budget_plot():
p = palette.Palette()
data = []
with open(FILE_BUDGET_DATA) as f:
for line in csv.reader(f):
data.append(line[4:])
ind = range(len(data[0]))
plt.bar(ind, [round(float(x), 2) for x in data[2]], color=p.blue(), label="World")
plt.bar(ind, [round(float(x), 2) for x in data[1]], color=p.orange(), label="India")
plt.ylabel("% of GDP")
plt.xlabel("Year")
plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05),
ncol=2, fancybox=True, shadow=True)
plt.xticks(ind, data[0], rotation=90)
plt.show()
def journal_over_years():
number_of_journals = 3
p = palette.Palette(remove_white=True)
year_data = defaultdict(Counter)
for d in get_data():
if 2019 > d.year > 1999:
year_data[d.year].update({d.journal})
year_label = []
year_values = []
year_journals = []
global_color = defaultdict(list)
for y in year_data:
total = 0
current_year = []
for t in year_data[y].most_common():
total += t[1]
if len(current_year) < number_of_journals:
current_year.append(t)
temp_values = []
temp_name = []
for c in current_year:
temp_values.append(c[1] * 100 / total)
temp_name.append(c[0])
global_color[c[0]].append(1)
year_label.append(f"{y}")
year_values.append(temp_values)
year_journals.append(temp_name)
zipped = zip(year_label, year_values, year_journals)
zipped = sorted(zipped)
year_label, year_values, year_journals = zip(*zipped)
color_palette = []
for f in p:
color_palette.append(f)
if len(color_palette) < len(global_color):
color_palette.extend(p.random(no_of_colors=len(global_color), grade=40))
colors = {}
for g in global_color:
colors[g] = color_palette[0]
color_palette.pop(0)
ind = range(len(year_label))
year_values = np.asanyarray(year_values).T
year_journals = np.asanyarray(year_journals).T
fig = plt.figure()
ax = fig.add_subplot(111)
bar_patch = []
left_margin = np.zeros(len(year_label))
for i, v in enumerate(year_values):
c = []
for journal in year_journals[i]:
c.append(colors[journal])
bar_patch.append(ax.barh(ind, v, left=left_margin, color=c))
left_margin += v
plt.yticks(ind, year_label)
for j in range(len(bar_patch)):
for i, patch in enumerate(bar_patch[j].get_children()):
point = patch.get_xy()
x = 0.5 * patch.get_width() + point[0]
y = 0.4 * patch.get_height() + point[1]
# ax.text(x, y, f"{round(patch.get_width(), 1)}%", ha='center')
legend_patches = []
for key in colors:
legend_patches.append(mpatches.Patch(color=colors[key], label=key))
plt.legend(handles=legend_patches, loc='center left', bbox_to_anchor=(1, 0.5))
plt.tight_layout()
plt.xlabel("Percentage of all paper published in given year")
plt.ylabel("Year")
plt.savefig("year_top_journals.png", type="png", dpi=300)
plt.show()